Saravanan666/pypi-org huggingface dataset:
Package: icanhasfile
icanhasfile is a Python module that helps you locate and operate on files using
glob searches.installashunRun the following command to install icanhasfile:$pipinstall--preicanhasfileusageTo get the help dialog, run one of the following commands:$icanhasfile--help$icanhasfile-h$icanhasfile-?The general command syntax is below. For a list of options, see the help
dialog. In the first form, you provide an explicit path to search; in the
second, the search is executed in the current directory.$icanhasfile[options]<path><filepattern>$icanhasfile[options]<filepattern>examplezAs an example, let’s say we have the following directory structure:+--pom.xml+--LICENSE.txt|+--dir1/|||\--pom.xml|+--dir2/|||+--assembly.xml|+--pom.xml|\--version.txt|+--dir3/|\--context.xmlIf you are searching forpom.xmland want to give the absolute path to the
top-level directory, issue the command below. You will be prompted to choose
from three matching files:$icanhasfile/path/to/top-level/dirpom.xml[0]pom.xml[1]dir1/pom.xml[2]dir2/pom.xmlChooseanumber(qtoquit):If you are already in the top-level directory and are searching forpom.xml,
issue the command below. You will be prompted to choose from three matching
files:$icanhasfilepom.xml[0]pom.xml[1]dir1/pom.xml[2]dir2/pom.xmlChooseanumber(qtoquit):If you are already in the top-level directory and are searching forallxml
files, issue the command below. You will be prompted to choose from five
matching files:$icanhasfile'*.xml'[0]pom.xml[1]dir1/pom.xml[2]dir2/assembly.xml[3]dir2/pom.xml[4]dir3/context.xmlChooseanumber(qtoquit):By default, the selected file will be opened using your $EDITOR. However, you
can also provide a–commandoption to use a different command on your
path. In the example below, we will justcatout the file. Also, as it is
run in the icanhasfile source directory and there is only a single matching
file, the file is cat’d without asking the user to choose from multiple
matching files:$icanhasfile.py--commandcat.MANIFEST.inincludeLICENSE.txtincludeREADME.rst
Package: icao-aircrft
Python client for lookup of ICAO aircraft (Doc
8643)
information, also known as “Aircraft Type Designators”.Installpip install icao-aircrftThe module requires requests and lxml to be installed.UsageExample for use via the command line:python -m icao-aircrft -t PAY3Example code for use as a library:>>> import icaoaircrft
>>> query = 'PAY3'
>>> for result in icaoaircrft.lookup(type_code=query):
>>>     print result
{'description': 'Landplane', 'wake_category': 'L', 'photo': None, 'type_code': 'PAY3', 'engine_count': 2, 'model': 'PA-42-720 Cheyenne 3', 'engine_type': 'Turboprop', 'manufacturer': 'AICSA'}
{'description': 'Landplane', 'wake_category': 'L', 'photo': None, 'type_code': 'PAY3', 'engine_count': 2, 'model': 'Cheyenne 3', 'engine_type': 'Turboprop', 'manufacturer': 'AICSA'}
...NotesThe module does not contain the actual database. Instead it issues
requests online to the icao.int server.The results of lookups are stored in a local cache file called
icaoaircrft_cache.db within the current directory. This file can be
deleted at any time to get rid of stale data.Due to the way the cache file is written, the module might not be
thread-safe.
Package: icao-nnumber-converter-us
ICAO – N-Number ConverterScript converting ICAO addresses to N-Numbers (Tail Numbers) and reciprocally. Only works for United States aircraft registrations.Using the converterRequirementsPython 3.xNothing else =)InstallUsingpip installpipinstallicao_nnumber_converter_usUsage of then_to_icaoandicao_to_nfunctions. Note that both functions returnNoneon invalid input.python3>>>fromicao_nnumber_converter_usimportn_to_icao,icao_to_n>>>n_to_icao("N12345")'a061d9'>>>icao_to_n("abcdef")'N86QU'Or simply clone the repo, and start converting.gitclonehttps://github.com/guillaumemichel/icao-nnumber_converter.gitcdicao-nnumber_converterConvert N-Number to ICAO address>python3convert.pyN12345
a061d9Convert ICAO address to N-Number>python3convert.pyabcdef
N86QUFormat & MappingICAO addresses are hexadecimal values of length 6. US registered ICAO addresses start with 'a'. N-Numbers, or Tail Numbers, are alphanumerical strings at most 6 characters long. US registered Tail Numbers start with 'N' (that's why they are called N-Number, yes!). Their format is as defined below.There is a direct sequential mapping from US ICAO addresses to N-Number, from a00001 for N1 to adf7c7 for N99999. The mapping is somewhat intuitive, but not convenient to implement.The mapping goes like this:ICAO addressN-Numbera00001N1a00002N1Aa00003N1AAa00004N1ABa00005N1AC......a0001aN1AZa0001bN1Ba0001cN1BAa0001dN1BB......a00259N1ZZa0025aN10a0025bN10Aa0025cN10AAa0025dN10AB......a004b2N10ZZa004b3N100a004b4N100Aa004b5N100AAa004b6N100AB......a0070bN100ZZa0070cN1000a0070dN1000Aa0070eN1000B......a00724N1000Za00725N10000a00726N10001a00727N10002......a0072eN10009a0072fN1001a00730N1001Aa00731N1001B......a00751N10019a00752N1002......a00869N10099a0086aN101a0086bN101Aa0086cN101AA......a00c20N10199a00c21N102a00c22N102A......a029d8N10999a029d9N11a029daN11Aa029dbN11AA......a05157N11999a05158N12......a18d4fN19999a18d50N2a18d51N2Aa18d52N2AA......a31a9eN29999a31a9fN3......ac6a79N9ac6a7aN9A......adf7c7N99999Useful links:Thisforum post.FAA N-Number Inquiry
Package: icartt
icartticarttis an ICARTT format reader and writerDocumentationPlease have a look at docs/source/usage.rst for usage examples. Full documentation is in preparation.ContributingWe are looking forward to receiving yournew issue report.If you'd like to contribute source code directly, pleasecreate a fork,
make your changes and thensubmit a merge requestto the original project.Installation of the development versionClone this repository / or your fork and install. We usepoetryfor packaging, which needs to be installed.git clone https://mbees.med.uni-augsburg.de/gitlab/mbees/icartt_pypackage.git or <URL of your fork>
cd icartt_pypackage
poetry install
poetry shellChangelog2.0.0 (2022-04-28)Compatible with ICARTT v2 standardFormats 1001 and 2110Complete internal overhaul1.0.0 (2017-12-19)Peer-reviewed version to be published in Knote et al., GMD0.1.0 (2017-08-12)Initial release
Package: icat-iml
Interactive Corpus Analysis ToolThe Interactive Corpus Analysis Tool (ICAT) is an interactive machine learning (IML) dashboard for unlabeled text datasets that allows a user to iteratively and visually define features, explore and label instances of their dataset, and train a logistic regression model on the fly as they do so to assist in filtering, searching, and labeling tasks.ICAT is implemented using holoviz'spanellibrary, so it can either directly be rendered like a widget in a jupyter lab/notebook instance, or incorporated as part of a standalone panel website.InstallationICAT can be installed viapipwith:pip install icat-imlDocumentationThe user guide and API documentation can be found athttps://ornl.github.io/icat.VisualizationThe primary ring visualization is called AnchorViz, a technique from IML literature. (See Chen, Nan-Chen, et al. "AnchorViz: Facilitating classifier error discovery through interactive semantic data exploration")We implemented an ipywidget version of AnchorViz and use it in this project, it can be found separately athttps://github.com/ORNL/ipyanchorvizCitationTo cite usage of ICAT, please use the following bibtex:@misc{doecode_105653,title={Interactive Corpus Analysis Tool},author={Martindale, Nathan and Stewart, Scott},abstractNote={The Interactive Corpus Analysis Tool (ICAT) is an interactive machine learning dashboard for unlabeled text/natural language processing datasets that allows a user to iteratively and visually define features, explore and label instances of their dataset, and simultaneously train a logistic regression model. ICAT was created to allow subject matter experts in a specific domain to directly train their own models for unlabeled datasets visually, without needing to be a machine learning expert or needing to know how to code the models themselves. This approach allows users to directly leverage the power of machine learning, but critically, also involves the user in the development of the machine learning model.},year={2023},month={apr}}
Package: icat-package-manager
ICAT Package Managericatpackagemanageris a python package that provides a CLI for automatically downloading and installing ICAT components on a local application server.UsageInvoke the CLI withpython -m icatpackagemanager, or simplyipmif installed with a tool likepipx. Use-hor--helpwith any of the commands for full details.Available commands:list- lists all components available from the repo, and which are installedlist <component>- lists available and installed versions of the componentinstall <component>:Checks for installed versions of the componentChecks for available versions of the componentIf there is no newer version, do nothing.Otherwise, download latest distro, and unzips it into install directoryIf there is no previous version, stop and prompt for configurationOtherwise, copy config from previous version, and (not implemented yet) run the setup scriptinstall <component> <version>- installs specific version of a packageTerminologyRepository/repo is a website hosting ICAT packages. By default, this is the officialrepo.icatproject.org.A component is one of the ICAT projects ie.icat.server,authn.simple, etc. Components can have many versions, in the formatx.y.z-<suffix>.A package is a specific version of a component. Packages are:'Available' if it exists in the repo'Installed' if it exists in the install directory'Deployed' if it's currently running on the application serverA distro is a zipfile containing a built package which can be installedThe distro cache is a directory containing distros downloaded from the repoThe install directory is where distros are unpacked so that they can be deployedPlanned workAdd adeploycommand or--deployoption oninstallto run setup scripts
Package: icat-sc
icatIdentifying Cell-states Across TreatmentsICAT is a tool developed to better identify cell states in scRNAseq experiments where perturbations or some other biologic heterogeneity is present, such as gene knock-outs.The method works by first identifying a set of conrol-defined cell states by performing unsupervised clustering. These identified cell states are then fed into a sparse gene weighting algorithm, Neighborhood Component Feature Selection (NCFS), to highly weight the most predictive genes, while also removing variance from non-explanatory genes. We then transform the data matrix using this weight vector, and perform semi-supervised clustering such that the originally identified control labels remain constant, but cells from experimental conditionsInstallationICAT can be installed usingpipand can be installed using the following command:pip install icat-scHow to useICAT makes heavy use of the excellentscanpylibrary along with the associatedAnnDatadata structure.An example code block walks through runningicaton a simulated dataset. The
final clustering is stored in thesslouvaincolumn of the returnedAnnDataobject.fromicatimportsimulatefromicatimportmodelsimportscanpyasscimportnumpyasnpdata_model=simulate.SingleCellDataset(populations=2,genes=1000,dispersion=np.random.choice([1,2,3],1000))controls=data_model.simulate()controls.obs['treatment']='control'perturbed=simulate.perturb(controls)perturbed.obs['treatment']='perturbed'adata=controls.concatenate([perturbed])sc.pp.log1p(adata)visualizing dataset# specify model parameters -- see documentation for more informationmodel=models.icat(ctrl_value="control",ncfs_kws={'reg':1,'sigma':3},neighbor_kws={'n_neighbors':15},cluster_kws={'resolution':0.75},)# cluster cells by providing treatment informationout=model.cluster(adata,adata.obs['treatment'])print(out.obs['sslouvain'].unique())visualizing results
Package: icbs
# icbs - Image Cut and (re)Build Squares
Package to cut an image in overlapping rectangles and to rebuild it. Can be used for pre-processing an image before a learning algorithm and then rebuild it or to create a data set from a large image.## Description
It is useful to cut an image like this one: ![](/img/pre_cut.png) To obtain an array of images: ![](/img/after_cut.png)It is useful because it handles well edge cases; from this image:
![](/img/edge_case.png) To obtain an array of images:
![](/img/after_cut_with_edge.png) With icbs you can also do the inverse operation, and given the images, rebuild the original one.## Installation & Requirements
To install this library you need to have at least python 3.6. You also need to installnumpy(pip install numpy)Runpip install icbs.## Usage
icbs has two methods, cut and rebuild. The parameters are similar for both images and are better explained through an image: ![](/img/pre_cut_labled.png) The methodcutreturns the n_img_per_col and the array with the images. n_img_per_col is particularly important for using therebuildmethod. You can find more info reading the parameter description under both methods.
Package: iccas
This repository contains:a helper package to get theICCAS dataset(Italian Coronavirus Cases by
Age group and Sex) and work with it;some Jupyter notebooks that you can run on Binder clicking on the badge
“launch notebooks” above.The packageThe package includes several submodules:ModuleDescriptionloadingObtain, cache and load the dataset(s)processingData (pre)processing. Fix data inconsistencies, resample data with interpolation.queriesSelect subsets of data, aggregate or extract useful values.chartsDraw charts and animations (in Italian or English).To install the package:pip install iccasIf you want to use the CLI:pip install iccas[cli]Free software: MIT licenseDocumentation:https://iccas.readthedocs.io.NotebooksNotebooks text is written in Italian but charts are available in English as well.
You just need to run in the first cell:ic.set_locale('en')To run notebooks locally, you need toinstall jupyter, for example with:pip install jupyterlabThen:pip install -r binder/requirements.txt
./binder/postBuildCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: iCCF
An implementation of common line profile indicators
measured from the cross-correlation function (CCF).To installDirectly from PyPIpip install iCCFOr from sourcegit clone https://github.com/j-faria/iCCF.git
cd iCCF
python setup.py installYou can append--userto both commands (pipandpython ...) in case you don't have root access.All the indicators are based on the works of others.
Please cite these works if you useiCCF.Queloz et al 2001Boisse et al 2011Nardetto et al 2006Figueira et al 2013Santerne et al 2015Lanza et al 2018See alsoSimilar codes were developed by Figueira et al. A&A 557, A93 (2013)with a Python package availablehere(described in Appendix A of Santos et al. A&A 566, A35 (2014))A similar package (in IDL) was developed by Lanza et al. A&A 616, A155 (2018).It is availablehere.
Package: icclim
icclim is a Python library to compute climate indices.
icclim name stands for index, calculation, climate.InstallationFrom pypi:pip install icclim.From conda-forge:conda install-cconda-forgeicclim.From sources:Clone the repositorygit clonehttps://github.com/cerfacs-globc/icclim.gitInstall icclimpython-msetup installHow to use icclimLet’s count the number of days above 25°C for each year, which corresponds to the indexSU, from atasmaxvariable scattered in multiple netcdf files.SUis one of the many index that can be computed with icclim. Seethe documentationto explore what other index you can compute with icclim.importicclimsummer_days=icclim.su("netcdf_files/tasmax_1990-2100.nc",out_file="summer_days.nc")For more examples on how to use icclim, see icclim’sHow to …documentation orour notebooks.Who use icclimicclim is part ofC4I platformbackend and is integrated inCLIPC Portal.
icclim is also used by some independent researchers.Who made icclimicclim has always been an open source project and was successfully made thanks to the joint effort of all its contributors.
The lead development is made atCERFACS, a research institution located in Toulouse, France.GrantsThis open-source project has been possible thanks to funding by the European Commission projects:FP7-CLIPC (2013-2016)FP7-IS-ENES2 (2013-2017)EUDAT2020 (2015-2018)H2020-IS-ENES3 (2019-2023)The beautiful icclim logo is a creation ofCarole Petetinand has been funded by the H2020IS-ENES3project grant agreement No 824084 (2019-2023).IndicesFor a detailed description of each ECA&D index, please visit:https://www.ecad.eu/documents/atbd.pdf
Package: icc-rt
Intel(R) Compiler RuntimeOne of many Intel(R) accelerated Python packages and performance library runtimes available on [PyPI](https://software.intel.com/en-us/articles/installing-the-intel-distribution-for-python-and-intel-performance-libraries-with-pip-and), and as part of [Intel(R) Distribution for Python](https://software.intel.com/en-us/distribution-for-python).For latest release updates and security notifications, please [subscribe](https://software.intel.com/en-us/forums/intel-distribution-for-python) to the Intel(R) Distribution for Python Community forum.Free to use and redistribute pursuant to the [Intel Simplified Software License](https://software.intel.com/en-us/license/intel-simplified-software-license).
Package: icd10-c2cdc
recodeConverts ICD-10 codes to CDC/NCHS Selected Causes of Death codes.ICD-10 stands for International Classification of Diseases v.10.CDC stands for Centers for Diseases Control and Prevention.NCHS stands for National Center for Health Statistics.To install run:pip install icd10_c2cdcHow this can be used:from icd10_c2cdc import Recoder
    recoder = Recoder('tables/358 ICD-10 Recodes.txt')  # Read about tables below
    print(recoder)  # Prints a tree of the recodes' hierarchy
    print(recoder.get_codes('G20'))  # Prints related Causes of Death codes: ['18500', '18800']Recoder is based on NCHS text files that were downloaded fromResDAC, each file is a table that specify the grouping classification for each ICD-10 code.
These files had a few errors (for example, some "1" were mistakenly typed as the letter "l"), which we fixed.
Download the fixed tables fromtables.
Package: icd10-c2d
IntroductionThis is an Open-Source Project intended to aid the developers in the Healthcare Industry.This package helps you to get the ICD-10 Code's Description when provided with a ICD-10 Code/ICD-10 Codes ListHow To UseInstall usingpip install icd10-c2dImport usingfrom icd10.code2description import Code2Description as c2dGet ICD Code's Description usingc2d.getICDDescription(["ICD10 Code 1","ICD10 Code 2","ICD10 Code N"])Finally, it looks like this :from icd10.code2description import Code2Description as c2d
c2d.getICDDescription(["ICD10 Code 1","ICD10 Code 2","ICD10 Code N"])Returns you a Map of ICD Codes with their Descriptions.Note : All the imports/dependencies are automatically installed.Bugs & IssuesIf you find a Bug or have an Idea, please feel free to drop a mail @sakethaux1111@gmail.com!
Package: icd10-cm
ICD-10 CMICD-10 is the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD),
a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms,
abnormal findings, complaints, social circumstances, and external causes of injury or diseases.Wikipedia: ICD-10Getting Startedpipinstallicd10-cmCommon UsageFind an ICD-10 codeimporticd10code=icd10.find("J20.0")print(code.description)# Acute bronchitis due to Mycoplasma pneumoniaeifcode.billable:print(code,"is billable")# J20.0 is billableprint(code.chapter)# Xprint(code.block)# J00-J99print(code.block_description)# Diseases of the respiratory systemCheck if an ICD-10 code existsimporticd10ificd10.exists("J20.0"):print("Exists")ChaptersChapterBlockTitleIA00-B99Certain infectious and parasitic diseasesIIC00-D48NeoplasmsIIID50-D89Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanismIVE00-E90Endocrine, nutritional and metabolic diseasesVF00-F99Mental and behavioural disordersVIG00-G99Diseases of the nervous systemVIIH00-H59Diseases of the eye and adnexaVIIIH60-H95Diseases of the ear and mastoid processIXI00-I99Diseases of the circulatory systemXJ00-J99Diseases of the respiratory systemXIK00-K93Diseases of the digestive systemXIIL00-L99Diseases of the skin and subcutaneous tissueXIIIM00-M99Diseases of the musculoskeletal system and connective tissueXIVN00-N99Diseases of the genitourinary systemXVO00-O99Pregnancy, childbirth and the puerperiumXVIP00-P96Certain conditions originating in the perinatal periodXVIIQ00-Q99Congenital malformations, deformations and chromosomal abnormalitiesXVIIIR00-R99Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classifiedXIXS00-T98Injury, poisoning and certain other consequences of external causesXXV01-Y98External causes of morbidity and mortalityXXIZ00-Z99Factors influencing health status and contact with health servicesXXIIU00-U99Codes for special purposesWikipedia: ICD-10
Package: icd9cms
ICD9 Diagnostic Code Helper UtilityA helper utility that offers fast lookup of ICD9-CM V32 (2015) codes,
complete with the entire hierarchy.Why does this utility exist?ICD9-CMS extends the original (already outdated) ICD9 code standard.The billable ICD9 codes are availablehere.
However, these flat files lack the non-billable hierarchical relations between codes and the intermediate code descriptions.For example, the above linked file only contains codes 0010, 0011 and 0019, and not the higher level codes 001 and 001-009.001-009: ntestinal Infectious Diseases
    001:Cholera
        0010:Cholera d/t vib cholerae:Cholera due to vibrio cholerae
        0011:Cholera d/t vib el tor:Cholera due to vibrio cholerae el tor 
        0019:Cholera NOS:Cholera, unspecified
    ...This intermediate non-billable nodes have been scraped fromhttp://www.icd9data.com/2015/Volume1/default.htmparsed into the hierarchy and merged with the above using cms.gov billable codes. The hierarchy have been
saved to disk.To install$ pip install icd9cmsTo use>>> from icd9cms.icd9 import search
>>>search('001')
001:Cholera:None
>>> # Codes can be searched for with or without the implied '.'  
>>> search('001.0') == search('0010')
True
>>> # The root node is under the code 'n/a' or None
>>> search('n/a')
n/a:root:NoneParent / Child  / Siblings Relations>>> code = search('001')
>>> code.parent
001-009:Intestinal Infectious Diseases:None
>>> code.children
>>> code.children
[0010:Cholera d/t vib cholerae:Cholera due to vibrio cholerae, 0011:Cholera d/t vib el tor:Cholera due to vibrio cholerae el tor, 0019:Cholera NOS:Cholera, unspecified]
>>> code.siblings
[002:Typhoid and paratyphoid fevers:None, 003:Other salmonella infections:None, 004:Shigellosis:None, 005:Other food poisoning (bacterial):None, 006:Amebiasis:None, 007:Other protozoal intestinal diseases:None, 008:Intestinal infections due to other organisms:None, 009:Ill-defined intestinal infections:None]Collect Billable (Leaf) codes / Is a Code Billable (leaf)>>> code = search('0010')
>>> code.is_leaf
True
>>> leaf_codes = search('001-009').leaves
>>> list(leaf_codes)
[0010:Cholera d/t vib cholerae:Cholera due to vibrio cholerae, 0011:Cholera d/t vib el tor:Cholera due to vibrio cholerae el tor, 0019:Cholera NOS:Cholera, unspecified, 0020:Typhoid fever:Typhoid fever, 0021:Paratyphoid fever a:Paratyphoid fever A, 0022:Paratyphoid fever b:Paratyphoid fever B, 0023:Paratyphoid fever c:Paratyphoid fever C, 0029:Paratyphoid fever NOS:Paratyphoid fever, unspecified, 00320:Local salmonella inf NOS:Localized salmonella infection, unspecified, 00321:Salmonella meningitis:Salmonella meningitis, 00322:Salmonella pneumonia:Salmonella pneumonia, ...