Saravanan666/pypi-org huggingface dataset:
Package: digimind
DigiMind - Remaing Cycle Time PredictorDigiMind is a library developed by master students from Politecnico di Milano for the challenge of MindSphere 2023. DigiMind is a remaining cycle time predictor for production system. According to a trained model, DigiMind is capable to predict the RCT for a given part id and for given scenario of the production system. For more details, get in touch with the author or join the GitHub community:https://github.com/pedrolbacelar/DigiMind
Package: dig-ini-editor
DIG ini-editorSimple command line INI editor.Get list of sectionsGet list of keys from sectionGet value of key from sectionAdd/Edit value of key from sectionDelete key from sectionDelete entire sectionCheck if section existsCheck if section and key existsBy default, all output will be written into stdout. It can be changed with option--output FILENAMEor--in-placewich will use the input file as output.The input filename"-"is reserved to read from stdin (cat file.ini | ini-editor ACTION - …)Usagepython-mdig_ini_editorACTIONorini-editorACTIONACTIONmust be one of:•getini-editorgetfilename[section[key]]Ifsectionis not set, will return a list of sections.Ifkeyis not set, will return a list of keys fromsection.Otherwise will return the value ofkeyfromsection.•setini-editorsetfilenamesectionkeyvalueWill add or replace, withvalue, the value ofkeyfromsection.•deleteini-editordeletefilenamesection[key]Ifkeyis not set, will remove entiresection.Otherwise will remove only thekeyfromsection.•existsini-editorexistsfilenamesection[key]Will return"true"and exit code0if the condition is satisfied, otherwise return"false"and exit code non-zero.Ifkeyis not set, will only check ifsectionexists.InstallationFrom PyPI (preferred):python-mpipinstalldig-ini-editorFrom github release:python-mpipinstall"https://github.com/DIG-/python-ini-editor/releases/download/1.0.1/dig_ini_editor-1.0.1-py3-none-any.whl"orpython-mpipinstall"https://github.com/DIG-/python-ini-editor/releases/download/1.0.1/dig_ini_editor.tar.gz"From github main branch:python-mpipinstall"git+https://github.com/DIG-/python-ini-editor.git@master#egg=dig_git_ignore"LicenseCC BY-ND 4.0You can use and redist freely.You can also modify, but only for yourself.You can use it as a part of your project, but without modifications in this project.
Package: diginlineprofiler
ProfileInlineInline data profiles to help you understand your data with one function.Check outAutoProfilerfor a version that updates automatically when you change your data.InstallationYou can install usingpip:pipinstalldiginlineprofilerUsageImport in a Jupyter notebook or lab then pass in any pandas dataframe.fromdiginlineprofilerimportVisualizerasplot# here we rename to plot but can be anything# df must be a pandas dataframe!plot(df)Development InstallationCreate a dev environment:condacreate-ndiginlineprofiler-dev-cconda-forgenodejsyarnpythonjupyterlabjupyter-packaging
condaactivatediginlineprofiler-devInstall the python. This will also build the TS package.pipinstall-e.When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
npm run buildHow to see your changesJupyter Lab:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when needednpmrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
Package: digip
Digip: A Digital Image Processing Python moduleThis module provides various digital image processing functions.Developed as course materials for the Digital Image Processing class forthe Institute for Environmental and Spatial Analysis(IESA)TODOManualLicenseCopyright (C) 2019, Huidae Cho <https://faculty.ung.edu/hcho/>This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: digipath-mltk
Parameterized large image pre-processing withpychunklbl.toolkitThe package modulepychunklbl.toolkitprovides eight parameterized functions designed to work with large image files and provide pre-processing for machine learning.The package module is intended developers to create machine learning datasets leveraging theopenslidelibrary for usage withtensorflow 2.0.Installationpip install digipath_mltk

# requires python 3.5 or later
pip3 install -r requirements.txtDevelopment install from clone in /DigiPath_MLTK/ directory:pip3 install -r requirements.txt
pip3 install --upgrade ../DigiPath_MLTKCommand line examplesImages used in the examples below was downloaded fromopenslide data, the other data files are in the repository data/ directory.Find the patches in a wsi file and write to an image file for preview.python3 -m digipath_mltk.cli -m write_mask_preview_set -i yourpath/images/CMU-1-Small-Region.svs -o resultsFind the patches in a wsi file and write to a directory.python3 -m digipath_mltk.cli -m wsi_to_patches_dir -i CMU-1-Small-Region.svs -o resultsFind the patches in a wsi file and write to a .tfrecords file.python3 -m digipath_mltk.cli -m wsi_to_tfrecord -i CMU-1-Small-Region.svs -o resultsView the patch locations in a .tfrecoreds file.python3 -m digipath_mltk.cli -m tfrecord_to_masked_thumb -i CMU-1-Small-Region.svs -r CMU-1-Small-Region.tfrecords -o results( test data not currently available in DigiPath_MLTK repository for the following examples )Find pairs of patches with registration offset in two wsi files and write to a directory.python3 -m digipath_mltk.cli -m registration_to_dir -i wsi_fixed.tiff -f wsi_float.tiff -D wsi_pair_sample_offset.csv -o resultsFind pairs of patches with registration offset in two wsi files and write to a tfrecords file.python3 -m digipath_mltk.cli -m registration_to_tfrecord -i  wsi_fixed.tiff -f wsi_float.tiff -D wsi_pair_sample_offset.csv -o resultsFind the patches in a wsi file defined in an annotations file with a priority file and write to a directory.python3 -m digipath_mltk.cli -m annotations_to_dir -i wsi_float.tiff -p wsi_float_annotation.csv -a wsi_float_annotation.xml -o resultsFind the patches in a wsi file defined in an annotations file with a priority file and write to a tfrecords file.python3 -m digipath_mltk.cli -m annotations_to_tfrecord -i wsi_float.tiff -p wsi_float_annotation.csv -a wsi_float_annotation.xml -o results
Package: digipathos
OverviewThis project is aimed to serve as a wrapper for the Digipathos dataset, in order to list and download public data from plant pathologies provided by Embrapa (Brazilian Agricultural Research Corporation).InstallationThe installation is pretty simple if you have a virtualenv already installed on your machine. If you don't please rely toVirtualEnv official documentation.pipinstalldigipathosDocumentationBesides the docstrings, major details about the documentation can be foundhere.TestingThis project is inteded to suit most of the existent needs, so for this reason, testability is a major concern. Most of the code is heavily tested, along withTravisas Continuous Integration tool to run all the unit tests once there is a new commit.UsageYou can use Digipathos in two different ways: via terminal or programatically.CLI (Command-Line Interface)This mode is highly recommended for those who are looking to explore a little bit the dataset. Here you can do the same operations from the programmatic mode, but with the advantage of being able to see all the data that is being retrieved.digipathosAnd then you're gonna be greeted by our dataset browser :-)An example listing all the datasets:Programmaticallydata_loader=DataLoader()# list all the datasetsdatasets=data_loader.get_datasets()# now lets give a look at the cropscrops=data_loader.get_crops()# how about getting all the datasets from a crop?datasets_from_crop=data_loader.get_datasets_from_crop('Pineapple')# now let's download a random datasetdataset_id=random.choice(list(datasets.keys()))data_loader.download_dataset(dataset_id=dataset_id)# download all from a given cropdata_loader.download_datasets_from_crop('Pineapple')# download all the datasetsdata_loader.download_all_datasets()Pretty simple, huh?A working example can be foundhere as a Python script.TroubleshootingIn case of any issue with the project, or for further questions, do not hesitate to open an issue here on GitHub.ContributionsContributions are really welcome, so feel free to open a pull request :-)
Package: digiplot
A Tkinter Code That Helps Users Digitize Curves From Plot Images.See the Code at:https://github.com/sonofeft/DigiPlotSee the Docs at:http://digiplot.readthedocs.org/en/latest/See PyPI page at:https://pypi.python.org/pypi/digiplotIf a 2D plot is available only on paper or an image file, but the user needs the x,y data pairs in digital form, DigiPlot will help to create those x,y data pairs.Keywords: digiplot setuptools development
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/x-rst
Provides-Extra: dev
Provides-Extra: test
Package: digipy
At Montreal-Python6, I have presented a real-time hand digit real-time demo.
This demo allows you to do real-time digit recognition from your digital camera.
It allows you to load any trained neural network and apply in real time the same
features extraction. This demo allows you to train, extract features, used
trained neural networks inside real-time demo, visualize features in 2D and
their frequency distribution and get feature discriminant weight.
Package: digirock
digirock -A set of classes for making flexible digital rock models.InstallationpipinstalldigirockUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensedigirockwas created by Tony Hallam. It is licensed under the terms of the MIT license.
Package: digiscreens-uib
DigiscreensDescriptionThe DigiScreens project is a python project that includes Python classes and modules for data visualization('MovieTVVis') and web scraping('Scraper). It is designed to do data analayzing and visualization on tv series and film data. It also offers tools for importing, processing, filtering and scraping metadata from web sources.StructureData: Contains various data files like CSVs and Excel files.Info: Contains various information files for using the project.Notebooks: Jupyter notebooks for visualising and testing the functionalities of the prosject.src:MovieTVVis: Python module for data visualization.NorwayTop10: Python script specific for finding top 10 film and tv serie data from every country.Scraper: Python module for web scraping.Tests: Unit tests for the different modulesInstallationClone the repository:git clonegit@git.app.uib.no:ii/inf219/23h/digiscreens.gitInstall all the required packages:pip install -r info/requirements.txtUsageIf you want a detailed description on how to use the different modules and functions in this project you can look at the user guidance docs:MovieTVVisWeb scraperNetflix popularity calculatorExample usage for the different modules:MovieTVVis: It can be used to visualize and data analayze on movie- and TV-seriedata.Scraper: Web scraping to get metadata from different sources.Other examples and demonstrations of how to use it. Can you find in jupyter notebooks in the notebooks- folder.TestingTo run tests, navigate to the tests directory and execute:python -m unittestSupportThis git repo is a delivery for the course INF219 at The University of Bergen. Users are free to add issues, but there is no guarantee that these will be taken care of.ContributingIf anyone wises to continue development of the code, they can contact one of the authors.AuthorsKristoffer BreivikKristoffer EikKavya MalhotraHenrik Johansen
Package: digisim
Welcome to DigiSim!This is a minimalistic library for scalable simulation of complex combinational digital logic in python. We have included various commonly used methods and techniques for dealing with digital logic and boolean algebra.PrerequisitesYou need to installtkinterfirst to work with logic gates functionality of the library.
Installtkinterby using command below-sudo apt-get install python-tk- For ubuntu versions 16x and below.sudo apt install python-tk- For ubuntu versions 18x and above.For windows, install tkinter by using this link--https://wiki.python.org/moin/TkInterInstallationCurrently we only have support for python 2.x. Use one of the following commands to install the library.pip install digisim- Make sure your default python interpreter is python 2.x.python -m pip install digisim- Can be used on windows.sudo pip install digisim- Try giving sudo access.pip2 install digisim- Try this in case above commands return error.To read documentation, visitHomepageof DigiSim.
Package: digisondeindices
Digisonde IndicesDigisonde Indices is a downloader and parser that retrieves Digisonde data fromDIDBase. Output datatype isxarray.DataSet. Missing data is returned as an empty dataset.Data Selection:CS is Autoscaling Confidence Score (from 0 to 100, 999 if manual scaling, -1 if unknown)foF2 (MHz) - F2 layer critical frequencyfoF1 (MHz) - F1 layer critical frequencyMUFD (MHz) - Maximum usable frequency for ground distance DfoE (MHz) - E layer critical frequencyhF (km) - Minimum virtual height of F tracehF2 (km) - Minimum virtual height of F2 tracehmE (km) - Peak height of E-layerhmF2 (km) - Peak height F2-layerhmF1 (km) - Peak height F1-layeryF2 (km) - Half thickness of F2-layer, parabolic modelyF1 (km) - Half thickness of F1-layer, parabolic modelB0 (km) - IRI thickness parameterTEC (m^-2) - Total electron contentExampleUse from other scripts like:importdigisondeindicesasdidbaseimportdatetimeasdt# timezone-unaware exampledate=dt.datetime(2012,1,1,5,0,0)# 2012-01-01 05:00:00 UTC, even though datetime object created in timezone of machine at runtimeinds=didbase.get_indices(date,'AH223')# retrieve data from Ahmedabad station# timezone-aware exampledate=dt.datetime(2012,1,1,5,0,0)# 2012-01-01 10:00:00 UTC for US/Eastern (UTC-05:00) given execution machine time zone is set to US/Easterninds=didbase.get_indices(date,'AH223',tzaware=True)# retrieve data from Ahmedabad stationReturned indices are always in UTC (asnumpy.datetime64).A list of available stations is provided by theGlobal Ionosphere Radio Observatory:DIDBase stations listData LicenseAll GIRO measurements are released under CC-BY-NC-SA 4.0 license.
Please follow theLowell GIRO Data Center RULES OF THE ROAD.
Requires acknowledgement of station data provider.
Package: digit
# digitDigit is the tool that help you to analyze git information from .git directory on website.I found the tool like [GitTools](https://github.com/internetwache/GitTools) to dump the source code from website with .git directory. GitTools have to dump objects from .git directory then use git command to analyze. But I only need the tool that can analyze .git directory without downloading and easy to dig into the git object.Github [https://github.com/bongtrop/digit](https://github.com/bongtrop/digit)## Requirements- Python 2 but i think that code can use in python 3- Python module that you can see in requirements.txt## InstallationIt's simple like the other python project.### pip```bashpip install digit# orpip install git+https://github.com/bongtrop/digit.git```### Manual```bashgit clone https://github.com/bongtrop/digit.gitcd digitpip install -r requirements.txtpython setup.py install```## Usagejust ```digit -h```.```bashUsage: digit.py [options] GIT_URLDig git information from .git directory on website.Options:-h, --help            show this help message and exit-o OBJECT, --object=OBJECTObject\'s sha1-w FILENAME, --write=FILENAMEWrite blob to file```## ContributionI dont mind the way that you will contribute. Just do it. below is example.- email- create issue- pull request- carrier pigeon- tell my friend to tell me- foo bar## LicensePlease see [LICENSE](LICENSE) file.
Package: digit2chinese
功能把数字转化为大写用法import digit2chinesechinese = digit2chinese.digit2chinese(1234567891)print(chinese) # 壹拾贰亿叁仟肆佰伍拾陆万柒仟捌佰玖拾壹orchinese = digit2chinese.digit2chinese(1234567891,style=1)print(chinese) # 十二亿三千四百五十六万七千八百九十一bug反馈如果发现bug请反馈至yanchengxin@yeah.net谢谢
Package: digitalai-release-sdk
Digital.ai Release SDKThe Digital.ai Release Python SDK (digitalai-release-sdk) is a set of tools that developers can use to create container-based tasks.Developers can use theBaseTaskabstract class as a starting point to define their custom tasks and take advantage of the other methods and attributes provided by the SDK to interact with the task execution environment.Installationpipinstalldigitalai-release-sdkTask Example: hello.pyfromdigitalai.release.integrationimportBaseTaskclassHello(BaseTask):defexecute(self)->None:# Get the name from the inputname=self.input_properties['yourName']ifnotname:raiseValueError("The 'yourName' field cannot be empty")# Create greetinggreeting=f"Hello{name}"# Add greeting to the task's comment section in the UIself.add_comment(greeting)# Put greeting in the output of the taskself.set_output_property('greeting',greeting)DocumentationRead more about Digital.ai Release Python SDKhere
Package: digitalarchive
Digital ArchiveA Python client for the Wilson Center'sDigital Archive("DA") of historical primary sources. This library provides an ORM for searching and accessing documents and other resources in the Digital Archive.InstallationThe client is available on pypi. It requires python 3.7+.pip install digitalarchiveUsage>>> import digitalarchive

# Search for documents:
>>> soviet_docs = digitalarchive.Document.match(title="soviet").all()

# Collections and other resource types are also searchable.
>> soviet_collections = digitalarchive.Collection.match(name="soviet")

# Grab a single, specific document:
>>> document = digitalarchive.Document.match(id="112566").first()

# Pull transcripts, translations, and original scans of documents:
>>> document.hydrate()
>>> document = test_doc.transcripts[0].html

# Pull the metadata and other assets for an entire resultset.
>>> chernobyl_docs = digitalarchive.Document.match(title="chernobyl")
>>> chernobyl_docs.hydrate()
>>> chernobyl_docs.all()

# Or just download all the documents!
>>> all_documents = digitalarchive.Document.match().all()Complete documentation for the client and the Digital Archive's models are availablehere.DisclaimersThis is an unofficial library. I am not presently affiliated with the Wilson Center. I understandthat the API is unlikely to change in the near future, but I cannot guarantee that this library won't break without warning.If you plan to scrape the DA, please be respectful.Planned FeaturesSupport for searching by date range.Asynchronous hydration of large result sets.For Collections, inlcude keyword hits inshort_descriptionfor searches. (modify collection searches to use therecord.jsoninstead ofcollection.jsonendpoint.
Package: digital-assistant-context
КонтекстВ ходе взаимодействия цифрового помощника с пользователем
исполнениесценарияв каждом моменте находится на
определенном акте. Нахождение исполнения сценария на определенном акте называется состоянием исполнения сценария.
Состояние исполнения сценария хранит исполнитель сценария для каждого отдельного пользователя. Состояние
определяется текущим актом и контекстом. Под контекстом понимается совокупность значений переменных сценария.Использование библиотекиНазначение библиотеки переводить структуру переменных из нотации сценария цифрового помощника в нотацию языка python
для манипуляции контекстом при исполнении сценария.Работа с контекстомИнициализация контекстаИнициализация контекста, например, из файла. Используется для восстановления состояния исполнения сценария.importjsonfromdigitalassistantcontext.contextimportContextwithopen('./context.json',encoding='utf-8')asf:context=Context(json.load(f))Изменение контекстаС помощью метода set объекта класса Context. Используется, например, при изменении контекста сценария.fromdigitalassistantcontext.contextimportContextvars_list=[{'name':'Услуги[0][Имя]','value':'Консультация'},{'name':'Услуги[0][Стоимость]','value':'500 р'}]context=Context()forvinvars_list:context.set(v.get("name"),v.get("value"))Наследование контекстаОбласть видимости переменных ограничена последовательностью и ее дочерними последовательностями первого уровня. При
создании объекта класса Context в параметре parent_ctx можно передать контекст родительской последовательностиimportjsonfromdigitalassistantcontext.contextimportContexttry:withopen('./parent_context.json',encoding='utf-8')asf:parent_context=Context(json.load(f))exceptFileNotFoundErrorase:print(e)try:withopen('./context.json',encoding='utf-8')asf:context=Context(json.load(f),parent_ctx=parent_context)exceptFileNotFoundErrorase:print(e)v=context.get('Услуги[1]')print(v)Сохранение состоянияТекущее состояние исполнения сценария в виде списка переменных в формате сценария можно преобразовать функцией
parse_var_to_list и сохранитьimportjsonfrompprintimportpprintfromdigitalassistantcontext.contextimportparse_var_to_list,Context,DICT_VAR_LIST_TYPEtry:withopen('./context.json',encoding='utf-8')asf:context=Context(json.load(f))exceptFileNotFoundErrorase:print(e)try:vars_list=parse_var_to_list(context.variables,list_type=DICT_VAR_LIST_TYPE)withopen('./variable.json','w',encoding='utf-8')asoutfile:json.dump(vars_list,outfile,ensure_ascii=False,indent=4)context_reload=Context()pprint(vars_list)forvinvars_list:context_reload.set(v,vars_list[v])withopen('./context_reload.json','w',encoding='utf-8')asfile:json.dump(context_reload.variables,file,ensure_ascii=False,indent=4)exceptNameErrorase:print(e)
Package: digitalclock
pyclockSeven segment display digital clock using tkinter.RequirementsPython >= 2.7Tkinter (Should be installed during Python installation)InstallationClone the project into a local repository, then navigate to the directory and
run:$ python setup.py installUsageimportpyclockif__name__=='__main__':pyclock.PyClock()TroubleshootingShould you run into any issues, refer to the projecthttps://gitlab.com/johncharlie/digitalclockDocumentationDocumentation follows theGoogle Python Style GuideTestingTo run tests, execute the following:# Unit tests
$ python setup.py testLicenseCopyright 2018 @johncharlie

Licensed under the GNU General Public License v3.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.gnu.org/licenses/gpl-3.0.en.html

Unless 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.See terms and conditionshere.