Saravanan666/pypi-org huggingface dataset:
Package: networkx-gdf
networkx-gdfPython package to read and write NetworkX graphs as GDF (Graph Data Format).GDF is a compact file format originally implemented byGUESS. Although the software itself is not anymore maintained, the format is still supported by active open-source projects such asGephi(see detailshere).RequirementsPython>=3.7networkx>=2.1pandas>=1.1.0InstallPackage is available to install onPyPI:pipinstallnetworkx-gdfUsageJust two functions are implemented, for reading from and writing data to file.fromnetworkx_gdfimportread_gdf,write_gdf# Builds NetworkX graph object from file.G=read_gdf("input_file.gdf")# Writes NetworkX graph object to file.write_gdf(G,"output_file.gdf")Detailed usageA few additional arguments are available both for reading and writing files:Building NetworkX graph object from fileG=read_gdf("input_file.gdf",# Required; file path to read data from.directed=None,# Optional; consider edges as directed or undirected.# True: returns a <nx.DiGraph> or <nx.MultiDiGraph> object.# False: returns a <nx.Graph> or <nx.MultiGraph> object.# None (default): decides based on "directed" edge attribute in file.multigraph=None,# Optional; consider multiple or single edges among nodes.# True: always returns a <nx.MultiGraph> or <nx.MultiDiGraph> object.# False: sums edge weights and returns a <nx.Graph> or <nx.DiGraph> object.# None (default): decides based on number of edges among the same pair of nodes.node_attr=None,# Optional; node attributes to import data from.# Accepts either a list containing attribute names or a boolean (True, False).# Defaults to True, which considers all node attributes.edge_attr=None,# Optional; edge attributes to import data from.# Accepts either a list containing attribute names or a boolean (True, False).# Defaults to True, which considers all edge attributes.)Writing NetworkX graph object to filewrite_gdf(G,# Required; graph object from NetworkX."output_file.gdf",# Required; file path to write data to.node_attr=None,# Optional; node attributes to export data from.# Accepts either a list containing attribute names or a boolean (True, False).# Defaults to True, which considers all node attributes.edge_attr=None,# Optional; edge attributes to export data from.# Accepts either a list containing attribute names or a boolean (True, False).# Defaults to True, which considers all edge attributes.)Note that any object types beyond integers/longs, floats/doubles, and booleans will be considered as strings.Importing as a classAlternatively, the module can also be imported as a class and inherited:fromnetworkx_gdfimportGDFclassMyClass(GDF):...G=MyClass.read_gdf("input_file.gdf")MyClass.write_gdf(G,"output_file.gdf")Both functions are implemented as static methods, so initializing the class as an object is not required.ReferencesNetworkXPandas
Package: networkxgmml
=============networkxxgmml=============XGMML parser for networkx-------Install-------pip install networkxgmmlThat's all!----APIs----XGMMLReader(graph_file)-----------------Argument: graph_file: a file-like object contains XGMMLReturn: a networkx.DiGraph objectXGMMLWriter(graph_file, graph, graph_name, directed=True)------------------------------------* file: a file-like object to write XGMML* graph: a networkx.Graph object* graph_name: a name of a network* directed: is directed graph or not-------License-------Distributed with a MIT license------------Contributors------------* Yasunobu OKAMURA* Anders Riutta* Jeff Yunes* Gustavo Pereira
Package: networkxtra
networkxtraExtra functions fornetworkx:Algorithmslargest_connected_componentConversionfrom_geojsonl,to_geojsonlDrawingdraw_cairo(much faster than the matplotlib backend)Functionsdescribe(G),remove_leafs(G)Layoutsbfs_layout(https://github.com/networkx/networkx/pull/5179)lgl_layout,lgl_layout_from_file(viaminilgl)Read/Writegenerate_geojsonl,parse_geojsonl,read_geojsonl,write_geojsonlgenerate_lgl,parse_lgl,read_lgl,write_lgl
Package: networkx-viewer
NetworkX Viewer provides a basic interactive GUI to viewnetworkxgraphs.  In addition to standard
plotting and layout features as found natively in networkx, the GUI allows
you to:Drag nodes around to tune the default layoutShow and hide nodesFilter nodesPan and zoomDisplay nodes only within a certain number of hops (“levels”) of
a “home node”Display and highlight the shortest path between two nodes.  Nodes
around the path can also be displayed within a settable number of
levelsIntelligently find and display nodes near displayed nodes using
“Grow” and “Grow Until” functionsUse attributes stored in the graph’s node and edge dictionaries to
customize the appearance of the node and edge tokens in the GUIMark nodes and edges for referenceSupport for bothnx.Graphandnx.MultiGraphSeehttps://github.com/jsexauer/networkx_viewerfor more details
Package: network-yuanzhe
Build A Neural Network From ScratchThe author builds a neural network structure from scratch. The neural network modules include:Multiple Hidden LayersKaiming InitializationWeight DecayBatch NormalizationDropoutLabel SmoothingReLU Activation FunctionTanh Activation FunctionGELU Activation FunctionSoftmax and Cross-entropy LossMomentum in SGDAdam OptimizerMini-batch Training
Package: networkz
NetworkZNetworkZ is a library of graph algorithms in Python. It is an extension of theNetworkX. It contains (by import) everything that is in NetworkX, plus some additional algorithms that were submitted into NetworkX but not merged yet. Currently, NetworkZ contains the following additional algorithms:Rank-maximal matching: by Oriya Alperin, Liel Vaknin and Amiel Lejzor.Social-aware coalition formation- by Victor Kushnir.Installationpip install networkzThis installs the latest version of networkx, and the new algorithms added in networkz.UsageRank Maximal Matching AlgorithmA rank-maximal matching is a matching that maximizes the number of agents who are matched to their 1st priority; subject to that, it maximizes the number of agents matched to their 2nd priority; and so on.import networkz as nx
G = nx.Graph()
G.add_nodes_from(["agent1", "agent2"], bipartite=0)
G.add_nodes_from(["product1", "product2"], bipartite=1)
G.add_weighted_edges_from([("agent1", "product1", 1), ("agent1", "product2", 1), ("agent2", "product2", 2)])
matching = nx.rank_maximal_matching(G, rank="weight")
print(matching)Seedemo websitefor more information.Social-aware coalition formation(TODO)ContributionAny additions or bug-fixes tonetworkxshould first be submitted there, according to theNetworkX Contributor Guide.If the pull-request is not handled, you are welcome to submit it here too.
Package: networkzero
Make it easy for learning groups to use simple networking in PythonDocs:http://networkzero.readthedocs.org/en/latest/Development:https://github.com/tjguk/networkzeroTests: to run the tests, run tox. All tests are run on Travis (Linux & Apple)
and Appveyor (Windows)APIaddress below refers to an IP:Port string eg “192.0.2.5:4567”Discoveryaddress = advertise(name, address=None)address = discover(name, wait_for_s=FOREVER)[(name, address), …] = discover_all()[(name, address), …] = discover_group(group_name, separator=”/”)Messagingreply = send_message_to(address, message, wait_for_reply_s=FOREVER)message = wait_for_message_from(address, [wait_for_s=FOREVER])send_reply_to(address, reply)send_news_to(address, news)wait_for_news_from(address[, pattern=EVERYTHING][, wait_for_s=FOREVER])Typical UsageOn computer (or process) A:importnetworkzeroasnw0address=nw0.advertise("hello")whileTrue:name=nw0.wait_for_message_from(address)nw0.send_reply_to(address,"Hello,%s"%name)On computer (or process) B and C and D…:importnetworkzeroasnw0server=nw0.discover("hello")reply=nw0.send_message_to(server,"World")print(reply)reply=nw0.send_message_to(server,"Tim")print(reply)
Package: netwrix-api
Netwrix Search API ModuleThis is a python3 library to interact withNetwrix AuditorExample:from netwrix_api import NetwrixAPI


filter_data = {
    "what": {"Contains": "GroupTest"},
    "datasource": "Active Directory",
    "objecttype": {"Contains": "Group"}
}
netwrix_host = "netwrixsv01.contoso.com"
username = "Username"
passwd = "ENTERPASSOWRD"
api = NetwrixAPI(netwrix_host, username, passwd)
results = api.queryDB(filter_data)
Package: netwulf
AboutSimple and interactive network visualization in Python.Network
visualization is an indispensable tool for exploring and communicating
patterns in complex systems. Netwulf offers an ultra-simple API forreproducible interactive visualizationof networks directly from a
Python prompt or Jupyter notebook. As a research tool, its purpose is to
allow hassle-free quick interactive layouting/styling for communication
purposes.The package is build around the philosophy that network manipulation and
preprocessing should be done programmatically, but that the efficient
generation of a visually appealing network is best done interactively,
without code.PaperIf you usenetwulffor your scientific work, consider citing us!
We’republished in JOSS.Installpipinstallnetwulfnetwulfwas developed and tested forPython 3.5Python 3.6Python 3.7So far, the package’s functionality was tested on Mac OS X, several
Linux distributions and Windows NT. Windows support cannot be guaranteed
as we do not have constant access to machines with this OS.Dependenciesnetwulfdirectly depends on the following packages which will be
installed bypipduring the installation processnetworkx>=2.0numpy>=0.14matplotlib>=3.0simplejson>=3.0DocumentationThe full documentation is available athttps://netwulf.rtfd.io.ExampleCreate a network and look at itimportnetworkxasnxfromnetwulfimportvisualizeG=nx.barabasi_albert_graph(100,m=1)visualize(G)ChangelogChanges are logged in aseparate
file.LicenseThis project is licensed under theMIT
License.ContributingIf you want to contribute to this project, please make sure to read thecode of
conductand thecontributing
guidelines.
In case you’re wondering about what to contribute, we’re always
collecting ideas of what we want to implement next in theoutlook
notes.Dev notesClone and install this repository asgitclone--recurse-submodules-j8git@github.com:benmaier/netwulf.gitmakeNote thatmakeper default letspipinstall a development
version of the repository.The JS base code in/netwulf/js/is a fork ofUlf Aslak’s
interactive web
app. If this
repository is updated, change to/netwulf/js/, then dogitfetchupstreamgitmergeupstream/mastergitcommit-m"merged"gitpushIf you want to upload to PyPI, first convert the newREADME.mdtoREADME.rstmakereadmeIt will give you warnings about bad.rst-syntax. Fix those errors inREADME.rst. Then wrap the whole thingmakepypiIt will probably give you more warnings about.rst-syntax. Fix those
until the warnings disappear. Then domakeupload
Package: netx-fdl-compiler
FDL CompilerThis Python module helps compile and decomplie flash device label (FDL) blobs used to configure theHilscher netX90.
This allows the configuration to be edited in a convienent text format (YAML) and then compiled into
a blob that can be flashed to the netX.This project includes the fdl_compiler module, which can compile or decompile files, as well as a schema for the YAML
configuration file for easier editing.If using Visual Studio Code, the included settings can help configure validation using the YAML plugin (must be
installed separately).Usagepython-mnetx_fdl_compilercompile{source}.yaml{destination}.fdlpython-mnetx_fdl_compilerdecompile{source}.fdl{destination}.yamlFor more information about the contents of a flash device label, please refer to thedocumentation from HilscherAn example of a YAML FDL description is below:Device:Manufacturer:1Class:NETX90Device Number:7833000Serial Number:20000Hardware Compatibility:1Hardware Revision:3Production Date:2017-W43MAC Addresses:-02:00:00:1e:99:00-02:00:00:1e:99:01-02:00:00:1e:99:02-02:00:00:1e:99:03APP MAC Addresses:[]OEM Identification:Options:SerialNumber:falseOrderNumber:trueHWRevision:falseProductionDate:falseSerial Number:''Order Number:TESTHardware Revision:''Production Date:''Custom Data:nullFlash Layout:-Type:HWCONFIGStart:1048576Size:8192Chip:INTFLASH0Name:HWConfigAccess:O_RDONLY-Type:FDLStart:1056768Size:4096Chip:INTFLASH0Name:FDLAccess:O_RDONLY-Type:FWStart:1060864Size:512000Chip:INTFLASH0Name:FWAccess:O_RDONLY-Type:FWUPDATEStart:1572864Size:389120Chip:INTFLASH1Name:FWUpdateAccess:O_RDWR-Type:MFW_HWCONFIGStart:1961984Size:8192Chip:INTFLASH1Name:MFW_HWConfigAccess:O_RDONLY-Type:MFWStart:1970176Size:86016Chip:INTFLASH1Name:MaintenanceAccess:O_RDONLY-Type:REMANENTStart:2056192Size:32768Chip:INTFLASH1Name:RemanentAccess:O_RDWR-Type:MANAGEMENTStart:2088960Size:8192Chip:INTFLASH1Name:ManagementAccess:O_RDWR-Type:NONEStart:0Size:0Chip:INTFLASH0Name:''Access:O_RDONLY-Type:NONEStart:0Size:0Chip:INTFLASH0Name:''Access:O_RDONLYChip Table:-Chip:INTFLASH0Driver:''Block:4096Size:524288Cycles:10000-Chip:INTFLASH1Driver:''Block:4096Size:524288Cycles:10000
Package: netx-hboot-image-compiler
netx_hboot_image_compilerThis generates second-stage bootloader configurations for the netX90 MCU from Hilscher.It is based on the hboot_image_compiler tool distributed by Hilscherhere.
It has been modified to be installable as a standalone module and to be compatible with Python 3.UsageTo generate a hwconfig XML patch file from a basic hwconfig XML description:python-mnetx_boot_image_compiler.hwconfigmake_hboot_xml-pnetx90_rev1_peripherals.xmlhardware_config.xmlhardware_config.hboot.xmlTo generate a hwconfig binary image:python-mnetx_hboot_image_compiler-nNETX90B-Ahw_config=hardware_config.hboot.xmltop_hboot_image_hwc.xmlhardware_config.hwc
Package: netzcfg
Retrieves network adapter configuration information using Windows Management Instrumentation (WMI) and return it as a Pandas DataFrame.Tested against Windows 10 / Python 3.10 / Anacondapip install netzcfgRetrievenetworkadapterconfigurationinformationusingWindowsManagementInstrumentation(WMI)andreturnitasaPandasDataFrame.Parameters:-ipenabled(bool,optional):IfTrue,filternetworkadaptersbythosewithIPenabled(defaultisFalse).Returns:-pd.DataFrame:ADataFramecontainingnetworkadapterconfigurationinformation,withcolumns:-Configurationparameternames(e.g.,'SettingID','IPAddress','SubnetMask',etc.).-Valuesassociatedwitheachconfigurationparameter.Example:>>>nic_info=get_nic_info(ipenabled=True)>>>print(nic_info.head())SettingIDIPAddressSubnetMaskDefaultIPGateway0{GUID}192.168.1.2255.255.255.0192.168.1.11{GUID}10.0.0.2255.255.255.010.0.0.1...
Package: netzero
NetZero Data Collection ToolThis tool is meant to make collecting data from various sources related to
energy usage much easier. The tool provides a simple command line interface
which orchestrates the entire process from collection to output.InstallationFirst, ensure that you have both Python 3 and pip installed:$python--version$pip--versionThen, all you have to do is run this command to installnetzeroand its dependencies:$pipinstallnetzeroIf you prefer to download the repository yourself you can run the following command
from within the repository to installnetzeroand its dependencies.$pipinstall.These commands will install all dependencies and installnetzeroon your
machine.In order to actually use the tool you'll need to set up anconfig.inifile.
A template with instructions is included in the fileexample_config.ini.UsageThis tool offers a simple command line interface that can be accessed by running$netzerousage: netzero [-h] <command> ...Collects and formats data from multiple sourcesoptional arguments:-h, --help  show this help message and exitavailable commands:<command>collect   Collect dataformat    Format dataExampleSay you wanted to collect Pepco, SolarEdge, and Weather data since August 2019.
First you need to ensure that your configuration file is set up. You can place
the configuration anywhere you want as along as you pass it in on the commandline.
In this example we will call itconfig.ini. Also, since we don't already have
a database for our data, we will tellnetzeroto create one callednetzero.db.
To collect our data we can run this command:$netzerocollect+psw-s2019-08-01-cconfig.ini-dnetzero.dbLets break this command down:netzero collect: tells netzero that we arecollectingdata with this command.+psw: adds Pepco (p), Solar (s) and Weather (w) data to the list of
sources to collect from (You can get a full list of sources fromnetzero collect -h).-s 2019-08-01: tellsnetzeroto only collect data after August 1st 2019.
The date must follow the formatYYYY-MM-DD.-c config.ini: specifies the file name of our configuration file.-d netzero.db: gives the filename of our database.For more options you can check out the help information usingnetzero -h.TODOTimezonesDeal with missing GSHP data
Package: netznoe-smartmeter-portal-api
NetzNÖ Smartmeter Portal APIThis project is an unofficial implementation of the NetzNÖ Smartmeter Portal API (https://smartmeter.netz-noe.at/).
It cleans up any null values from the API response and maps it to usable Python objects.Supports:Python 3.7+Installationpip3installnetznoe-smartmeter-portal-apiUsage examplefrompendulumimportDatefromnetznoe_smartmeter_portal_apiimportNetzNoeSmartmeterPortalApimeter_id='AT0020000000000000000000020xxxxxx'api=NetzNoeSmartmeterPortalApi(username='username',password='password')api.do_login()# ---# API Methods:# returns monthly aggregated datayearly_values=api.get_year(meter_id,2023)# SmartmeterResultYearly(#   consumption={#     Date(2023, 1, 1): 100.001,#     ...#   },#   grid_usage_leftover={},#   self_coverage={},#   self_coverage_renewable_energy={},#   joint_tenancy_proportion={},#   peak_demands={#     DateTime(2023, 2, 18, 17, 15, 0, tzinfo=Timezone('Europe/Vienna')): 2.101,#     ...#   }# )# returns daily aggregated data for the requested monthmonthly_values=api.get_month(meter_id,2023,3)# SmartmeterResult(#   consumption_metered={#     Date(2023, 3, 1): 1.810,#     ...#   },#   consumption_estimated={},#   grid_usage_leftover={},#   self_coverage={},#   self_coverage_renewable_energy={},#   joint_tenancy_proportion={},#   peak_demands_metered={#     DateTime(2023, 3, 1, 22, 0, 0, tzinfo=Timezone('Europe/Vienna')): 0.012,#     ...#   },#   peak_demands_estimated={}# )# returns daily aggregated data for the requested time rangeweekly_values=api.get_week(meter_id,start_date=Date(2023,1,2),end_date=Date(2023,1,8))# returns 15min values of the requested daydaily_values=api.get_day(meter_id,day=Date(2023,4,1))# logout of the apiapi.do_logout()Mapping between API fields and model fieldsFor easier usage and more meaningful naming of the fields provided by the NetzNÖ Smartmeter Portal API they have been
renamed in the implemented Python models.Python model "SmartmeterResult" (used by get_day(), get_week(), get_month()):API fieldPython model fieldUnitmeteredValuesconsumption_meteredkWhestimatedValuesconsumption_estimatedkWhgridUsageLeftoverValuesgrid_usage_leftoverkWhselfCoverageValuesself_coveragekWhselfCoverageRenewableEnergyValueself_coverage_renewable_energykWhjointTenancyProportionValuesjoint_tenancy_proportionkWhmeteredPeakDemandspeak_demands_meteredkWestimatedPeakDemandspeak_demands_estimatedkWPython model "SmartmeterResultYearly" (used by get_year()):API fieldPython model fieldUnitvaluesconsumptionkWhgridUsageLeftoverValuesgrid_usage_leftoverkWhselfCoverageValuesself_coveragekWhselfCoverageRenewableEnergyValueself_coverage_renewable_energykWhjointTenancyProportionValuesjoint_tenancy_proportionkWhpeakDemandspeak_demandskWis_mixed< ignored >LegalDisclaimer: This is not affiliated, endorsed or certified by Netz NÖ. This is an independent and unofficial API.
Provided as is. Use at your own risk.
Package: neucli
id: index
title: NeucliNeulabs CLIThe neulabs CLI is used to aggregate all those activities and commands useful to various developers, reducing development costs and creating shared standards.
The goal ofneucliis to facilitate the generation and use of standard components such as documentation structure, GitHub actions for releases, and more.Neucliis based onTaskfile, a task runner that allows the creation of modular CLIs through YAML configurations. Each command has its own structure, some of them are Python modules, while others are simple Bash scripts. This approach makes it easy to add additional modules in various programming languages, and you can also create your own custom commands locally and add them to the CLI.InstallationRequirementsPython >= 3.8 [install]Nodjs >= 16.14 [install]Homebrew (only on MacOS)GitHub access via SSHStepsInstallDonwloadtaskfiles/cli/cli.ymlfromneulabs-clirepositoryInstalltaskcommand[Linux]npm install -g @go-task/cli[MacOS]brew install go-taskInstallneucli:pip3 install neucliDeletecli.ymland restart terminalRunneucli cli:versionUpdateneucli cli:updateGetting startedHelp commandneucli helpSummary commandneucli summary -- commandPass args to commandneucli command -- -opt-1 value --opt-2 -x --zyxOwn cli commandsIn the file~/.neulabs-cli/Taskfile.local.ymlyou can add your own commands as desired.Then you can use your command withneucli local:command-nameDev modeSetupgit clonehttps://github.com/neulabscom/neulabs-cli.git&& cd neulabs-cli
git submodule update --init --recursivetask dev:setup:installer

source .activate
neucli --debug cli:deps:allTest neucli commandssource .activate
neucli --debug dev:utils:lintTest installer with debug modeneucli --debug cli:installer
Package: neucore
Table of ContentsInstallationUsageThis is the pypi package for Ailiverse AI modelsInstallationgit clone https://github.com/ailiverse/neucore

cd neucore

pip install .UsageThe following are the models implementedImage SegmentationImage ClassificationDeep Fake DetectionText Guided SegmentationNote Text Guided Segmentation does not have training or upload functionsimport neucore

authToken = "Your_AuthToken_Here" # visit console.ailiverse.com to obtained your auth token if you have not already

# defined the model
model = neucore.Model(authToken, model="Image Segmentation")
# If you still want to use the same model Id uncomment the following code
# model = neucore.Model(authToken, modelID = "YOUR_MODELID_HERE", model="Image Segmentation")

model.uploadFile("train.zip", "labelme")

model.train(epochs=1) # By default the number of epochs is 10

results = model.infer("hen.jpg")

print(results)Note you can use the modelID argument when initalizing the neucore model to if you want to use the previous models
Package: neufbox-client
Neufbox API ClientThis is a python API client library to interact with Neufbox NB4, NB5, NB6, NB6V, NB6VAC modem/routers sold by Neuf Telecom, SFR or Red by SFR companies.See theAPI specificationfor details about the API itself.InstallationTo install the package:pipinstallneufbox-clientGetting startedHere is a simple example that retrieves the neufbox "system" information:fromneufboximportClient,username_passwordfrompprintimportpprintclient=Client('192.168.1.1')system_info=client.system.get_info()pprint(system_info)We can also login to access private information from the neufbox. Ausername_passwordhelper is provided by the library, which sends the credentials (in hashed format) to validate the token received by the client from the neufbox (only if the neufbox allows username/password authentication):client.login(username_password('admin','p4ssw0rd'))wlan_info=client.wlan.get_info()pprint(wlan_info)The client also allows changing some parameters of the neufbox by using the setters available in the different namespaces. See theAPI specificationfor details.You can also have a look on thedemofile to see a working example.SupportThis project is hosted onGithub. Please report issues via the bug tracker.