Saravanan666/pypi-org huggingface dataset:
Package: dvalidator
Library for validating your object dictionaryfromdvalidator.validatorimportValidatorimportdvalidator.rulesasrulesvalidator=Validator({'a':rules.required(),'b':rules.required('Field "b" not found'),'c':[rules.required(),rules.is_type(str,'Field "c" is not str type')]})d={'a':1,'b':2,'c':'3'}res,err=validator.validate(d)print(res)TrueOther rules see in:https://github.com/kakabara/capybara/blob/master/capybara/rules.pyAlso you can create custom validation method:fromdvalidator.validatorimportValidatorimportdvalidator.rulesasrulesdefraise_on_exist(message='Found unexpected field in dict'):defcheck(field:str,dict_to_check:dict):iffieldindict_to_check:returnFalse,Noneelse:returnTrue,messagereturncheckvalidator=Validator({'a':rules.required(),'b':raise_on_exist()})d={'a':1,'b':2}res,err=validator.validate(d)print(res)False
Package: dvc-cc
DVC-CC is a wrapper for using the toolDataVersionControl (DVC)to make it possible to
use DVC to run your script in a cloud. To make this idea possible, we wrote a script that is part of a docker image
that can:download a git repository,download all required files with your DVC storage server,execute your script, andpush the results to GIT and to your DVC storage server.To assign the right hardware for your need in the cloud, we useCuriousContainers (CC). This Software runs on our cloud and manages the
cloud.Installation of DVC-CCDVC-CC is written in python so you can easily install DVC-CC by using pip.
We recommend that you install DVC-CC in a conda environment.
You can useanacondaor miniconda.For windows user We recommendthis websiteto install miniconda.Currently DVC-CC does not work under Windows!You can create, and activate an environment with the following lines:condacreate--namedvc_ccpythonpip
condaactivatedvc_ccIfconda activate dvc_ccdoes not work, trysource activate dvc_cc.Installation with pipThe following script will install the client on your computer:pipinstall--upgradedvc-ccIf you have problems on windows with "win32file", you need to install pywin32 withconda install -c anaconda pywin32.Installation from sourceIf you want to install the latest version from source you can install it withpoetry.gitclonehttps://github.com/deep-projects/dvc-cc.gitcddvc-cc/dvc-cc
poetrybuild
pipinstalldvc_cc-?????.whl# replace ????? with the current version that you build in the previous step.Get startedInstall DVC-CC and take a look atthis tutorial.TutorialsWorking with jupyter notebooksworking with sshfsDVC-CC SettingsWorking with pure DVC syntaxUsing live outputAn old tutorialStructure of this repositoryAcknowledgementsThe DVC-CC software is developed at CBMI (HTW Berlin - University of Applied Sciences). The work is supported by the
German Federal Ministry of Education and Research (project deep.TEACHING, grant number 01IS17056 and project
deep.HEALTH, grant number 13FH770IX6).
Package: dvc-data
FeaturesTODORequirementsTODOInstallationYou can installDVC dataviapipfromPyPI:$pipinstalldvc-dataUsageHashFileHashFileBased on dvc-object’sObject, this is an object that has a particular hash that can be used to verify its contents. Similar to git’sShaFile.fromdvc_data.hashfileimportHashFileobj=HashFile("/path/to/file",fs,HashInfo("md5","36eba1e1e343279857ea7f69a597324e")HashFileDBBased on dvc-object’sObjectDB, but storesHashFileobjects and so is able to verify their contents by theirhash_info. Similar to git’sObjectStore.fromdvc_data.hashfileimportHashFileDBodb=HashFileDB(fs,"/path/to/odb")IndexIndexA trie-like structure that represents data files and directories.fromdvc_data.indeximportDataIndex,DataIndexEntryindex=DataIndex()index[("foo",)]=DataIndexEntry(hash_info=hash_info,meta=meta)StorageA mapping that describes where to find data contents for index entries. Can be eitherObjectStorageforHashFileDB-based storage orFileStoragefor backup-like plain file storage.index.storage_map[("foo",)]=ObjectStorage(...)ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,DVC datais free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: dvc-objects
Featuresserverlesssupports multiple storages (local, s3, gs, gdrive, ssh/sftp, etc)based on fsspecRequirementsStorage must support these operations:uploadingdownloadinglistingcopyingquasiatomic renameInstallationYou can installDVC objectsviapipfromPyPI:$pipinstalldvc-objectsUsageThis is used indvcanddvc-data.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,DVC objectsis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: dvc-render
dvc-render is a library forrenderingdata stored inDVC plots formatinto different output formats, likeVega. It can also generate HTML and MarkDownreportscontaining multiple plots.It is used internally byDVC,DVCLive, andStudio.FeaturesRenderersTake data stored inDVC plots formatalongside plot properties in order to render a plot in different formats.ReportsTake multiple renderers and build an HTML or MarkDown report.TemplatesSupport for renderingVegaplots using custom of pre-defined templates.RequirementsThe basic usage of rendering Vega Plots doesn’t have any dependencies outsidePython>=3.8.Additional features are specified as optional requirements:https://github.com/iterative/dvc-render/blob/49b8f8a81c4e06b8f675197b8dd57e2a773cf283/setup.cfg#L27-L32InstallationYou can installDVC renderviapipfromPyPI:$pipinstalldvc-renderUsageRenderer & Templatesfromdvc_renderimportVegaRendererproperties={"template":"confusion","x":"predicted","y":"actual"}datapoints=[{"predicted":"B","actual":"A"},{"predicted":"A","actual":"A"},]renderer=VegaRenderer(datapoints,"foo",**properties)plot_content=renderer.get_filled_template()plot_contentcontains a validVegaplot using the confusion matrix template.Reportfromdvc_renderimportrender_htmlrender_html([renderer],"report.html")ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,DVC renderis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: dvc-streamlit
# dvc-streamlit-componentsStreamlit components for DVCTry online demohttps://share.streamlit.io/daavoo/dvc-streamlit-components/main/example/example_app.pyOr run locally:` git clonegit@github.com:iterative/dvc-streamlit-components.gitcddvc-streamlit-componentspip install-rrequirements.txt streamlit run example/example_app.py `## -dvc_streamlit.dvc_repoGets advc.api.Repoinstance from URL## -dvc_streamlit.rev_selectorMulti select revisions from advc.api.Repo.![init](img/init.gif)## -dvc_streamlit.dvc_metricsShow/Compare [DVC metrics](https://dvc.org/doc/command-reference/metrics) from different revisions.![metrics](img/metrics.gif)## -dvc_streamlit.dvc_paramsShow/Compare [DVC params](https://dvc.org/doc/command-reference/params) from different revisions.![params](img/params.gif)## -dvc_streamlit.plotsShow/Compare [DVC plots](https://dvc.org/doc/command-reference/plots) from different revisions.![plots](img/plots.gif)
Package: dvc-studio-client
Client to interact withDVC Studio.FeaturesModel Registryget_download_uris: Return download URIs for the specified model.Live Experimentspost_live_metrics: Post updates toapi/live.Studio authenticationget_access_token: Initiates the authentication process for a client application.InstallationYou can installdvc-studio-clientviapipfromPyPI:$pipinstalldvc-studio-clientLicenseDistributed under the terms of theApache 2.0 license,dvc-studio-clientis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: dvcx
What is DVCx?DVCx is a Python data manipulation library designed to work with unstructured AI datasets.
It provides a dataframe-like interface which can automatically reference data stored as files
(text, images, video) locally or in the cloud.Why use DVCx?Storage as a single source of truth.DVCx can organize unstructured data from storages
and datalakes (local files, S3, GCS, Azure ADLS) into overlapping datasets without
unnecessary file copies.Compute. DVCx supports local parallelization and external compute workers for efficient
data processing and AI metadata creation.Large scale.In contrast to in-memory frameworks (like Pandas data frame), DVCx can work
with datasets of millions and billions of records by using out-of-memory algorithms.Persistence and versioning. Your datasets, your computed metadata, and paid API call
results remain versioned and reusable.InstallationYou can installDQLviapipfromPyPI:$pipinstalldvcxUsageDQL can be used as a CLI (from system terminal), or as a Python library.TODO: CLI usageTo use it from Python code, import classdql.catalog.Catalog, which provides methods for all the same commands above, likels(),get(),find(),du()andindex().fromdql.catalogimportCatalogcatalog=Catalog()catalog.ls(["s3://ldb-public/remote/data-lakes/dogs-and-cats/"],update=True)How it’s related to DVC?DVCis an ML framework that helps connecting
unstructured data to ML models through pipelines to ensure reproducibility. DVCX,
created by DVC team, designed to handle the data preparation phase, thus functioning
upstream from DVC in the data management process.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,DQLis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: dv-data-generator
DV360 data GeneratorTo be able to retrieve different data reports from DV360 we use different API's. These API's are the DBM API and DV360 REST API. To make the code more maintanable we have turned the applied "builder" pattern into a package. This package can be used to retreive different types of reports or be extended to support reports, for different purposes.ExampleTo use the package and get our data from the platform, we must have a valid user oauth file. This can be created using the dqna authflow script.We need to init the director:director=DvDataDirector(credentials="../some-cerdentials-file.json")After this we can get different reports, see the methods ofdirector
Package: dvdfingerprint
This is a Python implementation of the disc fingerprinting technique used by theDiscidentDatabase. Works by traversing a directory (from a
DVD drive or ripped) and creating a unique hash for that disc.UsageCommand line:$ dvdfingerprint /Volumes/DVD_VIDEO/From your own code:import dvdfingerprint
dvdfingerprint.fingerprint("/Volumes/DVD_VIDEO/")Adapted from Erik Kastner’s Ruby implementationdvd_fingerprint.
Package: dvdp.ha-433
Home Assistant 433 MHz devicesTypes of devices supported:LightInstall$ sudo pip install dvdp.ha-433Use1 - RecordFirst you will have to record your 433 signal.For help:$ record_433 -hMinimal command:$ sudo record_433 <your_device_name> <ON or OFF>If you want to specify your recording BCM Pin (default is 15):$ sudo record_433 <your_device_name> <ON or OFF> -p <PIN_NR>Simply follow instructions. All recordings are saved to disk. Use--helpflag
for more info.2 - Test transmissionIf you used the default recordings directory it is quite simple.First check what recordings are available by using--help:$ transmit_433 -h 
usage: Transmit signal from recordings.

Recordings available in /usr/local/lib/python3.7/site-packages/dvdp/recordings:
        device: test_device_2, actions: ['ON', 'OFF']
        device: test_device, actions: ['ON', 'OFF']

...Then select the one you want to test from these recordings:$ sudo transmit_433 <your_device_name> <ON or OFF>e.g.:$ sudo transmit_433 test_device_2 ON3 - Start MQTT Client for Home assistantFor this to work you will have to install:Mosquitto broker for Hass.iohttps://github.com/home-assistant/hassio-addons/tree/master/mosquittoTo have Home Assistant Hass.io control your devices:$ ha_433 <BROKER IP>or if you require a password:$ ha_433 <BROKER IP> -u <USERNAME> -p <PASSWORD>For more options please refer to-h
Package: dvdp.utils
UtilitiesLibrary containing all kinds of python utilities.Install$ pip install dvdp.utilsDataDataSaverfrom dvdp.utils.data import DataSaverHelps with saving large datasets with images (or other np.arrays) to disk.
It also allows you to load them with DataSaver.load_data().fetch imagesfrom dvdp.utils.data import fetch_imagesFetch all images from a directory.Load them as a list of numpy arrays or a list Paths.
Package: dvdread
PyDvdRead -- A wrapper library around libdvdread4.This python module wraps libdvdread4 to provide Python access to DVD structural information. This does not fully wrap libdvdread4 as my needs are not that extensive.Please note that this module uses libdvdread4 to directly access a DVD disc if the device path is provided. This means that execution speed will depend on disc access, which can be slow. Just be aware of this.---------:Install:---------python3 setup.py buildpython3 setup.py install---------------:Documentation:---------------Use Python's online help to get the documentation from the classes.$ python3>>> import dvdread>>> help(dvdread.DVD)Help on class DVD in module dvdread.objects:class DVD(_dvdread.DVD)|  Entry class into parsing the DVD structure.|  Pass the device path to the init function, and then call Open() to initiate reading.|  Best to use the `with` keyword to ensure Python calls the Close() function when done.||  A DVD has titles.|  A title has chapters, audio tracks, and subpictures ("subtitles").||  Method resolution order:|      DVD|      _dvdread.DVD|      builtins.object||  Methods defined here:||  GetAllTitles(self)|      Gets a tuple of all the title objects starting with title one...............-------:Usage:-------A simple script is needed to parse the IFO structure in a DVD:import dvdreadwith dvdread.DVD("/dev/sr0") as d:d.Open()print("Number of titles on disc: %d" % d.NumberOfTitles)for t in d.GetAllTitles():print("Title %d has %d angles, %d audio tracks, %d chapters, %d subpictures, and runs for %s" % (t.TitleNum, t.NumberOfAngles, t.NumberOfAudios, t.NumberOfChapters, t.NumberOfSubpictures, t.PlaybackTimeFancy))Output:$ python3 foo.pyNumber of titles on disc: 7Title 1 has 1 angles, 4 audio tracks, 33 chapters, 3 subpictures, and runs for 02:23:11.00Title 2 has 1 angles, 4 audio tracks, 1 chapters, 3 subpictures, and runs for 00:00:12.00Title 3 has 1 angles, 4 audio tracks, 1 chapters, 3 subpictures, and runs for 00:00:12.00Title 4 has 1 angles, 4 audio tracks, 1 chapters, 3 subpictures, and runs for 00:00:21.19Title 5 has 1 angles, 1 audio tracks, 1 chapters, 3 subpictures, and runs for 00:02:04.00Title 6 has 1 angles, 4 audio tracks, 1 chapters, 3 subpictures, and runs for 00:00:07.00Title 7 has 1 angles, 1 audio tracks, 4 chapters, 0 subpictures, and runs for 00:04:50.21You must provide the device path to the DVD constructor, and then call Open() to parse the device structure. Doing this within the `with` keyword in Python ensures that DVD.Close() is called and cleanup is performed. The above script shows how to iterate through titles.--------------:Organization:--------------This module contains two parts:1) C implementation to wrap libdvdread4 and provide basic PyObject types for use within Python2) Python implementation to wrap PyObject types created in CThe C objects are defined within the /src/ directory and the Python objects in the /dvdread/ directory. The C objects are defined in the _dvdread module and the Python objects in the dvdread module.
Package: dve
Python Data Versatile EditorCopyright (c) 2015-2018 Jeremie DECOCK (http://www.jdhp.org)Web site:http://www.jdhp.org/projects_en.htmlSource code:https://github.com/jeremiedecock/dveIssue tracker:https://github.com/jeremiedecock/dve/issuesDVE on PyPI:https://pypi.python.org/pypi/dveDVE on Anaconda Cloud:https://anaconda.org/jdhp/dveDescriptionDVE is a library to make data versatile editors.WarningThis project is in beta stage.DependenciesPython >= 3.0Qt5 for PythonInstallationGnu/LinuxYou can install, upgrade, uninstall DVE with these commands (in a
terminal):pip install --pre dve
pip install --upgrade dve
pip uninstall dveOr, if you have downloaded the DVE source code:python3 setup.py installWindowsYou can install, upgrade, uninstall DVE with these commands (in acommand prompt):py -m pip install --pre dve
py -m pip install --upgrade dve
py -m pip uninstall dveOr, if you have downloaded the DVE source code:py setup.py installMacOSXNote:The following installation procedure has been tested to work with Python
3.5 under MacOSX 10.9 (Mavericks).
It should also work with more recent MacOSX systems.You can install, upgrade, uninstall DVE with these commands (in a
terminal):pip install --pre dve
pip install --upgrade dve
pip uninstall dveOr, if you have downloaded the DVE source code:python3 setup.py installDVE requires Qt5 and its Python 3 bindings plus few additional
libraries to run.Bug reportsTo search for bugs or report them, please use the DVE Bug Tracker at:https://github.com/jeremiedecock/dve/issuesLicenseThis project is provided under the terms and conditions of theMIT License.
Package: dveo
This module tries to make using theDVEOREST API easier to use with
Python.Example of usage:>>>fromdveoimportAPI>>>dveo=API('203.0.113.12','p455w0rd')>>>dveo.system_status()['system']['uptime']OrderedDict([('days','20'),('hours','7'),('mins','51')])>>>dveo.list_inputs()['net_stream1','net_stream2','net_stream3','net_stream4',...]>>>dveo.input_config('net_stream2')['inputname']'BigTV'>>>dveo.input_by_output_param('rtmptargeturi','acme',partial=True,include_value=True)[('net_stream1','rtmp://vid1.acme.net/stream'),('net_stream2','rtmp://stream10.acme.com/rtmp'),('net_stream3','rtmp://stream11.acme.com/rtmp'),...]Check out the variables and commands of your DVEO device API onhttp(s)://YOURDVEODEVICE:25599/metadata. The URL isn’t protected.
The port is set in the DVEO configuration.Installation$pipenvinstalldveoExample scriptsIn theexamplesdirectory you’ll find some implementation examples.retrieve_stream_settings.pyloads input and output settings for
one or more encoders and prints them and writes them to a json file.DocumentationFind autogenerated documentation at:https://dveo.readthedocs.io/Tested platformsThis code has been tested against the following devices:MultiStreamer DIG/IPBrutus IIBrutus IIIIf you have tested this on another device, please let me know.How to enable APITo enable the REST API on your DVEO device, follow these steps:Log in to web interface.Click on theSYSTEM SETUPtab.Open theWeb Server Setupmenu on the left.Select theAPI Web Serversubmenu.Check theEnable API Web Serverbox.If you choose to setEnable User SecuritytoYes:Fill in a password atAPI User Password (apiuser).Click theUpdatebutton.Restartthe service in theService Controlmenu to the right.
If it’s stopped it’ll be started.
Package: dvf
dual_vector_foilA dual vector foil（二向箔） that squashes any Python objects into your consoleIntroductionSimply speaking,dvf(dual vector foil) is a recursive pretty printer for any objects in Python. It allows you to inspect Python object in a simple and comprehensive way. Checkout the following example:Example on Flask appFlaskapp is very complex Python object, anddvfcan use paging (less) to wrap the output. If the GIF doesn't show immediately, be patient, it's about 10 MB large (click to zoom in):If your eyes are sharp enough, you'll find a warning at the end of the gif. That's becausedvftries to access some attributes ofFlaskthat are only valid in a request context. The warning is quite common for complex objects.Installationpip install dvfThe project is still under development, so any report on bugs is highly appreciated.
The development is under Python 3.7 and Python 3.6 is also tested. The package providesno Python <= 3.5 support.PhilosophyWhy notdiror__dict__There is already an amazing inspection packagepdir, which emphasize on theusageof modules and objects, whiledvfis aiming atdata and internal structureof objects.
As a result,dvfwill by default omit any object attributes that have type of function, module or class, and will try its best to expand any iterable to see what really lies in .Safety concernAs you might have gaused, it's not wise to usedvfon untrusted object becausedvfwill have to evoke some methods of the object to evaluate attributes. Is this a foundamental flaw ofdvf? I think not.
Because if an object is really malicious, it can delete your system when it's imported, why wait untilldvfto check it?Deal with loopsThe biggest problem ofdvfis loops in objects. The following class has a pointer points to himself. A simple recursion implementation ofdvfwill result in an infinite loop.classFoo:def__init__(self):self.another_me=selfTo solve this economically,dvfrecords every object it has visited and omit them next time it meet the object. That's why sometimes a complete view of certain objects is not possible.Another troublesome case is object creation during attribute access. A typical example isNumPyarray, which has an attribute ofTthat returns the transpose of the array,
which has anotherTthat returns another new array. So there is also an infinite loop. To solve thisdvfshould be very cautious toward data descriptors. Some result gained from descriptors will not be expanded.Todo listUser-custom searching and filteringDocs on output formatTestsUseprompt_toolkitto build an application that can handle wide output (horizontally scollable)
Package: dvf1312
dvf1312InstallationInstall dependencies (requests, OSMPythonTools)Utilisationin command line:
dvf1312 -m dvf1312.dvf1312 -f "31390 Carbonne" -p 31390in code:
from dvf1312 import dvf1312
result=dvf1312("31390 Carbonne", 31390)
print(result[0]) #json bati
print(result[1]) #json non batiIf you encounter an error with postcode on a specific commune,
you can try to rename the commune entered in input or tweak
the code so it downloads based on lat,lon.For fine tuning automation on downloading public data or
inquiry on developing algorithms to spatially identify
zones where prices are below or above a treshold,
you can contact me
Package: dvfile
dvfile-python介绍操作deepvocaldv文件的python库。本python库依赖numpy由于dv文件为二进制文件，且没有官方文档或解析器，本项目无法保证与deepvocal完美兼容，如遇到文件解析错误欢迎在issue中提出。安装pip install dvfile功能dv文件解析与保存dv文件目前可以解析的内容：曲速标记：位置、曲速节拍标记：位置、每小节拍数、音符分数音轨属性：音轨名、音轨音量、独奏、静音、双声道平衡区段属性：区段名、区段音源名、起点、长度音符属性：起点、长度、音高、歌词汉字、歌词拼音、滑音(弯曲深度、弯曲长度、头部滑音长度、尾部滑音长度)、颤音(颤音长度、颤音幅度、颤音速度、渲染出的颤音曲线)伴奏音轨：音轨名、音轨音量、独奏、静音、文件名、起点区段参数：音量、音调、气声、声线目前不能解析的内容（保存时将还原默认值）：音符属性：音素导入、导出ust、nn文件（需要utaufile）导入、导出mid文件（需要mido）导入、导出music21对象（需要music21、utaufile）导出五线谱（需要music21、utaufile和musescore(独立软件)）批量获取歌词量化（将音符对齐到节拍线）移调（音符与pit批量上下移动）自动修复工程（删除区段两端的无效音符和无效参数，音符按开始时间排序，修复音符重叠）示例importdvfileasdf#打开dv文件d=df.opendv("myproject.dv")#导出mid文件(需要mido)d.to_midi_file().save("myproject.mid")#每个音轨单独导出ust文件for(i,t)inenumerate(d.track):t.to_ust_file().save('myproject{}.ust'.format(i))#每个音轨单独导出nn文件(需要utaufile)for(i,t)inenumerate(d.track):t.to_nn_file().save('myproject{}.nn'.format(i))#导出五线谱(需要music21和musescore)d.to_music21_score().show()#获取第0音轨第0区段的歌词（拼音与汉字）列表tr=d.track[0]seg=tr.segment[0]pinyin=seg.getlyric()hanzi=seg.getlyric(use_hanzi=True)#将第0音轨的所有区段合并tr.segment=[sum(tr.segment)]#工程整体降低3keyd.transpose(-3)#保存dv文件d.save("myproject2.dv")#打开dvtb文件dt=df.opendvtb("myvoicebank.dvtb")参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request相关链接deepvocal官网sharpkey（deepvocal前身）视频教程deepvocal toolbox 文档