Saravanan666/pypi-org huggingface dataset:
Package: dmi-instascraper
DMI Instagram ScraperThis package provides a simple GUI on top ofinstaloader. Instaloader is a great Instagram
scraper, but it requires familiarity with command-line interfaces or Python to
use. For people who do not have that familiarity, this GUI makes it possible to
scrape Instagram and easily collect all scraped data in a single CSV file with
metadata.The scrape runs from the user's own computer. This can be contrasted with tools
like4CAT, which run
scrapes from a central server. The disadvantage of that approach is that
Instagram's aggressive rate limiting kicks in quickly if many scrapes are run
from a central server. By using this app, all scrapes are run from the local
computer, hopefully preventing rate limiting from being too obstructive.CSV files generated by the DMI Instagram Scraper can be imported into 4CAT for
further analysis.There is aFAQwith some more information on the application.InstallationVia pipThis is recommended if you have some familiarity with Python, since it makes it
easier to ensure you are running the latest version of the software.pip3 install dmi-instascraperThen to run itpython3 -m dmi_instascraperVia pre-packaged binariesThis is the simplest way of running the app; download the application file and
run it.One-file binaries for Windows and macOS are (or will soon be) provided on thereleasespage.LicenseThis software was developed by theDigital Methods Initiative, and is distributed
under the Mozilla Public License 2.0 license. See LICENSE for details.
Package: dmind
DMindDMind是一个Jupyter notebook的思维导图插件.利用百度脑图的开源库实现渲染, 支持json,markdown,text格式.安装通过pip安装pipinstalldmind使用text 格式%%dmind text

DMind
    是一个 jupyter notebook 插件
    是一个思维导图插件markdown 格式, 逻辑结构图%%dmind markdown right

# DMind使用文档
## 安装
### pip install dmind
## 使用
### 载入插件
#### %load_ext dmind
### 载入需要的附件
#### %dmindheader
### 渲染脑图
#### %%dmind datatype template theme 换行后输入内容json 格式 , 目录组织图, 文艺绿%%dmind json filetree fresh-green

{
    "root": {
        "data": {
            "text": "Dmind参数说明"
        },
        "children": [
...查看示例
Package: dmi-open-data
DMI Open Data APISimple Python interface to the The Danish Meteorological Institute's (DMI) v2Open Data API.Weather data from Denmark and Greenland are publicly available through DMI's Open Data API. Fetch raw weather observations from all available weather stations in Denmark and Greenland. Parameters available includetemperature,windspeed,humidity,pressure, _precipitation__, and many more.RequirementsPython 3.6+API Key formetObs v2fromDMI Open Data(Optional) API Key forclimateData v2fromDMI Open DataInstallation$pipinstalldmi-open-dataExamplefromdatetimeimportdatetimeimportosfromdmi_open_dataimportDMIOpenDataClient,Parameter,ClimateDataParameter# Get 10 stationsclient=DMIOpenDataClient(api_key=os.getenv('DMI_API_KEY'))stations=client.get_stations(limit=10)# Get all stationsstations=client.get_stations()# Get DMI stationdmi_station=next(stationforstationinstationsifstation['properties']['name'].lower()=='dmi')# Get closest stationclosest_station=client.get_closest_station(latitude=55.707722,longitude=12.562119)# Get available parametersparameters=client.list_parameters()# Get temperature observations from DMI station in given time periodobservations=client.get_observations(parameter=Parameter.TempDry,station_id=dmi_station['properties']['stationId'],from_time=datetime(2021,7,20),to_time=datetime(2021,7,24),limit=1000)# Init climate data clientclimate_data_client=DMIOpenDataClient(api_key=os.getenv('DMI_CLIMATE_DATA_API_KEY'))# Get climate dataclimate_data=climate_data_client.get_climate_data(parameter=ClimateDataParameter.MeanTemp,station_id=dmi_station['properties']['stationId'],from_time=datetime(2021,7,20),to_time=datetime(2021,7,24),time_resolution='day',limit=1000)API KeyAPI Key can be obtained for free at theDMI Open Data.TestsRun tests$python-munittestdiscovertests
Package: dmiparse
# dmiparseparse dmidecode with python=======[![Build Status](https://api.travis-ci.org/xmonader/dmiparse.svg?branch=master)](https://travis-ci.org//xmonader/dmiparse)[![codecov](https://codecov.io/gh/xmonader/dmiparse/badge.svg)](https://codecov.io/gh//xmonader/dmiparse)# dmiparseConvert dmidecode output into clean queryable structure## Installationpip3 install dmiparse## Example```ipythonIn [7]: print(sample1)# dmidecode 3.1Getting SMBIOS data from sysfs.SMBIOS 2.6 present.Handle 0x0001, DMI type 1, 27 bytesSystem InformationManufacturer: LENOVOProduct Name: 20042Version: Lenovo G560Serial Number: 2677240001087UUID: CB3E6A50-A77B-E011-88E9-B870F4165734Wake-up Type: Power SwitchSKU Number: Calpella_CRBFamily: Intel_MobileIn [8]: from dmiparse import parse_dmiIn [9]: parse_dmi(sample1)Out[9]: {'System Information': <dmiparse.Section at 0x7f88b0a5add8>}In [10]: from json import dumpsIn [11]: print(dumps(parse_dmi(sample1), default=lambda o: o.__dict__, indent=4)){"System Information": {"props": {"Manufacturer": {"val": "LENOVO","items": []},"Product Name": {"val": "20042","items": []},"Version": {"val": "Lenovo G560","items": []},"Serial Number": {"val": "2677240001087","items": []},"UUID": {"val": "CB3E6A50-A77B-E011-88E9-B870F4165734","items": []},"Wake-up Type": {"val": "Power Switch","items": []},"SKU Number": {"val": "Calpella_CRB","items": []},"Family": {"val": "Intel_Mobile","items": []}},"title": "System Information","handleline": "Handle 0x0001, DMI type 1, 27 bytes"}}```## running tests`tox` or `pytest`
Package: dmi-scheduler
dmi-schedulerA lightweight task scheduler in Python.Installationpip3 install dmi-schedulerPostgreSQL is used to keep track of jobs. As such, you need to have a
PostgreSQL database that the scheduler can interact with. Database
tables will be created automatically if they don't exist yet. You can
pass the database connection parameters to theScheduler()constructor with thedbname,dbhost,dbuser,dbpasswordanddbportkeyword arguments.Exampleimport time
from dmi_scheduler.scheduler import Scheduler

scheduler = Scheduler(config="scheduler.yml")
for i in range(0, 100):
    scheduler.queue.add_job("log_number.py")

while scheduler.has_jobs():
    time.sleep(1)

scheduler.end()
print("Done! Results can be found in the log file.")log_number.py:from random import choice
from dmi_scheduler.worker import BasicWorker

class SomeWorker(BasicWorker):
	max_workers = 3

	def work(self):
		self.log.info("Here is a random number: %i" % choice(range(0,1000)))LicenseThis scraper was developed by theDigital Methods Initiative, and is distributed
under the MIT license. See LICENSE for details.
Package: dmit
#DMIMET
Tools commonly used in Meteorological software that I dont want to replicate all the times#Documentation:https://dmit.readthedocs.io/en/latest/DocstringsFor the sake of making the documentation as automatic as possible the standard of numpy docstring is followed.https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standardThis is supported also by sphinx.The docs is made with sphinx and can be found here:https://dmit.readthedocs.io/en/latest/
Package: dm.iter
A small collection of iterator utilities.Currently, there is a single utility family related to relations.Relation utilitiesThese utilies are implemented indm.iter.relation.The relation is represented by a map from an element to an
iterator of its directly related elements.
The map is either an object with__getitem__method,
a callable raisingValueErrorfor undefined
input arguments or an object supporting subscription.
Elements must be allowed as set elements.Available utilies aredepth_first_searchandbreath_first_search.
They take as arguments a relation and an element iterator ‘’roots’’
and generate the relation’s transitive closure for ‘’roots’’
in depth first or breath first order, respectively.Lets look at a trivial example. Our relation has the integers between
0 and 11 as domain and maps an element to three times this element.>>> def relation(x):
...   if not (isinstance(x, int) and 0 <= x < 11): raise ValueError
...   return (3 * x,)
...
>>> from dm.iter.relation import depth_first_search, breadth_first_search
>>> tuple(depth_first_search(relation, ()))
()
>>> tuple(breadth_first_search(relation, ()))
()
>>> tuple(depth_first_search(relation, (1, 2, 3)))
(1, 3, 9, 27, 2, 6, 18)
>>> tuple(breadth_first_search(relation, (1, 2, 3)))
(1, 2, 3, 6, 9, 18, 27)
>>> dfs = depth_first_search(relation, (1, 2, 3))
>>> dfs.next()
1
>>> dfs.next()
3We now let our relation mapxto(2*x, 3*x).>>> def relation(x):
...   if not (isinstance(x, int) and 0 <= x < 11): raise ValueError
...   return (2 * x, 3 * x)
...
>>> tuple(depth_first_search(relation, (1,)))
(1, 2, 4, 8, 16, 24, 12, 6, 18, 3, 9, 27)
>>> tuple(breadth_first_search(relation, (1,)))
(1, 2, 3, 4, 6, 9, 8, 12, 18, 27, 16, 24)The relation can also be specified by a dictionary.>>> relation = dict((i, (2*i, 3*i)) for i in range(11))
>>> tuple(depth_first_search(relation, (1,)))
(1, 2, 4, 8, 16, 24, 12, 6, 18, 3, 9, 27)
>>> tuple(breadth_first_search(relation, (1,)))
(1, 2, 3, 4, 6, 9, 8, 12, 18, 27, 16, 24)Or an object with__getitem__method:>>> from UserDict import UserDict
>>> relation = UserDict(relation)
>>> tuple(depth_first_search(relation, (1,)))
(1, 2, 4, 8, 16, 24, 12, 6, 18, 3, 9, 27)
>>> tuple(breadth_first_search(relation, (1,)))
(1, 2, 3, 4, 6, 9, 8, 12, 18, 27, 16, 24)
Package: dmk-packages
DMK Packages패키지 배포하기(PyPI)PyPI · The Python Package Index회원가입 or 로그인 (테스트가 필요하다면TestPyPI · The Python Package Index회원가입 or 로그인)Packaging Python Projects - Python Packaging User Guide에 자세하게 적혀있으니 읽고 따라한다.아래는 가이드 문서에서 필요한 부분을 발췌하였다.패키지 배포시 참고사항배포시 API token을 사용하기 위해$HOME/.pypirc파일을 아래와 같이 작성한다.[testpypi]
  username = __token__
  password = pypi-AgENdGVzdC5weXBpLm9yZw......패키지 빌드 & 배포 & 설치 명령어 (Unix/macOS)python3 -m pip install --upgrade buildpython3 -m buildpython3 -m pip install --upgrade twinepython3 -m twine upload dist/*테스트 환경에 배포하고 싶다면python3 -m twine upload --repository testpypi dist/*python3 -m pip install dmk_packages테스트 환경에서 설치하고 싶다면python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps dmk_packages패키지 사용하기fromdmk_packagesimportdatabaseasdbdb.get_engine("KOREAINVESTMENT_DMK")
Package: dmlab2d
DeepMind Lab2DA learning environment written in C++ and Lua for the creation of grid worlds.AboutDeepMind Lab2Dis a system for the creation of 2D environments for machine
learning. The main goals of the system are ease of use and performance: The
environments are "grid worlds", which are defined with a combination of simple
text-based maps for the layout of the world, and Lua code for its behaviour.
Machine learning agents interact with these environments through one of two
APIs, the Pythondm_envAPI or a custom
C API (which is also used byDeepMind Lab).
Multiple agents are supported.If you useDeepMind Lab2Din your research and would like to cite it, we
suggest you cite theaccompanying whitepaper.Getting startedWe provide an example "random" agent inpython/random_agent, which performs
random actions. This can be used as a base for creating your own agents, and as
a simple tool to preview an environment.bazelrun-coptdmlab2d/random_agent----level_name=clean_upExternal dependencies, prerequisites and porting notesDeepMind Lab2Dcurrently ships as source code only. It depends on a few
external software libraries, which we ship in several different ways:Thedm_env,eigen,luajit,lua5.1,lua5.2,luajit,pngandzliblibraries are referenced as external Bazel sources, and Bazel
BUILD files are provided. The dependent code itself should be fairly
portable, but the BUILD rules we ship are specific to Linux on x86. To build
on a different platform you will most likely have to edit those BUILD files.A "generic reinforcement learning API" is included in//third_party/rl_api.Several additional libraries are required but are not shipped in any form;
they must be present on your system:Python 3.6or above withNumPy,PyGame, andpackaging.The build rules are using a few compiler settings that are specific to
GCC/Clang. If some flags are not recognized by your compiler (typically those
would be specific warning suppressions), you may have to edit those flags.DisclaimerThis is not an official Google product.
Package: dmlcloud
Python Project TemplateThis is a quickstart project template for Python that already comes attached with the following features:Packaging and metadata supportFormatting and linting viapre-commit,black,usort, andflake8Testing viapytestCI via github-actionsConfigurationTo tailor this template to your needs, the following steps must be taken:Rename themyprojectpackage folder to your project nameChange metadata and project name insetup.cfg.Do not forget to change the version attribute to point to your new package name as well.Add dependencies torequirements.txtAdjust theLICENSEfile to your liking.Adjust thisREADME.mdfile to your liking.Formatting and lintingInstallpre-commitandpytestviapip install -r ci_requirements.txtTo format and lint the entire codebase run:pre-commit run --all-filesTo perform this step automatically during each commit (and fail on errors) run:pre-commit installTestingTo run the tests execute:pytestin the top-level directory.
Tests can also be executed individually by running them as regular python script. This requires you to add a small main function to them, c.f.test/test_myproject.py.Github ActionsThis project defines the following workflows:run_linting.ymlwill runpre-commit run --all-fileson every push to develop and pull requestrun_tests.ymlwill runpyteston Windows, Ubuntu, and MacOS on every push to develop and pull_requestrelease_public.ymlandrelease_test.ymlcan be triggered manually to build a wheel distribution and publish it to PyPI or TestPyPI respectivelyFor the publising to work, you need to add the PyPI API token as Github secrets:PYPI_TOKENfor the official PyPI indexTEST_PYPI_TOKENfor the TestPyPI index
Package: dm-logger
DM-LoggerUrlsPyPIGitHubParametersOptions for creating individual loggersParameterTypeDefault ValueDescriptionnamestr(required)The name associated with the class instancelevelstr"DEBUG"The logging level (e.g., "DEBUG", "INFO")write_logsboolTrueWhether to write logs to a fileprint_logsboolTrueWhether to print logs to the consolefile_namestrNoneThe name of the log fileCommon class parameters for all loggersParameterTypeDefault ValueDescriptionlogs_dir_pathstr"logs"Path to the directory where the logs will be storedfile_namestr"main.log"The name of the log filewrite_mode"a" | "w""w"File write mode ('a' for append, 'w' for overwrite)max_MBint5Maximum size of the log file in MBmax_countint10Maximum number of log filesshow_name_labelboolTrueWhether to display the name label in log entriesshow_location_labelbool | "auto""auto"Whether to display the location label in log entriesformat_stringstrNoneThe format string of the logUsageSetup class configThis config usage for all loggersfromdm_loggerimportDMLoggerDMLogger.config.show_name_label=FalseDMLogger.config.max_count=5Recordfromdm_loggerimportDMLoggerlogger=DMLogger("main")logger.info("test message",tag="test tag")logger.debug("new message",id=123312)logger.info("only mess")logger.critical(env="production")logger.warning({"key":"value"})logger.error(["item1","item2",3])logger.info()Output19-11-2023 00:49:03.406 [INFO] {tag: 'test tag'} -- test message
19-11-2023 00:49:03.406 [DEBUG] {id: 123312} -- new message
19-11-2023 00:49:03.407 [INFO] -- only mess
19-11-2023 00:49:03.407 [CRITICAL] (test.<module>:8) {env: 'production'}
19-11-2023 00:49:03.408 [WARNING] -- {'key': 'value'}
19-11-2023 00:49:03.408 [ERROR] (test.<module>:10) -- ['item1', 'item2', 3]
19-11-2023 00:49:03.409 [INFO]Several loggersAll loggers will write to one filefromdm_loggerimportDMLoggerlogger=DMLogger("main",level="INFO")logger2=DMLogger("only_file",print_logs=False)logger.info("Start app")logger2.debug(process_id=123123)logger.error("App crashed!")Output19-11-2023 00:56:36.879 [INFO] [main] -- Start app
19-11-2023 00:56:36.880 [ERROR] [main] (test.<module>:10) -- App crashed!Log file19-11-2023 00:56:36.879 [INFO] [main] -- Start app
19-11-2023 00:56:36.880 [DEBUG] [only_file] {process_id: 123123}
19-11-2023 00:56:36.880 [ERROR] [main] (test.<module>:10) -- App crashed!RequirementsPython 3.8 or higher.
Package: dmm-api
DMM Affiliate API Client for PythonThis SDK is unofficialAPI Guide ishere.Installpipinstalldmm-apiUsageExamplesimportosfromdmm_apiimportDMMApiClientAPI_ID=os.environ.get('DMM_API_ID','')AFFILIATE_ID=os.environ.get('DMM_AFFILIATE_ID','')client=DMMApiClient(API_ID,AFFILIATE_ID)res=client.get_floor()print(res.json())Supported API listv3商品情報 API (ItemList)フロア API (FloorList)女優検索 API (ActressSearch)ジャンル検索 API (GenreSearch)メーカー検索 API (MakerSearch)シリーズ検索 API (SeriesSearch)作者検索 API (AuthorSearch)For developersSetupRequire: poetrygitclonegit@github.com:takelushi/dmm-api-py.gitcddmm-api-py
poetryinstallLint and Testflake8src/tests/exportAPI_ID='...'exportAFFILIATE_ID='...'pytestzBuildpoetrybuildRegister PyPI and install.poetrypublish
pip--no-cache-dirinstall--upgradedmm-api
Package: dmme
Diffusion Models Made EasyDiffusion Models Made Easy(dmme) is a collection of easy to understand diffusion model implementations in Pytorch.Documentation is available athttps://diffusion-models-made-easy.readthedocs.io/en/latest/InstallationInstall from pippipinstalldmmeInstall for customization or developmentpipinstall-e".[dev]"Install dependencies for testingpipinstalldmme[tests]Install dependencies for editing documentationpipinstalldmme[docs]Train Diffusion ModelsdmmeusesLightningCLIas a cli interface for training and evaluation.You can find sample configuration file in theconfigsdirectoryUsing config files you can train DDPM by runningdmme.trainerfit--configconfigs/ddpm/cifar10.yamlOr you can manually specify configurations for trainingdmme.trainerfit--seed_everything1337\--trainer.acceleratorgpu--trainer.precision16--trainer.benchmarktrue\--trainer.logger=pytorch_lightning.loggers.WandbLogger\--trainer.logger.project="CIFAR10_Image_Generation"\--trainer.logger.name="DDPM"\--trainer.gradient_clip_val=1.0\--trainer.max_steps800_000\--modelLitDDPM--dataCIFAR10Supported Diffusion ModelsDDPMDDIMIDDPM(WIP)Classifier Guidance
Package: dmm-search3
dmm-search3DMM Web API Version 3.0 Wrapper for Python3RequirementsPlease get ithere.api_idaffiliate_idInstall$ pip3 install dmm-search3Initialize# モジュールのインポートimportdmm# API ID と アフィリエイトIDをセットapi_id="your_api_id"affiliate_id="your_affiliate_id"# インスタンスの作成api=dmm.API(api_id=api_id,affiliate_id=affiliate_id)Feature#商品検索item_search=api.item_search(site="FANZA",hits=1,keyword="バレンタイン")#フロア一覧floor_list=api.floor_list()#女優検索actress_search=api.actress_search()#ジャンル検索genre_search=api.genre_search(floor_id=91)#メーカー検索maker_search=api.maker_search(floor_id=91)#シリーズ検索series_search=api.series_search(floor_id=91)#作者検索author_search=api.author_search(floor_id=91)# サンプル動画ドウンロードdmm.sample_download(content_id="abgktk_0012",file_name="sample",size="big")SamplePlease referhere.
Package: dmn_python
Python library enabling importing and exporting a DMN model (as an XML file) and visualizating it.Project structuredefinitions - classes composing DMN model
examples - examples of DMN models
img - elements representation images needed for diagram visualisation
tests
DMNExport.py - class used to export DMN model as an XML file
DMNImport.py - class used to import DMN model from an XML file
DMNVisualisation.py - class providing functions to display diagrams and decision tables
Package: dMO
dMOdMO is a JAX-powered differentiable cutting-plane algorithm for mixed-integer linear optimizationCitingPlease cite this paper if you work with dMO@article{differentiablecp_2023,
  title        = {Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization},
  author       = {Dragotto, Gabriele and Clarke, Stefan and Fernández Fisac, Jaime and Stellato, Bartolomeo},
  year         = 2023,
  journal      = {arXiv},
  volume       = {2311.03350},
  url          = {https://arxiv.org/abs/2311.03350}
}
Package: dmodelspy
Python conversion of Matlab dmodels routines (Battaglia et al., JVGR, 2013)Convertedsill: Made Sill class (v0.1.1)sphere: Made Sphere2D and Sphere3D classes (v0.1.2)spheroid: Make Spheroid class (v0.1.3)AddedMogi class (v0.1.3)Class principalsClasses use the dataclass decoratorEach class should have the dependant propertydVand the functions:calc_displ(x,y,z): returns displacements (u, v, w)calc_tilts(x,y,z): returns tilts (East and North)calc_strain(x,y,z): returns strains (areal, shear1 and shear2)calc_all(x,y,z): returns all values: u, v, w, tiltE, tiltN, areal, shear1, shear2x, y and z can be vectors or 1d numpy arrays.  x and y should have the same
length.  z should have the same length or be a scalar, in which case the same z
will be used for each (x, y) pair)ExceptionsMogi and Sphere2D classes only use (x, y) on inputMogi class has dV as input
Package: dmongo
dmongoA python library for communicating and Querying mongodbhow to useInstall packagepip install dmongoimport the packagefrom dmongo.dmongo import mongoQueryinitializing dmongoTo initialize dmongo, create a MONGO_URI, you either create the Database or dmongo will automatically creates it, if it doesn"t exist.Query=mongoQuery(MONGO_URI="localhost:27017",Database="geof",collections="movies")creating a recordTo create a new record, send in a data in this Dictionary format, where the key pair is the record name and the value pair is the value to be stored.Query.create({"name":"JohnDoe","email":"jdoe@gmail.com","username":"JohnD"})get all recordReturns all the record available in the defined collection of the specified DatabaseQuery.getAll()get one recordReturns a particular record from the defined collection of the specified database base on it's ID. which is unique across the collection.Query.getOne(ID="620843809255d268ce967245")updating a recordreplaces the existing value of the specified key pair with a new value of the specified value pair according to the ID specified.Query.update(ID="620843809255d268ce967245",data={"name":"JamesDoe"})filtering of recordReturns all the record record with the same value as specifiedQuery.filterBy(data={"name":"JamesDoe"})deleting a recordDeletes a record according to the ID specified.Query.delete(ID="620843809255d268ce967245")
Package: dmotd
DMOTDDynamicMessageOfTheDay is a simple piece of software that can be used to broadcast a MOTD to a group of computers or to join a server and listen for it.InstallingThis current version is made up of aPython Package, which can be installed withgitclonehttps://github.com/monolix/dmotd# Clone the repositorycddmotd# Change working directorypythonsetup.pyinstall# Install the packageIf you want to install it in a virtual environment, just run before installingsource<envdir>/bin/activateDocumentationYou can find all the docs relative to this project on itswiki.ContributingIf you want to contribute to this open source project, you're free to pull-request us! We'll review your submission as soon as we can.Did you arrive over here? Go somewhere else! This project is useless.