Saravanan666/pypi-org huggingface dataset:
Package: centralGo
# CentralGoCentralGo (CGo) is multithreaded ans user-friendly scheduler for running scripts and routine tasks.*   CGo works like Linux Crontab*   The definition of a job is quite easy, just add a similar line to jobs.conf :    define "MyJob" run "echo 'I am the job number 0'" every "1 seconds" notify "me@email.com";ORdefine "MyJob" run "./run.sh" every "sunday at 23:55" notify "me@email.com";*   CGo runs as a daemonThis could help you to> Run routine tasks like checking if a website is up or down every 5 minutes> Schedule tasks for with email notification> Keep logs on scheduled tasks> Replace your system crontab with a user-friendly definition languageVersion----1.0.1Utilities-----------CentralGo uses a number of open source projects to work properly:* [Python] - Of course* [Schedule] - An in-process scheduler for periodic jobsInstallation--------------You can find the package on Pypi, so you will be able to install it using :`pip install centralgoeasy_install centralgoorgit clone [git-repo-url] CentralGocd CentralGopython setup.py install`## LicenseGNU/GPL V2**Free Software, Hell Yeah!**
Package: central-system
simplify data retrieving on centralSystem.orgdownload fileimportcentral_systemfromcentral_systemimportClient,ApiRequest,Methodc=Client(data_endpoint="",access_key="",secret_key="",authorized_endpoint="")api_request=ApiRequest(data_address="1754C67BE4821000",request_path="api-provider/download/local/123456",method=Method.POST)try:# 模式一r=c.call_api(api_request)withopen('./img8.jpg','wb')asf:f.write(r.content)# 模式二f=open('./img5.jpg','wb')response_stream=c.call_api_stream(api_request)withresponse_streamasr:forchunkinr.iter_bytes(chunk_size=4096):f.write(chunk)finally:f.close()do httpimportcentral_systemfromcentral_systemimportClient,ApiRequest,Methodc=Client(data_endpoint="",access_key="",secret_key="",authorized_endpoint="")params={'pkey1':'value1','pkey2':'value2'}json={'jkey1':'value1','jkey2':'value2'}headers={'user-agent':'my-app/0.0.1'}api_request=ApiRequest(data_address="178589E198821000",request_path="post",method=Method.POST,headers=headers,params=params,json=json,timeout=10)response=c.call_api(api_request)print(response.content)do pgsqlimportcentral_systemfromcentral_systemimportClient,ApiRequest,Methodc=Client(data_endpoint="",access_key="",secret_key="",authorized_endpoint="")json={"payload":"{\"sql\":\"select * from zs.student\"}"}api_request=ApiRequest(data_address="1785CB2070C21000",request_path="pg",method=Method.POST,json=json,timeout=10)response=c.call_api(api_request)print(response.content)do odpsimportloggingimportcentral_systemfromcentral_systemimportClient,ApiRequest,Methodc=Client(data_endpoint="",access_key="",secret_key="",authorized_endpoint="")json={"payload":"{\"selectSql\":\"select * from user\"}"}api_request=ApiRequest(data_address="174FB94000C21000",request_path="get/odps",method=Method.POST,json=json,timeout=10)response=c.call_api(api_request)print(response.content)enable logimportloggingimportcentral_systemcentral_system.set_file_logger("../log/central.log",level=logging.DEBUG)
Package: centra-py-client
centra-py-clientPython client for Centra API access. VisitGuardicore’s website for more information about Centra!Find the full client documentation athttps://centra-py-client.readthedocs.io(locally,/docsfolder).Quick start guideInstallationReleased versionTo install centra-py-client, run this command in your terminal:$pipinstallcentra_py_clientThis is the preferred method to install centra-py-client, as it will always install the most recent released version.If you don’t havepipinstalled, thisPython installation guidecan guide
you through the process.From sourcesThe sources for centra-py-client can be downloaded from theGithub repo.You can either clone the public repository:$gitclonegit://github.com/guardicode/centra_py_clientOr download thetarball:$curl-OJLhttps://github.com/guardicode/centra_py_client/tarball/mainOnce you have a copy of the source, you can install it with:$pythonsetup.pyinstallUsageFirst, create a CentraClient:from centra_py_client import CentraClient, CentraSession
client = CentraClient(CentraSession("my.centra.address", "username", "password"))Then use the client to interact with Centra, e.g.:client.delete_label_by_name("Environemnt: TemporaryEnv")Stability statusThis package is currently considered unstable and there is no backward compatibility guaranteed.This status will continue as long as the package is in the 0.x.y versions. When the package
stabilizes and we can guarantee backward compatibility, version 1.0.0 will be released.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: centreonapi
Make a Python LIB for Centreon APIForked fromhttps://github.com/centreon/centreon-sdk-pythonInstall$pipinstallcentreonapiUsagesConnect to Centreon platformfromcentreonapi.centreonimportWebservicecentreon=centreon("https://centreon.mydomain.tld, "admin", "centreon")List all hosts on Centreon platformcentreon.hosts.list()>>>{'Centeon-central':Centeon-central,'server1':myserver1}Add new hostcentreon.hosts.add(name='server1',alias='DBServ',ip=127.0.0.1,template="DB-Host-Template",hg="DB Servers")Get host_,myhost=centreon.hosts.get('server1')myhost.name>>>server1MacrosGet macros_,macros=myhost.getmacro()macros>>>{'$_HOSTMODULESTATSFILE$':$_HOSTMODULESTATSFILE$,'$_HOSTMYSQLPASSWORD$':$_HOSTMYSQLPASSWORD$,'$_HOSTMYSQLPORT$':$_HOSTMYSQLPORT$,}mymacro=macros.get('$_HOSTMYSQLPORT$')mymacro.value>>>3306Set Macrosmyhost.setmacro('SECRETMACRO','pass',1,'secret macro pass')HostTemplate on hostmyhost.gettemplate()>>>{'App-Monitoring-Centreon-Central-custom':App-Monitoring-Centreon-Central-custom,'App-Monitoring-Centreon-Database-custom':App-Monitoring-Centreon-Database-custom,'App-Monitoring-Centreon-Poller-custom':App-Monitoring-Centreon-Poller-custom}DocumentationComplete documentationhereFeaturesTODODocumentationThe full documentation is athttp://centreonapi.rtfd.org.History0.1.5 (2019-02-16)Beta version0.1.0 (2018-12-14)First release on PyPI.
Package: centreseq
InstallationCreate a new conda environment (optional, but strongly recommended)conda env create -n centreseq python=3.6
conda activate centreseqInstall via biocondaconda install -c bioconda centreseqUsageUsage: centreseq [OPTIONS] COMMAND [ARGS]...

  centreseq builds an annotated core genome using assemblies as input.

Options:
  --version   Print the version and exit.
  --citation  Print the citation for this software and exit.
  --help      Show this message and exit.

Commands:
  core  Given an input directory containing assemblies, establishes a core
        genome
  tree  Produces output for phylogenetic tree softwareIssuesThe conda distribution might have an issue installing the correct version ofminced,
a Prokka dependency, so you might need to run the following command within your conda environment after installing centreseq:conda install mincedExternal DependenciesThese programs will be automatically installed with the conda package.ProkkaMMseqs2SeqKitMUSCLESNP-sitescyvcf2
Package: centresurv
Centresurv 0.1.2---------Geoserv is a python package that allows for the easy assessment of distribution of services in an area.It also allows for easy integration with population distribution data.---------DEPENDENCIES:osmnx, networkx, pandas, sqlite3, python_igraph, numpy, matplotlib, geopandas, foliumInstallation of osmnx, igraph and geopandas via pip is often problematic, particularly on Windows OS. It may be possibleto do so using conda, or to manually install from the wheelfiles, as shown in this tutorial:https://geoffboeing.com/2014/09/using-geopandas-windows/N.B. If following this method for installation of geopandas, it is then possible to simply pip install osmnxAlternatively, it may be more convenient to use windows subsystem for linux.---------Geoserv consists for four main modules:postcode_setup -> This module contains all the functions required to import and save a graph for a given area ofinterest, taking the map data from the Open Street Map APIs.Given a csv file containing postcode data for the same area (for the UK available from:https://www.doogal.co.uk/), a database can be created and shortest path analysis performed.analyse -> This contains functions to calculate very basic statistics regarding the shortest path analysis completedsuch as average distances, total person-meters in an area, and the gini coefficient for the entire areamaps -> This uses the data generated in the analyse module to create high quality visualisations of the data onchoropleth maps using folium, and saves these as html files. The boundaries for use in this must be downloadedas shapefiles (for UK these are available from: http://geoportal.statistics.gov.uk/ or https://data.gov.uk/)N.B. The shapefiles used must have a column entitled "name" containing the same sub-area names as are found inthe database. It is likely that these will require some degree of cleaning in order to match---------ACKNOWLEDGEMENTS:This package builds upon and integrates the functionality of several much better packages. Much of thefunctionality represents only small incremental modifications on the dependencies listed above, in particular osmnx.
Package: centrifuge-python
centrifuge-pythonThis is a WebSocket real-time SDK forCentrifugoserver (and anyCentrifuge-basedserver) on top of Python asyncio library.[!TIP]
If you are looking for Centrifugoserver APIclient – check outpycentinstead.Before starting to work with this library check outCentrifugo client SDK API specificationas it contains common information about Centrifugal real-time SDK behavior. This SDK supports all major features of Centrifugo client protocol - seeSDK feature matrix.Installpip install centrifuge-pythonThen in your code:from centrifuge import ClientSeeexample codeandhow to run itlocally.JSON vs Protobuf protocolsBy default, SDK uses JSON protocol. If you want to use Protobuf protocol instead then passuse_protobuf=Trueoption.When using JSON protocol:all payloads (data to publish, connect/subscribe data) you pass to the library are encoded to JSON internally usingjson.dumpsbefore sending to server. So make sure you pass only JSON-serializable data to the library.all payloads received from server are decoded to Python objects usingjson.loadsinternally before passing to your code.When using Protobuf protocol:all payloads you pass to the library must bebytesorNoneif optional. If you pass non-bytesdata – exception will be raised.all payloads received from the library will bebytesorNoneif not present.don't forget that when using Protobuf protocol you can still have JSON payloads - just encode them tobytesbefore passing to the library.Callbacks should not blockEvent callbacks are called by SDK usingawaitinternally, the websocket connection read loop is blocked for the time SDK waits for the callback to be executed. This means that if you need to perform long operations in callbacks consider moving the work to a separate coroutine/task to return fast and continue reading data from the websocket.Run exampleTo runexample, first start Centrifugo with config like this:{"token_hmac_secret_key":"secret","namespaces":[{"name":"example","presence":true,"history_size":300,"history_ttl":"300s","join_leave":true,"force_push_join_leave":true,"allow_publish_for_subscriber":true,"allow_presence_for_subscriber":true,"allow_history_for_subscriber":true}]}And then:python-mvenvenv
.env/bin/activate
makedev
pythonexample.pyRun testsTo run tests, first start Centrifugo server:dockerpullcentrifugo/centrifugo:v5
dockerrun-d-p8000:8000-eCENTRIFUGO_TOKEN_HMAC_SECRET_KEY="secret"-eCENTRIFUGO_PRESENCE=1\-eCENTRIFUGO_JOIN_LEAVE=true-eCENTRIFUGO_FORCE_PUSH_JOIN_LEAVE=true\-eCENTRIFUGO_HISTORY_TTL=300s-eCENTRIFUGO_HISTORY_SIZE=100\-eCENTRIFUGO_FORCE_RECOVERY=true-eCENTRIFUGO_USER_SUBSCRIBE_TO_PERSONAL=true\-eCENTRIFUGO_ALLOW_PUBLISH_FOR_SUBSCRIBER=true-eCENTRIFUGO_ALLOW_PRESENCE_FOR_SUBSCRIBER=true\-eCENTRIFUGO_ALLOW_HISTORY_FOR_SUBSCRIBER=truecentrifugo/centrifugo:v5centrifugoAnd then (from cloned repo root):python-mvenvenv
.env/bin/activate
makedev
maketest
Package: centuri-course
Turing Centre for living systems's "Introduction to biological data analysis" courseThe website versionhereThis is the repository for the introduction of the CENTURI "Introduction to biological data analysis" course.This first day is split in 2 main parts:Introduction to coding with Turing patternsData handling and visualisation0. Requirements for the course0.1 Recommended softwareThis course is made on ajupyter notebookrunning on Python 3.8 or newer.To install Python and the required dependencies we strongly recommend to useconda,mambaorpipenv(the teachers will use conda)0.2 Installing condaConda can be installed multiple ways. We do not have any recommendations about how to but one can readtherefor a likely exhaustive list on ways to install conda.Note that we do not necessarily recommend installing Anaconda, we do have a slight preference towardsMinicondabut that's just us.Moreover, we advise to start jupyter notebooks from a shell/terminal/prompt to be able to better see the error messages.0.3 DependenciesWhile we tried to keep the dependencies as small as possible, few are still required:Jupyter notebookNumPyScipyMatplotlibNote that other libraries might be necessary for the courses after.To install them one can for example run the following command lines in a terminal, assuming that conda is installed:condacreate--nameCenTuri-Courseto create the environment for the course. Then:condaactivateCenTuri-Courseto activate the course environment. And finally:condainstallnotebooknumpyscipymatplotlibAll dependencies should now be installed!Enjoy!0.4 Troubleshooting for MacOs M1 chipsThe newly introduced M1 chips in the latest macbooks can create some difficulties for installation.One way to solve the issue is to installMiniforgeand to then use it as Miniconda.0.5 Testing your configurationIf you would like to test your configuration, you can run the following python file:Configuration-test.py.One way to run it is the following:From a terminal in the folder that containsConfiguration-test.py:pythonConfiguration-test.pyYou should get an output similar to the following one:You are using Python version (3.10.4). It is recent enough for this courseNumpy version (1.21.5) is installedScipy version (1.8.0) is installedMatplotlib version (3.5.1) is installedEverything's goodIf your python version is too old or if you failed to install one of the libraries, you will get an error message.
Package: centurypy
CenturyPyCenturyPy is a Python library for training and running the Century (Soil Organic Matter) model. For more information about Century, take a look at thislink.This is the first version of the library and only simulates the dynamic for the carbon.InstallationUse the package managerpipto install centurypy.pipinstallcenturypyUsagefromcenturypyimportCenturyPy# define the model, the first parameter is the file with the samples's data.# the second parameter is a dictionary containing the initial conditions# from where the samples were takenmodel=CenturyPy('./data.csv',{"necromasa":2140,# Necromass"acto":50.04,# Initial value for the Active container"leno":2250,# Initial value for the Slow container"paso":19150,# initial value for the Pasive container"respo":0,# initial value for the Respiration"ln":0.33,# Lignin"frala":0.33,# Fine soil fraction})# begin training, default 500 generationsmodel.fit_model()# in case you want more precission, you can define# the number of generations using an integermodel.fit_model(1000)# or using the parameter's namemodel.fit_model(generations=1000)# by default, the library draws a chart containing the result of the training, in case you# don't want to show that chart, you can disable it using the second parameter as followsmodel.fit_model(generations=500,chart=False)The supported intput's file format is colon separated CSV (,). The content of that file must be the sample's data without the headers.
It must have only three columns, the first one represents the time when the sample was taken, the second one represents the value for the active container and the last one represents the value for the
respiration.An example is shown bellow:1, 243, 118.52
     2, 192, 512.245
     3, 240, 840.725
     4, 235, 1396.19
     5, 314, 1895.14625
     8, 241, 2195.29625
    11, 213, 2509.59625
    18, 185, 2892.02125
    25, 177, 3147.39625
    30, 114, 3203.66125ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.AuthorThis library was developed by Gerardo Vivas. Any questions about it have no doubt in contact me by emailvivas.fermin24@gmail.comLicenseMIT
Package: cenvars-client
Cenvars ClientWhat is it?A client library and scripts for cenvars.What problem does it solve?This fetches from a cenvars server environment variablesHow do I install it?pip install cenvars-clientHow do I use it?LibraryfromcenvarsimportapiLibrary$cenvars_newkey-s"https://cenvarsserver.example.com/cenvars/$# This will return an encoded cenvars_key, however in normal operation this$# will be provided by the cenvar server. For now we assume that the key is$# stored in the environment under the key CENVARS_KEY$$source<(cenvars)$# This will contact the cenvars server, fetch the environment variables and$# add it to the current shell session.What license is this?Two-clause BSDHow can I get support?Please use the repo’s bug tracker to leave behind any questions, feedback,
suggestions and comments. I will handle them depending on my time and what looks
interesting. If you require guaranteed support please contact me via
e-mail so we can discuss appropriate compensation.Signing OffIs my work helpful or valuable to you? You can repay me by donating via:https://paypal.me/MartinHellwig-or-https://www.patreon.com/hellwigThank you!
Package: cenv-script
conda environment helper scriptWrapper around conda that automatically persists changes to environment.yml
It is quite dummy but useful in simple case scenarios.Free software: MIT licenseDocumentation:https://cenv-script.readthedocs.io.FeaturesYou runcenv-scriptinstall numpyIt callsconda install numpy, checks for errors and then updates corresponding environment.ymlCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2020-05-08)Initial github commit
Package: ceODBC
Open Source Python/ODBC Utility - ceODBCceODBC is a Python extension module that enables access to databases using the
ODBC API and conforms to the Python database API 2.0 specifications with a
number of additions. Python 3.7 and higher is required as of version 3.1.Seehttps://www.python.org/dev/peps/pep-0249for more information on the Python
database API specification.For feedback or patches, please use GitHub issues:https://github.com/anthony-tuininga/ceODBC/issuesInstallationpython -m pip install ceODBC --upgradeAdd the --user option if you do not have system access. Binaries are available
for Linux and Windows as a convenience.Usage ExampleNote that theDSNin the example below should be replaced with an
appropriate string that ODBC understands. For example, if a system DSN called
"MYDATA" has been created in the ODBC manager, use "DSN=MYDATA" to connect.
For what is termed DSN-less connections, search the Internet for appropriate
strings.importceODBCconnection=ceODBC.connect(DSN)cursor=connection.cursor()cursor.execute("""select Col1, Col2, Col3from SomeTablewhere Col4 = ?and Col5 between ? and ?""",["VALUE",5,10])forcolumn_1,column_2,column_3incursor:print("Values:",column_1,column_2,column_3)NotesIf your driver is not capable of transactions (often indicated by the exception
"driver not capable" when connecting) then use the following statement to
connect instead:connection=ceODBC.connect(DSN,autocommit=True)For further information seehttp://ceodbc.readthedocs.org
Package: ceotr-kml-tool
metadata-xmlkml_toolis a Python3 tool for converting telemetry data from Telemetry file or log files into XMLIt can be used as a command line tool.InstallationDownload codeCreate ~/code/ if it does not exist cd into it
git clonegit@gitlab.oceantrack.org:ocean-gliders-canada/sensor_tracker.git
cd sensor_trackerDownload codeCreate ~/code/ if it does not exist cd into it
git clonegit@gitlab.oceantrack.org:ocean-gliders-canada/sensor_tracker.git
cd sensor_trackerSetupRefer to run.sh for most setup operationsSetup python envconda env create -f environment.yml
Package: cep
Esta biblioteca acessa o site dos Correios para fazer pesquisa por CEP.USO>>> from cep import Correios
>>> c = Correios()
>>> resultados = c.consulta('Juarez')
>>> len(resultados)
100
>>> resultados[64]
{'Localidade': u'Jo\xe3o Pessoa', 'Bairro': u'Torre', 'UF': u'PB', 'Logradouro': u'Avenida Juarez T\xe1vora - de 1147/1148 a 1911/', 'CEP': u'58040-021'}
>>> c.detalhe(64)
{'Localidade': u'Jo\xe3o Pessoa', 'Bairro': u'Torre', 'UF': u'PB', 'Logradouro': u'Avenida Juarez T\xe1vora - de 1147/1148 a 1911/1912', 'CEP': u'58040-021'}
>>> c.consulta('91370000', primeiro=True)
{'Localidade': u'Porto Alegre', 'Bairro': u'Vila Ipiranga', 'UF': u'RS', 'Logradouro': u'Rua Alberto Silva - at\xe9 965/966', 'CEP': u'91370-000'}REQUIREMENTSBeautifulSoupLICENSEMIT LicenseTODODocumentaçãoCriar setup.py e registrar no PyPiHospedar API pública (json/xml)
Package: cepa
For tutorials and API documentation seeStem’s homepage.Quick StartTo install you can either use…pip install stem… or install from the source tarball. Stem supports both the python 2.x and 3.x series. To use its python3 counterpart you simply need to install using that version of python.python3 setup.py installAfter that, give sometutorialsa try! For questions or to discuss project ideas we’re available onircand thetor-dev@ email list.
Package: cepan
cepanRetrieves data from aws cost explore as a pandas dataframe.Main featuresSupport for input with type hintsRetrieving results as pandas.DataframeInstallationpip install cepanUsagefromdatetimeimportdatetimeimportcepanascedf=ce.get_cost_and_usage(time_period=ce.TimePeriod(start=datetime(2020,1,1),end=datetime(2020,1,2),),granularity="DAILY",filter=ce.And([ce.Dimensions("SERVICE",["Amazon Simple Storage Service","AmazonCloudWatch"],),ce.Tags("Stack",["Production"]),]),metrics=["BLENDED_COST"],group_by=ce.GroupBy(dimensions=["SERVICE","USAGE_TYPE"],),)print(df)All paginated results will be returned as a Dataframe.Time                        SERVICE  BlendedCost
0   2020-01-01  Amazon Simple Storage Service   100.000000
1   2020-01-01               AmazonCloudWatch    10.000000List of currently supported APIsget_dimension_valuesget_tagsget_cost_and_usageAlias of aws service nameNormally, the Cost Explorer API requires complex and long names to filter by service name.
For example, if you only need the value of an EC2 instance, you would need to specifyAmazon Elastic Compute Cloud - Compute.df=ce.get_cost_and_usage(time_period=ce.TimePeriod(start=datetime(2020,1,1),end=datetime(2020,1,2),),granularity="DAILY",filter=ce.Dimensions("SERVICE",["Amazon Elastic Compute Cloud - Compute"],),group_by=ce.GroupBy(dimensions=["SERVICE","USAGE_TYPE"],),)cepan supports aliases with short service names.
If you only need the value of the EC2 instance, you can specify it withEC2.df=ce.get_cost_and_usage(time_period=ce.TimePeriod(start=datetime(2020,1,1),end=datetime(2020,1,2),),granularity="DAILY",filter=ce.Dimensions("SERVICE",["EC2"],),group_by=ce.GroupBy(dimensions=["SERVICE","USAGE_TYPE"],),)Correspondence table of aliases is shown inservice_alias.tsv.
You can also run theshow_service_aliasmethod to get the table.print(ce.show_service_alias())LicenseMIT License