def test_fit_wcs_from_points_CRPIX_bounds():
# Test CRPIX bounds requirement
wcs_str = """
WCSAXES = 2 / Number of coordinate axes
CRPIX1 = 1045.0 / Pixel coordinate of reference point
CRPIX2 = 1001.0 / Pixel coordinate of reference point
PC1_1 = 0.00056205870415378 / Coordinate transformation matrix element
PC1_2 = -0.00569181083243 / Coordinate transformation matrix element
PC2_1 = 0.0056776810932466 / Coordinate transformation matrix element
PC2_2 = 0.0004208048403273 / Coordinate transformation matrix element
CDELT1 = 1.0 / [deg] Coordinate increment at reference point
CDELT2 = 1.0 / [deg] Coordinate increment at reference point
CUNIT1 = 'deg' / Units of coordinate increment and value
CUNIT2 = 'deg' / Units of coordinate increment and value
CTYPE1 = 'RA---TAN' / Right ascension, gnomonic projection
CTYPE2 = 'DEC--TAN' / Declination, gnomonic projection
CRVAL1 = 104.57797893504 / [deg] Coordinate value at reference point
CRVAL2 = -74.195502593322 / [deg] Coordinate value at reference point
LONPOLE = 180.0 / [deg] Native longitude of celestial pole
LATPOLE = -74.195502593322 / [deg] Native latitude of celestial pole
TIMESYS = 'TDB' / Time scale
TIMEUNIT= 'd' / Time units
DATEREF = '1858-11-17' / ISO-8601 fiducial time
MJDREFI = 0.0 / [d] MJD of fiducial time, integer part
MJDREFF = 0.0 / [d] MJD of fiducial time, fractional part
DATE-OBS= '2019-03-27T03:30:13.832Z' / ISO-8601 time of observation
MJD-OBS = 58569.145993426 / [d] MJD of observation
MJD-OBS = 58569.145993426 / [d] MJD at start of observation
TSTART = 1569.6467941661 / [d] Time elapsed since fiducial time at start
DATE-END= '2019-03-27T04:00:13.831Z' / ISO-8601 time at end of observation
MJD-END = 58569.166826748 / [d] MJD at end of observation
TSTOP = 1569.6676274905 / [d] Time elapsed since fiducial time at end
TELAPSE = 0.02083332443 / [d] Elapsed time (start to stop)
TIMEDEL = 0.020833333333333 / [d] Time resolution
TIMEPIXR= 0.5 / Reference position of timestamp in binned data
RADESYS = 'ICRS' / Equatorial coordinate system
"""
wcs_header = fits.Header.fromstring(wcs_str, sep='\n')
ffi_wcs = WCS(wcs_header)
yi, xi = (1000, 1000)
y, x = (10, 200)
center_coord = SkyCoord(ffi_wcs.all_pix2world([[xi + x // 2, yi + y // 2]],
0),
unit='deg')[0]
ypix, xpix = [arr.flatten() for arr in np.mgrid[xi:xi + x, yi:yi + y]]
world_pix = SkyCoord(*ffi_wcs.all_pix2world(xpix, ypix, 0), unit='deg')
fit_wcs = fit_wcs_from_points((ypix, xpix), world_pix, proj_point='center')
assert (fit_wcs.wcs.crpix.astype(int) == [1100, 1005]).all()
assert fit_wcs.pixel_shape == (200, 10)
def recomputeXylsPixelPositions(originalXylsPath, originalWcsPath,
newWcsPathOrHeader):
"""
Return pixel coordinates valid for `newWcsPathOrHeader` for
the reference stars found in `originalXylsPath` (belonging to `originalWcsPath`).
:rtype: tuple (x,y) with x and y being ndarrays
"""
# Step 1: compute RA,Dec of reference stars (as this is not stored in .xyls)
originalWCS = WCS(readHeader(originalWcsPath))
x, y = readXy(originalXylsPath)
ra, dec = originalWCS.all_pix2world(x, y, 0)
# Step 2: compute pixel positions of reference stars in new WCS solution
if isinstance(newWcsPathOrHeader, string_types):
newWCS = WCS(readHeader(newWcsPathOrHeader))
else:
newWCS = WCS(newWcsPathOrHeader)
# all_world2pix raised a NoConvergenc error
# As we don't use SIP, we don't need to use all_world2pix.
# wcs_world2pix doesn't support any distortion correction.
xNew, yNew = newWCS.wcs_world2pix(ra, dec, 0)
return xNew, yNew
def pix2world(wcsHeader,
width,
height,
startX=0,
startY=0,
corner=True,
ascartesian=False):
"""
Calculate RA, Dec coordinates of a given pixel coordinate rectangle.
ra, dec = pix2world(..)
ra and dec are indexed as [y,x]
Each array element contains the RA,Dec coords of the top left corner of the
given pixel if corner==True, otherwise the coords of the pixel center.
If corner==True, an additional row and column exists at the bottom and right so that
it is possible to get the bottom and right corner values for those pixels.
:param dictionary wcsHeader: WCS header
:param width: width of rectangle
:param height: height of rectangle
:param startX: x coordinate of rectangle, can be negative
:param startY: y coordinate of rectangle, can be negative
If ascartesian=False:
:rtype: tuple(ra, dec) with arrays of shape (height+1,width+1) if corner==True, else (height,width)
If ascartesian=True:
:rtype: array of shape (height[+1],width[+1],3) with x,y,z order
"""
if corner:
startX -= 0.5 # top left corner instead of pixel center
startY -= 0.5
x, y = np.meshgrid(np.arange(startX, startX + width + corner),
np.arange(startY, startY + height + corner))
# check if TAN projection and use our fast version, otherwise fall-back to astropy
if wcsHeader['CTYPE1'] == 'RA---TAN' and wcsHeader['CTYPE2'] == 'DEC--TAN' and \
wcsHeader['LATPOLE'] == 0.0:
res = tan_pix2world(wcsHeader, x, y, 0, ascartesian=ascartesian)
else:
wcs = WCS(wcsHeader)
ra, dec = wcs.all_pix2world(x, y, 0, ra_dec_order=True)
if ascartesian:
np.deg2rad(ra, ra)
np.deg2rad(dec, dec)
res = spherical_to_cartesian(None, dec, ra, astuple=False)
else:
res = ra, dec
return res
def pix2world(wcsHeader, width, height, startX=0, startY=0, corner=True, ascartesian=False):
"""
Calculate RA, Dec coordinates of a given pixel coordinate rectangle.
ra, dec = pix2world(..)
ra and dec are indexed as [y,x]
Each array element contains the RA,Dec coords of the top left corner of the
given pixel if corner==True, otherwise the coords of the pixel center.
If corner==True, an additional row and column exists at the bottom and right so that
it is possible to get the bottom and right corner values for those pixels.
:param dictionary wcsHeader: WCS header
:param width: width of rectangle
:param height: height of rectangle
:param startX: x coordinate of rectangle, can be negative
:param startY: y coordinate of rectangle, can be negative
If ascartesian=False:
:rtype: tuple(ra, dec) with arrays of shape (height+1,width+1) if corner==True, else (height,width)
If ascartesian=True:
:rtype: array of shape (height[+1],width[+1],3) with x,y,z order
"""
if corner:
startX -= 0.5 # top left corner instead of pixel center
startY -= 0.5
x, y = np.meshgrid(np.arange(startX,startX+width+corner), np.arange(startY,startY+height+corner))
# check if TAN projection and use our fast version, otherwise fall-back to astropy
if wcsHeader['CTYPE1'] == 'RA---TAN' and wcsHeader['CTYPE2'] == 'DEC--TAN' and \
wcsHeader['LATPOLE'] == 0.0:
res = tan_pix2world(wcsHeader, x, y, 0, ascartesian=ascartesian)
else:
wcs = WCS(wcsHeader)
ra, dec = wcs.all_pix2world(x, y, 0, ra_dec_order=True)
if ascartesian:
np.deg2rad(ra, ra)
np.deg2rad(dec, dec)
res = spherical_to_cartesian(None, dec, ra, astuple=False)
else:
res = ra, dec
return res
def recomputeXylsPixelPositions(originalXylsPath, originalWcsPath, newWcsPathOrHeader):
"""
Return pixel coordinates valid for `newWcsPathOrHeader` for
the reference stars found in `originalXylsPath` (belonging to `originalWcsPath`).
:rtype: tuple (x,y) with x and y being ndarrays
"""
# Step 1: compute RA,Dec of reference stars (as this is not stored in .xyls)
originalWCS = WCS(readHeader(originalWcsPath))
x, y = readXy(originalXylsPath)
ra, dec = originalWCS.all_pix2world(x, y, 0)
# Step 2: compute pixel positions of reference stars in new WCS solution
if isinstance(newWcsPathOrHeader, string_types):
newWCS = WCS(readHeader(newWcsPathOrHeader))
else:
newWCS = WCS(newWcsPathOrHeader)
# all_world2pix raised a NoConvergenc error
# As we don't use SIP, we don't need to use all_world2pix.
# wcs_world2pix doesn't support any distortion correction.
xNew, yNew = newWCS.wcs_world2pix(ra, dec, 0)
return xNew,yNew
def test_fit_wcs_from_points():
header_str_linear = """
XTENSION= 'IMAGE ' / Image extension
BITPIX = -32 / array data type
NAXIS = 2 / number of array dimensions
NAXIS1 = 50
NAXIS2 = 50
PCOUNT = 0 / number of parameters
GCOUNT = 1 / number of groups
RADESYS = 'ICRS '
EQUINOX = 2000.0
WCSAXES = 2
CTYPE1 = 'RA---TAN'
CTYPE2 = 'DEC--TAN'
CRVAL1 = 250.3497414839765
CRVAL2 = 2.280925599609063
CRPIX1 = 1045.0
CRPIX2 = 1001.0
CD1_1 = -0.005564478186178
CD1_2 = -0.001042099258152
CD2_1 = 0.00118144146585
CD2_2 = -0.005590816683583
"""
header_str_sip = """
XTENSION= 'IMAGE ' / Image extension
BITPIX = -32 / array data type
NAXIS = 2 / number of array dimensions
NAXIS1 = 50
NAXIS2 = 50
PCOUNT = 0 / number of parameters
GCOUNT = 1 / number of groups
RADESYS = 'ICRS '
EQUINOX = 2000.0
WCSAXES = 2
CTYPE1 = 'RA---TAN-SIP'
CTYPE2 = 'DEC--TAN-SIP'
CRVAL1 = 250.3497414839765
CRVAL2 = 2.280925599609063
CRPIX1 = 1045.0
CRPIX2 = 1001.0
CD1_1 = -0.005564478186178
CD1_2 = -0.001042099258152
CD2_1 = 0.00118144146585
CD2_2 = -0.005590816683583
A_ORDER = 2
B_ORDER = 2
A_2_0 = 2.02451189234E-05
A_0_2 = 3.317603337918E-06
A_1_1 = 1.73456334971071E-05
B_2_0 = 3.331330003472E-06
B_0_2 = 2.04247482482589E-05
B_1_1 = 1.71476710804143E-05
AP_ORDER= 2
BP_ORDER= 2
AP_1_0 = 0.000904700296389636
AP_0_1 = 0.000627660715584716
AP_2_0 = -2.023482905861E-05
AP_0_2 = -3.332285841011E-06
AP_1_1 = -1.731636633824E-05
BP_1_0 = 0.000627960882053211
BP_0_1 = 0.000911222886084808
BP_2_0 = -3.343918167224E-06
BP_0_2 = -2.041598249021E-05
BP_1_1 = -1.711876336719E-05
A_DMAX = 44.72893589844534
B_DMAX = 44.62692873032506
"""
# A known header that failed before
header_str_prob = """
NAXIS = 2 / number of array dimensions
WCSAXES = 2 / Number of coordinate axes
CRPIX1 = 1024.5 / Pixel coordinate of reference point
CRPIX2 = 1024.5 / Pixel coordinate of reference point
CD1_1 = -1.7445934400771E-05 / Coordinate transformation matrix element
CD1_2 = -4.9826985362578E-08 / Coordinate transformation matrix element
CD2_1 = -5.0068838822312E-08 / Coordinate transformation matrix element
CD2_2 = 1.7530614610951E-05 / Coordinate transformation matrix element
CTYPE1 = 'RA---TAN' / Right ascension, gnomonic projection
CTYPE2 = 'DEC--TAN' / Declination, gnomonic projection
CRVAL1 = 5.8689341666667 / [deg] Coordinate value at reference point
CRVAL2 = -71.995508583333 / [deg] Coordinate value at reference point
"""
header_linear = fits.Header.fromstring(header_str_linear, sep='\n')
header_sip = fits.Header.fromstring(header_str_sip, sep='\n')
header_prob = fits.Header.fromstring(header_str_prob, sep='\n')
true_wcs_linear = WCS(header_linear, relax=True)
true_wcs_sip = WCS(header_sip, relax=True)
true_wcs_prob = WCS(header_prob, relax=True)
# Getting the pixel coordinates
x, y = np.meshgrid(list(range(10)), list(range(10)))
x = x.flatten()
y = y.flatten()
# Calculating the true sky positions
world_pix_linear = true_wcs_linear.pixel_to_world(x, y)
world_pix_sip = true_wcs_sip.pixel_to_world(x, y)
world_pix_prob = true_wcs_prob.pixel_to_world(x, y)
# Fitting the wcs, no distortion.
fit_wcs_linear = fit_wcs_from_points((x, y),
world_pix_linear,
proj_point='center',
sip_degree=None)
# Fitting the wcs, with distortion.
fit_wcs_sip = fit_wcs_from_points((x, y),
world_pix_sip,
proj_point='center',
sip_degree=2)
# Fitting the problematic WCS
fit_wcs_prob = fit_wcs_from_points((x, y),
world_pix_prob,
proj_point='center',
sip_degree=None)
# Validate that the true sky coordinates calculated with `true_wcs_linear`
# match sky coordinates calculated from the wcs fit with only linear terms
world_pix_linear_new = fit_wcs_linear.pixel_to_world(x, y)
dists = world_pix_linear.separation(world_pix_linear_new)
assert dists.max() < 7e-5 * u.deg
assert np.std(dists) < 2.5e-5 * u.deg
# Validate that the true sky coordinates calculated with `true_wcs_sip`
# match the sky coordinates calculated from the wcs fit with SIP of same
# degree (2)
world_pix_sip_new = fit_wcs_sip.pixel_to_world(x, y)
dists = world_pix_sip.separation(world_pix_sip_new)
assert dists.max() < 7e-6 * u.deg
assert np.std(dists) < 2.5e-6 * u.deg
# Validate that the true sky coordinates calculated from the problematic
# WCS match
world_pix_prob_new = fit_wcs_prob.pixel_to_world(x, y)
dists = world_pix_prob.separation(world_pix_prob_new)
assert dists.max() < 7e-6 * u.deg
assert np.std(dists) < 2.5e-6 * u.deg
# Test 360->0 degree crossover
header_linear["CRVAL1"] = 352.3497414839765
header_sip["CRVAL1"] = 352.3497414839765
header_prob["CRVAL1"] = 352.3497414839765
true_wcs_linear = WCS(header_linear, relax=True)
true_wcs_sip = WCS(header_sip, relax=True)
true_wcs_prob = WCS(header_prob)
# Calculating the true sky positions
world_pix_linear = true_wcs_linear.pixel_to_world(x, y)
world_pix_sip = true_wcs_sip.pixel_to_world(x, y)
world_pix_prob = true_wcs_prob.pixel_to_world(x, y)
# Fitting the wcs, no distortion.
fit_wcs_linear = fit_wcs_from_points((x, y),
world_pix_linear,
proj_point='center',
sip_degree=None)
# Fitting the wcs, with distortion.
fit_wcs_sip = fit_wcs_from_points((x, y),
world_pix_sip,
proj_point='center',
sip_degree=2)
# Fitting the problem WCS
fit_wcs_prob = fit_wcs_from_points((x, y),
world_pix_prob,
proj_point='center',
sip_degree=None)
# Validate that the true sky coordinates calculated with `true_wcs_linear`
# match sky coordinates calculated from the wcs fit with only linear terms
world_pix_linear_new = fit_wcs_linear.pixel_to_world(x, y)
dists = world_pix_linear.separation(world_pix_linear_new)
assert dists.max() < 7e-5 * u.deg
assert np.std(dists) < 2.5e-5 * u.deg
# Validate fit with SIP
world_pix_sip_new = fit_wcs_sip.pixel_to_world(x, y)
dists = world_pix_sip.separation(world_pix_sip_new)
assert dists.max() < 7e-6 * u.deg
assert np.std(dists) < 2.5e-6 * u.deg
# Validate the problematic WCS
world_pix_prob_new = fit_wcs_prob.pixel_to_world(x, y)
dists = world_pix_prob.separation(world_pix_prob_new)
assert dists.max() < 7e-6 * u.deg
assert np.std(dists) < 2.5e-6 * u.deg
# Test CRPIX bounds requirement
wcs_str = """
WCSAXES = 2 / Number of coordinate axes
CRPIX1 = 1045.0 / Pixel coordinate of reference point
CRPIX2 = 1001.0 / Pixel coordinate of reference point
PC1_1 = 0.00056205870415378 / Coordinate transformation matrix element
PC1_2 = -0.00569181083243 / Coordinate transformation matrix element
PC2_1 = 0.0056776810932466 / Coordinate transformation matrix element
PC2_2 = 0.0004208048403273 / Coordinate transformation matrix element
CDELT1 = 1.0 / [deg] Coordinate increment at reference point
CDELT2 = 1.0 / [deg] Coordinate increment at reference point
CUNIT1 = 'deg' / Units of coordinate increment and value
CUNIT2 = 'deg' / Units of coordinate increment and value
CTYPE1 = 'RA---TAN' / Right ascension, gnomonic projection
CTYPE2 = 'DEC--TAN' / Declination, gnomonic projection
CRVAL1 = 104.57797893504 / [deg] Coordinate value at reference point
CRVAL2 = -74.195502593322 / [deg] Coordinate value at reference point
LONPOLE = 180.0 / [deg] Native longitude of celestial pole
LATPOLE = -74.195502593322 / [deg] Native latitude of celestial pole
TIMESYS = 'TDB' / Time scale
TIMEUNIT= 'd' / Time units
DATEREF = '1858-11-17' / ISO-8601 fiducial time
MJDREFI = 0.0 / [d] MJD of fiducial time, integer part
MJDREFF = 0.0 / [d] MJD of fiducial time, fractional part
DATE-OBS= '2019-03-27T03:30:13.832Z' / ISO-8601 time of observation
MJD-OBS = 58569.145993426 / [d] MJD of observation
MJD-OBS = 58569.145993426 / [d] MJD at start of observation
TSTART = 1569.6467941661 / [d] Time elapsed since fiducial time at start
DATE-END= '2019-03-27T04:00:13.831Z' / ISO-8601 time at end of observation
MJD-END = 58569.166826748 / [d] MJD at end of observation
TSTOP = 1569.6676274905 / [d] Time elapsed since fiducial time at end
TELAPSE = 0.02083332443 / [d] Elapsed time (start to stop)
TIMEDEL = 0.020833333333333 / [d] Time resolution
TIMEPIXR= 0.5 / Reference position of timestamp in binned data
RADESYS = 'ICRS' / Equatorial coordinate system
"""
wcs_header = fits.Header.fromstring(wcs_str, sep='\n')
ffi_wcs = WCS(wcs_header)
yi, xi = (1000, 1000)
y, x = (10, 200)
center_coord = SkyCoord(ffi_wcs.all_pix2world([[xi + x // 2, yi + y // 2]],
0),
unit='deg')[0]
ypix, xpix = [arr.flatten() for arr in np.mgrid[xi:xi + x, yi:yi + y]]
world_pix = SkyCoord(*ffi_wcs.all_pix2world(xpix, ypix, 0), unit='deg')
fit_wcs = fit_wcs_from_points((ypix, xpix), world_pix, proj_point='center')
assert (fit_wcs.wcs.crpix.astype(int) == [1100, 1005]).all()
assert fit_wcs.pixel_shape == (200, 10)
