Saravanan666/pypi-org huggingface dataset:
Package: cvxcla
cvxclaCritical line algorithmThe critical line algorithm is a method to compute the efficient frontier of a
portfolio optimization problem.The algorithm has been introduced by Markowitz inThe Optimization of Quadratic Functions Subject to Linear Constraintsand subsequently described in his bookPortfolio Selection.The algorithm is based on the observation that the efficient frontier is a piecewise
linear function if expected return is plotted over expected variance.
The critical line algorithm computes the turning points, e.g. the corners
of the efficient frontier.LiteratureWe are using the following sourcesNiedermayer and NiedermayerThey suggested a method to avoid the expensive inversion of the covariance matrix.Applying Markowitz's critical line algorithmIt turns out that implementing their method in Python is not significantly faster
than the explicit inversion of the covariance matrix relying on LAPACK vianumpy.linalg.inv.Bailey and Lopez de PradoWe have initially started with their code published inAn Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization.
We have updated their original code and covered it in tests. We have made a few
noteworthy changes:Use a boolean numpy array to indicate whether a weight is free or blocked.Rewrote the computation of the first turning point.Isolated the computation of $\lambda$ and the update of weights to make them testable.Use modern and immutable dataclasses throughout.The code is not part of the published package though.
It is only used for testing purposes. We recommend it for educational purposes only.Markowitz et alInAvoiding the Downside: A Practical Review of the Critical
Line Algorithm for Mean-Semivariance Portfolio OptimizatioMarkowitz and a team of researchers from Hudson Bay Capital Management and Constantia
Capital provide a step-by-step tutorial on how to implement the critical line algorithm.We address a problem they oversaw: After finding the first starting point
all variables might be blocked. We need to enforce that one variable is labeled
as free even it sits on a boundary otherwise the matrix needed is singular.Rather than using their involved construction of the sparse matrix
to estimate the weights we bisect the weights into a free and a blocked part.
We then use a linear solver to compute the weights only for the free part.We alter some of their Python code. Our experiments to combine it with Niedermayer's
ideas to accelerate the computation of the matrix inverses did not yet justify
the additional complexity.PoetryWe assume you share already the love forPoetry.
Once you have installed poetry you can performmakeinstallto replicate the virtual environment we have defined inpyproject.tomland locked inpoetry.lock.JupyterWe installJupyterLabon fly within the aforementioned
virtual environment. Executingmakejupyterwill install and start the jupyter lab.
Package: cvxcli
cvxcliWe demonstrate how to deploy to simple command line apps using pipx.Install pipxThe current way to deploy apps is to usepipx.
The tool creates a virtual environment for each app.For the installation of pipx please refer to thepipx documentation.Install cvxcli with pipxcvxcli has been deployed to PyPI like any other Python package. It could be
installed via pip but we advise against that.cvxcli is not exactly a package you should use as a dependency in your own code.
cvxcli is the home for two apps that are meant to be used from the command line.
So please install via pipxpipxinstallcvxcliThis command will create a virtual environment for cvxcli and install the package
into that environment.Please note that youdo not need poetryto be installed on your machine.Using the appsUninstall cvxcli with pipx
Package: cvxcooker
cvxcookerWhat is cvxcooker?This is code to automate the often tedious process of writing
boilerplate files such asREADME.mdCONTRIBUTING.mdCODE_OF_CONDUCT.mdbook/sphinx/conf.pybook/_config.ymlAll those files are created from Jinja2 templates, e.g.
the file 'book/_config.yml' is created from the template# Book settings# Learn more at https://jupyterbook.org/customize/config.htmltitle:{{name}}author:{{authors[0]}}only_build_toc_files:trueexecute:execute_notebooks:forcetimeout:240parse:myst_enable_extensions:-substitution-linkify-dollarmathmyst_substitutions:book_url:{{homepage}}# needed for plotlysphinx:config:html_js_files:-https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js# Information about where the book exists on the webrepository:url:{{repository}}path_to_book:bookbranch:main# Add GitHub buttons to your book# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repositoryhtml:use_issues_button:trueuse_repository_button:trueextra_navbar:Powered by <a href="https://jupyterbook.org">Jupyter Book</a># Will be displayed underneath the left navbar.The data in double curly braces is replaced by the data injected into the template.
We get the data from parsing the projects 'pyproject.toml' in the root of the repository.Of course, we do not want to entertain the idea of having copies of all those
templates in all the repositories. Instead, we have this central repository
and every repository that wants to use this functionality adds in its Makefile
the job.PHONY:boilboil:## Update the boilerplate code@poetryrunpipinstallcvxcooker@poetryruncookpyproject.toml
Package: cvxfit
CvxFitCvxFit is a package which provides classes for fitting convex functions to given data.UsageTypical usage looks like this:#!/usr/bin/env python

from cvxfit import CvxFit
import scipy as sp

# Generate data.
N = 1000
n = 3

def f_actual(x):
    return sp.sum(x*x)

X = sp.randn(N, n)
Y = sp.array([f_actual(pt) for pt in X])

# Initialize object with 10 affine functions
# with regularization 0.001, and maximum
# number of iterations 20.
fit_object = CvxFit(X=X, Y=Y, type='pwl', extra_param=[10, 0.001, 20])

# Perform fit.
fit_object.fit()

# See training error; repeat fit if high.
print 'Training error: ' + str(fit_object.mean_training_error)

# Compare quality of fit at a random point.
pt = sp.randn(1, n)
print 'Actual value: ' + str(f_actual(pt))
print 'Approximate value: ' + str(fit_object.evaluate(pt)[0])AuthorsThis package was originally written and tested by Mainak Chowdhury, Alon Kipnis and Milind Rao.AcknowledgementsThis package came out of a course project for EE364b at Stanford University, Spring 2014, taught by Prof. Stephen Boyd. We would like to thank all members of the awesome teaching staff for their feedback and suggestions.
Package: cvxgraphalgs
CVX Graph AlgorithmsIntroductionModern convex optimization-based graph algorithms.Convex optimization presents an exciting new direction in designing exact and
approximate graph algorithms. However, these algorithms are often overlooked in
practice due to limitations in solving large convex programs quickly.
Convex optimization-based graph algorithms nonetheless achieve impressive
theoretical performance, and often provide a beautiful geometric interpretation.
This package implements some of these algorithms and provides corresponding
graph generators to test performance---hopefully highlighting how simple,
elegant and effective these can be for many real-world problems.DetailsIn this package, we provide implementations of the following algorithms. Note
featured convex optimization-based algorithms are in bold and references are
provided when available.This package also provides functions to generate graphs drawn from the planted
independent set distribution and stochastic block model.Maximum Cut ProblemGoemans-Williamson MAX-CUT Algorithm[1]Random MAX-CUT AlgorithmGreedy MAX-CUT AlgorithmIndependent Set AlgorithmCrude SDP-based Independent Set[2]Greedy Independent Set AlgorithmSpectral Algorithm for Independent SetInstall and UsageYou can install this directly from the Python Package Index (PyPI).pip install cvxgraphalgsBelow, we show how to run the Goemans-Williamson MAX-CUT Algorithm on a graph
drawn from the stochastic block model distribution. For more examples, explore
the jupyter notebooks available with the package documentation availablehere.>>> import cvxgraphalgs as cvxgr
>>> graph, _ = cvxgr.generators.bernoulli_planted_independent(
...     size=50, independent_size=15, probability=0.5
... )
>>> recovered = cvxgr.algorithms.crude_sdp_independent_set(graph)
>>> len(recovered)
15References[1]: Goemans, Michel X., and David P. Williamson. "Improved approximation
algorithms for maximum cut and satisfiability problems using semidefinite
programming."Journal of the ACM (JACM)42, no. 6 (1995): 1115-1145.[2]: McKenzie, Theo, Hermish Mehta, and Luca Trevisan. "A New Algorithm for the
Robust Semi-random Independent Set Problem."arXiv:1808.03633(2018).
Package: cvxmg
CVXMGCVXMG is a python based package that allows the users to compute the sizing of Isolated/Islanded MicroGrids (IMGs). Additionally to the sizing of the IMGs, CVXMG returns the optimal dispatch of the energy sources and the optimal tariffs for the energy. CVXMG allows implementing Demand Side Management (DSM) in the sizing of the IMGs. The package uses a plug and play approach, allowing its users to choose energy sources, DSM strategies, and types of solvers (deterministic, stochastic, multiyear, and stochastic multiyear). Here a brief description of the capabilities of the package:Obtain the sizing of the energy sources.Perform optimal dispatch of the energy sources.Compute the tariffs for the energy.Build different business models for the microgrid.And more.CVXMG has its own documentation that can be found at: link.First release!Just launch the first release! More to come!RequirementsCVXMG uses a number of open source projects to work properly:NumpyPandasScipyCVXPYMatplotlibAnd of course cvxpymg itself is open source with apublic repositoryon GitHub.InstallationYou can use "pip install cvxpymg" from your command window. Or clone the GitHub repository.DevelopmentWant to contribute? Great!TodosWrite more examples.Create Electrolizer-Tank-Fuel cell system source.Create biomass generator source.And more!LicenseMITFree Software, Hell Yeah!
Package: cvxopt
CVXOPT is a free software package for convex optimization based on the
Python programming language. It can be used with the interactive Python
interpreter, on the command line by executing Python scripts, or
integrated in other software via Python extension modules. Its main
purpose is to make the development of software for convex optimization
applications straightforward by building on Python’s extensive standard
library and on the strengths of Python as a high-level programming
language.
Package: cvxoptklu
CVXOPT is a free software package for convex optimization based on the
Python programming language. It can be used with the interactive Python
interpreter, on the command line by executing Python scripts, or
integrated in other software via Python extension modules. Its main
purpose is to make the development of software for convex optimization
applications straightforward by building on Python’s extensive standard
library and on the strengths of Python as a high-level programming
language.
Package: cvxpnpl
CvxPnPLA convex Perspective-n-Points-and-Lines method.Title:CvxPnPL: A Unified Convex Solution to the Absolute Pose Estimation Problem from Point and Line CorrespondencesAbstract:We present a new convex method to estimate 3D pose from mixed combinations of 2D-3D point and line correspondences, the Perspective-n-Points-and-Lines problem (PnPL). We merge the contributions of each point and line into a unified Quadratic Constrained Quadratic Problem (QCQP) and then relax it into a Semi Definite Program (SDP) through Shor's relaxation. This makes it possible to gracefully handle mixed configurations of points and lines. Furthermore, the proposed relaxation allows us to recover a finite number of solutions under ambiguous configurations. In such cases, the 3D pose candidates are found by further enforcing geometric constraints on the solution space and then retrieving such poses from the intersections of multiple quadrics. Experiments provide results in line with the best performing state of the art methods while providing the flexibility of solving for an arbitrary number of points and lines.URL to the Paper:arXivLicense:Apache 2.0InstallingThe easiest way to install the package is through pippip install cvxpnplAlternatively, you can clone this repo and invoke from its root folderpython setup.py installSDP Solver, BLAS and LAPACKcvxpnpl makes use ofSCSto obtain a solution to the underlying SDP problem. SCS requires BLAS and LAPACK which can be painful to set up for Windows users. As of version 2.0.0, SCS is permanently linking with LAPACK and BLAS and it is possible that the process is now easier for Windows users as well.ExamplesThe library exposes 3 public functions:pnp,pnlandpnpl. You can find a couple of examples showing how to use each in theexamples folder.BenchmarksA number of benchmarks were conducted in order to validate the robustness of cvxpnpl in various scenarios and compare it against other methods. Please check the dedicatedREADMEpage on the topic.
Package: cvxpy-leximin
CVXPY + LeximinThecvxpy_leximinpackage extendscvxpyby adding two objectives:LeximinandLeximax.
Each of these objectives takes as an argument a list of expressions.
Solving a problem with theLeximinobjective follows theleximin order, that is:Find the solutions in which the smallest expression is as large as possible (subject to the constraints).If there are two or more such solutions, then among all these solutions, find the ones in which the next-smallest expression is as large as possible.If there are two or more such solutions, then among all these solutions, find the ones in which the third-smallest expression is as large as possible.
And so on.TheLeximaxobjective is solved in the opposite way: find the solutions thatminimizethelargestexpression (subject to the constraints); among them,  minimize the next-largest expression; and so on.Note that the current implementation works only when domain (as defined by the constraints) is convex. In particular, it does not work for integer programming.Installationpip install cvxpy_leximinUsage exampleLeximin optimization can be used to find an egalitarian allocation of resources among people (seeEgalitarian item allocation.)import cvxpy, logging
from cvxpy_leximin import Problem, Leximin

# There are four resources to allocate among two people: Alice and George.
# The variables a[0], a[1], a[2], a[3] denote the fraction of each resource given to Alice:
a = cvxpy.Variable(4)

# The following constraint represents the fact that the allocation is feasible:
feasible_allocation = [x >= 0 for x in a] + [x <= 1 for x in a]

# Alice values the resources at 5, 3, 0, 0:
utility_Alice = a[0] * 5 + a[1] * 3 + a[2] * 0

# George values the resources at 2, 4, 9, 0:
utility_George = (1 - a[0]) * 2 + (1 - a[1]) * 4 + (1 - a[2]) * 9

# The leximin objective is: maximize the smallest utility, and subject to that, the next-smallest utility.
objective = Leximin([utility_Alice, utility_George])

# A problem is defined and solved like any cvxpy problem:
problem = Problem(objective, constraints=feasible_allocation)
problem.solve()
print("Problem status: ", problem.status)   # Should be optimal
print("Objective value: ", objective.value)  
   # It is (8, 9). It maximizes the smallest utility (8), and subject to that, the next-smallest one (9).
print("Allocation: ", a.value)
   # It is [1, 1, 0, 0]: Alice gets resources 0 and 1 (utility=8) and George resources 2 and 3 (utility=9).For more examples, see theexamples folder.CreditsThe algorithm is based on:Stephen J. Willson,
"Fair Division Using Linear Programming" (1998).
Part 6, pages 20--27.I am grateful to Sylvain Bouveret for his help with the algorithm. All remaining errors and bugs are my own.StatusThe functionality was tested only on fair allocation problems, only on objectives with linear expressions, and only on the default solver (SCIPY).If you would like to contribute, it could be great to test leximin optimization on other kinds of problems, expressions and solvers.
Package: cvxreg
cvxregis a Python package for machine learing with convex regression models built onCVXPY.The core aims of this package are:make convex regression models "easy to call" from python,interface withCVXPY,focus on a "machine learning" perspective, i.e.: predictive task, hyper-parameters should be obtained by a data-driven method such as cross-validation.InstallationThecvxregpackage is now avaiable on PyPI and the latest development version can be installed from the Github repositoryConvexRegression. Please feel free to download and test it. We welcome any bug reports and feedback.PyPIpip install cvxreg
Package: cv-xtractor
CV-XtractorCV-Xtractor is a Python package designed for parsing information from CVs (resumes). It leverages spaCy for natural language processing, providing features to extract key details such as names, phone numbers, email addresses, skills, education information, and more.InstallationInstall CV-Xtractor using pip:pipinstallcv-xtractorAdditionally, download the spaCy English language model:python-mspacydownloaden_core_web_smUsageHere's a simple example of how to use CV-Xtractor in your Python code:fromcv_xtractor.extract_entitiesimportextract_entities_from_file# Replace 'your_cv.pdf' with the path to your CV filecv_file_path='your_cv.pdf'# Extract entities from the CVentities=extract_entities_from_file(cv_file_path)# Print the extracted informationprint(entities)This will output a dictionary containing various details extracted from the CV, such as names, contact information, skills, education, and more.For more advanced usage and customization, please refer to the [documentation]
Package: cvzoomwindow
CvZoomWindowCvNameWindow class that adds zoom and pan functions to OpenCV's namedWindowInstallpip install cvzoomwindowSampleimportcv2importcvzoomwindow# Image loadingimg=cv2.imread("image.bmp")# Instance of CvZoomWindow classzw=cvzoomwindow.CvZoomWindow("Zoom Window"# Name of the window)# Displays an imagezw.imshow(img)# Waits for a pressed key.cv2.waitKey()How to operatemouse operationoperationDouble click of left buttonDisplay the entire imageDouble click of right buttonEqual-size display of imagesLeft button dragMoving ImagesMouse wheel upZoom upMouse wheel downZoom downMore informationBlog) CvZoomWindow classhttps://imagingsolution.net/program/python/opencv-python/cvzoomwindow_class/
Package: cw2
cw2 - ClusterWork 2ClusterWork 2 is a python framework to manage experiments using YAML config files. It also enables users to easily deploy multiple experiments using different configurations on computing clusters, which support theslurm workload manager.Installationpipinstallcw2QuickstartPlease refer to theQuickstart Guide.Program ExecutionTo start an experiment locally, e.g. for testing:python3YOUR_MAIN.pyYOUR_CONFIG.ymlTo start an experiment on a slurm cluster:python3YOUR_MAIN.pyYOUR_CONFIG.yml-s
Package: cwaddressformatter
地址格式規則https://pypi.org/project/cwaddressformatter/Release Note0.1.5:  20231004 修正一些奇怪地址的邏輯例如 鳳凰之聲 會被誤判成 鳳凰-聲新光三越左營店10樓 被誤判成 新光3越左營店10樓移除門號以前的逗號0.1.2:  20230605 調整邏輯更改地址資料庫為郵局版本(2022/07)，理論上更準，修正了「中正路四維巷」這種中文巷名的問題。0.1.0:  20230307 調整邏輯F改為樓，應用在dash參數上(-x)半型改全型（）-0.0.8:  20221202 更新路名OpenData 110版0.0.5: 20221202 文字轉數字機制優化之 改為 -文字轉數字機制優化，增加exception時，改用取代的方式將文字轉數字移除路名前綴的數字(郵遞區號)移除地址中間的空格移除typo的疊字0.0.4: 20200914 根據需求調整１１７號８樓-１==>	全形要改半形F前是數字才改樓之和 - 不轉換0.0.3: 20200831 修正一些錯誤將地址中的F轉成樓，侷限在floor這個參數0.0.2: 20200831 修正一些錯誤村、鄰的數字改為alpha字母增加例外判斷，凡是路名分析出來，小於2, 大於7 地址皆不做轉換增加郵政信箱判斷0.0.1: 地址格式規則路名包含數字時,十以下已國字表示.例如：工業十路、三多三路；十以上以數字表
示，EX：工業13路但路名像是十三北路、四維路、八德路、六張犁路、二十張路則不在上述規則中X段中的X請一律用國字表示，如忠孝東路三段巷、弄、號、樓一律以阿拉伯數字表示，樓不可打F，”之”請以”-“示。
EX：3樓-2地址內僅允許存在3種符號：括號 ( )、-、空格，星號、斜線、加號都不行哦！國外地址要加國名，避免郵局退件。
EX：(1)中國上海市….、(2)12520 12TH AVE NE SEATTLE WA 98125 USA主檔的地址請建在地址1的欄位中即可(不要使用地址2)主檔中的台除發票抬頭(ex:臺灣大學)外，統統以“台”表示。工業區的話要保留。沒有路名的話，要保留里樓層後面 建議不處理，或是手動更新後重新處理路名參考台灣OpenData提供的地址路名訂單到ERP後，客服檢查發現地址錯誤  更正地址、找回正確的customerNo，提供API 更新訂單中的receiver.member <=> Customer 對應資料套件更新更新套件python3 -m pip install --user --upgrade setuptools wheel生成distpython3 setup.py sdist bdist_wheel安裝/更新 twinepython3 -m pip install --user --upgrade twine上傳套件python3 -m twine upload dist/cwaddressformatter-0.1.5-py3-none-any.whl參考：https://medium.com/kenny-chang/python-%E7%99%BC%E5%B8%83%E7%AC%AC%E4%B8%80%E5%80%8Bpython%E5%A5%97%E4%BB%B6-gaql-tool-c6c7c357f623awg5LMXD8QZPGw!
Package: cwave
cwave is a Python2/3 library implementing the Continuous Wavelet Transform (CWT)
using FFT as inA Practical Guide to Wavelet Analysisby C. Torrence and G.P.
Compo. cwave is an open-source, GPLv3-licensed software.Homepage and documentationIssuesGithub pageFinancial ContributionsComputational Biology Unit - Research and Innnovation Center at Fondazione
Edmund Mach
Package: cwbplot
cwbplot: Useful tools for meteorology dataThere are several useful tools for meteorology data visualization and data processing in this package.This package includesTools for CWB rfs team daily workCWB open data (https://opendata.cwb.gov.tw/index) IO and visualizationOthers這個套件包含氣象局區域模式小組日常工作工具氣象局開放資料(https://opendata.cwb.gov.tw/index) 讀取及視覺化其它Documentation:https://cwbplot.readthedocs.io/en/dev/index.html參考文件:https://cwbplot.readthedocs.io/en/dev/index.html
Package: cwb-python
This is a Python wrapper to the low-level API of CQP which allowsyou to access CQP corpora in the same way as Perl's CWB::CLIf you installed CQP in a non-standard location (which is the default fornewer versions of CQP), point the setup in the right direction with, e.g.export CWB_DIR=/usr/local/cwb-3.4.10To install the module, use the standardpython setup.py buildsudo python setup.py installcommand sequence.As a prerequisite, install Cython withpip install CythonIf you use an old version of CQP (CWB 2.99 and older), you need tochange the value of the "extra_libs" variable in setup.py.To give you an idea how to use the library, see the following sample:--- 8< ---from CWB.CL import Corpus# open the corpuscorpus=Corpus('TUEPP')# get sentences and wordssentences=corpus.attribute('s','s')words=corpus.attribute('word','p')postags=corpus.attribute('pos','p')# retrieve offsets of the 1235th sentence (0-based)s_1234=sentences[1234]for w,p in zip(words[s_1234[0]:s_1234[1]+1],postags[s_1234[0]:s_1234[1]+1]):print "%s/%s"%(w,p)--- 8< ---In order to test the CWB.CL module's correct installationindependently of any CQP corpora, you can do apython -m doctest tests/idlist.txtwhich should terminate with no output when everything is well.
Package: cwc
cwc  by Python LibraryOverviewコピーした文章からワードクラウドを作成するImplementation分かち書きでMeCab or Janomeを使ってます。Install$ pip install cwcUnInstall$ pip uninstall cwcRequirementpython3.7 later.LiscenseMIT Liscenserun command EnvironmentWindowsUsageテキスト文章をコピーするcwc.exeを実行するワードクラウド画像が自動的に起動する
Package: cwcwidth
Python bindings for wc(s)widthcwcwidthprovides Python bindings forwcwidthandwcswidthfunctions defined in POSIX.1-2001
and POSIX.1-2008 based onCython. These functions compute the printable
length of a unicode character/string on a terminal. The module provides the same functions aswcwidthand its behavior is compatible.On systems not conforming to POSIX.1-2001 and POSIX.1-2008, Markus Kuhn'simplementationis used to provide the
functionality.DependenciesCython >= 0.28(optional, only for building). If Cython is not available, the C files are not
regenerated from their source.Quick installation guidecwcwidthcan be installed viapip:pipinstallcwcwidthor by running:python3setup.pyinstallUsage>>>importcwcwidth>>>cwcwidth.wcwidth("a")1>>>cwcwidth.wcswidth("コ")2>>>cwcwidth.wcswidth("コンニチハ, セカイ!")19>>>cwcwidth.wcswidth("コンニチハ, セカイ!",5)10Comparison withwcwidth>>>importwcwidth,cwcwidth,timeit>>>timeit.timeit(lambda:wcwidth.wcswidth("コンニチハ, セカイ!"))19.14463168097427>>>timeit.timeit(lambda:cwcwidth.wcswidth("コンニチハ, セカイ!"))0.16294104099506512LicenseThe code is licensed under the MIT license.