Saravanan666/pypi-org huggingface dataset:
Package: vaccontrib
Code to quantify the contributions unvaccinated and vaccinated
subpopulations make towards the effectie reproduction number and new
infections.repository:https://github.com/benmaier/vaccontrib/documentation:http://vaccontrib.readthedocs.io/>>>fromvaccontrib.covidimportget_reduced_vaccinated_susceptible_contribution_matrix_covid>>>R0=6>>>C=get_reduced_vaccinated_susceptible_contribution_matrix_covid(R0,variant='delta')>>>C/C.sum()array([[0.38159051,0.17360365],[0.28493897,0.15986686]])Also, check out thetutorial
notebookand anexplanatory notebook including stochastic
simulations.Installgitclonegit@github.com:benmaier/vaccontrib.gitpipinstall./vaccontribvaccontribwas developed and tested forPython 3.6Python 3.7Python 3.8So far, the package’s functionality was tested on Mac OS X and CentOS
only.Dependenciesvaccontribdirectly depends on the following packages which will be
installed bypipduring the installation processnumpy>=1.17matplotlib>=3.3bfmplot>=0.0.11ManuscriptResults found using this software package were reported in apreprint.
To replicate the results, use the scripts in thepaper_analysisdirectory. E.g. to get a comprehensive overview of results for a
parameterset, runpythoncompute.pyDIRNAME1DIRNAME2e.g.pythoncompute.py00_lower01_upperSee the help text:usage:compute.py[-h][-uRU][-vRV][-f][-RR0]dirs[dirs...]Computecontributionmatrices.positionalarguments:dirsdirectoriesforwhichcontributionsmatricesshouldbecomputedoptionalarguments:-h,--helpshowthishelpmessageandexit-uRU,--RuRUBaseR-valueofunvaccinated-vRV,--RvRVBaseR-valueofvaccinated-f,--save-figurescreate,show,andsaveillustrations-RR0,--R0R0BaseR0towhichthecontributionwillbescaledMake sure you havenumpyarray_to_latexinstalled.ChangelogChanges are logged in aseparate
file.LicenseThis project is licensed under theMIT
License.
Note that this excludes any images/pictures/figures shown here or in the
documentation.ContributingIf you want to contribute to this project, please make sure to read thecode of
conductand thecontributing
guidelines.
In case you’re wondering about what to contribute, we’re always
collecting ideas of what we want to implement next in theoutlook
notes.Dev notesFork this repository, clone it, and install it in dev mode.gitclonegit@github.com:YOURUSERNAME/vaccontrib.gitmakeIf you want to upload to PyPI, first convert the newREADME.mdtoREADME.rstmakereadmeIt will give you warnings about bad.rst-syntax. Fix those errors inREADME.rst. Then wrap the whole thingmakepypiIt will probably give you more warnings about.rst-syntax. Fix those
until the warnings disappear. Then domakeupload
Package: vacdec
vacdecPython script to decode the EU Covid-19 vaccine certificate, asspecified by the EU.This script takes an image with a QR code of a vaccine certificate as
the parameter and will show the certificate's content.It will not validate the signature.Afork of this script with signature verification exists.The code is very short and should provide an easy way to understand
how these certificates are encoded:The QR code encodes a string starting with "HC1:".The string following "HC1:" is base45 encoded.Decoding the base45 leads to zlib-compressed data.Decompression leads to a CBOR Web Token structure.setupYou will need the python pillow, pyzbar, cbor2 and base45 packages. Additionally, you need zbar. For Mac OS X, it can be installed viabrew install zbar, on Debian systems viaapt install libzbar0.SourceYou can install them via your distribution or via pip:pip install base45 cbor2 pillow pyzbarusageRun:./vacdec [image][image] can be an image in any format pillow supports, including of
course PNG and JPG.A test certificate can be foundhere.authorWritten byHanno Böck.
Package: vacefron.py
VACEfron.pyA Wrapper forvacefron.nl/apiwritten in Python.RequirementsPython 3.8 or aboveaiohttp (python3 -m pip install -U aiohttp)InstallationInstall the package by doing one of the following commands:Using pip (recommended):pip install vacefron.py -Upython -m pip install vacefron.py -UDocumentationSee the full and detaileddocs hereLinksAPI|Changelogs|Github|PyPiMade byThis wrapper is made bySoheab_#6240, DM me on Discord or join my serverherefor anything related to
this wrapper.You can join VAC Efron's serverhereto suggest or report anything on the API.
Package: vac-templater
VAC (Varnish Administration Console) Templater is a simple server and web UI
designed to allow non-technical users to graphically modify and deploy Varnish
Cache configurations. VAC Templater depends on the VAC API to discover VCL files
and to execute deployments. A Varnish Plus subscription is required to get
access to the VAC.Check outhttps://github.com/allenta/vac-templaterfor a detailed description,
extra documentation, and other useful information.copyright:2015 by Allenta Consulting, see AUTHORS.txt for more details.license:GPL, see LICENSE.txt for more details.
Package: vacumm-data
# VACUMM data (vacumm-data)[![Build Status](https://travis-ci.org/VACUMM/vacumm-data.svg?branch=master)](https://travis-ci.org/VACUMM/vacumm-data)Data used by the [vacumm](http://wwwifremer.fr/vacumm) python libraryand its tutorials and test scripts.## SourcesOn GitHub: https://github.com/VACUMM/vacumm-data## Installation```bash$ python setup.py install # from sources$ pip install vacumm-data$ conda install vacumm-data```Files are typically installed in the `<prefix>share/vacumm` folder.## Access to the data pathsThe main directory can be accessed with the`vacumm_data.get_vacumm_data_dir()` function:```pythonbase_path = get_vacumm_data_dir()```A particular file path is accessible with the`vacumm_data.get_vacumm_data_file()` function:```pythonncfile = get_vacumm_data_file('samples/menor.nc')```## Data### Samples (`samples`)Theses files are used by the vacumm tutorials and test scripts.### Shorelines (`shorelines`)Shapefiles used by the `vacumm.bathy.shorelines` module.#### HistolittSee: https://www.data.gouv.fr/fr/datasets/shom-ign-trait-de-cote-histolitt-r#### GSHHSSee: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html### Sea level (`sea_level`)Data used by the `vacumm.bathy.tide` module.
Package: vacuna
VacunaInject everything!Vacuna is a little library to provide dependency management for your python code.InstallpipinstallvacunaUsageimportvacunacontainer=vacuna.Container()@container.dependency(name='app')classApp:defrun(self):print('very important computation')@container.dependency()defmain(app):app.run()if__name__=='__main__':container.run(main)
Package: vacuum-cleaner
Deep Vacuum CleanerRadio telescope deconvolution based using a Conditional Generative Adversarial Deep Network.Based onpix2pix-tensorflowWhch is based onpix2pixby Isola et al.Article about this implementionpreparationsYou probably want to download a pretrained model.download:http://repo.kernsuite.info/vacuum/model.tar.xzAnd extract toshare/vacuum/model.Setup$ pip install vacuum-cleaneror if you want to try the GPU accelerated version:$ pip install "vacuum-cleaner[gpu]"But the tensorflow-gpu package is not the most portable package available.Usage$ vacuum-clean dirty.fits psf.fitsTrainingHave a look atvacuum-train --helpor at the source. Intended to be trained withspielas training data generator.
Package: vacuum-map-parser-base
Vacuum map parser - basePackage that contains base classes that should be extended by a vacuum-specific implementation of a map parser.Available implementations:vacuum-map-parser-roborockvacuum-map-parser-viomivacuum-map-parser-roidmivacuum-map-parser-dreameInstallationpipinstallvacuum-map-parser-baseSupportIf you want to support my work with a donation you can use one of the following platforms:PlatformPayment methodsLinkCommentKo-fiPayPalCredit cardNo feesSingle or monthly paymentbuycoffee.toBLIKBank transferPayPalPayPalNo feesRevolutRevolutCredit CardNo fees
Package: vacuum-map-parser-dreame
Vacuum map parser - DreameMap data parser that can be used to parse maps generated by Dreame vacuums.Installationpipinstallvacuum-map-parser-dreameUsagefromvacuum_map_parser_base.config.colorimportColorsPalettefromvacuum_map_parser_base.config.drawableimportDrawablefromvacuum_map_parser_base.config.image_configimportImageConfigfromvacuum_map_parser_base.config.sizeimportSizesfromvacuum_map_parser_base.config.textimportTextfromvacuum_map_parser_dreame.map_data_parserimportDreameMapDataParserpalette:ColorsPalette=ColorsPalette()sizes:Sizes=Sizes()drawables:list[Drawable]=[Drawable.PATH,Drawable.CHARGER]image_config:ImageConfig=ImageConfig()texts:list[Text]=[]model:str="dreame.vacuum.p2008"enc_key:str|None="secret_enc_key"# required just for vacuums with AES-encoded map dataraw_map:bytes=b''parser=DreameMapDataParser(palette,sizes,drawables,image_config,texts,model)unpacked_map=parser.unpack_map(raw_map,enc_key=enc_key)parsed_map=parser.parse(unpacked_map)Special thanksThe code of this library was initially created by@Neonox31as a part ofXiaomi Cloud Map Extractor.SupportIf you want to support my work with a donation you can use one of the following platforms:PlatformPayment methodsLinkCommentKo-fiPayPalCredit cardNo feesSingle or monthly paymentbuycoffee.toBLIKBank transferPayPalPayPalNo feesRevolutRevolutCredit CardNo fees
Package: vacuum-map-parser-roborock
Vacuum map parser - RoborockMap data parser that can be used to parse maps generated by Roborock vacuums.Installationpipinstallvacuum-map-parser-roborockUsagefromvacuum_map_parser_base.config.colorimportColorsPalettefromvacuum_map_parser_base.config.drawableimportDrawablefromvacuum_map_parser_base.config.image_configimportImageConfigfromvacuum_map_parser_base.config.sizeimportSizesfromvacuum_map_parser_base.config.textimportTextfromvacuum_map_parser_roborock.map_data_parserimportRoborockMapDataParserpalette:ColorsPalette=ColorsPalette()sizes:Sizes=Sizes()drawables:list[Drawable]=[Drawable.PATH,Drawable.CHARGER]image_config:ImageConfig=ImageConfig()texts:list[Text]=[]raw_map:bytes=b''parser=RoborockMapDataParser(palette,sizes,drawables,image_config,texts)unpacked_map=parser.unpack_map(raw_map)parsed_map=parser.parse(unpacked_map)Special thanksThis library wouldn't exist without following projects:openHAB miIO add-onby@marcelrvXiaomi Robot Vacuum Protocolby@marcelrvSupportIf you want to support my work with a donation you can use one of the following platforms:PlatformPayment methodsLinkCommentKo-fiPayPalCredit cardNo feesSingle or monthly paymentbuycoffee.toBLIKBank transferPayPalPayPalNo feesRevolutRevolutCredit CardNo fees
Package: vacuum-map-parser-roidmi
Vacuum map parser - RoidmiMap data parser that can be used to parse maps generated by Roidmi vacuums.Installationpipinstallvacuum-map-parser-roidmiUsagefromvacuum_map_parser_base.config.colorimportColorsPalettefromvacuum_map_parser_base.config.drawableimportDrawablefromvacuum_map_parser_base.config.image_configimportImageConfigfromvacuum_map_parser_base.config.sizeimportSizesfromvacuum_map_parser_base.config.textimportTextfromvacuum_map_parser_roidmi.map_data_parserimportRoidmiMapDataParserpalette:ColorsPalette=ColorsPalette()sizes:Sizes=Sizes()drawables:list[Drawable]=[Drawable.PATH,Drawable.CHARGER]image_config:ImageConfig=ImageConfig()texts:list[Text]=[]raw_map:bytes=b''parser=RoidmiMapDataParser(palette,sizes,drawables,image_config,texts)unpacked_map=parser.unpack_map(raw_map)parsed_map=parser.parse(unpacked_map)SupportIf you want to support my work with a donation you can use one of the following platforms:PlatformPayment methodsLinkCommentKo-fiPayPalCredit cardNo feesSingle or monthly paymentbuycoffee.toBLIKBank transferPayPalPayPalNo feesRevolutRevolutCredit CardNo fees
Package: vacuum-map-parser-viomi
Vacuum map parser - ViomiMap data parser that can be used to parse maps generated by Viomi vacuums.Installationpipinstallvacuum-map-parser-viomiUsagefromvacuum_map_parser_base.config.colorimportColorsPalettefromvacuum_map_parser_base.config.drawableimportDrawablefromvacuum_map_parser_base.config.image_configimportImageConfigfromvacuum_map_parser_base.config.sizeimportSizesfromvacuum_map_parser_base.config.textimportTextfromvacuum_map_parser_viomi.map_data_parserimportViomiMapDataParserpalette:ColorsPalette=ColorsPalette()sizes:Sizes=Sizes()drawables:list[Drawable]=[Drawable.PATH,Drawable.CHARGER]image_config:ImageConfig=ImageConfig()texts:list[Text]=[]raw_map:bytes=b''parser=ViomiMapDataParser(palette,sizes,drawables,image_config,texts)unpacked_map=parser.unpack_map(raw_map)parsed_map=parser.parse(unpacked_map)Special thanksThe code of this library was initially created by@halfbakeryas a part ofXiaomi Cloud Map Extractor.SupportIf you want to support my work with a donation you can use one of the following platforms:PlatformPayment methodsLinkCommentKo-fiPayPalCredit cardNo feesSingle or monthly paymentbuycoffee.toBLIKBank transferPayPalPayPalNo feesRevolutRevolutCredit CardNo fees
Package: vad
VAD - simple voice activity detection in PythonThis is a simple voice activity detection (VAD) algorithm in Python. It is based on simple energy-based thresholding and is intended to be used as a simple method for detecting speech in audio files when other methods cannot be used for both privacy, performance, or other reasons.InstallationYou can install the package using pip:pipinstallvadUsageThe package can be seamlessly integrated into your Python code. The following example shows how to use the package to detect speech in an audio file:fromvadimportEnergyVAD# load audio file in "audio" variablevad=EnergyVAD(sample_rate:int=16000,frame_length:int=25,# in millisecondsframe_shift:int=20,# in millisecondsenergy_threshold:float=0.05,# you may need to adjust this valuepre_emphasis:float=0.95,)# default values are used herevoice_activity=vad(audio)# returns a boolean array indicating whether a frame is speech or not# you can also use the following method to get the audio file with only speech# speech_signal is a numpy array of the same shape as audiospeech_signal=vad.apply_vad(audio)Audio samplesexample.wavis a sample audio file that can be used to test the package.example_vad.wavis the audio file with only speech after applying the VAD algorithm.example_vad_2.wavis the audio file with only speech direcly extracted from the original audio file using theapply_vadmethod.vad_output.pngis a plot of the voice activity detected by the VAD algorithm.test_vad.pyis the script that was used to generate the above audio files and plot.Known issuesThere is no additional VAD algorithm implemented in this package at the moment. It may be added in the future.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: vaddiocameras
vaddiocamerasConnect to Vaddio cameras over Telnet and retreive information.
Telnet must be enabled in the camera's web GUI -> Security.CameraConnect():init with no parametersset_cameras(cameras):List of dicts, with each dict being a separate camera.
Requires 'ip_address':'nnn.nnn.nnn.nnn; in the dict.
Any additional keys will be accepted but are not required.
Including 'room':'room_name' is recommended.
['room'] will also be parsed from the camera's hostname if the
naming convention is room-whatever, eg. ABC123-VADDIOconfig(user_name, password, write_results, output_path):user_name: name used when vaddio security was initially setuppassword: password to use with user_nametimeout: default 10. telnet timeout in seconds.output_path: dir to store telnet data is write_results is True.write_results: default True. Write telnet data to 'output_path/Vaddio room telnet.txt'.If you're going to be using ParseCameraResponse(), you must set write_results to True.set_requests(requests):Accepts a list of ASCII requests to send via telnet.
Requests that are already going to be sent:
'network settings get',
'streaming settings get',
'version',
'camera tilt get',
Any requests you provide will be appended to this list.
Tilt is polled because a reply of '0' probably means the camera has rebooted at some
point and is now sitting in the factory Home position.run():Called by gathering run() to an asyncio queue, or by using
asyncio.run(instance_name.run())
Begins Telnetting into the cameras provided, sending requests, and writing the
results to file if desired. If you're going to be using ParseCameraResponse(), you
must set write_results to True.ParseCameraResponse()input_path: dir containing the 'Vaddio room telnet.txt' files created by CameraConnect(). Try using input_path=camera_connect_instance.output_path if you're Telnetting and parsing in the same program.excel_path: default 'camera_rooms.xlsx'. Filename used when exporting camera information.run():Called by gathering run() to an asyncio queue, or by using
asyncio.run(instance_name.run())
Begins parsing information from the files located in input_path, then creates an Excel table with all of the camera data. Also returns the camera data as a list of dicts.
Package: vade-api
Welcome To Vadepark!Vadepark is a SAAS service that utilizes cameras across the country to capture realtime parking data. This python package is a wrapper for our API to help you get our data into your system as soon as possible.InstallationUse the package managerpipto install vade-api.pipinstallvade-apiQuickstartimportvade_api_srcasvaderealtime=vade.VadeRealtimeAPI("API_KEY",vade.ProductionLevel.BETA)#A list of zone_id's can be recived by emailing our support emailzone_id="dafd0c37-b7bb-4643-a23d-c4f1dc21e875"zone_spots=realtime.get_zone_occupancy(zone_id=zone_id)forspotinzone_spots:print("spot:{}in zone is occupied:{}".format(spot.uuid,spot.occupied))# spot: 8ab1fe79-dfd0-41b5-aa04-f2d42a1186c0 in zone is occupied: True# spot: 12eca230-23da-4145-8caf-3704798f6cb1 in zone is occupied: False# ...geo_spots=realtime.get_geo_occupancy(latitude=-78.685396,longitude=35.779223,radius=1000)forspotingeo_spots:print("spot:{}in geo location is occupied:{}".format(spot.uuid,spot.occupied))# spot: c54f2b8d-ca01-421f-ab94-ea56ee4853ee in geo location is occupied: True# spot: cf7fce25-7bb7-4b3d-8bfe-0154cac4e5cb in geo location is occupied: Falsesingle_spot=realtime.get_spot_occupancy(spot_id=geo_spots[0].uuid)print("spot:{}is occupied:{}".format(single_spot.uuid,single_spot.occupied))# spot: c54f2b8d-ca01-421f-ab94-ea56ee4853ee is occupied: NoneParking Spot StructureclassVadeSpotRealtime:uuid:str# database identifier for spotname:str# easy to read name for spotmdid:str# secondary easy to read name for spotlocation:GeoPoint# geo-location for the parking spotoccupancy_threshold:float# the raw occupancy confidence thresholdraw_score:float# the raw occupancy confidence scorelast_updated:datetime# the last time that parking spot was updated by our systemoccupied:bool# whether or not the parking spot is occupiedtype:VadeSpotType# "standard" or "curbspace"# standard - your average single car parking space# curbspace - a section of curb. We will return a list of vehicles parked on that section of curb if occupiedcurbspace_output:[CurbspaceOccupancy]# the list of vehicles within a definied curbspaceLicenseMIT
Package: vader
Voice Activity Detection with PythonInstallingpip install vaderBasic usageimportvader# use your own mono, preferably 16kHz .wav filefilename="audio.wav"# returns segments of vocal activity (unit: seconds)# note: it uses a pre-trained NN by defaultsegments=vader.vad(filename)# where to dump audio filesout_folder="segments"# write segments into .wav filesvader.vad_to_files(segments,filename,out_folder)You can also use different pre-trained models by specifying the method parameter# logistic methodsegments=vader.vad(filename,threshold=.1,window=20,method="logistic")# multi-layer perceptron methodsegments=vader.vad(filename,threshold=.1,window=20,method="nn")# Naive Bayes methodsegments=vader.vad(filename,threshold=.5,window=10,method="nb")# Random Forest methodsegments=vader.vad(filename,threshold=.5,window=10,method="rf")Thethresholdparameter is the ratio of voice frames above which a window of frames is counted as a voiced sample. Thewindowparameter controls the number of frames considered, and thus the length of the voiced samples.You can also train your own models:importvadermodel=vader.train.logistic_regression(mfccs,activities)model=vader.train.random_forest_classifier(mfccs,activities)model=vader.train.NN(mfccs,activities)model=vader.train.NB(mfccs,activities)The variablemfccsis a list of varying length mfcc features (num_samples,varying_lengths, 13), whileactivitiesis a list of binary vectors whose lengths match those of the mfcc features (num_samples,varying_lengths), equal to 1 when a frame is voiced, and 0 otherwise.AuthorsMaixent Chenebaux
Package: vader-multi
VADER Sentiment Analysis MultilanguageVADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.This version integrates the Google Translate API through thetranslattePython library. It requires an active Internet connection in order to work. Text language is automatically detected so it behaves exactly like the original version.InstallationUninstall first the original version so it is not instantiated instead of vader-multi:pipuninstallvaderSentiment
pipinstallvader-multiPolarity Scoresclass vaderSentiment.SentimentIntensityAnalyzerpolarity_scores(text)Returns a dictionary with the following keys:{'neg': float, 'neu': float, 'pos': float, 'compound': float}fromvaderSentiment.vaderSentimentimportSentimentIntensityAnalyzeranalyzer=SentimentIntensityAnalyzer()analyzer.polarity_scores("VADER is smart, handsome, and funny.")Examples>>>fromvaderSentiment.vaderSentimentimportSentimentIntensityAnalyzer>>>analyzer=SentimentIntensityAnalyzer()>>>analyzer.polarity_scores("VADER is VERY SMART, handsome, and FUNNY!!!"){'neg':0.0,'neu':0.233,'pos':0.767,'compound':0.9342}>>>analyzer.polarity_scores("¡¡¡VADER es MUY INTELIGENTE, guapo y DIVERTIDO!!!"){'neg':0.0,'neu':0.27,'pos':0.73,'compound':0.9387}>>>analyzer.polarity_scores("VADER è MOLTO INTELLIGENTE, bello e DIVERTENTE!!!"){'neg':0.0,'neu':0.256,'pos':0.744,'compound':0.9481}>>>analyzer.polarity_scores("VADER est TRÈS SMART, beau et drôle!!!"){'neg':0.0,'neu':0.276,'pos':0.724,'compound':0.9338}>>>analyzer.polarity_scores("Вейдер очень умный, красивый и смешной!!!"){'neg':0.0,'neu':0.314,'pos':0.686,'compound':0.8989}>>>analyzer.polarity_scores("ベイダーは非常にスマートで、ハンサムで面白いです!!!"){'neg':0.0,'neu':0.328,'pos':0.672,'compound':0.882}>>>analyzer.polarity_scores("வேடர் மிகவும் ஸ்மார்ட், அழகான மற்றும் வேடிக்கையானது!!!"){'neg':0.0,'neu':0.314,'pos':0.686,'compound':0.8989}
Package: vaderSentiment
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that isspecifically attuned to sentiments expressed in social media. It is fully open-sourced under the[MIT License](we sincerely appreciate all attributions and readily accept most contributions, but please don’t hold us liable).Citation InformationIf you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.Code examples are provided on the[VADER GitHub Repo]Details about the scoring are provided on the[VADER GitHub Repo]VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the[VADER GitHub Repo]Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,media
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Text Processing :: General
Description-Content-Type: text/x-rst