Saravanan666/pypi-org huggingface dataset:
Package: lamineml
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: lamine-root
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: laminhub_rest
laminhub-rest: Cross-instance managementNote: For more extensive documentation & testing, seedocs.SummaryInstallationCLIRun serverRun testsLaunch Jupyter labMigrateDeploymentUsageRelease process1. InstallationClone this repositoryNavigate to the repository and run:pip install .2. DependenciesSupabase CLITo use lnhub CLI on local environment you will first have to install and configureSupabase CLI.DockerYou must also have Docker installed to allow
Supabase CLI to create and run the relevant containers3. Local DevelopmentThelnhubscript serves as the entrypoint for all actions you need to run for local development.:warning: It is important that you use this entrypoint as it properly configures the settings.To ensure that the environment is properly configured you can do one of three things:SetLAMIN_ENV=localprior to runninglnhub. This will ensure that a local Supabase instance is started and that the connection strings are properly configured.If you want to connect to an external Supabase (either in the cloud or managed by another process), you can customizeLAMIN_ENV=foobar(orprodorstaging) and place a corresponding .env file in the root folder. In this case it would belaminhub-rest--foobar.env.The .env file must contain values for the following variables:POSTGRES_DSN
SUPABASE_API_URL
SUPABASE_ANON_KEY
SUPABASE_SERVICE_KEYRun the serverLAMIN_ENV=local lnhub runRun testsLAMIN_ENV=local lnhub test -s test_localAny parameters after thelnhub testcommand are passed directly tonoxLaunch Jupyter labLAMIN_ENV=local lnhub jupyterOpen an IPython shellLAMIN_ENV=local lnhub shellMigrateSeeMigrations3. DeploymentPush on thestagingbranch to deploy in staging.Push on themainbranch to deploy in production.4. UsageAccess API documentation from these endpoints.Locally:http://localhost:8000/docsOnstagingserver :https://laminhub-rest-cloud-run-staging-xv4y7p4gqa-uc.a.run.app/docsOnproductionserver:https://laminhub-rest-cloud-run-main-xv4y7p4gqa-uc.a.run.app/docs5. Release process
Package: lamini
LaminiCreate your own Generative AI.This python package is a client + SDK compatible with the Lamini API, documented here:https://lamini-ai.github.io/. For this reason, we recommend that most users stay up-to-date with the most recent stable version of our package available viapip install --upgrade --force-reinstall lamini.InstallationpipinstalllaminiSetup your keysGo tohttps://app.lamini.ai/accountand log in to get your API key.Create~/.lamini/configure.yamland put a key in it.production:key:"<YOUR-KEY-HERE>"DocumentationPackage documentation here:https://lamini-ai.github.io/GithubPackage source code here:https://github.com/lamini-ai/lamini
Package: laminlake
Lamin LakeLamin Lake is the biological data management system.Installation and configurationInstall the development version:flit install -s --deps developThen configure laminlake for use with Notion:laminlake configureand AWS (pip install awscli):aws configureTutorialsNotion integration:https://github.com/laminlabs/laminlake/blob/main/tutorials/notion.ipynbIngesting Eraslan21:https://github.com/laminlabs/usecases/blob/main/ingest-eraslan21.ipynb
Package: lammps
LAMMPS unofficial wheelsThis unofficial repository holds the code to buildLAMMPSplatform wheels for Linux, macOS, and Windows. LAMMPS can easily be installed usingpipinstalllammpsThe package requires Python 3.7 and above. Most packages are enabled in this distribution. For the usage of LAMMPS, seeLAMMPS documentation.Available BuildsOSArchBitConditionsMPILinuxx86_6464glibc >= 2.17MPICHLinuxaarch6464glibc >= 2.17MPICHLinuxppc64le64glibc >= 2.17MPICHmacOSx86_6464>= macOS-11MPICHmacOSarm6464>= macOS-11-Windowsamd6464Microsoft MPITo use MPI, it is required to use the same MPI as what the package was built against.UsagePythonfromlammpsimportPyLammpsCommand linelmp-hPluginsDevelopers should build plugins with the same MPI, and can register the plugin directory using the entry points:[project.entry-points.'lammps.plugins']some_plugin="some_package:get_plugin_dir"get_plugin_dirshould return a string which contains the plugin directory and can be added intoLAMMPS_PLUGIN_PATH. SeeLAMMPS plugin documentationfor details.LicenseThis package is distributed under the GNU General Public License, as the same as the original LAMMPS repository.
Package: lammps-cython
lammps-cythonA high-performance cython wrapper around LAMMPS. Lammps is a great
molecular dynamics package that has an unmatched set of potentials and
fixes. This package offers unique features such as minimizing I/O by
allowing direct access to thermostats and atom properties and allowing
interactive lammps within python interpreters such aipython.  The
goal of this project is to put an opinionated wrapper around LAMMPS
(the good parts) and allow the user to easily extend it's
functionality in python. The api should feel very similar toHOOMDand is being actively
developed.Latest ReleasePackage StatusLicenseBuild StatusBinder NotebooksDocumentationFull documentation can be found atlammps-cython.FeaturesFull MPI supportPythonic API inspired byHOOMDSupports Python 2 and 3Heavily documented and testedElimination of unnecessary file I/O for thermostats and atoms propertiesA neat feature of the wrapper is that lammps can be run regularly
using the following script (use "-i" instead of stdin). This is the
commandpylammpswhen the package is installed.fromlammpsimportLammpsimportsysLammps(args=sys.args)Installationlammps-cythonhas several options for installation. The easiest way
is using the provided docker containter imagecostrouc/lammps-cythonorconda install.There is no plan to support pip wheels. Other
methods require manual installation of lammps. Detailed installation
are provieded in thedocumentation. If
you have any issues with installation be submit an issue at thegitlab repository.conda:conda install -c costrouc -c conda-forge lammps-cythondocker:docker pull costrouc/lammps-cython:latestOtherwise the general path to installation is installLAMMPS as a
shared
librarythen
edit~/.config/lammps-site.cfgto include the paths of necissary
libraries. See example below.[lammps]lammps_include_dir=/usr/local/include/lammps/lammps_library_dir=/usr/local/lib/# true library filename is liblammps.so notice lib and .so are removedlammps_library=lammps# use mpic++ -showme to list libraries and includes[mpi]mpi_include_dir=/usr/lib/x86_64-linux-gnu/openmpi/includempi_library_dir=/usr/lib/x86_64-linux-gnu/openmpi/lib# no necissarily needed (default are mpi, mpi_cxx)mpi_library=mpiThenpip install lammps-cythonshould just work.Conda ImageThe conda image is built only for linux and python versions (3.5,
3.6). There is no plan to support Windows due toMPIbeing a
dependency. However, if OSX is needed please raise an issue.conda install -c costrouc -c conda-forge lammps-cythonDocker ImageThe docker imagecostrouc/lammps-cythonusespython3.5and has the library preinstalled with the executablespylammpsandlammpsavailable.TutorialsWork is being done to show how to use the features oflammps-cythonfor now just visit thetutorial page.These will turn to links when the tutorial exists.basic usagemodify atom positionsget forces and velocity for each atom and compute potential energyContributingAll contributions, bug reports, bug fixes, documentation improvements,
enhancements and ideas are welcome! These should be submitted at theGithub repository. Github is
only used for visibility.Contributors:Chris Ostrouchov(maintainer)LicenseMIT
Package: lammps-interface
LAMMPS InterfaceAuthorsPeter BoydMohamad MoosaviMatthew WitmanDescriptionThis program was designed for easy interface between the crystallographic
information file (.cif) and the Large-scale Atomic Molecular Massively
Parallel Simulator (Lammps).InstallationSimply install fromPyPI:pip install lammps-interfaceFor development purposes, clone the repository and install it from source:pip install -e .Note: In both cases, this addslammps-interfaceto yourPATH.UsageCommand line interfaceTo see the optional arguments type:lammps-interface --helpTo createLammpssimulation files for a given cif file type:lammps-interface cif_file.cifThis will createLammpssimulation files with UFF parameters.Jupyter notebookIn order to integrate lammps-interface into your project, check out the Jupyter notebooks provided in/notebooksfor usage examples.LicenseMIT license (seeLICENSE)CitationThe publication associated with this code is found here:Boyd, P. G., Moosavi, S. M., Witman, M. & Smit, B. Force-Field Prediction of Materials Properties in Metal-Organic Frameworks. J. Phys. Chem. Lett. 8, 357-363 (2017).https://dx.doi.org/10.1021/acs.jpclett.6b02532
Package: lammps-logfile
LAMMPS logfile readerTool to read a logfile produced byLAMMPSinto a simple python data structure with aget()-function providing the log data.InstallationFrom pypi (preferred/stable)pip install lammps-logfileDepending on your python installation, you may have to usepip3instead ofpip. This is usualy the case if you need to runpython3rather thanpythonto run python version 3.Install using pip directly from github to get the latest (possibly unstable) version:pip install git+https://github.com/henriasv/lammps-logfile.gitOr by cloning the repository:git clone https://github.com/henriasv/lammps-logfile.git
cd lammps-logfile
pip3 install .Basic usageimport lammps_logfile

log = lammps_logfile.File("path/to/logfile")

x = log.get("Time")
y = log.get("Temp")

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()This will give the concatenated log entries of all the runs where the style of the thermo output didn't change with respect to the last run. I.e. if the entries in thethermo_stylewas not changed between runs it will contain the log data for all the timesteps. If thethermo_stylewaschanged,xandywill contain the data from all the timesteps after thethermo_stylewas changed for the last time.Multiple runs in the same log fileIf multiple run statements have been made in a simulation, these can be retrieved bu supplying therun_numkeyword to theget()-functionimport lammps_logfile

log = lammps_logfile.File("path/to/logfile")

x = log.get("Time", run_num=N)
y = log.get("Temp", run_num=N)

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()In this case,xandywill contain the log data from theN'thruncommand in LAMMPS, counting from 0.Any invalid call to theget()-function will result in the function returningNone. This happes if the user asks for a thermo propery that does not exist in the log file, or if the user asks for arun_numlarger than the number of runs in the logfile.
Package: lammps-parse
lammps-parseInput file writers and output file readers for the molecular dynamics simulation code Lammps.Installationpip install lammps-parseFunctionality/roadmapFunctionality is currently rather limited and specific to my original use-cases. Pull-requests are welcome!Readers:✅read_lammps_log✅read_lammps_dump✅read_lammps_output✅check_successWriters:✅write_lammps_inputs✅write_lammps_atoms
Package: lammps-simulator
LAMMPS simulatorA light-weight Python package for launching LAMMPS simulations. Given a LAMMPS input script, the simulation is launched from a specified working directory. The default behavior is to copy the input script and all dependencies to the working directory, making it easy to redo the simulations. Simulations can be submitted directly to the Slurm simulation queue.InstallationInstall package from source using pip:$pipinstalllammps-simulatorPrerequisitesPython 3.3+ (subprocess.DEVNULL from 3.3 needed)LAMMPS(Any recent version)Basic usageTo run a LAMMPS script from the current directory, the script has to be specified and the way of running the simulation has to be defined. The easiest way of doing this is to use the default simulation object,sim:fromlammps_simulatorimportsimsim.set_input_script("script.in")sim.run(num_procs=4,lmp_exec="lmp")where the LAMMPS input scriptscript.inis launched on 4 CPU processes by calling the LAMMPS executablelmp. This corresponds to running$mpirun-n4lmp-inscript.inDefining working directory and copy files to itAssociating each simulation with a respective working directory is good practice, as it makes it easy to rerun the simulation. Create a simulator object associated with a directory by:fromlammps_simulatorimportSimulatorsim=Simulator("simulation",overwrite=False)The argumentoverwritecan be set to True if the contents of the simulation should overwrite a potentially existing simulation directory.Often, the LAMMPS script requires other files, like parameter files, data files or other LAMMPS scripts. The functioncopy_to_wdcan be used to copy any file to the working directory:sim.copy_to_wd("parameters.vashishta")sim.copy_to_wd("pos.data")sim.copy_to_wd("compute_something.in")or more compact:sim.copy_to_wd("parameters.vashishta","pos.data","compute_something.in")Assign variables to LAMMPS scriptIf your LAMMPS script takes command line variables, they can be specified bysim.set_input_script("script.in",var1=v1,var2=v2,...,varN=vN)orlmp_vars={'var1':v1,'var2':v2,...'varN':vN}sim.set_input_script("script.in",**lmp_vars)Variables might also be lists (index variables in LAMMPS terms):sim.set_input_script("script.in",var=[1,2,3])Slurm supportSimulations can be submitted to the Slurm queue by addingslurm=Trueand Slurm arguments to therun-method. Basic example:slurm_args={'job-name'='cpu','partition'='normal','ntasks'=16,'nodes'=1}sim.run(num_procs=16,lmp_exec="lmp",slurm=True,slurm_args=slurm_args)A job script,job.sh, will then be generated in the simulation directory, which is executed withsbatch job.shFor more examples, see the examples pages and thedocumentation.
Package: lammpstools
lammpstoolslammpstoolsis a small python package which can be used to analyse LAMMPS data.For documentation on the functions it contains, install the package:pip install lammpstoolsopen a python interpretter (by typingpythonin your shell)and then in the python interpreter writeimport lammpstoolsprint(lammpstools.__doc__)which will give a list of all functions. To access documentation of a specific function,
follow a similar route. E.g. for theDumpLoaderclass writeprint(lammpstools.DumpLoader.__doc__)which will tell you (probably) all you need to know.
Package: lampe
LAMPELAMPE is a simulation-based inference (SBI) package that focuses on amortized estimation of posterior distributions, without relying on explicit likelihood functions; hence the nameLikelihood-free AMortized Posterior Estimation(LAMPE). The package providesPyTorchimplementations of modern amortized simulation-based inference algorithms like neural ratio estimation (NRE), neural posterior estimation (NPE) and more. Similar to PyTorch, the philosophy of LAMPE is to avoid obfuscation and expose all components, from network architecture to optimizer, to the user such that they are free to modify or replace anything they like.As part of the inference pipeline,lampeprovides components to efficientlystore and load datafrom disk,diagnose predictionsanddisplay resultsgraphically.InstallationThelampepackage is available onPyPI, which means it is installable viapip.pip install lampeAlternatively, if you need the latest features, you can install it from the repository.pip install git+https://github.com/probabilists/lampeDocumentationThe documentation is made withSphinxandFuroand is hosted atlampe.readthedocs.io.ContributingIf you have a question, an issue or would like to contribute, please read ourcontributing guidelines.
Package: lampip
lampip: Simple CLI tool for creating python custom lambda layersFeaturesBuild Python(3.7, 3.8, 3.9)-compatible custom lambda layers using Docker and pip, and push it to AWS.Reduce the package size using some approachs.Byte-Compile(that remove source comments and docstrings).Remove*.dist-info.RequirementsPython3DockerAWS AccountInstallationYou can obtain this packages using pip.$pip3installlampipThen you can uselampipcommand.$lampip--helpUsage: lampip [OPTIONS] COMMAND [ARGS]...Simple CLI tool for creating python custom lambda layersOptions:--version  Show the version and exit.--help     Show this message and exit.Commands:deploy  Build and push lambda layernew     Create the scaffoldUsageAt the first create the scaffold$lampipnewscienceGo to the generated directory$cdscience$lslampip-config.toml  other_resources  requirements.txtEditrequirements.txtnumpy
scipy
pandasEditlampip-config.toml[lampip]layername="science"description="numpy, scipy, and pandas"pyversions=["3.7","3.8","3.9"][lampip.shrink]compile=truecompile_optimize_level=2remove_dist_info=true# [lampip.shrink.plotly]# remove_jupyterlab_plotly = true# remove_data_docs = trueBefore you deploy the lambda layer, be sure you have AWS credentials configured.(If you do not configure AWS credentials yet, ...)$awsconfigureAWS Access Key ID: ?????AWS Secret Acess Key: ?????Default region name: ?????(Option) You can switch the aws credentials using environments variables.(Case1: Using AWS CLI profile)$exportAWS_PROFILE="subaccount"(Case2: Using AWS access key directly)$exportAWS_ACCESS_KEY_ID=????$exportAWS_SECRET_ACCESS_KEY=?????$exportAWS_DEFAULT_REGION=?????Deploy$lampipdeployStart to make dist/science_1631253196_3.7.zip...Publish the custom layer: arn:aws:lambda:ap-northeast-1:XXXXXXXXXXXX:layer:science-py38:1DONE: dist/science_1631253312_3.8.zip created$ls-lhdist-rw-r--r-- 1 root root 73M  9月 10 23:54 science_1631253254_3.7.zip-rw-r--r-- 1 root root 73M  9月 10 23:55 science_1631253312_3.8.zip...(The --no-upload option suppress uploading zip files)$lampipdeploy--no-uploadThen you can check deployed layers on AWS Console.
Package: lamplight
This is a simple example of how I've been making OpenAI API calls.I've created pydantic objects to match the OpenAI objects, and then implement a couple of nice helpers on top.Most importantly.iterator(and optionally an async.aiterator) that automatically execute function as needed, if allowed. To go along with this, theFunctionBaseModel can be created from an existing annotated callable which really streamlines the whole process..
Package: lamport-256
lamport-256Simple single use Lamport signature scheme in pythonGreat for building toy blockchains and the like.DO NOT use in a security conscious production environment!Usage:LibraryTo install run>pipinstalllamport-256Importimportlamport_256Generate a private/public key pairkey_pair=lamport_256.generate_keys()private_key=key_pair.privpublic_key=key_pair.pubfrom here on out the library functions will appear as if they were imported directly e.g.from lamport_256 import sign_messageSign a messagesignature=sign_message(private_key,'Hello, World')Verify a messageifnotverify_signature(public_key,'Hello, World',signature):raiseException('Invalid signature')Dump keys to files# togetherexport_key_pair(key_pair,'pub.key','priv.key')#filenames can be named anything you'd like# or individually:export_key(key_pair.priv,'priv.key')Read keys from file# togetherkey_pair=parse_key_pair('location/of/pub.key','location/of/priv.key')# or individually:pub=parse_key('pub.key')CLIAlthough you can simply runpython location/to/lamport_256.py generate_keys, it's best to create an alias to run the python script.# In your .bashrc or equivalentaliaslamp='python location/of/lamport_256.py'Now you can run the script more conciselylampgenerate_keysTo specify where to save keys use the appropriate optionslampgenerate_keys--privlocation/to/save/key--publocation/to/save/keySign a messagelampsign--privlocation/of/private/key--msg'Hello, world'>signature.txt# or pass the message in as a filelampsign--privlocation/of/private/key--msglocation/of/message>signature.txtVerify a signaturelampverify--publocation/of/public/key--msg'message'--siglocation/of/signature# you can pass the message as a file here as welllampverify--publocation/of/public/key--msglocation/of/message--siglocation/of/signature