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Project Description

This file implements a matplotlib Normalize object which mimics the
functionality of image scaling functions in
`ds9 <>`__. It provides 5 ways of
configuring image scaling:

- Clipping the low and high intensities at specified percentiles
(``clip_lo``, ``clip_hi``)
- Specifying an intensity transfer function for ramping from black to
white (``stretch``)
- Setting the mid-point of this transfer function (``bias``, akin to
dragging the right mouse button left/right in ds9)
- Setting how sharp the transition from black-white is (``contrast``,
akin to dragging the right mouse button up/down in ds9)


See the `example
notebook <>`__

.. code:: python

data = fits.getdata('M51.fits')
norm = DS9Normalize(bias=0.2)
figure, axs = plt.subplots(ncols=3, nrows=3, squeeze=False, tight_layout=True)

for ax, bias in zip(axs[2], [.2, .5, .8]):
ax.imshow(data, norm=DS9Normalize(bias=bias))
ax.set_title('Bias = %0.1f' % bias)

for ax, contrast in zip(axs[1], [0.5, 1, 2]):
ax.imshow(data, norm=DS9Normalize(contrast=contrast))
ax.set_title('Contrast = %0.1f' % contrast)

for ax, stretch in zip(axs[0], ['linear', 'sqrt', 'arcsinh']):
ax.imshow(data, norm=DS9Normalize(stretch=stretch))

.. figure:: gallery.png
:alt: ds9norm demo

ds9norm demo
Build Status

[|Build Status|\ ]
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.. |Build Status| image::
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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
ds9norm-0.1.tar.gz (4.9 kB) Copy SHA256 Checksum SHA256 Source Aug 7, 2014

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