Saravanan666/pypi-org huggingface dataset:
Package: sc.contentrules.movebyattribute
Table of Contentssc.contentrules.movebyattributeOverviewUse caseRequirementsInstallationSponsoringCredits0.5 (2011-06-23)sc.contentrules.movebyattributeOverviewsc.contentrules.movebyattributeis a content rule action used to
organize content by moving it under a folderish object with the same id as
the value returned by a selected attribute.Use caseIn a portal content is created in a folder /post and must be moved to a
new location when it is published. This new location will be under another
folder /blogs where every user has its own sub folderA portal manager will add a content rule that when a Page is published
inside /post it will lookup the value of the method Creator and then move
the published content to a folderish under /blog. The id of this folderish
will match the value extracted from the Creator method.So, a user with username kirk, will add a new Page in /post and wait for
publication. When a reviewer publishes the content, it will be moved under
/blog/kirk, whichmustalready exist.RequirementsPlone 3.3.x and above (http://plone.org/products/plone)InstallationTo enable this product,on a buildout based installation:Edit your buildout.cfg and addsc.contentrules.movebyattributeto the list of eggs to install[buildout]
...
eggs =
    sc.contentrules.movebyattributeAfter updating the configuration you need to run the ‘’bin/buildout’’,
which will take care of updating your system.SponsoringDevelopment of this product was sponsored by:Simples Consultoria.Associacao PythonBrasil.CreditsErico Andrei (erico at simplesconsultoria dot com dot br)Changelog0.5 (2011-06-23)Brazilian Portuguese translation [erico_andrei]Initial release [erico_andrei]
Package: sc-corporate-analysis
A simple Python projectThis is a simple python project.InstallationIt is possible to install the tool withpip:pip install sc-corporate-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: scCPEP
scCPEPscCPEP is a Python package containing tools for scRNA-seq cell type identifiication based on an ensemble pruning model.Installation GuideInstall from PyPi:pip install scCPEPBefore running the tool, we recommend creating an environment locally using ‘’‘conda’‘’ first, then you need to install the following packages in your environment, scMGCA mainly depends on the followinb Python packages:python:python=3.7.12
pycaret=2.3.10
scanpy
rpy2
r-devtoolsAfter installing the python package, switch to '''R''' environment, and install the following tool:R:library(devtools)
install_github("suke18/FEAST")Usagecd scCPEP
mkdir MLmodels
mkdir tmp
python run.pyData AvailabilityThe data sets we used can be download indatas.LicenseThis project is covered under the MIT License.
Package: scc-pyteam-template-tools
InstallationRequire python >= 3.10using pip$ pip install scc-pyteam-template-toolsusing poetry$ poetry add scc-pyteam-template-toolsGuideRequire: using in global environmentcreate fastapi template with command then fill the questions$ create-fastapi-appcreate django template with command then fill the questions$ create-django-app
Package: sc-credit-card-apply-status-analysis
A simple Python projectThis project provides a simple utility to analysis credit card apply status.InstallationIt is possible to install the tool withpip:pip install sc-credit-card-apply-status-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sccross
scCrossA Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self enhancing and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:https://sccross.readthedocs.ioOverviewSingle-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.Key CapabilitiesCombine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.Generate cross-compatible single-cell data between two different types in the training set.Augment single-cell omics data through self-improvement techniques.Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.Maintain genomic integrity during omics perturbations and cross-generations effectively.InstallationYou may install scCross by the following command:pip install sccross
Package: scd
ContentsInstallationConfigurationUsageWhy scd?scd (something competely different) is a small tool with one
intention: make your version bumping underoverengineered.
It takes slightly different approach thanbumpversion: it does not make commits or
tags and do not updates version by command. It takes configuration file
and adjust your version regarding this file.It may seems a little bit complex, but it works really-really well if
you have a complex setup, where you need to manage versions not only in
literal format, but in different, complex ways in absolutely different
files. Also, it can eliminate a huge amount of copypasting in your.bumpversion.cfg. Also, it works with regular expressions therefore
it can eliminate design restrictions of bumpversion.Please checkofficial documentationfor
details. This README file is just a whirlwind tour.InstallationAs any Python package, you can install scd with pip or sources.pipinstallscd[yaml,colors]orgitclonehttps://github.com/9seconds/scdcdscdpythonsetup.pyinstallConfigurationHere is an example how can configuration file may looks like:version:number:1.0.1scheme:semversearch_patterns:full:"{{semver}}"vfull:"v{{semver}}"major_minor_block:"\\d+\\.\\d+(?=\\s\\#\\sBUMPVERSION)"replacement_patterns:full:"{{full}}"major_minor:"{{major}}.{{minor}}"major_minor_p:"{{major}}.{{minor}}{%ifpatch%}.{{patch}}{%endif%}"defaults:search:fullreplace:fullfiles:setup.py:-search_raw:"(?>=version\\s=\\s\\\"){{full}}"docs/conf.py:-default-search:vfullreplace:major_minor_p-search:major_minor_blockreplace_raw:"{{next_major}}"Yes, YAML! But you can use JSON or TOML also.Mustaches areJinja2templates (like inAnsible, for example). Also, as you can
see, it is possible to have a list of replacements per file.And yes, versioning is done by schemes.You can find athorough explanationsin docs.UsageWell, you would not belive, butscdor more verbosescd-v>>>Use/home/sergey/dev/pvt/scd/.scd.yamlasconfigfile>>>ParsedconfigasYAML>>>Versionis0.1.0.dev34+342f2c2>>>Starttoprocess/home/sergey/dev/pvt/scd/setup.py>>>Modify'version="0.0.1",'to'version="0.1.0.dev34+342f2c2",'>>>Starttoprocess/home/sergey/dev/pvt/scd/docs/source/conf.py>>>Modify"version = '1.0'"to"0.1'">>>Modify"release = '1.0.0b1'"to"0.1.0'">>>Starttoprocess/home/sergey/dev/pvt/scd/scd/__init__.py>>>Modify'__version__ = "0.1.0"'to'0.1.0.dev34"'Why scd?Because every version releases look the same.
Package: scd2
pandas_scdexecuting slowly changing dimension type 2 on pandas dataframes or parquet filespandas_scd arguments:src:pandas dataframe with the source of the SCDtgt:pandas dataframe with the target of the SCD (target can be
empty)cols_to_track:list of columns to track changes (default is allcolumns from the source table)tz:pytz time zone to use on start_ts and end_ts, default is None(will use local time)the return dataframe contain the entire target table with the new changes, ready for insert overwrite of the current target tableparquet_scd arguments:src:path to the source of the SCDtgt:path to the target of the SCD (target can be empty)cols_to_track:list of columns to track changes (default is all columns from the source table)tz:pytz time zone to use on start_ts and end_ts, default is None (will use local time)there is no return value, the tgt path that was provided will be overwrittenInstallationpip install scd2Getting startedfor working with pandas:from scd2 import SCD2
import pandas as df	  

tgt = pd.DataFrame.from_dict({'first_name': ["Chris"], 'last_name': ['Paul'], 'team': ["Clippers"], "start_ts": [datetime(2012, 1, 14, 3, 21, 34)], "end_ts": [None], "is_active": [True]}) 

src = pd.DataFrame.from_dict({'first_name': ["Chris"], 'last_name': ['Paul'], 'team': ['Suns']})

final_df = SCD2().pandas_scd2(src, tgt)pandas_scd2 will return a dataframe with the entire new targertgt:| first_name | last_name | team | start_ts | end_ts | is_active ||------------|-----------|----------|---------------------|--------|-----------|| Chris | Paul | Clippers | 2012-01-14 03:21:34 | | True |src:| first_name | last_name | team ||------------|-----------|----------|| Chris | Paul | Clippers |final_df:| first_name | last_name | team | start_ts | end_ts | is_active ||------------|-----------|----------|---------------------|---------------------|-----------|| Chris | Paul | Clippers | 2012-01-14 03:21:34 | 2018-01-01 00:00:00 | False || Chris | Paul | Suns | 2018-01-01 00:00:00 | | True |for working with parquet:src_parquet_path = '~/source.parquet'

tgt_parquet_path = '~/target.parquet'

SCD2().parquet_scd2(src, tgt)parquet_scd2 will overide the current target (tgt_parquet_path)src:pandas dataframe with the source of the SCDtgt:pandas dataframe with the target of the SCD (target can be empty)cols_to_track:list of columns to track changes (default is all columns from the source table)tz:pytz time zone to use on start_ts and end_ts, default is None (will use local time)
Package: scd30
scd30Python library for the Sensirion SCD30 co2, temperature, humidity sensor.I2C Clock stretchingMaster needs to support Clock Stretching up to 150ms. The default in Raspbian is too low, we have to increase it:To set it, download from here:https://github.com/raspihats/raspihats/tree/master/clk_stretchCompile:gcc -o i2c1_set_clkt_tout i2c1_set_clkt_tout.c
gcc -o i2c1_get_clkt_tout i2c1_get_clkt_tout.cexecute (add to /etc/rc.local to run on every boot):./i2c1_set_clkt_tout 20000 # for 200msThanksI borrowed some code and ideas from these two repos:https://github.com/UnravelTEC/Raspi-Driver-SCD30(python code, i2c clock stretching)https://github.com/sparkfun/SparkFun_SCD30_Arduino_Library(code structure, commands)
Package: scd4x
SCD4X C02 SensorPre-requisitesYou must enable:i2c:sudo raspi-config nonint do_i2c 0You can optionally runsudo raspi-configor the graphical Raspberry Pi Configuration UI to enable interfaces.InstallingStable library from PyPi:Just runpip3 install scd4xIn some cases you may need to usesudoor install pip with:sudo apt install python3-pipLatest/development library from GitHub:git clone https://github.com/pimoroni/scd4x-pythoncd scd4x-pythonsudo ./install.sh0.0.2Fix byte packing when sending data (unsigned char instead of signed char).Fix condition in set_temperature_offset.Fix reading data in get_temperature_offset.Add get/set_automatic_self_calibration_enabled.Add persist_settings.Repackage to latest boilerplate.0.0.1Initial Release
Package: scdata
Smart Citizen DataWelcome toSmartCitizen Data. This is a data analysis framework for working with sensor data in different ways:Interacting with several sensors APIsClean data, export and calculate metricsModel sensor data and calibrate sensorsGenerate data visualisations - matplotlib,plotlyoruplotGenerate analysis reports in html or pdf and upload them toZenodoA full documentation of the framework is detailed inthe Smart Citizen Docs.InstallationYou can check it out in thebefore installing if you want. Works withPython 3.*(tested untilpython 3.9.5).You can just run:pip install scdataWork on the source codeSimply clone the repository with:git clone https://github.com/fablabbcn/smartcitizen-data.git
cd smartcitizen-dataInstallscdatapackage with requirements:python setup.py installOr if you want to edit:cd scdata
pip install --editable .Tokens and configIf you want to upload data toZenodo, you will need to fill set an environment variable calledZENODO_TOKENin your environment. You can get more instructionshereand withthis example.A configuration file is available at~/.config/scdata/config.yaml, which contains a set of configurable variables to allow or not the local storage of relevant data in the data folder, normally in~/.cache/scdata:data:
  cached_data_margin: 2
  load_cached_api: true
  reload_metadata: true
  store_cached_api: true
paths:
  config: /Users/username/.config/scdata
  data: /Users/username/.cache/scdata
  export: /Users/username/.cache/scdata/export
  interim: /Users/username/.cache/scdata/interim
  inventory: ''
  models: /Users/username/.cache/scdata/models
  processed: /Users/username/.cache/scdata/processed
  raw: /Users/username/.cache/scdata/raw
  reports: /Users/username/.cache/scdata/reports
  uploads: /Users/username/.cache/scdata/uploads
zenodo_real_base_url: https://zenodo.org
zenodo_sandbox_base_url: http://sandbox.zenodo.orgAlso,.envfiles will be picked from~/.cache/scdata.Using with jupyter lab (optional)It can also be used withjupyter laborjupyter. For thisinstall juypterlab.ContributeIssues and PR more than welcome!FundingThis work has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreementNo. 689954
Package: scDB
Update Data ModelEGAWrite a function (named by theEGAID) to preprocess meta table under theEGAclass inscDB/EGA.pyfileEnter the following CMD to update data mode./bin/scDB [project] [workspace] EGA [path/to/delimited_maps] [EGAID]CCGProcess previous projectWrite a function (named by thecohortID) to preprocess meta table under theCCGclass inscDB/CCF.pyfileEnter the following CMD to update data mode./bin/scDB [project] [workspace] CCG [path/to/meta_table] [cohortID]Update BCL file to the shared workspace [SingleCellStorage_VALab]Copy the BCL file togs://fc-48fd2ee8-f6eb-4d07-b271-e8304d29fb1b/Raw_BCL_FilesCopy Manifest file togs://fc-48fd2ee8-f6eb-4d07-b271-e8304d29fb1b/Manifest_Files/[cohort_id]_Manifest.txtManually generate a[cohort_id].tsvfileEnter the following CMD to update data mode./bin/scDB nci-breardon-bi-org SingleCellStorage_VALab CCG meta/CCG/BCL/[cohort_id].tsv bclStorage
Package: scdecipher
DecipherDecipher is a single-cell analysis toolkit to jointly analyze samples from distinct
conditions (e.g. normal vs perturbed samples).Quickstart tutorials1. Example of Decipher on AML data.Directories.
├── decipher:       Source code
└── examples:       Examples and tutorialsHow to cite DecipherPlease cite our preprint: coming soon.Deep generative model Deciphers derailed trajectories in Acute Myeloid LeukemiaBibTexComing soonChicagoComing soon
Package: scdeepinsight
Additional dependencyPlease installpyDeepInsightat the same time. This additional dependency has not been published to PyPI and will not be installed automatically.python3 -m pip -q install git+https://github.com/alok-ai-lab/pyDeepInsight.git#egg=pyDeepInsightIntroduction of scDeepInsightscDeepInsight provides pre-trained cell type annotation model for scRNA-seq data. For further illustrations and vignettes, please refer to:scDeepInsight. A sample of how to use this package is also available intutorial. Users can directly use this tool to annotate PBMC datasets without performing training by themselves.31 cell types(celltype.l2) including the Doublet can be recognized through this pre-trained model. In future, we will provide more pretrained models for other types of tissues.Defined functionsImageTransform(query_path:str, barcode_path:str, image_path:str)query_path: The absolute path of the target scRNA-seq dataset (end with .h5ad).barcode_path: The absolute path where you wish to store the generated barcode file (end with .csv).image_path: The absolute path where you wish to store the generated image file (end with .npy).Annotate (barcode_path:str, image_path:str, batch_size:int = 128)barcode_path: The absolute path where you have stored the generated barcode file (end with .csv).image_path: The absolute path where you have stored the generated image file (end with .npy).batch_size: The batch size you wish to load to the converted image dataset when using the pretrained model for annotation. Default value is 128.Precautions for GPU usersThis package has built-in pretrained model trained by Efficientnet-b3 for annotation. scDeepInsight allows loading the model to GPU or GPU. Specifically, when loading the pretrained model to GPU, please make sure the correct version of PyTorch that matches the CUDA version has been installed previously:PyTorch versions.scdeepinsight (version 0.3.2): The default PyTorch version assigned in the dependencies of scdeepinsight is: 1.12.0, and the torchvision is 0.13.0, which is compatible with CUDA 10.2, 11.3, 11.6.scdeepinsight (version 1.3.2): The default PyTorch version assigned in the dependencies of scdeepinsight is: 1.13.0, and the torchvision is 0.14.0, which is compatible with CUDA 11.6, 11.7.scdeepinsight (version 2.3.2): The default PyTorch version assigned in the dependencies of scdeepinsight is: 2.0.0, and the torchvision is 0.15.0, which is compatible with CUDA 11.7, 11.8.
Package: scdef
Deep exponential families for single-cell data. scDEF learns hierarchies of cell states and their gene signatures from scRNA-seq data. The method can be used for dimensionality reduction, visualization, gene signature identification, clustering at multiple levels of resolution, and batch integration. The informed version (iscDEF) can additionally take known gene lists to jointly assign cells to types and find clusters within each type.To install scDEF and get started,read the documentation.
Package: scDenorm
scDenormInstallpipinstallscDenorm#orcondainstall-cchangebioscdenormHow to useUsing pbmc3k as an example datasetimportscanpyasscfromscipy.ioimportmmwritefromscDenorm.denormimport*ad=sc.datasets.pbmc3k()ad.layers['count']=ad.X.copy()adAnnData object with n_obs × n_vars = 2700 × 32738
    var: 'gene_ids'
    layers: 'count'sc.pp.normalize_total(ad,target_sum=1e4)sc.pp.log1p(ad)smtx=ad.X.tocsr().asfptype()smtx.dataarray([1.6352079, 1.6352079, 2.2258174, ..., 1.7980369, 1.7980369,
       2.779648 ], dtype=float32)ad.write_h5ad('data/pbmc3k_norm.h5ad')write out as sparse matrixmmwrite('data/scaled.mtx',smtx[1:10,])In jupyterInput Anndatascdenorm('data/pbmc3k_norm.h5ad',fout='data/pbmc3k_denorm.h5ad',verbose=1)INFO:root:Reading input file: data/pbmc3k_norm.h5ad
INFO:root:The dimensions of this data are (2700, 32738).
INFO:root:select base
INFO:root:denormlizing ...
100%|██████████| 2700/2700 [00:00<00:00, 2900.90it/s]
INFO:root:Writing output file: data/pbmc3k_denorm.h5adreturn a new anndata if there is no output path.new_ad=scdenorm('data/pbmc3k_norm.h5ad')100%|██████████| 2700/2700 [00:00<00:00, 2969.22it/s]new_adView of AnnData object with n_obs × n_vars = 2700 × 32738
    var: 'gene_ids'
    uns: 'log1p'ad.layers['count'].dataarray([1., 1., 2., ..., 1., 1., 3.], dtype=float32)new_ad.X.dataarray([1.       , 1.       , 2.0000002, ..., 1.       , 1.       ,
       3.       ], dtype=float32)Input sparse matrix with cell by geneIf it is gene by cell, setgxc=True.scdenorm('data/scaled.mtx',fout='data/scd_scaled.h5ad')100%|██████████| 9/9 [00:00<00:00, 2883.12it/s]In command lineInput Anndata!scdenormdata/pbmc3k_norm.h5ad--foutdata/pbmc3k_denorm.h5ad100%|█████████████████████████████████████| 2700/2700 [00:00<00:00, 2719.59it/s]Input sparse matrix with cell by gene!scdenormdata/scaled.mtx--foutdata/scd_scaled_c.h5ad100%|███████████████████████████████████████████| 9/9 [00:00<00:00, 1333.31it/s]or outputmtxformat.!scdenormdata/scaled.mtx--foutdata/scd_scaled_c.mtx100%|███████████████████████████████████████████| 9/9 [00:00<00:00, 1290.78it/s]
Package: scDesign3Py
scDesign3Py: python interface forscDesign3RpackageAboutscDesign3Pyis a python interface developed for thescDesign3Rpackage based onrpy2, enabling python users to easily use thescDesign3functions and share almost the same usage as theRversion.For detailed tutorial of this package, please visit thedocument website.To find out more details aboutscDesign3, you can check out manuscript on Nature Biotechnology:Song, D., Wang, Q., Yan, G. et al. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat Biotechnol (2023).For tutorial ofRpackagescDesign3, you can check thescDesign3 tutorial website.InstallationYou can install the most latest version fromGithubby running the following commandgitclonehttps://github.com/DILIU899/scDesign3Py.gitcdscDesign3Pypipinstall.Or quick install from PyPI:pipinstallscDesign3PyLicenseThis pacakge is licensed under the terms of theMIT License.
Package: sc.dev.core
IntroductionChangelog20110707.3 (2011-07-07)Fix entry point (again…) [erico_andrei]20110707.2 (2011-07-07)Fix entry point [erico_andrei]20110707.01 (2011-07-07)Added an entry point to upload this doc to packages.python.org [erico_andrei]Added Sphinx-PyPI-upload as dependency [erico_andrei]New version of sc.paster.package [erico_andrei]Improved documentation related to policy products and some shortcuts [davilima6]20110530.1 (2011-05-30)Update sc.paster.theme, sc.paster.package and sc.paster.buildout [erico_andrei]20110420.1 (2011-04-20)Using zest.releaser 3.21 [erico_andrei]Improve documentation related to package releases [erico_andrei]Added setuptools_hg to dependencies [erico_andrei]Added Mercurial reference [erico_andrei]Improved basic environment and configuration docs [erico_andrei]20110225.1 (2011-02-25)Adding a basic portuguese documentation [erico_andrei]Addind dependencies to i18ndude, Sphinx and rst2pdf [erico_andrei]20100908.02 (2010-09-08)New versions, support for zest.releaser improved [erico_andrei]20100818.01 (2010-08-18)Bump dependecies versions [erico_andrei]Add dependencies to collective.dist and setuptools-git [erico_andrei]Add dependencies to zest.releaser and collective.dist [erico_andrei]20100809.01 (2010-08-09)Bump sc.paster.theme version [erico_andrei]20100805.02 (2010-08-05)Fixes unicode error [erico_andrei]20100805.01 (2010-08-05)Initial release [erico_andrei]