python3-aplpy 1.1.1-1 / usr / lib / python3 / dist-packages / aplpy / rgb.py

from __future__ import absolute_import, print_function, division

from distutils import version
import os
import warnings

import tempfile
import shutil

import numpy as np
from astropy.extern import six
from astropy import log
from astropy.io import fits

from . import image_util
from . import math_util


def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
                  stretch='linear', vmid=None, exponent=2):

    min_auto = not math_util.isnumeric(vmin)
    max_auto = not math_util.isnumeric(vmax)

    if min_auto or max_auto:
        auto_v = image_util.percentile_function(image)
        vmin_auto, vmax_auto = auto_v(pmin), auto_v(pmax)

    if min_auto:
        log.info("vmin = %10.3e (auto)" % vmin_auto)
        vmin = vmin_auto
    else:
        log.info("vmin = %10.3e" % vmin)

    if max_auto:
        log.info("vmax = %10.3e (auto)" % vmax_auto)
        vmax = vmax_auto
    else:
        log.info("vmax = %10.3e" % vmax)

    image = (image - vmin) / (vmax - vmin)

    data = image_util.stretch(image, stretch, exponent=exponent, midpoint=vmid)

    data = np.nan_to_num(data)
    data = np.clip(data * 255., 0., 255.)

    return data.astype(np.uint8)


def make_rgb_image(data, output, indices=(0, 1, 2), \
                   vmin_r=None, vmax_r=None, pmin_r=0.25, pmax_r=99.75, \
                   stretch_r='linear', vmid_r=None, exponent_r=2, \
                   vmin_g=None, vmax_g=None, pmin_g=0.25, pmax_g=99.75, \
                   stretch_g='linear', vmid_g=None, exponent_g=2, \
                   vmin_b=None, vmax_b=None, pmin_b=0.25, pmax_b=99.75, \
                   stretch_b='linear', vmid_b=None, exponent_b=2, \
                   make_nans_transparent=False, \
                   embed_avm_tags=True):
    '''
    Make an RGB image from a FITS RGB cube or from three FITS files.

    Parameters
    ----------

    data : str or tuple or list
        If a string, this is the filename of an RGB FITS cube. If a tuple
        or list, this should give the filename of three files to use for
        the red, green, and blue channel.

    output : str
        The output filename. The image type (e.g. PNG, JPEG, TIFF, ...)
        will be determined from the extension. Any image type supported by
        the Python Imaging Library can be used.

    indices : tuple, optional
        If data is the filename of a FITS cube, these indices are the
        positions in the third dimension to use for red, green, and
        blue respectively. The default is to use the first three
        indices.

    vmin_r, vmin_g, vmin_b : float, optional
        Minimum pixel value to use for the red, green, and blue channels.
        If set to None for a given channel, the minimum pixel value for
        that channel is determined using the corresponding pmin_x argument
        (default).

    vmax_r, vmax_g, vmax_b : float, optional
        Maximum pixel value to use for the red, green, and blue channels.
        If set to None for a given channel, the maximum pixel value for
        that channel is determined using the corresponding pmax_x argument
        (default).

    pmin_r, pmin_r, pmin_g : float, optional
        Percentile values used to determine for a given channel the
        minimum pixel value to use for that channel if the corresponding
        vmin_x is set to None. The default is 0.25% for all channels.

    pmax_r, pmax_g, pmax_b : float, optional
        Percentile values used to determine for a given channel the
        maximum pixel value to use for that channel if the corresponding
        vmax_x is set to None. The default is 99.75% for all channels.

    stretch_r, stretch_g, stretch_b : { 'linear', 'log', 'sqrt', 'arcsinh', 'power' }
        The stretch function to use for the different channels.

    vmid_r, vmid_g, vmid_b : float, optional
        Baseline values used for the log and arcsinh stretches. If
        set to None, this is set to zero for log stretches and to
        vmin - (vmax - vmin) / 30. for arcsinh stretches

    exponent_r, exponent_g, exponent_b : float, optional
        If stretch_x is set to 'power', this is the exponent to use.

    make_nans_transparent : bool, optional
        If set AND output is png, will add an alpha layer that sets pixels
        containing a NaN to transparent.

    embed_avm_tags : bool, optional
        Whether to embed AVM tags inside the image - this can only be done for
        JPEG and PNG files, and only if PyAVM is installed.
    '''

    try:
        from PIL import Image
    except ImportError:
        try:
            import Image
        except ImportError:
            raise ImportError("The Python Imaging Library (PIL) is required to make an RGB image")

    if isinstance(data, six.string_types):

        image = fits.getdata(data)
        image_r = image[indices[0], :, :]
        image_g = image[indices[1], :, :]
        image_b = image[indices[2], :, :]

        # Read in header
        header = fits.getheader(data)

        # Remove information about third dimension
        header['NAXIS'] = 2
        for key in ['NAXIS', 'CTYPE', 'CRPIX', 'CRVAL', 'CUNIT', 'CDELT', 'CROTA']:
            for coord in range(3, 6):
                name = key + str(coord)
                if name in header:
                    header.__delitem__(name)

    elif (type(data) == list or type(data) == tuple) and len(data) == 3:

        filename_r, filename_g, filename_b = data
        image_r = fits.getdata(filename_r)
        image_g = fits.getdata(filename_g)
        image_b = fits.getdata(filename_b)

        # Read in header
        header = fits.getheader(filename_r)

    else:
        raise Exception("data should either be the filename of a FITS cube or a list/tuple of three images")

    # are we making a transparent layer?
    do_alpha = make_nans_transparent and output.lower().endswith('.png')

    if do_alpha:
        log.info("Making alpha layer")

        # initialize alpha layer
        image_alpha = np.empty_like(image_r, dtype=np.uint8)
        image_alpha[:] = 255

        # look for nans in images
        for im in [image_r, image_g, image_b]:
            image_alpha[np.isnan(im)] = 0

    log.info("Red:")
    image_r = Image.fromarray(_data_stretch(image_r, \
                                            vmin=vmin_r, vmax=vmax_r, \
                                            pmin=pmin_r, pmax=pmax_r, \
                                            stretch=stretch_r, \
                                            vmid=vmid_r, \
                                            exponent=exponent_r))

    log.info("Green:")
    image_g = Image.fromarray(_data_stretch(image_g, \
                                            vmin=vmin_g, vmax=vmax_g, \
                                            pmin=pmin_g, pmax=pmax_g, \
                                            stretch=stretch_g, \
                                            vmid=vmid_g, \
                                            exponent=exponent_g))

    log.info("Blue:")
    image_b = Image.fromarray(_data_stretch(image_b, \
                                            vmin=vmin_b, vmax=vmax_b, \
                                            pmin=pmin_b, pmax=pmax_b, \
                                            stretch=stretch_b, \
                                            vmid=vmid_b, \
                                            exponent=exponent_b))

    img = Image.merge("RGB", (image_r, image_g, image_b))

    if do_alpha:
        # convert to RGBA and add alpha layer
        image_alpha = Image.fromarray(image_alpha)
        img.convert("RGBA")
        img.putalpha(image_alpha)

    img = img.transpose(Image.FLIP_TOP_BOTTOM)

    img.save(output)

    if embed_avm_tags:

        try:
            import pyavm
        except ImportError:
            warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image")
            return

        if version.LooseVersion(pyavm.__version__) < version.LooseVersion('0.9.1'):
            warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image")
            return

        from pyavm import AVM

        if output.lower().endswith(('.jpg', '.jpeg', '.png')):
            avm = AVM.from_header(header)
            avm.embed(output, output)
        else:
            warnings.warn("AVM tags will not be embedded in RGB image, as only JPEG and PNG files are supported")


def make_rgb_cube(files, output, north=False, system=None, equinox=None):
    '''
    Make an RGB data cube from a list of three FITS images.

    This method can read in three FITS files with different
    projections/sizes/resolutions and uses Montage to reproject
    them all to the same projection.

    Two files are produced by this function. The first is a three-dimensional
    FITS cube with a filename give by `output`, where the third dimension
    contains the different channels. The second is a two-dimensional FITS
    image with a filename given by `output` with a `_2d` suffix. This file
    contains the mean of the different channels, and is required as input to
    FITSFigure if show_rgb is subsequently used to show a color image
    generated from the FITS cube (to provide the correct WCS information to
    FITSFigure).

    Parameters
    ----------

    files : tuple or list
       A list of the filenames of three FITS filename to reproject.
       The order is red, green, blue.

    output : str
       The filename of the output RGB FITS cube.

    north : bool, optional
       By default, the FITS header generated by Montage represents the
       best fit to the images, often resulting in a slight rotation. If
       you want north to be straight up in your final mosaic, you should
       use this option.

    system : str, optional
       Specifies the system for the header (default is EQUJ).
       Possible values are: EQUJ EQUB ECLJ ECLB GAL SGAL

    equinox : str, optional
       If a coordinate system is specified, the equinox can also be given
       in the form YYYY. Default is J2000.
    '''

    # Check whether the Python montage module is installed. The Python module
    # checks itself whether the Montage command-line tools are available, and
    # if they are not then importing the Python module will fail.
    try:
        import montage_wrapper as montage
    except ImportError:
        raise Exception("Both the Montage command-line tools and the"
                        " montage-wrapper Python module are required"
                        " for this function")

    # Check that input files exist
    for f in files:
        if not os.path.exists(f):
            raise Exception("File does not exist : " + f)

    # Create work directory
    work_dir = tempfile.mkdtemp()

    raw_dir = '%s/raw' % work_dir
    final_dir = '%s/final' % work_dir

    images_raw_tbl = '%s/images_raw.tbl' % work_dir
    header_hdr = '%s/header.hdr' % work_dir

    # Create raw and final directory in work directory
    os.mkdir(raw_dir)
    os.mkdir(final_dir)

    # Create symbolic links to input files
    for i, f in enumerate(files):
        os.symlink(os.path.abspath(f), '%s/image_%i.fits' % (raw_dir, i))

    # List files and create optimal header
    montage.mImgtbl(raw_dir, images_raw_tbl, corners=True)
    montage.mMakeHdr(images_raw_tbl, header_hdr, north_aligned=north, system=system, equinox=equinox)

    # Read header in with astropy.io.fits
    header = fits.Header.fromtextfile(header_hdr)

    # Find image dimensions
    nx = int(header['NAXIS1'])
    ny = int(header['NAXIS2'])

    # Generate empty datacube
    image_cube = np.zeros((len(files), ny, nx), dtype=np.float32)

    # Loop through files
    for i in range(len(files)):

        # Reproject channel to optimal header
        montage.reproject('%s/image_%i.fits' % (raw_dir, i),
                          '%s/image_%i.fits' % (final_dir, i),
                          header=header_hdr, exact_size=True, bitpix=-32)

        # Read in and add to datacube
        image_cube[i, :, :] = fits.getdata('%s/image_%i.fits' % (final_dir, i))

    # Write out final cube
    fits.writeto(output, image_cube, header, clobber=True)

    # Write out collapsed version of cube
    fits.writeto(output.replace('.fits', '_2d.fits'), \
                   np.mean(image_cube, axis=0), header, clobber=True)

    # Remove work directory
    shutil.rmtree(work_dir)