Saravanan666/pypi-org huggingface dataset:
Package: npustat
npustat一个能够简洁的显示华为atlas设备状态的命令行工具；之前使用GPU时习惯了使用工具gpustat查看GPU状态，使用华为的Atlas设备之后发现并没有类似的工具，于是仿照gpustat自己写了一个；
显示效果如下图：主要解决命令npu-smi info的两个缺点：显示的信息总是特别长，笔记本单屏显示不下；watch下，同一芯片每次展示不在屏幕的同一位置，观察该芯片的变化较困难；依赖说明python版本要求：python>=3.6；基础软件方面依赖说明：华为Atlas设备目前有两种方式可以获取加速卡的状态信息：ascend-dmi -i和npu-smi info，这两者的区别为：命令ascend-dmi -i：优点：查询结果为json格式，易于解析，不会出错；查询结果中包含实时功率信息；缺点：速度较慢，每次查询耗时约2～3秒；需要安装了toolbox工具包并正确配置了环境变量；命令npu-smi info：优点：查询速度快，每次查询小于1秒；只要正确安装了驱动即可，不需要额外安装其他包；缺点：查询结果中不包含实时功率信息；查询结果无法以json格式返回，解析容易出错，遇到错误时欢迎反馈；默认使用命令ascend-dmi -i做查询，可以使用参数--use-npu-smi修改为使用命令npu-smi info做查询；目前仅在配备了4张Atlas 300I-3000推理卡的 Atlas 500 服务器上进行过测试；所以在其他设备上难免会有问题，欢迎反馈问题；有任何优化建议也欢迎反馈；注：若要安装toolbox需要注意，华为的toolbox工具包相关的版本体系出现过多次变迁：在CANN的v5.0.1及以前版本，toolbox工具包归属到CANN中，可在CANN的软件下载链接中进行下载；在CANN的v5.0.2及以后的版本中不再包含toolbox工具包，而是迁移到了mindx中，需要到mindx的软件下载链接中进行下载；安装说明从PyPI进行安装:pip install npustat使用说明使用如下命令单次获取当前atlas设备信息：npustat使用如下命令动态刷新当前atlas设备信息，默认每2秒刷新一次：npustat --watch可选参数如下：usage: cli.py [-h] [--json] [-i [INTERVAL]] [--no-header] [--no-title]
              [--use-npu-smi] [--show-power] [--compact] [--debug] [-v]

optional arguments:
  -h, --help            show this help message and exit
  
  --json                将所有结果输出为JSON格式；
  
  -i [INTERVAL], --interval [INTERVAL], --watch [INTERVAL]
                        动态刷新模式；INTERVAL为刷新间隔，单位：秒；默认每2秒刷新一次；
  
  --no-header           是否隐藏 header 信息；header 信息包含机器名称、当前时间、版本号；默认展示 header 信息，配置该参数后 header 信息不再展示；
  
  --no-title            是否隐藏 title 信息；title 信息为对当前设备状态值各字段的说明；默认展示 title 信息，配置该参数后 title 信息不再展示；
  
  --use-npu-smi         使用命令"npu-smi info"获取当前设备状态值；注意该命令无法获取到加速卡的实时功率信息；
  
  --show-power          是否展示加速卡的功率信息，默认为展示；配置了参数 "--use-npu-smi" 之后该参数无效；
  
  --compact             是否采用紧凑模式展示信息，默认为不采用；紧凑模式下会去掉空白行及其他无意义的行，适用于加速卡较多，显示器较小，屏幕显示不下的情况；
  
  --debug               Debug模式时允许在程序出错的情况下打印更多的调试信息；
  
  -v, --version         show program's version number and exit另外：比较遗憾的是，目前我没有在任何华为的官方文档中找到获取每个设备上运行了哪些进程的功能，在昇腾论坛的询问结果也是如此：【Atlas 300I 推理卡】怎样查看有哪些进程正在上面运行？所以本项目也不具有显示每个设备上运行了哪些进程的功能；如有人知道如何获取每个atlas上运行了哪些进程，还请告知；显示内容说明machine_name  Mon Dec 20 22:38:59 2021  ascend-dmi version: 2.0.3
========================================================
[加速卡ID], 加速卡类型, 功率
[芯片ID] [DeviceID] Health, 芯片名称 | 温度, AICore, 内存
========================================================

[1], Atlas 300I-3000, 16.30 W
[0] [1] OK, Ascend 310 | 51°C,   0 %, 2621 MB / 8192 MBheader：第1行为header，可以使用参数--no-header不展示该信息；展示的信息从左到右依次为机器名称、当前时间、软件ascend-dmi/npu-smi的版本；title：第2~5行为title，可以使用参数--no-title不展示该信息；title是对后面展示的信息的各字段的说明；[1], Atlas 300I-3000, 16.30 W：每个加速卡的信息：[1]：加速卡ID；Atlas 300I-3000：加速卡类型；16.30 W： 加速卡实时功率；[0] [1] OK, Ascend 310 | 51°C,   0 %, 2621 MB / 8192 MB：每个芯片的信息：[0]：芯片ID；[1]：DeviceID；OK：芯片健康状态；Ascend 310：芯片名称；51°C：温度；0 %：AICore；2621 MB / 8192 MB：内存；ReferencegpustatLicenseMIT License
Package: npv
NPV provides a very high level simulation facility for getting the NPV
(net presevent value) score and other key metrics any business idea.
By default, the simulation automatically generates:monthly and annual income statementmonthly and annual cashflow statementmonthly and annual growth statisticsSimulation is based on set of input variables and volatility
computations that can be adjusted as needed for each project.Additionally, facility for performing a Monte Carlo simulation is provided,
and a method for generating key financial metrics individually.
Package: np-vba
Allen Institute Visual Behavior Analysis packageThis is a re-packaging ofhttps://github.com/AllenInstitute/visual_behavior_analysiswith
scripts/notebooks/dev resources removed and minor updates to code/dependencies
to provide compatibility with Python >3.7See the link above for the original readme.Quickstart for this re-packaged version:python -m pip install --extra-index-url https://pypi.org/simple np_vbaOriginal contributors:Nicholas Cain - @nicainMarina Garrett -marinag@alleninstitute.org, @matchingsNile Graddis -nileg@alleninstitute.org, @nilegraddisJustin Kiggins - @neuromusicJerome Lecoq -jeromel@alleninstitute.org, @jeromelecoqSahar Manavi -saharm@alleninstitute.org, @saharmanaviNicholas Mei -nicholas.mei@alleninstitute.org, @njmeiChristopher Mochizuki -chrism@alleninstitute.org, @mochicDoug Ollerenshaw -dougo@alleninstitute.org, @dougollerenshawNatalia Orlova -nataliao@alleninstitute.org, @nataliaorlovaJed Perkins - @jfperkinsAlex Piet -alex.piet@alleninstitute.org, @alexpietNick Ponvert - @nickponvertKate Roll -kater@alleninstitute.org, @downtoncrabbyRyan Valenza - @ryvalFarzaneh Najafi -farzaneh.najafi@alleninstitute.orgIryna Yavorska -iryna.yavorska@alleninstitute.org
Package: npvcc2016
npvcc2016 - Python loader of npVCC2016Corpusnpvcc2016is a Python package for loader ofnpVCC2016 non-parallel speech corpus.For machine learning, corpus/dataset is indispensable - but troublesome - part.We need portable & flexible loader for streamline development.npvcc2016is the one!!DemoPython/PyTorchpipinstallnpvcc2016fromnpvcc2016.PyTorch.dataset.waveformimportNpVCC2016_wavedataset=NpVCC2016(train=True,download=True)fordatumindataset:print("Yeah, data is acquired with only two line of code!!")print(datum)# (datum, label) tuple providednpvcc2016transparently downloads corpus, structures the data and provides standarized datasets.What you have to do is only instantiating the class!APIsCurrentnpvcc2016support PyTorch.As interface, PyTorch'sDatasetand PyTorch-Lightning'sDataModuleare provided.npVCC2016 corpus is speech corpus, so we providewaveformdataset andspectrogramdataset for both interfaces.PyTorch(pure PyTorch) datasetwaveform:NpVCC2016_wavespectrogram:NpVCC2016_specPyTorch-Lightningwaveform:NpVCC2016_wave_DataModulespectrogram:NpVCC2016_spec_DataModuleDependency NotesPyTorch versionPyTorch version: PyTorch v1.6 is working (We checked with v1.6.0).For dependency resolution, we doNOTexplicitly specify the compatible versions.PyTorch have several distributions for various environment (e.g. compatible CUDA version.)Unfortunately it make dependency version management complicated for dependency management system.In our case, the systempoetrycannot handle cuda variant string (e.g.torch>=1.6.0cannot accept1.6.0+cu101.)In order to resolve this problem, we usetorch==*, it is equal to no version specification.Setup.pycould resolve this problem (e.g.torchaudio'ssetup.py), but we will not bet our effort to this hacky method.
Package: npvcm
非参数化VCM时间片轮转实验评估1. 更新增加指标增量delta估计（2021-12-01）说明：对于比例指标 $ratio = \frac{y}{x}$, 先用模型估计在拉齐分母$x$下的分子增量$\Delta_y$，然后用$\Delta_{ratio} = \frac{\Delta_y}{x_{treatment}}$ 计算比例的提升。2. 使用例子importpandasaspdimportnumpyasnpfromdatetimeimportdatetimefromscipy.statsimportnormfromNonParamVCMimportNonParamVCM############################ 评估比例指标 ############################np.random.seed(10)# 输入待评估数据数据data=pd.read_csv('./data/data_example.csv')# 响应变量，对比例指标的评估选择分子ycol=['wandan_cnt_pcl']# 潜在协变量xcols=['hujiao_cnt_pcl','like_gesake_online_dur','intercept','exp_group','intensity','temperature','is_weekend']# 对比例指标的评估选择分母，常规指标设置为空listdenominator=['hujiao_cnt_pcl']# 基函数数量knots=10# 正则化惩罚参数lamb=1e-4# 协变量选择阈值threshold=1e-2# bootstrap迭代次数runs=500# 评估时间区间（小时）sum_window=np.arange(6,23)# 非参VCM模型评估result=NonParamVCM(data=data,ycol=ycol,xcols=xcols,denominator=denominator,knots=knots,lamb=lamb,threshold=threshold,sum_window=sum_window,runs=runs)# 结果result 中包含p值，策略在各个小时上的效应，各个城市选择的协变量，实验组和对照组指标观测值平均以及策略带来的提升（delta）############################ 评估常规指标 ############################data=dataycol=['gmv']xcols=['call_cnt','temperature','online_time','is_weekend','exp_group']knots=10lamb=1e-4threshold=1e-2runs=100# number of bootstrap iterationsum_window=np.arange(6,24)# treatment time windowresult_gmv=NonParamVCM(data=data,ycol=ycol,xcols=xcols,knots=knots,lamb=lamb,threshold=threshold,sum_window=sum_window,runs=runs)详细的评估过程和数据导入，见 ‘评估脚本.ipynb’3. 输出结果分析NonParamVCM运行过程中会输出三个主要结果（对各个城市，以及所有城市汇总）分别展示：策略在各个小时上对指标的提升（比例指标则是对分子），以及置信区间；一天之内（或者sum_window指定的时间区间）策略提升总量和零假设（没有提升）下的估计值分布的比较；实验组和对照组指标观测值，策略带来的提升值（delta)，p值，以及对于该城市模型选择的协变量4. 主要参数简要说明参数名称参数含义ycol被评估的变量名称，如果评估比例指标则添加分子指标名称xcols所有潜在可能被拉齐的协变量，如果评估比例指标，必须包含分母变量denominator如果评估比例指标，则设置为分母变量名；对常规指标设定为空knots基函数个数（将协变量分时效应视为函数，将其在基函数（样条函数）上展开；通常选择一天中小时数的一半左右，比如24小时选择10个基函数）lamb正则化协变量选择过程中的惩罚参数threshold在正则化回归后，只有其效应函数对应的$l_1$距离大于该参数的协变量才会最终进入到模型。sum_window用于评估的区间（比如只在早高峰进行策略的实验，设置sum_window = [7,8,9] （小时））runsbootstrap构造置信区间时的迭代次数5. NonParamVCM.py 中主要函数及功能：函数名称作用NonParamVCM总的评估函数：正则化协变量选择+elastic net 超参数选择 + elastic net评估实验效应评估covariates_selection正则化模型选择elastic_tuningelastic net回归超参数选择elastic_predictelastic net回归估计bootstrapbootstrap构造参数置信区间
Package: npview
NPViewCLI tools for quickly viewing data in various formats (eventually). Currently only.npyand.csv/.tsvfiles are supported.InstallationpipinstallnpviewUsageCSVResults in a nicely-formatted and interactive view of the csv data with sidescrolling. Quit by pressingq. The number of rows and columsnare printed on exit.csv{PATH_TO_YOUR_FILE.csv}1Unnamed:0PDBchainsequenceinteracting_residues>21811815h9iAGPHMTGLAAISDALAADLAGLSFSSPVAHVYNPLLYAREPHVAYLSRFGSPPKEVLFVGMNPGPWGMAQTGVPFGEVAVVTEWLGINGTVTRPAGEHPKKRVDGFACRRSEVSGRRLWGFIRERFGTPERFFARFFVANYCPLLFLTAEGG>319191jdwACPVSSYNEWDPLEEVIVGRAENACVPPFTIEVKANTYEKYWPFYQKQGGHYFPKDHLKKAVAEIEEMCNILKTEGVTVRRPDPIDWSLKYKTPDFESTGLYSAMPRDILIVVGNEIIEAPMAWRSRFFEYRAYRSIIKDYFHRGAKWTTAP>491912irpANVELFKKFSEKVEEIIEAGRILHSRGWVPATSGNISAKVSEEYIAITASGKHKGKLTPEDILLIDYEGRPVGGGKPSAETLLHTTVYKLFPEVNAVVHTHSPNATVISIVEKKDFVELEDYELLKAFPDIHTHEVKIKIPIFPNEQNIPLL>528281m8zAGRSRLLEDFRNNRYPNLQLREIAGHIMEFSQDQHGSRFIQLKLERATPAERQLVFNEILQAAYQLMVDVFGNYVIQKFFEFGSLEQKLALAERIRGHVLSLALQMYGCRVIQKALEFIPSDQQNEMVRELDGHVLKCVKDQNGNHVVQKCI>697972jptAGSELETAMETLINVFHAHSGKEGDKYKLSKKELKELLQTELSGFLDAQKDADAVDKVMKELDEDGDGEVDFQEYVVLVAALTVACNNFFWENS>71251253ew8ALVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGEYGLGYLCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERI>81521523sjzAAWPKVQPEVNIGVVGHVDHGKTTLVQAITGIWTSKHSEETIKLGYAETNIGVCESCKKPEAYVTEPSCKSCGSDDEPKFLRRISFIDAPGHEVLMATMLSGAALMDGAILVVAANEPFPQPQTREHFVALGIIGVKNLIIVQNKVDVVSKE>91611614ehsASAPSLEFLEKLVIRYLLEDRSLLDLAVGYIHSGVFLHKKQEFDALCQEKLDDPKLVALLLDANLPLKKGGFEKELRLLILRYFERQLKEIPKSSLPFSEKXICLKKARQAIXKLKQGELVAILE>101151153bitAXEELNIDFDVFKKRIELLYSKYNEFEGSPNSLLFVLGSSNAENPYQKTTILHNWLLSYEFPATLIALVPGKVIIITSSAKAKHLQKAIDLFKDPESKITLELWQRNNKEPELNKKLFDDVIALINSAGKTVGIPEKDSYQGKFXTEWNPVW>111641644iu4AKKMSIVLAPFSGGQPHSGVELGPDYLLKQGLQQDMEKLGWDTRLERVFDGKVVEARKASDNGDRIGRVKRPRLTAECTEKIYKCVRRVAEQGRFPLTIGGDHSIALGTVAGVLSVHPDAGVIWVDAHADINTMSGTVSGNLHGCPLSILLG>

NumRows:11test.csv
NumColumns:8npp{PATH_TO_YOUR_FILE.npy}NumPYnpv{PATH_TO_YOUR_FILE.npy}{THRESHOLD}Where{THRESHOLD}is an integer specifying Total number of array elements which trigger summarization rather than full repr. Default issys.maxsize.npp{PATH_TO_YOUR_FILE.npy}Will attempt to print your saved matrix & itss dimensions usingprettymatrix:(2x2)┌┐│122││3334444│└┘
Package: np-workflows
np_workflowsThis package contains all the Python code required to run Mindscope Neuropixels
experiments.Experiment workflows and related tasks are coordinated by Jupyter notebooks maintained here:https://github.com/AllenInstitute/np_notebooksRunning the notebooks requires a Python environment with:Python >= 3.11np_workflowsJupyter / JupyterLabgit clone https://github.com/AllenInstitute/np_notebooks
conda create -n workflows python=3.11
pip install np_workflows
pip install jupyterlabKeep thenp_workflowspackage up-to-date by runningpip install np_workflows -Ubefore each useTo develop this packagegit clonethenpdm installNP. rig setup.venv is currently used, with PDM to update packages before each experiment:install JupyterLab desktop (Win 64-bit)clonenp_notebooks(notnp_workflows)cd c:\Users\svc_neuropix\Documents\github & git clone https://github.com/AllenInstitute/np_notebooksfind the path to python.exe 3.11.*, or install it manuallycreate a new venv in thenp_notebooksdir:cd np_notebooks & path-to-python.exe -m venv .venvactivate venv:.venv\scripts\activate(terminal should now report(np_notebooks-3.11))install PDM in venv:path-to-python.exe -m pip install pdminstallnp_notebooksrequirements:pdm installin JupyterLab, set the default to Python environment to usec:\Users\svc_neuropix\Documents\github\np_notebooks\.venv\scripts\python.exeopen up any workflow in thenp_notebooksdir and check that the initial cell with imports works
Package: npx
npxNumPyis a large library used everywhere in scientific computing.
That's why breaking backwards-compatibility comes at a significant cost and is almost
always avoided, even if the API of some methods is arguably lacking. This package
provides drop-in wrappers "fixing" those.scipyxdoes the same forSciPy.If you have a fix for a NumPy method that can't go upstream for some reason, feel free
to PR here.dotimportnpximportnumpyasnpa=np.random.rand(3,4,5)b=np.random.rand(5,2,2)out=npx.dot(a,b)# out.shape == (3, 4, 2, 2)Forms the dot product between the last axis ofaand thefirstaxis ofb.(Not the second-last axis ofbasnumpy.dot(a, b).)np.solveimportnpximportnumpyasnpA=np.random.rand(3,3)b=np.random.rand(3,10,4)out=npx.solve(A,b)# out.shape == (3, 10, 4)Solves a linear equation system with a matrix of shape(n, n)and an array of shape(n, ...). The output has the same shape as the second argument.sum_at/add_atnpx.sum_at(a,idx,minlength=0)npx.add_at(out,idx,a)Returns an array with entries ofasummed up at indicesidxwith a minimum length ofminlength.idxcan have any shape as long as it's matchinga. The output shape is(minlength,...).The numpy equivalentnumpy.add.atismuchslower:Relevant issue reports:ufunc.at (and possibly other methods)
slowuniqueimportnpxa=[0.1,0.15,0.7]a_unique=npx.unique(a,tol=2.0e-1)assertall(a_unique==[0.1,0.7])npx'sunique()works just like NumPy's, except that it provides a parametertol(default0.0) which allows the user to set a tolerance. The real line
is essentially partitioned into bins of sizetoland at most one
representative of each bin is returned.unique_rowsimportnpximportnumpyasnpa=np.random.randint(0,5,size=(100,2))npx.unique_rows(a,return_inverse=False,return_counts=False)Returns the unique rows of the integer arraya. The numpy alternativenp.unique(a, axis=0)is slow.Relevant issue reports:unique() needlessly slowisin_rowsimportnpximportnumpyasnpa=[[0,1],[0,2]]b=np.random.randint(0,5,size=(100,2))npx.isin_rows(a,b)Returns a boolean array of lengthlen(a)specifying if the rowsa[k]appear inb.
Similar to NumPy's ownnp.isinwhich only works for scalars.meanimportnpxa=[1.0,2.0,5.0]npx.mean(a,p=3)Returns thegeneralized meanof a
given list. Handles the cases+-np.inf(max/min) and0(geometric mean) correctly.
Also does well for largep.Relevant NumPy issues:generalized meanLicenseThis software is published under theBSD-3-Clause
license.
Package: npy
# NpyNormalize Python block and indentation technique[![PyPI](https://img.shields.io/badge/pypi-v3.12.0-green.svg?style=flat)](https://pypi.python.org/pypi/npy/)[![Twitter](https://img.shields.io/badge/twitter-ZviWex-4099FF.svg?style=flat)](https://twitter.com/ZviWex)[![GNU License](https://img.shields.io/badge/license-GNU-blue.svg?style=flat)](https://www.gnu.org/licenses/gpl-3.0.en.html)[![WebSite](https://img.shields.io/website-down-red/http/shields.io.svg?label=website)](https://ZviWex.com)# Usage## As runnerYou can write your program in .npy file with the right way to indexnt and to crate blocks, and then run the normalyze.py parser on the file/repo```shpython normalyze.py file_pathpython normalyze.py -d dir_pathpython normalyze.py -h```The  normalyze.py file will be at <Python dir>\ScriptsThis options is to convert the .npy file (normal indentation file) to a .py file (readable to python interpreter)## As moduleYou can write your code this way and run it directly from python interptreter```pythonimport npy; exec(npy.run("""# Your code goes here"-"-"Lined raw string will look like this"-"-""""),globals())```Note - this supports modules too.# Installation```shpython -m pip install --upgrade pippython -m pip install --upgrade npy```# ContactIf you have any questions feel free to ping meat [here](https://ZviWex.com/) to connect.There is also a [mail address](mailto:zvikizviki@gmail.com) for higher latency discussion.
Package: npy4chan
Very simple, minimalistic wrapper for 4chan’s API.To create an instance of the wrapper:api = npy4chan.load()Attributes and methods:api.boards # dictionary of board identifiers and title (i.e; {"b": "Random"})
api.getPosts(board, page=1) # Get list of thread on page <page> of <board>
api.getThread(board,threadno) # Get posts from <board>'s thread <threadno>
api.getArchived(board) # get archived threadno's from <board>
Package: nPYc
Toolbox for preprocessing of metabolic profiling datasets
	---------------------------------------------------------

	.. image:: https://travis-ci.org/phenomecentre/nPYc-Toolbox.svg?branch=master
	   :target: https://travis-ci.org/phenomecentre/nPYc-Toolbox
	   :alt: Travis CI build status

	.. image:: https://readthedocs.org/projects/npyc-toolbox/badge/?version=latest
	   :target: http://npyc-toolbox.readthedocs.io/en/latest/?badge=latest
	   :alt: Documentation Status

	.. image:: https://codecov.io/gh/phenomecentre/nPYc-Toolbox/branch/master/graph/badge.svg
	   :target: https://codecov.io/gh/phenomecentre/nPYc-Toolbox
	   :alt: Test coverage

	|

	The nPYc toolbox offers functions for the import, preprocessing, and QC of metabolic profiling datasets.

	Documentation can be found on `Read the Docs <http://npyc-toolbox.readthedocs.io/en/latest/?badge=latest>`_.

	Imports
	 - Peak-picked LC-MS data (XCMS, Progenesis QI, *&* Metaboscape)
	 - Raw NMR spectra (Bruker format)
	 - Targeted datasets (TargetLynx, Bruker BI-LISA, *&* BI-Quant-Ur)

	Provides
	 - Batch *&* drift correction for LC-MS datasets
	 - Feature filtering by RSD *&* linearity of response
	 - Calculation of spectral line-width in NMR
	 - PCA of datasets
	 - Visualisation of datasets

	Exports
	 - Basic tabular csv
	 - `ISA-TAB <http://isa-tools.org>`_

	The nPYc toolbox is `developed <https://github.com/phenomecentre/npyc-toolbox>`_ by the informatics team at `The National Phenome Centre <http://phenomecentre.org/>`_ at `Imperial College London <http://imperial.ac.uk/>`_.
Package: npyetl
Copyright (c) 2019 Justus SchockPermission 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.Description: # NPYETL
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Package: npyfile
npyfileis a a package that allows you to continuously write arrays to a.npyfile. You can use it like a
standard Python file with the well-known context manager.Warning: Due to the internal structure of.npyfiles, the arrays must have the sameshape&dtype.ExampleGenerate some artificial dummy data, e.g. images, & write them into a temporary file. A real world scenario reflecting
this is the continuous arrival of new images from a camera.importpathlibimporttempfiletmp_dir=tempfile.TemporaryDirectory()outfile=pathlib.Path(tmp_dir.name)/'images.npy'images=np.random.randint(low=0,high=255,size=(10,640,480,3))withNpyFile(outfile)asfile:forimginimages:file.write(img)np.testing.assert_array_equal(images,np.load(outfile))tmp_dir.cleanup()Creditsattrs: Project & infrastructure setupnumpy: File format
Package: npyi
NPyIAPI wrapper around the NPPES API.https://npiregistry.cms.hhs.gov/registry/help-apiDocumentation:https://npyi.readthedocs.io/en/latest/InstallpipinstallnpyiExamplesfrom npyi import npi

# sample search by first_name / last_name
response = npi.search(search_params={'first_name': 'Andrew', 'last_name': 'Jackson'})
print(response.keys()) # dict_keys(['result_count', 'results'])
first_entry = response['results'][0]
print(first_entry['basic']['first_name']) # ANDREW
print(first_entry['basic']['last_name']) # JACKSON

# sample search by NPI number
response = search(search_params={'number': '1417367343'})
print(response['result_count']) # 1
print(response['results'][0]['number']) # 1417367343

# sample search by city
response = search(search_params={'city': 'San Francisco'})
first_entry = response['results'][0]
print(len(first_entry['addresses'])) # 2 (different address purposes - LOCATION and MAILING)
print(first_entry['addresses'][0]['city']) # SAN FRANCISCO

# limit example
response = search(search_params={'first_name': 'Andrew', 'city': 'New York'}, limit=50)
print(response['result_count']) # 50
response = search(search_params={'first_name': 'Andrew', 'city': 'New York'}, limit=1000)
print(response['result_count']) # 200 (200 is the max)
response = search(search_params={'first_name': 'Andrew', 'city': 'New York'})
print(response['result_count']) # 10 (10 is the default)
Package: npym
NPyMNPyM is a service that translates NPM packages into Python wheels so that you
can install them using a Python package manager. This helps calling JS code from
Python, especially if you combine it withNode Edge.This package is a support package for NPyM, which is depended upon by the
converted NPM packages.It provides:A wrapper to allow JS bin to be installed by package managersA way to know where thenode_modulesis installedIn order to know this, you can easily do:fromnpymimportnode_modulesprint(node_modules)
Package: npynx
npynxtwisted based http server with multiple domain supportusagefrom test/TestServer.pyfromjinja2importTemplatefromnpynximportServer,Domain,responseclassDomainTest(Domain):root_template=Template("ON {{ ROOT }}")defroot(self,*args):returnresponse(body=self.root_template.render(content="/"))defanother(self,*args):returnresponse(body=self.root_template.render(content="/another"))def__init__(self):Domain.__init__(self,"127.0.0.1:4200",{"":self.root,"/":self.root,"/another":self.another,})if__name__=='__main__':server=Server("tcp:4200")domain_test=DomainTest()server.add_domain(domain_test)try:server.start()server.join()exceptKeyboardInterrupt:server.terminate()