Saravanan666/pypi-org huggingface dataset:
Package: census-pgeoloader
![POLITICO](https://rawgithub.com/The-Politico/src/master/images/logo/badge.png)# census_pgeoloaderCommand line utility to download Census TIGER/Line shapefiles by state from the U.S. Census Bureau and aggregate them into a single table in a PostGIS-enabled database.# RequirementsUses [shp2pgsql](http://www.bostongis.com/pgsql2shp_shp2pgsql_quickguide.bqg). Assumes PostgreSQL + PostGIS.# To install```bash$ pip install census_pgeoloader```# To usePass a connection URI to your PostgreSQL database as well as the states you'd like to aggregate. This will dowload tract-level shapefiles by default.```bash$ pgeoloader postgres://postgres@localhost:5432/database KS MO TX VA ...```Valid values for states are FIPS codes, postal abbreviations or names. Pass `+` to collect all states.# Options```bashOptions:-t, --table TEXT               Name of table to create in DB. Default is"census_shapes".-p, --temp TEXT                Directory to download files to. Default is"./shapefiles"-y, --year TEXT                Year. Default is "2016".-g, --geo [tract|group|block]  Geographic unit. Default is "tract".-s, --srid TEXT                Specify an SRID transform, e.g., "4269:4326".Default is "4269".--help                         Show this message and exit.```#### Notes:- `--table` Drops any existing table by that name in the database.- `--geo` Can pull tracts, block groups or blocks.- `--srid` Default SRID should be 4269, but you can specify a transform with this option as available in [shp2pgsql](http://www.bostongis.com/pgsql2shp_shp2pgsql_quickguide.bqg).©2017 POLITICO
Package: censusprotocollib
This library provides a simple interface for Census Protocol clients.  Read the white
paper and subscribe to the mailing lists before posting questions.http://censusprotocol.orghttp://groups.google.com/d/forum/cp-announcehttp://groups.google.com/d/forum/cp-core-devThis library is released under the MIT license.  You are free to create commercial
derivatives of this for your own projects and products.  Register your projects with the
Census Protocol Foundation for listing in our directory.Copyright (c) 2015 The Census Protocol Foundation, Lee Preimesberger, Cemil ‘James’ Ayvaz, and Eric Ramirez.Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: censys
Censys Python LibraryAn easy-to-use and lightweight API wrapper for Censys APIs (censys.io). Python 3.8+ is currently supported. This library has been tested on Python 3.8 and 3.x (Currently version 3.10).Notice:The Censys Search v1 endpoints are deprecated as of Nov. 30, 2021. Please begin using v2 endpoints to query hosts and certificates and check out oursupport centerfor resources.FeaturesSearch Censys dataBulk Certificate lookupsDownload Bulk DataManage assets, events, and seeds in Censys ASMCommand-line interfaceGetting StartedThe library can be installed usingpip.pipinstallcensysTo upgraded usingpip.pipinstall--upgradecensysAlternatively, you can install the library usingpoetry.gitclonehttps://github.com/censys/censys-python.gitcdcensys-python/
poetryinstallOptionally, you can enable tab completion for the CLI by adding this line to your~/.bashrc,~/.zshrc, or equivalent.Please note that autocomplete is supported for field names in thesearchcommand.eval"$(register-python-argcompletecensys)"To configure your search credentials runcensys configor set bothCENSYS_API_IDandCENSYS_API_SECRETenvironment variables.$censysconfig

CensysAPIID:XXX
CensysAPISecret:XXX
Doyouwantcoloroutput?[y/n]:y

Successfullyauthenticatedforyour@email.comIf you have a Censys ASM account, you can configure your ASM credentials by runningcensys asm configor set bothCENSYS_ASM_API_KEYenvironment variables.$censysasmconfig

CensysASMAPIKey:XXX
Doyouwantcoloroutput?[y/n]:y

SuccessfullyauthenticatedExamplesThe examples located in theexamples/directory are a great place to start. You can also find more examples in theusage documentation.DocumentationResourcesSourceIssue TrackerChangelogDocumentationDiscussionsCensys HomepageCensys SearchContributingAll contributions (no matter how small) are always welcome. SeeContributing to Censys PythonDevelopmentThis project usespoetryfor dependency management. Please ensure you haveinstalled the latest version.gitclonegit@github.com:censys/censys-python.gitcdcensys-python/
poetryinstallTesting# Run testspoetryrunpytest# With coverage reportpoetryrunpytest--cov-reporthtmlLicenseThis software is licensed underApache License, Version 2.0Copyright (C) 2024 Censys, Inc.
Package: censys-bigquery-cli
Censys BigQuery Command Line ToolThis script allows users to query the data in Censys Data BigQuery Project from the command line. The results from the query can be exported as JSON, CSV, or viewed from the terminal screen.Note: the Censys Data BigQuery Project is available to enterprise customers and approved academic researchers. For more information on product tiers, contactsales@censys.io.Setting Up a Service Account in BigQueryPrior to using the BiqQuery Command Line Tool, you'll need to set up a service account that is associated with your Google Cloud Platform.Google provides documentation on how to create a service account, either via the GCP Console or the Command Line. VisitGetting started with authenticationfor full documentation.Be sure to set theGOOGLE_APPLICATION_CREDENTIALSenvironmental variable.exportGOOGLE_APPLICATION_CREDENTIALS="[PATH]"InstallThe library can be installed usingpip.pipinstallcensys-bigquery-cliORpipinstallgit+https://github.com/censys/censys-bigqueryUsageThe script allows you to input SQL queries as arguments in the script, returning the results as screen output (default), JSON, or CSV.Here are some example queries:censys_bq'SELECT ip, ports, protocols, tags FROM `censys-io.ipv4_public.current` WHERE location.city = "Ann Arbor" and REGEXP_CONTAINS(TO_JSON_STRING(tags), r"rsa-export") LIMIT 25'censys_bq'with user_ports as (SELECT [443, 3306, 6379] as selected_ports)SELECT DISTINCT ip, TO_JSON_STRING(user_ports.selected_ports)FROM `censys-io.ipv4_banners_public.current`, user_ports, UNNEST(services) as sWHERE (SELECT LOGICAL_AND(a_i IN (SELECT port_number FROM UNNEST(services))) FROM UNNEST(user_ports.selected_ports) a_i) LIMIT 15'--formatcsvcensys_bq'SELECT COUNT(ip), p80.http.get.body_sha256FROM `censys-io.ipv4_public.current`WHERE REGEXP_CONTAINS(p80.http.get.body, r"(?i)coinhive.min.js>")GROUP BY p80.http.get.body_sha256ORDER BY 1 DESC'--formatjsoncensys_bq'with Data as (SELECTdistinct fingerprint_sha256FROM`censys-io.certificates_public.certificates`, UNNEST(parsed.subject.organization) as po, UNNEST(parsed.names) as parsed_namesWHEREREGEXP_CONTAINS(TO_JSON_STRING(parsed.names), r"[.]example[.]"))SELECTdistinct ipFROM`censys-io.ipv4_banners_public.current` as c, UNNEST(services)JOIN Data as d on d.fingerprint_sha256 = certificate.fingerprints.sha256LIMIT 20'--formatjsonResourcesSourceIssue TrackerCensys HomepageContributingAll contributions (no matter how small) are always welcome.Developmentgitclonegit@github.com:censys/censys-bigquery.gitcdcensys-bigquery/
pipinstall-e".[dev]"LicenseThis software is licensed underApache License, Version 2.0Copyright (C) 2021 Censys, Inc.
Package: censys-client
Advanced Censys ClientAdditional InformationsThis client is more fast than the officialYou don't limit of api keys(10 keys -> 100k ips)Getting StartedThe library can be installed usingpip3.pip3install-ihttps://test.pypi.org/simple/censys-clientTo upgraded usingpip.pip3install-ihttps://test.pypi.org/simple/--upgradecensys-clientTo configure your search credentials runcensys-client --config.$censys-client--config
CensysAPIID(********************************98d1):CENSYS_API_ID
CensysAPISecret(****************************aucc):CENSYS_API_SECRET
YourAPIIDandAPISecrethavebeensuccessfullysavedto./config.txt

WouldyouliketoaddanotherAPIIDandAPISecret?(y/n):
Package: censys-cloud-connectors
Censys Unified Cloud ConnectorThe Censys Unified Cloud Connector is a standalone connector that gathers
assets from various cloud providers and stores them in Censys ASM. This
Connector offers users the ability to supercharge our ASM Platform with total
cloud visibility. This connector currently supports the following cloud
providers: AWS, Azure, and GCP. Please see oursupported providers.DocumentationPlease view our documentation onRead the Docs.ContributingAll contributions (no matter how small) are always welcome. SeeContributing to the Cloud Connectorto change or
test the code or for information on the CI/CD pipeline.Submit an issue or pull request onGitHub.LicenseThis software is licensed underApache License, Version 2.0.Copyright (C) 2023 Censys, Inc.
Package: censys-maltego
Censys Maltego IntegrationWelcome to Censys transforms for Maltego! Now you can use Censys data to easily perform investigations, quickly search or discover assets, and evaluate your company's digital asset risk by identifying server misconfigurations and rogue services.InstallInstallation requires a few pre-requisites:You will need aCensys accountandAPI Key.A working instance ofMaltego. Any supported version will work, but the CE edition is limited in how many results can be returned to the graph.A working python3 installation.Python 3.6+is currently supported.$pipinstallcensys-maltego
$canaricreate-profilecensys_maltego
...
Successfullycreatedcensys_maltego.mtz.YoumaynowimportthisfileintoMaltego.

INSTRUCTIONS:
-------------1.OpenMaltego.2.Clickonthehomebutton(Maltegoicon,top-leftcorner).3.Clickon'Import'.4.Clickon'Import Configuration'.5.Followprompts.6.Enjoy!Edit~/.canari/censys_maltego.confto include your Censys API ID and Secret. Alternatively you can configure your Censys credentials with thecensys configcommand.ResourcesCensys HomepageSourceIssue TrackerContributingAll contributions (no matter how small) are always welcome.LicenseThis software is licensed underApache License, Version 2.0Copyright (C) 2021 Censys, Inc.
Package: centapp
Python client library for interacting withCentAppAPI.You can find more information on the officialCentApp website.README also available in:РусскийInstallingCentApp SDK is available on PyPI:$python-mpipinstallcentappCentApp SDK officially supports Python 3.7+.API Version and ReferenceBase api url: https://cent.app/api/v1API reference and official docs:https://cent.app/en/reference/apiThe list of available modules:/api/v1/bill//api/v1/payment//api/v1/merchant//api/v1/payout/API ExamplesYou can find more examplesin examples folder.Bill creation example:token=''amount=100shop_id=''cent_app=CentApp(token)res=cent_app.bill().create(amount,shop_id)print(res)Merchant balance example:token=''cent_app=CentApp(token)res=cent_app.merchant().balance()print(res)
Package: centaur-client
Centaur Client Python LibraryCentaur provides fully-managed, serverless, scalable vector-database service for building various machine learning applications. The Centaur client SDK is your gateway to access the Centaur service.InstallationTo install the Centaur client Python SDK, simply run:pipinstallcentaur-clientQuickStartYou can useClientapi to communicate with Centaur service.importcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')Sample CodeCreate a CollectionClienthost variousCollectionAPIs for interacting with Centaur service.importcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')client.create('YOUR-COLLECTION-NAME',dimension=4)List Collectionsimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collections=client.list()print(collections)Describe Collectionimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')rsp=client.describe('YOUR-COLLECTION-NAME')print(rsp)Delete Collectionimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')client.delete('YOUR-COLLECTION-NAME')Get a Collection InstanceCollectionprovides APIs for accessingDocandPartitionimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')ifcollection:print('collection:',collection)Describe Collection Statisticsimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')rsp=collection.stats()print(rsp)Insert/Update/Upsert Docsimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')collection.upsert(('YOUR-DOC-ID',[0.1,0.2],{'price':100,'type':'dress'}))Query a Collectionimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')match_docs=collection.query([0.1,0.2,0.3,0.4],topk=100,include_vector=True)ifmatch_docs:fordocinmatch_docs:print(doc)Delete Docsimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')collection.delete('YOUR-DOC-ID')Fetch Docsimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')fetch_docs=collection.fetch(['YOUR-DOC-ID1','YOUR-DOC-ID2'])iffetch_docs:fordoc_idinfetch_docs:print(fetch_docs[doc_id])Create a Collection Partitionimportcentaurclient=centaur.Client(api_key='YOUR-CENTAUR-API-KEY')collection=client.get('YOUR-COLLECTION-NAME')collection.create_partition('YOUR-PARTITION-NAME')Centaur ResponsefromcentaurimportCentaurCode@dataclassesclassCentaurResponse(object):code:CentaurCodemessage:strrequest_id:stroutput:AnyLicenseThis project is licensed under the Apache License (Version 2.0).
Package: centaurminer
This package is a wrapper onselenium, a package that “automates web browser interaction from Python”. In this package, we add the following features:Automatically determine and install the correct chromedriver version, using webdriver-manager.Add a class used to identify where elements are located on the DOMAdd a class that specifies if any extra processing should be done to the elements in the previous class, rather than just extract an attribute.Create helper classes to simplify the process of identifying elementsHelper functions are included to make processing and storing data easier.Although this package is tailored to data mining scientific literature, is can be used in other applications in data mining.
Package: centerit
centeritA python library for centering the tkinter and PyQt windows.You can use this library to center your Tkinter or PyQt windows both horizontally and vertically. That means your window will show in the middle of the screen.How to installFirst clone the repository usinggitclonehttps://github.com/MrinmoyHaloi/centeritordownloadas a zip file and extract it.or pip install centeritHow to useimport everything from the library usingfrom centerit import *Then Call centertk for Tkintercentertk(root,width,height)and centerqt for PyQt5centerqt(root,width,height)You just have to pass the root window name and the dimensions you want for your root window.I used python 3.9.5 to develop it. I test it out with python 3.9.2. I used windows 10 20H2 and parrot os 4.11.1(Debian Based) to test it. I will apreciate if someone test it out with other versions of python that is lower than 3.9.2. I will try to add support for other gui libraries in the future, Like wxPython, PyQt4, PySide2 etc.
Package: centerline
Roads, rivers and similar linear structures are often represented by
long and complex polygons. Since one of the most important attributes of
a linear structure is its length, extracting that attribute from a
polygon can prove to be more or less difficult.This library tries to solve this problem by creating the the polygon’s
centerline using theVoronoi diagram. For more info on how
to use this package, see theofficial documentation.FeaturesA command-line script for creating centerlines from a vector source file and saving them into a destination vector file:create_centerlines$create_centerlinesinput.shpoutput.geojsonTheCenterlineclass that allows integration into your own workflow.>>>fromshapely.geometryimportPolygon>>>fromcenterline.geometryimportCenterline>>>polygon=Polygon([[0,0],[0,4],[4,4],[4,0]])>>>attributes={"id":1,"name":"polygon","valid":True}>>>centerline=Centerline(polygon,**attributes)>>>centerline.id==1True>>>centerline.name'polygon'>>>centerline.geometry.geoms<shapely.geometry.base.GeometrySequenceobjectat0x7f7d24116210>
Package: center-tk-window
Center tk windowA small library for centering tkinter windows on their parent or on the screen.InstallationTo install run the following command:pip install center_tk_windowUsageimporttkinterimportcenter_tk_windowroot_window=tkinter.Tk()window_to_center=tkinter.Toplevel(root_window)# Center a window on its parent unless it is the root window in which case it will be centered on screen.center_tk_window.center(root_window,window_to_center)# Center a window on its parentcenter_tk_window.center_on_parent(root_window,window_to_center)# Center a window on the screencenter_tk_window.center_on_screen(window_to_center)
Package: centpy
centpyCentral schemes for conservation laws in Python.The schemes are translated into Python fromCentPackwritten byJorge BalbásandEitan Tadmor.UsageCentpy provides to the user three main classes for parameters, equations, and solvers. Examples of instances for parameters and equations are intests/example_parameters.pyandtests/example_equations.py.The numerical solution of a one-dimensional Burgers equation is discussed below.ParametersThe parameter classes are simpledata classeswithout methods:Pars1dandPars1ddefined inparameters.py. Each attribute has a default
variable, but it is recommended that all attributes are set explicitly. The attributes are:AttributeDescriptionx_initleft grid pointx_finalright grid pointt_finalevolution timedt_outtime step of storageJnumber of interior grid pointscflCFL numberschemesolver scheme (fd2,sd2, orsd3)An instance of the parameter class can be created as follows.pars_burgers1d = centpy.Pars1d(
    x_init=0.0,
    x_final=2.0 * np.pi,
    t_final=10,
    dt_out=0.05,
    J=400,
    cfl=0.75,
    scheme="sd3")Note that the parameter data class does not have a member for the time stepdt, because it is calculated dynamically during the solution of the equation based on the CFL number and maximum spectral radius.EquationsThe equations areabstract base classeswhich require methods for setting initial data, boundary conditions, fluxes, and spectral radius. Additional helper methods and parameters can be added depending on the problem. The equations class inherits all attributes of the parameters class.  The space-time grid is constructed in this step based on the parameters. The Burgers equation class is defined below.class Burgers1d(centpy.Equation1d):
    def initial_data(self):
        return np.sin(self.x) + 0.5 * np.sin(0.5 * self.x)

    def boundary_conditions(self, u):
        u[0] = u[-4]
        u[1] = u[-3]
        u[-2] = u[2]
        u[-1] = u[3]

    def flux_x(self, u):
        return 0.5 * u * u

    def spectral_radius_x(self, u):
        return np.abs(u)The boundary conditions are periodic, so the data on the ghost points are copied from the interior points on the opposite end.SolutionThere are two solver classes:Solver1dandSolver2ddefined insolver1d.pyandsolver2d.pyrespectively. To construct the solution, we create an instance of theBurgers1dclass with the parameters, and give the equation instance as input to the solver class.eqn_burgers1d = Burgers1d(pars_burgers1d)
soln_burgers = centpy.Solver1d(eqn_burgers1d)
soln_burgers.solve()After the solver step, the instancesoln_burgersincludes the solution arrayu_n. Depending on the shape of the array, plots and animations can be easily constructed. Examples are given in the animations notebooktests/animations.ipynb.The options for the central solver arefd2for second order fully-discrete method,sd2for second order semi-discrete method, andsd3for third order semi-discrete method. Information about these solvers is given at the appendix of theCentPack User Guide.LaTeX formulas and animations for the examples are given in the Jupyter notebooktests/animations.ipynb.
Package: central50
simplify data retrieving on centralSystem.orgdownload fileimportcentral50fromcentral50importClient,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 httpimportcentral50fromcentral50importClient,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 pgsqlimportcentral50fromcentral50importClient,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 odpsimportloggingimportcentral50fromcentral50importClient,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 logimportloggingimportcentral50central50.set_file_logger("../log/central.log",level=logging.DEBUG)
Package: centraldogma-python
Central Dogma client in PythonPython client library forCentral DogmaInstall$ pip install centraldogma-pythonGetting started>>>fromcentraldogma.dogmaimportDogma>>>dogma=Dogma("https://dogma.yourdomain.com","token")>>>dogma.list_projects()[]Please seeexamplesfolderfor more detail.DevelopmentTestsUnit test$ pytestIntegration testRun local Central Dogma server with docker-compose$ docker-compose up -dRun integration tests$ INTEGRATION_TEST=true pytestStop the server$ docker-compose downLintPEP 8$ black .DocumentationPEP 257To build sphinx at local$ pip install sphinx sphinx_rtd_theme
$ cd docs && make html