Saravanan666/pypi-org huggingface dataset:
Package: custom-json-encoder
Custom JSON EncoderA JSON encoder that allows customizing the indentation based on the content and the width of the line.Seethe command-line toolto understand how to use theCustomJSONEncoder class. This tool is a patch fromjson/tool.py, so follow the#regionand#endregioncomments to understand the differences.Command Line InterfaceInstead of using the standardJSON tool$python-mjson.tooldemo.json--indent4{"menu":{"id":"file","value":"File","popup":{"menuitem":[{"value":"New","onclick":"CreateNewDoc()"},{"value":"Open","onclick":"OpenDoc()"},{"value":"Close","onclick":"CloseDoc()"}]}}}you can use thecustom JSON encoderinstead with the same flags$python-mcustom_json_encoderdemo.json--indent4{"menu":{"id":"file","value":"File","popup":{"menuitem":[{"value":"New","onclick":"CreateNewDoc()"},{"value":"Open","onclick":"OpenDoc()"},{"value":"Close","onclick":"CloseDoc()"}]}}}This tool provides the same functionality as the standard JSON tool$python-mcustom_json_encoder-h
usage:custom_json_encoder[-h][--sort-keys][--no-ensure-ascii][--json-lines][--indentINDENT|--indent-afterKEY|--tab|--compact][--indent-after-widthAMOUNT][--indent-after-indentationAMOUNT][infile][outfile]Asimplecommandlineinterfaceforjsonmoduletovalidateandpretty-printJSONobjects.

positionalarguments:infileaJSONfiletobevalidatedorpretty-printedoutfilewritetheoutputofinfiletooutfile

options:-h,--helpshowthishelpmessageandexit--sort-keyssorttheoutputofdictionariesalphabeticallybykey--no-ensure-asciidisableescapingofnon-ASCIIcharacters--json-linesparseinputusingtheJSONLinesformat.Usewith--no-indentor--compacttoproducevalidJSONLinesoutput.--indentINDENTseparateitemswithnewlinesandusethisnumberofspacesforindentation--indent-afterKEYindentafterthegivenkeyusing--indent-after-indentationspaces--tabseparateitemswithnewlinesandusetabsforindentation--compactsuppressallwhitespaceseparation(mostcompact)--indent-after-widthAMOUNTsetthewidthoftheoutputlinewhen--indent-afterisactive--indent-after-indentationAMOUNTusethisnumberofspacesforindentationwhen--indent-afterisactiveexcept for the--indent-after,--indent-after-widthand--indent-after-indentationflags, which allow indenting ONLY after the given key or after reaching the given width.$python-mcustom_json_encoderdemo.json--indent-aftermenuitem--indent-after-width50--indent-after-indentation4{"menu":{"id":"file","value":"File","popup":{"menuitem":[{"value":"New","onclick":"CreateNewDoc()"},{"value":"Open","onclick":"OpenDoc()"},{"value":"Close","onclick":"CloseDoc()"}]}}}
Package: custom-logger-cloudcity
Custom Logger CloudCityTo unify the way we log and alert to Google Cloud Logger and Sentry, we have created this package.Changelog1.0.2 Removed the explicit requirement of havint the CLOUD_LOGGER_NAME environment var set.
When none is set, fall back on the default loggerExampleHow to use:Include the package in your project by doingpip install custom-logger-cloudcitySimply import the module!Below is an examlpe how to useimportosos.environ.update({"GOOGLE_APPLICATION_CREDENTIALS":"{Location to you Service account key}"})os.environ.update({"CLOUD_LOGGER_NAME":"nice_test_logger"})# Give a name the app will log toos.environ.update({"ENV":"development"})# We need to set the environment vars before importing the custom_logger# You can do so as this example or just set it in the machine wide environmentfromcustom_loggerimportcustom_loggerif__name__=='__main__':# A message as a string will end up in as a textPayload in Google Cloud Loggingcustom_logger.info("Hello My Friend")# Below will not be logged to Google Cloud Loggingcustom_logger.error("not ok")# A message as an argument will end up in as a jsonPayload in Google Cloud Loggingcustom_logger.info(some_message="This is a test log in a jsonPayload")# # An example with multiple argumentscustom_logger.info(some_message="This is a test log in a jsonPayload",more_info="It has more info",and_even_more="These can all be metrics and stuff",but="We need to make sure that we catch the logging exceptions and print the whole lot",)# With message overwritecustom_logger.info(message="Let's overwrite the message node",some_message="This is a test log in a jsonPayload",more_info="It has more info",and_even_more="These can all be metrics and stuff",but="We need to make sure that we catch the logging exceptions and print the whole lot",)# You can even nest data, as this is stored as a struct in Google Cloud Loggingcustom_logger.info(message="This is message overwrite",amount_transfered=10,amount_failed=10,errors=["a","b","c"])
Package: custom-logger-xzf8971
custom-logger-xzf8971Just a very simple wrapper to configure a thread safe logger.UsageThe logger_name must be exclusive!fromcustom_loggerimportCustomLoggerWrapperloggerWrapper=CustomLoggerWrapper(logger_name="example_logger"logger_level=logging.INFO,logger_propagate=False,logger_fmt="%(asctime)s-%(levelname)s-%(message)s",log_filename="mylog.log")loggerWrapper.InitLogger()logger=loggerWrapper.GetInitedLogger()logger.info("example")
Package: custom-mantine-datepicker
Dash Mantine Components is an extensive (55+) Dash components library based onMantineReact Components Library. It makes it easier to create good quality dashboards with very well designed components out of the box.DocumentationTable of contentsInstallationQuickstartContributingTODOInstallationpipinstalldash-mantine-componentsQuickstartimportcustom_mantine_datepickerasdmcfromdashimportDash,Input,Outputapp=Dash(__name__)app.layout=html.Div([dmc.DatePicker(id="datepicker",format="dddd, MMMM D, YYYY"),dmc.Text(id="text"),dmc.Button("Click Me!")])@app.callback(Output("text","children"),Input("datepicker","date"))defdatepicker(date):returndateif__name__=="__main__":app.run_server(debug=True)ContributingJoin ourDiscordcommunity.Install virtual environment:python-mvenvvenvsourcevenv/bin/activate
pipinstall-rrequirements.txtInstall npm dependenciesnpminstallAdd your new component insrc/lib/components. Make sure to include it in thesrc/lib/index.jsas well.Ensure that component props are sorted in ascending order. Refer to existing components.Add a line in the CHANGELOG.md under one of these: Added, Changed, Deprecated, Removed, Fixed.Raise a PR, including an example to reproduce the changes contributed by the PR.TODOAdd more components from Mantine library and enhance already added ones.Add contributing guide.Add testing.Enhance docs.Add lazy loading to the components.
Package: custom-maya
项目说明此工具内含各种实用工具。仅为测试用！！！仅支持Maya2023及以上版本。参照如何使用mayapy运行venvMaya Python 解释器 mayapy安装并获取更新使用 pip 安装pipinstallcustom-maya需要使用 mayapy 环境安装。测试用Xcopycustom_maya"C:\Program Files\Autodesk\Maya2023\Python\Lib\site-packages\custom_maya"/E/H/C/I如何设置IDE制作从名为 python 的命令创建指向 mayapy 的软链接cd"C:\Program Files\Autodesk\Maya2023\bin"mklinkpython.exemayapy.exe将IDE的解释器设置为"C:\Program Files\Autodesk\Maya2023\bin\python.exe"例子获取某根目录下所有Maya文件的信息打包上传到pipy升级python.exe-mpipinstall--upgradebuild
python.exe-mpipinstall--upgradepippython-mbuild上传测试包python-mtwineupload--repositorytestpypidist/*上传正式版本包python-mtwineuploaddist/*
Package: custom-menu
custom-menucustomize menuRequirementsJupyterLab >= 3.0Installpipinstallcustom-menuContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the custom-menu directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou 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 extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallcustom-menu
Package: custom-mpl
Custom Matplotlib StylesheetsThis repository contains a collection of custom stylesheets for Matplotlib, a powerful visualization library in Python.The stylesheets in this repository are designed to provide a consistent and professional look to your Matplotlib visualizations. Each stylesheet is optimized for a different use case, so you can choose the one that best fits your needs.StylesheetsHere are the stylesheets currently included in this repository:powerbi.mplstyle: This style is optimized for use with Power BI visualizations, and contains colors related to Shell company colors.powerbi_dark.mplstyle: This style is optimized for use with Power BI visualizations with a dark background, and also contains colors related to Shell company colors.How to UseTo use either of these stylesheets, copy them into your working directory or a directory in your Matplotlib style path. You can then use the plt.style.use() function to apply the style to your plots.For example, to use thepowerbi.mplstylestylesheet, you would use the following code:importcustom_mplimportmatplotlib.pyplotaspltplt.style.use('custom_mpl.powerbi')To use thepowerbi_dark.mplstylestylesheet, you would use the following code:importcustom_mplimportmatplotlib.pyplotaspltplt.style.use('custom_mpl.powerbi_dark')ContributingContributions to this repository are welcome. If you have a new style to add or an improvement to an existing style, please feel free to submit a pull request.Please ensure that your style follows the same format as the existing stylesheets and that it has a descriptive name that accurately reflects its use case.LicenseThis repository is licensed under the MIT License.
Package: custom-nester
custom_nesterWhat is this?This is a module created basically for test and learning purposes.print_lol function takes a positional argument called 'the_list', which is any python list
(of, possibly nested lists). Each data item in the provided list is recursively printed
to the screen on its own line. (or into a file)

---Arguments----------(default value)
1st - 'the_list'
2nd - 'indent' is used to activate the indentation. (False)
3rd - 'level' is used to insert tab-stops when a nested list is encontered. (0)
4th - 'print_in' gives us the ability to print the output in separated files or screen. (sys.stdout)Requirementspython >= 3.9CreditsAriel Denaro
Package: custom-neural-net-creator
OverviewCustom-Neural-Net-Creator is an easy, to use tool that allows developers and machine learning enthusiasts to create and deploy their own custom deep neural networks for various purposes. This innovative tool makes the power of learning more accessible, than before.Neural networks are advanced machine learning algorithms that can detect complex patterns in large amounts of
data and make predictions based on the information they have been trained on.
These systems are designed to mimic the structure of the human brain, with interconnected layers of
“neurons” that transmit information to each other.
An artificial neural network typically consists of an input layer,
one or more hidden layers, and an output layer, each with a specific number of neurons connected to
preceding and following layers. Deep neural networks are a subtype of artificial neural networks
that can learn from large datasets and make predictions based on them.To view more information visit the documentation.Quickstartimportnumpyasnpfromcustom_neural_net_creator.modelimportModelfromcustom_neural_net_creator.denseimportDensefromcustom_neural_net_creator.activation_layerimportActivationLayerfromcustom_neural_net_creator.activation_functionsimportrelu,relu_derivative,sigmoid,sigmoid_derivative,tanh,tanh_primefromcustom_neural_net_creator.loss_functionsimportmean_squared_error,mean_squared_error_derivative#Input data for XORx=[[0,0],[0,1],[1,0],[1,1]]y=[[0],[1],[1],[0]]model=Model()model.add(Dense(2,10))#Input takes in two inputsmodel.add(ActivationLayer(relu,relu_derivative))#First hidden layer has 10 neurons and uses RELUmodel.add(Dense(10,10))model.add(ActivationLayer(relu,relu_derivative))#Second hidden layer has 10 neurons and uses RELUmodel.add(Dense(10,1))model.add(ActivationLayer(sigmoid,sigmoid_derivative))#Output layer is one neuron with Sigmoid as activation#Train on training datamodel.fit(x,y,mean_squared_error,mean_squared_error_derivative,epochs=1000,learning_rate=0.1,verbosity=3)#Loss of Epoch #1000: 0.0002757698731393589#Test modelpredictions=model.predict(x[0:3])print("Predicted: ")print(predictions)#Predicted: [array([[0.02610931]]), array([[0.98778214]]), array([[0.9873547]])]print("Actual:")print(y[0:3])# Actual:# [[[0]]# [[1]]# [[1]]]
Package: custom-onnx
custom_onnxA Custom Jupyter Widget LibraryInstallationYou can install usingpip:pipinstallcustom_onnxIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]custom_onnxDevelopment InstallationCreate a dev environment:condacreate-ncustom_onnx-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivatecustom_onnx-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"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 .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py custom_onnx
jupyter nbextension enable --sys-prefix --py custom_onnxNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript: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 neededyarnrunwatch# 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.Updating the versionTo update the version, install tbump and use it to bump the version.
By default it will also create a tag.pipinstalltbump
tbump<new-version>
Package: custom-operators
Novigi Custom Airflow OperatorsThis Repo contains custom airflow operators which were writen by Novigi Pty LtdVersion No - 1.0Bitbucket Link -https://Prasadi1994@bitbucket.org/novigi/novigi_operators.gitHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesWriting testsCode reviewOther guidelinesWho do I talk to?Repo owner or adminOther community or team contact
Package: custom-orm
inf_0008_custom_orm - Team 1Установка:pipinstallcustom-ormПример использования:fromdatetimeimportdatetimefromcustom-ormimportdbclassUser(db.Model):table_name='asdfasdf'id=db.IntegerField(primary_key=True,auto_increment=True)name=db.StringField(max_length=255)surname=db.StringField(max_length=255)age=db.IntegerField(name_in_db='my_age')birth=db.DateTimeField()is_agent_007=db.BooleanField()# You can get User instance by creating database entryu=User(name='Ruslan',surname='Gazizov',age=18,is_agent_007=True,birth=datetime(year=2003,month=1,day=21))# Or by finding it in databaseu=User.where(name='Ruslan',my_age=18)# Directly change the properties without savingu.name='Другое имя'u.surname='Другая фамилия'u.age='22'u.birth=datetime(year=2021,month=11,day=27)u.is_agent_007=False# Save changesu.save()# Delete database entryUser.delete(primary_key_value=u.id)User.save()
Package: custom-output
Custom OutputValue output customization.Function returns custom values, takes two parameters, code parameter receives the abbreviations! below you will see all types in the acronym table.abbreviationsReset
- Reset Color_Off='0m'#Text 

Regular Colors
-------------- 
- Black="0;30m"# Black
- Red="0;31m"# Red
- Green="0;32m"# Green
- Yellow="0;33m"# Yellow
- Blue="0;34m"# Blue
- Purple="0;35m"# Purple
- Cyan="0;36m"# Cyan
- White="0;37m"# White

Bold
----
- BBlack="1;30m"# Black
- BRed="1;31m"# Red
- BGreen="1;32m"# Green
- BYellow="1;33m"# Yellow
- BBlue="1;34m"# Blue
- BPurple="1;35m"# Purple
- BCyan="1;36m"# Cyan
- BWhite="1;37m"# White

Underline
---------
- UBlack="4;30m"# Black
- URed="4;31m"# Red
- UGreen="4;32m"# Green
- UYellow="4;33m"# Yellow
- UBlue="4;34m"# Blue
- UPurple="4;35m"# Purple
- UCyan="4;36m"# Cyan
- UWhite="4;37m"# White

Background
----------
- On_Black="40m"# Black
- On_Red="41m"# Red
- On_Green="42m"# Green
- On_Yellow="43m"# Yellow
- On_Blue="44m"# Blue
- On_Purple="45m"# Purple
- On_Cyan="46m"# Cyan
- On_White="47m"# White

High Intensty
-------------
- IBlack="0;90m"# Black
- IRed="0;91m"# Red
- IGreen="0;92m"# Green
- IYellow="0;93m"# Yellow
- IBlue="0;94m"# Blue
- IPurple="0;95m"# Purple
- ICyan="0;96m"# Cyan
- IWhite="0;97m"# White

Bold High Intensty
--------------------
- BIBlack="1;90m"# Black
- BIRed="1;91m"# Red
- BIGreen="1;92m"# Green
- BIYellow="1;93m"# Yellow
- BIBlue="1;94m"# Blue
- BIPurple="1;95m"# Purple
- BICyan="1;96m"# Cyan
- BIWhite="1;97m"# White

High Intensty backgrounds
-------------------------
- On_IBlack="0;100m"# Black
- On_IRed="0;101m"# Red
- On_IGreen="0;102m"# Green
- On_IYellow="0;103m"# Yellow
- On_IBlue="0;104m"# Blue
- On_IPurple="0;95m"# Purple
- On_ICyan="0;106m"# Cyan
- On_IWhite="0;107m"# White
Package: customplotting
Custom-PlottingCustom-plotting is a python package written with the aim of producing scientific publication quality figures as quickly as possible with as little code as possible(checkout examples).So instead of typing8 lines of codein matplotlib everytime:importmatplotlib.pyplotaspltfrommatplotlib_scalebar.scalebarimportScaleBar"""Plot image"""plt.figure()plt.imshow(img_data)cb=plt.colorbar()cb.set_label("Height (nm)",fontsize=20,fontweight='bold')plt.gca().axes.get_yaxis().set_visible(False)plt.gca().axes.get_xaxis().set_visible(False)"""Add scalebar"""scalebar=ScaleBar(dx=0.5,units='nm')# 1 pixel = 0.5 nmplt.gca().add_artist(scalebar)You only need1 line of codeto get the same plot:fromcustomplotting.mscopeimportplot_confocal"""Plot image with scalebar"""plot_confocal(img_data,stepsize=0.5,units='nm',cbar_label="Height (nm)")It saves you from a lot of unnecessary typing (if you often plot data)The functions wrap aroundmatplotliblibrary for plotting with the customizable options. For instance, you can directly add a scalebar to your image without additional code. The figures can customized (colorbar, axis, linewidth, labels, etc) using the same key word arguments as inmatplotlib. See function docstrings for more information about customization.Installpip install customplottingExample Usage"""Make a publishable image with scalebar"""fromcustomplotting.mscopeimportplot_confocalplot_confocal(img_data,stepsize=0.5,units='nm',cbar_label="Height (nm)")"""Make a publishable X-Y plot"""fromcustomplotting.generalimportplot_xyplot_xy([1,2,3],[4,5,6],xlabel="Random X",ylabel="Random Y")Check out thejupyter notebook for more examplesWhy use?Matplotlibis anAMAZINGpackage but if you are like me and are tired of typing 5-7 lines of code everytime you want a good publishable quality figure, thencustomplottingis the answer.Customplottingbasically wraps the matplotlib functions to allow you to quickly plot commonly used plots like x-y, images with scalebars, etc with only one line of code.What's New?Plot X-Y with custom settings (from v0.1.4)Scalebar can be added to the plot using a new dependencymatplotlib_scalebar(from v0.1.3)For widefield (pixera) plots, scale can automatically be selected by choosing the objective usedColorbar can be customized with labels (and also removed if needed)X-axis and Y-axis ticks can be added/removed nowContributeContributions are always welcome! SeeCONTRIBUTING.mdIssuesOpen an issue if you come across any!LicenseMIT LicenseDependenciesmatplotlibmatplotlib_scalarnumpy
Package: custom-poling
Custom PolingThecustom_polingmodule can be used to design the domain configuration of a nonlinear crystal to approximate a desired phase-matching function.InstallationInstall via pip:pipinstallcustom-polingExamples and related materialFor guidance on how to use the module, check out the jupyter notebooks and python scripts in theexamplesdirectory.The directory also contains the following supporting material:PublicationSupporting materialDrago, Christian, and Agata M. Brańczyk. "Tunable frequency-bin multimode squeezed vacuum states of light." Physical Review A 106.4 (2022): 043714.jupyter notebookDrago, Christian, and Agata M. Brańczyk. "Tunable frequency-bin multi-mode squeezed states of light." arXiv preprint arXiv:2204.10079 (2022).jupyter notebookReferencesThis module is based on the theory described inthis paper:Francesco Graffitti, Dmytro Kundys, Derryck T Reid, Agata M Brańczyk and Alessandro Fedrizzi, "Pure down-conversion photons through sub-coherence-length domain engineering." Quantum Science and Technology 2.3 (2017): 035001.If you usecustom-polingin your research, please cite the above paper and link to this repo.