'python' 검색 결과

Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Key Vault SDK for Python. Use for secrets, keys, and certificates management with secure storage.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Event Grid SDK for Python. Use for publishing events, handling CloudEvents, and event-driven architectures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Identity SDK for Python authentication. Use for DefaultAzureCredential, managed identity, service principals, and token caching.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Web PubSub Service SDK for Python. Use for real-time messaging, WebSocket connections, and pub/sub patterns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure API Management SDK for Python. Use for managing APIM services, APIs, products, subscriptions, and policies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.