Saravanan666/pypi-org huggingface dataset:
Package: np-config
np_configFor use on internal Allen Institute networkFetch configs from ZooKeeper nodes or .yaml/.json files:importnp_configzk_config:dict[str,str|int]=np_config.from_zk('/rigs/NP.1')file_config:dict[str,Any]=np_config.from_file('local_config.yaml')If running on a machine attached to a Mindscope Neuropixels rig (NP.0, ...,NP.3), get rig-specific config info with:rig=np_config.Rig()name:str=rig.id# "NP.1"index:int=rig.idx# 1acquisition_pc_hostname:str=rig.acq# "W10DT713843"config:dict[str,str|int]=rig.config# specific to NP.1paths:dict[str,pathlib.Path]=rig.paths# using values from rig.configIf not running on a rig-attached machine, get the config for a particular rig by supplying rig-index as aninttoRig:np1=np_config.Rig(1)np1_mvr_data_root:pathlib.Path=np.paths['MVR']the Mindscope ZooKeeper server is ateng-mindscope:2181configs can be added via ZooNavigator webview:http://eng-mindscope:8081or more conveniently, via an extension for VSCode such asgaoliang.visual-zookeeper
Package: npc-samstim
npc_samstimTools for processing stim data from Sam's DynamicRoutingTask, compatible with data in the cloud.Usagecondacreate-nnpc_samstimpython>=3.9
condaactivatenpc_samstim
pipinstallnpc_samstimPython>>>importnpc_samstimDevelopmentSee instructions inhttps://github.com/AllenInstitute/npc_samstim/CONTRIBUTING.mdand the original template:https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md
Package: npc_session
npc_sessionneuropixelscloudsessionBasic tools for parsing subject, session, date and time associated with data from the
Mindscope Neuropixels team, in the cloud.quickstartpipinstallnpc_sessionParse a normalized ID from a path or string:>>>fromnpc_sessionimportSessionRecord;>>>s=SessionRecord('//allen/programs/mindscope/workgroups/templeton/TTOC/2022-07-26_14-09-36_366122')>>>s'366122_2022-07-26'>>>s.idx0>>>s.subject366122>>>s.date'2022-07-26'>>>s.date.dtdatetime.date(2022,7,26)>>>s.date.year2022>>>s_1=SessionRecord('//allen/programs/mindscope/workgroups/templeton/TTOC/2022-07-26_14-09-36_366122_1')>>>s_1.idx1>>>s_2=s_1.with_idx(2)>>>s_2.idx2
Package: npc_shields
npc_shieldsTools for Neuropixels probe insertion into SHIELD implants: providing suggested targets, recording actual insertions, storing notes.creating new implant drawingsexport 2D view of 3D modelget a .sldpart CAD model from MPEopen in Solidworksrotate the view to face the top of the implant, matching the view from the rig scopeSave As -> .dxfcheck "current" view onlyedit 2D drawing for widgetopen a copy of one of the existing .svg drawings in Inkscape/Illustrator, to use the same page size and colorsadd the new .dxf fileif the lines are too thick to see any detail, change them to 1pt for nowresize the new drawing to fill the page, like the existing drawingwith the two drawings side by side, update the new drawing to match:the outline is fragmented and needs to be joined together so a white fill can be appliedholes that have straight edges will need joining to be fill-ablecircular holes should already be fill-ablecheck the CAD model for probe-letter designations (A1, A2, B1, ...)when finished, export as .svg
Package: npc-stim
npc_stimTools for accessing and processing stim data, compatible with data in the cloud.Usagecondacreate-nnpc_stimpython>=3.9
condaactivatenpc_stim
pipinstallnpc_stimPython>>>importnpc_stimDevelopmentSee instructions inhttps://github.com/AllenInstitute/npc_stim/CONTRIBUTING.mdand the original template:https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md
Package: npc-sync
npc_syncRepackaging ofAllenSDK.SyncDatasetwith extra functionality, compatible with data in the cloud.Usagecondacreate-nnpc_syncpython>=3.9
condaactivatenpc_sync
pipinstallnpc_syncPython>>>importnpc_sync>>>d=npc_sync.SyncDataset('s3://aind-ephys-data/ecephys_676909_2023-12-14_12-43-11/behavior_videos/20231214T124311.h5')>>>d.line_labels[0]'barcode_ephys'>>>d.validate(opto=True,audio=True)# note: some methods from the original `SyncDataset` are now property getters, including `line_labels`DevelopmentSee instructions inhttps://github.com/AllenInstitute/npc_sync/CONTRIBUTING.mdand the original template:https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md
Package: npctypes
npctypesA Python package for working with NumPy arrays and ctypes arrays.Free software: BSD 3-ClauseDocumentation:https://npctypes.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thenanshe-org/nanshe-cookiecutterproject template.History0.1.0 (2016-10-03)First release on PyPI.
Package: npc-utils
npc_utilsCollection of MindScope Neuropixels packages, simplifying installation and keeping versions updated.Usagecondacreate-nnpc_utilspython>=3.9
condaactivatenpc_utils
pipinstallnpc_utilsPython>>>importnpc_utilsDevelopmentSee instructions inhttps://github.com/AllenInstitute/npc_utils/CONTRIBUTING.mdand the original template:https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md
Package: npd-category-correlation
IntroductionThis package has been created for the purpose of integrating Bloomberg Data with NPD Equity Watch data.InstallationIMPORTANT: you must separately install the Bloomberg Python API before using certain functions (first line below)python-mpipinstall--index-url=https://bcms.bloomberg.com/pip/simpleblpapi

python-mpipinstallnpd-category-correlation==0.1.3Use Case: Creating Category Correlation DatasetThe get-correlation-dataset command takes two parameters: the path to your local EW flat file (must be CSV readable) and your desired output path (if left blank will be directory in which you run the command)get-correlation-datasetpath/to/FlatFile/EW_DATA.gzpath/to/output/correlation_data.csvIMPORTANT: You must login have Bloomberg Anywhere Terminal running on your computer for this to work.
Package: npdchecker
NPDCheckerИнструмент для проверки статуса плательщика налога на профессиональный доход (НПД) по ИННСинхронный режим:fromnpdcheckerimportNPDCheckerchecker=NPDChecker()inn=144075719327status=checker(inn)# проверка актуального статусаstatus=checker(inn,"2020-07-25")# Проверка статуса на 25 июля 2020 годаАсинхронный режим:fromnpdcheckerimportNPDCheckerchecker=NPDChecker()inn=144075719327asyncdefcheck():asyncwithchecker:status=awaitchecker.a(inn)status=awaitchecker.a(inn,"2019-12-31")checker.run(check())ОшибкиВсе ошибки API идут классомNPDError. Класс имеет атрибутыcodeиmessageсогласноДокументации.
Package: npd-data
Introductionnpd_data is a module that extract petroleum data from the NPD website using API's
It does also extract shape files for map plotting and further posptocessing
Detailed documentations and examples located on the notebookes:npd wraperdocsSuggestions are welcomeFeature summary:Python 3.6+ supportFocus on high speed with API servingOutput mainly based on pandas and geopandascould be combined with shape files for better data insightsSupported in both Linux, Windows and Mac OS (might be some issues with VPN activated)These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.Installationpip install -i https://test.pypi.org/simple/ npd-dataQuick Use> from npd_data.npd_wraper import fi``eld
>
>df =field.get_field_production_yearly()
>LicenseUsingMIT license
Package: npd-fs-data
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: npdl
NumpyDL: Numpy Deep Learning LibraryDescriptionsNumpyDLis:Based on Pure Numpy/PythonFor DL EducationAnd for My HomeworkFeaturesIts main features are:Purein NumpyNativeto PythonAutomatic differentiationsare basically supportedCommonly used modelsare provided: MLP, RNNs, LSTMs and CNNsAPIlikeKeraslibraryExamplesfor several AI tasksApplicationfor a toy chatbotMobile friendlydocumentsDocumentationAvailable online documents:latest docsdevelopment docsstable docsAvailable offline PDF:latest PDFInstallationInstall NumpyDL using pip:$>pipinstallnpdlInstall from source code:$>pythonsetup.pyinstallExamplesNumpyDLprovides several examples of AI tasks:sentence classificationLSTM inexamples/lstm_sentence_classification.pyCNN inexamples/cnn_sentence_classification.pymnist handwritten recognitionMLP inexamples/mlp-mnist.pyMLP inexamples/mlp-digits.pyCNN inexamples/cnn-minist.pylanguage modelingRNN inexamples/rnn-character-lm.pyLSTM inexamples/lstm-character-lm.pyOne concrete code example inexamples/mlp-digits.py:importnumpyasnpfromsklearn.datasetsimportload_digitsimportnpdl# preparenpdl.utils.random.set_seed(1234)# datadigits=load_digits()X_train=digits.dataX_train/=np.max(X_train)Y_train=digits.targetn_classes=np.unique(Y_train).size# modelmodel=npdl.model.Model()model.add(npdl.layers.Dense(n_out=500,n_in=64,activation=npdl.activation.ReLU()))model.add(npdl.layers.Dense(n_out=n_classes,activation=npdl.activation.Softmax()))model.compile(loss=npdl.objectives.SCCE(),optimizer=npdl.optimizers.SGD(lr=0.005))# trainmodel.fit(X_train,npdl.utils.data.one_hot(Y_train),max_iter=150,validation_split=0.1)ApplicationsNumpyDLprovides one toy application:Chatbotseq2seq inapplications/chatbot/model.pyAnd its final result:SupportsNumpyDLsupports following deep learning techniques:LayersLinearDenseSoftmaxDropoutConvolutionEmbeddingBatchNormalMeanPoolingMaxPoolingSimpleRNNGRULSTMFlattenDimShuffleOptimizersSGDMomentumNesterovMomentumAdagradRMSpropAdadeltaAdamAdamaxObjectivesMeanSquaredErrorHellingerDistanceBinaryCrossEntropySoftmaxCategoricalCrossEntropyInitializationsZeroOneUniformNormalLecunUniformGlorotUniformGlorotNormalHeNormalHeUniformOrthogonalActivationsSigmoidTanhReLULinearSoftmaxElliotSymmetricElliotSoftPlusSoftSignChangelog0.4.0 (2017.-06-18)Version 0.4.0.Embedding backwardMomentumNesterovMomentumAdagradRMSpropAdadeltaAdamAdamax0.3.0 (2017-06-15)Version 0.3.0.Add chatbot application.Add more examples.Support LSTM.Support GRU.0.2.5 (2017-05-30)Version 0.2.5.Add almost all test.0.2 (2017-05-10)Second release.Support Layers:Batch Normalization LayerEmbedding LayerMeanPooling LayerFlatten LayerSupport Activations:SymmetricElliotLReLUSoftPlusSoftSignSupport Initializations:HeNormalHeUniformOrthogonalAdd more tutorials.Add more API comments.0.1 (2017-04-11)First release.Support layers:Dense (perceptron) LayerSoftmax LayerDropout LayerConvolution LayerMaxPooling LayerSimpleRNN LayerSupport Activations:SigmoidTanhReLUSoftmaxElliotSupport Initializations:UniformNormalLecunUniformGlorotUniformGlorotNormalSupport Objectives:MeanSquaredErrorHellingerDistanceBinaryCrossEntropySoftmaxCategoricalCrossEntropySupport Optimizers:SGD
Package: npdoc
# npdoc.pyI do a lot of work entirely in an IPython notebook and it can be annoying to switch back and forth from a browser to the command line to look up function usage.  I therefore wrote this little utility.  Given a numpy function name, it will look up the function's source on GitHub, and parse the usage comment at the top of the function.This code requires the BeautifulSoup 4 library, which is readily pip'd.Here's an example:```>> import npdoc>> npd('meshgrid')>> Return coordinate matrices from coordinate vectors.Make N-D coordinate arrays for vectorized evaluations ofN-D scalar/vector fields over N-D grids, givenone-dimensional coordinate arrays x1, x2,..., xn... versionchanged:: 1.91-D and 0-D cases are allowed....```(Not showing the full output because there's a lot)You can also specify to only see the first or last lines with the ```nl``` argument.  A positive argument gives the first N lines:```>> import npdoc>> npd('meshgrid', nl = 4)>> Return coordinate matrices from coordinate vectors.Make N-D coordinate arrays for vectorized evaluations ofN-D scalar/vector fields over N-D grids, given```And a negative argument gives the last N lines:```>> import npdoc>> npd('meshgrid', nl = -4)>>  >>> y = np.arange(-5, 5, 0.1)>>> xx, yy = np.meshgrid(x, y, sparse=True)>>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)>>> h = plt.contourf(x,y,z)```Note:  in the case of the base NumPy functions, like ```np.array```, the source is more difficult to get to and to parse (those functions are written in C, too), so in that case, ```npd()``` will just open a browser with the formatted webpage.  If you just want to go straight to the formatted webpage anyway, just do ```npd('meshgrid', browser = True)```.
Package: npd-whale-wisdom
IntroductionLibrary for integrating Whale Wisdom and NPD data.Installation for Running LocallyInstallation Python packagespipinstallnpd-whale-wisdom=0.1.9Add WW Keys to Envionment
For running locally, you must add the Whale Wisdom shared and secret keys to Windows environemnt variables. These can be found in control pannel under properties. You can also search environment variables in the search bar. The shared key must be named "WW_SHARED_KEY" and the secret key must be named "WW_SECRET_KEY".UsageRunning Full Report Builder
Simply navigate to the project directory and runpythonmain.pyYou'll need to input the path to some input files. The web app hosting this code will pull these from Azure Storage.Calling Whale Wisdom API
If you want to make a custom Whale Wisdom API call do the following:fromWhaleWisdom.Common.py_whale_wisdomimportcall_api#the input is string representation of a python dictionaryapi_input='{"command":"stock_lookup","symbol":"GOOGL"}'api_res=call_api(api_input)
Package: nPDyn
nPDynnPDyn is a Python based API for analysis of neutron backscattering data.The API aims at providing a lightweight, user-friendly and modular tool
to process and analyze quasi-elastic neutron scattering (QENS) and
fixed-window scans (FWS) obtained with backscattering spectroscopy.nPDyn can be used in combination with other software for neutron data analysis
such asMantid.An important feature of nPDyn is the modelling interface, which is designed
to be highly versatile and intuitive for multidimensional dataset with global
and non-global parameters.
The modelling in nPDyn is provided by builtin classes,params.Parameters,model.Modelandmodel.Component.
nPDyn provides also some helper functions to uselmfitas modelling backend.
SeeFit datasection in documentation for details.Eventually, some plotting methods are available to examine processed data,
model fitting and optimized parameters.Installation:Prior to building on Windows, the path to Gnu Scientific Library (GSL) should
be given in setup.cfg file (required by libabsco)If not, the package can still be installed but paalman-ping corrections won’t
work.Unix and WindowsFor installation within your python framework:with pip:python3-mpipinstallnPDynwith source code:gitclonehttps://github.com/kpounot/nPDynnpdyncdnpdynpython3setup.pyinstallFull documentationSeehttps://npdyn.readthedocs.io/en/latest/SupportAgoogle groupis available for any
question, discussion on features or comment.In case of bugs or obvious change to be done in the code use GitHub Issues.ContributionsSeecontributing.Getting startedThe nPDyn API is organized around aSampleclass.
This class inherits from the NumPy ndarray class with some extra
features added, such as neutron scattering-specific attributes, binning,
data correction algorithm, automatic error propagation and data fitting.In a neutron backscattering experiment, there is not only the measurement of
samples but also some calibration measurements like vanadium, empty cell
and solvent signal (often D2O). Some methods of theSampleclass can be used to perform normalization or
absorption correction using the dataset corresponding to vanadium
or empty cell, respectively. These calibration dataset can be used also
in thefitfunction to automatically add a background or perform
a convolution with the resolution function.Details regarding importation of data are available in thedataImportsection of the documentation.Importantly, nPDyn provides versatile tools for model building and fitting
to the data. See the sectiondataFittingfor details.Finally, aplotmethod is provided for easy visualisation
of the data and the fit results.
Package: npe2
npe2napari plugin refactorsee alsohttps://github.com/napari/napari/issues/3115DocumentationFor documentation on authoring npe2 plugins, see thenapari plugin docs.  These include:themanifest referencethecontribution guideCommand line toolIncludes a command line toolnpe2with the following commands:Commands:cacheCacheutilsconvertConvertfirstgenerationnapariplugintonew(manifest)format.parseShowparsedmanifestasyamlvalidateValidatemanifestforadistributionnameormanifestfilepath.examples:# convert current directory to an npe2-ready plugin# (note: the repo must also be installed and importable in the current environment)npe2convert.npe2validateyour-plugin-packagenpe2parseyour-plugin-package
Package: npeg
Warning: project is unstable now.NpegIt is a small and blazingly fast python library for image processing.Why is it fast?Npeg core written on C.Npeg support cpu hardware acceleration, for example, avx. (In the process)Npeg support gpu gardware acceleration, for example, cuda, opencl. (In the future)Supported formatsPNG (In the future)JPG (In the future)WEBP (In the future)TIFF (In the future)PBMPGM (In the future)PPM (In the future)PPA (In the future)Examplesfromnpegimportimg_read,Interpolation# img rotation.withimg_read('cats.pbm')asimg:img.rotate(10)img.rotate(-10,Interpolation.INTER_LINEAR)img.save('rotated_cat.pbm')# Or you can read file manually.img=img_read('dogs.pbm')img.save('dods_cipy.bpm')# Kind of img copying.img.close()Installation$pipinstallnpegInstallation from sourceDependencies:poetrymeson$gitclonehttps://github.com/neothebestdeveloper/npeg
$poetryrunbuild_pkg
$pipinstall"dist/npeg-0.0.1-py3-none-any.whl"
Package: npend
Provides a convenient way to append numpy arrays to a file.The NpendWriter and NpendReader classes are used to write and read numpy arrays respectively.RequirementsnumpysipUsage#! -*- coding:utf-8 -*-
    from npend import NpendWriter, NpendReader
    import numpy as np
    if __name__=="__main__":
        filePath="test.npd"
        nw=NpendWriter(filePath)
        arr=np.random.random((34,5,6))# Write an array that has three dimensions.
        nw.append(arr)# Keep the size of the last two dimensions the same as the data written before.
        arr=np.random.random((10,5,6))
        nw.append(arr)
        arr=np.random.random((5,6))# Write an array with two dimensions.
        nw.append(arr)
        nw.close()
        nr=NpendReader(filePath)
        arr=nr.read()# Load numpy array from file.
        print(arr.shape,arr.dtype)
Package: npeuclid
Descriptionnpeuclid is a library that provides vector and matrix classes for 2D and 3D
graphics applications.npeuclid is a port of thehttps://github.com/ezag/pyeuclidlibrary to numpy.
Instead of doing matrix multiplications in native python as euclid is doing,
npeuclid is delegating all calculations to numpy.Differences to pyeuclidThe base type of all pyeuclid types are numpy ndarrays.2D and 3D geometry is supported. Currenly there is no quaternion support.All operaters return their transformed values, i.e. there are no inline operators. E.g.t.scale(sx,sy)will return a scaled version oft, and won't modify t.There is no vector point and vector support like in euclid. There is only one typeVec2, which behaves like a point under transformation. To get a vector transformation dot.linear()*p(like in Eigen).TypesHere are the types defined by npeuclid:2DVec2- Holds a 2D vector (or point).Vec2Array- Holds a list of points. All operations that can be done on a signal should be available on a Vec2Array as well. In particular, anAffine2operation may be applied on aVec2Arraywhich will transform all the points.Affine2- An 2D affine transformation.3DVec3- Holds a 3D vector (or point).Vec3Array- Holds a list of points. All operations that can be done on a signal should be available on a Vec3Array as well. In particular, anAffine3operation may be applied on aVec3Arraywhich will transform all the points.Affine3- An 3D affine transformation.Usage2Dp = Vec2(2,3)
q = Vec2(5,6)
angle = math.radians(33)
t = Affine2.new_translate(p.x,p.y).rotate(angle).translate(-p.x,-p.y)
print(t*q)
t = Affine2.new_rotate_around(angle, p.x, p.y)
print(t*q)
t = Affine2.rotate_around(angle, p.x, p.y)
print(t*q)
t = Affine2.new_translate(p.x,p.y)
print(t*p, t.linear()*p) 

# Use of Vec2Array is using numpy and is *much* faster than looping.
pp = Vec2Array([[5,6],[7,8],[3,4]])
print (t*pp)[0]3Dp = Vec2(2,3,4)
q = Vec2(5,6,7)
angle = math.radians(33)
t = Affine2.new_translate(p.x,p.y).rotatex(angle).translate(-p.x,-p.y)
print(t*q)
Package: npfc
npfc: Natural Product Fragment Combinationsnpfcis a chemoinformatics tool for classifying Natural Product (NP) fragment
combinations into predefined categories and therefore identifying pseudo-NPs.Pseudo-NPs are novel NP-inspired compound classes that combine the biological
relevance of NPs with the efficient exploration of chemical space by
fragment-based drug design.The npfc tool is written in Python and based on several key packages:RDKitfor handling chemistrypandasfor managing data into DataFramesNetworkXfor modelling graphsSnakemakefor encapsuling scripts into reproducible workflowsInstallationThe npfc tool can be installed using PyPi. In your Python environment, run:>>> pip install npfcWarning!!! There is currently an issue when pytables is installed via pip.
It was removed from the dependency but is required for the npfc workflow
(reference file used during deduplication). Instead, please install pytables via
conda:>>> conda create -n npfc_env python pytables
>>> conda activate npfc_env
>>> pip install npfcDocumentationThe full documentation is available at:https://npfc.readthedocs.io/.
It describes the API, as well as the different workflows implemented.
Moreover, a few tutorials are being included to show how to use this library in
some practical use.ContributionFeedback from the community is warmly welcomed. It can be in the form of bug
reports and feature requests submitted via github or code contribution via
forking this repo and submitting pull requests.Licensenpfc is licensed under theMIT license.
Package: npgamma
npgammais deprecated. A known bug in this version
ofnpgammais that the evaluation and reference grid
definitions are flipped. This has flow on effects
to parameters such as thelower_dose_thresholdand
simply swapping evaluation and reference grids may
not achieve the desired results.npgammais superceded bypymedphys.gamma.To installpymedphysseehttps://pymedphys.com/en/latest/getting-started/installation.htmlOn usage of the gamma function withinpymedphysseehttps://pymedphys.com/en/latest/user/gamma.html
Package: npgbq
ChangelogVersion 2.0.0:Change all of the API codeAllowed to set detailed configuration for schema of a BigQuery tableAdded more features to work with BigQueryVersion 2.0.1:Changed some methods to becomestaticmethodVersion 2.0.2:Fixed minor errorVersion 2.0.3:Added new methodgenerate_dummy_schema_from_dfto generrate dummy schema from pandas DataFrameVersion 2.0.4:Allowed to create Hive partitionsVersion 2.0.5:Unloaded the pyodbc in default connectorsVersion 2.0.9:Added a static method to get the full qualified table nameVersion 2.1.0:Added a method to update target schema with easeVersion 2.1.1:Added Biglake connection support
The steps to work with Biglake =Create an external connectionGet theservice accountfrom the connection and add to IAMTry create a BigLake manually using the command belownow you can use the code to do ittest_create_biglake.pyVersion 2.1.2:Bug FixedVersion 2.1.3:Bug FixedVersion 2.1.4:Bug FixedVersion 2.1.5 to 2.1.11:Bug FixedVersion 2.1.12:Improved readabilityVersion 2.1.13 14 15:Add tranformation config support