Saravanan666/pypi-org huggingface dataset:
Package: lmm-tools
Large Multimodal Model ToolsLMM-Tools (Large Multmodal Model Tools) is a minimal library that helps you utilize multimodal models to organize and structure your image data. One of the problems of dealing with image data is it can be difficult to organize and quickly search. For example, you might have a bunch of pictures of houses and want to count how many yellow houses you have, or how many houses with adobe roofs. This library utilizes LMMs to help create these tags or descriptions and allow you to search over them, or use them in a database to do other operations.Getting StartedLMMsTo get started you can create an LMM and start generating text from images. The following code will grab the LLaVA-1.6 34B model and generate a description of the image you pass it.importlmm_toolsaslmtmodel=lmt.lmm.get_model("llava")model.generate("Describe this image","image.png")>>>"A yellow house with a green lawn."WARNINGWe are hosting the LLaVA-1.6 34B model, if it times out please wait ~5-10 min for the server to warm up as it shuts down when usage is low.DataStoreYou can use theDataStoreclass to store your images, add new metadata to them such as descriptions, and search over different columns.importlmm_toolsaslmtimportpandasaspddf=pd.DataFrame({"image_paths":["image1.png","image2.png","image3.png"]})ds=lmt.data.DataStore(df)ds=ds.add_lmm(lmt.lmm.get_model("llava"))ds=ds.add_embedder(lmt.emb.get_embedder("sentence-transformer"))ds=ds.add_column("descriptions","Describe this image.")This will use the prompt you passed, "Describe this image.", and the LMM to create a new column of descriptions for your image. Your data will now contain a new column with the descriptions of each image:image_pathsimage_iddescriptionsimage1.png1"A yellow house with a green lawn."image2.png2"A white house with a two door garage."image3.png3"A wooden house in the middle of the forest."You can now create an index on the descriptions column and search over it to find images that match your query.ds=ds.build_index("descriptions")ds.search("A yellow house.",top_k=1)>>>[{'image_paths':'image1.png','image_id':1,'descriptions':'A yellow house with a green lawn.'}]You can also create other columns for you data such asis_yellow:ds=ds.add_column("is_yellow","Is the house in this image yellow? Please answer yes or no.")which would give you a dataset similar to this:image_pathsimage_iddescriptionsis_yellowimage1.png1"A yellow house with a green lawn.""yes"image2.png2"A white house with a two door garage.""no"image3.png3"A wooden house in the middle of the forest.""no"
Package: lmnt
LMNT Python LibraryThe LMNT Python library provides convenient access to the LMNT API from applications written in the Python language.[Documentation]InstallationInstalling from PyPI is the quickest way to get started:pipinstall--upgradelmntInstall from source with:pythonsetup.pyinstallGetting startedThe most common operation you'll perform is asynthesizerequest. Given some text and a voice, it will return an audio
file that you can play back. Take a look at ourdocumentationfor a deeper dive into the SDK.importasynciofromlmnt.apiimportSpeechLMNT_API_KEY=...# fill in your API key hereasyncdefmain():asyncwithSpeech(LMNT_API_KEY)asspeech:synthesis=awaitspeech.synthesize('Hello, world.',voice='lily',format='wav')withopen('output.wav','wb')asf:f.write(synthesis['audio'])asyncio.run(main())While you can provide anapi_keyargument, we recommend usingpython-dotenvto addLMNT_API_KEY="My API Key"to your.envfile so that your API key is not stored in source control.More examplesYou can find more examples in thedemodirectory.LicenseApache 2.0
Package: lmo
Lmo - Trimmed L-moments and L-comomentsIs your tail too heavy?
Can't find a moment?
Are the swans black?
The distribution pathological?

... then look no further: Lmo's got you covered!

Uniform or multi-dimensional, Lmo can summarize it all with one quick glance!Unlike the legacymoments,L-momentsuniquely describea
probability distribution, and are more robust and efficient.
The "L" stands for Linear; it is a linear combination of order statistics.
So Lmo is as fast as sorting your samples (in terms of time-complexity).Key FeaturesCalculates trimmed L-moments and L-comoments, from samples or anyscipy.statsdistribution.Full support for trimmed L-moment (TL-moments), e.g.lmo.l_moment(..., trim=(1/137, 3.1416)).Generalized Method of L-moments: robust distribution fitting that beats MLE.Fast estimation of L-comoment matrices from your multidimensional data
or multivariate distribution.Goodness-of-fit test, using L-moment or L-moment ratio's.Exact (co)variance structure of the sample- and population L-moments.Theoretical & empirical influence functions of L-moments & L-ratio's.Completedocs, including detailed API
reference with usage examples and with mathematical $\TeX$ definitions.Clean Pythonic syntax for ease of use.Vectorized functions for very fast fitting.Fully typed, tested, and tickled.Optional Pandas integration.Quick exampleEven if your data is pathological likeCauchy, and the L-moments
are not defined, the trimmed L-moments (TL-moments) can be used instead.
Let's calculate the TL-location and TL-scale of a small amount of samples:>>>importnumpyasnp>>>importlmo>>>rng=np.random.default_rng(1980)>>>x=rng.standard_cauchy(96)# pickle me, Lmo>>>lmo.l_moment(x,[1,2],trim=(1,1)).array([-0.17937038,  0.68287665])Now compare with the theoretical standard Cauchy TL-moments:>>>fromscipy.statsimportcauchy>>>cauchy.l_moment([1,2],trim=(1,1))array([0.        , 0.69782723])See thedocumentationfor more examples and
the API reference.RoadmapAutomatic trim-length selection.Plotting utilities (deps optional), e.g. for L-moment ratio diagrams.InstallationLmo is onPyPI, so you can do something like:pipinstalllmoRequired dependenciesThese are automatically installed by your package manager, alongsidelmo.PackageMinimum versionPython3.10NumPy1.22SciPy1.9Optional dependenciesPackageMinimum versionNotesPandas1.4Lmo extendspd.Seriesandpd.DataFramewith convenient methods, e.g.df.l_scale(trim=1). Install aspip install lmo[pandas]to ensure compatibility.Foundational LiteratureJ.R.M. Hosking(1990) – L-moments: Analysis and Estimation of
Distributions using Linear Combinations of Order StatisticsE.A.H. Elamir & A.H. Seheult(2003) – Trimmed L-momentsE.A.H. Elamir & A.H. Seheult(2004) – Exact variance structure of
sample L-momentsJ.R.M. Hosking(2007) – Some theory and practical uses of trimmed
L-momentsR. Serﬂing & P. Xiao(2007) – A contribution to multivariate
L-moments: L-comoment matricesW.H. Asquith(2011) – Univariate Distributional Analysis with
L-moment Statistics
Package: lmodule
Lmodule (Lmod Module)Lmodule is a Python 3 API forLmodmodule system. Lmodule can assist HPC facilities in testing their
software stack viamodule loadtesting. The API can be used by developers and system-administrators for interacting with module system.
Developers can utilize the Lmodule API for interacting  with module system in their python code, while System Administrators can leverage
the Lmodule API for testing their Software Stack.Authorlmodule was created byShahzeb Siddiqui.Contributing BackWe encourage community feedback and participation to help improve the lmodule API. To get started you can do the following:Join the slack communityTest the API and provide feedback in the form ofIssueorPull Request.SlackClick theJoin Slack Channelto join the community.
If you already have an account then access the Slack ChannelhereLICENSElmodule is released under the MIT license. See theLICENSEfile for more details.TalksAutomate Module Testing with LmoduleatEasyBuild User Meeting-Slides,Video
Package: lmp
Launch My Project is a simple CLI that allows its users to quickly launch projects via the smp CLI. A single function call can begin all processes and development tools needed to start working on a project. This includes starting up an IDE, a command prompt at the projects path, run scripts, open websites, open a file explorer at the projects path, and much more. LaunchMyProject is in a very early stage, but its basic functionality will simplify every developers life.
Package: lmproof
lmproof - Language Model Proof ReaderLibrary to do proof-reading corrections for Grammatical Errors, spelling errors, confused word errors and other errors using pre-trained Language Models.UsageInstall spacy model bypython -m spacy download en.
Then try out this snippet.importlmproofproof_reader=lmproof.load("en")source="The foxes living on the Shire is brown.'"corrected=proof_reader.proofread(source)# "The foxes living in the Shire are brown."How it works?We use the language model based scoring approach mentioned inChristopher Bryant and Ted Briscoe. 2018with few changes.Unlike many approaches to GEC, this approach does NOT require annotated training data and mainly depends on a monolingual language model. The program works by iteratively comparing certain words in a text against alternative candidates and applying a correction if one of these candidates is more probable than the original word. These correction candidates are variously generated by a word inflection library or are otherwise defined manually. Currently, this system only corrects:Non-words (e.g. freind and informations)
Morphology (e.g. eat, ate, eaten, eating, etc.)
Common Determiners and Prepositions (e.g. the, a, in, at, to, etc.)
Commonly Confused Words (e.g. bear/bare, lose/loose, etc.)This work builds uponhttps://github.com/chrisjbryant/lmgec-lite/ComponentsLanguage ModelstransformersInflection generatorsLemmInflectis used to lemmatize and generate inflections for candidate proposals to the language model.Spell Checkersymspellpyis used for obtaining spell check candidates.The components are highly modularised to facilitate experimentation with newer scorers and support more languages.
Pre-trained language models for other languages, inflectors, common error patterns can be easily added to support more languages.TODOsUse edits in existing GEC corpus to generate candidates.TestsPublish benchmarks of the model.Think of simple ways to generate insertion candidates.Add more languages.
Package: lmrt4u-dbneal
let-me-render-that-4u (lmrt4u)Python Package for the lmrt4u agile artefact render toolPackageBasic structure of package is├── README.md
├── lmrt4u
│   ├── __init__.py
│   ├── __main__.py
│   ├── controller.py
│   ├── entry.py
│   ├── helpers.py
│   ├── initializer.py
│   ├── interface.py
│   ├── loader.py
│   ├── parser.py
│   ├── renderer.py
│   ├── tuples.py
│   └── validator.py
├── requirements.txt
└── setup.pyInstallationTo install the tool without the use of  the code repositorypip install lmrt4u-dbnealUsageTo run the tool dopython -m lmrt4uThis generates a sample Lmrt4ufile in current directory. File can be modified manually then re-run to display updated sprint information.To run the tool without leveraging to user interface you can pass arguments directlypython -m lmrt4u Burndown ChartLmrt4uFileThe Lmrt4uFile contains the backlog information that will be used when the burndown charts are generated. Please note that all sprint properties are enforced through a verification module
so make sure to follow the configuration guide below.Here's a brief example  of the file (All information in parenthesis is for the user and should NOT be included in the file):sprints: (List of sprints contained in the backlog, sample file contains one but you can include as many as you'd like)
  Sample Sprint: (This is the name of a sprint)
    active: true (A boolean property, if a sprint is active (True) then a burndown chart will be generated)
    end: '2019-04-16' (End date for the sprint, *must occur after the start date*, *must match format 'yyyy-MM-DD'*)
    start: '2019-04-02' (Start date for the sprint, *must match format 'yyyy-MM-DD'))
    stories: (List of arguments with each unique item representing a story)
    - - Story Name 1 (This is the name of a story in the sprint)
      - This is the description (This is the description for the story)
      - 10 (This is the amount of story points assigned to the story)
      - '2019-04-03' (This is the completion date, put null if the story is yet to be completed)RequirementsPackage requirements are handled using pip. To install them dopip install -r requirements.txtCredits & ReferencesPython module template taken frommtchavez/python-package-boilerplateSDD template taken fromCSUF Course COP3331 Template
Package: lmrtfy
LMRTFY stands forLet Me Run That For You.✅ Create functions that run in thecloud, onyour serversor even onyour laptop.✅ Call them from code that runs somewhere else, justlike a regular function.✅Share functionswith friends and colleagues, track their usage andmonetizetheir usage.✅ Works with Python, but more languages will be added in the future.Quickstart GuideTutorialExamplesAPI ReferenceHow to report issuesandhow to contributeIntroductionLMRTFY is a tool to share scripts via the cloud. Your scripts can run on your laptop, on your server
or in the cloud. You and everybody you shared your deployed script with can call the function straight
from their own code using thelmrtfy packageWe strive to provide a frictionless developer experience:Change as little code as possible to use LMRTFYCall deployed function like any other function provided by a local libraryQuickstart - TL;DRinstall withpip install lmrtfylogin/sign up withlmrtfy loginrun$ ipythonandfrom lmrtfy.functions import catalogcall the provided example withjob = catalog.examples.free_fall_lmrtfy(100.)get the results withjob.resultsAs you can see in step 4, it's as simple as calling a regular function from any other library
you have installed locally.ExamplesTheexamplesare provided in theexamples/directory. They arework in progress. As lmrtfy
matures, more and more examples will be added.If you miss an example for a specific use case, please let us know, and we will add one!License
Package: lmrun
lmrunA CLI to run Groovy or PowerShell scripts on a LogicMonitor collector for debugging purposes.InstallationFrom PyPi:pip install lmrunFrom source:python setup.pyUsageSign InFirst use thelogincommand to log into LogicMonitor by specifying your API ID and KeyC:\> lmrun login
Please enter your LogicMonitor company name:
Please enter the API access id:
Please enter the API access key:or via cli parameterslmrun login --company company_name --access_id abc123 --access_key def456This will create a config.json file in the~\.lmrundirectory.Run a scriptThen use theexecuteorexecommand by passing a .groovy or .ps1 file, and the name of the monitored device to run the script against.lmrun execute test.groovy SRV-01lmrun exe test.ps1 SRV-01lmrun exe test.groovy SRV-01LogoutIf you wish to delete the saved credentials located in~\.lmrun\config.jsonsimply run:lmrun logout
Package: lmsg
lmsgDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nlmsg--fileenv.ymlInstallpipinstall-e.# install from pypipipinstalllmsgnbdev# activate conda environment$condaactivatelmsg# make sure the lmsg package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the lmsg package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/lmsg.gitor fromconda$condainstall-cdsm-72lmsgor frompypi$pipinstalllmsgDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.NumPy Documentation:np.DataSourcePyTorch Documentation:TorchDataHow to Package PyTorch
Modelstorch.monitor.Eventtorchvisiontorchvision.Datasets.VisionDatasettorchvision.utils.flow_to_imagePyTorch Models to Consider:Diffusion Video
AutoEncodersConvLSTM
AutoEncoderRecurrent All Pairs Field Transforms for Optical
FlowOptical Flow Toolbox
(mmflow)torchvision.models.optical_flow.raft
Package: lmso-algorithm
# lmso_algorithmThe least-mean-square (LMS) and the normalized least-mean-square (NLMS) algorithms require a trade-off between fast convergence
and low misadjustment, obtained by choosing the control parameters. In general, time variable parameters are proposed
according to different rules. Many studies on the optimization of the NLMS algorithm imply time variable control parameters
according some specific criteria.The optimized LMS (LMSO) algorithm [1] for system identification is developed in the context of a state variable model, assuming
that the unknown system acts as a time-varying system, following a first-order Markov model [2].The proposed algorithm follows an optimization problem and introduces a variable step-size in order to minimize the system misalignment[1] A. G. Rusu, S. Ciochină, and C. Paleologu, “On the step-size optimization of the LMS algorithm,” in Proc. IEEE TSP, 2019, 6 pages.[2] G. Enzner, H. Buchner, A. Favrot, and F. Kuech, “Acoustic echo control,” in Academic Press Library in Signal Processing,
vol. 4, ch. 30, pp. 807–877, Academic Press 2014.
Package: lms-prepaid
lms-prepaidA python script to retrieve balances for last mile solutions electric car charging prepaid card (this includes Entega prepaid cards)Installationpip install lms-prepaidDevelopmentlms-prepaidusespoetryUsagelms-prepaid -c AB-123456-XSee also:lms-prepaid --helpPoetry setupInstall poetry:pip install poetry(see the poetry homepage for alternatives)Installlms-prepaid:poetry installRun:poetry run lms-prepaid --helpEnvironment variablesThe script will use the following environment variables:LMS_PREPAID_DATABASEfor the path to the sqlite databasedefaults tolms-prepaid.sqliteint the CWDLMS_PREPAID_CARDfor the card numberLMS_PREPAID_GOTIFY_URLfor the gotify notification serverLMS_PREPAID_GOTIFY_TOKENfor the gotify app tokenLMS_PREPAID_GOTIFY_PRIOfor the gotify notification priorityDocker usageBuild the docker image:docker build -t lms-prepaid .Launch the image with with set environment variables:docker run -e LMS_PREPAID_CARD=AB-CDE-123456-X lms-prepaid
Package: lms-synergy-library
lms-synergy-libraryThis unofficial library is a collection of useful functions for the LMS Synergy platform.Installationpipinstalllms-synergy-libraryUsagefromlms_synergy_libraryimportLMSlms=LMS(login="demo",password="demo")# verifylms.verify()# get amount messageslms.get_amount_messages()# get amount notificationslms.get_amount_notifications()# get information about userlms.get_info()# get shedulelms.get_schedule()# get newslms.get_news()# get disciplineslms.get_disciplines()# get amount unverified worklms.get_amount_unverified_work()# get pesonal curatorslms.get_pesonal_curators()# get tutorslms.get_tutors()# get notificationslms.get_notify()# get notifications archivelms.get_notify_archive()# get unread messageslms.get_unread_messages()# get markslms.get_marks()# get eventslms.get_events()ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMITAuthors@kotorkovsciy
Package: lmstfy
lmstfyis an Apache2 licensed Python CLI for searching for error messages.TODOSupport stdout and stderr output.Filter output to only error messages.Filter output to last N error messages.QuickstartInstallpipx.Then installlmstfy.System RequirementsPython 3.6+ is required to installlmstfy. Don’t have Python 3.6 or later?
See thePython 3 Installation & Setup Guide.You also need to have pip installed on your machine for python3. Installing it
varies from system to system. Consult pip’s installation instructions.
Installing on Linux works best with a Linux Package Manager.Installing via Python$python3-mpipinstall--userpipx$python3-mpipxensurepath$python3-mpipxinstalllmstfyInstalling via BrewPipx works on MacOS, Linux, and Windows.Pipx can be installed on macOS usingbrew.$brewinstallpipx$pipxensurepath$pipxinstalllmstfyTutorialFollow theInstallationsteps to installlmstfy.Running CommandsInvoke a build system using the “run” subcommand:$lmstfyrunmake$lmstfyruncmake$lmstfyrungradlewbuild$lmstfyrunmavenmypackageInvoke a hot-reloading webserver using the “run” subcommand:$lmstfyrunpython3manage.pyrunserverInvoke an interpreter using the “run” subcommand:$lmstfyrunpython3The stdin, stdout, and stderr streams are wrapped around the executed
process. Signals are also passed to the subprocess.Piping InputPipe output tolmstfy.$./script.sh|grep-i-C10error|lmstfypipeIt’s also possible to provide metadata:$./script.sh|lmstfypipe--hintlanguage=python3Parsing OutputParse log files using cat:$lmstfycatlog.txtOr parse log files using curl:$lmstfycurlhttps://path/to/log.txtOr parse the output in your terminal:CMD-a CMD-c
$ lmstfy pipe << EOF
CMD-v EOF RETReferencelmstfy Documentationlmstfy at PyPIlmstfy at GitHublmstfy Issue TrackerLicenseCopyright 2020 Grant JenksLicensed 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 athttp://www.apache.org/licenses/LICENSE-2.0Unless 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: lmt8x
Transfer tables for LMT8x Temperature Sensors.LMT8x (LMT84, LMT85, LMT86, LMT87) are a series ofanalog temperature sensorsmade by Texus Instrument.
These sensors can provide -50°C ~ 150°C with ±0.4°C accuracy. Although the output voltage is nearly linear to the temperature, it does have a slight umbrella parabolic shape.
Therefore transfer tables are required to convert the voltage to the temperature.Datasheets:LMT84LMT85LMT86LMT87This package provides tranfer functions for LMT84, LMT85, LMT86, and LMT87 based on their transfer tables.
These funcitons basically do a binary search through the transfer tables and return the match.
If no match found, linear interpolation will be used to generate an approximate value.Installationpip install lmt8xYou can also install the in-development version with:pip install https://github.com/charlee/python-lmt8x/archive/master.zipUsagelmt8xpackage provides functionslmt84_v2t,lmt85_v2t,lmt86_v2t, andlmt87_v2tfor converting voltage to temperature.input voltage must be in mV.output temperature is in celsius.from lmt8x import lmt87_v2t     # or `lmt86_v2t`, `lmt85_v2t`, `lmt84_v2t`

# read voltage from sensors.
# v = read_sensor()

# convert v to temperature.
# the pamaeter must be in mV. Return value is in celsius.
temp = lmt87_v2t(v * 1000)

print('Temperature is %s C.' % temp)Documentationhttps://python-lmt8x.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog1.0.0 (2021-04-02)First release on PyPI.
Package: lmt-india-covid19-risk-assessment
lmt-india-covid19-risk-assessmentDefines survey typeLmtIndiaCovid19RiskAssessmentand responseLmtIndiaCovid19ResponseusingcoconutInstallationpip install lmt-india-covid19-risk-assessmentUsagefromcoconutimportLimeAPI,Workbookfromlmt_india_covid19_risk_assessmentimportLmtIndiaCovid19RiskAssessment# Create a LimeAPI instancelime=LimeAPI(url="https://surveys.my-lime-survey-instance.org",username="admin",password="password")# Create the survey instancesurvey=LmtIndiaCovid19RiskAssessment(survey_id=119618,lime_api=lime)# Load questions, responses, survey infosurvey.load_data()# Save the data to an Excel filesurvey.to_excel("survey.xlsx")# Save response data to a CSV filesurvey.to_csv("survey.csv")# Update a Google Sheets workbookworkbook=Workbook(workbook_id="abc123",survey=survey,service_account_json_path="google-cloud-creds.json")workbook.sync()