Saravanan666/pypi-org huggingface dataset:
Package: cvmmlst
cvmmlst__     __
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\___/ |___/_/ /_/ /_/_/ /_/ /_/_/____/\__/cvmmlst is a bacteria mlst analysis tool that could run on Windows, Linux and MAC os. Some of the code ideas in cvmmlst draw on Torsten Seemanns excellentmlsttool.InstallationUsing pippip3 install cvmmlstUsing condacomming soon...DependencyBLAST+ >2.7.0you should add BLAST in your PATHBlast installationWindowsFollowing this tutorial:Add blast into your windows PATHLinux/MacThe easyest way to install blast is:conda install -c bioconda blastUsageInitialize reference databaseAfter finish installation, you should first initialize the reference database using following commandcvmmlst -initusage: cvmmlst -i <genome assemble directory> -o <output_directory>

Author: Qingpo Cui(SZQ Lab, China Agricultural University)

optional arguments:
  -h, --help      show this help message and exit
  -i I            <input_path>: the PATH to the directory of assembled genome files. Could not use with -f
  -f F            <input_file>: the PATH of assembled genome file. Could not use with -i
  -o O            <output_directory>: output PATH
  -minid MINID    <minimum threshold of identity>, default=90
  -mincov MINCOV  <minimum threshold of coverage>, default=60
  -init           <initialize the reference database>
  -t T            <number of threads>: default=8
  -v, --version   Display versionOutputyou will get a text file and a summray file in csv format in the output directory.The text file likedatbglAcatldhabcZdapElhkASTSchemeFILE314391214487listeria_2665The content in csv summary file likedatbglAcatldhabcZdapElhkASTSchemeFILE314391214487listeria_2sample0124414353listeria_2sample0266837781121listeria_2sample03314391214487listeria_2sample0424414353listeria_2sample0566837781121listeria_2sample06
Package: cvmodels
Auto Documentation for developersAuthor: Shaon MajumderUtilitiesAuto DocumentationInstallationpip install documentationFunction UsagesFunction Usages EndVersioningmajor.minor[.maintenance[.build]](example:1.4.3.5249)adoption: major.minor.patch.maintenance.status.trials_for_successThe last position0 for alpha (status)1 for beta (status)2 for release candidate3 for (final) releaseFor instance:1.2.0.1 instead of 1.2-a11.2.1.2 instead of 1.2-b2 (beta with some bug fixes)1.2.2.3 instead of 1.2-rc3 (release candidate)1.2.3.0 instead of 1.2-r (commercial distribution)1.2.3.5 instead of 1.2-r5 (commercial distribution with many bug fixes)
Package: cvnet
# cvnet
Build Model for Computer Vision(CV) Neural Network.## 图像分类## 图像分割语义分割实例分割全景分割### 技术演化路径2010年前，传统分割：1）边缘检测；2）遗传算法2010-2015年，机器学习：1）随机森林；2）支持向量机2015年后，深度学习：1）经典分割算法：FCN, U-Net, SegNet, DeepLab; 2)实时分割算法：ENet, LinkNet, BiSeNet, DFANet, Light-Weight RefineNet; 3)RGB-D分割算法：RedNet, RDFNet### Networks implemented[PSPNet](https://arxiv.org/abs/1612.01105) - With support for loading pretrained models w/o caffe dependency[ICNet](https://arxiv.org/pdf/1704.08545.pdf) - With optional batchnorm and pretrained models[FRRN](https://arxiv.org/abs/1611.08323) - Model A and B[FCN](https://arxiv.org/abs/1411.4038) - All 1 (FCN32s), 2 (FCN16s) and 3 (FCN8s) stream variants[U-Net](https://arxiv.org/abs/1505.04597) - With optional deconvolution and batchnorm[Link-Net](https://codeac29.github.io/projects/linknet/) - With multiple resnet backends[Segnet](https://arxiv.org/abs/1511.00561) - With Unpooling using Maxpool indices#### Upcoming[E-Net](https://arxiv.org/abs/1606.02147)[RefineNet](https://arxiv.org/abs/1611.06612)### DataLoaders implemented[CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)[Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/segexamples/index.html)[ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/)[MIT Scene Parsing Benchmark](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip)[Cityscapes](https://www.cityscapes-dataset.com/)### Demodemo site:https://www.remove.bg/upload演示效果：demo1:<img src=”./docs/7.jpg” width=”600” />remove background:<img src=”./docs/7-removebg-preview.png” width=”600” />demo2:<img src=”./docs/red_car.png” width=”600” />remove background:<img src=”./docs/red_car-removebg-preview.png” width=”600” /># Reference
1. [ClassyVision](https://github.com/facebookresearch/ClassyVision)
2. [Deep-Learning-Project-Template](https://github.com/L1aoXingyu/Deep-Learning-Project-Template)
3. [pytorch-semseg](https://github.com/meetshah1995/pytorch-semseg)
4. [torchcv](https://github.com/donnyyou/torchcv)
5. [pytorch-cnn-finetune](https://github.com/creafz/pytorch-cnn-finetune)
6. [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
Package: cvnqplyobj
Cvnqplyobj=================This API SDK was automatically generated by [APIMATIC Code Generator](https://apimatic.io/).This SDK uses the Requests library and will work for Python ```2 >=2.7.9``` and Python ```3 >=3.4```.How to configure:=================The generated code might need to be configured with your API credentials.To do that, open the file "Configuration.py" and edit its contents.How to resolve dependencies:===========================The generated code uses Python packages named requests, jsonpickle and dateutil.You can resolve these dependencies using pip ( https://pip.pypa.io/en/stable/ ).1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r requirements.txt```Note: You will need internet access for this step.How  to test:=============You can test the generated SDK and the server with automatically generated testcases. unittest is used as the testing framework and nose is used as the testrunner. You can run the tests as follows:1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r test-requirements.txt```3. Invoke ```nosetests```How to use:===========After having resolved the dependencies, you can easily use the SDK following these steps.1. Create a "cvnqplyobj_test.py" file in the root directory.2. Use any controller as follows:```pythonfrom __future__ import print_functionfrom cvnqplyobj.cvnqplyobj_client import CvnqplyobjClientapi_client = CvnqplyobjClient()controller = api_client.clientresponse = controller.get_basic_auth_test(<required parameters if any>)print(response)```
Package: cvnum
The Python modulecvnumI beg your pardon for my english...English is not my native language, so be nice if you notice misunderstandings, misspellings, or grammatical errors in my documents and codes.Last version: 0.0.0This version was made on 2022-08-10.AboutcvnumHere are the features proposed bycvnum.Conversion of natural integers from one base to another.Textual version of natural integers in several languages.Proposing new translations can be done without being a programmer.
Package: cvpack
Collective Variable PackageOverviewCollective variables (CVs) are functions of the coordinates of a molecular system and provide a
means to project its conformational state onto a lower-dimensional space. By stimulating the
dynamics of a judiciously chosen set of CVs, one can obtain an enhanced sampling of the
configuration space, including regions that are otherwise difficult to access. The system's
free energy as a function of these CVs can be used to characterize the relative stability of
different states and to identify pathways connecting them.CVPack is a Python package that provides pre-defined CVs for the powerful molecular dynamics engineOpenMM. All these CVs are subclasses of OpenMM'sForceclass and, as such, can be directly added
to aCustomCVForceor used to define aBiasVariableforMetadynamics, for instance.Collective VariablesThe CVs implemented in CVPack are listed in the table below.Collective VariableDescriptionAngleangle formed by three atomsAtomic Functiona user-defined function of the coordinates of a group of atomsAttraction Strengthstrength of the attraction between two groups of atomsCentroid Functiona user-defined function of the centroids of groups of atomsComposite RMSDmultibody RMSD with concerted-rotation alignmentDistancedistance between two atomsHelix angle contentalpha-helix angle content of a sequence of residuesHelix H-bond contentalpha-helix hydrogen-bond content of a sequence of residuesHelix RMSD contentalpha-helix RMSD content of a sequence of residuesHelix torsion contentalpha-helix Ramachandran content of a sequence of residuesNumber of contactsnumber of contacts between two groups of atomsPathInCVSpaceprogress along (deviation from) a predefined path in CV spaceRadius of gyrationradius of gyration of a group of atoms(Radius of gyration)^2square of the radius of gyration of a group of atomsResidue coordinationnumber of contacts between two disjoint groups of residuesRMSDroot-mean-square deviation with respect to a reference structureSheet RMSD contentbeta-sheet RMSD content of a sequence of residuesTorsiontorsion angle formed by four atomsTorsion similaritydegree of similarity between pairs of torsion anglesBelow is a list of new CVs already available in the development version:Collective VariableDescriptionPathInCVSpaceprogress along (or deviation from) a predefined path in CV spaceInstallation and UsageCVPack is available as a conda package on themdtoolschannel. To install it, run:condainstall-cconda-forge-cmdtoolscvpackOr:mambainstall-cmdtoolscvpackTo use CVPack in your own Python script or Jupyter notebook, simply import it as follows:importcvpackDocumentationDocumentation for the latest CVPack version is available atGithub Pages.CopyrightCopyright (c) 2023-2024C. Abreu&Redesign ScienceAcknowledgementsInitial project based on theCMS Cookiecutterversion 1.1.
Package: cvpack-alkasm
cvpackOpenCV extensions for more Pythonic interactions.Installpipinstallcvpack-alkasmTypescvpackincludes types that exist in the main C++ OpenCV codebase, but that aren't included in the Python bindings. They are compatible as arguments to OpenCV functions, and they implement the same interfaces (with some new additions). The types that are included arePoint,Point3,Rect,RotatedRect,Size,TermCriteria. They are implemented as namedtuples, and as such are immutable.importcvpackimg=cvpack.imread("img.png")p1=cvpack.Point(50,50)p2=cvpack.Point(100,100)rect=cvpack.Rect.from_points(p1,p2)roi=img[rect.slice()]roi_size=cvpack.Size.from_image(roi)assertroi_size==rect.size()The overloaded constructors are available asfrom_classmethods, likefrom_pointsshown above. They also follow the same operator overloads that OpenCV has: two points summed is a point, adding a point to a rectangle shifts it, you can&two rectangles to get the intersection as a new rectangle, and so on.Image IOWrappers forimread,imwrite, andimshowsimplify usage by checking errors and allowing path-like objects for path arguments. Additionally,cvpackprovides functions to read images from a URL (imread_url), display to a browser (imshow_browser) for statically serving images while working in an interpreter, and displaying images in a Jupyter notebook (imshow_jupyter) as HTML directly rather than the typicalplt.imshowfrommatplotlib. Some other utilities related to display are also included.frompathlibimportPathimportcvpackforpathinPath("folder").glob("*.png"):img=cvpack.imread(path)big=cvpack.add_grid(cvpack.enlarge(img))cvpack.imshow_browser(img,route=str(path))Video IOWorking with video requires acquiring and releasing resources, socvpackprovides context managers for video readers and writers which wrap the classes from OpenCV. Reading video frames is simplified to iterating over the capture object.importcv2importcvpackwithcvpack.VideoCapture("video.mp4")ascap:withcvpack.VideoWriter("reversed.mp4",fourcc=int(cap.fourcc),fps=cap.fps)aswriter:forframeincap:flipped=cv2.flip(frame,0)writer.write(flipped)
Package: cv-parser
CV ParserCreate and parse a resume in YAML format.Dependencies:Pyyaml: for the parsing of the .yaml file.PyPDF: for the pdf creation.Pillow: for the image operation over the profile picture.Jinja2: for the creation of the web page resume.Installation:You can install it using PIP:pip install cv-parserUsage:In order to create the initial structure of the CV you can execute this command:cv_parser  generateThis will createmy_cv.ymlandme.jpgfiles.Once you have edited this files you can do:cv_parser my_cv.ymlThis will take themy_cv.ymlfile and generate the resume files into theoutputfolder.Usually thecv_parsecommand generate a pdf and html file, if you only want
one of those you can run fo web:cv_parser --render-type web my_cv.ymlorcv_parser --render-type pdf my_cv.ymlfor pdf creation.
Package: cvpartnerpy
CVPartner scraperThis is a package for scraping the cvpartner apiGetting startedOnce this is in the package index:$ pip install cvpartnerpyUsageThe package has support for getting user metadata and CVs attached to users.
This can be used to scrape out all user CVs from the api:cvp=CVPartner(org='myorg',api_key=os.environ['CVPARTNER_API_KEY'])withopen('user.ndjson','w')asf:foruincvp.get_users_from_api():user_id=u['user_id']cv_id=u['default_cv_id']user_cv=cvp.get_user_cv(user_id,cv_id)f.write(json.dumps(user_cv))
Package: cvplayer
cvplayercvplayer is a video player written in python that provides easy video playback using ffpyplayer and OpenCVInstallationpipinstallcvplayerthere are two ways to use cvplayer, either throughstarting interactive (direct) playback of a video from the command-line:cvplay filanamesimply starts the video in a different window where it's playback can be controlledby using keymapsthe default keymaps for controlling the video playback are -scripts (getting a VideoPlayer instance and doing whatever you want to)scripted accesscvplayer provides the VideoPlayer class which is the main wrapper around ffpyplayer's MediaPlayer.Initializing a VideoPlayer instance will start playing the video according to the options specified. The Playback can be controlled from within the script as well as using keymaps.the VideoPlayer class initiates playback and provide methods to control/access the state of the player.Here's and example to play a videofromcvplayerimportVideoPlayerplayer=VideoPlayer(filename)whileplayer.state!='eof':print(player.get_pts())time.sleep(1)parameters for a VideoPlayer instance:parametertypeDescriptionfilenamestrThe filename of the mediapausedboolIf True, the player will be in a paused state after creation, otherwise it will immediately start playing. Defaults to FalsefullscreenboolIf True, the video playback will be in fullscreen mode immediately after it's creation. Defaults to Truekey_controlsboolIf True, the video playback can be controlled using specified keymaps. Defaults to Trueskip_intervalintSpecify the numebr of seconds to move ahead or beind when navigating the video using the keymapsvolumefloatThe default volume. A value between 0.0 - 1.0. Defaults to 1.0muteboolIf True, the player will be muted by default after creation. Defaults to FalsetintPlay onlytseconds of the audio/video. Defaults to the full audio/video. Defaults to the full audio/videossintSeek to posssinto the file when starting. Defaults to the beginning of the fileblockingboolIf True, the playback will block the current thread (the one it's being run from) until the playback ends. Defaults to FalseplaybackboolIf False, only the player will be initialized and no video/audio will be played, only audio can be played by unmuting the player. Useful for just reading the frames form a video to display them using something else
Package: cvplot
Contribution-Value plotsThe Contribution-Value plot is a visual encoding for interpreting machine learning models.[more information]DemoInstallationTo install use pip:$ pip install cvplotIf you use jupyter lab, also run:$ jupyter labextension install cvplotfor classic jupyter notebooks, run:jupyter nbextension install --py --symlink --overwrite --sys-prefix cvplot
jupyter nbextension enable --py --sys-prefix cvplotDevelopmentFor a development installation (requires npm or yarn),$ git clone https://github.com/iamDecode/cvplot.git
$ cd cvplotYou may want to (create and) activate a virtual environment before continuing with:$ pip install -e .
$ jupyter labextension install js
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix cvplot
$ jupyter nbextension enable --py --sys-prefix cvplotWhen actively developing your extension, build Jupyter Lab with the command:$ jupyter lab --watchThis takes a minute or so to get started, but then automatically rebuilds JupyterLab when your javascript changes.CitationIf you want to refer to our visualization, please cite our paper using the following BibTeX entry:@article{collaris2021comparative,title={Comparative Evaluation of Contribution-Value Plots for Machine Learning Understanding},author={Collaris, Dennis and van Wijk, Jarke J.},journal={Journal of Visualization},year={2021},issn={1875-8975},doi={10.1007/s12650-021-00776-w},url={https://doi.org/10.1007/s12650-021-00776-w}}LicenseThis project is licensed under the BSD 2-Clause License - see theLICENSEfile for details.
Package: cvpolation
Interpolation of ImagesUsageTo Install this package. Use :pip install cvpolationUse the below snipit to start using the package.from cvpolation import *NQueens Possible Solution Spaceir = Interpolation()
ir.get_user_input()Visit our GitHub Repository for more information: https://github.com/toshihiroryuu/About the Author: http://athulmathew.com
Package: cv-pruner
CV-PrunerInstallationCV-Pruner is available atthe Python Package Index (PyPI).
It can be installed with pip:$pipinstallcv-prunerLicensingCopyright (c) 2021 Sigrun May, Helmholtz-Zentrum für Infektionsforschung GmbH (HZI)Copyright (c) 2021 Sigrun May, Ostfalia Hochschule für angewandte WissenschaftenLicensed under theMIT License(the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License by reviewing the fileLICENSEin the repository.
Package: cv-pytorch
CV-PyTorchInstallationpipinstallcv-pytorchorcondainstall-cjmenjivarcv-pytorchUse CasesCV-PyTorch is a collection of tools to help facilitate computer vision operations.Some of the tools in this library can be very useful to pre-process images before feeding to deep networks. However, the uses are not limited to pre-processing, you can perform a wide variety of operations that are helpful in many fields of work.Why?The re-implementation of these tools in PyTorch has the advantage of being significantly faster than other libraries using NumPy, etc.Supported OperationsCorner Detection (Harris)Pair Matching (SIFT)Inlier Detection (RANSAC)Pixel MatchingN Image StitchingThis is an example of 3 images stitched together:Gaussian SmoothingSharpeningOrientationMagnitudeScaling (No Interpolation)Scaling (Bilinear Interpolation)Edge Detection (Canny)Line Detection (Hough)
Package: cvr
Python API for cvr.devThe officialcvr.devPython client library.cvr.devis a web service that maintains a live-updated cache of the Danish CVR
registry.We aim to provide a much simpler and more modern API compared to
Virk/CVR's own Elastic Search solution.
Our focus is on high availability and robustness, making it as easy and
reliable as possible to retrieve data about Danish companies from the CVR
database.Installationcvrcurrently requires python version 3.5 or above.Using pipMake sure that you have pip installed, then run the following:pipinstallcvrFrom sourceMake sure that you have pip installed, then run the following in your
project folder:pipinstall.DocsThe HTTP API is available atdocs.cvr.dev.UsageIn theexamples/example.pydir there's a simple example program that verifies
that your API key works and fetches different data from the server.#!/usr/bin/env pythonimportcvrclient=cvr.Client(api_key='your api key')client.test_api_key()forvirksomhedinclient.cvr.virksomheder(cvr_numre=[10103940]):print('Found virksomhed',virksomhed.cvr_nummer)forpenhedinclient.cvr.produktionsenheder(p_numre=[1003388394]):print('Found penhed',penhed.p_nummer)TestThis project has two types of tests: live tests and local tests. The live tests
are run against our live servers and require that you set a valid API key in
the environment variable CVR_DEV_TEST_API_KEY. Note: the live tests will count
towards your usage.We use theunittestmodule for testing. You should use the following to run
the tests:# Run simple tests$python-munittest# Also all tests$CVR_DEV_TEST_API_KEY="your key"python-munittestAlternativesWe want you to have the best experience possible; if for some reason didn't find
what you were looking for at cvr.dev, reach out to us atkontakt@cvr.dev.If you just want to check out the market, these are potential alternatives:Virk's official integrationcvrapi.dkrisika.dkroaring.ioeanapi.dk
Package: cvraft
CvraftInstead of writing your CV or resumé in Microsoft Word, Google Docs, or some proprietary tools, you can just write it in Markdown. As a Markdown file is just plain text, you can easily track version with Git or your VCS of choice. Copy and paste with ease.cvrafttransforms your Markdown file to a ready-to-use HTML file. You can alsocustomize it with ease.InstallpipinstallcvraftUsageOutput HTML file to abuilddirectory in current directory. It also copiesstaticdirectory (if it exists in the same directory as the source Markdown file) tobuild.cvraftbuild<path/to/file.md>View the HTML file in a local web server athttp://localhost:9000cvraftserveCustomizationThe different with standard Markdown tool is that the output HTML is tweaked to wrap different parts of your CV in propersectiontags. This will ease your cutomization with CSS.The generated HTML structure could look like thisWith this structure, you can write your custom CSS in thestatic/styles/main.css. This path is the default CSS path in the generated HTML file.
Package: cvs2svn
cvs2svnis a tool for migrating a CVS repository toSubversion,git,Bazaar, orMercurial. The main design goals are robustness and 100% data
preservation. cvs2svn can convert just about any CVS repository we’ve ever
seen, including gcc, Mozilla, FreeBSD, KDE, GNOME…cvs2svn infers what happened in the history of your CVS repository and
replicates that history as accurately as possible in the target SCM. All
revisions, branches, tags, log messages, author names, and commit dates are
converted. cvs2svn deduces what CVS modifications were made at the same time,
and outputs these modifications grouped together as changesets in the target
SCM. cvs2svn also deals with many CVS quirks and is highly configurable.
See the comprehensivefeature list.Please read thedocumentationcarefully before using cvs2svn.Latest development versionFor general use, the most recent released version of cvs2svn is usually the
best choice. However, if you want to use the newest cvs2svn features or if
you’re debugging or patching cvs2svn, you might want to use the trunk version
(which is usually quite stable). To do so, use Subversion to check out a
working copy fromhttp://cvs2svn.tigris.org/svn/cvs2svn/trunk/using a command
like:svn co --username=guest http://cvs2svn.tigris.org/svn/cvs2svn/trunk cvs2svn-trunk(the password is empty; i.e., just press return).