Saravanan666/pypi-org huggingface dataset:
Package: gloss
glossGloss (short for glossary) is a different kind of dictionary.
All values also automatically become keys and so can be looked
up in the Gloss to find its corresponding 'key'. This makes it
easy to lookup and maintain one-to-one relationships such as a
label to a certain magnitude.Gloss is a MutableMapping and supports all the same methods
thatdictdoes.ExamplesfromglossimportGlossexample=Gloss()example["stdin"]=0example.update({"stdout":1,"stderr":2})print(example)#Gloss({"stdin": 0, "stdout": 1, "stderr": 2})print(example[1],"goes to"example["stderr"])#stdout goes to 2example[3]="config.toml"example[1]="shell pipe"print(example)#Gloss({"stdin": 0, "stderr": 2, "config.toml": 3, "shell pipe": 1})print([fd_or_descforfd_or_descinexample])#["stdin", "stderr", "config.toml", "shell pipe", 0, 1, 2, 3]As both the key and value are gettable items from the Gloss and
are indistinguishable once added,termis the preferred name
for items in a Gloss, and aterm pairis the 1-1 mapping each
term belongs to.Differences from EnumEnumis a wonderful
data structure that also supports 1-1 mappings and it's already built
into Python. However, these limitations of Enums are solved by Gloss:enum members are static. They are defined all at once in the
class and their values cannot change. A Gloss on the other hand
can be added to, altered, even have members deleted or popped,
all at runtimeAccessing Enum member names and their values are different
operations. Looking up a member by name is done with either dot
dereference or getitem; looking up a member by value is done
with a call. With a Gloss you don't have to know which side of
the mapping your key is on (if there even is a distinction to the
mapping), it is all done by getitemOne side of an enum mapping must be a string. Because member
names are attributes they must follow Python identifier naming
rules. In a Gloss, all terms may be any hashable objectDifferences from DictWhile the classic dict solves some of the limitations of using an Enum,
primarily being mutable at runtime, it is not a 1-1 mapping.enforcing uniqueness among dict values is a lot of extra worklooking up a dict value is O(n). Therefore so is updating, deleting,
popping, etc that value. Searching a Gloss for any term is O(1) timeupdating any term in a Gloss takes a single operation
Package: glossanea
GlossaneaA language learning program. Requires external data to function.LicenseMozilla Public License 2.0This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.AcknowledgementsPython section in global .gitignore taken fromgithub/gitignoreunder theCC0-1.0 License.
Package: gloss-collection
This is the final release to gloss-colletion.Find future updates athttps://pypi.org/project/gloss/gloss-collectionGloss (short for glossary) is a different kind of dictionary.
All values also automatically become keys and so can be looked
up in the Gloss to find its corresponding 'key'. This makes it
easy to lookup and maintain one-to-one relationships such as a
label to a certain magnitude.As both the key and value are gettable items from the Gloss and
are indistinguishable once added,termis the preferred name
for items in a Gloss, and aterm pairis the 1-1 mapping each
term belongs to.Gloss is a MutableMapping and supports all the same methods
thatdictdoes.ExamplesfromglossimportGlossexample=Gloss()example["stdin"]=0example.update({"stdout":1,"stderr":2})print(example)#Gloss({"stdin": 0, "stdout": 1, "stderr": 2})print(example[1],"goes to"example["stderr"])#stdout goes to 2example[3]="config.toml"example[1]="shell pipe"print(example)#Gloss({"stdin": 0, "stderr": 2, "config.toml": 3, "shell pipe": 1})print([fd_or_descforfd_or_descinexample])#["stdin", "stderr", "config.toml", "shell pipe", 0, 1, 2 , 3]Shouldn't I just use an enum?Great thought! You can, and often should, use anEnumfor these
sorts of relationsips. Besides being built in to Python, Enums
are probably faster and more space efficient. However, some
limitations of Enums solved by Gloss are:enum members are static. They are defined all at once in the
class and their values cannot change. A Gloss on the other hand
can be added to, altered, even have members deleted or popped,
all at runtimeAccessing Enum member namess and their values are differnt
operations. Looking up a member by name is done with either dot
dereference or getitem; looking up a member by value is done
with a call. With a Gloss you don't have to know which side of
the mapping your key is on (if there even is a distinction to the
mapping), it is all done by getitemOne side of an enum mapping must be a string. Because member
names are attributes they must follow Python identifier naming
rules. In a Gloss, all keys may be any hashable objectShouldn't I just use a dict?While the classic dict solves some of limitations of using an Enum,
primarily being mutable at runtime, looking up any value is O(n) and
therefore so is updating, deleting, poping, etc that value. In a
Gloss searching for any value is O(1) time, and updating that value
(for example chaning the key it maps to) can be done in a single
operation.
Package: glossia.comparator
Go-Smart Simulation Architecture (aka. GSSA)Glossia is a standalone set of tools for simulation orchestration, allowing remote control of
computational numerics software in Docker containers. It is administrated via WAMP and
simulations are configured using [GSSA-XML](reference/gssa-xml.rst), a conceptual description
format facilitating easy interchange of physical model components around a numerical model.The framework is used to provide a simulation backend for the [Go-Smart](http://smart-mict.eu/)
web-based Minimally Invasive Cancer Treatment (MICT) platform. Using this technology, researchers
and technicians can dynamically alter simulation strategies and equipment/physical parameters
through the web-interface.While existing technologies allow hypermodel modification through
tools such as [Apache Taverna](http://incubator.apache.org/projects/taverna.html), Go-Smart, through
Glossia, is unusual in that it provides interactive support for collaborative simulation at a
hypomodel level. At present, this is tested within a small number of frameworks (corresponding
to container images) including Python/Numpy/[FEniCS](https://fenicsproject.org) and
[Elmer](https://www.csc.fi/web/elmer).Primary authors: [NUMA Engineering Services Ltd](http://www.numa.ie>) (NUMA), Dundalk, IrelandProject website: [http://www.gosmart-project.eu/](http://www.gosmart-project.eu/)This project is co-funded by the European Commission under grant agreement no. 600641.DocumentationDocumentation for this component is available at
<https://go-smart.github.io/gssa>InstallationCMake installation is recommended from an out-of-source build directory.UsageThe simulation server (GSSA) may be launched by the command` {.sourceCode .sh} crossbar--debugstart--cbdirpath/to/directory(web)[defaultgssf-release/web]go-smart-simulation-server--hostHOSTADDRESS/localhost--websocket-portPORTNUMBERgo-smart-simulation-client--gssa-fileXMLFILE--websocket-portPORTNUMBER--hostHOSTADDRESS/localhost--definitionspath/to/file.py--skip-clean--outputLesion.vtp `Adding –help will show documentation of command line arguments. You
should start Crossbar.io in the gssf/web directory of the build folder
before launching this script. Ensure that the configuration in the web
directory matches the port and host to which go-smart-simulation-server
will connect for WAMP interaction.
Package: glossolalia
glossolalia is a GPL Licensed cli wrapper around the Psuedo Translations
available fromTransifexpseudo translations. The available types arebrackets,unicode,planguage,extend, andmixed.glossolalia is a fork ofpotpie, which is
unmaintained.InstallationTo install requests, simply:$ pip install glossolaliaOr, if you absolutely must:$ easy_install glossolaliaBut, you really shouldn’t do that.UsageTo create a pseudo translated po file, simply:$ glossolalia [--type] infile outfileAn example for a Django project might be:$ glossolalia locale/en/LC_MESSAGES/django.po locale/xx_pseudo/LC_MESSAGES/django.poThe default type is mixed, if you wish to use another type simplify specify it
as an option like:$ glossolalia --type brackets locale/en/LC_MESSAGES/django.po locale/xx_pseudo/LC_MESSAGES/django.poTypesBracketsAdds square brackets around the string (e.g. [translated text])UnicodeConverts all characters into look alike unicode charactersPLanguageIncreases the length of a string by around 30-50% by replacing the vowels with
unicode chars that look alike.This is based on a P-language, which is a simple vowel-extending language.Extend:Increases the length of a string by around 20-700% by appending special
chars (Greek, Chinese, etc) to the end of the string.Mixed:Combines Extend, Unicode and Brackets into one Type.
Package: gloss.theme
Gloss ProjectA design processOne of the key tools of the project is a theme which uses special gloss classes.CONSIDERED ALPHA: This project is considered in an alpha stage. This is because  we still need more comprehensive documentation. We are adding documentation as we go and when it is ready we will move the status to Beta.For the most up-to-date reference documentation visithttp://the-gloss-project.readthedocs.org/en/latest/See theINSTALLdocsGloss for Plone 5Gloss 0.8.x is for Plone 5.Gloss for Plone 4.3.xIf you are on Plone 4.3.x make sure to use a version less than 0.8.x.Gloss ClassesThe general approach is to use a gl namespace.
So class will be always named in the manner below:{gl namespace}-{what}-n{qualifier}classes provided:core classes:
==============
a.gl-logo (must be placed on an a tag, this is to preserve home link)
{gl namespace}-{what}-n{qualifier}
.gl-menu-link - add style to individual menu items
.gl-inner-only - (deprecated will be removed soon) only show on inner pages
.gl-front-only - (deprecated will be removed soon) only show on the frontpage
.gl-editbar - adds an edit bar
.gl-content - inserts the site content
.gl-content-body
.gl-content-byline
.gl-content-description
.gl-content-title
.gl-footer
.gl-below-content

drop classes:
==============
.gl-drop
.gl-front-drop
.gl-inner-drop

layout classes:
================
.gl-one-column-layout
.gl-two-column-layout.gl-right-sidebar
.gl-two-column-layout.gl-left-sidebar
.gl-two-column-layout.gl-right-column (deprecated will be removed soon)
.gl-two-column-layout.gl-left-column (deprecated will be removed soon)
.gl-three-column-layout

     .gl-second-column
     .gl-first-columnChangelog0.8.5Fixed the gloss preview.png0.8.4Added a .gl-search-box class0.8.3Updated the gloss logo that shows in the theme controlpanelAdded a custom.less file, this is the recommended location for stylesheet based customizations0.8.2it is now possible to use arbitrary references to .gl-first-column .gl-second-column, for supporting the left and right portlets0.8.1added support for Plone 5 (this version does not work with Plone 4.x)0.7.1added support for webflow dropdown/webcouturier.dropdownmenu menus0.7.0there are enough changes to warrant bumping to 0.7.0added jquery migrate to the registrycssregistry now disables public.css and reset.css when theme is activeremoved call to bootstrap css also added sensible webflow defaultsadded rules to inject webflow current on the menu when a section is activeUpdate custom.xmlCreate grid-webflow.xmlUpdate README.rstadded support for gl-sidebar-left and gl-sidebar-right0.5.4reverted the factoring out of the inline xsl as it does not workfactored out plone.app.toolbar rules into an optional plone.app.toolbar.xml filestarted keeping a changelog0.1dev (unreleased)Initial release
Package: glossy
Installationpip install glossyStart Decoratingimportglossyimporttime@glossy.decoratordeftimer(func,*args,**kwargs):"""TimerPlace this decorator on functions to see how longthey take to execute."""start=time.time()result=func(*args,**kwargs)secs=time.time()-startname=func.__name__print(f"Function{name}took{secs}seconds")returnresultFeaturesFlatter DecoratorsDiscoverabiltyTesting & Mocking
Package: glotaran
This project was renamed to pyglotaran on pypiThis is the last release of the glotaran packge under this name, and won't be updated further.
This release corresponds to thepyglotaranrelease version 0.1.0
Go topyglotaranto get instruction on how to install the latest versionOld readme kept for posterity (expect broken links):pyGloTarAnpyGloTarAn is a python library for global and target analysisWarningThis project is still in a pre-release stage and should only be used with care.Additional warning for scientistsThe algorithms provided by this package still need to be validated and reviewed, pending the official release it should not be used in scientific publications.InstallationFrom SourcePrerequisites:Python 3.6 or higher(Python 2 isnotsupported)On Windows only 64bit is supportedNote for Windows Users: The easiest way to get python for Windows is viaAnaconda$ git clone https://github.com/glotaran/pyglotaran.git
$ cd pyglotaran

$ pip install .
# To enforce python3 on systems where python2 is also installed
$ pip3 install .Note for Anaconda Users: Please make sure to update your distribution prior to install since some packages managed by Anaconda cannot be updated bypip.Mailinglistmailing-listCreditsThe credits can be found in the documentationscredits section
Package: glotio-api
(Unofficial) Python Bindings for the glot.io APIInstallation$ pip install glotio-apiBasic UsagefromglotioimportRunnerg=Runner("[YOUR_API_KEY]")# language listprint(Runner.get_lang().keys())g.set_lang("python")code="import sys;print(input() + ', ' + sys.argv[1])"g.set_code(code,filename="main.py")g.set_stdin("input text")g.set_command("python3 main.py 'command line argument'")result=g.run()print(result)LICENCEglotio-api is made available under an MIT License.
Package: glotter2
Glotter2The programming language icons were downloaded frompngegg.comThis is a fork of the originalGlotterrepository, which
appears to be unmaintained.Glotter2 is an execution library for collections of single file scripts. It uses Docker to be able to build, run, and optionally test scripts in any language without having to install a local sdk or development environment.For getting started with Glotter2, refer to ourdocumentation.ContributingIf you'd like to contribute to Glotter2, read ourcontributing guidelines.ChangelogGlotter2 releases0.8.0:Add ability to split lines on the expected value0.7.2:Make sure temporary directory used for docker is world accessible0.7.1:Remove work-in-progress from changelog0.7.0:Addtry/finallyto auto-generated project fixture to make sure docker
container is cleaned upAddtry/finallytoruncommand to make sure docker container is
cleaned upAddbatchcommand0.6.1:Updatedockerdependency to 6.1.0 to supporturllib32.x0.6.0:Add test documentation generation0.5.0:Add test generationAddpydanticdependency0.4.5:Add link to documentation0.4.4:Fix bug that would indicate "No tests were found" when filtering tests0.4.2:Remove call totime.sleepwhen pulling image0.4.1:Bump version since wrong version pushed to pypi0.4.0:Change test ID from<filename>to<language>/<filename>Speed up test collection by about 1 min and total test time by about
5 min insample-programsby caching list of sourcesModifydownload,run, andtestcommands so that-p,-l, and-sare no longer mutually exclusiveAdd--paralleltodownloadcommand to parallelize image downloadsAdd--paralleltotestcommand to parallelize tests0.3.0:Fix crash when running tests forsample-programswith glotter 0.2.xUpgrade dependencies to latest version:docker >=6.0.1, <7Jinja >=3.1.2, <4pytest >=7.2.1, <8PyYAML >=6.0, <7Upgrade python to 3.8 or aboveOriginal Glotter releases0.2.x: Add reporting verb to output discovered sources as a table in stdout or to a csv0.1.x: Initial release of working code.
Package: glove
# GloveCython general implementation of the Glove multi-threaded training.GloVe is an unsupervised learning algorithm for generating vector representations for words.Training is done using a co-occcurence matrix from a corpus. The resulting representations contain structure useful for many other tasks.The paper describing the model is [here](http://nlp.stanford.edu/projects/glove/glove.pdf).The original implementation for this Machine Learning model can be [found here](http://nlp.stanford.edu/projects/glove/).@author Jonathan Raiman## ExampleTo use this package you need a sparse co-occurence matrix.This matrix is represented by nested dictionaries that use ints as keyswith a 0-index.For instance below we have a corpus of 3 indices. Below 0 co-occurs with 2, 3.5 times:```pythonimport glovecooccur = {0: {0: 1.0,2: 3.5},1: {2: 0.5},2: {0: 3.5,1: 0.5,2: 1.2}}model = glove.Glove(cooccur, vocab_size=3, d=50, alpha=0.75, x_max=100.0)for epoch in range(25):err = model.train(batch_size=200, workers=9, batch_size=50)print("epoch %d, error %.3f" % (epoch, err), flush=True)```The trained embeddings are now present under `model.W`.## UsageThe model is controlled by setting several hyperpameters.### Glove.__init__()* `cooccurence` dict<int, dict<int, float>> : the co-occurence matrix* `alpha` float : (default 0.75) hyperparameter for controlling the exponent for normalized co-occurence counts.* `x_max` float : (default 100.0) hyperparameter for controlling smoothing for common items in co-occurence matrix.* `d` int : (default 50) how many embedding dimensions for learnt vectors* `seed` int : (default 1234) the random seed### Glove.train* `step_size` float : the learning rate for the model* `workers` int : number of worker threads used for training* `batch_size` int : how many examples should each thread receive (controls the size of the job queue)
Package: glovepy
# GlovePyA Python implementation with Cython of the [GloVe](http://www-nlp.stanford.edu/projects/glove/) algorithm with multi-threaded training.
this Python package mainly contains two Python classes. The first Python class (Corpus) builds the co-occurrence matrix given a collection of
documents; while the second Python class (Glove) will generate vector representations for words.GloVe is an unsupervised learning algorithm for generating vector representations for words developed by Stanford NLP lab.
The paper describing the model is [here](http://nlp.stanford.edu/projects/glove/glove.pdf). In contrast with
[Word2Vec](https://code.google.com/p/word2vec/) (there is a great Python implementation in [gensim](http://radimrehurek.com/gensim/models/word2vec.html))
which is often referred as prediction method, GloVe is called counting method which the embedding is produced by factorizing the logarithm of the
corpus word co-occurrence matrix.The original implementation for this Machine Learning model can be [found here](http://nlp.stanford.edu/projects/glove/). This work is based on the work of [glove-python](https://github.com/maciejkula/glove-python) and [glove](https://github.com/JonathanRaiman/glove).## Installation### Build on WindowsIf you are installing this package on Windows (tested on Windows 10), you will need to install Visual C++ 2015 Build Tools. If you have already installed Microsoft Visual Studio, you cannot install the Visual C++ Build Tools. It will ask you to uninstall your existing VS when you tried to install the Visual C++ build tools using the standalone installer. However, since you already have the VS, you can go to Control Panel—Programs and Features and right click the VS item and Change-Modify, then check the option of those components that relates to the Visual C++ Build Tools, like Visual C++, Windows SDD, then install them. Please also make sure the Build tools path has been added to the system path.Once you have the Visual C++ Build Tools, you can build the package as follows:`python python setup.py build_ext--inplace--compiler=msvcpython setup.py install `### LinuxInstall from the source code:`python python setup.py install `
Package: glove-py
glove-py is an implementation of the GloVe algorithm for learning word vectors from a corpus.Installationusing pip:pip install glove-pyinstall with github code :git clone git@github.com:f20500909/glove_py.git
python3 setup.py installusing in shellRunwith python:fromgloveimport*model=Glove(params)model.train(input_file)model.to_txt()words=model.most_similary("one",10)print(words)List of availableparamsand their default value:help info......
  -input_file               input_file [small_text]
  -vocab_size               vocabulary size to train  [0]
  -max_size                 maximum size to keep in courpus [10000000]
  -min_count                minimum times  [1]
  -window                   the window size to get common occurrence [10]
  -embed_size               embedded size  [80]
  -epoch                    train epoch [1]
  -threads                  train thread [40]
  -memory_limit             memory used to tain (GB) [1]
  -lr                       learning rate [0.05]with shell:
./scripts/demo.sh我去掉了很过我看上去用处不大的功能(只是我认为的用处打大的功能,有些细节并不理解也移除了),如提取共现矩阵的symmetric功能忽略了大小写,统一改大写为小写TODO:修复有时会发生的错误:   read err...: Connection reset by peer在合并共现词时，高频和低频矩阵好像写在了一个文件里面，感觉有点问题.....在编译pybind_11时不能让对应文件被include 必须用cmake加载ReferenceGloVeGloVe-cppJeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.
Package: glow
Glow LibrarySet of functional tools for easier prototypingOverview...InstallationFor basic installation use:pipinstallglowSpecific versions with additional requirementspipinstallglow[io]# For I/O extraspipinstallglow[all]# For allGlow is compatible with: Python 3.10+.
Tested on ArchLinux, Ubuntu 20.04/22.04, Windows 10/11.Structureglow.*- Core parts, available out the boxglow.io.*- I/O wrappers to access data in convenient formatsCore featuresglow.mapped- convenient tool to parallelize computationsglow.memoize- use if you want to reduce number of calls for any functionIO featuresglow.io.Sound- playable sound wrapperfromdatetimeimporttimedeltaimportnumpyasnpfromglow.ioimportSoundarray:np.ndarraysound=Sound(array,rate=44100)# Wrap np.ndarraysound=Sound.load('test.flac')# Load sound into memory from file# Get propertiesrate:int=sound.rateduration:timedelta=sound.durationdtype:np.dtype=sound.dtype# Plays sound through default device, supports Ctrl-C for interruptionsound.play()