Saravanan666/pypi-org huggingface dataset:
Package: cpca
chinese_province_city_area_mapperchinese_province_city_area_mapper：一个用于识别简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块举个例子:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
         ↓ 转换
|省    |市   |区    |地址                 |
|上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
|福建省|泉州市|洛江区|万安塘西工业区        |chinese_province_city_area_mapper: built to be recognize Chinese province,city and area in simplified Chinese string, it can automaticall map area to city
and map city to province.for example:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
         ↓ transform
|省    |市   |区    |地址                 |
|上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
|福建省|泉州市|洛江区|万安塘西工业区        |完整文档见该模块的Github，
GitHub:https://github.com/DQinYuan/chinese_province_city_area_mapper安装说明代码目前仅仅支持python3pip install cpcaGet Started本模块中最主要的方法是cpca.transform,
该方法可以输入任意的可迭代类型（如list，pandas的Series类型等），
然后将其转换为一个DataFrame，下面演示一个最为简单的使用方法:location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "北京朝阳区北苑华贸城"]
import cpca
df = cpca.transform(location_str)
df输出的结果为:省     市    区          地址              adcode
0 上海市 上海市  徐汇区     虹漕路461号58号楼5楼  310104
1 福建省 泉州市  洛江区     万安塘西工业区        350504
2 北京市 市辖区  朝阳区     北苑华贸城           110105如果还想知道更多的细节，请访问该
模块的github地址https://github.com/DQinYuan/chinese_province_city_area_mapper，
在那里我写了更多的细节
Package: cpca-fix
chinese_province_city_area_mapperchinese_province_city_area_mapper：一个用于识别简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块举个例子:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
         ↓ 转换
|省    |市   |区    |地址                 |
|上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
|福建省|泉州市|洛江区|万安塘西工业区        |chinese_province_city_area_mapper: built to be recognize Chinese province,city and area in simplified Chinese string, it can automaticall map area to city
and map city to province.
for example:["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区"]
         ↓ transform
|省    |市   |区    |地址                 |
|上海市|上海市|徐汇区|虹漕路461号58号楼5楼  |
|福建省|泉州市|洛江区|万安塘西工业区        |完整文档见该模块的Github，
GitHub:https://github.com/DQinYuan/chinese_province_city_area_mapper特点基于jieba分词进行匹配，同时加入了一些额外的校验匹配逻辑保证了准确率因为jieba分词本身只有80%的准确率，经常会分错，所以引入了全文匹配的模式，这种模式下能够提高准确率，但会导致性能降低，关于如何开启这个模式见Github上的使用文档如果地址数据比较脏的，不能指望依靠这个模块达到100%的准确，本模块只能保证尽可能地提取信息，如果想要达到100%准确率的话，最好在匹配完后再人工核验一下自带完整的省，市，区三级地名及其经纬度的数据支持自定义省，市，区映射输出的是基于pandas的DataFrame类型的表结构，易于理解和使用封装了简单的绘图功能，可以很方便地进行简单的数据可视化MIT 授权协议安装说明代码目前仅仅支持python3pip install cpcaGet Started本模块中最主要的方法是cpca.transform,
该方法可以输入任意的可迭代类型（如list，pandas的Series类型等），
然后将其转换为一个DataFrame，下面演示一个最为简单的使用方法:location_str = ["徐汇区虹漕路461号58号楼5楼", "泉州市洛江区万安塘西工业区", "朝阳区北苑华贸城"]
from cpca import *
df = transform(location_str)
df输出的结果为:区    市      省         地址
0  徐汇区  上海市  上海市   虹漕路461号58号楼5楼
1  洛江区  泉州市  福建省   万安塘西工业区
2  朝阳区  北京市  北京市   北苑华贸城全文模式：jieba分词并不能百分之百保证分词的正确性，在分词错误的情况下会造成奇怪的结果，比如下面:location_str = ["浙江省杭州市下城区青云街40号3楼","广东省东莞市莞城区东莞大道海雅百货"]
from cpca import *
df = transform(location_str)
df输出的结果为:区    市    省            地址
城区  东莞市  广东省  莞大道海雅百货自然堂专柜
城区  杭州市  浙江省  下青云街40号3楼这种诡异的结果因为jieba本身就将词给分错了，所以我们引入了全文模式，不进行分词，直接全文匹配，使用方法如下:location_str = ["浙江省杭州市下城区青云街40号3楼","广东省东莞市莞城区东莞大道海雅百货"]
from cpca import *
df = transform(location_str, cut=False)
df输出结果:区    市    省        地址
下城区  杭州市  浙江省  青云街40号3楼
莞城区  东莞市  广东省    大道海雅百货这些就完全正确了，不过全文匹配模式会造成效率低下，我默认会向前看8个字符(对应transform中的lookahead参数默认值为8)，这个是比较保守的，因为有的地名会比较长（比如“新疆维吾尔族自治区”），如果你的地址库中都是些短小的省市区名的话，可以选择将lookahead设置得小一点，比如:location_str = ["浙江省杭州市下城区青云街40号3楼","广东省东莞市莞城区东莞大道海雅百货"]
from cpca import *
df = transform(location_str, cut=False, lookahead=3)
df输出结果与上面一样。如果还想知道更多的细节，请访问该
模块的github地址https://github.com/DQinYuan/chinese_province_city_area_mapper，
在那里我写了更多的细节.
Package: cpc_api
[![Build Status](https://travis-ci.org/regardscitoyens/cpc-api.svg)](https://travis-ci.org/regardscitoyens/cpc-api)# CPC-API
A python api forhttp://nosdeputes.frandhttp://nossenateurs.frCPC is forControle Parlementaire Citoyens.See full API documentation here:https://github.com/regardscitoyens/nosdeputes.fr/blob/master/doc/api.md## ExamplesDéputes`python from cpc_api import CPCApi api =CPCApi(legislature='2012-2017')# synthese of legislature synthese = api.synthese() # search for a depute cope =api.search_parlementaires('Cope')[0][0]# get all info of cope all_info =api.parlementaire(cope['slug'])`Sénateurs & députés legislature 2007-2012…`python from cpc_api import CPCApi # do the same with senateurs api =CPCApi(ptype='senateur')larcher =api.search_parlementaires('larcher')[0][0]# 'or with legislature2007-2012'api =CPCApi(ptype='depute',legislature='2007-2012')morano =api.search_parlementaires('morano')[0][0]# ... `
Package: cpcax
AboutThis is a forked version of the cpca project (https://github.com/DQinYuan/chinese_province_city_area_mapper).PurposeFrom Github actitivy, it seems cpca has stopped maintenance since 2021.During usage, we encountered several problems with cpca.Some of the problems are:Inaccessible map data.cpca uses the map patches from openstreetmap.org, which can not be accessed now.Incompatible issues due to dependency versions.e.g.,jinja2andpyechartshave import problems for newer versions.In this repo, we try to revise and fix the above issues.Main RevisionsUse map tiles from AMapdef heatmap_folium(adcodes, file_path, 
                   tiles= 'https://wprd01.is.autonavi.com/appmaptile?x={x}&y={y}&z={z}&lang=zh_cn&size=1&scl=1&style=7', 
                   attr = 'amap'):
    ...
    map_osm = folium.Map(location=[35, 110], zoom_start=5, 
                         tiles = tiles, attr=attr)Fix some issue due to incompatible versions.Issue:ImportError: cannot import name 'environmentfunction' from 'jinja2'Fix:jinja2==3.0.0Issue:cannot import name 'Geo' from 'pyecharts'Fix: support the latest pyecharts version (We tested for 2.0.x. The old version is 0.5.x).Change map styles.Use white background maps inecharts_heatmap()andheatmap_folium().Installpip install cpcaxUsePrepare a list of address codes. The frequency of each code is used the value.
e.g.,['110000','110000','310018']folium heatmapfrom cpca import drawer
drawer.folium_heatmap(邮编列表, "geos.html")

from IPython.display import IFrame
IFrame(src='geos.html', width=1600, height=900)echarts heatmapfrom cpca import drawer
drawer.echarts_heatmap(邮编列表, "echarts_geos.html")

from IPython.display import IFrame
IFrame(src='echarts_geos.html', width=800, height=600)TODOExpose more style parameters for map visualization.
Package: cpcctool
cpcc-toolSome method for Software copyright application in China, including the generation of source code Word documents, source code statistics, etc.pip install cpcctoolExample: Generate documentation for the first and last 40 pages of source codeimportuuidimportosfromcpcctoolimportgen_code_docx,docx_first_40_pages,docx_last_40_pagessrc_dirs=[r"F:\workspace\xxx\code1\",r"F:\workspace\xxx\code2\"]dst_file=str(uuid.uuid4())+".docx"gen_code_docx(src_dirs,dst_file)dst_file=os.path.abspath(dst_file)docx_first_n_pages(dst_file,r"xxxSystem XXX V2.0.0 Source Code first 40 pages.docx",40)docx_last_n_pages(dst_file,r"xxxSystem XXX V2.0.0 Source Code last 40 pages.docx",40)Example: Statistics source code total linesfromcpcctoolimportcount_code_linessrc_dirs=[r"F:\workspace\xxx\code1\",r"F:\workspace\xxx\code2\"]cloc_exe=r"F:\workspace\l2m2\cpcc-tool\cloc-1.84.exe"count=count_code_lines(cloc_exe,src_dirs)print(count)Publish to PyPIpip install --user --upgrade setuptools wheel twine
python setup.py sdist bdist_wheel
python -m twine upload dist/*ReferenceCLOCpython-docxHow do I delete a range (pages) in a document using VBA.
Package: cpc-fusion
cpc-fusionA Python package based onweb3.pyto interact with
cpchain.Python 3.5+ supportInstallationcpc_fusion.py can be installed (preferably in a virtualenv) usingpipas follows:pipinstallcpc-fusionInstallation from source can be done from the root of the project with
the following command.pipinstall.Using FusionTo use the web3 library you will need to initialize theWeb3class.Use theautomodule to guess at common node connection options.fromcpc_fusionimportWeb3cf=Web3(Web3.HTTPProvider('http://127.0.0.1:8501'))cf.cpc.blockNumber>>>34341NoteIf you get the resultUnhandledRequest: No providers responded to the RPC requestthen you
are not connected to a node.Read more in thedocumentation on
CPChain.
Package: cpc.geofiles
[![image](https://img.shields.io/pypi/v/cpc.geofiles.svg)](https://pypi.python.org/pypi/cpc.geofiles)[![Build Status](https://travis-ci.org/noaa-nws-cpc/cpc.geofiles.svg?branch=master)](https://travis-ci.org/noaa-nws-cpc/cpc.geofiles)CPC geospatial file interaction (reading, writing, etc.)Free software: Creative Commons licenseFeaturesTODOCreditsThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [v0.1.0] - 2016-07-06### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: cpc.geogrids
[![Build Status](https://travis-ci.org/noaa-nws-cpc/cpc.geogrids.svg?branch=master)](https://travis-ci.org/noaa-nws-cpc/cpc.geogrids) [![Coverage Status](https://coveralls.io/repos/github/noaa-nws-cpc/cpc.geogrids/badge.svg?branch=master)](https://coveralls.io/github/noaa-nws-cpc/cpc.geogrids?branch=master)What is a Geogrid?The purpose of this package is to create and manipulate Geogrid objects. A Geogrid object stores
information about a geospatial grid, such as the resolution, lat/lon values of each grid point, etc.InstallationYou must have [Anaconda](https://anaconda.org) installed in order to installcpc.geogrids. Follow their instructions for installing on your system. Once you have Anaconda installed, installingcpc.geogridsis simple:$ make installMore documentationVisit our [documentation site](https://noaa-nws-cpc.github.io/cpc.geogrids)ContributingSee our [CONTRIBUTING document](.github/CONTRIBUTING.md)# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [v0.1.0] - 2016-07-06### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: cpc.geoplot
[![Build Status](https://travis-ci.org/noaa-nws-cpc/cpc.geoplot.svg?branch=master)](https://travis-ci.org/noaa-nws-cpc/cpc.geoplot) [![Coverage Status](https://coveralls.io/repos/github/noaa-nws-cpc/cpc.geoplot/badge.svg?branch=master)](https://coveralls.io/github/noaa-nws-cpc/cpc.geoplot?branch=master)What is a GeoGrid?The purpose of this package is to create and manipulate GeoGrid objects. A GeoGrid object stores
information about a geospatial grid, such as the resolution, lat/lon values of each grid point, etc.InstallationYou must have [Anaconda](https://anaconda.org) installed in order to installcpc.geoplot. Follow their instructions for installing on your system. Once you have Anaconda installed, installingcpc.geoplotis simple:$ make installMore documentationVisit our [documentation site](https://noaa-nws-cpc.github.io/cpc.geoplot)ContributingSee our [CONTRIBUTING document](.github/CONTRIBUTING.md)# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [v0.1.0] - 2016-07-06### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: cpc.mpp
[![image](https://img.shields.io/pypi/v/cpc.mpp.svg)](https://pypi.python.org/pypi/cpc.mpp)[![image](https://img.shields.io/travis/mikecharles/cpc.mpp.svg)](https://travis-ci.org/mikecharles/cpc.mpp)Model post processing packageFree software: Creative Commons licenseFeaturesTODOCreditsThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [0.1.0] - 2016-1-22### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: cpctl
Cooper Control ToolThis is the CLI tool to control cooperInstallingYou can installcpctldirectly from PyPI:sudopip3install-UcpctlNote: You may need to usesudobefore the command - it depends on the operating system used...UsageThis tool has built-in help system. Just run:>>cpctl--help
Usage:cpctl[OPTIONS]COMMAND[ARGS]...CooperControlTool.

Options:-d,--deviceDEVICEDevicepath.--zmqHOST:PORTZMQ--versionShowtheversionandexit.--helpShowthismessageandexit.

Commands:configConfigdevicesPrintavailabledevices.infoInfonodeManagethenodesresetResetstatusPrintstatusLicenseThis project is licensed under theMIT License- see theLICENSEfile for details.
Package: cpctools
cpctoolsWe are no longer developing in this project. Future works will be indynsight.cpctoolsis a python 3.8/3.9/3.10 library aimed at simplifying the analysis of Molecular Dynamics simulations.cpctoolsstands forComputationalPhysicalChemistryTOOLS, or, if you prefer, forChemicalPhysicsComputationalTOOLS.It contains two packages,SOAPifyandSOAPify.HDF5er.cpctoolsusesh5pyto store the trajectories, the SOAP fingerprints, and the analysis results in a binary format, facilitated byHDF5er.The documentation is available onread the docs. There you can consult the documentation for each available version of the package.How To InstallTo install the stable version ofcpctoolsjust type:pipinstallcpctoolsIf you want to usedscribeorquippyfor calculating the SOAP features you should install them separately, since they are quite heavy packages on their own, and usually you would use only one of these packages:pipinstall"dscribe<=1.2.2,>1.2.0"pipinstall"quippy-ase==0.9.10"package: SOAPifyThis package contains:a toolbox to calculate theSOAP fingerprintsof a system of atoms. The principal aim is to simplify the setup of the calculation. This toolbox depends ondscribeorquippyand 'unify' the output of the two codes.a toolbox to calculate the distances between SOAP fingerprintsa simple analysis tool for trajectories of classified atomspackage: SOAPify.HDF5erThis package is a toolbox to createhdf5 fileswithh5pyfrom the trajectory and topology files. The format we usedo notalign withh5mdOur format is thought to speed up the calculations without occupying too much RAM, thanks to the hdf5 dataset chunking capabilities.The data within the files are organized into Group categories:"Trajectories" contains subgroups that represent the various stored trajectories, each trajectory subgroup contains three datasets:"Types" contains the types of atoms in the simulation"Box" contains the history of the box dimensions"Trajectory" contains the history of the particle positions"SOAP" group contains the Dastasets of the calculated SOAP fingerprints, each SOAP Dataset contains attributes with the settings to reproduce the results."Classification" contain a group per trajectory, the format of the Dataset contained within is not fixedThe user can choose to use a single file per project or to store separately the results of the various steps of the analysis project (this is more recommended).
SOAPify.HDF5er contains a tool for exporting the trajectories from the hdf5 file toextended xyzformat, compatible withovito
Package: cpc.units
[![Build Status](https://travis-ci.org/mikecharles/cpc.units.svg?branch=master)](https://travis-ci.org/mikecharles/cpc.units) [![Coverage Status](https://coveralls.io/repos/github/mikecharles/cpc.geogrids/badge.svg?branch=master)](https://coveralls.io/github/mikecharles/cpc.geogrids?branch=master)InstallationYou must have [Anaconda](https://anaconda.org) installed in order to installcpc.geogrids. Follow their instructions for installing on your system. Once you have Anaconda installed, installingcpc.geogridsis simple:$ make installMore documentationVisit our [documentation site](https://mikecharles.github.io/cpc.units)ContributingSee our [CONTRIBUTING document](.github/CONTRIBUTING.md)# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [0.1.0] - 2016-1-22### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: cpdalp
Pure Numpy Implementation of the Coherent Point Drift Algorithm.MIT License.IntroductionThis is a pure numpy implementation of the coherent point driftCPDalgorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.The registration methods work for 2D and 3D point clouds. For more information, please refer to myblog.Pip InstallpipinstallpycpdInstallation From SourceClone the repository to a location, referred to as therootfolder. For example:gitclonehttps://github.com/siavashk/pycpd.git$HOME/pycpdInstall the package:pipinstall.For running sample registration examples underexamples, you will needmatplotlibto visualize the registration. This can be downloaded by running:pipinstallmatplotlibUsageEach registration method is contained within a single class inside thepycpdsubfolder. To try out the registration, you can simply run:pythonexamples/fish_{Transform}_{Dimension}.pywhereTransformis eitherrigid,affineordeformableandDimensionis either2Dor3D. Note that examples are meant to be run from therootfolder.
Package: cpdb-api
A Python API for NewClimate Institute's ClimatePolicy DataBase (CPDB). You can also view the database via the website athttps://climatepolicydatabase.org/.InstallationInstallation is simple, via pip.pip install cpdb-apiIf youneedto specify a version, please do this (we strongly recommend against this unless you know what you're doing):pip install cpdb-api==<version e.g. 1.0.5>Usagefrom cpdb_api import request 

r = request.Request()

# set filters
r.set_country("IND")
r.set_decision_date(2010)
r.set_policy_status("In force")
r.add_sector("Electricity and heat")
r.add_sector("Coal")
r.add_policy_instrument("Energy and other taxes")
r.add_mitigation_area("Renewables")

# Issue the request (this returns a pandas dataframe, if you want to parse it programmatically)
r.issue()

# save the result to CSV file
r.save_csv("filtered_cpdb.csv")ReleasingTo release to PyPi (pip), do the following:Increment the version number in pyproject.tomlRun the following commands:$ python3 -m build
$ python3 -m twine upload dist/*
Package: cpdetect
Change-point detection with cpdetectcpdetectis a python package designed for change point-detection using statistical methods. This is a first version and considers only one change-point model which is normal mean model. This assumes normally distributed time series values and changes only in the mean (mean shift). The package offers three detection methods which are Binary Segmentation (BS), Backward Detection (BWD) and Screening and Ranking algorithm (SaRa).InstallTo install the package usepip install cpdetectExample usageLet's import some useful libraries first.importscipy.statsasspimportnumpyasnpfrommatplotlibimportpyplotaspltNow we can create an example time series with three change-points.# mean shifts between 0 and 3, sigma = 2Y1=sp.norm.rvs(0,2,200)Y2=sp.norm.rvs(3,2,200)Y3=sp.norm.rvs(0,2,200)Y4=sp.norm.rvs(3,2,200)Y=np.hstack((Y1,Y2,Y3,Y4))plt.plot(Y)To find the change-points location we can useBinSegwhich contains binary segmentation implementation.fromcpdetectimportBinSegbs=BinSeg()# creating objectbs.fit(Y,stat='Z',sigma=2)# fitting to dataplt.plot(bs.stat_values)# statistic plotbs.predict(0.01)# change-point detectionIf we don't know what the standard deviation (sigma) is, we can use T statistic.bs.fit(Y,stat='T')# fitting to dataplt.plot(bs.stat_values)# statistic plotbs.predict(0.01)# change-point detectionIf we don't know the distribution of time series values, we can't use normal mean models. Then we can use bootstrap which finds the statistic distribution by itself.bs.predict(0.01,bootstrap_samples=1000)Libraries usednumpypandasscipy
Package: cpdf
cpdfcpdf is a pure-python cli application for manipulating PDF files.Installationpipinstall-UcpdfAscpdfis an application, you might want to install it withpipx.Usage$cpdf--helpUsage: cpdf [OPTIONS] COMMAND [ARGS]...cpdf is a pure-python cli application for manipulating PDF files.Options:--version--help     Show this message and exit.Commands:2-up            Create a booklet-style PDF from a single input.cat             Concatenate pages from PDF files into a single PDF file.extract-images  Extract images from PDF without resampling or altering.You can see the help of every subcommand by typing:$cpdf2-up--helpUsage: cpdf 2-up [OPTIONS] PDF OUTCreate a booklet-style PDF from a single input.Pairs of two pages will be put on one page (left and right)usage: python 2-up.py input_file output_fileArguments:PDF  [required]OUT  [required]Options:--help  Show this message and exit.
Package: cpdflow
cpdflow is a declarative approach to model lifecycle management on Cloud Pak for Data.In a nutshell, cpdflow consolidates APIs from various Cloud Pak for Data modules in a dependency graph aligned to the 4 model lifecycle stages fromFactsheets Model Inventory; so that you can simply declare the end state of each model and cpdflow infers the necessary steps and runs them.Think of it as usingKubernete's applyto manage model lifecycles. Simply declare the finallifecycle stagefor each model and let cpdflow handle the rest.For example, todeploytwo models (German Credit Risk-SVCandGerman Credit Risk-RF) in a development space, simply define the model names in the respective lifecycle stage.cpdflow apply deploy -c config.json -m "German Credit Risk-SVC" -m "German Credit Risk-RF"And tovalidateanother model (German Credit Risk-GBC) on OpenScale in a development environment.cpdflow apply validate -c config.json -m "German Credit Risk-GBC"Note: Although there are prerequisites steps such as training, deploying and subscribing before OpenScale can evaluate the model, it was not explictly defined as cpdflow handles dependencies automatically.cpdflow infers the necessary steps that needs to be actioned upon to achieve the final state.Under the hood, cpdflow generates an execution path based on adependency graphand runs the necessary steps to achieve the model's desired lifecycle stage.Here is what the entire graph looks like,This is a living graph and is updated consistently to add new features and update API changes.
Package: cpe
Common Platform Enumeration for PythonCPE(this code) is a LGPL licensed Python package, implementing the
CPE standards.About the CPE standardCommon Platform Enumeration (CPE) is a standardized method of describing
and identifying classes of applications, operating systems, and hardware
devices present among an enterprise’s computing assets.For more information, please visit the official website of CPE,
developed byMITREand maintained byNIST.FeaturesCPE rich comparison.CPE cross-version conversion.CPE Language parsing and evaluation.LGPL Licensed.Semantic versioning.Tests.InstallationTo installCPEexecute:$pipinstallcpeThe latest stable version is always inPyPI.DocumentationDocumentation is available atReadTheDocs.CompatibilityPython: 2.7, 3.4CPE: 1.1, 2.2, 2.3CPE Formats: WFN, URI, FS.ContributeFollow the steps on thehow to contributedocument.
Package: cpematcher
# cpematcher[![Build Status](https://travis-ci.com/alertot/cpematcher.svg?branch=master)](https://travis-ci.com/alertot/cpematcher)  [![PyPIersion](https://badge.fury.io/py/cpematcher.svg)](https://badge.fury.io/py/cpematcher)## OverviewMatch CPEs between them.## Installation / UsageTo install use pip:$ pip install cpematcher