def baseline(hdu,
chan,
windows,
order=0,
subtract=True,
returnrms=True,
kms=True):
#this function needs to be passed a FITS HDU
#with data in the (V,X,Y) format
header = None
if isinstance(hdu, pyfits.hdu.image.PrimaryHDU):
# get data and header from the hdu
header = hdu.header
data = hdu.data
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, pyfits.header.Header):
raise SculptArgumentError(
'header',
"Since you passed in data that is a numpy array, set header to a pyfits header type"
)
data = hdu
shape = data.shape
#windows over which to do the test for sigma values
#basically excluse major lines you observe
lenv, lenx, leny = shape
#defaultswindow = ((100,200),(650,750))
sigma = numpy.zeros((lenx, leny))
x = numpy.arange(lenv)
c_loop = 0
# this commented section is the makermsimage way of selecting them
# if someone wants to make a change regarding one being better, do it
# velax = getaxes(header, 1)
# indices = numpy.zeros(velax.shape, dtype=bool)
# for v1, v2 in window:
# if chan:
# indices[v1:v2] = True
# else:
# ind = numpy.logical_and(velax>= v1, velax<= v2)
# indices = numpy.logical_or(indices, ind)
# indices = numpy.logical_not(indices) # for indices to include in std calc
for win in windows:
if (len(win) != 2):
print "Each element in the window list must be a 2-tuple"
c1, c2 = win
c1, c2 = sorted((c1, c2))
ind = numpy.logical_and(x >= c1, x <= c2)
if (c_loop == 0):
final_ind = numpy.logical_or(ind, ind)
else:
final_ind = numpy.logical_or(final_ind, ind)
x_windows = x[final_ind]
for ix in range(lenx):
for iy in range(leny):
spectra = data[:, ix, iy]
spec_windows = spectra[final_ind]
p = numpy.polyfit(x_windows, spec_windows, order)
spec_windows -= numpy.polyval(p, len(x_windows))
sigma[ix, iy] = spec_windows.std()
if (subtract):
data[:, ix, iy] -= numpy.polyval(p, lenv)
# this is the original input - with data reduced as needed
hdu_orig = pyfits.hdu.image.PrimaryHDU(header=header, data=data)
if returnrms:
#the following grabs relevant information from the original header
#and reproduces it in the RMSMap header shifted to account for
#the different shape of the data
crpix1 = sxpar(header, "CRPIX1")
crval1 = sxpar(header, "CRVAL1")
cdelt1 = sxpar(header, "CDELT1")
if kms:
#convert velocity to km/s
crval1 = crval1 / 1000.
cdelt1 = cdelt1 / 1000.
ctype1 = sxpar(header, "CTYPE1")
crpix2 = sxpar(header, "CRPIX2")
crval2 = sxpar(header, "CRVAL2")
cdelt2 = sxpar(header, "CDELT2")
ctype2 = sxpar(header, "CTYPE2")
crpix3 = sxpar(header, "CRPIX3")
crval3 = sxpar(header, "CRVAL3")
cdelt3 = sxpar(header, "CDELT3")
ctype3 = sxpar(header, "CTYPE3")
nv = sxpar(header, "NAXIS1")
nx = sxpar(header, "NAXIS2")
ny = sxpar(header, "NAXIS3")
blank = sxpar(header, "BLANK")
hnew = header.copy()
sxaddpar(hnew, "CRVAL1", crval2, comment="DEGREES")
sxaddpar(hnew, "CRPIX1", crpix2)
sxaddpar(hnew, "CDELT1", cdelt2, comment="DEGREES")
sxaddpar(hnew, "CTYPE1", ctype2)
sxaddpar(hnew, "CRVAL2", crval3, comment="DEGREES")
sxaddpar(hnew, "CRPIX2", crpix3)
sxaddpar(hnew, "CDELT2", cdelt3, comment="DEGREES")
sxaddpar(hnew, "CTYPE2", ctype3)
sxaddpar(hnew, "NAXIS", 2)
sxaddpar(hnew, "NAXIS1", nx)
sxaddpar(hnew, "NAXIS2", ny)
sxaddpar(hnew, "NAXIS3", 1)
sxaddpar(hnew, "NAXIS4", 1)
if chan:
vorc = 'CHANNEL'
else:
vorc = 'VELOCITY'
sxaddhist(hnew,
"WINDOW : %s; Window %s LIMITS" % (repr(windows), vorc))
#sxaddpar(hnew, "BUNIT", units, "Units")
sxdelpar(hnew, "CRVAL3")
sxdelpar(hnew, "CRPIX3")
sxdelpar(hnew, "CDELT3")
sxdelpar(hnew, "CTYPE3")
sxdelpar(hnew, "NAXIS3")
sxdelpar(hnew, "NAXIS4")
hdu_rms = pyfits.hdu.image.PrimaryHDU(data=sigma, header=hnew)
return (hdu_orig, hdu_rms)
else:
return hdu_orig
def momentcube(hdu,
v1,
v2,
header=None,
chan=False,
dontblank=False,
kms=True,
moment=0,
returnrms=False,
window=None):
"""
Create 2d moment image from cube. The input cube is expected
to be in the VLM format
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
This cube is expected to be in vlm format. If numpy format
data cube is passed, then the header parameter should also be
passed in.
@type hdu: pyfits HDU or numpy array
@param header: pyfits style header object that corresponds to the
hdu data variable. This header parameter is only considered if
the hdu parameter is a numpy array type rather than a pyfits HDU
type
@type header: pyfits header object
@param v1: lower velocity to be used (If chan is set to True, this
should be an integer). If chan is False, this is expected to be
velocity expressed in kms (if the input parameter kms is True). If
chan is False, and kms is False, v1 is treated with the same
units as the FITS header describes.
@param v2: upper velocity to be used. Same explanation as v1
@param chan: If True, v1 and v2 are treated as channels. If False,
they are treated as velocity
@type chan: Boolean
@param dontblank: By default, momentcube will use the BLANK fits
header value and automatically zero blanked values in order
to compute moments. If dontblank keyword is set, then
such blanking is not performed.
@type dontblank: Boolean
@param kms: If True velocity units is in kms.
@type kms: Boolean
@param moment: which moment to extract. default is moment=0
- moment=0 - integrated intensity
- moment=1 - centroid velocity
- moment=2 - width
@type moment: int
@param returnrms: If True, return image of the noise in the moment map
@type returnrms: Boolean
@param window: list of tuples (pairs) of channels/velocity to exclude
from polynomial calculation in determining rms. If chan is set to
True, these should be integers. If chan is False, this is expected to be
velocity expressed in kms (if the input parameter kms is True). If
chan is False, and kms is False, window is treated with the same
units as the FITS header describes.
@return: A HDU instance with 2d output map in the data attribute of the HDU,
and the corresponding header in header attribute of the HDU. If
returnrms is True, returns the momentmap and rms image as a tuple.
"""
if moment not in range(3):
raise SculptArgumentError('moment', "moment can only be one of 0,1,2")
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header,
astropy.io.fits.header.Header):
raise SculptArgumentError(
'header',
"Since you passed in data that is a numpy array, set header to a pyfits header type"
)
data = hdu
else:
raise SculptArgumentError(
'hdu',
"can only be one of pyfits.PrimaryHDU type or numpy ndarray")
if returnrms and window is None:
raise SculptArgumentError(
'rms',
'if returnrms is True, you need to specify a window to do the rms calculation in'
)
if returnrms and moment != 0:
raise SculptArgumentError('rms',
'For now, only moment=0 return rms image')
# calculate the x-axis (velocity)
crpix1 = sxpar(header, "CRPIX1")
crval1 = sxpar(header, "CRVAL1")
cdelt1 = sxpar(header, "CDELT1")
if kms:
#convert velocity to km/s
crval1 = crval1 / 1000.
cdelt1 = cdelt1 / 1000.
ctype1 = sxpar(header, "CTYPE1")
crpix2 = sxpar(header, "CRPIX2")
crval2 = sxpar(header, "CRVAL2")
cdelt2 = sxpar(header, "CDELT2")
ctype2 = sxpar(header, "CTYPE2")
crpix3 = sxpar(header, "CRPIX3")
crval3 = sxpar(header, "CRVAL3")
cdelt3 = sxpar(header, "CDELT3")
ctype3 = sxpar(header, "CTYPE3")
nv = sxpar(header, "NAXIS1")
nx = sxpar(header, "NAXIS2")
ny = sxpar(header, "NAXIS3")
blank = sxpar(header, "BLANK")
#cube = data.copy()
if not dontblank and blank is not None:
ind = numpy.where(data == blank)
data[ind] = 0.0
vind = numpy.zeros(nv, dtype=bool)
if not chan:
#get velocity axis
velax = getaxes(header, 1)
if v2 < v1:
v1, v2 = v2, v1
if chan:
vind[v1:v2 + 1] = True
else:
vind = numpy.logical_and(velax >= v1, velax <= v2)
N = len(numpy.where(vind)[0])
print "The number of spectral channels used, N: %d" % N
T = data[:, :, vind].sum(axis=2) #T is integ intensity image now
if moment >= 1:
C = (data[:, :, vind] * velax[vind]).sum(axis=2)
C = C / T #centroid velocity definition
if moment == 2:
W = (data[:, :, vind] * velax[vind]**2.).sum(axis=2)
W = W / T - C**2.
hnew = header.copy()
sxaddpar(hnew, "CRVAL1", crval2, comment="DEGREES")
sxaddpar(hnew, "CRPIX1", crpix2)
sxaddpar(hnew, "CDELT1", cdelt2, comment="DEGREES")
sxaddpar(hnew, "CTYPE1", ctype2)
sxaddpar(hnew, "CRVAL2", crval3, comment="DEGREES")
sxaddpar(hnew, "CRPIX2", crpix3)
sxaddpar(hnew, "CDELT2", cdelt3, comment="DEGREES")
sxaddpar(hnew, "CTYPE2", ctype3)
sxaddpar(hnew, "NAXIS", 2)
sxaddpar(hnew, "NAXIS1", nx)
sxaddpar(hnew, "NAXIS2", ny)
sxaddpar(hnew, "NAXIS3", 1)
sxaddpar(hnew, "NAXIS4", 1)
if chan:
vorc = 'CHANNEL'
else:
vorc = 'VELOCITY'
sxaddpar(hnew, "VMIN", v1, "LOWER %s LIMIT" % vorc)
sxaddpar(hnew, "VMAX", v2, "UPPER %s LIMIT" % vorc)
sxaddpar(hnew, "MOMENT", moment, comment="Order of Moment")
if moment == 0:
units = "K.km/s"
else:
units = "km/s"
sxaddpar(hnew, "BUNIT", units, "Units")
for axis in xrange(3, 5):
for attr in ('CRVAL', 'CRPIX', 'CDELT', 'CROTA', 'CTYPE', 'NAXIS'):
sxdelpar(hnew, '%s%1d' % (attr, axis))
if moment == 0:
dt = T * abs(cdelt1)
elif moment == 1:
dt = C
else:
dt = W
hdu = pyfits.PrimaryHDU(dt, header=hnew)
if moment == 0 and returnrms:
specrms = mkrmsimage(data, window=window, header=header)
rms_data = specrms.data * abs(cdelt1) * math.sqrt(N)
hrms = hnew.copy()
sxaddhist(hrms, "WINDOW : %s; Window %s LIMITS" % (repr(window), vorc))
rms = pyfits.PrimaryHDU(rms_data, header=hrms)
return hdu, rms
return hdu
def baseline(hdu, chan, windows, order = 0, subtract = True, returnrms = True, kms = True):
#this function needs to be passed a FITS HDU
#with data in the (V,X,Y) format
header = None
if isinstance(hdu, pyfits.hdu.image.PrimaryHDU):
# get data and header from the hdu
header = hdu.header
data = hdu.data
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, pyfits.header.Header):
raise SculptArgumentError('header', "Since you passed in data that is a numpy array, set header to a pyfits header type")
data = hdu
shape = data.shape
#windows over which to do the test for sigma values
#basically excluse major lines you observe
lenv, lenx, leny = shape
#defaultswindow = ((100,200),(650,750))
sigma = numpy.zeros((lenx, leny))
x = numpy.arange(lenv)
c_loop = 0
# this commented section is the makermsimage way of selecting them
# if someone wants to make a change regarding one being better, do it
# velax = getaxes(header, 1)
# indices = numpy.zeros(velax.shape, dtype=bool)
# for v1, v2 in window:
# if chan:
# indices[v1:v2] = True
# else:
# ind = numpy.logical_and(velax>= v1, velax<= v2)
# indices = numpy.logical_or(indices, ind)
# indices = numpy.logical_not(indices) # for indices to include in std calc
for win in windows:
if (len(win) != 2):
print "Each element in the window list must be a 2-tuple"
c1, c2 = win
c1, c2 = sorted((c1, c2))
ind = numpy.logical_and(x>=c1, x<=c2)
if (c_loop == 0):
final_ind = numpy.logical_or(ind,ind)
else:
final_ind = numpy.logical_or(final_ind,ind)
x_windows = x[final_ind]
for ix in range(lenx):
for iy in range(leny):
spectra = data[:,ix,iy]
spec_windows = spectra[final_ind]
p = numpy.polyfit(x_windows,spec_windows,order)
spec_windows -= numpy.polyval(p,len(x_windows))
sigma[ix, iy] = spec_windows.std()
if (subtract):
data[:,ix,iy] -= numpy.polyval(p,lenv)
# this is the original input - with data reduced as needed
hdu_orig = pyfits.hdu.image.PrimaryHDU(header = header, data = data)
if returnrms:
#the following grabs relevant information from the original header
#and reproduces it in the RMSMap header shifted to account for
#the different shape of the data
crpix1 = sxpar(header,"CRPIX1")
crval1 = sxpar(header,"CRVAL1")
cdelt1 = sxpar(header,"CDELT1")
if kms:
#convert velocity to km/s
crval1 = crval1/1000.
cdelt1 = cdelt1/1000.
ctype1 = sxpar(header,"CTYPE1")
crpix2 = sxpar(header,"CRPIX2")
crval2 = sxpar(header,"CRVAL2")
cdelt2 = sxpar(header,"CDELT2")
ctype2 = sxpar(header,"CTYPE2")
crpix3 = sxpar(header,"CRPIX3")
crval3 = sxpar(header,"CRVAL3")
cdelt3 = sxpar(header,"CDELT3")
ctype3 = sxpar(header,"CTYPE3")
nv = sxpar(header,"NAXIS1")
nx = sxpar(header,"NAXIS2")
ny = sxpar(header,"NAXIS3")
blank = sxpar(header,"BLANK")
hnew = header.copy()
sxaddpar(hnew, "CRVAL1", crval2, comment="DEGREES")
sxaddpar(hnew, "CRPIX1", crpix2)
sxaddpar(hnew, "CDELT1", cdelt2, comment="DEGREES")
sxaddpar(hnew, "CTYPE1", ctype2)
sxaddpar(hnew, "CRVAL2", crval3, comment="DEGREES")
sxaddpar(hnew, "CRPIX2", crpix3)
sxaddpar(hnew, "CDELT2", cdelt3, comment="DEGREES")
sxaddpar(hnew, "CTYPE2", ctype3)
sxaddpar(hnew, "NAXIS", 2)
sxaddpar(hnew, "NAXIS1", nx)
sxaddpar(hnew, "NAXIS2", ny)
sxaddpar(hnew, "NAXIS3", 1)
sxaddpar(hnew, "NAXIS4", 1)
if chan:
vorc = 'CHANNEL'
else:
vorc = 'VELOCITY'
sxaddhist(hnew, "WINDOW : %s; Window %s LIMITS" % (repr(windows), vorc))
#sxaddpar(hnew, "BUNIT", units, "Units")
sxdelpar(hnew, "CRVAL3")
sxdelpar(hnew, "CRPIX3")
sxdelpar(hnew, "CDELT3")
sxdelpar(hnew, "CTYPE3")
sxdelpar(hnew, "NAXIS3")
sxdelpar(hnew, "NAXIS4")
hdu_rms = pyfits.hdu.image.PrimaryHDU(data=sigma, header=hnew)
return (hdu_orig, hdu_rms)
else:
return hdu_orig
def mkrmsimage(hdu,
window,
header=None,
chan=False,
dontblank=False,
kms=True,
moment=0):
"""
Create rms image from spectral cube. The input cube is expected
to be in the VLM format
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
This cube is expected to be in vlm format. If numpy format
data cube is passed, then the header parameter should also be
passed in.
@type hdu: pyfits HDU or numpy array
@param header: pyfits style header object that corresponds to the
hdu data variable. This header parameter is only considered if
the hdu parameter is a numpy array type rather than a pyfits HDU
type
@type header: pyfits header object
@param window: list of tuples (pairs) of channels/velocity to exclude
from polynomial calculation in determining rms. If chan is set to
True, these should be integers. If chan is False, this is expected to be
velocity expressed in kms (if the input parameter kms is True). If
chan is False, and kms is False, v1 is treated with the same
units as the FITS header describes.
@param chan: If True, window variables are treated as channels. If False,
they are treated as velocity
@type chan: Boolean
@param dontblank: By default, mkrmsimage will use the BLANK fits
header value and automatically zero blanked values in order
to compute rms. If dontblank keyword is set, then
such blanking is not performed.
@type dontblank: Boolean
@param kms: If True velocity units is in kms.
@type kms: Boolean
@return: A HDU instance with 2d output map in the data attribute of the HDU,
and the corresponding header in header attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header,
astropy.io.fits.header.Header):
raise SculptArgumentError(
'header',
"Since you passed in data that is a numpy array, set header to a pyfits header type"
)
data = hdu
else:
raise SculptArgumentError(
'hdu',
"can only be one of pyfits.PrimaryHDU type or numpy ndarray")
#check for list or tuple type of window
if type(window) not in (types.ListType, types.TupleType):
raise SculptArgumentError('window',
'has to be a List Type or Tuple Type')
# calculate the x-axis (velocity)
crpix1 = sxpar(header, "CRPIX1")
crval1 = sxpar(header, "CRVAL1")
cdelt1 = sxpar(header, "CDELT1")
if kms:
#convert velocity to km/s
crval1 = crval1 / 1000.
cdelt1 = cdelt1 / 1000.
ctype1 = sxpar(header, "CTYPE1")
crpix2 = sxpar(header, "CRPIX2")
crval2 = sxpar(header, "CRVAL2")
cdelt2 = sxpar(header, "CDELT2")
ctype2 = sxpar(header, "CTYPE2")
crpix3 = sxpar(header, "CRPIX3")
crval3 = sxpar(header, "CRVAL3")
cdelt3 = sxpar(header, "CDELT3")
ctype3 = sxpar(header, "CTYPE3")
nv = sxpar(header, "NAXIS1")
nx = sxpar(header, "NAXIS2")
ny = sxpar(header, "NAXIS3")
blank = sxpar(header, "BLANK")
cube = data.copy()
if not dontblank and blank is not None:
ind = numpy.where(cube == blank)
cube[ind] = 0.0
#get velocity axis
velax = getaxes(header, 1)
indices = numpy.zeros(velax.shape, dtype=bool)
for v1, v2 in window:
if chan:
indices[v1:v2] = True
else:
ind = numpy.logical_and(velax >= v1, velax <= v2)
indices = numpy.logical_or(indices, ind)
indices = numpy.logical_not(indices) # for indices to include in std calc
rms = cube[:, :, indices].std(axis=2)
hnew = header.copy()
sxaddpar(hnew, "CRVAL1", crval2, comment="DEGREES")
sxaddpar(hnew, "CRPIX1", crpix2)
sxaddpar(hnew, "CDELT1", cdelt2, comment="DEGREES")
sxaddpar(hnew, "CTYPE1", ctype2)
sxaddpar(hnew, "CRVAL2", crval3, comment="DEGREES")
sxaddpar(hnew, "CRPIX2", crpix3)
sxaddpar(hnew, "CDELT2", cdelt3, comment="DEGREES")
sxaddpar(hnew, "CTYPE2", ctype3)
sxaddpar(hnew, "NAXIS", 2)
sxaddpar(hnew, "NAXIS1", nx)
sxaddpar(hnew, "NAXIS2", ny)
sxaddpar(hnew, "NAXIS3", 1)
sxaddpar(hnew, "NAXIS4", 1)
if chan:
vorc = 'CHANNEL'
else:
vorc = 'VELOCITY'
sxaddhist(hnew, "WINDOW : %s; Window %s LIMITS" % (repr(window), vorc))
#sxaddpar(hnew, "BUNIT", units, "Units")
sxdelpar(hnew, "CRVAL3")
sxdelpar(hnew, "CRPIX3")
sxdelpar(hnew, "CDELT3")
sxdelpar(hnew, "CTYPE3")
sxdelpar(hnew, "NAXIS3")
sxdelpar(hnew, "NAXIS4")
hdu = pyfits.PrimaryHDU(rms, header=hnew)
return hdu
def extract_posvel_angle(hdu, p1, angle, header=None, gauss_width=2):
"""
Given a hdu in vlm format, and one position (in pixel
coordinates), and an angle, obtains a position velocity cut about
that position with that angle, along that line
Returns p-v image as a pyfits hdu unit.
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
If numpy format data cube is passed, then the header parameter
should also be passed in. The cube is expected to be in vlm format.
@type hdu: pyfits hdu type or numpy nd-array
@param p1: (x, y) pixel location of position 1. The x,y position can be a
two-tuple, a list of two elements, or numpy array of two elements
@type p1: tuple, list or array of floats or ints
@param angle: angle of PV cut with respect to X-axis in degrees
@type angle: float
@param header: pyfits header object if needed
@gauss_width: the width of the gaussian kernel to use in weighting
neighboring pixels with when extracting spectra
@type gauss_width: int
@return A HDU instance with 2d position-velocity image in the data
attribute of the HDU, and the corresponding header in header
attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, astropy.io.fits.header.Header):
raise SculptArgumentError('header', "Since you passed in data that is a numpy array, set header to a pyfits header type")
data = hdu
else:
raise SculptArgumentError('hdu', "can only be one of pyfits.PrimaryHDU type or numpy ndarray")
hdr = header.copy()
dt = data.copy()
hdu = pyfits.PrimaryHDU(dt, header=hdr)
if gauss_width <= 0 or type(gauss_width) != types.IntType:
raise SculptArgumentError('gauss_width', "should be positive and non-zero integer")
try:
if len(p1) != 2:
raise SculptArgumentError('positions', "Positions p1 and p2 should be 2-element tuples, lists or arrays")
except:
raise SculptArgumentError('positions', "Positions p1 and p2 should be 2-element tuples, lists or arrays")
a, b = p1
#x2, y2 = p2
m = math.tan(math.radians(angle)) #slope of line
xmin, ymin = 0, 0
y1, x1, v1 = hdu.data.shape
ymax = y1-1
xmax = x1-1
print "xylims: ", xmin, ymin, xmax, ymax
# #check for x-limits first
# xl = None
# xh = None
# if m > 0.0:
# #line sloped right of vertical
# yl = m*(xmin-a) + b
# else:
# #line sloped left of vertical
# yl = m*(xmax-a) + b
# if ymin <= yl and yl <= ymax:
# if m > 0.0:
# xl = xmin
# else:
# xl = xmax
# if m > 0.0:
# yh = m*(xmax-a) + b
# else:
# yh = m*(xmin-a) + b
# print "yl, yh = ", yl, yh
# if ymin <= yh and yh <= ymax:
# if m > 0.0:
# xh = xmax
# else:
# xh = xmin
# if xl is None:
# yl = None
# if m > 0.0:
# xl = ((ymin-b)/m) + a
# else:
# xl = ((ymax-b)/m) + a
# if xmin <= xl and xl <= xmax:
# yl = ymin
# if xh is None:
# yh = None
# if m > 0.0:
# xh = ((ymax-b)/m) + a
# else:
# xh = ((ymin-b)/m) + a
# if xmin <= xh and xh <= xmax:
# yh = ymax
i1, i2 = line_rectangle_intersection(xmin, xmax, ymin, ymax, a, b, angle)
xl,yl = i1
xh,yh = i2
print xl, xh, yl, yh
d = math.sqrt((xl-xh)**2 + (yl-yh)**2.)
posvel = numpy.zeros((int(d), dt.shape[2]), dtype='float')
dist = numpy.arange(int(d))
if xl <= xh:
x = xl + numpy.sqrt(dist**2./(1+m**2.)) #2nd term is deltax
else:
x = xl - numpy.sqrt(dist**2./(1+m**2.)) #2nd term is deltax
y = yl + m*(x-xl)
for i in range(int(d)):
posvel[i,:] = extract_spec(hdu, x[i], y[i],
gauss_width=gauss_width).data
r, d = xyad(header, a, b)
sxaddpar(hdr, "CRVAL2", r)
dmid = math.sqrt((xl-a)**2 + (yl-b)**2)
sxaddpar(hdr, "CRPIX2", int(dmid))
sxaddpar(hdr, "NAXIS", 2)
sxdelpar(hdr, "NAXIS3")
for name in ('CTYPE', 'CRVAL', 'CDELT', 'CRPIX'):
sxdelpar(hdr, "%s3" % name)
sxaddhist(hdr, "Extracted posvel image from (%.1f, %.1f) with angle %.2f with gauss_width=%s" % (a, b, angle, gauss_width))
return pyfits.PrimaryHDU(posvel, header=hdr)
def extract_posvel(hdu, p1, p2, header=None, gauss_width=2):
"""
Given a hdu in vlm format, and two positions (in pixel
coordinates), obtains a position velocity cut along that line
Returns p-v image as a pyfits hdu unit.
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
If numpy format data cube is passed, then the header parameter
should also be passed in. The cube is expected to be in vlm format.
@type hdu: pyfits hdu type or numpy nd-array
@param p1: (x, y) pixel location of position 1. The x,y position can be a
two-tuple, a list of two elements, or numpy array of two elements
@type p1: tuple, list or array of floats or ints
@param p2: (x, y) pixel location of position 2. The x,y position can be a
two-tuple, a list of two elements, or numpy array of two elements
@type p2: tuple, list or array of floats or ints
@param header: pyfits header object if needed
@gauss_width: the width of the gaussian kernel to use in weighting
neighboring pixels with when extracting spectra
@type gauss_width: int
@return A HDU instance with 2d position-velocity image in the data
attribute of the HDU, and the corresponding header in header
attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, astropy.io.fits.header.Header):
raise SculptArgumentError('header', "Since you passed in data that is a numpy array, set header to a pyfits header type")
data = hdu
else:
raise SculptArgumentError('hdu', "can only be one of pyfits.PrimaryHDU type or numpy ndarray")
hdr = header.copy()
dt = data.copy()
hdu = pyfits.PrimaryHDU(dt, header=hdr)
if gauss_width <= 0 or type(gauss_width) != types.IntType:
raise SculptArgumentError('gauss_width', "should be positive and non-zero integer")
try:
if len(p1) != 2 and len(p2) != 2:
raise SculptArgumentError('positions', "Positions p1 and p2 should be 2-element tuples, lists or arrays")
except:
raise SculptArgumentError('positions', "Positions p1 and p2 should be 2-element tuples, lists or arrays")
x1, y1 = p1
x2, y2 = p2
m = (y1-y2)/(x1-x2) #slope of line
d = math.sqrt((x1-x2)**2 + (y1-y2)**2.)
posvel = numpy.zeros((int(d), dt.shape[2]), dtype='float')
dist = numpy.arange(int(d))
if x1 <= x2:
x = x1 + numpy.sqrt(dist**2./(1+m**2.)) #2nd term is deltax
else:
x = x1 - numpy.sqrt(dist**2./(1+m**2.)) #2nd term is deltax
y = y1 + m*(x-x1)
for i in range(int(d)):
posvel[i,:] = extract_spec(hdu, x[i], y[i],
gauss_width=gauss_width).data
xmid = (x1+x2)/2.
ymid = (y1+y2)/2.
r, d = xyad(header, xmid, ymid)
sxaddpar(hdr, "CRVAL2", r)
mid = posvel.shape[0]/2.
sxaddpar(hdr, "CRPIX2", mid)
sxaddpar(hdr, "NAXIS", 2)
sxdelpar(hdr, "NAXIS3")
for name in ('CTYPE', 'CRVAL', 'CDELT', 'CRPIX'):
sxdelpar(hdr, "%s3" % name)
sxaddhist(hdr, "Extracted posvel image from (%.1f, %.1f) to (%.1f, %.1f) with gauss_width=%s" % (x1, y1, x2, y2, gauss_width))
return pyfits.PrimaryHDU(posvel, header=hdr)
def extract_spec(hdu, x0, y0, header=None, gauss_width=2):
"""
Given a hdu in vlm format, and a x0,y0 location (in pixel
coordinates), obtains a gaussian-convolved spectra
around x0, y0 with width gauss_width.
Returns spectrum as a pyfits hdu unit as well.
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
If numpy format data cube is passed, then the header parameter
should also be passed in. The cube is expected to be in vlm format.
@type hdu: pyfits hdu type or numpy nd-array
@param x0: x pixel location where spectrum will be centered
@type x0: float or int
@param y0: y pixel location where spectrum will be centered
@type y0: float or int
@param header: pyfits header object if needed
@gauss_width: the width of the gaussian kernel to use in weighting
neighboring pixels with.
@type gauss_width: int
@return A HDU instance with 1d spectrum map in the data attribute of the HDU,
and the corresponding header in header attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header,
astropy.io.fits.header.Header):
raise SculptArgumentError(
'header',
"Since you passed in data that is a numpy array, set header to a pyfits header type"
)
data = hdu
else:
raise SculptArgumentError(
'hdu',
"can only be one of pyfits.PrimaryHDU type or numpy ndarray")
hdr = header.copy()
dt = data.copy()
if gauss_width <= 0 or type(gauss_width) != types.IntType:
raise SculptArgumentError('gauss_width',
"should be positive and non-zero integer")
gk = gauss_kern(gauss_width)
kernx, kerny = gk.shape
ysize, xsize, vsize = dt.shape
if x0 < 0 or x0 >= xsize:
raise SculptArgumentError(
'x0', "x0=%s is out of bounds of x-limits: (0, %d)" % (x0, xsize))
if y0 < 0 or y0 >= ysize:
raise SculptArgumentError(
'y0', "y0=%s is out of bounds of y-limits: (0, %d)" % (y0, ysize))
#print gk.shape
xmin = int(round(x0)) - kernx / 2
xmax = xmin + kernx
if xmin < 0:
xmin = 0
if xmax >= xsize:
xmax = xsize - 1
ymin = int(round(y0)) - kerny / 2
ymax = ymin + kerny
if ymin < 0:
ymin = 0
if ymax >= ysize:
ymax = ysize - 1
specdata = numpy.zeros(vsize, dtype='float')
#print "Gk = " , gk
#print xmin, xmax, ymin, ymax
#print gk.sum().shape
if xmax - xmin != kernx or ymax - ymin != kerny:
specdata = dt[y0, x0, :]
else:
for i in range(vsize):
specdata[i] = (gk * dt[ymin:ymax, xmin:xmax, i]).sum() / gk.sum()
sxaddpar(hdr, "NAXIS", 1)
sxdelpar(hdr, "NAXIS2")
sxdelpar(hdr, "NAXIS3")
for i in range(2, 4):
for name in ('CTYPE', 'CRVAL', 'CDELT', 'CRPIX'):
sxdelpar(hdr, "%s%i" % (name, i))
sxaddhist(
hdr, "Extracted spectrum at (%s, %s) with gauss_width=%s" %
(x0, y0, gauss_width))
return pyfits.PrimaryHDU(specdata, header=hdr)
def mkrmsimage (hdu, window, header=None,
chan=False, dontblank=False,
kms=True,
moment=0):
"""
Create rms image from spectral cube. The input cube is expected
to be in the VLM format
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
This cube is expected to be in vlm format. If numpy format
data cube is passed, then the header parameter should also be
passed in.
@type hdu: pyfits HDU or numpy array
@param header: pyfits style header object that corresponds to the
hdu data variable. This header parameter is only considered if
the hdu parameter is a numpy array type rather than a pyfits HDU
type
@type header: pyfits header object
@param window: list of tuples (pairs) of channels/velocity to exclude
from polynomial calculation in determining rms. If chan is set to
True, these should be integers. If chan is False, this is expected to be
velocity expressed in kms (if the input parameter kms is True). If
chan is False, and kms is False, v1 is treated with the same
units as the FITS header describes.
@param chan: If True, window variables are treated as channels. If False,
they are treated as velocity
@type chan: Boolean
@param dontblank: By default, mkrmsimage will use the BLANK fits
header value and automatically zero blanked values in order
to compute rms. If dontblank keyword is set, then
such blanking is not performed.
@type dontblank: Boolean
@param kms: If True velocity units is in kms.
@type kms: Boolean
@return: A HDU instance with 2d output map in the data attribute of the HDU,
and the corresponding header in header attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, astropy.io.fits.header.Header):
raise SculptArgumentError('header', "Since you passed in data that is a numpy array, set header to a pyfits header type")
data = hdu
else:
raise SculptArgumentError('hdu', "can only be one of pyfits.PrimaryHDU type or numpy ndarray")
#check for list or tuple type of window
if type(window) not in (types.ListType, types.TupleType):
raise SculptArgumentError('window', 'has to be a List Type or Tuple Type')
# calculate the x-axis (velocity)
crpix1 = sxpar(header,"CRPIX1")
crval1 = sxpar(header,"CRVAL1")
cdelt1 = sxpar(header,"CDELT1")
if kms:
#convert velocity to km/s
crval1 = crval1/1000.
cdelt1 = cdelt1/1000.
ctype1 = sxpar(header,"CTYPE1")
crpix2 = sxpar(header,"CRPIX2")
crval2 = sxpar(header,"CRVAL2")
cdelt2 = sxpar(header,"CDELT2")
ctype2 = sxpar(header,"CTYPE2")
crpix3 = sxpar(header,"CRPIX3")
crval3 = sxpar(header,"CRVAL3")
cdelt3 = sxpar(header,"CDELT3")
ctype3 = sxpar(header,"CTYPE3")
nv = sxpar(header,"NAXIS1")
nx = sxpar(header,"NAXIS2")
ny = sxpar(header,"NAXIS3")
blank = sxpar(header,"BLANK")
cube = data.copy()
if not dontblank and blank is not None:
ind = numpy.where(cube == blank)
cube[ind] = 0.0
#get velocity axis
velax = getaxes(header, 1)
indices = numpy.zeros(velax.shape, dtype=bool)
for v1, v2 in window:
if chan:
indices[v1:v2] = True
else:
ind = numpy.logical_and(velax>= v1, velax<= v2)
indices = numpy.logical_or(indices, ind)
indices = numpy.logical_not(indices) # for indices to include in std calc
rms = cube[:,:,indices].std(axis=2)
hnew = header.copy()
sxaddpar(hnew, "CRVAL1", crval2, comment="DEGREES")
sxaddpar(hnew, "CRPIX1", crpix2)
sxaddpar(hnew, "CDELT1", cdelt2, comment="DEGREES")
sxaddpar(hnew, "CTYPE1", ctype2)
sxaddpar(hnew, "CRVAL2", crval3, comment="DEGREES")
sxaddpar(hnew, "CRPIX2", crpix3)
sxaddpar(hnew, "CDELT2", cdelt3, comment="DEGREES")
sxaddpar(hnew, "CTYPE2", ctype3)
sxaddpar(hnew, "NAXIS", 2)
sxaddpar(hnew, "NAXIS1", nx)
sxaddpar(hnew, "NAXIS2", ny)
sxaddpar(hnew, "NAXIS3", 1)
sxaddpar(hnew, "NAXIS4", 1)
if chan:
vorc = 'CHANNEL'
else:
vorc = 'VELOCITY'
sxaddhist(hnew, "WINDOW : %s; Window %s LIMITS" % (repr(window), vorc))
#sxaddpar(hnew, "BUNIT", units, "Units")
sxdelpar(hnew, "CRVAL3")
sxdelpar(hnew, "CRPIX3")
sxdelpar(hnew, "CDELT3")
sxdelpar(hnew, "CTYPE3")
sxdelpar(hnew, "NAXIS3")
sxdelpar(hnew, "NAXIS4")
hdu = pyfits.PrimaryHDU(rms, header=hnew)
return hdu
def extract_spec(hdu, x0, y0, header=None, gauss_width=2):
"""
Given a hdu in vlm format, and a x0,y0 location (in pixel
coordinates), obtains a gaussian-convolved spectra
around x0, y0 with width gauss_width.
Returns spectrum as a pyfits hdu unit as well.
@param hdu: input pyfits style HDU (header data unit) or just the numpy
numpy format data cube from pyfits HDU data attribute.
If numpy format data cube is passed, then the header parameter
should also be passed in. The cube is expected to be in vlm format.
@type hdu: pyfits hdu type or numpy nd-array
@param x0: x pixel location where spectrum will be centered
@type x0: float or int
@param y0: y pixel location where spectrum will be centered
@type y0: float or int
@param header: pyfits header object if needed
@gauss_width: the width of the gaussian kernel to use in weighting
neighboring pixels with.
@type gauss_width: int
@return A HDU instance with 1d spectrum map in the data attribute of the HDU,
and the corresponding header in header attribute of the HDU.
"""
if isinstance(hdu, astropy.io.fits.hdu.image.PrimaryHDU):
#get data and header from the hdu
data = hdu.data
header = hdu.header
elif isinstance(hdu, numpy.ndarray):
if header is None or not isinstance(header, astropy.io.fits.header.Header):
raise SculptArgumentError('header', "Since you passed in data that is a numpy array, set header to a pyfits header type")
data = hdu
else:
raise SculptArgumentError('hdu', "can only be one of pyfits.PrimaryHDU type or numpy ndarray")
hdr = header.copy()
dt = data.copy()
if gauss_width <= 0 or type(gauss_width) != types.IntType:
raise SculptArgumentError('gauss_width', "should be positive and non-zero integer")
gk = gauss_kern(gauss_width)
kernx, kerny = gk.shape
ysize, xsize, vsize = dt.shape
if x0 < 0 or x0 >= xsize:
raise SculptArgumentError('x0', "x0=%s is out of bounds of x-limits: (0, %d)" % (x0, xsize))
if y0 < 0 or y0 >= ysize:
raise SculptArgumentError('y0', "y0=%s is out of bounds of y-limits: (0, %d)" % (y0, ysize))
#print gk.shape
xmin = int(round(x0))-kernx/2
xmax = xmin+kernx
if xmin<0:
xmin = 0
if xmax >= xsize:
xmax = xsize-1
ymin = int(round(y0))-kerny/2
ymax = ymin+kerny
if ymin<0:
ymin = 0
if ymax >= ysize:
ymax = ysize-1
specdata = numpy.zeros(vsize, dtype='float')
#print "Gk = " , gk
#print xmin, xmax, ymin, ymax
#print gk.sum().shape
if xmax-xmin != kernx or ymax-ymin != kerny:
specdata = dt[y0, x0, :]
else:
for i in range(vsize):
specdata[i] = (gk*dt[ymin:ymax, xmin:xmax, i]).sum()/gk.sum()
sxaddpar(hdr, "NAXIS", 1)
sxdelpar(hdr, "NAXIS2")
sxdelpar(hdr, "NAXIS3")
for i in range(2,4):
for name in ('CTYPE', 'CRVAL', 'CDELT', 'CRPIX'):
sxdelpar(hdr, "%s%i" % (name, i))
sxaddhist(hdr, "Extracted spectrum at (%s, %s) with gauss_width=%s" % (x0,y0,gauss_width))
return pyfits.PrimaryHDU(specdata, header=hdr)