Saravanan666/pypi-org huggingface dataset:
Package: akatsuki-pp-py
akatsuki-pp-pyDifficulty and performance calculation for allosu!modes.This is a python binding to the Rust libraryrosu-ppwhich was bootstrapped throughPyO3.
Since all the heavy lifting is done by Rust, rosu-pp-py comes with a very fast performance.
Check out rosu-pp'sREADMEfor more info.Exposed typesThe library exposes the following classes:Calculator: Contains various parameters to calculate strains or map, difficulty, or performance attributesBeatmap: Contains a parsed beatmapBeatmapAttributes: Contains various attributes about the map itselfDifficultyAttributes: Contains various attributes about the difficulty based on the modePerformanceAttributes: Contains various attributes about the performance and difficulty based on the modeStrains: Contains strain values for each skill based on the modeAdditionally, the following error types are exposed:ParseError: Failed to parse a beatmapKwargsError: Invalid kwargs were providedHow to use akatsuki-pp-pyThe first step is to create a newBeatmapinstance by providing appropriate kwargs.
Either of the kwargspath,content, orbytesmustbe given. The kwargsar,cs,hp, andodare optional.
With the settersset_ar,set_cs,set_hp, andset_odyou can specify custom attributes.map=Beatmap(path="/path/to/file.osu",ar=9.87)map.set_od(1.23)withopen("/path/to/file.osu","rb")asfile:map=Beatmap(bytes=file.read())withopen("/path/to/file.osu")asfile:map=Beatmap(content=file.read())Next, you need to create an instance ofCalculatorby providing the appropriate kwargs again.
Any of the following kwargs are allowed:mode,mods,acc,n_geki,n_katu,n300,n100,n50,n_misses,combo,passed_objects,clock_rate, anddifficulty.
Each of these also have a setter method e.g.set_n_misses.calc=Calculator(mode=2,acc=98.76)calc.set_mods(8+64)# HDDTThe last step is to call any of the methodsmap_attributes,difficulty,performance, orstrainson the calculator and provide them aBeatmap.Examplefromakatsuki_pp_pyimportBeatmap,Calculatormap=Beatmap(path="./maps/100.osu")calc=Calculator(mods=8)# Calculate an SS on HDmax_perf=calc.performance(map)# The mods are still set to HDcalc.set_acc(99.11)calc.set_n_misses(1)calc.set_combo(200)# A good way to speed up the calculation is to provide# the difficulty attributes of a previous calculation# so that they don't need to be recalculated.# **Note** that this should only be done if neither# the map, mode, mods, nor passed objects amount changed.calc.set_difficulty(max_perf.difficulty)curr_perf=calc.performance(map)print(f'PP:{curr_perf.pp}/{max_perf.pp}| Stars:{max_perf.difficulty.stars}')map_attrs=calc.map_attributes(map)print(f'BPM:{map_attrs.bpm}')strains=calc.strains(map)print(f'Maximum aim strain:{max(strains.aim)}')Installing rosu-pp-pyInstalling rosu-pp-py requires asupported version of Python and Rust.OncePythonandRustand ready to go, you can install the project with pip:$pipinstallakatsuki-pp-pyor$ pip install git+https://github.com/osuAkatsuki/akatsuki-pp-pyLearn MoreRust documentation.PyO3 documentation.Python documentation.
Package: akbase2233
akbase2233 可以使用自己的密码表进行内容的加密与解密，其中最主要的特点是加密结果不是唯一的。
虽然同样的内容加密后的结果有所差异，但是却不影响正常的解密，所以其安全性有很大的提升。关键是可以设计
私有的密码表，密码表不公开的情况下，基本上是不可解的。
Package: ak_cache
ak_cacheCache any data for your python projectsView Demo·Documentation·Report Bug·Request FeatureTable of Contents1. About the Project1.1. Features2. Getting Started2.1. Prerequisites2.2. Installation3. Usage4. Roadmap5. License6. Contact7. Acknowledgements1. About the Project1.1. FeaturesRead and write data to a Cached Pickle File2. Getting Started2.1. Prerequisites2.2. InstallationInstall my-project with flitgitclonehttps://github.com/rpakishore/ak_cache.gitcdak_cache
pipinstallflit
flitinstallAlternatively, you can use pippipinstallak_cache3. UsageUse this space to tell a little more about your project and how it can be used. Show additional screenshots, code samples, demos or link to other resources.$fromak_cacheimportCache$cache_file=Cache(r'Path\to\Cache\file.pkl')$cache_file.write('This is a text')$cache_file.read()'This is a text'Encrypt your pickle file as below$cache_file=Cache(r'Path\to\Cache\encr_file.pkl',password="Strong_Password")$cache_file.write('This is an encrypted text')$cache_file.read()'This is an encrypted text'4. RoadmapAdd encryption option to the cache file5. LicenseSee LICENSE.txt for more information.6. ContactArun Kishore -@rpakishoreProject Link:https://github.com/rpakishore/7. AcknowledgementsUse this section to mention useful resources and libraries that you have used in your projects.Awesome README TemplateBanner MakerShields.ioCarbon
Package: akcompress
This is a text compression library implemented on the lines of HUFFMAN CODING.
The code base is implemeted and maintained by Akshay Kumar Singh.Procedure:pip3 install akcompressimport akcompressfrom akcompress import compressfrom akcompress import expandYou are good to go.To access documentation use the doc() method.Change Log0.0.1 (30/06/2021)First Release
Package: ak-construct
Construct is a powerfuldeclarativeparser (and builder) for binary data.Instead of writingimperative codeto parse a piece of data, you declaratively
define adata structurethat describes your data. As this data structure is not
code, you can use it in one direction toparsedata into Pythonic objects,
and in the other direction, convert (“build”) objects into binary data.The library provides both simple, atomic constructs (such as integers of various sizes),
as well as composite ones which allow you form hierarchical structures of increasing complexity.
Construct featuresbit and byte granularity, easy debugging and testing, aneasy-to-extend subclass system, and lots of primitive constructs to make your
work easier:Fields: raw bytes or numerical typesStructs and Sequences: combine simpler constructs into more complex onesAdapters: change how data is representedArrays/Ranges: duplicate constructsMeta-constructs: use the context (history) to compute the size of dataIf/Switch: branch the computational path based on the contextOn-demand (lazy) parsing: read only what you requirePointers: jump from here to there in the data streamNoteConstruct3is a rewrite of Construct2;
the two are incompatible, thusconstruct3will be released as adifferent package.
Construct 2.5 is the last release of the 2.x codebase.Construct 2.5 drops the experimental text parsing support that was added in Construct 2.0;
it was highly inefficient and I chose to concentrate on binary data.ExampleAPascalStringis a string prefixed by its length:>>> from construct import *
>>>
>>> PascalString = Struct("PascalString",
...     UBInt8("length"),
...     Bytes("data", lambda ctx: ctx.length),
... )
>>>
>>> PascalString.parse("\x05helloXXX")
Container({'length': 5, 'data': 'hello'})
>>> PascalString.build(Container(length = 6, data = "foobar"))
'\x06foobar'Instead of specifying the length manually, let’s use an adapter:>>> PascalString2 = ExprAdapter(PascalString,
...     encoder = lambda obj, ctx: Container(length = len(obj), data = obj),
...     decoder = lambda obj, ctx: obj.data
... )
>>> PascalString2.parse("\x05hello")
'hello'
>>> PascalString2.build("i'm a long string")
"\x11i'm a long string"See more examples offile formatsandnetwork protocolsin the repository.ResourcesConstruct’s homepage ishttp://construct.readthedocs.org, where you can find all kinds
of docs and resources. The library itself is developed ongithub;
please usegithub issuesto report bugs, and
github pull-requests to send in patches. For general discussion or questions, please use thenew discussion group.RequirementsConstruct should run on any Python 2.5-3.5 or PyPy implementation.Its only requirement issix, which is used to overcome the
differences between Python 2 and 3.
Package: ak-django-activity-stream
Django Activity StreamWhat is Django Activity Stream?Django Activity Stream is a way of creating activities generated by the actions on your site.It is designed for generating and displaying streams of interesting actions and can handle following and unfollowing of different activity sources.
For example, it could be used to emulate the Github dashboard in which a user sees changes to projects they are watching and the actions of users they are following.Action events are categorized by four main components.Actor. The object that performed the activity.Verb. The verb phrase that identifies the action of the activity.Action Object.(Optional)The object linked to the action itself.Target.(Optional)The object to which the activity was performed.Actor,Action ObjectandTargetareGenericForeignKeysto any arbitrary Django object and so can represent any Django model in your project.
An action is a description of an action that was performed (Verb) at some instant in time by someActoron some optionalTargetthat results in anAction Objectgetting created/updated/deleted.For example:justquick(actor)closed(verb)issue 2(object)ondjango-activity-stream(target)12 hours agoNomenclature of this specification is based on the Activity Streams Spec:http://activitystrea.ms/For complete documentation seeDjango Activity Stream DocumentationContributorsThis project exists thanks to all the people who contribute!SponsorsGet supported django-activity-stream with the Tidelift Subscription
Package: ak-django-datadog
# Django DatadogA simple Django middleware for submitting timings and exceptions to Datadog.## InstallationDownload the code into your project and install it.`bash git clonegit://github.com/conorbranagan/django-datadog.gitcddjango-datadogpython setup.py install `Adddatadogto your list of installed apps.`python INSTALLED_APPS += ('datadog') `Add the following configuration to your projects’settings.pyfile:`python DATADOG_API_KEY = 'YOUR_API_KEY' DATADOG_APP_KEY = 'YOUR_APP_KEY' DATADOG_APP_NAME = 'my_app' # Used to namespace metric names `The API and app keys can be found athttps://app.datadoghq.com/account/settings#apiAdd the Datadog request handler to your middleware insettings.py.`python MIDDLEWARE_CLASSES += ('datadog.middleware.DatadogMiddleware') `## UsageOnce the middlewhere installed, you’ll start receiving events in your Datadog
stream in the case of an app exception. Here’s an example:![example django exception](https://dl.dropbox.com/u/126553/django-datadog.png)You will also have new timing metrics available:my_app.request_time.{avg,max,min}my_app.errorsMetrics are tagged withpath:/path/to/viewNote:my_appwill be replaced by whatever value you give forDATADOG_APP_NAME.
Package: ak-djangorestframework-jsonapi
OverviewJSON API support for Django REST FrameworkDocumentation:http://django-rest-framework-json-api.readthedocs.org/Format specification:http://jsonapi.org/format/By default, Django REST Framework will produce a response like:{
    "count": 20,
    "next": "http://example.com/api/1.0/identities/?page=3",
    "previous": "http://example.com/api/1.0/identities/?page=1",
    "results": [{
        "id": 3,
        "username": "john",
        "full_name": "John Coltrane"
    }]
}However, for anidentitymodel in JSON API format the response should look
like the following:{
    "links": {
        "prev": "http://example.com/api/1.0/identities",
        "self": "http://example.com/api/1.0/identities?page=2",
        "next": "http://example.com/api/1.0/identities?page=3",
    },
    "data": [{
        "type": "identities",
        "id": 3,
        "attributes": {
            "username": "john",
            "full-name": "John Coltrane"
        }
    }],
    "meta": {
        "pagination": {
          "count": 20
        }
    }
}RequirementsPython (2.7, 3.4, 3.5, 3.6)Django (1.11, 2.0)Django REST Framework (3.6, 3.7)InstallationFrom PyPI$ pip install djangorestframework-jsonapiFrom Source$ git clone https://github.com/django-json-api/django-rest-framework-json-api.git
$ cd django-rest-framework-json-api
$ pip install -e .Running the example app$ git clone https://github.com/django-json-api/django-rest-framework-json-api.git
$ cd django-rest-framework-json-api
$ pip install -e .
$ django-admin.py runserver --settings=example.settingsBrowse tohttp://localhost:8000Running TestsIt is recommended to create a virtualenv for testing. Assuming it is already
installed and activated:$ pip install -e .
$ pip install -r requirements-development.txt
$ py.testUsagerest_framework_json_apiassumes you are using class-based views in Django
Rest Framework.SettingsOne can either addrest_framework_json_api.parsers.JSONParserandrest_framework_json_api.renderers.JSONRendererto eachViewSetclass, or
overridesettings.REST_FRAMEWORKREST_FRAMEWORK = {
    'PAGE_SIZE': 10,
    'EXCEPTION_HANDLER': 'rest_framework_json_api.exceptions.exception_handler',
    'DEFAULT_PAGINATION_CLASS':
        'rest_framework_json_api.pagination.PageNumberPagination',
    'DEFAULT_PARSER_CLASSES': (
        'rest_framework_json_api.parsers.JSONParser',
        'rest_framework.parsers.FormParser',
        'rest_framework.parsers.MultiPartParser'
    ),
    'DEFAULT_RENDERER_CLASSES': (
        'rest_framework_json_api.renderers.JSONRenderer',
        'rest_framework.renderers.BrowsableAPIRenderer',
    ),
    'DEFAULT_METADATA_CLASS': 'rest_framework_json_api.metadata.JSONAPIMetadata',
}IfPAGINATE_BYis set the renderer will return ametaobject with
record count and alinksobject with the next and previous links. Pages
can be specified with thepageGET parameter.This package provides much more including automatic inflection of JSON keys, extra top level data (using nested serializers), relationships, links, and handy shortcuts like MultipleIDMixin. Read more athttp://django-rest-framework-json-api.readthedocs.org/
Package: ak_docx
ak-docxA base module to manipulate docx filesView Demo·Documentation·Report Bug·Request FeatureTable of Contents1. About the Project1.1. Features2. Getting Started2.1. Prerequisites2.2. Dependencies2.3. Installation3. Usage4. License5. Contact6. Acknowledgements1. About the ProjectA base project to simplifydocxfile manipulation1.1. FeaturesFeature 1Feature 2Feature 32. Getting Started2.1. Prerequisites2.2. DependenciesThe repo comes pre-compiled with all dependencies.2.3. InstallationInstall from pypipython-mvenv.venv

.venv\Scripts\activate

pipinstallak_docx3. Usage4. LicenseSee LICENSE.txt for more information.5. ContactArun Kishore -@rpakishoreProject Link:https://github.com/rpakishore/ak-docx6. AcknowledgementsAwesome README TemplateBanner MakerShields.ioCarbon
Package: akello
Setup DynamoDB on your local environmentWarningFor local development you will need a free AWS account with cognito pools created. Please reference AWS docs.Setup NoSQL WorkbenchDownload and install NoSQL Workbench over here:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/workbench.settingup.htmlOnce you installed NoSQL Workbench, run DynamoDB locallySet your environment variablesexport AWS_REGION=## 
export AWS_SECRET_NAME=##  
export AWS_ACCESS_KEY_ID=##
export AWS_SECRET_ACCESS_KEY=##
export DYNAMODB_TABLE=##
export AWS_COGNITO_USERPOOL_ID=##
export AWS_COGNITO_APP_CLIENT_ID=##Run the Fast API serverpython3 -m venv venv 
source venv/bin/activate
pip install -r requirements.txt
uvicorn akello.main:app --reloadCall akello servicesCreate a new registryfromakelloimportregistrymoderate_depression=registry.create_registry('Moderate Depression')Refer a patientfromakelloimportregistryfromakello.dynamodb.models.registryimportPatientRegistry# build a patient object using the PatientRegistry modelpatient_registry=PatientRegistry(id='registry id',# .. other attributes)registry.refer_patient(patient_registry)Add a patient encounterfromakelloimportregistryfromakello.dynamodb.models.registryimportTreatmentLogtreatment_log=TreatmentLog(patient_mrn='<patients mrn>',phq9_score=16,gad7_score=12,minutes=4,# .. other required attributes)registry.add_treatment_log('<registry_id>','<patient_id>',treatment_log)Publish a packagepython3 -m build
twine upload dist/*Run testspython3 -m unittest
Package: akeneo
A Python wrapper for the Akeneo REST API. Easily interact with the Akeneo REST API using this library.InstallationpipinstallakeneoGetting startedGenerate API credentials (Consumer Key & Consumer Secret) following this instructionshttps://api.akeneo.com/getting-started-admin.html.Check out the Akeneo API endpoints and data that can be manipulated inhttps://api.akeneo.com/api-reference-index.html.Basic setupBasic setup for the Akeneo REST API:fromakeneoimportAkeneoAPIakeneo=AkeneoAPI(url="AKENEO_INSTANCE_URL",client_id="YOUR_CLIENT_ID",secret="YOUR_SECRET",username="YOUR_USERNAME",password="YOUR_PASSWORD",)ResponseAll methods will directly return the JSON response from the API.Changelog0.0.1Initial version: Every endpoint should be ok, except these ones for ‘Media files’.
Package: akeneo-api-client
Akeneo API Python ClientA simple Python client for the Akeneo PIM APIInstallationpip install akeneo-api-clientUsageInitialise the clientfromakeneo_api_client.client_builderimportClientBuilderfromakeneo_api_client.client.akeneo_api_errorimportAkeneoApiErrorcb=ClientBuilder(uri)api=cb.build_authenticated_by_password(username,password,client_id,secret)Or if you already have a cached version of the token you can use:api=cb.build_authenticated_by_token(client_id,secret,token,refresh_token)Fetch a producttry:response=api.product_uuid_api.get(uuid)print(response)exceptAkeneoApiErrorase:print(e.response.status_code)print(e.response_body)Iterate over a list of productsfromakeneo_api_client.search.search_builderimportSearchBuildersb=SearchBuilder()sb.add_filter('updated','>','2023-11-29 00:00:00')sb.add_filter('completeness','=',100,{"scope":"ecommerce"})sb.add_filter('enabled','=',True)search=sb.get_filters()try:forpageinapi.product_uuid_api.all(query_params={"search":search}):foriteminpage:print(item["uuid"])exceptAkeneoApiErrorase:print(e.message)Create a producttry:response=api.product_uuid_api.create(data={"family":"my_family"})print(response.headers.get("location"))exceptAkeneoApiErrorase:print(e.response_body)Upsert a productThis call will create a product if it doesn't exist or update it if it doesdata={"values":{"Product_name":[{"scope":None,"locale":"en_GB","data":"My product"}]}}try:api.product_uuid_api.upsert(uuid,data)exceptAkeneoApiErrorase:print(e.message)Upsert a list of productsproducts=[{"uuid":str(uuid.uuid4()),"values":{"Product_name":[{"scope":None,"locale":"en_GB","data":"Product 1"}]}},{"values":{"Product_name":[{"scope":None,"locale":"en_GB","data":"Product 2"}]}}]try:response=api.product_uuid_api.upsert_list(products)foriteminresponse:ifitem['status_code']>=400:print(item)exceptAkeneoApiErrorase:print(e.response.reason)Delete a producttry:api.product_uuid_api.delete(uuid)exceptAkeneoApiErrorase:print(e.response_body)
Package: akeneo-cli
Akeneo CLIYou'll need to get an app credentials for Akeneo as explainhereThis package use generic calls to Akeneo api. To know the list of available endpoints and how the API work please refer to the officialdocumentationCLIThe CLI itself is a work in progress. Currently only the product can be retrieved with a command like.source.env#Create your own .env from env.exampleakeneogetproductCodeExamples of usage from codefromakeneo_cli.clientimportAkeneoClientakeneo_client=AkeneoClient(os.getenv(AKENEO_URL),os.getenv(AKENEO_CLIENT_ID),os.getenv(AKENEO_CLIENT_SECRET),)withakeneo_client.login(os.getenv(AKENEO_USERNAME),os.getenv(AKENEO_PASSWORD))assession:product_list=session.get("products")product=session.get("products",code="my-product")product-model=session.get("product-models",code="some-model")response=session.patch("products",code="my-product",data=product_data)response=session.post("products",code="my-product",data=product_data)response=session.bulk("products",data=[product_data1,product_data2,product_data3])response=session.put_product_file("my-product","my-attribute","my-filepath",is_model=False,locale=None,scope=None)response=session.put_asset_file("my-asset-filepath")response=session.delete("products",code="my-product")
Package: akerbp.models
AkerBP.modelsMachine Learning Models for Petrophysics.Classification ModelsWrapper for XGBoost classifierHierarchical and nested models for lithology (outdated)Regression ModelsWrapper for XGBoost regressorRule-based models - several methods for badlog detection, including:Crossplots outlier detection (supported: OCSVM, Elliptic envelope and Isolation Forest)Logtrend, outliers, DENC and washout (based on the crossplots above)FlatlineResistivitiesCasingUMAP 3D segmentationcrosscorrelationHow to useExample of how to use the badlog model class.import akerbp.models.rule_based_models as models
    # instantiate a badlog model object
    model = models.BadlogModel()

    # define which methods to run for badlog detection and run prediction
    # on data from one well
    methods = ['casing', 'flatline', 'dencorr', 'logtrend']
    model_predictions = model.predict(
        df_well,
        methods=methods,
        settings=None,
        mappings=None,
        folder_path=None
    )Example of how to use the regression model class (wrapper of XGBoost).import akerbp.models.regression_models as models
    # instantiate an XGBoost regression model object with parameters as model_settings
    reg_model = models.XGBoostRegressionModel(
        settings=model_settings,
        model_path=folder_path
    )
    results = reg_model.predict(df_well)
    reg_model.save()  # it saves the model to specified folder pathThis library is closely related and advised to be used together withakerbp.mlpet, also developed by AkerBP.Rule-based modelsThe dataframe returned from running predictions on data from one well will contain new columns named in the following format "TYPE_METHOD_VAR", where:TYPE: either "flag" or "agg". Flag can be 0 or 1 for regular or badlog samples respectively. Agg is the aggregation type, or score. It indicates how anomalous is the sample (used as a way for the user to set thresholds per method).METHOD: method for the column flag. It should be as in the methods given to the predictions. An exception is the crossplots method, that will instead have [vpden, vpvs, aivpvs] as output method column names.VAR: variable or curve that the column flags. It should be one of the following: den, ac, acs, rmed, rdep, rmic, calib_bs (one column only).LicenseAkerBP.models Copyright 2021 AkerBP ASALicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: akerun-sum
入退出集計プログラムNFCカードのドアキーアケルンの入退出記録から、勤務日数や勤務時間を集計するプログラムです。以下の環境で動作確認Windows 10 Home, Python 3.4.3Ubuntu 16.04.2 LTS, Python 3.5.2使用方法akerun-sum.py -i inputfile -o outputfile -d yyyymm [-f n]引数-i  入力ファイル名
-o  出力ファイル名
-d  集計期間 yyyymm の形式で指定
-f  出力タイプ 初期値は0
    0  出力パターン1
    1  出力パターン2実行例akerun-sum.py -i input-euc.csv -o output-euc.csv -d 201610
akerun-sum.py -i input-anotherformat.csv -o output-anotherformat.csv -d 201610　-f 1社員数やレコードの数はリストで管理しているため無制限想定している入力ファイルDATE,AKERUN,USER,LOCK,CLIENTのカラムを持つCSVファイルDATE日付データyyyy/mm/dd hh:mmとyyyy-mm-dd hh:mm:ssの2パターンに対応昇順にソートされていることが前提AKERUN本プログラムでは使用していないUSER社員名データLOCK入室：オフィスに入った退室：オフィスから出た解錠：オフィスに入室したか退室のどちらか施錠：鍵を締めた（本プログラムでは使用していない）CLIENT鍵の種類（本プログラムでは使用していない）出力ファイル出力ファイルは2パターンあり、引数によって切替可能文字コードは入力ファイルに合わせる出力パターン1Excelファイルで開くことを想定氏名就業日数就業時間yyyy/mm/dd入yyyy/mm/dd退yyyy/mm/dd時…山田太郎213.58:4710:121.25…山田次郎220.58:4720:1211.25…:::::::出力パターン2通常のCSVファイル氏名山田太郎集計期間yyyymm就業日数2就業時間13.5月日入室時刻退室時刻就業時間yyyy/mm/dd8:4710:121.25::::氏名山田次郎集計期間yyyymm就業日数2就業時間13.5月日入室時刻退室時刻就業時間yyyy/mm/dd8:4720:1211.25::::