Saravanan666/pypi-org huggingface dataset:
Package: omg-dosimetry
With OMG Dosimetry, you can easily perform film calibration, film-to-dose conversion, and dose analysis.Demonstration files are provided for each module to get you started quickly.
Code is heavily commented so you can follow along and adapt it for your personal usage.DocumentationTo get started, install the package, view the API docs, visit theFull Documentationon Read The Docs.Gafchromic Calibration ModuleThe calibration module computes multichannel calibration curves from scanned films.Scanned films are automatically detected and selected, or ROIs can be drawn manually.The lateral scanner response effect (inhomogeneous response of the scanner along the detector array) can be accounted for by creating separate calibration curves for each pixel along the array.
This requires exposing long film strips and scanning them perpendicular to the scan direction (see demonstration files).
To account for non-flat beam profiles, a text file containing the relative beam profile shape along the film strips can be given as input to correct for non-uniform dose on the film.
Alternatively, the lateral scanner response correction can be turned off in which a single calibration curve is computed for all pixels. This simpler calibration is adequate if scanning only small films at a reproducible location on the scanner.FeaturesAutomatically loads multiple images in a folder, averages multiple copies of the same image and stacks different scans together.Automatically detects film position and size, and defines ROIs inside these films.Daily output correctionBeam profile correctionLateral scanner response correctionSave/Load LUT filesPublish PDF reportFilm-to-Dose ModuleThe film-to-dose module performs optimized multichannel conversion from scanned gafchromic films to absolute dose.
It uses the optimized multichannel method from Mayeret al(https://doi.org/10.1118/1.3694100) and calibration curves obtained with the calibration module.FeaturesMultiple scans of same film are loaded and averaged automaticallyAutomatic film detection and cropMultichannel optimized conversion to absolute dose (reduced film inhomogeneities/artefacts)Lateral scanner response is accounted for if this feature was turned on during calibrationCalibration curve interpolation performed by fitting either a rational function or spline curveOutput individual channels dose (R/G/B), as well as optimized dose, mean channel dose and average doseOutput metrics for evaluation of dose conversion quality: disturbance map, residual error, consistency mapPublish PDF reportDose Analysis ModuleThe dose analysis module performs in-depth comparison from film dose to reference dose image from treatment planning system.FeaturesPerform registration by identifying fiducial markers to set isocenterInteractive display of analysis results (gamma map, relative error, dose profiles)Gamma analysis: display gamma map, pass rate, histogram, pass rate vs dose bar graph, pass rate vs distance to agreement (fixed dose to agreement), pass rate vs dose to agreement (fixed distance to agreement)Publish PDF report
Package: omglinegraphs
linegraphsTheomglinegraphs packageis a Python library for creating customizable line graphs. It provides an easy-to-use API for plotting data and adding labels, units, and increments to the axes. This package is ideal for data visualization and analysis in scientific and engineering applications.FeaturesCustomizable line graphsSupport for multiple data setsAutomatic determination of axis unitsAdjustable axis incrementsSave graphs to files in various formatsAPI for creating and modifying graphsInstallationYou can install theomglinegraphs packageusing pip:pip install omglinegraphsUsageTo create a line graph, first import theLineGraphclass from theomglinegraphsmodule:fromlinegraphsimportLineGraphgraph=LineGraph()Then, create an instance of theLineGraphclass and call the relevant methods to customize the graph:graph=LineGraph()# Add data pointsdata=[(1,2),(3,4),(5,6)]graph.plot(data)# Set title and axis labelsgraph.title('My Graph')graph.x_title('X-axis')graph.y_title('Y-axis')# Set axis units and incrementsgraph.x_unit('s')graph.y_unit('m')graph.x_incrementsby(1)graph.y_incrementsby(0.5)# Create the graphgraph.create()# Save the graph to a filegraph.save('mygraph.png')ContributionsContributions to the omglinegraphs are welcome! If you find a bug, have a feature request, or want to contribute code, please open an issue or pull request on the GitHub repository.LicenseThe Graphing Package is licensed under the MIT License.
Package: omg-micro
omg.pyA micro-framework for the excellentOpen Microservices Guide, for suppportive code written in Python 3.6+.Note: this is pre-release software, and is subject to improvement. Contributions are welcome! This framework is being developed forother languages, as well. If you'd like to help,let us know!Intended / Example Usage$catservice.pyimportomgfromuuidimportuuid4service=omg.Microservice(name='uuid')@service.register()defnew(prefix:str)->str:"""Generates a UUID, with a given prefix."""returnf'{prefix}{uuid4().hex}'if__name__=='__main__':service.serve()registertakes some optional arguments:nameandpath. You can also callservice.add(f=new), instead.Next, run the command$ omg-generatemicroservice.ymlfiles will automatically be generated, for your application:$omg-generateservice:service
…'microservice.yml'writtentodisk!$ cat microservice.ymlactions:new:help:Generates a UUID, with a given prefix.arguments:prefix:in:queryrequired:truetype:stringhttp:method:getpath:/newport:8080output:type:stringlifecycle:startup:command:-python3-/app/service.pyomg:1$catDockerfile
FROMkennethreitz/pipenv
COPY./app
CMD["python3","service.py"]Now, run your microservice!$pythonservice.py2019-05-0914:45:39,342-micro-DEBUG-Initiating'uuid'service.2019-05-0914:45:39,344-micro-DEBUG-RegisteringFlaskendpoint:'/new'2019-05-0914:45:39,344-micro-DEBUG-Dockerfile'./Dockerfile'alreadyexists!2019-05-0914:45:39,345-micro-DEBUG-MicroserviceManifest'./microservice.yml'alreadyexists!2019-05-0914:45:39,346-micro-INFO-Servingon:'*:8080'This will spawn a Flask application (using the production-readywaitress web server), preconfigured to serve the masses!Or, use theomg-cli:$omgrunnew-aprefix='user-'…Installation$pipinstallomg.pyP.S.Do provide feedback, if you desire! :)✨ 🍰 ✨
Package: omgpy
omg-pyPython helper for implementing the Onion Mirror Guidelines (OMG)The Onion Mirror Guidelines are a set of rules enforced by the operator ofDarkDotFail- a dark net directory of confirmed and official Tor .onions - to help
improve the reliability of the information displayed on the website.Practically, implementing the OMG shows a commitment to user safety by proving ownership of PGP keys on a regular basis.Reference Specificationhttps://dark.fail/spec/omg.txtInstallationThis will install theomgpypackage globally withpipgit clone https://code.samourai.io/walletguy/omg-py.git
cd omg-py
pip3 install .UsageImport OMGfrom omgpy import OMGStart OMG with the email address of the PGP key you want to sign with. This key must exist already and be correctly loaded ingpg. You can also use a key ID instead of an email address.omg = OMG("email@example.com")Define atemplate_path. If this directory exists then you must manually populate it with templates of the files that will be signed. If this directory doesn't exist currently, it will be created automatically in the next step and the default template files will be copied.You can always add new template files to the Template directory, you are not limited by the default set.omg.template_path = "/Desktop/omg/templates"You can copy the default set of files that need to be signed in order to comply with the OMG by callingcreate_template_filesafter setting thetemplate_path. This only needs to be done once (and it is optional - if you do not do this, you will need to manually create the template files and place them in yourtemplate_pathdirectory.)omg.create_template_files()You can optionally declare where you would like the output saved. If this is not set the default location of the files will be in the current working directory where the script is called from.omg.public_path = "/Desktop/omg/public"You are ready to sign.omg.sign()When the files are signed, the output will be saved in a directory calledpublic. In this directory will be two additional directories:signedandunsigned. Theunsigneddirectory will contain the raw.txtfile of what is to be signed and thesigneddirectory will contain the corresponding clearsigned.txt.ascfile.
Package: omg.py
omg.pyA micro-framework for the excellentOpen Microservices Guide, for suppportive code written in Python 3.6+.Note: this is pre-release software, and is subject to improvement. Contributions are welcome! This framework is being developed forother languages, as well. If you'd like to help,let us know!Intended / Example Usage$catservice.pyimportomgfromuuidimportuuid4service=omg.Microservice(name='uuid')@service.register()defnew(prefix:str)->str:"""Generates a UUID, with a given prefix."""returnf'{prefix}{uuid4().hex}'if__name__=='__main__':service.serve(ensure=True)registertakes some optional arguments:nameandpath. You can also callservice.add(f=new), instead.If not available on disk, the requiredDockerfileandmicroservice.ymlfiles will automatically be generated, for your application:$catmicroservice.yml
actions:new:help:GeneratesaUUID,withagivenprefix.arguments:prefix:in:queryrequired:truetype:stringhttp:method:getpath:/newport:8080output:type:string
lifecycle:startup:command:-python3-/app/service.py
omg:1$catDockerfile
FROMkennethreitz/pipenv
COPY./app
CMD["python3","service.py"]Now, run your microservice!$pythonservice.py2019-05-0914:45:39,342-micro-DEBUG-Initiating'uuid'service.2019-05-0914:45:39,344-micro-DEBUG-RegisteringFlaskendpoint:'/new'2019-05-0914:45:39,344-micro-DEBUG-Dockerfile'./Dockerfile'alreadyexists!2019-05-0914:45:39,345-micro-DEBUG-MicroserviceManifest'./microservice.yml'alreadyexists!2019-05-0914:45:39,346-micro-INFO-Servingon:'*:8080'This will spawn a Flask application (using the production-readywaitress web server), preconfigured to serve the masses!Or, use theomg-cli:$omgrunnew-aprefix='user-'…Installation$pipinstallomg.pyP.S.Do provide feedback, if you desire! :)✨ 🍰 ✨
Package: omicexperiment
The omicexperiment package is an open-source, BSD-licensed, Python 3
package for analysis of dataframes of ‘omic experiments’, built upon the
solid foundations of the Python scientific stack.The omicexperiment package has the goal of providing a pleasant API forrapidanalysis of ‘omic experiments’ in interactive environments.The R bioinformatics community has already provided similar
implementations for similar functionality. Examples include DESeqDataSet
(from the package DeSeq2), MRExperiment (from the package
metagenomeSeq), phyloseq-class (from the package phyloseq). To my
knowledge, there exists no similar powerful functionality available to
users of python.The philosophy of this package is to build upon solid foundations of the
python scientific stack and try not to re-invent the wheel. Packages
such as numpy and pandas are powerful optimized libraries in dealing
with matrix and tabular data, respectively. This package’s backend thus
consists almost entirely of pandas DataFrames and pandas APIs.Example codefrom omicexperiment.experiment.microbiome import MicrobiomeExperiment
from omicexperiment.filters import Sample, Taxonomy

mapping = "example_map.tsv"
biom = "example_fungal.biom"
tax = "blast_tax_assignments.txt"

#our Experiment object
exp = MicrobiomeExperiment(biom, mapping,tax)

#include only samples with more than 90000 counts
exp.filter(Sample.count > 90000)

##collapse the OTUs in the _data_ DataFrame to genus level assignment
exp.filter(Taxonomy.groupby('genus')) #any taxonomic rank can be passedInstallationJust clone the git repository. Hopefully very shortly, the package will
be uploaded to PyPi and will be pip-installable.Due to bugs in handling numpy installation in setuptools, it is better
to install numpy first using ‘pip install numpy’.DependenciesCurrent omicexperiment dependencies:numpyscipypandasscikit-biobiom-formatlxmlpygalLicenseThis package is released as open-source, under a BSD License. Please see
COPYING.txt.DocumentationPlease see the doc folder in the package
(https://github.com/bassio/omicexperiment/tree/master/doc), it contains
Jupyter notebooks that runs you through most of the current package
functionality.AcknowledgementsThank you to all the great python bioinformatics and data science
community for releasing their code as open source.Contributing and use in your researchPlease be advised that at this stage, this package is still alpha
software. Testing is still not implemented so be careful. But if you are
interested in contributing to this project, please contact. Contributors
are welcome to improve the software. Nowadays, I expect the package to
continue growing slowly as I add functionality and improvements as long
as I need it in my own research. I will be initially focusing on
developing the MicrobiomeExperiment part.0.1.2-devDevelopment version(s) dedicated to fixing PyPI installation.0.1.1Initial version
Package: omicron-gap
Omicron gap related utilitiesThis package is a collection of utilities to compliment LIGO's detector
characterization group Omicron trigger generator task. Our goal is to
visualize the times when we should have triggers and any gaps.There are also programs to submit htCondor jobs to fill in
those gaps.omicron-batch-merge-dir- to reduce the number of files, this program
safely merges contiguous trigger files in a metric dayomicron-channel-check- examines each channel in our configuration file
to confirm it is available in current frame files.omicron-compare-dirs- Confirm that 2 directories have trigger files that cover
the same time period and have the same number of triggers. Works with merge programsomicron-find-gaps- Check DQ segments, frame availability and trigger file
ecistance to determine which segments if any need to be reprocessedomicron-gapsomicron-gap-analysisomicron-merge-daysomicron-metric-day-mergeomicron-plot-gapsomicron-segfile-print
Package: omicscope
OmicScope: OmicScope: from quantitative proteomics to systems biology.OmicScopeis a comprehensive workflow designed to analyze and provide insights on quantitative proteomics data. To date,OmicScopeworks with data generated fromProgenesis QI for Proteomics,MaxQuant,PatternLab V, and DIA-NN. Additionally, a fourth generic input can be used, enabling users to runOmicScopewith data from different platforms, such as transcriptomics. For users that previously performed statistical analysis, OmicScope provides the Snapshot method to quickly import data.OmicScopecan perform differential expression analysis in both static and longitudinal experimental designs. For static experiments, proteins that are differentially regulated are determined by t-tests (for 2 group comparison) or One-way ANOVA (for >2 group comparison); while for longitudinal analysis,OmicScopeperforms the pipeline suggested by Storey, 2005.Once the differential expression data is obtained, the user can perform Over-Representation Analysis (ORA) or Gene-Set Enrichment Analysis (GSEA), which are implemented according toGSEApyin the EnrichmentScope module.Both differential expression and enrichment analyses have a comprehensive visualization toolkit generated byOmicScope, including dotplots, networks, heatmaps, etc.Finally, for each experiment performed byOmicScope, it is possible to export an omics file (.omics extension) for further use in theNebula workflow. Nebula is a specialized module that enables analysis of multiple studies or comparisons, providing several types of analysis to compare those groups and find similarities among them.Installationpip install omicscopeYou can also install the in-development version with:pip install https://github.com/guireiso/omicscope/archive/main.zipNote: OmicScope uses the package SciPy, which requires a fortran compiler to be installed to build its source,which can be tricky on some systems. An alternative to this is to use Anaconda environment. OmicScope can then be more easily installed within this environment, using the command line or preferred method.Note2: Installing OmicScope may take some time on certain systems. If you observe the command line halting, please be patient, as the installation process may require seconds to minutes to complete.Documentationhttps://omicscope.readthedocs.io/DemoIn the repository, specifically in tests/data/proteins, you’ll discover raw protein quantification data for executing both OmicScope and EnrichmentScope pipelines. Furthermore, within tests/data/MultipleGroups/omics_file, you’ll locate the omics outputs suitable for utilization in the Nebula pipeline.Changelog1.0.0 (2022-11-10)First release on PyPI.
Package: omics_pipe
Omics pipe is an open-source, modular computational platform that automates best practice multi-omics data analysis pipelines published in Nature Protocols and other commonly used pipelines, such as GATK.  It currently automates and provides summary reports for two RNAseq and miRNAseq pipelines, variant calling from whole exome sequencing (WES), variant calling and copy  number variation analysis from whole genome sequencing (WGS), two ChIPseq pipelines and a custom RNAseq pipeline for personalized genomic medicine reporting.  It also provides automated support for interacting with the The Cancer Genome Atlas (TCGA) datasets, including automatic download and processing of the samples in this database.
Package: omicvarsel
OmicVarSelPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://omicvarsel.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-08-29)First release on PyPI.
Package: omidb
OMI-DBA command-line interface and Python package to work with theOPTIMAM Mammography Image Database.Note: This project is still in early development, and should be considered experimental (pre-alpha) until the OMI-DB NBSS data has been fully modelled, and a stable API has been established. Issues and feedback are welcome: please raise an issue.InstallationYou will need version >=3.7 of Python.For the CLI only, we recommend pipx:pipxinstallomidbTo install the package in your project, use your package/dependency manager of choice and addomidb.For development, we recommend cloning the repository and installing usingpdmgitclonehttps://bitbucket.org/scicomcore/omi-db.git
pdminstallYou can then run the utility scripts usingpdm run, for examplepdmrunomidbTests can be executed usingpdmrunpytesttestsExamplesPlease see the example scripts under./examples. A local copy of OMI-DB JSON data will be required.
Package: omidentd
Based on Twisted-2.0. Has the following features:- Easily extensible- Supports multiple profiles to allow changes in behaviourThe profiles that are implemented so far include behaviours:- "octomonkey" profile:- Looks up usernames and maps them to idents from a config file- Can return a static ident if the user is not listed in the config file- Can respond to invalid ident requests (e.g. if the request is for a host behind a NAT router)- "standard" profile:- Behaves as a standard, non-proxying identd- "static" profile:- Always returns the same, static identd, even for invalid requests- "random" profile:- Returns a random ident, even for invalid requestsThe basic identd (that these profiles extend) can:- Proxy ident requests to hosts currently being NAT'd- Drop root privileges immediately after starting
Package: omiga
Omiga is a free, versatile third-party Python library. It can be used
to make 2D game projects such as pixels, single-player, multiplayer,
etc. And cooperate with the pyinstaller library to make programs and
publish them.If you want to join us, please come and visit our website
shadowoffice.mysxl.cn, we really need your help to make the omiga
library more excellentInstallationFirst make sure you have the latest version of Python installed
(minimum Python 3.9)If you want to download our latest version, enter the following
instructions in the command prompt (Windows system).pip install omigaIf your computer system is Mac or Linux, enter the following
instructions in the built-in terminalpip install omigaWhen you have downloaded omiga, please enter the following code in
Python to make sure you have downloaded omiga normallytry:
    import omiga.default
except:
    print("The omiga library is not installed")If you do not output the statement in the print function, you have
finished installingEnter the following command at the command prompt or terminal to view
the version of the librarypip show omigaCongratulations, you鈥檝e installed the omiga library and it is now
ready to use!HelpIf you are a beginner to omiga, we recommend that you first check out
omiga鈥檚 beginner鈥檚 tutorial and enter the following code in Pythonimport omiga.default
help(omiga)In this way, you can learn most of omiga鈥檚 codeIf you want to upgrade the omiga version, enter the following command
at the command prompt or terminalpip install omiga --upgradeIn this way, the upgrade and update of the omiga library can be
completedKey featuresomiga has many practical functions, such as creating windows, window
drawing, function definition, playing WAV sound files, creating
sprites, physics engine, keyboard, mouse interaction, etc.In the future, we will also introduce features such as GameAI (which
can be used to develop AI in games through various algorithms),
OmigaUI (used to create UI interfaces), and update various previous
tools.Soon, we will launch interaction classes and mobile phone adaptation
functions for controller operation.ContributeIf you want to contribute to the development of Omiga, please go to
our official website shadowoffice.mysxl.cn and join our DingTalk
group to get the source code. We welcome your improvement of the
omiga library.If you would like to know more, please send an email tochenjinhao2008@hotmail.comThankWe thank everyone who contributed to the Omiga library, and below is
our list of thanks.Shadow Studio and all its membershttps://pypi.orghttps://pypi.org/project/turtle/https://pypi.org/project/pygame/https://pypi.org/project/Pillow/other librariesgithub.comshequ.codemao.cnkitten4.codemao.cn