Saravanan666/pypi-org huggingface dataset:
Package: sci-annot-eval
Sci-Annot Evaluation ComponentThis package was developed as part of my master's thesis and used in the evaluation stage.Its main purpose is to produce per-page confusion matrices with multiple classes for predictions in the field of Page Object Detection, with inter-object dependencies also supported.
To be more precise, it was used to compare predictions in the task of figure, table and caption extraction, but the project can somewhat easily be extended to other object types.FeaturesThis tool currently supports the following commands:rasterize- Rasterize all pdfs in input folder and additionally produce a summary parquet file called render_summary.parquet in the output folder.split-pdffigures2- Take original pdffigures2 output and split it into validator-friendly per-page files.benchmark- Evaluate predictions against a ground truth and produce TP, FP, and FN metrics for each page.deepfigures-predict- Use deepfigures to detect elements from each pdf in the input folder.transpile- Take a folder of predictions in one format and output them in another.Currently, the following prediction formats are supported:Sci-Annot- The corresponding annotation front-end.PDFFigures 2.0DeepFiguresConsider contributing a parser/exporter for your system of choice!How the Validation WorksThe comparison of two sets of bounding boxes is modelled as an optimal assignment problem,
with the cost function being the distance between the centres of bounding boxes.
The matching algorithm runs inside each class (Figues, Tables, Captions) individually,
and uses the Intersection over Union (IoU) to decide if two bounding boxes match.
This means that if two bounding boxes look the same, but have different classes,
no True Positives will be counted towards either of those classes.
This is in contrast to some other validation schemes which award partial points in such cases.The reference validation runs for all referenced classes at the same time (Figures and Tables in our case),
and does not take the bounding boxes' shape or class into account,
only if its reference matches the closest bounding box in the corresponding prediction set.
For more information on how this works, refer to the thesis which spawned this project.Installation & UsageThis tool is packaged under the namesci-annot-eval.You can install it likepip install sci-annot-eval, orconda install sci-annot-eval.Once installed, call the package from your clisci-annot-eval COMMAND, or use it as a library in your python project.Development SetupIf you wish to work on this project locally, you'll need:python3.9+pipenvTo set up the dependencies, just runpipenv installin the project root.
From that point on, you can dopipenv shell, which will launch your custom python environment with all of the dependencies installed.When developing, you can callpython3 cli.pyin the project root to execute the local version of sci-annot-eval, instead of the installed one.TODOFix loggingAdd more tests
Package: scibag
Data science meta-packageThis package used to be called pylab but was renamed due to name collision with the pylab interface of matplotlib.History0.2 (2016-09-12)Renamed to scibag to avoid name collisions0.1.4 (2016-05-24)Dropped wheel dependency (#2)fixed version numbers throughout the project (#3)marked package as “inactive” to prepare for the name transition0.1.3 (2015-04-23)Added jsonschema dependency0.1.1 (2015-04-23)Added tornado dependency0.1.0 (2015-04-23)First release on PyPI.
Package: scibd
scIBDscIBD is a doublet detection tool for scCAS data.scIBD is totally produced by Python.The depending packages used in scIBD can be installed by the command pip/pip3 install -r requirements.txtInstallationpipinstall-rrequirements.txtgitclonegit://github.com/Ying-Lab/scIBDcdscIBD
pythonsetup.pyinstallRunningimportscibdassiKNNITER=si.KNNIter(input)result=KNNITER.IterCall()Parametersinput: the AnnData; or the count matrix(numpy or scipy)output:if the input is an obeject of AnnData, the output is also an AnnData, the obs of returned AnnData adds two columns: obs['PredDBL'] is the predicted results where 1 indicates the predicted doublets and 0 indicates the singlets; obs['DBLscore'] is the doublet scores of all droplets.if the input is the count matrix, the output are the idx of predicted doublets and the doublet scores of all dropletsother parameters:exprate: The expected calling rate of doublets, default 0.1.strategy: The KNN graphing strategy, scIBD can adaptively opt a KNN graphing strategy. Users can also manually set it as 'PCoA' or 'PCA'.core: The number of threads, default is the max core number depending on the terminals.sim_rate: The ratio of simulated doublets in each iteration, default is 0.3.nPC: The number of used principal components, default is 5.neighbors: The number of neighbors used to construct KNN graph, default is 40.n_tree: The number of trees in KNN constrcution, default is 30.
Package: scibeam
SciBeamSciBeamis an open source library for analyzing time series beam
measurement data. Using pandas dataframe and series as its base
classing, additional time series related features are added for quick
analysis, such as file name matching, gaussian fitting, peak analysis,
noise filtering, plotting, etc. The flexible method chain enables fast
data analysis on any time series data.SciBeam is originally designed for experimental physics data analysis.
The library has been tested on the daily lab data analysis and is under
active development in terms of bredth and deepth of scientific
computation.InstallationDependenciesSciBeam requires:Python( >= 3.4)Numpy( >= 1.8.2)Scipy( >= 0.13.3)pandas ( >= 0.23.0)matplotlib ( >= 1.5.1)reosUser installationCurrently only avaliable through downloading from Github, will be
avaliable for installation through pip soon:Using PyPIpipinstallscibeamUsing souce codeDownload the souce code:gitclonehttps://github.com/SuperYuLu/SciBeam`Change to the package directory:cdscibeamInstall the package:python setup.py installReleasev0.1.0: 08/19/2018 first release !DevelopmentUnder active development.TODO:Increase test coverageAdd more plotting functionsAdd config.py for global configuratureAdd AppVeyorContributeComing soon…TestingThe testing part is based on unittest and can be run through setuptools:pythonsetup.pytestormaketestStatusVersion 0.1.0 onPyPI
Package: scibench
SciBenchExperimental general-purpose benchmarking framework for research.InstallTo install from GitHub:pip install git+https://github.com/marcofavorito/scibench.gitTestsTo run tests:toxTo run only the code tests:tox -e py3.7To run only the linters:tox -e flake8tox -e mypytox -e black-checktox -e isort-checkPlease look at thetox.inifile for the full list of supported commands.DocsTo build the docs:mkdocs buildTo view documentation in a browser:mkdocs serveand then go tohttp://localhost:8000Licensescibench is released under the GNU Lesser General Public License v3.0 or later (LGPLv3+).Copyright 2022 Marco FavoritoAuthorsMarco Favorito
Package: sciberia
Sciberia helper libraries v0.1.6Libraries include reader and process under MIT LicenseInstallpython3-mpipinstall-UsciberiaHOWTOimportnumpyasnpfromsciberiaimportProcess,Readerpath="/data/scans"reader=Reader(path)print(f"{len(reader.filenames)}studies in{path}directory")data=np.eye(4)process=Process(data)dilated=process.dilation(data)print(dilated)
Package: scibib
SciBibA Python package to collect scientific bibliographical data.InstallationThis package can be installed with pip.$ pip install scibibFurthermore, you will need to edit a configuration file to pass your orcid_token.
This can be done interactively runningpython -m scibib.data_queryYou will be prompted to give your Orcid /read-public API token:Please provide the value for orcid_token in your config file.
▯If you prefer to edit the file directly, cancel with Ctrl+C. The message in
an ImportError will tell you which file to edite.g.[...]
ImportError: Please edit your module configuration file /home/gael/.config/scibib/scibib_config.py to define the orcid_token variable.This token is needed to use orcid's /read-public API.UsageA basic demo is provided as test/demo.py on the GitHub repository.A complete documentation of the package is provided in pdfhere,
the html version is served onGitHub Pages.
Package: scibiomart
scibiomartThis is just a simple wrapper around the API from BioMart, but I found existing packages were not quite sufficent
for what I was wanting to do i.e. cli interface and python interface with tsv API.Here you can simply get the list of all genes and perform other biomart functions such as mapping between human and
mouse.Have a look at thedocswhich explains things in more detail.Installationpip install scibiomartUsageFor the most simple usage, use API which will get the latest mouse and human and map gene IDs to gene names.Examplesfrom scibiomart import SciBiomartApi

sb = SciBiomartApi()

# Get only the default for those genes
results_df = sb.get_mouse_default({'ensembl_gene_id': 'ENSMUSG00000029844,ENSMUSG00000032446'})

# Select attributes
results_df = sb.get_mouse_default({'ensembl_gene_id': 'ENSMUSG00000020875,ENSMUSG00000038210'},
                                     attr_list=['entrezgene_id'])
# Get all genes
results_df = sb.get_mouse_default()

# Sort the results based on TSS (takes direction into account)
results_df = sb.sort_df_on_starts(results_df)

# Get human
results_df = sb.get_human_default()Examples extendedIf you are interested in more than the simple API, see the tests for all examples, however, you can list the datasets
etc, and query other attributes.Print martssb = SciBiomart()
marts = sb.list_marts()
print('\n'.join(marts))Print datasetssb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
err = sb.list_datasets()List attributessb = SciBiomart()  
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('fcatus_gene_ensembl')
err = sb.list_attributes()List configssb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('fcatus_gene_ensembl')
err = sb.list_configs()List filterssb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('fcatus_gene_ensembl')
err = sb.list_filters()Run generic queryHere we show a generic query for two genes (as a comma separated list) and the attributes we're interested in are
'ensembl_gene_id', 'hgnc_symbol', 'uniprotswissprot'.Run query:def run_query(self, filter_dict: dict, attr_list: list):i.e. you can pass it a filter dictionary and a list of attributes. This will make it quicker, you can also run it and it
will get all genes (i.e. if filter_dict is empty).sb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('hsapiens_gene_ensembl')
results = sb.run_query({'ensembl_gene_id': 'ENSG00000139618,ENSG00000091483'},
                       ['ensembl_gene_id', 'hgnc_symbol', 'uniprotswissprot'])
print(results)Match mouse to humanGet mouse orthologs for human genessb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('hsapiens_gene_ensembl')
attributes = ['ensembl_gene_id', 'mmusculus_homolog_ensembl_gene', 'mmusculus_homolog_perc_id_r1']
results = sb.run_query({'ensembl_gene_id': 'ENSG00000139618,ENSG00000091483'},  attributes)
print(results)See docs for more info
Package: sciblox
sciblox was designed to make data science and machine learning easier.It features similar modules to R’s CARET, and draws inspiration from many other packages.To use it:from sciblox import *
%matplotlib notebookWhat’s included?CARET like preprocessing (scaling, normalising, dummify, outlier removal, unskew, etc)Processor modules - you can fit onto testing data!LightGBM and RandomForest support - later will add moreMore analysis methods: outlier detection, skewness methods, auto unskewing etc3D automatic plots in NEW cool analyse function!BPCA, MICE, KNN, Boosting, Forest, SVD imputationEasy sequential text mining and automatic text miningJupyter Notebooks integratedWhat’s in construction?More machine learning libraries - Extra Trees, Neural Networks, SVM etcAdvanced data and text miningCV, Auto machine learningMultiprocessing supportDemonstrationIf you want to see sciblox in action, please visithttps://danielhanchen.github.io/GITHUB:https://github.com/danielhanchen/sciblox
Package: scib-metrics
scib-metricsAccelerated and Python-only metrics for benchmarking single-cell integration outputs.This package contains implementations of metrics for evaluating the performance of single-cell omics data integration methods. The implementations of these metrics useJAXwhen possible for jit-compilation and hardware acceleration. All implementations are in Python.Currently we are porting metrics used in the scIBmanuscript(andcode). Deviations from the original implementations are documented. However, metric values from this repository should not be compared to the scIB repository.Getting startedPlease refer to thedocumentation.InstallationYou need to have Python 3.9 or newer installed on your system. If you don't have
Python installed, we recommend installingMiniconda.There are several options to install scib-metrics:Install the latest release on PyPI:pipinstallscib-metricsInstall the latest development version:pipinstallgit+https://github.com/yoseflab/scib-metrics.git@mainTo leverage hardware acceleration (e.g., GPU) please install the apprpriate version ofJAXseparately. Often this can be easier by using conda-distributed versions of JAX.Release notesSee thechangelog.ContactFor questions and help requests, you can reach out in thescverse Discourse.
If you found a bug, please use theissue tracker.CitationReferences for individual metrics can be found in the corresponding documentation. This package is heavily inspired by the single-cell integration benchmarking work:@article{luecken2022benchmarking,
  title={Benchmarking atlas-level data integration in single-cell genomics},
  author={Luecken, Malte D and B{\"u}ttner, Maren and Chaichoompu, Kridsadakorn and Danese, Anna and Interlandi, Marta and M{\"u}ller, Michaela F and Strobl, Daniel C and Zappia, Luke and Dugas, Martin and Colom{\'e}-Tatch{\'e}, Maria and others},
  journal={Nature methods},
  volume={19},
  number={1},
  pages={41--50},
  year={2022},
  publisher={Nature Publishing Group}
}
Package: scicamera
SciCameraConsistent and reliable imaging for scientific applications.WhySciCamera?Scientific imaging applications often require minimal post-processing pipelines,
precise capture timing, near-gapless sequential frames, and easily
configurable settings like gain, resolution, bit-depth, and exposure
length.This project, which began as fork of the webcam/video-focusedpicamera2library, aims to make it easy to configure and use cameras for scientific applications,
with a focus onperformance, reliability, code quality, and maintainability.Why notSciCamera?SciCamera currently focuses on high-quality, timing-sensitive, minimally-processedstill images. For low-bandwidth, real-time image and video streaming, we recommend
thepicamera2library.Platform supportSciCamerasupportsRaspberry Pi OS (Bullseye or later), 64-bit.x86 UbuntuOther debian flavors are likely to be supported. We welcome pull requests to extend
the testing toolchains to cover your platform.InstallationSciCamerais a pure python package, but relies on the python
c++ wrapper oflibcamera.SciCameracan be installed simply with:pip install scicameraInstalling libcamera + python bindingsImport and use of the above pacakge requires thatlibcamerato be built
with the python package enabled. On rasbian, this is accomplished by
installing thelibcamerapackage from apt. In x86 it must be built
using something like the following:gitclonehttps://github.com/Exclosure/libcamera.gitcdlibcamera
gitcheckoutv0.0.4
mesonsetupbuild-Dpycamera=enabled
ninja-Cbuild
sudoninja-CbuildinstallBugs/ContributingOpen an issue/PR to discuss your bug or feature. Once a course of action
has been identified, open a PR, discuss the changes.Feature creep is not of interest, but we would be happy
to help you build your more complicated project on top of this.If we like them, and the tests pass we will merge them.
CI requires code has been processedisortandblacktoolchains.Doing this is pretty easy:isort .
black .Great work.Publishing to PYPIShould be added to github action laterAdd your pypi token$poetryconfigpypi-token.pypimy-tokenCut a new tag$gittag-av0.1.0-m"Version 0.1.0"$gitpushoriginv0.1.0Publish$poetrypublish--build
Package: scicast
scicast is a python utility that automates many of the repetitive steps of analyzing single cell sequencing data.-k-means clustering to identify clusters-Clustering and subclustering of data to identify ‘stable’ sets of cells.-Statistical testing to identify top genes that indentify stable cluster.-Correlation search and analysis to identify gene networks driving cluster identity.-Outputs both plots for visualization (PCA and heatmap) cell and gene lists that can be used to refine analysis.
Package: scicheat
Sci-CheatBasic analytics for working with a new datasetNotesSee examples folder for demo of functionalityVery much a work in progress!Works best in a Jupyter Notebook environmentInstallationgit clone https://github.com/andrewjkuo/scicheat.git
python setup.py installUsageImport the PrimaryAnalysis classLoad in a pandas dataframe and specify the target variableYou can run specific methods or just use run_all()from scicheat.dataloader import PrimaryAnalysis

pa = PrimaryAnalysis(df=df, target='y_col')
pa.run_all()To DoError handlingDeal with NAs intelligentlyDeal with dates intelligentlyData normalisationOptionally limit rows for speedHyperparameter tuningUnsupervised analysis
Package: sci-clone
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            \__ \/ /    / /_____/ /   / /   / / / /  |/ / __/
           ___/ / /____/ /_____/ /___/ /___/ /_/ / /|  / /___
          /____/\____/___/     \____/_____/\____/_/ |_/_____/

               A simple tool for cloning from Sci-Hub.Python Version > 3.6Procedure (by ISSN)Query theDigital Object Identifier(DOI)/URL/PMID from crossref.org;Download articles from Sci-Hub with the DOI/URL/PMID.InstallationThe simple command:pip install sci-cloneor provide PyPI index if the above command fails:pip install sci-clone -i https://pypi.org/simpleor install from this repository if you cannot connect to PyPI:pip install git+https://github.com/f10w3r/sci-cloneDefault SourcesSci-Hub:sci-hub.wfDOI/URL/PMID:crossref.orgUsageI. Download by DOI/URL.sci-clone DOI/URL/PMID [DOI/URL/PMID] ...ExamplesDownload two articles with DOI/URL/PMID:sci-clone 10.1126/science.1248506 https://www.jstor.org/stable/27854031Download with the DOI/URL/PMID within a file:papers.txtorpapers.bibsci-clone examples/papers.txtsci-clone examples/papers.bibII. Download by Year (from a journal).sci-clone ISSN YEAR_FROM [YEAR_TO]ExamplesDownload articles from journalAmerican Journal of Sociology(ISSN: 0002-9602) in year 2020:sci-clone 0002-9602 2020Download articles from journalSociology of Education(ISSN: 0038-0407) from year 2010 to 2012:sci-clone 0038-0407 2010 2012Useful ConfigsDownload and save the files to directorypapers(should be created before download):sci-clone 0038-0407 2010 2012 -d papersIf the default Sci-Hub URL is invalid, change it to another valid one:sci-clone 10.1126/science.1248506 -s sci-hub.twUpgradepip install sci-clone --upgradeUninstallationpip uninstall sci-cloneNotesSci-Hub does not have every article that has DOI, the ones that not found are logged in filemissing.logunder each sub-directory.
Package: scicloud
Science VMis a scicloud-computing platform that integrates into the Python Programming Language. It enables you to leverage the computing power of your datacenter and/or your choice of scicloud providers without having to manage, maintain, or configure virtual servers.When using this Python library known asscicloud, Science VM will integrate seamlessly into your existing code base. To offload the execution of a function to our servers, all you must do is pass your desired function into thescicloudlibrary. ScienceVM will run the function on its high-performance cluster. As you run more functions, our cluster auto-scales to meet your computational needs.Before using this package, you will need to sign up aScience VMaccount.Thescicloudlibrary also features a simulator, which can be used without a Science VM account.  The simulator uses themultiprocessinglibrary to create a stripped down version of the Science VM service.  This simulated service can then run jobs locally across all CPU cores.Quick command-line example:>>> import scicloud
>>> def square(x):
...     return x*x
...
>>> jid = scicloud.call(square,3)  #square(3) evaluated on Science VM
>>> scicloud.result(jid)
9Full package documentation is available athttp://docs.scivm.com.  Some dependencies may be required depending on your platform and Python version; see INSTALL for more information.
Package: scico
SCICO is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX, which provides features such as automatic gradient calculation and GPU acceleration.
Package: scida
scidascida is an out-of-the-box analysis tool for large scientific datasets. It primarily supports the astrophysics community, focusing on cosmological and galaxy formation simulations using particles or unstructured meshes, as well as large observational datasets.
This tool uses dask, allowing analysis to scale up from your personal computer to HPC resources and the cloud.FeaturesUnified, high-level interface to load and analyze large datasets from a variety of sources.Parallel, task-based data processing with dask arrays.Physical unit support via pint.Easily extensible architecture.RequirementsPython 3.9, 3.10, 3.11DocumentationThe documentation can be foundhere.Installpip install scidaFirst StepsAfter installing scida, follow thetutorial.LicenseDistributed under the terms of theMIT license,scidais free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.AcknowledgementsThe project structure was adapted fromWoltandHypermodern Pythoncookiecutter templates.
Package: scidash-api
SciDash Python API to upload data to SciDash from any Python environmentHow to use itfromscidash_apiimportclient# instantiate your client with defaultsclient_instance=client.ScidashClient()# or with optional build_info and hostname attributesclient_instance=client.ScidashClient(build_info="SUPER BUILD",hostname="EXTRAORDINARY HOST")# or with custom config to point to a specific scidash deployment such as 'http://localhost:8000' and hostnameclient_instance=client.ScidashClient({'base_url':'http://localhost:8000'},hostname="EXTRAORDINARY HOST")# login into Scidash Serverclient_instance.login({'username':'my_username','password':'my_secret_password'})# and then call upload method for score object from sciunitclient_instance.upload_test_score(score)# or call upload_suite method passing suite and score_matrix sciunit objectsclient_instance.upload_suite_score(suite,score_matrix)
Package: scidatetime
What?scidatetime is python module for DateTime handle.InstallpipinstallscidatetimeUsagefromscidatetimeimportDateTime# 当前时间print(DateTime())# 2023-09-19 14:50:43.570560+08:00# 指定日期print(DateTime('2023-09-19'))# 2023-09-19 00:00:00+08:00# 指定时间print(DateTime('2023-09-19 14:50:43'))# 2023-09-19 14:50:43+08:00# 指定时间戳print(DateTime(1695106243000))# 2023-09-19 14:50:43+08:00# 指定时间及时区print(DateTime('2023-09-19T14:50:43+07:00'))# 2023-09-19 14:50:43+07:00# 计算时间差print((DateTime('2023-09-20')-'2023-09-19').total_seconds())# 86400.0print((DateTime('2023-09-20')-60*1000))# 2023-09-19 23:59:00+08:00# 获取相对时间print(DateTime('2023-09-20').toRelativeTime('2023-09-20 14:23'))# 14小时前print(DateTime('2023-09-20 10:00:00').toRelativeTime('2023-09-20 10:01:00'))# 1分钟后print(DateTime('2023-01-20 10:00:00').toRelativeTime())# xxx年前Status