Saravanan666/pypi-org huggingface dataset:
Package: echopy
Echopy is a multipurpose library containing common algorithms for fisheries acoustic data processing, such as background noise correction, removal of seabed and corrupted pings, target detection, multifrequency analysis, and binning. The library provides building blocks that can be assembled for a variety of fisheries acoustics applications and is committed to transparency, cooperation, and universal access in fisheries acoustics software.InstallationEchopy requiresPython 3.6To install echopy and all itsrequirementstype the following in your preferred command-line terminal:pip install echopyor install the latest version available in the develop branch by cloning the repository:git clone https://github.com/open-ocean-sounding/echopy.git
pip install ./echopyContributingEchopy has essentially grown from code that was initially implemented to support specific research projects, and later made publicly available for the benefit of all. If you work in fisheries acoustics, there are several ways you can contribute to improve Echopy. You might create anissue, propose an improvement or report a bug, or even create your own repository branch, work on it, andsend pull requeststo the main branch. By doing so, you can  give visibility to your algorithms in fisheries acoustics, while making life easier for your colleagues. Echopy has anacknowledgments policyto make sure that your work is credited. Check out thecontributionsection if you want to be part of Echopy.MaintainersAlejandro Ariza-French National Research Institute for Sustainable Development (IRD)ContributorsAlejandro Ariza-French National Research Institute for Sustainable Development (IRD)Robert Blackwell-British Antarctic SurveySophie Fielding-British Antarctic SurveyEtienne Pauthenet-LOCEAN, Sorbonne UniversitéXinliang WANG-Yellow Sea Fisheries Research instituteAcknowledgmentsEchopy does not currently have any direct funding, but benefits from the hard work, time  and support of researchers and their institutions. We acknowledgeall contributors and institutions involvedwith special mention of the British Antarctic Survey projectRapidKrill, andThe Antarctic Wildlife Research Fund (AWR)for releasing the first version and giving life to Echopy.LicenseThis software is licensed under theMIT License.Contactechopy@protonmail.com
Package: echopy-lib
Python ECHO library can be used to construct, sign and broadcast transactions and to easily obtain data from the blockchain via public apis.InstallationInstall with pip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-dev python3-pip
$ pip3 install echopy-libManual installation:$ git clone https://github.com/echoprotocol/echopy-lib.git
$ cd echopy-lib
$ python3 setup.py install
or
$ pip3 install .PreparationLaunched echo node (https://github.com/echoprotocol/echo-core) with open port.UsagefromechopyimportEchourl='ws://127.0.0.1:9000'echo=Echo()echo.connect(url)accounts=echo.api.database.get_objects(['1.2.0'])echo.disconnect()
Package: echopype
EchopypeEchopype is a package built to enable interoperability and scalability in ocean sonar data processing. These data are widely used for obtaining information about the distribution and abundance of marine animals, such as fish and krill. Our ability to collect large volumes of sonar data from a variety of ocean platforms has grown significantly in the last decade. However, most of the new data remain under-utilized. echopype aims to address the root cause of this problem - the lack of interoperable data format and scalable analysis workflows that adapt well with increasing data volume - by providing open-source tools as entry points for scientists to make discovery using these new data.Watch theechopype talkat SciPy 2019 for background, discussions and a quick demo!DocumentationLearn more about echopype in the official documentation athttps://echopype.readthedocs.io. Check out executable examples in the companion repositoryhttps://github.com/OSOceanAcoustics/echopype-examples.ContributingYou can find information about how to contribute to echopype at ourContributing Page.Echopype doesn't run on your data?Please report any bugs bycreating issues on GitHub.Pull requestsare always welcome!ContributorsWu-Jung Lee (@leewujung) founded the echopype project in 2018. It is currently led by Wu-Jung Lee and Emilio Mayorga (@emiliom), who are primary developers together with Landung "Don" Setiawan (@lsetiawan), and previously Brandon Reyes (@b-reyes), Kavin Nguyen (@ngkavin) and Imran Majeed (@imranmaj). Valentina Staneva (@valentina-s) is also part of the development team.Other contributors are listed inechopype documentation.We thank Dave Billenness of ASL Environmental Sciences for
providing the AZFP Matlab Toolbox as reference for our
development of AZFP support in echopype.
We also thank Rick Towler (@rhtowler)
of the NOAA Alaska Fisheries Science Center
for providing low-level file parsing routines for
Simrad EK60 and EK80 echosounders.LicenseEchopype is licensed under the open sourceApache 2.0 license.Copyright (c) 2018-2023, Echopype Developers.
Package: echoregions
EchoRegionsEchoRegions is a tool used for parsing and utilizing region files created in EchoView.EchoRegions is designed withEchopypein mind, and parsed region files will be able to mask or be overlaid on top of Echopype echograms. However, the EVR and EVL parsers do not require Echopype to be installed.Functionality and UsageThere are functions for reading EVR and EVL files.importechoregionsaserevr=er.read_evr('data/x1.evr')# Read an EVR fileevl=er.read_evl('data/data/x1.bottom.evl')# Read an EVL fileAll three of these functions return an object specific to the filetype being parsed, but
they all store the result of the reading intodata, such as evr.data using the above example.
The data is stored as a Pandas DataFrame, which allows users to leverage the powerful indexing
and computational tools that Pandas provides.Please see theAPI documentationfor all of the classes and functions available in echoregions.
Package: echo-server
echo-serverNetwork service that send messages back to client. Used in network testing.Installpipinstallecho-serverInstalled Commandsecho-serverUsageC:\Users\test>echo-server--help
Usage:echo-server[OPTIONS][PORT]...Startechoserverongivenports.PressCTRL+Ctostop.Thedefaultlistenningportis3682.Youcanlistenonmanyportsatthesametime.Example:echo-server800180028003Options:-i,--ignore-failed-ports-b,--bindingTEXTDefaultto0.0.0.0.--helpShowthismessageandexit.Releasesv0.2.0 2020/09/03Add parameter binding, so that we can set the binding ip address.v0.1.3 2020/03/27Add ignore_failed_ports option.v0.1.2 2020/03/27Fix data print problem.v0.1.1 2020/03/27Fix port str typed problem.v0.1.0 2020/03/27First release
Package: echoss-image-utils
labelme2yolo소개labelme를 이용하여 어노테이션 작업을 한 josn파일을 yolo학습이 가능하도록 txt파일로 변환해주는 기능사용법Commad linepython labelme2yolo.py --json_dir=<s3데이터를 사용한다면 None, 아니면 json파일들이 있는 폴더 경로> --s3_data=<s3데이터를 사용하는지 아닌지에대한 bool값>\
    --s3_yaml=<s3접속 정보가 담긴 .yaml파일의 경로>  --json_db_table=<사용하려는 json 데이터 베이스의 테이블 명>--s3_data에 입력되는 .yaml파일의 양식 및 필수 키 값# labelme2yolo config
    db_region: 'kr_local'
    image_data_path: images/bb_seg_data/
    json_data_path: json_labels/bb_seg_data/
    yolo_data_path: yolo_labels/bb_seg_data/
    detect_save_path: 'results/'

    # s3 config
    s3: True  # True or False

    bucket: 'bucket-name'
    endpoint_url: 'https://kr.object.ncloudstorage.com'
    region_name: 'kr-standard'
    access_key: 'access-key'
    access_token: 'secret-token-key'사용 예시.59 서버에서 작동 시cd jupyter_notebooks/image_utils
    
    python labelme2yolo.py --json_dir=None --s3_data=True --s3_yaml=../data/45_abalone_data/yolo_data/ai_solution_dataset_test.yaml --json_db_table='p1_json_info'img_dataset_split소개어노테이션 작업이 완료 된 json 혹은 txt 파일을 기준으로 Train, Validation, Test를 원하는 비율에 맞게 나누어서 목록을 생성해주는 기능사용법Commad linepython img_dataset_split.py --yaml_file_path=<s3접속 정보가 담긴 .yaml파일의 경로> --ratio=<train,val,test 비율 기입 예) 8,1,1 > \
    --db_config_file_path=<db 접속정보가 담긴 config파일 경로> --save_path=<저장하고 싶은 경로 및 이름> --random_seed=55 \
    --json_db_table=<json data의 정보가 있는 테이블 명> --image_db_table=<image data의 정보가 있는 테이블 명> --use_s3=<s3데이터를 사용하는지 아닌지에대한 bool값>--yaml_file_path 입력되는 .yaml파일의 양식 및 필수 키 값# labelme2yolo config
    db_region: 'kr_local'

    # s3 config
    s3: True  # True or False

    bucket: 'bucket-name'
    endpoint_url: 'https://kr.object.ncloudstorage.com'
    region_name: 'kr-standard'
    access_key: 'access-key'
    access_token: 'secret-token-key'## 사용 예시
.59 서버에서 작동 시cd jupyter_notebooks/image_utils

python img_dataset_split.py --yaml_file_path=../data/45_abalone_data/yolo_data/ai_solution_dataset_test.yaml --ratio=8,1,1 \
--db_config_file_path=../echoss_query/config/config.yaml --save_path=split_dataset_list.json --random_seed=55 \
--json_db_table=p1_json_info --image_db_table=p1_image_info --use_s3=True내부 함수를 직접사용하고자 할 때sd = SplitDataset(<solution config yaml file path>, (8,1,1), <db config yaml file path>, <json data db table>, <image data db table>, True or False)

sd.save_data_split_json(<save file path>, <random seed : int>)
Package: echoss-query
echoss_query사용 전 config(인증 정보) 폴더의 유무를 확인한 뒤 사용해야한다.InstallaionTo install this package, please use Python 3.8 or higher.Install from Source:git clone https://gitlab.echoss.net/big-data/solution-center/echoss_query
    cd ~/echoss_query
    pip install -r requirements.txtQuick Startfrom echoss_query import MysqlQuery, MongoQuery, ElasticSearch

    mysql = MysqlQuery('CONFIG_FILE_PATH' or dict)
    mongo = MongoQuery('CONFIG_FILE_PATH' or dict)
    elastic = ElasticSearch('CONFIG_FILE_PATH' or dict)


    #CREATE
    mysql.create('QUERY_STRING')
    elastic.index(index='INDEX_NAME')

    #DROP
    mysql.drop('QUERY_STRING')
    elastic.delete_index(index='INDEX_NAME')

    #TRUNCATE
    mysql.truncate('QUERY_STRING')

    #ALTER
    mysql.alter('QUERY_STRING')

    #SELECT

    mysql.select('QUERY_STRING')
    mongo.select('COLLECTION_NAME','QUERY_STRING or DICTIONARY')
    elastic.get(id='ID')
    elastic.search(field='FIELD_NAME',value='VALUE')

    elastic.get_source(id='ID') #-> Extract Source
    mysql.faster_select('QUERY_STRING') #-> Use SSCursor
    mysql.select_list('QUERY_STRING') #-> list

    #INSERT

    mysql.insert('QUERY_STRING')
    mongo.insert('COLLECTION_NAME','QUERY_STRING or DICTIONARY')
    elastic.create(id='ID', body='JSON_BODY')

    #UPDATE

    mysql.update('QUERY_STRING')
    mongo.update('COLLECTION_NAME','QUERY_STRING or DICTIONARY')
    elastic.update(id='ID', body='JSON_BODY')
    
    #DELETE

    mysql.delete('QUERY_STRING')
    mongo.delete('COLLECTION_NAME','QUERY_STRING or DICTIONARY')
    elastic.delete(id='ID')
 
    #Check Database

    mysql.databases()
    mongo.databases()

    #Check Tables
    mysql.tables('DB_NAME')

    #-MySQL Util Function-

    # Ping
    mysql.ping()
    elastic.ping()

    # Connection Information
    mysql.conn_info()
    elastic.info()

    # Close
    # crash process close
    mysql.close()Code QualityWhen creating new functions, please follow the Google style Python docstrings. See example below:def example_function(param1: int, param2: str) -> bool:
    """Example function that does something.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        The return value. True for success, False otherwise.

    """
Package: echoss-s3handler
import 방법from echoss_s3handler import s3_handlercustom yolov7과 image_utils의 함수 사용시 S3에 있는 데이터를 사용하고자 하여 만든 Class크게 두가지의 class가 존재함client를 활용한 clasresource를 활용한 classClient Class내부 주요 함수# s3 config 파일 읽기
    with open("../s3 config file path/ai_solution_dataset_test.yaml") as f:
        data_dict = yaml.load(f, Loader=yaml.SafeLoader)
    # class 할당
    s3_client = S3ClientHandler(data_dict)
    
    
    s3_client.get_bucket_list()
     - 현재 config 파일에 해당하는 접속 계정에 있는 bucket들의 정보를 출력해준다.(버킷명, 버킷 생성날짜 등)
    
    s3_client.get_object_info()
     - parameter로 입력된 object의 마지막 수정일시(YYYY-MM-DD HH:MM:SS 꼴)와 s3link를 출력해준다.
    
    s3_client.get_object_list()
     - parameter로 입력된 prefix와 after_ts(기준이되는 시간 입렵 포맷:"2023-02-28 00:00:00")를 확인하여 object들의 path를 list로 반환한다.Resource Class내부 주요 함수# s3 config 파일 읽기
    with open("../s3 config file path/ai_solution_dataset_test.yaml") as f:
        data_dict = yaml.load(f, Loader=yaml.SafeLoader)
    # class 할당
    s3_resource = S3ResourceHandler(data_dict)
    
    
    s3_resource.get_bucket_list()
     - 현재 config 파일에 해당하는 접속 계정에 있는 bucket들의 정보를 출력해준다.(버킷명, 버킷 생성날짜 등)
     - data_info를 True로 하게 되면 출력이 버킷명이 key값, 마지막 생성일자를 vlaue로 하는 Dict형태로 출력되며,
     false로 하면 버킷의 목록만 list형태로 출력.
    
    s3_resource.get_object_info()
     - parameter로 입력된 object의 마지막 수정일시(YYYY-MM-DD HH:MM:SS 꼴)와 s3link를 출력해준다.
    
    s3_resource.get_object_list()
     - parameter로 입력된 prefix와 after_ts(기준이되는 시간 입렵 포맷:"2023-02-28 00:00:00")를 확인하여 object들의 path를 list로 반환한다.
    
    s3_resource.download_object()
     - S3에 있는 object를 local에 다운로드하기 위한 함수
    
    s3_resource.upload_object()
     - locla에 있는 데이터를 S3에 업로드하기 위한 함수
    
    s3_resource.put_object()
     - 메모리상으로 저장되어있는 json혹은 txt 데이터를 S3 object로 업로드하는데 사용하는 함수
     - 이미지도 업로드할 수 있으나 이때에는 아래와 같이 이미지를 PIL, io라이브러리를 이용하여 가공한 뒤 업로드를 해야합니다.
        img0 = Image.fromarray(im0, mode='RGB')
        out_img = io.BytesIO()
        img0.save(out_img, format='png')
        out_img.seek(0)
    
    s3_resource.s3tos3_put_object()
     - 서로 다른 S3계정간의 데이터를 local에 따로 다운로드 한 뒤 업로드 하는 것이 아닌 메모리상으로 불러 업로드하는 함수
    
    s3_resource.read_file()
     - S3에 있는 파일을 메모리로 읽기 위한 함수
    
    s3_resource.read_image()
     - S3에 있는 이미지 파일을 메모리로 읽는 함수
     - 이후 사용되는 이미지 형식에 따라 옵션에 "cv2", "PIL"을 선택하여 사용 가능
Package: echostream-botocore
echostream-botocoreProvides abotocore.session.Sessionimplementation for accessing EchoStream Tenant resourcesThis package provides two primary mechanisms to create abotocore.session.Sessionobject in your EchoStream Tenant;ApiSessionorAppSession. These session objects will automatically refresh both your Cognito credentials and your botocore credentials (using the EchoStream AppSync API).Installationpipinstallechostream-botocoreCommon parametersParameterENV VARDescriptionRequiredappsync_endpointAPPSYNC_ENDPOINTThe EchoStream AppSync endpointIfcognitonot providedclient_idCLIENT_IDThe Cognito Client Id for the provideduser_pool_idIfcognitonot providedcognitoN/AApycognito.CognitoobjectIf other parameters are not provideddurationN/AThe length of time that the underlying credentials should be good for in seconds; shoudl be greater than900Defaults to3600passwordPASSWORDThe password associated withusernameIfcognitonot providedtenantTENANTThe name of the EchoStream TenantYesuser_pool_idUSER_POOL_IDThe Cognito User Pool IdIfcognitonot providedusernameUSER_NAMEThe username of theApiUserIfcognitonot providedApiSessionApiSessionobjects are used to gain a Tenant-levelbotocore.session.Sessionin your Tenant using an EchoStreamApiUser.ApiSessions may be created using apycognito.Cognitoinstance or via a combination of environment variables and parameters. The environment variables or parameters are interchangeable. All parameters/environment variables are required if aCognitoobject is not provided. If aCognitoobject is provided, then all parameters/environment varaiables are ignored and it is assumed that theCognitoobject references anApiUser.Usage (assuming correct ENV setup)fromboto3importSessionfromechostream_botocoreimportApiSessionsession=Session(botocore_session=ApiSession(),region_name="us-east-1")ddb_client=session.client("dynamodb")...AppSessionAppSessionobjects are used to gain a App-levelbotocore.session.Sessionin your Tenant using an EchoStreamAppUser.AppSessions may be created using apycognito.Cognitoinstance or via a combination of environment variables and parameters. The environment variables or parameters are interchangeable. All parameters/environment variables are required if aCognitoobject is not provided. If aCognitoobject is provided, then all parameters/environment varaiables are ignored and it is assumed that theCognitoobject references anAppUser.Additional ParametersParameterENV VARDescriptionRequiredappAPPThe name of the EchoStream AppYesUsage (assuming correct ENV setup)fromboto3importSessionfromechostream_botocoreimportAppSessionsession=Session(botocore_session=AppSession(),region_name="us-east-1")ddb_client=session.client("dynamodb")...
Package: echostream-function-context
echostream-function-contextMicro library for typing function contexts in EchoStream functionsInstallationFor productionpipinstallechostream-function-contextProduction install has no requirements.For developmentpipinstallechostream-function-context[dev]Development install will also installboto3-stubs[dynamodb].Usagedefprocessor(*,context,message,source,**kwargs):fromtypingimportTYPE_CHECKING,castifTYPE_CHECKING:fromechostream_function_contextimportContextelse:Context=objectcontext=cast(Context,context)
Package: echostream-managed-app
echostream-managed-appEchoStream managed app is a part of your EchoStream tenant. It maintainsechostream managed nodesthroughout their lifecycle. It runs as daemon service on your or your partner's compute environment.It is packaged as a part of your seed.iso (boot image), it can be downloaded from EchoStream application UI or through an api call. The boot image hardens the VM and configures all necessary things to let the managed app run.What echostream-managed-app does?It starts up app change receiver node and starts receiving messages from app change router.Pull Docker images from private EchoStream ecr or public-ecr.Startup/kill managed-node docker containers as they are added/removed in/through the UI/API.Restart managed-nodes if there is any part of node config got updated or edges between the nodes are modified.It runs containers on isolated docker network.Upstream the app/node logs to cloudwatch.ConfigurationRequirements and other configuration for testing the managed app in local env.Env varsBelow are the required env vars. These can be found inuser-datafile in your app's boot image (seed.iso)APPAPPSYNC_ENDPOINTAWS_DEFAULT_REGIONCLIENT_IDLOG_GROUP_NAMEPASSWORDTENANTUSER_NAMEUSER_POOL_IDother requirementsThe necessary pip packages for this app are inrequirements.dev.txt.Docker engine must be installed.This app looks for aws credentials in environment variables or at~/.aws/credentials.
These aws credentials should have access to login/pull images from ecr and write access to cloudwatch logs."ecr-public:DescribeImages",
"ecr-public:GetAuthorizationToken"
"ecr:BatchCheckLayerAvailability"
"ecr:BatchGetImage",
"ecr:DescribeImages",
"ecr:DescribeRepositories",
"ecr:GetAuthorizationToken",
"ecr:GetDownloadUrlForLayer",
"ecr:GetRepositoryPolicy",
"ecr:ListImages",
"logs:CreateLogStream"
"logs:PutLogEvents",
"sts:GetServiceBearerToken",UsageAfter environment variables are sourced and Docker engine is running, the app can be started by python interpreterpython echostream_managed_app/__init__.py.The app starts to receive messages from app change receiver and starts managing the nodes on the docker.The logs are written to/var/log/echostream/echostream-managed-app.logNote:AWS credentials can be passed into environment using aws-vault.If access is denied to write to the log file, either run as sudo or change the log destination which is accessible.For more verbose logs, set the logging level toDEBUGMore details here:Managed-app
Package: echoviz-MALOU
EchoVizVisualization of (3D) echocardiograms in various ways usingplotly. This does not include pre-processing of the data from DICOMs to the voxel grid.InstallationRun$ pip install echoviz-malou. This will automatically install needed dependencies.Features (= Documentation attempt)echovizallows you to view 3D voxel grids in different ways:2D slice of the gridplot_slice2D slice of a voxel grid along chosen axis and slice, saved as PNGsliced_sequence2D slice of several voxel grids along the same chosen axis and slice, saved as GIFsliced_volume2D slices of one voxel grid along chosen axis for all slices (you can specify a stride), saved as GIF3D (interactive plot or "static", i.e. screenshot of the interactive view)interactive_3d3D view of a voxel grid that the user can rotate and zoom in, saved as HTMLstatic_3dScreenshot of above function output4D for a sequence of voxel gridsanimated_3d3D view of several voxel grids that the user can rotate and zoom in, saved as HTML
It is possible to add a target and/or a prediction on top of the input plot. In 2D they will be plotted side by side, in 3D they'll be plotted on top of each other.In 3D it is also possible to plot the Signed Distance Function as a heatmap between the target and the prediction (negative when the prediction is inside the target). "ASD" is also available, it's the absolute value of the SDF.sdf_[interactive|static|animated]_3dasd_[interactive|static|animated]_3dAll functions expect to receive inputs asVoxelGridor a list ofVoxelGrid. Annotations/labels and predictions aredictionariescontaining aVoxelGridor list ofVoxelGridper key (keys are used to get the plot colour, chose between "all", "anterior" or "posterior".ExamplesSeetests/test.pyandtests/test.ipynb.NB: Some additional packages are needed to run the test scripts,Fortests/test.py, run$ pip install h5py clickFortests/test.ipynbrun$ pip install jupyterlab ipywidgets jupyter-dashTroubleshootingKaleido permission deniedYou may need to make Kaleido executable files as executable manually:chmod +x ~/<venv_name>/lib/python3.9/site-packages/kaleido-<version_number>.egg/kaleido/executable/kaleido ~/<venv_name>/lib/python3.9/site-packages/kaleido-<version_number>.egg/kaleido/executable/bin/kaleidoFor more detail seehereOtherPlease report any issues via the GitHubissues tracker.This package was built to plot TEE images and has not been tested on anything else, if you want to participate, you are more than welcome!
Package: echovr-api
Echo VR APIPython bindings for Echo VR's HTTP API.InstallationIf you haven't already,install Python 3andPipenv.Now, in your project directory, run:pipenv install echovr-apiUsageBasic usage example:from requests.exceptions import ConnectionError
import json
import echovr_api

try:
    game_state = echovr_api.fetch_state()

    print(f"Game status: {game_state.game_status}")
    print(f"Seconds on clock: {game_state.game_clock}")

    if (game_state.blue_team.score > game_state.orange_team.score):
        print("Blue team is winning!")
    elif (game_state.orange_team.score > game_state.blue_team.score):
        print("Orange team is winning!")
    else:
        print("It's tied!")

    print(f"Score: {game_state.blue_team.score} - {game_state.orange_team.score}")

except ConnectionError as e:
    print("Connection refused. Make sure you're running Echo VR with the -http option and that you're in a match.")
except json.decoder.JSONDecodeError as e:
    print("Could not decode response. (Not valid JSON.)")For comprehensive documentation of the available methods and classes, please see thefull API Documentation on Read The Docs.ContributingTo get everything you need to start making changes to this package, firstinstall Python 3andPipenv, clone this repository, then run:pipenv installTry itTo play around with the API, open an instance of Echo VR with the -http flag, then run:pipenv run python -i ./test.pyRelease processFirst, updateCHANGELOG.mdand the version number insetup.pyanddocs/source/conf.py. Then commit, tag, and push these changes.Next, build the package:pipenv install --dev
pipenv run python setup.py sdist bdist_wheelFinally, upload the built packages to PyPi. You can do this usingtwine(pip install twine):twine upload dist/*