Metadata-Version: 1.1
Name: ModestImage
Version: 0.1
Summary: Friendlier matplotlib interaction with large images
Home-page: UNKNOWN
Author: Chris Beaumont
Author-email: cbeaumont@cfa.harvard.edu
License: MIT
Download-URL: http://github.com/ChrisBeaumont/mpl-modest-image
Description: ModestImage
        ===========
        
        *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
        -  Large ``numpy.memmap`` arrays can be visualized, without making an
           in-memory copy of the entire array. This enables visualization of
           images too large to fit in memory.
        
        Using ModestImage
        -----------------
        
        The easiest way is to use the modified ``imshow`` function:
        
        ::
        
            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 ``vmin`` and ``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)[0].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 (e.g.
        after ``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)
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
