Matplotlib figure customization

matplotlib-figure-customization

Summary

Customize and configure matplotlib figures for mass spectrometry data visualization by adjusting plot appearance (colors, labels, legends), axis ranges, and output format. This skill ensures publication-ready spectrum plots with proper annotation and interactive or file-based display.

When to use

When rendering spectrum data (m/z vs. intensity arrays) from MZA files and need to control visual presentation: applying m/z range windows, setting line colors and labels for legend identification, sizing the figure, or choosing between interactive display versus file export.

When NOT to use

• Input arrays are not aligned (different lengths or incompatible data types)—verify array shapes and dtypes before calling.
• m/z range window is invalid (e.g., mz_min >= mz_max or outside data bounds)—validate range before passing.
• Figure already fully rendered and closed—the Axes object must remain open to accept customization.

Inputs

• m/z array (1D numpy float array)
• intensity array (1D numpy float array, same length as m/z array)
• optional m/z range tuple (mz_min, mz_max) for windowing
• optional matplotlib axis object (ax) for reuse
• optional figure size tuple (figsize)
• optional color string (e.g., 'blue', '#FF5733')
• optional label string for legend
• optional figname string ('show' or file path)

Outputs

• matplotlib Axes object (customized and ready for display or further modification)
• interactive plot window (if figname='show')
• saved image file in PNG/PDF/other format (if figname contains valid path)

How to apply

After initializing a matplotlib figure and axis (via mzapy's _setup_and_save_or_show_plot decorator or by accepting an existing ax), apply customization in the following order: (1) Plot intensity data as a line or stem plot against m/z values using matplotlib.pyplot, respecting the m/z range window by slicing or filtering input arrays; (2) Set axis labels, title, and other textual properties; (3) Apply color and line style to match the provided color parameter; (4) Conditionally add a legend if a label string is provided; (5) Return the ax object for further downstream customization, or trigger display by passing figname='show' for interactive viewing, or pass figname with a file path to save via plt.savefig(). The choice between line and stem plot depends on spectral resolution and density—stem plots clarify individual peaks in sparse spectra.

Related tools

• matplotlib (Core library for creating and customizing static and interactive 2D plots of m/z vs. intensity spectra) — https://matplotlib.org/
• numpy (Provides efficient array slicing, filtering, and masking for windowing m/z and intensity data before plotting) — https://numpy.org/
• mzapy (Loads MZA-format MS data files and provides integration with matplotlib via _setup_and_save_or_show_plot decorator and plot_spectrum function) — https://github.com/PNNL-m-q/mzapy

Examples

import numpy as np
import matplotlib.pyplot as plt
from mzapy.view import plot_spectrum
mz_data = np.array([100.0, 101.5, 103.2])
intensity_data = np.array([500, 1200, 300])
ax = plot_spectrum(mz_data, intensity_data, mz_range=(100, 104), color='blue', label='MS1 Scan 630', figsize=(10, 6))
plt.show()

Evaluation signals

• Plotted data visually spans the specified m/z range window without truncation artifacts or data loss outside bounds.
• Axis labels, title, and legend (if provided) are correctly formatted, readable, and positioned.
• Color and line style are applied as specified; stem vs. line plot choice matches spectral density (sparse → stem, dense → line).
• Output file (if figname is a path) is created with correct format, non-zero file size, and readable by standard image viewers.
• Interactive display (figname='show') opens a responsive window; returned ax object can be queried for limits, labels, and data (e.g., ax.get_xlim(), ax.get_lines())

Limitations

• Large spectra (>100k points) may render slowly or consume significant memory; consider downsampling or using stem plot for clarity.
• m/z and intensity arrays must be 1D and same length; no automatic broadcasting or resampling is performed.
• Figure resolution and DPI depend on matplotlib backend and figsize; output quality may vary across platforms and display contexts.
• Legend placement is automatic; overlapping labels or crowded legends may obscure plot data—manual positioning via ax.legend(loc=...) may be required.

Evidence

• [other] Plot intensity as a line or stem plot against m/z values using matplotlib.pyplot, respecting the specified m/z range window: "Plot intensity as a line or stem plot against m/z values using matplotlib.pyplot, respecting the specified m/z range window."
• [other] Accept input arrays (m/z values, intensity values) and optional parameters (m/z range, color, label, figsize): "Accept input arrays (m/z values, intensity values) and optional parameters (m/z range, color, label, figsize)."
• [other] Customize plot appearance (color, label, axis labels) and conditionally add legend if label is provided: "Customize plot appearance (color, label, axis labels) and conditionally add legend if label is provided."
• [other] Return ax for further customization, display interactively if figname='show', or save to file if figname contains a path using plt.savefig: "Return ax for further customization, display interactively if figname='show', or save to file if figname contains a path using plt.savefig."
• [other] mzapy provides a Python interface to unprocessed MS data in the MZA format, with matplotlib as a dependency for visualization operations: "mzapy provides a Python interface to unprocessed MS data in the MZA format, with matplotlib as a dependency for visualization operations."