Saravanan666/pypi-org huggingface dataset:
Package: res-manager
This project helps you to handle your experimental results efficiently when doing your research. Basically it usespickleandsqlite3to save, load and manage the data, now we provide you with some high-level interfaces so that you can get rid of pickle dumping and loading from disk all the time.Since it is based onpickle, in theory you can use it to manage all types of data in python, you can even save an object as shown in the following examples.Installationpip installres-managerUsageJust start withfrom res_manager import ResultManager, now you can create an object withrm = ResultManager(path)and then use it to save, load, delete and preview your data.>>> from res_manager import ResultManager
>>> rm = ResultManager('data')
>>> rm.save([1, 2, 3], topic='test saving', name='data1', comment='Test saving a list')
>>> rm.save(65535, topic='test saving', comment='Test saving a number without a name')
>>> rm.save(rm, topic='topic 2', name="object of \"ResultManager\"", comment='Saving an object')
>>> rm.save({0:1,1:'string'}, name="hongshan's dict without topic")
>>> rm.print_meta_info()
    ...
>>> rm.load(3)
    <res_manager.manager.ResultManager object at 0x7f29c8587470>
>>> rm.load(3, version='first')
    [1, 2, 3]
>>> rm.delete_by_id(3, version='latest')
>>> rm.update_meta(1, name='b', topic='topic 5')ResultManagerThis project mainly provides:A class namedResultManagerthat provides all functions.ResultManager.save: Save your data.ResultManager.load: Loading data by ID.ResultManager.print_meta_info: Print all meta info of saved data, including data id, name, topic.ResultManager.delete_by_id: Delete data from yor storage.ResultManager.update_meta: Update meta information.ResultManager.print_namesandResultManager.print_comments： Print names and comments.ResultManager.print_data_info: Print details of the requested data.Future PlanAdding support to save and load figures for each piece of dataAdd more interfacesAuthorsAuthor and lead developer:Hongshan LiRequirementsprettytable>=0.7.2NoteResultManageris developed by Python 3.5 and tested on python 2.6, 2.7, 3.4-3.7Key release notes1.0.3Save data to pickle files, initial version.2.0.0Introduce SQLite to save and manage data.2.1.0Securely closing SQLite connections under any circumstances.2.2.0Add support to quotation marks, addupdate_metainterface, simplifier some interfaces.3.0.0Add version control to manage data at different versions with the same name.
Package: resman-client
ResMan ClientIntroductionThis is the client ofResman Server.Usepip install resman-clientto install it.Initializefromresman_clientimportResmanClientclient=ResmanClient("https://resman.xxx.com/","user-name","password")Image Listfromresman_clientimportImageList,DefaultS3Image,ImageListClientimage_list:ImageListClient=client.create_image_list(ImageList(title="title",description="content",data={# jsonable object}))image_list.append_s3_images([# DefaultS3Image objects# Can be different bucket, but must be same server as Resman Server])image_list.upload_images([# filenames, binary data or binary IOs])Video Listfromresman_clientimportVideoListClient,VideoListvideo_list:VideoListClient=client.create_video_list(VideoList(title="title",description="whatever",data={# jsonable object}))# Upload videovideo_list.upload_mp4_video("mp4 file path")Novelfromresman_clientimportNovelClient,Novelnovel:NovelClient=client.create_novel(Novel(title="title",data={# jsonable object}),text="Content of the novel")Some NoticeSupport mp4 (h264/h265) video onlydata is a JSON body to store some metadata like original URL, won't be displayed in websiteVideo and Image list have an attribute order to control the order, auto increasing (+1) will be applied while uploading multi objects, the order of objects with sameorderfield can't be predicted.
Package: resmcseg
Library for Deep Residual Multiscale Segmenter (resmcseg)The python library of Deep Residual Multiscale Segmenter (autonet).
Current version just supports the KERAS package of deep learning and
will extend to the others in the future.Major modulesmodelgResMCSeg: major class to obtain a deep extensive residual multiscaleFCN. You can setup its aruments. See the class and its
member functions' help for details.gResMCSegPre: major class to make semantic segmentation for binary and multi class.pretrainedmodel: function to download the pretrained models using the DSTL and
ZURICH datasets from the Google cloudutilsegmetrics: main metrics including jaccard index, MIoU, and loss functions etc.helper: helper functions including color mapping etc.datadata: function to access one image for Zurich to test the model's prediction.InstallationYou can directly install it using the following command for the latest version:sudopipinstallresmcsegNote for installation and useCompiler requirementsresmcseg requires a C++11 compliant compiler to be available.Runtime requirementsresmcseg requires installation of Keras with support of Tensorflow as the
backend system of deep learning (to support Keras). Also Pandas and Numpy should
be installed.
Specifics: Keras>=2.2.2; opencv2>=3.4.1,tensorflow,numpy,sklearn, pandas,
gdal,tifffile etc.Use caseThe homepage of the github for the package, resmcseg provides specific
examples for use of the library:https://github.com/lspatial/resmcsegpubLicenseThe resmcseg is provided under a MIT license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.Test callimportosimportcv2fromkeras.modelsimportload_model,model_from_jsonfromresmcseg.util.segmetricsimportcompute_iou,jaccard,mean_iouC,miou,mean_ioufromresmcseg.model.gresmcseg_preimportgResMCSegPrefromresmcseg.model.resizelayerimportResizeLayerfromresmcseg.model.pretrainedmodelimportdownloadPretrainedModelfromresmcseg.util.helperimportbce_dice_loss,jaccard_coef,jaccard_coef_int,jaccard_coef1fromresmcseg.util.helperimportonehot_to_rgb,color_dictfromresmcseg.dataimportdloadmodelFl='/tmp/model_strwei.h5'ifnotos.path.isfile(modelFl):downloadPretrainedModel('ZURICH',destination=modelFl)model=load_model(modelFl,custom_objects={'ResizeLayer':ResizeLayer,'bce_dice_loss':bce_dice_loss,'mean_iou':mean_iou,'jaccard_coef':jaccard_coef,'jaccard_coef1':jaccard_coef1,'miou':miou,'jaccard_coef_int':jaccard_coef_int,'mean_iouC':mean_iouC})ppre=gResMCSegPre(patchsize=224,bordersize=16,overprop=0.3)img,mask=dload()imgres=ppre.preAImgMulti(img,model,9)mskImg=onehot_to_rgb(imgres,color_dict)fpath="/tmp/zurich1img_pre.jpg"cv2.imwrite(fpath,cv2.cvtColor(mskImg,cv2.COLOR_RGB2BGR),[int(cv2.IMWRITE_JPEG_QUALITY),100])y_pred=imgres.flatten()y_true=mask.flatten()iou=compute_iou(imgres,mask)jacard=jaccard(imgres,mask)print("iou : "+str(iou)+'; jacard is ',jacard)CollaborationWelcome to contact Dr. Lianfa Li (Email:lspatial@gmail.comorlilf@lreis.ac.cn).
Package: resmem
ResMemThis is a package that wrapsResMem. This is a residual neural network that
estimates the memorability of an image, on a scale of 0 to 1.How to UseTo install from PyPI:pipinstallresmemThe package contains two objects, ResMem itself, and a preprocessing transformer function built on torchvision.fromresmemimportResMem,transformermodel=ResMem(pretrained=True)Thetransformeris designed to be used with pillow.fromPILimportImageimg=Image.open('./path/to/image')# This loads your image into memoryimg=img.convert('RGB')# This will convert your image into RGB, for instance if it's a PNG (RGBA) or if it's black and white.model.eval()# Set the model to inference mode.image_x=transformer(img)# Run the preprocessing functionprediction=model(image_x.view(-1,3,227,227))# For a single image, the image must be reshaped into a batch# with size 1.# Get your prediction!For more advanced usage, see thesample.pyfile in this repository.Description of the ModelHistorically, the next big advancement in using neural networks after AlexNet, the basis for MemNet, was ResNet. This allowed for convolutional neural networks to be built deeper, with more layers, without the gradient exploding or vanishing. Knowing that, let's try to include this in our model. What we will do is take a pre-trained ResNet, that is the whole thing, not just the convolutional features, and add it as an input feature for our regression step. The code for this ishere.For the following discussion, while ResMem is initialized to a pre-trained optimum, I have allowed it to retrain for our problem. The thought is that given a larger set of parameters the final modelshouldbe more generalizable. Using weights and biases, we can run a hyperparameter sweep.Here we can see much higher peaks, reaching into the range of 0.66-0.67! All of these runs were both trained and validated on a dataset that was constructed from both MemCat and LaMem databases.Github InstructionsIf you want to fiddle around with the raw github sourcecode, go right ahead. But be aware that we use git lfs for the
model storage. You'll have to install the package athttps://git-lfs.github.com/and then run:gitlfsinstall
gitlfspullto get access to the full model. Also note that thetests.pyfile that's included with this repository references a
database that we, at this time, do not have permission to distribute, so consider that test file to be a guide rather
than a hard-and-fast test.Citation@inproceedings{ResMem2021,
title = {Embracing New Techniques in Deep Learning for Predicting Image Memorability},
author = {Needell, Coen D. and Bainbridge, Wilma A.},
booktitle = {Proceedings of the Vision Sciences Society, 2021},
year = {2021},
publisher = {ARVO Journals},
url = {https://www.coeneedell.com/publication/vss2021/vss2021.pdf}
}
Package: resn
resnresn (REdis Social Network) is a simple library to create social networks. It provides a backend for data models using Redis, supporting friends, followers, updates and a news feed out of the box. You can use it with any web framework like Django, Flask, Pylons etc. For examples see the filetests.py.Requires:Latest version of [redis-py](http://github.com/andymccurdy/redis-py)Latest version of [redis_wrap](http://github.com/amix/redis_wrap)Author: Vivek Narayanan <mail@vivekn.co.cc>License: BSD, see LICENSE for more info.InstallationFirst install redis_py and redis_wrap if you haven’t already:$ easy_install redis
$ easy_install resis_wrapThen install resn:$ easy_install resnSetting upTo use resn, import the module resn and add this to the beginning of the file.from resn import *
setup_system(‘default’, host, port) # host and port of your redis server, default is localhost:6379There are a couple of other settings you might want to change.resn_settings[‘Feed Size’] = 100 # default value = 1000,  No of messages in a user’s feed.
resn_settings[‘Token Validity’] = 72 * 3600 # This is the default value in secondsExamplesFor examples seehttps://github.com/vivekn/resn/blob/master/tests.pyFutureSupport for integrating with Facebook and Twitter APIs is planned.
Package: resnet50-pynq
Install theresnet50-pynqpackage usingpip:pipinstallresnet50-pynqAfter the package is installed, to get your own copy of the available notebooks
run:pynqget-notebooksResNet50You can then try things out by doing:cdpynq-notebooks
jupyternotebookThere are a number of additional options for thepynq get-notebookscommand,
you can list them by typingpynqget-notebooks--helpYou can also refer to the officialPYNQ documentationfor more information
regarding thePYNQ Command Line Interfaceand in particular theget-notebookscommand.Supported Boards/ShellsCurrently, we distribute the overlay only for the following Alveo boards and
shells:ShellBoardxilinx_u250_xdma_201830_2Xilinx Alveo U250Designs are built usingVitis 2019.2.
Package: resnet-simple
resnet-simpleresnet-simpleis a Python3 library that provides a well-documented and easy to use implementation of ResNet (and ResNetv1.5), together with its most basic use case of image classification.ResNetResidual Network, more commonly known as ResNet, is a deep learning model that is most commonly used for image recogition and classification. It was first introduced by He et al. in the paper titled "Deep Residual Learning for Image Recognition". Each ResNet has multiple residual blocks that takes an input $x$ and pass it through stacked non-linear layers which are fit to the residual mapping $F(x) := H(x) - x$, where $H(x)$ is the original unreferenced mapping which can be recast back via $H(x) = F(x) + x$. Intuitively speaking, ResNets should achieve higher accuracy than models that purely use convolutional networks (CNNs) such as VGGNet and AlexNet, as technically it should be easier to optimize and learn residual mapping than an unreferenced one.This module allows you to choose between the original ResNet (v1) and themodified ResNet (v1.5), in which the latter enjoys a slight increase in accuracy at the expense of a slower performance. To do so, setdownsample_3x3 = Trueunder theBottleNeck/ResNetclass to use ResNetv1.5, anddownsample_3x3 = Falsefor ResNetv1. This feature only works for ResNets that use BottleNeck blocks such as ResNet50, ResNet101 and ResNet152.How to use resnet-simpleresnet-simpleonly supports python 3.7 and above. If you want to useresnet-simplefor other versions of Python, do note that it may not function as intended.Module is mainly tested on Google Colab (GPU) and Windows (CPU). Issues when setting up in other environments, or when using multiple GPU cores, are still relatively unknown.DependenciesThe following libraries are compulsory in order to useresnet-simple, which is automatically installed together with it unless otherwise specified:tqdmtorchsafetensorsWhen pip installingresnet-simple, the following libraries will also be installed as they are used in our examples:matplotlibtorchvisiontensorboardscikit-learn (for Python 3.7, use version 1.0.2)User installationIn command prompt on Windows,pip install resnet-simpleIn your python file,fromresnet_simpleimport*ContributionsI appreciate if you are able to contribute to this project as I am the only contributor for now. If you think that something is wrong with my code, you can open an issue and I'll try my best to resolve it!CitationThe code from this module is re-implemented based on what was described in the following paper.@inproceedings{inproceedings,
author ={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
year ={2016},
month ={06},
pages ={770-778},
title ={Deep Residual Learning for Image Recognition},
doi ={10.1109/CVPR.2016.90}}@misc{he2015deep,
      title={Deep Residual Learning for Image Recognition}, 
      author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
      year={2015},
      eprint={1512.03385},
      archivePrefix={arXiv},
      primaryClass={cs.CV}}
Package: resolv3r
OverviewResolv3r is a simple python package aimed at resolving the vendor associated with a given ipNote: this package only resolves devices as either cisco or aruba!To install using pip, simply run:pip3 install resolv3rDependenciesTested in 3.10 (Use in older versions of python at your own risk)Look in pyproject.toml for more details into the project dependencies:asyncsshnetmikoHow to use?In your python project, simply write:from resolv3r import ResolverNow, to resolve a given ip to a vendor, you first need to create a Resolver object:resolver = Resolver("192.168.0.1", "username", "password")Now, begin the resolution process using:device_vendor = resolver.detect_vendor()This should return the correct vendor of the ip in question: either "cisco_ios" or "hp_procurve"How does this work?First, resolv3r uses asyncssh to connect to the device in question and determine device type using a single command ("sh version")If that fails, resolv3r moves onto using netmiko's autodetection feature to resolve the vendor.Finally, if that doesn't work, resolv3r raises a LookupError, indicating that the vendor resolution process was not successful.
Package: reSolved
reSolved python libraryreSolved is an upcoming captcha solving service, capable of solving reCaptcha, hCaptcha, GeeTest as fast as sub 5 seconds. reSolved is designed to handle heavy workloads, unlike other captcha solving services.We strive to improve the reSolved experience every single day - which is why we are releasing libraries that make an already simple integration process completely seamless.Getting StartedInstallationpip install reSolvedImport the libraryfromresolved.domlabsimportResolvedInitialize an objectresolved=ReSolved(COMPANY_API_KEY,USER_API_KEY')Create a task and get the solved captcha tokentask=resolved.createTask(SITE_KEY",              # stringSITE_URL,# stringCAPTCHA_SERVICE",       # string - e.g., reCaptcha, hCaptcha etcCAPTCHA_TYPE,# string - e.g, v2Invis/v2SESSION_CLONE,# booleanSMART_MOVEMENT,# booleanPROXY)# string - e.g., http://ip:port:username:passwordtaskID=task['data']['taskID']token=resolved.getToken()Full code example# import libraryfromsrc.reSolvedimportReSolved# create a resolved objectresolved=ReSolved(COMPANY_API_KEY,USER_API_KEY')# create a tasktask=resolved.createTask(SITE_KEY",              # stringSITE_URL,# stringCAPTCHA_SERVICE",       # string - e.g., reCaptcha, hCaptcha etcCAPTCHA_TYPE,# string - e.g, v2Invis/v2SESSION_CLONE,# booleanSMART_MOVEMENT,# booleanPROXY)# string - e.g., http://ip:port:username:passwordtaskID=task['data']['taskID']# store the tokentoken=resolved.getToken()
Package: resolve-ec2-id
resolve-ec2-idLinksGitLabGitHubPyPIIntroductionThis is a simple command-line tool which looks up the EC2 instance ID given an instance name:$resolve-ec2-idmy-named-instance
i-1234567890abcdef0Currently this tool only works with default credentials. Consider configuring them with environment variables or using a program likeaws-vault.AlternativesThis package is perhaps not so useful because very similar functionality can be accomplished with AWS CLI as follows:awsec2describe-instances--filters'Name=tag:Name,Values=my-named-instance'--query'Reservations[*].Instances[*].{Instance:InstanceId}'--outputtextHowever, this tool has slightly better error-handling; the above AWS CLI command will not generate an error in the case that no instance is found.InstallationIn order to install in a clean and isolated Python environment, it is recommended to usepipx:pipxinstallresolve-ec2-idExamplesAssuming you are using the Bash shell, to start an instance if you know the name but not the ID:$awsec2start-instances--output=yaml--instance-ids="$(resolve-ec2-idmy-named-instance)"StartingInstances:
-CurrentState:Code:0Name:pendingInstanceId:i-1234567890abcdef0PreviousState:Code:80Name:stoppedThis can also be used from within Python:fromresolve_ec2_idimportresolve_ec2_idec2_id=resolve_ec2_id('my-named-instance')RequirementsBeyond the base dependencies which install automatically, this requires eitherAWS CLIorboto3. (I did not makeboto3a dependency because it isn't needed if AWS CLI is already installed.)
Package: resolve-hash
resolve-hashAllow to resolve a hash to a known URL representation.Usageresolve-hash <hash>outputs URL matching the hashOutput:URL matching the hash, if foundExit code:0 if the hash has been found1 if the hash has NOT been foundExample:$resolve-hash8d8645468228
https://devcentral.nasqueron.org/rKERUALD8d8645468228

$resolve-hash00000000000000(git)-[main]https://github.com/seungwonpark/ghudegy-chain/commit/00000000000000c06d2e8c36f247206a9a4b1c63

$resolve-hashnot_a_hash
$echo$?1Why this package?Terminator has a comprehensive plugins' system to offer extra features,
like resolve console output as links.Meanwhile, it's sometimes convenient to open a link in a browser,
especially if the VCS hash is resolved to the code review system.Hash sourcesVCSPhabricator, browsing your .arcrc file to know the instances you work withGerrit, if explicitly configuredGitHubGitLab, if you provide a token, as search queries must be authenticatedConfigurationYou can provide a configuration by creating a$HOME/.config/resolve-hash.conffile.Configuration is a YAML file.VariableDescriptionFormatgerritURL to your Gerrit instancesList of stringsgitlab_public_tokenPersonal token for GitLab.comstringExample:gerrit:-https://gerrit.wikimedia.org/r/gitlab_public_token:glpat-sometokenUse as a libraryYou can use the package as a library to resolve hashes in your application:fromresolvehash.vcsimportphabricatorurl=phabricator.query_phabricator_instances("/home/luser/.arcrc","8d8645468228")print(url)Extend the codeHow to add a new VCS source?If you wish to add a new VCS source, add a method in VcsHashSearch,
then add it toget_search_methods.How to add a hash source?If you wish to extend this script by searching Foo in addition to VCS,
you can create a class FooHashSearch with the following methods:__init__(self, config, needle_hash): constructor called by the scriptsearch(self): perform your search, return a URL or NoneHow can I contribute?You can commit your changes to the upstream by following instructions athttps://agora.nasqueron.org/How_to_contribute_codeThe canonical repository ishttps://devcentral.nasqueron.org/source/resolve-hash.gitLicenseBSD-2-Clause, seeLICENSEfile.
Package: resolve-hosts
resolve-hostsResolve list of DNS hostnames.This is a Python module designed to fit the need of a simple app that can
query an input list of DNS names and output their resolved IP addresses. It
defaults to using the local system resolver for lookups, but can instead
use a list of one or more custom DNS resolvers supplied on the command line.InstallationThis is a setuptools package; install using pip:pip install resolve-hostsIt's recommended to usepipxfor easy setup and
isolation:pipx install resolve-hostsUsageThe module installs a command line tool calledresolve-hoststhat should be
in yourPATH. For a usage overview, see the output ofresolve-hosts -h.Feed it a newline-separated list of names as a parameter. To read from
standard input, omit the parameter or use-for the input.A file with a few sample names to resolve is in thetests/folder.Example using the local system resolver:$ resolve-hosts tests/testnames.txt 
www.example.com             93.184.216.34
blocked.test.on.quad9.net   NXDOMAIN
mxs.mail.ru                 94.100.180.31 217.69.139.150
magnolia.ns.cloudflare.com  172.64.34.214 108.162.194.214 162.159.38.214The above system is clearly configured to useQuad9resolvers, as the local resolver returned NXDOMAIN for the test FQDN.Using specified resolvers and debug output enabled:$ resolve-hosts -s 8.8.4.4 -s 8.8.8.8 -d tests/testnames.txt 
[DEBUG] configured to use resolver(s): ['8.8.4.4', '8.8.8.8']
[DEBUG] effective resolver address(es): ['8.8.4.4', '8.8.8.8']
www.example.com             93.184.216.34
blocked.test.on.quad9.net   127.0.0.1
mxs.mail.ru                 217.69.139.150 94.100.180.31
magnolia.ns.cloudflare.com  172.64.34.214 108.162.194.214 162.159.38.214JSON output from data on stdin:$ resolve-hosts -j < tests/testnames.txt 
{
    "data": [
        {
            "www.example.com": [
                "93.184.216.34"
            ]
        },
        {
            "blocked.test.on.quad9.net": [
                "NXDOMAIN"
            ]
        },
        {
            "mxs.mail.ru": [
                "217.69.139.150",
                "94.100.180.31"
            ]
        },
        {
            "magnolia.ns.cloudflare.com": [
                "162.159.38.214",
                "108.162.194.214",
                "172.64.34.214"
            ]
        }
    ]
}