Saravanan666/pypi-org huggingface dataset:
Package: chronos-forecast
ChronosSimple time series prediction model. Implemented usingPyroand greatly inspired byProphet.Requirements:python >= 3.7pandas >= 1.1numpy >= 1.19matplotlib >= 3.2torch >= 1.5pyro-ppl >= 1.3InstallationTo install chronos, run the following command from your terminal:pipinstallchronos-forecastSimple Use CaseWith the files included, you can load theDivvy bike dailydata (the data has been aggregated since the original file is 2GB) as follows:importpandasaspdimportnumpyasnpdivvy_data=pd.read_csv('data/divvy_daily_rides.csv')divvy_data['ds']=pd.to_datetime(divvy_data['ds'])print(divvy_data.head())ds          y
0 2014-01-01  105421324
1 2014-01-02  123221770
2 2014-01-03    6662107
3 2014-01-04  201035389
4 2014-01-05   35549270You can call Chronos as follows:>>>fromchronosimportChronos>>>importchronos_plotting>>>>>>my_chronos=Chronos(seasonality_mode="mul",distribution="Gamma")>>>my_chronos.fit(divvy_data)EmployingMaximumAPosteriori100.0%-ELBOloss:-1.5903|MeanAbsoluteError:11152849920.0000>>>predictions=my_chronos.predict(period=365)Predictionno:1000>>>chronos_plotting.plot_components(predictions,my_chronos)Notice we can specify the distribution of the ride shares to be a gamma distribution to ensure they are never negative. Additionally, we made the seasonality multiplicative to make sure that its affect increases as the absolute number of
rides increases.
Package: chronospy
Chronos is a mutil-thread/mutil-process task scheduler drive by Tornado IOLoop.ContentsInstallHello WorldAPIsetupscheduleremove_taskstart_taskstop_taskstartstophow to use every toolsevery_secondevery_ateveryLICENSEInstallInstall the extension with the following command:$ easy_install chronospyor alternatively if you have pip installed:$ pip install chronospyor clone it form github then run the command in shell:cdchronos# the path to the projectpythonsetup.pyinstallHello Worldimporttornadoimportchronosimportosimporturllib2deftest_process():print("process pid%s"%(os.getpid()))deftest(word):print("an other task, say '%s'"%(word))defsay():response=urllib2.urlopen('https://www.google.com/')html=response.read()print(html[:10])definit():# bind a ioloop or use default ioloopchronos.setup()# chronos.setup(tornado.ioloop.IOLoop())chronos.schedule('say',chronos.every_second(1),say)chronos.schedule('say2',chronos.every_second(1),test_process,once=True,process=True)chronos.schedule('say3',chronos.every_second(1),lambda:test("test3"))chronos.start(True)if__name__=='__main__':init()APIsetupsetup(io_loop=None)bind a io_loop  or use default ioloop.scheduleschedule(name, timer, func, once=False, start=False, process=False, max_executor=5)add task into chronos:name:uniqe task name,timer:every timer objectfunc:the task functiononce:set True will run only once time.start:when chronos start and schedule a new task, if set to True will add to Tornado IOLoop and schedule to run at time.process:if process is True, then the job will run in on a procees, otherwise defaultly running in thread.max_executor:the max threads(or processes) to run a task.remove_taskremove_task(task_name)stop and remove the task from chronosstart_taskstart_task(task_name)start the task in chronosstop_taskstop_task(task_name)stop the task in chronosstartstart(start_ioloop=False)add tasks in ioloop, if you use chronos in a tornado web server, you can set start_ioloop to “False”, then start your custom ioloop later.stopstop(stop_ioloop=False, clear=True)stop the task in ioloopstop_ioloop:will stop the ioloop if set to “True”.clear:will remove tasks from chrons if set to “True”.how to use every toolsevery_secondset eveny seconds to run a job:every_second(5) # run job every 5 secondsevery_atset every hourly or mintuely run a job:every_at(hour=1, minute=10, second=0) # run at 01:10:00 every day
every_at(minute=10, second=0) # run at run at 10 mintue every houreveryevery(10).minutesevery().hourevery().day.at("10:30")every().mondayevery().wednesday.at("13:15")LICENSECopyright (C) 2015 Thomas HuangThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 2 of the License.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: chronous
Chronousis a asynchronous python library designed to make asynchronous event-driven architectures on discord.py[Example]importasynciofromtypingimportNoReturnimportdatetimefromchronous.ArchitectureimportBaseArchitecturefromchronous.eventsimportEventContext,Setup,Init,Start,CloseclassSampleArchitecture(BaseArchitecture):def__init__(self)->None:super(SampleArchitecture,self).__init__(name="sample")# Registering eventsself.register_event(event=Setup())self.register_event(event=Init())self.register_event(event=Start())self.register_event(event=Close())defrun(self)->None:# Registering default lifecycle events# Start process.print("Starting process...")asyncio.run(self.process())asyncdefprocess(self)->NoReturn:awaitself.dispatch("setup")awaitself.dispatch("init")print('='*20)awaitself.dispatch("start",datetime.datetime.now())index:int=0whileindex<10:print("Looping!")index+=1awaitself.dispatch("close")print('='*20)sample=SampleArchitecture()# Multiple listener sample@sample.listen()asyncdefsetup(ec:EventContext):print("{e.name}phase - listener 1".format(e=ec.event))@sample.listen()asyncdefsetup(ec:EventContext):print("{e.name}phase - listener 2".format(e=ec.event))# EventContext  sample@sample.listen()asyncdefinit(ec:EventContext):print("Initialization phase")print("Event :{e}".format(e=ec.event))# Additional arguments sample@sample.listen()asyncdefstart(ec:EventContext,time:datetime):print("Starting process...")print("Starting at :{time}".format(time=time))# Exception sample@sample.listen()asyncdefclose(ec:EventContext):print("Closing process...")print(f"Make an error :{1/0}")sample.run()
Package: chronqc
ChronQC: A Quality Control Monitoring System for Clinical Next Generation SequencingFree software: MIT licenseDocumentation:http://chronqc.readthedocs.io/en/latest/.ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics, which allows comparison of the current run to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds, and historical observations within a specified period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping.FeaturesSuited for different assays in a clinical laboratoryGenerates interactive time series plots for various metricsRecords users’ notes and corrective actions onto the graphs to facilitate long-term recordkeepingProvides high level of customization: works with local databases and generates different chart typesLeverages existing standard tools such asMultiQCExample live ChronQC reporthttps://nilesh-tawari.github.io/chronqcExamplesChronQC plots can be generated from,A custom SQLite database.For example see,examples/custom_db_example.The output ofMultiQC.For example on creating the ChronQC database and plots see,examples/multiqc_example_1.For example on creating, updating the ChronQC database and plots see,examples/multiqc_example_2.For complete command line reference see thedocumentation.For details of chart types see thedocumentation.RequirementChronQC is implemented in Python (tested with v2.7 / v3.5 / v3.6) and runs on all common operating systems (Windows, Linux and Mac OS X).InstallationYou can install ChronQC from PyPI using pip as follows:pip install chronqcAlternatively, you can install from GitHub:git clone https://github.com/nilesh-tawari/ChronQC.git
cd ChronQC
pip install -r requirements.txt
pip install --editable .If you would like the development version instead, the command is:pip install --upgrade --force-reinstall git+https://github.com/nilesh-tawari/ChronQC.gitLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsHistoryDec 21, 2017 - Released version 1.0.4 software package
Nov 15, 2017 - Released version 1.0.3 software package
Aug 04, 2017 - Released version 1.0.2 software package
July 13, 2017 - Released version 1.0.1 software package
June 20, 2017 - Released version 1.0.0 software package
Package: chronumental
ChronumentalChron​ologies from mon​umentalphylogenetic treesChronumental is a tool for creating a "time-tree" (where distance on the tree represents time) from a phylogenetic divergence-tree (where distance on the tree reflects a number of genetic substitutions).What sets Chronumental apart from most other tools is that it scales to extremely large trees, which can contain millions of nodes. Chronumental uses JAX to represent the task of computing a time tree in a differentiable graph for efficient calculation on a CPU or GPU.📝 Read the preprint📚 View the documentationInstallationMethod 1: Using pipx (recommended for basic use - installs in its own isolated environment)pip install --local pipx
pipx install  chronumentalMethod 2: In your python environmentpip install chronumentalMethod 3: BiocondaChronumental isnowavailable on biocondaconda config --add channels bioconda
conda install chronumentalUsageThis demo uses trees and metadata collated by theUShERteam.wget https://hgwdev.gi.ucsc.edu/~angie/UShER_SARS-CoV-2/2021/10/06/public-2021-10-06.all.nwk.gz
wget https://hgwdev.gi.ucsc.edu/~angie/UShER_SARS-CoV-2/2021/10/06/public-2021-10-06.metadata.tsv.gz
chronumental --tree public-2021-10-06.all.nwk.gz --dates public-2021-10-06.metadata.tsv.gz --steps 100📚 Pleasevisit our documentation pageto learn more about the parameters you can use to control Chronumental.IntegrationsTaxoniumcan automatically call Chronumental, and generate a combined visualisation that allows switching between distance and time phylogeniesSimilar toolsTreeTimeis a more advanced tool for inferring time trees. If you have a dataset of e.g. <10,000 rather than millions of nodes you are definitely best off trying it. The TreeTime readme also links to other similar tools.TroubleshootingChronumental uses the earliest date in your dataset as an anchor to calibrate everything else. If this earliest date is wrong due to a metadata error things won't work well. You can set the reference node manually with--reference_nodeto avoid this issue.
Package: chroptiks
chroptiksDescriptionchroptiks is a Python package that offers advanced plotting utilities, making it easier to create complex and informative visualizations. It extends the functionality of libraries like matplotlib and scipy, providing a user-friendly interface for a variety of plotting needs.RequirementsPython libraries: matplotlib, numpy, scipyOther: LaTeX. If you don't want to use LaTeX in the plotting, edit theplotting_utils.pyfile and either comment out the line :plt.rc('text', usetex=True), or set theusetexkeyword to beFalsebefore installing the package.InstallationTo install chroptiks from source, run:gitclonehttps://github.com/cagostino/chroptiks.gitcdchroptiks
pythonsetup.pyinstallOtherwise simply install from pip:pipinstallchroptiksUsageHere's how you can use your_package_name in your projects:from chroptiks.plotting_utils import hist2d, hist1d, scatter, plot3d, plotbarFeatures---2D Histograms (hist2d): Easily create 2D histograms for data analysis.---1D Histograms (hist1d): Simplify the process of creating and customizing 1D histograms.---Scatter Plots (scatter): Enhanced functionality for scatter plot creation.---3D Plots (plot3d): Intuitive tools for 3D data visualization.---Bar Charts (plotbar): Quick and customizable bar chart creation.Example usagehist2d, hist1d, scatter, plot3d, plotbar are now ready to be used as per their defined functionalities and plots are generated through each of their plot methods. For example:importnumpyasnpfromchroptiks.plotting_utilsimporthist2dx=np.linspace(-1,1,100000)+np.random.normal(0,.1,size=100000)y=x**(3)+np.random.normal(0,0.3,size=100000)z=np.random.normal(size=100000)hist2d.plot(x,y,nx=200,ny=200)hist2d.plot(x,y,nx=40,ny=40,ccode=z,ccodename='Z',xlabel='X',ylabel='Y')hist2d.plot(x,y,nx=200,ny=200,bin_y=True,size_y_bin=0.1,xlabel='X',ylabel='Y',percentiles=False)fromchroptiks.plotting_utilsimporthist1dhist1d.plot(z,range=(-2,2),bins=100,xlabel='Z',ylabel='Counts')hist1d.plot(z,range=(-2,2),bins=100,xlabel='Z',ylabel='Counts',normed=True)hist1d.plot(z,range=(-4,4),bins=100,xlabel='Z',ylabel='Cumulative Count',cumulative=True)fromchroptiks.plotting_utilsimportscat#scatscat.plot(x,z)#scat with color-codescat.plot(x,y,ccode=z,color=None,edgecolor=None,vmin=-0.5,vmax=0.5)#note if you use ccode to color the points, you must set color to None, and I would advise you to set edgecolor to None as well or else each will have outlines.#scat with y-binningscat.plot(x,y,bin_y=True,size_y_bin=0.1,percentiles=True,xlabel='X',ylabel='Z',aspect='auto')
Package: chrpa
chrpaClimate Hazards Return Period Analysischrpa contains a series of functions to support climate hazard analyses. The functions facilitate analyses to understand how return periods and exceedance probability for straight-line wind and precipidation at key locations change under future climate conditions predicted by climate models.  The package also contains example datasets and tutorials to help demonstrate how the functions can be used.Installationpvops can be installed usingpip. See more information atreadthedocs.Package Layout and DocumentationThe package is delineated into the following directories. Refer to theexamplesdirectory for a full run-through of the available functionality.├───chrpa
│   ├───main
│   │ 
│   ├───tests
│      
│   
├───docs
│   
└───ExamplesMore information about these modules is available atreadthedocs.CitingIf using this package, please citeour paperusing the following:ContributingThe long-term success of chrpa requires community support. Please see theContributing pagefor more on how you can contribute.Copyright and Licensechrpa is copyrighted through Sandia National Laboratories. The software is distributed under the Revised BSD License. Seecopyright and licensefor more information.
Package: chrphb-contacts
chrphb-contactsThis file will become your README and also the index of your
documentation.Installpipinstallchrphb_contactsHow to useFill me in please! Don’t forget code examples:external_data={'id':1,'name':'John Doe','email':'john@doe.com','phone':'00000000000','met_ts':'2019-06-01 12:22'}person=Person(**external_data)person.name'John Doe'foo??1+12
Package: chrplotlib
chrplotlibPlot graphs using charsinstall:$pipinstallchrplotlibFor the current developer version:gitclonegit@github.com:guofei9987/chrplotlib.gitcdchrplotlib
pipinstall.examplefromchrplotlibimportanimationdefupdate_bar(i):data=10*np.random.rand(20)print(bar(data,label=range(20),max_height=10))print('\033[{}A'.format(12))animation(update_func=update_bar,interval=range(100),pause=0.2)data=10*np.random.rand(20)label=range(20)print(bar(data))Print this:▃▃                         
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██ ██ ██ ██ ▃▃ ██ ██ ██ ██ ██ ██ ██ ██ ▃▃ ██ ██ ██ ██ ██ ██print(bar(data=data,label=range(20)))Print this:▃▃                         
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0  1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16 17 18 19print(bar(data,kind='bar'))Get this:█▌█▌█▌█▌█▌█▌█▌█▌▌   
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Package: chrw
An MIT licensed, python wrapper for thechrurl shortener API.This depends on the requests module, but that’s easily installed.Includes rate limiting (with exceptions!), and currently supports chr APIv1.It’s fairly simple, but you can read thedocsonline.InstallationSince it’s on PyPi, you can simplypip install chrw, or if you really have to,easy_install chrw.Easy-peasy, lemon squeezy.Authorsplausibility
Package: chrys
chrysA collection of color palettes for visualisation in JavaScript, Python and Sass.Demonetbek.github.io/chrysInstallationNode.js and SassnpminstallchrysPython 3.6 and uppipinstallchrysUsageFor instructions, refer tothe docs.Development: InstallationBuild the Docker container:./docker/install.shCreate.pypirc:cpexample.pypirc.pypircEnter aPyPI API tokenas the password in.pypirc.Development: UsageUpgrade Node dependencies:./docker/run.sh./docker/scripts/upgrade_node_deps.shBuild the palette data, JavaScript and Python distribution packages, Sass, CSS, Illustrator scripts:./docker/run.sh./docker/scripts/build.shPublish the JavaScript and Python distribution packages:npmpublish
npmrunpublish-pyCreditPalettes from:Bokeh(BSD 3-Clause)Vega(BSD 3-Clause)LicenseCopyright (c) 2017 Hein Bekker. Licensed under the BSD 3-Clause License.
Package: chrysalis-st
chrysalisis a powerful and lightweight method designed to identify and visualise tissue compartments in spatial
transcriptomics datasets, all without the need for external references.chrysalisachieves this by identifying spatially variable genes (SVGs) through spatial autocorrelation.
It then employs dimensionality reduction and archetypal analysis to locate extremal points in the low-dimensional
feature space, which represent pure tissue compartments.
Each observation (i.e. capture spot) in the gene expression matrix is subsequently represented as a proportion of these
distinct compartments.chrysalisfeatures a unique approach based on maximum intensity projection, allowing the simultaneous visualization
of diverse tissue compartments.
Moreover, it seamlessly integrates intoscanpybased pipelines.If you likechrysalis, consider citing ourpreprint.chrysaliscan define distinct tissue compartments and cellular niches with specific gene expression signatures,
highlighted with specific colors. For instance, in theV1_Human_Lymph_Nodedataset,chrysalisidentifies
various regions, such as germinal centers (yellow), B cell follicles (dark orange), and T cell compartments
(lime). You can find more examples in thegallerysection.Packagechrysaliscan be used with any pre-existinganndatasnapshot of 10X Visium, Slide-seqV2 and Stereo-seq datasets
generated withscanpy, and on new samples without the need of preprocessing. It is designed to be as lightweight as
possible, however currently it relies onlibpysalfor its fast implementation of Moran's I.chrysalisrequires the following packages:numpypandasmatplotlibscanpypysalarchetypesscikit_learnscipytqdmseabornTo installchrysalis:pip install chrysalis-stDocumentation, Tutorials and API detailsUser documentation is available on:https://chrysalis.readthedocs.io/Basic tutorials covering the main functionality ofchrysalisare available on the documentation site.first step-by-step tutorial:https://chrysalis.readthedocs.io/en/latest/tutorials/lymph_node_tutorial.htmlUsageimportchrysalisaschimportscanpyasscimportmatplotlib.pyplotaspltadata=sc.datasets.visium_sge(sample_id='V1_Human_Lymph_Node')sc.pp.calculate_qc_metrics(adata,inplace=True)sc.pp.filter_cells(adata,min_counts=6000)sc.pp.filter_genes(adata,min_cells=10)ch.detect_svgs(adata)sc.pp.normalize_total(adata,inplace=True)sc.pp.log1p(adata)ch.pca(adata)ch.aa(adata,n_pcs=20,n_archetypes=8)ch.plot(adata)plt.show()ReferenceChrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysisDemeter Turos, Jelica Vasiljevic, Kerstin Hahn, Sven Rottenberg, Alberto Valdeolivasdoi:https://doi.org/10.1101/2023.08.17.553606
Package: chssh
Inspired by ec2-ssh from instagram, this uses the pychef library to allow you to address your hosts by their chef node name instead of the dns name.This differs fromknife sshbecause these tools operate with interactive shells, and are meant for working with 1 node at a time.PrerequisitiesYou need to have an installed and configured chef client on your machine. The code uses the pychef autoconfigure() to discover and load the chef configurationUsagechssh node1# ssh to chef node node1chssh user@node1# ssh as user to chef node node1chscp file user@node1:path# scp file to chef node node1chscp user@node1:path. # scp file from chef node node1Enjoy!
Package: cht
Notify to the slack channel using the incoming webhooks when the
previous command has ended.RequirementsPython 2Installpypi$ pip install chtDownloadIf you don’t want to install cht with pip, download the main script.# assume that ~/bin is included in $PATH
$ curl https://raw.githubusercontent.com/suzuki-shunsuke/cht/master/cht.py -o ~/bin/cht
$ chmod a+x ~/chtUsage$ <previous command>; cht $? [-u <url>] [-c <channel>] [-m <message>]if you want to send the previous command’s output to the slack channel,
use pipe.$ <previous command> | cht [-u <url>] [-c <channel>] [-m <message>]Environment VariableYou can use the following environment variables to set the channel and
webhook url.CHT_URL: webhook urlCHT_CHANNEL: channel nameCommand line arguments are prefered to the environment variables.ContributingFork (https://github.com/suzuki-shunsuke/cht/fork)Create a feature branchCommit your changesRebase your local changes against the master branchCreate a new Pull RequestLicenseMITAuthorSuzuki Shunsuke