Saravanan666/pypi-org huggingface dataset:
Package: scalems
SCALE-MS[DockerHub]NSF Awards1835449Matteo Turilli, Rutgers University New Brunswick1835607Kristen Fichthorn, Pennsylvania State University1835720Michael Shirts, University of Colorado at Boulder1835780Peter Kasson, University of VirginiaDownloadingGitThis project is managed with agitrepository hosted athttps://github.com/SCALE-MS/scale-msTest data submoduleData for some examples and tests is stored in a separatetestdata repository,
but the external repository is embedded as a git submodule for convenience.
(testdata/)
This fact should be uninteresting to most readers,
but if you need the extra files, you will appreciate the convenience of a couple of
extra arguments available togitcommands.When cloning this repository, use the--recursiveargument togit clone.When updating a local copy of this repository, use the--recurse-submodulesargument togit pullorgit checkout.DocumentationRefer tohttps://scale-ms.readthedocs.io/en/latest/for the most up-to-date documentation.To build the documentation locally, clone this repository and go to the root directory of the local repository, thenInstallSphinxand additional requirements:pip install -r docs/requirements.txtInstall thescalemspackage so that Sphinx can import it when run:pip install .Optionally, installplantumlfor rendering diagrams.
(e.g. on Ubuntu, dosudo apt-get install plantuml)Build with Sphinx using thedocs/conf.pyfile in this repository.ExampleFrom the root of a local copy of the repository, build HTML documentation for thescalemspackage into a new./webdirectory (or some directory of your choosing)../docsis the input directory.python3 -m sphinx docs web -b htmlThen open./web/index.htmlNote: Depending on your Sphinx installation, you may have asphinx-buildexecutable
or you may access thesphinxmodule with the-mPython interpreter option
(python -m sphinx ...).
Package: scaleogram
# scaleogramScaleogram is a user friendly plot tool for 1D data analysis with Continuous Wavelet Transform
built on the [PyWavelets library](https://github.com/PyWavelets/pywt).I started this project when realizing how harsh it can be to build nice plots
of wavelets scaleogram with axes ticks and labels consistent with the actual location of features.
Hence this module aim to provide a reliable tool for either quick data analysis or publication.It has the following features:simple call signature for complete beginnersreadable axes and clean matplotlib integrationmany options for changing scale, spectrum filter, colorbar integration, etc…support for periodicity and frequency units, consistent with labellingspeed, uses a N*log(N) algorithm for transformsportability: tested with python2.7 and python3.7comprehensive error messages and documentation with examplessupport for Cone Of Influence mask![Example](https://github.com/alsauve/scaleogram/blob/master/doc/example.png)## Install with pipInstallation should be straightforward with` pip install scaleogram `## Install from github` git clonehttp://github.com/alsauve/scaleogramcd scaleogram python ./setup.py install--user`### PrerequisitesThis module depends onPyWavelet >= 1.0matplotlib >= 2.0numpy >= 1.0## DocumentationA lot of documentation and examples are available online from the docstringsJupyter notebook are also provided for quickstartingA gentle introduction to CWT based data analysis [TODO][scale to frequency relationship](https://github.com/alsauve/scaleogram/blob/master/doc/scale-to-frequency.ipynb)[Example of scaleogram with the NINO3.4 SST seasonal time series](https://github.com/alsauve/scaleogram/blob/master/doc/El-Nino-Dataset.ipynb)[Graphical output of the test set](https://github.com/alsauve/scaleogram/blob/master/doc/tests.ipynb)## Running the testsA features test matrix can be plotted with` # launch graphical tests python-mscaleogram.test `## Built With[ViM](https://www.vim.org/) - The editor[Spyder](https://www.spyder-ide.org/) - The Scientific Python Developement Environment[Jupyter](https://jupyter.org/) - The Jupyter Notebook## RealeasesSee the [Releases page](https://github.com/alsauve/scaleogram/releases).## ContributingPlease read [CONTRIBUTING.md](https://gist.github.com/PurpleBooth/b24679402957c63ec426) for details on our code of conduct, and the process for submitting pull requests to us.## VersioningWe use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/alsauve/scaleogram/tags).## AuthorsAlexandre sauve-Initial work- [Scaleogram](https://github.com/alsauve/scaleogram)## LicenseThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details## AcknowledgmentsThe team behind PyWavelet for their nice job into making wavelet transform availableThe Matlab environement for inspiration and good documentationMabel Calim Costa for the waipy package and inspiration
Package: scale-sensor-fusion-io
sensor-fusion-ioSDK for working with sensor fusion scenesInstallationpip install scale-sensor-fusion-ioRequirementsMinimum Python version supported is 3.8.Code samplesEnd-to-End Example From PandaSetHereis a link to a Jupyter notebook example.Constructing an SFS scenePosePath DataframeEncoding a SFS sceneimport scale_sensor_fusion_io as sfio
from scale_sensor_fusion_io.model_converters to_scene_spec_sfs
from scale_json_binary import write_file

scene = sfio.Scene() # your scene here
scene_sfs = to_scene_spec_sfs(scene)
import scale_sensor_fusion_io as sfio

write_file(f'~/scene.sfs', scene_sfs)Loading sfs fileThere is a SFSLoader class that provides helper functions for loading a scene from url. There are few variations of loading function, depending on your use case.from scale_sensor_fusion_io.loaders import SFSLoader
scene_url = "~/scene.sfs"

# Not recommended, but possible
raw_scene = SFSLoader(scene_url).load_unsafe() # scene is dict
sfs_scene = SFSLoader(scene_url).load_as_sfs() # scene is SFS.Scene
scene = SceneLoader(scene_url).load() # scene is models.SceneValidating SFSBefore you upload a scene for task creation, you'll want to validate that your sfs scene is well formed. You can do this in a variety of ways.Validating scene objectIf you're working with the model.Scene object, you can use thevalidate_scenemethod available under scale_sensor_fusion_io.models.validationimport scale_sensor_fusion_io as sfio
import pprint

scene = sfio.Scene() #

errors = validate_scene(scene)
if errors:
    pp.pprint(asdict(errors))
else:
    print("Scene validated successfully")Validating from urlIf you've already generated a .sfs file, you can also validate that it is well formedfrom scale_sensor_fusion_io.validation import parse_and_validate_scene
from scale_json_binary import read_file
import pprint

pp = pprint.PrettyPrinter(depth=6)

scene_url = "your_scene.sfs"

raw_data = read_file(scene_url)
result = parse_and_validate_scene(raw_data)

if not result.success:
    pp.pprint(asdict(result))
else:
    print("Scene parsed and validated successfully")FAQResulting scene file is too largeFor scenes that span a large timeframe, the size of the resulting .sfs file may increase to multi-GBs. This is not ideal for loading onto LidarLite.Video encodingOne easy way to reduce scene size is to encode camera content as video, as the video content can be more easily compressed. The tradeoff is the potentially reduced quality of images, but for labeling 3D scenes, this is often sufficient.See .utils/generate_video.py for helper functionsDownsample point cloudsAnother option is to downsample lidar point clouds. If your scene is used primarily for cuboid annotation, we recommend voxel downsampling using voxel sizes of at most 20mm.A good heuristic for efficient loading and labeling is to have a scene contain no more than 100,000 points.
Package: scales.py
scalesA tool to generate stringed instrument visual aidsInstallationUsageOther helpful usesDependenciesInstallationInstall using:pip install scales.pyThen import it like so:fromscalesimportScalesUsageScales objects take at least thescaleparameter, which should be a list of notes (the first note is treated as the root). They can be drawn using thedrawfunction, wherestartandstopare the range of frets to show (defaults to showing 15 frets if not specified).For example:a_mixo=['A','B','C#','D','E','F#','G']six_string=Scales(title='A Mixolydian',scale=a_mixo)six_string.draw(start=11,stop=15)By default, it will assume a 6 string guitar in standard tuning, but you can specify other tunings like:c_major=['C','D','E','F','G','A','B']ukulele=Scales(title='C Major on Ukulele',strings=['G','C','E','A'],scale=c_major)ukulele.draw()Scales can be drawn without making it an object:g_chord=['G','B','D']Scales(title='Open G',scale=g_chord).draw(stop=3)Other helpful usesScales can be used to generate blank visuals to print out. Heres a blank 6 string fretboard:Scales([]).draw()Or a blank open chord chart:Scales([]).draw(stop=3)DependenciesLibraryDescriptionmatplotlib2D plotting librarynumpyFundamental package for scientific computing
Package: scale-v2
# SCALE v2: Single-cell integrative Analysis via latent Feature Extraction## Installation
#### install from PyPIpip install scale-v2#### install from GitHubgit clone git://github.com/jsxlei/scale_v2.git
cd scale_v2
python setup.py installSCALE v2 is implemented in [Pytorch](https://pytorch.org/) framework.
Running SCALE v2 on CUDA is recommended if available.
Installation only requires a few minutes.## Quick Start### 1. Command lineSCALE.py –data_list data1 data2 –batch_categories batch1 batch2data_list: data path of each batch of single-cell dataset
batch_categories: name of each batch#### Output
Output will be saved in the output folder including:
*checkpoint:  saved model to reproduce results cooperated with option –checkpoint or -c
*adata.h5ad:  preprocessed data and results including, latent, clustering and imputation
*umap.png:  UMAP visualization of latent representations of cells
*log.txt:  log file of training process#### Useful options
* output folder for saveing results: [-o] or [–outdir]
* filter rare genes, default 3: [–min_cell]
* filter low quality cells, default 600: [–min_gene]
* select the number of highly variable genes, keep all genes with -1, default 2000: [–n_top_genes]#### Help
Look for more usage of SCALE v2SCALE.py –help### 2. API functionfrom scale import SCALE
adata = SCALE(data_list, batch_categories)Function of parameters are similar to command line options.
Output is a Anndata object for further analysis with scanpy.#### TutorialSee document## Previous version [SCALE](https://github.com/jsxlei/SCALE)SCALE is still available in SCALE v2### Command lineSCALE.py –d data –version 1### APIfrom scale.extensions import SCALE_v1
SCALE_v1(data)All the usage is the same with previous SCALE version 1.
Package: scaleway-api
Scaleway APIThis is an unofficial wrapper around the Scaleway APIs based onHammock.Usagefrom scaleway_api import Scaleway

scw = Scaleway("nl-ams", "<SECRET KEY>")
servers = scw.instance.servers.GET()

for server in servers.json().get("servers", {}):
    print(f'Server Name: {server["name"]}')Environment VariablesThe Scaleway API will try to pull the following environment variables for convencience.Environment VariableDescriptionSCW_SECRET_KEYYour Scaleway key. More information can be foundin Scaleway's documentationSCW_DEFAULT_REGIONShould be eithernl-amsorfr-par(Default:fr-par)SCW_DEFAULT_ZONEShould be eithernl-ams-1orfr-par-1(Default:REGION-1/fr-par-1)SCW_API_VERSIONCurrently onlyv1is available. Added to future-proof the library.DevelopmentHappy to receive pull requests.Setuppython3 setup.py install
pip install -r requirements-dev.txtTestpython3 setup.py testCoverage Reportcoverage run -m unittest discover && coverage report
Package: scalewiz
A graphical user interface designed to work withTeledyne SSI MX-class
HPLC pumpsfor the purpose of calcite scale inhibitor chemical
performance testing.If you are working with Teledyne SSI Next Generation pumps generally, please check outpy-hplc!If you notice something weird, fragile, or otherwise encounter a bug, please open anissue.InstallationScaleWiz is packaged and run as a GUI, but can be installed like a command-line tool.python -m pip install --user scalewizOr, if you usepipx(try it!😉)pipx install scalewizUsagepython -m scalewizIf Python is on your PATH (or you usedpipx😎), simplyscalewizFurther instructions can be viewed in thedocssection of this repo or with the Help button in the main
menu.AuthorWritten by@teauxfuforPremier Chemical Technologies, LLC.Acknowledgements@balaclafor support and invaluable help in brainstorming
Package: scalg
Scoring Algorithm (SCALG)This algorithm works based on a percentual range proximity principle. Initially it was developed for a personal project, however later I found out it is a form of Newton's method used in statistics to solve maximum likelihood equations.Usagepip install scalgAs of15 september 2020it contains two methods (scoreandscore_columns) which will be described and demonstrated in the examples below.import scalgExamplesThis will be the sample dataset used as source_data withing the examples with the corresponding indexes and column weights.Columns ->  0     1      2      3
Weights ->  1     0      0      1
        1[[2016 ,21999 ,62000  ,181],
Sets -> 2 [2013 ,21540 ,89000  ,223],
        3 [2015 ,18900 ,100000 ,223],
        4 [2013 ,24200 ,115527 ,223],
        5 [2016 ,24990 ,47300  ,223]]Score MethodThe output if you pass in source_data and weights:scalg.score(source_data, [1, 0, 0, 1])[[2016, 21999, 62000,  181, 2.2756757812463335],
 [2013, 21540, 89000,  223, 1.9553074815952338],
 [2015, 18900, 100000, 223, 2.894245191297678],
 [2013, 24200, 115527, 223, 1.1297208538587848],
 [2016, 24990, 47300,  223, 3.0]]The output if you pass in source_data, weights and get_scores=True:scalg.score(source_data, [1, 0, 0, 1], get_scores=True)[2.2756757812463335, 1.9553074815952338, 2. 894245191297678, 1.1297208538587848, 3.0]The output if you pass in source_data, weights and get_score_lists=True:scalg.score(source_data, [1, 0, 0, 1], get_score_lists=True)[[1.0                 ,0.0, 0.6666666666666666 ,0.0                      ,1.0]
 [0.49113300492610834 ,0.5665024630541872      ,1.0, 0.12972085385878485 ,0.0]
 [0.7845427763202251  ,0.38880501854104677     ,0.22757852463101114      ,0.0]
 [0.0                 ,1.0                     ,1.0                      ,1.0]]This gives out the score of each element in the list compared to other elements, keeping it's position.Score Columns MethodHere you may use the same weights which you would use inscalg.score, or you may specify the weights of each column in the corresponding order. In this example using the weights argument[1, 0, 0, 1]or[0, 1]would make no difference.The output if you pass in source_data, columns and weights:scalg.score_columns(source_data, [0, 1], [1, 0, 0, 1])Scored columns            Scores for corresponding columns
   0|    1|                  |
[[2016 ,21999 ,62000  ,181 ,1.4911330049261085],
 [2013 ,21540 ,89000  ,223 ,0.5665024630541872],
 [2015 ,18900 ,100000 ,223 ,1.6666666666666665],
 [2013 ,24200 ,115527 ,223 ,0.12972085385878485],
 [2016 ,24990 ,47300  ,223 ,1.0]]The score was computed only based on columns 0 and 1.
Package: scaling
scalingConvert quantities between model and prototype scale using Froude and Reynolds similitude.Installationpip install scalingUsage>>>fromscalingimportFroudeConverter>>>froude=FroudeConverter()>>># Convert model value of 200 mm to prototype value (m) with scale of 10>>>froude.model_to_proto(200,Âlength_scale=10,Âinput_unit='mm',Âtarget_unit='m')2.0>>># Get Froude scaling exponent for quantities of time>>>Âfroude.scaling_exponent('s')0.5>>># Get length, mass and time dimensions for quantities of energy>>>Âfroude.dimensions('kJ')'L^2Â M^1Â T^-2'Dataframes are also accepted, and specific units can be specified for the values in the index.>>>T=2>>>H=100>>># Generate regular waves with height=100mm, and period=2s>>>t=np.arange(0,10.1,0.1)>>>eta=0.5*H*np.sin(t*2*np.pi/T)>>>df_model=pd.DataFrame(index=t,data=eta)>>>df_model.columns=['$\eta$ (mm)']>>>df_model.index.name='Time (s)'>>>df_model.plot()>>># Convert to prototype dimensions, with length scale=25>>>df_proto=froude.model_to_proto(df_model,length_scale=25,input_unit='mm',target_unit='m',index_input_unit='s',index_target_unit='s')>>>df_proto.columns=['$\eta$ (m)']>>>df_proto.plot()scalingusespintfor unit and dimension conversions.pintis able to interpret a wide range of different input units.>>># Convert water head model value (mm) to prototype pressure value (kPa)>>>froude.model_to_proto(10,Âlength_scale=100,'mm.H20',Â'kPa')9.80665>>># Demonstrate different ways of specifying units of newtons>>>Âfroude.dimensions('N')'L^1Â M^1Â T^-2'>>>Âfroude.dimensions('newton')'L^1Â M^1Â T^-2'>>>Âfroude.dimensions('kg.m/s/s')'L^1Â M^1Â T^-2'>>>Âfroude.dimensions('kilogram.metre/second^2')'L^1Â M^1Â T^-2'Froude scaling referenceQuantityDimensionsScaling exponentLengthL^1Î»^1MassM^1Î»^3TimeT^1Î»^0.5VelocityL^1 T^-1Î»^0.5AccelerationL^1 T^-2Î»^0ForceL^1 M^1 T^-2Î»^3PressureL^-1 M^1 T^-2Î»^1OvertoppingL^2 T^-1Î»^1.5Reynolds scaling referenceQuantityDimensionsScaling exponentLengthL^1Î»^1MassM^1Î»^3TimeT^1Î»^2VelocityL^1 T^-1Î»^-1AccelerationL^1 T^-2Î»^-3ForceL^1 M^1 T^-2Î»^0PressureL^-1 M^1 T^-2Î»^-2OvertoppingL^2 T^-1Î»^0
Package: scallopy
scallopy, the Python binding for ScallopSample usagefromscallopyimportScallopContext# Create new contextctx=ScallopContext()# Construct the programctx.add_relation("edge",(int,int))ctx.add_facts("edge",[(0,1),(1,2),(2,3)])ctx.add_rule("path(a, c) = edge(a, c)")ctx.add_rule("path(a, c) = edge(a, b), path(b, c)")# Run the programctx.run()# Inspect the resultpaths=ctx.relation("path")for(s,t)inpaths:print(f"elem: ({s},{t})")BuildMake sure you are currently inside of a python virtual env, and havematurininstalled. Then do$maturindevelop--release# release is important for speedThat's it! You can then run one of our example$pythonexamples/incr_edge_path.pyDocumentationPlease check out the following filescallopy/scallopy.pyifor documentation.
Package: scalpel
Scalpel is an audio editor for Linux written inPython. It aims at
providing a simple-to-use and easy-to-extend audio editor. Sound
hackers, get started translating your Matlab routines into
Python/Numpy functions!Scalpel usesPyGTKfor the user interface,Numpyfor the internal
processing,ALSAfor the audio playing andlibsndfilefor reading
and writing files. A minimal part of the code is written inCythonfor better performance.Scalpel still has some rough edges but is quite usable. Try it now and
be sure to send your feedback. Installing is as easy as (seehttp://scalpelsound.online.fr/?page_id=457for more details):pip install scalpelScalpel is released under the BSD License.Links:Homepage:http://scalpelsound.online.frSource:http://gitorious.org/scalpelPypi:http://pypi.python.org/pypi/scalpel
Package: scalpels
# Scalpels
This is a project on digging OpenStack for performance or complecated issues.## Background
OpenStack is made of multiple Python-based projects. Each project has similiar but different architecture. Scalpels gathers useful scripts or 3rd tools to help operator find what happen in your cloud.## Contribute
This project is prototype now and under ‘quick’ development. If you have interests in this work, please contact @kun_huang, at #openstack-chinese channel.## Ideas
Each project will have scripts working:on python callson sql querieson filesystem I/Oon RPC calls if needon necessary system callson common system statistics
Package: scalpg-dump
Scalingo PostgresSQL dumperDumps your Scalingo PostgreSQL database without the hustleTo dump the database go to a local directory that’s associated with a Scalingo appScalingo PostgresSQL dumper requires a working installation of the Scalingo CLI
Install Scalingo CLI:http://doc.scalingo.com/app/command-line-tool.htmlFree software: MIT licenseDocumentation:https://scalpg-dump.readthedocs.io.UsageDump to file:scalpg_dump > dump.sqlPass version to scalingos dbclient-fetcher:scalpg_dump -v11Run for specific version / region:SCALINGO_APP=your-app SCALINGO_REGION=agora-fr1 scalpg_dumpCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-06-01)First release on PyPI.
Package: scalr-api
Install$pipinstallscalr-apiExamplesHere is an example of how to delete a Scalr role:fromscalrimportScalrUserAPIscalr=ScalrUserAPI(url='https://your-scalr-host/',key_id='your_scalr_key_id',secret_key='your_scalr_secret_key',env_id=your_scalr_environment_id)scalr.role_delete(role_id=your_role_id)CreditsScalrfor providing examples on how to use Scalr API v2.
Package: scalr-ctl
scalr-ctl is a command-line interface to your Scalr accountVersion 1.0 works with the new Scalr JSON API.Install:$ pip install scalr-ctlConfigure:$ scalr-ctl configureUse:$ scalr-ctl –help$ scalr-ctl <tab> <tab>Update:$ pip install –upgrade scalr-ctl$ scalr-ctl update
Package: scalyca
This package aims to provide a simple common framework for console jobs in Python.
There is nothing really new about the whole approach and everything can be done
using existing tools and packages. SCALYCA just wraps it all together into a reusable
class that should be easy to extend for use in batch jobs or simple pipeline stages.It is supposed to be very lightweight and easily integrable.
More complex tasks are probably better suited for custom solutions using the same modules.
The programs are not supposed to be interactive, everything has to be initialized
either from the command line or, in case of SCALYCA, also from a selectable configuration file.SCALA -- Simple Console Application with Logging and ArgparseThe main class is called Scala. It has two main components:Logging, primarily tostdoutduring the execution of the program.
This is implemented with theloggingmodule, using just a single logger.Parsing and processing command line arguments, that configure the initial state of the application
and tell it what and where should exactly be done. The tool for the job isargparse.SCALYCA -- Simple Console Application with Logging, YAML Configuration and ArgparseScalyca is a subclass of Scala that adds persistent configuration
to the program, which is read from aYAMLfile during the initialization process,
then stored as a staticDotMapfrom moduledotmap.
This is similar to a Python dictionary except that it allows for dot access, which is much more legible.All of the properties can be overridden from the command line.
The resulting object is accessible through theconfigvariable of the class.UsageJust extend theScalaclass:Static attributeapp_namesets the name of the application that will be displayed byargparse.Static attributedescriptionsets the long description forargparse.Inadd_argumentsyou are supposed to add all arguments to the built-inArgumentParser.
Package: scam
Source Code Analyzing MachineGitHub Repo LinkA locally run application that demonstrates different matching algorithmsCurrent release compares files as a one to one connectionOutputs given percentage of similarity and highlighted visualization of the matching sections of input documentsSupports C, C++, Java, and Python filesGetting StartedInstall PySide2:pip install scamRun from directly outsidesourcedirectory:python3 ./source/scam.pyGetting StartedSTEP 1it is recommended to create and navigate to virtual environemnt using python to run scriptpython3.8 -m venv <dir> 
source <dir>/bin/activateSTEP 2install source_analyzer python package from Python Package Index.Note:must be running python version 3.8 or greaterpip install scamORDownload the latest built compressed file release fromsource_analyzer-X.X.X.tar.gzthen install downloaded filepip3 install /<path_to_file>/scam-0.0.2.tar.gzSTEP 3(make sure xlaunch is running)
run scriptscamKnown Errors/IssuesIssues-
Python files featuring a heavy amount of print statements may cause skewed data.Project Group: CodalyzersDjoni Austin | @dcaust1nJared Dawson | @lukinator1Shane Eising | @seising99Julian Marott | @jmmorattaReferences:https://theory.stanford.edu/~aiken/publications/papers/sigmod03.pdf