Saravanan666/pypi-org huggingface dataset:
Package: tamarack
Tamarack is a quantitative web application profiler. It will track the
performance of all requests made to your app, aggregate them, and
present them so you can identify bottlenecks and track improvements
and regressions.Tamarack consists of three parts: acollector librarywhich resides
in your application. The collector will feed thereceiver serverdata, which is then presented in theexplorer. The data is stored inPostgreSQLand can be scaled up depending on the needs of your
application. The collector is designed to cause minimal overhead in
your application.The data is available in near real time, with only a few minutes of
delay to give all servers time to gather the statistics.There is currently only an implementation of the Tamarack Collector in
Python primarily for Django, with Flask support coming soon. The
receiver API is a simple HTTP API, so writing bindings for your
language should not be hard.Setting It UpYour system will need some basic packages before getting Tamarack up
and running:Python 3.4, with development libraries (python3.4,python3.4-dev)Python package installer (python3-pip)PostgreSQL 9.3 or later, with extension modules (postgresql-9.3,postgresql-contrib-9.3)Once those packages have been installed, you can begin setting up
Tamarack. The first thing you will need is a virtual environment where
all Python packages will be installed. With Python 3.4 this should be
easy easy, since Virtualenv now is bundled with the Python
installation. However, this clashes with some Linux distributions’
package management and might require some manual setup:## Skip this if you have pyvenv-3.4 available:# Install virtualenv globallypip3installvirtualenv# Create a Tamarack environment in /var/libvirtualenv-p`whichpython3.4`/var/lib/tamarackIf you havepyvenv-3.4available, you can simply use it instead:## Skip this if you installed virtualenv above:# Create a Tamarack environment in /var/libpyvenv-3.4/var/lib/tamarackCommon for both setups is how you use them to install Tamarack:# Step into this virtual environmentsource/var/lib/tamarack/bin/activate# Install Tamarackpipinstalltamarack# Create a configuration filetamarackinit/etc/tamarack.pyThis will generate a configuration file at/etc/tamarack.py, which
you will need to edit with the settings for your system. When you’re
done, you’re ready to start Tamarack:tamarack--config=/etc/tamarack.pyserverThis will start an HTTP server on port 3000, which you can visit in a
web browser.Where to Go From Here?If you followed the instructions above and visited the page, you
probably noticed that the list of applications is empty. Now you will
need to integrate theTamarack Collectorin your application in
order to start receiving profiling data.
Package: tamarco
TamarcoMicroservices framework designed for asyncio and Python.FeaturesLifecycle management.Standardized settings via etcd.Automatic logging configuration. Support for sending logs to an ELK stack.Application metrics via Prometheus.Designed for asyncio.Messaging patterns. The framework comes with support for AMQP and Kafka via external resources. The AMQP resource has
implemented publish/subscribe, request/response and push/pull patterns.Custom encoders and decoders.Pluging oriented architecture. Anyone can create a new resource to add new functionality. External resources are integrated
into the framework transparently for the user.Graceful shutdown.ResourcesThe framework allows to write external resources and integrate them in the lifecycle of a microservice easily. List with
the available resources:MetricsRegistryStatusProfilerMemory analizerHTTPKafkaAMQPPostgres (not released yet)Influxdb (not released yet)Redis (not released yet)Websocket (not released yet)Let us know if you have written a resource.DocumentationThe documentation is availablehere.ExamplesThere are several examples in theexamplesfolder.To run them, install tamarco, launch the docker-compose (not necessary for all the examples) and run it.pipinstalltamarcodocker-composeup-dpythonexamples/http_resource/microservice.pyRequirementsSupport for Python >= 3.6.
Package: tamarco-kafka
tamarco-kafkaKafka resource for Tamarco microservice framework. It runs a confluent-kafka client in a thread.This repository is a plugin for Tamarco, for more information go toTamarco main repository.SettingsThis resource depends on the following configuration:system:resources:kafka:bootstrap_servers:kafka:9092The bootstrap servers are the address of the members of a kafka cluster separated by coma.Input and outputsThe inputs and outputs need to be declared in the resource.InputThe input can be used with two different patterns, as decorator and as async stream.This resource only supports balanced consumer groups with auto commit.Async streamThis usage case uses the input as asynchronous iterator to consume the metric stream.classMyMicroservice(Microservice):name="input_example"metrics_input=KafkaInput(topic='metrics',codec=JsonCodec)kafka=KafkaResource(inputs=[metrics_input])@taskasyncdefmetrics_consumer(self):asyncformetricinself.metrics_input:self.logger.info(f'Consumed message from metrics topic:{metric}')DecoratorThis usage case declares a function as handler of the messages, and the resource is going to open automatically a
coroutine to consume each message.classMyMicroservice(Microservice):name="input_example"kafka=KafkaResource(inputs=[metrics_input])@KafkaInput(resource=kafka,topic='metrics',codec=JsonCodec)asyncdefmetrics_handler(self,message):self.logger.info(f'Consumed message from metrics topic:{message}')OutputIt is a Kafka producer very simple to use.classMyMicroservice(Microservice):name="output_example"metrics_output=KafkaOutput(topic='metrics',codec=JsonCodec)kafka=KafkaResource(outputs=[metrics_output])@task_timer(interval=1000,autostart=True)asyncdefmetrics_producer(self):metrics_message={'metrics':{'cat':'MEOW'}}awaitself.metrics_output.push(metrics_message)self.logger.info(f'Produced message{metrics_message}to metrics topic')How to run the examplesTo run them you just need to launch the docker-compose, install the requirements and run it.pipinstall-rexamples/requirements.txtdocker-composeup-dpythonexamples/microservice_stream_input.py
Package: tamarin
Tamarin is a Django app used for parsing storing S3 bucket log data in local
models. Said data can be used for monitoring:Resource usage for each bucketWhich hosts are consuming the most bucketsWhich files are being downloaded most frequentlyTamarin’s only purpose is to retrieve the logs from S3, parse them, and store
them locally in a Django model. You’ll need to write your own queries and
reports, or use another third party app that builds on Tamarin.DocumentationVisit the following URL for complete documentation:http://duointeractive.github.com/tamarin/LicenseTamarin is licensed under theBSD License.
Package: tamarind
tamarind🥭manage multiple ephemeral neo4j containerstamarindis a system that manages Neo4j databases by provisioning Docker container resources for you. The user-facing API provides access to a dictionary-like lookup for your databases, and allows you to start and stop database servers on the fly.InstallationInstall from PyPI using pip:pip3installtamarindYou will need Docker already installed if you intend to use the Docker provisioning service. You can install Docker with the script fromhttps://get.docker.com/.UsageCreating a new dbfromtamarindimportNeo4jDockerProvisionerN=Neo4jDockerProvisioner()N.start("MyDatabase")Now you can access this graph database through py2neo:>>>N["MyDatabase"]<py2neo.Graph>The object returned is apy2neo.database.Graphobject:>>>N["MyDatabase"].run("MATCH (a:Person) RETURN a.name, a.born LIMIT 4").data()[{'a.born':1964,'a.name':'Keanu Reeves'},{'a.born':1967,'a.name':'Carrie-Anne Moss'},{'a.born':1961,'a.name':'Laurence Fishburne'},{'a.born':1960,'a.name':'Hugo Weaving'}]List all>>>N.ps().keys()['MyDatabase','OldDatabase']Stop a service>>>N.stop("OldDatabase")>>>N.ps().keys()['MyDatabase']Custom ProvisionersIf you provision Neo4j instances via resources other than Docker (e.g. via AWS EC2 AMIs), you can extend theNeo4jProvisionerclass withstartandpsfunction calls (stopis an optional implementation in Tamarind).LegalLicensed under Apache 2.0. Reach out toopensource@fitmango.comwith questions.Copyright 2021 FitMango.Licensed under the Apache License, Version 2.0 (the "License");
you may not use this codebase except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.Made with ❤️ at🥭
Package: tamarin-iac
Tamarin - Infrastructure as CodeTamarin is a tool to provide Virtual Machines on any operating systems.It was created initally to replace vagrant for the Mac M1, once the VirtualBox was not compatible with this new processor.Installation on Macbrew install qemu
python3 -m pip install tamarin-iacHow to use itFirst you have to create a base image, like you do with any normal Linux installation:tamarin image create debian -iso debian.isoFirst create a file called inventory.ymlExample:inventory:
    image: debian
    ssh-password: alisson
    ssh-user: root
    vms:
        - name: sandbox
          memory: 512
          port-forward:
          - '2222:22'Then you can check your inventory using:$ tamarin status

+-----------+---------+
| Name      | status  |
+-----------+---------+
| webserver | Stopped |
| database  | Stopped |
+-----------+---------+Run your VM:$ tamarin start
[+] Creating VM: webserver
[+] Setting up VM: webserver on port 4022
[!] Waiting port 4022 to be open
[+] VM Ready!
[+] Setting the hostnameAccess via ssh:(base) alissonmachado@Alissons-Air Tamarin % ssh root@localhost -p 4022
root@localhost's password: 

Linux webserver 5.10.0-11-amd64 #1 SMP Debian 5.10.92-1 (2022-01-18) x86_64
root@webserver:~#
Package: tambotapi
ttbotIt's a beautiful project that helps developers program.Installation :pip install ttbotLogan Usagefromttbotimport*## Follow us on social media[![Github](https://img.shields.io/badge/Github-NovaTools-orange?style=for-the-badge&logo=github)](https://github.com/NovaTools4/)[![Telegram](https://img.shields.io/badge/Telegram-NovaTools-orange?style=for-the-badge&logo=Telegram)](https://t.me/VZX_TEAM)
Package: tambus
Bambus Template EngineUsageTo get started, follow the example below to render a template:importtambust=tambus.TambusEngine()# Rendering HTMLwithopen("/templates/index.html")asf:print(t.translate(f.read(),hello="world!"))Imagine the content of/templates/index.htmlis as follows:<!DOCTYPE html><htmllang="en"><head><title>My Page</title></head><body><h1>Hello {hello}</h1></body></html>The output would be:<!DOCTYPE html><htmllang="en"><head><title>My Page</title></head><body><h1>Hello world!</h1></body></html>Contribution GuidelinesContributions to Tambus are welcome! If you have any bug fixes, improvements, or new features to add, please submit a pull request. Make sure to follow the coding style and include tests for any new functionality.LicenseTambus is licensed under the MIT License. See theLICENSEfile for more information.
Package: tameris
Tameris - Python Discord API wrapper!Tameris is focused on customizability in your Discord bots as well as flexibility and expandability!ExamplesCreating a simple bot with Tamerisbot=Client(bot_token='token',command_prefix='+')classHelloCommand(Command):asyncdefrun(self,context:Context,call_arguments):awaitbot.send_message(content=f'Hello! @{context.author.name}#{context.author.discriminator}',channel_id=context.channel.id)asyncdefready():print('Logged in.')asyncdefon_message(message):awaitbot.process_commands(message)bot.events.on_ready=readybot.events.on_message_create=on_messagebot.register_command(HelloCommand,'hello')bot.run()
Package: tamilinayavaani
Tamilinaiyam - Pizhaithiruthi (Spellchecker)Python PackagePython Port of TamilInaiya spell checker is named ‘tamilinayavaani’
and available as Python module form same name. It can be used with a UTF-8 encoded text file as follows,Installation$python3-mpipinstall--upgradetamilinayavaani>=0.13Usage - in-memoryfromtamilinaiyavaaniimportSpellCheckerflag,suggs=SpellChecker.REST_interface('வாழை','பழம்')expected=['வாழைப்','பழம்']assertnotflagassertexpected[0]==suggs[0]Usage - file-basedAn file-based use of the library would look like,fromtamilinaiyavaaniimportSpellChecker,SpellCheckerResultresult=SpellChecker(fname).run()#fname is a full filename# result is a list of SpellCheckerResult objects.SourceTamil Virtual Academy release sources atlink.LicenseThis code is licensed under terms of GNU GPL V2. Checklinkfor license info.CreditsThanks to Tamil Virtual Academy, Chennai for releasing ths source code of this application. This work is open-source
publication of Vaani fromlinkYou can support the close-source Vaani project, an 8 yr effort
as of 2020, by donating heredonate(link)Python Port was enabled by Kaniyam Foundation, T. Shrinivasan, @manikk, Ashok Ramachandran, and others.
You can support Kaniyam Foundation and its mission by donating via instructions
here,KaniyamThe Python port depends on tamilsandhichecker projectlinkand the Open-Tamil
projectlink:Additional source data, of 5000 common errors, from Vaani project provided at “Error Annotated Tamil Corpus” is added to the checker under the Creative Commons License. “Annotated with Major Grammar & Spelling Errors generated by VaaniNLP” Ref: Neechalkaran Raja,link:
Package: tamil-nlp-package-test
tamil-nlp-packageIt is a repository for Tamil NLP packages as pip installablesteps for installation and dependenciesInstall and update using pip:pipinstalltamil_nlp_package_testTamil Sentence Tokenizer Examplefromtamil_nlp_package_testimportsent_tokenizerasttinput_text="ஒவ்வொருவருக்கும் கல்வி கற்பதற்கான உரிமையுண்டு. குறைந்தது ெதாடக்க அடிப்படைக் கட்டங்களிலாவது கல்வி இலவசமானதாயிருத்தல் வேண்டும். தொடக்கக் கல்வி கட்டாயப்படுத்தல் வேண்டும். தொழில் நுட்பக் கல்வியும் உயர் தொழிற் கல்வியும் பொதுவாகப் பெறப்படத்தக்கனவாயிருத்தல் வேண்டும். உயர் கல்வியானது யாவருக்கும் திறமையடிப்படையின் மீது சமமான முறையில் கிடைக்கக் கூடியதாக்கப்படுதலும் வேண்டும். கல்வியானது மனிதனின் ஆளுமையை முழுதாக விருத்தி ெசய்யுமுகமாகவும் மனிதவுரிமைகளுக்கும் அடிப்படைச் சுதந்திரங்களுக்குமான மரியாதையை வலுப்படு்த்துமுகமாகவும் ஆற்றப்படுத்தப்படல் வேண்டும். அது சகல நாடுகளுக்கிடையேயும், இன அல்லத மதக் குழுவினருக்கிைடயேயும் மன ஒத்திசைவு, பொறுதியுணர்வு, தோழமை, ஆகியவற்றை மேம்படுத்துதல் வேண்டுமென்பதுடன், சமாதானத்தைப் பேணுவதற்காக ஐக்கிய நாடுகள் சபையின் முயற்சிகளை மேற்கொண்டு செல்லுவதற்குதவவும் வேண்டும்."output=tt.tokenize(input_text)print(output)Outputஒவ்வொருவருக்கும் கல்வி கற்பதற்கான உரிமையுண்டு.
குறைந்தது ெதாடக்க அடிப்படைக் கட்டங்களிலாவது கல்வி இலவசமானதாயிருத்தல் வேண்டும்.
தொடக்கக் கல்வி கட்டாயப்படுத்தல் வேண்டும்.
தொழில் நுட்பக் கல்வியும் உயர் தொழிற் கல்வியும் பொதுவாகப் பெறப்படத்தக்கனவாயிருத்தல் வேண்டும்.Tamil Word Tokenizer Exampleinput file - sample.txtஒவ்வொருவருக்கும் கல்வி கற்பதற்கான உரிமையுண்டு.
குறைந்தது ெதாடக்க அடிப்படைக் கட்டங்களிலாவது கல்வி இலவசமானதாயிருத்தல் வேண்டும்.
தொடக்கக் கல்வி கட்டாயப்படுத்தல் வேண்டும்.
தொழில் நுட்பக் கல்வியும் உயர் தொழிற் கல்வியும் பொதுவாகப் பெறப்படத்தக்கனவாயிருத்தல் வேண்டும்.fromtamil_nlp_package_testimportword_tokenizeraswtlang='tam'# tam - Tamil, Hindi - hin ...input_file_name='sample.txt'# input filename as string.output_file_name='output'# output filename as string.output=wt.tokenize(lang,input_file_name,output_file_name)# It'll return a result in a text file as output_file_name.output file - output.txt# Sent_id = 1
# text = தொடக்கக் கல்வி கட்டாயப்படுத்தல் வேண்டும்.
# transliteration =  toṭakkak kalwi kaṭṭāyappaṭuttal wēṇṭum.
# url = ஒவ்வொருவருக்கும் கல்வி கற்பதற்கான உரிமையுண்டு.	குறைந்தது ெதாடக்க அடிப்படைக் கட்டங்களிலாவது கல்வி இலவசமானதாயிருத்தல் வேண்டும்.
# wtok =தொடக்கக் கல்வி கட்டாயப்படுத்தல் வேண்டும் .
1 தொடக்கக் unk
2 கல்வி unk
3 கட்டாயப்படுத்தல் unk
4 வேண்டும் unk

# Sent_id = 2
# text = தொழில் நுட்பக் கல்வியும் உயர் தொழிற் கல்வியும் பொதுவாகப் பெறப்படத்தக்கனவாயிருத்தல் வேண்டும்.
# transliteration =  toẓil nuṭpak kalwiyum uyar toẓiṟ kalwiyum potuwākap peṟappaṭattakkaṉawāyiruttal wēṇṭum.
# url = ஒவ்வொருவருக்கும் கல்வி கற்பதற்கான உரிமையுண்டு.	குறைந்தது ெதாடக்க அடிப்படைக் கட்டங்களிலாவது கல்வி இலவசமானதாயிருத்தல் வேண்டும்.
# wtok =தொழில் நுட்பக் கல்வியும் உயர் தொழிற் கல்வியும் பொதுவாகப் பெறப்படத்தக்கனவாயிருத்தல் வேண்டும் .
1 தொழில் unk
2 நுட்பக் unk
3 கல்வியும் unk
4 உயர் unk
5 தொழிற் unk
6 கல்வியும் unk
7 பொதுவாகப் unk
8 பெறப்படத்தக்கனவாயிருத்தல் unk
9 வேண்டும் unk
Package: tamilspellchecker
Tamil Spell CheckerIdea for building simple Tamil Spell Checker came from a conversation with T Shrinivasan from open-tamil team.Tamil Spell Checker uses below approach to suggest different spellings for a wordCheck whether it is a valid Tamil word using Bloom FilterUse Levenstein Distance (edit distance of 2) to suggest words when it is not a tamil wordProject Madurai CrawlerProject Madurai has good collection of tamil works. Use Project Madurai Crawler to generate Tamil unique word list.To run it use the below command` python ProjectMaduraiCrawler.py `Create Bloom Filter FileBloom Filter is a space efficient and compute optimized probablistic datastructure designed to tell whether an item is present in a set or not. More information on Bloom Filter can be found in [wiki](https://en.wikipedia.org/wiki/Bloom_filter).Spellchecker is using Bloom Filter to check whether a word is a valid tamil word or not.Bloom Filter Datastructure file has to be created first before using to check validity of a wordTo generate Bloom Filter file use the below command` python TamilBloomFilterCreator.py `## Sample code to check whether a word is valid tamil word` from TamilwordChecker import TamilwordChecker unique_word_count = 2043478 tamilwordchecker =TamilwordChecker(unique_word_count,"tamil_bloom_filter.txt")print(tamilwordchecker.tamil_word_exists("மேகம்"))`Sample code to check get spell check corrections` from TamilSpellingAutoCorrect import TamilSpellingAutoCorrect spellchecker  =TamilSpellingAutoCorrect("tamil_bloom_filter.txt","tamilwordlist.txt")from_spell_checker_list =spellchecker.tamil_correct_spelling("மேக்ம்")print(from_spell_checker_list) `Norvig AlgorithmNorvig algorithm can run faster than exhaustive search method; you
can use it as follows,fromtamilspellchecker.TamilSpellingAutoCorrectimportTamilSpellingAutoCorrect,get_datafrompprintimportpprintfromtamil.utf8importget_lettersspellchecker=TamilSpellingAutoCorrect(get_data("tamil_bloom_filter.txt"),get_data("tamilwordlist.txt"))results=spellchecker.tamil_Norvig_correct_spelling("தமிழ்னாடு")#தமிழ்நாடு என்பது சரியான சொல்.results=list(filter(lambdax:len(get_letters(x))>=4,results))#filter for words >= 4 lettersresults=list(filter(lambdax:len(get_letters(x))<=6,results))#and for words <= 6 letterspprint(results)assert'தமிழ்நாடு'inresultsAccuracy IssuesAccuracy of Tamilwordchecker depends on the list of unique words that is there in tamilwordlist.txt. Need to add more unique words from other sources.
Package: tamnun
Tamnun MLtamnunis a python framework for Machine and Deep learning algorithms and methods especially in the field of Natural Language Processing and Transfer Learning. The aim oftamnunis to provide an easy to use interfaces to build powerful models based on most recent SOTA methods.For more abouttamnun, feel free to readthe introduction to TamnunML on Medium.Getting Startedtamnundepends on several other machine learning and deep learning frameworks likepytorch,kerasand others. To installtamnunand all it's dependencies run:$ git clone https://github.com/hiredscorelabs/tamnun-ml
$ cd tamnun-ml
$ python setup.py installOr using PyPI:pip install tamnunJump in and try out an example:$ cd examples
$ python finetune_bert.pyOr take a look at the Jupyer notebookshere.BERTBERTstands for Bidirectional Encoder Representations from Transformers which is a language model trained by Google and introduced in theirpaper.
Here we use the excellentPyTorch-Pretrained-BERTlibrary and wrap it to provide an easy to usescikit-learninterface for easy BERT fine-tuning. At the moment,tamnunBERT classifier supports binary and multi-class classification. To fine-tune BERT on a specific task:fromtamnun.bertimportBertClassifier,BertVectorizerfromsklearn.pipelineimportmake_pipelineclf=make_pipeline(BertVectorizer(),BertClassifier(num_of_classes=2)).fit(train_X,train_y)predicted=clf.predict(test_X)Please seethis notebookfor full code example.Fitting (almost) any PyTorch Module using just one lineYou can use theTorchEstimatorobject to fit anypytorchmodule with just one line:fromtorchimportnnfromtamnun.coreimportTorchEstimatormodule=nn.Linear(128,2)clf=TorchEstimator(module,task_type='classification').fit(train_X,train_y)Seethis filefor a full example of fittingnn.Linearmodule on theMNIST(classification of handwritten digits) dataset.Distiller Transfer LearningThis module distills a very big (like BERT) model into a much smaller model. Inspired by thispaper.fromtamnun.bertimportBertClassifier,BertVectorizerfromtamnun.transferimportDistillerbert_clf=make_pipeline(BertVectorizer(do_truncate=True,max_len=3),BertClassifier(num_of_classes=2))distilled_clf=make_pipeline(CountVectorizer(ngram_range=(1,3)),LinearRegression())distiller=Distiller(teacher_model=bert_clf,teacher_predict_func=bert_clf.decision_function,student_model=distilled_clf).fit(train_texts,train_y,unlabeled_X=unlabeled_texts)predicted_logits=distiller.transform(test_texts)For full BERT distillation example seethisnotebook.SupportGetting HelpYou can ask questions and join the development discussion onGithub IssuesLicenseApache License 2.0 (Same as Tensorflow)
Package: tamodify
TA 接口文件修改库可以读出文件，修改一些字段或者是增加、删除某些行，再写入一个新文件。也可以从零生成数据文件、索引文件，可以读数据文件、索引文件。安装方法pip install tamodify注意仅在debian linux下开发测试，windows下编译有问题安装好后在/usr/local/tamodify目录下有样例代码，可以参照开发。在python环境下执行import tamodify
help(tamodify)可以看到模块的一些帮助信息。
Package: tamos
tamosWhat does "tamos" stand for?"tamos" stands for Thermal Architectures Modelling and Optimization Software.What is it?tamosis a non-GUI tool that aims at facilitating the study of thermal architectures.
A thermal architecture is a set of energy production, storage and distribution components that operate together in order to meet some energy demands.How does it work?tamosperforms the optimal sizing and operation of various energy components gathered in energy hubs.
It relies on the description of these components using the Mixed-Integer Linear Programming formalism (MILP), i.e. mathematical programming.What are the main software components?tamosheavily relies on thedocplexapi, that eases the formulation of MILP problems.
Once declared, the problems are solved using the Cplex solver. It can also be exported to.LPand.MPSformats and be solved using non-proprietary solvers.Installation notesThe packaged version oftamosis available on PyPi. Please run:pip install tamosWhere is project hosted?Sources are managed on GitHub:https://github.com/BNerot/tamos/tree/main/src/tamosThe fileBatch analysis.ipynbis only on GitHub. It provides the user with an easy way to analyse large volumes of results.Is it difficult to use?Please follow one of the two examples inexamplesas a quick start guide.
You can also find a web version of the documentation indocs/build/html. Once this directory is downloaded, please open 'index.html'.CopyrightThe code is distributed under an Apache-2.0 license.
Most of the development work was done in the context of a PhD thesis.
This thesis was funded by two French institutions:Commissariat à l'Energie Atomique:https://www.cea.fr/englishAgence de la transition écologique:https://www.ademe.fr/en/frontpage/