Saravanan666/pypi-org huggingface dataset:
Package: nannypi
nannypiThis project is to create a telegram-enabled nanny cam using a raspberry pi and a usb webcam.Project InstallationRun the scriptEdit the /etc/default/motion file:sudo vim /etc/default/motion# set to 'yes' to enable the motion daemon
start_motion_daemon=yes

TELEGRAM_BOT_TOKEN=<telegram_bot_token>
TELEGRAM_CHAT_ID=<telegram_chat_id>

export TELEGRAM_BOT_TOKEN
export TELEGRAM_CHAT_IDEdit motion config file:sudo vim /etc/motion/motion.confThe optionon_picture_saveis commented out.  Remove the comment and add the following.on_picture_save /opt/nannypi/venv/bin/send_telegram_media.py --delete --file %fPython Script infoThis is part of a larger repo created to create a telegram-enabled nanny camera.  See the main repo for full information:nannypi.Install Python Scriptspipinstallnannypisend_telegram_media.pyThis script will send media to telegram using cli arguments.pythonsend_telegram_media.py-h
usage:send_telegram_media.py[-h][-f[FILE[FILE...]]][-tTOKEN][-cCHAT_ID][-d]SendmediaimagestoaTelegramChat.

optionalarguments:-h,--helpshowthishelpmessageandexit-f[FILE[FILE...]],--file[FILE[FILE...]]Filestosend-tTOKEN,--tokenTOKENtelegramtoken.CanalsobespecifiedwiththeenvvariableTELEGRAM_BOT_TOKEN.-cCHAT_ID,--chat-idCHAT_IDtelegramchatid.CanalsobespecifiedwiththeenvvariableTELEGRAM_CHAT_ID-d,--deletedeletefileaftersending
Package: nano
This is a set of nano-size tools and apps for Django 1.8 and later.Currently included:activationA place to store activation-codes for e.g. authenticationbadgeUser-badges worth certain points ala. StackOverflowblogA very basic blog-appcommentsUnmoderated comments for logged-in userschunkTemplates stored in the databasefaqJust about as simple a FAQ as is possibleprivmsgPrivate messages with separate archives for sent an receiveduserA very basic user-registration- and password-handling app/tooltoolsUtility-functions used by the above appsInstallationSee INSTALL.txt for installation-instructions and TODO.txt for
what’s missing.UsageCommon for all appsAppendnano.<subapp>to your INSTALLED_APPS, wheresubappis any of the tools listed above excepttools.blogBlog entries can be tagged usingdjango-taggitif it is in
INSTALLED_APPSandNANO_BLOG_USE_TAGS is set to True in settings.chunkAdd ‘nano.chunk.loader.Loader’ to TEMPLATE_LOADERS.userDoesn’t have any models so just hook up the views in an urls.py:signup()password_change()password_reset()Settings for userNANO_USER_EMAIL_SENDERThe From:-address on a password-reset email. If unset, no
email is sent.Default:Not setNANO_USER_TEST_USERSSpecial-cased usernames for live testing.Default:()NANO_USER_BLOG_TEMPLATETemplate used for auto-blogging new users.Default:blog/new_user.htmlVersion:0.10.0
Package: nanoAPI
NANO : A Nano Web FrameworkNano is an Express inspired nano web framework which is under development. Currently, it has features of adding routes, controllers and sending API responses (Refer theexample blog appfor reference). Defining models with custom field types is also available but is under development.Status: Under developementTo run example app$pip3install-rrequirements.txt

$python3main.pyrun:<appname>DocumentationStep 1: Install nanoAPI and create folder structure$pip3installnanoAPI

$touchmodels.py
$touchcontrollers.py
$touchroutes.py
$touchmain.pyStep 1: Install nanoAPI and create folder structure# models.pyfromnanoAPI.dbimportmodelclassUser(model.Model):username=model.StringType('username',max_length=50,min_length=4,unique=True,required=True)# define your model hereStep 2: Define your controllers# controllers.pyfromnanoAPI.handlerimportResponsedefindex(req):# define your controller herereturnResponse(status=200,data={"msg":"message"})Step 3: create a router and assign controllers with routes# routes.pyfromnanoAPI.handlerimportRouterfrom.controllersimportindexrouter=Router()router.get("/",index)Step 4: create nanoAPI app and assemble your router and models# main.pyfromnanoAPIimportnanoAPI,runfrom.routesimportrouterfrom.modelsimportUserapi=nanoAPI()api.set_router('/api',router)api.set_models(User)run(api)Step 5: Boot your models to db and run the server$python3main.pyrun:db
$python3main.pyrun:apiRefer theexample blog appfor folder structure. Working on a better documentation
Package: nanobind
nanobindis a small binding library that exposes C++ types in Python and
vice versa. It is reminiscent ofBoost.Pythonandpybind11and uses near-identical
syntax. In contrast to these existing tools, nanobind is more efficient:
bindings compile in a shorter amount of time, produce smaller binaries, and
have better runtime performance.More concretely,benchmarksshow up
to~4× fastercompile time,~5× smallerbinaries, and~10× lowerruntime overheads compared to pybind11. nanobind also outperforms Cython in
important metrics (3-12×binary size reduction,1.6-4×compilation time
reduction, similar runtime performance).Please see the following links for tutorial and reference documentation inHTMLandPDFformats.
Package: nanobind-stubgen
nanobind-stubgenGenerate Python stub files (pyi) for code completion in IDEs fornanobindmodules.Installationpip install nanobind-stubgenUsageNanobind stubgen uses theinspectmodule to reverse engineer the module structure and detects nanobind types. The doc
string of the nanobind types contains the function signature, which will be used to create the stub files.To create pyi files for a module, first install the module and callnanobind-stubgenwith the module name as first argument.
Here is an example on how to generate stubs fornanogui:nanobind-stubgennanoguiIt is possible to change the output path (by default it is the current directory) by specifying the parameter--out. To
create the pyi files directly in the nanogui package directory, the following command can be used (note the
changing python version):nanobind-stubgennanogui--outvenv/lib/python3.9/site-packagesLimitationsThe stub generator does not use the nanobind project, but the actual compiled python module. This means, that the generator can only detect module and function information that has been writen into the__doc__string by nanobind.Only some imports in the pyi files are addedMethod overloading is supported but only to some extentHelppositionalarguments:moduleModuletocreatestubs(e.g.nanogui).

optionalarguments:-h,--helpshowthishelpmessageandexit--packagePACKAGEOptionalpackagepathtoimportmodulefrom.--outOUTOutputpathforthegeneratedpyifiles.AboutMIT License
Package: nanobt
Package nanobt (v 0.1.0)A quickly backtesting library for inversion strategiesInstall packagepip install nanobtData as pd.DataFrameColumns required = ['datetime', 'open', 'high', 'low', 'close', 'volume']Column 'datetime' must be datetime object.Code examplefromnanobt.backtestingimportBacktestingfromnanobt.tradesimportTradeHistory,SideOrderimportpandasaspdINIT_PORTFOLIO=1000classBuyAndHoldStrategy(Backtesting):defnext(self):ifnotself.position:self.entry(SideOrder.BUY)data=pd.read_csv('./data/binance_BTCUSDT_5m.csv')data['datetime']=pd.to_datetime(data['time'],unit='s')data=data.drop(columns=['time'])strategy=BuyAndHoldStrategy()strategy.setdata(data)trades=strategy.run()th=TradeHistory(trades=trades)print("Init Portfolio: ",INIT_PORTFOLIO)print("Buy and Hold Strategy: ",th.study(cash=INIT_PORTFOLIO,sizer=1,commision=0.04,show_plot=False))
Package: nanoCEM
nanoCEMThe nanopore current events magnifier (nanoCEM) is a python command line to facilitate the analysis of DNA/RNA modification sites by visualizing statistical features of current events.
NanoCEM can be used to showcase high confidence sites and observe the difference based on the modification sample and the low or no modification sample.It supports two re-squiggle pipeline(Tomboandf5c) and supportR9andR10.
If you want to view single read signal or raw signal,Squigualiseris recommended.InstallationBefore pip install, make sure you have installed thesamtools(>=1.16) ,f5c(>=1.3),slow5tools(>=1.1.0) andminimap2(>=2.17),conda install samtools=1.16 minimap2 f5c=1.3 slow5tools -c conda-forge -c biocondaTo install the latest nanoCEMpip install nanoCEMSee our Installation page for details.To check the version of nanoCEM, run:pip list | grep nanoCEMNotes:Additionally, we do not rely on any re-squiggle or eventalign packages. We only need their index files for the sequencing data.Data releaseFor the data we used and related commands in our paper, please view ourwikiDocumentsFull documentation is available athttps://nanocem.readthedocs.io/Workflow
Package: nanoclient
Nanoclient PythonThis project was made to make it easier to interact with nano blockchain in python.Getting StartedimportNanoClientfromnanoclient# get your nanswap node api here, https://nanswap.com/nodesclient=NanoClient({"nodeUrl":"https://nodes.nanswap.com/XNO","currency":"xno","nanswapApi":"your_nanswap_api","workUrl":"https://nodes.nanswap.com/XNO"});# this should only be called once and seed should be backed upclient.setRetrieveSeed(client.randomSeed())# a file is created with the seed in your directory. i.e, after running this code once, remove this line# 0 is the index, you can continue deriving as many wallets as you need, the number is the index and each derives unique wallet on the seed stored in filewallet=client.wallet(0)# while 0=0 and 1=1, returns json with keysKey Commandsseed=client.randomSeed()privateKey=client.derivePrivateKey(seed,0)publicKey=client.derivePublicKey(privateKey)deriveAddress=client.deriveAddress(publicKey)RPC Commandsnode actions after '#'client.rpc.account_info(nanoAddress)# account_infoclient.rpc.work_generate(blockHash)# work_generateclient.rpc.recievable(nanoAddress)# pendingclient.rpc.process(block,"(send/recieve)")# processclient.rpc.req(any_json_rpc_command)
Package: nanocom
nanocomAn ultra simple command line serial client.Getting startedInstall it usingpipinstallnanocomand connect to your serial port usingnanocom--port/dev/ttyUSB0--baudrate115200UsageRunningnanocom --helpwill display all the cli options. The following options
are available-p, --portThe path to the serial port. On macOS it will be something like/dev/tty.usbserialand on Linux probably something like/dev/ttyUSB0. This
option is required.-b, --baudrateThe baudrate of the serial port. This is typically something like9600or115200but any integer is accepted. If not given, this option defaults to115200.-m, --mapA character map,key,valuepair, such that when thekeycharacter is
entered into the client thevaluestring will be sent instead. Thevaluestring can be multiple characters and multiple character maps are allowed. An
example would be if you wanted to send a carriage return and a new line when a
new line character was given you would use--map "\n" "\r\n".-c, --exit-charThe exit character where the Ctrl key and this character are used to exit the
client. The character must be one ofAtoZ,[,\,], or_. For
example if you gave the option--exit-char \\then the client would exit when
you pressed Ctrl +\.LicenseThis project is licensed under the MIT license (LICENSEorhttp://opensource.org/licenses/MIT).
Package: nanocompore
Nanocompore identifies differences in ONT nanopore sequencing raw signal corresponding to RNA modifications by comparing 2 samplesNanocompore compares 2 ONT nanopore direct RNA sequencing datasets from different experimental conditions expected to have a significant impact on RNA modifications. It is recommended to have at least 2 replicates per condition. For example one can use a control condition with a significantly reduced number of modifications such as a cell line for which a modification writing enzyme was knocked-down or knocked-out. Alternatively, on a smaller scale transcripts of interests could be synthesized in-vitro.Full documentation is available athttp://nanocompore.rna.rocksCompanion repositoriesNanoCompore_pipeline: Nextflow pipeline to preprocess data for NanoComporeNanocompore_analysis: Analyses performed with Nanocompore for the BioRxiv preprintNanopolishComp: Collapse Nanopolish eventalign output per kmer, required before running NanoComporeMain authorsTommaso Leonardi - tom {at} tleo.ioAdrien Leger - aleg {at} ebi.ac.uk
Package: nanoconf
NanoConfNanoConf is a tiny, opinionated, and easy to use configuration library for Python. It is designed to be used in small to medium sized projects where a full blown configuration library is overkill.InstallationpipinstallnanoconfUsagefromnanoconfimportNanoConf# or if NanoConf if too long of a namefromnanoconfimportNC# Create a new configuration objectconfig=NanoConf("/path/to/config.nconf")# Use the configuration objectprint(config["key"])# Since all values are also loaded as attributes, you can also doprint(config.key)Configuration File FormatNanoConf uses a simple configuration file format that is easy to read and write.
Each File is YAML formatted and contains a single top-level dictionary.
Even though the top-level must be a dictionary, you can nest dictionaries and lists as deep as you want.
Each config file also must have the .nconf extension. This ensures that NanoConf will only load files that are meant to be configuration files.key:valuetest:1overriden:falsethings:-thing1-thing2-thing3top:v1:1middle:v2:2inner:v3:3deep:v4:4If you have multiple config files you want to load into a single config object, you can put them all in the same directory and pass that directory to NanoConf.
NanoConf will automatically place sub-files by their filename as an attribute of the parent file.
The contents of that file will be accessible as you'd expect under the corresponding filename attribute.<project root>
conf_dir
  |__ cfg1.nconf
  |__ cfg2.nconf
  |__ cfg3.nconf# load an entire directoryproj_config=NanoConf("/path/to/conf_dir")print(proj_config.cfg1.test)Or you can import additional files or directories from within any config file by using the_importkeyword.# main.nconf_import:-/path/to/project/more_configkey:valuetest:1<project root>
main.nconf
more_config
  |__ subcfg1.nconf
  |__ subcfg2.nconf
  |__ subcfg3.nconf# loading the main config file will also load the sub-configsproj_config=NanoConf("/path/to/project/main.nconf")print(proj_config.more_config.subcfg1.test)Notice how the directory structure was also maintained in the attribute path. This makes it easier to find the file that a value came from.Environment VariablesNanoConf supports environment variables either as overrides to existing values or as additions to the loaded config.
Envars are evaluated on a per-file basis, so you can have different envars for different config files.
The way we manage this is by having a special_envar_prefixkey in the config file.Note:NanoConf will not modify the case of any environment varable name or value. It is up to you to ensure that the case is correct._envar_prefix:myappkey:valueoverrideme:originalexportmyapp_overrideme=changedconfig=NanoConf("/path/to/config.nconf")print(config.overrideme)You can also pass complex data structures as JSON strings in environment variables.exportmyapp_abc='{"a": 1, "b": 2, "c": 3}'config=NanoConf("/path/to/config.nconf")print(config.abc.b)
Package: nanodip
NanoDiPThis project is a fork ofhttps://github.com/neuropathbasel/nanodipand stillbuggy and under construction.DisclaimerImplementation of this software in a diagnostic settings occurs in the sole responsibility of the treating physician. Usage of this software occurs at the risk of the user. The authors may not be held liable for any damage (including hardware) this software might cause.InstallationThe intended location for the installation is/applications/nanodip. For a installation within a virtual environment you can use:mkdir-p/applications/nanodip_env
virtualenv--python3.7/applications/nanodip_env
gitclonehttps://github.com/brj0/nanodip/applications/nanodipcd/applications/nanodipsource/applications/nanodip_env/bin/activate
pipinstall-Usetuptools
pipinstall-e.NanoDiP can be started withpython /applications/nanodip/nanodip/main.pyor simply with the console commandnanodip.
Package: nano-duration
nano_duration: Operations with ISO 8601 durations.What is this.ISO 8601 is most commonly known as a way to exchange date-times in textual format.
to have more precision duration this package included milliseconds, microseconds, and nanoseconds
A lesser-known aspect of the standard is the representation of durations. They have a
shape similar to this:P3Y6M4DT12H30M5S80m90u120nwhich symbols defined as blow:"Y" -> "years"
"M" -> "months"
"D" -> "days"
"H" -> "hours"
"M" -> "minutes"
"S" -> "seconds"
"m" -> "miliseconds"
"u" -> "microseconds"
"n" -> "nanoseconds"As this module maps ISO 8601 dates/times to standard Python data type.Parse:parses an ISO 8601 duration string into Duration object.fromnano_durationimportparseduration=parse("P3Y6M4DT12H24M12S10m80u12n")Generate:generate a duration object into ISO 8601 duration stringfromnano_durationimportDuration,generategenerate(Duration(years=3,months=2,days=3,hours=5,seconds=57,miliseconds=8,microseconds=30,nanoseconds=20,))
Package: nanoepiseg
NanoEpiSeg 1.0.0b3NanoEpiSeg is a tool forde novosegmentation of  a methylome from read-level methylation calls (such as Nanopolish).NanoEpiSeg is currently in development. Please do not hesitate to report bugs or feature requests.A detailed documentation is in the works, stay tuned!PrerequisitesNanoEpiSeg assumes that your methylation calls are stored inMetH5format.InstallationThrough pip:pip install nanoepisegThrough anaconda:conda install -c snajder-r nanoepisegUsageNanoEpiSeg is meant to be parallelizable, which is why you would typically call nanoepiseg on a cluster system in parallel.
In order to best accomplish load-balancing, it is done per hdf5 chunk.You can list the number of chunks per chromosome via:nanoepiseglist_chunks--m5fileINPUT_FILE.m5To then perform segmentation for a certain chunk, you can run:nanoepisegsegment_h5--m5fileINPUT_FILE.m5\--out_tsvOUTPUT_FILE\--reader_workersNUM_READER_WORKERS\--workersNUM_SEGMENTATION_WORKERS\--chromosomeCHROMOSOME\--chunksCHUNK1[CHUNK2...]There are further options available. Please check out the help to discover them.
Package: nanoevent
NanoeventNanoevent is a terrifically small and simple event dispatching library for
Python 3.6+. It's key features:Easy enough to start using inminutes.Pretty feature complete.Designed to be super simple to use.Installationpip install nanoeventUser guideimportnanoeventd=nanoevent.Dispatcher()defhandler(value):print(value)d.attach('event:name',handler)# The following results in "Hi, this is a test" being printed.d.emit('event:name','Hi, this is a test')
Package: nanofootball-sdk
Библиотека для работы с nanofootball.comПозволяет заскриптовать самые простые действия, и легко создавать удалять упражнения, искать повторы и другое.Установкаpip install nanofootball-sdkАвторизацияЧерез файл nanofootball-credentials.yaml(рекомендуется)Создайте файл nanofootball-credentials.yaml следующего содержанияusername: my_name@yandex.ru
password: qwerty111и положите в домашнюю или локальную папку, тогда при вызову всех апишек можно будет пользоваться простоvideos=VideoService()
for video in videos:
    print(video)Через кодfrom nanofoot import Api
api=Api(username='my_name@yandex.ru', password='qwerty123.')В таком случае все сервисы нужно будет создавать вручную указывая апиfrom nanofoot import Api, VideoService

api=Api(username='my_name@yandex.ru', password='qwerty123.')

videos=VideoService(api=api)

for video in videos:
print(video)Видеоfrom nanofoot import VideoService

videos=VideoService()
print(videos[2554])

# prints video with id 2554Упражненияfrom nanofoot import ExerciceService

exercises=ExercisesService()
print(exercises[122])

# prints exercise with id 122

for exercise in exercises['Z3']
    print(exercise)

# will print all exercises in Z3 folderКомандная строкаПозволяет выполнять простые фукции с сайтом, создавать, удалять, просматривать видео и упражненияnf video info 2334
nf video delete 2334
nf --help
Package: nanoget
nanogetThis module provides functions to extract useful metrics from Oxford Nanopore sequencing reads and alignments.FUNCTIONSData can be presented in the following formats, using the following functions:A sorted bam fileprocess_bam(bamfile, threads)A standard fastq fileprocess_fastq_plain(fastqfile, 'threads')A fastq file with metadata from MinKNOW or Albacoreprocess_fastq_rich(fastqfile)A sequencing_summary file generated by Albacoreprocess_summary(sequencing_summary.txt, 'readtype')Fastq files can be compressed using gzip, bzip2 or bgzip. The data is returned as a pandas DataFrame with standardized headernames for convenient extraction. The functions perform logging while being called and extracting data.INSTALLATIONpipinstallnanogetorconda install -c bioconda nanogetCopyright: 2016-2020 Wouter De Costerdecosterwouter@gmail.com
Package: nanograd
nanogradA lightweight deep learning framework.Description•Features•TODO•LicenseDescriptionAfter verification, nanograd is not a city in Russia...However, it is a PyTorch-like lightweight deep learning framework. Use it to implement any DL algo you want with little boilerplate code.Essentially, Nanograd is a continuously updated project. The goal is to implement as many features as possible while using as few abstraction layers as possible (only Numpy functions are allowed). Any contribution to the repo is welcome.The library has a built-in auto-differentiation engine that dynamically builds a computational graph. The framework is built with basic features to train neural nets: basic ops, layers, weight initializers, optimizers and loss functions. Additional tools are developed to visualize your network: computational
graph visualizers or activation map visualizers (SOON!).The repo will be updated regularly with new features and examples.Inspired fromgeohot's tinygrad.FeaturesPyTorch-like autodifferentiation engine (dynamically constructed computational graph)Weight initialization: Glorot uniform, Glorot normal, Kaiming uniform, Kaiming normalActivations: ReLU, Sigmoid, tanh, Swish, ELU, LeakyReLUConvolutions: Conv1d, Conv2d, MaxPool2d, AvgPool2dLayers: Linear, BatchNorm1d, BatchNorm2d, Flatten, DropoutOptimizers: SGD, Adam, AdamWLoss: CrossEntropyLoss, Mean squared errorComputational graph visualizer (see example)A quick side-by-side comparison between PyTorch and Nanograd for tensor computationsBasic tensor calculationsPyTorcha=torch.empty((30,30,2)).normal_(mean=3,std=4)b=torch.empty((30,30,1)).normal_(mean=10,std=2)a.requires_grad=Trueb.requires_grad=Truec=a+bd=c.relu()e=c.sigmoid()f=d*ef.sum().backward()print(a.grad)print(b.grad)Nanograda=Tensor.normal(3,4,(30,30,2),requires_grad=True)b=Tensor.normal(10,2,(30,30,1),requires_grad=True)c=a+bd=c.relu()e=c.sigmoid()f=d*ef.backward()print(a.grad)print(b.grad)Training a CNN on MNIST# Model, loss & optimmodel=CNN()loss_function=CrossEntropyLoss()optim=SGD(model.parameters(),lr=0.01,momentum=0)# Training loopBS=128losses,accuracies=[],[]STEPS=1000foriintqdm(range(STEPS),total=STEPS):samp=np.random.randint(0,X_train.shape[0],size=(BS))X=tensor.Tensor(X_train[samp])Y=tensor.Tensor(Y_train[samp])optim.zero_grad()out=model(X)cat=out.data.argmax(1)accuracy=(cat==Y.data).mean()loss=loss_function(out,Y)loss.backward()optim.step()loss,accuracy=float(loss.data),float(accuracy)losses.append(loss)accuracies.append(accuracy)Y_test_preds=model(tensor.Tensor(X_test)).data.argmax(1)print((Y_test==Y_test_preds).mean())Visualizing a computational graphVisualizing a computational graph has never been that easy. Just callplot_forwardandplot_backward.f.plot_forward()f.plot_backward()TODOSolve batchnorm issuesAdd GRU, LSTM cellsCode example with EfficientNet-B0, CIFAR-10, MNISTCode a transformer with Nanograd and train it on GPULicenseMITGitHub@PABannier· 
Twitter@el_PA_B