Saravanan666/pypi-org huggingface dataset:
Package: dwilib
NEW: Switched to Python version 3, it is now required and assumed everywhere!Tools in development to support analysis of Diffusion-Weighted Imaging (DWI)
data, with focus on prostate cancer.This software is being developed as part of a research project at theMagnetic
Resonance Imaging Research Center, Turku, Finland.Note: This is very much under construction. The program code is being written on
demand basis, for custom needs at in-house projects. It has grown organically at
the same time as the programmer has been studying the subject and learning the
tools. So the code is quite messy in many places.FeaturesRead input data as DICOM, or in custom ASCII or MATLAB formatsPerform model fitting (Monoexponential ADC, Kurtosis, Stretched exponential,
Biexponential)Calculate correlation with Gleason scoreCalculate and compare diagnostic ROC AUCsCalculate reproducibility measuresPlotting schemesViewer for multi-slice, multi-b-value DWI DICOM files (uses the Matplotlib GUI
widget)Build tool for automated pipelining of data processing tasksTodo or In ProgressImproved documentationImproved build toolAutonomous tumor delineation/ROI placementRegression classificationRequirementsPython 3.4pathlib2 (or newer Python)NumPySciPyScikit-LearnOptional requirementsleastsqbound-scipy(fitting)Matplotlib(plotting)Pydicom(reading DICOM files)h5py(handling HDF5 files)scikit-image(texture analysis)Mahotas(texture analysis)PyDoIt(task management)
Package: dwipy
dwipyDWI analysis pipelineMade for  the initial analysis of diffusion data, especially for extracting the b-values and b-vectors from DICOM data, to allow for further processing.InstallationThis package requires the installation of Tesseract 4.1.1:https://medium.com/quantrium-tech/installing-tesseract-4-on-ubuntu-18-04-b6fcd0cbd78fAnd mtrix:https://mrtrix.readthedocs.io/en/latest/pipinstalldwipy
Package: dwlver
===============================dwlver===============================.. image:: https://img.shields.io/pypi/v/dwlver.svg:target: https://pypi.python.org/pypi/dwlver.. image:: https://img.shields.io/travis/digwanderlust/dwlver.svg:target: https://travis-ci.org/digwanderlust/dwlver.. image:: https://readthedocs.io/projects/dwlver/badge/?version=latest:target: https://readthedocs.io/projects/dwlver/?badge=latest:alt: Documentation Status.. image:: https://requires.io/github/digwanderlust/dwlver/requirements.svg?branch=master:target: https://requires.io/github/digwanderlust/dwlver/requirements?branch=master:alt: Dependenciesversion testing* Free software: BSD license* Documentation: https://dwlver.readthedocs.io.Features--------* TODOCredits---------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`digwanderlust/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage=======History=======0.1.0 (2016-07-07)------------------* First release on PyPI.
Package: dwm-pracs
dwm-pracsClassification Algorithm(Naive Bayes)Bhushan PythonVinay PythonRishabh PythonLinear RegressionRishabh PythonVinay PythonBhushan PythonClustering Algorithm (K means)Bhushan PythonVinay Python (1-d data)Association Rule Mining (Apriori)Bhushan PythonVinay PythonPage RankBhushan PythonVinay Python
Package: dwnld
dwnldMotivationA library to download stuff given urls with different protocols, likeftp://...orssh://....UsageThere is only one interesting module level function in this repo, it can download (or move) stuff from A to B:fromdwnldimportdownloaddownload("file://stuff.txt","somewhere_else.txt")download("ssh://some-remote-server:/home/reptile/stuff.txt","here.txt")download("https://cool.com/nice.pdf","here.pdf")download("http://cool.com/nice.pdf","here.pdf")# supports tor viadownload("onion://cool.onion/here.pdf","here.pdf",proto="https")TodosSupport ftp/sftpSupport torrentSupport non-default ports for tor/socks4/socks5 serverLicenseThis project is licensed under the GPL-3 license.
Package: dwolla-clt
## Version
1.2.4## Requirements
- [Python](http://www.python.org/)## InstallThe recommended way to install dwolla-clt is [through
pip](http://pypi.python.org/). Just type:[sudo] pip install dwolla-clt## UsageLogin to your account by typing:dwolla-clt loginThen, use the different methods to interact with your account:dwolla-clt balance
dwolla-clt send
dwolla-clt request
dwolla-clt transactions
…To get help, or see a list of supported commands:dwolla-clt –help## CreditsMichael Schonfeld &lt;michael@dwolla.com&gt;## SupportMichael Schonfeld &lt;michael@dwolla.com&gt;Dwolla API &lt;api@dwolla.com&gt;## References / Documentationhttp://developers.dwolla.com/dev## License(The MIT License)Copyright (c) 2013 Dwolla &lt;michael@dwolla.com&gt;Permission 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: dwong
dwong, a package for DarkQuest data analysis.dwong is a comprehensive Python package, created by student Dowling Wong, tailored for data analysis and neural network-based particle identification in the DarkQuest experiment. The aim of this project is to streamline DarkQuest's data analysis process by providing exemplary data-processing functions.The package mainly contains four modules: dwong, dplot, dcsv and dkeras.Contents and useful functions.dwongemcal_bytuplemulti_clustersh4_bytupleprepare_data_bytupledplotemcal_evt(x, y, eng)emcal_pdf(ntuple_name, fname, absolute_path)dkerastrain_model(x, y)save_model(model, fname)load_model(mname)plot_confusion_matrix(cm, names, title='Confusion matrix', cmap=plt.cm.Blues)plot_roc(pred,y)dcsvgen_csv(filename)Smaple of use.dwong, main module for data analysis.import dwong
 
 dq_events = dwong.getData(filename, "Events") #data acquisition from n-tuple.
 (x, y, eng, labels, labels_decrease, seeds, seed_labels) = dwong.multi_clusters(dq_events)#here performed clustering
 (h4x, h4y) = h4_bytuple(dq_events)
 dq_st23 = dq_events["st23"]
 dq_track = dq_events["track"]
 gpz = dq_events["gen"]["pz"]
 trkls_coord = np.stack((dq_st23["x"], dq_st23["y"], dq_st23["z"], dq_st23["px"], dq_st23["py"], dq_st23["pz"]), axis=1)
 trkls_cal = np.stack((dq_st23["Cal_x"], dq_st23["Cal_y"]), axis=1)
 track_st3 = np.stack((dq_track["x"], dq_track["y"], dq_track["pz"]), axis=1)

 folded_list=dwong.prepare_data_bytuple(filename)#return a list of events, each event may contain multiple particles.   
 flat_list = [particle for event in folded_list for particle in event]
 labels = [0] * len(flat_list)
 labeled_flat_list = [[label, *particle] for label, particle in zip(labels, flat_list)]#list of particles, in a flat list.dplot, plot module.import dwong
 from dwong import dplot

 dq_events = dwong.getData(filename, "Events") #data acquisition from n-tuple.
 (x, y, eng) = emcal_bytuple(dq_events)
 fig = emcal_evt(x, y, eng)
 #The emcal_evt will plot the emcal page in jupyter notebook, and if you have further consideration, it returns fig

 #save a pdf booklet for all the emcal plots for events in a root file
 if ntuple_name.endswith(".root")& (ntuple_name not in train):
        emcal_pdf(ntuple_name, fname, absolute_path)

 #or you can plot all n-tuple under a directory
 target_dir = os.listdir("/Users/dwong/Desktop/n-tuples/5_80_training/")
 for file_name in target_dir:
     os.chdir(taregt_dir)
     if file_name.endswith(".root")& (file_name not in train):
         emcal_pdf(ntuple_name, fname, absolute_path)External Link*DarkQuest Snowmass paper.
*DarkQuest Collaboration code collection.
*The source for this project is available here.
*Dowling's code collection for data analysis, model training, particle ID and samples.
*Analysis package dwong's Pypi page.
*Dowling's personal website.
Package: dword
Welcome to dwordAn amazing library to create synthetic videosInstallationInstall the dword librarypipinstalldwordMake sure you haveffmpeg installed.Quick startStep 1: Use api keys to loginStart bylogging into your DeepWord accountand generating API keysUse these keys to login to your DeepWord account via the Python apifrom dword.core import DeepWordacc = DeepWord(API_KEY, SECRET_KEY)login successfulNow, make sure that you have enough credits available to generate synthetic videos.acc.available_credits9975.5Step 2: Start creating videosThat's it!!!!You can now start creating synthetic videos. All you need is a video of the person talking and the audio you want them to say. In this quick start tutorial, we will use a video and audio we already have.To learn more about different ways to use video and audio, refer ourtutorial here.acc.generate_video('Anna.mp4', 'my_audio.mp3', title = 'first_deepword_video'){'status': True,
 'message': 'Your video has been added to the queue for processing. It should be complete within 10-15 minutes',
 'url': 'https://login.deepword.co/video/b47y6a4xnhkuey7ak6'}{% include important.html content='The video can take a few minutes to generate. You can find the status of the video by retrieving a list of all videos on your account' %}acc.list_videos()[-1]{'email': 'test_dword_api@yopmail.com',
 'thumbnail': 'video_b47y6afmn0kri14srr.mp4',
 'title': 'video_0',
 'video_url': 'https://videos-deep-word123.s3.us-east-2.amazonaws.com/output_data/b47y6afmn0kri14srr.mp4',
 'video_duration': '30.0000',
 'video_id': 'b47y6afmn0kri14srr',
 'generate_date': '2021-07-24T17:11:42.000Z',
 'output_status': 'Complete'}Once the status changes from 'Queued' to 'Completed' you can either useacc.download_videooracc.download_all_videosto download your video.
Package: dwpose
Easy DWPose DetectorEffective Whole-body Pose Estimation with Two-stages DistillationDWPose officialInstallation#need install pytorch for your platform#pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118pip3installonnxruntime==1.13.1
pip3installonnxruntime_gpu==1.13.1
pip3installmatplotlib==3.6.2
pip3installopencv_python==4.7.0.68
pip3installscipy==1.11.4
pip3installscikit_image==0.19.3
pip3installhuggingface_hub==0.20.1pip3installdwposeEasy to useimportjsonimportosfromPILimportImage#if u need proxy#os.environ['http_proxy'] = "http://127.0.0.1:1080"#os.environ['https_proxy'] = "http://127.0.0.1:1080"img=Image.open("test.jpg")#your image filefromdwposeimportDwposeDetectormodel=DwposeDetector.from_pretrained_default()imgOut,j,source=model(img,include_hand=True,include_face=True,include_body=True,image_and_json=True,detect_resolution=512)#openpose jsonf=open("keypoints.json","w")f.write(json.dumps(j))f.close()#openpose imageimgOut.save("openpose.jpg")#detected resolution imagesource.save("source.jpg")delmodelReferencesDWPose officialcontrolnet_auxcomfyui_controlnet_aux
Package: dwq
# dwq - a Disque based work queuedwq (Disque Work Queue) is a tool that can be used to distribute Jobs on git
repositories across multiple machines.## Installation# pip install dwq## UsageStart a worker:# dwqwExecute a command on worker:# dwqc -r <repository> -c <commit> lsMore sophisticated:# export DWQ_REPO=<repository> DWQ_COMMIT=<commit>
# for i in $(seq 10); do echo ‘echo $DWQ_WORKER:$DWQ_WORKER_THREAD’; done | dwqc
Package: dwriteshapepy
ProjectDWriteShapePy is a Python extension built using Cython. It provides streamlined bindings for
the DirectWrite shaping engine. The model is similar to the model provided by uHarfBuzz but some
differences because of differences in the API models between DirectWrite and HarfBuzz.DWriteShapePy was initially developed to support a Python based font testing environment where similar tests are
executed using both DirectWrite and HarfBuzz.ExampleimportsysimportdwriteshapepyasdwfrompathlibimportPathwithopen(sys.argv[1],'rb')asfontfile:fontdata=fontfile.read()text=sys.argv[2]face=dw.Face(fontdata)font=dw.Font(face)upm=font.upemprint(upm)buf=dw.Buffer()buf.add_str(text)features={"kern":True,"liga":True}dw.shape(font,buf,features)glyph_names=[font.glyph_to_string(g.codepoint)forginbuf.glyph_infos]infos=[(g.codepoint,g.cluster)forginbuf.glyph_infos]print(glyph_names)print(infos)advance=[(adv.x_advance,adv.y_advance,adv.x_offset,adv.y_offset)foradvinbuf.glyph_positions]print(advance)infos=buf.glyph_infospositions=buf.glyph_positionsforinfo,posinzip(infos,positions):gid=info.codepointcluster=info.clusterx_advance=pos.x_advancex_offset=pos.x_offsety_offset=pos.y_offsetprint(f"gid{gid}={cluster}@{x_advance},{x_offset}+{y_offset}")TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: dwrpy
dwrpyPython Doppler Weather Radar TolkitFree software: MIT licenseDocumentation:https://dwrpy.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-09-09)First release on PyPI.
Package: dwscripter
DW-ScripterDW-Scripter (lib name : dwscripter) is a tool/library made to generate Dataweave Script based on a input CSV file.Installationpip install"python -m pip install dwscripter"UsageguideLogs the Instruction/ManualmapperGenerates the Dataweave mapping. Uses a CSV file as inputtrialDisplays a sample CSV file & generates mapping based on it
Package: dws-repo2docker
repo2dockerrepo2dockerfetches a git repository and builds a container image based on
the configuration files found in the repository.See therepo2docker documentationfor more information on using repo2docker.This version is a fork that adds support forData Workspaces.For support questions please search or post tohttps://discourse.jupyter.org/c/binder.See thecontributing guidefor information on contributing to
repo2docker.Please note that this repository is participating in a study into sustainability
of open source projects. Data will be gathered about this repository for
approximately the next 12 months, starting from 2021-06-11.Data collected will include number of contributors, number of PRs, time taken to
close/merge these PRs, and issues closed.For more information, please visitour informational pageor download ourparticipant information sheet.Using repo2dockerPrerequisitesDocker to build & run the repositories. Thecommunity editionis recommended.Python 3.6+.Supported on Linux and macOS.See documentation note about Windows support.InstallationThis a quick guide to installingrepo2docker, see our documentation fora full guide.To install from PyPI:pipinstalljupyter-repo2dockerTo install from source:gitclonehttps://github.com/jupyterhub/repo2docker.gitcdrepo2docker
pipinstall-e.UsageThe core feature of repo2docker is to fetch a git repository (from GitHub or locally),
build a container image based on the specifications found in the repository &
optionally launch the container that you can use to explore the repository.Note that Docker needs to be running on your machine for this to work.Example:jupyter-repo2dockerhttps://github.com/norvig/pytudesAfter building (it might take a while!), it should output in your terminal
something like:Copy/pastethisURLintoyourbrowserwhenyouconnectforthefirsttime,tologinwithatoken:http://0.0.0.0:36511/?token=f94f8fabb92e22f5bfab116c382b4707fc2cade56ad1ace0If you copy paste that URL into your browser you will see a Jupyter Notebook
with the contents of the repository you had just built!For more information on how to userepo2docker, see theusage guide.Repository specificationsRepo2Docker looks for configuration files in the source repository to
determine how the Docker image should be built. For a list of the configuration
files thatrepo2dockercan use, see thecomplete list of configuration files.The philosophy of repo2docker is inspired byHeroku Build Packs.Docker ImageRepo2Docker can be run inside a Docker container if access to the Docker Daemon is provided, for example seeBinderHub. Docker images arepublished to quay.io. The oldDocker Hub imageis no longer supported.
Package: dwtls
dwtls: Discrete Wavelet Transform LayerS|Paper|Demodwtlsis a downsampling/pooling layer library of discrete wavelet transform (DWT) layers with fixed and trainable wavelets presented in [1].
The use of DWT has an anti-aliasing filter and the perferct reconstruction property, at least either of which is lacked in conventional pooling layers.
The two properties enables the DWT layers to propagate entire information of an input feature.The library includes the following layers.DWT layers with fixed wavelets (e.g., Haar, CDF22, CDF26, CDF15, and DD4 wavelets)DWT layers with trainable wavelets (trainable DWT layers)Weight-normalized trainable DWT layers
The library works with Python>=3.7 and pytorch>=1.0.Installationdwtlscan be installed with:pipinstalldwtlsorgitclonegit@github.com:TomohikoNakamura/dwtls.git
pipinstall-edwtlsHow to useAll DWT layers are implemented as subclasses oftorch.nn.Moduleof PyTorch, so we can use them in many DNNs, using PyTorch scripts.
We preparea short tutorial notebook, which makes it easy to use our DWT layers in your codes.CitationIf you usedwtlsin your work, please cite:@article{TNakamura202104IEEEACMTASLP,author={Nakamura, Tomohiko and Kozuka, Shihori and Saruwatari, Hiroshi},title={Time-Domain Audio Source Separation with Neural Networks Based on Multiresolution Analysis},journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},volume=29,pages={1687--1701},month=apr,year=2021,doi={10.1109/TASLP.2021.3072496},}References[1] Tomohiko Nakamura, Shihori Kozuka, and Hiroshi Saruwatari, “Time-Domain Audio Source Separation with Neural Networks Based on Multiresolution Analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1687–1701, Apr. 2021.pdf,demoLicensedwtlsis MIT licensed (seeLICENSEfile).
Package: dwupload
The dwupload program is a Demandware cartridge file watcher. The script watches the file system and synchronizes
files with your sandbox via webdav. The script assumes you are watching from the cartridges root path e.g.
/some/path/cartridges/ this path would then contain your dw cartridges below.The script assumes that you have all your cartridges in one folder, which may change depending on use cases. Currently
for my use case our git repository contains all of our cartridges in one folder.If the cartridge path is not set correctly the program can wipe all the files on your sandbox. To fix this simply run
a clean using Demandware Studio and then try fixing the root watch path. You may also run into issues if both Demandware
Studio and the dwupload script are both running, but I haven’t had a problem so far.This script has been tested on both 10.8.5 and Windows XP/7. Your mileage may vary.Getting StartedInstall Python 2.7 if it’s not already installedInstall setuptoolsRun python setup.py installCreate a configuration file there is an example in the dwupload package.On unix based systems you can create .dwsettings in your home folder. This might work on windows, but has not been testedIf you have .dwsettings just run dwupload to start the file system watcher.If you do not have .dwsettings you can specify the -c flag to supply the path to the config fileAlternatively you can add the DWUPLOAD_CONFIG_PATH environment variable so you do not have to specify the config path every timeUse dwupload -h to see the program’s help text0.1.4
—
- Added multiple configurations in one config file with section_name argument
- Prompt user for password if password is not stored in config for security reasons0.1.3
—Added connection test on startup to make sure connection is working or will fail with an error0.1.2
—Fixed bug that locked files on Windows 7 preventing script from working on consecutive uploads0.1.1
—Bug fixes for url paths on WindowsFixed bug not allowing config to be overridden via the command lineRemoved logging statements0.1Initial version
Package: dwython
dwythonDwython is a Datawave API interconnect within Python.Exampleuser_query=query.Query(query=query,cert_path=cert,key_path=key,ca_cert=cacert,key_password=key_pass,url=url)result=user_query.create()print("{},{},{}".format(line,result.operation_time,result.wall_time))result=user_query.next()print("{},{},{}".format(line,result.operation_time,result.wall_time))
Package: dx-base
dx-basepackageA collection of base packages and modules for structural design software.FeaturesStructural elements in plane.Exceptions with error-code.Nodal forces.Structural materials.Material safety factors.Sample usage>>> from dx_base.safety_factors import SafetyFactor
>>> sf = SafetyFactor(1.24, 'persistent', 'serviceability')
>>> sf
1.24
>>> sf.design_situation
'persistent'
>>> sf.limit_stte
'serviceability'ContributeSource code lives inhttps://gitlab.com/d-e/dx-base.Public APISee thedocumentation pages.
Package: dxcam-cpp
DXCam-CPPA high performance screen capturing library for Windows
rewritingDXcamin C++.Usagepipinstalldxcam-cppThe interface of the Python module of DXCam_CPP is designed to befully
compatiblewith the original Python DXcam as an alternative.
Seethe documentation of DXcamfor more details.Working in progress