Saravanan666/pypi-org huggingface dataset:
Package: mabelbeta
mabel is a Data Engineering platform designed to run in serverless environments.mabeljust runs when you need it, scaling to zero, making it efficient and ideal
for deployments to platforms like Kubernetes, GCP Cloud Run, AWS Fargate and Knative.DocumentationGitHub WikiBug ReportsGitHub IssuesFeature RequestsGitHub IssuesSource CodeGitHubDiscussionsGitHub DiscussionsFocus on What MattersWe've builtmabelto enable Data Analysts to write complex data engineering tasks
quickly and easily, so they could get on with doing what they do best.frommabelimportReaderdata=Reader(dataset="test_data")print(data.count())Key FeaturesOn-the-fly compressionLow-memory requirements, even with terabytes of dataIndexing and partitioning of data for fast readsCursors for tracking reading position between processesPartial SQL DQL (Data Query Language) supportSchema anddata_expectationsvalidationInstallationFrom PyPI (recommended)pip install --upgrade mabelFrom GitHubpip install --upgrade git+https://github.com/mabel-dev/mabelA preview release of mabel is available from PyPIpip install --upgrade mabelbetaYou may need to manually uninstall mabel before the test version will install.These versions are usually labelled with ana(signifying alpha status) in the
library version. Alpha versions are more likely to have functional issues.GuidesHow to Read DataDependenciesorjsonfor JSON (de)serializationbitarrayfor handling high density boolean datasiphashcfor non-cryptographic hashingpydanticto define internal data modelszstandardfor real-time on disk compressionLZ4for real-time in memory compressionsimdjsonfor fast JSON deserializationcythonfor precompilationThere are a number of optional dependencies which are usually only required for
specific features and functionality. These are listed intests/requirements.txt.Integrationsmabel comes with adapters for the following data services:ServiceGoogle Cloud StorageMinIOAWS S3Azure Blob StorageLocal StorageMabel is extensible with adapters for other data services as required.Deployment and Executionmabel supports running on a range of platforms, including:PlatformDockerKubernetesWindows (1)Linux (2)1 - Some non-core features are not available on Windows.2 - Tested on Debian (WSL) and Ubuntu.How Can I Contribute?All contributions, bug reports, bug fixes, documentation improvements,
enhancements, and ideas are welcome.If you have a suggestion for an improvement or a bug,raise a ticketor start adiscussion.Want to help build mabel? See thecontribution guidance.LicenseApache 2.0
Package: mabfs
Multi armed bandit feature selectionFeature selector based on Thompson sampling algorithm
Based on:https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.36DescriotionThis package is used to select optimal subset of features to maximize selected metric.
Optimization could be used for both regression and classification.How it workscalculate information relevance and information redundancy for every featureinit beta distribution for every featuresample every beta distribution and select desired number of featurecross validate modelcalculate resulting score based om CV metric and information relevance and redundancyupdate beta distributionsUsageimportpandasaspdimportnumpyasnpfromsklearn.datasetsimportmake_regressionfromsklearn.metricsimportmake_scorerfromsklearn.metricsimportmean_absolute_errorfromsklearn.linear_modelimportLinearRegressionfrommabfs.ts_selectorimportThompsonSamplingFeatureSelectionx,y,coef=make_regression(n_samples=1000,n_features=500,n_informative=10,effective_rank=5,tail_strength=0.7,noise=0.05,shuffle=True,bias=100,coef=True,random_state=666)x=pd.DataFrame(x)y=pd.Series(y)true_features=np.where(coef>0)[0]model=LinearRegression()tsfs=ThompsonSamplingFeatureSelection(model=model,scoring=make_scorer(mean_absolute_error),desired_number_of_features=10,x=x,y=y,cv_splits=3,exploration_coef=0.3,optimization_steps=100000,is_regression=True,n_jobs=36)tsfs.select_best_features()
Package: maboss
pyMaBoSSpyMaBoSS is a python API for theMaBoSSsoftware.
It can be used inside theColomoto docker.
The API is loaded by running the following line in Python:import mabossOnce loaded, it allows you to quickly load a MaBoSS model:sim = maboss.load("metastasis.bnd", "metastasis.cfg")And then to simulate the model and plot the simulation results:res = sim.run()
res.plot_piechart()Installation instructionspyMaBoSS can be installed either via Conda, PyPi or GitHub.Installation with CondaThis is the recommended option, as it also allows the simple installation of all the dependencies.conda -c colomoto install pymabossThis will install the library, which will already be ready to use.Installation with PyPiThis is not the recommended option as it cannot yet package the MaBoSS binaries, but if you already have then install you can just install pyMaBoSS usingpip install mabossTo download the MaBoSS binaries, if you have conda and if you are using linux or macosx, you can run :python -m maboss-setupOtherwise, you can download them using the following links, forLinux,MacOSXorWindows. Once downloaded, you need to extract them and make them accessible by putting them in a folder configured in your PATH.Installation with GitHubFinally, you can also install pyMaBoSS directly from the official GitHub repository :git clonehttps://github.com/colomoto/pyMaBoSScd pyMaBoSSpython setup.py installNote that this also comes without the MaBoSS binaries, so to install them you’ll have to follow the steps described above.
Package: mab-ranking
mab-rankingA library for Online Ranking with Multi-Armed-Bandits. This library is useful to find the best Top N items among a relatively small candidate set.For example:Show the Top 5 News Articles among 200 articles every dayRecommend the Top 10 Trending Products every weekRank components on websites to drive more engagement (Whole Page Optimization)InstallationPrerequisitesmab-ranking requires the following:Python (3.6, 3.7, 3.8)Install mab-rankingAt the command line:pip install mab-rankingGetting startedLet's say that you want to recommend the top 5 trending products to your website visitors every week among the 300 most selling products. Every Monday at some time T you'll define a newRankBanditimplementation. For example:num_ranks = 5
num_arms = 300
rank_bandit = IndependentBandits(num_ranks, BetaThompsonSampling, num_arms=num_arms)Then, every time a visitor X is landed on your home page for the rest of the week, you need to select which 5 products to show them in sectionTop Trending Products. So, you'll do the following:selected_arms = rank_bandit.choose()Let's say that theselected_armsequals[30, 2, 200, 42]. That means that you need to show products, 30, 2, 200 and 42 in this order. You can keep your own mapping from product UUIDS to integer arm ids in your app's business logic.The visitor X viewed this selected order. Let's say that (s)he clicked on products 2 and 42. Then therewardslist will be[0.0, 1.0, 0.0, 1.0]. So:rewards = [0.0, 1.0, 0.0, 1.0]
rank_bandit.choose(selected_arms, rewards)Implementation DetailsImplementations ofRankBandit:IndependentBandits: fromMicrosoft Paper, "A Fast Bandit Algorithm for Recommendations to Users with Heterogeneous"Implementations ofBandit:BetaThompsonSampling: Beta Thompson Sampling MAB
Package: maby
Morphology annotation for budding yeastsInstallationpipinstallmabyOnce installed, use commandmaby-initto download the pre-trained models and example images. These will be stored in your python environment, so you may need to use special permissions. We recommend using a pythong environment, such asconda.TrainingOnce the package is installed, the commandmaby-trainwill be accessible from the command line.maby-train--directory/path/to/imageEvaluatingTo evaluate the performance of the trained model on the validation data, use the commandmaby-evaluate.It requires a path to a results directory as input.maby-evaluate--directory/path/to/directory--checkpoint/path/to/model.ptVisualizing results with napariWe visualize results withnapariusing the command:maby-visualizeBy default, it loads a network to predict the location of the nucleus, as well as an example time-lapse.
ClickRunto make a prediction on the example. This may take up to a minute, depending on your hardware.You can choose which model to run the prediction with in the dropdown menu. Thenucleusandvacuolemodels are pre-trained, but thecustommodel is loaded with random initial weights.If you want to predict on a different.tiffile, you can choose an image and pressLoad Imageto load it into the viewer. The bright field data will be automatically normalized between -1 and 1.If you want to use a different model trained withmaby-trainon your own dataset, locate the checkpoint file and clickLoad model. Note that this will overwrite which ever model is currently loaded, so we suggest that you first switch to thecustommodel in the model picking pane.
Package: mac
macManageacloud is a flexible orchestration platform. It allows you to create, destroy and organise servers and infrastructures.Fork me onGitHub!FeaturesOrchestrate serversOrchestrate production ready infrastructure (golden images, autoscaling groups, load balancers, etc)Versioning servers and infrastructureEmpower Continuous DeliveryView who is the owner of the resources (servers and infrastructure)Interact with your servers using a Command Line Interface, REST Api or Web InterfaceManage access to serversConvert your infrastructure into codeOrchestrate servers using your existing automation codes (eg Docker, Puppet, Chef, SaltStack, etc)Test server configurations easilyIntegrate any technology that can be operated from the command line interface or an APIKeep the history (including logs) of servers and infrastructureTrigger events via WebHooksAlthough Manageacloud is technology agnostic, we have created some shortcuts for easier operation with Amazon Web Services, Google Compute Engine, Rackspace and Digital OceanAccess to thedocumentationor to thequickstart guideto learn more.InstallationCommand Line InterfaceInstall vispip3 install:pip3installmacExamplesStandalone applicationsTo create a new server and install apache for Ubuntumacinstancecreate-b"apt-get update && apt-get install apache2 -y"-rubuntu:trustyTo create a new server and install apache for CentOSmacinstancecreate-b"apt-get update && apt-get install apache2 -y"-rcentos:7To install Wordpressmacinstancecreate-cbasic_wordpress_installationInfrastructuresYou can learn more about how to orchestrate infrastructure using our [quickstart guide](https://manageacloud.com/quickstart)The following example:Creates an instance and a load balancer in AWSDeploys an application and versionversion_2This infrastructure has anameand aversion, which makes it ideal for some scenarios such as blue-green deployments.mac-sinfrastructuremacfilehttps://goo.gl/ezRWx1-pINF_VERSION=2APP_BRANCH=version_2
Package: mac2vendors
Convert mac addresses to vendor information/ names !Installationpip install mac2vendorsUsagemtv --help

usage: mtv [-h] {mac} ...

positional arguments:
  {mac}  [command] help
    mac        Translates the mac address to a vendor mapping.

optional arguments:
  -h, --help   show this help message and exitRaw UsageUse from codefrom mac2vendors import get_mac_vendor
vendor_list = get_vendor(mac_address="00:00:00")
print(vendor_list)
[['00:00:00', '00:00:00', 'Officially Xerox, but 0:0:0:0:0:0 is more common']]
Package: mac-4mater
Get mac address, correct and return in required format
DOES NOT CHECK THE INPUT MAC ADDRESS FORMAT, just delete invalid characters and check len = 12
:param mac: mac address
:param delimiter: separator character
:param chunk: the number of characters before the separator output, mast be in [1, 2, 3, 4, 6]. 0 or None for output without delimiter
:param capital: lowercase or uppercase return output str. default lowercase
:return: mac address in required formatexample:
print(mac_4mater(‘ab.bg.55.55.55.53’, delimiter=’.’, chunk=4, capital=False))
ValueError: Invalid mac address ab.bg.55.55.55.53print(mac_4mater(‘ab.bc.55.55.55.53’, delimiter=’.’, chunk=4))
abbc.5555.5553print(mac_4mater(‘ab.bc55.55.55bbbbbbbbbbbbbb.53’, delimiter=’:’, chunk=4, capital=False))
ValueError: Invalid mac address ab.bc55.55.55bbbbbbbbbbbbbb.53print(mac_4mater(‘ABrrrrrrrrrrrr.bc.55.55.55.53’, delimiter=’-’, chunk=2, capital=True))
AB-BC-55-55-55-53print(mac_4mater(‘AB.bc.55.55.55.53’, delimiter=’:’, chunk=2, capital=True))
AB:BC:55:55:55:53
Package: macabake
MacabakeA tool for creating custom macaroonsLND Config Environment VariablesPut these environment variables into your pathexport CRED_PATH=/home/lnd_user/Documents/creds/awesome-node/lnd
export LND_CRED_PATH=/home/lnd_user/Documents/creds/awesome-node/lnd
export LND_NODE_IP=awesome-node.m.voltageapp.io
export LND_NODE_PORT=10009This script expects there to be a validadmin.macaroonand atls.certfile located atCRED_PATH/LND_CRED_PATH.ExampleBake a custom macaroonmacabake bakemacaroon \
    --apis Lightning OpenChannel \
    --apis Lightning SubscribeChannelEvents \
    --apis Lightning ConnectPeer \
    --output_filename=mycustommacaroon.macaroonThe macaroon is saved into the same directory asadmin.macaroon, which is at the$CRED_PATHenvironment variable.
The--apisflag can be passed multiple times, and is two strings: Subsystem API_name. The subsystems are listed on the LND docs website, like in the image below or using the command:macabake list-lnd-subsystemsYou can view which APIs are in each subsystem like so:macabake list-lnd-apis --subsystem WalletKitLND Subsystems from LND DocsLink to LND API Docs:
Package: macad
MacadThe Tool For Changing Your MAC Address for Any InterfaceTo install this package, usesudopipinstallmacadNote:this library requires root privileges, you need to use sudoRUNWant to contribute? Great!Open your favorite Terminal and run these commands.See All Arguments and Options for more information:sudomacad-hFind your interface and put cammandsudomacad-i<interface>-m<newmacaddress>
ORsudomacad--interface<interface>--mac<newmacaddress>Example:sudomacad-iwp2s1-m00:11:22:33:44:55Note:sometimes assigned mac not be available so for that start mac from 00:XX:XX:XX:XX:XXThe output  look like this[+]MACAddresswasSuccessfullyChangedto00:XX:XX:XX:XX:XXLicenseMITFree Software, Hell Yeah!AuthorDeepak Patidar&RishiRaj Singh Chawda
Package: macaddr
macaddrAs a network engineer you many have to deal with MAC address values.  In some
cases a network operating system wants the MAC address in a particular format.
The following are common formats:* aa:bb:cc:dd:ee:ff
* aabb-ccdd-eeff
* aabb.ccdd.eeffAs well as their upper-case variants, AA:BB:CC:DD:EE:FF for example.This package provides theMacAddressclass definition and a supportingformatmethod that allows you to develop programs using a MAC address in different formats.InstallationpipinstallmacaddrUsageBy default the MacAddress is formated in the "2 byte colon separated" format.frommacaddrimportMacAddressmac=MacAddress("aabb.ccdd.eeff")print(mac)aa:bb:cc:dd:ee:ffYou can obtain differnt formats using theformatmethod.frommacaddrimportMacAddressmac=MacAddress("aabb.ccdd.eeff")print(mac.format(size=4,sep='-',to_case=str.upper))AABB-CCDD-EEFFTheformatmethod values are cached so that repeated calls toformatwith
the same arguments return the cached value.  For example, if you had a network
of 1000 devices, 500 of them required the "2-colon" format and 500 of them
required the "4-dot" format, then these two formats are only computed twice.
Package: macaetr
DownloadMACA(Multivariate Adaptive Constructed Analogs) downscaled climate data and estimate ASCE standardized reference evapotranspiration.Given a point locationMACA-ETrsimplifies the process of downloading downscaled climate variables from the MACA dataset via a simple Python API. Data that can be retrieved (and used to calculate reference ET) includes global climate models from the Coupled Model Inter-Comparison Project 5 (20 models) whose output is downscaled using the MACA methodologies. The MACA downscaling model is trained from both the Livneh et al. 2014 dataset and the gridMET dataset, output from both can be downloaded using theMACA-ETrpackage. The historical (1950-2005) and future predictions (rcp4.5 and rcp8.5 emissions trajectories from 2006-2099) datasets are included in MACA. TheMACA-ETrpackage can also be used to calculate ASCE short and tall reference ET time series using data downloaded from MACA at a given location within the conterminous United States.DocumentationUnder developmentInstallationYou may install the dependencies using the conda virtual environment (recommended), the environment file can be downloadedhereand installed and activated bycondaenvcreate-fenvironment.ymlcondaactivatemacaetrOnce activated install with PIP:pipinstallmacaetr
Package: mac-agents
Installation$[sudo]pipinstallmac-agentsExamples~/Library/LaunchAgents/file.pyimportmac_agentsclassAgent(mac_agents.Agent):StartInterval=5# capital letterdefrun(self):passif__name__=="__main__":Agent().run()$find~/Library/LaunchAgents-name"*.py"|xargspython-mmac_agents.create"$@"$find~/Library/LaunchAgents-name"*.plist"|xargslaunchctlload# or launchctl unload$launchctllist|grep.py$|awk'{print $3}'|xargs-I'{}'launchctlremove{}$find~/Library/LaunchAgents-name"*.py.plist"-execrm{}+Linkslaunchd.plistreadme42.com
Package: mac-alias
What is this?mac_aliaslets you generate or read binary Alias and Bookmark records
from Python code.While it is written in pure Python, some OS X specific code is required to
generate a proper Alias or Bookmark record for a given file, so this module
currently is not portable to other platforms.Credit is due to Wim Lewis <wiml@hhhh.org>, whose work on reverse engineering
the alias format was extremely useful in writing this code.  Alastair Houghton
<alastair@alastairs-place.net> was responsible for reverse engineering the
bookmark format.See thedocumentationfor more information.
Package: macapi
======macapi======|Python 27|**What is macapi?** macapi is a tool used to easily access MongoDB Atlas by leverating its resful api. It's primarily used to easilty adminsiter Atlas clusters and groups... figure:: https://github.com/dmcna005/macapi/blob/master/macapi.png:alt: macapi--------------------------------------------------Installation------------.. code-block:: console$ pip install macapi**The toolkit constists of the following tools**:``alerts``~~~~~~~~~~Used to get and create alert configuration for a single group... code-block:: console$ alerts -husage: alerts [-h] [-G] [-U] [-C] [-D] [-f FILE] -g GROUP_ID -u API_USER -kAPI_KEYoptional arguments:-h, --help            show this help message and exit-G, --get             get a group alert and monitoring configuration-U, --update          update current alerts configuration-C, --create          replace current alerts confifuration-D, --delete          delte a single alert-f FILE, --file FILE  write file to current directory unless absolute pathis provided-g GROUP_ID, --group_id GROUP_ID-u API_USER, --api_user API_USER-k API_KEY, --api_key API_KEY``cluster``~~~~~~~~~~~Used to create and modify cluster configurations for a given group... code-block:: console$ cluster -husage: cluster [-h] [-G] [-C] [-f FILE] -n NAME -g GROUP_ID -u API_USER -kAPI_KEY [-D] [--resize] [--size SIZE] [--nodes NODES][--shards SHARDS]optional arguments:-h, --help            show this help message and exit-G, --get             get's the currnet group cluster configuration-C, --create          creates a new cluster-f FILE, --file FILE  write file to current directy or path-n NAME, --name NAME  the name of the cluster-g GROUP_ID, --group_id GROUP_IDid of the group that you are trying to make thechanges for-u API_USER, --api_user API_USERthe email address you use to login-k API_KEY, --api_key API_KEYYour Atlas api key-D, --delete          deletes a cluster from a project--resize              resizes an instace--size SIZE           size of an instance in ['M10',...,'M60']--nodes NODES         number of nodes per shard or replicaSet--shards SHARDS       number of replicaSets to deploy``ip_whitelist``~~~~~~~~~~~~~~~~Used to create, delete, update and getip whitelist information for a single group in Atlas... code-block:: console$ ipwhitelist -husage: ipwhitelist [-h] [-G] [-C] [-D] [-f FILE] -g GROUP_ID -u API_USER -kAPI_KEYoptional arguments:-h, --help            show this help message and exit-G, --get             get's the currnet group IP Whitelist-C, --create          replace current alerts confifuration-D, --delete          delte a single alert-f FILE, --file FILE  write file to current directory unless absolute pathis provided-g GROUP_ID, --group_id GROUP_IDid of the group that you are trying to make thechanges for-u API_USER, --api_user API_USERthe email address you use to login-k API_KEY, --api_key API_KEYYour Atlas api key.. |Python 27| image:: https://img.shields.io/badge/Python-2.7-brightgreen.svg?style=flat:target: http://python.org
Package: macApp
macAppA macOS .app information retriever for Python 3Installationpip install macApp --upgradeWorks for Python >=3.1Usage>>>importmacApp>>>macApp.App(<path>)'App Name'# Example:>>>VSCode=macApp.App("/Applications/Visual Studio Code - Insiders.app")>>>VSCode.architectures['arm64']>>>VSCode.used_dates['2020-12-24 08:00:00 +0000','2020-12-24 23:00:00 +0000','2020-12-25 23:00:00 +0000','2020-12-26 23:00:00 +0000','2020-12-27 23:00:00 +0000','2020-12-28 23:00:00 +0000','2020-12-29 23:00:00 +0000']>>>VSCode.size'276.41MB'>>>VSCode.copyright'Copyright (C) 2019 Microsoft. All rights reserved'>>>VSCode.creation_date'2020-12-16 00:00:00 +0000'>>>VSCode.bundle_id'com.microsoft.VSCodeInsiders'>>>VSCode.bundle_id.vendor'microsoft'>>>VSCode.bundle_id.app'VSCodeInsiders'>>>VSCode.bundle_id.version'1.53.0-insider'>>>VSCode.bundle_id.version.major1>>>VSCode.as_dict{'displayName':'Visual Studio Code - Insiders.app','alternateNames':['Visual Studio Code - Insiders.app'],'category':'public.app-category.developer-tools','categoryType':None,'bundleID':{'id':'com.microsoft.VSCodeInsiders',[...]'minor':53,'patch':0,'other':None}}Methodskill(signal=None)Closes the app if running (is_running == True)The 'signal' parameter is used specify a special signal used withkillallrelatives(starting_path=OPTIMIZEDPATH, no_warning=False)Searches for all of the files which might be related to the appThe 'starting_path' parameter is used specify which directory (folder) to start searching fromThe 'no_warning' parameter is used specify if macApp should display warnings or notOPTIMIZEDPATH or macApp.utils.OptimizedPath() is a variable used to indicate relatives() that we want to search through predefined paths which are more likely to have files related to the appuninstall(starting_path=OPTIMIZEDPATH, no_warning=False)Moves all of the files found by relatives() to a folder and creates a report'starting_path' is the same as the one on relatives()'no_warning' is the same as the one on relatives()How does it workmacApp parses the result from the shell macOS built-in shell commandmdlsto retrieve information about the appDependenciesThis module uses one dependency to ensure that created reports are safely written tosafeIOCreditmacApp by Anime no Sekai

© Anime no Sekai — 2020
Package: mac-app
Installation$[sudo]pipinstallmac-appClassesclass__doc__mac_app.AppMac app generatorExamplescreate app from python file>>>mac_app.App(app_script="file.py",app_name="name").appify().mkalias("~/name.app")create app from class>>>importmac_app>>>classMyApp(mac_app.App):defrun(self):passif__name__=="__main__":MyApp().run()>>>MyApp().appify().mkalias("~/MyApp.app")readme-generator
Package: mac-app-bundleid
Installation$[sudo]pipinstallmac-app-bundleid$[sudo]npmi-gmac-app-bundleidExamples$app-bundleid"Finder"com.apple.finder
$app-bundleid/System/Library/CoreServices/Finder.app
com.apple.finder$app-bundleidpath/to/my-app.app"me.my-app.app"$app-bundleidpath/to/my-app.app
me.my-app.appreadme42.com
Package: mac-app-env
Installation$[sudo]pipinstallmac-app-envHow it works.envfilePATH=path/to/custom/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin<app>/Contents/Info.plist<key>LSEnvironment</key><dict><key>PATH</key><string>path/to/custom/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string></dict>Examples$find."*.app"|xargspython-mmac_app_env.envpaths with spaces:$find."*.app"-execpython-mmac_app_env.env{}\;readme42.com