Saravanan666/pypi-org huggingface dataset:
Package: cigar
Cigar=====cigar is a simple library for dealing with cigar strings. the most usefulfeature now is soft-masking from left or right. This allows one to adjusta SAM record only by changing the cigar string to soft-mask a number of basessuch that the rest of the SAM record (pos, tlen, etc.) remain valid, butdownstream tools will not consider the soft-masked bases in further analysis.```Python>>> from cigar import Cigar>>> c = Cigar('100M')>>> len(c)100>>> str(c)'100M'>>> list(c.items())[(100, 'M')]>>> c = Cigar('20H20M20S')>>> len(c)40>>> str(c)'20H20M20S'>>> list(c.items())[(20, 'H'), (20, 'M'), (20, 'S')]>>> c.mask_left(29).cigar, c.cigar('20H9S11M20S', '20H20M20S')>>> c = Cigar('10M20S10M')>>> c.mask_left(10).cigar'30S10M'>>> c.mask_left(9).cigar'9S1M20S10M'>>> Cigar('10S').mask_left(10).cigar'10S'>>> Cigar('10H').mask_left(10).cigar'10H'>>> Cigar('10H').mask_left(11).cigar'10H'>>> Cigar('10H').mask_left(9).cigar'10H'>>> Cigar('1M10H').mask_left(9).cigar'1S10H'>>> Cigar('5M10H').mask_left(9).cigar'5S10H'>>> c = Cigar('1S1H1S5H1S5M10H')>>> c.mask_left(9).cigar == c.cigarTrue>>> c = Cigar('1S1H1S5H1S5M10H')>>> c.mask_right(9).cigar == c.cigarTrue>>> c.mask_right(11).cigar'1S1H1S5H1S4M1S10H'```Installation============pip install cigar
Package: cigar-coordinates
cigar_coordinates1 Introductioncigar_coordinatesis a tool To get the coordinates of a given CIGAR string. CIGARs are in respect of the reference sequence. By Guanliang MENG, seehttps://github.com/linzhi2013/cigar_coordinates.cigar_coordinatesunderstands the following CIGAR characters:seq - - - N N N O O O
refseq - N O N - O O N -
 cigar P n D B u U M N I2 Installationpip install cigar_coordinatesThere will be a commandcigar_coordinatescreated under the same directory as yourpipcommand.3 Usage$ cigar_coordinates
usage: cigar_coordinates [-h] -c <STR> [-s <INT>] [-q] [-d {+,-}]

To get the coordinates of a given CIGAR string.
By Guanliang MENG, see https://github.com/linzhi2013/cigar_coordinates.

I understand the following CIGAR types:

     seq - - - N N N O O O
  refseq - N O N - O O N -
   cigar P n D B u U M N I

The output coordinates are closed intervals.

optional arguments:
  -h, --help  show this help message and exit
  -c <STR>    input CIGAR string
  -s <INT>    the start coordinate on the sequence, whatever the gene
              direction is, this option is always the smaller one. [1]
  -q          the '-s' option is about query sequence, not refseq [True]
  -d {+,-}    the gene direction [+]4 ExampleFor a gene: ND1;len=945;[2444:3389](+)$ cigar_coordinates -c 6D166M268N511M -s 2444
6D  2444  2444
166M  2444  2609
268N  2610  2877
511M  2878  33885 AuthorGuanliang MENG6 CitationCurrently I have no plan to publishcigar_coordinates.
Package: ci-generator
ci generatorCI generator is a tool to generate CI configuration files for your project.
It's a command line tool that can be used to generate CI configuration files for your project. It's written in Python and uses templates to generate the files.Possible CI systems are:Github ActionsJenkinsDockerGitlab CIThey can integrate with the following tools:SonarQubeSonarCloudTest coverageDockerFileDockerComposeInstallationMultiplatform Linux, Windows, MacOspipinstallci-generatorUsagecigen--helpGithub Actionscigengithub-actions--helpoutput:Usage:cigen[OPTIONS]COMMAND[ARGS]...ciGenisaContinuousIntegrationGenerator

Options:--helpShowthismessageandexit.

Commands:dockerThisisthemaincommandfortheDockergithub-actionThisisthemaincommandfortheGitHubActionsgitlabThisisthemaincommandfortheGitLabjenkinsThisisthemaincommandfortheJenkinsgithub-actions subcommand can be used to generate Build and Test Github Actions configuration files.Github Actions commands availablegogithub-actions Golang example:cigengithub-actionsgo-nmyproject-bpushmain-a1-v1.21.1github-actions Python example:cigengithub-actionspython-nmyproject-bpushmain-a1-v3.9.6Commands not yet availableJenkinscigenjenkins--helpjenkins subcommand can be used to generate Build and Test Jenkins configuration files.jenkins Golang example:cigenjenkinsgo-nmyproject-bpushmain-a1-v1.21.1jenkins Python example:cigenjenkinspython-nmyproject-bpushmain-a1-v3.9.6Dockercigendocker--helpdocker subcommand can be used to generate Docker configuration files.docker example:cigendocker-ndockerfile-igolang-v1.21.1-smultiGitlab CIcigengitlab-ci--helpgitlab-ci subcommand can be used to generate Build and Test Gitlab CI configuration files.gitlab-ci Golang example:cigengitlab-cigo-nmyproject-bpushmain-a1-v1.21.1gitlab-ci Python example:cigengitlab-cipython-nmyproject-bpushmain-a1-v3.9.6NOTE: ci generator is not yet fully stable, so it's not recommended to use it in production. It's still in development.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: cigetcert
R E A D M EProduct Name:           cigetcertProduct Version:        1.19Date (yyyy/mm/dd):      2019/07/20------------------------------------------------------------------------ORIGIN======AuthorsDave DykstraFermi National Accelerator Laboratory - Mail Station #120P.O Box 500Batavia, IL 60510Internet: cigetcert-support@fnal.govFermilab Standard Government Sponsorship NoticeThis work was performed at Fermi National Accelerator Laboratory,which is operated by Universities Research Association, Inc., undercontract DE-AC02-76CHO3000 with the U.S. Department of Energy.------------------------------------------------------------------------TERMS AND CONDITIONS====================This software has been made freely available with a request forcollaborative input subject to standard Fermitools Terms and Conditions.Small parts of it come from others outside of Fermilab and are coveredby other open source licenses (GNU GPL, python, and BSD).  Details arein the COPYING file.------------------------------------------------------------------------PRODUCT DESCRIPTION===================cigetcert gets an X.509 certificate from a SAML 2.0 Service Provider(SP) such as CILogon using the Enhanced Client or Proxy (ECP)profile. Optionally it can also get a grid proxy certificate and/ortransfer a grid proxy to MyProxy. It was developed for the FermilabDistributed Computing Access with Federated Identities (DCAFI) projectbut is intended to be usable by other projects.Documentation-------------See the home pagehttp://redmine.fnal.gov/projects/fermitools/wiki/cigetcertA formatted man page is available athttps://git.io/vgcZmRequired Software-----------------python >= 2.4, including add-on packages m2crypto, pyOpenSSL,python-lxml, and python-kerberos------------------------------------------------------------------------SUPPORT=======Help with the installation or use of cigetcert is available by sendingmail tocigetcert-support@fnal.govwhich is auto-forwarded to the group in charge of cigetcert support atFermilab.This software is furnished by Fermilab on an "as is" basis as a serviceto the wider community with the goal of stimulating collaborative useand further development.Provision of the software implies no commitment of support by Fermilab.The Fermilab Computing Division is open to discussing other levels ofsupport for use of the software with responsible and committed users andcollaborators.Use of the software in experiments at Fermilab will be assured of alevel of support as agreed to through the standard Memorandum OfUnderstanding process.Users may send mail to the mail address given above, with theexpectation that it will be read and noted.------------------------------------------------------------------------
Package: cigit
Welcome toCi-GitScriptingbuild(s) forContinuousIntegration andContinuousDelivery (CI/CD)Installationpython-mpipinstallcigitCredentials# Git Configurl:https://github.com/hlop3z/cigit.gitpath:./demo# Auth Configusername:hlop3zpassword:(P)ersonal-(A)ccess-(T)okenPipeline# Define Stagesstages:-build-test-deploy# Define { build } stage jobsbuild:stage:buildscript:-echo "Build"# Define { test } stage jobsunit_tests:stage:testscript:-echo "Unit-Tests"integration_tests:stage:testscript:-echo "Integration-Tests"# Define { deploy } stage jobsdeploy_to_staging:stage:deployscript:-echo "Deploy-To-Staging"deploy_to_production:stage:deployscript:-echo "Deploy-To-Production"File"ci-bot.py"importcigitcigit.cli()CommandsCommandDescriptionrunRunthe pipelinepullGit Pulla remote repository & run the pipelinecloneGit Clonea remote repository & run the pipelineCheatsheetClonepythonci-bot.pyclonePullpythonci-bot.pypullRunpythonci-bot.pyrun
Package: cigram
CiGRAM is a generator for random complex networks with community structure and assortative connections.
CiGRAM can be used as a benchmark for community detection algorithms.This package also includes LFR benchmark models.Installation - currently only tested on linux.
Requires C++ and libboost installed and in library paths.To install visithttp://www.boost.org/There is no reason this shouldn’t compile on windows if the required libs are present.
Package: cihai
cihai ·Python library forCJK(chinese, japanese,
korean) data.This project is under active development. Follow our progress and check back for updates!QuickstartAPI / Library (this repository)$pipinstall--usercihaifromcihai.coreimportCihaic=Cihai()ifnotc.unihan.is_bootstrapped:# download and install Unihan to dbc.unihan.bootstrap()query=c.unihan.lookup_char('好')glyph=query.first()print("lookup for 好:%s"%glyph.kDefinition)# lookup for 好: good, excellent, fine; wellquery=c.unihan.reverse_char('good')print('matches for "good":%s'%', '.join([glph.charforglphinquery]))# matches for "good": 㑘, 㑤, 㓛, 㘬, 㙉, 㚃, 㚒, 㚥, 㛦, 㜴, 㜺, 㝖, 㤛, 㦝, ...SeeAPIdocumentation and/examples.CLI (cihai-cli)$pipinstall--usercihai-cliCharacter lookup:$cihaiinfo好char:好kCantonese:hou2 hou3kDefinition:good, excellent, fine; wellkHangul:호kJapaneseOn:KOUkKorean:HOkMandarin:hǎokTang:"*xɑ̀u*xɑ̌u"kTotalStrokes:"6"kVietnamese:háoucn:U+597DReverse lookup:$cihaireverselibrarychar:圕kCangjie:WLGAkCantonese:syu1kCihaiT:'308.302'kDefinition:librarykMandarin:túkTotalStrokes:'13'ucn:U+5715--------UNIHAN dataAll datasets that cihai uses have stand-alone tools to export their data. No library required.unihan-etl-UNIHANdata exports for csv, yaml and json.Developing$gitclonehttps://github.com/cihai/cihai.git`$cdcihai/Bootstrap your environment and learn more about contributing. We use the same conventions / tools across all cihai projects:pytest,sphinx,mypy,ruff,tmuxp, and file watcher helpers (e.g.entr(1)).Python versions0.19.0: Last Python 3.7 releaseQuick linksQuickstartDatasetsa full list of current and future data setsPythonAPIRoadmapPython support: >= 3.8, pypySource:https://github.com/cihai/cihaiDocs:https://cihai.git-pull.comChangelog:https://cihai.git-pull.com/history.htmlAPI:https://cihai.git-pull.com/api.htmlIssues:https://github.com/cihai/cihai/issuesTest coverage:https://codecov.io/gh/cihai/cihaipypi:https://pypi.python.org/pypi/cihaiOpenHub:https://www.openhub.net/p/cihaiLicense: MIT
Package: cihai-cli
cihai-cli ·Command line interface to thecihaiCJK-language library.This project is under active development. Follow our progress and check back for updates!Installation$pipinstall--usercihai-cliDevelopmental releasesYou can test the unpublished version of cihai-cli before its released.pip:$pipinstall--user--upgrade--precihai-clipipx:$pipxinstall--suffix=@nextcihai-cli--pip-args'\--pre'--include-deps--forceThen usecihai@next info 好.For more information seedevelopmental releasesCharacter lookupSeeCLIin the documentation for full usage information.$cihaiinfo好char:好kCantonese:hou2 hou3kDefinition:good, excellent, fine; wellkHangul:호kJapaneseOn:KOUkKorean:HOkMandarin:hǎokTang:"*xɑ̀u*xɑ̌u"kTotalStrokes:"6"ucn:U+597DRetrieve all character information (including book indices):$cihaiinfo好-achar:好kCangjie:VNDkCantonese:hou2 hou3kCihaiT:"378.103"kDefinition:good, excellent, fine; wellkFenn:552AkFourCornerCode:"4744.7"kFrequency:"1"kGradeLevel:"1"kHKGlyph:0871kHangul:호kHanyuPinlu:hǎo(6060) hāo(142) hào(115)kHanyuPinyin:21028.010:hǎo,hàokJapaneseKun:KONOMU SUKU YOIkJapaneseOn:KOUkKorean:HOkMandarin:hǎokPhonetic:"481"kRSAdobe_Japan1_6:C+1975+38.3.3 C+1975+39.3.3kRSKangXi:"38.3"kTang:"*xɑ̀u*xɑ̌u"kTotalStrokes:"6"kVietnamese:háokXHC1983:0445.030:hǎo 0448.030:hàoucn:U+597DReverse lookup$cihaireverselibrarychar:圕kCantonese:syu1kDefinition:librarykJapaneseOn:TOSHOKAN SHOkMandarin:túkTotalStrokes:'13'ucn:U+5715--------char:嫏kCantonese:long4kDefinition:the place where the supreme stores his books; librarykJapaneseOn:ROUkMandarin:lángkTotalStrokes:'11'ucn:U+5ACF--------Developing$gitclonehttps://github.com/cihai/cihai-cli.git$cdcihai-cliBootstrap your environment and learn more about contributing. We use the same conventions / tools across all cihai projects:pytest,sphinx,mypy,ruff,tmuxp, and file watcher helpers (e.g.entr(1)).Python versionsFinal Python 3.7 version: 0.16.0Quick linksQuickstartPythonAPI2017 roadmapPython support: >= 3.8, pypySource:https://github.com/cihai/cihai-cliDocs:https://cihai-cli.git-pull.comChangelog:https://cihai-cli.git-pull.com/history.htmlAPI:https://cihai-cli.git-pull.com/api.htmlIssues:https://github.com/cihai/cihai-cli/issuesTest coveragehttps://codecov.io/gh/cihai/cihai-clipypi:https://pypi.python.org/pypi/cihai-cliOpenHub:https://www.openhub.net/p/cihai-cliLicense: MIT
Package: cihaidata-unihan
cihaidata-unihan- tool to buildunihanintosimple data formatCSV format. Part of thecihaiproject.Unihan’s data is disperved across multiple files in the format of:U+3400      kCantonese      jau1
U+3400      kDefinition     (same as U+4E18 丘) hillock or mound
U+3400      kMandarin       qiū
U+3401      kCantonese      tim2
U+3401      kDefinition     to lick; to taste, a mat, bamboo bark
U+3401      kHanyuPinyin    10019.020:tiàn
U+3401      kMandarin       tiàncihaidata_unihan/process.pywill download Unihan.zip and build all files into a
single tabular CSV (default output:./data/unihan.csv):char,ucn,kCantonese,kDefinition,kHanyuPinyin,kMandarin
丘,U+3400,jau1,(same as U+4E18 丘) hillock or mound,,qiū
㐁,U+3401,tim2,"to lock; to taste, a mat, bamboo bark",10019.020:"tiàn,tiàn"process.pysupports command line arguments. Seecihaidata_unihan/process.py CLI
argumentsfor information on how you can specify custom columns, files,
download URL’s and output destinations.Being built against unit tests. See theTravis BuildsandRevision History.UsageTo download and build your ownunihan.csv:$./cihaidata_unihan/process.pyCreatesdata/unihan.csv.Seecihaidata_unihan/process.py CLI argumentsfor advanced usage examples.Structure# dataset metadata, schema information.datapackage.json# (future) when this package is stable, unihan.csv will be provideddata/unihan.csv# stores downloaded Unihan.zip and it's txt file contents (.gitignore'd)data/build_files/# script to download + build a SDF csv of unihan.cihaidata_unihan/process.py# unit tests to verify behavior / consistency of buildertests/*# python 2/3 compatibility modulescihaidata_unihan/_compat.pycihaidata_unihan/unicodecsv.py# python module, public-facing python API.__init__.pycihaidata_unihan/__init__.py# utility / helper functionscihaidata_unihan/util.pyCihai isnotrequired for:data/unihan.csv-simple data formatcompatible csv file.cihaidata_unihan/process.py- create adata/unihan.csv.When this module is stable,data/unihan.csvwill have prepared
releases, without requires usingcihaidata_unihan/process.py.process.pywill not require external libraries.Exampleshttps://github.com/datasets/gdphttps://github.com/datasets/country-codesRelated links:CSVSimple Data Format(SDF):http://data.okfn.org/standards/simple-data-formatTools:http://data.okfn.org/toolsPython supportPython 2.7, >= 3.3, pypy/pypy3Sourcehttps://github.com/cihai/cihaidata-unihanDocshttps://cihaidata-unihan.git-pull.comChangeloghttps://cihaidata-unihan.git-pull.com/en/latest/history.htmlAPIhttps://cihaidata-unihan.git-pull.com/en/latest/api.htmlIssueshttps://github.com/cihai/cihaidata-unihan/issuesTravishttps://travis-ci.org/cihai/cihaidata-unihanTest coveragehttps://codecov.io/gh/cihai/cihaidata-unihanpypihttps://pypi.python.org/pypi/cihaidata-unihanOpenHubhttps://www.openhub.net/p/cihaidata-unihanLicenseMIT.git repo$gitclonehttps://github.com/cihai/cihaidata-unihan.gitinstall dev$gitclonehttps://github.com/cihai/cihaidata-unihan.gitcihai$cd./cihai$virtualenv.env$source.env/bin/activate$pipinstall-e.tests$pythonsetup.pytest
Package: ci-hash
Project Title
One Paragraph of project description goes hereGetting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.Prerequisites
What things you need to install the software and how to install themGive examples
Installing
A step by step series of examples that tell you how to get a development env runningSay what the step will beGive the example
And repeatuntil finished
End with an example of getting some data out of the system or using it for a little demoRunning the tests
Explain how to run the automated tests for this systemBreak down into end to end tests
Explain what these tests test and whyGive an example
And coding style tests
Explain what these tests test and whyGive an example
Deployment
Add additional notes about how to deploy this on a live systemBuilt With
Dropwizard - The web framework used
Maven - Dependency Management
ROME - Used to generate RSS Feeds
Contributing
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.Versioning
We use SemVer for versioning. For the versions available, see the tags on this repository.Authors
Billie Thompson - Initial work - PurpleBooth
See also the list of contributors who participated in this project.License
This project is licensed under the MIT License - see the LICENSE.md file for detailsAcknowledgments
Hat tip to anyone whose code was used
Inspiration
etc
Package: cihpc
CI & HPC ·Asimpleframework which can monitor the performance and scalability of software packages.
The framework presented here combinesContinuous Integation&High Performance Computingtogether with a minimalistic set of Python scripts.
The results can be visualised in form of static Jupyter notebook or in an interactive web page.Featuresautomatically run benchmarks of your projectinspect performance and scalability of your projectcreate complex configurations with travis-like syntax build matrix capabilities (YAMLformat)How to use ci-hpc?First install the framework by executing:pip3install--userci-hpcCreate a configuration (fileconfig.yaml) for your repository.
In this cofiguration, you should specify several options.ci-hpcneeds to know, which repositories are part of your project.
You can specify one or more repositories. Each repository will be
cloned and checked out when installing.You also need to tell theci-hpchow to install your project.It can be as simple as./configure; make; make installorpip install ./foo/But it can be also quite complex, you can even simplify
entire process with usage of install file:with something like this!sh install.sh(install.shin this case is a shell script, which contains
your installation process)Next thing is testing section. Here,ci-hpcneeds to know,
what benchmarks you want to run under what configuration.
You can create complex build matrices so your configuration
can be kept simple and transparent.When you are done with the configuration. You should probably verify,
it is working. Assuming the following structure:.
└── hello-world
   └── config.yamlExecuteci-hpcwithcihpc --project hello-worldIf everything works, setup a mongoDB server and add collection
section (once again fileconfig.yaml):ci-hpcneeds to know, what results you want to store.
Is it a somejsonprofiler oryamlresults?
or are the timings save in axmlformat? In theci-hpcthere
is some general support for thejsonandyamlformats, but you can write your ownartifactmodule and simply
put it in the correct folder.The most of the heavy lifting is already done in a parent class
so adding a new collection module should be relatively easy.Now to display the results, setup a visualization server.What's new?now supporting multiple repositories within single projectspeed up testing process by using multiple cores on a computing nodeeasily run ci-hpc on a previous commits by usingcommit-browsermoduleautomatically determine which tests to run based on the previous resultswebhook support, automatically startci-chpupon new commitWhat's to come?run extra tests when suspecting significant performance changecreate web visualisation configuration from analyzing records in database
Package: ci-info
ci-infoA Python implementation ofwatson/ci-info.
Get details about the current Continuous Integration environment.Pleaseopen an issueif your CI server isn't properly detected :)Supported CI toolsOfficially supported CI servers:NameisPRAWS CodeBuild🚫AppVeyor✅Azure Pipelines✅Appcircle🚫Bambooby Atlassian🚫Bitbucket Pipelines✅Bitrise✅Buddy✅Buildkite✅CircleCI✅Cirrus CI✅Codefresh✅Codeship🚫Drone✅dsari🚫Expo Application Services🚫GitHub Actions✅GitLab CI✅GoCD🚫Hudson🚫Jenkins CI✅LayerCI✅Magnum CI🚫Netlify CI✅Nevercode✅Render✅Sail CI✅Screwdriver✅Semaphore✅Shippable✅Solano CI✅Strider CD🚫TaskCluster🚫TeamCityby JetBrains🚫Travis CI✅Vercel🚫Visual Studio App Center🚫Installationpip install ci-infoUsageimportci_infoifci_info.is_ci():print(ci_info.name())"My CI Name"APIci_info.name()Returns a string containing name of the CI server the code is running on.
If CI server is not detected, it returnsNone.Don't depend on the value of this string not to change for a specific
vendor.ci_info.is_ci()Returns a boolean. Will beTrueif the code is running on a CI server,
otherwiseFalse.Some CI servers not listed here might still trigger theci_info.is_ci()boolean to be set toTrueif they use certain vendor neutral
environment variables. In those casesci_info.name()will beNoneand no
vendor specific boolean will be set toTrue.ci_info.is_pr()Returns a boolean if PR detection is supported for the current CI server. Will
beTrueif a PR is being tested, otherwiseFalse. If PR detection is
not supported for the current CI server, the value will beNone.ci_info.info()Returns a dictionary of all above values in key/value pairs.LicenseMIT
Package: ciipromol
# CIIProMol## ciipro.rutgers.edu### Outline -1. From a set of SMILES* obtain CIDS* Get bioassays for each compound2. Concatenate bioassays into a bioprofile* eliminate bioassays based of number of actives* remove invivo assays* remove highly correlated assays3. Find invitro/invivo correlations* remove assays based on different stats* use those stats to find bio nearest neighbors* find the assays that minimize the difference in activity between target cmp and nnA python class for extending [CIIPro](ciipro.rutgers.edu) functionality.CIIProMol looks to extend the [Python rdkit API](http://www.rdkit.org/docs/api/index.html).CIIProMol requires several packages (e.g., rdkit, Pandas, Numpy).These dependencies are listed in the file `explicit-spec-file.txt` andcan be loaded directly into a new [conda](http://conda.pydata.org/docs/) environment.Use the following code to install the required Python packages into anew conda environment.```bash$ conda create --name ciipromol --file explicit-spec-file.txt```CIIProMol is broken down into three modules:1) `ciipromol.py`2) `ciiprofiler.py`3) `ciipredictor.py`### ciipromolContains `class CIIProMol`.  A `CIIProMol` object can by instantiatedwith an rdkit `Mol` object.```pythonfrom ciipromol import *from rdkit import Chemmol = Chem.MolFromSmiles('CC(=O)OC1=CC=CC=C1C(=O)O')cpm = CIIProMol(mol, activity=20.0)```If `cids=None` (default), all PubChem Compound ID's (CIDs) associated withthat structure will be retrieved.  Biological assays associated with those CIDscan be retrieved.```pythoncpm.GetBioAssays()```If `attribute=True` (default), the BioAssays can be acccessed as a Pandas DataFrameusing the attribute, `cpm.BioAssays`### ciiprofilerContains `class CIIProfiler`.  A `CIIProfiler` object can be instantiated by passinga list of `CIIProMol` objects.```pythonciiprofiler = CIIProfiler([cpm1, cpm2, cmp3])```The function `MakeBioProfiler()` will concatenate the attributes `cpm1.BioAssays`,`cpm2.BioAssays`, etc., into one matrix.