Saravanan666/pypi-org huggingface dataset:
Package: netcdf4_pydap
This code provides enhancements to thepydapclient package:An updated pydap client that uses therequestspackage to handle Central Authentication Services (such as ESGF).A (partial) compatibility layer withnetcdf4-python.Frederic Laliberte
University of Toronto, 2016DocumentationESGFRegister athttps://pcmdi.llnl.gov/user/add/?next=https://pcmdi.llnl.gov/projects/esgf-llnl/.
When registering, you will create apasswordand receive anopenid.Go tohttps://pcmdi.llnl.gov/esg-orp/home.htmand login with youropenidusing yourpassword.Go tohttps://pcmdi.llnl.gov/esg-orp/registration-request.htm?resource=http%3A%2F%2Fpcmdi.llnl.gov%2Fthredds%2FfileServer%2Fesg_testroot%2Fregister%2Fcmip5_research.nc.
Your browser will likely ask you to trust some certificates. CLICKALWAYS ALLOW. If you don’t do this, it won’t work.Register forCMIP5 Research. You do not need to download data.Connect to OPeNDAP links as follows:import matplotlib.pyplot as plt
import numpy as np

import netcdf4_pydap
import netcdf4_pydap.cas.esgf as esgf
openid = YOUROPENID
password = YOURPASSWORD

credentials={
              'password' : password,
              'authentication_url' : esgf._uri(openid)}

url = ('http://cordexesg.dmi.dk/thredds/dodsC/cordex_general/cordex/'
       'output/EUR-11/DMI/ICHEC-EC-EARTH/historical/r3i1p1/DMI-HIRHAM5/'
       'v1/day/pr/v20131119/'
       'pr_EUR-11_ICHEC-EC-EARTH_historical_r3i1p1_DMI-HIRHAM5_v1_day_19960101-20001231.nc')

with netcdf4_pydap.Dataset(url,**credentials) as dataset:
    data = dataset.variables['pr'][0,:,:]
    plt.contourf(np.squeeze(data))
    plt.show()If usinghttps://ceda.ac.ukinstead ofhttps://pcmdi.llnl.gov, yourusernamemust be passed along with your credentials use:credentials={
              'username' : YOURCEDAUSERNAME,
              'password' : password,
              'authentication_url' : esgf._uri(openid)}URS NASA EarthdataThere are a few steps required to connect to NASA Earthdata:Create an account athttps://urs.earthdata.nasa.govAuthorize the data to see your profile (NASA calls this adding an app, a la Facebook).
Followhttp://disc.sci.gsfc.nasa.gov/registration/authorizing-desidsc-data-access-in-earthdata_loginLoad OPeNDAP data:import matplotlib.pyplot as plt
import numpy as np
import netcdf4_pydap

credentials={'username': YOURUSERNAME,
             'password': YOURPASSWORD,
             'authentication_url':'https://urs.earthdata.nasa.gov/'}
url = ('http://goldsmr3.gesdisc.eosdis.nasa.gov:80/opendap/'
       'MERRA_MONTHLY/MAIMCPASM.5.2.0/1979/MERRA100.prod.assim.instM_3d_asm_Cp.197901.hdf')

with netcdf4_pydap.Dataset(url, **credentials) as dataset:
    data = dataset.variables['SLP'][0,:,:]
    plt.contourf(np.squeeze(data))
    plt.show()Version History0.2.6:  Another bug fix when getting cookies.0.2.5:  Bug fix when getting cookies.0.2.4:  Bug fix for ESGF.0.2.3:  New authentication method. Support URS NASA Earthdata0.2.2:  Pydap 3.1.1 is now a requirement.0.2.1:  Removed support for NASA Earthdata.0.2:    Travis-CI integration. Limited test suite. Code reorganization.0.1.4:  Bug fix and more robust error handling.0.1.1:  Added special Dataset class to mimic wget.0.1 :   Initial commit.
Package: netcdf4_soft_links
Python code to create soft links to netcdf4 files.Intended to process data from the CMIP5 and CORDEX archives distributed
by the Earth System Grid Federation.This package was developed by F. B. Laliberte and P. J. Kushner as part of the “ExArch: Climate analytics
on distributed exascale data archives” G8 Research Initiative grant.Note that at the moment testing ofnetcdf4_soft_linksis done entirely throughcdb_query.
The build status shown above actually reflects the build status ofcdb_query.Frederic Laliberte, Paul J. Kushner
Univerty of Toronto, 2016The Natural Sciences and Engineering Research Council of Canada (NSERC/CRSNG) funded
FBL and PJK during this project.Version History0.7.x:  Minor bug fixes for handling years prior to 1900.Performance improvements in reading soft links.0.7:    Improved pydap - requests interactions. Cleaner temp file handling.0.6:    Major overhaul in certificate handling management. In the future, certficatesmight become unnecessary.0.5.x:  Minor bug fixes. Bug fix with fixed time variables. Fixed download_files.Added subset function. Fixed import errors. Minor changes to read_soft_links API.0.5 :   Version in support of upcoming cdb_query 2.00.3 :   Several updates. Bug fixes and streamlining. Inclusion of pydap.0.2 :   First fully functional version.0.1 :   Initial commit.
Package: netcdf-extraction-util
This is a simple script to extract time-series from a NetCDF file to a Comma Separated Value (CSV) file.To use:netcdf_extraction_util <input_netcdf_file> <selected_reach>This will produce a <selected_reach>.csv file with columns date and flow for the selected reach. If the
reach does not exist on <input_netcdf_file>, the script will error and show you the available reaches.
Package: netcdf-meta
netCDF 轉換工具這個專案包含兩個 Python 模組，用於操作 netCDF 格式的檔案。第一個模組netcdf_to_csv.py可將 netCDF 檔案轉換成 CSV 格式。第二個模組netcdf_meta.py可用來檢視 netCDF 檔案的元數據信息。安裝首先，使用以下命令安裝所需的套件：python3-mpipinstallnetcdf-to-csvnetcdf-meta模組使用netcdf_to_csv這個模組將 netCDF 檔案轉換成 CSV 格式。以下為使用方法和範例：python3-mnetcdf_to_csv<nc_file1><nc_file2>...範例：python3-mnetcdf_to_csvexample1.ncexample2.nc這將會將example1.nc和example2.nc轉換成對應的 CSV 檔案example1.csv和example2.csv。netcdf_meta這個模組用來檢視 netCDF 檔案的元數據信息。以下為使用方法和範例：python3-mnetcdf_meta<nc_file1><nc_file2>...範例：python3-mnetcdf_metaexample1.ncexample2.nc這將會顯示example1.nc和example2.nc的元數據信息，包括全局屬性、維度、變量和前三行數據。
Package: netcdf-scm
netCDF-SCMBasicsCitationsRepository healthLatest releasesnetCDF-SCM is a Python package for processing netCDF files.
It focusses on metrics which are relevant to simple climate models and is built on top of theIrispackage.LicensenetCDF-SCM is free software under a BSD 3-Clause License, seeLICENSE.
If you make any use of netCDF-SCM, please cite theGeoscience Data Journal (GDJ)paper (Nicholls et al., GDJ 2021) as well as the relevantZenodo release.InstallationThe easiest way to install netCDF-SCM is withconda# if you're using a conda environment, make sure you're in it
conda install -c conda-forge netcdf-scmIt is also possible to install it withpip# if you're using a virtual environment, make sure you're in it
pip install netcdf-scmHowever installing with pip requires installing all ofIris’s dependencies yourself which is not trivial.
As far as we know,Iriscannot be installed with pip alone.DocumentationDocumentation can be found at ourdocumentation pages(we are thankful toRead the Docsfor hosting us).ContributingPlease see theDevelopment section of the docs.
Package: netcdf-to-csv
netCDF 轉換工具這個專案包含兩個 Python 模組，用於操作 netCDF 格式的檔案。第一個模組netcdf_to_csv.py可將 netCDF 檔案轉換成 CSV 格式。第二個模組netcdf_meta.py可用來檢視 netCDF 檔案的元數據信息。安裝首先，使用以下命令安裝所需的套件：python3-mpipinstallnetcdf-to-csvnetcdf-meta模組使用netcdf_to_csv這個模組將 netCDF 檔案轉換成 CSV 格式。以下為使用方法和範例：python3-mnetcdf_to_csv<nc_file1><nc_file2>...範例：python3-mnetcdf_to_csvexample1.ncexample2.nc這將會將example1.nc和example2.nc轉換成對應的 CSV 檔案example1.csv和example2.csv。netcdf_meta這個模組用來檢視 netCDF 檔案的元數據信息。以下為使用方法和範例：python3-mnetcdf_meta<nc_file1><nc_file2>...範例：python3-mnetcdf_metaexample1.ncexample2.nc這將會顯示example1.nc和example2.nc的元數據信息，包括全局屬性、維度、變量和前三行數據。
Package: netcdf-unpacker
NetCDF UnpackerNetCDF Unpacker is a Python library designed to simplify the process of working with NetCDF files containing satellite weather data. With this library, you can easily explore the contents of NetCDF files and extract specific weather variables, making it convenient for data analysis and visualization. NetCDF files are commonly used for storing geospatial and time-series data, including atmospheric and climate data.FeaturesVariable Information:Explore the available variables in NetCDF files, along with their shapes and attributes.Variable Extraction:Extract specific weather variables from the NetCDF file and create a pandas DataFrame for further analysis.Geospatial Filtering:Specify a location using latitude and longitude and extract data for that specific point.Time Handling:Utilize the time component in NetCDF files to create a time series in the DataFrame.User-Friendly Interface:A user-friendly command-line interface for selecting variables and setting extraction parameters.InstallationYou can install NetCDF Unpacker using pip:pipinstallnetcdf-unpackerUsage1. Initialize NetCDF Unpackerfromnetcdf-unpackerimportNetCDF_Unpacker# Provide the path to your NetCDF filenc_unpacker=NetCDF_Unpacker("path_to_your_file.nc")2. Explore Variable Information# Get information about the variables in the NetCDF filenc_unpacker.info()3. Extract Weather Variables# Extract weather variables for a specific location and time componentlatitude=40.0# Your desired latitudelongitude=-75.0# Your desired longitudevariable_index=4# Index of the variable to extracttime_variable="time"# The time component variable name# Extract the data and get a DataFrameweather_data=nc_unpacker.unpack(latitude,longitude,variable_index,time_variable)4. Analyze and Visualize DataWith the extracted data in a DataFrame, you can now perform data analysis, visualization, and other operations using popular Python libraries such as pandas, matplotlib, and seaborn.ExampleHere's a simple example of extracting and visualizing data from a NetCDF file:importmatplotlib.pyplotasplt# Extract temperature datatemperature_data=nc_unpacker.unpack(latitude,longitude,variable_index,time_variable)# Plot the temperature time seriesplt.figure(figsize=(12,6))plt.plot(temperature_data["Date"],temperature_data[variable_name])plt.title(f"Temperature at Lat{latitude}, Lon{longitude}")plt.xlabel("Date")plt.ylabel(f"{variable_name}")plt.grid(True)plt.show()Support and ContributionsIf you encounter any issues, have suggestions, or would like to contribute to this project, please visit the github repository or reach out to us via mail.-Ananya Giliyal -giliyal.ananya@gmail.com-Isha Paranjpe -ishaparanjpe.work@gmail.com-Mansi Katgire -katgiremansi@gmail.com-Atishay Gwari -atishay345@gmail.comLicenseThis library is open-source and available under theMIT License.NetCDF Unpacker simplifies working with NetCDF files, making it easy to extract and analyze weather data. Give it a try and unlock the potential of your satellite weather data for research, analysis, and visualization.
Package: netcenlib
NetCenLibNetCenLib (Network centrality library) is a tool to compute a wide range of centrality measures for a given network. The
library is designed to work with Python Networkx library.OverviewThe goal of NetCenLib is to offer a comprehensive repository for implementing a broad spectrum of centrality measures. Each
year, new measures are introduced through scientific papers, often with only pseudo-code descriptions, making it
difficult for researchers to evaluate and compare them with existing methods. While implementations of well-known
centrality measures exist, recent innovations are frequently absent. NetCenLib strives to bridge this gap. It references the
renowned CentiServer portal for well-known centrality measures and their originating papers, aiming to encompass all
these measures in the future.Code structureAll custom implementations are provided undernetcenlib/algorithmspackage. Each centrality measure is implemented in a separate file, named after the measure itself. Correspondingly, each file contains a function, named identically to the file, which calculates the centrality measure. This function accepts a NetworkX graph as input (and other params if applicable) and returns a dictionary, mapping nodes to their centrality values. Ultimately, every custom implementation is made available through thenetcenlib/algorithmspackage.Implemented centrality measures:AlgebraicAverage DistanceBarycenterBetweennessBottleNeckCentroidClosenessClusterRankCommunicability BetweennessCorenessCurrent Flow BetweennessCurrent Flow ClosenessDecayDegreeDiffusion degreeEigenvectorEntropyGeodestic k pathGroup Betweenness CentralityGroup ClosenessGroup DegreeHarmonicHeatmapKatzHubbellLaplacianLeverageLinLoadMncPagerankPdiPercolationRadialityRumorSecond OrderSemi LocalSubgraphTopologicalTrophic LevelsHow to useLibrary can be installed using pip:pipinstallnetcenlibCode usageProvided algorithms can be executed in the following ways:by invoking a specific function fromnetcenlib.algorithmspackage, which computes a given centrality measure for a
given graph.importnetworkxasnximportnetcenlibasncl# Create a graphG=nx.karate_club_graph()# Compute degree centralitydegree_centrality=ncl.degree_centrality(G)# Compute betweenness centralitybetweenness_centrality=ncl.betweenness_centrality(G)# Compute closeness centralitycloseness_centrality=ncl.closeness_centrality(G)# Compute eigenvector centralityeigenvector_centrality=ncl.eigenvector_centrality(G)invokingcompute_centralitymethod ofCentralityServiceclass, which allows to compute centrality for a given
centrality measure.fromtypingimportAnyimportnetworkxasnxfromnetworkximportGraphfromnetcenlib.centralityimportcompute_centralityfromnetcenlib.taxonomiesimportCentralityg:Graph=nx.karate_club_graph()centrality_centroid:dict[Any,float]=compute_centrality(g,Centrality.CENTROID)This method allows you not to directly specify centrality, making it easy to compute different centralities in a loop.ContributingFor contributing, refer to itsCONTRIBUTING.mdfile.
We are a welcoming community... just follow theCode of Conduct.MaintainersProject maintainers are:Damian FrąszczakEdyta Frąszczak
Package: netcfg
Simple network bridge configuration for Docker containers. It is similar in functionality
to thepipeworkscript, but has persistent configuration and  can automatically configure
networking when containers are started.InstallationNetcfg can be installed via pip:$ pip install netcfgUsageAfter installation, there are two parts to netcfg. First, a daemon process must be launched
with root privileges and given access to the Docker IPC socket:$ netcfg daemonBy default, netcfg stores configuration under/var/lib/netcfg/netcfg.json, but this location
may be overriden by using the--configcommand-line argument. After the daemon is running
in the background, one can setup netcfg configuration by calling thenetcfgscript.First, one should define one or more networks:$ netcfg create foo0 bridgeThe first argument specifies the network name and the other specifies the network type. Currently
only networks with typebridgeare supported, but netcfg implements different network types as
modules so new ones could be added.Then, we can attach networks to one or more containers:$ netcfg attach my_container_a foo0 --address 10.42.0.1/24
$ netcfg attach my_container_b foo0 --address 10.42.0.2/24Currently only static addressing can be configured (IPv4 and IPv6 are supported) and multiple
addresses may be specified. In case one only wants an address-less L2 veth device, no address
argument should be given.Existing configuration can be shown by using:$ netcfg show
{
  "containers": {
    "my_container_a": {
      "name": "my_container_a",
      "networks": {
        "foo0": {
          "address": [
            "10.42.0.1/24"
          ]
        }
      }
    },
    "my_container_b": {
      "name": "my_container_b",
      "networks": {
        "foo0": {
          "address": [
            "10.42.0.2/24"
          ]
        }
      }
    }
  },
  "networks": {
    "foo0": {
      "destroy_on_stop": false,
      "name": "foo0",
      "type": "bridge"
    }
  }
}If the containers are running, networks will be configured immediately. Otherwise, networks will
be configured when the named containers are started.
Package: netcfg-builder
Network Config BuilderAs a network automation engineer I need to template build network device
configurations.  I want to use Jinja2 as my templating language.  I want to be
able to use the Jinajincludedirective so that I can include relative
template files. I want the ability to dynamically create variable collections
for each host using native Python, and have a tool that correlates these
variables based on a priority ordering.This repostory provides such a tool.$netcfg-builder--template<template-file.j2>-e<extra-variables>When-e <varname>=<value>, then the variable namevarnameis defined with the value<value>.For example, define a varialbe calledvarsdirthat has the value/usr/local/myvars.$netcfg-build--template<template-file>-evardirs=/usr/local/lib/myvarsWhen<extra-varialbes>is a.pyfile, that file needs to decorator functions; each
of these functions will be called in priority order.  Priority 0 is first.For example:frompathlibimportPathfromnetcfg_builder.variablesimportingest,load_directory@ingest(0)defload_variables(tvars:dict,**extras):varsdir=Path(extras['varsdir'])tvars.update(load_directory(varsdir.joinpath('common')))tvars.update(load_directory(varsdir.joinpath(extras['hostname'])))The first parameter,tvarsto the decordated function is current set of template variables.
The second parameter,**extrasare any extra variables defined on the command line.
Package: netchat
netchatnetcat(socat) + chatRequirementspython modulesclickpexpectcommand line toolssocatUsage:Usage: netchat [OPTIONS] ADDRESS PORT [SCRIPT]

Options:
  -f, --file FILENAME
  -t, --timeout INTEGER
  -v, --verbose
  -e, --echo
  -d, --debug
  --help                 Show this message and exit.
Package: netcleanser
netcleanserThe library makes parsing and manipulation of URL🌐 and Email address📧 easy.InstallpipinstallnetcleanserHow to useEmail>>>fromnetcleanserimportEmail>>>email=Email('shinichi.takayanagi@gmail.com')>>>email.domain'gmail.com'>>>email.local_part'shinichi.takayanagi'>>>email.is_validTrue>>>email.value'shinichi.takayanagi@gmail.com'ThisEmailclass issettableanddictable# As a dict key>>>x={email:1}>>>x[email]1# As elemtns of set>>>email2=Email("nakamichiworks@gmail.com")>>>{email,email,email,email2,email2}{Email(value='nakamichiworks@gmail.com)',Email(value='shinichi.takayanagi@gmail.com)'}Email.build()allows you to create dummy email address specifing the only part oflocal_partordomain>>>Email.build(local_part="hoge")Email(value='hoge@dummy.com)'>>>Email.build(domain="hoge.com")Email(value='dummy@hoge.com)'Url>>>fromnetcleanserimportUrl>>>url=Url('https://www.google.com/search?q=auhuhe')>>>url.scheme'https'>>>url.host'www.google.com'>>>url.domain'www.google.com'>>>url.registered_domain'google.com'>>>url.netloc'www.google.com'>>>url.path'/search'>>>url.query'q=auhuhe'>>>url.is_validTrue>>>url.is_accessibleTrue>>>url.value'https://www.google.com/search?q=auhuhe'>>>str(url)'https://www.google.com/search?q=auhuhe'>>>url.contains_wwwTrue>>>url.remove_query()Url(host='www.google.com',username='None',password='None',scheme='https',port='None',path='/search',query='',fragment='')>>>url.remove_www()Url(host='google.com',username='None',password='None',scheme='https',port='None',path='/search',query='q=auhuhe',fragment='')ThisUrlclass issettableanddictable>>>x={url:123}>>>x[Url('https://www.google.com/search?q=auhuhe')]123>>>{url,url,Url('https://google.com'),url}{Url(host='www.google.com',username='None',password='None',scheme='https',port='None',path='/search',query='q=auhuhe',fragment=''),Url(host='google.com',username='None',password='None',scheme='https',port='None',path='',query='',fragment='')}ThanksUrlclass strongly depends on awesomepurlpackage, thanks!
Package: netcli
NETCLInetcli is the CLI for the people who doesn't want to remember every command from every vendor gear.Networking nowadays is moving away from traditional CLI operation, but there is still some debugging use-cases when a CLI interaction gives some benefits. However, when you spend more time coding than CLIing, you end up forgetting about specific CLI syntax, still necessary when you are managing a multi-vendor fleet.To ovecome this issue,netclilets you create your own CLI language, for interact with any device using your own custom commands.netcli solves this problem using a simple approach:You have aconfigmode to handle your custom commands and the translation for all the specific vendors you are interested in.You have aconnectmode to run an interactive CLI against your devices and enjoy your commandsInstallationpip install netcliHow to run itConfigYour customs commands will be stored in~/.my_netcli_commands.jsonAdd$ netcli config add "bgp received routes neighbor:192.0.2.1"Note:Use quotes to add your commandArguments should come at the end, using the pattern<arg_name>:<default_value>This will enter an interactive mode to provide:Description: Useful to remember what this command is doingVendor specific implementations, using this format:<vendor_type> - <vendor_command>importantwithin<vendorcommand>you can place the arguments provided using[arg_name]Example:show bgp summary [instance (vrf)]Every argument is between square brackets[]Within the brackets, the parenthesis()is the keyword that will be replacedIf an argument keyword has the string value "None", the argument (and related text) will be omitted<vendor_type>comes fromNetmiko libraryDelete$ netcli config delete "bgp received routes"Show$ netcli config showIt listes the custom commands with the description and ports.
If you need the vendor implementation, use--verboseInteractive CLI$ netcli connect <target> -v <vendor_type>Enjoy it!Notes:To overwrite a default value use[<arg_name>:<new_value>]Execution ofraw commandsis possible starting the command withr-Matching outputof custom commands can be achieved by adding|and the string to matchUsehelp/hto get extra helpDefault optionsYou can overwrite default behaviours by using file~/.netcli.cfgto define some custom values to easy your operation:{
        "dns_suffix": ".opendns.com",
        "global_delay_factor": 6,
        "max_lines": 20
}Supported values:dns_suffixis concatenated at the end of your target namemax_lineslimit the maximum number of lines to be returned to the CLI (this doesn't affect the logs)log_pathfull path to store the logging of the session. By default is the current directory with the timestamp (i.e. netcli_20190303-194937)timeoutNetmiko timeoutglobal_delay_factorNetmiko global_delay_factor
Package: netclop
Network clustering operationsNetworkclusteringoperations (netclop) is a command line interface for geophysical fluid transport network construction and associated clustering operations (e.g., community detection, significance clustering).InstallationUsepipxto install and run in an isolated environment.brew install pipx
pipx ensurepathTo install:pipx install netclopTo upgrade:pipx upgrade netclopUseParticle trajectories must be decomposed into initial and final latitude and longitude coordinates and stored in a positions file in the forminitial_latitude,initial_longitude,final_latitude,final_longitude. Positions are binned withH3. Community detection usesInfomap.netclop [GLOBAL OPTIONS] COMMAND [ARGS] [OPTIONS]Runnetclop --helpto see all available global options and commands andnetclop COMMAND --helpfor the arguments and options of a command.
Package: netconf
This package supports creating both netconf clients and servers. It also
provides a CLI netconf client utility. An example server is included under
theexamplesubdirectory.Documentation is available at:http://netconf.readthedocs.io/The following modules are present:__main__- Netconf CLI client utility.base- Shared netconf support classes.error- Netconf error classes.client- Netconf client classes.server- Netconf server classes.util- Netconf utility functions.master branch status:
Package: net_conf
Net-Conf is a commandline-based network manager. It is specifically
designed for use in a root-shell. It will not flip-flap around
networks unless you explicitly tell it to!InstallationTo install Net-Conf into a virtualenv simply type:` # git clone github.com/baccenfutter/netconf.git # ln-s$PWD/net_conf/netconf.sh /usr/local/bin/netconf # netconf--help`ConfigurationThe default configuration directory is ~/.config/net_confFollow the instructions in ~/.conf/net_conf/howto.txt