Saravanan666/pypi-org huggingface dataset:
Package: ci-jira-releaser
# ci-jira-releaserSimple way to release past(or current) commits within JiraCredit to @shamrin for the name generator code [https://github.com/shamrin/namesgenerator](https://github.com/shamrin/namesgenerator)```usage: ci-jira-releaser [-h] -p PROJECT_KEY -u JIRA_URL [-r REPO_ROOT][-U JIRA_USERNAME] [-P JIRA_PASSWORD] --from-commitFROM_COMMIT [--to-commit TO_COMMIT][--project-name PROJECT_NAME][--build-number BUILD_NUMBER] [--development][--production]A simple way to label development tags or auto make a releaser within Jiraoptional arguments:-h, --help            show this help message and exit-p PROJECT_KEY, --project-key PROJECT_KEYThe Jira project key: e.g PROJ-u JIRA_URL, --jira-url JIRA_URLThe url Jira is located at including http://-r REPO_ROOT, --repo-root REPO_ROOTThe location of the git repo, must have a .git inside-U JIRA_USERNAME, --jira-username JIRA_USERNAMEenvironment_var[JIRA_USER]-P JIRA_PASSWORD, --jira-password JIRA_PASSWORDenvironment_var[JIRA_PASSWORD]--from-commit FROM_COMMITHash of git commit to start grabbing issues from--to-commit TO_COMMITEnd commit to parse through--project-name PROJECT_NAMEThe name of the project to help generate the label--build-number BUILD_NUMBERFor build systems usually an environment variable.Usually passed by Jenkins as a ENV variable.--development         Pass flag if using for development--production          Pass flag if using for productionDon't forget you can use environment variables```Examples--Development---```ci-jira-releaser -p PROJ -u http://site.atlassian.net --from-commit SHA -r ~/my-project --project-name my-project --build-number 123 --development```Production---```ci-jira-releaser -p PROJ -u http://site.atlassian.net --from-commit SHA -r ~/my-project --production```
Package: cijoe-pkg-example
cijoe-pkg-example: cijoe package for ‘example’InstallThe package is distributed via PyPi, run the following to command to install:pip3install--usercijoe-pkg-exampleTo install the development preview, install:pip3install--user--precijoe-pkg-exampleSee thecijoefor additional documentation.If you find bugs or need help then feel free to submit anIssue. If you want
to get involved head over to theGitHub pageto get the source code and
submit aPull requestwith your changes.
Package: cijoe-pkg-liblightnvm
cijoe-pkg-liblightnvm: cijoe testcases and testplans for liblightnvmInstallThe package is distributed via PyPi, run the following to command to install:Installcijoe-pkg-liblightnvmsystem-wide via the pip:sudopipinstallcijoe-pkg-liblightnvmOr install it user-level:pipinstall--usercijoe-pkg-liblightnvmNoteWhen doing user-level install, then include thepipbinary install
path in yourPATHdefinition. For examplePATH="$PATH:$HOME/.local/bin"Create environment definition for CIJOERun CIJOE interactively and define the target environment:# Start cijoecijoe# Use refence definitions as a template for defining your environmentcat$CIJ_ENVS/refenv-u1604.sh>target_env.shtail-n+2$CIJ_ENVS/refenv-liblightnvm.sh>>target_env.sh# Open up your favorite editor and modify accordinglyvimtarget_env.shCreate virtual environment definition for CIJOERun CIJOE interactively and define the target environment:# Start cijoecijoe# Use refence definitions as a template for defining your environmentcat$CIJ_ENVS/refenv-u1604.sh>target_env.sh# The following is the qemu variables to definetail-n+2$CIJ_ENVS/qemu.sh>>target_env.sh# Lastly, the variables used by liblightnvm testcasestail-n+2$CIJ_ENVS/refenv-liblightnvm.sh>>target_env.sh# Open up your favorite editor and modify accordinglyvimtarget_env.shRunning testsStart the test-runner and view the report:# Create directory to store resultsRESULTS=$(mktemp-dtrun.XXXXXX-p/tmp)# Run using the testplan for Open-Channel 2.0 devicescij_runner\$CIJ_TESTPLANS/OCSSD_S20_IOCTL.plan\target_env.sh\--output$RESULTS# Create test reportcij_reporter$RESULTS# Inspect the test-reportxdg-open$RESULTS/report.htmlIf you find bugs or need help then feel free to submit anIssue. If you want
to get involved head over to theGitHub pageto get the source code and
submit aPull requestwith your changes.
Package: cijoe-pkg-lightnvm
cijoe-pkg-lightnvm: cijoe package for the lightnvm subsystem, PBLK, and ZBLKInstallThe package is distributed via PyPi, run the following to command to install:Installcijoe-pkg-lightnvmsystem-wide via the pip:sudopipinstallcijoe-pkg-lightnvmOr install it user-level:pipinstall--usercijoe-pkg-lightnvmNoteWhen doing user-level install, then include thepipbinary install
path in yourPATHdefinition. For examplePATH="$PATH:$HOME/.local/bin"Create environment definition for CIJOERun CIJOE interactively and define the target environment:# Start cijoecijoe# Use refence definitions as a template for defining your environmentcat$CIJ_ENVS/refenv-u1604.sh>target_env.shtail-n+2$CIJ_ENVS/refenv-pblk.sh>>target_env.sh# Open up your favorite editor and modify accordinglyvimtarget_env.shCreate virtual environment definition for CIJOERun CIJOE interactively and define the target environment:# Start cijoecijoe# Use refence definitions as a template for defining your environmentcat$CIJ_ENVS/refenv-u1604.sh>target_env.sh# The following is the qemu variables to definetail-n+2$CIJ_ENVS/qemu.sh>>target_env.sh# Lastly, the variables used by pblk testcasestail-n+2$CIJ_ENVS/refenv-pblk.sh>>target_env.sh# Open up your favorite editor and modify accordinglyvimtarget_env.shRunning testsStart the test-runner and view the report:# Create directory to store resultsRESULTS=$(mktemp-dtrun.XXXXXX-p/tmp)# Run e.g. the pblk smoketestcij_runner\$CIJ_TESTPLANS/pblk_000_smoketest.plan\target_env.sh\--output$RESULTS# Create test reportcij_reporter$RESULTS# Inspect the test-reportxdg-open$RESULTS/report.htmlIf you find bugs or need help then feel free to submit anIssue. If you want
to get involved head over to theGitHub pageto get the source code and
submit aPull requestwith your changes.
Package: cijoe-pkg-spdk
cijoe-pkg-spdk: cijoe testcases and testplans for SPDKInstallThe package is distributed via PyPi, run the following to command to install:pipinstall--usercijoe-pkg-spdkTo install the development preview, install:pipinstall--user--precijoe-pkg-spdkSee theCijoefor additional documentation.If you find bugs or need help then feel free to submit anIssue. If you want
to get involved head over to theGitHub pageto get the source code and
submit aPull requestwith your changes.Test-target EnvironmentOn the test-target, make sure you install:lshw
screen
timeBuild FIO on the test-target:cd /opt/fio
./configure
makeBuild SPDK on the test-target:cd /opt/spdk
./configure --with-fio=/opt/fio
make
Package: cijoe-pkg-xnvme
cijoe-pkg-xnvme: cijoe testcases and testplans for xNVMeInstallThe package is distributed via PyPi, run the following to command to install:pipinstall--usercijoe-pkg-xnvmeTo install the development preview, install:pipinstall--user--precijoe-pkg-xnvmeSee theCijoefor additional documentation.If you find bugs or need help then feel free to submit anIssue. If you want
to get involved head over to theGitHub pageto get the source code and
submit aPull requestwith your changes.Test-target EnvironmentOn the test-target, make sure you install:lshw
screen
time
Package: cilantro-ee
Running your RCNet nodeGet a computer with Ubuntu 18.04.DigitalOcean droplets are our favorites if you are new.Install prerequisites:Install Git if your OS doesn't have it preinstalledCent OSsudo yum install gitUbuntusudo apt-get install git-coreInstall Node requirementssudo apt update
sudo apt install -y python3-pip redis-server mongodbOpen your ports:sudo ufw allow 443/tcp
sudo ufw allow 8080/tcp
sudo ufw allow 10000:10999/tcpDownload Cilantrogit clone https://github.com/Lamden/cilantro-enterprise.git
cd cilantro-enterprise
git checkout demoInstall Cilantrosudo python3 setup.py develop
sudo make installNOTE: Capnproto takes averylong time to install because it compiles from source. Please be patient!!Setup Node Configurationpython3 scripts/setup_node.pyEnter your Signing Key (Private Key) as a Hex StringFind your Signing Key / Private Key in the Lamden Vault Chrome Plugin. If you don't have a wallet yet, get one by following the instructions here:https://docs.lamden.io/lamden-vault/Enter your wallet password to expose your Private Key, as shown above.Enter Constitution File NameThis is the file name of the initial configuration. Constitution files are located in theconstitutionsfolder. If you don't know which one to use, ask an admin.Example names:rcnet.json,nojan.json,nohup.json.If you are not part of the constitution, this step will fail. You can't join the network if you are not a participant.Start your nodemake stop-db
make start-db
python3 scripts/bootstrap.pyIf you want to leave your node up and running while you log out ofssh, addnohupand&like this:make stop-db
make start-db
nohup python3 scripts/bootstrap.py &Your node is now up and running!
Package: ciles
CILES: Continuous Interval Langevin Equation SimulatorLangevin integrator for SDEs with constant drift and diffusion on
continuous intervals with circular boundary conditions.CILES is written in Cython and uses GSL for interpolation of drift &
diffusion fields, to be able to simulate continuous variables.DescriptionGiven a discretized drift field A(x) and a (position dependent)
diffusion coefficient B(x) this tool performs simple time-forward
integration of the SDE:dx(t)/dt = A(x(t)) + sqrt(B(x(t))) * eta(t)where eta(t) is a gaussian white noise term and x is a variable on an
interval with circular boundaries (commonly 0 <= x < 2PI).Both drift field A and diffusion B need to be arrays of the same
dimension. They are internally interpolated (usinggsl_interp_cspline_periodic) to provide continuous fields, which are
then used in the forward integration.Forward integration is performed with theEuler-Murayama
scheme:
x(t+dt) = x(t) + dt * A(x(t)) + r * sqrt(dt * B(x(t))), where r is a
normally distributed random number with zero mean and unit variance.DependenciesNumpyCythonCython-gslInstallationClone repositorypython setup.py installTo test (usingnosetests):nosetestsExample usefromciles.integratorimportLangevinIntegratorasLIimportnumpyasnpdrift=np.zeros(100)# no drift fielddiff=np.ones(100)# constant diffusion with 1 deg^2/sdt=1e-3# 1 ms timesteptmax=1.# simulate until 1s# initialize the integratorli=LI(drift,diff,dt=dt,tmax=tmax)# simulate a single trajectoryli.run()out=li.outMore examplesBelow are the plot results of the currently available examples fromciles.examples.Final distributions after 2s diffusionSee thesourceTrajectories for drift-field with 2 fixed pointsSee thesource
Package: cil-estimator
cil_estimatoris nowecdf_estimator!This software package is deprecated! Thus, we do not recommend to use the version ofcil_estimatorthat is provided here. Nonetheless,cil_estimatoris further developed and can be obtained fromGitHubandPyPIunder its new nameecdf_estimator.Welcome to cil_estimatorIt contains a Python based framework for parameter estimation, which is currently under
construction.It may be installed using$ pip install git+https://github.com/AndreasRupp/ecdf_estimator.gitfor the latest version, which is located in the GitHub repository. Alternatively, you can use$ python3 -m pip install ecdf_estimatorto obtain the latest stable version fromPyPI.Copyright, License, and Contribution PolicyThis directory contains the ecdf_estimator library.The ecdf_estimator library is copyrighted by the authors ofecdf_estimator. This term currently
refers to Andreas Rupp.This library is free software; you can use it, redistribute it, and/or modify it under the terms
of theGNU Lesser General Public Licenseas published by the Free Software Foundation; eitherversion 2.1of the License, or (at your option) any later version. The full text of the GNU
Lesser General Public version 2.1 is quoted inLicense.txt.ContributionsAs a contributor to this project, you agree that all of your contributions be governed by theDeveloper Certificate of Origin version 1.1. This project does not require copyright
assignments for contributions. This means that the copyright for code contributions in this project
is held by its respective contributors who have each agreed to release their contributed code under
a compatible open source license (LGPL v2.1 for library code). The full text of the Developer
Certificate of Origin version 1.1 is quoted inDeveloperCertificateOfOrigin.txt.ContactFor further questions regarding licensing and commercial use please contact Andreas Rupp directly
usingEmail.LinksThe license can be found inLicense.txt. It contains theGNU Lesser
General Public License version 2.1.The developer certificate of origin can be found inDeveloperCertificateOfOrigin.txt. It
contains theDeveloper Certificate of Origin version 1.1.
Package: cilin
CilinInstallpipinstallcilinUsagefromcilinimportCilinC=Cilin(trad=False)# trad=True to convert results to traditional Chinese# Category label>>>C.get_tag('A')'人'>>>C.get_tag('Aa')'人-泛称'>>>C.get_tag('Aa01')'人 人民 众人 角色'# Terms under a certain category>>>C.get_members('Ca01B'){'己巳','回历','戊辰','丙寅','太阴历','子丑寅卯','庚午','地支','乙丑','夏历','甲午','旧历','天干','巳','子午卯酉','阴历','戊寅','戊戌','伊斯兰教历','丁卯','农历','庚子','庚申','癸','辛未','辛亥'}>>>C.get_members('Ca01B01='){'旧历','农历','太阴历','阴历','夏历'}# Divide terms into categories at different levels (1-5)>>>C.category_split(level=1){'A':{'荷兰人','巡视员','人夫','农妇','王子',...},'B':{'赌具','湖','敞篷车','灰黄色','圆桌',...},...'L':{'费心','失敬','劳神','恭贺','借问',...}}Acknowledgent同義詞詞林資料：https://github.com/yaleimeng/Final_word_Similarity/tree/master/cilin/V3
Package: cilissa
ComputerImageLikeness AssessingAutomationOverviewCILISSA allows for the use of various metrics to perform full-reference image comparisons.It features the most popular full-reference image quality metrics, image transformations and translations.
CILISSA is also very extensible and new operations can be easily added.CILISSA has an optional Qt-based graphical interface that lets you experiment with various operations, their orders and properties.RequirementsPython >= 3.7InstallationInstall from PyPI$pipinstallcilissaReleasesBinaries for Windows and Linux can be found onGitHub releases.UsageGUIInformation about the GUI can be found in thecilissa_gui/README.mdfile.CLICurrently the CLI only supports working with a single pair of images.The parameters of metrics and transformations can be modified by passing them to the--kwargsargument using the following format:<operation-name>-<parameter-name>=<value>whereparameter-nameuses hyphens (-) instead of underscores (_)DocumentationDocumentation is hosted onRead the Docs.
Package: cilium-microscope
Cilium MicroscopeCilium microscope allows you to seecilium monitoroutput from all your cilium nodes.
This allows you to have one simple to use command to interact with your cilium nodes
within k8s cluster.Running microscope in your Kubernetes clusterkubectl create-fhttps://raw.githubusercontent.com/cilium/microscope/master/docs/microscope.yamlwill create a pod in your kube-system namespace to which you can connect to runmicroscopewithkubectl exec-it-nkube-systemmicroscope sh. This will also create RBAC objects whichmicroscopeneeds in order to do its work.Alternatively, you can usekubectl run-i--ttymicroscope--imagecilium/microscope--restart=Never--sh. This won’t work if you have RBAC enabled in your cluster.In any case you will end up with a shell insidemicroscopepod.microscope-hinside this shell will showmicroscopehelp.Running microscope locallyTo runmicroscopelocally, you need to have Python 3.5 or newer installed. Using virtualenv is recommended, but not necessary.microscopeis available as a package in PyPI, so all you need to do is runpip installcilium-microscope.microscopeexecutable should be available in your path.Alternatively you can runmaketo build self-contained Python archive which will container all dependencies and requires only Python to run.The archive will be located indist/microscope.pyz, and should be executable directly.
Package: cim-11-certic
CIM-11Classification Internationale des Maladies, Onzième RévisionPackage fournissant en français une liste de maladies associées à leurs codes internationaux,
tels que diffusés par l'OMS (Organisation Mondiale de la Santé), ainsi qu'une recherche
plein-texte sur les labels de ces maladies.Pour plus de renseignements, voir la page officielle de la CIM-11:https://icd.who.int/frAvertissementsCe package n'est pas affilié à l'OMS, merci de ne pas les contacter pour un quelconque support à son sujetCe package est destiné à fournir une solution simple, rapide et surtout hors-ligne au codage des maladiesIl ne se soustrait pas aux outils fournis par l'OMS et en particulier son APICertaines catégories ont été ignorées, en particulier:la section V, "section supplémentaire pour l'évaluation du fonctionnement"la section X, "codes d'extension"Dans sa dernière version, les données de ce package sont figées au 28 août 2023Installationpip install cim-11-CERTICUtilisationfrom cim_11 import root_concepts, label_search

# Parcours de l'arbre:

for item in root_concepts():
    print(f"-- {item.icode + ' ' if item.icode else ''}{item.label}")
    for child in item.children:
        print(f"-- {child.icode + ' ' if child.icode else ''}{child.label}")
        for sub in child.children:
            print(f"---- {sub.icode + ' ' if sub.icode else ''}{sub.label}")

# Recherche full-text sur label:

for item in label_search("tumeur foie métastase"):
    print(f"{item.icode} {item.label}")

# Résultats de la recherche:

# 2D80 Métastase de tumeur maligne, dans le foie ou les voies biliaires intrahépatiques
# 2D80 Métastase de tumeur maligne, dans le foie ou les voies biliaires intrahépatiques
# 2D80 Métastase de tumeur maligne, dans le foie ou les voies biliaires intrahépatiques
# 2D80.0 Métastase de tumeur maligne, dans le foie
Package: cima-comutations-factors-4
cima h commutations factorsThis package aim to provide commutations factors computed based on CIMA H table for actuarials projectsThe current commutations factors list are:lx: Number of survivors at age x according to the mortability tabledx: Number of death at age x according to the mortability tableW: The age where there is no survivors according the MTqx: Death probality betwwen age x and x+1Cx: Present value of survivor numbersDx:Mx:Rx:Sx:Nx:A_k:  Certain present value for a annuity of 1F payable in advance over k yearsA_xn: Probable present value at age x over n yearsnEx: Deferred capital at age x over n years
Package: cima-comutations-factors-5
cima h commutations factorsThis package aim to provide commutations factors computed based on CIMA H table for actuarials projectsThe current commutations factors list are:lx: Number of survivors at age x according to the mortability tabledx: Number of death at age x according to the mortability tableW: The age where there is no survivors according the MTqx: Death probality betwwen age x and x+1Cx: Present value of survivor numbersDx:Mx:Rx:Sx:Nx:A_k:  Certain present value for a annuity of 1F payable in advance over k yearsA_xn: Probable present value at age x over n yearsnEx: Deferred capital at age x over n years
Package: cImage
cImage  -  A simple image processing library for PythonInstallationIf using Thonny, go to Tools -> Manage Packages, then entercs20-image. This should install both the image module, and the Pillow module (so you can use any type of image you’d like).If you are not using Thonny, copy image.py to your site-packages directory, or just keep it in the same folder as the Python scripts that import it.UsageThis image library is not going to give you fancy high performance operations on images.  It allows you to read in an image and manipulate its pixels.  Then you can save the new image to a file, or you can display the image in a window.  Thats really about it, but its really all you want to do if you are teaching an introductory computer science course.Image Types SupportedIf you have PIL installed on your system (if you are using Thonny, this was installed along with the image module):jpeggiftiffpngetc.If you do not have PIL installed then you are stuck with GIF images only.Exampleimport image

win = image.ImageWin(480, 640, "Image Processing")
original_image = image.FileImage('lcastle.gif')

width = original_image.get_width()
height = original_image.get_height()
print(width, height)

original_image.draw(win)
my_image = original_image.copy()

for row in range(height):
    for col in range(width):
         v = my_image.get_pixel(col,row)
         v.red = 255 - v.red
         v.green = 255 - v.green
         v.blue = 255 - v.blue
         my_image.set_pixel(col,row,v)

my_image.draw(win)
print(win.get_mouse())
my_image.save('lcastle-inverted.gif')
print(my_image.to_list())
win.exit_on_click()
Package: ciman
CIMANContainerImageMANagerAboutThis package provides aContainerImageMANagement Python library and command line utilityRequirementsPython 3.6+InstallpipinstallcimanHow to use (command ine tool)# show info for the bash image on githubcimaninfobash# show info for the bash image from the microsoft registrycimaninfomcr.microsoft.com/dotnet/samples# show tags for an imagecimantagsmcr.microsoft.com/dotnet/samplesEnvironment variablesDOCKER_REGISTRY: name of the docker registry to use for images names not containing a registry prefix (default: registry-1.docker.io)SSL_CERT_FILE: load CA certificate from the specified file instead of the default location
Package: cimcb
cimcbcimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.InstallationDependenciescimcb requires:Python (>=3.5)Bokeh (>=1.0.0)KerasNumPy (>=1.12)SciPyscikit-learnStatsmodelsTensorFlowtqdmUser installationThe recommend way to install cimcb and dependencies is to usingconda:conda install -c cimcb cimcborpip:pip install cimcbAlternatively, to install directly from github:pip install https://github.com/KevinMMendez/cimcb/archive/master.zipTutorialOpen with Binders:APIFor futher detail on the usage refer to the docstring.cimcb.modelPLS_SIMPLS: Partial least-squares regression using the SIMPLS algorithm.PCR: Principal component regression.PCLR: Principal component logistic regression.RF: Random forest.SVM: Support Vector Machine.RBF_NN: Radial basis function neural network.NN_LinearLinear: 2 Layer linear-linear neural network.NN_LinearLogit: 2 Layer linear-logistic neural network.NN_LogitLogit: 2 Layer logistic-logistic neural network.cimcb.plotboxplot: Creates a boxplot using Bokeh.distribution: Creates a distribution plot using Bokeh.pca: Creates a PCA scores and loadings plot using Bokeh.permutation_test: Creates permutation test plots using Bokeh.roc_plot: Creates a rocplot using Bokeh.scatter: Creates a scatterplot using Bokeh.scatterCI: Creates a scatterCI plot using Bokeh.cimcb.cross_valkfold: Exhaustitive search over param_dict calculating binary metrics.cimcb.bootstrapPerc: Returns bootstrap confidence intervals using the percentile boostrap interval.BC: Returns bootstrap confidence intervals using the bias-corrected boostrap interval.BCA: Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.cimcb.utilsbinary_metrics: Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.ci95_ellipse: Construct a 95% confidence ellipse using PCA.knnimpute: kNN missing value imputation using Euclidean distance.load_dataXL: Loads and validates the DataFile and PeakFile from an excel file.nested_getattr: getattr for nested attributes.scale: Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.table_check: Error checking for DataTable and PeakTable (used in load_dataXL).univariate_2class: Creates a table of univariate statistics (2 class).wmean: Returns Weighted Mean. Ignores NaNs and handles infinite weights.Licensecimcb is licensed under the ___ license.AuthorsKevin MendezDavid BroadhurstCorrespondenceProfessor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
E-mail:d.broadhurst@ecu.edu.auCitationIf you would cite cimcb in a scientific publication, you can use the following: ___