Metadata-Version: 1.1
Name: ModestImage
Version: 0.2
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.
        
        ## Installation
        
        ```
        pip install ModestImage
        ```
        or
        ```
        easy_install ModestImage
        ```
        
        ## 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
