2-D Visualization: Gridded Images [ I = f(x,y) ]

[ericdill]

Description

2-D data, in this context, refers to data with 2 independent variables and a single dependent variables. For an example, consider a 2-D detector which has two orthogonal spatial directions (call them x and y) and 1 dependent variable (intensity, fluorescence energy, etc...). The dependent variable is usually shown with false coloring using tools like mpl.imshow(), mpl.contour(), mpl.contourf(), etc... There are many different scientific use cases for the display of 2-D data. Some involve viewing raw experimental data as it comes off of the detector, some are results of analysis pipelines where a raw 2-D image has been modified in some way (e.g., corrected for experimental issues like dark current, background, point distortions, etc...), or a 2-D image has been constructed from a series of measurements.

Visualization Requirements

• View 2-time correlation
• Look at stacks of 2-D images from different reductions of spectrum
• View raw data from 2-D detector
• View slices through reciprocal space reconstruction
• Browse 2-D image stack
• Represent data as contour plot
• IXS: Plot summed spectrum minus elastic peak as 2-theta is scanned

[tacaswell]

matplotlib/matplotlib#3291 (comment) (scroll down for pictures) In-process PR at mpl.

Hill shading is pretty cool.