Saravanan666/pypi-org huggingface dataset:
Package: unicore.content
Generate the models from theAvroschema:$ virtualenv ve
$ source ve/bin/activate
(ve)$ pip install elastic-git
(ve)$ eg-tools load-schema \
    ./_schemas/unicore.content.models.Page.avsc \
    ./_schemas/unicore.content.models.Category.avsc \
    ./_schemas/unicore.content.models.Localisation.avsc \
    --map-field uuid=elasticgit.models.UUIDField \
    > ./unicore/content/models.py
Package: unicore.distribute
unicore.distributeDistribution tools for Universal Core content.Run with Docker$ docker run -dP universalcore/unicore.distribute:latest
1cbc5ea16c08f9787c8d1546f21c489b27bab1c2fb63d7bb97c59565d1d4a2fa
$ docker port 1cbc5ea16c08
6543/tcp -> 0.0.0.0:49166
$ curl 0.0.0.0:49166/repos.json
[]
$
Package: unicore-gitmodels
Unicore Gitmodels=================Definition of models used for python-gitmodel.. image:: https://travis-ci.org/praekelt/unicore-gitmodels.svg:target: https://travis-ci.org/praekelt/unicore-gitmodelsInstallation------------::$ pip install unicore-gitmodelsUsage-----::# models.pyfrom unicore_gitmodels.models import GitPageModel, GitCategoryModelws = Workspace('/path/to/repo/.git')GitPage = ws.register_model(GitPageModel)GitCategory = ws.register_model(GitCategoryModel)::# views.pypage = GitPage(title='a title', slug='a-title', content='some sample content')page.save(True, message="sample git commit")Authors=======Praekelt Foundation-------------------* Milton Madanda* Simon de HaanChangelog=========1.7.0-----#. Add default sorting to `all()` using position field on GitPageModel1.6.0-----#. Add default sorting to `all()` using position field1.5.0-----#. Add `related_pages` and ListField() type.1.4.0-----#. Feature category in navbar1.3.1-----#. Ensure language is returned in dto.1.3.0-----#. Added featured boolean field.1.2.0-----#. Added featured_in_category boolean field.1.1.0-----#. Added language and source fields.1.0.3-----#. Add slugify to page model.1.0.2-----#. Add slugify to model for cases when no slug is provided.1.0.1-----#. Ensure all files included in pypi package.1.0.0-----#. Initial version from unicore-cms/unicore-cms-django.
Package: unicorn
Unicorn is a lightweight, multi-platform, multi-architecture CPU emulator framework
based onQEMU.Unicorn offers some unparalleled features:Multi-architecture: ARM, ARM64 (ARMv8), M68K, MIPS, PowerPC, RISCV, SPARC, S390X, TriCore and X86 (16, 32, 64-bit)Clean/simple/lightweight/intuitive architecture-neutral APIImplemented in pure C language, with bindings for Crystal, Clojure, Visual Basic, Perl, Rust, Ruby, Python, Java, .NET, Go, Delphi/Free Pascal, Haskell, Pharo, and Lua.Native support for Windows & *nix (with Mac OSX, Linux, *BSD & Solaris confirmed)High performance via Just-In-Time compilationSupport for fine-grained instrumentation at various levelsThread-safety by designDistributed under free software license GPLv2Further information is available athttp://www.unicorn-engine.org
Package: unicorner
# unicornerGM season standings and fixtures parser as a reusable library.This library can change at any time. Same applies to GM’s actual websites. Use at your own risk.### Installpip install unicorner### Usage#### Parsing Standings and Fixtures PagesStandings page has to parsed before fixtures can be parsed.from unicorner import SeasonParsesp = SeasonParse()
sp.parse_standings_page(path=”standings.html”)
sp.parse_fixtures_page(path=”fixtures.html”)
print(sp.game_days[0])#### Extracting to CSVpython -m unicorner extract_all –help### GM Data Model IssuesGM does not store the historical team names - only the latest version of the name is preserved.In the past, GM would reuse the same team object for unrelated groups of people so you would
have one seasonTeamId=23point to toTeam Aand the next season, if all the people ofTeam Aleft,TeamId=23could point to another group of playersTeam B. You would see this in team history page
which would show past games that the new group of players had never heard of.Both of the above are caused by not having aseason-teammodel.We work around this by first introducing
the concept ofFranchise- the identity of a group of players playing together that spans over
more than one season. Each franchise should be given an ID which is independent from GM IDs.
These can be maintained in afranchises.csvfile.Then, for each season that a franchise joins, we create a separateTeamobject whose ID is a
concatenation of zero-padded GM season’s ID and team ID.
For example, team identified by GM withTeamId=23playing in seasonSeasonId=101gets ID0101.23Each such team can have its own name so every season a franchise can use a different name. The mapping
from teams to franchises is maintained in afranchise_seasons.csvfile.Examples of both files can be found undertests/data/### GM Data IssuesGM data is very much like their operations. Sometimes there’s no score or the score is incorrect, the winner
is wrong, or the game is marked incorrectly say as a final when it isn’t a final etc. etc. To fix these issues
usescore_overrides.csvfile.
Package: unicornhat
Learn more:https://shop.pimoroni.com/products/unicorn-hatLearn more:https://shop.pimoroni.com/products/unicorn-phat2.2.2Bugfix: Switched to DMA channel 10 to avoid SD card locking + freezing2.2.1Refactor: Switched to using rpi_ws218x 3.x.x2.1.3Feature: Added set_all and shade_pixels which now drives set_pixelsFeature: Added PHAT_VERTICAL optional orientationBugfix: get_brightness now returns valueBugfix: get_pixels now takes into account pHAT/HAT shape2.1.2Added AUTO set_layout which attempts to detect uHAT via EEPROMAdded get_shape() which returns a tuple of width/height2.1.1Initialized to 128 brightness (max)2.1.0Added Unicorn pHAT support2.0.7Added warning on low brightness2.0.6Indent bugfix2.0.5Max brightness capped at 50%Added requirement for latest 1.0.0 rpi_ws281x ( dubbed 2.0.0 on pip )2.0.0Major switch over to rpi_ws281x libraryRaspberry Pi 2 compatibility1.0.1Fix to rotation()1.0.0Initial Release
Package: unicornhathd
http://shop.pimoroni.com/products/unicorn-hat-hdInstallingFull install (recommended):We’ve created an easy installation script that will install all
pre-requisites and get your Unicorn HAT HD up and running with minimal
efforts. To run it, fire up Terminal which you’ll find in Menu ->
Accessories -> Terminal on your Raspberry Pi desktop, as illustrated
below:Finding the terminalIn the new terminal window type the command exactly as it appears below
(check for typos) and follow the on-screen instructions:curlhttps://get.pimoroni.com/unicornhathd|bashIf you choose to download examples you’ll find them in/home/pi/Pimoroni/unicornhathd/. To prototype and try out your code
without having to deploy it onto a Raspberry Pi every time, you can use
theUnicorn HAT
simulatorto run a mock
Unicorn HAT on your computer.Manual install:SPI needs to be enabled to communicate with the Unicorn Hat HD. If the
SPI on your Pi is not enabled or you are unsure if it is:sudoraspi-confignonintdo_spi0sudorebootLibrary install for Python 3:sudoapt-getinstallpython3-pippython3-devpython3-spidevsudopip3installunicornhathdLibrary install for Python 2:sudoapt-getinstallpython-pippython-devpython-spidevsudopipinstallunicornhathdDevelopment:If you want to contribute, or like living on the edge of your seat by
having the latest code, you should clone this repository,cdto the
library directory, and run:sudoapt-getinstallpython-devpython-setuptoolssudopython3setup.pyinstall(orsudo python setup.py installwhichever your primary Python
environment may be)In all cases you will have to enable the SPI bus.Documentation & SupportGuides and tutorials -https://learn.pimoroni.com/unicorn-hat-hdFunction reference -http://docs.pimoroni.com/unicornhathd/GPIO Pinout -http://pinout.xyz/pinout/unicorn_hat_hdGet help -http://forums.pimoroni.com/c/support0.0.4Added support for display addressing (for Ubercorn)0.0.3Deferred IO setup until first call to showChanged sys.exit to ImportError for warning about missing dependencies0.0.2Tweaked Python package MANIFEST0.0.1Initial release
Package: unicornhatmini
Unicorn HAT MiniRequirementsYou must enable SPI on your Raspberry Pi:Run:sudo raspi-config nonint do_spi 0InstallingStable library from PyPi:Just runsudo pip3 install unicornhatminiOr for Python 2:sudo pip install unicornhatminiLatest/development library from GitHub:git clone https://github.com/pimoroni/unicornhatmini-pythoncd unicornhatmini-pythonsudo ./install.shChangelog0.0.2Fix for Pi kernel 5.4.51, removed cshigh setting from SPI setup0.0.1Initial Release
Package: unicorn-hat-sim
Simulates an Unicorn HAT HD (and should work for the 8x8 HAT and the 8x4
PHAT as well) using pygame.UsageIf you want your code to run on your computer as well as your Pi, you
could do something like this:pip installunicorn-hat-sim(orpip2orpip3depending on
your setup)Adjust yourimport unicornhathdstatement as follows:try:importunicornhathdasunicornprint("unicorn hat hd detected")exceptImportError:fromunicorn_hat_simimportunicornhathdasunicornYou can choose fromimport unicornhathd(16x16),import unicornhat(8x8) andimport unicornphat(8x4).Demo(Note that this gif has a low framerate, the simulator runs nice and
smooth in real life)DemoTODO[ ] find a python person who shows me how this would be done properly[ ] fix/check rotation[ ] add a proper LED glow effect so it looks more like a real unicorn
HAT[ ] publish via pip
Package: unicornhatsimulator
Unicorn HAT (HD) simulatorv1.0.4Unicorn HAT (HD) simulator - Originally by Jannis Hermanns <jannis@gmail.com>, modified by Mark Pitman <mark.pitman@gmail.com> and adjusted by me (Matt Brash <matt@brash.ca>)Simulates a Unicorn HAT HD (and should work for the 8x8 HAT and the 8x4 PHAT as well) using pygame.
The version you see here includes the following edits:Addedset_all(r, g, b)to set the entire buffer to one colourModified theclear()function to act like the real HAT (clears the buffer, not the screen)Addedget_pixel(x, y)to return a tuple of the r, g, b values of a pixelAddedget_pixels()which returns the entire buffer as an array of arrays of arrays ([y][x][r, g, b])Modifiedset_pixel()to accept a colour string, for exampleset_pixel(x, y, "red")seeset_pixel.mdAdded the non-standardset_pixel_size(value)which is only for the simulator to increase or decrease the dot sizeFixed the rotation code to rotate counter-clockwise correctlyCommented the code thoroughlySize the initial window based on the size of the HAT and pixel sizeUsageIf you want your code to run on your computer as well as your Pi, you could do something like this:pip install unicornhatsimulator(orpip2orpip3depending on your setup)Adjust yourimport unicornhathdstatement as follows:'''Note: The alias "as unicorn" can be whatever you want (or nothing at all).For example: as u'''try:importunicornhathdasunicornprint("unicorn hat hd detected")exceptImportError:fromunicornhatsimulatorimportunicornhathdasunicornYou can choose fromimport unicornhathd(16x16),import unicornhat(8x8) andimport unicornphat(8x4).Demo(Note that this gif has a low framerate, the simulator runs nice and smooth in real life)TODOfind a python person to show me how this would be done properly. For example - leaving the window open at the end.fix/check rotationadd a proper LED glow effect so it looks more like a real unicorn HATpublish via pip (now online at pypi)addget_pixel(x, y)andget_pixelsadd non-standardset_pixel_size(p)to increase or decrease dot sizesDisclaimerI amnotapygameprogrammer. I'm barely apythonprogrammer. I found the original code for the simulator and tried to modify it to match the current state of thePimoroni UnicornhatHDlibrary. This was in order to have my students be able to work on their projects inReplitwhen they don't have access to their physical raspberry pi's from class.Feel free to leave suggestions (issues, bugs) but do not expect a timely response.~ M. Brash
Package: unicorn-sdk
unicorn-sdk is the python sdk of unicorn-api.we provide api to bypass bot-detect product:akamaikasadatls clientcaptchacloundflarecontact us at:https://t.co/HWHs1FurZaInstallation$pipinstall-Uunicorn-sdkDemoshttps://github.com/unicorn-aio/unicorn-sdk-demos
Package: unictelecomes
Send message from unictelecom provider, this library for send message from unic telecome.for installation please usepip install unictelecomesfor init libraryfrom unictelecome import unictelecomeunictelecome = unictelecome(apikey=”api_key”, sender=”sender”, user=”user_name”)create messageunictelecome.createmessage(message=”text”, recipients=”number_phone”)change message priorityunictelecome.urgent = Truefor testing message we give result but message not sending to phoneunictelecome.test = Truesend messageunictelecome.sendmessage()
Package: unicum
Python libraryunicumunicumconsists of multiple object implementations that implement various factory pattern.
All types merge into on typeVisibleObjectand each type contributes bits and piece.The visible obj focus on robust and simple construction from a dictionary viaPersistentObjecthaving values only simple types or containers containers of simple types.These values are translated viaFatoryObjectinto more complex structures which are take from a factory.Or, alternatively, usingDataRangeinto something similar to adata_frametype inR,
a table with column and row names as well as common types for each column values.Inheriting fromLinkedObjectprovides functionality to swap or update attributes at runtimeExample UsageUsingFactoryObject:>>>fromunicumimportFactoryObject>>>classCurrency(FactoryObject):__factory=dict()>>>classEUR(Currency):pass>>>classUSD(Currency):pass>>>EUR().register()# registers USD() instance with class name 'EUR'>>>eur=Currency('EUR')# picks instance with key 'EUR' from currency cache>>>eur==EUR()# picks instance with key given by class name 'EUR' from currency cache, too.True>>>eur2=eur.__class__('EUR')# picks instance with key 'EUR' from currency cache>>>eur==eur2True>>>usd=USD().register()# registers USD() instance with class name 'USD'>>>usd.register('usd')# registers usd with name 'usd'>>>usd==USD()True>>>eur==eur.__class__('USD')False>>>usd==eur.__class__('USD')True>>>usd==Currency('usd')TrueUsingLinkedObject:>>>fromunicumimportLinkedObjectDevelopment VersionThe latest development version can be installed directly from GitHub:$pipinstall--upgradegit+https://github.com/sonntagsgesicht/unicum.gitContributionsIssuesandPull Requestsare always welcome.LicenseCode and documentation are available according to the Apache Software License (seeLICENSE).
Package: unidata-blocks
Unidata BlocksA library that helps query unicode blocks byBlocks.txt.Installationpipinstallunidata-blocksUsageimportunidata_blocksblock=unidata_blocks.get_block_by_chr('A')assertblock.code_start==0x0000assertblock.code_end==0x007Fassertblock.name=='Basic Latin'DependenciesLangcodesLicenseUnder theMIT license.
Package: unidbm
Dict-style key value wrapper around some embeded databases==========================================================Usage=====>>> import unidbm>>> db = unidbm.open('sqlite', path=':memory:')Set and get data:>>> db[u'foo'] = ['bar', {'baz': 1}]>>> db[u'foo']['bar', {'baz': 1}]Iterate:>>> [key for key in db] == [u'foo']TrueDelete:>>> del db[u'foo']>>> len(db)0Backends--------- sqlite- kyoto cabinet- semidbmCustom backend--------------Backend works with bytes (str in py2) keys and values.It should implement next methods:- __init__(self, path, **any_options)- def __getitem__(self, key):- def __setitem__(self, key, value):- def __len__(self):- def __iter__(self):- def close(self):
Package: unide-python
This Python package is part of theEclipse Unide Projectand provides an API for generating,
parsing and validating PPMP payloads. PPMP, the“Production
Performance Management Protocol”is a simple,
JSON-based protocol for message payloads in (Industrial) Internet of
Things applications defined by theEclipse IoT Working Group. Implementations for other programming
languages are available from the Unide web site.The focus of the Python implementation is ease of use for backend
implementations, tools and for prototyping PPMP
applications. Generating a simple payload and sending it over MQTT
usingEclipse Pahois
a matter of just a few lines:import unide
import paho.mqtt.client as mqtt

client = mqtt.Client()
client.connect("localhost", 1883, 60)
device = unide.Device("Device-001")
measurement = device.measurement(temperature=36.7)
client.publish(topic="sample", measurement)InstallationThe latest version is available in the Python Package Index (PyPI) and
can be installed using:pip install unide-pythonunide-pythoncan be used with Python 2.7, 3.4, 3.5 and 3.6.Source code, including examples and tests, is available on GitHub:https://github.com/eclipse/unide.pythonTo install the package from source:git clone git@github.com:eclipse/unide.python.git
cd unide.python
python setup.py installContributingThis is a straightforward Python project, usingsetuptoolsand the
standardsetup.pymechanism. You can run the test suite usingsetup.py:python setup.py testThere also is a top-levelMakefilethat builds a development
environment and can run a couple of developer tasks. We aim for 100%
test coverage and usetoxto
test against all supported Python releases. To run all tests against
all supported Python versions, build the documentation locally and an
installable wheel, you’ll requirepyenvand a decent implementation of
make.make allwill create a virtualenvenvin the project
directory and install the necessary tools (seetools.txt).For bug reports, suggestions and questions, simply open an issue in
the Github issue tracker. We welcome pull requests.DocumentationDetailed documentation is available on Read the Docs:http://unidepython.readthedocs.io/en/latest/.
Package: unidic-combo
UniDic-COMBOUniDic2UD+COMBO-pytorchwrapper forspaCyBasic Usage>>>importunidic_combo>>>nlp=unidic_combo.load("kindai")>>>doc=nlp("澤山居つた兄弟が一疋も見えぬ")>>>print(unidic_combo.to_conllu(doc))# text = 澤山居つた兄弟が一疋も見えぬ1澤山沢山ADV副詞_2advmod_SpaceAfter=No|Translit=タクサン2居つ居るVERB動詞-非自立可能_4acl_SpaceAfter=No|Translit=オッ3たたAUX助動詞_2aux_SpaceAfter=No|Translit=タ4兄弟兄弟NOUN名詞-普通名詞-一般_9nsubj_SpaceAfter=No|Translit=キョウダイ5ががADP助詞-格助詞_4case_SpaceAfter=No|Translit=ガ6一一NUM名詞-数詞_7nummod_SpaceAfter=No|Translit=イチ7疋匹NOUN接尾辞-名詞的-助数詞_9obl_SpaceAfter=No|Translit=ピキ8ももADP助詞-係助詞_7case_SpaceAfter=No|Translit=モ9見え見えるVERB動詞-一般_0root_SpaceAfter=No|Translit=ミエ10ぬずAUX助動詞_9aux_SpaceAfter=No|Translit=ヌ>>>importdeplacy>>>deplacy.render(doc,Japanese=True)澤山ADV<══╗advmod(連用修飾語)居つVERB═╗═╝<╗acl(連体修飾節)たAUX<╝║aux(動詞補助成分)兄弟NOUN═╗═══╝<╗nsubj(主語)がADP<╝║case(格表示)一NUM<╗║nummod(数量による修飾語)疋NOUN═╝═╗<╗║obl(斜格補語)もADP<══╝║║case(格表示)見えVERB═╗═══╝═╝ROOT(親)ぬAUX<╝aux(動詞補助成分)>>>fromdeplacy.depreljaimportdeprelja>>>forbinunidic_combo.bunsetu_spans(doc):...fortinb.lefts:...print(unidic_combo.bunsetu_span(t),"->",b,"("+deprelja[t.dep_]+")")...澤山->居つた(連用修飾語)居つた->兄弟が(連体修飾節)兄弟が->見えぬ(主語)一疋も->見えぬ(斜格補語)unidic_combo.load(UniDic,BERT=True)loads spaCy Language pipeline for UniDic2UD + COMBO-pytorch. AvailableUniDicoptions are:UniDic="gendai": Use現代書き言葉UniDic.UniDic="spoken": Use現代話し言葉UniDic.UniDic="novel": Use近現代口語小説UniDic.UniDic="qkana": Use旧仮名口語UniDic.UniDic="kindai": Use近代文語UniDic.UniDic="kinsei": Use近世江戸口語UniDic.UniDic="kyogen": Use中世口語UniDic.UniDic="wakan": Use中世文語UniDic.UniDic="wabun": Use中古和文UniDic.UniDic="manyo": Use上代語UniDic.UniDic=None: Useunidic-lite(default).BERT=True/BERT=Falseoption enables/disables to usebert-base-japanese-whole-word-masking.Installation for Linuxpip3installunidic_comboInstallation for Cygwin64Make sure to getpython37-develpython37-pippython37-cythonpython37-numpypython37-cffigcc-g++mingw64-x86_64-gcc-g++gcc-fortrangitcurlmakecmakelibopenblasliblapack-devellibhdf5-devellibfreetype-devellibuv-develpackages, and then:curl-Lhttps://raw.githubusercontent.com/KoichiYasuoka/UniDic-COMBO/master/cygwin64.sh|shInstallation for macOSg++--version
pip3installunidic_combo--user
python3-mspacydownloaden_core_web_sm--userIf you fail to installJsonnet, try below before installing UniDic-COMBO:(echo'#! /bin/sh';echo'exec gcc `echo $* | sed "s/-arch [^ ]*//g"`')>/tmp/clang
chmod755/tmp/clang
envPATH="/tmp:$PATH"pip3installjsonnet--userIf you fail to installfugashi, try to installMeCabbefore installing UniDic-COMBO:cd/tmp
gitclone--depth=1https://github.com/taku910/mecabcdmecab/mecab
./configure--with-charset=UTF8
make&&sudomakeinstallBenchmarksResults of舞姬/雪國/荒野より-Benchmarks舞姬LASMLASBLEXUniDic="kindai"84.9177.7885.19UniDic="qkana"83.0277.7885.19UniDic="kinsei"75.9367.8671.43雪國LASMLASBLEXUniDic="qkana"87.5082.3578.43UniDic="kindai"83.1978.4374.51UniDic="kinsei"78.5773.0869.23荒野よりLASMLASBLEXUniDic="kindai"78.5359.4659.46UniDic="qkana"77.4959.4659.46UniDic="kinsei"76.0459.4659.46Reference安岡孝一:TransformersのBERTは共通テスト『国語』を係り受け解析する夢を見るか, 東洋学へのコンピュータ利用, 第33回研究セミナー (2021年3月5日), pp.3-34.
Package: unidic-lite
Unidic LiteThis is a version ofunidic-pythat is
designed to be installable with pip alone, not requiring any extra downloads.At the moment it uses Unidic 2.1.2, from 2013, which is the most recent release
of UniDic that's small enough to be distributed via PyPI.Note this package takes roughly 250MB on disk after being installed.In order to use this you will need to install a MeCab wrapper such asmecab-python3orfugashi.Differences from the Official UniDic ReleaseThis has a few changes from the official UniDic release to make it easier to use.entries for 令和 have been addedsingle-character numeric and alphabetic words have been deletedunk.defhas been modified so unknown punctuation won't be marked as a nounLicenseThis code is licensed under the MIT or WTFPL license, as you prefer. Unidic
2.1.2 is copyright the UniDic Consortium and distributed under the terms of theBSD license.
Package: unidic-lite-imitator
Unidic Lite ImitatorThis package imitates Japanese morphological analysis of mecab and unidic_lite with a small onnx model.You can add tokenization and part-of-speech estimation to your environmentwith only 2MB additoinal disk space if you already have onnxruntime in your environment.Installation$ pip install unidic_lite_imitatorUsage Examples>>importunidic_lite_imitator>>tagger=unidic_lite_imitator.Tagger()>>sample_text='使い方のサンプルです。'>>tagger.parse(sample_text)[('使い','動詞'),('方','接尾辞'),('の','助詞'),('サンプル','名詞'),('です','助動詞'),('。','補助記号')]Input string length must be 256 or less.
Package: unidic-lite-imitator-transformers
Unidic Lite Imitator TransformersThis package imitates Japanese morphological analysis of mecab and unidic_lite with a small transformers model.You can add tokenization and part-of-speech estimation to your environmentwith only 2MB additoinal disk space if you already have transformers in your environment.Installation$ pip install unidic_lite_imitator_transformersUsage Examples>>importunidic_lite_imitator_transformers>>tagger=unidic_lite_imitator_transformers.Tagger()>>sample_text='使い方のサンプルです。'>>tagger.parse(sample_text)[('使い','動詞'),('方','接尾辞'),('の','助詞'),('サンプル','名詞'),('です','助動詞'),('。','補助記号')]Input string length must be 256 or less.
Package: unidip
UniDip Python PortSee reference paper:http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noiseUniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality.Installcoming soon...pip3.6 install unidipExamplesBasic UsagefromunidipimportUniDip# create bi-modal distributiondat=np.concatenate([np.random.randn(200)-3,np.random.randn(200)+3])# sort data so returned indices are meaningfuldat=np.msort(dat)# get start and stop indices of peaksintervals=UniDip(dat).run()Advanced Optionsalpha: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise.mrg_dst: Defines how close intervals must be before they are merged.ntrials: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer.intervals=UniDip(dat,alpha=0.001,ntrials=1000,mrg_dst=5).run()