Saravanan666/pypi-org huggingface dataset:
Package: admin_logs
AboutThis small module for django allows you to store requests in your database(now supports only database) and then look at them in django admin.
This module was inspired by logs in Google Application EngineConfigureInclude this lines to your settings.py:INSTALLED_APPS += ('admin_logs', )
MIDDLEWARE_CLASSES = ('admin_logs.middleware.LogRequestMiddleware', ) + MIDDLEWARE_CLASSES  # place middleware as early as possible

ADMIN_LOGS_BACKEND = 'admin_logs.backends.database.DatabaseBackend'  # now supports only database

from admin_logs import setup_level
setup_level('INFO')  # set minumum log level that will be written to logs

from admin_logs.decorators import log

# use full for celery tasks
@log(name='test')
def task_name():
  logging.warning('logging in task')And this warning will be written to logs and you can check it later.
Package: adminpy
adminpy - Python Admin access for most platformsThis module makes it easy to check system platform type and gain admin rights for scripts that require it.This utilises PyUAC on the Windows platform and requires the use of Sudo on *NIX systems for admin rights escalation.The purpose of this is to try and make people's life easier when needed more rights for python scripts.adminpy on GithubUsage of the moduleThere are 3 functions of this moduleThe sysinfo() function outputs whether the script recognises which type of system it's running on.Example: On a Windows system, the script will output,"You are running on a Windows-based system."The admincheck() function will output a "Yes" or "No", depending on if it has root or admin privilegeThe runadmin() function will utilise ctypes function or Sudo (for linux) to relaunch script if does not have root or admin privilegeRequirementsThis module requiresPyUACandPyWin32package to be installed on a Windows system.This module requires Python 3, and does not support Python 2.
Package: admin-search-plus
Admin Search PlusAdmin Search Plus is a AdminMixin for Django that limits searches to specific fields which greatly enhances performance when working on large datasets.Installation$ pip install admin-search-plusOr through github:$ pip install -e git://github.com/Lenders-Cooperative/admin-search-plus#egg=admin-search-plusBuilding from source$ python -m build$ pip install admin_search_plus.whlUsageAddadmin_search_plusto yourINSTALLED_APPSbeforedjango.contrib.admin:INSTALLED_APPS = [
    'app_to_be_overrided',
    ...
    'admin_search_plus',
    ...
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
]NOTE: To override a template, the app should be listed beforeadmin_search_plusInadmin.pyimportAdminSearchPlusMixinto add search functions toModelAdmin.from admin_search_plus import AdminSearchPlusMixin

class YourModelAdmin(AdminSearchPlusMixin, admin.ModelAdmin):
    admin_search_plus = True
    show_full_result_count = False
    show_result_count = False

admin.site.register(YourModel, YourModelAdmin)
Package: admin-tool-button
django-admin-tool-buttonCustom tool buttons for Django adminRationaleDefine extra actions buttons for Django admin.SupportSupports: Python 3.9.Supports Django Versions: 4.2.7.Installation$pipinstalladmin_tool_buttonUsageAddadmin_tool_buttontoINSTALLED_APPS. In youradmin.pyadd your custom actions like so:fromadmin_tool_button.contrib.adminimportButtonActionAdminclassMyAdmin(ButtonActionAdmin):button_actions=['my_action']defmy_action(self,request):# Perform your custom action
Package: admin-torch
Admin-TorchTransformers Training **Stabilized**What's New?•Key Idea•How To Use•Docs•Examples•Citation•LicenseHere, we provide a plug-in-and-play implementation ofAdmin,
which stabilizes previously-diverged Transformer training and achieves better performance,without introducing additional hyper-parameters. The design of Admin is half-precision
friendly and can bereparameterized into the original Transformer.What's New?Beyond theoriginal admin implementation:admin-torchremoved the profilling stage and isplug-in-and-play.admin-torch's implementation ismore robust(see below).Comparison w. theDeepNet Initand theOriginal Admin Init(on WMT'17).Regular batch size (8x4096)Huge batch size (128x4096)Original Admin✅❌DeepNet❌✅admin-torch✅✅More details can be found inour example.Key IdeaWhat complicates Transformer training?For Transformer f, input x, randomly initialized weight w, we describe its stability (output_change_scale) asInour study, we show that, an original N-layer Transformer'soutput_change_scaleisO(n), which unstabilizes its training. Admin stabilize Transformer's
training by regulating this scale toO(logn)orO(1).More details can be found in ourpaper.How to use?installpip install admin-torchimportimport admin-torchenjoydef __init__(self, ...):
...+(residual = admin-torch.as_module(self, self.number_of_sub_layers))+...

def forward(self, ):
...-!x = x + f(x)!-+(x = residual(x, f(x)))+x = self.LN(x)
...An elaborated example can be found atour doc, and a real working example can be found atLiyuanLucasLiu/fairseq(training recipe is available atour example).CitationPlease cite the following papers if you found our model useful. Thanks!Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, and Jiawei Han (2020). Understanding the Difficulty of Training Transformers. Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP'20).@inproceedings{liu2020admin,
  title={Understanding the Difficulty of Training Transformers},
  author = {Liu, Liyuan and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu and Han, Jiawei},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
  year={2020}
}Xiaodong Liu, Kevin Duh, Liyuan Liu, and Jianfeng Gao (2020). Very Deep Transformers for Neural Machine Translation. arXiv preprint arXiv:2008.07772 (2020).@inproceedings{liu_deep_2020,
 author = {Liu, Xiaodong and Duh, Kevin and Liu, Liyuan and Gao, Jianfeng},
 booktitle = {arXiv:2008.07772 [cs]},
 title = {Very Deep Transformers for Neural Machine Translation},
 year = {2020}
}
Package: admin-totals
Django Admin TotalsModule to show totals in Django Admin List.Installationvirtualenv .
source bin/activate
pip install admin-totalsOrpip install git+https://github.com/douwevandermeij/admin-totals.gitUsageIn settings.pyINSTALLED_APPS=['admin_totals',]In admin.py:fromadmin_totals.adminimportModelAdminTotalsfromdjango.contribimportadminfromdjango.db.modelsimportSum,Avgfromdjango.db.models.functionsimportCoalesce@admin.register(MyModel)classMyModelAdmin(ModelAdminTotals):list_display=['col_a','col_b','col_c']list_totals=[('col_b',lambdafield:Coalesce(Sum(field),0)),('col_c',Avg)]Make sure to at least have the columns oflist_totalsinlist_display.Testspython runtests.pyContributingPlease make sure to run the following commands before pushing and making a PR:pip install -r requirements/test-ci.txt
isort --recursive admin_totals tests
flake8isortwill sort the imports andflake8will lint the code. Please fix any errors before committing. Also, make sure to write passing tests.
Package: adminui
Python AdminUIFull Readme at GithubDocumentation中文文档Write professional web interface with Python.If you need a simple web interface and you don't want to mess around with
HTML, CSS, React, Angular, Webpack or other fancy Javascript frontend stuff,
this project is for you. Now you can write web pages, forms, charts and dashboards with only Python.This library is good for: data projects, tools and scripts, small IT systems and management systems,
Hacker or Hackathon projects. Basically if you need an interface for your system and you don't
care much about customizing the style or performance for large traffic, consider this package.This project is based on Flask/FastApi and Ant Design Pro.FeaturesNo HTML, CSS, JS neededDatabase agnostic: feed content at your own, no matter it's MySql, Sqlite, Excel tables, Firebase or some IoT hardwareJWT based authentication/login system with a neat login pageForms and detail pagesLine and Bar ChartCreate decent looking menusData tables with paginationAdaptive to small screens and mobile devicesSupport both Flask and FastApiInstallation and quick startinstall the package with pip:pip install adminuiThe basic "form page" example:# example_form.py
from adminui import *

app = AdminApp()

def on_submit(form_data):
    print(form_data)

@app.page('/', 'form')
def form_page():
    return [
        Form(on_submit = on_submit, content = [
            TextField('Title', required_message="The title is required!"),
            TextArea('Description'),
            FormActions(content = [
                SubmitButton('Submit')
            ])
        ])
    ]

if __name__ == '__main__':
    app.run()Run the Python file:python example_form.pyThen visithttp://127.0.0.1:5000/to see the result.Use FastApi instead of FlaskSetuse_fastapi=Truewhen creating theapp; andprepare()instead ofrun()to expose the app to uvicorn. See python/example_fastapi.py for details# instead of app = AdminApp(), use
app = AdminApp(use_fastapi=True)

# ... other stuff you do with adminui app

# in the end of the main file, use
fastapi_app = app.prepare()

# in command line, run:
# uvicorn example_fastapi:fastapi_appDocumentationHosted onRead the DocsContributing and DevelopmentThis is a personal project. So please create issues to tell me what you need from this project.You may also give stars to let me know if this project is worthy to invest more time on.To work with the source code:This project has a Typescript front-end and a Python backend.The front-end is in the/srcfolder.The back-end is in the/pythonfolder.To start developing:cd into/pythonfolder and runpip install -r requirements.txtto install requirementsrun one of the example_xxx.py file in the/pythonfolderOpen another terminal, runnpm install&npm startat the root folder to start the frontend;Under this development mode, requests from front-end will forward to the backend.When you are done with developing:runnpm run buildwill build the project.The front-end is based on the amazingAnt Design Prolibrary, you may consult their documentation during the development.The Python backend is located in/python/adminui. It is Flask based. There are some examples in the/pythonfolder.
Package: admix-kit
admix-kit: Toolkit for analyzing genetic data of admixed populationsThis package aims to facilitate analyses and methods development of genetics data from admixed populations. We welcomefeature requests or bug reports.Installationpipinstall-Uadmix-kitUsecondaff you wish to create separate environment to avoid potential conflicts with other packages with:gitclonehttps://github.com/KangchengHou/admix-kitcdadmix-kit

condaenvcreate--fileenvironment.yaml
condaactivateadmix-kitGet startedOverviewPython APICommand line interfaceUse casesThe following manuscripts contain more motivation and details for functionalities withinadmix-kit:Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. bioRxiv 2023Impact of cross-ancestry genetic architecture on GWASs in admixed populations. AJHG 2023Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nature Genetics  2023On powerful GWAS in admixed populations. Nature Genetics 2021Upload to PyPI (for developers)python setup.py sdist
twine upload dist/*
Package: admk
Algebraic Dynamics Monge Kantorovich solver for solution of Optimal Transport on
Graphs. It contains (part of) the work described inFast Iterative Solution of the Optimal Transport Problem on Graphs. Consider citying this paper if you find the code inside this repository useful.Free software: MIT licenseInstallationpip install admkYou can also install the in-development version with:pip install https://github.com/enricofacca/admk/archive/main.zipDocumentationhttps://admk.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.1 (2022-06-10)Working code.0.1.0 (2022-06-10)Working code.0.0.0 (2022-01-12)First release on PyPI.
Package: admmsolver
A fast and general ADMM solverInstallationpip3 install admmsolverImplementation noteImplementation noteHow to develop using VS code + dockerInstallRemote Developmentextension of VS code.Clone the repository and open the cloned directory by VS code.You will be asked if you want to reopen the directory in a container. Say yes!(The first time you start a docker container, a docker image is built. This takes a few minutes).Once you login in the container, all required Python packages are pre-installed andPYTHONPATHis set tosrc.To run all unit tests and static type checkes bymypy, use./bin/runtests.sh.Note:The cloned git repository on your host file system is mounted on the working directory in the container. Created files in the container will be persisted even after the container stops.The full list of what are installed in the container will be found in.devcontainer/Dockerfile.If you customizeDockerfileand build an image for debugging, execute the command shown below on your host machine.docker build -f .devcontainer/Dockerfile .
Package: admobilize-malos
A simplePythoncoroutine based driver for communicating with malos-vision.LicenseThis application follows the GNU General Public License, as described in theLICENSEfile.InstallingThe package is available on PyPI, so you can easily install via pip:$pipinstallmatrix-io-malosRunning the CLI clientThe library includes a simple command line client to start reading data from
your MALOS service right away.#Getthemalosclienthelpscreen$malosclient--help#GetIMUdatatoSTDOUTfromalocallyrunningMALOSservice$malosclientIMU#GetHUMIDITYdatatoSTDOUTfromaremotelyrunningMALOSservice$malosclient-h192.168.0.100HUMIDITY#GetFACEdetectiondatausingaserializeddriverconfigfile$malosclient--driver-config-file~/driver_config.protoVISIONUsing the MalosDriverTo use the MALOS driver works as an async generator so in your code
you can do the following:importasyncioimportsysfrommatrix_io.malos.driverimportIMU_PORT,UV_PORTfrommatrix_io.proto.malos.v1importdriver_pb2frommatrix_io.proto.malos.v1importsense_pb2frommatrix_io.malos.driverimportMalosDriverasyncdefimu_data(imu_driver):asyncformsginimu_driver.get_data():print(sense_pb2.Imu().FromString(msg))awaitasyncio.sleep(1.0)asyncdefuv_data(uv_driver):asyncformsginuv_driver.get_data():print(sense_pb2.UV().FromString(msg))awaitasyncio.sleep(1.0)asyncdefstatus_handler(driver):type_mapping={driver_pb2.Status.MESSAGE_TYPE_NOT_DEFINED:"Not Defined",driver_pb2.Status.STARTED:"Started",driver_pb2.Status.STOPPED:"Stopped",driver_pb2.Status.CONFIG_RECEIVED:"Config Received",driver_pb2.Status.COMMAND_EXECUTED:"Command Executed",driver_pb2.Status.STATUS_CRITICAL:"Critical",driver_pb2.Status.STATUS_ERROR:"Error",driver_pb2.Status.STATUS_WARNING:"Warning",driver_pb2.Status.STATUS_INFO:"Info",driver_pb2.Status.STATUS_DEBUG:"Debug"}asyncformsgindriver.get_status():print(type_mapping[msg.type])ifmsg.uuid:print("UUID:{}".format(msg.uuid))ifmsg.message:print("MESSAGE:{}".format(msg.message))awaitasyncio.sleep(1.0)# Driver configurationdriver_config=driver_pb2.DriverConfig()# Create the driversimu_driver=MalosDriver('localhost',IMU_PORT)uv_driver=MalosDriver('localhost',UV_PORT)# Create loop and initialize keep-aliveloop=asyncio.get_event_loop()loop.run_until_complete(imu_driver.configure(driver_config))loop.run_until_complete(uv_driver.configure(driver_config))loop.create_task(imu_driver.start_keep_alive())loop.create_task(uv_driver.start_keep_alive())# Initialize data and error handlersloop.create_task(imu_data(imu_driver))loop.create_task(uv_data(uv_driver))loop.create_task(status_handler(imu_driver))loop.create_task(status_handler(uv_driver))try:loop.run_forever()exceptKeyboardInterrupt:print('Shutting down. Bye, bye !',file=sys.stderr)finally:loop.stop()asyncio.gather(*asyncio.Task.all_tasks()).cancel()loop.run_until_complete(loop.shutdown_asyncgens())loop.close()Who can answer questions about this library?Heitor Silva <heitor.silva@admobilize.com>Maciej Ruckgaber <maciek.ruckgaber@admobilize.com>More Documentation…
Package: adnap
ADNAPReverse engineer the Panda dynamics model.Installpip install adnapRequirementsThe dependencypanda-modelrequiresPOCO C++ librariesandEigen3to be installed. On Ubuntu install them by running:sudo apt-get install libpoco-dev libeigen3-devUsagePoint the environment variable to the libfranka shared library downloaded withpanda-modelexport PANDA_MODEL_PATH=<path-to-libfrankamodel.so>Run optimization with 10 random samples from the Panda state-space and save results in params.npy:adnap-optimize -n 10 -o params.npyEvaluate the optimized physical parameters against the shared library on 1000 random samples:adnap-evaluate -n 1000 params.npy
Package: adnd2e-combat-simulator
SummaryThe AD&D Second edition Combat Simulator will run simple simulated battles to
determine the statistical likelyhood of success or failure by the party. The
simulator does not account for player creativity and uses a very simple method
to determine how the battle will go.UsageFirst create acombatants.yamlfile with the players and monsters
information. The examplecombatants.example.yamlfile included in the package
is a good place to start.Next simulate the warbattle [BATTLES]The BATTLES argument indicates how many times to simulate the battle. Default
is 1.ConfigurationAdd all details about the combatants into the combatants.yaml file. The
example combatants.example.yaml illustrates the syntaxacThe values in the AC dictionary are added together to detemrine the AC. For
example a shield would have an AC value of 1 because it reduces AC by 1.
Studded leather would have a value of 3 because it gives AC 7. If a
combatant had both (3 + 1), their AC would be 6. Default is no modifier or AC
10.attackThis list enumerates the attacks that the combatant will use. The values are
the names of the attacks in the attacks section. Each combatant usesallof
their attacks each round. For example a monster with an attack list of “claw”,
“claw”, “bite” would make all 3 attacks in a single round.attacksThis list contains all the possible attacks a combatant might use.damageThis can either be a string or a dictionary. If it’s a string it applies to
targets of all sizes. If it’s a dictionary the size of the target is mapped to
a damage string.tohitThe values in the To Hit dictionary are added together to determine the total
modifier for the to hit role. For example if a fighter specialized in a bastard
sword and had a magical bastard sword +1, the 1 from specialization and 1 from
magic would be added to the d20 die roll. Default is no modifier.rofRate of fire can be a number of attacks/shots per round (e.g. 2 or 3) or a
ratio of attacks/shots per round (2/1 or 3/2). Default is 1/1.qtyThe number of the given type of monster to include in the battle.hdThe hit dice of the monster. This can be a traditional Hit Die number (e.g. 3)
which is the number of 1d8 dice to roll to determine the monsters hit points,
or it can be a traditional Hit Die number with a modifier (e.g. 3 + 2), or it
can just be a description of dice and modifiers (e.g. 1d6 + 2 or 2d8)
Package: adnipy
adnipyProcess ADNI study data with adnipy.Adnipy is a python package designed for working with theADNI database.
It also offers some handy tools for file operations.Free software: MIT licenseDocumentation:https://adnipy.readthedocs.ioCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2019-09-05)First release on GitHub.First release on PyPI.0.1.0 (2019-10-25)Improved documentation.Added pandas dataframe class extension for ADNI
Package: adnmtf
StatusCompatibilitiesContactNMTFNon-Negative Matrix and Tensor FactorizationsDevelopped in collaboration withAdvestis(Github)NMF ExamplefromadnmtfimportNMFimportnumpyasnpw=np.array([[1,2],[3,4],[5,6],[7,8],[9,10],[11,12]])h=np.array([[0.1,0.2,0.3,0.4,0.5,0.6],[0.9,0.8,0.7,0.6,0.5,0.4]])m0=w.dot(h)my_nmfmodel=NMF(n_components=2)estimator_=my_nmfmodel.fit_transform(m0)estimator_=my_nmfmodel.predict(estimator_)In this example, the matrix to be factorized is generated by the dot product of:W123456789101112andH0.10.20.30.40.50.60.90.80.70.60.40.4NMFinstantiates a NMF class with 2 components.fit_transformcalls the functions below in the given order:nmtf_basemodule:non_negative_factorization,nmf_init,r_ntf_solvenmtf_coremodule:ntf_solve,ntf_solve_simple,ntf_updatepredictderives fromfit_transformoutputs ordered sample and
feature indexes for future use in ordered heatmaps.predictcallsnmf_predictandbuild_clustersin thenmtf_basemoduleNTF ExamplefromadnmtfimportNTFimportpandasaspdDATA_PATH=...df=pd.read_csv(DATA_PATH)m0=df.valuesn_blocks=5my_ntfmodel=NTF(n_components=5)estimator_=my_ntfmodel.fit_transform(m0,n_blocks)estimator_=my_ntfmodel.predict(estimator_)In this example, the tensor to be factorized is read in filedata_ntf.csv.
The tensor has 5 layers in the 3rd dimension and is formatted as a table
with 5 blocks concatenated horizontally.NTFinstantiates a NTF class with 5 components.fit_transformcalls the functions below in the given order:nmtf_basemodule:non_negative_tensor_factorization,ntf_init,r_ntf_solvenmtf_coremodule:ntf_solve,ntf_solve_simple,ntf_updatepredictderives fromfit_transformoutputs ordered sample and
feature indexes for future use in ordered heatmaps.predictcallsnmf_predictandbuild_clustersin thenmtf_basemoduleArticlesPeer-reviewed articles(researchgate) A Tale of Two Matrix Factorizations(researchgate) Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations(nature) Learning the parts of objects by non-negative matrix factorizationBlog articles(Medium) Using Non-negative matrix factorization to classify companies(offconvex) Tensor Methods in Machine Learning
Package: ad-notify
# notify“notify”は通知を簡単にするための自前ライブラリ
現在はEmail通知が可能。# DEMOnone# Featuresnone# RequirementPython 3.6.8`bash `# Usagenone`bash `# Notenone# AuthorToshiaki.Kosuga
Aderans Internal Information System Department# License“ecbeing-downloader” is under [MIT license](https://en.wikipedia.org/wiki/MIT_License).
“ecbeing-downloader” is Confidential.
Package: adnpy
ADNpy aims to be an easy-to-use Python library for interacting with theApp.net API.InstallationTo install Requests, simply:$pipinstalladnpyDocumentationDocumentation is available athttp://adnpy.readthedocs.org/.Quick StartIn order to use ADNpy, You’ll to need an access token. If you don’t already have one, firstcreate an app, and then generate an access token for your app.importadnpyadnpy.api.add_authorization_token(<AccessTokenHere>)# Create a postpost,meta=adnpy.api.create_post(data={'text':'Hello App.net from adnpy!'})# Take a look at recent checkinsposts,meta=adnpy.api.get_explore_stream('checkins')forpostinposts:printpost# You can even paginate through checkins using the cursor method.# Cursors will obey rate limits (by blocking until retries are# permitted), and will allow you to page through the entire stream.forpostinadnpy.cursor(adnpy.api.get_explore_stream,'checkins'):printpost