Saravanan666/pypi-org huggingface dataset:
Package: despace
DespaceIntroductionHierarchical spatial decomposition has been shown to be super useful in many geometric problems like geological analysis and N-body simulations. The general goal is to index multi-dimensional data points while keeping spatially proximal points as close as possible. Traditionally,k-Dtrees and space filling curves (Hilbert/Peano and Morton curves) are constructed to divide thekdimensional space first. Fork-Dtrees, every data point will be assigned to the tree leaves and leaves close to each other in the tree structure are also spatially close to each other. For space filling curves, each data point in the space is assigned to the nearest space filling curve vertex. The whole data set can therefore be sorted along the space filling curve direction.Constructing and traversing thek-Dtrees andk-dimensional space filling curves can be annoying in some certain cases. Here I am instead trying to use a simple sorting scheme to index or sortkdimensional data so that close data points are stored closely in the final data structure.Note: 'dimension' here means physical dimensions like in 2D and 3D cartesian coordinates. It's different from the definition of 'dimension' innumpy.ndarraywhich refers to number of axes to describe the data layout. Mostly we can think of the 'dimension' in this repo as the number of columns in a 2d numpy array (matrix).AlgorithmSort the entire data points in the first dimension.Divide the data into two halves and sort each half in the second dimension.For each sorted half data, divide it into two halves and sort it in the third dimension.Continue the above procedure by circulating the dimension indices until dividing into single data points.Reconstruct the whole data set with the above pseudo-sorted dataInstallpip install despaceCurrent statusFor a 8X8 grid, we get a Morton curve if we init thesort_typeasMorton(the default):We get a Hilbert curve if we init thesort_typeasHilbert:k-Dcases are primarily done for theMortontype. Visualization for 2D and 3D cases is supported. Below shows plots ofN=10000random points in a 2D square space and a 3D cube. The data points are blue-->red colored according to their indices from 0 to 9999.For theHilberttype, only 1D and 2D cases have been implemented.Try it outYou can play with the python scriptgenerate_random.pyin theexamplesfolder like changing the number of data pointspython generate_random.py 50 2 # use 50 data points for 2D.And we get a figure like below:Use in your codefromdespaceimportSortNDimportnumpyasnps=SortND()s(np.random.rand(50,2))s.plot(show_plot=False)# Set show_plot=True in jupyter-notebookWe shall get a similar figure as the above example.Try in Google ColabClick the buttonand follow the examples in the Google Colab file.
Package: despasito
DESPASITODESPASITO: Determining Equilibrium State and Parametrization Application for SAFT, Intended for Thermodynamic OutputFirst open-source application for thermodynamic calculations and parameter fitting for the Statistical Associating Fluid Theory (SAFT) EOS and SAFT-𝛾-Mie coarse-grained simulations. This software has two primary facets.The first facet is a means to evaluate implicit equations of state (EOS), such as the focus of this package, SAFT-𝛾-Mie. This framework allows easy implementation of more advanced thermodynamic calculations as well as additional forms of SAFT or other equations of state. Feel free to contribute!The second facet is parameterization of equations of state (EOS), some of which are useful for coarse-grained (CG) simulations. The SAFT-𝛾-Mie formalism is an attractive source of simulation parameters as it offers a means to directly link the intermolecular potential with thermodynamic properties. This application has the ability to fit EOS parameters to experimental thermodynamic data in a top down approach for self and cross interaction parameters.In another work nearing publication, we present a method of predicting cross-interaction parameters for SAFT-𝛾-Mie from multipole moments derived from DFT calculations. This method is easily implemented in using the package,MAPSCIas a plug-in. It should be noted that additional, iterative fine tuning in a simulation parameters may be desired, but previous works have found close agreement between simulation parameters and those fit to the EOS.Need Assistance?Check out ourDocumentationfirst.DocumentationCheck out ourDocumentation:InstallationNOTE:DESPASITO is not yet available in conda-forge, but it is available with pip.Prerequisites:Python: Available for python 3.6 to 3.8 (limited by current compatibility issues with Numba)NumPy: needed for running setup (distutils). Follow instructions outlinedherefor installation.OptionsOption 1:Install from pip:pip install despasitoOption 2:Install locally with pip.Step 1: Install the prerequisites listed above.Step 2: Download the master branch from our github page as a zip file, or clone it with git viagit clone https://github.com/jaclark5/despasitoto your working directory.Step 3: Install withpip install despasito/., or change directories and runpip install ..NOTEIfpipis unavailable, follow the instructions outlinedherefor installation.Option 3:Install locally with python.Step 1: Install the prerequisites listed above.Step 2: Download the master branch from our github page as a zip file, or clone it with git viagit clone https://github.com/jaclark5/despasitoto your working directory.Step 3: After changing directories, install withpython setup.py install --user.Command Line UseThis package has been primarily designed as a command line tool but can be used as an imported package.In any directory with the appropriate input files in .json format, run DESPASITO withpython -m despasito -i input.jsonSeeexamplesdirectory for input file structures.CopyrightCopyright (c) 2019, Jennifer A ClarkAcknowledgementsProject based on theComputational Molecular Science Python Cookiecutterversion 1.0.
Package: desper
desperA Python3 game development toolkit for resource and logic management.
Meant to be used coupled with open source projects.Inspired by the wonderful ECS library:esper. A special thanks to its author, Benjamin.AbstractDesper provides a component based logic system, designed with game development in mind. The objective is to fill the lack of game logic management systems in the open source Python landscape.In particular, while there are many valuable media libraries out there (pyglet,SDL2, etc.) they mostly provide APIs for window and graphic management. Consequently, often times game logic has to be implemented and reinvented from scratch. Desper provides a minimal expandable API that aims at obtaining interoperation with these powerful media libraries. These expansions will be implemented over time and provided through separate packages.The project started as an expansion ofesper, hence many aspects of the API will result similar.InstallationSimple installation from PyPi (recommended):pip install desperOr installation from master branch:git clone https://github.com/Ball-Man/desper
cd desper
pip install .ExpansionsExpansion libraries the can offer interoperation with existing media libraries will be implemented over time and provided through separate packages. Current ones:pyglet-desper(work in progress), forpygletinteroperationProject status(updated 2022-12-29)A first draft of the docs (API reference only) can be found atdesper.readthedocs.io. Examples and a user guide are in the making.desper in actioncurrent project, closed source:Lone Planetandroid game experiment, open source (uses an old alpha version of desper):monospace
Package: despike
Python package to remove spikes in 2D imagesDesciptionThe spikes in 2D-images correspond to high-energy pixels generated
by cosmic rays, sensor noise or dead pixels. They use to have values
very different from the rest of their neighboor.To find them, we use a moving box (5×5 pixels by default) on the
image and we compare the mean/median of this sub-image to the central
pixel. If the value is n (3 by default) times larger than the observed
standard deviation we use the median value a the surrounding pixels
(8 pixels by default) to replace the spike.InstallWithpip:$pipinstalldespikeWith thesource files:$gitclonehttps://github.com/seignovert/despike.git$cddespike;pythonsetup.pyinstallUsage>>>importdespike>>>despike.spikes(img)# Search the location of spikes in the image>>>despike.clean(img)# Clean the image from spikesAn example can be find in thisJupyter NoteBook.
Package: despoilerizer
DespoilerizerThis script reads from stdin and prints out the contents of stdin to stdout,
without the lines that are in between the:SPOILERS BEGIN:and the:SPOILERS END:tags.It uses stdin and stdout so that it can be easily integrated in bash
scripting, for instance:$<my_file.tex|python-mdespoilerizer|pdflatexYou can also redefine the tags:$<my_file.tex|\python-mdespoilerizer--begin="# spoiler"--end="# unspoiler"|\pdflatexInstallation$pipinstalldespoilerizerExamplesstdinThis is a simple file.
:SPOILERS BEGIN:
It contains spoilers.
:SPOILERS END:
And also other information.stdoutThis is a simple file.
And also other information.stdin\begin{document}
% :SPOILERS BEGIN:
important stuff...
% :SPOILERS END:
\end{document}stdout\begin{document}
\end{document}Usage as importable packageimportdespoilerizerfrompathlibimportPathasPforpathin[P('file1.tex'),P('file2.py'),P('file3.txt')]:out_path=P(path.stem+'_nospoilers'+path.suffix)withopen(path,'r')asfin:withopen(out_path,'w')asfout:despoilerizer.despoilerize_file(fin,fout)contents="""This is a very important string.such spoilersI really don't want anyone to know.much wow"""withopen(P('some_file.txt'),'w')asf:f.write(despoilerizer.despoilerize(contents,begin='such spoilers',end='much wow'))MotivationI have a LaTeX file for a document I'm sharing with my friends.
This document contains some information for a DnD campaign both for stuff the
players have encountered and stuff that's being worked on.Therefore, I want to have two different PDF documents: one with the spoilers
and one without them.
Package: despot
DespotA fast and simple client to free your music from that green streaming service.SetupInstall viapipx:pipxinstalldespotDespot followssemverversioning. It is also available as container imagecodeberg.org/drunkontee/despot. Images are tagged asv1.2.3,v1.2, andv1, as well aslatestpointing to the most recent version, for example:dockerrun--tty--rmcodeberg.org/drunkontee/despot:v0
dockerrun--tty--rmcodeberg.org/drunkontee/despot:v0.6
dockerrun--tty--rmcodeberg.org/drunkontee/despot:v0.6.0
dockerrun--tty--rmcodeberg.org/drunkontee/despot:latestUsageDownload songs like so:despothttps://open.….com/track/07ZCaJfuutIaoDxYrkdzQYYou can pass multiple URLs at once. Partial URLs (just the path) work, too:despot/track/07ZCaJfuutIaoDxYrkdzQYBy default, despot will place files in./downloads. You can use--destinationto change the destination directory.Use--helpto see all other available options:NotesDespot does not transcode audio. Transcoding is terrible, so you'll have to do that yourself.Despot uses fixed pattern for naming files within the destination directory:Singular Tracks:{artist} - {title}.{ext}Entire shows/podcasts and singular episodes:{show}/{publish_time} - {o.title}.{ext}Albums and entire arists:{album_artist}/{album} ({album_year})/{disc:02d}-{track:02d} {title}.{ext}Playlists:{playlist_name}/{idx:03d} {artist_or_show} - {name}.{ext}These are the only patterns. Deal with it.DisclaimerUsing this code to connect to the API of that green streaming service is probably forbidden by them, and downloading unencrypted files violates their TOS. You should not use this code. Pretty please with sugar on top.
Package: des-sdk-py
Installpip install des-sdk-pyUsageMerchant client//initializing
client = MerchantClient('5K8iH1jMJxn8TKXXgHJHjkf8zGXsbVPvrCLvU2GekDh2nk4XXXX', accountID, url)
//creat data exchange and return request id
result = client.createDataExchangeRequest(9, {
    "bankCardNo": "bankID"
})
//get result thourgh reuqest id
response = client.getResult(result)
if response.get("datasources") is not None:
    for data in response.get("datasources"):
        print(data.get("data"))
else:
    print("response is None, please try more")Datasource client//initializing
client = BlacklistGatewayClient('5KFachrDu7yHqhDeqdshedh6cWasLDv8d8Rko2JuvKM12XXXXXX', account)
print(client.getQuestionReport("GXC4ywUcU8h6zPqESvAMkGREmmg9r54etHTpEtBHp8Rg2WYXXXXXX"))Dev Documentshttps://doc.gxb.io/des/
Package: dessert
OverviewDessert is a utility library enabling Python code to introspect assertions raised via theassertstatement.It is a standalone version of the introspection code frompytest, and all credit is due to Holger Krekel and the pytest developers for this code.UsageUsing dessert is fairly simple:>>>withopen(tmp_filename,"w")asf:..._=f.write("""... def func():...     def x():...         return 1...     def y():...         return -1...     assert y() == x()... """)>>>importemport>>>importdessert>>>withdessert.rewrite_assertions_context():...module=emport.import_file(tmp_filename)>>>module.func()# doctest: +IGNORE_EXCEPTION_DETAIL +ELLIPSISTraceback(mostrecentcalllast):...asserty()==x()AssertionError:assert-1==1+where-1=<functionyat...>()+and1=<functionxat...>()LicenceDessert is released under the MIT license. It is 99% based on pytest, which is released under the MIT license.
Package: dessia-common
Dessia commonThe common parts of the dessia SDK.PurposeThe dessia SDK consists in a framework to build python apps that can be uploaded and interpreted by dessia platform instances. This package give a base class to make your object inherit.Installationpip(3) install dessia_commonpip can be named pip3 by your distribution.
Python at a version > 3.8 is required.UsageThe main usage is to make your objects inherit from dessia_common.DessiaObject.
This will give them the powers to be handled by dessia platforms.DocumentationsFor user documentation on how to use this SDK:https://documentation.dessia.techFor developement documentation or precise questions on classes and method:https://documentation.dessia.tech/dessia_common/Changelog & roadmapSee markdown files in the repo CHANGELOG.md and ROADMAP.md for details.
Package: dessiccate
dessicate: A library to stay DRY.IntroductionDessiccate(verb): remove the moisture from (something); cause to become completely dry.Dry(adj): without moisture. Also, an acronym forDON'T REPEAT YOURSELF.This project was started to house functions the author uses a lot so he doesn't have to writ them over and over again. I hope you find some of them useful!Installpip install dessiccate
Package: dessinemoi
Dessine-moi, a simple Python factoryS'il vous plaît, dessine-moi un mouton.The narrator of Antoine de Saint-Exupéry's Little Prince probably dreamt of a factory like this one...MotivationDessine-moiis a simple Python implementation of the factory pattern. It was
born from the need to create dynamically object trees from nested dictionaries
(e.g.a JSON document).FeaturesCreate aFactoryobject and register types to itUse dictionaries to create objects from the factoryCreateattrs-compatible converters to automatically convert dictionaries to
instances of registered typesCustomise factories to your needsCheck thedocumentationfor more detail.LicenseDessine-moiis distributed under the terms of theMIT license.AboutDessine-moiis written and maintained byVincent Leroy.The development is supported byRayference.Dessine-moiis a component of theEradiate radiative transfer model.
Package: desssign
desssignFeaturesTODORequirementsTODOInstallationYou can installdesssignviapipfromPyPI:pip install desssignUsagePlease see theReference Guidefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,desssignis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated fromStatistics Norway'sSSB PyPI Template.
Package: dester
dester-linterDescriptionA linter for LaTeX files.How to installpip install desterRequirementspython 3.7 or laterHow to usedester-linter <filename>Arguments:<filename> - path to the file you want to execute the linter on.
Excepted file extenstions are .tex, .bib, .tikz.Options:--comment=int - passes how many spaces should be inserted after a comment sign;--newline=bool - if False is passed the new line will not be inserted after a sentence;--indent=int - how many spaces should be inserted at the beginning of the line within a block;--blank_lines=int - how many blank lines should be inserted before section/chapter etc;--overwrite=bool - to overwrite the input file.Default values:--comment=1 - one space is inserted after a comment sign;--newline=True - a newline is inserted after a sentence;--indent=4 - four spaces are inserted at the beginning of a line within a begin{item}\end{item} block;--blank_lines=1 - one blank line is inserted before section\subsection\subsubsection\chapter\paragraph\subparagraph;--overwrite=False - a new output file is created in the same folder.LicenceMIT License
Package: destination
Destination is a framework agnostic regular expression based path routing
library.Installation$pipinstall-UdestinationThread SafetyCurrently, destination is not thread safe. Hence, you should deepcopy
instances or add a mutex lock before using dispatchers and rules in the
other threads.UsageThe default implementation of url parsing uses regular expressions. This is
similar to Django and Tornado. You can create rules and dispatchers to
resolve and parse your url using regular expressions set in the rules.Generally, you should start withReRuleandDispatcher. You can create aReRulewith the regular expression
that is going to be used to parse (and possibly compose) the url, and an
optional identifier to help you identify which rule is parsed, if an identifier
is not provided or its value is set toNone, the rule itself will be
used as an identifier. ADispatchermay be instantiated with no
arguments as a storage of multiple rules. You can add or remove rules at
any time.ReSubDispatcheris a sub-dispatcher that can be added to a dispatcher
as a rule. It uses regular expressions to chop off the part matched to the
regular expression and dispatches the rest part to the rules added to it.BaseRuleandBaseDispatchercan be used to create custom rules
and dispatchers.LicenceMIT LicenseCopyright (c) 2021 Kaede HoshikawaPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: destined
DEclarative Specification for Test INstancE DistributionsDeclare a distribution of instances by specifying function parameters.{"generator":"graphs.erdos_renyi","parameters":{"nodes":{"value":100},"edges":{"value":2500}}}Nest distribution specifications to vary parameters.{"generator":"graphs.erdos_renyi","parameters":{"nodes":{"value":100},"edges":{"generator":"randint","parameters":{"low":{"value":100},"high":{"value":4000}}}}}Sample from the distribution using system seeds.destinedevaluateexamples/random-graphs.json1000-ExamplesMeasure connectivity of a particular model of random graphs:{"instances":{"generator":"graphs.undirected_noloop_erdos_renyi_np","parameters":{"nodes":{"generator":"choice","parameters":{"seq":{"value":[10,20,50,100]}}},"prob":{"generator":"uniform","parameters":{"low":{"value":0},"high":{"value":0.6}}}}},"attributes":"graphs.features"}Evaluating this data shows probability of connectedness of these graphs given their density:Measure satisfiability of the uniform random 3-sat model:{"instances":{"generator":"sat.uniform_k_sat","parameters":{"nvariables":{"value":100},"nclauses":{"generator":"randint","parameters":{"low":{"value":10},"high":{"value":1000}}},"clause_length":{"value":3}}},"attributes":"sat.features"}Evaluating this data shows probability of feasibility of these formulae given the clause/variable ratio:Free software: MIT license
Package: destiny_bot
This repo contains some scraping scripts to retrieve [Destiny Tracker Events](http://destinytracker.com/destiny/events)Additionally, it contains a module with [Helga](http://helga.readthedocs.org/en/latest/) plugins to output this information to IRC.Currently, the commands consist of:nightfall - Returns name and modifiers on the current week’s nightfall strike. (scraped from destinyplanet.com)heroic - Returns name and modifiers on the current week’s heroic strike. (scraped from destinyplanet.com)daily - Returns name and modifiers on the current week’s story mission (scraped from destinytracker.com)xur - Returns a URL for a www.vg247.com page that lists Xur’s location and inventory.bounties - Returns a list of available bounty missions from the specified vendor.The project also comes with acheck.pyexecutable script that allows you to check the daily, heroic, and nightfall commands
from a command line shell.
Package: destripegui-gpu
Live Destriping GUI with GPU-accelerated destripingInstallation instructionsInstructions for installing GPU Live Destriping GUI:
0. Ensure Anaconda is installed on the computer.Go to Go tohttps://github.com/LifeCanvas-Technologies/destripegui/tree/gpu-destripeand click the green "<> Code" button.Download it by clicking "Download Zip"Extract the folder somewhereNavigate to the folder (named "destripegui") in File Explorer, and then double click "install.bat". This will do all the necessary installationsRun the GUI by opening a command line and typingconda activate destripeguifollowed bydestripegui
Package: destruct
Destruct is a tiny library to declaratively define data structures
and parse them to Python objects.The library is not mature by any means. Things may break, and API may
significantly change in future.ExampleData structures are declared in a manner somehow similar to Django models:from destruct import StructBase
import destruct.fields as f

class MyStruct(StructBase):
    timestamp = f.TimestampField()
    value = f.U32Field()Then, parsing is just creating object:data = MyStruct(b"\0\0\0\0\0\0\0\xFF")
assert data.timestamp == datetime.datetime.utcfromtimestamp(0)
assert data.value == 255CopyrightCopyright (c) 2012, Aleksey Zhukov. Distributed under MIT (Expat) license.Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the “Software”), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.The software is provided“as is”,without warranty of any kind, express or
implied, including but not limited to the warranties of merchantability, fitness
for a particular purpose and noninfringement.In no eventshall the authors or
copyright holders be liable for any claim, damages or other liability,
whether in an action of contract, tort or otherwise, arising from, out of
or in connection with the software or the use or other dealings in the software.