"""
Copyright (c) 2010-2018 CNRS / Centre de Recherche Astrophysique de Lyon
Copyright (c) 2019 Simon Conseil <simon.conseil@univ-lyon1.fr>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import astropy.units as u
import logging
import numpy as np
__all__ = ('FormatCoord', 'get_plot_norm', 'plot_rgb')
[docs]def get_plot_norm(data, vmin=None, vmax=None, zscale=False, scale='linear'):
from astropy import visualization as viz
from astropy.visualization.mpl_normalize import ImageNormalize
# Choose vmin and vmax automatically?
if zscale:
interval = viz.ZScaleInterval()
if data.dtype == np.float64:
try:
vmin, vmax = interval.get_limits(data.filled(np.nan))
except Exception:
# catch failure on all NaN
if np.all(np.isnan(data.filled(np.nan))):
vmin, vmax = (np.nan, np.nan)
else:
raise
else:
vmin, vmax = interval.get_limits(data.filled(0))
# How are values between vmin and vmax mapped to corresponding
# positions along the colorbar?
if scale == 'linear':
stretch = viz.LinearStretch
elif scale == 'log':
stretch = viz.LogStretch
elif scale in ('asinh', 'arcsinh'):
stretch = viz.AsinhStretch
elif scale == 'sqrt':
stretch = viz.SqrtStretch
else:
raise ValueError('Unknown scale: {}'.format(scale))
# Create an object that will be used to map pixel values
# in the range vmin..vmax to normalized colormap indexes.
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch(), clip=False)
return norm
[docs]def plot_rgb(images, title=None, scale='linear', vmin=None, vmax=None,
zscale=False, show_xlabel=False, show_ylabel=False, ax=None,
unit=u.deg, use_wcs=False, **kwargs):
"""Plot the RGB composite image with axes labeled in pixels.
For each color, final intensity values are assigned to each pixel as
follows. First each pixel value, ``pv``, is normalized over the range
``vmin`` to ``vmax``, to have a value ``nv``, that goes from 0 to 1, as
follows::
nv = (pv - vmin) / (vmax - vmin)
This value is then mapped to another number between 0 and 1 which
determines the final value to give the displayed pixel. The mapping from
normalized values to final value can be chosen using the scale argument,
from the following options:
- 'linear': ``color = nv``
- 'log': ``color = log(1000 * nv + 1) / log(1000 + 1)``
- 'sqrt': ``color = sqrt(nv)``
- 'arcsinh': ``color = arcsinh(10*nv) / arcsinh(10.0)``
By default the image is displayed in its own plot. Alternatively
to make it a subplot of a larger figure, a suitable
``matplotlib.axes.Axes`` object can be passed via the ``ax`` argument.
Note that unless matplotlib interative mode has previously been enabled
by calling ``matplotlib.pyplot.ion()``, the plot window will not appear
until the next time that ``matplotlib.pyplot.show()`` is called. So to
arrange that a new window appears as soon as ``plot_rgb`` is
called, do the following before the first call to ``plot_rgb``::
import matplotlib.pyplot as plt
plt.ion()
Parameters
----------
images : [`~mpdaf.obj.Image`, `~mpdaf.obj.Image`, `~mpdaf.obj.Image`]
The three [blue, green, red] images to be used. i.e. ordered by
increasing wavelength.
title : str
An optional title for the figure (None by default).
scale : 'linear' | 'log' | 'sqrt' | 'arcsinh'
The stretch function to use mapping pixel values to
final values (The default is 'linear'). The same scaling is applied to
all three imasges. The pixel values are
first normalized to range from 0 for values <= vmin,
to 1 for values >= vmax, then the stretch algorithm maps
these normalized values, nv, to a position p from 0 to 1
along the colorbar, as follows:
linear: p = nv
log: p = log(1000 * nv + 1) / log(1000 + 1)
sqrt: p = sqrt(nv)
arcsinh: p = arcsinh(10*nv) / arcsinh(10.0)
vmin : [float, float, float]
Lower limits corresponding to the [blue, green, red] images.
Pixels that have values <= vmin are assigned a value of 0.
Pixel values between vmin and vmax are scaled according
to the mapping algorithm specified by the scale argument.
vmax : [float, float, float]
Upper limits corresponding to the [blue, green, red] images.
Pixels that have values >= vmax are assigned a value of 1.
Pixel values between vmin and vmax are scaled according
to the mapping algorithm specified by the scale argument.
zscale : bool
If True, vmin and vmax are automatically computed
using the IRAF zscale algorithm.
ax : matplotlib.axes.Axes
An optional Axes instance in which to draw the image,
or None to have one created using ``matplotlib.pyplot.gca()``.
unit : `astropy.units.Unit`
The units to use for displaying world coordinates
(degrees by default). In the interactive plot, when
the mouse pointer is over a pixel in the image the
coordinates of the pixel are shown using these units,
along with the pixel value.
use_wcs : bool
If True, use `astropy.visualization.wcsaxes` to get axes
with world coordinates.
kwargs : matplotlib.artist.Artist
Optional extra keyword/value arguments to be passed to
the ``ax.imshow()`` function.
Returns
-------
ax : matplotlib AxesImage
images_aligned : `~mpdaf.obj.Image`, `~mpdaf.obj.Image`, `~mpdaf.obj.Image`
The input images, but all aligned to that with the highest resolution.
"""
if vmin is None:
vmin = [None, None, None]
if vmax is None:
vmax = [None, None, None]
# Default X and Y axes are labeled in pixels.
xlabel = 'q (pixel)'
ylabel = 'p (pixel)'
if ax is None:
import matplotlib.pyplot as plt
if use_wcs:
ax = plt.subplot(projection=images[0].wcs.wcs)
xlabel = 'ra'
ylabel = 'dec'
else:
ax = plt.gca()
elif use_wcs:
logging.getLogger(__name__).warning(
'use_wcs does not work when giving also an axis (ax)')
# find which image has the highest pixel resolution
# also find bbox that encloses all 3 images
steps = np.full([3, 2], np.nan, dtype=float)
corners = np.full([3, 4, 2], np.nan, dtype=float)
for i_im, im in enumerate(images):
wcs = im.wcs
step = wcs.get_axis_increments(unit=u.deg)
corn = wcs.wcs.calc_footprint(axes=[wcs.naxis1, wcs.naxis2])
steps[i_im] = step
corners[i_im] = corn
idx_best_res = np.argmin(np.mean(np.abs(steps), 1))
im_best_res = images[idx_best_res] # image with highest res
# get bounding pixel coords in best image
corners = np.vstack(corners)
corners = im_best_res.wcs.wcs.all_world2pix(corners, 0)
new_shape = np.array([[np.min(corners[:, 0]), np.max(corners[:, 0])],
[np.min(corners[:, 1]), np.max(corners[:, 1])]])
new_shape = np.around(new_shape).astype(int)
new_dim = new_shape[:, 1] - new_shape[:, 0] + 1
new_start = new_shape[:, 0].reshape(1, 2)
new_start = im_best_res.wcs.wcs.all_pix2world(new_start, 0)[0]
new_dim = new_dim[::-1] # naxis2, naxis1
new_start = new_start[::-1] # dec, ra
old_inc = im_best_res.get_axis_increments(unit=u.deg)
# expand the reference image so that it now covers the footprints of the
# other 2 images
im_best_res = im_best_res.resample(new_dim, new_start, old_inc,
unit_step=u.deg)
# create BGR stack
data_stack = np.full(im_best_res.shape + (3,), np.nan, dtype=float)
data_stack = np.ma.array(data_stack)
images_aligned = []
for i, im in enumerate(images):
# align all images to image with best res
im = im.align_with_image(im_best_res)
images_aligned.append(im)
data = im.data
norm = get_plot_norm(data, vmin=vmin[i], vmax=vmax[i], zscale=zscale,
scale=scale)
data = norm(data)
data_stack[:, :, i] = data
data_stack = np.ma.clip(data_stack, 0, 1)
data_stack = data_stack.filled(np.nan)
# reverse BGR to RGB order
data_stack = data_stack[:, :, ::-1]
# mask all NaNs and plot transparent
mask = np.all(np.isnan(data_stack), axis=2)
alpha = ~mask * 1. # no transparency where data is good
data_stack = np.concatenate([data_stack, alpha[..., np.newaxis]], axis=2)
# Display the RGBA image.
ax.imshow(data_stack, interpolation='nearest', origin='lower', **kwargs)
# Keep the axis to allow other functions to overplot
# the image with contours etc.
for im in images_aligned:
im._ax = ax
# Label the axes if requested.
if show_xlabel:
ax.set_xlabel(xlabel)
if show_ylabel:
ax.set_ylabel(ylabel)
if title is not None:
ax.set_title(title)
# Change the way that plt.show() displays coordinates when the pointer
# is over the image, such that world coordinates are displayed with the
# specified unit, and pixel values are displayed with their native
# units.
ax.format_coord = FormatCoord(images_aligned[0], data_stack)
for im in images_aligned:
im._unit = unit
return ax, images_aligned