Saravanan666/pypi-org huggingface dataset:
Package: crms
CRMS CLI Installation(0) Preparation(0.1) PIP3 must be prepared.sudoapt-getupdate-y
sudoapt-getinstallpython3-pip(0.2) Git must be prepared and configuredsudoapt-getinstallgit
gitconfig--globaluser.name“YourName”
gitconfig--globaluser.emailyour-email@xxx.com(1) Install gcloud sdk(1.1) Downloadhttps://cloud.google.com/sdk/docs/install(Linux 64-bit package)(1.2) Extract the downloaded package to ~/google-cloud-sdktar-xfgoogle-cloud-cli-395.0.0-linux-x86_64.tar.gz(1.3) Add path to .bashrc./google-cloud-sdk/install.sh(1.4) Setup Google CredentialDownload Google Credential file (contact administrator of your Google Cloud Project)Access permission of credential may be one of reading or writing (writing permission is necessary for a full test)In case of reading permission, crms push command is not allowed (crms list/desc/pull commands are allowed)Default credential for the reader of google cloud robot project is cloudrobotai-reader-cred.zipunzip cloudrobotai-reader-cred.zipSet GOOGLE_APPLICATION_CREDENTIALS into ~/.bashrcexportGOOGLE_APPLICATION_CREDENTIALS=”/path/to/<google-cloud-project-credential>.json”(1.5) (optional) Set an environment variable (CRMS_META_REPOSITORY) for Google Firestore (Default='croudrobotai')exportCRMS_META_REPOSITORY=your-google-cloud-project(2) Install crms from Github or PyPi(2.0) pip upgrade is required before install crmspip3install--upgradepip[rebootifnecessary,andtrytoinstallagain](2.1) For full test : Download crms from Github and install with pip3gitclonehttps://github.com/jangcs/crms.gitcdcrms
pip3install.(2.2) For just install (not for test): Install from PyPipip3installcrms(3) Install DVC for Google Cloud Storagepip3installdvc[gs][rebootifnecessary](4) Prepare ssh key for github(4.1) SSH key has to be generated for secure connection with github.comssh-keygen
ssh-keyscan github.com > ~/.ssh/known_hosts(4.2) And, the generated SSH key has to be registered to github.com in order to push a modelLogin github.com -> Settings/SSH and GPG keys -> [New SSH Key] Button -> Copy the contents of ~/.ssh/id_rsa.pub to Key box -> [Add SSH Key] Button(5) Test crms cli(5.1) Modify the line in test.sh, 'crms confgit@github.com:IdToBeReplaced/...' to your github accountsed-i's/IdToBeReplaced/<your_github_account>/g'test.sh(5.2) Add a repository to github (ex. model_test)Login github.com -> [New] Button -> Create a New Repository -> Enter a repository name(ex. model_test) -> [Create repository] Button(5.3) Execute test.sh with the name of the added github repository/test.shmodel_test
Package: crn
# Python CRNA CRN simulator based on[David Soloveichik's Mathematica CRN Simulator](http://users.ece.utexas.edu/~soloveichik/crnsimulator.html)## Version & PromisesCurrently this is in a pre-alpha state, but already has some reasonablefeatures. No guarantees are made for backward-compatibility. That is, someof the API calls might change, some of the reaction literals syntax mightchange, etc.## DependenciesThe package is written for Python 3.6+ and definitely won't work on an olderPython version.The current list of dependencies are```numpyscipysympystochpy```## ExamplesA simple example of creating a crn, simulating it, and plotting it, is givenhere. An explanation of this code in detail can be found in`crn/examples/basic_example.py`.```pythonfrom crn import *a, a1, a2, b, c, t, z = species("A A1 A2 B C T Z")sys = CRN(a >> a1 + a2,a1 + b >> t,c >> z,(a2 >> z).k(2.5),z + t >> 0)sys.simulate({a: 2.5, b: 2.0}, t=5).plot("sim.png", title="Example Simulation")```More examples will be added soon in the `crn/examples/` folder.## InstallationInstall using `pip` or `pip3` depending on your setup.```pip3 install crn```Note: [StochPy](https://github.com/SystemsBioinformatics/stochpy) is notPython 3 ready. So, when `crn` is imported, it actually runs `lib2to3` on thefiles that need to be modified within StochPy.
Package: cr-nimble
CR-NimbleCR-Nimbleconsists of fast linear algebra
and signal processing routines.
Most of the routines have been implemented using
Google JAX. Thus, they can be easily run on
a variety of hardware (CPU, GPU, TPU).Functionality includes:Utility functions for working with vectors, matrices and arraysLinear algebra functionsDigital signal processing functionsData compression functionsTest data generation functionsInstallationpython -m pip install cr-nimbleFor Windows, you can use unofficial JAX builds
fromhere.Importimport cr.nimble as crnSeedocumentationfor library usage.CR-Nimbleis part ofCR-Suite.Related libraries:CR-WaveletsCR-Sparse
Package: crnt4sbml
CRNT4SBMLCRNT4SBML is an easily installable Python based package available on MacOS and Windows. CRNT4SBML is concentrated on
providing a simple workflow for the testing of core CRNT methods directed at detecting bistability in cell signaling
pathways endowed with mass action kinetics.Free software: Apache Software License 2.0Documentation:https://crnt4sbml.readthedocs.io.FeaturesRoutine for testing of the Deficiency Zero and One Theorems.Routine for running the mass conservation approach.Routine for running the semi-diffusive approach.Citing CRNT4SBMLIf you use CRNT4SBML in your research, we would appreciate it if you use the following citation in any works you publish:Brandon C Reyes, Irene Otero-Muras, Michael T Shuen, Alexandre M Tartakovsky, Vladislav A Petyuk,CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks,
Bioinformatics.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (08-22-2019)First release on PyPI.0.0.2 (08-23-2019)Addition of Cytoscape functionality.0.0.3 (08-26-2019)Improvements to the plots produced by the continuity analysis.0.0.4 (09-5-2019)Addition of safety precautions for numerical continuation.0.0.5 (09-16-2019)Addition of routines to parse catalysis in SBML.Addition of routines to automatically generate physiological bounds.0.0.6 (09-23-2019)Updating safety wrapper to smoothly work on Windows and Mac.0.0.7 (10-11-2019)Adding output to continuity routine to make post-processing simpler.0.0.8 (10-23-2019)Adding functionality to allow use of Jupyter notebooks.0.0.9 (11-6-2019)Improving stability when creating the equilibrium manifold in the mass conservation approach.0.0.10 (11-6-2019)Adding routine for an exhaustive equilibrium manifold creation in the mass conservation approach.0.0.11 (4-23-2020)Adding a general approach for mass conserving systems.0.0.12 (5-21-2020)Adding a different layout for installation of package.0.0.13 (8-10-2020)Improving usability on Windows machines.0.0.14 (1-28-2021)Improving the output of plots produced by direct simulation.0.0.15 (8-25-2021)Updated tolerances for Bayesian stopping rule to 0.1.
Package: cro3n
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 ╚═════╝ ╚═╝  ╚═╝  ╚═════╝  ╚═╝  ╚═══╝Another onecron expressionutils package for python, wrapped on rust cratezslayton/cron.Installationpipinstall-Ucro3nExampleseeexample/pythonDevyou need config:rustpython 3.7+pyo3 tools
more details seehttps://pyo3.rs/v0.20.0/Related Projectszslayton/cronPyO3/maturinPyO3/pyo3LICENSEMIT
Package: crockford
crockford - Encode and Decode using the Crockford Base32 schemeInstallationUse:> sudo pip install crockfordor:> sudo easy install crockfordor:> git clone git://github.com/ingydotnet/crockford-py.git
> cd crockford-py
> sudo make installUsageimport crockfordbase32 = crockford.b32encode(string)
string = crockford.b32decode(base32)AuthorsIngy dot Net <ingy@ingy.net>Copyrightcrockford is Copyright (c) 2011, Ingy dot Netcrockford is licensed under the New BSD License. See the LICENSE file.
Package: crockford32
DescriptionAn implementation of Crockford's Base32 encoding, including checksums. Intended for use on unique resource identifiers in APIs.
Does not apply automatic conversions on decode input as described in "API Design Patterns" by JJ Geewax p.92.Created usingpybind11andsetuptools.Usageencode(string input [, checksum = False])importcrockford32ascr32encode_input_one="Hello"encode_input_two="Tree"a=cr32.encode(encode_input_one)b=cr32.encode(encode_input_two,True)decode(string input [, checksum = False])throws ValueError- invalid input charactersthrows ValueError- invalid checksum (requires checksum=True)importcrockford32ascr32decode_input_one="91JPRV3F"decode_input_two="AHS6AS8U"try:a=cr32.decode(decode_input_one)exceptValueError:passtry:b=cr32.decode(decode_input_two,True)exceptValueError:pass
Package: crockode
crockodecrockodeis a python package for encoding a string message into a DNA sequence and decoding .Installation$pipinstallcrockodeUsageimportcrockode# returns 'GCCAGCGGGCTAGCTAGCTT'crockode.encode_string('hello')# returns 'hello'crockode.decode_DNA('GCCAGCGGGCTAGCTAGCTT')ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecrockodewas created by Michela Palamin, Simone Procaccia, Erin Chung, Joshua Talks. It is licensed under the terms of the MIT license.Creditscrockodewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.Project statusThe creators have run out of time, energy, and motivation to develop this project. Rest In Peacecrockode05/02/2024-09/02/2024.
Package: croexp
Crontab Expression:https://crontab.guru/.vifcroexp library will allow you to check if the crontab parameters are correct.
you need to follow the order of the schedule expression like in the web site: (minute, hour, day_month, month, day_week)minute: allowed values 0-59.hour: allowed values 0-23.day_month: allowed values 1-31.month: allowed values 1-12.day_week: allowed values 1-7.'*' any value.Parameter:crontab_parameter => need to be a string.Return:If the parameters are valid, otherwise it will raise an error: 'Parameters are not correct'.>>> from croexp import vifcroexp>>> vifcroexp("*,0,1,4,4")
Package: croissance
DescriptionA tool for estimating growth rates in growth curves. The tool fits λ ⋅ eμ⋅x+ N0to any candidate growth phases of the growth curve that have increasing growth, i.e. where both the first and second derivative of the growth function are positive. To identify these phases reliably, the tool utilizes a custom smoothing function that addresses problems other smoothing methods have with growth curves that have regions with varying levels of noise (e.g. lots of noise in the beginning, then less noise after growth starts, then more noise in the stationary phase).The parameter N0represents the background/blank OD reading (not seeding OD) and can optionally be constrained. This is recommended if the value is known.The growth rate in calculated growth phases can only be properly compared if their seeding OD (when the organism is at its initial population) points to a similar stage of actual growth.Intercept (λ) reported by this package can be used as indicator of lag if SNR is sufficiently high.InstallationTo installcroissance, use Python 3.xpip:pip3 install croissanceUsageCroissance can be used from command-line or as a Python library. The input to the command-line tool is generally one or more*.tsvfiles (tab-separated values) with the following format:timeA1A2…0.00.00.01…0.170.140.06……………Each sample should be recorded in its own column with the sample name in the header row. The time unit is hours and the value unit should be OD or some value correlating with OD.To process this file, enter:croissance example.tsvThe output will be generated atexample.output.tsv. The output is formatted with column headers:name(sample name),phase(nth growth phase),start(start time),end(end time),slope(μ),intercept(λ),n0(N0) and a few others. By default, each sample is represented by at least one row, containing phase “0”. This is simply the highest ranking phase if one was found for this curve; otherwise the remaining columns are empty.To also output a PDF files with figures (example.output.pdf), enter:croissance --figures example.tsvTo see a description of all the command-line options available, entercroissance--help.For use from Python, provide your growth curve as apandas.Seriesobject. The growth rates are estimated usingcroissance.process_curve(curve). The return value is anamedtupleobject with attributesseries,outliersandgrowth_phases. Each growth phase has the attributesstart(start time),end(end time),slope(μ),intercept(λ),n0(N0), as well as other attributes such asSNR(signal-to-noise ratio of the fit) andrank.from croissance import process_curve

result = process_curve(my_curve)
print(result.growth_phases)
Package: croissant-sim
# CROISSANT: spheriCal haRmOnics vISibility SimulAtor iN pyThon[![codecov](https://codecov.io/gh/christianhbye/croissant/branch/main/graph/badge.svg?token=pj1hkgcazd)](https://codecov.io/gh/christianhbye/croissant)CROISSANT is a rapid visiblity simulator in python based on spherical harmonics. Given an antenna design and a sky model, CROISSANT simulates the visbilities - that is, the perceived sky temperature.CROISSANT uses spherical harmonics to decompose the sky and antenna beam to a set of coefficients. Since the spherical harmonics represents a complete, orthormal basis on the sphere, the visibility computation reduces nicely from a convolution to a dot product.In frequency domain, CROISSANT uses Discrete Prolate Spheroidal Sequences as a rapid linear interpolation scheme. Being linear, this interpolation can be done directly on the spherical harmonics coefficients, avoiding redoing the most expensive part of the computation.Moreover, the time evolution of the simulation is very natural in this representation. In the antenna reference frame, the sky rotates overhead with time. To account for this rotation, it is enough to rotate the spherical harmonics coefficients. In the right choice of coordinates (that is, one where the z-axis is aligned with the rotation axis of the earth or the moon), this rotation is simply achieved by multiplying the spherical coefficient by a phase.Overall, this makes CROISSANT a very fast visibility simulator. CROISSANT can therefore be used to simulate a large combination of antenna models and sky models - allowing for the exploration of a range of propsed designs before choosing an antenna for an experiment.## Installation
For the latest release, dopip install croissant-sim(seehttps://pypi.org/project/croissant-sim). Git clone this repository for the newest changes (this is under activate development, do so at your own risk!).CROISSANT depends on the package pygdsm which is not uploaded to PyPI. This package will therefore not automatically be installed with CROISSANT, but is available on GitHub athttps://github.com/telegraphic/pygdsm.## Demo
Jupyter Notebook:https://nbviewer.org/github/christianhbye/croissant/blob/main/notebooks/example_sim.ipynb## Contributing
Contributions are welcome - please see the [contribution guidelines](https://github.com/christianhbye/croissant/blob/add_contributing/CONTRIBUTING.md).
Package: cromdriver
CromdriverAuto downloader for chromedriverCromdriver is a library to download automatically the latest Chromedriver release for Selenium.Import the lib in your Selenium project and it will download and the AppData directory the latest release fromhttp://chromedriver.storage.googleapis.com/index.html.You can get any version published in the website.Drivers are downloaded :Mac OS X:~/Library/Application Support/cromdriverUnix:~/.local/share/cromdriverWindows:C:\Users\<username>\AppData\Local\cromdriver\cromdriverInstallationInstall the library with PIPpipinstallcromdriverUsage exampleThere are two ways to use the library :Selenium python filefromseleniumimportwebdriverimportcromdriverdriver=webdriver.Chrome()driver.get('https://www.google.com')importosfromseleniumimportwebdriverimportcromdriverpath=os.path.join(cromdriver.get_chromedriver_path(),'chromedriver.exe')driver=webdriver.Chrome(executable_path=path)driver.get('https://www.google.com')CLI~$ cromdriver getDownloading version : 86.0.4240.22Path : C:\Users\gauth\AppData\Local\cromdriver\cromdriver\RELEASE\86.0.4240.22~$ cromdriver get --version 85.0.4183.87 --platform linuxDownloading version : 85.0.4183.87Path : C:\Users\gauth\AppData\Local\cromdriver\cromdriver\RELEASE\85.0.4183.87~$ cromdriver testLast release on your machine : 86.0.4240.22Last release on http://chromedriver.storage.googleapis.com/index.html : 86.0.4240.22~$ cromdriver listChromedrivers downloaded :- 85.0.4183.87- 86.0.4240.22~$ cromdriver del --version 85.0.4183.87Version 85.0.4183.87 deletedWhen you use this CLI, PATH environnement variable is not updated. You will need to add the directory path to the ENV.Release History0.4.0CHANGE: Update README.mdADD: Github action to deploy in Pypi0.3.0ADD : First working versionMetaGauthier Chaty –@gokender–gauthier.chaty+github@outlook.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/Gokender
Package: cromlech.container
This package defines interfaces of container components, and provides
a basic container implementation. The available component is a generic
and non-persistent ccontainer.The package also provides some utilities to handle containers :
an implementation of azope.size.ISizedcomponent and an
implementation of azope.location.ISublocationscomponent. They can
both be registered as adapters, butcromlech.containerdoesn’t do it
by default.This package is the fruit of a fork fromzope.container, in order to
free it from the heavy zope dependencies and from the C
module. Therefore, the behavior is different : the ContainedProxy
component does not allow a transparent persistency.Changelog0.1 (2011-06-14)Initial release
Package: cromlech.marshallers
cromlech.marshallerscromlech.marshallersprovides ways to convert
data between Python objects and other formats.Currently,cromlech.marshallerssupports :JSON (native & RapidJSON)pickle (native)marshal (native)msgpackYAMLBSONMarshallers using third party libraries use conditionnal import.
Check thesetup.pyextra_requiresto include them transparently.Examples>>> from cromlech.marshallers import JSONMarshaller
>>> marshaller = JSONMarshaller()
>>> struct = [1, "two", 3]
>>> data = marshaller.dumps(struct)
>>> assert marshaller.loads(data) == structFeaturesEach marshaller can handle strings or bytes (according to their binary
nature), streams and files.>>> marshaller.dumps(struct)
>>> marshaller.loads(data)
>>> marshaller.dump(struct, stream)
>>> marshaller.load(stream)File access is watched by a lock that prevents concurrency. This lock
can be configured to include a comprehensive timeout.>>> marshaller.dump_to(struct, path, timeout=2)
>>> marshaller.load_from(path, timeout=2)In addition to that, the marshaller can be used as a decorator, in
order to marshal the result of a function or method :>>> @marshaller.wraps
... def hello():
...     return 'world'You can read the detailed usageshereChangelog0.1 (2020-11-27)First release
Package: cromo
cromoContraint Reasoning Over MOdelsInstallationpip install cromoExample UsageClone the repositoryGo to the cromo directoryExample run of cromo on rough terrain
`cromo search start Fire src/cromo/test/georgetown.geojson "2020-10-20" "2020-10-30"Example run of cromo on flat terraincromo search start Fire src/cromo/test/whiterock.geojson "2021-03-01" "2021-03-31"
Package: cromosim
cromosimCROMOSIM is a Python Library for Crowd Motion Simulation.Website:http://www.cromosim.frSource code:https://github.com/sylvain-faure/cromosimThe aim of this open source project is to make it possible to users to run simulations based from several models (cellular automata, microscopic simulations or using compartments), to test new configurations, and even to investigate the possibility to program their own model in complex geometry : do-it yourself !This package proposes Python implementations of the numerical methods detailed in the book “Crowds in equations: an introduction to the microscopic modeling of crowds” by B. Maury (ENS Ulm & Univ. Paris-Sud) and S. Faure (CNRS), World Scientific 2018, Advanced textbooks in mathematics.How to use cromosim ?First you have to install cromosim, either by using pip:~$ pip install cromosimor by manually installing the package:cromosim$ python setup.py installOnce cromosim is installed, you can verify that it is possible to import it
into Python:~$ python
Python 3.7.7 (default, Mar 10 2020, 15:43:33)
[Clang 11.0.0 (clang-1100.0.33.17)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import cromosim
>>> print(cromosim.__version__)
2.0.0
>>>Now to make a first simulation, you can download one of the examples found in:https://github.com/sylvain-faure/cromosim/tree/master/examplesor retrieve all the examples available using the following command (svn
is the Subversion command):~$ svn export https://github.com/sylvain\-faure/cromosim/trunk/examples my-cromosimand then run a first example:~$ cd my-cromosim/micro/granular
granular$ python micro_granular.py --json input_room.jsonThese examples will allow you to start your own calculations.
Package: cromp
✏️ CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters)This repo provides the official implementation of CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters) along with the test dataset and the test pipeline code to reproduce the benchmarking results as described in the CROMP paper ("Constrained Regression with Ordered and Margin-sensitive Parameters: Application in improving interpretability for wage models with prior knowledge").Author:Kaushik Bar🎯 Quick InfoCROMP allows:User-defined order among the coefficientsUser-defined minimum margins (i.e., percentage gaps) between the coefficientsUser-defined lower and upper bounds for each coefficientCoefficients without any order or margin restrictionsThis package is both interpretable and accurate.
There is no other package / library available in the public domain today with these capabilities.💻 InstallationInstall usingPyPI:pipinstallcromp💻 DependenciesThe package depends on:python >= 3.6scipynumpypandasThe tests depend additionally on:scikit-learnData scientist salary dataAmes House price dataSCB Sweden wage data🚀 UsageBelow is a minimal usage guide. This library aloows flexibility for more fine-grained controls for each coefficient separately.
Please go through thetestsin addition to see more examples.>>>fromcrompimportCROMPTrain,CROMPPredict>>>df=pd.read_csv("tests/data/ames_house_prices_data.csv")>>>df_train=df.iloc[:-50,:]>>>df_test=df.iloc[-50:,:]>>>target_col='SalePrice'>>>feats_in_asc_order=['1stFlrSF','TotalBsmtSF','GrLivArea']>>>feats_in_no_order=[]>>># Instantiate model>>>model=CROMPTrain()>>># Configure constraints>>>ret_success=model.config_constraints(feats_in_asc_order,min_gap_pct=0.5,feats_in_no_order,lb=0.0,ub=100.0,no_intercept=True)>>># Train>>>ret_success,cromp_model=model.train(df_train,target_col)>>># Predict>>>model=CROMPPredict(cromp_model)>>>result=model.predict(df_test)☕ ContactFeel free to contactKaushik Barif you have any further questions / feedback.Author Affiliation:Inxite Out Pvt LtdCitationTo cite CROMP in your work, please use the following bibtex reference:@software{Bar_CROMP_Constrained_Regression_2022,author={Bar, Kaushik},license={MIT},month={10},title={{CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters)}},url={https://github.com/kb-open/CROMP},year={2022}version={v0.0.3},publisher = {Zenodo},doi = {10.5281/zenodo.7152807},url = {https://doi.org/10.5281/zenodo.7152807}}
Package: cromulatron
A Twitter bot that posts sentences for grammaticality judgments.Free software: BSD licenseInstallationpip install cromulatronDocumentationhttps://cromulatron-bot.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2016-10-10)First release on PyPI.