Saravanan666/pypi-org huggingface dataset:
Package: tsiconv
tsiconvtsiconv is a cli tool I created to take an input time and convert to UTC (optionally a destination timezone).Many tools I ran accross log in UTC time. Additionally SOC's are now run by people in different timezones including PST, MST, EST, CST, or international timezones in Africa, India, Europe, Asia.0InstallationI highly recommend you usepipxto install this, as it creates the virtualenv for you and seamlessly handles the loading of the virtual environment when running this tool. If you choose not to usepipx, you should create a virtualenv and possibly a wrapper script to launch this in the virtualenv.pipxinstalltsiconvUsageThe following is the help for the programusage: __main__.py [-h] [-V] [-v] -t TIME [-s SOURCE] [-d DESTINATION] [-l]

 a program to convert timezones
    ex: python tsiconv.py

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -v, --verbose
  -t TIME, --time TIME  A datetime to convert. This should be in ISO-8601 format
  -s SOURCE, --source SOURCE
                        A source timezone to translate from. Only required if --time is a naive format
  -d DESTINATION, --destination DESTINATION
                        A destination timezone to translate from
  -l, --list            List out the timezones supported by this applicationWithout providing a destination timeformat, the default is to convert to UTC.Examples# print help/usagetsiconv-h# print all available timezone formatstsiconv-l# Convert from Central European Time to Eastern Standard Timetsiconv-t"2011-12-03T10:15:30+01:00"-dAmerica/New_York# Convert from Central Standard Time to Eastern Standard Timetsiconv-t"2011-12-03T10:15:30"-sAmerica/Chicago-dAmerica/New_York# Convert to UTC verboselytsiconv-v-t"2011-12-03T10:15:30+05:00"CreditsThis package was created withCookiecutterand thetbennett6421/pythoncookieproject template.
Package: tsid
TSID - Task Space Inverse DynamicsTSID is a C++ library for optimization-based inverse-dynamics control based on the rigid multi-body dynamics libraryPinocchio.DocumentationTake a look at the projectwikifor an overview of the design of the library.In the exercises folder you can find several examples of how to use TSID in Python with robot manipulators, humanoids, or quadrupeds.On thewebsite of Andrea Del Preteyou can find slides and video lessons on TSID.Memmo 2020 summer schoolInstallation with CondaIf you want to directly dive into TSID in Python, only one single line is sufficient (assuming you have Conda installed):conda install tsid -c conda-forgeInstallation from Debian/Ubuntu packages, with robotpkgIf you have never added robotpkg's software repository you can do it with the following commands:sudo tee /etc/apt/sources.list.d/robotpkg.list <<EOF
deb [arch=amd64] http://robotpkg.openrobots.org/packages/debian/pub $(lsb_release -sc) robotpkg
EOF

curl http://robotpkg.openrobots.org/packages/debian/robotpkg.key | sudo apt-key add -
sudo apt updateYou can install TSID and its python bindings (replace * with you Python version) with:sudo apt install robotpkg-py3*-tsidInstallation from sourcesFirst you need to install the following dependencies:boost (unit_test_framework)eigen3pinocchioeiquadprogexample-robot-data(only for running the examples)To install eigen3 on Ubuntu you can use apt-get:sudo apt-get install libeigen3-devTo installpinocchiofollow the instruction on its website.To compile TSID:cd $DEVEL/openrobots/src/
git clone --recursive git@github.com:stack-of-tasks/tsid.git
cd tsid
mkdir _build-RELEASE
cd _build-RELEASE
cmake .. -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=$DEVEL/openrobots
make installPython BindingsTo use this library in python, we offer python bindings based on Boost.Python and EigenPy.To install EigenPy you can compile the source code:git clone https://github.com/stack-of-tasks/eigenpyor, on Ubuntu, you can use apt-get:sudo apt-get install robotpkg-py3*-eigenpyFor testing the python bindings, you can run the unit test scripts in thescriptfolder, for instance:ipython script/test_formulation.pyTo run the demo using gepetto-viewer:ipython demo/demo_romeo.pyCreditsThis package is authored by:Andrea Del Prete(University of Trento)Justin Carpentier(INRIA)It includes key contributions from:Julian Viereck(Max Planck Institute, New  York  University)Sanghyun Kim(Seoul National University)Eloise Dalin(LORIA, INRIA Lorraine)Noelie Ramuzat(LAAS, CNRS)Pierre Fernbach(LAAS, CNRS)Aurelie Bonnefoy(LAAS, CNRS)Etienne Arlaud(INRIA)Fabian Schramm(INRIA)And is maintained by:Guilhem Saurel(LAAS-CNRS)CitingIf you are (or not) happy with TSID and want to cite it, please use the following citation:@inproceedings {adelprete:jnrh:2016,
    title = {Implementing Torque Control with High-Ratio Gear Boxes and without Joint-Torque Sensors},
    booktitle = {Int. Journal of Humanoid Robotics},
    year = {2016},
    pages = {1550044},
    url = {https://hal.archives-ouvertes.fr/hal-01136936/document},
    author = {Andrea Del Prete, Nicolas Mansard, Oscar E Ramos, Olivier Stasse, Francesco Nori}
}
Package: ts-ids-es-json-generator
ts-ids-es-json-generatorIntroductionThis simple utility generateselasticsearch.jsonusingschema.json.How to UseTo run the generator, simply provide the program with the location of the IDS root directory:$ pipenv run python -m ids_es_json_generator ~/path/to/ids/folderelasticsearch.jsonmay be manually edited after automatic generation (e.g. to update non-searchable fields).OutputThe script saveselasticsearch.jsonto the same directory you provided.AssumptionsThe generator operates under the following assumptions:It's the IDS designer's responsibility to make sure the schema andelasticsearch.jsonare correct.
The generator will not validate the IDS for you.RestrictionsJSON pointers are dereferenced to create the mapping, so schemas with circular references or missing references will fail.TestsRun the tests using the following command:$ pipenv run python -m pytestHow it worksAnywhere an array of objects appears in the schema, anestedfield type will be created in the outputelasticsearch.json, including arrays of objects contained anywhere within other arrays of objects.
The steps are:The schema is loaded into a dict with all of its JSON pointers dereferenced usingjsonref, so that nested fields involvingdefinitionsare included in the elasticsearch mapping.find_array_objectwill find all arrays of objects and create a dict representing their locations in the schema (see test casetest_array_simple_nested_casein__tests__/unit/test_find_array_object.py).generate_es_mappinguses this dict to create the elasticsearch mapping (see test casetest_nested_keys_casein__tests__/unit/test_generate_es_mapping.py).See the example files and test cases for more example usage.
Package: tsig
TESS Simulated Image GeneratorAlso known as TSIG, this python module provides the ability to generate star field data algorithmically, for testing information processing systems. Live data is expected to come from satellites and the format data stream from this hardware is the target output of this module.InstallationThe TSIG module can be installed using pip using the following command:pip install tsigAll dependancies are pulled in and installed within the system or virtual environment for you.Quick Usage GuideThe TSIG module includes a number of scripts to help generating data quickly. These should be documented here.
Package: tsih
TSIH - A dict with a HISTorytsih.Dictis a type ofUserDictthat allows versioning, backed up by asqlite3database.Transparent operationOnly changes (deltas) are stored.Forward-filling of values. A value is reused in future versions, unless it changes.Auto-versioning option (off by default), to produce a new version every time a value change happens.Ability to store related entries as separate dictionaries. Eachtsih.Dicthas adict_namethat is used in the database to identify the dictionary.Tuple-based indexing. Get and set values bydict_name,versionandkey.Usage and examplestsih.Dictobjects can be used just like regular dictionaries:>>>fromtsihimportDict>>>a=Dict()>>>a['test']=True>>>a{'test':True}>>>a.get('missing',5)5>>>a['missing']Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>KeyError:'missing'But at any point, new versions can be produced:>>>a.version0>>>a['start']='now'>>>a{'test':True,'start':'now'}>>>a.version=1>>>a['start']='one version ago'>>>a{'test':True,'start':'one version ago'}Previous values can be accessed using tuple keys, i.e., (version, key):>>>a[(0,'start')]'now'>>>a[(1,'start')]'one version ago'Each version only "records" changes, but later versions (even if they don't exist yet) inherit unchanged values from the previous ones:>>>a[(5,'start')]'one version ago'>>>a.version=5>>># Until the value is changed>>>a['start']='4 versions ago'>>>a[(5,'start')]'4 versions ago'You can accesseverystate of the Dict usingNonein place of the version and/or the key.
In that case, we will get an iterator, which we can turn into a list explicitly or with the.valuemethod.For example, here we get all the changes to thestartkey:>>>a[(None,'start')].value()#[(0.0,'now'),(1.0,'one version ago'),(5.0,'4 versions ago')]Similarly, to get the keys and values at a specific version:>>>list(a[(0,None)])[('start','now'),('test',True)]Or, we can combine both to get the keys and values at every version:>>>a[(None,None)].value()[(0.0,'start','now'),(1.0,'start','one version ago'),(5.0,'start','4 versions ago'),(0.0,'test',True),(1.0,'test',True),(5.0,'test',True)]Use casesTsih was originally part of theSoilAgent-Based Social Simulation framework, where both the environment and the agents need to keep track of state (i.e., attribute) changes.
Package: tsimcne
Unsupervised visualization of image datasets using contrastive learningThis is the code for the paper “Unsupervised visualization of image datasets using contrastive learning” (ICLR 2023).If you use the code, please cite our paper:@inproceedings{boehm2023unsupervised,
  title={Unsupervised visualization of image datasets using contrastive learning},
  author={B{\"o}hm, Jan Niklas and Berens, Philipp and Kobak, Dmitry},
  booktitle={International Conference on Learning Representations},
  year={2023},
}We show that it is possible to visualize datasets such as CIFAR-10 and CIFAR-100 in 2D with a contrastive learning objective, while preserving a lot of structure! We call our method t-SimCNE.InstallationInstallation should be as easy as calling:pipinstalltsimcneThe package is now available onPyPI.  If you want to install it
from source, you can do as follows.gitclonehttps://github.com/berenslab/t-simcnecdt-simcne
pipinstall.Since the project uses apyproject.tomlfile, you need to make sure that pip  version is at leastv22.3.1.Usage exampleThe documentation is available atreadthedocs.  Below is a
simple usage example.importtorchimporttorchvisionfrommatplotlibimportpyplotaspltfromtsimcne.tsimcneimportTSimCNE# get the cifar dataset (make sure to adapt `data_root` to point to your folder)data_root="experiments/cifar/out/cifar10"dataset_train=torchvision.datasets.CIFAR10(root=data_root,download=True,train=True,)dataset_test=torchvision.datasets.CIFAR10(root=data_root,download=True,train=False,)dataset_full=torch.utils.data.ConcatDataset([dataset_train,dataset_test])# create the object (here we run t-SimCNE with fewer epochs# than in the paper; there we used [1000, 50, 450]).tsimcne=TSimCNE(total_epochs=[500,50,250])# train on the augmented/contrastive dataloader (this takes the most time)tsimcne.fit(dataset_full)# map the original images to 2DY=tsimcne.transform(dataset_full)# get the original labels from the datasetlabels=[lblforimg,lblindataset_full]# plot the datafig,ax=plt.subplots()ax.scatter(*Y.T,c=labels)fig.savefig("tsimcne.png")CIFAR-10CIFAR-100ReproducibilityFor reproducing the results of the paper, please see theiclr2023branchin this
repository.
Package: tsincluder
MotivationI need to generate a changelog based on git content. I want to controle
the structure of the changelog but I don’t want to manage the git
content.### from 2016-01 to 2016-05

  We implements feature activation ...

  * 2016-04-13 : Fabien Arcellier : feat: activate ...
  * 2016-04-10 : Fabien Arcellier : feat: add index ...
  * 2016-04-10 : Fabien Arcellier : feat: add search ...
  * ...I need to manage the format and the place of content myself and let git
generate some contents from a fileCHANGELOG.md.in### from 2016-01 to 2016-05

  We implements feature activation ...

  * @tsincluder git log --date=short --pretty=format:'%ad : %aN : %s'  --abbrev-commit --since="1/1/2016" --until="4/31/2016" | grep featTo generateCHANGELOG.md, runtsincluderCHANGELOG.md.in>CHANGELOG.mdSynopsistsincludermeans Text Script Inclusion Manager.tsincluderis acliand alibraryyou can call from a python script that will
replace a markup as@tsincluder echo hello worldbyhello world.It keep the content that prefix the markup to generate a valid format as
markdown and add it on all the lines if tsincluder generate many lines.The latest versionYou can find the latest version to …git clone https://github.com/FabienArcellier/tsincluder.gitUsagerun tsincluder to replace the markers @tsinclude (text script include)
by the content generate by the command on stdoutpython-mtsincluderfile.into use it as a libraryfromtsincluderimportProcessorline=" * @tsincluder git log --date=short --pretty=format:'%ad :%aN :%s'  --abbrev-commit --since="1/1/2016" --until="4/31/2016" | grep feat"processor=Processor()content=processor.process(line)print(content)As tsincluder executes a subprocess command, the script can configure
the working directory and markupfromtsincluderimportProcessorprocessor=Processor(working_directory="/tmp",markup="@other_markup")InstallationInstall tsincluder using PyPipipinstalltsincluderInstall tsincluder from the sourcepythonsetup.pyinstallTestsUse make to execute unit tests.make testsContributorsFabien ArcellierLicenseThis Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
Package: tsinfer
tsinferInfer a tree sequence from genetic variation dataThedocumentationcontains details of how to use this software, includinginstallation instructions.The algorithm, its rationale, and results from testing on simulated and real data are described in the followingNature Genetics paper:Jerome Kelleher, Yan Wong, Anthony W Wohns, Chaimaa Fadil, Patrick K Albers and Gil McVean (2019)Inferring whole-genome histories in large population datasets. Nature Genetics51: 1330-1338Please cite this if you usetsinferin your work. Code to reproduce the results in the paper is present in aseparate GitHub repository.Note thattsinferdoes not attempt to infer node times (i.e. branch lengths of the
inferred trees). If you require a tree sequence where the dates of common ancestors
are expressed in calendar or generation times, you should post-process thetsinferoutput using software such astsdate.
Package: tsingspider
DataSpiderA spider framework with several internal spiders.ThanksThanksJetBrainsprovided FREEPyCharmProfessional for this project.Installpipinstall--upgradetsingspiderFeaturesLight-weight: do not have to start browser simulator, won't cost lots of resourcesBut not all the website can download in this wayLazy: won't download anything before you actually use the dataUseful UtilitiesSupport HLS downloadSupport cookies from firefoxSupport ProxiesGenerate magnet link from torrent dataWrite Your Own SpiderTo define a resource, you can useLazySouporLazyContent.LazyContentis for binary data, basically all kinds of the data are binary.LazySoupis for the XML format resource, widely be used for downloading web-page.For example:fromtsing_spider.utilimportLazySoup,LazyContentclassYourOwnSpider(LazySoup):def__init__(self,url:str):LazySoup.__init__(self,url)@propertydefsome_info(self)->str:"""Extract information from self.soupthe data will be downloaded at the first time of using it"""pass
Package: tsip
About Python-TSIPPython-TSIP is a Python package for parsing and creating TSIP packets. The Trimble Standard
Interface Protocol (TSIP) is the binary protocol spoken by the GPS receivers sold by Trimble Navigation Ltd.
(http://www.trimble.com).Python-TSIP is available under the “BSD 2-Clause Simplified License”.StatusAlmost the full set of TSIP command and report packets understood by the Copernicus II receiver has been implemented but
so far only some of them have been tested against the actual GPS. Implementing a complete set of tests against an actual
Copernicus II receiver is currently work in progress. Presumably Trimble Thunderbolt and Thunderbolt-E are also
supported as they appear to implement a subset of the commands/reports of the (newer) Copernicus II receiver.
I don’t have access to any other Trimble products.Python-TSIP is automatically tested against the following Python versions.Python 2.6Python 2.7Python 3.3Python 3.4Python 3.5pypypypy3The tests currently fail on the following Python versions.Python 3.2 (syntax error in the coverage module, it may work otherwise)Jython (can’t get Tox to work with jython)The master branch equals the latest release. The develop branch represents the
latest development but may not always pass all tests.ExampleThe following code shows how to receive the current GPS time from the receiver.Command packet 0x21 requests the current GPS time.Report packet 0x41 contains the current GPS time. Its fields are accessible by index.importtsipimportserial# Open serial connection to Copernicus II receiverserial_conn=serial.Serial('/dev/ttyS0',38400)gps_conn=tsip.GPS(serial_conn)# Prepare and send command packet 0x21command=tsip.Packet(0x21)gps_conn.write(command)whileTrue:# should implement timeout here!!!report=gps_conn.read()ifreport[0]==0x41:print'GPS time of week .......:%f'%(report[1])print'Extended GPS week number:%d'%(report[2])print'GPS UTC offset .........:%f'%(report[3])breakMore examples can be found in thedocs/examples/folder.History0.3.2 (28-Oct-2017)Added Report Packet 0x58: GPS System Data from the Receiver.Fixed Report Packet 0x47: Signals Levels for Tracked Satellites.Fixed Report Packet 0x6D: Satellite Selection List.xAddeddocs/examples/example2.pywich provides a more comprehensive
template program than the example in the README.0.3.1 (26-Sep-2017)Fixed README.rst so it renders properly on PyPi.0.3.0 (26-Sep-2017)Argument totsip.Packet()can now also be a tuple or list
(inspired by Criss Swaim).Changed development status from alpha to beta.Cleaned up lots of code to satisfy flake8.0.2.0 (03-Dec-2015)Rewritten from scratch.Implements almost complete set of TSIP commands supported by
Trimble Copernicus II and Thunderbolt/Thunderbolt-E GPS
receivers.0.1.0 (20-Jun-2015)First release on PyPI.
Package: tsipy
TSIPYPython package for processing TSI signals.Full documentation is availableonline.Installationpip install tsipyInstallation for Developers# Clone repository
git clone https://github.com/roksikonja/tsipy.git

# Setup venv
python -m venv venv
source venv/bin/activate

pip install --upgrade pip
pip install wheel

# For development
pip install -e .[dev]

# For documentation
pip install -e .[docs]UsageDemos can be found in./scripts.python ./scripts/exp_virgo.py
python ./scripts/exp_fusion.pyReferencesReferences can be found in./references.
Package: tskit-book-theme
sphinx-book-themeAn interactive book theme for Sphinx.This is a lightweight Sphinx theme designed to mimic the look-and-feel of an
interactive book. It has the following primary features:Bootstrap 5for visual elements and functionality.Flexible content layoutthat is inspired by beautiful online books,
such asthe Edward Tufte CSS guideVisual classes designed for Jupyter Notebooks. Cell inputs, outputs,
and interactive functionality are all supported.Launch buttons for online interactivity. For pages that are built with
computational material, connect your site to an online BinderHub for interactive content.Get startedTo get started withsphinx-book-theme, first install it withpip:pip install sphinx-book-themethen, activate the theme in your Sphinx configuration (conf.py):...
html_theme = "sphinx_book_theme"
...This will activate the Sphinx Book Theme for your documentation. Note that you may
need to change yourhtml_theme_optionsconfiguration depending on your previous
theme. See the pages to the left for information about what you can configure withsphinx-book-theme.DocumentationSeethe Sphinx Book Theme documentationfor more information.
Package: tskmanager
TaskerTask manager for pythonQuickstartimportsysimporttimefromtasker.managerimportManagerfromtasker.taskimportTaskif__name__=="__main__":defexample1(attached_outstream=sys.stdout):print("1",file=attached_outstream)time.sleep(1)print("2",file=attached_outstream)time.sleep(1)print("3",file=attached_outstream)defexample2(attached_outstream=sys.stdout):print("console 2",file=attached_outstream)manager=Manager()manager.run_attached_stream()manager.run_task(Task(example1))time.sleep(1)manager.run_task(Task(example2))
Package: tsl2561
Python library for TSL2561This is python library for working with Adafruit’s TSL2561 luminosity sensor.RequirementsPython >=3.5Adafruit GPIO library (https://github.com/adafruit/Adafruit_Python_GPIO)InstallationInstall the latest version using pip:pipinstalltsl2561Examplefromtsl2561importTSL2561if__name__=="__main__":tsl=TSL2561(debug=True)print(tsl.lux())LicenseCopyright (c) 2015 Kevin Townsend for Adafruit Industries.
All rights reserved.Copyright (c) 2016-present, Georges Toth
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holders nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ‘’AS IS’’ AND ANY
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: tslite
tsliteA Simple Time Series Database Implemented By Python.Installpip install tsliteUsage# -*- coding: UTF-8 -*'''Created on 2020-02-19'''from__future__importprint_functionimportrandom,timeimporttslitepath='/tmp/tsdb/test'# the database pathdb=tslite.Database(path)tabname='t1'db.drop_table(tabname)# clern table# get tabletab=db.get_table(tabname)# define the table structtab.define({'field_lock':True,# lock the table struct'fields':[{'name':'name','type':'string','index':True},{'name':'group','type':'int','index':True},{'name':'x','type':'int','index':True},{'name':'v','type':'int'},{'name':'y','type':'float'},{'name':'z','type':'float'},{'name':'t','type':'float'},]})# count for testingcount=10tm=time.time()foriinrange(count):# write datatab.write_data({'x':i,'time':tm+i,'name':random.choice(['a','aa','bb']),'y':random.random()})# query dataq=tab.query()forrinq:print(r)print('name is "a":')# query field 'name' == "a"q=tab.query(eqs={'name':'a'})forrinq:print(r)Click to view more information!CHANGES1.0.0first version