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name: visualization-convolution description: "Use Astropy visualization and convolution for image normalization, stretches, WCSAxes, RGB rendering, FITS bitmap export, kernels, direct convolution, and FFT convolution." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Visualization and Convolution Router

Use this sub-skill when a task centers on displaying astronomy images or convolving array data with Astropy kernels.

Load This When

• The user needs image scaling, ImageNormalize, intervals, stretches, ZScaleInterval, PercentileInterval, AsinhStretch, LogStretch, or Matplotlib normalization.
• The task mentions WCSAxes plotting, world-coordinate axes, overplotting coordinates, or publication-style FITS image display.
• The task needs RGB composites, make_lupton_rgb, or fits2bitmap.
• The task uses convolution kernels, Gaussian2DKernel, convolve, convolve_fft, NaN interpolation, boundary modes, or kernel normalization.

Route Away When

• Constructing or validating the WCS object is the main challenge; use ../wcs-nddata/SKILL.md.
• FITS/table reading and writing is the main task; use ../tables-io/SKILL.md.
• Statistical clipping or model fitting is central; use ../modeling-stats-timeseries/SKILL.md.
• Optional dependency installation or CLI availability is the main issue; use ../cli-config-data/SKILL.md.

First Actions

1. Identify the input: NumPy array, FITS image, WCS object, RGB channels, masked/NaN data, or kernel.
2. For display, separate data extraction, normalization, plotting, and file output.
3. Choose interval and stretch based on data distribution; avoid hard-coded min/max until inspecting the image.
4. For WCS plotting, create/validate WCS first, then pass it as Matplotlib projection.
5. For convolution, choose direct convolve for small kernels or convolve_fft for larger FFT-friendly kernels.
6. Decide boundary, fill value, NaN treatment, and kernel normalization.
7. Validate output shape, finite values, and whether NaNs should remain masked or interpolated.

References

• references/api-reference.md lists verified visualization and convolution API signatures.
• references/workflows.md covers normalization, WCSAxes plotting, RGB, CLI bitmap export, and convolution recipes.
• references/troubleshooting.md covers NaNs, zero-sum kernels, optional Matplotlib/SciPy issues, WCSAxes pitfalls, and unsafe file overwrites.

Safety and Validation

• Use temporary output paths for generated PNG/JPEG/FITS experiments.
• Do not overwrite user image outputs without explicit permission.
• Preserve units and WCS decisions from sibling routes; this sub-skill owns display/convolution choices, not file semantics.
• For convolution, assert shape and inspect edge behavior before using output in downstream science.

Native-Backed Validation Ideas

• Normalize a small image with ImageNormalize(..., interval=ZScaleInterval()) and assert finite output.
• Convolve a tiny array with Gaussian2DKernel and assert output shape.
• Run fits2bitmap --help or use a temporary FITS fixture for a bounded CLI smoke.