Saravanan666/pypi-org huggingface dataset:
Package: maskedtensor
maskedtensorMaskedTensor is a library that is part of thePyTorchproject and is an extension oftorch.Tensorby providing the ability to mask out the value for any given element. Elements with masked out values are ignored during computation and give the user access to advanced semantics such as masked reductions, safe softmax, masked matrix multiplication, filtering NaNs, and masking out certain gradient values.InstallationBinariesTo install MaskedTensor via pip, use the following command:pip install maskedtensorNote that MaskedTensor requires PyTorch >= 1.11, which you can get on thethe main websiteFrom SourceTo install from source, you will need Python 3.7 or later, and we highly recommend that you use an Anaconda environment. Then run:python setup.py developDocumentationPlease find documentation on theMaskedTensor Website.Building documentationPlease follow the instructions in thedocs README.NotebooksFor an introduction and instructions on how to use MaskedTensors and what they are useful for, there are a nubmer of  tutorials on theMaskedTensor website.Licensemaskedtensor is licensed under BSD 3-Clause
Package: maskedtensor-nightly
maskedtensorWarning: This is a prototype library that is actively under development. If you have suggestions or potential use cases that you'd like addressed, please
open a Github issue; we welcome any thoughts, feedback, and contributions!MaskedTensor is a prototype library that is part of thePyTorchproject and is an extension oftorch.Tensorthat provides the ability to mask out the value for any given element. Elements with masked out values are ignored during computation and give the user access to advanced semantics such as masked reductions, safe softmax, masked matrix multiplication, filtering NaNs, and masking out certain gradient values.InstallationBinariesTo install the official MaskedTensor via pip, use the following command:pip install maskedtensorFor the dev (unstable) nightly version that contains the most recent features, please replacemaskedtensorwithmaskedtensor-nightly.Note that MaskedTensor requires PyTorch >= 1.11, which you can get on thethe main websiteFrom SourceTo install from source, you will need Python 3.7 or later, and we highly recommend that you use an Anaconda environment. Then run:python setup.py developDocumentationPlease find documentation on theMaskedTensor Website.Building documentationPlease follow the instructions in thedocs README.NotebooksFor an introduction and instructions on how to use MaskedTensors and what they are useful for, there are a nubmer of tutorials on theMaskedTensor website.Licensemaskedtensor is licensed under BSD 3-Clause
Package: maskerade.rides2-secure-bucket
A CDK L3 Construct for a Secure BucketThis is an AWS CDK L3 Construct used to demonstrate the development and publishing process with the AWS CDK.UsageJust import and use it.# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826importaws_cdk.coreascdkfromsecure_bucketimportSecureBucketclassSandboxCdkStack(cdk.Stack):def__init__(self,scope,id,*,description=None,env=None,stackName=None,tags=None):super().__init__(scope,id,description=description,env=env,stackName=stackName,tags=tags)SecureBucket(self,"myBucket")Encryption optionsThis is just a wrapper around an S3 Bucket and thepropsare shared.However, you cannot supply anUNENCRYPTEDoption for theencryptionproperty. If you do, or don't set it at all, it will use theBucketEncryption.KMS_MANAGEDvalue by default.Integration TestIf you want to see full usage, you can run$cdksynthto produce a basic stack with one SecureBucket resourceLicenseMIT License
Package: maskerade.rides3-secure-bucket
A CDK L3 Construct for a Secure BucketThis is an AWS CDK L3 Construct used to demonstrate the development and publishing process with the AWS CDK.UsageJust import and use it.# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826importaws_cdk.coreascdkfromsecure_bucketimportSecureBucketclassSandboxCdkStack(cdk.Stack):def__init__(self,scope,id,*,description=None,env=None,stackName=None,tags=None):super().__init__(scope,id,description=description,env=env,stackName=stackName,tags=tags)SecureBucket(self,"myBucket")Encryption optionsThis is just a wrapper around an S3 Bucket and thepropsare shared.However, you cannot supply anUNENCRYPTEDoption for theencryptionproperty. If you do, or don't set it at all, it will use theBucketEncryption.KMS_MANAGEDvalue by default.Integration TestIf you want to see full usage, you can run$cdksynthto produce a basic stack with one SecureBucket resourceLicenseMIT License
Package: mask-gpu
Mask GPUA simple tool to expose only specified number of GPUs with desired memory to Tensorflow (and a few more apps, read further). This tool queries GPU free memory values usingnvidia-smiand assignsCUDA_VISIBLE_DEVICESenvironment variable based on the specified memory and number of GPUs to expose, to expose specific GPUs. Apps such astensorflow-gpuuse this information and utilize only the GPUs that are exposed. If your app does not make use ofCUDA_VISIBLE_DEVICES, then this is probablynotwhat you would need.Dependenciesnvidia-smiInstallationpipinstallmask-gpuQuick StartTo show available GPUs infomask-gpu--info# Outputs the following# Finding GPUs with a minimum of 1024MiB free memory...# GPUs available: 6 -> [0, 1, 4, 5, 6, 7]Simply runmask-gputo expose 1 GPU (with a minimum of 1024MiB free memory)`mask-gpu`# Remember to wrap mask-gpu in ` symbol# Or else mask-gpu will print the command# and you will have to manually execute itSpecifying OptionsBy defaultmask-gpuseraches for GPUs with atleast 1024M free memory and allots 1 GPUmask-gpu--info--min_memory1024# mask-gpu -i -m 1024                <--- Same as above# Info does not execute any commands`mask-gpu--expose1--min_memory1024`# `mask-gpu -e 3 -m 1024`            <--- Same as above# Remember to wrap mask-gpu in ` symbolYou can specify your own options using the above as the templateUnmask all (Revert)To unmask all GPUs, i.e, to revert to what it was before usingmask-gpu`mask-gpu--unmask-all`# `mask-gpu -u`                      <--- Same as above# Remember to wrap mask-gpu in ` symbolNOTE: This command will clearCUDA_VISIBLE_DEVICESand hence it will be erased when executing the above commandLicenceThe MIT LicenseCopyright (c) 2019 Saravanabalagi RamachandranPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, 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 IN
THE SOFTWARE.
Package: masking
This package contains a function 'maskstring' to mask a given input string based on the index positions passed as arguments.Project resources are available athttps://github.com/sangram11/MASKING.gitSteps to install the package: -$pip install maskingExample to show usage of this package: -import masking.maskstring as msk
print(msk.maskstring('helo','*',2))Output: -*lAlternatively you can supply multiple index values where you don't intent to mask.print(mask.maskstring('Password','*',0,7))Output: -P******d
Package: maskit
masKIT: Ensemble-based gate dropouts for quantum circuitsMasKIT is a framework that provides masking functionality in the context of
parameterized quantum circuits (PQC) for PennyLane. It targetsscientistsand
simplifies researching trainability and expressivity of circuits by enabling to
dynamically mask gates within the circuit. The framework is designed to act as
a drop-in replacement and therefore allows to enhance your existing PennyLane
projects with low effort.The masking is supported on different axes, i.e. layers, wires, parameters, and
entangling gates, for different modes, i.e. adding, removing, inverting.The current version is still in a development stage and therefore does not cover
the whole functionality one might imagine for masking PQCs.
Please feel invited to submit your contributions and ideas.InstallationThe framework can be installed via pypi:python-mpipinstallmaskitContributingYou love research as much as we do? Anything missing? We welcome all support,
whether on bug reports, feature requests, code, reviews, tests, documentation,
blog posts, and more.
Please have a look at ourcontribution guidelines.Contributors ✨Thanks goes to these wonderful people (emoji key):cDenius💻🤔🚧🐛👀Eileen Kuehn💻🤔🚧⚠️📖Max Fischer👀Niklas Metz💻⚠️This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: mask-plasmid
mask-plasmid: Create a quick BED file from an assembled genomeBackgroundWhen building phylogenetic trees with microbial genomic data, it is essential to
get as close as possible to the clonal frame.A common technique for identifying the clonal frame is to map reads to a reference
genome, and then filter out any sites that are not present in all the samples of
interest.In general, plasmids should not be part of the clonal frame. While theoretically
it is possible they are part of the clonal frame, with short read data it is
hard to say if the plasmids are all the same. More importantly, however, it is
quite impossible to say that the plasmid(s) and the chromosomes have been
vertically inherited from the most recent common ancestor of a sample.Thus, it is generally recommended that plasmids be removed from the analyses.The problem arises in small read data when it is not quite possible to say with
certainty if a read belongs in the plasmid or in the chromosomes. Sometimes plasmids
get inserted in chromosomes, sometimes reads should map to a plasmid but erroneously
map to the chromosome because the plasmid was not included in the analysis.
Thus, in the context of mapping reads to a reference to identify potential variants and
the clonal frame, the ambiguous reads (i.e., that could either map on the chromosome or
on a plasmid) should be removed from the pool of potential reads used to identify variant sites.If one removes the plasmids from the reference dataset before attempting to map the
reads then it is not possible to identify ambiguous reads. Thus, a better strategy
might be to keep the plasmids in the reference dataset, map all the reads, identify
variable sites, and then mask the plasmid sites.This can be achieved usingSnippy 4by using the--maskoption and giving it a BED file.What does this tool do?This tool will produce a BED file with every locus in a Genbank file, which can
be easily edited and then used to--maskplasmids when usingSnippy 4.Installationpip install mask-plasmidRunningmask-plasmid <my_gb.gbk[.gz]> > plasmids.bedDevelopmentPushing to PypiThe following command will:run testsclean up the current branchbump the versiongenerate the distributionsclean up the current branchtag the commit with the versionpush to githubpush to pypigit commit -a -m <message>
pipenv run inv deploy <new_version_number> [<patch|minor|major>]
Package: maskprocessor
Python MaskProcessorPython maskprocessor implementation which inspired byhashcat maskprocessorInstallationpipinstallmaskprocessorUsagePrint helpmaskprocessor--help# or -hVia command line$maskprocessor'?d'0123456789Import as python module>>>frommaskprocessorimportmaskprocessorasmaskproc>>>g=maskproc('?l')>>>print(next(g))a>>>print(next(g))bLicenseMIT License
Package: maskpw
MaskpwA simple library to ask the user for a password. Similar to getpass.getpass() but allows to specify a default mask (like * instead of blank).Features 🧰Customize prompt showed to the user.Use a custom mask to hide the password.Installation 📦⬇Usepipto install it.python3-mpipinstallmaskpwUsage 🛠To use it, just import theget_passwordfunction and call it with a prompt ("Password: "by default):frommaskpwimportget_passwordif__name__=="__main__":username=input("Username: ")password=get_password()print(f"Username:{username}; Password:{password}")This will show the following:$pythontest.pyUsername: John DoePassword: *****Username: John Doe; Password: 12347If you want to change the mask:frommaskpwimportget_passwordif__name__=="__main__":username=input("Username: ")password=get_password(mask="#")print(f"Username:{username}; Password:{password}")Now it will look like:$pythontest.pyUsername: John DoePassword: #####Username: John Doe; Password: 12347Contributing ✍Pull requestsare welcome. For major changes,bug fixesorfeature requests, please open an issue first to discuss what you would like to change.License 📜⚖This project uses theMITlicense.
Package: mask-rcnn-documents
Train and test a MASK-RCNN modelTools to train and test a Mask-RCNN model using PyTorch.He, Kaiming, Georgia Gkioxari, Piotr Dollár, et Ross Girshick. « Mask R-CNN ».
arXiv, 24 janvier 2018. http://arxiv.org/abs/1703.06870.InstallationThe scripts should be installed in your environment using pip by including the following line in yourrequirements.txtfile.git+ssh://git@gitlab.univ-lr.fr/acadiie/document_layout_analysis/mask-rcnn.git#egg=mask-rcnnUsagetrain_mask_rcnnusage:train_mask_rcnn[-h]--data-pathDATA_PATH--experiment-nameEXPERIMENT_NAME--epochsEPOCHS[--batch-sizeBATCH_SIZE][--deviceDEVICE][--log-pathLOG_PATH][--mlflow-endpointMLFLOW_ENDPOINT]TrainaMask-RCNNmodelbasedonResNet-50

options:-h,--helpshowthishelpmessageandexit--data-pathDATA_PATHPathtothedirectorywherearestoredtheimagesandlabels.Inthisdirectory,thereshouldbeatrainandavalsubdirectory,eachcontaininganotherimagesandlabelssubdirectories.--experiment-nameEXPERIMENT_NAMENameoftheexperiment--epochsEPOCHS,-eEPOCHSNumberofepochs--batch-sizeBATCH_SIZE,-bsBATCH_SIZEBatchsizeforthemodel--deviceDEVICE,-dDEVICEDeviceonwhichtoruntheneuralnetwork--log-pathLOG_PATHWheretologthemodelsteps--mlflow-endpointMLFLOW_ENDPOINTURLtoaMLFlowtrackingsystemtest_mask_rcnnusage:TestMask-RCNNmodel.[-h]--data-pathDATA_PATH--model-checkpointMODEL_CHECKPOINT[--thresholdTHRESHOLD]options:-h,--helpshowthishelpmessageandexit--data-pathDATA_PATHPathtothedirectorywherearestoredtheimagesandlabels.Inthisdirectory,thereshouldbeatestdirectorycontaininganotherimagesandlabelssubdirectories.--model-checkpointMODEL_CHECKPOINTPathtothemodelcheckpointtotest--thresholdTHRESHOLDConfidencethresholdinthemodeltoexportthemasks.
Package: mask-recognition
Project DescriptionFace Mask Recognition using PyTorch.

It creates a bounding box around the face of the person present in the image and put a text
at the top of the bounding box shows probability of mask on the face.Install:pip install mask_recognitionRequirements:pytorch >= 1.2.0torchvision >= 0.3.0Usage:from mask_recognition import MaskRecognition

import cv2 as cv

mr = MaskRecognition(device=0)  # -1 for cpu, 0 for gpu

frame = mr.recognize(frame, color="rgb")    # color="bgr" for BGR color scheme. frame is the RGB image.

########################## To use webcam ##########################

mr.cam_capture(device_id=0)     # 0 for default or 1, 2, 3, if more than one webcam

## press esc to stop
Package: masksdk
image-obfuscation-sdkan obfuscation server contains two parts. one is inference, another is maskthis repo stands for the mask part standalone SDKTODOadd resize mask technologymore lightweight API, such as numpy-op directly instead of PIL-opHow toimportiofrommasksdk.call_infer_and_maskimportmask_one_image,ModelName,MaskName# confinfer_host='your_ip:your_port'# mask in local and infer in serverraw_img_file='path_to_your_image_file/image_file_name.jpg'withopen(raw_img_file,"rb")asimg_content:mask_img,bbox=mask_one_image(infer_host=infer_host,image_file=io.BytesIO(img_content.read()),model_names=[ModelName.face_ai,ModelName.plate_ai],mask_name=MaskName.blur,degree=0.5,down_sampling_ratio=1,pixel_gap=10,api_version='v2',)# showmask_img.show()print('bbox={}'.format(bbox))
Package: masksemi
masksemiCode for converting a label list in a scikit-like semi-supervised label list.Getting StartedDependenciesYou need Python 3.7 or later to usemasksemi. You can find it atpython.org.You also need numpy package, which is available fromPyPI. If you have pip, just run:pip install numpyInstallationClone this repo to your local machine using:git clone https://github.com/caiocarneloz/masksemi.gitFeaturesGiven the label list and a certain percentage, mask the amount of unlabeled data based on percentage and also encode the data for scikit usage with semi-supervised models. The percentage split is considered by class. This way, all classes will have the given percentage as labeled data.UsageConsidering iris dataset labels:array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
        ...,
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
        ...,
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', ...], dtype=object)The maskData function is called by passing labels and the percentage of labeled data:masked_labels = maskData(labels, 0.1)The labels are encoded and masked:array([-1, -1, -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0, -1, -1, -1,
       -1,  0,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1,  1, -1,  1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1,  2, -1, -1, -1, -1,  2, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  2,  2, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  2])
Package: maskslic
maskSLICSimple linear iterative clustering (SLIC) in a region of interestOutlineThis code demonstrates the adaption of SLIC for a defined region of interest.
The main contribution is in the placement of seed points within the ROI.The code is a modification of the SLIC implementation provided by Scikit-image (http://scikit-image.org/)An online demo is available at:http://maskslic.birving.com/indexAn outline of the method is available at:http://arxiv.org/abs/1606.09518Figure 1: An example region and the automatically placed seed points using this method.Figure 2: The final superpixel regions within the ROIGetting startedInstall dependenciespip install -r requirements.txtbuild cython codepython setup.py build_ext --inplacerun examplepython run_example.pyUsing a pythonvirtualenvis reccommended on linux to get the latest versions of the dependencies.DevelopmentThis code is still a work in progress.
Package: mask-stats
Compute summary statistics between two sets of N-dimensional binary masks.Installation instructionspip install mask_statsUsagefrommask_statsimportcompute_evaluations_for_mask_pairstrue_masks=[mask1,mask2,...,maskN]pred_masks=[pred1,pred2,...,predN]true_eval,pred_eval=compute_evaluations_for_mask_pairs(true_masks,pred_masks)TodoDocumentationMore testsFunction to evaluate single mask pair
Package: maskstr
maskstrHashUses acleanstring as argument and generate a hash.HashCheckUses acleanstring andhash(generated usingHashfunction) as arguments and validate itmaskUses acleanstring as argument and mask itmasked(mk):Uses amaskedas arguments and unmask itmaskedh(mk, hsh):Uses amaskedstring andhash(generated usingHashfunction) as arguments and unmask itsimplemUses acleanstring and do a simple mask based on base64simplemkdUses amaskedstring masked bysimplemfunction and unmask it.
Package: masktools
Utilities for designing DEIMOS slitmasksContact: <adwasser@ucsc.edu>Website: <https://github.com/adwasser/masktools>Required packagesnumpy (tested on 1.11.0)astropy (tested on 1.1.1)matplotlib (tested on 1.5.0)astroquery (tested on 0.3.1)masktools.superskimsThis package is for creating distributions of SKiMS (Stellar Kinematics with Multiple Slits)
which optimally sample the integrated stellar light of a galaxy.Authors: Nicola Pastorello and Asher WassermanThis packages installs thesuperskimsscript, which creates dsim input files for a given galaxy.> usage: superskims [-h] [-m MU_EFF] [-r RA] [-d DEC] [-q]
>                   name R_eff axial_ratio position_angle num_masks
>
> This script generates dsim input files from the inputted galaxy parameters
>
> positional arguments:
>   name                  Name of galaxy for output and NED lookup
>   R_eff                 Effective radius of galaxy in arcseconds
>   axial_ratio           Ratio of minor axis to major axis
>   position_angle        In degrees counter-clockwise from North
>   num_masks             Number of masks for the galaxy
>
> optional arguments:
>   -h, –help            show this help message and exit
>   -m MU_EFF, –mu_eff MU_EFF
>                         Effective surface brightness in mag/arcsec^2, default
>                         is 22
>   -r RA, –ra RA        Right ascension, in degrees J2000. If name is findable
>                         by NED, will use that one instead.
>   -d DEC, –dec DEC     Declination, in degrees J2000. If name is findable by
>                         NED, will use that one instead.
>   -q, –quiet           If toggled, suppress console output.For more fine-grained control, you can access the Mask and Galaxy classes through themasktools.superskimspackage while within a python environment, e.g.,:from masktools import superskims
from astropy.coordinates import SkyCoord
center = SkyCoord('12h35m37.9s +12d15m50s')
galaxy = superskims.Galaxy(name='N4551', center=center, r_eff=16.6,
                           axial_ratio=0.75, position_angle=70.5)
galaxy.optimize(num_masks=4)
for mask in galaxy.masks:
    output_file = mask.name + '_PA{:0.1f}_superskims.dsim'.format(mask.mask_pa)
    superskims.outputs.save_to_dsim(mask, galaxy.center, output_file)masktools.starsThis package is for obtaining good guide stars and align stars for a mask.  Work in progress.
Theget_tablemethod in thequery_usnomodel is likely to be reorganized at some point.