Saravanan666/pypi-org huggingface dataset:
Package: nwbindexer
nwbindexer and search_nwbThis repository contains two tools for searching within NWB (HDF5) files:nwbindexer- builds an SQLite database (also called the 'index') containing metadata in
a collection of NWB files and and allows searching the metadata in the database.search_nwb.py- searches within one or more NWB files directly (without building an index).A related third tool is theNWB Query Engine.  It is at:https://github.com/jezekp/NwbQueryEngine.  The two tools in this repository
use a query syntax similar to the one used in the NWB
Query Engine.Documentation for nwbindexer is at:https://nwbindexer.readthedocs.ioInstallation & UsageInstructions for installation and usage are at:https://nwbindexer.readthedocs.ioLicenseLicense terms are in filelicense.txt(reproduced below).Copyright ©2019. The Regents of the University of California (Regents).  All Rights Reserved.Permission to use, copy, modify, and distribute this software
and its documentation for educational, research, and not-for-profit purposes, without
fee and without a signed licensing agreement, is hereby granted, provided that the
above copyright notice, this paragraph and the following two paragraphs appear in all
copies, modifications, and distributions.  Contact The Office of Technology Licensing,
UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620,
(510) 643-7201,otl@berkeley.edu,http://ipira.berkeley.edu/industry-infofor commercial licensing opportunities.IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL,
INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE
OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, PROVIDED HEREUNDER IS PROVIDED
"AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES,
ENHANCEMENTS, OR MODIFICATIONS.
Package: nwbinspector
Inspect NWB files for compliance withNWB Best Practices. This inspector is meant as a companion to the PyNWB validator, which checks for strict schema compliance. In contrast, this tool attempts to apply some common sense to find components of the file that are technically compliant, but possibly incorrect, suboptimal in their representation, or deviate from best practices. This tool is meant simply as a data review aid. It does not catch all best practice violations, and any warnings it does produce should be checked by a knowledgeable reviewer.InstallationpipinstallnwbinspectorUsage# supply a path to an NWB filenwbinspectorpath/to/my/data.nwb# supply a path to a directory containing NWB filesnwbinspectorpath/to/my/data/folder/
Package: nwbmatic
nwbmaticNWB creator from various data streamsOverviewThis package started as a main feature ofpynappleIO module. It is now a standalone package to help create NWB from various data streams from electrophysiological and calcium imaging pipelines. It supports outputs from :ElectrophysiologyCalcium imagingBehaviorPhymatlab CNMF-EDeepLabCutNeurosuiteInscopix CNMF-EOptitrackMinianSuite2PWarningA larger choice of data format is available fromneuroconvUsageThe general workflow of loading a session is described by the infographic below. As it is challenging to accomodate all possible types of format, we aimed to keep the IO of nwbmatic minimal while allowing the user to inherit the base loader and import their own custom io functions.The base loader is thus responsible for initializing the NWB file containing the tracking data, the epochs and the session informations.Getting StartedInstallationThe best way to install nwbmatic is with pip within a newcondaenvironment :$ conda create --name nwbmatic pip python=3.8
$ conda activate nwbmatic
$ pip install nwbmaticif you would like the option to use our feature that loads data from the Allen Institute (optional):$ pip install nwbmatic[allen]or directly from the source code:$ conda create --name nwbmatic pip python=3.8
$ conda activate nwbmatic
$ # clone the repository
$ git clone https://github.com/pynapple-org/nwbmatic.git
$ cd nwbmatic
$ # Install in editable mode with `-e` or, equivalently, `--editable`
$ pip install -e .This procedure will install all the dependencies includingpynapplepandasnumpypynwb 2.0h5pyallensdkExampleIn this example, a session preprocessed withphywill be copied to NWB and loaded.importnwbmaticasntmdata=ntm.load_session("path/to/my/session","phy")CreditsThanks to Selen Calgin, Sara Mahallati and Luigi Petrucco for their contributions.
Package: nwb_schema_language
nwb-schema-languageTranslation of the nwb-schema-language to LinkMLWebsitehttps://p2p_ld.github.io/nwb-schema-languageRepository Structureexamples/- example dataproject/- project files (do not edit these)src/- source files (edit these)nwb_schema_languageschema-- LinkML schema
(edit this)datamodel-- generated
Python datamodeltests/- Python testsDeveloper DocumentationUse the `make` command to generate project artefacts:make all: make everythingmake deploy: deploys siteCreditsThis project was made withlinkml-project-cookiecutter.
Package: nwb-web-gui
NWB Web GUIWeb graphical user interface for NWB conversion and visualization.1. InstallationFrom PyPI:$ pip install nwb-web-guiFrom a local copy of the repository:$ git clone https://github.com/catalystneuro/nwb-web-gui.git
$ cd nwb-web-gui
$ pip install .2. RunningFrom command line shortcut:$ nwbguiFrom repository local copy:$ python wsgi.pyNWB Web GUI by default runs onlocalhost:5000.3. Running on docker container (referencing a local folder)Change on config.ini file the NWB_GUI_ROOT_PATH to /usr/src/nwb_web_gui/filesbuild docker with:$ docker build -t latest .run the docker with:$ docker run -it -p 5000:5000 -p 8866:8866 -v /host/path/to/filesFolder:/usr/src/nwb_web_gui/files <image_id>4. Run NWB Web GUI for a specific NWB ConverterNWB Web GUI can be set to run with any specific NWB converter:fromnwb_web_guiimportinit_appimportos# Set ENV variables for app# Set root path from where to run the GUIdata_path='/source_path'os.environ['NWB_GUI_ROOT_PATH']=data_path# Set which NWB GUI pages should be displayedos.environ['NWB_GUI_RENDER_CONVERTER']='True'os.environ['NWB_GUI_RENDER_VIEWER']='True'os.environ['NWB_GUI_RENDER_DASHBOARD']='False'# Choose NWB converter to be usedos.environ['NWB_GUI_NWB_CONVERTER_MODULE']='my_lab_to_nwb'os.environ['NWB_GUI_NWB_CONVERTER_CLASS']='MylabNWBConverter'# Choose port where the GUI will run. Default is 5000port=5000print(f'NWB GUI running on localhost:{port}')print(f'Data path:{data_path}')# Initialize appapp=init_app()# Run appapp.run(host='0.0.0.0',port=port,debug=False,use_reloader=False)5. DocumentationDocumentation
Package: nwbwidgets
Explore NWB data in JupyterExplore our documentation »Table of ContentsAboutInstallationUsageDemoDocumentationAboutA library of widgets for visualization NWB data in a Jupyter notebook (or lab). The widgets allow you to navigate through the hierarchical structure of the NWB file and visualize specific data elements. It is designed to work out-of-the-box with NWB 2.0 files and to be easy to extend.Installationnwbwidgetsrequires Python >= 3.7.The latest published version can be installed by running:pipinstallnwbwidgetsNote that there are some optional dependencies required for some widgets.
If an NWB data file contains a data type that requires additional dependencies,
you will see a list of extra modules needed for that specific widget.
All other widgets in the file will still work.UsageUsingPanelThe easiest way to use NWB widgets is with the interactivePanel:fromnwbwidgets.panelimportPanelPanel()Usingnwb2widgetIf you’re working directly with a NWB file object in your Jupyter notebook, you can also explore it with NWB Widgets usingfrompynwbimportNWBHDF5IOfromnwbwidgetsimportnwb2widgetio=NWBHDF5IO('path/to/file.nwb',mode='r')nwb=io.read()nwb2widget(nwb)Using DockerYou can also run the NWB Widgets Panel using Docker:$dockerrun-p8866:8866ghcr.io/NeurodataWithoutBorders/nwbwidgets-panel:latestDemoDocumentationSee ourReadTheDocs pagefor full documentation, including a gallery of all supported formats.How it worksAll visualizations are controlled by the dictionaryneurodata_vis_spec. The keys of this dictionary are pynwb neurodata types, and the values are functions that take as input that neurodata_type and output a visualization. The visualizations may be of typeWidgetormatplotlib.Figure. When you enter a neurodata_type instance intonwb2widget, it searches theneurodata_vis_specfor that instance's neurodata_type, progressing backwards through the parent classes of the neurodata_type to find the most specific neurodata_type inneurodata_vis_spec. Some of these types are containers for other types, and create accordian UI elements for its contents, which are then passed into theneurodata_vis_specand rendered accordingly.Instead of supplying a function for the value of theneurodata_vis_specdict, you may provide adictorOrderedDictwith string keys and function values. In this case, a tab structure is rendered, with each of the key/value pairs as an individual tab. All accordian and tab structures are rendered lazily- they are only called with that tab is selected. As a result, you can provide may tabs for a single data type without a worry. They will only be run if they are selected.ExtendingTo extend NWBWidgets, all you need to a function that takes as input an instance of a specific neurodata_type class, and outputs a matplotlib figure or a jupyter widget.Used ingiocomo-lab-to-nwbbuffalo-lab-data-to-nwbaxel-lab-to-nwb
Package: nwce
nwcenwceis a Python library that can parse Cisco configuration files. I wrote it mainly for Nexus devices but it
should work for any indented configuration files.DisclaimerThis library is still in the very early development stages and the API could be broken without notice.Getting StartedTests provide a very good starting point before I start writing the documentation.Installingpip install nwceRunning the testspython -v tests/LimitationsContributingPlease readCONTRIBUTING.mdfor details on our code of conduct.Buildingpoetry build
poetry publishVersioningWe useSemVerfor versioning. For the versions available, see
thetags on this repository.AuthorsOlivier Desnoë-Initial work-Albatross NetworksSee also the list ofcontributorswho participated in this
project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: nwc-pattern-miner
nwc-pattern-miningNon-compliant Window Co-occurrence pattern mining in temporal dataA Python library to find sequential association patterns in time-series data, co-occurring with target anomalous windows.Anomalous windows are sequences in which a target variable defies expected behavior (e.g. Emissions from a vehicle).The patterns are found in co-occurrence with a specific non-compliant feature (to decipher the reason behinds it's irregular behavior).The package uses various pruning methodologies to speed up pattern mining, and hashing for quick support count.The algorithm is based on research: (Discovering non-compliant window co-occurrence patterns)[https://link.springer.com/article/10.1007/s10707-016-0289-3]API-description:from nwc_pattern_miner import mine_sequence_patternsAPI Parameters:series_df:pd.DataFrame; Input DataFrame (Only features [discretized] and Target [binarized] columns)nc_window_col:str; Column Name with Binary Target (Anomalous Windows)support_threshold:float; Support threshold for sequence co-occurrence patternscrossk_threshold:float; Ripley's Cross-k threshold for sequence co-occurrence patternspattern_length:int; length of feature sequences co-occurring with anomalous windowsconfidence_threshold:float, default=-1; Confidence threshold for sequence co-occurrence patternslag:int, default= 0; lag consideration between sequence patterns and anomalous windowsinvalid_seq_indexes:list, default=list(); list of indexes across which sequence patterns would be invalidatedoutput_metric:{'crossk', 'support'}, default='crossk'; Metric used to sort patterns minedoutput_type:{'topk', 'threshold'}, default='topk';Type of output for sequence patterns minedoutput_threshold:float, default= -1; Threshold cutoff used to get output sequence patterns, ifoutput_type='threshold'topk:int, default=100; Top-k sequence patterns obtained based onoutput_metric, ifoutput_type='topk'pruning_type:str, default=apriori; Between[apriori, br-dr], both have same run-time, 'br-dr' does more enumerations but enumeration speed is much faster due to UB pruning.Sample Input DataFrame:engrpmEGRkgphMSPhumEngTqNCWindow91153131540Sample Output DataFrame:engrpmEGRkgphMSPhumEngTqCountSupportKvalueConfidenceFirst Occurrence Index4 4 45 5 52 2 21460.005282.3771.0471674 4 47 7 72500.006432.3571.041984
Package: nwdiag
nwdiaggenerate network-diagram image file from spec-text file.FeaturesGenerate network-diagram from dot like text (basic feature).Multilingualization for node-label (utf-8 only).You can get some examples and generated images onblockdiag.com.SetupUse easy_install or pip:$ sudo easy_install nwdiag

Or

$ sudo pip nwdiagspec-text setting sampleFew examples are available.
You can get more examples atblockdiag.com.simple.diagsimple.diag is simply define nodes and transitions by dot-like text format:nwdiag {
  network dmz {
      address = "210.x.x.x/24"

      web01 [address = "210.x.x.1"];
      web02 [address = "210.x.x.2"];
  }
  network internal {
      address = "172.x.x.x/24";

      web01 [address = "172.x.x.1"];
      web02 [address = "172.x.x.2"];
      db01;
      db02;
  }
}UsageExecute nwdiag command:$ nwdiag simple.diag
$ ls simple.png
simple.pngRequirementsPython 3.7 or laterblockdiag 1.5.0 or laterfuncparserlib 0.3.6 or laterreportlab (optional)wand and imagemagick (optional)setuptoolsLicenseApache License 2.0
Package: nwe
O nwe é programa usado para gerar cards doANKIe pdf, a partir do siteNews Web Easy, ele vai pegar as palavras e procurar o significado noJishoe depois traduzir para português usando ogoogletrans.InstalaçãoPara instalar, use o pip3 com o pacote “nwe”.$pip3installnweFuncionamentoa partir do siteNews Web Easy, você pode ir nesse site e escolher um artigo, como esse do link deexemplocopia essa url e insere eminsira o link:, ai clicar no botãoiniciar, espera abarra de progressocompletar, logo depois desse processo, você já pode gerar o Pdf e o card do anki.Licença::MIT LicenseCopyright (c) 2020 narutolavoPermission 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: nwebclient
NWeb ClientClient to access the NWeb-APIInstallationpip install --upgrade nwebclient

# oder

pip install .python -m nwebclient helpMoreDocumentationBeispielimport nwebclientclass NWebClient
  """ Anstatt url kann auch ein Pfad zur einer JSON-Datei, die die Schluessel enthaelt, angegeben werden. """
  __init__(url, username,password)
  doc(id)
  docs(q)
  group(id)
  getOrCreateGroup(guid, title)
  downloadImages()

metric_val(endpointUrl:string, metricName:string, val:numeric)Links:Gitlab-RepoPyPi-PackageGit-URL:git@gitlab.com:bsalgert/nwebclient.gitPackaging:https://packaging.python.org/tutorials/packaging-projects/
Package: nwebsocket
Simple and easy to use syncronous WebSocket client, no async.Usable withJupyter Notebook.InstallationInstall using pippip install nwebsocketPython versionPython 3.7+ is required.UsageRaw APIA simple example without classes# example_minimal.pyimporttimefromnwebsocketimportWebSocketwscn=WebSocket("wss://ws.postman-echo.com/raw")wscn.onmessage=lambdam:print(m)wscn.onopen=lambda:print("Opened connection")wscn.onclose=lambda:print("Closed connection")wscn.onerror=lambdae:print("Connection error",e)while(notwscn.readyState):time.sleep(1e-4)wscn.send('text')time.sleep(1.)wscn.close()Class protocolExample of extending the WebSocket class.# example_class.pyimporttimefromnwebsocketimportWebSocketclassWSProtocolLogic(WebSocket):def__init__(self,url):super().__init__(url)self.messages=[]# wait for connection, close or errorwhile(notself.readyState):time.sleep(1e-4)defonopen(self):print("Opened connection")defonclose(self):print("Closed connection")defonerror(self,e):print("Connection error",e)defonmessage(self,m):self.messages.append(m)wscn=WSProtocolLogic("wss://ws.postman-echo.com/raw")wscn.send('text')time.sleep(1.)print(wscn.messages)wscn.close()MotivationThere are many asynchronous Python WebSocket client packages out there, and
almost of them require your code to use the async syntax. This is unfortunate,
since it will lead to arun_until_completecall eventually, which will
block the main thread from performing other operations in parrallel.InspirationThis package was inspired by the ultra-simple WebSocket API in the JavaScript
language, which it replicates one-to-one.https://developer.mozilla.org/en-US/docs/Web/API/WebSocket/urlGuidelineSince the callback-style API is quite unusual when it comes to pythonicity,
your task will be to:defineonmessage,onopen,oncloseandonerrorhandle reconnection/simplement the TX/RX specification for working with the endpointisolate the callback pattern from the rest of your codeTake a chat service as an example, there are operations which are following
the traditional request/response pattern such as posting messages, and there
are other messages which are received without issuing a request (chat posting
by other users).These latter messages must be handled by your code as soon as they are received.
Class instances can help with that, by storing the received information (chat
posts). Keep your callbacks short, fast and serializable.LimitationsThis library is not suitable for high throughput, as the queue mechanism in Python
is notoriously slow due to serialization.License (MIT)Copyright (C) 2022Adapta Robotics|MATT Robot
Package: nwhy
The NWhy library providesPybind11APIs for analysis of complex data set intepret as hypergraphs.To install in an Anaconda environment>>> conda create -n <env name> python=3.8Then activate the environment>>> conda activate <env name>Install Intel Threading Building Blocks(TBB)To installTBB:>>> conda install tbbIf a local TBB has been install, we can specify TBBROOT>>> export TBBROOT=/opt/tbb/Install using PipFor installation:>>> pip install nwhyFor upgrade:>>> pip install nwhy --upgradeor>>> pip install nwhy -UQuick test with importFor quick test:>>> python -c "import nwhy"If there is no import error, then installation is done.
Package: nwkfmt
Newick Tree Validator and FormatterSimple utility to validate and format Newick trees.UsageValidation without formatting:./main.py<tree.nwk>In place formatting:./main.py<tree.nwk>--inplaceOutputting formatted tree to a separate file:./main.py<tree.nwk>--outfile<output.nwk>Outputting list of terminal nodes into a text file:./main.py<tree.nwk>--terminals<terminals.txt>RequirementsPythonTyperBioPythonReleaseThis project usesPoetryfor dependency management and packaging.To build a release, run:poetrybuildTo publish a release to PyPI, run:poetrypublish
Package: nwm
nwmPython library to fetch and process the National Water Model (NWM) NetCDF datasets.If you have any suggestion to improve the current function, please create a github issuehere.Get StartedInstall package$ pip install nwmDownload NWM DataYou can launch binder to test and run the code below.Example 1: use NwmHs class to download data (Recommended method)importmatplotlib.pyplotaspltfromnwmimportNwmHs# get data from National water model HydroShare Appnwm_data=NwmHs()dataset=nwm_data.get_data(archive='harvey',config='short_range',geom='channel_rt',variable='streamflow',comid=[5781915],init_time=0,start_date='2017-08-23')# show metadatadataset.attrs# plot dataplt.figure(figsize=(9,5))dataset.plot()plt.xlabel('Year 2017')plt.ylabel('{}({})'.format(dataset.variable_name,dataset.variable_unit))plt.title('Short range streamflow forecast for Channel 5781915 during Harvey Hurricane Event')Example 2: use BmiNwmHs class to download data (Demonstration of how to use BMI)importmatplotlib.pyplotaspltimportnumpyasnpimportcftimefromnwmimportBmiNwmHs# initiate a data componentdata_comp=BmiNwmHs()data_comp.initialize('config_file.yaml')# get variable infovar_name=data_comp.get_output_var_names()[0]var_unit=data_comp.get_var_units(var_name)print(' variable_name:{}\nvar_unit:{}\n'.format(var_name,var_unit))# get time infostart_time=data_comp.get_start_time()end_time=data_comp.get_end_time()time_step=data_comp.get_time_step()time_unit=data_comp.get_time_units()time_steps=int((end_time-start_time)/time_step)+1print(' start_time:{}\nend_time:{}\ntime_step:{}\ntime_unit:{}\ntime_steps:{}\n'.format(start_time,end_time,time_step,time_unit,time_steps))# initiate numpy arrays to store datastream_value=np.empty(1)stream_array=np.empty(time_steps)cftime_array=np.empty(time_steps)foriinrange(0,time_steps):data_comp.get_value(var_name,stream_value)stream_array[i]=stream_valuecftime_array[i]=data_comp.get_current_time()data_comp.update()time_array=cftime.num2date(cftime_array,time_unit,only_use_cftime_datetimes=False,only_use_python_datetimes=True)# plot dataplt.figure(figsize=(9,5))plt.plot(time_array,stream_array)plt.xlabel('Year 2017')plt.ylabel('{}({})'.format(var_name,var_unit))plt.title('Short range streamflow forecast for Channel 5781915 during Harvey Hurricane Event')
Package: nwmurl
URL generator tool for National Water Model dataOverviewnwmurlis a Python package that allows you to generate URLs for National Water Model (NWM) data.UsageInstall the package using pip:pipinstallnwmurlThen, you can use it in your Python code:fromnwmurlimportgenerate_urlurl=generate_url(model="short_range",comid=12345,datetime="2023-10-29T12:00:00Z")