Saravanan666/pypi-org huggingface dataset:
Package: bhive
bhive - Unofficial Python Library for Hivebhive is an unofficial python library for hive, which is created new from beem
The library name is a play on word from beem and hive making a bee hive. bhive includespython-graphenelib.InstallationThe minimal working python version is 2.7.x. or 3.4.x
Tested on python versoin 3.7.5bhive can be installed parallel to python-hive and beem.For Debian and Ubuntu, please ensure that the following packages are installed:sudoapt-getinstallbuild-essentiallibssl-devpython-devFor Fedora and RHEL-derivatives, please ensure that the following packages are installed:sudoyuminstallgccopenssl-develpython-develFor OSX, please do the following:brew install openssl
export CFLAGS="-I$(brew --prefix openssl)/include $CFLAGS"
export LDFLAGS="-L$(brew --prefix openssl)/lib $LDFLAGS"For Termux on Android, please install the following packages:pkginstallclangopenssl-devpython-devSigning and Verify can be fasten (200 %) by installing cryptography:pipinstall-Ucryptographyor:pipinstall-Usecp256k1prpInstall or update bhive by pip:pip install -U bhiveYou can install bhive from this repository if you want the latest
but possibly non-compiling version:git clone https://github.com/thecrazygmn/bhive.git
cd bhive
python setup.py build

python setup.py install --userRun tests after install:pytestCLI tool bhivepyA command line tool is avail1able. The help output shows the available commands:bhivepy --helpStand alone version of CLI tool bhivepyWith the help of pyinstaller, a stand alone version of bhivepy was created for Windows, OSX and linux.
Each version has just to be unpacked and can be used in any terminal. The packed directories
can be found under release. Each release has a hash sum, which is created directly in the build-server
before transmitting the packed file. Please check the hash-sum after downloading.ChangelogCan be found in CHANGELOG.rst.LicenseThis library is licensed under the MIT License.Acknowledgementsbeem created by holger80python-bitsharesandpython-graphenelibwere created by Fabian Schuh (xeroc).
Package: bhmm
This project provides tools for estimating the number of metastable states, rate
constants between the states, equilibrium populations, distributions
characterizing the states, and distributions of these quantities from
single-molecule data. This data could be FRET data, single-molecule pulling
data, or any data where one or more observables are recorded as a function of
time. A Hidden Markov Model (HMM) is used to interpret the observed dynamics,
and a distribution of models that fit the data is sampled using Bayesian
inference techniques and Markov chain Monte Carlo (MCMC), allowing for both the
characterization of uncertainties in the model and modeling of the expected
information gain by new experiments.
Package: bhmsh
BHMshA lightweight OpenAI-powered Bash actuator.Installationpython3 -m pip install --upgrade bhmshBHMsh requires Python 3.8 or greater and a Unix-based OS (macOS or Linux).With that being said, there are currently build issues with the latest Python 3.12: use 3.8 - 3.11.Python 3 Setup (Supplementary)Download an acceptable version of Python 3here.To verify:python3 --versionIf you don't see the correct version after running this, you may need to consult the interent on Python installation locations for your specific OS.
And then you'll need to update yourPATHenvironment variable accordingly.Once you have an acceptable version of Python 3, doing the following doesn't hurt:python3 -m pip install --upgrade pip setuptools wheelUsageMost basic execution explicitly using your OpenAI API key:bhmsh -t <your-openai-api-key>You can also execute BHMsh in the following manner:bhmshThis will check your current environment for an exported variableOPENAI_API_KEYcontaining your OpenAI API key.If you want to see a log of BHMsh's interation with GPTsh:bhmsh --logThis will create a tail-able log file (tail -f <path-to-log>in a concurrent terminal).
Its path is given upon execution.For full execution parameter details:bhmsh --helpMajor runtime commandsCommandDescription(c)learStarts a new ChatGPT session(q)uit, (e)xitQuit BHMsh
Package: bh_oauth
Dependencies:requests- HTTP for HumansQuick Example:auth = dict(
    client = "Bullhorn OAuth Client ID",
    secret = 'Bullhorn OAuth Client Secret',
    usr = "Bullhorn Username",
    pwd = "Bullhorn Password")

bhr = BHRest(**auth)
# If in the sandbox environment - update URLs
bhr._auth_url = "https://auth9.bullhornstaffing.com/oauth"
bhr._rest_url = "https://rest9.bullhornstaffing.com/rest-services"

print bhr.access_token
print bhr.find(query="+12345678")Features:Automatically refreshrest_token/urlifaccess_tokenhas expired.Search the following entity types given a string containing search terms:ClientContactJobOrderCandidateClientCorporationLead (if leadsAndOpportunitiesEnabled = true)Opportunity (if leadsAndOpportunitiesEnabled = true)To search through the phone numbers start query with the “+” and append last 8 digits.
Package: bhoonidhi
bhoonidhiA Python library for Bhoonidhi, ISRO's Open data access portal (https:/bhoonidhi.nrsc.gov.in). The current version contains AI helper called "smart search" using NLP. The functionality can enable the power of searching in bhoonidhi using common search sentences and fetch the bhoonidhi results.Installation (https://pypi.org/project/bhoonidhi/0.1.0/)pip install bhoonidhiImporting and using the modulefrom bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "Get me Landsat 8 data from the region of hyderabad and Guntur from the last 1 month"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)Smart search for Event Basedfrom bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "Get me available data from siachen avalanche and kerala floods 2020"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)Multiple query parametersfrom bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)Downloading a product requires a valid login from bhoonidhi or uops. For free registration visithttps://uops.nrsc.gov.in/ImgeosUops/FinalImgeosUops/OdapUserRegister.htmlDownload a single product from the searched productsfrom bhoonidhi.SmartSearch import bhoonidhiSmartSearch
from bhoonidhi.SmartSearch import bhoonidhiDownload

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiProducts = bhoonidhiSmartSearch(user_query)
# Want to download the 12th product from the response, pass the 12th response product object
bhoonidhiDownload(bhoonidhiProducts[12],user_id='bhoonidhi_user', password='password', output_path='D:\\bhoonidhiDownloads')Bulk Download searched products (Batch downloading with custom number of concurrent/parallel downloads [maximum limit of 5 by https:/bhoonidhi.nrsc.gov.in])from bhoonidhi.SmartSearch import bhoonidhiSmartSearch
from bhoonidhi.SmartSearch import bhoonidhiBatchDownload

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiProducts = bhoonidhiSmartSearch(user_query)
# Want to download all (maximum downloads allowed by website is 5)
bhoonidhiBatchDownload(bhoonidhiProducts,user_id='bhoonidhi_user', password='password', parallelDownloads=5, output_path='D:\\bhoonidhiDownloads')The module fetches the results seperately and concatenates the results in a list format.One can download products easily from this library.
This module shall be enabled for bhoondihiAPI which is currently under construction. Thematic based search, on-demand-LULC classification module libraries shall be released soon.
Package: bhp
bhpbhp is a BloodHound user file parser!Installation•Getting started•Usage•Coming SoonInstallationbhp supports all major operating systems and can be installed for the PyPi using the following command:pipx install bhpIf this tool is not yet availible via PyPi, you can install it directly from the repository using:git clone https://github.com/puzzlepeaches/bhp.git
cd bhp && pip3 install .For development, clone the repository and install it locally using poetry.git clone https://github.com/puzzlepeaches/bhp.git && cd bhp
poetry shell && poetry installGetting startedbhp supports the latest BloodHound user json file format. Let's say you ran a BloodHound export on an enagement last summer and now the client is back and asking for social engineering servies. If you want to cheat and get as much coverage as possible, you can parse your previous BloodHound export for users with mailboxes for phishing.bhp gophish 20210414091456_users.json acmecorp.gophish.csvWith the output file, you can go into the Gophish web application and add the users to the campaign super easily.UsageThe bhp help menu is shown below:Usage: bhp [OPTIONS] COMMAND [ARGS]...

  Parse BloodHound JSON userfiles for external use.

Options:
  -h, --help  Show this message and exit.

Commands:
  gophish  Outputs a gophish import compatible csv file.
  stdout   Outputs specified type to stdout.
  txt      Outputs specified type to a text file.The stdout and txt modules allow the user to specify a desired output type. Let's say you want to get an idea of the most common title for users in your export. You can do something like:bhp stdout title 20210414091456_users.json | sort -uThanksThe BloodHound team for making me stare a graphs all dayTheGophishteam for making me mod their project to land phishing emails.The Sprocket team member that created the first iteration of this tool way back.
Package: bhpq
BHPQ - Binary Heap Priority QueueA binary heap priority queue implementation, thread safeInstallationYou can install bhpq fromPyPI:pip install bhpqbhpq is supported on Python 3.7Usagefrom bhpq import BinaryHeapPriorityQueue

# The BinaryHeapPriorityQueue constructor takes two input params:

# - prefer (required param)
#    the preferred object is pushed to the top of the queue
# the prefer input is a lambda function eg:
# prefer=(lambda lhs, rhs: lhs if lhs.val >= rhs.val else rhs)

# - size
#    The initial size allocation of the queue, default value is 10Exampleclass Node(object):
def __init__(self, val):
    self.val = val

A = BinaryHeapPriorityQueue(
        prefer=(lambda lhs, rhs: lhs if lhs.val >= rhs.val else rhs), size=5
    )

A.add(Node(1))
A.add(Node(4))
A.add(Node(3))
A.add(Node(5))
A.add(Node(2))

assert 5 == A.pop().val
assert 4 == A.pop().val
assert 3 == A.pop().val
assert 2 == A.pop().val
assert 1 == A.pop().val
assert None == A.pop()Methodssize()returns the current size of the priority queuepeek()returns the object at the topof the priority queue if it exists else returns Nonepop()removes and returns the object at the top of the priority queue if it exists else returns Noneadd(val)adds an element to the priority queueMaintainerⓘAayush Uppal
Package: bhsenti
基于三分类的文本情感分析基于bert模型，能够识别文本postive, neutral, negative三种情感。安装pip install bhsenti使用importbhsentipre=bhsenti.predict("待预测文本")pre_info=bhsenti.predict_info("待预测文本")print(pre)# 积极print(pre_info)# {'text': '待预测文本', 'result': '积极', 'classes': 2, 'score': [0.3605044484138489, 0.009216712787747383, 0.6302788257598877]}text: str 接收到的文本,result: str 预测结果,classes: int 情感类别序号： 0.消极 1.中性 2.积极,score: list[double] 每个类别的预测得分
Package: bhuiyans-dataset-builder
# Dataset-builder ![Build](https://img.shields.io/badge/Design-passing-Green)
A video processing tool for visual data collection, end-to-end preprocessing, ready-to-go for model training.### Input
A video or a set of videos. Currently supports`.mp4`only.### FeaturesAudio extraction :v:
-`python src/app.py--fileName001-01.mp4--extractAudioyes`Audio detection :v:
-`python src/app.py--fileName001-01.wav--detectAudioyes`- To show plot:`python src/app.py--fileName001-01.wav--detectAudioyes--plottrue`Audio-video split :v:
- To separate audio:`python src/app.py--fileName001-01.wav--separateAudioyes`- To separate video:`python src/app.py--fileName001-01.mp4--separateVideoyes`Audio-video merge :v:
-`python src/app.py--fileName001-01.mp4--mergeAudioVideoyes`Video player :v:
- Play the separated videos to see all is good:`python src/app.py--fileName001-01.mp4--playAll`Video compression :v:
-`python src/app.py--fileName001-01.mp4--compressBySize2`Face detection and region extraction :v:
-`python src/app.py--fileName001-01.mp4--detectLipyes--speaker001`Video data processing utilities :v:### How to use it?Clone the repository:`https://github.com/MasumBhuiyan/visual-data-manager.git`Open terminal and <b>cd</b> to the directory where <b>requirements.txt</b> is located.Create, activate, and install packages in a virtual environment
-`pip install virtualenv`-`virtualenv env`-`source env/bin/activate`-`pip install-rrequirements.txt`To split a video run:`will be updated`
Package: b-i
b-iBI tools  for data developer use Python Language. 更快捷的BI数据开发工具用法安装pip3 install b-i修改数据库配置文件vim ~/.b-i快速查询from b_i import hive_client
hive_client.read_sql("show tables")

from b_i import mysql_client
mysql_client.read_sql("show tables")高级示例b_i 数据开发工具包维护了三个操作对象:hive数据库操作对象：hive_clientmysql数据库操作对象：mysql_clientelasticsearch数据库操作对象：elastic_client提供两个常用方法:read_sql()exec_sql()
Package: bianary-decision-parameter
this module is used to find out best parameters of desision tree.#Bianary_classification##Aim
The module aims to create a library that will help users to build trees that can help classify binary classes easily. But, it is essential to note that this module should be used after the process (cleaning, transformation, and reduction) of the data.##Advantages
1.)Autopruneing the tree no need to hyperparameter tuneing
2.)very easy to use.
3.) Brier score to evaluate to find out best paraameter as  is that it is focused on the positive class, which for imbalanced classification is the minority class. A perfect classifier has a Brier score of 0.0.##Limitation
1.) Preprocessing must requires.
2.) do not shows good perfformance for high dimentional data ,feature reduction before use this module is adviceable.
3.)little time consumeing.##code example :importbianary_decision_parameterfrombianary_decision_parameterimportfromsklearnimportpreprocessingfromsklearn.datasetsimportload_breast_cancerfromsklearn.model_selectionimporttrain_test_splitLoadbreast_cancerdatasetsplititintotrainandtestsetda=load_breast_cancer()X1=da.datay1=da.targetscaled_X_1=preprocessing.scale(X1)X_train1,X_test1,y_train1,y_test1=train_test_split(scaled_X_1,y1,test_size=0.3)p2=bianary_decision_parameter('auto',X_train1,y_train1,X_test1,y_test1,1,1)p3=bianary_decision_parameter('sqrt',X_train1,y_train1,X_test1,y_test1,1,10)p4=bianary_decision_parameter('log2',X_train1,y_train1,X_test1,y_test1,1,10)p2.parameter()accuracy_scoremissclassificationspecificityRecallprecisiondepthccp_alphamax_features00.9122810.0877190.9122810.838710.91228120.004307autoExplanationp2=bianary_decision_parameter('auto',X_train1,y_train1, X_test1, y_test1,1,1)
Feature_selection = 'auto' \n
class_weight = 0_class ,1_class
Package: biano
biano键盘按键钢琴，监听按键发出声音，可以后台运行
keyboard piano, listen key press to play sound, can run background
运行(run):
python -m biano

按键对应音符:
keys to musical note:

左手(left hand):
 q  w  e  r  t 
 1 #1  2 #2  3
 a  s  d  f
 4 #4  5 #5
 z  x  c
 6 #6  7

右手(right hand):
 y  u  i  o  p
 1 #1  2 #2  3
 h  j  k  l
 4 #4  5 #5
 n  m  ,
 6 #6  7

数字键1,2,3,4,5修改左手基准音调
数字键6,7,8,9,0修改右手基准音调

number keys 1,2,3,4,5 modify base tone of left hand
number keys 6,7,8,9,0 modify base tone of right hand

按键-和=修改数字键音调偏移
keys - and = modify number key tone offset
Package: biapol-taurus
BiA-PoL taurus toolsCollection of tools to work on Bio-image Analysis tools on thetaurus cluster of TU Dresden.Before you can start working on the taurus cluster, you need to file aproject request.ProjectFileTransferIf you own a project space on taurus and want to transfer files to it, you can askHPC supportto enable a connection to your fileserver.
Afterwards, you can useTUD ZIH datamoverto transfer files from the fileserver to the cluster. If you want to do this conveniently from python,
see theProjectFileTransfer demo.ContributingContributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of the BSD-3 license, "biapol-taurus" is free and open source software.SupportIf you need support with the tools listed here, pleaseopen an issue.
Package: biapol-utilities
biapol-utilitiesCollection of utility functions from the BiA-PoL group at the DFG Cluster of Excellence "Physics of Life" (PoL) @ TU Dresden, Germany.UsageThis library contains functions for comparing and visualizing label images. For example, comparing label images is as easy as this:importbiapol_utilitiesasbiaubiau.label.compare_labels(ground_truth,segmented)In another example, you can visualize a label image with a nice color map like this:importbiapol_utilitiesasbiaufromskimage.ioimportimshowimshow(label_image,cmap=biau.labels_colormap(),vmin=0,vmax=labels.max())We provide examples to most of our functionality on ourdocumentation. The examples are also available asjupyter notebooks.Installationbiapol-utilitiescan be installed using pip:pip install biapol-utilitiesIf you want to developbiapol-utilitiesyou can download and install it like this:git clone https://github.com/BiAPoL/biapol-utilities
cd biapol-utilities
pip install -e .RessourcesDocumentationSource codeBug reports are welcome hereHow to contributeAcknowledgementsDeveloping biapol-utilities is a community effort. We would like to thank everybody who helped developing and testing.
Furthermore, we are gladly re-using code from other open source projects.
See the thethirdparty-licenses folderfor details.
Package: biasaway
BiasAwaya tool to generate composition-matched background sequence setsDocumentationA detailed documentation is available in different formats:HTML|PDF|ePUBInstallationQuick installation usingcondaBiasAway is available onBiocondafor installation viaconda.condainstall-cbiocondabiasawayInstall usingpipBiasAway is also available onPyPifor installation viapip.pipinstallbiasawayBiasAway is developed in Python and Python packagesbiopythonandnumpy. Bothpipandcondawill install these dependencies and you are ready to use BiasAway. It is tested with Python versions3.4,3.5,3.6, and3.7.Install BiasAway from sourceYou can install a development version by usinggitfrombitbucket.Install development version fromBitbucketIf you havegitinstalled, use this:gitclonehttps://bitbucket.org/CBGR/biasaway.gitcdbiasawaypythonsetup.pysdistinstallHow to use BiasAwayOnce you have installed biasaway, you can type:biasaway--helpThis will show the main help, which lists the four subcommands/modules:k,w,g, andc.usage:biasaway<subcommand>[options]positionalarguments<subcommand>:{k,w,g,c}Listofsubcommandskk-mershufflinggeneratorwk-mershufflingwithinaslidingwindowgeneratorg%GCdistribution-basedbackgroundchoosercGCdistributionand%GCcompositionwithinaslidingwindowbackgroundchooseroptionalarguments:-h,--helpshowthishelpmessageandexit-v,--versionshowprogram'sversionnumberandexitAnd to view the help for the six subcommandsk,w,g, andctype:biasawayk--helpbiasawayw--helpbiasawayg--helpbiasawayc--helpInteractive web-serverBiasAway web-server is freely available at:http://biasaway.uio.noSupportIf you have questions, or found any bug in the program, please write to us atazizk[at]uio.nooranthony.mathelier[at]ncmm.uio.no
Package: biascorrection
This is a function to do bias correction of precipitation using linear scaling method only. Additionally, this function requires three input files to add it in the function. The first file, it is the observed precipitation data and set its following “https://www.youtube.com/watch?v=uEnTc5MK4uQ&t=29s”.
The second file, it is the climate precipitation data.
The third file, it is the datetime of precipitation.This package must work with numpy and pandas package, users should install all of them also. Thank you very muchChange log0.0.4 (10/14/2021)Third Release
Package: bias-correction
AboutThe modulebias_correctionconsists of functions to perform bias correction of datasets to remove biases across datasets. Implemented methods includequantile mapping,modified quantile mapping,scaled distribution mapping (Gamma and Normal Corrections).Installationpip install bias-correctionDocumentationLatest documentation is availablehere.Usagebias_correctionis easy to use. Just import:frombias_correctionimportBiasCorrectionInstantiate the bias correction class as:bc=BiasCorrection(reference,model,data_to_be_corrected)Perform correction specifying the method to be used:corrected=bc.correct(method='gamma_mapping')
Package: biasedclassifier
Biased ClassifierBiased ClassifierCurrent version: 0.3.0InstallDirectly fromPyPiservers:pip install biasedclassifierInterfaceEstimator's constructor:BiasedClassifier(
    p=[0.0],
    unbiased_estimator=None,
    knn=None
)whereunbiased_estimatoris the base estimator to use (and to biased towards critical set). We pass ak-NearestNeighborobject directly via the paramterknn.UseExample using Random Forests fromscikit-learn.AssumeX, yis a training set with three classes and two heavily inbalanced classes. In this case, we'd like to bias two classifiers into these subsets. We've decided that0.3and0.2proportions are enough for the minority classes (from smaller up) andk=10neighbors to collect for critical set. Our unbiased estimator will be a random forest of size 200.from biasedclassifier import BiasedClassifier
from sklearn.neighbors import NearestNeighbors
from sklearn.ensemble import RandomForestClassifier

clf = BiasedClassifier(
    p=[0.3, 0.2], 
    unbiased_classifier=RandomForestClassifier(n_estimators=200), 
    knn=NearestNeighbors(n_neighbors=10)
)

# Train
clf.fit(X,y)

# Obtain probabilities for each class
prob = clf.predict_proba(X)

# Predicted values
y_pred = clf.predict(X)

# Average accuracy score
score = clf.score(X, y)It is important to note thatBiasedEstimatordoes not change the state of both objectsunbiased_classifierandknn. Instead, it uses clones internally to do its operations.CompatibilityThis model is compatible with all of the capabilities offered byscikit-learnrequiringget_paramsandscoremethods.
Package: biased-stop-words
ContentsOverviewAvailable genresInteractive NotebookInstallationBasic usageRunning TestPython compatibilityOverviewBiases are bugsStop words are words which are filtered out before processing of natural language data. Often in text analysis there are non-casual correlations, consider the following documents:He is an astronaut, he is on VenusHe is an accountant, he is on EarthShe is an astronaut, she is on MarsProcessing these documents into two topics will result in gendered clustering. If we remove the gendered terms:is an astronaut, is on Venusis an accountant, is on Earthis an astronaut, is on MarsProcessing will result in job clustering. Both clusterings are valid, however if you are interested in employing an astronaut, you don’t want male accountants showing up. There are many other examples of non casual relationships occurring in natural language; religion, ethnicity, and age to name but a few.Available genresGendered TermsUS NamesReligious Terms (Partial)More will be available soon. Contribute athttps://github.com/gregology/biased-wordsInteractive NotebookExplore this package in anInteractive NotebookHosted bybinderInstallationbiased-stop-wordsis available on PyPIhttp://pypi.python.org/pypi/biased-stop-wordsInstall viapip$ pip install biased-stop-wordsOr viaeasy_install$ easy_install biased-stop-wordsOr directly frombiased-stop-words’sgit repo <https://github.com/gregology/biased-words>$ git clone --recursive git://github.com/gregology/biased-stop-words.git
$ cd biased-stop-words
$ python setup.py installBasic usage>>> from biased_stop_words import genres, get_stop_words
>>> genres()
'religious, gendered, us-common-names, us-names, us-male-names, us-female-names, gendered-nouns'
>>> get_stop_words('gendered', 'us-common-names')
[u'trenton', u'augustine', u'khalil', u'aiden', u'elisabeth', u'andre', u'khanum', u'elva', u'fran...Running Test$ python biased_stop_words/tests.pyPython compatibilityDeveloped for Python 2 & 3.