Saravanan666/pypi-org huggingface dataset:
Package: dasp-stacker
Dimensionally Aligned Signal Projection (DASP)SummaryDimensionally aligned signal projection (DASP) algorithms are used to analyze fast Fourier transforms (FFTs) and generate visualizations that help focus on the harmonics for specific signals. At a high level, these algorithms extract the FFT segments around each harmonic frequency center, and then align them in equally sized arrays ordered by increasing distance from the base frequency. This allows for a focused view of the harmonic frequencies, which, among other use cases, can enable machine learning algorithms to more easily identify salient patterns. This work seeks to provide an effective open-source implementation of the DASP algorithms as well as functionality to help explore and test how these algorithms work with an interactive dashboard and signal-generation tool.The DASP library is implemented in Python and contains four types of algorithms for implementing these feature engineering techniques: fixed harmonically aligned signal projection (HASP), decimating HASP, interpolating HASP, and frequency aligned signal projection (FASP). Each algorithm returns a numerical array, which can be visualized as an image. For consistency FASP, which is the terminology used for the short-time Fourier transform, has been implemented as part of the library to provide a similar interface to the STFT of the raw signal. Additionally, the library contains an algorithm to generate artificial signals with basic customizations such as the base frequency, sample rate, duration, number of harmonics, noise, and number of signals.Finally, the library provides multiple interactive visualizations, each implemented using IPyWidgets and work in a Jupyter environment. A dashboard-style visualization is provided, which contains some common signal-processing visual components (signal, FFT, spectogram) updating in unison with the HASP functions (see Figure 1 below). Seperate from the dashboard, an independent visualization is provided for each of the DASP algorithms as well as the artifical signal generator. These visualizations are included in the library to aid in developing an intuitive understanding how the algorithms are affected by different input signals and parameter selections.A detailed breakdown of how these algorithms work can be seen in 'example/algorithm_walkthrough'FeaturesDashboard-style interactive visualization (Figure 1)Numerical array output for each algorithmInteractive visualization for FASP onlyInteractive visualization for HASP algorithms (selecting one at a time)Interactive visualization for sine wave generationExamplesAn example of each feature available, and how to use them, can be seen in 'examples/dasp_example_notebook.ipynb'Installationpipinstalldasp-stackerCitation
Package: daspuml-compiler
The main goal was to implement a language and compilator that allows generating UML sequence diagram based on friendly and human-readable source code.
Home-page:https://gitlab.com/agh-dasp/daspuml-languageAuthor: Dominik Bober, Adam Klekowski, Szymon Duda, Przemysław Ziaja
Author-email: UNKNOWN
License: MIT
Description: UNKNOWN
Keywords: UML
Platform: UNKNOWN
Package: daspython
Welcome to the DAS Python packageTheRoyal Netherlands Institute for Sea Researchhas its data management system to help scientists archive and access their data. This tool is called:Data Archive System (DAS)and this package is its Python client.NOTE: This package requires Python 3.11 and plusTo install using pip$ pip install daspythonTo install this package locally use the following commands:Create a virtual environment first:Install virtualenv if not installed.$ pip install virtualenvCreate the virtual enviroment$ virtualenv .venvActivate your virtual environment (for Windows)$ .\.venv\Scripts\activate.ps1Deactivate your virtual environment (for Windows)$ deactivateNow install the dependencies$pipinstall-r.\requirements.txtThe best way to see how each method is used is visiting outautomated test scriptspage.AuthenticationUse this class to authenticate and keep your token that will be needed to use with all other service classes.Usagefromdaspython.auth.authenticateimportDasAuthauth=DasAuth('DAS url','Your user name','Your password')if(auth.authenticate()):print('You are connected ...')Unit Testspython -m unittest tests/test_something.pyDeploying this package:$ pip install twineInstall twinetwine upload dist/*Than follow the instructions to publish/upload the distribution files to pypi.org
Package: dasscostorageclient
DaSSCo Storage ClientA simple client library used to call the DaSSco Storage APIInstallationRequires Python 3.10+python -m pip install dasscostorageclientGetting startedfrom dasscostorageclient import DaSSCoStorageClient

client_id = 'CLIENT_ID'
client_secret = 'CLIENT_SECRET'

client = DaSSCoStorageClient(client_id, client_secret)

res = client.institutions.get_institutions()

institutions = res.get('data')
Package: dastro-bot
Discord Astro BotDiscord Bot for Star Citizen playersThis project contains python bot project for Discord, useful for Star Citizen players.Available features:Organization fleet information. Each member can add/remove ships that he owns.
Database is stored for everyone. There are commands to display whole organization fleet or
ships owned by specific member.Ship matrix data. There are commands to view ship details, comparison of multiple ships or check
prices of multiple ships that match expression (e.g. all DRAKE ships)Displaying of Road Map data with filters on specific expression (e.g. when searching for a ship release).
Displaying info for a specific release or Road Map category.Displaying current SC releases (PU and PTU) according to Road Map version.Thread monitoring changes in current SC release on Road Map page and Spectrum forums.Trade assistant shows best trade routes for given conditions (budget, cargo, start/end location).Mining assistant showing resources prices.If your bot is running use thehelpcommand to see all available commands.PrerequisitesPython 3.5+MongoDB - for storing data cacheSome SQL database if you don't want to use default SQLiteDependenciesaiohttp 3.4.4async-timeout 3.0.1disco-py 0.0.12pafy 0.5.4pymongo 3.7.2SQLAlchemy 1.2.12tabulate 0.8.2youtube-dl 2018.10.5Installationpipinstalldastro_bot
python-mdastro_bot.installDIRECTORY_NAMEThe second command generates default configuration files to run your own BOT:discord_bot.json - disco-py bot configuration filediscord_bot.py - basic bot classdiscord_bot.service - systemd unit filelanguages.py - named tuples with translationsettings.py - custom settings for your botBasic ConfigurationHere's what you absolutely need to configure to run the bot:discord_bot.jsonset thetokenvalue to your generated discord tokensettings.pyset the CHANNELS dict values to channels IDs from your serveractually in basic config only themainchannel is requiredyou can set all three channels with the same valuediscord_bot.serviceif you want to setup a systemd serviceTrade and mining dataAll raw data for these features is pulled from this project API:https://scm.oceandatarat.orgI strongly encourage to use this page for prices and other data reporting or to
contribute in any other way to the linked project.
Please create your own account on that page and set theSCM_TOKENin
settings.py accordingly.SQL DatabaseAstro Bot uses SQL Alchemy to handle database and SQLite database is
used by default. If you want to use different database then please
adjustsettings.pyfile accordingly.There are two parameters there which are used to configure database:DATABASE_NAME
DATABASE_DIALECTMongoDBMongo is used to store cache data (in case if external data sources are unavailable).
It works with default settings. If you need to customize it findMONGO_CONNECTION_STRINGin settings.py
Package: dastyletools
dastyletools使用dastyletoolspip install dastyletoolsfromdastyletoolsimporttools# 加tools.add(1,1)# 2# 减tools.sub(1,1)# 0# 乘tools.mul(1,1)# 1# 除tools.div(1,1)# 1.0# 从列表1的n坐标处拼接列表2a=[iforiinrange(10)]# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]b=[iforiinrange(5,15)]# [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]c=tools.rl1n_to_l2(a,b,3)# [0, 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]# 返回拉平的多维数组a=[[1,2,3],[4,5,6],[7,8,9]]tools.flatten_list(a)# [1, 2, 3, 4, 5, 6, 7, 8, 9]# 从源数组的值中随机分配出不重复的包含源数组值n个数组a=[iforiinrange(10)]tools.random_list_n(a,5)# [[6, 9], [1], [5, 0, 7], [2], [4, 3, 8]]# 生产从0到n的数组tools.arange(10)# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]打包python模块安装必要的模块pip install setuptools
pip install wheel
pip3 install twine创建~/.pypirc文件并且进行一些配置在pypi注册你的账号[distutils]
index-servers = pypi

[pypi]
username:xxx
password:xxx# 生成gz压缩包跟wheel文件，可以直接用pip install安装
python setup.py sdist bdist_wheel       
# 直接源码编译与安装    
python setup.py build install上传源码到pypitwine upload dist/*.whl
Package: das_unsupervised
Tools for unsupervised classification of acoustic signalsDAS-unsupervisedprovides tools for pre-processing acoustic signals for unsupervised classification:extract waveforms or spectrograms of acoustic events from a recordingnormalize the duration, center frequency, amplitude, or sign of waveform/spectrogramsUnsupervised classification itself is performed using existing libraries:dimensionality reduction:umapclustering:hdbscanorscikit-learnCan be used in combination withDAS, a deep learning based method for the supervised annotation of acoustic signals.Installationpipinstalldas-unsupervisedDemosIllustration of the workflow and the method using vocalizations from:fliesmicebirdsAcknowledgementsCode from the following open source packages was modified and integrated into das-unsupervised:avgn(Sainburg et al. 2020)noisereducefly pulse classifier(Clemens et al. 2018)Data sources:flies:David Stern(Stern, 2014)mice: data provided by Kurt Hammerschmidt (Ivanenko et al. 2020)birds:Bengalese finch song repository(Nicholson et al. 2017)ReferencesT Sainburg, M Thielk, TQ Gentner (2020) Latent space visualization, characterization, and generation of diverse vocal communication signals. Biorxiv .https://doi.org/10.1101/870311J Clemens, P Coen, F Roemschied, T Perreira, D Mazumder, D Aldorando, D Pacheco, M Murthy (2018) Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior. Current Biology 28, 2400–2412.e6 (2018).https://doi.org/10.1016/j.cub.2018.06.011D Stern (2014). Reported Drosophila courtship song rhythms are artifacts of data analysis. BMC BiologyA Ivanenko, P Watkins, MAJ van Gerven, K Hammerschmidt, B Englitz (2020) Classifying sex and strain from mouse ultrasonic vocalizations using deep learning. PLoS Comput Biol 16(6): e1007918.https://doi.org/10.1371/journal.pcbi.1007918D Nicholson, JE Queen, S Sober (2017). Bengalese finch song repository.https://doi.org/10.6084/m9.figshare.4805749.v5
Package: data
datais a small Python module that allows you to treat input in a singular
way and leave it up to the caller to supply a byte-string, a unicode object, a
file-like or a filename.>>>open('helloworld.txt','w').write('hello, world from a file')>>>fromdataimportDataasI>>>a=I(u'hello, world')>>>b=I(file='helloworld.txt')>>>c=I(open('helloworld.txt'))>>>printunicode(a)hello,world>>>printunicode(b)hello,worldfromafile>>>printunicode(c)hello,worldfromafileThis can be made even more convenient using thedatadecorator:>>>fromdata.decoratorsimportdata>>>@data('buf')...defparse_buffer(buf,magic_mode=False):...return'buf passed in as '+repr(buf)...>>>parse_buffer('hello')"buf passed in as Data(data='hello', encoding='utf8')">>>rv=parse_buffer(open('helloworld.txt'))>>>assert'file='inrvFitting inAll instances support methods likereador__str__that make it easy to
fit it into existing APIs:>>>d=I('some data')>>>d.read(4)u'some'>>>d.read(4)u' dat'>>>d.read(4)u'a'>>>e=I(u'more data')>>>str(e)'more data'Note howreadreturns unicode. Additionally,readbis available:>>>f=I(u'I am\xdcnicode.')>>>f.readb()'I am\xc3\x9cnicode.'Everydataobject has an encoding attribute which is used for converting
from and to unicode.>>>g=I(u'I am\xdcnicode.',encoding='latin1')>>>g.readb()'I am\xdcnicode.'Iteration and line reading are also supported:>>>h=I('I am\nof many\nlines')>>>h.readline()u'I am\n'>>>h.readlines()[u'of many\n',u'lines']>>>i=I('line one\nline two\n')>>>list(iter(i))[u'line one\n',u'line two\n']Extrassave_toSome useful convenience methods are available:>>>j=I('example')>>>j.save_to('example.txt')Thesave_tomethod will use the most efficient way possible to save the
data to a file (copyfileobjorwrite()). It can also be passed a
file-like object:>>>k=I('example2')>>>withopen('example2.txt','wb')asout:...k.save_to(out)...temp_savedIf you need the output inside a secure temporary file,temp_savedis
available:>>>l=I('goes into tmp')>>>withl.temp_saved()astmp:...printtmp.name.startswith('/tmp/tmp')...printl.read()...Truegoesintotmptemp_savedfunctions almost identically totempfile.NamedTemporaryFile,
with one difference: There is nodeleteargument. The file is removed only
when the context manager exits.Where it is usefuldatacan be used on both sides of an API, either while passing values in:>>>importjson>>>fromdataimportDataasI>>>m=I('{"this": "json"}')>>>json.load(m){u'this':u'json'}or when getting values passed (see the data decorator example above). If
necessary, you can also support APIs that allow users to pass in filenames:>>>classParser(object):...@data('input')...defparse(self,input,parser_opt=False):...returninput...defparse_file(self,input_file,*args,**kwargs):...returnself.parse(I(file=input_file),*args,**kwargs)...>>>p=Parser()>>>p.parse_file('/dev/urandom')Data(file='/dev/urandom',encoding='utf8')See the documentation athttp://pythonhosted.org/datafor an API reference.Python 2 and 3dataworks the same on Python 2 and 3 thanks tosix, a few compatibility functions and a
testsuite.Python 3 is supported from 3.3 onwards, Python 2 from 2.6.
Package: data247
# python-data247An API client for [Data 24-7](https://www.data24-7.com/).## InstallTo install the library, you can use[pip](http://www.pip-installer.org/en/latest/).```bash$ pip install data247```## UsageEssentially all you need to do is create a `Data247` client object--this holdsyour account credentials and also specifies which data you're requesting.You can then make queries using the `get` method, passing in the search term.```python>>> from data247 import Data247, IP>>>>>> client = Data247('myusername', 'mypassword', IP)>>> client.get('174.134.29.20'){u'city': u'Bakersfield',u'areacode': u'661',u'country': u'US',u'dst': u'1',u'zipcode': u'93312',u'longitude': u'-119.227203',u'state': u'CA',u'cost': u'0.0030',u'billable': u'1',u'latitude': u'35.383202',u'timezone': u'-8',u'ip_address': u'174.134.29.20'}```## Changelogv0.4: 12-17-2012- Updating dependencies for python-requests.- Updating codebase to make use of requests 1.0+ (this release introducedbackwards-incompatible changes).v0.3: 12-14-2012- Making `get` method return `{}` on error.v0.2: 12-14-2012- Adding support for extra data fields.v0.1: 11-27-2012- First release!
Package: data2es
Data2es数据导入工具本项目是一个用来把数据从mysq导入到es中的小工具。程序使用pip 安装，程序作为后台程序执行，可以脱离当前命令行环境。启动时需要指定配置文件。程序的日志文件默认为/tmp/data2es.log此文件也可以在启动时指定。命令行执行方法如下usage:data2esd{start|stop|restart|kill}[-c]positionalarguments:{start|stop|restart|kill}Thiscontrolcommandcanbeusedwithstartorstoporrestartorkill.Usedtocontroltherunningofdata2esddaemons

optionalarguments:-h,--helpshowthishelpmessageandexit-cCONFIGFILE_PATH,--configCONFIGFILE_PATHSpecifyconfigurationfilepath.-lLOG_FILE_PATH,--logfileLOG_FILE_PATHSpecifylogfilepath.-v,--versionshowprogram'sversionnumberandexit安装pipinstalldata2es启动data2esdstart-c'config.conf'其中config是必须指定的停止data2esdstop重启data2esdrestart配置文件范例以下配置文件所有配置项都要有，值可以为空。input:{
    mysql:{
        provider = "mysql" #数据源类型        
        db_host = "127.0.0.1"
        db_port=3110
        db_user="root"
        db_password="root"
        db_name="db_test"
        #是否记录上次执行结果, 如果为真,将会把上次执行到的 tracking_column 字段的值记录下来,保存到 last_run_metadata_path 指定的文件中
        record_last_run = true
        #追踪的字段
        tracking_column = "id"        
        #上一个sql_last_value值的存放文件路径, 必须要在文件中指定字段的初始值
        last_run_metadata_path = "/tmp/base_parameter.txt"
        #是否清除 last_run_metadata_path 的记录,如果为真那么每次都相当于从头开始查询所有的数据库记录
        #clean_run = false
        #是否将 column 名称转小写
        lowercase_column_names = false
        # sql 语句文件,分页和增量跟新都写在sql语句中
        statement = "select *from table where `id` > {last_run_id} order by `id` limit 5000"
        trigger = "schedule" #定时器触发
        #trigger = "webhook" #webhook触发
        # 设置监听间隔  各字段含义（由左至右）分、时、天、月、年，全部为*默认含义为每分钟都更新
        schedule = "* * * * * *"
        #设置webhook的访问令牌,为空时采用默认的令牌 ”esdata“
        webhook_token=""
        #设置webhook的访问令牌,为0时采用默认的端口号9899
        webhook_port=0        
        #指定对应的输出
        output_name="elasticsearch"
    }   
}

output:{ #elasticsearch服务器和index的配置
      elasticsearch:{
        hosts="127.0.0.1:9200"        
        index="test-index1"
        document_id = "id"
        username="elastic"
        password="elastic"
      }       
    }
webhook:{#有两种事件，一个是开始执行一次任务，一个是任务完成,如果指定了url那么在事件发生时会通知到这两个地址
  start=""
  finished=""
  error=""
}执行触发提供两种触发方式，一种是定时任务，一种是webhook触发。定时任务触发采用类似corn的方式设置。webhook的方式下，系统会运行一个http服务器，监听指定的端口，调用地址为http://<ipaddress>:port/hook?token=''。webhook如果在配置文件中配置了webhook地址，那个在事件发生时会通知设置的地址。通知内容如下(兼容钉钉和企业微信群机器人)：start{"msgtype":"text","text":{"content":"0000-00-00 00:00:00 从第 xxxxx 开始同步数据。"}}finished{"msgtype":"text","text":{"content":"0000-00-00 00:00:00 完成数据从 xxxx 到 xxxx 的同步。"}}
Package: data2image-alpha
Data2ImageData2Imageis a state-of-the-art library that wraps the most important techniques for the construction ofSynthetic ImagesfromSorted Data(also known asTabular Data).FeaturesInput data formats (2 options):Pandas DataframeFiles with the following formatTabular files: The input data must be inCSV, taking into account theTidy Dataformat.Tidy Data: Thetarget(variable to be predicted) should be set as the last column of the dataset. Therefore, the first columns will be the features.All data must be in numerical form.Runs onLinux,WindowsandmacOSsystems.Compatible withPython3.7 or higher.ModelsModelClassFeaturesHyperparametersTINTOtinto()blurproblemalgorithmpixelsbluramplificationdistancestepsoptionseedtimesverboseSuperTMLSuperTML()problemverboseIGTDIGTD()problemscalefea_dost_methodsave_image_sizemax_stepval_steperrorswitch_tmin_gainseedverboseDocumentationRead the documentation.Getting StartedYou can install Data2Image usingPypi(test):pip install data2image-alphaTo import a specific model usefrom data2Image.models import tintoCreate the model. If you don't set any hyperparameter, the model will use the default values (read documentation).model = tinto(blur=True)To generate the synthetic images use.genereateImages(data,folder)method.model.generateImages(data, resultsFolderPath)LicenseData2Image is available under theApache License 2.0.AuthorsBorja Reinoso-borjareinoso@gmail.comManuel Castillo-Cara-jcastillo@fi.upm.esRaúl García-Castro
Package: data2latex
Data2LaTeXThis project is a part of my bachelor thesis which deals with data representation usingPythonandLaTeX. You can find the source code on myGitHub.The idea behind this package prototype is that generating LaTeX documents containing scientific data from Python should not be difficult and require many steps. Currently the package supports the creation of simple tables and two types of plots: scatter plots and line plots. The package uses thePyLaTeXpackage to handle the document creation and compilation process. The main data sources are arrays and data tables from the popular packagesnumpyandpandas. A major inspiration for the module syntax is thematplotlib.pyplotmodule, which allows plots to be created in a few lines of code. The tables are created using thetblrenvironment from thetabularraypackage. The plots are created using thetikzpicture/axisenvironment from thetikz/pgfplotspackage.ExamplesExamples with results can be found in thedocumentation.Simple featuresimportdata2latexasdtoldtol.section("Data2LaTeX")dtol.text("This project is part of my bachelor thesis which deals with data representation using Python and LaTeX.")dtol.finish("simple_features")Simple tableimportdata2latexasdtoldata=[[1,2,3],[4,5,6],[7,8,9]]dtol.table(data)dtol.finish("simple_table")Simple plotimportdata2latexasdtolX=[0,1,2,3,4,5,6,7,8,9,10]Y=[84,13,94,37,80,89,90,45,55,26,92]dtol.plot(X,Y,line="-",mark="*")dtol.finish("simple_plot")Installationpython-mpipinstall--upgradepip
python-mpipinstall--upgradedata2latexDevelopmentpython-mvenv.venv
./.venv/Scripts/activate
python-mpipinstall--upgradepip
python-mpipinstall.[dev]Generating documentationsphinx-apidoc-odocssrc/data2latex
./docs/makehtmlPackagingpythonclear.py
python-mpipinstall--upgradebuild
python-mbuildPublishingpython-mpipinstall--upgradetwine
python-mtwineuploaddist/*
Package: data2model
data2modelPython library and CLI tool for generating different Python data classes from data.Supported data formats:CSVSupported data classes:dataclassesRequirementsPython 3.8+Installationpipinstalldata2modelUsageAs libraryimportasyncioimportpathlibfromdata_to_modelimportModelGeneratorfiles=[{"input":pathlib.Path("example.csv"),"output":pathlib.Path("example.py")},]asyncdefmodel_generator(input_file:pathlib.Path,output_file:pathlib.Path):mg=ModelGenerator(input_file)model=awaitmg.get_model()awaitmodel.save(output_file)asyncdefmain():tasks=[model_generator(i["input"],i["output"])foriinfiles]awaitasyncio.gather(*tasks)if__name__=="__main__":asyncio.run(main())As CLIfrom single file$catdata.csv
col1,col21,2
,2.0

$data2modelfile-ifdata.csv-ofdata.py
$catdata.py# This file was generated by data_to_model.fromdataclassesimportdataclass
fromtypingimportAny,Dict,Optional,Union


@dataclass
classData:col1:Optional[int]col2:Union[float,int]@classmethoddeffrom_dict(cls,data:Dict[str,Any])->"Data":col1=data["col1"]col2=data["col2"]returncls(col1=col1,col2=col2)defto_dict(self)->Dict[str,Any]:return{"col1":self.col1,"col2":self.col2}from directory$data2modelfolder-ifdata-ofmodels

$lsdata
data1.csvdata2.csv

$lsmodels
data1.pydata2.py
Package: data2rdf
data2rdfA pipeline for generating data representation in RDF out of raw data given in ASCII, CSV or EXCEL format.https://data2rdf.readthedocs.io/en/latest/InstallationInstall for using the packageEither install the package from the pypipipinstalldata2rdfInstall for developmentInstall the package from the source code:gitclonegit@github.com:MI-FraunhoferIWM/data2rdf.gitcddata2rdf
pipinstall-e.Windows specificIn windows it might be necessary to install curses manually. This can be done with:pip install windows-cursesDebug a Chowlk XML FileIn some cases when the syntax of the draw.io file is not correct (e.g.: missing label on arrow, bracket in class file) chowlk crashes. The only (pretty annoying but working) way to find the wrong syntax is to execute chowlk with the command line for that file and inclemently remove elements from the draw.io diagram. This way you can find the wrong syntax by process of elimination.Version UpdatesChowlk is installed via pip through the dependenciesRunning csv and excel pipelineAbox pipeline CLIUnnittest for csv and excel pipeline and abox pipelineBuilding the docs locallyHTMLA server will start, generate the docs and listen for changes in the source files.
This can be done by using docker or installing the development environment directly on the you machine. Next are installation guides for Docker and Linux OS.DockerFirst, build the Docker image by running the following command:$dockerbuild-fdocs.Dockerfile-tdata2rdf-docs.Then, start the program by running:$dockerrun-it--rm-v$PWD:/app-p8000:8000data2rdf-docsLinuxAt an OS level (these commands work on Linux Debian):$sudoaptinstallpandocgraphvizdefault-jre
$sudoapt-getinstalltexlive-latex-recommended\texlive-latex-extra\texlive-fonts-recommended\latexmkThe python dependencies:$pipinstall.[docs]Now you can start the server and render the docs:$ sphinx-autobuild docs/source docs/build/htmlThe documentation will be available onhttp://127.0.0.1:8000.PDF (LaTeX)To generate a PDF of the documentation, simply run (from the root project folder):make-CdocslatexpdfThe generated PDF can be found under docs/build/latex/data2rdf_docs.pdf