Saravanan666/pypi-org huggingface dataset:
Package: pyannotators-duckling
pyannotators_ducklingAnnotator based on Facebook's DucklingInstallationYou can simplypip install pyannotators_duckling.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_ducklingRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-keybert
pyannotators_keybertAnnotator based on Facebook's KeyBERTInstallationYou can simplypip install pyannotators_keybert.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_keybertRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-patterns
pyannotators_patternsAnnotator based on Facebook's PatternsInstallationYou can simplypip install pyannotators_patterns.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_patternsRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-spacymatcher
pyannotators_spacymatcherAnnotator based on Facebook's SpacyMatcherInstallationYou can simplypip install pyannotators_spacymatcher.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_spacymatcherRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-spacyner
pyannotators_spacynerAnnotator based on Facebook's SpacyNERInstallationYou can simplypip install pyannotators_spacyner.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_spacynerRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-stefan
pyannotators_stefanMy new Stefan AnnotatorInstallationYou can simplypip install pyannotators_stefan.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_stefanRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-trankitner
pyannotators_trankitnerAnnotator based on Facebook's TrankitNERInstallationYou can simplypip install pyannotators_trankitner.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_trankitnerRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-trfclassifier
pyannotators_trfclassifierClassifier based on Huggingface Text Classification pipelineInstallationYou can simplypip install pyannotators_trfclassifier.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_trfclassifierRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotators-zeroshotclassifier
pyannotators_zeroshotclassifierAnnotator based on Facebook's ZeroShotClassifierInstallationYou can simplypip install pyannotators_zeroshotclassifier.DevelopingPre-requesitesYou will need to installflit(for building the package) andtox(for orchestrating testing and documentation building):python3 -m pip install flit toxClone the repository:git clone https://github.com/oterrier/pyannotators_zeroshotclassifierRunning the test suiteYou can run the full test suite against all supported versions of Python (3.8) with:toxBuilding the documentationYou can build the HTML documentation with:tox -e docsThe built documentation is available at `docs/_build/index.html.
Package: pyannotebook
🎹 pyannote + 🗒 notebook = pyannotebookpyannotebookis a customJupyter widgetbuilt on top ofpyannote.coreandwavesurfer.js.This is a work in progress, try it here:Feedback andpull requestswelcome!Development InstallationCreate a dev environment:condacreate-npyannotebook-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivatepyannotebook-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py pyannotebook
jupyter nbextension enable --sys-prefix --py pyannotebookNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.Updating the versionTo update the version, install tbump and use it to bump the version.
By default it will also create a tag.pipinstalltbump
tbump<new-version>
Package: pyannote.features.shennong
pyannote-shennong-featuresA feature extractor for Pyannote that is based on the Shennong libraryInstallationThis library depends on Shennong, which is available on conda.
Thus, make sure you're in a conda environmentwhere pyannote-audio has been installed alreadyand runconda install -c coml shennongThen you can pip-install the library in a regular way:pip install pyannote.features.shennongPyannote ConfigurationTo use features from this library, this is the kind of configuration you
should add yourconfig.ymlfile:feature_extraction:name:pyannote.features.ShennongMfccparams:coefs:19e:TrueD:TrueDD:TruemfccWindowType:'hanning'mfccLowFreq:20mfccHighFreq:-100# Real value will be (f_nyquist - 100)with_pitch:Truewith_cmvn:Falseduration:0.025step:0.010sample_rate:16000pitchFmin:20pitchFmax:500
Package: pyannote.server
# PyAnnote REST API## Installation$ pip install pyannote.server## Running the server$ python -m pyannote.server.run## Using the API### Parsers* `/parser/` returns list of supported file formats$ curl -X GET http://localhost:5000/parser/["mdtm", "uem"]* `/parser/<format>` parses `POST`ed file and returns its content in PyAnnote JSON format.### Evaluation metrics* `/metric/` returns list of available evaluation metrics$ curl -X GET http://localhost:5000/metric/["detection", "diarization", "identification"]* `/metric/<name>` compares `POST`ed reference and hypothesis annotations in JSON format and returns the corresponding evaluation metric.**Input format (JSON)**{"reference": [...],"hypothesis": [...]}**Output format (JSON)**{METRIC: {METRIC: value,COMPONENT_1: value_1,COMPONENT_2: value_2,... # components are values from... # which the final value is computed},... # one call to /parser/<metric> may... # return more than one sub-metrics}### Error analysis* `/error/diff` compares `POST`ed reference and hypothesis and returns their differences.**Input format (JSON)**# same format as for metric/<name>{"reference": [...],"hypothesis": [...]}* `/error/regression` compares `POST`ed reference with two hypotheses and returns regressions and/or improvements brought by the second one (`after`) over the first one (`before`).**Input format (JSON)**{"reference": [...],"before": [...],"after": [...]}
Package: pyanoboard
PyanoboardPyanoboardis a simple piano to keyboard emulator, allowing you to use any MIDI enabled device as a virtual keyboard.Pyanoboardactually simulates key events rather than sending characters to be typed, which means you can use it for anything such as gaming and not just typing.InstallationEitherClone this repository, and usepyanoboard.pyfrom thepyanoboarddirectoryYou must havepygameinstalledInstall the package via pippip install pyanoboardPrerequisitesMIDI enabled device such as a digital pianoMIDI to USB cablepythonWindowsUsageTo use this package, enterpython-mpyanoboardconfigWhere config is the path to your configuration file using the JSON format. An example configuration file has been included underexample/config.jsonexample config{"device_id":1,"bindings":[{"note":"C3","key":"h"}]}pyanoboardcan also take the following argumentsArgumentMeaning-verboseEnables verbose logging-devicesShows a list of the connected MIDI devices instead of starting pyanoboardIn order to find thedevice_idof your device, use the-devicesargumentpython-mpyanoboard-devices
Package: pyanoled
pyanoledPiano LED visualizer based in Python 3.x. Inspired by theonlaj/Piano-LED-Visualizerproject.Table of ContentsHardware SetupLEDRaspberry PiInstall/RunTODOHardware SetupSetup instructions for the hardware are covered inonlaj/Piano-LED-Visualizer.
It lists out all of the parts and equipment, and references a lot of other sites with useful information on how
to setup and configure the hardware. Here are some of my takeaways:LEDFor a standard 88-key piano, the distance from lowest and highest key is just 4 meters. The WS2818B LEDs
have enough LED density to align with every key, so 4 meters of that will be around 172 LEDs.It is recommended to purchase the longer-lengthed strip and cut it down to 4 meters. Soldering two
shorter strips together is more tedious. Here is agood instructional videoon how to do that.Finding vendors selling 4 meter long aluminum casing was also troublesome. Here is avendorthat offers custom-length cases and lids.Get a 5V power supply that draws 10A of current.Raspberry PiThe instructions for setting up the Raspberry Pi are pretty straight forward, but here are some issues that I ran into:Installing RasbianTo enable wifi: do not need to set country code value inwap_supplicant.confif you have Raspbery Pi ZeroConfiguring Raspbian to work with WS2128To disable sound: if/etc/modprobe.d/alsa-base.confis not found, try/usr/share/alsa/alsa.confEnabling SPIConfiguring MIDIMissing Jack module errors:sudo apt-get install libjack-devPython 3 packages:libatlas-base-devAdditional packages that might be neededsudo apt-get install libjpeg-dev zlib1g-devInstall/RunSetup your virtual environmentvirtualenv<pathtoenv>source<pathtoenv>/bin/activateInstall project from package or sourceTo install from pypi:# in raspberry pi, install the project from pypipip3installpyanoledTo install from source:# in raspberry pi, clone repositorygitclone<repository># in project root directory, run setuptools# note: some dependencies (like numpy) can take a long time to install this way. can install those# dependencies through pip first before running this steppython3setup.pydevelopRun project# console script setup as entrypointpyanoledTODOPersist PyanoLED app configuration changesDockerize PyanoLEDMIDI playbackSynthesia supportAdditional color schemes and effectsAdditional configuration exposed in UI
Package: pyanom
This library is Python projects for anomaly detection. This contains
these techniques.Kullback-Leibler desity estimationSingular spectrum analysisGraphical lassoCUMSUM anomaly detectionHoteling T2Directional data anomaly detectionREQUIREMENTSnumpypandasscikit-learnscipyINSTALLATIONpipinstallpyanomUSAGEKullback-Leibler desity estimationimportnumpyasnpfrompyanom.density_ratio_estimationimportKLDensityRatioEstimatorX_normal=np.loadtxt("./data/normal_data.csv",delimiter=",")X_error=np.loadtxt("./data/error_data.csv",delimiter=",")model=KLDensityRatioEstimator(band_width=h,lr=0.001,max_iter=100000)model.fit(X_normal,X_error)anomaly_score=model.score(X_normal,X_error)Singular spectrum analysisimportnumpyasnpfrompyanom.subspace_methodsimportSSAy_error=np.loadtxt("./data/timeseries_error2.csv",delimiter=",")model=SSA(window_size=50,trajectory_n=25,trajectory_pattern=3,test_n=25,test_pattern=2,lag=25)model.fit(y_error)anomaly_score=model.score()Graphical lassoimportnumpyasnpfrompyanom.structure_learningimportGraphicalLassoX_normal=np.loadtxt("./data/normal_data.csv",delimiter=",")X_error=np.loadtxt("./data/error_data.csv",delimiter=",")model=GraphicalLasso(rho=0.1)model.fit(X_normal)anomaly_score=model.score(X_error)Direct learning sparse changesfrompyanom.structure_learningimportDirectLearningSparseChangesmodel=DirectLearningSparseChanges(lambda1=0.1,lambda2=0.3,max_iter=10000)model.fit(X_normal,X_error)pmatrix_diff=model.get_sparse_changes()CUSUM anomaly detectionimportnumpyasnpfrompyanom.outlier_detectionimportCADy_normal=np.loadtxt("./data/timeseries_normal.csv",delimiter=",").reshape(-1,1)y_error=np.loadtxt("./data/timeseries_error.csv",delimiter=",").reshape(-1,1)model=CAD(threshold=1.0)model.fit(y_normal)anomaly_score=model.score(y_error)Hoteling T2importnumpyasnpfrompyanom.outlier_detectionimportHotelingT2X_normal=np.loadtxt("./data/normal_data.csv",delimiter=",")X_error=np.loadtxt("./data/error_data.csv",delimiter=",")model=HotelingT2()model.fit(X_normal)anomaly_score=model.score(X_error)Directional data anomaly DirectionalDataAnomalyDetectionimportnumpyasnpfrompyanom.outlier_detectionimportAD3X_normal=np.loadtxt("./data/normal_direction_data.csv",delimiter=",")X_error=np.loadtxt("./data/error_direction_data.csv",delimiter=",")model=AD3()model.fit(X_normal,normalize=True)anomaly_score=model.score(X_error)
Package: pyanomaly
pyanomalyConjunto de algoritmos para detectar anomalias em Series Temporais.Instalaçãopip install pyanomalyComo usarIremos realizar os testes no dataset contendo temperaturas diarias da cidade de Melbourne.dataset:https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv# data
import numpy as np
import pandas as pd
# plot
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

df = pd.read_csv('./dados/daily-min-temperatures.csv', parse_dates=['Date'])
df.set_index('Date', inplace=True)print(df.head(5).T)Date  1981-01-01  1981-01-02  1981-01-03  1981-01-04  1981-01-05
Temp        20.7        17.9        18.8        14.6        15.8df.plot(figsize=(8, 4));Madmad = MAD()
mad.fit(df['Temp'])
outliers = mad.fit_predict(df['Temp'])

outliers.head()Date
1981-01-15    25.0
1981-01-18    24.8
1981-02-09    25.0
1982-01-17    24.0
1982-01-20    25.2
Name: Temp, dtype: float64fig, ax = plt.subplots(1, 1, figsize=(12, 6))

sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers, 
                color='r', ax=ax)

plt.title('Zscore Robusto', fontsize='large');Tukeytu = Tukey()

tu.fit(df['Temp'])
outliers = tu.predict(df['Temp'])

outliers.head()Date
1981-01-15    25.0
1981-01-18    24.8
1981-02-09    25.0
1982-01-17    24.0
1982-01-20    25.2
Name: Temp, dtype: float64fig, ax = plt.subplots(1, 1, figsize=(12, 6))

sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers, 
                color='r', ax=ax)

plt.title('Tukey Method', fontsize='large');Twitter - S-MADoutliers = twitter(df['Temp'], period=12)
outliers.head()Date
1981-01-15    25.0
1981-01-18    24.8
1981-02-09    25.0
1982-01-20    25.2
1982-02-15    26.3
Name: Temp, dtype: float64fig, ax = plt.subplots(1, 1, figsize=(12, 6))

sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers, 
                color='r', ax=ax)

plt.title('Tukey Method', fontsize='large');
Package: pyanova
pyanovaAn Anova Precision Cooker (Sous Vide) Bluetooth API Python WrapperDescirptionThis is a Python wrapper of theAnova Precision Cooker(Sous Vide) API via the Bluetooth LE connection. Under the hood, it utilizespeplin/pygattfor the Bluetooth LE communications.The library is tested onC.H.I.Pwith Debian + Python 2.7 and Python 3.4AcknowledgementsThis work relies heavily on the researches fromdfrankland/sous-vide, a very nice API for Node JS. Commands used inpyanovaare based on itsresearch on the Android APP APK.Bluetooh LE communication is made easy withpygattInstallationPrerequisitespygatt- specifically, it uses thegatttool backendbluez- specifically, the gatttool backend requireshcitool, make sure you can findhcitoolin your system.Install using Python pipOnce the prerequisite are satisfied, you can installpyanovawith pip:pip install pyanovaUsageThe GATTTool backend normally requiresrootpermission. Hence you might need to run your program withrootpermission orsetup passwordless sudo for 'hcitool'.AutomodeYou can initialize PyAnova easily with automode which will automatically discover and connect to the first available Anova devicefrompyanovaimportpyanovapa=pyanova.PyAnova()pa.get_status()Manual modeAlternatively, you can fully control the discover and connect phrases of an PyAnova objectfrompyanovaimportpyanovapa=pyanova.PyAnova(auto_connect=False)devices=pa.discover(list_all=False,timeout=3)pa.connect_device(devices[0])pa.get_status()TODOUnit testsAutogen docsProbably swtich topygattlib?Demosee: samples/pyanova_terminal.pyDisclaimerThis software may harm your device. Use it at your own risk.THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT
WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
CORRECTION.
Package: pyanova-nano
PyAnova-NanoControl the Anova Nano via BLE.This is a rough translation ofdengelke/node-sous-vide.Notes:The code should be considered experimental at this point.Presumably this library is not compatible with any other Anova model!The PyAnova class is not compatible withc3V6a2Vy/pyanova.ExamplesAutomatic discoveryThe device should be found automatically based on the service uuid it provides.Context manager: auto connect and disconnectimportasynciofrompyanova_nanoimportPyAnovaasyncdefprint_device_sensors():asyncwithPyAnova()asclient:print(awaitclient.get_sensor_values())asyncio.run(print_device_sensors())No context managerimportasynciofrompyanova_nanoimportPyAnovaasyncdefget_unit():client=PyAnova()awaitclient.connect()print(awaitclient.get_timer())awaitclient.disconnect()asyncio.run(get_unit())Manual connectionTo use a custom address, first discover all relevant devices, then usePyAnova.connect(device=my_anova)withmy_anovabeing thebleak.BLEDeviceyou want to connect to.importasynciofrombleakimportBLEDevicefrompyanova_nanoimportPyAnovaasyncdefprint_target_temp():client=PyAnova()devices:list[BLEDevice]=awaitclient.discover(connect=False,list_all=True)# Select the device to use.my_anova=next(iter(devices))print(f"Found:{my_anova.address}")awaitclient.connect(device=my_anova)temperature=awaitclient.get_target_temperature()print(temperature)awaitclient.disconnect()asyncio.run(print_target_temp())DisclaimerThis software may harm your device. Use it at your own risk.THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
Package: pyans
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 \______   \___.__._____    ____   ______   
  |     ___<   |  |\__  \  /    \ /  ___/   
  |    |    \___  | / __ \|   |  \\___ \    
  |____|    / ____|(____  /___|  /____  >   
            \/          \/     \/     \/    
       /-|                             |-
  -.'.'.-|  Python Console ANSI Viewer |- 
       \-|                             |- 

  PYANS - Python Console ANSI Viewer

  Display ANSI graphics in Your terminal.
  Written in python.

  u, n       - scroll up, down more
  k, j, h, l - scroll up, down, left, right
  0,G        - goto begin, end of file
  $          - display sauce information
  ?          - display help
  a          - autoscroll
Package: pyansiescapes
PyANSIEscapes - Console Manipulation using ANSI Escapes SequencesThis package provides human readable functions for adding ANSI escapes sequences to your python console outputs.
It's loosy based onansiescapes, however offers more convenience functions
for formatting Rich Text.Update: Ships withemojis, so you can use :smile: in your
messages(strings) pass to ansi.format()-calls.InstallationpipinstallpyansiescapesUsageimportpyansiescapes.commandsasansi# Print blue text on white background:print(ansi.format("Hello ANSI!",color='blue',background='white'))API-ReferenceThe documentation is hosted onreadthedocs.ioYou can also access the docs usinghelp(ansi)in a python REPL.LicenseThis repository is licensed under the MIT License. SeeLICENSEfor details.