Saravanan666/pypi-org huggingface dataset:
Package: vectors
CHANGES0.0.9from __future__ import divisionadded to fix division operator to use
true division as in Python 3.0 instead of classic division.VectorsVectors is a simple library toolkit dealing with common vector and point
logic in the 3-dimensional space.Supports commonly used vector math functions including:Vector magnitudeAddition with another vector or a real number.Multiplication by another vector or a real number.Dot productCross/scalar productAngle between vectorsCheck if two vectors are perpendicular, parallel or non-parallelGithub RepositoryInstallationpip install vectorsDocumentationUsageThere are multiple ways to create our vector instances using the vectors
module.We can first initialize some vectors and points calling their repsective
class contructors as follows.fromvectorsimportPoint,Vectorv1=Vector(1,2,3)#=> Vector(1, 2, 3)v2=Vector(2,4,6)#=> Vector(2, 4, 6)p1=Point(1,2,6)#=> Point(1, 2, 3)p2=Point(2,0,2)#=> Point(2, 4, 6)We can also create a Point instance or a Vector instance with a list
using the class method from_list().components=[1.2,2.4,3.8]v=Vector.from_list(components)#=> Vector(1.2, 2.4, 3.8)We can also create our Vectors from two Point instances using the
classmethod from_points().v=Vector.from_points(p1,p2)#=> Vector(1, -2, -4)We can also get access to the vector array to use it with other
libraries.v1.vector#=> [1, 2, 3]MagnitudeWe can get the magnitude of the vector easily.v1.magnitude()#==> 3.7416573867739413AdditionWe can add a real number to a vector or compute the vector sum of two
vectors as follows.v1.add(2)#=> Vector(3.0, 4.0, 5.0)v1.sum(v2)#=> Vector(3.0, 6.0, 9.0)Both methods return a Vector instance.MultiplicationWe can multiply a vector by a real number.v1.multiply(4)#=> Vector(4.0, 8.0, 12.0)The above returns a Vector instance.Dot ProductWe can find the dot product of two vectors.v1.dot(v2)#=> 0.0We can also use angle theta on the dot function.v1.dot(v2.180)Dot product returns a real number.Cross/Scalar ProductWe can find the cross product of two vectors.v1.cross(v2)#=> Vector(0, 0, 0)Cross product returns a Vector instance, which is always perpendicular
to the other two vectors.Angle ThetaWe can also find the angle theta between two vectors.v1.angle(v2)#=> 0.0Angle is a measured in degrees.Parallel, Perpendicular, Non-ParallelWe can check if two vectors are parallel, perpendicular or non-parallel
to each other.v1.parallel(v2)#=> Truev1.perpendicular(v2)#=> Falsev1.non_parallel(v2)#=> FalseAll of the above return either True or False.TODOCreate Analytic Geometry Toolkit based on the vectors toolkit.
Package: vectors2d
Vectors2DIt's a small module called to facilitate work with vectors in Python. Adds class Vector2D and operations with vectors in 2 dimensions.
All functions take Vector2D. They also take tuples or lists of int or float with len == 2.
Vectors can be added, subbed, multiplied by other vector or by int/float and more. You can find all the functions of the module below.InstallingSimply runpip install vectors2dfrom your command line.Class:Vector2D: takes ints, floats, tuple, list when creating. If not given, creates zero vector (0;0). If given only one number, assigns it to "x" coordinate. Also can take other Vector2D object.Class methods:is_zero(): check if vector is a zero vector.is_parallel_to(vector): check if the vector is parallel to the given vector. Takes Vector2D, list ot tuple.is_perpendicular_to(vector): check if the vector is perpendicular to the given vector. Takes Vector2D, list ot tuple.is_codirectional_to(vector): check if vector is co-directional to the given vector. Takes Vector2D, list ot tuple.Global functions:absolute_vector(vector): calculates an absolute value of given vector. Takes Vector2D object, list or tuple. Returns float;sum_vectors(*vectors): returns the sum of all the given vectors. Takes Vector2D objects, lists and tuples in any combination. Returns Vector2D;sub_vectors(*vectors): returns the subtraction of all the given vectors. Takes Vector2D objects, lists and tuples in any combination. Returns Vector2D;mult_vector(vector, multiplier): returns the multiplication of the given vector by the given number (can be int or float). "vector" takes Vector2D object, list and tuple as vector; "multiplier" takes int and float as multiplier. Returns Vector2D;scalar_mult_vectors(vectors*): calculates a scalar multiplication of the given vectors. Takes Vector2D objects, lists and tuples in any combination. Returns float;get_angle(vector1, vector2): returns the angle between the two given vectors in radians. Takes Vector2D objects, lists and tuples in any combination. Returns float;vector_from_dots(dot1, dot2): calculates a vector from the two given dots. Returns Vector2D.For more information, see GitHub documentation:https://github.com/MitryP/vectors
Package: vectors32
IntroductionThis module, written in pure Python 3.2, does not require other modules, only
Python 3.2 or higher. Consequently the module is as portable as Python3 itself. The well documented module is easy to use and allows use of the usual
scalar variable syntax for the more complex vector operations. Thus, for vector scalar multiplication (aka dot product) of vectorsvaandvbone simply writes:scalarresult = va * vbFor the vector product of vectorsvaandvb, one only needs to write:vectorresult = va ** vbVector naming is left to the end user.  The module is well documented with pdf filesquickStart,userManualandreferenceVectors32.Documentation and ServicingQuick Startwill get an experienced user up to speed very quickly.User Manualhas an explanation and examples of most capabilities of the package, whilst theReference of Vectors32lists all the methods and functions of the module with a brief explanation of each. In addition to the supplied pdf files, the information may also be accessible on the web at:http://akabaila.pcug.org.au/vectors32/index.htmlThe author plans to spend his retirement years to program open source software. He will be interested in your comments of this project and will consider all reasonable requests for extensions and improvements and/or bug fixes of the module. Your emails will be appreciated:algis.kabaila@gmail.comThe development and initial testing of the package was on a kubuntu 11.04 “natty” platform.LicenceThe module is licensed under LGPL and is Open Source Freeware. You are welcome to copy it and to share it.Algis Kabaila, Canberra, 2011.
Package: vector-shortcuts
vxVector and linear algebra toolbelt forNumPy.Featuresnormalizenormalizes a vector.sprojcomputes the scalar projection of one vector onto another.projcomputes the vector projection of one vector onto another.rejectcomputes the vector rejection of one vector from another.reject_axiszeros or squashes one component of a vector.magnitudecomputes the magnitude of a vector.anglecomputes the unsigned angle between two vectors.signed_anglecomputes the signed angle between two vectors.almost_zerotests if a vector is almost the zero vector.almost_collineartests if two vectors are almost collinear.pad_with_onesadds a column of ones.unpadstrips off a column (e.g. of ones).apply_homogeneousapplies a transformation matrix using homogeneous
coordinates.Complete documentation:http://vx.readthedocs.io/Installationpipinstallnumpyvector_shortcutsUsageimportnumpyasnpimportvxprojected=vx.sproj(np.array([5.0,-3.0,1.0]),onto=vx.basis.neg_y)ContributeIssue Tracker:https://github.com/metabolize/vx/issuesSource Code:https://github.com/metabolize/vxPull requests welcome!SupportIf you are having issues, please let us know.AcknowledgementsThis collection was developed at Body Labs byPaul Melnikowand extracted
from the Body Labs codebase and open-sourced as part ofblmathbyAlex
Weiss. blmath was subsequentlyforkedby Paul Melnikow and later
this namespace was broken out into its own package.LicenseThe project is licensed under the two-clause BSD license.
Package: vector-vis-graph
Vector Visibility GraphThis repository contains theNumbaJIT-compiled implementation of theVector Visibility Graphs (VVGs), which are a generalization of the Visibility Graphs (VGs) for multivariate time series. For a single time series,ts2vg(GitHub,PyPI) provides a detailed and thorough construction of VGs with aCython-based approach for performance. However, this implementation is not directly applicable to multivariate time series. Therefore, in this package, we implement the construction of VVGs for multivariate time series using aNumba-based approach for performance.InstallationYou can install the package directly fromPYPIusingpiporpoetryas follows:pipinstallvector-vis-graphorpoetryaddvector-vis-graphor directly fromCondacondainstall-cconda-forgevector-vis-graphUsageGiven a multivariate time series, thevector_vis_graphpackage can be used to construct a vector visibility graph (VVG). The package provides two functionsnatural_vvg()andhorizontal_vvg()with the same input types for constructing Natural and Horizontal VVGs. They take a multivariate time series where the rows correspond to the time steps and the columns correspond to the vector components such that for a multivariate time series,mts,mts[i]is the vector at time stepi. The functions also take the following optional arguments:timeline: The timeline of the multivariate time series. If not provided, the timeline is assumed to be[0, 1, 2, ...].weight_method: The method used to calculate the weight of the edges. The default isWeightMethod.UNWEIGHTED. There are a few other options available in theWeightMethodenum.penetrable_limit: The penetrable limit of the "visibility" of the vectors. For two vectors at different time steps to be visible to each other, the vectors at in-between time steps must satisfy certain conditions. The penetrable limit is the number of in-between time steps that can violate the conditions. The default is0.directed: Whether the graph is directed or undirected. The visibility of the vectors is calculated in aleft-to-rightdirected manner. Ifdirected, the calculated graph adjacency matrix is returned, else its sum with its transpose is returned. The default isFalse, so undirected.importnumpyasnpfromvector_vis_graphimportWeightMethod,horizontal_vvg,natural_vvg# Multivariate Time SeriesTIME_LENGTH=100VEC_SIZE=64multivariate_ts=np.random.rand(TIME_LENGTH,VEC_SIZE)# Natural Vector Visibility Graph with Default Parameters# Timeline: [0, 1, 2, ...]# Weight Method: Unweighted# Penetrable Limit: 0# Undirected Graphnvvg_adj=natural_vvg(multivariate_ts)# Horizontal Vector Visibility Graph with All Custom Parameters# Timeline: [0, 2, 4, ...]# Weight Method: Cosine Similarity# Penetrable Limit: 2# Directed Graphhvvg_adj=horizontal_vvg(multivariate_ts,timeline=np.arange(0,2*TIME_LENGTH,2),# [0, 2, 4, ...]weight_method=WeightMethod.COSINE_SIMILARITY,penetrable_limit=2,directed=True,)
Package: vecto-sdk
Docs•Blog•Discord•TutorialsVecto Python SDKOfficial Python SDK forVecto, the database software that puts intelligent search and powerful models at your fingertips, allowing you to leverage the full potential of AI in mere minutes.InstallationYou can install the package from our latest GitHubrelease.pip install vecto-sdkAlternatively you can also download the latest wheel file from the releases page.For the token, sign up for your accesshere.Building the WheelIf you would like to build your own wheel, runpython setup.py bdist_wheel --universalwhich creates a .whl file in the dist folder. You can install that wheel file withpip install dist/vecto-*.whlinto your current environment (if the file is in the current working directory).Sample UsageFor first time users, we recommend using ourVectorSpaceinterface.Find Nearest Neighborsimport vecto
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("my-cool-ai")

for animal in ["lion", "wolf", "cheetah", "giraffe", "elephant", "rhinoceros", "hyena", "zebrah"]:
    vector_space.ingest_text(animal, { 'text': animal, 'region': 'Africa' })

similar_animals = vector_space.lookup_text("cat", top_k=3)
                        
for animal in similar_animals:
    print(f"{animal.attributes['text']} similarity: {animal.similarity:.2%}")

# Prints: "lion similarity: 84.91%"Ingest Text or Imagesimport vecto
from pathlib import Path
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("my-cool-image-ai")

if not vector_space.exists():
    vector_space.create(model='CLIP', modality='IMAGE') 

for animal in ["lion.png", "wolf.png", "cheetah.png", "giraffe.png", "elephant.png", "rhinoceros.png", "hyena.png", "zebra.png"]:
    vector_space.ingest_image(Path(animal), { 'text': animal.replace('.png', ''), 'region': 'Africa' })

similar_animals = vector_space.lookup_image(Path("cat.png"), top_k=1)

for animal in similar_animals:
    print(f"{animal.attributes['text']}")

# Prints: lionLooking up by Analogyimport vecto
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("word_space")

if not vector_space.exists():
    vector_space.create(model='SBERT', modality='TEXT') 

for word in ["man", "woman", "child", "mother", "father", "boy", "girl", "king", "queen"]:
    vector_space.ingest_text(word, { 'text': word })

analogy = vector_space.compute_text_analogy("king", { 'start': 'man', 'end': 'woman' }, top_k=3)

for word in analogy:
    print(f"{word.attributes['text']} similarity: {word.similarity:.2%}")

# Prints: "queen similarity: 93.41%"For more advanced capabilities including management access, we recommend using the core Vecto class.TutorialWe have a new Vecto tutorial! Checkout theVecto tutorials repository.Developers DiscordHave any questions? Feel free to chat with the devs at ourDiscord!
Package: vectra-api-tools
IntroductionVectra API Toolsis set of resources that is designed to save time and repetitive work by providing a python library that simplifies interaction with the Vectra API, scripts that can be run from the command-line, and additional resources that can help with the Vectra API. It was built to be a project to allow not only Vectra, but the Vectra community to contribute to the success of its customers.The current repository is broken down into the following:modules- this directory contains the modules associated with the VAT libraryscripts- collection of scripts to interact with the Vectra api. These scripts can be used as-is or as a reference on how to leverage the VAT librarytest- collection of tests that can be used to validate the VAT libraryVectra_APIv1.postman_collectionis a Postman collection for the Vectra API. It has all of the current endpoints and and associated parameters for each endpointWikihttps://github.com/vectranetworks/vectra_api_tools/wikiCurrent version2.4.1LicenseApache 2Installationpip install from pypi:pip install vectra_api_toolspip install develop branch from github:pip install git+https://github.com/vectranetworks/vectra_api_tools.git@developsource:python setup.py install
Package: vecvis
A tool for visualization and filtering of vectors.FeaturesSimple to use APIClear visualization of vectors in a starchart.Filtering of vectors.Only show Pareto-optimal vectors.Constrain the vectors by valueranges for each dimension.How to useAfter you have installed VecVis you are ready to go.
The only way to start the GUI and add vectors is via API.
The available functions are listed atreadthedocs.
This version does not work on Mac OS X.Example use:fromvecvisimportAPIapp=API(3)app.add_vector((1,0.4,1.3))
Package: vedadb
Vedadb - Base de donnée de VedadbCe module est la base de donnée de vedadb
Vous pouvez l'installer avec pip:pip3 install vedadbPour installer le paqueage:python setup.py installPour installer le paqueage:-->Toutes les version:

    sudo rm -r vedadb-*-py3.7.egg
-->Une version spécifique ici le 0.0.2:
    sudo rm -r vedadb-0.0.2-py3.7.egg
Package: veda-pytorch
VEDA PyTorchVEDA PyTorch is a library to add device support for the NEC SX-Aurora TSUBASA into PyTorch.Release NotesVersionCommentv8Addedtorch.logical_notv7Support for PyTorch v2.0.0Support for PyTorch v1.13.0Addedtorch.log1pv6Support for PyTorch v1.12.0 and v1.12.1v5Addedtorch.clamptorch.clamp_maxtorch.clamp_mintorch.exptorch.logtorch.normtorch.powtorch.whereFixed conversion from numeric value to boolFixed callingtorch.ve.memory_allocated()without device idPreventing 0-byte allocations from PyTorch to be passed on to VEDAv4fixed possible segfault in Tensor resize if no storage is initializedfixed dtype handling in Scalar to Tensor operationsv3added squeeze and unsqueeze handlersv2Minor changes to enable PyTorch v1.11.0Fixed vedaInit error checking to ignore if already initializedv1Initial Release
Package: veda-tensorflow
VEDA TensorFlowVEDA TensorFlow is a library to add device support for the NEC SX-Aurora TSUBASA
into TensorFlow using the Pluggable Device API.Release NotesVersionCommentv7Added TF v2.13.* supportAdded TF v2.12.* supportFixed <v2.10.* supportv6Added TF v2.11.* supportAdded TF v2.10.* supportUpgraded to VEDA CPP APIv5Added TF v2.9.* supportv4Added BroadcastTo operationIncreasedhost_memory_allocatealignment to be 64, as lower values keep failing inisAligned()v3Bugfixes for loss functionsAdded missing optimizers: SGD, Adadelta, Adagrad, Adam, and AdamaxFixed possible segfault in PluggableDevicehost_memory_allocatev2Minor changes to enable TF v2.7.1 and v2.8.0Fixed vedaInit error checking to ignore if already initializedv1Initial ReleaseF.A.Q.I get the error message: "Internal: platform is already registered with name: "NEC_SX_AURORA"This error is caused by the combination of RH-Python38 package and using a
VirtualEnv. Due toimproper checking for symlinks in
TensorFlowthe device
support library gets loaded and initialized twice causing this error message.You can use the following workaround as long as the bug is not resolved in
TensorFlow.# BEGIN BUGFIXimportsysimportossys.path=list(set(os.path.realpath(p)forpinsys.path))importsitegetsitepackages=site.getsitepackagesdefgetsitepackages_(prefixes=None):returnlist(filter(lambdax:'lib64'notinx,getsitepackages(prefixes)))site.getsitepackages=getsitepackages_# END BUGFIXimporttensorflow...I get the error message "tensorflow.python.framework.errors_impl.InvalidArgumentError: 'visible_device_list' listed an invalid Device id '1' but visible device count is 1"This is a known problem within TF due toTF throws: "'visible_device_list'
listed an invalid Device id" when using non-GPU
PluggableDeviceswhen
using CUDA and VE devices at the same time. The VE devices get added to list of
GPUs, ultimately creating invalid devices indices.Either you need to manually patch your TF installation (see the TF issue), or
useVEDA_VISIBLE_DEVICES=100orCUDA_VISIBLE_DEVICES=to disable either the
CUDA or VE devices.
Package: veda-tensors
VEDA Tensors LibraryVEDA Tensors is a library to provide generic usable high performance kernels
usually used in AI frameworks. VEDA Tensors is still beta and far from being
complete.Release NotesVersionCommentv0.1.5Added unaryNotBugfixes for NCC >= 5v0.1.4Upgraded to VEDA 2.0AddedLog1Pv0.1.3Fixed dtype checking inveda_tensors_unary_cv0.1.2Added L0, L1, L2 toveda_tensors_reduce_dimAddedveda_tensors_unary_tsAddedveda_tensors_unary_tssAddedveda_tensors_unary_tttAddedveda_tensors_whereAddedveda_tensors_binary_sAddedveda_tensors_softmaxAddedVEDA_TENSORS_UNARY_CLAMPAddedVEDA_TENSORS_UNARY_POWImproved performance of several functions.v0.1.1Added AdaDelta, AdaGrad, Adam and AdaMax optimizer functionsv0.1.0Initial Release
Package: vedicpy
vedicpyFor humans, through regular mathematical steps, solving problems sometimes are complex and time-consuming. But using Vedic Mathematic’s General Techniques (applicable to all sets of given data) and Specific Techniques (applicable to specific sets of given data), numerical calculations can be done very fast.This package is a python implementation of Vedic mathematical sutras. It uses the Vedic mathematics for performing basic mathematical operations like multiplication, division, square roots, cube roots etc.This can be used to perform large multiplications and divisions. Though python does not have any restrictions on the size of integers, floats.Since Vedic maths sutras work on individual digits in a number as opposed to the whole number, the implementation works slower on small digit numbers but works faster on larger digit numbers and some other operations like finding the square root or the cube root of a number.Documentation:https://vedicpy.readthedocs.io/en/latest/?badge=latestSource Code:https://github.com/utkarsh0702/vedicpyImplementation1. Installationpipinstallvedicpy2. Usageimportvedicpyasvedica=vedic.cube.cube_2digit_number(43)print(a)Available FunctionalitiesVedic MultiplicationVedic DivisionSquareSquare Root*CubeCube Root*DivisibilityRecurring fractionsCompliment*Vedic Mathematics doesnot provide a way to caluclate square root and cube root accurately. So, if it says that the number is a perfect square or a perfect cube there is still some chance that it is not.
Package: vedirect
python-VEDirectWhat is this lib ?Small library to read Victron's VE.Direct frames.This is useful in order to read Victron's MPPT charge controllers.You need to use a VE.Direct to USB cableHow to use this lib ?First of all, install this library using pip:pip3installvedirectThen, you simply need to import the lib and start asking values:>>>importvedirect>>>device=vedirect.VEDirect()>>>print(device.battery_volts)27.5The list of available parameters is:battery_volts
battery_amps
solar_volts
solar_power
device_serial
device_MPPT_state
Package: ved-reader
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:pip install realpython-readerThe reader is supported on Python 2.7, as well as Python 3.4 and above.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorialssimply call the program:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]