Saravanan666/pypi-org huggingface dataset:
Package: ddbimp
dynamodb-dev-importer (ddbimp)Easily load data from CSV to test out your DynamoDB table design.When working with DynamoDB, it is common practice to minimise the number of tables used, ideally down to just one.Techniques such as sparse indexes and GSI overloading allow a lot of flexibility and efficiency.Designing a good schema that supports your query patterns can be challenging. Often it is nice to try things out with a small amount of data.
I personally find it convenient to enter data into a spreadsheet and play around with it there.This utility eases the process of populating a DynamoDB table from a CSV file, exported from a spreadsheet, that follows a specific format common to DynamoDB modelling patterns.InstallYou can install it with$ pip3 install ddbimpRunAssuming tablepeople(pk:S, sk:S)is provisioned in your default region.$ ddbimp --table people --skip 1 example.csvExpected input formatpkskdataPERSON-1sales-Q1-2019Alexjan: 12012feb: 1927Your spreadsheet (and exported CSV) should contain columns for:pkskdata (optional)anything after those three can contain arbitrary attributes of formattribute_name: valuei.e.city: EdinburghExample row:PERSON-1,sales-Q1-2019,Alex,jan: 12012,feb: 1927Will yield an item like this:{
    pk: 'PERSON-1',
    sk: 'sales-Q1-2019',
    data: 'Alex',
    jan: 12012,
    feb 1927
}For a full example CSV, take a look atexample.csv.Key ideasTable consists of partition keypk: Sand sort keysk: S- their meaning varies depending on the itemA secondary index swaps the sort and partition keys, so the partition key issk: Sand sort keypk: SA final secondary index usessk: Sanddata: Swhere data is an arbitrary value you might want to search for, the meaning ofdatadepends on the item it is part ofGroup items through a shared partition key, storesubitems with a sort key e.g.e.g.pk:PERSON-1, sk:sales-Q1-2019, jan:12012, feb:1927AWS recentlyreleased a preview build of a tool called NoSQL Workbench. It builds on the above ideas. I've tried it out and it seems pretty good, but I am a luddite and am faster working in a spreadsheet right now. I'd certainly recommend giving it a try.Useful resourceshttps://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-indexes.htmlhttps://docs.aws.amazon.com/amazondynamodb/latest/developerguide/workbench.htmlhttps://www.youtube.com/watch?v=6yqfmXiZTlMCaveats, TODOUses your default AWS profileRegion needs to be setMake work directly with a Google Sheets via sheets API
Package: ddb-local
# ddb-localPython wrapper for DynamoDB Local.## What is it for?This is a convenient Python wrapper for [DynamoDB Local](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DynamoDBLocal.html). All the instructions online currently involves manually downloading the tarball and installing it, which is a huge hassle and is a barrier to automation. It wasn't really easy to integrate the local DDB nicely into tests (e.g., pytest) so I wrote this library.## PrerequisiteYou must have `java` in `PATH` or specify `JAVA_HOME`. You need a working Internet connection to download DynamoDBLocal tarball. Optionally, you can get it yourself, and point `unpack_dir` to the root of the unpacked directory.## ExamplesGet a throwaway in-memory DDB for testing:```pythonimport boto3from ddb_local import create_new_inmemory_ddbwith create_new_inmemory_ddb() as local_ddb:ddb = boto3.client('dynamodb', endpoint_url=local_ddb.endpoint)```Use it in a context manager:```pythonimport boto3from ddb_local import LocalDynamoDBwith LocalDynamoDB() as local_ddb:ddb = boto3.client('dynamodb', endpoint_url=local_ddb.endpoint)```Outside a context manager, you can also call `start()` and `stop()` manually. In this case, it's your responsibility to make sure `stop()` is called.```pythonfrom ddb_local import LocalDynamoDBdb = LocalDynamoDB()db.start()print(f"Endpoint is at {db.endpoint}")db.stop()```Example usage with [pytest](https://pytest.org/):```pythonimport pytestfrom ddb_local import LocalDynamoDB@pytest.fixturedef local_ddb():with LocalDynamoDB():yield ddbdef test_with_local_ddb(local_ddb):ddb = boto3.client('dynamodb', endpoint_url=local_ddb.endpoint)# ...```Use `extra_args` escape hatch to specify custom arguments to the underlying Java process:```pythonfrom ddb_local import LocalDynamoDBdb = LocalDynamoDB(extra_args=["-sharedDb"])```Troubleshooting with debug flag:```pythonfrom ddb_local import LocalDynamoDBdb = LocalDynamoDB(debug=True)db.start()# expect stderr/out from DynamoDbLocaldb.stop()```Use a pre-installed binary that is already unpacked:```pythonfrom ddb_local import LocalDynamoDBdb = LocalDynamoDB(unpack_dir="/opt/dynamodb_local")```## Development* `make` to run test, coverage and distribution build.* `make coverage` to run coverage.* `make test` to run test.* `pytest` to run test.* `make clean` to delete all files.* `make upload` to upload to PyPI.* Populate `.pypirc` with the API token.## SupportSupport is provided on a best-effort basis.Create an issue in the [Github repo](https://github.com/wbkang/ddb_local).
Package: ddb-pop-n-lock
ddb-pop-n-lock - A simple reasonable lock for AWS dynamodbThe MIT License (MIT)Copyright (c) 2016 Paul J DeCourseyPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: ddbpy
dalmatiner-python-clientpip install --upgrade ddbpyA client that sends metrics into Dalmatiner DB over the binary protocol and wraps the http front end for queries.Docs for the binary protocol:https://docs.dalmatiner.io/en/latest/proto.htmlDocs for the http api:https://docs.dalmatiner.io/en/latest/http_api.htmlThis client aims for correctness and simplicity first. Therefore it will remain pure python and synchronous. If you need an asynchronous client then please fork and modify and we’ll be happy to link back from this page below.Other known libraries:Erlang:https://github.com/dalmatinerdb/ddb_clientstatusmetric sending worksbucket and metric listing works via the binary protocol (this needs to be removed)query via the http api has not been added yetPull requests welcome.example usageimport time
from ddbpy.client import Send

dfe = ('127.0.0.1', 5555)

bucket = 'test'

with Send(dfe) as send:
    send.switch_streaming(bucket)
    while True:
        ts = int(time.time())
        value = "123.456"
        metric = "float"
        send.send_payload(metric, ts, value)
        time.sleep(1)
Package: ddbpy3
dalmatiner-python-clientpip install --upgrade ddbpyA client that sends metrics into Dalmatiner DB over the binary protocol and wraps the http front end for queries.Docs for the binary protocol:https://docs.dalmatiner.io/en/latest/proto.htmlDocs for the http api:https://docs.dalmatiner.io/en/latest/http_api.htmlThis client aims for correctness and simplicity first. Therefore it will remain pure python and synchronous. If you need an asynchronous client then please fork and modify and we’ll be happy to link back from this page below.Other known libraries:Erlang:https://github.com/dalmatinerdb/ddb_clientstatusmetric sending worksbucket and metric listing works via the binary protocol (this needs to be removed)query via the http api has not been added yetPull requests welcome.example usageimport time
from ddbpy.client import Send

dfe = ('127.0.0.1', 5555)

bucket = 'test'

with Send(dfe) as send:
    send.switch_streaming(bucket)
    while True:
        ts = int(time.time())
        value = "123.456"
        metric = "float"
        send.send_payload(metric, ts, value)
        time.sleep(1)
Package: ddbsizemetric
Pythonfromaws_cdkimportcorefromddbsizemetricimportDDBSizeLibclassMyStack(core.Stack):def__init__(self,scope:core.Construct,id:str,**kwargs)->None:super().__init__(scope,id,**kwargs)# The polling frequency defaults to once every 6 hours, which is how frequently DynamoDB updates the Table information.DDBSizeLib(self,"MyDynamoTableScanner")Javascriptimportddbsizemetric=require('@richardhboyd/ddbsizemetric');importcdk=require('@aws-cdk/core');exportclassDdbTestJsStackextendscdk.Stack{constructor(scope:cdk.App,id:string,props?:cdk.StackProps){super(scope,id,props);newddbsizemetric.DDBSizeLib(this,"MyDynamoTableScanner")}}
Package: ddcafe
Copyright (c) 2018 Ikorin24Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: # Daydream CafeYou can play ‘Daydraem cafe’ on the terminal.Daydream cafe is the opening song of [Gochi-usa](http://www.gochiusa.com/), Japanese Anime.![result](https://github.com/ikorin24/ddcafe/blob/dev/cap.gif)# Requirementspython3.x# Install` pip install ddcafe `# Usage` ddcafe [options] `## Options-h,–help: show help-v,–version: show version of this app-s <SPEED>,–speed <SPEED>: ratio of playing speed (range: 0.1 ~ 10.0, default: 1.0)-d <DATA>,–data <DATA>: movie source json data (default movie is “daydream cafe”)-r,–reverse: reverse pixel color; black to white, white to black## How to play sound ?If you imagine it, you would listen in your head.# Authorikorin24([github](https://github.com/ikorin24))Platform: UNKNOWN
Package: ddcal
OverviewA heuristic one dimensional clustering algorithm called DDCAL (Density Distribution Cluster Algorithm) that is based on iterative feature scaling.
The algorithm aims as first order to even distribute data into clusters by considering as well as second order to minimize the variance inside each cluster and maximizing the distances between clusters.The algorithm is designed to be used for visualization, e.g., on choropleth maps.Basic Usagepip install -i https://pypi.org/simple/ ddcalfrom clustering.ddcal import DDCAL

# load data
frequencies = [0, 1, 1, 1, 5, 5, 5, 30, 88]

# initialize parameters
ddcal = DDCAL(n_clusters=3, feature_boundary_min=0.1, feature_boundary_max=0.49,
                  num_simulations=20, q_tolerance=0.45, q_tolerance_increase_step=0.5)

# execute DDCAL algorithm
ddcal.fit(frequencies)

# print/use results
print(ddcal.sorted_data)
print(ddcal.labels_sorted_data)Supplemental MaterialSupplemental material for the paperDDCAL: Evenly Distributing Data into High Density Clusters based on Iterative Feature Scalingcan be found in the folder:supplementalSynthetic Data SetsThe synthetic data sets, which were used in the paperDDCAL: Evenly Distributing Data into High Density Clusters based on Iterative Feature Scalingwhich includes a description on each data set, can be found in the folder:tests/dataAcknowledgements"ICT of the Future" program - an initiative of the Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK)
Package: ddc-api-sdk
叮当猫 HTTP SDK 1.0使用示例Install Packagepython3 -m pip install ddc-api-sdk==1.0.5Used CodefromDDCCore.APICoreimportAPICoreif__name__=='__main__':sdk=APICore("https://api.xxxxx.com","Your API Key")data=sdk.高级查询_姓名_省市县_性别_年龄范围查询("杨晓霞","江苏省","女",18,25)data1=sdk.高级查询_模糊姓名_精准手机号前3位_精准后4位查询("杨晓霞","136","8823")print(data)print(data1)
Package: ddcci-plasmoid-backend
KDE Plasma Widget for external monitor brightness adjustmentThis widget allows you to adjust the brightness of external monitors. We accomplish that usingDDC/CI, a protocol that allows your computer to control monitors and change options like the brightness or contrast.A seamless integration into the Plasma desktop is a major goal of this project. The widget is versatile and can be used as a standalone widget or integrated into the system tray.Notebook monitors are currently unsupported because they use different interfaces to communicate with the operating system.RequirementsPython 3.8 or newerfastenersddcutil 1.4.1 or newer (older versions may work but caused issues in the past)This package acts as the backend for the widget.For a full installation guide, refer to theGitHub repository.
Package: ddcheck
Check URLs and manage the respective records in DynDNS.Descriptionddcheckaccepts a list of healthckeck URLs, resolve them to IPs and makes
HTTP requests directly to the IPs. When given IP is recognized as down, it will
remove the record from DynDNSFeaturesIPv4 and IPv6 support (A and AAAA records in DNS)Traversing over CNAMEs to target A/AAAA recordsIP is marked as failed when it either timeouts (configurable in seconds) or
returns defined HTTP code (for example 500, by default turned off)Dry run mode: Will not change the DynDNS recods. Outputs onlycurlcommands
instead.Will not delete IPs from DynDNS, when all the IPs in the dns record fail.Installationpip install'git+https://github.com/whiskybar/ddcheck.git#egg=ddcheck'UsageCLIDynDNS credetials can be defined either by env variables, or as parameters.# ddcheck --help

usage: ddcheck [-h] [-d] [-e ERROR_CODES] [-t TIMEOUT] [-D]
               [--dynect-customer DYNECT_CUSTOMER] [--dynect-user DYNECT_USER]
               [--dynect-password DYNECT_PASSWORD]
               URL [URL ...]

Run a ddcheck.

positional arguments:
  URL                   URL to check

optional arguments:
  -h, --help            show this help message and exit
  -d, --debug           Debug logging on
  -e ERROR_CODES, --error-codes ERROR_CODES
                        HTTP codes considered as non-OK
  -t TIMEOUT, --timeout TIMEOUT
                        URL timeout
  -D, --dry-run         Do not really update the dyndns. Just print records to
                        delete.
  --dynect-customer DYNECT_CUSTOMER
                        Customer name in DynEct (defaults to
                        DYNECT_CUSTOMER_NAME env variable)
  --dynect-user DYNECT_USER
                        Username in DynEct (defaults to DYNECT_USER_NAME env
                        variable)
  --dynect-password DYNECT_PASSWORD
                        Password in DynEct (defaults to DYNECT_PASSWORD env
                        variable)Exampleexport DYNECT_PASSWORD=aaa
export DYNECT_USER_NAME=bbb
export DYNECT_CUSTOMER_NAME=ccc
ddcheck -e 500 --debug --dry-run http://root.example1.com/health/ http://cname.example2.com/check/PythonExample
Package: ddc-indices
DDC Meteorological IndicesThis is the Danube Data Cube Meteorological Indices library,
a python implementation for calculating common meteorological indices,
utilizing data cubes.The following indices are available:SPI, Standardized Precipitation Index, utilizing both gamma and Pearson Type III distributionsSPEI, Standardized Precipitation Evapotranspiration Index, utilizing both gamma and Pearson Type III distributionsPDSI, Palmer Drought Severity IndexPNI, Percentage of Normal Precipitation IndexInstallation$ pip install ddc-indicesExample$ python

# Calculating the Standardized Precipitation Index (SPI)
>>> import xarray as xr
>>> from ddc_indices import DdcIndices
>>> from ddc_indices import SpiConfig

# import my dataset containing precipitation data
>>> dataset = xr.open_zarr('path_to_zarr')
>>> config = SpiConfig(variables={'prec': 'prec'}, periodicity='daily')
>>> spi = DdcIndices().initialize(dataset, config)
>>> spi.compute()
>>> spi.index
Package: ddcmaker
## ddcmaker 0.0.28文档#### 介绍
ddcmaker开发专属库,为zsylmaker系列系统提供依赖的第三方库#### 软件安装 ->python3.5.2以上版本
pip install ddcmaker#### 依赖flask
requests
marshmallow
pyserial# 安装依赖
pip install -r requirements.txt
# 安装ddcmaker
pip install ddcmaker## 参与贡献
1.herunlei2.奔跑的大猪3.fastbiubiu4.快乐风男####特别寄语
感谢中科大源与清华源的技术与资源支持！！！
Package: ddcompiler
ddcompilerddcompiler is the parser used by AutomationEdge AI Studio dialog designer. In dialog designer, conditions can be used either in an action dialog element or a branch dialog element. A condition is an expression which evaluates either to True or False. ddcompiler has a lexer and a parser which respectively tokenizes and parses the given expression.Installationpip install ddcompilerwill install the library. It has been tested with Python 3.9.6, but should work with any Python 3.* version.Basic Usageimportddcompilerasddcvar_dict={'conv.lang':'fr',}# Variables used in the expression of the form ${var_name} should be present in the var_dict.expression='${conv.lang}== "fr" or ${conv.lang}== "de" and contains("aistudio", "studio")'# Instantiate parser and lexer, pass expression to the lexer.ddparser=ddc.DDParser(ddc.DDLexer(expression))tree=ddparser.parse()# Prints parse treetree.traverse()# Get list of variables used in the expression in form of a setprint(f"VARIABLES in the expression:{ddparser.get_variables()}")print(f"INPUT:{expression}")print(f"EVALUATION:{tree.evaluate(var_dict)}")Expression LanguageVisitAI Studiodocumentation to get list of supported constructs.More ExamplesGo throughparser test fileto see different examples of expressions.
Package: ddcrelight
RequirementsA yoctopuce light sensor (https://www.yoctopuce.com)ddcutilSetupA udev rule is likely required to allow the tool to run as non-root. The
following can be added as the file/etc/udev/rules.d/50-yoctopuce.rules:# udev rules to allow write access to all users for Yoctopuce USB devices
SUBSYSTEM=="usb", ATTR{idVendor}=="24e0", MODE="0666"Additionally, udev rules must be configured for ddcutil to operate without root
permissions:https://www.ddcutil.com/i2c_permissions/Then a daemon should be setup:ddcrelight.service:[Unit]
Description=Update monitor brightness intelligently using light sensors.

[Service]
ExecStart=<path-to-script>/ddcrelight daemon
Restart=on-failure
RestartSec=5

[Install]
WantedBy=default.target
Package: ddc-utility
DDC UtilityThis is the Danube Data Cube Utility library, for retrieving data from the Danube Data Cube portal.Installationpipinstallddc-utilityUsageUsers must have a valid DDC registration.Example$ python

# Importing packages
>>> from ddc_utility.ddc import DanubeDataCube
>>> from ddc_utility.config import DdcCubeConfig, CustomCubeConfig
>>> from ddc_utility.cube import open_cube

# Setting DDC credentials
>>> os.environ['DDC_CLIENT_ID'] = ""
>>> os.environ['DDC_CLIENT_SECRET'] = ""

# Initialize DDC object 
>>> DDC = DanubeDataCube()

# Retrieve meteorological data from DDC
>>> cube_config = DdcCubeConfig(dataset_name='METEOROLOGY', variable_names=['prec', 'temp_min', 'temp_max'], danube_data_cube=DDC)
>>> cube = open_cube(cube_config)

# Retrieve custom cube based on users's AOI
>>> cube_config2 = CustomCubeConfig(dataset_name='<my_aoi_1.zarr>', variable_names=['NDVI', 'temp_avg', 'B04', 'B08'], danube_data_cube=DDC)
>>> cube2 = open_cube(cube_config2)
Package: ddd-api-gateway
DDD Api Gateway FrameworkInstallationFrom source code:python setup.py installFrom pypi:pip install ddd-api-gatewayUsagefrom ddd_base.value_object import ValueObject


class TheValueObject(ValueObject):

    def __init__(self, name):
        super(TheValueObject, self).__init__()
        self.name = name

    def __eq__(self, other):
        if not isinstance(other, ValueObject):
            return NotImplemented

        return self.name == other.name


def test_value_object_compare():
    a_value_object = TheValueObject("name")
    b_value_object = TheValueObject("name")

    assert a_value_object.same_as(b_value_object)LicenseThis software is licensed under theMIT licenseSeeLicense fileChangelog0.0.7Route specification need ssl0.0.6Add endpoint hostname property0.0.5Expose exceptions message0.0.4Expose exceptions0.0.3Add entity repository0.0.2Adjust for travis test and cover all percentage0.0.1Initial public release
Package: ddd-base
DDD Base FrameworkInstallationFrom source code:python setup.py installFrom pypi:pip install ddd-baseUsagefrom ddd_base.value_object import ValueObject


class TheValueObject(ValueObject):

    def __init__(self, name):
        super(TheValueObject, self).__init__()
        self.name = name

    def __eq__(self, other):
        if not isinstance(other, ValueObject):
            return NotImplemented

        return self.name == other.name


def test_value_object_compare():
    a_value_object = TheValueObject("name")
    b_value_object = TheValueObject("name")

    assert a_value_object.same_as(b_value_object)LicenseThis software is licensed under theMIT licenseSeeLicense fileChangelog0.0.5Add use example0.0.4Friendly readme0.0.3Smart setup0.0.2Minimise description0.0.1Initial public release
Package: ddd-core
Domain Driven Design CoreDescriptionThe application of Domain Driven Design is not easy in general. The goal of this library is to be easy and well guided to develop ,.
Not only ideas or DDD are used. CQRS and event sourcing are good suplements to this theory and it is a bit merged in this library.InstallationThis project has an automated deployment to pypi, so only is needed to use pip command:>> pip install ddd-coreThere is no external requirements for this library.UsageThe use of this library is by inheritance. If you want to create a Value Object you only have to inherit from a Value class. If you want to have an aggregate, inherit from a Aggregate class, if a service from a Service class.
It is planned to be widely docummented the behaviour is inherited from these classes.I know composition is better than inheritance but a base class for each concept is the clearer way to implement DDD with OO programming.SupportSend any suggestion tosruiz@indoorclima.comorsalvador.ruiz.r@gmail.com. Any ideas or support is well recieved.RoadmapIncrement versioning when pushingIntegrate with gitlab continuous integration to publish to pypi as libraryImprove coverage rate to > 96%Improve usage documentation with sphinxUpload to readthedocsContributingState if you are open to contributions and what your requirements are for accepting them.For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.Authors and acknowledgmentFor sure the ideas of Eric Evans and Domain Design Development are the base of this project to be used with python language. Without this seed it has not sensseLicenseThis is under LGPL lincense. You can use and modify this library.Project statusIt is used in projects developed currently by the company IndoorClima.