Results for “pastebin”

Create publication-quality statistical graphics in Python with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and multi-panel figures.

Create publication-quality static, animated, and interactive plots with fine-grained control over every element, from basic charts to multi-panel figures, with export to PNG, PDF, and SVG.

Create publication-quality statistical graphics from tabular datasets with minimal code, supporting multivariate analysis, statistical estimation, and complex multi-panel figures.

Create publication-quality statistical graphics in Python with dataset-oriented plotting, semantic mapping, and automatic statistical estimation.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

Create publication-quality statistical graphics using Seaborn, with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures.

Create publication-quality statistical graphics directly from tabular datasets, covering relational, distribution, categorical, regression, and matrix plots with minimal code.

Create publication-quality statistical graphics in Python with dataset-oriented plotting, semantic mapping, and built-in statistical estimation.