Saravanan666/pypi-org huggingface dataset:
Package: dcm2bids
dcm2bidsYour friendly DICOM converter.dcm2bidsreorganises NIfTI files usingdcm2niixinto theBrain Imaging Data Structure(BIDS).Major upgrade with dcm2bids >=3.0.0⚠️ Breaking changes alert ⚠️dcm2bids>=3.0.0 is not compatible with config files made for v2.1.9 and below.
In order to develop dcm2bids new features we had to rewrite some of its code.Since v3.0.0, dcm2bids has becomemore powerfulandmore flexiblewhile reducing the burden of creating config files. Porting your config file should be relatively easy by following theHow-to upgradepage.
If you have any issues with it don't hesitate to report it onNeurostars.Scopedcm2bidsis a community-centered project. It aims to be a friendly,
easy-to-use tool to convert your dicoms. Our main goal is to make the dicom
to BIDS conversion as effortless as possible. Even if in the near future
more advanced features will be added, we'll keep the focus on your day
to day use case without complicating anything. That's the promise of thedcm2bidsproject.DocumentationPlease take a look at thedocumentationto:Learn about bidswith some datasetexamplesInstall dcm2bidsFollow the tutorialSeek for more advanced usageIssues and QuestionsWe work hard to make suredcm2bidsis robust and we welcome comments and questions to make sure it meets your use case! Here's our preferred workflow:If you have a usage question :raising_hand:, we encourage you to post your question onNeurostarswithdcm2bidsas an optional tag. The tag is really important becauseNeurostarswill notify thedcm2bidsteam only if the tag is present.Neurostarsis a question and answer forum for neuroscience researchers, infrastructure providers and software developers, and free to access.Before posting your question, you may want to first browse through questions that were tagged with thedcm2bids tag. If your question persists, feel free to comment on previous questions or ask your own question.If you think you've found a bug :bug:, please open an issue onour repository. To do this, you'll need a GitHub account. See ourcontributing guidefor more details.Please cite dcm2bidsIf you use dcm2bids in your research or as part of your developments, please always cite the reference below.APABoré, A., Guay, S., Bedetti, C., Meisler, S., & GuenTher, N. (2023). Dcm2Bids (Version 3.1.0) [Computer software].https://doi.org/10.5281/zenodo.8342572BibTeX@software{Bore_Dcm2Bids_2023,
author = {Boré, Arnaud and Guay, Samuel and Bedetti, Christophe and Meisler, Steven and GuenTher, Nick},
doi = {10.5281/zenodo.8342572},
month = aug,
title = {{Dcm2Bids}},
url = {https://github.com/UNFmontreal/Dcm2Bids},
version = {3.1.0},
year = {2023}
Package: dcma
Deep Codon Mutation Analyser (DCMA)DCMA analyses each column of codons within .fasta files, from a target file, generating a report with all possible mutations listed.InstallationpipinstalldcmaExamplesSimple ExecutionGroup ExecutionUsageCommand Lineusage:run-dcma[-h][--reportNameREPORTNAME][--reportPathREPORTPATH][--searchKPSEARCHKP][--debug]TARGETREPORTTYPE

Analysemutationonallnucleotidealignment.fastafilesfromatarget.

positionalarguments:TARGETTarget.fastafiletobeanalysed.REPORTTYPEOutputreportfiletype.

optionalarguments:-h,--helpshowthishelpmessageandexit--reportNameREPORTNAMEOutputreportcustomfilename.--reportPathREPORTPATHOutputreportcustomfilepath.--searchKPSEARCHKPCustomkeyphrasetodetectsearchablesequences.--debugTurndebugmessageson.Pythonimportdcma.coreassolvertarget_path='example.fasta'report_name='myrep'report_type='all'# 'xls', 'csv' or 'all'report_path='results-folder'# optionalsolver.set_debug_mode(True)# optional# results[0] -> polarity results dataframe# results[1] -> alerts dataframeresults=solver.run(target_path)# option 1: export to the current foldersolver.export(results,report_type,report_name)# option 2: export to custom foldersolver.export(results,report_type,report_name,report_path)
Package: dcm-classifier
IntroductionIn this work, we developed a robust, easily extensible classification framework that extracts key features from well-characterized DICOM header fields to identify image modality and acquisition plane.
Utilizing classical machine learning paradigms and a heterogeneous dataset of 9121 scans collected at 12 sites, using 23 scanners from 6 manufacturers, we achieved 99.4% accuracy during the K-Fold Cross-Validation for classifying 11 image modalities and 99.96% accuracy on image acquisition plane classification. Furthermore, we demonstrated model generalizability by achieving 98.6% accuracy on out-of-sample animal data. Our proposed framework can be crucial in eliminating error-prone human interaction, allowing automatization, and increasing imaging applications' reliability and efficiency.This work was submitted for publication at the 2024SPIE Medical Imagingconference.This project was supported byBotimageai.InstructionsBelow are useful commands to start using the tool.Clone git repo$ git clone https://research-git.uiowa.edu/SINAPSE/dicomimageclassification.gitNavigate to the cloned repo$ cd <repo path>Setup virtual environment$ python3 -m venv <venv_path> && source <venv_path>/bin/activateInstall required packages$ pip install -r REQUIREMENTS.txtRun the script!python3 classify_study.py -m models/rf_classifier.onnx -d <path_to_dicom_session>TestingpytestCoverage Analysiscoveragerun--concurrency=multiprocessing--parallel-mode-mpytesttests--junitxml=tests/pytest.xmlcoveragecombinecoveragereport--format=text-m|teetests/pytest-coverage.txtcoveragexml-otests/coverage.xmlcoveragexml-otests/coverage.xmlAuthorsMichal Brzusgithub:mbrzus, email:michal-brzus@uiowa.eduHans J. Johnsongithub:BRAINSia, email:hans-johnson@uiowa.edu
Package: dcmetro
DC Metro Command Line ToolWhat is itA command line app for sending commands to get live information on the DC Metro. You can find the shortest path between two stations, an estimation of the rail time between two stations, and the current arrival times for your metro station.PyPi page hereSetupSetup a python virtual environment.python3 -m venv venvActivate your python virtual environment.source venv/bin/activateInstall withpip install dcmetro.(OPTIONAL, BUT RECOMMENDED) Skipping this step means you will be using a community API key. Creating your own API token will be more reliable. Setup a WMATA API tokenhere. Runecho 'API_KEY = "<YOUR TOKEN HERE>"' > .envRundcmetroto start.Useful commands include:when <location>to view incoming trains.length <from_location> to <to_location>to get an estimated length of rail time (not including stops, which will vary)path <from_location> to <to_location>to get the shortest path from one location to the other.How it worksThe DC Metro has anAPI! This API includes some useful endpoints with the information needed to build out commands, such as the ones above.The commandpath <from_location> to <to_location>uses a graph respresentation of the DC Metro. Because it is fully connected, it should be possible to get the path from any spot to another. Additionally, if the distance between two nodes was provided, then the shortest path can even be calculated. Luckily, the API has some endpoints that provide enough information to build this. One endpoint provides all the stations on a specific train colors route, from one of its ends to the other. The distance to the previous station was also provided.
So, having the stations, in order, of a specific route color, and with distances to the previous station, you are able to create a graph with weighted edges, by iterating through all route colors' list of stations and distances between stations.This was enough to complete a fully connected graph with weighted edges.From here, Dijkstra's algorithm is utilized to find the shortest path between any two nodes in the graph.Finally, theTextualPython library is used for the terminal user interface.
Package: dcmfetch
DICOM Server AccessDcmfetch is a python package for retrieving images from aDICOMserver. The package does not provide a DICOm trnasport itself but wraps either the command line utilies fromdcm4che3or the native python librarypynetdicom. In addition, bundled standalone versions of the required dcm4che3 tools are included so the only mandatory external (non-python) dependency for the dcm4che3 option is a working java runtime. Further details on installation and use are availablehere.The DICOM server must support the dicomc-findandc-getprotocols (not justc-move).
This is the case for thedcm4cheeserver, though several other common servers do not as yet supportc-get. In addition though,dcmfetchsupports access to servers such asorthancusing theQIDO-RSandWADO-RSwebRESTAPIs instead.DICOM servers are identified by keys in the configuration filedcmnodes.cf. This file encodes the server details (AET, most, port) together with a string representing facilities supported by the server (including the web API if available). This file is typically installed to the directory/etcon a unix system.As well as the python packagedcmfetchtwo utilities are provided:dcmfetcha command line program to retrieve series anddcmfetchtoolaPyQtbased graphical tool.LicencesThis package is made available under theMITlicence. The included dcm4che3 programs are distributed under theMozillapublic licence; the only modification here has been to bundle them into single executables.
Package: dcmfile-parser
IntroductionParser for DCM files Data Conversion Format. The package has two main classesDCMParserandDCMObject, which provide a  toolkit to deal with DCM  files used in automotive software development. While theDCMParseris designed to read and interpret DCM files, theDCMObjectallows for structured representation and manipulation of the parsed data.Installpipinstalldcmfile_parserDCMParserFeaturesFile Reading: Read and parse DCM files.Create DCM Object: Translate raw DCM file content into structured data and create a DCMObject.UsageParsing the DCM file:fromdcmfile_parserimportDCMParser,DCMObjectdcmfile_parser=DCMParser("path/to/dcm/file")Creating a DCM object:dcm_obj=dcmfile_parser.create_dcm_object()DCMObjectFeaturesInitialization and Attribute Sorting: Auto-sorts parameters alphabetically by name upon initialization.Parameter Management: Remove, update (from another DcMObject), or add parameters.Exporter: Write theDCMObjectback to a dcm file .UsageWrite to a File:dcm_obj.write()Update Parameters from Another DCMObject:updated_names,missing_names=dcm_obj.update_from(other_dcm_obj)Add New Parameters from Another DCMObject:dcm_obj.add_new_parameters_from(other_dcm_obj)Remove a Parameter:result=dcm_obj.remove_parameter_by_name("parameter_name")Cleanup All Parameters:dcm_obj.cleanup_parameters()DependenciesSpecified in requirements.txt
Package: dcm-parser
DCM-ParserA effecient tool for extract dicom meta data from files.This tool offers:convert dicom to png (if pixel array is 2D) / mp4 (3D)extract all meta data (dicom header) to a single csv fileexamize corrupted files and files with missing valuesmulti-thread speed up exact HUNDREDS files in secondsInstallationpipinstall-rrequirements.txt
pipinstalldcm-parserUsageONE line for everything!fromdcm_parserimportbatch_extractionbatch_extraction(source_dir=[YOUR_SOURCE_DIR],dest_dir=[YOUR_TARGET_DIR],save_img=True)How to cite this workIf you find this git repo useful, please consider citing it using the snippet below:@misc{DCM-parser,author={Le Peng},title={DCM-parser: A Lightning DICOM Parser},howpublished={\url{https://github.com/PL97/DICOM_Parser}},year={2022}}
Package: dcmpi
DCMPI- DICOM Preprocessing Interface:
explore/distill DICOM data.This software implements an automated information extraction tool from DICOM
files containing MRI acquisitions. It is specifically targeted at the output
of the Siemens scanners, although it should be easily adaptable to other
vendors and eventually even different acquisition modalities.It features a Graphical User-Interface to manage the most used export options.
This is also mirrored by a CLI of the same software.Additionally, several other command-line tools are provided, which offer a
much finer control over the single tasks.The available tasks are:DICOM copy/moving and sorting according to acquisition
(this includes a JSON summary of the acquisitions and series)extraction of DICOM metadata and experimental storing as JSONextraction of acquisition protocol informationextraction of human-friendly information from the DICOM, stored as JSONextraction of the image data and conversion to the popular NIfTI format
(this is supported through the softwaredcm2nii,
with partial support for other  programs, e.g.isis)creation of HTML reports from the DICOM information
(PDF conversion is supported viawkhtmltopdf)backup of the DICOM files as compressed archivesmonitor of a folder to detect appearance of new DICOM filesThe subsequent data analysis is probably best performed usingPyMRT, which natively understands the
output of DCMPI.NotesThis work is based on reverse engineering of the DICOM files and as such should
not be used as a reliable source of information.
Some of the features are only partially supported.
Please get in contact if you would like to suggest additional features.WARNINGThis is a research tool and it is provided ‘as is’.
DONOTBASE ANY DIAGNOSIS ON THE INFORMATION PROCESSED BY THIS SOFTWARE!InstallationThe recommended way of installing the software is throughPyPI:$pipinstalldcmpiAlternatively, you can the clone the source repository fromGitHub:$mkdirdcmpi$cddcmpi$gitclonegit@github.com:norok2/dcmpi.git$pythonsetup.pyinstallFor more details see alsoINSTALL.rst.LicenseThis work is licensed through the terms and conditions of theGPLv3+The use of this software for scientific purpose leading to a publication
should be acknowledged through citation of the upcoming paper.AcknowledgementsThis software originated as part of my Ph.D. work at theMax Planck Institute for Human Cognitive and Brain Sciencesand theUniversity of Leipzig.For a complete list of authors please seeAUTHORS.rst.People who have influenced this work are acknowledged inTHANKS.rst.This work was partly funded by the European Union
through the Seventh Framework Programme Marie Curie Actions
via the “Ultra-High Field Magnetic Resonance Imaging: HiMR”
Initial Training Network (FP7-PEOPLE-2012-ITN-316716).
Package: dcmReader
dcmReaderParser for the DCM (Data Conservation format) format used by e.g. Vector, ETAS,...Basic usageThe read function of DcmParser parses the given DCM-file and stores internally the parsed values.Sample program:
from dcmReader.dcm_reader import DcmReaderdcm = DcmReader()
dcm.read("tests/Sample.dcm")

readFunctions = dcm.get_functions()
for fnc in readFunctions:
    print(fnc)Will return:ParameterFunction
  Version: 1.0
  Description: Function for parameters
BlockParameterFunction
  Version: 2.0
  Description: Function for block parameters
CharacteristicLineFunction
  Version: 3.0
  Description: Function for characteristic line functions
FixedCharacteristicLineFunction
  Version: 3.1
  Description: Function for fixed characteristic line functions
GroupCharacteristicLineFunction
  Version: 3.2
  Description: Function for group characteristic line functions
CharacteristicMapFunction
  Version: 4.0
  Description: Function for characteristic map functions
FixedCharacteristicMapFunction
  Version: 4.1
  Description: Function for fixed characteristic map functions
GroupCharacteristicMapFunction
  Version: 4.2
  Description: Function for group characteristic map functions
DistributionFunction
  Version: 5.0
  Description: Function for distribution functionsYou can also save a DCM object to a file with the write function. The output is sorted first by FUNKTION then by LANGNAME.
This can also be used to sort a DCM file:from dcmReader.dcm_reader import DcmReader

dcm = DcmReader()
dcm.read("tests/Sample.dcm")
dcm.write("tests/Sample_sorted.dcm")UnitTestsThe UnitTests can be run in the tests directory by running
python Tests.py
Package: dcmri
Note:dcMRI is under construction. The first stable version with more comprehensive functionality and documentation will be v1.0.0. Before that the API may still change and features may be deprecated without warning.dcMRI is a python toolbox for dynamic contrast MRI.Documentation:https://qib-sheffield.github.io/dcmriSource:https://github.com/QIB-Sheffield/dcmriPyPi release:https://pypi.org/project/dcmri/InstallInstall the latest version of dcMRI:$ pip install dcmriLicenseReleased under the Apache 2.0 license (https://opensource.org/licenses/Apache-2.0):Copyright (C) 2023-2024 dcMRI developers
Ebony Gunwhy <e.gunwhy@sheffield.ac.uk>
Steven Sourbron <s.sourbron@sheffield.ac.uk>
Package: dcmrtstruct2nii
dcmrtstruct2niiDICOM RT-Struct to nii-mask. This is a naïve approach to rasterizing rt-struct to masks in nii format. If there's holes in your RT-Struct then this approach will most likely not work. The RT-Struct needs to be within the bounds of the slices of the original DICOM. Rasterization is done on a slice-by-slice basis, interpolation between slices is currently unsupported.InterpolationInterpolation of the mask between slices is currently unsupported. Send us an algorithm or a pull requests and we'll happly add it.Input file formatThe DICOM and RT-Struct inputs need to be unzipped in a directory. Currently this is the only way to read the input files.CLI Tool# install using pip and show tool help
pip install dcmrtstruct2nii
dcmrtstruct2nii --help

# list structures in DICOM RT Struct
dcmrtstruct2nii ls -r /path/to/rtstruct/file

# convert help output
dcmrtstruct2nii convert --help

# convert DICOM RT Structs to .nii.gz masks
dcmrtstruct2nii convert -r /path/to/rtstruct/file.dcm -d /path/to/original/extracted/dicom -o /output/pathPython API# install using pip and show tool help
pip install dcmrtstruct2nii# lets test it
from dcmrtstruct2nii import dcmrtstruct2nii, list_rt_structs

print(list_rt_structs('/path/to/dicom/rtstruct/file.dcm'))

dcmrtstruct2nii('/path/to/dicom/rtstruct/file.dcm', '/path/to/original/extracted/dicom/files', '/output/path')License and academic useThe software is licensedMIT License.For academic use, use a presistent copy from.Please cite:Thomas Phil, Thomas Albrecht, Skylar Gay, & Mathis Ersted Rasmussen. (2023). Sikerdebaard/dcmrtstruct2nii: dcmrtstruct2nii v5 (Version v5). Zenodo. https://doi.org/10.5281/zenodo.4037864
Package: dcmstack
This package provides DICOM to Nifti conversion with the added ability
to extract and summarize meta data from the source DICOMs. The meta data
can be injected it into a Nifti header extension or written out as a JSON
formatted text file.InstallingYou can the latest release from PyPI by doing:$pipinstalldcmstackDocumentationDocumentation can be read online:https://dcmstack.readthedocs.org/You can build the HTML documentation under build/sphinx/html with:If you have thesphinxandnumpydocpackages and amakecommand you
can build the documentation by running themakecommand in thedoc/directory. For example, to create the HTML documentation you would do:$pythonsetup.pybuild_sphinx$makehtmlAnd then view doc/_build/html/index.html with a web browser.Running TestsYou can install dcmstack along with any test dependencies by installing thetestextra. Then you can run thepytestcommand to run the test-suite:$pipinstalldcmstack[test]$pytest
Package: dcmt
This module is a thin Python wrapper over C librarydcmt, created by Makoto Matsumoto and Takuji Nishimura, authors of Mersenne twister RNG algorithm.
The original library provides functions for creating multiple independent RNGs (which can be used, for example, in parallel Monte-Carlo simulations).The package contains intactdcmtsources (except forexamplefolder) and exports some functions and structures from its API.
For licensing on original library and other information please see the link above, orsrc/dcmt/READMEfile and sources.The version of the module corresponds to the version of original library, with the addition of the build number.
For example, the wrapper on top ofdcmt 0.6.1may have version like0.6.1-10.
Module documentation can be foundhereChangelog0.6.1-5 (in development)Fix issues with setuptools/distutils (one of them does not support Cython,
other does not support Sphinx)0.6.1-4 (in development)Fixed bug when mt_range() return ‘common’ parameters dict
with non-common parameters included;Fixed bug when DcmtRandomState.rand() returned array
instead of Python float;Added raw integer randoms generation to DcmtRandomState;Added ‘inplace’ function to fill provided numpy array with randoms0.6.1-3Using slightly optimized random number generation taken fromnumpy1.5.1;Module is rewritten using Cython;Exporting analogues of Python standardrandom.Randomandnumpy’snumpy.random.mtrand.RandomState;Renamedcreate_mtstomt_range, which now returns dictionary andnumpyarray instead ofctypesstructures;0.6.1-2Shortened function names: create_mts(), init_mt()Removed get_random() and added rand(), which returns numpy arrayAdded create_mts_stripped(), currently as a ‘half-documented’ feature0.6.1-1Three basic functions: create_generators(), init_generator() and get_random() (returns integer)
Package: dcmutils
Table of ContentsOverviewpfdcmInstallationPython Virtual EnvironmentUsing thefnndsc/ubuntu-python3dockUsing thefnndsc/pfdcmdockUsagepfdcmusageOverviewThis repository providespfdcm– a controlling service that speaks to a PACS and handles DICOM data management.pfdcmMost simply,pfdcmprovides a REST-type interface to querying a PACS as well as managing DICOM data received from a PACS.InstallationInstallation is relatively straightforward, and we recommend using either python virtual environments or docker.Python Virtual EnvironmentOn Ubuntu, install the Python virtual environment creatorsudoaptinstallvirtualenvThen, create a directory for your virtual environments e.g.:mkdir~/python-envsYou might want to add to your .bashrc file these two lines:exportWORKON_HOME=~/python-envssource/usr/local/bin/virtualenvwrapper.shThen you can source your .bashrc and create a new Python3 virtual environment:source.bashrcmkvirtualenv--python=python3python_envTo activate or “enter” the virtual env:workonpython_envTo deactivate virtual env:deactivateUsing thefnndsc/ubuntu-python3dockWe provide a slim docker image with python3 based off Ubuntu. If you want to play inside this dock and installpmanmanually, dodockerpullfnndsc/ubuntu-python3This docker has an entry pointpython3. To enter the dock at a different entry and install your own stuff:dockerrun-ti--entrypoint/bin/bashfnndsc/ubuntu-python3Now,aptupdate&&\aptinstall-ylibssl-devlibcurl4-openssl-devlibrtmp-dev&&\pipinstallpfdcmIf you do the above, remember tocommityour changes to the docker image otherwise they’ll be lost when you remove the dock instance!dockercommit<container-ID>local/ubuntu-python3-pfdcmwhere``<container-ID>``istheIDoftheabovecontainer.Using thefnndsc/pfdcmdockThe easiest option however, is to just use thefnndsc/pfdcmdock.dockerpullfnndsc/pfdcmand then rundockerrun--namepfdcm-v/home:/Users--rm-tifnndsc/pfdcm--forever--httpResponseUsageFor usage ofpfdcm, consult the relevant wiki pages.pfdcmusageForpfdcmdetailed information, see thepfdcm wiki page.