Saravanan666/pypi-org huggingface dataset:
Package: bpmagic
# bpmagicbpmagic is a library to save and load boilerplate code. With a few simple commands, you can save and load bits of frequently used code to speed up programming in iPython and Jupyter.## Installbpmagic is up on pip, so you can simply call:`pip install bpmagic`## Loading bpmagicTo load the bpmagic namespaces, you can use the load_ext magic function:`%load_ext bpmagic`To prevent typing this everytime, you can add this to your ipython profile startup (mine is in `/.ipython/profile_default/startup/`) and place the above command into a `*.ipy` file (you can name it whatever you want, I used `bpmagic.ipy`)## SaveUsing the `%%isave` magic function, you can write and save a chunk of code into a new bpmagic namespace. For example:```%%isave dataimport pandas as pdimport numpy as npimport scipy as sp```This will save your current cell of code into the namespace `data`. If the namespace is already defined, you can overwrite using the -o flag:```%%isave plot -oimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inline```## LoadUsing the `%iload` function, you can load up 1 or more profiles at once. For example:`%iload data plot`This will load the boiler plate code for `data` and `plot` and replace the cell that contained `%iload` with:```# dataimport pandas as pdimport numpy as npimport scipy as sp# plotimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inline```## DeleteIf you pass a profile name after `%idelete`, you will remove that profile name. Example:`%idelete data`## RenamePassing in the old name followed by the new profile name will change the name of a profile:`%irename plot plotting`## ListTo get a list of current profiles, simply call `%ilist` in ipython, and it will return a list of all of your current profiles.
Package: bpm-ai
bpm-aiAI task automation for BPM engines.Seecamunda-8-connector-gptfor actual integration with the Camunda Platform 8 BPM engine using custom BPMN elements (Connectors).InstallationRequires Python 3.11.Default$pipinstallbpm-aiFor Local InferenceInstall PyTorch (remove index url for CUDA GPU support) and spaCy:$pipinstalltorch--index-urlhttps://download.pytorch.org/whl/cpu
$pipinstallspacyFor Apple Silicon:$pipinstalltorchspacy[apple]Install bpm-ai with inference extra:$pipinstallbpm-ai[inference]LicenseThis project is developed underSponsors and Customers
Package: bpmappers
bpmappers is a Python moudle that maps Python dictionary values and object properties to different dictionary.InstallInstall using pip.$ pip install bpmappersUsageAn example of mapping an instance of the Person class to a dictionary:>>>classPerson:...def__init__(self,name,age):...self.name=name...self.age=age...def__repr__(self):...return"<Person name={}, age={}>".format(self.name,self.age)...>>>p=Person("Spam",25)>>>p<Person name=Spam, age=25>>>>frombpmappersimportMapper,RawField>>>classPersonMapper(Mapper):...mapped_name=RawField('name')...mapped_age=RawField('age')...>>>PersonMapper(p).as_dict()OrderedDict([('mapped_name', 'Spam'), ('mapped_age', 25)])RequirementsTarget Python version is 3.6, 3.7, 3.8, 3.9Django 2.2, 3.0, 3.1 (When use Django support)LicenseThis software is licensed under the MIT License.DocumentationThe latest documentation is hosted at Read The Docs.https://bpmappers.readthedocs.io/ja/stable/DevelopThis project is hosted at GitHub:https://github.com/beproud/bpmappersRelease Procedure:https://github.com/beproud/bpmappers/blob/master/release_checklist.rstAuthorBeProud, IncMaintainerShinya Okano <tokibito@gmail.com>
Package: bpmll
bp-mll-tensorflowEfficient (vectorized) implementation of the BP-MLL loss function in TensorFlow (bp_mll.py).BP-MLL is a loss function designed for multi-label classification using neural networks. It was introduced by Zhang & Zhou in [1]. Note that in line with [1], every sample needs to have at least one label and no sample may have all labels.Installationpip3 install bpmllUsagefrom bpmll import bp_mll_lossThen simply use it as a function in your tensorflow or keras models.Check outfull_example.pyfor an example of training a simple multilayer perceptron using Keras with BP-MLL.References[1] Zhang, Min-Ling, and Zhi-Hua Zhou. "Multilabel neural networks with applications to functional genomics and text categorization." IEEE transactions on Knowledge and Data Engineering 18.10 (2006): 1338-1351.
Package: bpmodels
BrainPy-ModelsNote:We welcome your contributions for model implementations.BrainPy-Modelsis a repository accompany withBrainPy, which is a framework for spiking neural network simulation. With BrainPy, we implements the most canonical and effective neuron models and synapse models, and show them inBrainPy-Models.Here, users can directly import our models into your network, and also can learn examples of how to use BrainPy fromDocumentations.We provide the following models:Neuron modelsSynapse modelsLearning rulesNetworksLeaky integrate-and-fire modelAlpha SynapseSTDPContinuous attractor networkHodgkin-Huxley modelAMPA/NMDABCM ruleE/I balance networkIzhikevich modelGABA_A/GABA_BOja's rulegamma oscillationsMorris--Lecar modelExponential Decay SynapseGeneralized integrate-and-fireDifference of Two ExponentialsExponential integrate-and-fireShort-term plasticityQuadratic integrate-and-fireGap junctionadaptive Exponential IFVoltage jumpadaptive Quadratic IFHindmarsh--Rose modelWilson-Cowan modelInstallationInstall from source code:python setup.py installInstallBrainPy-Modelsusingconda:conda install bpmodels -c brainpyInstallBrainPy-Modelsusingpip:pip install bpmodelsThe following packages need to be installed to useBrainPy-Models:Python >= 3.7Matplotlib >= 2.0brainpy-simulator >= 0.3.0Quick StartThe use ofbpmodelsis very convenient, let's take an example of the implementation of the E-I balanced network.We start by importing thebrainpyandbpmodelspackages and set profile.importbrainpyasbpimportbpmodelsimportnumpyasnpimportmatplotlib.pyplotasplt# set profilebp.profile.set(jit=True,device='cpu',numerical_method='exponential')The E-I balanced network is based on leaky Integrate-and-Fire (LIF) neurons connecting with single exponential decay synapses. As showed in the table above,bpmodelsprovides pre-defined LIF neuron model and exponential synapse model, so we can usebpmodels.neurons.get_LIFandbpmodels.synapses.get_exponentialto get the pre-defined models.V_rest=-52.V_reset=-60.V_th=-50.neu=bpmodels.neurons.get_LIF(V_rest=V_rest,V_reset=V_reset,V_th=V_th,noise=0.,mode='scalar')syn=bpmodels.synapses.get_exponential(tau_decay=2.,mode='scalar')# build networknum_exc=500num_inh=500prob=0.1JE=1/np.sqrt(prob*num_exc)JI=1/np.sqrt(prob*num_inh)group=bp.NeuGroup(neu,geometry=num_exc+num_inh,monitors=['spike'])group.ST['V']=np.random.random(num_exc+num_inh)*(V_th-V_rest)+V_restexc_conn=bp.SynConn(syn,pre_group=group[:num_exc],post_group=group,conn=bp.connect.FixedProb(prob=prob))exc_conn.ST['w']=JEinh_conn=bp.SynConn(syn,pre_group=group[num_exc:],post_group=group,conn=bp.connect.FixedProb(prob=prob))exc_conn.ST['w']=-JInet=bp.Network(group,exc_conn,inh_conn)net.run(duration=500.,inputs=(group,'ST.input',3.))# visualizationfig,gs=bp.visualize.get_figure(4,1,2,10)fig.add_subplot(gs[:3,0])bp.visualize.raster_plot(net.ts,group.mon.spike,xlim=(50,450))fig.add_subplot(gs[3,0])rates=bp.measure.firing_rate(group.mon.spike,5.)plt.plot(net.ts,rates)plt.xlim(50,450)plt.show()Then you would expect to see the following output:
Package: bpm-renamer
BPM Detector in PythonImplementation of a Beats Per Minute (BPM) detection algorithm, as presented in the paper of G. Tzanetakis, G. Essl and P. Cook titled: "Audio Analysis using the Discrete Wavelet Transform".You can find it here:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.5712Based on the work done in the MATLAB code located at github.com/panagiop/the-BPM-detector-python.Process .wav file to determine the Beats Per Minute.Dependencies: scipy, numpy, pywavelets, matplotlib
Package: bpmscan
BPMScanEver imagined that from a video footage can you measure heart rate of a person? Well, Yeah! It is possible and the technology behind that is Remote-photoplethysmography (rPPG) and Face tracking. Try Out :)Installation$pip3installbpmscanUsage$bpmscan#Defaults to webcam(camera_id=0)$bpmscan[camera_id,ipcamera_address,video_file_path]#Optional to useDisclaimerThis project is made for a coursework and represents only educational usage. This should not be used as a means of medical instrument, software or as a reference to measure any health vitals as this is under development and may give errenous result. The author is not responsible for any incidents occuring after violating this Disclaimer.
Package: bpm-tools
READMEThis README would normally document whatever steps are necessary to get your application up and running.What is this repository for?Quick summaryVersionLearn MarkdownHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesWriting testsCode reviewOther guidelinesWho do I talk to?Repo owner or adminOther community or team contact
Package: bp-neurotools
neurotoolsCreative and original name? Maybe not. Lots of cool and helpful tools for working with neuroimaging data? Yes.What's Included?neurotools is split into a number of different directories / sub-packages, each including different functionality (though key pieces from different sub-directories are shared allowing for some cool functionality!). Be low are some brief descriptions of the different sub-packages.loading :
This directory encompasses a set of utilities for loading generic neuroimaging data, as well as some ABCD study specific loading utilities.misc :
A set of misc. functions that don't fit in nicely anywhere else.plotting :
Tools and functions for plotting surface representations of the data. Also
smart functions for easy quick plots and collages of surface representations.
Still a big work in progress as plotting can be messy, but in general these
tools are designed to help make it easier and more stream-lined.random :
Different from misc... this includes functions that are based on randomness.
These include utilities for generating constrained permutations based on block structures
and also for generating random / null surface parcellations.rely :
Included here are utilities for running 'reliability' like tests. These
were featured inhttps://github.com/sahahn/ABCD_Consortium_Analysisstats :
Featured here are wrappers and other code for running statical tools,
from basic functions (i.e., calculating cohen's d effect size with or without NaN's)
to running mixed linear models.transform :
Tools for transforming data - that is to say, tools for extracting ROIs from surface
level data are provided, with utility for inverse_transforming them as well. Also included,
but still more as a work in progress, are some utilities for converting between different
common surfaces, e.g., from fs LR 32k surface parcellations to a version without medial
walls and with the sub-cortical regions included.Besides these main functionalities, another main folder is included, called "slurm". This folder
will store examples for using the neurotools package on a SLURM cluster is a massively paralell way.
Included are, and in the future will be, examples with extra scripts and pieces responsible for submitting and collecting jobs.InstallThe easiest way to install is through pypip viapip install bp-neurotoolsYou can also  install the latest version via github directly, by cloning (git clone https://github.com/sahahn/neurotools) and then pip installing (navigate into the directory then runpip install .) the repository.This will install for you the latest version. Note that on running it for the first time, it will install for you automatically the required external data that the library leverages.
Package: bpnotify
bpnotifyis notification routing for Django. Application notify function with targets, notify_type, media, extra_data, then bpnotify send notify with backend (example: mail).RequirementsPython (3.9, 3.10, 3.11, 3.12)Celery (5.2, 5.3)Django (3.2, 4.2)sixLinksrelease checklist
Package: bpolicy
A collection of basic policies for resources anti-spammer.Install from Pypipip install bpolicyBasic Exampleimport memcache
from bpolicy.consts import MINUTE, HOUR
from bpolicy import RatedPolicyFactory, ClockPolicyFactory, GenerationedPolicyFactory
from bpolicy import chained_policy, silent_check

store = memcache.Client(['127.0.0.1:9013'])
rated_factory = RatedPolicyFactory(quota=10, interval=1 * MINUTE)
clock_factory = ClockPolicyFactory(start_time=time(hour=1), end_time=time(hour=6), discount=0.3)
generationed_factory = GenerationedPolicyFactory(quota=10, interval=1 * HOUR, discount=0.5, max_keep_traking=3)


def check_policy(identity):
    return silent_check(chained_policy('hello', [generationed_factory, clock_factory, rated_factory], store), identity)


identity = "88.88.23.1"
for i in range(100):
    if check_policy(identity):
        print("success")
    else:
        print("banded")
Package: bpoptimizer
BlockParty Floor OptimizerThis package allows for reading a BlockParty floor as a 48x48 pixel image and provides analytics including optimized standing positions.Free software: MIT licenseDocumentation:https://radioactivedroid.github.io/bpoptimizer/FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-01-21)First release on PyPI.
Package: bpp-anubis
Framework for running bpp qaUsageGeneral structure of anubis commandanubis-d<path tobehave-likedirectory>-e<environments>-c<mcat, lsat>-p<number of processes>-b<browser to use>-it<tags to include in the run>-et<tags to exclude from the run>-r<results destination json>-rt<number of retries for failed scenarios>-a<path to accounts file>-s<section of accounts file to read; usually "course.env">-ofoutexact parameters vary from project to project.Installationpip installbpp-anubisRequirementsassertpyauth0-pythonbeautifulsoup4behave-html-formatterconfigparserhtml5liblxmlnested_lookuppandaspdfkitrequestsseleniumslack_sdkuuidclipboardCompatibilityLicenceMITAuthorsanubiswas written bymatthew bahloul.
Package: bpp-env-helpers
environment-helperspackage for generalizing qa project setupMaking Queriesfunction to read query filedef get_query_string(path_to_query_file) -> str:
    # implementationfunction to send the request with parametersdef make_query(gql_url, query_string, query_file_path, variables) -> Response:
    # implementationValidating parameters for test runs (WIP)Required Parametersfaculty-admin-portalenv = tst|stgappbrowser = chromium|firefox|webkitfaculty-portalenv = tst|stgappbrowser = chromium|firefox|webkitqbank-student-portalenv = tst|stgappbrowser = chromium|firefox|webkitqbank-admin-portalenv = tst|stgappbrowser = chromium|firefox|webkitcf-student-portalenv = tst|stgapp|betabrowser = chromecf-admin-portalenv = tst|stgapp|betabrowser = chromemcat/lsat-admin-portalenv = tst|stgappbrowser = chromecourse = mcat|lsatmcat-student-portalenv = tst|stgapp|beta|prodbrowser = chromelsat-student-portalenv = tst|stgapp|beta|prodbrowser = chromerev-techenv = tst|stgappbrowser = chromeBooleans to Set Upcheck if there's user/password, if so, tell the run to log in with that datacreate a boolean calledlog_in_with_cookie = FalseReading and returning environment data (WIP)Reading and returning account data (WIP)Triggering functions (WIP)Properties (maybe) (WIP)Resetting (maybe) (WIP)
Package: bp.preferences
bp.preferences is based on the Preference package available on PyPI (http://pypi.python.org/pypi/Preferences).It overwrites theload_filesmethod to change the behaviour of loading preferences from different files. On initialization preferences are always loaded in this order:package_location/file_name.cfg  (contains default preferences)/etc/file_name~/.file_namecurrent_working_direcory/file_nameAdditionaly some methods are added to make conversion of preference values to Python types easier:def _toString(self, value):Converts a string enclosed with single or double quotes to a Python string without quotes. This is needed e.g. to support preference values with leading/trailing whitespace characters.CHANGELOG0.1Initial release
Package: bpptkg-bulletinclient
bpptkg-bulletinclientbpptkg-bulletinclient is a Python library primarily used to send WebObs update
signals to the BPPTKG bulletin web services.InstallationInstall from PyPI by running this command:pip install -U bpptkg-bulletinclientGuidesYou can see the guides by viewing command help:bulletinclient -hFailed Request DataIf bulletinclient failed to send the request, it will store the data (JSON
format) in the~/.bulletin/failedrequest/directory. You can use the data to
investigate the problem or send the data again to the bulletin web services.LicenseMIT
Package: bpptkg-meteo
bpptkg-meteoBPPTKG weather station data fetcher and schema tables library.InstallationInstall the latest version from PyPI by typing this command:pip install -U bpptkg-meteoDocumentationFull documentation is available atdocs/directory. You can build the
documentation by running these commands:cd /path/to/bpptkg-meteo/
pip install -r requirements.txt
sphinx-build -b html docs/ /path/to/build/LintingUse black formatter to lint and fixes code files if you made changes to the
scripts.ContributingSeeCONTRIBUTING.mdto learn how to contribute to this project.LicenseBy contributing to the project, you agree that your contributions will be
licensed under its MIT license. SeeLICENSEfor
details.
Package: bpptkg-querybulletin
bpptkg-querybulletinSimple Python library to query seismic bulletin database.InstallationInstall the latest version from PyPI:pip install -U bpptkg-querybulletinInstall database vendor dependent client package, e.g. for MySQL:pip install mysqlclientDatabase CredentialsCreate JSON file in~/.bulletin/querybulletin/config.jsonto store your
seismic bulletin database credentials. For example:{"dburl":"mysql://user:password@localhost/seismic_bulletin"}If you have custom config file path, add-cor--configoption to the full
path of JSON config file in the script arguments.ExamplesQuery bulletin for certain time range:querybulletin -s "2021-08-01 06:00:00" -e "2021-08-10 06:00:00"Query bulletin for certain time range and event type:querybulletin -s "2021-08-01 06:00:00" -e "2021-08-10 06:00:00" -t VTBQuery bulletin for certain event ID:querybulletin -u "2021-07#2355"Store output to the CSV file:querybulletin -s "2021-08-01 06:00:00" -e "2021-08-10 06:00:00" -t VTB -o bulletin.csvLicenseMIT
Package: bpptkg-richter
bpptkg-richterbpptkg-richteris a Python library for computing Richter local magnitude
scales on BPPTKG seismic network.InstallationInstall using pip:pip install -U bpptkg-richterRichter Magnitude ScalesYou may want to installObsPypackage, because this
package only work on ObsPy stream type. Default network isVGand default
component isZ:fromobspyimportreadimportrichter# Read single station or multiple stations streamsstream=read('/path/to/stream.mseed')# Compute Richter local magnitude for station MEPASml=richter.compute_ml(stream,'MEPAS',network='VG',component='Z')# Compute Wood-Anderson zero-to-peak amplitude in meter for station MEPASwa_ampl=richter.compute_wa(stream,'MEPAS',network='VG',component='Z')# Compute count amplitude peak-to-peak for station MEPASapp=richter.compute_app(stream,'MEPAS',network='VG',component='Z')or for short:fromobspyimportreadimportrichterstream=read('/path/to/stream.mseed')ml=richter.compute_ml(stream,'MEPAS')wa_ampl=richter.compute_wa(stream,'MEPAS')app=richter.compute_app(stream,'MEPAS')For current version, on computing local magnitude (compute_ml) and
Wood-Anderson amplitude(compute_wa), the only supported component isZcomponent.compute_appsupport other components, for example:app=richter.compute_app(stream,'MELAB',component='E')DocumentationFull documentation and guides are available atdocs/directory and online athttps://bpptkg-richter.readthedocs.io/en/latest/.LicenseBy contributing to the project, you agree that your contributions will be
licensed under its MIT license. SeeLICENSEfor
details.
Package: bppy
BPpy: Behavioral Programming In PythonA Python implementation for the Behavioral Programming paradigmInstallationYou can installbppywith pip:pipinstallbppyThis does not include dependencies for gym integration. To installbppywith gym integration, use:pipinstallbppy[gym]If installation is not working, you can try upgrading pip:pipinstall--upgradepipbefore submitting an issue.In case you want to get started instantly and usebppy(withpynusmvand all dependencies) without installing it, you can use thedocker image:dockerrun-ittomyaacov/bppy-dockerDocumentationBPpy documentationGeneral introduction to Behavioral Programming can be found inthisBPjs tutorial and othersCiting BPpyTo cite this repository in publications:@article{yaacov_bppy_2023,
title = {BPpy: Behavioral programming in Python},
journal = {SoftwareX},
volume = {24},
pages = {101556},
year = {2023},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2023.101556},
url = {https://www.sciencedirect.com/science/article/pii/S2352711023002522},
author = {Tom Yaacov}
}
Package: bpqcrypto
# bpqcrypto## Test` python3-mbpqcrypto.test `## Install### Windows` pip3 installbpqcrypto-1.0.2-py3-none-win_amd64.whl`### Linux` pip3 installbpqcrypto-1.0.2-py3-none-linux_amd64.whl`
Package: bp-query-tool
Query ToolQuery Tool- a powerful and user-friendly streamlit component designed to simplify your data analysis. With its intuitive interface, you can easily select the desired databases, columns, and tables, and apply filters, dimensions, and measures to analyze your data. Whether you're a beginner or an experienced analyst, "query_tool" is the perfect tool for your data analysis needs.InstallationYou can install theData Gridpackage from PyPI using pip:pipinstallbp-query-toolUsageTo use the Query Tool component in your Streamlit application, follow these steps:Import the necessary modules:importquery_toolasqtAdd theQuery Toolcomponent to your application:qt.query_tool(query=query,title="Query Tool",databases=databases,tables=tables,columns=columns,on_database_change=on_database_change,on_table_change=on_table_change,on_generate_query=on_generate_query,on_copy_query=on_copy_query,on_execute_query=on_execute_query,key="query_builder",)Run your Streamlit application:streamlitrunyour_app.pyExampleimportquery_toolasqtdatabases=['COMPANY_DB','SALES_DB']tables=[{'database':'COMPANY_DB','tables':['EMPLOYEES','DEPARTMENTS']},{'database':'SALES_DB','tables':['CUSTOMERS','ORDERS']}]columns=[{'fqtn':'COMPANY_DB.EMPLOYEES','columns':['ID','NAME','EMAIL','POSITION']},{'fqtn':'COMPANY_DB.DEPARTMENTS','columns':['ID','NAME','LOCATION','MANAGER']},{'fqtn':'SALES_DB.CUSTOMERS','columns':['ID','NAME','EMAIL','PHONE']},{'fqtn':'SALES_DB.ORDERS','columns':['ID','PRODUCT','QUANTITY','DATE']}]defon_database_change(database):print(database)defon_table_change(database,table):print(database,table)defon_generate_query(selection,query):print(selection,query)defon_copy_query(query):print(query)defon_execute_query(query):print(query)qt.query_tool(query=query,title="Query Tool",databases=databases,tables=tables,columns=columns,on_database_change=on_database_change,on_table_change=on_table_change,on_generate_query=on_generate_query,on_copy_query=on_copy_query,on_execute_query=on_execute_query,key="query_builder",)Screenshot
Package: bp.rename
Usagebp_rename COMMAND <options>Commandsremovebp_rename remove <pattern>[...]removes every occurance of <pattern> from all filenamesreplacebp_rename replace <pattern> <replace>[...]replace every occurance of <pattern> with <replace> in all filenamesWARNINGDO NOT USE THIS PACKAGE ON DATA THAT IS NOT COMPLETELY USELESS FOR YOU!Onlyverybasic tests have been run on OS X 10.6 and Ubuntu 10.4.It is not utterly ouf question that the universe might collapse if you run this code.You have been warned. Don’t complain!Changelog0.1.1Recreated the project with modern-package-template (http://pypi.python.org/pypi/modern-package-template/1.0).Fixedhttp://github.com/disko/bp.rename/issues#issue/1(thanks to Sridhar Ratnakumar for the pointer)added check for empty files arguments and empty todo lists0.1Initial release