Saravanan666/pypi-org huggingface dataset:
Package: resistant-documents-client
Resistant documents clientThis library provides a Python client for aResistant.ai document forgery analysis service.
For a detailed description of the API please seeAPI reference docs.PrerequisitesDuring the customer onboarding process, you should be provided with the following:CLIENT_ID : strCLIENT_SECRET : strNote: Those credentials are connected with specific environment (e.g. test, production etc.). Together with secret credentials for
non-production environment you should be provided with url links:api_urltoken_urlBasic usageSubmit a document for analysis with default pipeline configuration.fromresistant_documents_client.clientimportResistantDocumentsClientclient=ResistantDocumentsClient(client_id="CLIENT_ID",client_secret="CLIENT_SECRET")withopen("local_file.pdf","rb")asfp:report=client.analyze(fp.read(),query_id="local_file.pdf")print(report["score"])For non-production environment, provideapi_urlandtoken_urlprovided together with client secrets to client creation (ResistantDocumentsClient).Customized usageSubmit a document for analysis with customized process parameters or select a different type of analysis.Step 1: Create a client with your credentialsclient=ResistantDocumentsClient(client_id="CLIENT_ID",client_secret="CLIENT_SECRET")Note: If your client secrets are for different environment than production you have to provide followingapi_urltoken_url. To setup proxy server URL, you can provide optionalproxy.Step 2: Create submission with pipeline setupwithopen("local_file.pdf","rb")asfp:my_submission_id=client.submit(fp.read(),query_id="local_file.pdf",pipeline_configuration="CONTENT_AFTER_FRAUD_AFTER_QUALITY")Possible pipeline configurations are listed inREST API docsStep 3: Retrieve analysis resultYou can retrieve only those types of analysis which were requested in the previous step aspipeline_configurationoption.result_content=client.content(submission_id=submission_id)result_fraud=client.content(submission_id=submission_id)result_quality=client.quality(submission_id=submission_id)result_classification=client.classification(submission_id=submission_id)print(result_content)print(result_fraud)print(result_quality)print(result_classification)These methods also acceptmax_num_retries, which represents how many times the client will poll the server before failing (because the communication is asynchronous). It might be customized but has a default
value. Other parameters correspond to the ones in the REST API docs.Other supported endpointssubmission/{submission_id}/decisionsubmission/{submission_id}/feedbacksubmission/{submission_id}/characteristics
Package: resist_hotlinking
python curl反防盗链抓取连接
===curl 反盗链抓取网页内容源码地址:https://github.com/maicong/OpenAPI/blob/master/curl_get.php翻译为python版依赖
—
需要安装pycurlpycurl安装时如果ssl backend不同会报错import pycurlImportError: pycurl: libcurl link-time ssl backend (openssl) is differ
ent from compile-time ssl backend (gnutls)pip uninstall pycurl
export PYCURL_SSL_LIBRARY=openssl
pip install pycurlSimple Usage
—from resist_hotlinking import curl_getprint curl_get(’http://www.baidu.com’)
Package: resistics
WelcomeResistics is a native Python 3 package for the processing of magnetotelluric (MT) data. It incorporates standard robust processing methods and adopts a modular approach to processing which allows for customisation and future improvements to be quickly adopted.AboutResistics began as a set of python classes to help analyse noisy MT timeseries data acquired in northern Switzerland through increased use of statistics and time window based features. Since then, it has grown into a MT data processing package. The name is an amalgamation of resistivty and statistics...resistics!AudienceResistics is intended for people who use magnetotelluric methods to estimate the subsurface resistivity. This may be for the purposes of furthering geological understanding, for geothermal prospecting or for other purposes.Getting startedResistics can be installed using pip,python -m pip install --user resisticsor possibly,python3 -m pip install --user resisticsdepending on how your Python 3 install is named.The next step after installing is to visitwww.resistics.ioand read the documentation. For those unfamiliar with resistics, the features, conventions and tutorial sections are a good way to become more comfortable using the package.Open-sourceResistics is available for free under the MIT licence. The resistics source code can be found in theGitHub repository. Contributors are welcome.Support and feature requestsSupport and feature requests can be submitted on the in theGitHub repositorypage.
Package: resistify
🕵️ ResistifyResistify is a program which classifies plant NLRs by their protein domain and motif architecture.
It is designed to be lightweight - no manual database installations or tricky dependencies here!InstallationTo get started with Resistify:pip install resistifyResistify requiresbiopythonandscikit-learn==0.24.2.
It also requireshmmsearchandjackhmmer- install thesevia condaor any other means.
I'd recommend creating an environment for it specifically, as scikit-learn dependencies are a bit busted:mamba create -n resistify python==3.9 pip hmmer
pip install resistifyA conda distribution is in progress!UsageTo run Resistify:resistify <input.fa> <output_directory>Yourinput.fashould contain the amino acid sequences of your proteins of interest.
Multiline and sequence description fields are allowed.
Stop codons "*" are permitted at the end of sequences - internal stop codons are not.Anoutput_directorywill be created which will contain the results of your run:results.tsv- A table of the length, classification, and predicted functionality of each sequence, as well as the presence of any MADA motif or CJID domainmotifs.tsv- A table of all the NLRexpress motifs for each sequencedomains.tsv- A table of all the domains identified for each sequencenbarc.fasta- A fasta file of all the NB-ARC domains identified.How does it work?Resistify is a two step process.First, all sequences are searched for CC, RPW8, TIR, and NB-ARC domains.
This is used to quickly filter out any non-NLR sequences and identify the primary architecture of each NLR.Secondly, each potential NLR sequence is scanned for CC, TIR, NB-ARC, and LRR associated motifs via NLRexpress.
These are used as an additional layer of evidence to reclassify each NLR by predicting LRR domains, and predicting any CC or TIR domains which may have been missed in the initialhmmsearch.Resistify will also search for N-terminal MADA motifs and CJID domains that are common to CNLs and TNLs respectively.ContributingContributions are greatly appreciated!
If you experience any issues running Resistify, please get in touch via the Issues page.
Package: resistorcalc
ResistorCalcResistorCalc is a graphical user interface (GUI) application built with Tkinter for calculating the resistance value and tolerance of resistors based on their color bands. This tool provides an interactive way to input color bands, visualize the resistor code, and obtain accurate resistance information.FeaturesColor Code Visualization:The application visually represents the color code of resistors on a canvas for easy interpretation.Flexible Band Selection:Users can select the number of bands (4, 5, or 6) to accommodate various resistor types.Calculation Accuracy:The tool follows the standard resistor color code to accurately calculate resistance values and tolerances.Clear and Calculate:Clear input fields and results with the "Clear" button, and calculate the resistor values with the "Calculate" button.Getting StartedInstallation:Ensure you have Python3 installed on your system.Clone this repository:https://gitlab.com/harichella005/resistorcalc.gitNavigate to the project directory:cd ResistorCalccd lib && chmod +x resistorcalc.pysudo ln -s "$(pwd)/resistorcalc.py" /usr/bin/resistorcalcresistorcalcRun the Application:Execute the following command:python3 resistorcalc.pyUsage:Enter the color bands of the resistor in the entry field.Select the number of bands using checkboxes.Click the "Calculate" button to obtain the resistor value and tolerance.Use the "Clear" button to reset the input and results.Happy Resistor Calculations!ResistorCalc is designed to simplify the process of resistor value and tolerance calculations. Enjoy exploring the colorful world of resistors, and may your electronic projects be as harmonious as a well-calculated resistor network!
Package: resistors
ResistorsA Python library for decoding resistor color codes and encoding resistance values.Converts colors to valueConverts value to colorsSupports four and five-band resistorsZero dependenciesThe librarystrictlyfollows the following chart:Using colors not listed in this chart will result in aKeyErrororValueError.Installationpip3 install resistorsUsagefrom resistors import Resistor

r1 = Resistor("red", "red", "black", "black", "brown")
print(r1.get_resistance())  # {'value': 220.0, 'tolerance': 1.0}
r2 = Resistor.with_resistance(1230, 2, 5)   # create resistor object with value of 1230Ω, tolerance of +-2%, and 5 bands
print(r2.get_colors())  # ['brown', 'red', 'orange', 'brown', 'red']
Package: resize
Permission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included inall copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS INTHE SOFTWARE.Description: ===============================resize===============================.. image:: https://badge.fury.io/py/resize.png:target: http://badge.fury.io/py/resizer.. image:: https://travis-ci.org/bdabrowski/resize.png?branch=master:target: https://travis-ci.org/bdabrowski/resizer.. image:: https://pypip.in/d/resize/badge.png:target: https://crate.io/packages/resizer?version=latestSimple command line utility for re-sizing images in a batch operation.Usage:resize.py <path> [--height=<px>] [--width=<px>]resize.py -h | --helpresize.py --versionOptions:-h --help           Show this screen.-v --version        Show version.-O --output-dir     Full path to directory with photos (./processed is default)-H --height=<px>    Set resulting height in pixels as number. [Default 1024]-W --width=<px>     Set resulting width in pixels as number. [Default 768]Example:resize.py . --height=1024 --width=768Results in creating subdirectory "processed" with all processed images inside.Requirements------------- Python >= 2.7 or >= 3.7License-------MIT licensed. See the bundled `LICENSE <https://github.com/bdabrowski/resize/blob/master/LICENSE>`_ file for more details.Keywords: resizerPlatform: UNKNOWNClassifier: Development Status :: 2 - Pre-AlphaClassifier: Intended Audience :: DevelopersClassifier: License :: OSI Approved :: MIT LicenseClassifier: Natural Language :: EnglishClassifier: Programming Language :: Python :: 2Classifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.4Classifier: Programming Language :: Python :: 3.5Classifier: Programming Language :: Python :: 3.6Classifier: Programming Language :: Python :: 3.7Classifier: Programming Language :: Python :: Implementation :: CPythonClassifier: Programming Language :: Python :: Implementation :: PyPyProvides-Extra: test
Package: resize-and-crop
Resize and CropDescriptionResize and crop an image to fit the specified size.Installationpipinstallresize-and-croporpipenvinstallresize-and-cropMethodsresize_and_crop(path, size, crop_origin) - Resize the image located at path, into the size specified, cropping the image starting at the crop_origin.resized_image_file(image_field, format, dimensions, crop_origin) - Returns a new image file that has the resized image. Can be used to save to a Django ImageField.Usagefromresize_and_cropimportresize_and_cropimage=resize_and_crop("/path/to/image",(200,200),"middle")fromdjango.dbimportmodelsfromresize_and_crop.utilsimportresized_image_fileclassExampleModel(models.Model):image=models.ImageField(upload_to="images/")thumbnail=models.ImageField(upload_to="thumbnails/")defsave(self,*args,**kwargs):ifself.imageandnotself.thumbnail:self.thumbnail=resized_image_file(self.image,"jpeg",(200,200),)returnsuper().save(*args,**kwargs)
Package: resize-img
A simple image resizing CLI - PyPi package deployment CI exampleAboutThis project is used as an example of how to deploy a Python package to PyPi using GitHub Actions. The example package is a simple CLI tool I built many moons ago that allows you to easily resize any image file from the command line. Feel free to use this if you think it would be useful but you're probably better off withImageMagick.Installationpip install resize-imgCommandsresize-img $IMG_PATHSaves a new, resized image of the specified image path to the CWD, using the default size of 512x512px.resize-img $IMG_PATH smallSaves specified image as 250x250px in CWD.resize-img $IMG_PATH mediumSaves specified image as 1000x1000px in CWD.resize-img $IMG_PATH bigSaves specified image as 2000x2000px in CWD.resize-img $IMG_PATH custom <int> <int>Saves specified image as pxpx in CWD.resize-img --helpPrints the CLI instructions to your terminal.Deploy your own PyPi package with GitHub ActionsPrerequisites:APyPi API tokenAGitHub accountSetup instructionsExamples of each of the following steps can be found in this repository in the referenced locations.Add your PyPi username as password to a GitHub repo's environment as secrets.Package the code you want to deploy into a folder(the package in this example is called img_resizer).Create a file named__init__.pyin your package and import the main function for your package.Create a file namedsetup.pyin the root folder of your project. In this file, you specify the name of the PyPi package and any CLI entry points. Package requirements and markdown description can also be specified if you want this to show on the PyPi platform,see mine for example.Create a GitHub Actions workflow to trigger your desired event that will build your package with thewheellibrary and upload your package using thetwinelibrary. Your PyPi username and password secrets will need to be set as environment variables for this to work(see my example).update your project repo in GitHub to trigger your workflow and upload your package to PyPi. This will then be easily installable on any machine with the commandpip install <package name>
Package: resizeImgForML
RequirementsPython 3.xFeaturesnothingHistory0.0.1 (2020-10-27)0.0.2 (2020-11-02)
0.0.3 (2020-11-02)
0.0.4 (2020-11-02)
0.0.5 (2020-11-02)
0.0.6 (2020-11-04)
0.0.7 (2020-11-04)first releaseUsageplease look at Github READMEPlan to upgradeopen image through one unipue path(it is useful for WebApp like Django)open one directory and all of image at the directoryresize & save images for MNISTand more, if i canfirst release
Package: resizePixel
resizePixelchange quality of the imagePackageBasic structure of package is.
â”œâ”€â”€ CHANGELOG.md
â”œâ”€â”€ CONTRIBUTING.md
â”œâ”€â”€ LICENSE
â”œâ”€â”€ README.md
â”œâ”€â”€ resizePixel
â”‚   â”œâ”€â”€ __init__.py
â”‚   â”œâ”€â”€ resizePixel.py
â”‚   â””â”€â”€ version.py
â”œâ”€â”€ script
â”‚   â””â”€â”€ test
â”œâ”€â”€ setup.py
â””â”€â”€ tests
    â”œâ”€â”€ __init__.py
    â”œâ”€â”€ __pycache__
    â”‚   â””â”€â”€ __init__.cpython-39.pyc
    â”œâ”€â”€ img
    â”‚   â””â”€â”€ test.png
    â””â”€â”€ unit
        â”œâ”€â”€ __init__.py
        â”œâ”€â”€ test_resizePixel.py
        â””â”€â”€ test_version.pyRequirementsPackage requirements are handled using pip. To install them dopip install -r requirements.txtTestsStart the testsUsage# import library 

from resizePixel.resizePixel import rpImage,reduce_qualtiy
from PIL import Image
import os
# image path
image_path = r"C:\Users\mkshgh\Downloads\img.png"
# I am saving in the same directory 
# img_path,img_name > C:\Users\mkshgh\Downloads, img.png
img_path,img_name = os.path.split(image_path)
output_img_name = 'output_'+img_name

# open the image and check the current quality
image_file = Image.open(image_path)
my_image = rpImage(image_path,image_file)
info = my_image.get_image_meta()
print(info)

# decrease quality explained above
# Use quality from 1 to 99; 99 being the best and 1 being the worst quality
# If the quality of the image is already low use 1 to 20; works great with larger files.
new_image = reduce_qualtiy(output_img_name,image_file,quality=50,outputdir=img_path)
print(new_image)
Package: reskeeper
# Reskeeper[![version](https://img.shields.io/static/v1.svg?label=version&message=0.1.1&color=blue)](https://github.com/qige96/reskeeper)[![docs](https://img.shields.io/static/v1.svg?label=docs&message=rtds&color=green)](https://reskeeper.readthedocs.io/en/latest/)A lightweight management system for applying and releasing exclusive resources.## IntroductionConsidering these scenarios: you are doing web crapping thatrequiring proxies with multi-threads, and each thread can onlyuse one proxy and one proxy can only be used by one thread atone time; or you are doing monkey tests for login process ofyour several applications at one time, each application canonly use one test account and each account can only be usedby one application at one time. You will need to manage theapplying and releasing of these **exclusive resources**, andhere is this little system taht can help.## InstallationYou can install it by just one command```shellpip install reskeeper```or```shellgit install git+https://github.com/qige96/reskeeper.git```## Quick Start```pythonfrom reskeeper import ResourceKeeperresources = ["resource1","resource2",]rk = ResourceKeeper()     # create a resource keeper instancerk.load(resources)        # load a batch of datares1 = rk.get()           # get a resourceres2 = rk.get()           # get one more resourceres3 = rk.get()           # None, no resource availableprint(res1)               # <res_id: 1>print(res2.res_id,res2.data)        # (2, "resource2")print(res3)               # Nonerk.release(res1)          # release a resourceres4 = rk.get()           # one resource availableprint(res4.to_dict())     # {'res_id': 1, 'data': 'resource1'}```## Documentationsee https://reskeeper.readthedocs.io
Package: reskin-sensor
# Reskin Sensor Library
This is a python library to interface with [ReSkin](https://openreview.net/forum?id=87_OJU4sw3V) sensors. We provide two classes for interfacing with [ReSkin](https://openreview.net/forum?id=87_OJU4sw3V). TheReSkinBaseclass is good for standalone data collection: it blocks code execution while data is being collected. TheReSkinProcessclass can be used for non-blocking background data collection. Data can be buffered in the background while you run the rest of your code.Latest stable release is v2.0.0## InstallationThis package can be installed using pip:` pip install reskin_sensor `Alternatively, if you would like the latest (potentially unstable) version,
1. Clone this repository using` $ git clonehttps://github.com/raunaqbhirangi/reskin_sensor.git--recursive`2. Install this package using` $ pip install-e. `## UsageConnect the 5X board to the microcontroller.Connect the microcontroller (we recommend the Adafruit Trinket M0 or the Adafruit QT PY) to the computer using a suitable USB cable3. Use the [Arduino IDE](https://www.arduino.cc/en/software) to upload code to a microcontroller. The code as well as upload instructions can be found in the [arduino](./arduino) folder.
If you get acan’t open device “<port-name>”: Permission deniederror, modify permissions to allow read and write on that port. On Linux, this would look like` $ sudo chmod a+rw<port-name>`4. Run test code on the computer` $ python tests/sensor_proc_test.py-p<port-name>`## Credits
This package is maintained by [Raunaq Bhirangi](https://www.cs.cmu.edu/~rbhirang/). We would also like to cite the [pyForceDAQ](https://github.com/lindemann09/pyForceDAQ) library which was used as a reference in structuring this package.
Package: resmali
reSMALIScoring matrix-assisted ligand identification (SMALI) rewrittenA tool for predicting SH2 domain binding from peptide sequences with the format xx-Y-xxxxThe original SMALI was described in Huang H, Li L, Wu C, et al. Defining the specificity space of the human SRC homology 2 domain. Mol Cell Proteomics. 2008;7(4):768-784. doi:10.1074/mcp.M700312-MCP200 . However the tool is no longer hosted and is therefore no longer accessible. As the SMALI prediction tool was based on a experimental dataset, the loss of the tool represents the loss of a unique datapoint for making SH2 domain prediction.The complete OPAL array dataset upon which the original SMALI tool used was not published. However normalised intensinty values making up 20% or more of the total intensity for a given position within the peptide sequence was published. reSMALI uses this data to generage its own 'reSMALI' scores which are an attempt to emulate the scores provided by the original tool.Installingpip install resmaliPredict SH2 domain bindingTo predict SH2 domain binding with a compredict_protein(protein_seq,sh2=None,local_threshold='preset')Where 'protein_seq' is your protein sequence. When 'sh2' is set to 'None', predictions will be made for all SH2 domains.This function will return a list with the columns [positon, sequence, sh2motif, sh2_threshold, score ]The local threshold is precalculated as the top 5% of scores for all valid phospho-tyrosines within the human proteome for that SH2 domain.Note this function will only return SH2 domains where the reSMALI score is above the set local_thresholdTo show the reSMALI scores for all valid phospo-tyrosine motifs and SH2 domains the  'local_threshold' can be set to '0' when using the predictProtein function.###Exampleimportresmaliresmali.predict_protein("EEVVARyLNRNyWEkKQEEARyLN")>>>[[6,'ARYLNRN','GADS',1.88,2.0],[6,'ARYLNRN','GRAP',1.33,2.96],[6,'ARYLNRN','GRB2',0.99,1.73],[6,'ARYLNRN','GRB7',0.85,0.98],[6,'ARYLNRN','HSH2D',0.73,2.77],[6,'ARYLNRN','SH2D2A',1.72,2.87],[6,'ARYLNRN','SHE',1.19,1.2]]Just give me predictionscondensed_list(protein_seq, sh2 = None, local_threshold='preset')This function will return a list with only SH2 domain predictions in the format '[Position, SH2 domain]'###Exampleimportresmaliresmali.condensed_list("EEVVARyLNRNyWEkKQEEARyLN")>>>[[6,['GADS','GRAP','GRB2','GRB7','HSH2D','SH2D2A','SHE']]]