Saravanan666/pypi-org huggingface dataset:
Package: nntpserver
nntpserver.pyNo-dependency, single file NNTP server library for developing modern, rfc3977-compliant (bridge) NNTP servers for python >=3.7. Developed as part oftade, a web discussion forum with mailing list/NNTP interfaces which powers thehttps://sic.pmlink aggregator.Included example servers are:example_server.pyreturning hard-coded articleshnnntp.pyquerying news.ycombinator.com (hackernews) API and caching results in an sqlite3 database. A public instancemightbe online at nessuent.xyz:564 (TLS only)https://sic.pm NNTP server that uses `nntpserver.py`accessed on a Commodore Amiga withNewsCoasterclientRunningexample_server.py:$python3example_server.py--connect-with-nntplib
Listeningonlocalhost:9999
Connectingwithnntplib...
Newconnection.
sending201NNTPServiceReady,postingprohibited
got:CAPABILITIES
sending101Capabilitylist:
sendingVERSION2sendingREADER
sendingHDR
sendingLISTACTIVENEWSGROUPSOVERVIEW.FMTSUBSCRIPTIONS
sendingOVER
sending.
got:GROUPexample.all
Groupnameexample.all
sending211111example.all
got:XOVER1-1
sending224Overviewinformationfollows(multi-line)sending1Helloworld!epilys<epilys@example.com>Wed,01Sep202115:06:01+0000<unique@example.com>161sending.
got:LISTOVERVIEW.FMT
sending215Orderoffieldsinoverviewdatabase.
sendingSubject:
sendingFrom:
sendingDate:
sendingMessage-ID:
sendingReferences:
sendingBytes:
sendingLines:
sending.1epilys<epilys@example.com>Helloworld!(1)Donewithnntplib.
Package: nn-tuning
Neural Network Tuning Analysis ToolkitAnalyse neural networks for feature tuning.DocumentationInstallation$ pip install nn_analysisDepending on your use you might need to install several other packages.The AlexNet network requires you to install PyTorch and PyTorch vision using:$ pip install torch torchvisionPredNet requires a more specific configuration. For PredNet you need to be using python version 3.6 and TensorFlow version < 2.FeaturesFitting tuning functions to recorded activations of a neural network,Automatic storage of large tables on disk in understandable folder structures,Easilyextendableto other neural networks and stimuli.The above features are explained in more detail in nn_analyis'documentation.
Package: nn-tuning-prednet
Neural Network Tuning Analysis ToolkitAnalyse neural networks for feature tuning.DocumentationInstallation$ pip install nn_analysisDepending on your use you might need to install several other packages.The AlexNet network requires you to install PyTorch and PyTorch vision using:$ pip install torch torchvisionPredNet requires a more specific configuration. For PredNet you need to be using python version 3.6 and TensorFlow version < 2.FeaturesFitting tuning functions to recorded activations of a neural network,Automatic storage of large tables on disk in understandable folder structures,Easilyextendableto other neural networks and stimuli.The above features are explained in more detail in nn_analyis'documentation.
Package: nnusf
NNSFνNNSFνis a python module that provides predictions for neutrino structure functions. 
  It relies onYADISMfor the large-Q region 
  while the low-Q regime is modelled in terms of a Neural Network (NN). The NNSFν 
  determination is also made available in terms of fast interpolationLHAPDFgrids that can be accessed through an independent
  driver code and directly interfaced to theGENIEMonte Carlo
  neutrino event generators.Quick linksInstallation instructionsTutorialsDelivery & UsageCitationTo refer to NNSFν in a scientific publication, please use the following:@article{Candido:2023utz,author="Candido, Alessandro and Garcia, Alfonso and Magni, Giacomo and Rabemananjara, Tanjona and Rojo, Juan and Stegeman, Roy",title="{Neutrino Structure Functions from GeV to EeV Energies}",eprint="2302.08527",archivePrefix="arXiv",primaryClass="hep-ph",reportNumber="Nikhef 2022-014, Edinburgh 2022/27, TIF-UNIMI-2023-5",month="2",year="2023"}And if NNSFν proved to be useful in your work, consider also to reference the codes:@misc{https://doi.org/10.5281/zenodo.7657132,doi={10.5281/ZENODO.7657132},url={https://zenodo.org/record/7657132},author="Candido, Alessandro and Garcia, Alfonso and Magni, Giacomo and Rabemananjara, Tanjona and Rojo, Juan and Stegeman, Roy",title="{Neutrino Structure Functions from GeV to EeV Energies}",publisher={Zenodo},year={2023},copyright={Open Access}}
Package: nn-utils
Typical usage
often looks like this:#!/usr/bin/env python
import ml_utils as mlu
mlu.imutils.resize_keep_aspect(img_arr):Some functions for supporting machine learning, generally and caffe-specific. Currently just a dump, ideally will clean up and add install.py, examples etccaffe - python layers forimage read (file or lmdb) - single / multi label, pixel level labelson-the-fly augmentationsupport for pixel-level segmentation (read mask as label, etc)controlling and reporting acc/loss of solver with solver.stepimage processingimage augmentation - including bounding boxes and pixel levelacccuracy/precision/recall reporting for single label, multilabel , bounding box, and pixel levelutilities e.g. read from anywhere (url/db/local file/img array)grabcutread/write various file formatslmdb, hd5yolodeepfashiontamara bergILSRVCetctagging toolsmultilabelpixel level
Package: nnv
Neural Network Visualizer (NNV)Simple and easy to use tool to generate Neural Network Visualizations.Installationpip install nnvUsagefromnnvimportNNVlayersList=[{"title":"input\n(relu)","units":3,"color":"darkBlue"},{"title":"hidden 1\n(relu)","units":3},{"title":"hidden 2\n(relu)","units":3,"edges_color":"red","edges_width":2},{"title":"output\n(sigmoid)","units":1,"color":"darkBlue"},]NNV(layersList).render()It is possible to customize the node size/colors, title font size, spacing between nodes and layers and maximum number of nodes to show,...fromnnvimportNNV# Let's increase the size of the plotimportmatplotlib.pyplotaspltplt.rcParams["figure.figsize"]=(200,10)layers_list=[{"title":"input\n(relu)","units":300,"color":"darkBlue"},{"title":"hidden 1\n(relu)","units":150},{"title":"hidden 2\n(relu)","units":75},{"title":"Dropout\n(0.5)","units":75,"color":"lightGray"},{"title":"hidden 4\n(relu)","units":18},{"title":"hidden 5\n(relu)","units":9},{"title":"hidden 6\n(relu)","units":4},{"title":"output\n(sigmoid)","units":1,"color":"darkBlue"},]NNV(layers_list,max_num_nodes_visible=8,node_radius=10,spacing_layer=60,font_size=24).render(save_to_file="my_example_2.pdf")DocumentationNNV documentation is still being created. For now, if you have any question, please look directly the library source code or open an Issue.Future addittionsSome useful features that may be added in the future (help is welcome):add labels to each nodeimport layers info directly from a keras modelCitationIf you use this library and would like to cite it, you can use:R. Cordeiro, "NNV: Neural Network Visualizer", 2019. [Online]. Available: https://github.com/renatosc/nnv. [Accessed: DD- Month- 20YY].or:@Misc{,
  author = {Renato Cordeiro},
  title  = {NNV: Neural Network Visualizer},
  month  = may,
  year   = {2019},
  note   = {Online; accessed <today>},
  url    = {https://github.com/renatosc/nnv},
}
Package: nnvisualizertf
nn_visualizerVisualizing Tensorflow neural networks.Please see theexamples.ipynbnotebook for an example on how to use the package.Currently supporting:Input layers:For dense networks with the shape (x_size)For convolution with the shape (x_size, y_size, n_channels)Convolutional Network Layers:Conv2DMaxPooling2DAveragePooling2DDense Layers:Dense (also as output)FlattenChanges0.7Fixed cases, in which the layers descriptions have overlapped.0.6Supporting dense layers as input.0.5Changed build tool to flit, now available as package from PyPi under the namennvisualizertf.0.4Changed build configuration.0.3Changed build tool to hachtling.0.2Renaming to comply with the python naming conventions.0.1Initial version.
Package: nn_wtf
# nn-wtf - Neural Networks Wrapper for TensorFlownn-wtf aims at providing a convenience wrapper to Google's[TensorFlow](http://www.tensorflow.org/) machine learning library.Its focus is on making neural networks easy to set up, train and use.The library is in pre-alpha right now and does not do anything seriously usefulyet.## Installationnn-wtf runs under Python3.4 and above.### DependenciesYou need to install TensorFlow manually. The installation process depends onyour system. Install the version of TensorFlow built for Python 3.4.Seehttps://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#download-and-setupfor details.Example installation for Linux without GPU support:```$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0rc0-cp34-cp34m-linux_x86_64.whl```### NN-WTF itselfSimple:```$ pip install nn_wtf```## DocumentationSorry the documentation is absolutely minimal at this point. More usefuldocumentation will be ready by the time this package reaches alpha status.### List of useful classes and methods* `NeuralNetworkGraph`: Base class for defining and training neural networks* `__init__(self, input_size, layer_sizes, output_size, learning_rate)`* `set_session(self, session=None)`* `train(self, data_sets, max_steps, precision, steps_between_checks, run_as_check, batch_size)`* `get_predictor().predict(input_data)`* `MNISTGraph`: Subclass of NeuralNetworkGraph suitable for working on MNIST data* `NeuralNetworkOptimizer`: Optimize geometry of a neural network for fast training* `__init__( self, tested_network, input_size, output_size, training_precision,layer_sizes, learning_rate, verbose, batch_size)`* `brute_force_optimal_network_geometry(self, data_sets, max_steps)`### Usage exampleIf you want to try it on MNIST data, try this sample program:```pythonfrom nn_wtf.mnist_data_sets import MNISTDataSetsfrom nn_wtf.mnist_graph import MNISTGraphimport tensorflow as tfdata_sets = MNISTDataSets('.')graph = MNISTGraph(learning_rate=0.1, layer_sizes=(64, 64, 16), train_dir='.')graph.train(data_sets, max_steps=5000, precision=0.95)image_data = MNISTDataSets.read_one_image_from_url('http://github.com/lene/nn-wtf/blob/master/nn_wtf/data/7_from_test_set.raw?raw=true')prediction = graph.get_predictor().predict(image_data)assert prediction == 7```>From there on, you are on your own for now. More functionality and documentationto come.
Package: no2foryou
no2foryouReplaces allpython2*andpip2*commands with error messages.Why? Because it's 2020 - stop using Python 2!InstallationRequires Python 3 (duh)$ pip install no2foryouUsage$ python2
error: this version of python has expired. shame on you!

$ pip2
error: this version of python has expired. shame on you!
Package: noaaco2
noaaco2This is a tutorial to debut a PyPI application and to validate software reproducibility for climate professions.noaaco2 is a PyPI application that scrapes the latest dataset from NOAA via the Internet and
displays a graph for a specified number of months.The PyPI environment allows PyPI applications to run on Windows, MacOS, and Linux operating systems.
As long as Python is installed on the system, you can use it without being aware of the operating system.noaaco2 scrapes the latest csv file from the following NOAA site:ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.csvnoaaco2 can generate a graph with specified months and save it in result.csv and noaaco2.png.In order to run noaaco2 application, Python must be installed on your system.How to install Python and noaaco2Follow the Python installation steps for Windows, WSL on Windows, MacOS, and Linux operating systems.
1. Download a right installation file from miniconda site:
https://docs.conda.io/en/latest/miniconda.html

2. X-server installation is needed.
For Windows 11 or 10,WSL on Windows 11 or 10, MacOS, X-server must be installed.
X-server for Windows and WSL on Windows, install VcXsrv Windows X Server.
You can download it from the following site:
https://sourceforge.net/projects/vcxsrv/
For MacOS, you must install XQuartz via Homebrew or brew command.
For Linux operating systems, X-server is ready so that you don't need to install it.

3. PyPI libraries and applications can be installed by the pip installation command.
($) sign indicates the prompt from your system in the terminal. Open the terminal.
And enter the following command to install noaaco2 application.
$ pip install noaaco2
Since noaaco2 uses PyPI libraries such as numpy, pandas and matplotlib, you must install them.
$ pip install numpy pandas matplotlib
noaaco2 uses bash commands such as wget command so that you must install it.
$ sudo apt install wgetHow to run noaaco2noaaco2 takes a single parameter for specifying the number of months.
Without the number of months, noaaco2 shows a graph with all months in the dataset.$ noaaco2 <number-of-months>or$ noaaco2For example, 24 months, run the following command.$ noaaco2 24
Package: noaadata-py
Library for handling marine Automatic Identification System (AIS)
messages and NOAA marine data messages fromhttp://opendap.co-ops.nos.noaa.gov/axis/.
Has extensions to handle the USCG N-AIS receive fields.  The SOAP code is a bit rusty and now
uses pydap.For AIS ship traffic, noaadata provides bridges to sqlite3 and
postgresql/postgis databases.  Is able to generate Google Earth KML
files for AIS.Part of the UNH/NOAA Chart of the Future project.Still in development.  Some rough edges.
Package: noaa-ftp
NOAA-FTPI needed to work with data from NOAA, so I write a code in jupyter notebook and solved my problem for viewing and downloading data.
Then I decided to convert that code to a python package.My Personal Website:Water Directory.To import, use command below:fromnoaa_ftpimportNOAAAvailable functions:dir()download()Get list of files and foldersnoaa_dir=NOAA("ftp.ncdc.noaa.gov",'pub/data/ghcn/daily').dir()noaa_dirDownload custom file from the directorynoaa=NOAA("ftp.ncdc.noaa.gov",'pub/data/ghcn/daily').download('ghcnd-stations.txt')
Package: noaa_radar
This package provides tools for downloading and overlaying radar images from the NOAA Ridge radar sites.From the command lineFrom the command line, it is easy to request images with the provided utility script.  This example
fetches the radar image from the site near Atlanta.  It includes all of the available layers.fetch_radar_image_cli--composite_reflectivity--counties--cities--topo--rivers--legend--warnings--riversffcsample.jpgSome simple code examples.fromnoaa_radarimportRadarnoaa=Radar()image=noaa.get_composite_reflectivity('HTX')image.save("composite_reflectivity.jpg")image=noaa.get_base_reflectivity('HTX')image.save("base_reflectivity.jpg")image=noaa.get_storm_relative_motion('HTX')image.save("storm_relative_motion.jpg")image=noaa.get_base_velocity('HTX')image.save("base_velocity.jpg")image=noaa.get_one_hour_precipitation('HTX')image.save("one_hour_precipitation.jpg")image=noaa.get_storm_total_precipitation('HTX')image.save("storm_total_precipitation.jpg")For an map of radar sites see, the noaa map athttps://www.roc.noaa.gov/wsr88d/Images/WSR-88DCONUSCoverage1000.jpg.
On the map, Radar site names start with a K, just remove it and you have the name string of the site
to request.  For example, ‘KHTX’ becomes ‘HTX’.
Package: noaareport
# noaareportA library used to read the noaa’s solar report. It uses pandas to store the data into a dataframe making it easy to manipulate.## Installation### Python package`bash pip install noaareport `### CLI toolThere is a very basic cli tool that can be used once the package is installed.`bash python-mnoaareport ~/reports/2005_events 2005 03 10 `You can use the grabit.sh script to download all the files from 1998 to 2018.
Package: noaa-sdk
NOAA Python SDKSDK for NOAA Weather Service REST API.Fully unit tested SDK for NOAA Weather Service REST API.https://www.weather.gov/documentation/services-web-apihttps://github.com/paulokuong/noaaRequirementsPython 3.4 (tested)GoalTo provide a generic wrapper for the latest V3 NOAA weather service API.Keep on changing this SDK when NOAA updates their API.Class can be extended / decorated.Code sampleThere are 3 types of forecast responses: forecast, forecastHourly, forecastGridData.To get weather forecast with postal code and country code.fromnoaa_sdkimportNOAAn=NOAA()res=n.get_forecasts('11365','US',type='forecastGridData')foriinres:print(i)To get weather observation data from all nearest stations in 11375, US between 2017-12-01 00:00:00 (UTC) to 2017-12-01 05:00:00 (UTC)fromnoaa_sdkimportNOAAn=NOAA()observations=n.get_observations('11365','US')forobservationinobservations:print(observation)To get weather forecast for a coordinate in USAfromnoaa_sdkimportNOAAn=NOAA()n.points_forecast(40.7314,-73.8656,type='forecastGridData')ContributorsPaulo Kuong (@pkuong)
Package: noaatsi
noaatsinoaatsi is a PyPI package application to scrape the updated data from NOAA on TSI and to display the TSI graph.
Data is stored in the following site which will be automatically scraped by noaatsi:https://www.ncei.noaa.gov/data/total-solar-irradiance/access/yearly/tsi_v02r01_yearly_s1610_e2021_c20220202.ncNOAA file is based on netCDF4.
In order to read .nc file, netCDF4 library is needed. To install it, run the following command.$ pip install netCDF4In order to install noaatsi, run the following command.$ pip install noaatsiTo generate TSI file (tsi.csv) and the graph on the screen (tsi.png), run the following command.$ noaatsinoaatsi.png is finally generated.
Package: noaaweather
===========NOAA Weather===========A handy utility to pull weather from NOAA services in a sane manner.#!/usr/bin/env pythonfrom noaaweather import weathersfWeather = weather.noaa()sfWeather.getByZip('94109')print sfWeather.precipitation.liquid.tomorrow.max.valueprint sfWeather.temperature.apparent.tomorrow.min.valueprint sfWeather.temperature.apparent.valueSupport for Temperature, Humidity, Wind, Precipitation, Cloud CoverPulls from http://graphical.weather.gov/xml/rest.phpFirst release not really tested...
Package: noaa-weather
noaa-weather-apiYou can access historical weather data with NOAA Weather API.Getting StartedMethod :get_all_stations_in_location():Returns all stations in that given country.df_sis a data frame that contains information such as station code, latitude and longitude.from noaa import get_all_stations_in_location as gas

df_s = get_all_stations(dataset, location, n_token)df_slooks like this:Method :get_bulk_weather_in_station():Returns one historical weather report for a given station between two dates, using one of the resulting station id's.Date: Must be inStringandYYYY-MM-DDformat. Example:'2019-06-26'df_w = get_bulk_weather_in_station(dataset, location, s_date, e_date, n_token)df_wlooks like this:For more information about abbreviations:click here.
Package: no-action
no_actionAn incremental automation library to eliminate toil in processes.DescriptionIdeologically based on Dan Slimmon'sDo-nothing scriptingarticle from 2019.no_actionprovides aStepclass that can be sub-classed and customized as well as aProdecureclass that
wraps a series of ordered steps.First, one writes a series of Steps with the manual details in their docstring and the procedure
prints the docstrings, a glorified checklist. Over time though, with each Step encapsulated in
a discrete class, Steps of a procedure can be automated by writing code that actually does what is
described in the docstring (override theStep.execute()method). Soon, your procedures are fully
automated and can be hooked into an Internal Developer Platform (IDP), a runner, or other automation
orchestration system.InstallationThis library can be found on PyPI. Run the following command to install the library into your
project.python3 -m pip install no-actionLicenseThis project is licensed under the GNU GPL 3. See;LICENSEfor the preamble.COPYINGfor the full text.
Package: noah-cli
NoahNoah is a project management tool specifically designed for bioinformatics projects. It enables reproducible analysis of large datasets, making it effortless to share and collaborate with others.FeaturesA human-readable configuration fileGit integrationGuaranteed reproducibility and portabilityManaging data, containers, and workflows, all in one placeInstallationThe easiest way to install Noah is to use pip:pipinstallnoah-cliQuickstartTo get started with Noah, use thenoah initcommand to create a new project:noahinitmy_project# This will by default create a Git repositorycdmy_projectTo add a new dataset to your project, use thenoah addcommand:noahaddSRP123456# ENCSR123ABC, GSE123456, etc. are also supportedYou can also add privately hosted datasets:noahaddme@some_host:path/to/my_datasetftp://ftp.example.com/another_datasetIf you share your project with others, they can easily retrieve the datasets you added:noahinstallFor more information, please refer toexamples.LicenseThis software is licensed under the MIT license. SeeLICENSEfor more details.
Package: noah-vega
Vega ver1.8.5 released:IntroductionVega is an AutoML algorithm tool chain developed by Noah's Ark Laboratory, the main features are as follows:Full pipeline capabilities: The AutoML capabilities cover key functions such as Hyperparameter Optimization, Data Augmentation, Network Architecture Search (NAS), Model Compression, and Fully Train. These functions are highly decoupled and can be configured as required, construct a complete pipeline.Industry-leading AutoML algorithms: Provides Noah's Ark Laboratory's self-developedindustry-leading algorithms (Benchmark)andModel Zooto download the State-of-the-art (SOTA) models.Fine-grained network search space: The network search space can be freely defined, and rich network architecture parameters are provided for use in the search space. The network architecture parameters and model training hyperparameters can be searched at the same time, and the search space can be applied to Pytorch, TensorFlow and MindSpore.High-concurrency neural network training capability: Provides high-performance trainers to accelerate model training and evaluation.Multi-Backend: PyTorch, TensorFlow, MindSpore.InstallationInstall Vega and the open source softwares that Vega depends on:pip3 install --user --upgrade noah-vegaCooperation and ContributionWelcome to use Vega. If you have any questions or suggestions, need help, fix bugs, contribute new algorithms, or improve the documentation, submit an issue in the community. We will reply to and communicate with you in a timely manner.