Saravanan666/pypi-org huggingface dataset:
Package: metanl
This package provides wrappers around NLTK and other systems to provide
convenient natural language tools, such as:TokenizersStopword removersWord frequency lookupLemmatizers (which reduce words to their root form, possibly taking
part-of-speech tags into account)Analyzers for East Asian languages (for example, we currently use a MeCab
process to find word breaks in Japanese)For word frequencies in some language, metanl uses corpora from the University
of Leeds Center for Translation Studies (http://corpus.leeds.ac.uk/list.html),
whose data is released under the Creative Commons Attribution license.Author: Rob Speer
Package: metaNNvis
MetaNNvisMetaNNvis is a tool for accessing introspection methods for neural networks regardless of the
framework in which the neural network has been built. It is easily extendable and currently supports models from
TensorFlow 2.0 and PyTorch in combination with methods fromCaptumandtf-keras-vis. For more details, see theproject report.InstallationThe latest version of metaNNvis can be installed via pip:pip install metaNNvisAdditionally, you need the following dependencies:torch,seaborn,numpy,tensorflow,onnx2torch,onnx,captum,tf-keras-vis,torchvision,matplotlibandonnx2keras. All dependencies can be installed via pip except for onnx2keras, which can be downloaded from the
project'sGitHub pageUsageFor instructions on how to use cross-framework introspection and how to extend it by new methods, see
theuser guide.Available methodsCross-Framework Introspection currently supports most methods from Captum and all methods from tf-keras-vis. The
supported methods are:MethodCategoryCaptumIntegrated Gradientsprimary, layer, neuronSaliencyprimaryDeepLiftprimary, layer, neuronGradientShapprimary, layer, neuronInput X GradientprimaryGradient X ActivationlayerDeconvolutionprimary, neuronFeature Ablationprimary, layer, neuronFeature PermutationprimaryConductancelayer, neuronLayer ActivationlayerGradCAMlayerNeuron Gradientneurontf-keras-visActivation Maximizationfeature visualizationVanilla Saliency / SmoothGradattributionGradCAMattributionGradCAM++attributionScoreCAMattributionLayerCAMattributionCurrently not supported are:MethodCategoryCaptumDeepLiftShapprimary, layer, neuronGuided Backpropagationprimary, neuronGuided GradCAMprimaryOcclusionprimaryShapley Value SamplingprimaryLimeprimaryKernelShapprimaryLayer Relevance Propagationprimary, layerInternal Influencelayer
Package: metano
MetanoFor installation instructions and further information about Metano,
please visitthe project homepage, where we also provide a detaileddocumentation.MMTBTheMetano Modeling Toolbox (MMTB)is a web-based toolbox that assists during model creation and analysis.
Many Metano tools (FBA, FVA, split-ratio, ...) can be used via MMTB without installation. Furthermore, MMTB offers different visualizations for your metabolic models, and a platform for the distribution of models.License informationMetano is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.Metano is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See theGNU General Public Licensefor more details.
Package: metantic
MetanticMetaprogramming with pydantic Models (creating types from types)PartialCreate a partial model (all fields optional)frommetanticimportPartialclassUser(BaseModel):id:strname:strage:intPartial(User).model_validate(dict(id="id",name="name"))# PartialUser(id='id', name='name', age=None)FieldsObtain aLiteraltype for the model's fields (which can be validated)frommetanticimportFieldsclassUser(BaseModel):id:strname:strage:intFields(User)# typing.Literal['id', 'name', 'age']classQuery(BaseModel):fields:Fields(User)Query(fields=["id","name"])# OKQuery(fields=["email"])# ValidationError: fields Input should be 'id', 'name' or 'age' [...]OmitCreate a model with a subset of fieldsfrommetanticimportFieldsclassUser(BaseModel):id:strname:strage:int# the ID is unchangeabledefupdate(patch:Omit(User,["id"])):...TakeCreate a model with a subset of fields (recursively)classFullName(BaseModel):first:str;middle:str;family:strclassUser(BaseModel):id:strfull_name:FullNamefriends:list[str]# field -> take the field# (field, subfields) -> take field but with subfields onlyUserInfo=Take(User,['id',('full_name':['first','family'])])# UserInfo is equivalent toclassUserInfo(BaseModel):id:strfull_name:TakeFullNameclassTakeFullName(BaseModel):first:str;family:strPathsType-safe pathsValidatefrommetanticimportpathsclassFullName(BaseModel):first:str;middle:str;family:strclassUser(BaseModel):id:strfull_name:FullNamefriends:list[str]paths.validate(['full_name','first'],User.model_json_schema())# Nonepaths.validate(['bad_name','first'],User.model_json_schema())# ValueError("Key 'bad_name' doesn't exist in ['id', 'full_name', 'friends'])"TypeCreate a pathRootModelwith validationfrommetantic.pathsimportPathUserPath=Path(User)UserPath(root=['full_name','first'])# PathT(root=['full_name', 'first'])UserPath(root=['bad_name','first'])# raises ValueError(...)BuilderCurrently under construction. May fail for some generic/union typesfrommetanticimportpathsps=paths.builder(User)# PathBuilder objectps['id'].path# ['id']ps['full_name']['first'].path# ['full_name', 'first']ps['friends'][69].path# ['friends', 69]ps['full_name']['second'].path# ERRORWorks with tuples, lists, dicts, classes and pydantic modelsType unions stop path generation
Package: meta-onnx
metaonnxmetaonnx部署通用框架1、安装最新版 meta-cvpip install meta-cv2、安装最新版 meta-onnxpip install meta-onnx3、图像分类示例（参考classification_demo.py代码）import cv2, platform
import metaonnx as m

Classification = m.Classification

y = Classification(model_path='models/mixnet_xl_bs24_4.onnx',
                   input_width=224,
                   input_height=224,
                   use_preprocess=True,
                   normal=True,
                   mean=[0.485, 0.456, 0.406],
                   std=[0.229, 0.224, 0.225],
                   swap=(2, 0, 1),
                   class_names=classnames)

batch_size = 24
img = cv2.imread('models/bottle.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets, _scores, _labels)
cv2.imwrite("models/bottle.png", img)4、目标检测示例（参考detection_demo.py代码）import platform, cv2
import metaonnx as m

Detection = mo.Detection

y = Detection(model_path='models/yolov8m.onnx',
              input_width=640,
              input_height=480,
              use_preprocess=True,
              pad=True,
              normal=True,
              swap=(2, 0, 1),
              confidence_thresh=0.5,
              nms_thresh=0.3,
              class_names=classnames)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)5、实例分割示例（参考segment_demo.py代码）import cv2
import metaonnx as m

Segment = mo.Segment

y = Segment(model_path='models/yolov8m-seg.onnx',
            input_width=640,
            input_height=480,
            use_preprocess=True,
            pad=True,
            normal=True,
            swap=(2, 0, 1),
            confidence_thresh=0.5,
            nms_thresh=0.3,
            class_names=classnames)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)6、人脸检测示例（参考face_detection_demo.py代码）import cv2
import metaonnx as m

FaceDetection = m.FaceDetection

y = FaceDetection(model_path='models/face_detection.onnx',
                  input_width=480,
                  input_height=640,
                  use_preprocess=True,
                  pad=True,
                  normal=True,
                  mean=[0.485, 0.456, 0.406],
                  std=[0.229, 0.224, 0.225],
                  swap=(2, 0, 1),
                  confidence_thresh=0.5,
                  nms_thresh=0.4,
                  class_names=['face'])

batch_size = 1
img = cv2.imread('models/face.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/face.png", img)
Package: metaopt
MetaOpt is a library that optimizes black-box functions using a limited
amount of time and utilizing multiple processors. The main focus of MetaOpt
is the parameter tuning for machine learning and heuristic optimization.MetaOpt has been developed by theComputational Intelligence Groupat the University of Oldenburg.
The authors are: Renke Grunwald, Bengt Lüers, Jendrik Poloczek, Justin Heinermann, Oliver Kramer.For a user guide, seethe documentation.
The following describes basic operations with the repository.StatusPyPInGitHubmasterdevelopDownloadMetaOpt is obtainable viaarchives of past releases,
but you can also get the sources by cloning the repository.To get a working copy of the MetaOpt repository:$gitclonehttps://github.com/cigroup-ol/metaopt.gitInstallationMetaOpt isavailable on PyPI, but you can also install it from source.To install MetaOpt from PyPI using pip:$sudopipinstallmetaoptTo install MetaOpt from a working copy:$cdmetaopt$sudopythonsetup.pyinstallTo verify MetaOpt was installed correctly:$pythonmetaopt--versionmetaopt0.1.0.0TestsMetaOpt hasautomated online tests, but you can also run them locally.To run MetaOpt’s test suite from a working copy:$cdmetaopt$sudopipinstall-rrequirements_test.txt$maketests-allCoverageMetaOpt hasautomated online test coverage reports, but you can also create them locally.To create MetaOpt’s coverage reports from a working copy:$cdmetaopt$sudopipinstall-rrequirements_coverage.txt$makecoverageExamplesMetaOpt comes withexamples, which you can view in the docs, but you can also run them locally.To install MetaOpt’s example dependecies and run an example from a working copy:$cdmetaopt$sudopipinstall-rrequirements_examples.txt$PYTHONPATH=.pythonexamples/svm_saes_global_timeout.pyDocumentationMetaOpt hasautomatically generated online documentation, but you can build
yourself a local copy.To build MetaOpt’s html documentation from a working copy:$cdmetaopt$sudopipinstall-rrequirements_docs.txt$makedocs0.1.0 – initial release
Package: metapack
Metapack, Metatab Data PackagingParse and manipulate structured data and metadata in a tabular format.Metatabis a data format that allows structured
metadata – the sort you’d normally store in JSON, YAML or XML – to be stored
and edited in tabular forms like CSV or Excel. Metatab files look exactly like
you’d expect, so they are very easy for non-technical users to read and edit,
using tools they already have. Metatab is an excellent format for creating,
storing and transmitting metadata. For more information about metatab, visithttp://metatab.org.Metapack is a packaging system that uses Metatab to create Zip, Excel and
filesystem data packages.This repository has a Python module and executable. For a Javascript version,
see themetatab-jsrepository.See thedocumentation for full detailsInstallMetapack requires geographic libraries, most importantly gda, pyproj, shapely
and geopandas. These libraries can be difficult to install, often requiring
compilation. By far, the easiest way to install them properly is with Anaconda.
And, because, metapack has a lot of dependencies, you’ll want to install it in
a virtual environment. Metapack only works with Python 3.5 or later:$condacreate--namemetapackpython=3.7$condaactivatemetapack$condainstallnumpypandasgdalgeospyproj=1.9.5.1fionashapelygeopandas$pipinstallmetapackVerify that the install worked by running `` mp config``Getting StartedSeeGetting Startedfor an initial tutorial, or the other guides in thedocs directory on
GithubDevelopment NotesClearing the CacheSome tests can pass despite errors if the file the test is looking for is
cached. The cache can be set with an evironmental variable and cleared before
the tests to solve this problem$cache_dir=/tmp/some/dir$rm-rf$cache_dir$mkdir-p$cache_dir$APPURL_CACHE=$cache_dirpythonsetup.pytestDevelopment Testing with DockerTesting during development for other versions of Python is a bit of a pain,
since you have to install the alternate version, and Tox will run all of the
tests, not just the one you want.One way to deal with this is to install Docker locally, then run the docker
test container on the source directory. This is done automatically from the
Makefile in appurl/tests$cdmetapack/metapack/test$makebuild# to create the container image$makeshell# to run bash the containerYou now have a docker container where the /code directory is the appurl source
dir. Since the Docker container is running code from your host machine, you can
edit it normally.Now, run tox to build the tox virtual environments, then enter the specific
version you want to run tests for and activate the virtual environment.To run one environment. for example, Python 3.4# tox -e py34To run one test in one environment environment. for example, Python 3.4# tox -e py34 -- -s
Package: metapack-github
Initialize a git repository for a Metapack package and commit it to Github.InstallationConfigurationUseDevelopmentCommittingThe project scaffold gets the version number for the module from git. To get the version number:$ python setup.py --versionSet the version with a tag. The version numbers are specified withPEP440$ git tag 0.0.1PublishingPublish the project to toPyPIwith twine:pip install twine
twine upload dist/*ScaffoldingThis project has been set up using PyScaffold 3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: metapack-wp
Wordpress extension for the Metapack data management system.http://metatab.orgDescriptionThis extension provides Metapack commands for:DevelopmentThis project has been set up using PyScaffold 3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.CommittingThe project scaffold gets the version number for the module from git. To get the version number:$ python setup.py --versionSet the version with a tag. The version numbers are specified withPEP440$ git tag 0.0.1PublishingPublish the project to toPyPIwith twine:pip install twine
twine upload dist/*ScaffoldingThis project has been set up using PyScaffold 3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: meta-paddle
cvpaddlepaddle识别通用框架安装 paddlepaddleCPUpython -m pip install paddlepaddle -ihttps://mirror.baidu.com/pypi/simpleCUDA 11.1python -m pip install paddlepaddle-gpu==2.2.2.post111 -fhttps://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.htmlCheckimport paddle
paddle.utils.run_check()
Package: metaparams
metaparamsmetaparamsis a MetaClass/Class infrastructure to define params
without invoking objects and have them automatically parse/remove thekwargspassed to the class in which they are intalledDocumentationRead the full documentation at readthedocs.org:metaparams documentationPython 3 OnlyYes it is time to move forwardInstallationFrom pypi:pip install metaparamsFrom source:Place themetaparamsdirectory found in the sources inside your projectFeatures:ParamsBasea class from which to subclass to also be params-enabledMetaParamsa MetaClass for more complex usage pattern
Package: metaparse
# metaparseThis is to be a machine learning project to extract metainformation about information sources, such as agents online that share information.## Problem specificationGiven some data from an information sourceI, recognize its [intent about content](https://wefindx.net) towards the world, answering following questions about the information source and its information content:F(domain).assets:.Agent(who?).Place(where?).Event(when?).Topic(about what?)X(process).actions:.doing(doing what?)Y(range).targets:.Goal(why?).Idea(how in principle?).Plan(what specifically?).Step(in what order?)This will allow to treat every information source as an instance of equation optimization processF(X)=Y, where the actions of agents are explained by theirintents (Y)about content their worldmodel (F).
Package: meta-patterns
meta-patternsDesign patterns for Python implemented with decorators and classes.Getting StartedCurrently only one design pattern is implemented:Listener.Listener PatternTheListenerpattern (otherwise known as theObserverorPublish-Subscribepattern) is a behavioral design pattern that lets you define a subscription mechanism to notify multiple objects about any events that happen to the object they’re observing (source).Its use is demonstrated here:frommetapatterns.listenerimportListenable,listenableclassSubject(Listenable):@listenabledefmyfunc(self,arg1):"""@listenable indicates this function can be 'listened in on'.It allows Listeners to hook into it (see MyListener)"""print("myfunc called with arg",arg1)return"Hoozah"defmyfunc2(self,arg1):print("myfunc called with arg",arg1)classMyListener(Subject.Listener):"""Identify this class as a listener of `Subject` through inheritance.This makes it so not all listenable methods need to be implemented (they have a default empty implementation in `Subject.Listener`)."""defon_myfunc(self,subject,arg1):print("listened in on call to myfunc with arg",arg1)defon_myfunc_finished(self,subject,result,arg1):print("listened in on result of myfunc with arg",arg1,"and result",result)# This cannot be defined because myfunc2 is not a listenable function in Subject#def on_myfunc2(self, arg1):#passWe can run this as follows:if__name__=="__main__":subject=Subject()print("# Calling myfunc without listener")subject.myfunc(3)listener=MyListener()subject.add_listener(listener)print("\n# Calling myfunc with listener")subject.myfunc(5)print("\n# Calling myfunc2 with listener")subject.myfunc2(7)subject.remove_listener(listener)print("\n# Calling myfunc again with listener removed")subject.myfunc(5)which gives the output:#Callingmyfuncwithoutlistenermyfunc called with arg 3#Callingmyfuncwithlistenerlistened in on call to myfunc with arg 5myfunc called with arg 5listened in on result of myfunc with arg 5 and result Hoozah#Callingmyfunc2withlistenermyfunc called with arg 7#Callingmyfuncagainwithlistenerremovedmyfunc called with arg 5Subclassing fromSubject.Listenerhas the advantage of raising aTypeErrorwhen anon_function has no matching counterpart inSubject. This can help detect difficult to find problems that arise when changing the name of a function inSubject. Our method includes this check to guard against some issues that come with the loose coupling of theListenerpattern.Future WorkImplement more design patterns.Suggestions? Contactme!
Package: metapensiero.asyncio.tasklocal
author:Alberto Berticontact:alberto@metapensiero.itlicense:GNU General Public License version 3 or laterAn asyncio’s Task-local variable containerUsageA drop-in replacement forthreading.localobject that stores
per-task variables (instead of per-thread). You can use it like this:import asyncio
from metapensiero.asyncio.tasklocal import Local

mylocal = Local()

@asyncio.coroutine
def task_a():
    mylocal.a_var = 'foo'
    yield from asyncio.sleep(0.001) # switches to task_b
    assert mylocal.a_var == 'foo'
    print(mylocal.a_var)

@asyncio.coroutine
def task_b():
    mylocal.a_var = 'bar'
    yield from asyncio.sleep(0.001) # switches back to task_a
    assert mylocal.a_var == 'bar'
    print(mylocal.a_var)

loop = asyncio.get_event_loop()
a = asyncio.Task(task_a())
b = asyncio.Task(task_b())
loop.run_until_complete(asyncio.wait((a, b)))

# this prints out:
# foo
# barReally the kind of object to be tracked as “current” is configurable
by passing-in a differentDiscriminatorinstance to theLocalclass.By default it will use theTaskDiscriminatorsupplied with the
package. It will use standardasyncio’s loop detection techniques
(by delegating it toasyncio.Task.current_task()) but if you want
to track tasks of a particular event loop, you can supply your ownTaskDiscriminatorinstance to theLocalinstance
initialization, like this:from metapensiero.asyncio.tasklocal import Local, TaskDiscriminator

mylocal = Local(discriminator=TaskDiscriminator(loop=my_loop))You can also track another set of object by subclassing theBaseDiscriminatorclass:from metapensiero.asyncio.tasklocal import BaseDiscriminator

class ASampleSessionDiscriminator(BaseDiscriminator):

    def current(self):
        return current_session()

mylocal = Local(discriminator=ASampleDiscriminator())You have to implement at least thecurrentmethod.Likethreading.localclass, this class supports subclassing, in
which case it will re-execute the__init__method for each one of
the tracked objects:call_counter = 0

class CustomLocal(Local):

    a_value = None

    def __init__(self, a_value):
        self.a_value = a_value
        nonlocal call_counter
        call_counter += 1

mylocal = CustomLocal('foo')Heremylocal.a_valuewill be initialized tofoofor every
tracked object (asyncio’s tasks by default). Herecall_counterwill count the number of every tracked object in which themylocalobject has been accessed.TestingTo run the tests you should run the following at the package root:python setup.py testBuild statusChanges0.1 (2015-12-14)First release complete with tests0.2 (2015-12-26)Fix CHANGES and packaging
Package: metapensiero.deform.semantic-ui
Author:Lele GaifaxContact:lele@metapensiero.itlicense:GNU General Public License version 3 or laterThis package replaces standardDeformBootstrapbased widgets withSemantic-UIequivalents.Changes0.9 (2020-08-23)Align with Deform 2.0.12, add autofocus option to most fields0.8 (2019-10-08)Align with Deform 2.0.8, add HTML5 attributes to buttons0.7 (2019-06-23)Fix typo in an inline styleUse twine to upload to PyPI0.6 (2019-06-23)Stylistic tweaks, in particular:use Semantic-UI’s popups instead of HTML5 title, for easier readability of long
descriptionsunclutter sequences, removing redundant segments0.5 (2018-11-26)Unbreak checkbox0.4 (2018-11-16)Align with Deform 2.0.7, in particular:addtagsoption support toSelect2Widgetadd support for HTML5 attributes0.3 (2018-04-20)Better templates for CheckboxWidget and CheckboxChoiceWidget0.2 (2018-03-10)Minor tweaks to the form’s buttons, aligning them to the right and using new “link” type
introduced with Deform 2.0.5Better template for DatePartsWidgetRemove field description, it appeared twice0.1 (2018-02-16)First release0.0 (unreleased)Initial effort