Saravanan666/pypi-org huggingface dataset:
Package: cde-diagnostics
CDE-diagnosticsThe ''CDE-diagnostics'' package provides tools for validating the quality of conditional density estimators in Python. It contains source code for the paper ''Diagnostics for Conditional Density Estimators and Bayesian Inference Algorithms'' (https://arxiv.org/abs/2102.10473).Author: David ZhaoInstallationCDE-diagnostics is available from PyPI; to install usepip install cde-diagnosticsReferencesIf you use this code in your research, please cite:@InProceedings{zhao2021diagnostics,
    title = {{Diagnostics for conditional density models and Bayesian inference algorithms}},
    author = {Zhao, David and Dalmasso, Niccol\`{o} and Izbicki, Rafael and Lee, Ann B.},
    abstract = {There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, it is challenging to assess conditional density estimates and gain insight into modes of failure. While existing diagnostic tools can determine whether an approximated conditional density is compatible overall with a data sample, they lack a principled framework for identifying, locating, and interpreting the nature of statistically significant discrepancies over the entire feature space. In this paper, we present rigorous and easy-to-interpret diagnostics such as (i) the ``Local Coverage Test'' (LCT), which distinguishes an arbitrarily misspecified model from the true conditional density of the sample, and (ii) ``Amortized Local P-P plots'' (ALP) which can quickly provide interpretable graphical summaries of distributional differences at any location x in the feature space. Our validation procedures scale to high dimensions and can potentially adapt to any type of data at hand. We demonstrate the effectiveness of LCT and ALP through a simulated experiment and applications to prediction and parameter inference for image data.},
    booktitle = {Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI)},
    year = {2021},
    volume = {125},
    series = {Proceedings of Machine Learning Research},
    month = {26--29 Jul},
    publisher = {PMLR}
}
Package: cdeid
A framework for training de-identification models to automatically remove protected health information (PHI) from the free text.cDeid is a customized de-identification method. The users can easily train their own de-identification Models on the
data sets which are extracted from their own free text corpus. cDeid is based on 3 popular NLP toolkits:spaCy,StanzaandFLAIR.InstallationThis project is based on Python 3.7+. Please install it if you do not have. PyTorch is used by FLAIR and Stanza toolkits.
It needs to be installed fromherebefore you install this project.pipinstallcdeidUsage exampleWe are using thepre-trained word2vec embeddingsreleased from the CoNLL 2017 Shared Task. It is important
to specify the customized PHI types in the corpus otherwise it will cause runtime error during training the models.Using the Python APITrain the modelsfromcdeid.models.trainerimportTrainerphi_types=['PHONE','PERSON','ADDRESS','IDN','DOB']nlp=Trainer("C:/data","C:/workspace",phi_types,"C:/wordvec/English/en.vectors.xz")nlp.train()De-identify a sample documentfromcdeid.deidentifier.phi_deidimportPHIDeiddeider=PHIDeid("C:/workspace","C:/output")doc=deider("C:/raw/example.txt")deider.output(doc)Using the command lineTrain the modelspython-mcdeid--commandtrain--workspaceC:/workspace--data_dirC:/data--phi_typesPHONEPERSONADDRESSIDNDOB--wordvec_fileC:/wordvec/English/en.vectors.xzDe-identify a sample documentpython-mcdeid--commanddeid--workspaceC:/workspace--deid_output_dirC:/output--deid_fileC:/raw/example.txtRelease History0.1.1The first release0.1.2Modify Readme and Setup0.1.3Update model trainersContributorsLeibo Liu - initial work -leiboliuLicenseApache License, Version 2.0
Package: cd-email
Creating a README file is an essential part of documenting your Python project for PyPI and GitHub. A good README should provide clear instructions on how to install, configure, and use your software. Below is a template for yourSMTPClientproject:CD Emailcd-emailis a Python package that simplifies the process of sending emails via SMTP. It supports sending plain text emails, HTML emails, and emails with attachments. It also uses IMAP to receive emails too.FeaturesSend plain text and HTML emails.Attach files to your emails.Support for SMTP servers with authentication.Flexible and easy to use.InstallationTo installCD Email, you can use pip:pipinstallcd-emailUsageImport theSMTPClientclass from the package and initialize it with your SMTP server details:fromcd_email.send_smtpimportSMTPClientclient=SMTPClient("smtp.example.com",587,"your_username","your_password")Sending a Simple EmailTo send a simple email, use thesend_emailmethod:client.send_email(sender="sender@example.com",recipients=["recipient@example.com"],subject="Test Subject",body_text="This is a simple text email.")Sending HTML EmailTo send an HTML email, include thebody_htmlparameter:client.send_email(sender="sender@example.com",recipients=["recipient@example.com"],subject="Test HTML Email",body_html="<h1>This is an HTML email</h1>")Sending an Email with AttachmentsTo send an email with attachments, include theattachmentsparameter with a list of file paths:client.send_email(sender="sender@example.com",recipients=["recipient@example.com"],subject="Email with Attachments",attachments=["path/to/attachment1.txt","path/to/attachment2.jpg"])RequirementsPython 3.xsmtplibandemailpackages (standard library)IMAP Python ClientA simple and straightforward IMAP client written in Python for interacting with IMAP servers. This client allows you to login, search, fetch, delete emails, and manage folders on an IMAP server.FeaturesConnect to an IMAP server with SSLLogin and logoutSelect IMAP foldersSearch for emails with various criteriaFetch full email contentDelete emailsList all foldersClose selected folderInstallationCurrently, this IMAP client is provided as a standalone Python class. You can integrate it into your project by copying theIMAPClientclass fromimap_client.py.UsageHere's a quick example of how to use the IMAPClient:fromcd_email.read_imapimportIMAPClient# Create an instance of the IMAPClientclient=IMAPClient("imap.example.com")# Login with your credentialsifclient.login("your@email.com","yourpassword"):print("Logged in successfully!")# Select a folder (defaults to INBOX)client.select_folder("INBOX")# Search for all emailsemails=client.search()# Fetch and print the subject of each emailforemail_idinemails:email=client.fetch_email(email_id)print(f"Subject:{email['subject']}")# Remember to logoutclient.logout()else:print("Login failed.")## ContributingContributionsarewelcome!Pleasereadour[ContributingGuide](CONTRIBUTING.md)formoreinformation.## LicenseThisprojectislicensedunderthe[MITLicense](LICENSE).## More documentation at:[CodeDocta](https://codedocta.com"Code Docta")
Package: cdent
C’Dent - A Portable Module Programming LanguageC’Dent is a programming language that:Is primarily intended to write portable OO modules. C’Dent modules
are written once, and then compiled to equivalent port modules in any
number of supported programming languages.Has multiple input syntaxes. Including defined subsets of:
- Perl and Perl 6
- Python and Python 3000
- Ruby
- JavaScript
- JavaCompiles to a Common’DENominaTor tree form known as C’Dent. C’Dent is
an OO model that has the notions of modules, classes, methods, and
expressions.Emits C’Dent trees to several existing programming languages,
including:
- C’Dent - the compiled tree form serialized as YAML or XML
- Perl and Perl 6
- Python and Python 3000
- PHP
- Ruby
- JavaScript
- Scala
- Java
- ActionScript
- Go
- PIR (Parrot Intermediate Runtime)
- C and C++
- CIL (.NET Common Intermediate Language)Uses static implicit typing to assign types to all objects at
compile time, and throw syntax errors for type conflicts. Strong
typing is required to generate equivalent code in the various
emitted port languages.INSTALLATIONCurrently the best way to install C’Dent is to get the source code and install
it like so:> git clone git://github.com/ingydotnet/cdent.git
> cd cdent
> sudo python setup.py installYou will need setuptools for Python to run the last command. On Debian-based systems, this can be installed with:> sudo apt-get install python-setuptoolsUSAGEAfter you install C’Dent, you will have acdentcompiler in your Unix path.
Try running this command:cdent --helpYou’ll need a program written in C’Dent. There are some in your C’Dent
repository clone. One example istests/modules/world.cd.pywhich looks like
this:"""\
This is World class :)
"""

class World():
    def greet(self):
        print "Hello, world"You can compile to Ruby with this command:cdent --compile --in=tests/modules/world.cd.py --to=rbWhich produces:# *** DO NOT EDIT ***  This is a C'Dent generated Ruby module.
###
# This is World class :)
###

class World
    def greet
        puts("Hello, world")
    end
endYou can compile it to many other languages by changing the value of–to=.DEVELOPMENT STATUSC’Dent can currently parse modules with a simplistic grammar of Module/Classes/Methods/Print/Comments to an AST form and generate equivalent output modules in many languages:C’Dent can currently parse:PythonJavaScriptPerl 6PIRC’Dent/YAML (a C’Dent AST in YAML form)C’Dent can currently produce:PerlPythonPHPRubyJavaScriptScalaJavaActionScriptPerl 6Python 3GoPIRC’Dent/YAMLNext Steps:Add variables and assignmentsAdd type detectionAdd Ruby and Perl as inputAdd Scala and C++ as outputLots of other stuffCOMMUNITYJoin #cdent on irc.freenode.net.COPYRIGHTC’Dent is Copyright (c) 2010, Ingy dot NetC’Dent is licensed under the New BSD License. See the LICENSE file.
Package: cdev-assembly-line
Pre-stepsGet pyenv going and install multiple python versions (.python-version) fileStepsInstall your Poetry environmentpoetry installAdd the test repo for Pypipoetry config repositories.testpypi https://test.pypi.org/legacy/Add your test key for the Test Pypi repopoetry config http-basic.testpypi __token__ pypi-your-api-token-hereAdd your production ket for Pypi repopoetry config pypi-token.pypi pypi-your-token-hereDevOpen a Poetry shellpoetry shellBuildsTest your buildpoetry buildPublish the packagetest ->poetry publish -r testpypiprod ->poetry publish
Package: cdev-forge
Pre-stepsGet pyenv going and install multiple python versions (.python-version) fileStepsInstall your Poetry environmentpoetry installAdd the test repo for Pypipoetry config repositories.testpypi https://test.pypi.org/legacy/Add your test key for the Test Pypi repopoetry config http-basic.testpypi __token__ pypi-your-api-token-hereAdd your production ket for Pypi repopoetry config pypi-token.pypi pypi-your-token-hereDevOpen a Poetry shellpoetry shellBuildsTest your buildpoetry buildPublish the packagetest ->poetry publish -r testpypiprod ->poetry publish
Package: cdf2cim
Python client for mapping netCDF files to CIM documents.What is ES-DOC ?ES-DOC stands for Earth Science - Documentation.  It’s goal is to
provide software tools and services in order to support the
distribution of earth science documentation.What is esdoc-cdf2cim ?esdoc-cdf2cim is a python client library for automating the
publication of simulation documents in CIM v2.0 format.Why esdoc-cdf2cim ?A documentation eco-system is only as good as the available client
tools.  ES-DOC provides the cdf2cim client tool so as to simplify
leveraging the ES-DOC eco-ssytem in the python programming language.Who uses esdoc-cdf2cim ?ESG-F data node managers.How to install esdoc-cdf2cim ?pip install cdf2cimHow to integrate esdoc-cdf2cim into the ESG Publisher?https://github.com/ES-DOC/esdoc-cdf2cim/blob/master/ESG_Publisher_Integration.pdfFurther Information?Seehttp:es-doc.org
Package: cdf-ifs-file-extractor
CDF IFS file extractorIn progressGeneralThe cdf-ifs-file-extractor allows continuous uploading, changing and deletion of files stored in a local directory
and connect those files to the corresponding assets with some metadata.
Running on a continuous schedule the extractor moves old mapping_files into an Archive and uploads new files if it is
fed with a new mapping file. Feedback of unsuccessful upload attempts is given via the two files unknown_files.csv and
unknown_assets.csv.Structure of the Mapping fileThe extractor expects the mapping file to have a specific format. This requires the following columns:FILE_NAMEDOC_NODOC_REVDOC_SHEETDOC_CLASSDOC_CLASS_NAMEDOC_FORMATDOC_FORMAT_NAMEDOC_TITLEDOC_RESP_SIGNLETTER_DATELAST_UPDATED: to be checked if this column is really includedDOC_REFERENCE_OBJECTOPERATION: allows one of the three values N, C, D to upload a new file or change or delete an existing oneRequirements local directoryThe local directory needs to containthe desired files to operate ona folder with the name Archivethe mapping fileParametersThe parameters for the function areproject:api_key_name: name of the environment variable storing the api keybase_urlclient_nameclient_timeoutlog_pathlog_levelpath: path of the local directorymapping_file: file nameFeedback of the extractorFor non developing users the extractor provides feedback to the user for unsuccessful file operation attempts in two
casesThe asset_id specified in the mapping file does not exist: these ids are stored together with the corresponding file
name in unknown_assets.csvThe specified file does not exist: unknown_files.csvBoth files will be created in the end of a run of the extractor and archived as soon as the extractor is running the
next time.
Package: cdflib
CDFlibcdflibis a python module to read/write CDF (Common Data Format.cdf) files without needing to install theCDF NASA library.Python >= 3.8 is required.
The core of this package uses only numpy, with no complicated compiler requirements.InstallTo install, open up your terminal/command prompt, and type:pipinstallcdflibDocumentationThe full documentation can be found here:https://cdflib.readthedocs.io/en/latest/
Package: cdflow
cdflow - create and manage software services using continuous deliveryThis repository contains the cdflow command that you can install on your system (e.g. on your dev machine or CI server) in order to release, deploy and (eventally) decommission software services with ease.The code here is just a thin wrapper for thecdflow docker imageused to ensure you get the latest version when you release (with the option to pin should you need to) and that the image used remains consistent through your pipeline.Full documentation is here:https://mergermarket.github.io/cdflow/Installpip install cdflowRunningIf you are using thecdflow wrapper commandmentioned above, you can get usage information by running:$ cdflow --help

cdflow

Create and manage software services using continuous delivery.

Usage:
    cdflow release --platform-config <platform_config> <version> [options]
    cdflow deploy <environment> <version> [options]
    cdflow destroy <environment> [options]

Options:
    -c <component_name>, --component <component_name>
    -v, --verbose
    -p, --plan-onlyTests./test.sh
Package: cdfs
FeaturesTODORequirementsTODOInstallationYou can installcdfsviapipfromPyPI:$pipinstallcdfsUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,cdfsis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: cdft1d
Classical density functional theory code: 1D version (cdft1d)Marat Valiev and Gennady ChuevSimulates inhomogenous molecular liquid system in the presence of
Lennard-Jones (LJ) solute particleIn this release only RISM solver is provided.The program can be ran asrism <input_file>The input file is of the following form<solute>
# site   sigma(Angs)  eps(kj/mol)    charge(e)
Na       2.16         1.4755         1.0
<simulation>
temp 300
solvent 2site
tol 1.0E-7
max_iter 100It contains two sections:<solute>and<simulation>.The<solute>section specifies solute parameters: name, LJ parameters
and charge.The<simulation>section describes general parameters of the systemtemp 300       - temperature in K (in this case 300K)
solvent 2site  - solvent model (in this case two site water model)
tol 1.0E-7     - tolerance for convergence (1.0E-7 in this case)
max_iter 100   - maximum number of iterations (100 in this case)Upon successfull the program will generate RDF files (with
extension rdf).
In particular,
for the example provided above the following rdf files will be generatedNaH.rdf		NaO.rdfThe rdf files contains two column - distance (r) and RDF g(r). E.g.# r        g(r)
0.005      0.0
0.015      7.973e-10
0.025      2.395e-09
0.035      4.797e-09
0.045      8.01e-09
0.055      1.204e-08
....Available solvent models:2site

Kippi M. Dyer, John S. Perkyns, George Stell, 
and B. Montgomery Pettitt,Mol Phys. 2009 ; 107(4-6): 423–431. doi:10.1080/00268970902845313.References:Gennady N Chuev, Marina V Fedotova and Marat Valiev,
Renormalized site density functional theory,
J. Stat. Mech. (2021) 033205Chuev GN, Fedotova MV, Valiev M.
Chemical bond effects in classical site density
functional theory of inhomogeneous molecular liquids.
J Chem Phys. 2020 Jan 31;152(4):041101. doi: 10.1063/1.5139619. PMID: 32007044.Marat Valiev and Gennady N Chuev,
Site density models of inhomogeneous classical molecular liquids,
J. Stat. Mech. (2018) 093201
Package: cdfutils
cdfutilsA library of miscellaneous utility functions and classes that I've created for my projects.ConfigConfigStore: A generic configuration class that persists values in a text file.DotNetSome general helper classes for use with .NET Windows.FormsCustomMainMenu: Builds a full MainMenu from a supplied configuration.CustomToolBar: Builds a ToolBar from a supplied configuration.TextfileUtility functions for reading from text filesrandomLine(): Returns a random line from a file.randomSection(): Returns a random section (delimited by lines starting with #) from a file.
Package: cdhistory
## CDHISTORY[![Build Status](https://travis-ci.org/jdowner/cdhistory.svg)](https://travis-ci.org/jdowner/cdhistory)This package is used to record the frequency of visits to particular directories
through the ‘cd’ function. The goal is to use this information to provide
a way to quickly navigate to commonly visited directories.To incorporate this functionality, install the package in the usual way,python setup.py installThis will add the cdhistory packacge to your python install area, and a
cdhistory script to /usr/local/bin. To incorporate cdhistory into your normal
‘cd’ use, there is a bash script installed in /usr/local/share called
cdhistory.bash that provides an function to wrap ‘cd’.The cdhistory.bash snippet replaces the existing ‘cd’ function in bash with a
wrapped version. The function behaves like the builtin cd command normally, but
records the directories that are visited. However, if you prepend the first
argument to ‘cd’ with a colon it will go to the best match using cdhistory, e.g.cd :reposwill ‘cd’ to the path that best matches the string ‘repos’. In effect, this is
like creating aliases for commonly visited directories.
Package: cdhit-reader
cdhit-parserCD-HIT file reader.Read CD-HIT .clstr fileBasic usagefromcdhit_readerimportread_cdhitinput="cluster.fa.clstr"forclusterinread_cdhit(input):print(f"{cluster.name}refSequence={cluster.refname}size={len(cluster)}")formemberincluster.sequences:print(f"{member.name}({member.length}) identity={member.identity}%{'(Reference sequence)'ifmember.is_refelse''}")Load all clusters in to a list:# Load all clusters to a listclusters=read_cdhit(input).read_items()Read FASTA fileifos.path.exists(fileName):forseqincdhit_reader.read_fasta(fileName,line_len=60):print(seq)# will be wrapped at 60 chars per line, use 0 to disable wrapping# to access individual attributes:# print(">" + seq.name + " " + seq.comment + "\n" + seq.sequence)Installpipinstallcdhit-readeror viaMiniconda, which will also install cd-hitcondainstall-cbioconda-cconda-forgecdhit-readerDemo applicationsCluster statsThe module ships a demo program calledcdhit-reader.py.Compare two fasta files:warning: This requires cd-hit installed and available in the system path.cdhit-compareallows to compare two fasta files and print the sequences that are in common, those which are only
present in one of the files or those which are redundant.Example:cdhit-comparedata/input1.faadata/input2.faa--id0.99will produce:input1  BJJOHBJ_00007
input2  BJJOHBJ_00007
input2  BJJOHBJ_00002
both    BJJOHBJ_00003:BJJOHBJ_00003
both    BJJOHBJ_00005:BJJOHBJ_00005
both    BJJOHBJ_00004:BJJOHBJ_00004
multi   input1#IBJJOHBJ_00006,input1#BBJJOHBJ_000B6,input1#CBJJOHBJ_000C6,input2#IBJJOHBJ_00006,input2#BBJJOHBJ_000B6,input2#CBJJOHBJ_000C6
dupl_input1     BJJOHBJ_00001:BJJOHBJ_000Fwhere records starting withfile1orfile2are only present in one of the files,
records starting withbothare present in both files (one per file),
records starting withduplare duplicates (two in one of the files),
and records starting withmultiare present multiple times in at least one of the datasets.AuthorAndrea TelatinLicenseThis project is licensed under the MIT License.AcknowledgmentsThis module was based onfasta_readerbyDanilo Horta
Package: cdh-lava-demo
CDC Data Hub LAVA Demo DocumentationCDC Data Hub LAVA Demo (CDH-LAVA-DEMO)Project OverviewProject Name: CDC Data Hub LAVA Demo (CDH-LAVA-DEMO)GitHub Link:cdh-lava-demoPoint of contact:Nishant NepalorJohn BowyerOrganizational unit: DDPHSS/ADBRelated projects: OPHDST/ADB/CDHRelated investments:  Pending Public ReleaseGovernance status: Pending Public ReleaseProgram official:Sachin Agnihotri
Package: cdhmm-ashishmokalkar
An easy to use python library consisting implementation of Continuous Density Hidden Markov Models.After studying Hidden Markov Models(HMM) for a while now, I have came across many python libraries which implements HMM algorithms like forward,  backward, Viterbi and Baum-Welch. However, most of these libraries work on discrete observations. Continuous Density Hidden Markov Models(CD-HMM) are a type of HMM which consists of Emission probabilities in the form of a distribution like gaussian or uniform distribution. Despite its use in Speech processing, very less codes are available on the internet regarding CD-HMM. This library has implementation of all HMM algorithms applied on continuous density observations.
Package: cdh-ref-python
CDC Data Hub Reference Data DocumentationCDC Data Hub Reference Data Accelerator Python (CDH-REF-LAVA)Project OverviewProject Name: CDC Data Hub Reference Data Accelerator Python (CDH-REF-LAVA)GitHub Link:cdc-davPoint of contact:Nishant NepalorJohn BowyerOrganizational unit: DDPHSS/ADBRelated projects: OPHDST/ADB/CDHRelated investments:  Pending Public ReleaseGovernance status: Pending Public ReleaseProgram official:Sachin Agnihotri