Friendlier matplotlib interaction with large images
ModestImage extends the matplotlib AxesImage class, and avoids unnecessary calculation and memory when rendering large images (where most image pixels aren't visible on the screen). It has the following benefits over AxesImage:
- Draw time is (roughly) independent of image size
numpy.memmaparrays can be visualized, without making an in-memory copy of the entire array. This enables visualization of images too large to fit in memory.
pip install ModestImage
The easiest way is to use the modified
import matplotlib.pyplot as plt from modest_image import ModestImage, imshow ax = plt.gca() imshow(ax, image_array, vmin=0, vmax=10) plt.show()
imshow accepts all the keyword arguments that the matplotlib
function does. The
vmax keywords aren't necessary
but, if they are not provided, the entire image will be scanned to
determine the min/max values. This can be slow if the array is huge.
To create a ModestImage artist directly:
artist = ModestImage(data=array)
Looking at very big FITS images
import matplotlib.pyplot as plt import pyfits from modest_image import imshow ax = plt.gca() huge_array = pyfits.open('file_name.fits', memmap=True).data artist = imshow(ax, huge_array, vmin=0, vmax=10) plt.show()
This opens almost instantly, with a modest memory footprint.
Why is Matplotlib Image Drawing Slow?
For the first draw request after setting the color mapping or data array, AxesImage (the default matplotlib image class) calculates the RGBA value for every pixel in the data array. That's a lot of work for large images, and usually overkill given that the final rendering is limited by screen resolution (usually 100K-1M pixels) and not image resolution (often much more).
AxesImage compensates for this by saving the results of this scaling. This means that subsequent renderings that only change the position or zoom level are very fast. However, in interactive situations where the data array or intensity scale change often, AxesImage wastes lots of time calculating RGBA values for every pixel in a (potentially large) data set. It also makes several temporary arrays with size comparable to the original array, wasting memory.
How is ModestImage faster?
ModestImage resamples the image array at each draw request, extracting a smaller image whose resolution and extent are matched to the screen resolution. Thus, the RGBA scaling step is much faster, since it takes place only for pixels relevant for the current rendering.
This scheme does not take advantage of AxesImage's caching, and thus redraws after move and zoom operations are slightly slower. However, draws after colormap and data changes are substantially faster, and most redraws are fast enough for interactive use.
Performance and Tests
speed_test.py compares the peformance of ModestImage and
AxesImage. For a 1000x1000 pixel image:
Performace Tests for AxesImage time_draw: 186 ms per operation time_move: 19 ms per operation time_move_zoom: 28 ms per operation Performace Tests for ModestImage time_draw: 25 ms per operation time_move: 20 ms per operation time_move_zoom: 28 ms per operation
time_draw is the render time after the cache has been cleared
set_data has been called, or the colormap has been
changed). ModestImage is slightly slower than, though still
competetive with, AxesImage for move and zoom operations where
AxesImage uses cached data.
Unit tests can be found in the
tests directory. ModestImage does not
always produce results identical to AxesImage at the pixel level, due to
how it downsamples images. The discrepancy is minor, however, and disappears
if no downsampling takes place (i.e. a screen pixel samples <= 1 data pixel)