Saravanan666/pypi-org huggingface dataset:
Package: cusir
CuSIRIntroductionCuSIR is a Python code built on top of CuPy, a NumPy-like library for GPU-accelerated computing. It provides a solver for the two-dimensional diffusive SIR model, described by the following system of reaction-diffusion equations:$$
\begin{align}\partial_t S &= -\beta_{\mathbf{r}} S I - \gamma I + D_I \nabla^2 I \\\\
\partial_t I &= \beta_{\mathbf{r}} S I + D_S \nabla^2 S - \mathbf{v} \cdot \nabla I,
\end{align}
$$where $S$ is the density of susceptible individuals, $I$ is the density of infected individuals, $\beta_{\mathbf{r}}$ is the transmission rate that depends on the location $\mathbf{r}$, $\gamma$ is the recovery/removal rate, $D_I$ and $D_S$ are diffusion coefficients, and $\mathbf{v}$ is the convection field.RequirementsTo use the CuSIR package, you will need the following software and hardware:A CUDA-compatible GPU: A graphics processing unit (GPU) that supports CUDA. Check the list of CUDA-compatible GPUs on the NVIDIA website (https://developer.nvidia.com/cuda-gpus) to see if your GPU is supported.CUDA Toolkit: A parallel computing platform and programming model developed by NVIDIA for general-purpose computing on GPUs. You can download CUDA from the NVIDIA website (https://developer.nvidia.com/cuda-downloads).Note: Currently (January, 2023), the last version of CUDA (12) is not supported by CuPy. You will need to install any previous version of CUDA (recommended 11.2) to use CuSIR.CuPy: A NumPy-like library for GPU-accelerated computing. You can install CuPy by following the instructions in the CuPy documentation (https://docs-cupy.chainer.org/en/stable/install.html).InstallationTo install the CuSIR package, you can use pip by running the following command in your command prompt or terminal:pipinstallcusirThis command will install the latest version of the CuSIR package. It is mandatory to meet the requirements listed above for CuSIR to work properly.UsageAlmost everything is implemented in thesystemclass, which is located in thesystemmodule. The following code shows how to use thesystemclass to solve the diffusive SIR model:importcusir.systemascs# Define the spatial domainLx=2**10Ly=2**10# Create the system objects=cs.system(Lx,Ly)# Define the system parameterss.beta=1# Transmission rates.gamma=0.1# Recovery/removal rates.D_I=1# Diffusion coefficient for infected individualss.D_S=1# Diffusion coefficient for susceptible individuals# Define the initial conditionss.set_plane_initial_conditions()# Solve the systemfor_inrange(10000):s.update()# Update the systems.rigid_x()# Apply rigid boundary conditions in the x-direction#You can also use the following to do the same:#s.solve(10000)# Get the solutionS=s.S.get()# get() pulls the data from the GPU to the CPU as a NumPy arrayI=s.I.get()LicenseCuSIR is licensed under the MIT license. See theLICENSEfile for more details.
Package: cuspy
Config for Python1. IntroductionParse config from json file2. Usage2.1 Basicconfig.json{"a":1,"b":2}python filefromcuspyimportConfigUtilsconfig=ConfigUtils("config.json")a=config.aaa_config.ab=config.aaa_config.b2.2 File/Directory Pathconfig.json{"aaa_config":{"root":"/home/aaa","data_dn":"data_dir","data_fp":"data.tsv"}}python filefromcuspyimportConfigUtilsconfig=ConfigUtils("config.json")data_dp=config.aaa_config.data_dp# data_dp = "/home/aaa/data_dir"data_fp=config.aaa_config.data_fp# data_fp = "/home/aaa/data_dir/data.tsv"
Package: cusser
cusser🤬A curses wrapper that understands ANSI escape code sequencescusser is a lightweight Python package for teachingcurseshow to useANSI escape code sequences. It
works by wrapping the curses standard window object and intercepting escape code
sequences.Features♻️ Easily integrate with thestandardcursesmodule🕹️ Use the same escape code sequencesas you would with Colorama🖍️ Only one dependency:stransi(for
actuallly parsing escape code sequences)🐍 Python 3.8+Installation$pipinstallcusserUsageIn[1]:importcursesIn[2]:fromcusserimportCusserIn[3]:defapp(stdscr)->None:...:"""Start a new application."""...:ifnotisinstance(stdscr,Cusser):...:stdscr=Cusser(stdscr)...:...:ultra_violet=(100,83,148)...:x,y=34,12...:stdscr.addstr(...:f"\033[2J\033[{x};{y}H"...:"\033[1;32mHello "...:f"\033[;3;38;2;{';'.join(map(str,ultra_violet))}m"...:"cusser"...:"\033[m 🤬!"...:)...:stdscr.refresh()...:stdscr.getch()...:In[4]:curses.wrapper(app)CreditsPhotobyGwendal CottinonUnsplash.
Package: cuss-inspect
cuss_inspectHow It Workscuss_inspectis a logistic regression based model trained on 180K+ reviews and tested on 24K+ reviews.Performance1 Prediction (ms)10 Predictions (ms)100 Predictions (ms)1000 Predictions (ms)10000 Predictions (ms)cuss_inspect0.20.30.84.324.7AccuracyPrecisionRecallF1 Score00.840.940.8910.990.960.98Accuracy0.96macro avg0.910.950.93weighted avg0.960.960.96Receiver Operating CharacteristicsInstallation$ pip install cuss_inspectUsagefromcuss_inspectimportpredict,predict_prob# for simple stringtext_0="this is simple review. you have done a good job"print(predict(text_0))# [0]print(predict_prob(text_0)# [0.05]text_1="son of a bitch"print(predict(text_1))# [1]print(predict_prob(text_1)# [1.]# for list of inputstest=['who are you?','what do you want?','son of a dog','how the hell can you say that','fuck it']print(predict(test))# [0 0 1 1 1]print(predict_prob(test))# [0.12 0.22 0.55 0.96 1.]*predict()andpredict_probreturnnumpyarrays.
Package: custard
📚简介custard是一个小而全的Python工具类库，通过静态方法封装，降低相关API的学习成本，提高工作效率，使Python拥有函数式语言般的优雅，让Python语言也可以“甜甜的”。custard中的工具方法来自每个用户的精雕细琢，它涵盖了Python开发底层代码中的方方面面，它既是大型项目开发中解决小问题的利器，也是小型项目中的效率担当；custard是项目中“util”包友好的替代，它节省了开发人员对项目中公用类和公用工具方法的封装时间，使开发专注于业务，同时可以最大限度的避免封装不完善带来的bug。🍺custard如何改变我们的coding方式custard的目标是使用一个工具方法代替一段复杂代码，从而最大限度的避免“复制粘贴”代码的问题，彻底改变我们写代码的方式。以计算MD5为例：👴【以前】打开搜索引擎 -> 搜“Python MD5加密” -> 打开某篇博客-> 复制粘贴 -> 改改好用👦【现在】引入custard  -> Kerberos.md5_encode()custard的存在就是为了减少代码搜索成本，避免网络上参差不齐的代码出现导致的bug。🛠️包含组件一个Python基础工具类，对文件、流、加密解密、转码、正则、线程、XML等JDK方法进行封装，组成各种Util工具类，同时提供以下组件：模块介绍core核心，包括Bean操作、各种Util等crypto加密解密模块，提供对称、非对称和摘要算法封装curlcurl实现hitfilter敏感词过滤器limiter拦截器time日期kaptcha图片验证码实现swagger自动解析swagger生成request请求pagination分页组件📝中文文档🚽编译安装访问custard的Github主页：https://github.com/kamalyes/custard下载整个项目源码（master或dev分支都可）然后进入custard项目目录执行：pip3install-rrequirements.txt-ihttps://pypi.douban.com/simple
pipinstallbuild
python-mbuild然后就可以使用引入了。🏗️添砖加瓦🎋分支说明custard的源码分为两个分支，功能如下：分支作用master主分支，release版本使用的分支，与中央库提交的jar一致，不接收任何pr或修改dev开发分支，默认为下个版本的SNAPSHOT版本，接受修改或pr🐞提供bug反馈或建议提交问题反馈请说明正在使用的JDK版本呢、custard版本和相关依赖库版本。GitHub issue🧬贡献代码的步骤在Gitee或者Github上fork项目到自己的repo把fork过去的项目也就是你的项目clone到你的本地修改代码（记得一定要修改dev分支）commit后push到自己的库（dev分支）登录Gitee或Github在你首页可以看到一个 pull request 按钮，点击它，填写一些说明信息，然后提交即可。等待维护者合并📐PR遵照的原则custard欢迎任何人为custard添砖加瓦，贡献代码，不过维护者是一个强迫症患者，为了照顾病人，需要提交的pr（pull request）符合一些规范，规范如下：注释完备，尤其每个新增的方法应按照Python文档规范标明方法说明、参数说明、返回值说明等信息，必要时请添加单元测试，如果愿意，也可以加上你的大名。custard的缩进按照IDEA 默认（tab）缩进，所以请遵守（不要和我争执空格与tab的问题，这是一个病人的习惯）。新加的方法不要使用第三方库的方法，custard遵循无依赖原则（除非在extra模块中加方法工具）。请pull request到dev分支。master是主分支，表示已经发布中央库的版本，这个分支不允许pr，也不允许修改。我们如果关闭了你的issue或pr，请不要诧异，这是我们保持问题处理整洁的一种方式，你依旧可以继续讨论，当有讨论结果时我们会重新打开。
Package: custardpy
CustardPy: Docker image for 3D genome analysisMajor Release! (version 1)This repository containsSource code ofCustardPy(PyPI),Dockerfile ofCustardPyDocker image,Full Manual, andTutorial data of Hi-C and Micro-C analysis using demo data.0. ChangelogSeeChangelog1. InstallationDocker image is available atDockerHub.1.1 DockerTo use docker command, type:# pull docker image
docker pull rnakato/custardpy

# container login
docker run --rm -it rnakato/custardpy /bin/bash
# execute a command
docker run --rm -it -v (your directory):/opt/work rnakato/custardpy <command>When calling loops using Juicer HICCUPS, suppy--gpus alloption to allow GPU computation (GPU card needed):docker run --gpus all -it --rm -it -v (your directory):/opt/work rnakato/custardpy call_HiCCUPS.sh1.2 SingularitySingularity can also be used to execute the docker image:# build image
singularity build custardpy.sif docker://rnakato/custardpy

# execute a command
singularity exec custardpy.sif <command>Singularity mounts the current directory automatically. If you access the files in the other directory, please mount by--bindoption, for instance:singularity exec --bind /work custardpy.sif <command>This command mounts/workdirectory.When calling loops using Juicer HICCUPS, suppy--nvoption to allow GPU computation (GPU card needed):singularity exec --bind /work custardpy.sif call_HiCCUPS.sh2. Quickstart# download Churros/tutorial directory
git clone https://github.com/rnakato/CustardPy.git
cd CustardPy/tutorial/Hi-C/

# download fastq and genome data and make index
bash 00_getdata.sh

# Execute Juicer pipeline
bash QuickStart_juicer.sh3. UsageSeehttps://custardpy.readthedocs.iofor the detailed Manual.4. Build Docker image from DockerfileFirst clone and move to the repositorygit clone https://github.com/rnakato/CustardPy.git
cd CustardPy/DockerThen type:docker build -f Dokerfile.<version> -t <account>/custardpy_juicer .6. ReferenceNakato R, Sakata T, Wang J, Nagai LAE, Nagaoka Y, Oba GM, Bando M, Shirahige K, Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors,Nature Communications, 2023. doi:10.1038/s41467-023-41316-47. ContactRyuichiro Nakato: rnakato AT iqb.u-tokyo.ac.jp
Package: custatevec-cu11
NVIDIA cuStateVecis a high-performance library dedicated to operations with state vectors for expressing quantum algorithms.
cuStateVec is a component of theNVIDIA cuQuantum SDK.In addition to C APIs, cuStateVec can also be accessed in Python viacuQuantum Python.DocumentationPlease refer tohttps://docs.nvidia.com/cuda/cuquantum/custatevec/index.htmlfor the cuStateVec documentation.InstallationThe cuStateVec wheel can be installed as follows:pipinstallcustatevec-cuXXwhere XX is the CUDA major version (currently CUDA 11 & 12 are supported).NoteTo use cuQuantum’s Python APIs, please directly installcuQuantum Python.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: custatevec-cu12
NVIDIA cuStateVecis a high-performance library dedicated to operations with state vectors for expressing quantum algorithms.
cuStateVec is a component of theNVIDIA cuQuantum SDK.In addition to C APIs, cuStateVec can also be accessed in Python viacuQuantum Python.DocumentationPlease refer tohttps://docs.nvidia.com/cuda/cuquantum/custatevec/index.htmlfor the cuStateVec documentation.InstallationThe cuStateVec wheel can be installed as follows:pipinstallcustatevec-cuXXwhere XX is the CUDA major version (currently CUDA 11 & 12 are supported).NoteTo use cuQuantum’s Python APIs, please directly installcuQuantum Python.Citing cuQuantumPleae click this Zenodo badge to see the citation format:
Package: custCLI
Python-CLIA Command Line Interface library for PythonInstallation:To use the library, just download theCLI.pyfile in custCLI and drop it in to you'r working folderOr install the library using pip:pip install custCLIWhen using microPython, copy theCLI.pyfile from the microPython folderMicroPython filesHow to use:Import the necessary classes and enums:fromCLIimportCLI,Command,Parameter,DataTypeDefine your custom functions that will be executed when a command is called. For example:deftestFunction(params):print("testFunction")forparaminparams:print(f"\t{param}")Create instances of theCommandclass for each command you want to add. For example:command1=Command("test1","test1 description",testFunction,[Parameter("param1","param1 description",DataType.STRING),Parameter("param2","param2 description",DataType.INTEGER)])To add a default value for a parameter, just add it to the endParameter("param2","param2 description",DataType.STRING,"TheDefaultValue")Optionally, define more custom functions and create additionalCommandinstances.Create an instance of theCLIclass, passing in the name, description, and a list of commands. For example:cli=CLI("Test CLI","Test CLI description",[command1,command2])Print the header of the CLI using theprintHeader()method:cli.printHeader()Enter a loop to continuously accept user commands using thenewCommand()method:whileTrue:cli.newCommand()How to use:fromCLIimportCLI,Command,Parameter,DataTypedeftestFunction(params):print("testFunction")forparaminparams:print(f"\t{param}")command1=Command("test1","test1 description",testFunction,[Parameter("param1","param1 description",DataType.STRING),Parameter("param2","param2 description",DataType.INTEGER)])deftestFunction2(params):print("testFunction2")forparaminparams:print(f"\t{param.name}-{param.dataType.value}-{param.value}")command2=Command("test2","test2 description",testFunction2,[Parameter("param1","param1 description",DataType.STRING),Parameter("param2","param2 description",DataType.INTEGER)])cli=CLI("Test CLI","Test CLI description",[command1,command2])cli.printHeader()whileTrue:cli.newCommand()
Package: custodes
App guardians.UsageimportasynciofromtypingimportAny,Dictfromcodefast.asyncio.rabbitmqimportconsumefromrichimportprintfromcustodes.serverimportgetfromcustodes.clientimportpostasyncdefmain():returnawaitasyncio.gather(post('custodes server',{'code':0,'message':'OK'},loop=True,expire=120),get())if__name__=='__main__':cf.info('custodes server started...')asyncio.run(main())
Package: custom-actions
Create a JavaScript Action using TypeScriptUse this template to bootstrap the creation of a TypeScript action.:rocket:This template includes compilation support, tests, a validation workflow, publishing, and versioning guidance.If you are new, there's also a simpler introduction.  See theHello World JavaScript ActionCreate an action from this templateClick theUse this Templateand provide the new repo details for your actionCode in MainFirst, you'll need to have a reasonably modern version ofnodehandy. This won't work with versions older than 9, for instance.Install the dependencies$npminstallBuild the typescript and package it for distribution$npmrunbuild&&npmrunpackageRun the tests :heavy_check_mark:$npmtestPASS./index.test.js✓throwsinvalidnumber(3ms)✓wait500ms(504ms)✓testruns(95ms)...Change action.ymlThe action.yml contains defines the inputs and output for your action.Update the action.yml with your name, description, inputs and outputs for your action.See thedocumentationChange the CodeMost toolkit and CI/CD operations involve async operations so the action is run in an async function.import*ascorefrom'@actions/core';...asyncfunctionrun(){try{...}catch(error){core.setFailed(error.message);}}run()See thetoolkit documentationfor the various packages.Publish to a distribution branchActions are run from GitHub repos so we will checkin the packed dist folder.Then runnccand push the results:$npmrunpackage
$gitadddist
$gitcommit-a-m"prod dependencies"$gitpushoriginreleases/v1Note: We recommend using the--licenseoption for ncc, which will create a license file for all of the production node modules used in your project.Your action is now published! :rocket:See theversioning documentationValidateYou can now validate the action by referencing./in a workflow in your repo (seetest.yml)uses:./with:milliseconds:1000See theactions tabfor runs of this action! :rocket:Usage:After testing you cancreate a v1 tagto reference the stable and latest V1 action
Package: custom-admin
Custom Admin BaseA base toolkit for building a Django-Suit compatible Admin theme.Installationpipinstalldjango-custom-admin-basesettings.pyThis app should come before other admin style modifiers, since it replaces admin/base.html and admin/base_site.htmlINSTALLED_APPS=["...","custom_admin","your_app_to_extend_custom_admin","...","django.contrib.admin",]Migrations and Static filesmanage.pymigratecustom_admin
manage.pycollectstatic
Package: custom-conda-create
Custom conda createSmall CLI tool that customizes the conda create command.Free software: MIT licenseDocumentation:https://custom-conda-create.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-05-21)First release on PyPI.
Package: custom-diffusion
Custom Diffusion: Creating Video from Frame Using DiffusionInstallationpipinstallcustom_diffusionUsage# Importing the required librariesfromcustom_diffusion.utils.data_utilsimportload_images_from_folderfromcustom_diffusionimportStableDiffusionControlNetGeneratorfromcustom_diffusion.utils.video_utilsimportconvert_images_to_videofromcustom_diffusion.demoimportvideo_pipeline# Creating a video from a video fileframes_path=video_pipeline(video_path="test.mp4",output_path="output.mp4",start_time=0,end_time=5,frame_rate=1,)# Creating a video from a folder of imagesimages_list=load_images_from_folder(frames_path)prompt="a anime boy"negative_prompt="bad"list_prompt=[prompt]*len(images_list)list_negative_prompt=[negative_prompt]*len(images_list)# Generating images from a list of imagesgenerator=StableDiffusionControlNetGenerator()generated_image_list=generator.generate_image(stable_model_path="andite/anything-v4.0",controlnet_model_path="lllyasviel/control_v11p_sd15_canny",scheduler_name="EulerAncestralDiscrete",images_list=images_list,prompt=list_prompt,negative_prompt=list_negative_prompt,height=512,width=512,guess_mode=False,num_images_per_prompt=1,num_inference_steps=30,guidance_scale=7.0,controlnet_conditioning_scale=1.0,generator_seed=0,preprocess_type="Canny",resize_type="center_crop_and_resize",crop_size=512,)# Converting the generated images to a videoframe2video=convert_images_to_video(image_list=generated_image_list,output_file="output.mp4",frame_rate=5,)
Package: custom-django-validation
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent
[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6
[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: customdl
Custom Deep LearningCreate a customized Feedforward Neural NetworkAvailable options:Weight initialization: Random, Xavier, HeActivation functions: Identity, Sigmoid, Softmax, Tanh, ReLULoss functions: MSE, Cross EntropyOptimizers: GD, Momentum based GD, Nesterov accerelated GDLearning mode: online, mini-batch, batchRefer to the documentation of any class/method by using help(class/method) Eg: help(FNN), help(FNN.compile)For a high-level overview of the underlying theory refer:Feedforward Neural NetworkOptimizersInstallation$[sudo]pip3installcustomdlDevelopment Installation$gitclonehttps://github.com/Taarak9/Custom-DL.gitUsage>>>fromcustomdlimportFNNHandwritten Digit Recognition exampleimportnumpyasnpfrommatplotlibimportpyplotaspltfrommnist_loaderimportload_data_wrapperfromcustomdlimportFNN# MNIST data splittraining_data,validation_data,test_data=load_data_wrapper()# Loss function: Cross Entropyhdr=FNN(784,"ce")hdr.add_layer(80,"sigmoid")hdr.add_layer(10,"sigmoid")hdr.compile()hdr.fit(training_data,validation_data)hdr.accuracy(test_data)The mnist_loader used could be foundhere.Features to be addedPlots for monitoring loss and accuracy over epochsRegularization techniques: L1, L2, dropoutOptimizers: Adam, RMSPropRBF NN
Package: customer-churn-classification-model
CUSTOMER CHURN PREDICTIONA machine learning program that predicts customer churn (yes(1) or no(0)) from a telecos data company.This program usesextreme gradient boostingtechnique in predicting outcomes.Data set and its descriptionDataDescriptionCustomer IDCustomer unique identifierGenderWhether the customer is a male or a femaleSenior CitizenWhether the customer is a senior citizen or not (1, 0)PartnerWhether the customer has a partner or not (Yes, No)DependentsWhether the customer has dependents or not (Yes, No)TenureNumber of months the customer has stayed with the companyPhone serviceWhether the customer has a phone service or not (Yes, No)Multiple linesWhether the customer has multiple lines or not (Yes, No, No phone service)Internet ServiceCustomer’s internet service provider (DSL, Fiber optic, No)Online securityWhether the customer has online security or not (Yes, No, No internet service)Dependecies and packgages*numpy>=1.20.0,<1.21.0
*xgboost
*pandas>=1.3.5,<1.4.0
*pydantic>=1.8.1,<1.9.0
*scikit-learn>=1.0.2,<1.1.0
*strictyaml>=1.3.2,<1.4.0
*ruamel.yaml==0.16.12
*feature-engine>=1.0.2,<1.1.0
*joblib>=1.0.1,<1.1.0Source code linkSource code link:Github link
Package: customer.krainrealestate
.. contents::customer.krainrealestate========================Package that extends Plone Member Data for custom needsContributors============Note:  place names and roles of the people who contribute to this packagein this file, one to a line, like so:- Jens Krause (jens@propertyshelf.com), Original AuthorChangelog=========0.8 (2014-04-21)----------------- save AgentPortrait as contentleadimage for AgentProfilePages0.7 (2014-04-09)----------------- make Agents sortable by a parameter in the user settings (admin view)0.6 (2014-03-31)----------------- adust AgentPortrait scaling to 266x266 (square format)0.5 (2014-03-30)----------------- versioning cleanup0.4 (2014-03-30)----------------- fix agent zope-auth error for Agents0.3 (2014-03-24)----------------- automated Agent Folder Generation & Setup (en/es/de)0.2 (2014-03-19)----------------- add custom user actions (personal bar) to agents folders0.1 (2014-03-13)----------------- Package created using zopeskel[]