Saravanan666/pypi-org huggingface dataset:
Package: nanslice
Not Another Neuroimaging Slicer - nansliceCredit / Blame / Contact - Tobias Wood -tobias.wood@kcl.ac.ukThis Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one athttp://mozilla.org/MPL/2.0/.If you find the tools useful the author would love to hear from you.Brief DescriptionThis is a pure Python module for creating slices through neuro-imaging datasets.
The main motivation for building this was to implement the 'Dual-Coding'
visualisation method that can be found in this paper:http://dx.doi.org/10.1016/j.neuron.2012.05.001. However, it then expanded to
include standard visualisation methods, and an interactive viewer for Jupyter
notebooks.Documentation can be found athttps://nanslice.readthedocs.io/en/latest/.A Jupyter Notebook demonstrating the module can be found athttps://mybinder.org/v2/gh/spinicist/nanslice/master?filepath=doc%2Fexample.ipynb.In dual-coding instead of plotting thresholded blobs of T-statistics or p-values
on top of structural images, transparency (or alpha) is used to convey the
p-value of T-statistic, while color can be used to convey the effect size or
difference in group means etc. Finally, contours can be added at a specific
p-value, e.g. p < 0.05. In this way, 'dual-coded' overlays contain all the
information that standard overlays do, but also show much of the data that is
'hidden' beneath the p-value threshold.Whether you think this is useful or not will depend on your attitude towards
p-values and thresholds. Personally, I think that sub-threshold but
anatomically plausible blobs are at least worthshowingto readers, who can
then make their own mind up about significance.This is a sister project tohttps://github.com/spinicist/QUIT. I mainly work
with quantitative T1 & T2 maps, where group mean difference or "percent change"
is a meaningful, well-defined quantity. If you use these tools to plot "percent
BOLD signal change", I hope you know what you what you are doing and wish you
luck with your reviewers.InstallationNaNSlice is available onPyPI. Runpip install nansliceto install the
stable version. Alternatively, clone the repository from Github and then runpip install -e .to use the development version.PerformanceThese are Python scripts. The core sampling/blending code was written over 3
evenings while on the Bruker programming course. Most of nanviewer was written
in literally 4 hours across a Monday and Tuesday. After a refactoring, it is
surprisingly responsive on my MacBook. The Jupyter viewer, on the other hand,
is not wildly performant. Patches are welcome!
Package: nantic_activity
trytond-activity================The activity module of the Tryton application platform.Installing----------See INSTALLSupport-------For more information or if you encounter any problems with this module,please contact the programmers atNan-Tic--------------website: http://www.nan-tic.com/email: info@nan-tic.comIf you encounter any problems with Tryton, please don't hesitate to askquestions on the Tryton bug tracker, mailing list, wiki or IRC channel:http://doc.tryton-erp.es/http://bitbucket.org/nantic/trytond-activityhttp://groups.tryton.org/http://wiki.tryton.org/irc://irc.freenode.net/trytonLicense-------See LICENSECopyright---------See COPYRIGHTFor more information please visit the NaN·tic web site:http://www.nan-tic.com/
Package: nanto
nantonanto makes working with values that might be NaN safer and easierFree software: MIT licenseDocumentation:https://nanto.readthedocs.io.fromnantoimportisanan,nantonone,nantozeroisanan(7.0)# Falseisanan('string')# Falseisanan(None)# Falseisanan(float('nan'))# Truenanto(float('nan'),'fallback')# returns 'fallback'nanto(1.7,'fallback')# returns 1.7nanto(float('inf'),'fallback')# returns infnanto(None,'fallback')# returns Nonenanto('hello','fallback')# returns 'hello'nantonone(float('nan'))# returns Nonenantonone(1.7)# returns 1.7nantonone(float('inf'))# returns infnantonone(None)# returns Nonenantonone('hello')# returns 'hello'nantozero(float('nan'))# returns 0nantozero(1.7)# returns 1.7nantozero(float('inf'))# returns infnantozero(None)# returns Nonenantozero('hello')# returns 'hello'CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.0 (2022-02-22)First release on PyPI.
Package: nanugo
NanugoNanugo(나누고)means "cut, and-" in Korean.Convert your Handwritten(or any) PDF files to Anki decks.I like to write stuff than to type stuff, especially when I'm studying. So I use my tablet for note-taking. But doing both writing and typing in Anki is just time consuming and... stupid. And I am not screen capturing, clicking a bunch of buttons for, like, an hour a day just to make an Anki deck from what I already spent a lot of time making. I'm also not using AI to further complicate stuff.I JUST WANT MY HANDWRITINGS CONVERTED TO ANKIDECK.Nanugo allows you to generate Anki decks from PDF files using both an easy-to-use CLI or as a Python module. You can think of it as creating a real-life flashcard—similar to folding (or, in this case, cutting) a piece of paper in half. How handy.FeaturesConvert each pages in PDF file(s) into Anki deck(s) in seconds.Convert bulk of files with one command line.Fast conversions, even with larger files.Split PDFs into flashcards both vertically and horizontally, with custom ratio, for flexible content arrangement.Automate your workflow by using nanugo directly from your Python script.InstallationYou can install nanugo withpip$ pip install nanugoUsageUsing with CLInanugo can be used with cli without any compromises.$ nanugo your_apkg_name /path/to/pdf/file.pdf

# You can also provide wildcard path or multiple paths.

$ nanugo your_apkg_name /path/with/pdf/files/*.pdf /path/with/single/file.pdf

# There are few options you can set.

$ nanugo --ratio 0.6 0.4 --vertical --inversed your_apkg_name /path/to/pdf/file.pdf

# You can make multiple set of cards from single page too.

$ nanugo --rows 5 multiple_rows_pkg /path/to/pdf/file.pdf

# For further help, you can read the source code or you can just ask for

$ nanugo --helpPython ModuleYou can also use nanugo as a Python module to further suit your needs. Read (not that good but better than nothing)documentationfor all the arguments and stuff.import nanugo

builder = nanugo.nanugo.Builder()
path = '/home/foobar/'

deck = builder.build_deck("foo", "./bar.pdf", vertical = True)
pkg = builder.build_pkg([deck]) # It can take multiple decks, ergo list should be provided.

pkg.write_to_file(path)Changelogv1.0.3Fixed a cli error occurred when no scale value was provided.Some docstring changesv1.0.2Fixedrowsoption having issues with conflicting media file names.You can now usescaleandwidthoptions.scaleoption might help image quality of cards created with multiple rows feature.Some docstring changes and refactors here and there.v1.0.1You can now create multiple cards from a single page of pdf withrowsoption. 5 rows template is also provided.Broken. Use v1.0.2 instead.All images are now centered withtext-align: center;css snippet by default.Author@lewisleedevContributingContributions are always welcome. SeeCONTRIBUTING.md
Package: naogi
naogi_modelNaogiModel it is an abstract class for the naogi.com ML deployment platformHow to deploy via naogi.comAddnaogito your project requirements.txtcreate filenaogi.pyin the root directory (copypaste file fromnaogi.py)implement your logic of model loading, prepareing and callinggo to you naogi.com profile, create project and connect gitHow it works? (What to implement in naogi.py)Loading model (server starting time)When naogi server is starting, it callload_model(self)-- you have to implement model loading logic in that function (loading from file, internet, etc.)Here you have to load and init your model and save the model object to some variableExampledefload_model(self):self.model=__get_model()self.model.load_weights()Prepareing (request time)When you call [GET/POST] /prepare of your APIprepare(self, params_dict)is calling first.All request params can be found inparams_dict. Here you can prepare you params: open and modify Image, transform and normalize text and safe data forprepareto self attribute.Example# now you can make GET /predict?text_data=My-long-text# (and not worry about spaces)defprepare(self,params_dict):self.text=params_dict['text_data'].strip()Predicting (request time)After request params prepareingpredict(self)is calling.defpredict(self):raw=self.model.predict(self.text)return__from_raw_to_some(raw)Here you have to return the value, that valid for some Renderer class (json, file, custom)Rendering (request time)And the last step is callingrenderer().render(...)and passing the result ofpredictOut of the box you can useJsonRendererandFileRendereroryou can create custom renderer fromAbstractRendererclassMyRenderer(AbstractRenderer):defrender(data):return...defrenderer(self):returnMyRendererJsonRendereraccepts any json.dumps valid dataFileRendereruses flask'ssend_fileunder the hood, so you can pass any bytes.Additional params can watch hereFinAnd finally you can make API calls to<your-naogi-project-url>/predictwith paramsDevelopment...TestingBefore testing you should installpytestFrom root folderPYTHONPATH='./'pytesttests/renderers/pil_image_renderer.pyDeployrm-rfdist/*
python3-mbuild
python3-mtwineupload--repositorypypidist/*
Package: naomi-paginator
Naomi PaginatorSimple embed paginator written in Python fordiscord.pybots. Used in theNaomi bot project.Simple usage:importdiscordfromdiscord.extimportcommandsfromnaomi_paginatorimportPaginatorintents=discord.Intents.default()intents.message_content=Truebot=commands.Bot(command_prefix='!',intents=intents)@bot.eventasyncdefon_ready():print("I'm ready.")@bot.command()asyncdefpaginate(ctx):"""Paginator test command."""p=Paginator(ctx)embeds=(discord.Embed(color=0xff0000,title='Embed #1',description='Test starts here'),discord.Embed(color=0x00ff00,title='Embed #2',description='Second embed...'),discord.Embed(color=0x0000ff,title='Embed #3',description='Last embed'))forxinembeds:p.add_page(x)awaitp.call_controller()bot.run('token')Installing# From source:$pip3installgit+https://codeberg.org/The-Naomi-Developers/naomi-paginator# From PyPI:$pip3installnaomi-paginatorLicenseMIT License

Copyright (c) 2020-present The-Naomi-Developers

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: napa
NAPA - Network Analysis of Protein AdaptationThe NAPA package performs intra-protein residue coevolution network construction and analysis.The package supports two input types:A FASTA alignment of (functionally related) homologous protein sequencesA FASTA alignment of (functionally related) homologous protein sequences AND
a Phylogenetic Tree ensemble (trees in newick format)NAPA produces the following outputs:A mutation pair network (undirected for alignment only, directed/undirected for phylogeny ensemble)Network analysis - network communities, network node centralities, network path centralitiesDocumentationPlease see thedocumentationon github for complete usage details.InstallationUse pip to install the napa package:$pipinstallnapaNAPA depends on several other packages and these will be installed automatically by pip if they
are not already installed.Required packages:numpyscipyjoblibnetworkxete2pyyamlNote the pip scipy installation can sometimes have issues.  If so, alternate methods of installing scipy may be preferable.  See scipyinstallfor details.  Then rerun pip install napa.
Package: napalm25-ios-alternative-drv
alternative unofficial napalm ios driverDue the purpose of NAPALM the included IOS Drivers are designed to require Privilege Exec Mode(aka Privilege Level 15)
to ensure the full feature-set is always available.If this privilege level is not available the driver will not work.This limits its use for certain purposes where priv exec is not required. (like using the getters() or sending show commands)This driver is a fork of the original driver, it changes the behavior of the driver and implements privilege level checking.
It will allow to connect without privilege level 15 and instead raise permission-errors whenever a function is called and the required privilege level is not available.It will neither change function arguments or return values nor implement new functions.installpip install napalm-ios-alternative-drvusagewithout secret:#!/usr/bin/env python3
from napalm import get_network_driver
driver = get_network_driver('ios_ad')      
device = driver('1.1.1.1', 'username', 'password')
device.open()    
vals = device.get_facts()   
print(vals)    
device.close()with secret:#!/usr/bin/env python3
from napalm import get_network_driver
optional_args = { 'secret': '<enable_secret>'}
driver = get_network_driver('ios_ad')      
device = driver('1.1.1.1', 'username', 'password', optional_args=optional_args)
device.open()    
vals = device.get_facts()   
print(vals)    
device.close()Authors:The largest part of the codebase is sync'd from and belongs to theNAPALM Project.Privilege Patches are done by me.Original Authors(NAPALM Project)David Barroso (dbarrosop@dravetech.com)Elisa Jasinska (elisa@bigwaveit.org)Many others, check thenapalm contributorspage for details.Privilege Patchesreminguremingu@techturn.de
Package: napalm-aruba505
Napalm-aruba505Driver implementation for ArubaOS Access PointsCurrently supported Napalm methods:get_config()get_facts()get_lldp_neighbors()get_interfaces()get_environment()get_vlans()cli()How to installpip install napalm-aruba505How to use itimport napalmfrom napalm import get_network_driverdriver = napalm.get_network_driver("napalm_aruba505")device = driver("my-ap-1", "my_username", "my_password")config = device.get_config()print(config)Useful info:sshFRIEND
Package: napalm-aruba-cx
Napalm-aruba-cxNAPALMDriver implementation for AOS-CX Switches.Supported DevicesAll AOS-CX devices are supported, however the AOS-CX switch firmware should be version 10.05 or later, as some modules
may have not been properly tested in older versions.Current Support Functionality* get_arp_table - Get the ARP table from a device.
* get_config - Get configuration from the device.
* get_facts - Get the version, serial number, vendor, model, and uptime from a device.
* get_interfaces - Get list of interfaces from a device.
* get_interfaces_ip - Get list of interface IP addresses from a device.
* get_lldp_neighbors - Get the list of LLDP Neighbors from a device.
* get_lldp_neighbors_detail - Get LLDP Neighbor details from a device.
* get_mac_address_table - Get the MAC Address table from a device.
* get_ntp_servers - Gets NTP information from a network device.
* get_route_to - Shows an IP route on a device.
* get_snmp_information - Get the SNMP information of a device.
* is_alive - Check to see if the connection to the device is up.
* ping - Execute a ping command from the device.

Early versions of this driver will focus on the Get functions.  Configuration handling is in current development.Getting StartedFor more information and guides to help get started, check out theAruba Developer Hub section for NAPALM.PrerequisitesThe following software is required:Python3PipPython modules specified inrequirements.txtpyaoscx v1.0.0requestsurllib3Note that the current version of this driver utilizes pyaoscx v1 only.  Future versions of this driver will be
compatible with pyaoscx v2InstallingTo install simply run:pip3 install napalm-aruba-cxSwitch configurationThe AOS-CX driver utilizes the REST API on the switches.
Depending on the switch model, this may already be enabled.  If not, please use the CLI commands below to enable REST on
the specified VRF.There must be a user on the switch who belongs to the "administrators group" and has a password set.  This user
would then be allowed to access the REST API.  In the below example, replace the username "admin" and password
"mypassword" with your own username and password respectively.8320(config)# user admin group administrators password plaintext mypasswordSet the access mode for the HTTPS server to "read-write" and enable the HTTPS server on the VRF through which the
client making the NAPALM calls can reach the switch.  In the below example, this specifies both the management VRF and
the default VRF.8320(config)# https-server rest access-mode read-write
8320(config)# https-server vrf default
8320(config)# https-server vrf mgmtContributingPlease readCONTRIBUTINGfor details on our process for submitting issues and requests.LicenseThis project is licensed under the Apache License - see theLICENSEfile for details
Package: napalm-asa
napalm-asaThis is aNAPALMcommunity driver for the Cisco ASA platform.Minimum RequirementsThis driver makes use of the Cisco ASA REST API.  The REST API is only available from software version 9.3.2 and up, and on the 5500-X series, ASAv, ASA on Firepower and ISA 3000 platforms.For latest information on the ASA REST API Compatibility check thecompatibility matrix(PDF)Quick startpipinstallnapalm-asafromnapalmimportget_network_driverdriver=get_network_driver("asa")device=driver(hostname='192.168.1.1',username='cisco',password="cisco",optional_args={'port':8443})device.open()facts=device.get_facts()device.close()Check the fullNAPALM Docsfor more detailed instructions.Supported GettersGetterSupportget_arp_table✅get_bgp_config❌get_bgp_neighbors❌get_bgp_neighbors_detail❌get_config✅get_environment❌get_facts✅get_firewall_policies❌get_interfaces✅get_interfaces_counters❌get_interfaces_ip✅get_ipv6_neighbors_table❌get_lldp_neighbors❌get_lldp_neighbors_detail❌get_mac_address_table❌get_network_instances❌get_ntp_peers❌get_ntp_servers❌get_ntp_stats❌get_optics❌get_probes_config❌get_probes_results❌get_route_to❌get_snmp_information❌get_users❌is_alive✅ping❌traceroute❌Setting up a Lab EnvironmentMock tests are usefull for quickly iterating when writing a new getter of fixing a bug, but you do want to test on a 'real' device to make sure everything works as expected. One of the most convenient ways is to use an ASAv running on Virtualbox + Vagrant. @bobthebutcher has a nice write up onhow to setup an ASAv with Vagrant.
Package: napalm-ce
napalm-ceThis is aNAPALMcommunity driver for the Huawei CloudEngine Switch.RequirementsPython 3.6+, napalm 3+Quick startpipinstall-ihttps://test.pypi.org/simple/napalm-cefromnapalmimportget_network_driverdriver=get_network_driver("ce")device=driver(hostname='192.168.1.1',username='admin',password="Huawei123",optional_args={'port':22})device.open()facts=device.get_facts()device.close()Check the fullNAPALM Docsfor more detailed instructions.Implemented APIcli(commands)close()commit_config()compare_config()discard_config()get_arp_table(vrf='')get_config(retrieve='all', full=False, sanitized=False)get_environment()get_facts()get_interfaces()get_interfaces_counters()get_interfaces_ip()get_lldp_neighbors()get_mac_address_table()get_users()is_alive()load_merge_candidate(filename=None, config=None)load_replace_candidate(filename=None, config=None)open()ping(destination, source=u'', ttl=255, timeout=2, size=100, count=5, vrf=u'')rollback()
Package: napalm-comware-ssh
napalm-comware-sshNAPALMNAPALM(Network Automation and Programmability Abstraction Layer with Multivendor support) is a Python library that implements a set of functions to interact with different router vendor devices using a unified API.NAPALM supports several methods to connect to the devices, to manipulate configurations or to retrieve data.napalm-comware-sshNAPALM driver for H3C/HPE Comware V7 network devices.About H3C.Supported devicesS5100、S5500、S6800、S12500 Series Data Center Switches.Supported functions:white_check_mark: is_alive():white_check_mark: get_facts():white_check_mark: get_interfaces():white_check_mark: get_lldp_neighbors():white_check_mark: get_environment():white_check_mark: get_interfaces_counters():white_check_mark: get_lldp_neighbors_detail():white_check_mark: cli():white_check_mark: get_arp_table():white_check_mark: get_mac_address_move_table():white_check_mark: get_mac_address_table():white_check_mark: get_vlans():white_check_mark: get_config():white_check_mark: get_interfaces():white_check_mark: get_irf_config():white_check_mark: is_irf()Getting StartedInstallpipinstallnapalm-comware-sshUpgradingpip install napalm-comware-ssh -UUsefromnapalmimportget_network_driverdriver=get_network_driver("comware")driver=driver("192.168.56.20","netdevops","NetDevops@01",)driver.open()ret=driver.is_alive()print(ret)
Package: napalm-control
Nonlinear Algebraic Approximation of Control SystemsA Python implementation of an algorithm for construction homogeneous
approximations of nonlinear control systems. For description of the algoritm refer to ...InstallationYou can install the package using pippip install napalm-controlHow to useThe main part of the package is implemented in a single class called ControlSystem. This class describes a control system and its methods implement the algorithm of approximation of given system.fromnapalm_control.approximation_toolsimportControlSystemGiven a systemyou can initialize it the following waysystem=ControlSystem(a,b)whereaandbare corresponding vectors.Note: the vectorsmustbe composed out of sympy symbols or numbers.
For example, consider a system:Then the implementation using the package would be as follows:importsympyassymfromnapalm_control.approximation_toolsimportControlSystemx1,t=sym.symbols('x_1 t')a=sym.Matrix([0,-sym.Rational(1,2)*x1**2-4*t*x1-3*t**2*x1,-x1**2-2*t*x1-3*t**2*x1])b=sym.Matrix([-1,0,0])system=ControlSystem(a,b)