Saravanan666/pypi-org huggingface dataset:
Package: lsquery
InstallationThe project is used to spin up docker game servers.You require bothpython 3andpipinstalled to run this command.To install:pip install lsqueryOnce installed you should be able to run it as a python module:python3 -m lsqueryRequirementsThese requirements should automatically be installed by pip, but incase they aren't you can run this command.pip install asyncio==3.4.3 docker==6.1.3 opengsq~=1.4.3 python-decouple~=3.6 pytz~=2022.6 rcon~=2.1.1 websocket-client==1.4.2 websockets~=10.4ConfigurationThe project depends on three environment variables. A sample environment file has been provided.env.exampleDOCKER_HOST=
WEBSOCKET_HOST=
WEBSOCKET_APP_KEY=When you first run the module, it will check if these environment variables have been set.
If not, it will prompt the user for each value. If the system detects a default value, it will suggest using it.If you ever want to reconfigure these values, you can run:python3 -m lsquery -cEnvironment VariableDescriptionSample ValuesDOCKER_HOSTDocker Hostunix:///var/run/docker.socktcp://192.168.1.11:2376WEBSOCKET_HOSTWebsocket Hostportal.lanslide.com.auWEBSOCKET_APP_KEYWebsocket Application Key-Post InstallationYou may want to consider running this package as a service. Here is a sample portal.service scriptNote:screenmust be installed./etc/systemd/system/portal.service[Unit]
Description=Portal Service
After=multi-user.target

[Service]
Type=forking
Restart=on-failure
ExecStart=/usr/bin/screen -L -dmS portal /usr/bin/python3 -m lsquery
ExecStop=/usr/bin/screen -X -S portal quit

[Install]
WantedBy=multi-user.targetTo enable this service on start up you can runsystemctl enable portalTo start this service you can runsystemctl start portal
Package: lsreader
LS-Reader ProjectThe LS-Reader is designed to read LS-DYNA results and can extract the data of more than 2000 such as stress, strain, id, history variable, effective plastic strain, number of elements, binout data and so on now. The LS-Reader provides an uniform interface for getting these data. For convenience, input parameters, like number of state, number of integration point, are designed as keyword arguments. More details please seeLS-Reader Tutorial.How to use# D3plotReaderfromlsreaderimportD3plotReader,DataTypeasdtdata_path=your/d3plot/file/pathdr=D3plotReader(data_path)shell_stress=dr.get_data(dt.D3P_SHELL_STRESS,ist=0,ipt=1)print(shell_stress[0].x())shell_eps=dr.get_data(dt.D3P_SHELL_EFFECTIVE_PLASTIC_STRAIN,ist=0,ipt=1)print(shell_eps[0])thickness=dr.get_data(dt.D3P_SHELL_THICKNESS,ist=11)print(thickness[0])num_solid_element=dr.get_data(dt.D3P_NUM_SOLID)print(num_solid_element)# Get d3plot data by partnum_solid_pid_4=dr.get_data(dt.D3P_NUM_SOLID,ipart_user=4)shell_stress_pid_4=dr.get_data(dt.D3P_SHELL_STRESS,ist=4,ipt=0,ipart_user=4)# Get data by part setnum_shells=dr.get_data(dt.D3P_NUM_SHELL,ipartset_user=[1,2])# Get numpy array(numpy is required to be installed, using "pip install numpy")shell_stress=dr.get_data(dt.D3P_SHELL_STRESS,ist=0,ipt=1,ask_for_numpy_array=True)print(shell_stress)# BinoutReaderfromlsreaderimportBinoutReader,BINOUT_DataTypeasbdtdata_path=your/binout/file/pathbr=BinoutReader(data_path)branches=br.get_data(bdt.BINOUT_BRANCHES)print(branches)ids=br.get_data(bdt.BINOUT_NODOUT_IDS)print(ids).........Latest TutorialThe examples and documents for this project is available here.
Package: lsrm
This git repository contains project code for the land surface recharge models (lsrm) developed at Environment Canterbury (ECan).
The current lsrm available was originally developed by David Scott in 2013 in Fortran, but has been transcribed and improved by Mike Kittridge into this Python package.ds moduleThe ds module contains the LSRM class for running the transcribed David Scott lsrm.Initialisation does not require any parameters:l1 = LSRM()Then follow the sequence of methods: soils_import, input_processing, and lsrm.irr1, psw1 = l1.soils_import()

mv, sites = self.input_processing(bound_shp, grid_res, buffer_dis, interp_fun, agg_ts_fun, time_agg)

results1 = self.lsrm()Documentation on the David Scott’s lsrm methods can be foundhere
Package: ls-s3-logs
A python library to parse S3 log files.WarningUnit tests currently require actual S3 credentials (and a bucket with logs) and can therefore only be run manually.
Mocking the relevant parts of boto.s3 is on the roadmap. Contributions are welcome :)PurposeDownload S3 logs from a bucket, and parse them.This application does not store the log objects generated and leaves that to other applications.InstallEventually from pypi …UseTo get logs, simply use the Downloader class:from itertools import chain
from pprint import pprint
from lss3logs.download import Downloader

MY_ACCESS_KEY_ID = 'XXX'
MY_KEY_SECRET = 'XXX'
MY_S3_BUCKET_NAME = 'XXX'

downloader = Downloader(
    connection=None,
    aws_access_key_id=MY_ACCESS_KEY_ID,
    aws_key_secret=MY_KEY_SECRET,
)

# download 10 logs
logs = downloader.download_files(
    MY_S3_BUCKET_NAME,
    prefix='logs/',
    max_logs=1)

entries = [ log.entries for log in logs ]
entries = list(chain.from_iterable(entries))
[pprint(entry.__dict__) for entry in entries]Running testsFirst you need to specify the test config, which contains the AWS credentials and details of bucket tot test with.
python-testconfig is used to manage the test configuration.Copy test_config.ini.sample to test_config.ini (in the same directory) and set correct values:export NOSE_TESTCONFIG_AUTOLOAD_INI=`pwd`/test_config.iniTo test with nose:python setup.py nosetestsor running nosetests directly:nosetests -s --exeDirectly and with coverage:nosetests -s --exe --with-coverage --cover-package=lss3logs(Note: the –exe includes python files whoch are executable, so it’s optional if you don’t have any.)Code qualityChecking code with pylint:pylint lss3logsTODOmock boto output (seehttps://github.com/eykd/duo/blob/master/test_duo.pyfor an example)fix as many errors as possbile reported by pylintbring test coverage to 100%use Sphinx for docsCreditsThe regular expression for parsing the log lines is copied from a script by “kkowalczyk” located athttp://code.google.com/p/kjk/source/browse/trunk/scripts/test_parse_s3_log.py
Package: ls.sampleproject
This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.InstallationInstall the package.$pipinstallls.sampleprojectCompatibilityls.sampleproject is known to work with the following versions of Python.PythonPythonPython3.5.43.5.43.5.43.5.43.5.43.5.4I am aiming to support the latest version of each.  Older versions may be dropped without much notice.  Let me know if that is a problem for you.  Other versions may work - YMMV.
Package: lsshipper
### DescriptionThis is very simple replacement for ElasticSearch filebeat.Usageinstallpipinstallgit+https://github.com/stavinsky/lsshipperafter that create config directory anywhere you want and put 2 config
files there.log_config.ini - this is config for logging system.example with
verbose logsconfig.ini - main config. Make sure thatpatternandnewlineparameters is doublequotted.example with verbose
logsrunningIt can be started as a module:python-mlsshipper--config_dir<dirwithconfigs>Why?There is windows software that adds preallocated block of zeroes(\x00)
to log file. Original filebeat skips such blocks. So big part of logs
can be lost.SpeedOn my mac mini late 2003 it can write to network about 2Mb/s. I will
test later on windows and linux. I believe on linux it should be much
faster.ProblemsOn very fast server reconnects it can loose one log string. Please
check test_connection.py
Package: lssr
lssrAlternative ls command.インストールPython 3.7 以上がインストールされていれば利用可能です。pipinstalllssr使い方# カレントディレクトリにあるアイテムを表示lssr# 指定したディレクトリにあるアイテムを表示（相対パス）lssrpath/to/target/dir# 絶対パスでの指定lssr/absolute/path/to/target/dir# ソート順を逆にするlssr-r
lssr--reversepath/to/target/dir# 最終内容更新時刻順の新しい順にソートlssr-t# 古い順lssr-tr# ファイルサイズの大きい順にソートlssr-S# 小さい順lssr-rS# オプションの並び順は、ある程度自由ですlssr-tpath/to/target/dir--reverse# ヘルプを表示lssr-h
lssr--help# バージョンを表示lssr-V
lssr--versionlsコマンドとの違いアイテムの並び順が異なります。デフォルトで、以下の順序でアイテムが表示されます。フォルダ -> ファイルUnicodeつまり、GitHub と同様の並び順になるはずです。ドットファイルを含んだリスト形式でのカラー表示がデフォルトです（ls -AGlと同等）。現在、パスを 2 つ以上指定することはできません。その代わり、オプションの指定はパスの前後どちらでも良いです。多くのオプションがまだ実装されていません。利用できるオプションは上記「使い方」やヘルプコマンドを参照してください。
Package: lsst-ctrl-bps
lsst-ctrl-bpsThis package provides a PipelineTask execution framework for multi-node processing for the LSST Batch Production Service (BPS).This is a Python 3 only package.SPIE Paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution SystemPyPI:lsst-ctrl-bpsThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-ctrl-bps-htcondor
ctrl_bps_htcondoris a package in theLSST Science Pipelines.It provides a HTCondor plugin for LSST PipelineTask execution framework, based onctrl_bps.SPIE paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution System.PyPI:lsst-ctrl-bps-htcondorThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-ctrl-bps-panda
ctrl_bps_pandais a package in theLSST Science Pipelines.It provides a PanDA plugin for LSST PipelineTask execution framework, based onctrl_bps.SPIE paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution System.User Guide.PyPI:lsst-ctrl-mpexecThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-ctrl-bps-parsl
ctrl_bps_parslThis package is aParsl-based plugin for theLSSTBatch Production Service (BPS)execution framework.
It is intended to support running LSSTPipelineTaskjobs on high-performance computing (HPC) clusters.
Parsl includesexecution providersthat allow operation on batch systems typically used by HPC clusters, e.g.,Slurm,PBS/TorqueandLSF.
Parsl can also be configured to run on a single node using athread pool, which is useful for testing and development.This is aPython 3 onlypackage (we assume Python 3.8 or higher).Documentation will be availablehere.This software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-ctrl-mpexec
ctrl_mpexecis a package in theLSST Science Pipelines.It provides a PipelineTask execution framework for single-node processing.SPIE paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution System.PyPI:lsst-ctrl-mpexecThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-daf-butler
lsst-daf-butlerLSST Data Access framework described inarXiv:2206.14941.This is aPython 3 onlypackage (we assume Python 3.10 or higher).SPIE Paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution SystemADASS paper from 2019:Abstracting the Storage and Retrieval of Image Data at the LSST.Early design note:DMTN-056PyPI:lsst-daf-butlerThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsstdesc-diffsky
lsstdesc-diffsky is a python library based on JAX
for producing mock catalogs tailored to LSST DESC science requirements.InstallationThe latest release of lsstdesc-diffsky is available for installation with pip.
To do so, we recommend starting from a base environment of jax managed by conda-forge:$ conda create -c conda-forge -n diffsky_env python=3.9 numpy jax pytest ipython jupyter matplotlib scipy h5py
$ conda activate diffsky_envTo install the latest release:$ pip install lsstdesc-diffskyTo install the current tip of the main branch, you will need to clone and install the source code:$ cd /path/to/root/lsstdesc-diffsky
$ pip install .DocumentationOnline documentation for lsstdesc-diffsky is available atlsstdesc-diffsky.readthedocs.ioTestingTo run the suite of unit tests:$ cd /path/to/root/lsstdesc-diffsky
$ pytestTo build html of test coverage:$ pytest -v --cov --cov-report html
$ open htmlcov/index.htmlAvailable CatalogsA mock catalalog is available at NERSC and is called roman_rubin_2023_v1.1.0.Working at NERSCIf you want to use the desc-python environment at NERSC,
you will need to add lsstdesc-diffsky and its dependencies yourself.
The directions for cloning and adding packages to the environment can be foundhere.
Once you have built the basic environment, you will need to activate it and install
lsstdesc-diffsky and its dependencies. Note that the appropriate version of the
diffsky codes can vary with the mock being used.If you wish to use the code in a jupyter notebook,
you will also need to install a python kernel as follows:$ python -m ipykernel install --user --name=my_kernel_namedemo_load_catalogis a demo notebook showing how to load the catalog from a notebook running at NERSC
and perform some basic operations.
Package: lsst-dochub-proto
Prototype of LSST DocHub (www.lsst.io) as a static website generator.Usage#/usr/bin/env python
from dochubproto import DocHubProto

p = DocHubProto()
idx = p.render_index()ConfigurationDocHubProtouses the following environment variables:KEEPER_URL(defaulthttps://keeper.lsst.codes).LOGLEVEL(defaultWARNING).TEMPLATE_DIR: directory containing Jinja2 templates (defaulttemplates).UL_TEMPLATE_NAME: relative path to template for individual document items (defaultdoclist.jinja2).IDX_TEMPLATE_NAME: relative path to theindex.htmltemplate (defaultindex.jinja2).MAX_DOCUMENT_DATA_AGE: maximum cache age in seconds of a document (default3600).DocHubProto API overviewcheck_state()returns one of:STATE_EMPTY('empty')STATE_READY('ready')STATE_REFRESHING('refreshing')STATE_STALE('stale')A document is ‘stale’ if it is older thanMAX_DOCUMENT_DATA_AGE.get_document_data()andget_fresh_document_data()return adictwhose keys are document sections (e.g.DMTN) and within each section, a list ordered by document handle (e.g.dmtn-038).render()returns an HTML unordered list entity created from the document data, encoded as UTF-8.render_index()returns an HTML document created from the document data, encoded as UTF-8.debug(),info(),warning(),error(), andcritical()each log a message at the specified level; it uses astructloglogger to log JSON output viaapikit.
Package: lsst-pex-config
pex_configis a package in theLSST Science Pipelines.
Thelsst.pex.configmodule provides a configuration system that is integral to the task framework, though it can be used on its own as well.Documentation:https://pipelines.lsst.io/v/daily/modules/lsst.pex.config/SPIE paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution System.PyPI:lsst-pex-configThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-pipe-base
lsst-pipe-basePipeline infrastructure code for theRubin Science Pipelines.SPIE Paper from 2022:The Vera C. Rubin Observatory Data Butler and Pipeline Execution SystemPyPI:lsst-pipe-baseThis software is dual licensed under the GNU General Public License (version 3 of the License, or (at your option) any later version, and also under a 3-clause BSD license.
Recipients may choose which of these licenses to use; please see the files gpl-3.0.txt and/or bsd_license.txt, respectively.
Package: lsst-projectmeta-kit
lsst-projectmeta-kit is a Python toolkit for extracting and transforming metadata about LSST’s code and documentation projects, and loading it into the LSST projectmeta database.This project is related to LSST Project Meta, an experimental project to gather LSST information artifacts from across the web, and make them discoverable through a single API.
Extract/transform/load workflows can use this library to create LSST JSON-LD (linked data) resources stored in our Project Meta database.lsst-projectmeta-kit is developed byLSST, the Large Synoptic Survey Telescope.
Learn more atwww.lsst.org.Licenselsst-projectmeta-kit is MIT licensed. SeeLICENSE.
Package: lsstream
A nifty utility to assist the tech-savvy students of ASU's SILC LSS with their Streaming Requests. Say goodbye to tedium! 😴 🖥️ 🎞️🚀 Getting StartedPrerequisitesPython (3.7 or higher)InstallationInstall thelsstreampackage via pip through your command line:>pipinstalllsstreamor>pip3installlsstream🎯 UsageTo runlsstream, type the following command in your command line:>python-mlsstream.runor>python3-mlsstream.runSimply follow the on-screen prompts to streamline your streaming experience! :sparkles:💡 FeaturesBatch File Generation: Generates multiple .txt files in a snap. Just input your media titles and links, andlsstreamwill use the correct template to create your files.Embed Code Testing: Say goodbye to waiting! Test the embed code almost instantaneously and make sure everything works perfectly. Codes are copied to your clipboards and📝 LicenseThis project is licensed under the MIT License.❗ IssuesIf you encounter any issues, please post themhere.🎉 Happy Streaming! 🎉
Package: lsst-resources
lsst.resourcesThis package provides a simple interface to local or remote files using URIs.from lsst.resources import ResourcePath

file_uri = ResourcePath("/data/file.txt")
contents = file_uri.read()

s3_uri = ResourcePath("s3://bucket/data/file.txt")
contents = s3_uri.read()The package currently understandsfile,s3,gs,http[s], andresource(Python package resource) URI schemes as well as a scheme-less URI (relative local file path).The package provides the main file abstraction layer in theRubin Observatory Data Butlerdatastore.PyPI:lsst-resources
Package: lsst-rsp
lsst-rspUser-facing Python classes and functions for use in the RSP environment.
Learn more athttps://lsst-rsp.lsst.ioInstall from PyPI:pipinstalllsst-rspbut, really, lsst-rsp is only useful inside an RSP JupyterLab container.See below for how to test new versions within such a container.lsst-rsp is developed by Rubin Observatory athttps://github.com/lsst-sqre/lsst-rsp.Developing lsst-rspThe best way to start contributing to lsst-rsp is by cloning this repository, creating a virtual environment, and running themake initcommand:gitclonehttps://github.com/lsst-sqre/lsst-rsp.gitcdlsst-rsp
makeinitYou can run tests and build documentation withtox:toxTo learn more about the individual environments:tox-avDeveloping lsst-rsp on the RSPTheLSSTkernel in the RSPsciplat-labimage already has a release
version oflsst-rspin it.  Therefore, there is some setup you need to
do in order to create a development environment you can use.
Specifically, you need to create a virtualenv for the editablelsst-rsp, installtoxandpre-commitfor its test machinery, and
then create a JupyterLab kernel pointing to it.Open a terminal session:VENV="lsst_rsp"mkdir-p${HOME}/venvs
python-mvenv${HOME}/venvs/${VENV}.${HOME}/venvs/${VENV}/bin/activate
mkdir-p${HOME}/srccd${HOME}/src
gitclonehttps://github.com/lsst-sqre/lsst-rsp# or git@github.com:lsst-sqre/lsst-rsp.git if you prefercdlsst-rsp
makeinit
pipinstallipykernel
python-mipykernelinstall--user--name=${VENV}Now you will need to shut down your lab and get a new container image.
That's because the process your Lab interface is running inside doesn't
know about the new kernel--but once you restart the Lab container, it
will.Once you're in your new container, you will notice that you have a new
kernel namedlsst_rsp.Now you've got an editable version installed in your custom kernel, and
you can still run all the usual tox environments too.If you start a notebook with your custom kernel,importlsst.rsplsst.rsp.__version__will show you your development version.  Note that you will still need
to restart the kernel to pick up changes you make to your copy oflsst_rsp.Uninstalling a development version from the RSPOpen a terminal window..$HOME/venvs/lsst_rsp/bin/activate
jupyterkernelspecuninstalllsst_rsp
y
deactivateShut down and restart your notebook as before.  When you come back in,
in a terminal window:rm-rf$HOME/venvs/lsst_rspYou will need to remove the virtualenv directory after restarting the
Lab container, because otherwise JupyterLab will be holding some files
open because it still believes it has a kernel there.
Package: lsst-sphgeom
sphgeom: spherical geometry primitivesOverviewThis low-level C++ library provides primitives for representing points and
regions on the unit sphere, as well as support for partitioning the sphere.
It can be used to answer the following sorts of questions:Is point X inside region Y?Do two regions A and B intersect?Which pieces of the sphere does region C overlap?Regions can be serialized to binary strings, so that they may be stored
efficiently in files or VARBINARY database columns. They can also be
approximated with simpler regions - for example, one can ask for the
bounding circle of a convex polygon.Python bindings that expose most of the C++ API are also provided viapybind11.PointsThere are 3 different classes for pointsLonLatfor spherical coordinates,Vector3dfor Cartesian vectors in ℝ³ (not constrained to lie on the unit sphere)UnitVector3dfor vectors in ℝ³ with unit ℓ² norm.RegionsFour basic sphericalRegiontypes are
provided:Box, a longitude/latitude angle boxCircle, a small circle defined by a center and opening angle/chord lengthEllipse, the intersection of an elliptical cone with the unit sphereConvexPolygon, a convex spherical polygon with unit vector vertices and great circle edgesIn addition to the spherical regions, there is a type for 3-D axis aligned
boxes,Box3d. All spherical regions know how
to compute their 3-D bounding boxes, which makes it possible to insert them
into a 3-DR-tree. This is used by the
exposure indexing task in thedaf_ingestpackage to spatially index exposure bounding polygons using theSQLite3R*tree module.A region can also determine its spatial
relationship to another region, and
test whether or not it contains a given unit vector.PixelizationsThis library also provides support for assigning points to pixels (a.k.a.
cells or partitions) in aPixelization(a.k.a. partitioning) of the sphere, and for determining which pixels
intersect a region.Currently, theChunkerclass implements
the partitioning scheme employed byQserv.
TheHtmPixelizationclass implements
the HTM (Hierarchical Triangular Mesh) pixelization. TheQ3cPixelizationandMq3cPixelizationclasses implement
the original Quad Tree Cube indexing scheme and a modified version with
reduced pixel area variation.Installing with pipA simple pip-compatible installer is available.  This only installs the
Python bindings and the resulting installation is not usable for linking
from C++.  Some metadata (in particular the version number) are not set
properly for the distribution.  The main purpose for now is to allow
other packages to pip install from the GitHub URL in their CI systems
where sphgeom is a dependency.See AlsoContributingFor instructions on how to contribute, seehttp://dm.lsst.org/#contributing(or just send us a pull request).SupportFor help, seehttp://dm.lsst.org/#support.