Saravanan666/pypi-org huggingface dataset:
Package: pheme
Pheme - Greenbone Static Report Generatorphemeis a service to create scan reports. It is maintained byGreenbone Networks.Phemeis the personification of fame and renown.Or in this case personification of a service to generate reports.Table of ContentsInstallationRequirementsDevelopmentUsageMaintainerContributingLicenseInstallationRequirementsPython 3.8 and later is supported.Besides pythonphemealso needs to havelibcairo2-devpango1.0installed.Developmentphemeusespoetryfor its own dependency management and build
process.First install poetry via pippython3 -m pip install --user poetryAfterwards runpoetry installin the checkout directory ofpheme(the directory containing thepyproject.tomlfile) to install all dependencies including the packages only
required for development.Afterwards activate the git hooks for auto-formatting and linting viaautohooks.poetry run autohooks activateValidate the activated git hooks by runningpoetry run autohooks checkUsageIn order to prepare the data structure the XML report data needs to be posted tophemewith a grouping indicator (either by host or nvt).E.g.:> curl -X POST 'http://localhost:8000/transform?grouping=nvt'\
    -H 'Content-Type: application/xml'\
    -H 'Accept: application/json'\
    -d @test_data/longer_report.xml

  "scanreport-nvt-9a233b0d-713c-4f22-9e15-f6e5090873e3"⏎The returned identifier can be used to generate the actual report.So far a report can be either in:application/jsonapplication/xmltext/csv
E.g.> curl -v 'http://localhost:8000/report/scanreport-nvt-9a233b0d-713c-4f22-9e15-f6e5090873e3' -H 'Accept: application/csv'For visual report likeapplication/pdftext/htmlthe corresponding css and html template needs to be put into pheme first:> curl -X PUT localhost:8000/parameter\
    -H 'x-api-key: SECRET_KEY_missing_using_default_not_suitable_in_production'\
    --form vulnerability_report_html_css=@path_to_css_template\
    --form vulnerability_report_pdf_css=@path_to_css_template\
    --form vulnerability_report=@path_to_html_templateafterwards it can be get as usual:> curl -v 'http://localhost:8000/report/scanreport-nvt-9a233b0d-713c-4f22-9e15-f6e5090873e3' -H 'Accept: application/pdf'MaintainerThis project is maintained byGreenbone AGContributingYour contributions are highly appreciated. Pleasecreate a pull requeston GitHub. Bigger changes need to be discussed with the development team via theissues section at GitHubfirst.LicenseCopyright (C) 2020-2023Greenbone AGLicensed under theGNU Affero General Public License v3.0 or later.
Package: phen.dns
Simple DNS server for sites based on Phen, or for keeping track of
computers with dynamic IP addresses. Other plugins can use the internal
extension mechanism to add records, e.g. the mail plugin can add MX and
SPF records.Example configuration file/device-id/system/dns.jcfg:{
  "verbose": false,     # log requests (default false)
  "http-path": "dns",   # for IP update through http(s) request (requires the
                        # http plugin to be loaded; default "", i.e. disabled)
  # file that keeps the dynamic IP associations:
  "dyn-state": "/path/to/state file",  # default /device-id/system/dns.state
  "recursive": false,   # for non-authoritative servers (default false)
  "zones": {
    "phen.eu": [
      {
        "mname": "ns1.phen.eu",         # type SOA implied
        "rname": "contact.phen.eu",
        "serial": 201512150,            # if -1, makes a serial based on date
        "refresh": "20 min",
        "retry": "5 min",
        "expire": "14 days",
        "minimum": "5 min"
      },
      {"type": "A", "address": "78.47.100.138"},
      {"type": "AAAA", "address": "2a01:4f8:c17:1bff::2"},
      {"type": "NS", "name": "ns1.phen.eu"},
      {"subdomain": "ns1", "type": "A", "address": "78.47.100.138"},
      {"subdomain": "www", "type": "A", "address": "78.47.100.138"},
      {"subdomain": "opo",
       "type": "CNAME", "name": "some.dyndns.moo", "ttl": "5 min"},

      {"subdomain": "cxs", "type": "A",
       # identity fingerprint to retrieve the IP address:
       "dynamic": "mlUDK5IPJYwAD0D6aINCAIbcc20QFqw4CHqwaUSJSIk",
       "ttl": 300},

      {"subdomain": "poa", "type": "A",
       # sha256(my-secret-password) for updates through http:
"dynamic": ">a9c90c47c231afb31950169ccb89951337eb0689d31660e32c34835bb7018c0c",
       "ttl": 300}
    ]
  }
}Phen’s shell has thesha256command that can be used to calculate
the value for thedynamicattribute. The to update the dynamic dns
entry, you can use a tool such as w3m or wget:wget "https://phen.eu/dns?pwd=my-secret-password&domain=poa.phen.eu&ip=4.3.2.1"If you are using a self-signed certificate, use the--no-check-certificateoption. You only have to inform the IP
address explicitly if it is different than the one from the originating
request.
Package: phenix-edge-auth
Phenix EdgeAuth Digest Tokens for PythonEasily generate secure digest tokens to use with the Phenix platform without requiring any networking activity.InstallationTo install Phenix Edge Authorization Digest Token with pip:$pipinstallphenix-edge-authTesting$pytest-vvExamplefromedgeauth.token_builderimportTokenBuilder# Create a token to access a channeltoken=TokenBuilder().with_application_id('my-application-id')\.with_secret('my-secret')\.expires_in_seconds(3600)\.for_channel('us-northeast#my-application-id#my-channel.1345')\.build()Command Line ExamplesDisplay the help information:$edgeauth--helpCreate a token for channel access:$edgeauth--application_id"my-application-id"--secret"my-secret"--expires_in_seconds3600--channel"us-northeast#my-application-id#my-channel.1345"
Package: phenoai
PhenoAI is a machine learning interface for applications in High Energy Physics Phenomenology. It allows the user to use trained machine learning algorithms from the PhenoAI algorithm library (see link below) via a consistent interface. Trained algorithms can be stored in a folder with PhenoAI structure using the maker module of the package and shared, making it possible to communicate full-dimensional results so that one does not have to flee to models with lower dimensionality or to project out informative dimensions of the full problem.Algorithm libraryThe current version of the package allows the user to use algorithms trained by scikit-learn and keras. These algorithms have to be created by the user, or have to be loaded from an external source like the algorithm library:http://hef.ru.nl/~bstienen/phenoai(work in progress).DocumentationDocumentation, a quick start guide and a range of examples can be found on the official PhenoAI website:http://hef.ru.nl/~bstienen/phenoai.Citation[coming soon]To DoAdd support for ROOT trained algorithms
Package: phenoapt
phenoaptPhenotype-based Gene Prioritization, modelled using Graph Embedding techniquesThis project contains the python client API library.InstallationYou install phenoapt with pip:pip install phenoaptOr, if you prefer, clone the git repo and install from source:git clone https://github.com/phenoapt/phenoapt.git
cd phenoapt
python setup.py installFeaturesRank by genes or diseasesOptional phenotype weights can be suppliedCommand line usageAn example of top 10 gene rankings given phenotypes and weights:$ phenoapt rank-gene -p 'HP:0001193,HP:0001231,HP:0002999,HP:0003621' -w '1,2,2,1' -n 10
  rank    score  entrez_id    gene_symbol
------  -------  -----------  -------------
     1  72.5097  EZ:8200      GDF5
     2  73.8103  EZ:2697      GJA1
     3  79.5231  EZ:51360     MBTPS2
     4  85.5202  EZ:2316      FLNA
     5  86.5767  EZ:10682     EBP
     6  86.7231  EZ:8481      OFD1
     7  86.9275  EZ:4010      LMX1B
     8  87.2089  EZ:3930      LBR
     9  91.1848  EZ:4000      LMNA
    10  92.2059  EZ:2273      FHL1and similarly, disease rankings can be obtained like this:$ phenoapt rank-disease -p 'HP:0001193,HP:0001231,HP:0002999,HP:0003621' -w '1,2,2,1' -n 10
  rank    score  disease_id    disease_name
------  -------  ------------  -------------------------------------------------------------------------------
     1  63.2341  OMD:228900    FIBULAR HYPOPLASIA AND COMPLEX BRACHYDACTYLY
     2  66.2116  OMD:215140    GREENBERG DYSPLASIA; GRBGD
     3  66.3988  OMD:161200    NAIL-PATELLA SYNDROME; NPS
     4  71.6026  OMD:308205    IFAP SYNDROME WITH OR WITHOUT BRESHECK SYNDROME
     5  73.3633  OMD:302960    CHONDRODYSPLASIA PUNCTATA 2, X-LINKED DOMINANT; CDPX2
     6  73.3904  OMD:135900    COFFIN-SIRIS SYNDROME 1; CSS1
     7  74.5251  OMD:228930    FIBULAR APLASIA OR HYPOPLASIA, FEMORAL BOWING AND POLY-, SYN-, AND OLIGODACTYLY
     8  75.0786  OMD:611816    TEMPLE-BARAITSER SYNDROME; TMBTS
     9  75.1829  OMD:228520    FIBROCHONDROGENESIS 1; FBCG1
    10  78.0241  OMD:609945    OMD:609945For more information about tool usage, run phenoapt with –help.Code usageSeejupyter notebook exampleHistory0.1.0 (2020-09-06)First release on PyPI.
Package: phenobench
PhenoBench Development KitPhenoBenchis a large dataset and benchmarks for the semantic interpretation of images of real agricultural fields. Together with the dataset, we provide a development kit that provides:a framework-agnostic data loader.visualization functions for drawing our data format.evaluation scripts,phenobench-eval, for all tasks (also used on the CodaLab servers).validator, calledphenobench-validator, checking CodaLab submission files for consistencyFor more information on the dataset, please visitwww.phenobench.org.Getting startedDownloadthe dataset.Install the development kit:pip install phenobench.Explore the data with thetutorial notebook.See thecode of our baselinesas a starting point or train your own models.See theFAQfor common questions and troubleshooting.If you discover a problem or have general questions regarding the dataset, don't hesitate to open an issues. We will try to resolve your issue as quickly as possible.Evaluation scripts (phenobench-eval)Important:Install all dependencies withpip install "phenobench[eval]".For evaluating and computing the metrics for a specific task, you can run thephenobench-evaltool as follows:$phenobench-eval--task<task>--phenobench_dir<dir>--prediction_dir<dir>--split<split>taskis one of the following options:semantics,panoptic,leaf_instances,plant_detection,leaf_detection, orhierarchical.phenobench_diris the root directory of the PhenoBench dataset, wheretrain,valdirectories are located.prediction_diris the directory containing the predictions as sub-folders, which depend on the specific tasks.splitis eithertrainorval.Note thatall ablation studies of your approach should run on the validation set. Thus, we also provide a comparably large validation set to enable a solid comparison of different settings of your approach.CodaLab Submission Validator (phenobench-validator)Before you submit a zip file to our CodaLab competitions, see also our availablebenchmarks, you can use thephenobench-validatorto check your submission for consistency. The tool is also part of the pip package, therefore after installing the package via pip, you can call thephenobench-validatoras follows:$phenobench-validator--task<task>--phenobench_dir<dir>--zipfile<zipfile>taskis one of the following options:semantics,panoptic,leaf_instances,plant_detection,leaf_detection, orhierarchical.phenobench_diris the root directory of the PhenoBench dataset, wheretrain,valdirectories are located.zipfileis the zip file that you want to submit to the corresponding benchmark on CodaLab.Frequently Asked QuestionsQuestion:What are the usage restrictions of the PhenoBench dataset?Answer:We distribute the dataset using the CC-BY-SA International 4.0 license, which allows research but also commercial usage as long as the dataset is properly attributed (via a citation of the corresponding paper) and distributed with the same license if altered or modified. See also ourdataset overview pagefor the full license text, etc.
Package: phenobert
PhenoBERTA novel tool for human clinical disease phenotype recognizing with deep learning.What is PhenoBERT?PhenoBERT is a method that uses advanced deep learning methods (i.e. convolutional neural networks and BERT) to identify clinical disease phenotypes from free text. Currently, only English text is supported. Compared with other methods in the expert-annotated test set, PhenoBERT has reached SOTA effect.In GSC+(Lobo et al., 2017) dataset:MethodPrecisionRecallF1-scoreSet  SimilarityNCBO  Annotator75.2249.0559.3872.22NeuralCR72.5158.7164.8878.93Clinphen55.2638.1345.1255.86MetaMapLite67.2144.2553.3767.07PhenoPro-NLP14.4753.3222.7634.64PhenoBERT77.9760.7068.2682.41How to install PhenoBERTpipPhenoBERT supports Python 3.6 or later. We recommend that you install PhenoBERT viapip, the Python package manager. To install, simply run:pipinstallphenobertThis should also help resolve all of the dependencies of PhenoBERT, for instancePyTorch1.3.0 or above.From SourcePretrained embeddings and modelsWe have trained
Package: phenocam-snow
PhenoCamSnowPhenoCamSnowis a Python package for quickly building deep learning models to classifyPhenoCam images.InstallationPhenoCamSnow supports Python 3.7+ and can be installed via pip:pip install phenocam-snowOptional dependencies for development and documentation purposes can be installed by specifying the extras[dev]and[docs], repsectively.Example UsageThe following code snippets show how to train and evaluate a model on classifying images from the canadaojp site into "snow", "no snow", and "too dark".python -m phenocam_snow.train \canadaojp \--model resnet18 \--learning_rate 5e-4 \--weight_decay 0.01 \--new \--n_train 120 \--n_test 30 \--classes snow no_snow too_darkThis will print out the file path of the best model, which can be substituted into the next command.python -m phenocam_snow.predict \canadaojp \[path/to/best_model.ckpt] \resnet18 \--categories snow no_snow too_dark--url https://phenocam.nau.edu/data/latest/canadaojp.jpgAdvanced usage details can be found in thedocumentation.CitationIf you use PhenoCamSnow for your work, please seeCITATION.cffor use the citation prompt provided by GitHub in the sidebar.AcknowledgementsProfessor Jochen StutzandZoe Pierrat.
Package: phenocellpy
PhenoCellPySeepublicationfor more informationPhenoCellPy is an open-source python package that defines methods for modeling changes of cell behaviors.
It also has pre-defined sequences of behaviors.Modeling of biological systems requires a degree of model complexity that can be daunting to new and experienced
modelers alike. Defining cellular behaviors or states and how one cellular state leads to the next is not trivial. To
make the development of new biological models we have created PhenoCellPy. PhenoCellPy defines methods and Python
classes that make the definition of sequence of cell behaviors easier. The main class is the Phenotype, which is the
container of cell behaviors. Phenotype here can mean the cell cycle, the stages of necrosis, the fact that the cell is
alive, the fact that it is dead,etc. The Phenotype is made of one or more Phases, each Phase defines the target
volume for the cell and the volume change rates it displays. It also defines which is the next Phase of the Phenotype,
what conditions trigger Phase change, if cell division occurs when exiting the Phase, what behaviors occur immediately
on Phase entry and just before Phase exit (e.g., changing the target volume). The cell volume dynamics are
handled by the Cell Volume class. The cell volume is subdivided among the solid and fluid cytoplasm, solid and fluid
nucleus, and a calcified fraction.By default, the Phase transition can be either stochastic (with a set Phase transition rate) or deterministic (with a
set Phase period). The user can also define a custom transition function. One of our pre-built Phenotypes, the Necrotic
Standard Model, uses a custom transition function for its first Phase (which represents the osmotic swelling of a
necrotic cell). The custom transition function monitors the cell volume and changes from the swelling Phase to the
ruptured cell Phase when the cell reaches its rupturing volume. The Phase class can, optionally, check if a cell should
exit the Phenotype and enter senescence.As mentioned, a Phenotype can be any sequence of Phases. For instance, the cell cycle is a Phenotype. The modeler using
PhenoCellPy must call the division methods of the modeling framework being used to divide the cell.PhenoCellPy is intended to be used with other python-based modeling framework,e.g., CompuCell3D, Tissue Forge, as
an embedded model. PhenoCellPy is inspired by the phenotype definitions of PhysiCell [1].[1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, PhysiCell: an Open Source Physics-Based
Cell Simulator for Multicellular Systems, PLoS Comput. Biol. 14(2): e1005991, 2018. DOI: 10.1371/journal.pcbi.1005991RequirementsPhenoCellPy only requirements arePython 3 supportNumPySciPyInstallationPhenoCellPy's v0.0.9-alpha and above are packaged throughpip. You can install it usingpipConda distribution coming soonHow to cite:Gianlupi, J. F., Sego, T. J., Sluka, J. P., & Glazier, J. A. (2023). PhenoCellPy: A Python package for biological cell
behavior modeling. bioRxiv, 2023-04.
Package: phenocv
phenocvIntroductionphenocvis a toolkits for rice high-throught phenotyping using computer vision.phenocvis still in early development stage, and more features will be added in the future.For label-studio semi-automatic annotation, please refer toplayground.For mmdetection training, please refer tommdetection.For yolo training, please refer toUltralytics.Support for mmdetection and label-studio will be added in the future.InstallationBefore install the package, make sure you have installedpytorchand install in the python environment with python>=3.8.Install with pip:pipinstallphenocvInstall in editable mode, allow changes to the source code to be immediately available:gitclonehttps://github.com/r1cheu/phenocv.gitcdphenocv
pipinstall-e.LicenseThis project is released under theAGPL 3.0 license.CitationIf you find this project useful in your research, please consider cite:@misc{2023phenocv,title={Rice high-throught phenotyping computer vision toolkits},author={RuLei Chen},howpublished={\url{https://github.com/r1cheu/phenocv}},year={2023}}
Package: phenomenal
PyPi:https://pypi.org/project/phenomenal/ສະບາຍດີ ແພ໊ຄເກຈນີ້ແມ່ນແພ໊ຄເກຈທີ່ອະທິບາຍ Profile ຂອງຂ້ອຍ ແລະ
ສາມາດນຳໄປໃຊ້ກັບຄົນອື່ນໄດ້ວິທີຕິດຕັ້ງເປີດ CMD / Terminalpipinstallphenomenalວິທີໃຊ້ແພ໊ຄເກຈນີ້ເປີດ IDLE ຂຶ້ນມາແລ້ວພິມ…fromphenomenalimportProfilemy=Profile('Hery')my.company='phenomenal'my.hobby=['Youtuber','Reading','Sleeping']print(my.name)my.Show_email()my.Show_myart()my.Show_hobby()my.Show_dog()ພັດທະນາໂດຍ: Hery PhenomenalFB:https://www.facebook.com/HeryPhongsavathGitHub:https://github.com/Thitphavanh
Package: phenom-xfel
phenomAphenomenological model of X-ray Free Electron Laser (XFEL) radiation.The PHENOM python package is designed to provide a simple, robust and computationally efficient method for generating representations of the complex wavefield of X-ray Free Electron Laser pulses. By making use of approximate representations of pulse wavefront andspectra, phenom allows large ensembles of photon pulses with arbitrary statistics to be generated in a truly python-ised manner.Getting StartedAt the command line::$ pip install phenom-xfelTo check that your instillation has worked, open iPython and try::$ import phenomExamplesPhenom has been designed to require minimal knowledge of the XFEL process prior to generating your first pulse.Getting StartedTutorials.Integrating with WPG.More details on generating these pulses can be found in thedocumentation.
Package: phenonaut
PhenonautA toolkit for multi-omic phenotypic space exploration.DescriptionPhenonaut is a framework for applying workflows to multi-omics data. Originally targeting high-content imaging and the exploration of phenotypic space, with different visualisations and metrics, Phenonaut allows now operates in a data agnostic manner, allowing users to describe their data (potentially multi-view/multi-omics) and apply a series of generic or specialised data-centric transforms and measures.Phenonaut operates in 2 modes:As a Python package, importable and callable within custom scripts.Operating on a workflow defined in either YAML, or JSON, allowing integration of complex chains of Phenonaut instructions to be integrated into existing workflows and pipelines. When built as a package and installed, workflows can be executed with:python -m phenonaut workflow.yml.StructrureDatasets are read into the dataset class, aided by a yaml file describing the underlying data (see config/ for example yaml data definition files). Pandas dataframes are created representing the data (a Phenonaut object may hold multiple dataset objects), along with two additional pieces of data.A features list, accessible with .features property of a dataframe. Initially defined by the data definition workflow.perturbation_column, optional column which gives a unique ID to the treatment performed on the well/vial/data.Metadata, optional dictionary containing metadata for the dataset.DocumentationStart hereUser guideAPI documentationPublication examplesWorkflow modeCopyright © The University of Edinburgh, 2023.Development has been supported by GSK.
Package: phenopedia-parser
PhenopediaParserA python API for parsingPhenopedia."Phenopedia provides a disease-centered view of genetic association studies summarized in the online Human Genome Epidemiology (HuGE) encyclopedia. Users can switch to a gene-centered view (Genopedia) or to other HuGE Tools."Getting StartedInstallpip install phenopedia_parserUsagefrom phenopedia_parser import PhenopediaParser

pheno_dataframe = PhenopediaParser.parse(search_term='lung', dataframe=True)
print(pheno_dataframe)LicenseMIT LicenseSupport
Package: phenoshift
PhenoShiftA simple and efficient library for mapping loinc test results to hpo terms.RequirementsPython 3.5+Installing with pippipinstallphenoshiftInstalling with Conda# Ensure conda-forge is in your channelscondaconfig--addchannelsconda-forge
condaconfig--setchannel_prioritystrict# Install the packagecondainstallphenoshift
Package: phenospy
Phenospy (= Phenoscript Python tools)Phenospyis a Python package that facilitates the automatic analysis and comparison of ontology-based (= semantic) descriptions of species and phenotypes. Phenospy works with descriptions written inPhenoscript, a computer language designed for describing species (support for other semantic approaches is under development).The Phenoscript language allows rapid coding of morphological and ecological traits using an individual-based approach (ontology's A-box). You can efficiently code Phenoscript by using theVS Code Phenoscript extensionthat provides syntax highlighting and snippet support. It can be installed from the Marketplace from within the VS Code. Its GitHub repository can be accessedhere. Afterward, Phenospy can be used to analyze and process the semantic descriptions.What can I do with Phenospy?Create snippets based on selected ontologies for writing semantic phenotypes with the VS Code Phenoscript extension.Convert the Phenoscript description into an OWL file.Convert the Phenoscript description into an annotated Natural Language description (Markdown format).Automatically compare species and phenotypes (under development).DocumentationSeeWiki for details.RequirementsPython >=3.0If you are a new user with Python 2 installed, you should uninstall it and install Python 3 instead. For more information, please refer tothis discussion.InstallPhenospy can be installed directly from thePyPI repositoryby running the following command in your terminal (macOS) or a command prompt (Windows):pip install phenospyIssuesSeeissues for details.