Saravanan666/pypi-org huggingface dataset:
Package: netpyne
NetPyNE (Python package)DescriptionNetPyNE is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.For more details, installation instructions, documentation, tutorials, forums, videos and more, please visit:www.netpyne.orgThis package is developed and maintained by Dura-Bernal lab (http://dura-bernal.org) and the Neurosim lab (www.neurosimlab.org).AcknowledgementsThis work was funded by grants from the NIH, NSF, NYS SCIRB, UK Welcome Trust and Australian Research Council. We are thankful to all the contributors that have collaborated in the development of this open source tool via GitHub.This project was supported by:HBP Brain Simulation Platform funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).This project has received a Voucher "Integration of NetPyNE into EBRAINS Platform (EBRnetpyne)" from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement:No. 785907 (Human Brain Project SGA2).No. 945539 (Human Brain Project SGA3).
Package: netpyne-py3
# NetPyNE (python package)
## Description
NetPyNE is a python package to facilitate the development, parallel simulation and analysis of biological neuronal networks using the NEURON simulator.For more details, installation instructions, documentation, tutorials, forums, videos and more, please visit: www.netpyne.orgThis package is developed and maintained by the Neurosim lab (www.neurosimlab.org)
Package: netpyne-ui
Experimental prototype for a new user interfacefor `NEURON <http://www.neuron.yale.edu/neuron/>`__ based on webtechnologies.This prototype is being developed in collaboration with the `NeurosimLab <http://neurosimlab.org/>`__ and the `SenseLab <https://senselab.med.yale.edu/>`__.The UI connects to`nrnpython <http://www.neuron.yale.edu/neuron/static/docs/help/neuron/neuron/classes/python.html>`__through a `Geppetto <http://git.geppetto.org>`__ extension for `JupyterNotebook <http://jupyter.org/>`__.See the `Repo <https://github.com/MetaCell/NetPyNE-UI>`__ and `Wiki <https://github.com/MetaCell/NetPyNE-UI/wiki>`__ for more=======NetPyNE-UI=========User Interface for NetPyNE.See the `Repo <https://github.com/MetaCell/NetPyNE-UI>`__ and `Wiki <https://github.com/MetaCell/NetPyNE-UI/wiki>`__ for moreinfo!Installation============.. code-block:: bashpip install netpyne_uijupyter nbextension enable --py jupyter_geppettoUsage=====.. code-block:: bashNetPyNE-UIor.. code-block:: bashjupyter notebook --NotebookApp.default_url=/geppetto --NotebookApp.token=''
Package: netqr
QR Code for NetDronesSample package that reads in QR code data, writes them into files for printing.
It also reads in a set of image files and looks for QR Codes.Python PIP Packaging NotesThe package layout has the source code insrc/netpyand note the
critical files there are:py.typed. Must be an attribute insetup.pywhich configures the
python package.init.py. This must be there to denote the package and import all
the modules.setup.py. The file that drives the packaging, it tells the build system where
to find the components when you do apython -m buildpip install -e. This is used for development and does an "editable"
installation
Package: netR
netR is a customizableHTTPframework forpython. netR is designed for Python users who want more control and customization. netR is composed from the ground up to ensure speed efficiency.netR is a newpythonframework that need much more development andfeedback. netR is currently not recommended for production use until version 1.0.0 is released.Installing$pipinstallnetRDocsCreating a simple HTTP serverimportnetR
Package: netra
# netraTrain & query MNIST dataset with different models using Tensorflow.# Installation$ pip install netra# UsageHelp:$ netra –helpQuerying model:$ netra query -ihttps://i.imgur.com/GbreNv2.png$ netra query -i /foo/bar/foo.pngTraining model:$ netra train –model regression
$ netra train –model regression –epochs 1000 –batch_size 100
$ netra train -m regression -e 1000 -b 100# Sample output:![netra](https://user-images.githubusercontent.com/4463796/26892117-e1a77a34-4bd4-11e7-91d2-11af0d1294f7.png)![netra2](https://user-images.githubusercontent.com/4463796/26892600-773b27fc-4bd6-11e7-9bfd-38b60b88b8bf.png)
Package: netrange
NetRangeA simple package for reading and ranging IPs and ports.InstallationRun the following to install:pipinstallnetrangeUsageFrom CLI$netrange--help
usage:IPRange[-h][--version][--verbose]{ip,port}...

AsimplepackageforreadingandrangingIPsandports.

positionalarguments:{ip,port}chooseoneoption

optionalarguments:-h,--helpshowthishelpmessageandexit--versionshowprogram'sversionnumberandexit--verboseFrom Moduleimportnetrangeips=ip.load(from_file=file)ranged_ips=ip.dump(ips)
Package: netranker
NetrankerThis python app provides the APIs for netranker, a netrunner card crowd-ranking
website.Installnetranker is on pypi. To install, simply runpip install netrankerDependenciesPython dependencies are managed bypipenv. To install dependencies, runpipenv installto install dev dependencies as well (required to run tests), usepipenv install -dThe default app config also expects a MongoDB server running on localhost:27017RunTo start the API server, runFLASK_APP=netranker/app.py pipenv run flask run
Package: netrics
netrics: a Python 2.7 package for econometric analysis of networksby Bryan S. Graham, UC - Berkeley, e-mail:bgraham@econ.berkeley.eduThis package includes a Python 2.7 implementation of the two econometric
network formation models introduced in Graham (2014, NBER).This package is offered “as is”, without warranty, implicit or otherwise. While I would
appreciate bug reports, suggestions for improvements and so on, I am unable to provide any
meaningful user-support. Please e-mail me atbgraham@econ.berkeley.eduPlease cite both the code and the underlying source articles listed below when using this
code in your research.A simple example script to get started is:>>>> # Import numpy in order to correctly read test data
>>>> import numpy as np

>>>> # Import urllib in order to download test data from Github repo
>>>> import urllib

>>>> # Append location of netrics module base directory to system path
>>>> # NOTE: only required if permanent install not made
>>>> # NOTE: edit path to location on netrics package on local machine
>>>> import sys
>>>> sys.path.append('/Users/bgraham/Dropbox/Sites/software/netrics/')

>>>> # Load netrics module
>>>> import netrics as netrics

>>>> # Download Nyakatoke test dataset from GitHub
>>>> download =  '/Users/bgraham/Dropbox/' # Edit to location on your machine
>>>> url = 'https://github.com/bryangraham/netrics/blob/master/Notebooks/Nyakatoke_Example.npz?raw=true'
>>>> urllib.urlretrieve(url, download + "Nyakatoke_Example.npz")

>>>> # Open dataset
>>>> NyakatokeTestDataset = np.load(download + "Nyakatoke_Example.npz")

>>>> # Extract adjacency matrix
>>>> D = NyakatokeTestDataset['D']

>>>> # Initialize list of dyad-specific covariates as elements
>>>> # W = [W0, W1, W2,...WK-1]
>>>> W = []

>>>> # Initialize list with covariate labels
>>>> cov_names = []

>>>> # Construct list of regressor matrices and corresponding variable names
>>>> for matrix in NyakatokeTestDataset.files:
>>>>     if matrix != 'D':
>>>>         W.append(NyakatokeTestDataset[matrix])
>>>>         cov_names.append(matrix)

>>>> # Apply tetrad logit procedure to dataset
>>>> [beta_TL, vcov_beta_TL, tetrad_frac_TL, success] = \
         netrics.tetrad_logit(D, W, dtcon=None, silent=False, W_names=cov_names)CODE CITATIONGraham, Bryan S. (2016). “netrics: a Python 2.7  package for econometric analysis ofnetworks,” (Version 0.0.1) [Computer program]. Available athttps://github.com/bryangraham/netrics(Accessed 04 September 2016)PAPER CITATIONSGraham, Bryan S. (2014). “An econometric model of link formation with degreeheterogeneity,” NBER Working Paper No. w20341.
Package: netrohomeapi
NetroHomeAPI Python WrapperThis is a Python wrapper for the NetroHome API. It allows you to interact with the NetroHome cloud service to manage your Netro smart watering system.InstallationYou can install the package from PyPI usingpip:pip install netrohomeapiUsagefromnetrohomeapiimportNetroHomeAPI# Initialize the API with your access tokenapi=NetroHomeAPI("YOUR_ACCESS_TOKEN")
Package: netron
Netron is a viewer for neural network, deep learning, and machine learning models.Netron supports ONNX, TensorFlow Lite, Core ML, Keras, Caffe, Darknet, MXNet, PaddlePaddle, ncnn, MNN and TensorFlow.js.Netron has experimental support for PyTorch, TorchScript, TensorFlow, OpenVINO, RKNN, MediaPipe, ML.NET and scikit-learn.
Package: netropy
NetropyNetropy is a python interface to theNIST Randomness Beacon.API Quick Reference# Get the current/latest recordrec=beacon.current()# Retrieve the previous record for a given recordrec_prev=beacon.previous(rec)# Retrieve the next record for a given recordrec_next=beacon.next(rec)# Retrieve the last record in the chainrec_last=beacon.last()# Retrieve the first record in the chainrec_first=beacon.start_chain(rec)# Verify a recordis_valid=record.verify_record(rec)# Verify two sequential recordsis_valid=record.verify_pair(rec1,rec2)ExamplesA record is a dictionary representation of the xml record returned from
the beacon:rec=beacon.current()pprint(rec){'frequency':60,'outputValue':u'742B8591BF3C291C129E4C84AD01DF17EFAE5EC18ECE5299C33F23BE9F88ED77A277CA7F161237EDB69D06D683A6501D314B7DF79307400AECC4298D46588DF8','previousOutputValue':u'D52003A06C14F6CC7386B48F5CEBFE6BA583E2E6040E0698C26704AAFD6A7FC8F89D11B838E80255DB0FD9BB5DD48DC76A6C05A097B0D5E7537D65C7431E7A31','seedValue':u'016BDBDA1F9D5A20D2350755B9A27F81B4B295007F998856C2DC30D2431E9082E6CD1DE77C70FD55B71DBF8807830E69F946635CE70781C535D6DE14E62E550B','signatureValue':u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statusCode':u'0','timeStamp':1396753680,'version':u'Version 1.0'}Get the current record and print the random seed value:importbeaconrec_current=beacon.current()random_value=rec_current['seedValue']print(random_value)E1DB9B9919DF258E21E3A04D2D52E9F320097710588F25472E87608BEA0C72D1295B1D5EA5F199AD4E87A227BB5A4939073EBFCD512137AAD371C31299896341Get the previous record:importbeaconrec_current=beacon.current()rec_previous=beacon.previous(rec_current)random_value=rec_previous['seedValue']print(random_value)299785EF23C8984459B6126222B6244E7AF0536C9EC5FF9A2D8105B0B7FB462CA0D691BCB99227D2E1337486AABF8169DE76F3D83FA2533AC9681174F926D9E3MiscExpects python 2.6 / 2.7
Package: netrunner
NetrunnerAn async network command runner built using Scrapli.
You provide and inventory and tasks (functions), and the runner does the rest.Currently only working with Cisco IOS and NXOS devices, but I will be adding others.Installationpip install netrunnerBasic UsageHere is a basic example. More examples can be found in the examples folder.importasynciofromnetrunnerimportRunnerhosts=[{"hostname":"SW1021","ip":"10.0.0.9","platform":"ios"},{"hostname":"SW1031","ip":"10.0.0.10","platform":"ios"},{"hostname":"NX1001","ip":"10.0.0.11","platform":"nxos"},{"hostname":"NX2001","ip":"10.0.0.12","platform":"nxos"},]# Here is a task. The first parameter is the host and must be provided.# Other parameters can also be passed into the tasks/functions.# Please see the examples in the example folder.asyncdeffirst_task(host):# Commands must be passed in via a list. By default the output is parsed using# Genie, but parse can be set to False if raw output is requiredresult=awaithost.send_command(["show version","show vlan"])returnresultasyncdefmain():# Hosts can either be passed here to be used globally, or can be passed per taskrunner=Runner(username="test_user",password="T3stpass",hosts=hosts)result=awaitrunner.run(name="Check Version and Vlan",task=first_task)print(result.run_time)print(result.result["show version"])print(result.result["show vlan"])print(result.failed)if__name__=="__main__":asyncio.run(main())
Package: netscaler-module
Netscaler ModuleNetscaler module for python 3.xRequirementsPython 3+Installation$pipinstallnetscaler_moduleQuickstartImport thenetscaler_modulelibrary to use the functionsfromnetscaler_moduleimportNitroClass....Create aDATABASEvariable and append the dicts response from nitro class.
Below an example.DATABASE=list()if__name__=='__main__':globalDATABASEns_pool=[{'ip':'192.168.2.100','hostname':'ns1','backup_name':'ns1',},{'ip':'192.168.2.101','hostname':'ns2','backup_name':'ns2',}]password={'username':'nsroot','password':'XXXXXXX'}backup={'backup_folder':'repo','backup_level':'full',}forns_iteminns_pool:kwargs=ns_item|password|backupns=NitroClass(**kwargs)ns.login()ifns.master:data=ns.get_lbvservers_binding_partitions()DATABASE.extend(data)ns.create_backup()ns.download_backup()ns.delete_backup()ns.logout()print(DATABASE)
Package: netscan-TD4B
netscanThis is an API adaption to the Original RustScan, which extends it's functionality for use as a native Python module e.g. Extension Module.All Credits go to the original Authors which can be found here:https://github.com/RustScan/RustScanAs of this commit, the Wheels distribution is built via RustScan version: 2.1.0 Release.How to Use.Using the module is simple. Simply install the latest version via pip.pipinstallnetscan-td4bThe default Batch Size is set to 10. The batch size is essentially the number of concurrent ports to check at a time e.g. check 10 ports at a time.The default timeout (time to wait for a TCP timeout) is 1.5 seconds (1500 miliseconds).Note that these are extremley conservative defaults and hence lead to low performance. To increase the performance for you're environment tweak these by setting the environment variables.Here is an example of how to use the NetScan extension module:>>>importos,netscan>>># Sets the concurrent number of ports to check at a time to 1500!>>>os.environ["SCAN_BATCH_SIZE"]="1500">>># Sets the TCP Timeout to 0.5 seconds>>>os.environ["SCAN_TIMEOUT"]="500">>>netscan.run_scan(["10.0.0.1","10.0.0.28"])>>>['10.0.0.1:53','10.0.0.1:80','10.0.0.28:135','10.0.0.28:139','10.0.0.28:445','10.0.0.1:443','10.0.0.28:3389','10.0.0.28:7680','10.0.0.1:8080','10.0.0.1:8181','10.0.0.1:21515','10.0.0.28:27036','10.0.0.1:49152','10.0.0.1:49153','10.0.0.1:49154','10.0.0.28:49670','10.0.0.28:57621']>>>exit()Additionally you can scan for only a specific set of ports or service(s) via an optional secondary argument, like so.>>>importos,netscan>>># Sets the concurrent number of ports to check at a time to 1500!>>>os.environ["SCAN_BATCH_SIZE"]="1500">>># Sets the TCP Timeout to 0.5 seconds>>>os.environ["SCAN_TIMEOUT"]="500">>>netscan.run_scan(["10.0.0.1"],[80,443])>>>['10.0.0.1:80','10.0.0.1:443']>>>exit()Cheers!
Package: netscape-cookies
Netscape Cookiesnetscape-cookiesis a Python extension that simplifies the process of exporting Selenium WebDriver cookies into the Netscape HTTP Cookie format. This is useful when you need to use cookies in various tools that require the Netscape format.Table of ContentsInstallationUsageBasic UsageExample with Selenium WebDriverFunctionsContributingLicenseInstallationTo installnetscape-cookies, simply run:pipinstallnetscape-cookiesUsageBasic Usagefromnetscape_cookiesimportsave_cookies_to_filefromnetscape_cookiesimportto_netscape_string# Your Selenium WebDriver cookiescookie_data=[{"domain":".example.com","expiry":1677649426,"path":"/","secure":True,"name":"cookie_name","value":"cookie_value"}]file_path="cookies.txt"# Save cookies to file in Netscape formatsave_cookies_to_file(cookie_data,file_path)# Get cookies as String in Netscape formatto_netscape_string(cookie_data)Example with Selenium WebDriverfromseleniumimportwebdriverfromselnet_cookiesimportsave_cookies_to_filefromnetscape_cookiesimportto_netscape_stringbrowser=webdriver.Firefox()browser.get("https://www.example.com")# Get cookies from Selenium WebDrivercookie_data=browser.get_cookies()file_path="cookies.txt"# Save cookies to file in Netscape formatsave_cookies_to_file(cookie_data,file_path)# Get cookies as String in Netscape formatto_netscape_string(cookie_data)browser.quit()Functionsnetscape-cookiesprovides the following functions:to_netscape_string(cookie_data: List[Dict[str, Any]]) -> str: Converts the given Selenium WebDriver cookie data into a Netscape HTTP Cookie formatted string.save_cookies_to_file(cookie_data: List[Dict[str, Any]], file_path: str) -> None: Saves the given Selenium WebDriver cookie data to a file in the Netscape HTTP Cookie format.ContributingContributions tonetscape-cookiesare welcome!Licensenetscape-cookiesis released under theMIT License.
Package: netschoolapi
netschoolapiNetSchoolAPI — это асинхронный клиент для «Сетевого города», который может получить дневник с домашними заданиями и оценками, объявления и просроченные задания.Библиотека не имеет отношения к «ИрТеху»ДокументацияА ещё у нас есть примеры в/examples.Жалобы и предложенияОбсуждение библиотеки ведётся вчатепроекта в телеграме.
Если вы хотите сообщить о баге или предложить фичу — создайтеишью.ЛицензияКод библиотеки распространяется под лицензиейMIT.
Это значит, что вы можете копировать, распространять, изменять, публиковать и продавать код netschoolapi без разрешения её авторов, но при условии добавления уведомления об авторских правах во все копии или существенные части Программного обеспечения. Проще говоря, развлекайтесь!
Package: netsci
netsciAnalyzing Complex Networks with PythonAuthorVersionDemoGialdettinetsciis a python package for efficient statistical analysis of spatially-embedded networks. In addition, it offers several algorithms and implementations (CPU and GPU-based) of motif counting algorithms.For other models and metrics, we highly recommend using existing and richer tools. Noteworthy packages are the magnificentNetworkX,graph-toolorBrain Connectivity Toolbox.A simple exampleAnalyzing a star network (of four nodes)importnumpyasnpimportnetsci.visualizationasnsvA=np.array([[0,1,1,1],[0,0,0,0],[0,0,0,0],[0,0,0,0]])nsv.plot_directed_network(A,pos=[[0,0],[-1,1],[1,1],[0,-np.sqrt(2)]])importnetsci.metrics.motifsasnsmf=nsm.motifs(A,algorithm='brute-force')print(f)# [1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]nsv.bar_motifs(f)Using GPU for the motif counting is easyfromnetsci.models.randomimporterdos_renyiA_er=erdos_renyi(n=1000,p=0.01,random_state=71070)# Create an Erdős–Rényi networkf_er=nsm.motifs(A_er,algorithm='gpu')# Count motifs using a GPUprint(f_er)# [156453141   9481653     37283     95997     47667     48287      1001       843#        769       334         2         9         5         9         0         0]InstallationInstall latest release version viapip$pipinstallnetsciInstall latest development versionvia pip$pipinstallgit+https://github.com/gialdetti/netsci.gitor in development mode$gitclonehttps://github.com/gialdetti/netsci.git
$cdnetsci
$pipinstall-e.TestingAfter installation, you can launch the test suite:$pytestHelp and SupportExamplesThemeMyBinderColabBasic network motifs demoConnectomics dataset, and 3-neuron motif embeddingCommunicationPlease send any questions you might have about the code and/or the algorithm toeyal.gal@mail.huji.ac.il.CitationIf you usenetsciin a scientific publication, please consider citing the following paper:Gal, E., Perin, R., Markram, H., London, M., and Segev, I. (2019).Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks.BioRxiv 656058.Bibtex entry:@article {Gal2019
    author = {Gal, Eyal and Perin, Rodrigo and Markram, Henry and London, Michael and Segev, Idan},
    title = {Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks},
    year = {2019},
    doi = {10.1101/656058},
    journal = {bioRxiv}
}
Package: netscud
netscudnetscud is an SSH and Telnet python library for network devices (mainly network switches and routers). It uses async techniques to speed up concurrent connections.As an overview here is an example of netscud code for a Cisco IOS device:# Python library importimportasyncio,netscudasyncdefmain():# Device parametersmy_device={"ip":"192.168.0.16","username":"cisco","password":"cisco","device_type":"cisco_ios",}# Creation of a deviceasyncwithnetscud.ConnectDevice(**my_device)assw1:# Sending commandoutput=awaitsw1.send_command("show interfaces description")# Display messageprint(output)# Main function callif__name__=="__main__":# Main async loopasyncio.run(main())The result of this script is:c:\>script.pyInterface              IP-Address      OK? Method Status                Protocol
FastEthernet0/0        192.168.0.16    YES NVRAM  up                    up
FastEthernet0/1        unassigned      YES manual up                    up
FastEthernet1/0        unassigned      YES manual administratively down down
FastEthernet1/1        unassigned      YES DHCP   up                    up
Ethernet2/0            unassigned      YES DHCP   up                    up
Ethernet2/1            unassigned      YES NVRAM  up                    up
Ethernet2/2            unassigned      YES NVRAM  up                    up
Ethernet2/3            unassigned      YES NVRAM  up                    up

c:\>Documentation can be found here:https://netscud.readthedocs.io/en/latest/
Package: netsdldv
basic knowledge这是什么?这是一个sql 执行中间件, 它可以从配置文件中读取,并加载配置按需生成相应的sql 并执行.怎么配置以 xmlFactory 举例.
xml 的文件格式如下
	<DV>
		<ID>1</ID> #dv_id
		<TableName>order_head</TableName>  # the table you will select from 
		<TopRecord>500</TopRecord> # euqal to select top 
		<Fields> # format will be {TrueField} as {DispField}, ${number} is a placeholder number will be replace by true table name
			<Field>
				<DispField>Order_date</DispField> 
				<TrueField>dbo.Fn_DispDateTime($0.Order_date)</TrueField>
				<WhereField>$0.Order_date</WhereField>
			</Field>
                </Fields>
		<Tables>
			<Table ID="88"> # $88
				<Name>V_Type</Name> # true table name
				<Alias>Tp_order</Alias> 
				<Relation> # relations that about with this alias
                                $0.Order_type=$88.Type_no    and  $88.Type_for='Order_type'  and   $88.Pj_id=$0.Pj_id and $88.Status&gt;=0

</Relation>
				<JoinType>left</JoinType>
                        </Table>
                </Tables>
        </Dv>1 how to useengine = create_engine(self.G2CN_str)
        session = sessionmaker(bind=engine)

        factory = XMLFactory("D:\\chenchu\\Python\\DvManager\\test.xml")
        dvm = factory.Build()

        dvm.addQueryField(['Cust_no', 'Order_type_name', 'Order_date'])
        # dvm.filter= DvFilter('Order_date').Equal_to('2019-10-11') #'Order_date'
        dvm.filter.field_name = 'Order_date'
        dvm.filter.LESS_THAN('2019-10-11')

        # add field
        dvm.fields['Pj_id'] = DvField(id="",disp_name='Pj_id',true_name="$0.Pj_id",where_name="$0.Pj_id") 

        pj_id  = DvFilter('Pj_id').Equal_to('82187')

        dvm.filter.AND(pj_id)
        dvm.top_record = 1000
        dvm.temp_tablename = "#ls"

        dvm.sql_adapter = sql_server(session()) # 或许可以设为 default
        print(dvm.Build()) 
        rows = dvm.Query()
        for row in rows:
            print(row)2.how to load from xmlfactory = XMLFactory("./test.xml")
        dvm = factory.Build()3.how to generate a xml from a dvid# self.AmimanagerDB == connection string
        tools = dv2xml.dv2xml(self.AmimanagerDB,1)
        tools.run("./test.xml")