Saravanan666/pypi-org huggingface dataset:
Package: provpy
PROV-JSON is a JSON representation for the Provenance Data Model (PROV-DM). The representation was developed internally at the University of Southampton as a common format for exchanging provenance information between research projects. In addition, the PROV-JSON representation also aims at facilitating client-side manipulations of provenance information and, at the same time, allowing the use of a generic client-side API in various web applications. However, as it is based on the Provenance Data Model (http://www.w3.org/TR/prov-dm/) being standardized at the W3C Provenance Working Group, it is hoped that the PROV-JSON representation will also find support from a wider community.Folder structure:- specs/json: The PROV-JSON specification document- examples: containing PROV-JSON examples (*.json)Plan for a PROV-JSON toolbox that facilitates accessing and manipulating PROV-JSON structures:In conjunction with the PROV-JSON specifications provided in this document, a PROV-JSON toolbox is being developed to facilitate working with PROV-JSON structures. The toolbox will provide the following functionalities:Querying: query support for- properties of an element or a relation,- elements and relations based on their types, and- including/excluding specific types of elements/relations.Traversing: the toolbox presents a PROV-JSON structure as a provenance graph of elements linked by their relations. By so doing, it provides a number of ways to traverse the graph (horizontally or vertically) from a particular element.Simple inference: transitivity closure support for transitive relations in PROV-DMEditing: creating and updating provenance graphsTyped literals: handle popular data types (e.g. XSD) and provide extension points for application-specific data typesUI widgets: generic JavaScript UI widgets to support the functionalities above
Package: provrandom
provrandom - Prove the outcome of randomness!provrandomis a Python package that provides a fair random number generator designed for applications that would be able to use fair randomness. The package allows you to generate random numbers and information in a way that can be verified by both the server and the client, ensuring fairness and transparency in the process.FeaturesGenerate provably fair random numbers based on server seeds, client seeds, and nonces.Easy-to-use interface for obtaining random numbers and related information.Transparent and verifiable randomness to guarantee fairness in any applications.Flexible customization of seeds and nonces for a wide range of use cases.InstallationYou can installprovrandomusing pip:pipinstallprovrandomUsage:fromprovrandomimportRandom# Create an instance of the Random classrandom_instance=Random()# Example usage of methods:print("Random information:",random_instance.random_information)print("Random hash:",random_instance._hash())print("Random number between 10 and 20:",random_instance.number(10,20))print("Random float between 1.0 and 5.0:",random_instance._float(1.0,5.0))print("Random string of length 8:",random_instance.string(8))# Example usage of _random_container method to generate a list of random numbersrandom_numbers_list=random_instance._random_container(list,5,0,100)print("Random numbers list:",random_numbers_list)
Package: prowav
ProWavYou can use this for preprocessing wave files.UsagePlease install prowav by using pip.pip install prowavUsageProWav can calculate mfcc spectrogram and pad for batch executionfromprowavimportProWavprowav=ProWav(["path/to/wave/data_1.wav","path/to/wave/data_2.wav"])frame_width=20# the length of a frame (ms)stride_width=20# the frame interval (ms)n_mfcc=26# the number of features by mfcc features (If you want to use mfcc preprocessing, you should specify this value)mode='MFCC'window_func='hamming'# the name for window functiondata=prowav.prepro(frame_width=frame_width,stride_width=stride_width,mode=mode,n_mfcc=n_mfcc,window_func=window_func)# >> (num_files, num_frames, n_mfcc)If you want use fft spectrogram, please specify the mode, "fft".prowav=ProWav(["path/to/wave/data_1.wav","path/to/wave/data_2.wav"])frame_width=20stride_width=20mode='fft'window_func='hamming'data=prowav.prepro(frame_width=20,stride_width=20,\mode=mode,window_func=window_func)# >> (num_files, num_frames, num_features)You can also use mel-spectrogram. Specify the mode, "mel_spec"prowav=ProWav(["path/to/wave/data_1.wav","path/to/wave/data_2.wav"])frame_width=50stride_width=50mode='MFCC'n_mfcc=26window_func='hamming'data=prowav.prepro(frame_widh=frame_width,stride_width=stride_width,mode=mode,window_func=window_func,n_mfcc=n_mfcc)You can use zero-padding or repeat-padding.prowav=ProWav(["path/to/wave/data_1.wav","path/to/wave/data_2.wav"])frame_width=50stride_width=50mode='mel_spec'n_mels=50window_func='hamming'data_zero=prowav.prepro(frame_widh=frame_width,stride_width=stride_width,mode=mode,n_mels=n_mels,window_func=window_func,zero_padding=True)# zero paddingdata_repeat=prowav.prepro(frame_widh=frame_width,stride_width=stride_width,mode=mode,ne_mels=n_mels,window_func=window_func,repeat_padding=True)# repeat paddingJust loading wave data is possible.prowav=ProWav(["path/to/wave/data_1.wav","path/to/wave/data_2.wav"])prowav.load_wav()# loading wav file into this class.prowav.data# the list of ndarray. Raw data are listed.You can choose parallel option.prowav.load_wav(parallel=True)prowav.load_wav(parallel=True,verbose=5)
Package: prox
Installationpip3install-UproxHow to check the proxy listYou have to provide at least the type and location of proxy list.If the proxylist located athttp://example.com/abc.txt:prox_checksocksURLhttp://example.com/abc.txtIf the list is in local file:prox_checksockspath/to/file.txtHow to check multiple listsCreate file foo.yml like:config:
  save: 1

task:
  - proxy_type: socks
    plist_url: var/plist1.txt
    limit: 100

  - proxy_type: http
    plist_url: http://example.com/servers.txt
    limit: 100It should by YML list of tasks. Each task contains key names same
as check_plist.py command line arguments.Run the command:prox_taskfoo.ymlThe tool to filter proxy list by countriesUseprox_geocommand with–statoption to see number of proxies grouped by country.Useprox_geocommand with-i,-xand–exclude-listoptions to filter proxy list
Package: proxapy
Simple API proxy that uses Flask/requests/gunicorn.Use casesCentralize API requests- Sometimes only a specific IP can access
APIs. Run Proxapy on that machine and access the API from everywhere.Implement custom pre/post operation on API request/response-
Fork the project, add routes and implement your changes. Common
changes include content filtering, add authentication key header not
known by the end user.UsageMake sure you havevirtualenvmake runVisithttp://<proxyhost>:<proxyport>/<API>to accesshttp://<API>By default<proxyhost>is bound to0.0.0.0and<proxyport>to5000, so for example you could run Proxapy and then visit:http://localhost:5000/api.left-pad.io/?str=proxied&len=20
Package: proxbias
proxbiasCode to support the publication "High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by
CRISPR-Cas9 editing". The preprint is on bioRxivhereInstallationThis package is installable viaNexus.pipinstallproxbiasNotebooksNotebooks to reproduce plots from public datasets are included in thenotebooksdirectory. Note that this doesn't include rxrx3 for IP reasons at this time.cpg0016_loading.ipynb- Load the JUMP CP data, apply PCA and proximity bias correction and save data locally.cpg0016_plots.ipynb- Create whole-genome plots from cpg0016, calculate Brunner-Munzel statistics and make bar plots.shinyDepMap_benchmark.ipynb- Load DepMap 19Q3 data and create plots showing an enrichment for within-chromosome arm relationships.DepMap_PB_Drivers.ipynb- Use DepMap 22Q4 data to look at differential proximity bias when TP53 and other genes are wild-type vs loss/gain of function.DepMap_22q4_vs_23q2.ipynb- Generate whole-genome plots and Brunner-Munzel probability plots for DepMap 22Q4 and 23Q2 data.scPerturb_tables_heatmaps.ipynb- Load scRNASeq datasets from scPerturb, run infercnv, compute chromosomal loss, generate result tables, and plot infercnv heatmaps for cells with specific loss.TODO:Code to search DepMap for drivers of proximity bias
Package: proxcat
proxcatA htop-like status monitor for Proxmox VE servers, powered by python curses, with support for multiple nodes, VM (QEMU) and Container (LXC) status and node status.ScreenshotInstallationpip install requests proxmoxer proxcat(optional)pip install PySensorsfor CPU temperature info support (only when ran directly on PVE host)Or, clone repository and runpip install ..UsageOn your PVE server, create an API token in WebUI under Datacenter -> Permissions -> API Tokens. Remember to deselect "Priviledge Separation". For security concerns, it is recommended to use it with a dedicated low-level user - for example, one with the "PVEAuditor" role assigned - instead of root or other administrators.Create a config file.proxcatwill look forconfig.iniunder$XDG_CONFIG_HOME/proxcat/or~/.proxcat/Or, you can specify a custom config file with the-c/--configflag.Then fill in the config file like this:[Account]# address of PVE hostHost=# Username (format: <username>@<realm)User=# name of your tokenTokenName=# your token valueToken=[Settings]# set data update interval (in ms), optionalUpdateInterval=# whether to enable host CPU temperature info (requires lm_sensors and PySensors). Disabled by default.ShowSensors=FalseThe options in [Settings] can be overriden by command-line flags; see-hor--helpfor details.run theproxcatcommand.Keybindings:qto quitnpfor switching between nodesany other key for force refreshing screenChangelogseeCHANGELOG.md.TodoSwitch entirely topyproject.tomlAdd optional lm_sensors support for HW monitoring (if ran on host)Change time display to difference between host and local clientMore functionality for curses UI (custom sorting, ...)Invoke VM/CT jobs within appOptional headless/daemon mode with WebUILicenseMIT
Package: prox-checker
prox-checkerAsync python proxy checkerFeaturesWorks in 2023No pycurl needed, OS independentHTTP, Socks4, Socks5AsyncFast(<= 1.5kb per check)ChecksavailabilityandanonymitySecure, no data collectingTypedInstallationpip install prox-checkerUsage...fromprox_checkerimportProxyChecker,ProxyProtocol,ProxyCheckerResultasyncdefcheck_my_proxies():proxies=['144.24.207.98:8080','103.169.130.51:5678','198.58.126.147:51576','45.79.155.9:3128','206.220.175.2:4145',]working_proxies:List[ProxyCheckerResult]=awaitProxyChecker().check_proxies(proxies=proxies,proxy_async_limit=1_000,protocol_async_limit=3,response_timeout=5,)print(working_proxies)'''Output:[<ProxyCheckerResult url=http://144.24.207.98:8080 proxy=144.24.207.98:8080 protocol=ProxyProtocol.http>,<ProxyCheckerResult url=socks4://198.58.126.147:51576 proxy=198.58.126.147:51576 protocol=ProxyProtocol.socks4>,<ProxyCheckerResult url=socks5://198.58.126.147:51576 proxy=198.58.126.147:51576 protocol=ProxyProtocol.socks5>,<ProxyCheckerResult url=socks4://206.220.175.2:4145 proxy=206.220.175.2:4145 protocol=ProxyProtocol.socks4>,<ProxyCheckerResult url=socks5://206.220.175.2:4145 proxy=206.220.175.2:4145 protocol=ProxyProtocol.socks5>]Leaves only anon working proxies, separated by protocols'''socks5_urls=[result.urlforresultinworking_proxiesifresult.protocol==ProxyProtocol.socks5]print(socks5_urls)# ['socks5://198.58.126.147:51576', 'socks5://206.220.175.2:4145']max_bandwidth_bytes_s=ProxyChecker.estimate_max_bandwidth_bytes_s(proxy_async_limit=1_000,protocol_async_limit=3,)max_bandwidth_mb_s=max_bandwidth_bytes_s/1024/1024print(max_bandwidth_mb_s)# 4.39453125custom_judges=['http://proxyjudge.us','http://azenv.net/',]custom_judges_working_proxies=awaitProxyChecker(judges=custom_judges).check_proxies(proxies=proxies,proxy_async_limit=1_000,protocol_async_limit=3,response_timeout=5,)print(custom_judges_working_proxies)# same as first...
Package: proxenos
A Python toolkit for Rendezvous/Highest Random Weight (HRW) hash based routing.>>>importproxenos.mappers.consul>>>importproxenos.rendezvous>>>mapper=proxenos.mappers.consul.ConsulClusterMapper(...host='localhost',port=8500)>>>mapper.update()>>>mapper.cluster{SocketAddress(host=IPAddress('::'), port=8000),
 SocketAddress(host=IPAddress('::'), port=8001),
 SocketAddress(host=IPAddress('::'), port=8300),
 SocketAddress(host=IPAddress('::'), port=8500)}>>>mapper.select('github.com',proxenos.rendezvous.HashMethod.SIPHASH)SocketAddress(host=IPAddress('::'), port=8001)$proxenosselect-bconsul-hlocalhost-p8500github.com0.0.0.0:8300FeaturesSupport for multiple service discovery backends possible.Currently
only support for Consul is implemented, but other backends coming
eventually.Supports many different hash functions and PRFs. The default hashing
method is theSipHashPRF.Provides a command-line interface for quickly testing/debugging
node selection.Installation$pipinstallproxenos$# If you'd like Murmur3 support, use:$pipinstallproxenos[murmur]Notesproxenosuses Python’s PRNG for calculating the weight of nodes in
a cluster, rather than using a linear congruential generator (LCG)
as described in theoriginal paper on Rendezvous hashing. Seeded
weights generated are limited tosys.maxsize, which means the
resulting weights will differ depending on the platform architecture.Similar projectsclandestinednikhilgarg28/rendezvous
Package: proxidize-wrapper
Wrapper for proxidize android app.Free software: MIT licenseInstall a Proxy ServerReadThisto host the server.We would need the http address which is in the format of “IP:PORT:USERNAME:PASSWORD” to use this wrapper.FeaturesUse Proxidize Android app to proxy your internet connection on your PC. Wrapper for it.How to UseInstall the app on your android phone. mentioned above.Install the package:pip install ec2_proxyfromproxidize_wrapperimportProxyp=Proxy("127.0.0.1:34517:USERNAME:PASSWORD","23412")#Initialize the class object.print(p.get_ip())# Print the current ip address.p.change_ip()# change the ip address.print(p.get_ip())# Print the current ip address.
Package: proxied
proxiedproxied package can be use to defer initialization of object.Suppose we have:__init__.pyfromconfigimportConfig,load_configconfig:Config=Nonedefinit_app():config=load_config()If we try to import config in another file, i.e.cool_staff.pyfrom.importconfig...init_staff(config.db_url)We will end up importing None. So, we will be forced to
make import not at the top of the file.Here comes the proxied package__init__.pyfromtypingimportUnionfromproxyimportProxyfromconfigimportConfig,load_configconfig:Union[Config,Proxy]=Proxy()definit_app():config.set_inner(load_config())Now we can easily import config at the top of the file work with
proxy object as it was the object of type Config.Installationpip install proxiedProxyProxy class can be initialized with inner or inner_constructor.
If inner_constructor is supplied, then it will be called once,
and the result will be cached.If Proxy class is initialized without inner and inner_constructor,
the inner should be set later with a help ofproxy.set_innermethod.fromproxyimportProxyproxy=Proxy()proxy.set_inner({})proxy["test_key"]=10It’s possible to set values for multiple proxies.fromproxyimportProxyproxies=[Proxy(),Proxy(),Proxy(),Proxy()]values=[10,11,list(),dict()]Proxy.set_proxies(proxies,values)There is a check, if proxy is initialized with proxied valuefromproxyimportProxyproxy=Proxy()ifnotproxy.initialized:data=get_needed_data(...)proxy.set_inner(data)ExamplefromproxyimportProxyclassNotAvailableDuringImport:@propertydefdata(self):return"Not Available during import"proxy:Union[NotAvailableDuringImport,Proxy]=Proxy()proxy.set_inner(NotAvailableDuringImport)assertproxy.data=="Not Available during import"LicenseCopyright Oleksii Petrenko, 2020.Distributed under the terms of theMITlicense,
json_modify is free and open source software.
Package: proxied-chromedriver-binary-auto
chromedriver-binaryDownloads and installs thechromedriverbinary version 88.0.4324.27 for automated testing of webapps. The installer supports Linux, MacOS and Windows operating systems.Alternatively the packageproxied-chromedriver-binary-autocan be used to automatically detect the latest chromedriver version required for the installed Chrome/Chromium browser.InstallationLatest and fixed versionsFrom PyPIpip install proxied-chromedriver-binaryFrom GitHubpip install git+https://github.com/imanazari70/proxied-chromedriver-binary-autoAutomatically detected versionsPlease make sure to install Chrome or Chromium first and add the browser to the binary search path.From PyPIpip install proxied-chromedriver-binary-autoTo redetect the required version and install the newest suitable chromedriver after the first installation simply reinstall the package usingpip install --upgrade --force-reinstall proxied-chromedriver-binary-autoFrom GitHubpip install git+https://github.com/imanazari70/proxied-chromedriver-binary-autoUsageTo use chromedriver justimport chromedriver_binary. This will add the executable to your PATH so it will be found. You can also get the absolute filename of the binary withchromedriver_binary.chromedriver_filename.Examplefrom selenium import webdriver
import chromedriver_binary  # Adds chromedriver binary to path

driver = webdriver.Chrome()
driver.get("http://www.python.org")
assert "Python" in driver.titleExporting chromedriver binary pathThis package installs a small shell scriptchromedriver-pathto easily set and export the PATH variable:$ export PATH=$PATH:`chromedriver-path`
Package: proxiedssl
IntroductionThis module provides a Django middleware class,proxiedssl.middleware.ProxiedSslWsgiMiddleware, that looks for one of two
headers and sets the WSGI URL scheme to the value of the header. This allows a
proxy server that handles SSL to notify the Django WSGI request handler that the
end user is accessing the site through SSL.InstallationAddproxiedssl.middleware.ProxiedSslWsgiMiddlewareto theMIDDLEWARE_CLASSEStuple in your settings file. It would be best if it was
first so that theis_securemethod is accurate for the other middleware
classes.RequirementsDjango and you currently must be using the WSGI handler either through a WSGI
server or through the FCGI server which is a wrapper around the WSGI server.TODOAdd a middleware class for the mod_python handlerAdd full unit tests0.1.1 (2012/01/05)Added debug logging output for all possibilities0.1 (2011/04/18)Initial code
Package: proxier
ProxierProxier is a library to provide free access Proxy server's hosts as code.
Please, checkhttps://proxier.io/.What does it doIt consumes data provided byproxier.ioAPIs.How to install?$ pip install proxierHow to use it?importproxierimportrequestsresponse=requests.get("http://bot.whatismyipaddress.com/",proxies=proxier.get_requests_proxy())print("my ip: "+response.text)
Package: proxies
|PyPI| |PyPID| |PyPIV| |PyPIL| |PyPIS|Get latest http proxies------------------Install^^^^::pip install proxiesOR:::git clone https://github.com/MyFaith/proxiespython setup.py installUsage^^^^::from Proxies import Proxiesp = Proxies()p.get_proxies(20, 1)result = p.get_result()print(result).. |PyPI| image:: https://img.shields.io/pypi/v/proxies.svg?maxAge=2592000:target: https://pypi.python.org/pypi/proxies.. |PyPID| image:: https://img.shields.io/pypi/dm/proxies.svg?maxAge=2592000:target: https://pypi.python.org/pypi/proxies.. |PyPIV| image:: https://img.shields.io/pypi/pyversions/proxies.svg?maxAge=2592000:target: https://pypi.python.org/pypi/proxies.. |PyPIL| image:: https://img.shields.io/pypi/l/proxies.svg?maxAge=2592000:target: https://pypi.python.org/pypi/proxies.. |PyPIS| image:: https://img.shields.io/pypi/status/proxies.svg?maxAge=2592000:target: https://pypi.python.org/pypi/proxies
Package: proxiestor
proxiestor (Automatic Tor IP Rotation)Author:Sptty Chan (Fanda)Github & Examples:proxiestorAbout ProxiestorProxiestor is a Python library designed to automate Tor. With Proxiestor, you can easily run Tor without the need to open a new terminal window. Just call the function from Proxiestor, and Tor will be up and running. You can even rotate the IP by adding arguments when calling the Proxiestor library function. Please take a look at the example below.example#import library
from proxiestor import Tor
import requests

#Class initiation
#Tor without IP rotation
tor = Tor(ip_rotation=False) #Change "False" to "True" if you want to enable IP rotation

#Start tor
tor.start()

#Your program should be executed after calling the "start" function and concluded by calling the "close" function
r=requests.get("https://httpbin.org/ip", proxies={"http": "socks5://127.0.0.1:9050","https": "socks5://127.0.0.1:9050"}).json()["origin"]
print(f"your IP: {r}")
#You can create a loop here

#Close/stop tor
tor.close()Check our GitHub for more examples.
Package: proxiflow
ProxiFlowProxiFlow is a data preprocessig tool for machine learning that performs
data cleaning, normalization, and feature engineering.The biggest advantage if this library (which is basically a wrapper overpolarsdata frame) is that it is configurable via YAML configuration file which makes it suitable for MLOps pipelines or for building API requests over it.DocumentationRead the full documentationhere.UsageTo use ProxiFlow, install it via pip:pipinstallproxiflowYou can then call it from the command line:proxiflow--config-filemyconfig.yaml--input-filemydata.csv--output-filecleaned_data.csvHere's an example of a YAML configuration file:input_format:csvoutput_format:csvdata_cleaning:#mandatory# NOTE: Not handling missing values can cause errors during data normalizationhandle_missing_values:drop:falsemean:true# Only Int and Float columns are handled# mode: true # Turned off for now.knn:truehandle_outliers:true# Only Float columns are handledremove_duplicates:truedata_normalization:# mandatorymin_max:#mandatory but values are not mandatory. It can be left empty# Specify columns:-Age# not mandatoryz_score:-Pricelog:-Floorsfeature_engineering:one_hot_encoding:# mandatory-Bedrooms# not mandatoryfeature_scaling:# mandatorydegree:2# not mandatory. It specifies the polynominal degreecolumns:# not mandatory-Floors# not mandatoryThe above configuration specifies that duplicate rows should be removed
and missing values should be dropped.APIProxiFlow can also be used as a Python library. Here's an example:importpolarsasplfromproxiflow.configimportConfigfromproxiflow.coreimportCleaner# Load the datadf=pl.read_csv("mydata.csv")# Load the configurationconfig=Config("myconfig.yaml")# Clean the datacleaner=Cleaner(config)cleaned_data=cleaner.clean_data(data)# Perform data normalizationnormalizer=Normalizer(config)normalized_data=normalizer.normalize(cleaned_data)# Perform feature engineeringengineer=Engineer(config)engineered_data=engineer.execute(normalized_data)# Write the output dataengineered_data.write_csv("cleaned_data.csv")Log[x] Data cleaning[x] Missing values handling[x] Mean[x] Drop[x] KNN Imputer[x] Median[x] Data normalization[x] Min Max normalization[x] Z-Score normalization[x] Logarithmic normalization[ ] Feature engineering[x] One Hot Encoding[x] Feature Scaling[ ] Recursive Feature Elimination[ ] SelectKBest[ ] LASSO regularization[ ] Text Preprocessing[ ] Tokenization[ ] Stemming[ ] Stopword removal[ ] Text Vectorization[ ] Bag of Words[ ] TF-IDF[ ] Word embeddings[ ] Word2Vec[ ] GloVe[ ] BERT[ ] Categorical Encoding[ ] Target encoding[ ] Count encoding[ ] Binary encoding[ ] Dimensionality reduction[ ] Principal Component Analysis (PCA)[ ] t-Distributed Stochastic Neighbor Embedding (t-SNE)[ ] Uniform Manifold Approximation and Projection (UMAP)