Saravanan666/pypi-org huggingface dataset:
Package: drstorage
drstorageUsingFrom python>>>importdrstorage>>>data=bytearray.fromhex("abab00471200c5120901000000000000000a10025810000000000000140d0a")>>>result=drstorage.models.F1_600.parse(data)>>>print(result)Container:humidity=7.1temperature=19.7model=600or from command line$python-c'import sys; sys.stdout.buffer.write(bytes(bytearray.fromhex("abab00471200c5120901000000000000000a10025810000000000000140d0a")))'|drstorageparse--modelF1_600
Container:humidity=7.1temperature=19.7model=600Supported Modelsgeneric(default, the "base" Dr. Storage model)F1_600F1_1200X2M_157InstallationIn a fresh virtual environment$ python -m pip install "git+https://github.com/kratsg/drstorage.git"The above is actually cloning and installing directly from the Git repository.
However, if you want to, you can of course also install it directly from the Git repository "locally" by first cloning the repo and then from the top level of it running$ python -m pip install .ContributingAs this library is experimental contributions of all forms are welcome.
You are of course also most welcome and encouraged to open PRs.DevelopingTo develop, use a virtual environment.
Once the environment is activated, clone the repo from GitHubgit clone git@github.com:kratsg/drstorage.gitand install all necessary packages for developmentpython -m pip install --ignore-installed --upgrade -e .[complete]Then setup the Git pre-commit hooks by runningpre-commit installAcknowledgementsMike HanceNoah PeakeWill JohanssonJames TranovichAnja BerensPaul IngemiMatthew Gignac
Package: drtest
pyDRtest:Data reduction test moduleThis is a test module that can perform data reduction in several ways.FeaturesCan perfrom data reduction on arraysSelect from several data reduction methodsTested with an .abf file using pyabf moduleInstallationpipinstalldrtestUsageImporting:importdrtestasdrUse the DataAnalysis class for some xdata, ydata:da=dr.DataAnalysis(xdata,ydata)Perform data reduction with different methods:xdec,ydec=da.data_reduction(method='decimate',reduction_factor=4)xavr,yavr=da.data_reduction(method='average',reduction_factor=4)xmin,ymin=da.data_reduction(method='min',reduction_factor=4)xmax,ymax=da.data_reduction(method='max',reduction_factor=4)xminmax,yminmax=da.data_reduction(method='min/max',reduction_factor=4)
Package: drucker
# Rekcurd[![Build Status](https://travis-ci.com/rekcurd/drucker.svg?branch=master)](https://travis-ci.com/rekcurd/drucker)
[![PyPI version](https://badge.fury.io/py/rekcurd.svg)](https://badge.fury.io/py/rekcurd)
[![codecov](https://codecov.io/gh/rekcurd/drucker/branch/master/graph/badge.svg)](https://codecov.io/gh/rekcurd/drucker“Non-generated packages only”)
[![pypi supported versions](https://img.shields.io/pypi/pyversions/rekcurd.svg)](https://pypi.python.org/pypi/rekcurd)Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like [Flask](http://flask.pocoo.org/).## Parent Projecthttps://github.com/rekcurd/drucker-parent## Components
- [Rekcurd](https://github.com/rekcurd/drucker) (here): Project for serving ML module.
- [Rekcurd-dashboard](https://github.com/rekcurd/drucker-dashboard): Project for managing ML model and deploying ML module.
- [Rekcurd-client](https://github.com/rekcurd/drucker-client): Project for integrating ML module.## Installation
From source:`bash $ git clone--recursivehttps://github.com/rekcurd/drucker.git$ cd drucker $ python setup.py install `From [PyPi](https://pypi.org/project/rekcurd/) directly:`bash $ pip install rekcurd `## How to use
Example code is available [here](https://github.com/rekcurd/drucker-example).## Unittest` $ python-munittest `## Kubernetes support
Rekcurd can be run on Kubernetes. See [here](https://github.com/rekcurd/drucker-parent).
Package: drucker-dashboard
# Rekcurd-dashboard[![Build Status](https://travis-ci.com/rekcurd/drucker-dashboard.svg?branch=master)](https://travis-ci.com/rekcurd/drucker-dashboard)
[![PyPI version](https://badge.fury.io/py/rekcurd-dashboard.svg)](https://badge.fury.io/py/rekcurd-dashboard)
[![codecov](https://codecov.io/gh/rekcurd/drucker-dashboard/branch/master/graph/badge.svg)](https://codecov.io/gh/rekcurd/drucker-dashboard“Non-generated packages only”)
[![pypi supported versions](https://img.shields.io/pypi/pyversions/rekcurd-dashboard.svg)](https://pypi.python.org/pypi/rekcurd-dashboard)Rekcurd dashboard is the project for managing ML model and deploying ML module. Any Rekcurd service is manageable. It can deploy the Rekcurd service to Kubernetes cluster.## Parent Projecthttps://github.com/rekcurd/drucker-parent## Components
- [Rekcurd](https://github.com/rekcurd/drucker): Project for serving ML module.
- [Rekcurd-dashboard](https://github.com/rekcurd/drucker-dashboard) (here): Project for managing ML model and deploying ML module.
- [Rekcurd-client](https://github.com/rekcurd/drucker-client): Project for integrating ML module.## Environment
- Python 3.6
- flask 1.0.x
- [Node.js](https://nodejs.org/) 8.x
- [yarn](https://yarnpkg.com/)## Installation
From source:`bash $ git clone--recursivehttps://github.com/rekcurd/drucker-dashboard.git$ cddrucker-dashboard/frontend$ yarn install && yarn run build && cd .. $ pip install-e. `From PyPi directly:`bash $ pip installrekcurd-dashboard`## How to boot
### Preparation
You need to create [settings.yml](./drucker_dashboard/settings.yml).### General users
Launched onhttp://0.0.0.0:18080as a default.#### command line`bash $ rekcurdui--settingssettings.yml db init $ rekcurdui--settingssettings.yml db migrate $ rekcurdui--settingssettings.yml server `#### docker-compose (Deprecated)`bash # For dev $docker-compose-fdocker-compose/docker-compose.develop.yamlup # For prod $docker-compose-fdocker-compose/docker-compose.production.yamlup `### For AWS users
If you run this on AWS (such as EKS), you need to configure aws-cli setting.
Follow the [official document](https://docs.aws.amazon.com/streams/latest/dev/kinesis-tutorial-cli-installation.html).Rekcurd-dashboard docker container will mount the configuration files,
so the IAM account used by configuration needs to have enough permissions to access to Kubernetes resources on AWS.`bash # For dev $docker-compose-fdocker-compose/aws/docker-compose.develop.yamlup # For prod $docker-compose-fdocker-compose/aws/docker-compose.production.yamlup `## How to use
See [docs](./docs/README.md).## Unittest
### Prerequisites`bash $ pip install-rrequirements.txt $ pip install-rtest-requirements.txt`If you don’t have VirtualBox, run it.`bash $ sudo yum install-ykernel-develkernel-headersmake patch gcc $ sudo wgethttps://download.virtualbox.org/virtualbox/rpm/el/virtualbox.repo-P/etc/yum.repos.d $ sudo yum install-yVirtualBox-5.2`### Test`bash $ sudo sh drucker_dashboard/e2e_test/startup.sh $ python-munittest $ sudo sh drucker_dashboard/e2e_test/cleanup.sh `## Kubernetes support
Rekcurd can be run on Kubernetes. See [here](https://github.com/rekcurd/drucker-parent).
Package: drugintfinder
A pipeline for identifying the most notable druggable interactors of a target within a e(BE:L) generated
Knowledge Graph. This feature will be integrated into the e(BE:L) package in the future.Installation$pipinstalldrugintfinderUsageFinding the direct causal interactors of the phosphorylated MAPT protein:$diffindMAPT-nprotein-ecausal-mphoFinding the direct causal interactors of the phosphorylated MAPT protein which are druggable:$diffindMAPT-nprotein-ecausal-mpho-dCreating a table of various parameters for each interactor by which to rank them:$difrankMAPT-mphoDisclaimerThe Druggable Interactor finder is a resource developed in an academic capacity funded by theMAVO projectand thus comes with no warranty or guarantee of maintenance or support.
Package: druglinker
DrugLinkerThis package is a simple python module to obtain all unique drug identifiers that match a given input string.
It does it by matching the input term with DrugBank open-data vocabulary :https://www.drugbank.ca/releases/latest#open-data.
It is useful to process candidate drug tokens after drug named-entity recognition (NER) or to directly match sentences after tokenization.To install the package simply run the following in a bash shell:pip install druglinker==0.1.1Example of use in a python environment:from druglinker import dbsearch
dbsearch.get_ids("amox")
# output: ["DB01060"]
dbsearch.get_ids("amoxicillin")
# output: ["DB01060"]
dbsearch.get_ids("vancomycin")
# output: ["DB00512"]

# Simple sentence tagging:

text = "20mg of midazolam were administered intravenously"
for term in text.split():
    ids = dbsearch.get_ids(term)
    if ids:
        print(term)
        print(ids)

# output:
# midazolam
# ['DB00683']For instance you can now easily find further information about the drug identifier detected (e.g. DB00683) athttps://www.drugbank.ca/drugs/DB00683
Package: drug-repurposing-extract
Project Description for drug-repurposing-extractThis is a Python package for drug repurposing data extraction. You can use it to retrieve and analyze data related to specific compound IDs (CIDs) for potential drug repurposing.InstallationYou can install the package usingpip:pipinstalldrug-repurposing-extractUsageImport the packageimportdrug_repurposing
fromdrug_repurposingimportget_dataGenerate CSV File from CID ListTo generate a CSV file from a list of CIDs (Compound IDs), you can use the generate_data_from_list function:get_data.generate_data_from_list([942,1070])Generate CSV List from Uploading a CSV File of CID ListYou can also generate a CSV file from a list of CIDs by uploading a CSV file containing the CID list. Here's how you can do it in a Google Colab environment:fromgoogle.colabimportfiles
importio
importpandasaspduploaded=files.upload()uploaded_file=list(uploaded.values())[0]df=pd.read_csv(io.BytesIO(uploaded_file))data=df["cid"].values

get_data.generate_data_from_list(data)Predicting the CIDs DataTo predict data based on CIDs, you can follow these steps:Make sure you have a CSV file (e.g., complete_data.csv) generated from the previous steps.Read the CSV file using pandas:importpandasaspddf=pd.read_csv("complete_data.csv")Use the predict_data function to perform predictions:get_data.predict_data(df)
Package: drug-smile-fet
Pre-requisitesInstall RdKit library:instllation throughanacondainstllation throughpipUsageMake sure you have Python installed in your system.Run Following command in the CMD.pip install drug-smile-fetExample# example.py
from dsfet import fe_1mol
import pandas as pd
train_smiles = {'DRUG_NAME': {0: 'Luminespib', 1: 'Trametinib', 2: 'Venetoclax', 3: 'Olaparib', 4: 'Axitinib'},
               'PUBCHEM_ID': {0: 135539077.0, 1: 11707110.0, 2: 49846579.0, 3: 23725625.0, 4: 6450551.0},
               'SMILES': {0: 'CCNC(=O)C1=NOC(=C1C2=CC=C(C=C2)CN3CCOCC3)C4=CC(=C(C=C4O)O)C(C)C',
                          1: 'CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC=CC(=C4)NC(=O)C)C5CC5',
                          2: 'CC1(CCC(=C(C1)C2=CC=C(C=C2)Cl)CN3CCN(CC3)C4=CC(=C(C=C4)C(=O)NS(=O)(=O)C5=CC(=C(C=C5)NCC6CCOCC6)[N+](=O)[O-])OC7=CN=C8C(=C7)C=CN8)C',
                          3: 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F',
                          4: 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4'}
               }
train_smiles_df = pd.DataFrame(data=train_smiles)

test_smile = train_smiles
test_smile_df = pd.DataFrame(test_smile)

#Example 1: to call NLP-based feature extraction method
#Train, Test, feature_sequences, feature_to_token_map = fe_1mol.oneMolFeatureExtraction(trainSMILES=train_smiles_df, testSMILES=train_smiles_df,ngram_list=[1,2,3,4,5,6,7,8])
Train, Test, feature_sequences, feature_to_token_map = fe_1mol.oneMolFeatureExtraction(trainSMILES=train_smiles_df, testSMILES=None,ngram_list=[1,2,3,4,5,6,7,8])

#Example 2: to call Morgan Fingerprints based feature extraction method
#nBits is the number of bits in the fingerprint
result= fe_1mol.morganFingerPrint(train_smiles_df, nBits=1024)Note:The input to the methodoneMolFeatureExtraction() and morganFingerPrint()must be a pandas DataFrame and the Drug SMILES column name must be in uppercase:e.g., SMILESCite us at:Rahul Sharma, & Jake Y. Chen. (2022). Drug SMILE Feature Extraction Tool (1.0.3). Zenodo.https://doi.org/10.5281/zenodo.7072304
Package: drugwars
DrugwarsThe DOS game from the 80s re-written in Python from Scratch!Play in your browserHereInstallationRecommended:Using pippip3 install drugwars

# To Update
pip3 install --upgrade drugwarsInstalling from sourcegit clone https://github.com/M4cs/drugwars
cd drugwars
python3 setup.py install
drugwarsHow to PlayGameplayThe game is played inside of your terminal and uses letters and numbers to select things and take part actions.You start with 2000 dollars and 5500 dollars in debt to the loan shark. The goal of the game is to pay off the loan shark and get as much money as possible while avoiding cops or getting mugged.Cops can shoot at you and you have 20 hit points until you die and the game ends. You are able to purchase guns occasionally which will give you a chance to kill the cops.You have 30 days to buy and sell as many drugs as possible to make a profit. Prices are randomly generated so it's all dependent on the market in the area you're at.There are events that can happen inbetween moving areas. These can either be good for you or bad, it's all random.You have access to a bank and stash in the Bronx which allows you to store money and drugs to keep them safe while getting mugged or caught by the cops.RulesYou cannot go back to a location if you have chosen jet, you must travel somewhere else.You can only go to the loan shark, bank, and stash in the Bronx.You only have 30 days to make your money and pay back the loan shark.Your debt increases each day with interest so pay it back ASAPYour savings will increase each day with interest as well.ControlsWhen asked yes or no questions:Respond withyornWhen asked to (B)uy, (S)ell, or (J)et:Respond withbto buy,sto sell, orjto jetWhen asked to (R)un or (F)ight:Respond withrto run, orfto fightWhen asked where to go:Respond with a number1-6, these correspond to a location on the tableTroubleshootingI pressed buy or sell and I didn't mean to!Simply choose a drug and set 0 as the quantity.I jetted but want to go back.You can't this is a game mechanic. Be careful with your choices!Why did I make this?Drugwars is a game I've been playing for a long time, well before I learned how to program. Now that I'm apt in Python I thought what the hell, why not rewrite a game I love in Python and release it. Well that's what I'm doing and here it is. Enjoy :)Changelogv1.2.1- Fix End Game
- Add Endgame
- Change Coat Probabilityv1.2.0- Optimized Drug Stashes
- Optimized Stash Menu
- Optimized Classes
- Optimized Helpers
- Bug Fixes
- Added Withdraw to Stash (it was missing lol)v1.1.10- Adds "a"/"all" and "h"/"half" features in Loan Shark and Bankv1.1.9- Fix infinite loop in Issue #2v1.1.8- Added difficulty screenv1.1.7- Fix loan shark balance
- Fix input bugsv1.1.6:- Fix borrowingv1.1.5:- Fix actions breakingv1.1.4:- Remove Auto updatev1.1.3:- Fix logic around pricing eventsv1.1.2:- Disable Actions from occuring first roundv1.1.1:- Adds auto update check (Set DO_NOT_UPDATE to 1 to skip)v1.1.0:- Adds upgrade trench coat
- Changes chances of getting away from Headass
- Fixes crashing bugs
Package: druida
DRUIDAThe master intelligence for metasurface designDruida is an artificial intelligence developed support the metasurfaces design process.Generative pipelines for metasurfaces design
The goal is to provide a stable version of the most important algorithmic pipelines to train and deploy AI for metasurfaces design.FeaturesDeep Neural Network StackGAN Generator StackGAN Discriminator StackUnconditional Diffusion ModelConditional Diffusion ModelGoalsConfigurable AI models
Easy to interface and use through jupyter notebooks.
Reproduceable models
API to future hyperparameters optimizationTechDillinger uses a number of open source projects to work properly:[Python] - Python 3.[PyTorch] - The framework to build our algorithms.[CLIP] - Pipelione to produce word encoding.InstallationInstall the dependencies and devDependencies and start the server.https://pypi.org/project/druida/pipinstalldruidaPluginsDillinger is currently extended with the following plugins.
Instructions on how to use them in your own application are linked below.PluginREADMEDropbox[plugins/dropbox/README.md][PlDb]GitHub[plugins/github/README.md][PlGh]Google Drive[plugins/googledrive/README.md][PlGd]OneDrive[plugins/onedrive/README.md][PlOd]Medium[plugins/medium/README.md][PlMe]Google Analytics[plugins/googleanalytics/README.md][PlGa]DevelopmentWant to contribute? Great!Dillinger uses Gulp + Webpack for fast developing.
Make a change in your file and instantaneously see your updates!Open your favorite Terminal and run these commands.First Tab:nodeappSecond Tab:gulpwatch(optional) Third:karmatestBuilding for sourceFor production release:gulpbuild--prodGenerating pre-built zip archives for distribution:gulpbuilddist--prodMITFree Software, Hell Yeah!
Package: druidaHFSS
druidaHFSSAnsys HFSS InterfaceSimulate, data generation, optimizationThis development is intended to provide an interface to access HFSS, in order to automate simulation, Data generation and optimization processes.FeaturesAutomate the re-creation of models in aedt files.Create intermediate files to run simulations.Run simulations and gather specific metrics from the simulator.Usagefrom dbuilder import Manager as MGBuilder=MG.Builder(ansysPath=ansysPath,modelName=_,projectName=_, designName=_,modelPath=_, exportPath=_)Builder.create()Builder.sim_file('', batch, iteration, simfile_path, **kwargs)Builder.simulate(simfile_path+simfileName)dbManager=MG.DBManager(ansysPath=ansysPath,
                       modelName=modelName,
                       projectName=project_name,
                       designName=designName,
                       modelPath=modelPath, 
                       dbPath=dBPath,
                      dbName=dbName)dbManager.load_df(columns=columnNames)


"""This object can be treated as a pandas dataframe"""
dbManager.dfdbManager.insert_row(data_to_store)image_rgb=tools.cropImage( file,image_path=path,
                              image_name=fileName_absolute,
                              output_path=imagesPath, 
                             resize_dim=(512,512))
Package: druid-cli
Druiddata store does some incredible things and
at the same time is a piece of software that can come in your darkest
nightmares.It is simply hard to work with druid: starting from deploying cluster
and ending on using the simplest task.druid-cliis a tool that aims to help with some tasks that are really
PITA in druid like:removing your datachecking if anything worksinspecting clusterProject is still WIP so you should be aware that: * some features are
not available (tasks, removing segments etc.) * it is very likely that
error messages (or even stack traces) won’t tell you what you’re doing
wrongInstallationInstall from sources or use pip to fetch latest version from PYPI:pip install druid-cliUsageUsage: druid-cli [OPTIONS] COMMAND [ARGS]...

Options:
  --overlord LOCATION     Hostname and port of overlord node
  --coordinator LOCATION  Hostname and port of coordinator node
  --broker LOCATION       Hostname and port of broker node
  --help                  Show this message and exit.

Commands:
  datasource  note: requires druid broker node
  rule        note: requires druid coordinator node
  server      note: requires druid coordinator node
  task        note: requires druid overlord nodedruid-cli assumes that each type of druid nodes listens onlocalhost:8080. This obviously can not be true so you have give todruid-clian explicit location of required druid node like:druid-cli --coordinator localhost:8082 datasource detailsThis obviuosly sucks but you can provide those locations using following
environment variables:DRUID_OVERLORDDRUID_COORDINATORDRUID_BROKERFor more detailed usage on commands usedruid-cli<command>--helpContributionsContributions are very welcome.Changelist0.3.1 (2014-12-10)fixed project requirements (python-dateutiladded)0.3.0 (2014-12-09)submitting tasks now requires specifying interval0.2.0 (2014-12-05)support for submitting delete task0.1.0 (2014-12-03)errors from druid API endpoints are now nicely formattedsome errors can give hints about what is incorrect (currently only
http 404 errors)html2textadded to requirementsinitial tasks support (kill and noop)Licencedruid-cliis licensed under LGPL license, version 3.
Package: druid-data
DruidDataA library to store and retrieve python objects in a DynamoDB database. It supports the basic CRUD operationsFeaturesConstantString- An enum that holds constant values for parameter namesDynamoDBGlobalConfiguration:- A singleton to define global parameters related to DynamoDBdynamo_entity- A decorator for classes whose objects will be stored in and retrieved from the databaseDynamoCrudRepository- An abstract class to execute CRUD operations on DynamoDB using the classes decorated with the dynamo_entity decorator, use the extensions of this class:SingleTableDynamoCrudRepositoryandMultiTableDynamoCrudRepositorydepending on your approachSingleTableDynamoCrudRepository- An extension of the DynamoCrudRepository specifically for SingleTable designMultiTableDynamoCrudRepository- An extension of the DynamoCrudRepository specifically for Mutitable designInstallationWithpipinstalled, run:pip install druid_dataLicenseThis library is licensed under the MIT-0 License. See the LICENSE file.
Package: druid-libs
Druid LibsA suite of Python utilities to ease adopting best practices such as test mock lambda functions.FeaturesMockLambda- A utility that help you to test Lambda function handlers locally with unity test, this utility allow you invoke your lambda passing dinamically env variables to your function, to test his behavior.FakeContext- A utility that help you to test Lambda function handlers locally with unity test, using a fake context.FakeEvent- A utility that help you to create a fake event for API Gateway, this help you to work with verbs: GET, POST, DELETE, and UPDATE.InstallationWithpipinstalled, run:pip install druid-libsLicenseThis library is licensed under the MIT-0 License. See the LICENSE file.
Package: druid-query
Apache Druid Query LibraryA simple library for creating and executing queries with Apache Druid.UsageSQL Queriesfromdruid_query.clientimportClientfromdruid_query.queriesimportSqlclient=Client(sql_endpoint='localhost:8082/druid/v2/sql/')query=Sql('SELECT * FROM wikipedia LIMIT 10')result=client.execute(query)print(result)Native Queriesfromdruid_query.clientimportClientfromdruid_query.componentsimport*fromdruid_query.queriesimportTimeseriesclient=Client(native_endpoint='localhost:8082/druid/v2/')query=Timeseries('wikipedia',[intervals.Interval('2015-09-11','2015-09-13')],granularities.Period('PT2H'),aggregations=[aggregations.Count('num_records')])result=client.execute(client,query)print(result)Async supportfromdruid_query.clientimportAsyncClientfromdruid_query.queriesimportSqlimportasyncioclient=AsyncClient(sql_endpoint='localhost:8082/druid/v2/sql/')query=Sql('SELECT * FROM wikipedia LIMIT 10')result=asyncio.run(client.execute(query))print(result)