Saravanan666/pypi-org huggingface dataset:
Package: echemdb
The echemdb Python package can interact with a database offrictionless datapackagescontaining electrochemical data followingechemdb's metadata schema.
Such a database can be generated from the data onechemdb.orgor from local files.Detailed installation instructions, description of the modules, advanced usage examples, including
local database creation, are provided in ourdocumentation.Installation instructionsInstall the latest stable version of echemdb from PyPI or conda.pip install echemdbconda install -c conda-forge echemdbPython APIThe currently available data shown onechemdb.orgcan be downloaded and stored in a database.>>>fromechemdb.cv.cv_databaseimportCVDatabase>>>db=Database()Filtering the database for entries having specific properties, e.g., containing Pt as working electrode material, returns a new database.>>>db_filtered=db.filter(lambdaentry:entry.system.electrodes.working_electrode.material=='Pt')A single entry can be retrieved with the identifiers provided on the website
(see for exampleengstfeld_2018_polycrystalline_17743_f4b_1)>>>entry=db['engstfeld_2018_polycrystalline_17743_f4b_1']Each entry has a set of descriptors such as itssourceor the electrochemicalsystem.>>>entry.source# or entry['source']{'citation key':'engstfeld_2018_polycrystalline_17743','curve':1,'url':'https://doi.org/10.1002/chem.201803418','figure':'4b','version':1}The data related to an entry can be returned as apandasdataframe (values are provided in SI units).>>>entry.dftEj00.000000-0.1969620.04300910.011368-0.1963930.051408...The dataframe can be returned with custom or original figure axes' units by rescaling the entry.>>>entry.rescale({'E':'mV','j':'uA / m2'}).dftEj00.000000-196.96173043008.84216210.011368-196.39332151408.199892...>>>entry.rescale('original').dftEj00.000000-0.1969624.30088410.011368-0.1963935.140820...The units and reference electrodes can be found in the resource schema. The units are updated upon rescaling an entry.>>>entry.package.get_resource('echemdb')['schema']{'fields':[{'name':'t','unit':'s','type':'number'},{'name':'E','unit':'V','reference':'RHE','type':'number'},{'name':'j','unit':'A / m2','type':'number'}]}The data can be visualized in a plotly figure:>>>entry.plot()LicenseThe contents of this repository are licensed under theGNU General Public
License v3.0or, at your option, any later version.
Package: echidnalib
EchidnaLibModern and modular open source python package used to create discord bots.About The ProjectThe goal of this project is to implement an easy to use system which can dynamically add commands and functionality to Discord Bots.I also plan to include a way to manage and control bots through the console during runtime.InstallationYou can install the current version using "python -m pip install echidnalib"RoadmapSee theopen issuesfor a list of proposed features (and known issues).ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.Acknowledgementsdiscord.py
Package: echo
echo: Callback Properties in PythonEcho is a small library for attaching callback functions to property
state changes. For example:class Switch(object):
    state = CallbackProperty('off')

def report_change(state):
    print 'the switch is %s' % state

s = Switch()
add_callback(s, 'state', report_change)

s.state = 'on'  # prints 'the switch is on'CalllbackProperties can also be built using decoratorsclass Switch(object):

      @callback_property
      def state(self):
        return self._state

      @state.setter
      def state(self, value):
          if value not in ['on', 'off']:
              raise ValueError("invalid setting")
          self._state = valueFull documentation is avilablehere
Package: echo1-coco-builder
Introductionecho1-coco-builderprovides a faster, safer way to build coco formatted data.See:https://cocodataset.org/#format-datafor more informationInstallation# If using pippipinstallecho1-coco-builder# If using poetrypoetryaddecho1-coco-builderExample use (building coco annotations)importpandasaspdfromecho1_coco_builder.annotations_builderimportCocoAnnotationsBuilder# Open a CSV using pandasdf=pd.read_csv("./tests/data/test.csv")# Initialize the coco buildercoco_builder=CocoAnnotationsBuilder()# For each row in the csvforannotation_id,rowindf.iterrows():# image_id must be an integerimage_id=row["image_name"]# image_name must be a stringfile_name=row["image_name"]# image_width and image_height must be an integerimage_width=row["image_width"]image_height=row["image_height"]# category_id must be an integercategory_id=row["category_id"]# category_name must be a stringcategory_name=row["category_name"]# bbox format: [x,y,width,height]bbox=row["bbox"].split(",")# add a new imagecoco_builder.add_image({"id":image_id,"file_name":file_name,"width":image_width,"height":image_height,})# add a new categorycoco_builder.add_category({"id":category_id,"name":category_name})# add a new annotationcoco_builder.add_annotation({"id":annotation_id,"image_id":image_id,"category_id":category_id,"bbox":bbox,"segmentation":segmentation,"iscrowd":0,"area":area,})# add infococo_builder.add_info({"year":2022,"version":"v1.0","contributor":"Echo1","description":"Contact for more info.","url":"https://echo1.io",})# print the data in the coco format as a python objectprint(coco_builder)# print the data in the coco format as jsonprint(coco_builder.get())# save the data in the coco format as jsonpython_file=open("example-data.json","w")python_file.write(coco_builder.get())python_file.close()Example use (building coco results)fromecho1_coco_builder.results_builderimportCocoResultsBuilder# Initialize the coco generatorresults_builder=CocoResultsBuilder()results_builder.add_result({"image_id":1,"bbox":[490,365,14,26],"score":0.8559583425521851,"category_id":1,"category_name":"My Category","segmentation":[],"iscrowd":0,"area":364,})# print the data in the coco results format as a python objectprint(results_builder)# print the data in the coco results format as jsonprint(results_builder.get())
Package: echo1-coco-preview
echo1-coco-previewInstallation & Use# Install echo1-coco-preview>pipinstallecho1-coco-preview

>coco-preview
usage:coco-preview[-h][--annotations_fileANNOTATIONS_FILE][--data_base_pathDATA_BASE_PATH][--scale_percentSCALE_PERCENT]--image_idIMAGE_ID
coco-preview:error:thefollowingargumentsarerequired:--image_id
Package: echo1-coco-split
echo1-coco-splitecho1-coco-split provides a faster, safer way to split coco formatted datasets into train, validation and test sets.Installation & Use# Install echo1-coco-splitpipinstallecho1-coco-split# Run the coco-splitcoco-split\--has_annotations\--valid_ratio.2\--test_ratio.1\--annotations_file./annotations/instances_default.jsoncoco-split helpusage:coco-split[-h]--annotations_fileANNOTATIONS_FILE--valid_ratioVALID_RATIO--test_ratioTEST_RATIO[--train_nameTRAIN_NAME][--valid_nameVALID_NAME][--test_nameTEST_NAME][--has_annotations][--seedSEED]Splitsacocoannotationsfileintoatraining,validation,andtestset.

options:-h,--helpshowthishelpmessageandexit--annotations_fileANNOTATIONS_FILEPathtoCOCOannotationsfile.--valid_ratioVALID_RATIOsetvaliddatasetratio--test_ratioTEST_RATIOsettestdatasetratio--train_nameTRAIN_NAMEWheretostoreCOCOtrainingannotations--valid_nameVALID_NAMEWheretostoreCOCOvalidannotations--test_nameTEST_NAMEWheretostoreCOCOtestannotations--has_annotationsIgnoreallimageswithoutannotations.Keeponlythesewithatleastoneannotation--seedSEEDsettheseedgenerator
Package: echo1-image-slicer
echo1-image-slicerecho1-image-slicer provides a fast way to slice an image into smaller imagesInstallation & Use# Install echo1-image-slicerpipinstallecho1-image-slicer# Run the image-slicerimage-slicer\-f./tests/test.jpg\-s./output\-spyolo-\-sw500\-sh5002022-02-1816:24:08.959|INFO|echo1_image_slicer.echo1_image_slicer:slice_image:17-Loadingthefile./tests/test.jpg2022-02-1816:24:09.015|DEBUG|echo1_image_slicer.echo1_image_slicer:slice_image:22-Theimageshapeis(1333,1333)2022-02-1816:24:09.015|DEBUG|echo1_image_slicer.echo1_image_slicer:slice_image:29-Calculatingthesliceboxpositions.2022-02-1816:24:09.068|INFO|echo1_image_slicer.echo1_image_slicer:slice_image:58-Saved16imageslicesto./outputimage-slicer helpusage:image-slicer[-h]-fFILE_NAME[-spSAVE_TO_FILE_PREFIX]-sSAVE_TO_DIR-swSLICE_WIDTH-shSLICE_HEIGHT[-owOVERLAP_WIDTH_RATIO][-ohOVERLAP_HEIGHT_RATIO]Slicesanimageintosmallerimages.

optionalarguments:-h,--helpshowthishelpmessageandexit-fFILE_NAME,--file_nameFILE_NAMEThefilenametoslice.-spSAVE_TO_FILE_PREFIX,--save_to_file_prefixSAVE_TO_FILE_PREFIXTheprefixforsavedslicefilenames.-sSAVE_TO_DIR,--save_to_dirSAVE_TO_DIRThedirectorytosavetheslicesto.-swSLICE_WIDTH,--slice_widthSLICE_WIDTHThewidthofeachslice.-shSLICE_HEIGHT,--slice_heightSLICE_HEIGHTTheheightofeachslice.-owOVERLAP_WIDTH_RATIO,--overlap_width_ratioOVERLAP_WIDTH_RATIOTheoverlapwidthratio.-ohOVERLAP_HEIGHT_RATIO,--overlap_height_ratioOVERLAP_HEIGHT_RATIOTheoverlapheightratio.ThanksPrevious work done by:GitHub - obss/sahi: A lightweight vision library for performing large scale object detection/ instance segmentation.
Package: echoAI
Echo-AIPython package containing all mathematical backend algorithms used in Machine Learning.
The full documentation for Echo is providedhere.Table of ContentsAbout the ProjectActivation FunctionsRepository StructureSetup InstructionsAboutEcho-AI Packageis created to provide an implementation of the most promising mathematical algorithms, which are missing in the most popular deep learning libraries, such asPyTorch,KerasandTensorFlow.Activation FunctionsThe package contains implementation for following activation functions (âœ… - implemented functions, ðŸ•‘ - functions to be implemented soon, :white_large_square: - function is implemented in the original deep learning package):#FunctionEquationKerasPyTorchTensorFlow-KerasTensorFlow - Core1Weighted Tanhâœ…âœ…âœ…ðŸ•‘2Swishâœ…âœ…âœ…ðŸ•‘3ESwishâœ…âœ…âœ…ðŸ•‘4Aria2âœ…âœ…âœ…ðŸ•‘5ELiSHâœ…âœ…âœ…ðŸ•‘6HardELiSHâœ…âœ…âœ…ðŸ•‘7Milaâœ…âœ…âœ…ðŸ•‘8SineReLUâœ…âœ…âœ…ðŸ•‘9Flatten T-Swishâœ…âœ…âœ…ðŸ•‘10SQNLâœ…âœ…âœ…ðŸ•‘11ISRUâœ…âœ…âœ…ðŸ•‘12ISRLUâœ…âœ…âœ…ðŸ•‘13Bent's identityâœ…âœ…âœ…ðŸ•‘14Soft Clippingâœ…âœ…âœ…ðŸ•‘15SReLUâœ…âœ…âœ…ðŸ•‘15BReLUðŸ•‘âœ…âœ…ðŸ•‘16APLðŸ•‘âœ…âœ…ðŸ•‘17Soft Exponentialâœ…âœ…âœ…ðŸ•‘18MaxoutðŸ•‘âœ…âœ…ðŸ•‘19Mishâœ…âœ…âœ…ðŸ•‘20Beta Mishâœ…âœ…âœ…ðŸ•‘21RReLUðŸ•‘â¬œðŸ•‘ðŸ•‘22CELUâœ…â¬œâœ…ðŸ•‘23ReLU6âœ…â¬œðŸ•‘ðŸ•‘24HardTanhâœ…â¬œâœ…ðŸ•‘25GLUðŸ•‘â¬œðŸ•‘ðŸ•‘26LogSigmoidâœ…â¬œâœ…ðŸ•‘27TanhShrinkâœ…â¬œâœ…ðŸ•‘28HardShrinkâœ…â¬œâœ…ðŸ•‘29SoftShrinkâœ…â¬œâœ…ðŸ•‘30SoftMinâœ…â¬œâœ…ðŸ•‘31LogSoftmaxâœ…â¬œâœ…ðŸ•‘32Gumbel-SoftmaxðŸ•‘â¬œðŸ•‘ðŸ•‘Repository StructureThe repository has the following structure:-echoAI# main package directory|-Activation# sub-package containing activation functions implementation||-Torch# sub-package containing implementation for PyTorch|||-functional.py# script which contains implementation of activation functions|||-weightedTanh.py# activation functions wrapper class for PyTorch|||-...# PyTorch activation functions wrappers||-Keras# sub-package containing implementation for Keras|||-custom_activations.py# script which contains implementation of activation functions||-TF_Keras# sub-package containing implementation for Tensorflow-Keras|||-custom_activation.py# script which contains implementation of activation functions|-__init__.py-Observations# Folder containing other assets-docs# Sphinx documentation folder-LICENSE# license file-README.md-setup.py# package setup file-Smoke_tests# folder, which contains scripts with demonstration of activation functions usage-Unit_tests# folder, which contains unit test scriptsSetup InstructionsTo installechoAIpackage from PyPI run the following command:$ pip install echoAICode Examples:Sample scripts are provided inSmoke_testsfolder.
You can use activation functions from echoAI as simple as this:# import PyTorchimporttorch# import activation function from echoAIfromechoAI.Activation.Torch.mishimportMish# apply activation functionmish=Mish()t=torch.tensor(0.1)t_mish=mish(t)
Package: echoai-dev
EchoAI DEV: The command line tool to interface with Generative AIInstallpipinstallechoai-devUsageYou can use this cli with either openai GPT or Anthropic Claude (through AWS Bedrock). To use GPT models, configure your OpenAI API token.exportOPENAI_API_KEY='sk-...'For Anthropic Claude models, use the aws cli to configure your environement by running the command:aws configUse the environment variableECHOAI_BACKENDto define which backend model to use. This variable can take two valuesbedrockoropenai. If the variable is not define, the default behavior isopenai.Once that's done, you can use theechoaicommand-line tool to interface with Generative AI. Here is an example of how you can use it:echoai"Why does the Python live on land?"This will generate a response from the Generative AI that hopefully answers the question. Of course, the quality of the response will depend on the specific model and prompt used.Disclaimer: AI jokes may not be funny to human beings. Use at your own risk.Azure OpenAI environment variables.Theechoaicli works with the environment variables set for OpenAI. If you use Azure endpoints, you can set yourOPENAI_API_TYPE,_VERSION,_ENGINEand_BASEaccording to your Azure project.New featuresSupport for AWS Bedrock (Claude models)Read files from mentioned pathsPrint code blocks to file
Package: echo_api
Welcome toecho_api’s documentation!This project is a work in progress, and contributions are encouraged. If
you have questions feels free to contact me atjjorissen52@gmail.comInstallationpip install echo_apiConfigurationSecretsecho_apiis configured to take credentials from a file namedecho.confthat is expected by default in your working directory.echo.confshould look like:[echo]username=UserNamepassword=Passwordwsdl_location=/path/to/wsdl.xmlendpoint=https://cloud.echooneappcloud.com/yourorganizationname/OneAppWebServiceIf you wantecho.confto be somewhere other than your project
directory, you will need to set it the location using an environment
variable.# LinuxexportINTERFACE_CONF_FILE=/absolute/path/to/conf_file.conf#name doesn't matter# Or set in Python before you import echo_apiimportosos.environ["INTERFACE_CONF_FILE"]='/absolute/path/to/conf_file.conf'Note that you must have credentials for a user that has access to the
API before you can proceed.SOAP API WSDL DefinitionDue to the possibility of some configuration issues on Echo’s side, you
will need to manually inspect the XML describing the API and ensure that
the endpoint definition is correct. Copy and paste this into the address
bar on your browser (you will need to change it to be your
organization):https://cloud.echooneappcloud.com/yourorganization/OneAppWebService.svc?singleWsdlCopy and paste the XML response into an XML file (wsdl.xml) in your
project directory and scroll all the way to the bottom until you see:<wsdl:portname="BasicHttpBinding_OneAppWebService_SSL"binding="tns:BasicHttpBinding_OneAppWebService_SSL"><soap:addresslocation="https://eoaapp0.echooneapp.com/YourOrganization/OneAppWebService.svc"/></wsdl:port></wsdl:service>You will want to change<soap:addresslocation="https://eoaapp0.echooneapp.com/YourOrganization/OneAppWebService.svc"/>to<soap:addresslocation="https://cloud.echooneappcloud.com/yourorganization/OneAppWebService"/>Once you’ve set up your wsdl and secrets files, test your connection.
For a secrets file that will remain in your project directory, simply
use:fromecho_apiimportapi# Connection() will log you in if everything is correctly configured.connection=api.BaseConnection()connection.session_id'61d63ecc7571430a9ead84dfc7f6301d'If you see a string like the one above, it means that a connection was
successfully established and you’ve got the hard part done…connection.API_Logout()'LoggedOut|kathleen.reynolds'UsageTheBaseConnectionobject has all of the API definitions provided by
the WSDL file. The API documentation can be found atread the docs.
Package: echo-artistry
EchoArtistry 🎨🔊EchoArtistry is an innovative tool that transforms spoken words into captivating visual stories 🌟. It takes voice messages and transforms them into comics, offering a unique blend of audio-visual artistry. Dive into the world of automated storytelling with EchoArtistry!Limitations ⚠️Results may vary based on the clarity and content of the input audio.Requirements 🛠️Before you get started, make sure you have the following installed on your machine:FFmpeg: A complete, cross-platform solution to record, convert, and stream audio and video.Installation 🖥️pipinstallecho-artistryUsage 🚀Start by recording a voice message. Then, run EchoArtistry with your audio file:echo-artistry"path_to_audio_file.wav""output_directory""YOUR_API_KEY"path_to_audio_file.wav: Path to your audio file.output_directory: Directory to save generated comics.YOUR_API_KEY: Your OpenAI API key.--model_name(optional): Specify the AI model, default is "gpt-3.5-turbo-1106".Output 🎁EchoArtistry will create in your output directory:Transcription File: Text transcription of your audio.Comic Story File (.txt): The narrative script of your comic.Comic Image File (.png): The final comic strip.Documentation 📖For more details on EchoArtistry's capabilities and how to use them, visit ourdocumentation.Join the EchoArtistry Community 🤝We love collaboration and feedback! 🚀 If you have ideas or encounter any issues, please contribute. Check out ourcontributing guidelinesto get started.
Package: echochamber
echochamberThis tiny project is an example of creating a time series crawler atwww.microprediction.orgUsing some code adapted
from pyESN written originally by Clemens Korndofer we create a "Crawler" which looks for univariate time
series and supplies predictions for them.Installpip install echochamber
pip install micropredictionUsageSee /examplesfrom microprediction import new_key
from echochamber import EchoCrawler

# Create and launch crawler

if __name__=="__main__":
    write_key = new_key(difficulty=10)    # Patience is a virtue
    print(write_key)
    crawler = EchoCrawler(n_reservoir=50)
    crawler.run()To check its position on the leaderboards just plug in the write_key athttps://www.microprediction.com/dashboard.htmlWrite_keyGenerating may take half an hour or so. Seewww.muid.orgfor more information. We suggest you alter the code to hardwire your write_key.
Package: echo-client
简介Echo-client 是一个为无声系虚拟主播设计的命令行终端，不过也可以用于快速制作视频中的字幕注意：本项目仍在活跃开发中，下方的所有信息可能会发生改变，请以代码为准。安装首先您需要正确地在 OBS 中安装 Echo-live。然后定位到config.js的 websocket 这部分，将websocket_enable改为true。下方的websocket_url中请填写服务端的 ip 地址与端口号。（如果您在一台电脑上同时运行 OBS 和本程序则 ip 可以写127.0.0.1）// 启用 WebSocket// * 如果没人要求您这么做，请不要动它。// * 广播模式下启用 WebSocket 可连接至服务器以从第三方软件获取消息。// * 可从服务器接收的消息和广播消息一致，发送的消息须使用类似于 JSON.stringify 的方法序列化。// * 详见：https://sheep-realms.github.io/Echo-Live-Doc/dev/broadcast/websocket_enable:true,// WebSocket 连接地址// websocket_url: 'ws://192.168.1.12:3000', // iPadwebsocket_url:'ws://127.0.0.1:3000',如果您在两台设备上分别运行 OBS 和本程序，请务必将server.py的host = "127.0.0.1"修改为host = "0.0.0.0"（监听局域网）。如果您有 python 环境，请使用如下命令安装（不推荐，还在开发）pipinstallecho-client使用您需要正确完成“安装”章节。正确运行程序，控制台输出应该类似这样（有颜色）：已经在 127.0.0.1:3000 监听 websocket 请求，等待 echo 客户端接入...
tips: 如果没有看到成功的连接请求，可以尝试刷新一下客户端
用户输入模块加载成功，您现在可以开始输入命令了，客户端连接后会自动执行！
请输入命令:然后刷新 OBS 中的预览窗口，您应该可以看见 websocket 连接成功的提示。命令应该怎么写呢？请看项目根目录下的 message_sample.txt，这是一个包含了目前可以使用的所有格式的示例文件。有关该文件的更多信息请点开查看。配置还没写。
Package: echocolor
EchocolorSimple Python interface for printing colored text to a terminal.For the time being, both Python 3 and Python 2.7 are supported. However,
only Python 3 support is guaranteed for the future. But since this
library probably won’t have many reasons to change, it’s likely that
Python 2.7 will be supported for awhile.InstallationInstall from PyPi:pip install echocolorTo install for development, clone this repo, then cd into the directory and dopip install-e.[dev]Running testsWithout the dev dependencies:python -m unittest discover tests/Or in a dev environment to include test coverage:pip install nose coverage
nosetests --nocapture --with-coverage --cover-erase --cover-package echocolor --cover-inclusive tests/ExamplesBasic usage would would involveechoanderr, which write to stdout and
stderr respectively.  The methods available on them are dynamically defined from
a list of colors (see full list at the end of readme).echoanderrprovide methods for normal foreground and background coloring.  The structure of
the method names isecho.<color>for foreground colors, andecho.<color>_on_<bgcolor>for foreground on background colors.fromechocolorimportecho,errecho.cyan("This text will be cyan")echo.magenta_on_yellow("This text will be magenta with a yellow background and will look terrible")err.light_red("This text will be red and written to stderr instead of stdout")In addition to colors, bold and underlined text are also possible:echo.bold("This text will be bold")echo.underline("And this will be underlined")If you want to color strings without printing them, importcolors:fromechocolorimportechofromechocolor.colorsimportlight_greengreen_text=light_green("This part will be green")print("This part will be regular. "+green_text)However, since the strings end with the escape code to reset all
styling, doing something like the following will result in the second
part of the line being unstyled:fromechocolorimportechofromechocolor.colorsimportmagentaecho.underline(magenta("This text will be magenta and underlined.")+" But this text will be unstyled.")By default, if stdout is being redirected then all the echo output will
be unformatted. That can be changed by settingecho.force_color = True.ColorsThe following colors are supported along with any combination of<colorX>_on_<colorY>.  The full list can also be seen from the code by checking the value ofechocolor.terminal_codes.COLORS.black
red
green
yellow
blue
magenta
cyan
light_gray
dark_gray
light_red
light_green
light_yellow
light_blue
light_magenta
light_cyan
white
Package: echod
Echod is a fully configurable mock server and an HTTP callback recorder.
It is perfect to test external services.It is easy to controlling Echod on the fly from your code or using your
testing framework setup mechanism.The main part of Echod is an HTTP server with an REST API, the Echo HTTP
server have a lot of flexibility and support many start up methods.Echod server can be run as:A standalone usingechodcommand line tool.A WSGI HTTP Server application.A Docker instance container.Mockimportechodmock_response={'status_code':200,'body':{...},}request_contain={'body':{...}}expectation={'method':'POST','path':'/v1/users/','request':request_contain,'response':mock_response,}withechod.mock(**expectation)asclient:response=client.post()response.status_code==200callbackimportrequestswithechod.callback()aswebhook:settings.callback_url=webhook.urlrequests.post()webhook.wait_callback(timeout=10)webhook.response.data=={...}