def get_finding_chart(source_ra,
source_dec,
source_name,
image_source='desi',
output_format='pdf',
imsize=3.0,
tick_offset=0.02,
tick_length=0.03,
fallback_image_source='dss',
**offset_star_kwargs):
"""Create a finder chart suitable for spectroscopic observations of
the source
Parameters
----------
source_ra : float
Right ascension (J2000) of the source
source_dec : float
Declination (J2000) of the source
source_name : str
Name of the source
image_source : {'desi', 'dss', 'ztfref'}, optional
Survey where the image comes from "desi", "dss", "ztfref"
(more to be added)
output_format : str, optional
"pdf" of "png" -- determines the format of the returned finder
imsize : float, optional
Requested image size (on a size) in arcmin. Should be between 2-15.
tick_offset : float, optional
How far off the each source should the tick mark be made? (in arcsec)
tick_length : float, optional
How long should the tick mark be made? (in arcsec)
fallback_image_source : str, optional
Where what `image_source` should we fall back to if the
one requested fails
**offset_star_kwargs : dict, optional
Other parameters passed to `get_nearby_offset_stars`
Returns
-------
dict
success : bool
Whether the request was successful or not, returning
a sensible error in 'reason'
name : str
suggested filename based on `source_name` and `output_format`
data : str
binary encoded data for the image (to be streamed)
reason : str
If not successful, a reason is returned.
"""
if (imsize < 2.0) or (imsize > 15):
return {
'success': False,
'reason': 'Requested `imsize` out of range',
'data': '',
'name': ''
}
if image_source not in source_image_parameters:
return {
'success': False,
'reason': f'image source {image_source} not in list',
'data': '',
'name': ''
}
fig = plt.figure(figsize=(11, 8.5), constrained_layout=False)
widths = [2.6, 1]
heights = [2.6, 1]
spec = fig.add_gridspec(ncols=2,
nrows=2,
width_ratios=widths,
height_ratios=heights,
left=0.05,
right=0.95)
# how wide on the side will the image be? 256 as default
npixels = source_image_parameters[image_source].get("npixels", 256)
# set the pixelscale in arcsec (typically about 1 arcsec/pixel)
pixscale = 60 * imsize / npixels
hdu = fits_image(source_ra,
source_dec,
imsize=imsize,
image_source=image_source)
# skeleton WCS - this is the field that the user requested
wcs = WCS(naxis=2)
# set the headers of the WCS.
# The center of the image is the reference point (source_ra, source_dec):
wcs.wcs.crpix = [npixels / 2, npixels / 2]
wcs.wcs.crval = [source_ra, source_dec]
# create the pixel scale and orientation North up, East left
# pixelscale is in degrees, established in the tangent plane
# to the reference point
wcs.wcs.cd = np.array([[-pixscale / 3600, 0], [0, pixscale / 3600]])
wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
if hdu is not None:
im = hdu.data
# replace the nans with medians
im[np.isnan(im)] = np.nanmedian(im)
if source_image_parameters[image_source].get("reproject", False):
# project image to the skeleton WCS solution
print("Reprojecting image to requested position and orientation")
im, _ = reproject_adaptive(hdu, wcs, shape_out=(npixels, npixels))
else:
wcs = WCS(hdu.header)
if source_image_parameters[image_source].get("smooth", False):
im = gaussian_filter(
hdu.data,
source_image_parameters[image_source]["smooth"] / pixscale)
norm = ImageNormalize(im, interval=ZScaleInterval())
watermark = source_image_parameters[image_source]["str"]
else:
# if we got back a blank image, try to fallback on another survey
# and return the results from that call
if (fallback_image_source is not None):
if (fallback_image_source != image_source):
print(f"Falling back on image source {fallback_image_source}")
return get_finding_chart(source_ra,
source_dec,
source_name,
image_source=fallback_image_source,
output_format=output_format,
imsize=imsize,
tick_offset=tick_offset,
tick_length=tick_length,
fallback_image_source=None,
**offset_star_kwargs)
# we dont have an image here, so let's create a dummy one
# so we can still plot
im = np.zeros((npixels, npixels))
norm = None
watermark = None
# add the images in the top left corner
ax = fig.add_subplot(spec[0, 0], projection=wcs)
ax_text = fig.add_subplot(spec[0, 1])
ax_text.axis('off')
ax_starlist = fig.add_subplot(spec[1, 0:])
ax_starlist.axis('off')
ax.imshow(im, origin='lower', norm=norm, cmap='gray_r')
ax.set_autoscale_on(False)
ax.grid(color='white', ls='dotted')
ax.set_xlabel(r'$\alpha$ (J2000)', fontsize='large')
ax.set_ylabel(r'$\delta$ (J2000)', fontsize='large')
obstime = offset_star_kwargs.get("obstime",
datetime.datetime.utcnow().isoformat())
ax.set_title(f'{source_name} Finder ({obstime})',
fontsize='large',
fontweight='bold')
star_list, _, _, _ = get_nearby_offset_stars(source_ra, source_dec,
source_name,
**offset_star_kwargs)
if not isinstance(star_list, list) or len(star_list) == 0:
return {
'success': False,
'reason': 'failure to get star list',
'data': '',
'name': ''
}
ncolors = len(star_list)
colors = sns.color_palette("colorblind", ncolors)
start_text = [-0.35, 0.99]
starlist_str = (
"# Note: spacing in starlist many not copy/paste correctly in PDF\n" +
f"# you can get starlist directly from" +
f" /api/sources/{source_name}/offsets?" +
f"facility={offset_star_kwargs.get('facility', 'Keck')}\n" +
"\n".join([x["str"] for x in star_list]))
# add the starlist
ax_starlist.text(0,
0.50,
starlist_str,
fontsize="x-small",
family='monospace',
transform=ax_starlist.transAxes)
# add the watermark for the survey
props = dict(boxstyle='round', facecolor='gray', alpha=0.5)
if watermark is not None:
ax.text(0.035,
0.035,
watermark,
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes,
fontsize='medium',
fontweight='bold',
color="yellow",
alpha=0.5,
bbox=props)
ax.text(
0.95,
0.035,
f"{imsize}\u2032 \u00D7 {imsize}\u2032", # size'x size'
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes,
fontsize='medium',
fontweight='bold',
color="yellow",
alpha=0.5,
bbox=props)
# compass rose
# rose_center_pixel = ax.transAxes.transform((0.04, 0.95))
rose_center = pixel_to_skycoord(int(npixels * 0.1), int(npixels * 0.9),
wcs)
props = dict(boxstyle='round', facecolor='gray', alpha=0.5)
for ang, label, off in [(0, "N", 0.01), (90, "E", 0.03)]:
position_angle = ang * u.deg
separation = (0.05 * imsize * 60) * u.arcsec # 5%
p2 = rose_center.directional_offset_by(position_angle, separation)
ax.plot([rose_center.ra.value, p2.ra.value],
[rose_center.dec.value, p2.dec.value],
transform=ax.get_transform('world'),
color="gold",
linewidth=2)
# label N and E
position_angle = (ang + 15) * u.deg
separation = ((0.05 + off) * imsize * 60) * u.arcsec
p2 = rose_center.directional_offset_by(position_angle, separation)
ax.text(p2.ra.value,
p2.dec.value,
label,
color="gold",
transform=ax.get_transform('world'),
fontsize='large',
fontweight='bold')
for i, star in enumerate(star_list):
c1 = SkyCoord(star["ra"] * u.deg, star["dec"] * u.deg, frame='icrs')
# mark up the right side of the page with position and offset info
name_title = star["name"]
if star.get("mag") is not None:
name_title += f", mag={star.get('mag'):.2f}"
ax_text.text(start_text[0],
start_text[1] - i / ncolors,
name_title,
ha='left',
va='top',
fontsize='large',
fontweight='bold',
transform=ax_text.transAxes,
color=colors[i])
source_text = f" {star['ra']:.5f} {star['dec']:.5f}\n"
source_text += f" {c1.to_string('hmsdms')}\n"
if (star.get("dras") is not None) and (star.get("ddecs") is not None):
source_text += \
f' {star.get("dras")} {star.get("ddecs")} to {source_name}'
ax_text.text(start_text[0],
start_text[1] - i / ncolors - 0.06,
source_text,
ha='left',
va='top',
fontsize='large',
transform=ax_text.transAxes,
color=colors[i])
# work on making marks where the stars are
for ang in [0, 90]:
position_angle = ang * u.deg
separation = (tick_offset * imsize * 60) * u.arcsec
p1 = c1.directional_offset_by(position_angle, separation)
separation = (tick_offset + tick_length) * imsize * 60 * u.arcsec
p2 = c1.directional_offset_by(position_angle, separation)
ax.plot([p1.ra.value, p2.ra.value], [p1.dec.value, p2.dec.value],
transform=ax.get_transform('world'),
color=colors[i],
linewidth=3 if imsize <= 4 else 2)
if star["name"].find("_off") != -1:
# this is an offset star
text = star["name"].split("_off")[-1]
position_angle = 14 * u.deg
separation = \
(tick_offset + tick_length*1.6) * imsize * 60 * u.arcsec
p1 = c1.directional_offset_by(position_angle, separation)
ax.text(p1.ra.value,
p1.dec.value,
text,
color=colors[i],
transform=ax.get_transform('world'),
fontsize='large',
fontweight='bold')
buf = io.BytesIO()
fig.savefig(buf, format=output_format)
buf.seek(0)
return {
"success": True,
"name": f"finder_{source_name}.{output_format}",
"data": buf.read(),
"reason": ""
}
def get_finding_chart(
source_ra,
source_dec,
source_name,
image_source='ps1',
output_format='pdf',
imsize=3.0,
tick_offset=0.02,
tick_length=0.03,
fallback_image_source='dss',
zscale_contrast=0.045,
zscale_krej=2.5,
extra_display_string="",
**offset_star_kwargs,
):
"""Create a finder chart suitable for spectroscopic observations of
the source
Parameters
----------
source_ra : float
Right ascension (J2000) of the source
source_dec : float
Declination (J2000) of the source
source_name : str
Name of the source
image_source : {'desi', 'dss', 'ztfref', 'ps1'}, optional
Survey where the image comes from "desi", "dss", "ztfref", "ps1"
defaults to "ps1"
output_format : str, optional
"pdf" of "png" -- determines the format of the returned finder
imsize : float, optional
Requested image size (on a size) in arcmin. Should be between 2-15.
tick_offset : float, optional
How far off the each source should the tick mark be made? (in arcsec)
tick_length : float, optional
How long should the tick mark be made? (in arcsec)
fallback_image_source : str, optional
Where what `image_source` should we fall back to if the
one requested fails
zscale_contrast : float, optional
Contrast parameter for the ZScale interval
zscale_krej : float, optional
Krej parameter for the Zscale interval
extra_display_string : str, optional
What else to show for the source itself in the chart (e.g. proper motion)
**offset_star_kwargs : dict, optional
Other parameters passed to `get_nearby_offset_stars`
Returns
-------
dict
success : bool
Whether the request was successful or not, returning
a sensible error in 'reason'
name : str
suggested filename based on `source_name` and `output_format`
data : str
binary encoded data for the image (to be streamed)
reason : str
If not successful, a reason is returned.
"""
if (imsize < 2.0) or (imsize > 15):
return {
'success': False,
'reason': 'Requested `imsize` out of range',
'data': '',
'name': '',
}
if image_source not in source_image_parameters:
return {
'success': False,
'reason': f'image source {image_source} not in list',
'data': '',
'name': '',
}
matplotlib.use("Agg")
fig = plt.figure(figsize=(11, 8.5), constrained_layout=False)
widths = [2.6, 1]
heights = [2.6, 1]
spec = fig.add_gridspec(
ncols=2,
nrows=2,
width_ratios=widths,
height_ratios=heights,
left=0.05,
right=0.95,
)
# how wide on the side will the image be? 256 as default
npixels = source_image_parameters[image_source].get("npixels", 256)
# set the pixelscale in arcsec (typically about 1 arcsec/pixel)
pixscale = 60 * imsize / npixels
hdu = fits_image(source_ra,
source_dec,
imsize=imsize,
image_source=image_source)
# skeleton WCS - this is the field that the user requested
wcs = WCS(naxis=2)
# set the headers of the WCS.
# The center of the image is the reference point (source_ra, source_dec):
wcs.wcs.crpix = [npixels / 2, npixels / 2]
wcs.wcs.crval = [source_ra, source_dec]
# create the pixel scale and orientation North up, East left
# pixelscale is in degrees, established in the tangent plane
# to the reference point
wcs.wcs.cd = np.array([[-pixscale / 3600, 0], [0, pixscale / 3600]])
wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
fallback = True
if hdu is not None:
im = hdu.data
# replace the nans with medians
im[np.isnan(im)] = np.nanmedian(im)
# Fix the header keyword for the input system, if needed
hdr = hdu.header
if 'RADECSYS' in hdr:
hdr.set('RADESYSa', hdr['RADECSYS'], before='RADECSYS')
del hdr['RADECSYS']
if source_image_parameters[image_source].get("reproject", False):
# project image to the skeleton WCS solution
log("Reprojecting image to requested position and orientation")
im, _ = reproject_adaptive(hdu, wcs, shape_out=(npixels, npixels))
else:
wcs = WCS(hdu.header)
if source_image_parameters[image_source].get("smooth", False):
im = gaussian_filter(
hdu.data,
source_image_parameters[image_source]["smooth"] / pixscale)
cent = int(npixels / 2)
width = int(0.05 * npixels)
test_slice = slice(cent - width, cent + width)
all_nans = np.isnan(im[test_slice, test_slice].flatten()).all()
all_zeros = (im[test_slice, test_slice].flatten() == 0).all()
if not (all_zeros or all_nans):
percents = np.nanpercentile(im.flatten(), [10, 99.0])
vmin = percents[0]
vmax = percents[1]
interval = ZScaleInterval(
nsamples=int(0.1 * (im.shape[0] * im.shape[1])),
contrast=zscale_contrast,
krej=zscale_krej,
)
norm = ImageNormalize(im, vmin=vmin, vmax=vmax, interval=interval)
watermark = source_image_parameters[image_source]["str"]
fallback = False
if hdu is None or fallback:
# if we got back a blank image, try to fallback on another survey
# and return the results from that call
if fallback_image_source is not None:
if fallback_image_source != image_source:
log(f"Falling back on image source {fallback_image_source}")
return get_finding_chart(
source_ra,
source_dec,
source_name,
image_source=fallback_image_source,
output_format=output_format,
imsize=imsize,
tick_offset=tick_offset,
tick_length=tick_length,
fallback_image_source=None,
**offset_star_kwargs,
)
# we dont have an image here, so let's create a dummy one
# so we can still plot
im = np.zeros((npixels, npixels))
watermark = None
vmin = 0
vmax = 0
norm = ImageNormalize(im, vmin=vmin, vmax=vmax)
# add the images in the top left corner
ax = fig.add_subplot(spec[0, 0], projection=wcs)
ax_text = fig.add_subplot(spec[0, 1])
ax_text.axis('off')
ax_starlist = fig.add_subplot(spec[1, 0:])
ax_starlist.axis('off')
ax.imshow(im, origin='lower', norm=norm, cmap='gray_r')
ax.set_autoscale_on(False)
ax.grid(color='white', ls='dotted')
ax.set_xlabel(r'$\alpha$ (J2000)', fontsize='large')
ax.set_ylabel(r'$\delta$ (J2000)', fontsize='large')
obstime = offset_star_kwargs.get("obstime",
datetime.datetime.utcnow().isoformat())
ax.set_title(
f'{source_name} Finder (for {obstime.split("T")[0]})',
fontsize='large',
fontweight='bold',
)
star_list, _, _, _, used_ztfref = get_nearby_offset_stars(
source_ra, source_dec, source_name, **offset_star_kwargs)
if not isinstance(star_list, list) or len(star_list) == 0:
return {
'success': False,
'reason': 'failure to get star list',
'data': '',
'name': '',
}
ncolors = len(star_list)
if star_list[0]['str'].startswith("!Data"):
ncolors -= 1
colors = sns.color_palette("colorblind", ncolors)
start_text = [-0.45, 0.99]
origin = "GaiaEDR3" if not used_ztfref else "ZTFref"
starlist_str = (
f"# Note: {origin} used for offset star positions\n"
"# Note: spacing in starlist many not copy/paste correctly in PDF\n" +
"# you can get starlist directly from" +
f" /api/sources/{source_name}/offsets?" +
f"facility={offset_star_kwargs.get('facility', 'Keck')}\n" +
"\n".join([x["str"] for x in star_list]))
# add the starlist
ax_starlist.text(
0,
0.50,
starlist_str,
fontsize="x-small",
family='monospace',
transform=ax_starlist.transAxes,
)
# add the watermark for the survey
props = dict(boxstyle='round', facecolor='gray', alpha=0.7)
if watermark is not None:
ax.text(
0.035,
0.035,
watermark,
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes,
fontsize='medium',
fontweight='bold',
color="yellow",
alpha=0.5,
bbox=props,
)
date_obs = hdr.get('DATE-OBS')
if not date_obs:
mjd_obs = hdr.get('MJD-OBS')
if mjd_obs:
date_obs = Time(f"{mjd_obs}",
format='mjd').to_value('fits', subfmt='date_hms')
if date_obs:
ax.text(
0.95,
0.95,
f'image date {date_obs.split("T")[0]}',
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes,
fontsize='small',
color="yellow",
alpha=0.5,
bbox=props,
)
ax.text(
0.95,
0.035,
f"{imsize}\u2032 \u00D7 {imsize}\u2032", # size'x size'
horizontalalignment='right',
verticalalignment='center',
transform=ax.transAxes,
fontsize='medium',
fontweight='bold',
color="yellow",
alpha=0.5,
bbox=props,
)
# compass rose
# rose_center_pixel = ax.transAxes.transform((0.04, 0.95))
rose_center = pixel_to_skycoord(int(npixels * 0.1), int(npixels * 0.9),
wcs)
props = dict(boxstyle='round', facecolor='gray', alpha=0.5)
for ang, label, off in [(0, "N", 0.01), (90, "E", 0.03)]:
position_angle = ang * u.deg
separation = (0.05 * imsize * 60) * u.arcsec # 5%
p2 = rose_center.directional_offset_by(position_angle, separation)
ax.plot(
[rose_center.ra.value, p2.ra.value],
[rose_center.dec.value, p2.dec.value],
transform=ax.get_transform('world'),
color="gold",
linewidth=2,
)
# label N and E
position_angle = (ang + 15) * u.deg
separation = ((0.05 + off) * imsize * 60) * u.arcsec
p2 = rose_center.directional_offset_by(position_angle, separation)
ax.text(
p2.ra.value,
p2.dec.value,
label,
color="gold",
transform=ax.get_transform('world'),
fontsize='large',
fontweight='bold',
)
# account for Shane header
if star_list[0]['str'].startswith("!Data"):
star_list = star_list[1:]
for i, star in enumerate(star_list):
c1 = SkyCoord(star["ra"] * u.deg, star["dec"] * u.deg, frame='icrs')
# mark up the right side of the page with position and offset info
name_title = star["name"]
if star.get("mag") is not None:
name_title += f" {star.get('mag'):.2f} mag"
ax_text.text(
start_text[0],
start_text[1] - (i * 1.1) / ncolors,
name_title,
ha='left',
va='top',
fontsize='large',
fontweight='bold',
transform=ax_text.transAxes,
color=colors[i],
)
source_text = f" {star['ra']:.5f} {star['dec']:.5f}\n"
source_text += f" {c1.to_string('hmsdms', precision=2)}\n"
if i == 0 and extra_display_string != "":
source_text += f" {extra_display_string}\n"
if ((star.get("dras") is not None) and (star.get("ddecs") is not None)
and (star.get("pa") is not None)):
source_text += f' {star.get("dras")} {star.get("ddecs")} (PA={star.get("pa"):<0.02f}°)'
ax_text.text(
start_text[0],
start_text[1] - (i * 1.1) / ncolors - 0.06,
source_text,
ha='left',
va='top',
fontsize='large',
transform=ax_text.transAxes,
color=colors[i],
)
# work on making marks where the stars are
for ang in [0, 90]:
# for the source itself (i=0), change the angle of the lines in
# case the offset star is the same as the source itself
position_angle = ang * u.deg if i != 0 else (ang + 225) * u.deg
separation = (tick_offset * imsize * 60) * u.arcsec
p1 = c1.directional_offset_by(position_angle, separation)
separation = (tick_offset + tick_length) * imsize * 60 * u.arcsec
p2 = c1.directional_offset_by(position_angle, separation)
ax.plot(
[p1.ra.value, p2.ra.value],
[p1.dec.value, p2.dec.value],
transform=ax.get_transform('world'),
color=colors[i],
linewidth=3 if imsize <= 4 else 2,
alpha=0.8,
)
if star["name"].find("_o") != -1:
# this is an offset star
text = star["name"].split("_o")[-1]
position_angle = 14 * u.deg
separation = (tick_offset +
tick_length * 1.6) * imsize * 60 * u.arcsec
p1 = c1.directional_offset_by(position_angle, separation)
ax.text(
p1.ra.value,
p1.dec.value,
text,
color=colors[i],
transform=ax.get_transform('world'),
fontsize='large',
fontweight='bold',
)
buf = io.BytesIO()
fig.savefig(buf, format=output_format)
plt.close(fig)
buf.seek(0)
return {
"success": True,
"name": f"finder_{source_name}.{output_format}",
"data": buf.read(),
"reason": "",
}
def reproject_to(self,
target_wcs,
algorithm='interpolation',
shape_out=None,
order='bilinear',
output_array=None,
parallel=False,
return_footprint=False):
"""
Reprojects this NDCube to the coordinates described by another WCS object.
Parameters
----------
algorithm: `str`
The algorithm to use for reprojecting. This can be any of: 'interpolation', 'adaptive',
and 'exact'.
target_wcs : `astropy.wcs.wcsapi.BaseHighLevelWCS`, `astropy.wcs.wcsapi.BaseLowLevelWCS`,
or `astropy.io.fits.Header`
The WCS object to which the ``NDCube`` is to be reprojected.
shape_out: `tuple`, optional
The shape of the output data array. The ordering of the dimensions must follow NumPy
ordering and not the WCS pixel shape.
If not specified, `~astropy.wcs.wcsapi.BaseLowLevelWCS.array_shape` attribute
(if available) from the low level API of the ``target_wcs`` is used.
order: `int` or `str`
The order of the interpolation (used only when the 'interpolation' or 'adaptive'
algorithm is selected).
For 'interpolation' algorithm, this can be any of: 'nearest-neighbor', 'bilinear',
'biquadratic', and 'bicubic'.
For 'adaptive' algorithm, this can be either 'nearest-neighbor' or 'bilinear'.
output_array: `numpy.ndarray`, optional
An array in which to store the reprojected data. This can be any numpy array
including a memory map, which may be helpful when dealing with extremely large files.
parallel: `bool` or `int`
Flag for parallel implementation (used only when the 'exact' algorithm is selected).
If ``True``, a parallel implementation is chosen and the number of processes is
selected automatically as the number of logical CPUs detected on the machine.
If ``False``, a serial implementation is chosen.
If the flag is a positive integer n greater than one, a parallel implementation
using n processes is chosen.
return_footprint: `bool`
Whether to return the footprint in addition to the output NDCube.
Returns
-------
resampled_cube : `ndcube.NDCube`
A new resultant NDCube object, the supplied ``target_wcs`` will be the ``.wcs`` attribute of the output ``NDCube``.
footprint: `numpy.ndarray`
Footprint of the input array in the output array. Values of 0 indicate no coverage or
valid values in the input image, while values of 1 indicate valid values.
Notes
-----
This method doesn't support handling of the ``mask``, ``extra_coords``, and ``uncertainty`` attributes yet.
However, ``meta`` and ``global_coords`` are copied to the output ``NDCube``.
"""
try:
from reproject import reproject_adaptive, reproject_exact, reproject_interp
from reproject.wcs_utils import has_celestial
except ModuleNotFoundError:
raise ImportError(
"The NDCube.reproject_to method requires the optional package `reproject`."
)
if isinstance(target_wcs, Mapping):
target_wcs = WCS(header=target_wcs)
low_level_target_wcs = utils.wcs.get_low_level_wcs(
target_wcs, 'target_wcs')
# 'adaptive' and 'exact' algorithms work only on 2D celestial WCS.
if algorithm == 'adaptive' or algorithm == 'exact':
if low_level_target_wcs.pixel_n_dim != 2 or low_level_target_wcs.world_n_dim != 2:
raise ValueError(
'For adaptive and exact algorithms, target_wcs must be 2D.'
)
if not has_celestial(target_wcs):
raise ValueError(
'For adaptive and exact algorithms, '
'target_wcs must contain celestial axes only.')
if not utils.wcs.compare_wcs_physical_types(self.wcs, target_wcs):
raise ValueError(
'Given target_wcs is not compatible with this NDCube, the physical types do not match.'
)
# If shape_out is not specified explicity, try to extract it from the low level WCS
if not shape_out:
if hasattr(low_level_target_wcs, 'array_shape'
) and low_level_target_wcs.array_shape is not None:
shape_out = low_level_target_wcs.array_shape
else:
raise ValueError(
"shape_out must be specified if target_wcs does not have the array_shape attribute."
)
self._validate_algorithm_and_order(algorithm, order)
if algorithm == 'interpolation':
data = reproject_interp(self,
output_projection=target_wcs,
shape_out=shape_out,
order=order,
output_array=output_array,
return_footprint=return_footprint)
elif algorithm == 'adaptive':
data = reproject_adaptive(self,
output_projection=target_wcs,
shape_out=shape_out,
order=order,
return_footprint=return_footprint)
elif algorithm == 'exact':
data = reproject_exact(self,
output_projection=target_wcs,
shape_out=shape_out,
parallel=parallel,
return_footprint=return_footprint)
if return_footprint:
data, footprint = data
resampled_cube = type(self)(data,
wcs=target_wcs,
meta=deepcopy(self.meta))
resampled_cube._global_coords = deepcopy(self.global_coords)
if return_footprint:
return resampled_cube, footprint
return resampled_cube
