Saravanan666/pypi-org huggingface dataset:
Package: cythonrgbasearch
RGBA color search with Cythonpip install cythonrgbasearchTested against Windows / Python 3.11 / AnacondaCython (and a C/C++ compiler) must be installedfromcythonrgbasearchimportfind_rgba_colors,find_rgb_colorsimportcv2importnumpyasnpdata=cv2.imread(r"C:\Users\hansc\Downloads\pexels-alex-andrews-2295744.jpg")# 4525 x 6623 x 3 picture https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/data=np.ascontiguousarray(np.dstack([data,np.full(data.shape[:2],0,dtype=np.uint8)]))colors1=np.array([(66,71,69),(62,67,65),(144,155,153),(52,57,55),(127,138,136),(53,58,56),(51,56,54),(32,27,18),(24,17,8),],dtype=np.uint8)colors1=np.array([list((x.tolist()))+[0]forxincolors1],dtype=np.uint8)ocol=np.array([[255,255,255,0]],dtype=np.uint8)r1=find_rgba_colors(pic=data,colors=colors1,dummy_alpha=0)r2=find_rgba_colors(pic=data,colors=ocol,dummy_alpha=0)r3=find_rgb_colors(pic=data,colors=colors1)r4=find_rgb_colors(pic=data,colors=ocol)# r1# Out[4]:# rec.array([(136, 138, 127, 0,    0,   38), ( 69,  71,  66, 0,    0, 4522),#            ( 65,  67,  62, 0,    0, 4523), ...,#            (  8,  17,  24, 0, 4524, 6620), (  8,  17,  24, 0, 4524, 6621),#            (  8,  17,  24, 0, 4524, 6622)],#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1'), ('x', '<i8'), ('y', '<i8')])# r2# Out[5]:# rec.array([(255, 255, 255, 0,  568, 5021), (255, 255, 255, 0,  586, 3064),#            (255, 255, 255, 0,  612, 2812), ...,#            (255, 255, 255, 0, 3752,  805), (255, 255, 255, 0, 3752,  806),#            (255, 255, 255, 0, 3775, 1291)],#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1'), ('x', '<i8'), ('y', '<i8')])# r3# Out[6]:# rec.array([(136, 138, 127,    0,   38), ( 69,  71,  66,    0, 4522),#            ( 65,  67,  62,    0, 4523), ..., (  8,  17,  24, 4524, 6620),#            (  8,  17,  24, 4524, 6621), (  8,  17,  24, 4524, 6622)],#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('x', '<i8'), ('y', '<i8')])# r4# Out[7]:# rec.array([(255, 255, 255,  568, 5021), (255, 255, 255,  586, 3064),#            (255, 255, 255,  612, 2812), ..., (255, 255, 255, 3752,  805),#            (255, 255, 255, 3752,  806), (255, 255, 255, 3775, 1291)],#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('x', '<i8'), ('y', '<i8')])
Package: cythonsequencefinder
Module for finding sequences in arrays using Cython and NumPy.pip install cythonsequencefinderTested against Windows / Python 3.11 / AnacondaCython (and a C/C++ compiler) must be installed to use the optimized Cython implementation.fromtimeimportperf_counterimportnumpyasnpfromcythonsequencefinderimportnp_search_sequencefromcythonsequencefinderimportfind_seqseq=np.array([1,2,4,5],dtype=np.int32)sequence_size=len(seq)arr=np.random.randint(low=0,high=10,size=1000000,dtype=np.int32)start=perf_counter()rax=find_seq(arr,seq,distance=1)print(perf_counter()-start)start=perf_counter()rax2=np_search_sequence(arr,seq,distance=1)print(perf_counter()-start)# 0.004291800003557 - cython# 0.007874400005675852 - numpy
Package: cython-setuptools
cython-setuptoolsEasier distribution and development of Cython modules.Features:Two distribution models: with C/C++ files included in the package, and
withoutCython modules are defined insetup.cfgInstall directly from Cython sources, without installing Cython in the target
environment (Cython is only included ininstall_requires)Installation$pipinstallcython-setuptoolsUsageHere is an example Python package using the default distribution model (only
Cython files are included in the source package).First install thecython-setuptoolsvendor module in the package, next tosetup.py.$cdyour-python-project/
$cython-setuptoolsinstallThen usecython-setuptools'setup()in yoursetup.py:fromcysetuptoolsimportsetupsetup()Note that we keep the defaultcythonize=Trueargument ofsetup()here,
meaning that C files are compiled from Cython files automatically.setup(cythonize=False)would mean we would need to distribute the C/C++ files
compiled from Cython in the source package.Define your Cython modules insetup.cfg.[metadata]name=your-python-projectversion=1.0[options]packages=find:install_requires=cython[options.extras_require]dev=cython[cython-defaults]include_dirs=include/[cython-module: foo.bar]sources=foo.pyxbar.cppinclude_dirs=eval(__import__('numpy').get_include())language=c++pkg_config_packages=opencvThen your Cython modules can be compiled and tested in-place with:$pythonsetup.pybuild_ext--inplaceThis automatically compile outdated Cython files. Ifsetup(cythonize=False)is used, you have to specifically tell the setup to recompile outdated Cython
files:$CYTHONIZE=1pythonsetup.pybuild_ext--inplace
Package: cython-sgio
cython-sgioCython-based wrapper for sending SCSI Generic I/O commands from
Python.LicenseCopyright © 2014-2020 The cython-sgio AuthorsThis program is free software; you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published
by the Free Software Foundation; either version 2.1 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License
along with this program; if not, seehttp://www.gnu.org/licenses/.
Package: cython-vst-loader
cython-vst-loaderA loader for VST2 audio plugins providing a clean python object-oriented interfaceSupported platforms:Linux 64bit,Windows 64bitSupported python versions:3.7,3.8,3.9In-depth documentation:Usage examplesSetting up development environmentBuilding and releasinghome page:https://github.com/hq9000/cython-vst-loaderProject goalsThe purpose is to have a simple wrapper for VST plugins to be used in higher-level projects, such ashttps://github.com/hq9000/py_headless_dawSupported pluginsRecreating a complete VST host environment in Python is a challenging task.
Because of that, not every plugin will work with this wrapper, many are known not to work.
Also, in case of a closed-source plugins, troubleshooting issues is almost impossible.Because of that, the loader "officially" supports (by testing) a limited number of free and (mostly) open source plugins.
Other plugins may or may not work, if you discover an open source plugin that is causing issues, feel free to write a bug.The list of (tested/known-not-to-work/reportedly-working) plugins will be refreshed as new information arrives.Note: only 64-bit/VST2 plugins are currently supported.Plugins tested on WindowsSynthsTAL NoiseMaker (https://tal-software.com/products/tal-noisemaker)TAL Elec7ro (https://tal-software.com/products/tal-elek7ro)DiscoDSP OB-Xd (https://www.discodsp.com/obxd/)Tunefish4 (https://www.tunefish-synth.com/download)EffectsDragonfly Reverb (https://michaelwillis.github.io/dragonfly-reverb/)TAL Reverb 2 (https://tal-software.com/products/tal-reverb)Plugins tested on LinuxSynthsamsynth (http://amsynth.github.io/)EffectsDragonfly Reverb (https://michaelwillis.github.io/dragonfly-reverb/)Plugins known not to work with the loaderSynth1TyrellN6Installationpip install cython_vst_loader
Package: cytimeutil
#--coding:utf-8--
时间字符串按照本身格式转为时间戳strToTimeStamp(timeStr, timeFmt):时间戳转为一定格式的时间字符串timeStampToStr(timeStamp, timeFmt):计算两个时间字符串之间的差值，可以按照秒(s)、分(m)、小时(h)返回，默认返回秒timeMinusStr(start_time_str, end_time_str, time_fmt, timeType):计算两个时间戳之间的差值，可以按照秒(s)、分(m)、小时(h)返回，默认返回秒timeMinusStamp(start_time, end_time, timeType):将时间戳转为世界时间字符串timeStrToUTCStr(time_str, timeFmt):时间字符串添加分钟和小时后返回时间字符串strTimeAddMinHour(str_p, mins, hours):时间字符串添加分钟和小时后返回时间字符串strTimeAddMinHourByFmt(str_p, mins, hours, fmt):获取两个时间字符串之间，所有的日期getDatesByTimes(sDateStr, eDateStr):
Package: cytocad
CytoCAD - Copy-number variation caller using low-depth whole-genome sequencing dataCytoCAD is a bioinformatics tool for the identification of large genomic copy-number variation through coverage anomaly detection
(CAD) using mapped whole-genome sequencing (WGS) data. It has been tested in low-depth (~8X) Oxford Nanopore WGS long-read
data. Its output displays chromosome illustrations demarcating regions of copy-number gains (Red) or losses (Blue). The above illustration shows a loss of one chromosome 7 copy, a gain of one chromosome 21 copy, a partial duplication of both chromosome 8 copies, and a loss of one chromosome 17 short arm. It also has two X chromosomes and no Y chromosome, suggesting a female sex.Basic information:Takes as input a mapped whole-genome sequencing BAM file and output a BED file and a chromosome ideogram-like figureUsesRupturespython package for change point detection of read coverage data per
chromosomeUsestagorefor chromosome ideogram illustrationsGetting StartedQuick runcytocad [Options] sample.bam working_dirArgumentCommentsample.bamInput mapped WGS BAM fileworking_dirWorking directoryOutputOutput fileComment${sample}.ideo.svgChromosome ideogram produced bytagore${sample}.CNV.bedBED file of chromosome regions with CNVFor more information, seewiki.Operating system:Linux (x86_64 architecture, tested in Ubuntu 16.04)Installation:There are three ways to install CytoCAD:Option 1: Conda (Recommended)# Installing from bioconda automatically installs all dependencies 
conda install -c bioconda cytocadOption 2: PyPI (See dependencies below)# Installing from PyPI requires own installation of dependencies, see below
pip install cytocadOption 3: GitHub (See dependencies below)# Installing from GitHub requires own installation of dependencies, see below
git clone https://github.com/cytham/cytocad.git 
cd cytocad
pip install .Installation of dependenciesbedtools >=2.26.0samtools >=1.3.0rsvg-convert >=2.40.13Please make sure each executable binary is in PATH.1.bedtoolsPlease visitherefor instructions to install.2.samtoolsPlease visitherefor instructions to install.3.rsvg-convertsudo apt-get update
sudo apt-get install librsvg2-binVersioningSeeCHANGELOGCitationNot available yetAuthorTham Cheng Yong-cythamLicenseThis project is licensed under GNU General Public License - seeLICENSE.txtfor details.LimitationsChromosome pairs illustrated by tagore may resemble sister chromatids, but they are in fact homologous pairsPhasing of CNVs for each chromosome homologous pair is not yet possible.The default minimum size of detectable CNV is about 500 kb. It can be adjusted by the 'interval' and 'rolling' parameters
following the equation: minimum size ~= interval*rollingOther chromosomal structural variations, such as inversions, have to be detected by other tools, such asNanoVar. NanoVar has incorporated CytoCAD in its pipeline from version 1.4.0 onwards.
Package: cytocipher
Cytocipher - detection of significantly different cell populations in scRNA-seqFor a complete tutorial that installs cytocipher & reproduces the pancreas development analysis, please seehere.Installation!pip install cytocipherimport cytocipher as ccExpected InputAn AnnData object,data, that has been processed similarly to thescanpy standard workflowto produce log-cpm normalised data with tentative cluster labels
(e.g. from Leiden clustering). It's better if the Leiden resolution is high,
so that there is alot of over-clustering.
Cytocipher merges the non-significantly different clusters.Code Scoring Minimal ExampleFunctions below run the marker gene identification, code scoring, &
subsequent visualisation of the resulting cell by cluster enrichment scores.cc.tl.get_markers(data, 'leiden')
cc.tl.code_enrich(data, 'leiden')
cc.pl.enrich_heatmap(data, 'leiden')In a jupyter notebook, you can see documentation using, for example:?cc.tl.get_markersCluster Merging Minimal ExampleBelow runs the cluster merging and visualises the heatmap of enrichment
scores per cell for each of the new merged clusters.cc.tl.merge_clusters(data, 'leiden')
cc.pl.enrich_heatmap(data, 'leiden_merged')To visualise the scores being compared for a given pair of clusters,
the following visualises the scores as violin plots of the enrichment scores
& prints the p-values determined by comparing the scores:cc.pl.sig_cluster_diagnostics(data, 'leiden', plot_pair=('3', '9'))Input pair ('3', '9')p=0.9132771265170103 (3 cells; 3 scores) vs (9 cells; 3 scores)p=0.8128313109661132 (3 cells; 9 scores) vs (9 cells; 9 scores)To get an sense of the upper- and lower- bounds for what is considered
a significant cluster, default parameters plot the violins illustrated above
for the upper- and lower- bounds of
significant versus non-significant cluster pairs:cc.pl.sig_cluster_diagnostics(data, 'leiden')See thepancreas tutorialfor more example Cytocipher functionality, including; visual bias checks,
Sankey diagrams to visualise cluster merging, volcano plots, and more!IssuesPlease feel free to post an issue on thegithubif there is a problem, & I'll help you out ASAP.
Package: cytocluster
CytoClusterOverviewWelcome! This is CytoCluster, a package for clustering high-dimensional cytometry data in Python.
CytoCluster started as a module in the first release ofCytoPy.CytoCluster is now a standalone package that provides functions and classes for:Popular clustering algorithms such as FlowSOM, Phenograph, SPADE etc.Plotting and visualising clustering results.Ensemble clustering for combining the results of many clustering algorithms into consensus clusters.Examples of using the CytoCluster package are provided in the example notebooks (see notebooks folder in this repo).Installationpip install cytoclusterRelease NotesThe CytoPy ecosystem is under development, see ### for details on release information.
Package: cytocoarsening
📦 cytocoarsening.pyWe want to identify cell-types that are enriched for both phenotype (e.g. cell phenotype) and relate to the external information. Graph-based approaches for identifying these modules can suffer in the single-cell setting because there is an extremely large number of cells profiled per sample and we often profile multiple samples with multiple different experimental conditions or timepoints. Here isCytocoarsening github link.OverviewInstallationIf you'd like to install by PYPI, please type this line on your command line(Cytocoarsening PYPI):pipinstallcytocoarseningOr you can clone the git repository by,git clone https://github.com/ChenCookie/cytocoarsening.gitOnce you've clone the repository, please change your working directory into this folder.cd cytocoarseningData accesspreeclampsiacovidNK cellTake preeclampsia for example, if you'd want to list all of the publicly available files for download,from lxml import html
import requests

r = requests.get(f'https://zenodo.org/record/6779483#.Yrygu-zMJhF')
content = html.fromstring(r.content)
hrefs = content.xpath('//a/@href')
files = [i for i in hrefs if i.endswith('?download=1')]
files = np.unique(files)
print(files)If you'd like to download any  preeclampsia dataset file from zenodo,curl 'https://zenodo.org/record/6779483/files/Han-FCS_file_list.xlsx?download=1' --output Han-FCS_file_list.xlsxParameter ExplanationThe function can be excute at one line.coarsening_group,group_edge,result_dicts=cytocoarsening(cell_data,cell_label,multipass,k_nearest_neighbors)inputcell_data- numpy.ndarray. The single cell data with several features. The shape of ndarray is (cell number,features number). Default:cell_data = None.cell_label- numpy.ndarray. The attribute of each cell data. The shape of ndarray is (cell number,). Default:cell_label = None.multipass- int. The pass number that what want the data size decrease. Default:multipass = 10.k_nearest_neighbors- int. Number of neighbors in the inisial graph in each pass. Default:k_nearest_neighbors = 5.outputcoarsening_group- dict. The dictionary that indicate supernode as key and the node number list of the group as value in coarsening graphgroup_edge- numpy.ndarray. The array that record the edge that combine two nodesresult_dicts- dict. The dictionary that save different result value, including accuracy, error rate, quadratic equation evaluation in feature and label, node number, edge number, runtime, and keypointToy Examplefrom cytocoarsening.cytocoarsening import cytocoarsening
import numpy as np
import random

cell_data=[[random.random() for i in range(33)] for j in range(4500)]
cell_data=np.array(cell_data)

cell_label = np.array([0] * 1000 + [1] * (3500))
np.random.shuffle(cell_label)

group,edge,diccts=cytocoarsening(cell_data,cell_label,3,5)
Package: cytogis
Cyto to GISConvert Cytoscape .cyjs files to GeoJSON objectsOverviewCyto to GIS is a Python package that facilitates the conversion of Cytoscape .cyjs files into GeoJSON objects to use in QGIS.
It provides functionality to extract node and edge data from Cytoscape JSON representations and transform them into GeoJSON features for geographical information systems (GIS) applications.UsageExample for Using cyto_to_qgis:fromcytogisimportGISManager# Configure your input and output pathsCONFIG={"cyto_path":"YOUR_PATH_TO_cyjs_FILE","coord_path":"YOUR_PATH_TO_COORDINATES_CSV","out_path_nodes":"YOUR_PATH_TO_OUTPUT_NODES","out_path_edges":"YOUR_PATH_TO_OUTPUT_EDGES","lat_long_cols":("your column name for latitude","your column name for longitude"),"cols_to_drop":["your","cols","to","drop"]# optional}# Instantiate GIS Managergis=GISManager(CONFIG)# Process edgesedges_collection=gis.create_features_edges()edges_collection.save_geojson(CONFIG["out_path_edges"])# Process nodesnodes_collection=gis.create_features_nodes()nodes_collection.save_geojson(CONFIG["out_path_nodes"])ContributingContributions are welcome!Bug Reports and Feature RequestsIf you encounter any issues or have ideas for improvements, please open an issue.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: cytohmm
CytoHMMA hidden Markov model for cytogenetic microarrays.Getting startedPython 3InstallingUsing the source code from github.  First install numpy and cythongit clone https://github.com/harry-zuzan/cytohmm.git
cd cytohmm
python setup.py installUsing pippip install numpy
pip install cython
pip install cytohmm
Package: cytomine-python-client
Cytomine-python-clientWhen using our software, we kindly ask you to cite our website url and related publications in all your work (publications, studies, oral presentations,...). In particular, we recommend to cite (Marée et al., Bioinformatics 2016) paper, and to use our logo when appropriate. See our license files for additional details.URL:http://www.cytomine.orgLogo:http://www.cytomine.org/sites/default/files/inline-images/logo-300-org.pngScientific paper: Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, Gilles Louppe, Rémy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts and Louis Wehenkel. Collaborative analysis of multi-gigapixel imaging data using Cytomine, Bioinformatics, DOI: 10.1093/bioinformatics/btw013, 2016.https://doi.org/10.1093/bioinformatics/btw013Cytomine (http://doc.cytomine.org) client and utilities in Python.Cytomine client contains functions to import/export data (projects, ontologies, images, users, annotations, softwares, ... ) from/to Cytomine core server based on the RESTful API.Cytomine utilities contains various functions including ones to access whole slide images (tiles, ...) and basic image processing tools.See examples inhttps://github.com/cytomine/Cytomine-python-client/tree/master/client/examplesandhttps://github.com/cytomine/Cytomine-python-client/tree/master/utilities/examplesThe client is automatically installed with the Docker/Bootstrap procedure, however it is possible to install it independently on remote computers. See installation instructions here :https://doc.cytomine.org/Python-client
Package: cytominer-eval
Cytominer-eval: Evaluating quality of perturbation profilesCytominer-eval contains functions to calculate quality metrics for perturbation profiling experiments.InstallationCytominer-eval is still in beta, and can only be installed from GitHub:pipinstallgit+git://github.com/cytomining/cytominer-evalSince the project is actively being developed, with new features added regularly, we recommend installation using a hash:# Example:pipinstallgit+git://github.com/cytomining/cytominer-eval@5c9fb860d1b27e746ee498d625d033475caceb7eUsageCytominer-eval uses a simple API for all evaluation metrics.# Working exampleimportpandasaspdfromcytominer_evalimportevaluate# Load Datacommit="6f9d350badd0a18b6c1a76171813aaf9a52f8d9f"url=f"https://github.com/cytomining/cytominer-eval/raw/{commit}/cytominer_eval/example_data/compound/SQ00015054_normalized_feature_select.csv.gz"df=pd.read_csv(url)# Define important function argumentsmeta_features=df.columns[df.columns.str.startswith("Metadata_")]features=df.drop(meta_features,axis="columns").columns.tolist()replicate_groups=["Metadata_broad_sample","Metadata_mg_per_ml"]# Evaluate profile qualityevaluate(profiles=df,features=features,meta_features=meta_features,replicate_groups=replicate_groups,replicate_reproducibility_return_median_cor=False,operation="replicate_reproducibility",)MetricsCurrently, four metric operations are supported:Replicate reproducibilityPrecision/recallmp-valueGrit
Package: cytonic
CytonicA Python framework to define REST APIs and automatically generate server side bindings and clients.FeaturesGenerate Python language bindings from YAML API definitionsUse the same bindings for clients and to implement the server sideQuickstartCheck out theexamples/todolistfolder.Why the name "Cytonic"?The name is inspired by the great Skyward book series by Brandon Sanderson, where "cytonics" are people
or other organisms that can enter the space of the delvers to teleport to any location in the galaxy.
Package: cytools
A software package for analyzing Calabi-Yau manifoldsCYTools is an open-source software package developed byLiam McAllister's groupwith the purpose of studying Calabi-Yau manifolds arising from the Kreuzer-Skarke database. The founding authors areMehmet DemirtasandAndres Rios-Tascon. It emerged from several years of effort towards exploring previously uncharted parts of the string landscape. It offers vastly superior computational performance compared to other software that are typically used in the field. Installation instructions and detailed documentation can be found in theCYTools website.Most of the code is written in Python, with wrappers to interface with various other open-source software. The full package comes in the form of a Docker image that includes all the necessary dependencies, so that it is easy to install and use on most operating systems.CYTools makes use of a variety of open-source projects. It includes a few code snippets fromSageMath[GPLv2], a modified version ofTOPCOM[GPLv2] that can be foundhere, theComputational Geometry Algorithms Library[LGPLv3], and multiple Python packages includingSciPy,NumPy,pplpy,OR-Tools,scikit-sparse, andflint-py.All original CYTools code is distributed under the terms of theGNU General Public License version 3. All other packages and code snippets are redistributed under their respective licenses.Questions, comments and/or suggestions can be directed tosupport@cy.tools.