abstract-base-class-implementation

name	abstract-base-class-implementation
description	Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
license	CC-BY-4.0
metadata	{"edam_operation":"http://edamontology.org/operation_3441","edam_topics":["http://edamontology.org/topic_3520","http://edamontology.org/topic_0091"],"tools":["Python","plotly","pyOpenMS-viz","Pandas","matplotlib","bokeh"],"techniques":["mass-spectrometry"],"license_tier":"open"}
derived_from	[{"doi":"10.1021/acs.jproteome.4c00873","title":"pyopenmsviz"}]
evidence_spans	["ms_data.plot(x=\"m/z\", y=\"intensity\", kind=\"spectrum\")","conda create --name=pyopenms-viz python=3.12","Multiple backends supported including matplotlib, bokeh, and plotly","Rendering is typically slower than the BOKEH backend","Chromatograms can be plotted using kind = chromatogram","pyOpenMS-Viz is a visualization package for mass spectrometry data directly from pandas dataframes"]
claims	[]
provenance	{"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_pyopenmsviz_cq","doi":"10.1021/acs.jproteome.4c00873","title":"pyopenmsviz"}],"dedup_kept_from":"coll_pyopenmsviz_cq"}
schema_version	0.2.0
attribution	{"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.jproteome.4c00873","all_source_dois":["10.1021/acs.jproteome.4c00873"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"}

abstract-base-class-implementation

Summary

Design and implement a multi-level abstract base class (ABC) hierarchy that separates domain-agnostic plotting logic from domain-specific (mass-spectrometry) concerns, and that delegates to backend-specific concrete implementations via a kind-dispatch mechanism. This pattern enables consistent APIs across multiple plotting backends while avoiding code duplication and supporting extension.

When to use

When building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations. Use this skill when you need to route a single user-facing API call (e.g., kind='spectrum') to the appropriate concrete plot class based on both plot kind and backend selection.

When NOT to use

• Input data is not tabular or does not fit the Pandas DataFrame schema expected by the library.
• You need to support plot kinds not explicitly recognized by the domain-specific ABC (e.g., a novel MS data type not covered by chromatogram, mobilogram, spectrum, or peakmap); extend the ABC hierarchy rather than bypass it.
• Your library has only one plotting backend; the abstraction overhead outweighs the benefit of code reuse.

Inputs

• Pandas DataFrame with mass-spectrometry data (columns for x-axis, y-axis, optional z-axis)
• kind parameter specifying plot type (string: 'spectrum', 'chromatogram', 'mobilogram', 'peakmap')
• backend parameter or default specifying which plotting library to use (string: 'matplotlib', 'bokeh', 'plotly')
• Configuration object (e.g., SpectrumConfig, ChromatogramConfig) with plot-specific parameters

Outputs

• Plot object (backend-specific: matplotlib Figure/Axes, bokeh Figure, plotly Figure)
• Rendered visualization of mass-spectrometry data suitable for static (matplotlib) or interactive (bokeh, plotly) display

How to apply

Establish a hierarchy: (1) define a domain-agnostic BasePlot ABC with shared configuration, initialization, and the abstract generate() method; (2) create an intermediate domain-specific ABC (e.g., BaseMSPlot inheriting from BasePlot) that adds mass-spectrometry-specific validation and methods for recognized plot kinds (chromatogram, mobilogram, spectrum, peakmap); (3) implement concrete plot subclasses (e.g., ChromatogramPlot, SpectrumPlot) inheriting from the domain-specific ABC, each overriding generate() with logic for axis labeling and data validation; (4) implement backend-specific abstract bases (e.g., BOKEHPlot, PLOTLYPlot) that override generate() to call backend-specific rendering; (5) implement backend–plot-type pairs as concrete classes (e.g., BOKEHSpectrumPlot, PLOTLYSpectrumPlot) inheriting from both the plot-kind class and the backend abstract class; (6) implement a kind-dispatch mechanism in a pandas plotting backend accessor that inspects the kind parameter and backend name, instantiates the appropriate concrete class, and calls its generate() method. This approach localizes backend-specific logic, centralizes MS-specific validation, and allows adding new backends or plot kinds with minimal changes to existing code.

Related tools

• Pandas (Provides DataFrame structure for storing and manipulating mass-spectrometry tabular data; supports custom accessor registration for the plot() method.)
• matplotlib (Backend for static 2D and 3D plot rendering; concrete implementations inherit from BOKEHPlot/PLOTLIBPlot abstract classes and override generate() to call matplotlib-specific rendering.)
• bokeh (Backend for interactive 2D plot rendering; concrete implementations inherit from BOKEHPlot abstract class and call bokeh-specific Figure and glyph methods.)
• plotly (Backend for interactive 2D and 3D plot rendering; concrete implementations inherit from PLOTLYPlot abstract class and call plotly-specific go.Figure methods.)
• pyOpenMS-viz (Reference implementation library demonstrating the full ABC hierarchy, backend dispatch, and pandas accessor registration.) — https://github.com/OpenMS/pyopenms_viz

Examples

ms_data.plot(x="m/z", y="intensity", kind="spectrum", backend="bokeh")

Evaluation signals

• Each concrete plot subclass successfully overrides generate() without raising NotImplementedError, and produces output matching its backend (matplotlib Figure, bokeh Figure, or plotly Figure object).
• Kind-dispatch mechanism routes 'spectrum' + 'matplotlib' to BOKEHSpectrumPlot (or equivalent), 'chromatogram' + 'bokeh' to BOKEHChromatogramPlot, etc., with no manual if–elif chains in user-facing code.
• BaseMSPlot validation catches unsupported plot kinds (e.g., 'unknown_plot') before instantiation, raising a clear error message.
• Adding a new plot kind (e.g., 'ion_mobility_plot') requires changes only to the domain-specific ABC and concrete subclasses; backend-specific code remains unchanged.
• Pandas accessor registration allows invocation via df.plot(x='m/z', y='intensity', kind='spectrum', backend='bokeh') without explicit class instantiation by the user.

Limitations

• Backend-specific rendering differences (e.g., 3D support available in matplotlib and plotly but not bokeh) require conditional logic in the kind-dispatch mechanism or separate concrete classes per backend, increasing maintenance burden.
• Multiple inheritance (plot-kind class + backend abstract class) can lead to method resolution order (MRO) ambiguities if not carefully managed; Python's C3 linearization helps but requires discipline.
• Adding a new backend requires implementing a new abstract base class and concrete implementations for each supported plot kind, which is labor-intensive for libraries with many plot types.
• Configuration objects (SpectrumConfig, etc.) must be kept in sync with backend-specific parameters; inconsistencies can lead to silent failures or unexpected plot output.

Evidence

• [other] Define BasePlot as an abstract base class with common configuration and initialization logic: "Define BasePlot as an abstract base class with common configuration and initialization logic inherited by all plot types."
• [other] BaseMSPlot intermediate abstract class for mass-spectrometry-specific methods: "Define BaseMSPlot as an intermediate abstract class inheriting from BasePlot to add mass-spectrometry-specific methods and validation for chromatogram, mobilogram, spectrum, and peakmap plot kinds."
• [other] Backend-specific plot classes override generate() to call backend-specific rendering methods: "Implement backend-specific plot classes (BOKEHPlot, PLOTLYPlot, MATPLOTLIBPlot as abstract bases; BOKEHLinePlot, BOKEHSpectrumPlot, etc. as concrete implementations) that override generate() to call"
• [other] Kind-dispatch mechanism routes plot kind to appropriate concrete class based on backend: "Implement a kind-dispatch mechanism in the pandas plotting backend accessor that routes kind='spectrum', 'chromatogram', 'mobilogram', 'peakmap' to the appropriate concrete plot class constructor"
• [readme] pyOpenMS-Viz integrates with multiple plotting library backends: "integrates seamlessly with various plotting library backends (matpotlib, bokeh and plotly) and leverages the power of Pandas for data manipulation and representation"