Saravanan666/pypi-org huggingface dataset:
Package: icdcodex
This is experimental software and a stable API is not expected until version 1.0What is it?A python library for building vector representations of ICD-9 and ICD-10 codes. Because it takes advantage of the hierarchical nature of ICD codes, it also provides these hierarchies in anetworkxformat.Motivationicdcodexwas the first prize winner in the Data Driven Healthcare Track of John Hopkins'MedHacks 2020. It was hacked together to address the problem ofICDmiscodes, which is a major issue for health insurance in the United States. Indeed, while ICD coding is tedious and labour intensive, it is not obvious how to automate because the output space is enourmous. For example, ICD-10 CM (clinical modification) has over 70,000 codes and growing.There aremany strategiesfor target encoding that address these issues.icdcodexhas two features that make ICD classification more amenable to modeling:Access to anetworkxtree representation of the ICD-9 and ICD-10 hierarchiesVector embeddings of ICD codes using thenode2vecalgorithm (including pre-computed embeddings and an interface to create new embeddings)Example Codefromicdcodeximporticd2vec,hierarchyembedder=icd2vec.Icd2Vec(num_embedding_dimensions=64)embedder.fit(*hierarchy.icd9())X=get_patient_covariates()y=embedder.to_vec(["0010"])# Cholera due to vibrio choleraeIn this case,yis a 64-dimensional vector close to otherInfectious And Parasitic Diseasescodes.Related Worknode2vecPaper,Website,Code,Alternate CodeLearning Low-Dimensional Representations of Medical Concepts:Paper,CodeThe Hackathon TeamJeremy Fisher (Maintainer)Alhusain AbdallaNatasha NehraTejas PatelHamrish SaravanakumarDocumentationSee the full documentation:https://icd-codex.readthedocs.io/en/latest/ContributionsContributions are always welcome!History0.4.9, 0.5.0 and 0.5.1 (2024-01-08)Added 2024 ICD-10-CMFixed a bug where the node descriptions were being discardedRegenerated the hierarchy files with the descriptions0.4.8 (2023-05-02)Use the newer scikit-learn PyPI locationDeprecate the Python 3.6 versionAdd 2023 ICD-10-CM0.4.6 (2022-10-13)Add 2022 ICD-10-CM (thank you @keithcallenberg!)0.4.4 and 0.4.5 (2020-10-18)Add the code descriptions for ICD9Add usage on how to recapitulate functionality of sirrice/icd9Make the hierarchy directed to allow simpler and more intuitive traversalFix issue where edges were not being formed between "Diseases Of The Blood And Blood-Forming Organs" and "Congenital Anomalies" and their children0.4.3 (2020-10-04)Fix issue where hierarchy jsons were not being shipped with the pypi distribution0.4.2 (2020-10-03)Add support for python <= 3.8 in thehierarchymodule by using theimportlib.resourcesbackport0.4.1 (2020-09-11)Update PyPI metadata0.4.0 (2020-09-11)ICD-10-CM (2019 to 2020) codes are now fully present (whereas hackathon version missed certain codes)Versions of the ICD 9 and ICD-10-CM hierarchies are now cached to thedatamoduleChanged the hierarchy API:hierarchy.icd9hierarchy()is nowhierarchy.icd9(). Ditto for ICD-10-CM.0.3.0 (2020-09-05)Finesse API, now consistent between documentation and implementation0.1.0 (2020-09-04)First release on PyPI, testing the waters during hackathon
Package: icdcrawl
InstallationpipinstallicdcrawlQuick Start>>>importicdcrawl>>>icdcrawl.icd10data_com("https://www.icd10data.com/ICD10CM/Codes/I00-I99/I10-I16")['I10','I11','I11.0','I11.9','I12','I12.0','I12.9','I13','I13.0','I13.1','I13.10','I13.11','I13.2','I15','I15.0','I15.1','I15.2','I15.8','I15.9','I16','I16.0','I16.1','I16.9']>>>icdcrawl.icd9data_com("http://www.icd9data.com/2015/Volume1/390-459/401-405/default.htm")['401','401.0','401.1','401.9','402','402.0','402.00','402.01','402.1','402.10','402.11','402.9','402.90','402.91','403','403.0','403.00','403.01','403.1','403.10','403.11','403.9','403.90','403.91','404','404.0','404.00','404.01','404.02','404.03','404.1','404.10','404.11','404.12','404.13','404.9','404.90','404.91','404.92','404.93','405','405.0','405.01','405.09','405.1','405.11','405.19','405.9','405.91','405.99']>>>icdcrawl.icdsearch_idv_tw(['430','431','432','4320','4321','4329'],trans_to=10)['I60.00','I60.01','I60.02','I60.10','I60.11','I60.12','I60.20','I60.21','I60.22','I60.30','I60.31','I60.32','I60.4','I60.50','I60.51','I60.52','I60.6','I60.7','I60.8','I60.9','I61.0','I61.1','I61.2','I61.3','I61.4','I61.5','I61.6','I61.8','I61.9','I62.00','I62.01','I62.02','I62.03','I62.1','I62.9']⚠️Don't request too frequently, or websites may prohibit crawlers from accessing.Supported Websitesicd10data.comicd9data.comicdsearch.idv.tw... (todo)
Package: icd-mappings
ICD-MappingsThis python tool enables a variety of mappings of ICD codes (International Classification of Diseases) to different medical conceptswith a single line of code.Supported MappingsFromICD-9 CMdiagnostic codes to:ICD-10 CM: International Classification of Diseases version 10 Clinical Modification.ICD-9 Chapters: 19 Chapters of ICD-9-CM.CCS: Clinical Classification Software. All 14k ICD-9-CM diagnostic codes can be mapped into just 283 clinical categories.CCI: Chronic Condition Indicator. True or False whether the diagnostic is chronic.FromICD-10 CMdiagnostic codes to:ICD-9 CM: International Classification of Diseases version 9 Clinical ModificationICD-10 CM Chapters: 22 Chapters of ICD-10 CM.CCS(R): Clinical Classification Software (Refined). All the 70k ICD-10-CM diagnostic codes can be mapped into just 530 clinical categories.CCI(R): Chronic Condition Indicator (Refined). True or False Whether the diagnostic is chronic.Installationpip install icd-mappingsUsageBelow are some examples on how to use this tool.MapperThis class allows you to map between ontologies.fromicdmappingsimportMappermapper=Mapper()icd9code='29410'mapper.map(icd9code,source='icd9',target='ccs')>>>'653'# you can pass any Iterable of codes (list, numpy array, pandas Series, you name it)icd9codes=['29410','5362','NOT_A_CODE','3669']mapper.map(icd9codes,source='icd9',target='ccs')>>>['653','141',None,'86']# which of these diagnostics are chronic?mapper.map(icd9codes,source='icd9',target='cci')>>>[True,False,None,True]# icd9 to icd10mapper.map(icd9codes,source='icd9',target='icd10')>>>['F0280','R111000',None,'H269']# And many more... You can check all available mappers this waymapper.show_mappers()>>>Fromicd9to:>>>-icd10>>>-chapter>>>-ccs>>>-cci>>>Fromicd10to:>>>-icd9>>>-chapter>>>-ccsr>>>-ccirValidatorThis class helps you validate codes for a given ontology. Currently supports ICD9 and ICD10 codes.fromicdmappingsimportValidatorvalidator=Validator()icd9code='3591'validator.validate(icd9code,expects='icd9_diagnostic')>>>Trueicd9codes=['3591','NOT_A_CODE','00321']validator.validate(icd9codes,expects='icd9_diagnostic')>>>[True,False,True]# can also check procedure codesicd9codes=['3582','5731','NOT_A_CODE']validator.validate(icd9codes,expects='icd9_procedure')>>>[True,True,False]# likewise for ICD10icd10code='B530'validator.validate(icd10code,expects='icd10_diagnostic')>>>TrueFeature requestsFeel free to request a new functionality or report a bug by creating anew issue.AcknowledgmentsTekaichifor building the initial version of the icd9->ccs pipeline
Package: icdump
IntroductionThis project aims at providing a cross-platform utility to dump Objective-C and Swift metadata
from 64-bits Mach-O binaries.Getting Startedimporticdumpmetadata=icdump.objc.parse("./RNCryptor.bin")# Iterate over all the Objective-C interfaces (classes)forclsinmetadata.classes:print(cls.demangled_name)PodsDummy_RNCryptor_iOS
RNCryptor.RNCryptor.Encryptor
RNCryptor.RNCryptor.Decryptor
RNCryptor.RNCryptor.FormatV3
RNCryptor.RNCryptor.EncryptorV3
RNCryptor.RNCryptor.DecryptorV3
RNCryptor.Engine
RNCryptor.(DecryptorEngineV3 in _FFCD8353248C1C3EE416689E2680CC5C)
RNCryptor.(HMACV3 in _FFCD8353248C1C3EE416689E2680CC5C)
RNCryptor.OverflowingBuffer# Dump the metadata as a header declarationprint(metadata.to_decl())@interface RNCryptor.RNCryptor.Encryptor{
    NSObject * encryptor;
}
@end
@interface RNCryptor.RNCryptor.Decryptor{
    NSObject * decryptors;
    NSObject * buffer;
    NSObject * decryptor;
    NSObject * password;
}
@end
...
Package: ice
Ice is a Python module with a WSGI microframework meant for developing
small web applications in Python. It is a single file Python module
inspired byBottle.Why Ice?This microframework was born as a result of experimenting with WSGI
framework. Since what started as a small experiment turned out to be
several hundred lines of code, it made sense to share the source code on
the web, just in case anyone else benefits from it.This microframework has a very limited set of features currently. It may
be used to develop small web applications. For large web applications,
it may make more sense to use a more wholesome framework such asFlaskorDjango.It is possible that you may find that this framework is missing a useful
API that another major framework provides. In such a case, you have
direct access to the WSGI internals to do what you want via the
documentedAPI.If you believe that a missing feature or a bug fix would be useful to
others, you mayreport an issue, or even better, fork thisproject
on GitHub, develop the missing feature or the bug fix, and send a
patch or a pull request. In fact, you are very welcome to do so, and
turn this experimental project into a matured one by contributing your
code and expertise.RequirementsThis module should be used with Python 3.3 or any later version of
Python interpreter.This module depends only on the Python standard library. It does not
depend on any third party libraries.InstallationYou can install this module using pip3 using the following command.pip3 install iceYou can install this module from source distribution. To do so,
download the latest .tar.gz file fromhttps://pypi.python.org/pypi/ice,
extract it, then open command prompt or shell, and change your current
directory to the directory where you extracted the source distribution,
and then execute the following command.python3 setup.py installNote that on a Windows system, you may have to replacepython3with
the path to your Python 3 interpreter.ResourcesHere is a list of useful links about this project.Documentation on Read The DocsLatest release on PyPISource code on GitHubIssue tracker on GitHubChangelog on GitHubSupportTo report bugs, suggest improvements, or ask questions, please create a
new issue athttp://github.com/susam/ice/issues.LicenseThis is free software. You are permitted to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of it, under the
terms of the MIT License. SeeLICENSE.rstfor the complete license.This software is provided WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. SeeLICENSE.rstfor the complete disclaimer.
Package: ice3x
ICE3X Python LibraryThis ice3x library is a Python package compliant with the ICE3X APi version 2.This package is essentially a port of the officialPHP client.QuickstartThe ICE3X API has two broad categories of resources, protected and unprotected resources.In order to access protected resources one needs to create a private and public key under theaccount managementsection of their platform.Sync clientfromice3x.clients.syncimportIceCubedSyncClientapi_key='public key'secret='private key'client=IceCubedSyncClient(api_key=api_key,secret=secret)client.get_public_trade_list()Async clientfrom__future__importannotationsfromtwisted.internetimportreactorfromice3x.clients.asynchronousimportIceCubedAsyncClientapi_key='public key'secret='private key'client=IceCubedAsyncClient(api_key=api_key,secret=secret)defprint_data(data:dict)->None:"""prints the json response from an API call"""print(data)d=client.get_public_trade_list()d.addBoth(print_data)reactor.run()InstallationThe library can be installed from PyPi as follows.pipinstallice3xThe async client is an optional extra and may be installed as follows.pipinstallice3x[async]To install the version on this repository follow the steps below.gitclonehttps://github.com/BradleyKirton/ice3xcdice3x
python-mvenvenv# virtualenv envsourceenv/bin/activate
pipinstall.#pip install .[async] for the async clientDevelopementClone the repo and install the package with it's development requirements.gitclonehttps://github.com/BradleyKirton/ice3xcdice3x
pipenvinstall-e.[dev]maketestTODONote this library is still in beta.Write documentation
Package: iceberg
ICEBERGThemasterbranch generally reflects the ICEBERG release on Pypi, and is
considered stable: it should work 'out of the box'. Please refer to the documentation.Thedevelbranch (and any other branches than master, for that matter)
may not correspond to the published documentation, and specifically may have
dependencies which need to be resolved manually.  Please contact us with an issue
if you need advice on the usage of any non-master branch.Installation:Create a Python Virtual Environment (VirtualEnvorConda)
and activate it.To install our package from source, you should clone the repo and execute pip install from its root folder:gitclonehttps://github.com/ICEBERG-project/ICEBERG-middleware.gitcdICEBERG-middeware
pipinstall.--upgradeTo install from PyPipipinstallicebergUsageBefore using ICEBERG connections to a MongoDB server and one to a RabbitMQ server are needed. To setup a password-protected database with Docker, see theseinstructions here. To setup RabbitMQ with Docker see theseinstructions. Once the two servers are available,
export the following variables with appropriate values:exportRADICAL_PILOT_DBURL=mongodb://usernane:password@mongodb_hostname:port/db_nameexportRMQ_ENDPOINT=ip_valueexportRMQ_PORT=port_numberexportRMQ_USERNAME=usernameexportRMQ_PASSWD=passwordexportVE_PENGUINS=ve_path/ve_nameThe ICEBERG command can be executed as follows:iceberg[-h][--resourceRESOURCE][--queueQUEUE][--cpusCPUS][--gpusGPUS][--input_pathINPUT_PATH][--output_pathOUTPUT_PATH][--walltimeWALLTIME][--analysisANALYSIS]{seals,penguins,4Dgeolocation,rivers,landcover}...The arguments shown are mandatory for the command to execute correctly. Executing with-hwill
print the command help page:positional arguments:
  {seals,penguins,4Dgeolocation,rivers,landcover}

optional arguments:
  -h, --help            show this help message and exit

Required Arguments:
  --resource RESOURCE, -r RESOURCE
                        Where the execution will happen
  --queue QUEUE, -q QUEUE
                        The queue of the resource
  --cpus CPUS, -c CPUS  How many CPUs will be required
  --gpus GPUS, -g GPUS  How many GPUs will be required
  --input_path INPUT_PATH, -ip INPUT_PATH
                        Where the input images are
  --output_path OUTPUT_PATH, -op OUTPUT_PATH
                        Where the results should be saved
  --walltime WALLTIME, -w WALLTIME
                        The estimated execution timeCurrently, the available analysis commands are:sealspenguins4DGeolocationlandcoverriversBased on the selected analysis further arguments may be required.To see further help messages per use case execute the ICEBERG command with all the arguments
and add a--helpafter it. It will print the arguments for the selected alanysis.0.0.0Simplifying the command line interface to support subcommands
Package: iceberg-diag
Iceberg Diagnostics ToolOverviewThe Iceberg Table Analysis CLI Tool evaluates Iceberg tables to identify how Upsolver optimizations can enhance efficiency.It presents a side-by-side comparison of current metrics against potential improvements in scan durations, file counts, and file sizes, providing a straightforward assessment of optimization opportunities.Installationiceberg-diagcan be installed using either Brew or PIP, as detailed below:Using PIPPrerequisitesPython 3.8 or higher: Verify Python's installation:python3--versionTo installiceberg-diagusing PIP, ensure you have the latest version ofpip:pipinstall--upgradepipThen, install the package withpippipinstalliceberg-diagUsing BrewExecute the following commands to installiceberg-diagvia Brew:brewtapupsolver/iceberg-diag
brewinstalliceberg-diagUsage Instructionsiceberg-diag[options]Command-Line Options-h,--help: Display the help message and exit.--profile PROFILE: Set the AWS credentials profile for the session, defaults to the environment's current settings.--region REGION: Set the AWS region for operations, defaults to the specified profile's default region.--database DATABASE: Set the database name, will list all available iceberg tables if no--table-nameprovided.--table-name TABLE_NAME: Enter the table name or a glob pattern (e.g.,'*','tbl_*').--remote: Enable remote diagnostics by sending data to the Upsolver API for processing.Provides more detailed analytics and includes information about file size reducations.UsageDisplaying help information:iceberg-diag--helpListing all available databases in profile:iceberg-diag--profile<profile>Listing all available iceberg tables in a given database:iceberg-diag--profile<profile>--database<database>Running diagnostics on a specific table in a specific AWS profile and region (completely locally):iceberg-diag--profile<profile>--region<region>--database<database>--table-name'*'Running diagnostics usingremoteoptioniceberg-diag--profile<profile>--database<database>--table-name'prod_*'--remote
Package: iceberg-est
Estimating Population Size via Musicological "Icebergs"This repo contains the source code for Iceberg, a Python library that implements two methods for estimating the true size of a population given a collection of samples taken without replacement.
For more details on how icebergs can be musicological, and what such objects have to do with estimating population size, seeMichael Scott Cuthbert, "Tipping the Iceberg: Missing Italian Polyphony from the Age of Schism," Musica Disciplina 54 (2009): 39–74.Installation & UseYou can install Iceberg from the command line, just like any other Python library.$pipinstalliceberg-estOnce installed, you can use the functionality by importing the relevant modules.importiceberg.estimateasestimporticeberg.simulateassimContentsicebergcontains the Python library, including two modulesiceberg.estimateexposes functions that implement the estimation algorithmsiceberg.simulateexposes functions that simulate situations illustrating the functionality of the estimation algorithmsdocscontains descriptions of the estimation algorithms, examples of their use, and comparisons of their effectiveness
Package: iceberg-tools
iceberg-schema-toolsCreate and maintain central iceberg schema.  Render and validate FHIR data.OverviewCode that generates the base schema from FHIR goes here.  Additional tools are provided to lints, validates and visualize the schema.Note: The actual schemas are stored inicebergSetuppip install iceberg-toolsUse$ iceberg schema
Usage: iceberg schema [OPTIONS] COMMAND [ARGS]...

  Manage bmeg or gen3 schemas from FHIR resources.

Options:
  --help  Show this message and exit.

Commands:
  generate  Generate from FHIR resources.
  compile   Create aggregated json file from individual yaml schemas

$ iceberg data

Usage: iceberg data [OPTIONS] COMMAND [ARGS]...

  Project data (ResearchStudy, ResearchSubjects, Patient, etc.).

Options:
  --help  Show this message and exit.

Commands:
  simplify             Renders PFB friendly flattened records.
  validate             Check FHIR data for validity and conventions.
  validate-simplified  Check simplified data for validity and conventions.
  pfb                  Write simplified FHIR files to a PFB.
  migrate              Migrate from FHIR R4B to R5.0.
  report               Aggregate avro pfb files into a cytoscape tsv.Note:pfb_fhirandicebergare synonymous in this context.ExamplesThe commands:pfb_fhir schema generate simplified
pfb_fhir data simplify --schema_path  iceberg/schemas/simplified/simplified-fhir.json tests/fixtures/simplify/study/ tmp/simplified
pfb_fhir data pfb tmp/simplified/ tmp/study.pfb
tree tmpWill generate the following output:INFO:'Records with relationships': 59413
INFO:'Records': 59460
tmp
├── simplified
│   ├── Condition.ndjson
│   ├── DocumentReference.ndjson
│   ├── Encounter.ndjson
│   ├── MedicationAdministration.ndjson
│   ├── Observation.ndjson
│   ├── Patient.ndjson
│   ├── ResearchStudy.ndjson
│   ├── ResearchSubject.ndjson
│   ├── Specimen.ndjson
│   └── Task.ndjson
└── study.pfbContributingSeeCONTRIBUTING.mdfor developer notes.
Package: icebox
icebox - Encrypting Cold Storage Clienticebox is a command-line client for secure online file storage. All metadata is
protected from unauthorized access, by usingicepackfor archive handling.Supported storage backends:Amazon S3 (Glacier Deep Archivestorage class)WebDAVLocal folder (for testing or third-party tools)InstallationRequirements:Python 3.8age 1.0OpenSSH 8.0Install withpiporpipx:$ pip install iceboxIfpigzorpbzip2are installed, they will be used for faster compression.AWS credentialsConfigure AWS credentials as described in theBotodocumentation. The
credentials should have write access to the S3 buckets you plan to use. See the
exampleIAM policyfor recommended permissions.UsageHelp for each command can be displayed by using the--helpoption.Create a new boxCreate the boxmyboxfor an S3 bucket calledmybucket:$ icebox init mybox s3 mybucketPlease note:encrypted metadata (several KB perputoperation) is stored
using theStandardstorage class.Create the boxmyboxfor a WebDAV URL:$ icebox init mybox webdav https://example.com/webdav/folderStore data in a boxTo store a file or directory, simply specify its location:$ icebox put mybox cat-pictures/grumpy.jpgRetrieve data from a boxThere are no directories in boxes, so you just specify the original name of the
source and a destination:$ icebox get mybox grumpy.jpg -d ~/DesktopBulkretrievals from S3 can take a long time. To perform aStandardretrieval, use theTieroption:$ icebox get mybox grumpy.jpg -d ~/Desktop -o Tier=StandardRetrieval operations are tracked by icebox, so you can interrupt a pending
retrieval and request the same source again later.Delete data from a boxTo delete a stored file or directory, use its original name:$ icebox delete mybox grumpy.jpgList data in a boxTo list the contents of a box:$ icebox list myboxRefresh data in a boxTo update local box information from the backend:$ icebox refresh myboxRefresh operations are tracked by icebox, so you can interrupt a refresh and
continue it later.Check the version and dependencies$ icebox version --dependencies
icebox 1.0.0
✅ age found. (Version: v1.0.0)
✅ age-keygen found.
✅ ssh found. (Version: OpenSSH_8.2p1)
✅ ssh-keygen found.