Saravanan666/pypi-org huggingface dataset:
Package: glog-pushover
GLogA logging system based on structlog with ntfy supportFree software: MIT licenseDocumentation:https://glog.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2022-09-24)Official release.Add pydantic supportNew structure0.1.0 (2022-09-24)First release on PyPI.
Package: glom
glomRestructuring data, the Python wayReal applications have real data, and real data nests. Objects inside
of objects inside of lists of objects.glom is a new and powerful way to handle real-world data, featuring:Path-based access for nested data structuresReadable, meaningful error messagesDeclarative data transformation, using lightweight, Pythonic specificationsBuilt-in data exploration and debugging featuresAll of that and more, available as afully-documented,
pure-Python package, tested on Python 3.7+, as well as
PyPy3. Installation is as easy as:pip install glomAnd when you install glom, you also gettheglomcommand-line
interface, letting you experiment at the console, but never limiting
you to shell scripts:Usage: glom [FLAGS] [spec [target]]

Command-line interface to the glom library, providing nested data access and data
restructuring with the power of Python.

Flags:

  --help / -h                     show this help message and exit
  --target-file TARGET_FILE       path to target data source (optional)
  --target-format TARGET_FORMAT
                                  format of the source data (json, python, toml,
                                  or yaml) (defaults to 'json')
  --spec-file SPEC_FILE           path to glom spec definition (optional)
  --spec-format SPEC_FORMAT       format of the glom spec definition (json, python,
                                    python-full) (defaults to 'python')
  --indent INDENT                 number of spaces to indent the result, 0 to disable
                                    pretty-printing (defaults to 2)
  --debug                         interactively debug any errors that come up
  --inspect                       interactively explore the dataAnything you can do at the command line readily translates to Python
code, so you've always got a path forward when complexity starts to
ramp up.ExamplesWithout glom>>>data={'a':{'b':{'c':'d'}}}>>>data['a']['b']['c']'d'>>>data2={'a':{'b':None}}>>>data2['a']['b']['c']Traceback(mostrecentcalllast):...TypeError:'NoneType'objectisnotsubscriptableWith glom>>>glom(data,'a.b.c')'d'>>>glom(data2,'a.b.c')Traceback(mostrecentcalllast):...PathAccessError:couldnotaccess'c',index2inpathPath('a','b','c'),goterror:...Learn moreIf all this seems interesting, continue exploring glom below:glom TutorialFull API documentation at Read the DocsOriginal announcement blog post (2018-05-09)Frequently Asked QuestionsPyCon 2018 Lightning Talk (2018-05-11)All of the links above are overflowing with examples, but should you
find anything about the docs, or glom itself, lacking,please submit
an issue!In the meantime, just remember: When you've got nested data, glom it! ☄️
Package: glom-dict
glom-dictCustom Dictionary with glom path compatible get, set and delete methods.https://glom.readthedocs.io/en/latest/For easy access to and operations on nested data.Installationpython-mpipinstallglom-dictExamples>>>fromglom_dictimportGlomDict>>>d=GlomDict(my_dict={"a":{"b":"c"}})>>>d["my_dict.a.b"]'c'>>>d["my_dict.a.b"]="C">>>d["my_dict.a.b"]'C'Better error messages.>>>d=GlomDict({'a':{'b':None}})>>>d["a.b.c"]Traceback(mostrecentcalllast):...PathAccessError:couldnotaccess'c',index2inpathPath('a','b','c'),goterror:...Glom Pathsfromglom_dictimportGlomDict,Path>>>my_path=Path("a","b",1)>>>d=GlomDict({"a":{"b":["it","works","with","lists","too"]}})>>>d[my_path]'works'For more examples refer to the excellentglomtutorial.https://glom.readthedocs.io/en/latest/tutorial.htmlDetailsBased oncollections.UserDictImplemented methods__getitem__-glom.glom()__setitem__-glom.assign()__delitem__-glom.delete()assign-glom.assign()- can passmissingcallable to automatically backfill missing structures.update- Works but no special behavior
Package: glom-tf
GLOM TensorFlowThis Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups
transformers, neural fields, contrastive representation learning, distillation and capsules to be combined. This was
suggested by Geoffrey Hinton in his paper"How to represent part-whole hierarchies in a neural network".Further,Yannic Kilcher's videoandPhil Wang's repowas very helpful for me to implement this project.InstallationRun the following to install:pipinstallglom-tfDevelopingglom-tfTo installglom-tf, along with tools you need to develop and test, run the following in your virtualenv:gitclonehttps://github.com/Rishit-dagli/GLOM-TensorFlow.git# or clone your own forkcdGLOM-TensorFlow
pipinstall-e.[dev]A bit about GLOMThe GLOM architecture is composed of a large number of columns which
all use exactly the same weights. Each column is a stack of spatially local
autoencoders that learn multiple levels of representation for what is happening
in a small image patch. Each autoencoder transforms the embedding at one level
into the embedding at an adjacent level using a multilayer bottom-up encoder
and a multilayer top-down decoder. These levels correspond to the levels in a
part-whole hierarchy.Interactions among the 3 levels in one columnAn example shared by the author was as an example when show a face image, a single column might converge on embedding
vectors representing a nostril, a nose, a face, and a person.At each discrete time and in each column separately, the embedding at a
level is updated to be the weighted average of:bottom-up neural net acting on the embedding at the level below at the previous timetop-down neural net acting on the embedding at the level above at the previous timeembedding vector at the previous time stepattention-weighted average of the embeddings at the same level in nearby columns at the previous timeFor a static image, the embeddings at a level should settle down over time to produce similar vectors.A picture of the embeddings at a particular timeUsagefromglomtfimportGlommodel=Glom(dim=512,levels=5,image_size=224,patch_size=14)img=tf.random.normal([1,3,224,224])levels=model(img,iters=12)# (1, 256, 5, 12)# 1 - batch# 256 - patches# 5 - levels# 12 - dimensionsUse thereturn_all = Trueargument to get all the column and level states per iteration. This also gives you access
to all the level data across iterations for clustering, from which you can inspect the islands too.fromglomtfimportGlommodel=Glom(dim=512,levels=5,image_size=224,patch_size=14)img=tf.random.normal([1,3,224,224])all_levels=model(img,iters=12,return_all=True)# (13, 1, 256, 5, 12)# 13 - time# top level outputs after iteration 6top_level_output=all_levels[7,:,:,-1]# (1, 256, 512)# 1 - batch# 256 - patches# 512 - dimensionsCitations@misc{hinton2021represent,title={How to represent part-whole hierarchies in a neural network},author={Geoffrey Hinton},year={2021},eprint={2102.12627},archivePrefix={arXiv},primaryClass={cs.CV}}
Package: glone
gloneA Python CLI to backup all your GitHub repositories.Developmentpoetry installpoetry shellTech StackClick(for the interface)Packaging and DevelopmentPoetryMypyisortBlackFlake8flake8-bugbearflake8-comprehensionspep8-namingflake8-builtinsBanditThis CLI was created withCookiecutterand thejoaopalmeiro/cookiecutter-templates/python-cliproject template.NotesBackup script.ghapi documentation(API).Commands:glone joaopalmeiro.glone joaopalmeiro -o ~/Downloads.glone joaopalmeiro -o ~/Downloads -f tar.glone --help.glone --version.GitPythonpackage.GitHub CLI(a.k.a.gh).humanizepackage.poetry add humanize.
Package: gloomhavenhelper
Gloomhaven Helper Server ClientSupports Gloomhaven Helper with network sync up to version8.4.xThis project provides an open source alternative to talk to the application"Gloomhaven Helper".A simple example would be to run a headless server (no GUI) and connect to it with your clients. The purpose of this would be to have a small inexpensive always-on computer with your gloomhaven game state (like a raspberry pi). With port-forwarding enabled in your router, your friends can connect over the internet and you don't have to forward internet traffic to your personal computer.Another use-case is to connect as a client and hook it up to a bounch of hardware that changes depending on the game state.The original purpose for this project was to connect an inexpensive wifi-enabled micro-controller to Gloomhaven Helper app and scan rfid/nfc tags to interact with the game state (like setting initiative or adding/removing characters). Because of this, the core protocol is implemented in c++ and wrapped in swig for python bindings. If you want to use this project with c++ directly, look at thec++codebase and theMakefileto figure out how to use it.InstallationRequires python 3.7 or higherFor an x86 (intel/amd cpu) running linux or macos, install with pip (3):pip install gloomhavenhelperFor arm64/aarch64 systems, install with:pip install https://github.com/Gronis/gloomhaven-helper-rfid/releases/download/0.1.1/gloomhavenhelper-0.1.1-py3-none-linux_aarch64.whlfor older arm versions, open an issue and I can see if I can provide a build for those systems as well.Windows is not supported or tested. If you have a windows machine and want to host a server, I recommend just to use the original Gloomhaven Helper java application as a server.Usage:Use as a severpython -m gloomhavenhelperUse as a client#!/usr/bin/env pythonimportglommhavenhelperasghhimportasyncioTCP_IP='127.0.0.1'# server ipTCP_PORT=58888# server portasyncdefon_game_state(message_number,game_state):# Do things with the game state.print("Got game state{}, round{}".format(message_number,game_state.round))# To understand what you can get out of the game state print it outghh.print(game_state)asyncdefmain():client=ghh.Client(TCP_IP,TCP_PORT)client.on_game_state=on_game_stateawaitclient.connect()print("Connected")asyncio.get_event_loop().create_task(main())asyncio.get_event_loop().run_forever()Example of projects:Videoof scanning a tag/card and changing initiativeBuildThis project requiresg++/clang++or another c++ compilerpython3swigwheels(build wheels binary)twine(upload wheels binary to pypi repository)How to buildRunmake pythonto build python code. The python module is placed in aoutdirectory. Change to this directory andimport gloomhavenhelperto try out your new build:cd out && python.Runmake wheelto build wheel library. The build will be located indistfolder. You can then install it withpip install dist/gloomhavenhelper-<some-more-stuff-here>.whlIf you want to clean up the workspace because your build is failing because of some old cached file, or you just want to clean up the workspace, usemake clean.License: MITContributers- Robin Grönberg
Package: gloopy
Gloopy provides the following services:Creation and manipulation of 3D, flat-surfaced, polyhedra, using the
Shape class.Factory functions to produce particular shapes, such as Cube or Icosahedron.Some basic algorithms to modify existing shapes, such as by subdividing or
extruding their surfaces.Conversion of shapes into Glyph instances, which
manage vertex arrays stored in a VBO.A simple Render class which renders glyphs
with given positions and orientations.A camera attribute on the single Gloopy instance,
that can be positioned, oriented, or told to look at a particular item or
position.DependenciesWritten mostly on Windows, tested occasionally on Ubuntu.Python 2.7Pyglet 1.1.4PyOpenGL 3.0.1DocumentationIn the Gloopy source, see gloopy/docs/html/index.htmland the scripts in the ‘examples’ directory.Documentation is not currently available online.LicenseGloopy is released under the New BSD License, the text of which is to be found
in the project’s LICENSE.txt. Alternatively, you may use it under the terms of
any other OSI-approved license.Status & Known IssuesIt works for me, but has not been used any real projects. The API is
a mess and may change substantially in later releases.No issue tracker is currently maintained, but the major shortfalls as
I percieve them are:Some algorithmic modifiers, such as face subdivision, extrusion, stellation,
do not currently work on MultiShapes. This is because these modifiers rely
on modifying attributes of the given shape in place, such as by inserting
new entries in the .faces collection. However, MultiShapes provide many of
these attributes by using generators to form a composite stream of their
children. I guess I ought to make all shape modifiers functional.The supplied ‘directional lighting’ shader is broken - rotating an object
does not modify the apparent illumination of its surfaces.No attempt is made to handle textures. All faces are plain colors.We don’t currently handle multiple shaders within a single scene.ThanksPyWeekparticipants ‘Scav’ and ‘Threads’ for showing me
how it should be done, and PyWeek message board users donal.h, Cosmologicon,
RB[0], PyTM30, Tee and saluk for cajoling me into accepting the merit of
allowing people to bring pre-existing codebases into PyWeek so long as they
are public.
Package: glop
GLOP - the Generalized Lpeg and Ometa-inspired Parsing and printing framework.IntroductionGLOP is an exploration of parsing expression grammars, mostly influenced by
Lua’sLPEGmodule and Alessandro Warth’sOMetaproject. It is of course
also heavily influenced by grep and friends.It is intended to provide several different things:A tool and with a friendly command line interface for searching for things
a la grep, and transforming them a la sed, awk, perl -e and so forth.Libraries in several languages to match the functionality of that tool.A tool for generating parsers in multiple languages for a given grammar.Libraries for the above as well.A study in implementing a non-trivial problem in multiple languages.What worksAt the moment, GLOP can accept grammars and execute them using averysimple language for executing actions on the parsed text.The code is only implemented in Python.Things that still need to be implementedparser generators.a generated parser that matches the hand-rolled one (glop needs to be
able to bootstrap itself up).Open issuesBasically, everything is still open.One of the distinctive aspects of LPEG is its focus on matching patterns,
rather than entire grammars. Focusing on patterns leads one to a syntax
more tuned for patterns, as terminals are more common than non-terminals.
Package: glopan
✨🥞✨🤷‍♂️ What?Glowing Pancake, glopan, is a simple Python package for converting figures and operating on pdfs.📦 InstallationGlowing Pancake is pip-installable.pip install glopan🧙‍♀️ UsageAfter a successful installation, Glowing Pancake can be imported as a regular python packageimport glopanor run from the command line by typingglopanorpython -m glopan.🪂 DependenciesGlowing Pancake depends on the following third-party tools:Inkscape>= v.1.0.1, for converting from PDF to various graphics formats.Ghostscript>= v.9.26, for converting from Postscript to PDF.Microsoft Word, for converting from DOCX to PDF.❓ What's up with the silly package name?Yup, the package is named after its Github repo, which got a random name when it was created...
Package: glopen
Open-like context managers for remote globus files.glopen(filename, mode, endpoint):Creates a temporary file[On a read mode:] Transfers the remote file to the temporary fileOpens the temporary file with the given modeYields the open temporary fileCloses the temporary file[On a write mode:] Transfers the temporary file to the remote fileDeletes the temporary fileglopen_many(filenames, mode, endpoint)takes a list of filenames and yeilds a list of open files.
The remote copies are grouped into a single globus transfer, improving performance for small files.Example>>>fromglopenimportglopen>>>withglopen("path/to/remote/file.anything",mode="r",endpoint="globusid#endpoint")asf:...lines=f.readlines()>>>fromglopenimportglopen_many>>>files=["file1","file2","file3"]>>>withglopen_many(files,mode="w",endpoint="globusid#endpoint")asfs:...forf,dinzip(fs,d):...f.write(d)Installglopenis on the Python Package Index (PyPI):pip install glopenIt depends onglobussh, a light-weight wrapper around the globus SSH interface.
Your local machine must be a globus endpoint, so you can either runGlobus connect personalorGlobus connect server.Configurationglopentransfers files to and from a temporary directory on your local machine,
so it needs to know the machine’s endpoint name and a directory that globus can access.
You tellglopenthe endpoint and temporary directory in a~/.glopenconfig file:{
  "local_endpoint" : "<globusid>#<endpoint_name>",
  "tempdir" : "/home/<username>/tmp"
}
Package: glored
Asynchronous GLObal REDis InterfaceImplements a small interface around the basicredis.Redis()client meant to be shared
across different files in the same process that need access to the same redis instance.
This implementation has two major goals:Allow sharing the same Client object across different files without having to pass
the object itself each time.Allow asynchronous calls to the redis interface, specially when submitting data to
avoid any disruption in running codeUsageThe usage of this library is straight forward and it implements the basicredis.Redis()methods:fromgloredimportredis_clientimporttime# Synchronousredis_client.set('some_key','some_value')result=redis_client.get('some_key')# Asynchronousredis_client.asynchronous.set('some_key','other_value')time.sleep(1)result=redis_client.get('some_key')
Package: gloriahousing
Predicts Median housing valueThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Change Log0.0.1 (13-11-2020)-First Release
Package: gloria-housing
Predicts Median housing valueThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Change Log0.0.1 (13-11-2020)-First Release
Package: glorpen-di
Another Dependency Injection library for Python.This package has following three guidelines:any class configured byContainermechanism should not be modified in any waythere is no need for external services definition files forContainernoContainercompiling and service tagging - we have introspection and dynamic language for this taskAnd so this package provides:noxml configurationnoannotations (more cons than pros)nochanges to services codeOfficial repositoriesFor forking and other funnies.BitBucket:https://bitbucket.org/glorpen/glorpen-diGitHub:https://github.com/glorpen/glorpen-diSupported design patternsService instance can be created by:factory servicecalling class object with argumentsInstance options can be altered by:constructor argumentssetterscalling methodsusing configurator serviceEach service has defined scope, service cannot request other service from narrower scope.Injecting services and parametersfromglorpen.diimportContainerclassMyParamService(object):passclassMyService(object):def__init__(self,obj,text,value):super(MyService,self).__init__()print("service instance:%s"%obj)print("container parameter:%s"%text)print("provided value:%s"%value)c=Container()c.add_service(MyParamService)c.add_parameter('my-param',"value from container")c.add_service(MyService).kwargs(obj__svc=MyParamService,text__param="my-param",value="defined value")c.get(MyService)Running snippet will print:service instance: <__main__.MyParamService object at 0x7f2fef6e9828>
container parameter: value from container
provided value: defined valueSee https://glorpen-di.readthedocs.io/en/latest/ for code examples and more documentation.
Package: glorpen-docker-exporter
Prometheus Docker ExporterExports stats from Docker API (/containers/<id>/statendpoint).Metrics are collected on each request, everything in single thread.
Fetching stats from Docker API for lots of containers on a small machine could take few seconds,
but this implementation will not use host CPU excessively (looking at you, cAdvisor!).Supported metricscpublock i/o (cgroups v2)memorynetworkUsageusage: glorpen-docker-exporter [-h] [-a ADDR] [-p PORT] [--sysfs SYSFS]

options:
  -h, --help            show this help message and exit
  -a ADDR, --addr ADDR  address to lsiten on, defaults to 0.0.0.0
  -p PORT, --port PORT  port to listen on, defaults to 8080
  --sysfs SYSFS, -s SYSFS
                        path to sysfs to use, defaults to /sysAccess to/sysis needed for resolving block device names (/sys/dev/block).Exporter needs r/w access to Docker socket (eg./run/docker.sock).Supported Docker envs:DOCKER_HOST: The URL to the Docker host.DOCKER_TLS_VERIFY: Verify the host against a CA certificate.DOCKER_CERT_PATH: A path to a directory containing TLS certificates to use when connecting to the Docker host.Dockerdockerrun-v/sys:/sys:ro-v/run/docker.sock:/run/docker.sock:rwglorpen/prometheus-docker-exporter:1.0.0Pypipipinstallglorpen-docker-exporter
python-mglorpen-docker-exporterExample Prometheus queriesCPU usage per container (as indocker statcommand):delta(container_cpu_seconds_total[2m]) / delta(container_cpu_system_seconds_total[2m]) * avg_over_time(container_cpu_online[2m])Container HDD reads and writes:rate(container_blkio_reads_bytes_total[2m]) > 0
rate(container_blkio_writes_bytes_total[2m]) > 0
Package: glorpen-wallpaper-picker
Wallpaper PickerRandomize your wallpaper in a smarter way.Picker allows you to set X11 wallpaper, picked at random from wallpaper dir.For each image you can define POI (x,y coords) and then image will always be centered on given point - e.g. in portrait mode.
Images will be scaled up or down to match screen resolution.Installationpipinstallglorpen-wallpaper-pickerUsageTo update wallpaper, including picking from images marked as offensive:$ wallpaper-picker wallpaper --offensiveTo change attrs:$ ./.env/bin/wallpaper-picker attr-set image.jpg --offensive y --poi 1,2
Details for image /tmp/image.jpg
POI: x:1, y:2
Offensive: yesTo list current attributes of given file:$ wallpaper-picker attr-get image.jpg
Details for image /tmp/image.jpg
POI: x:1, y:2
Offensive: yesTo remove attrs usenoneorunsetas values:$ wallpaper-picker attr-set image.jpg --offensive unset --poi unset
Details for image /tmp/image.jpg
POI: not set
Offensive: no
Package: glosario
glosarioPackage to define technical termsInstallation:pip install glosarioUsage>>> from glosario import glosario
>>> glosario.set_language('en')
>>> glosario.define('data frame')FeaturesDefines technical terms in multiple languagesAllows for setting multiple languages in one script (sequentially)Implements cosine similarity for matching words to closest slugDependenciesPyYamltextdistanceDocumentationThe official documentation is hosted on Read the Docs:https://glosario.readthedocs.io/en/latest/CreditsThis package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from thepyOpenSci/cookiecutter-pyopensciproject template and theaudreyr/cookiecutter-pypackage.