def line_sum(image, sumflag="c", nbgr=0, width=0, pow=2.0, tangent=False, compress=1):
"""
Sum down 'c'olumns or across 'r'ows
This returns the summed data and background
"""
if sumflag == "c":
data = image.sum(axis=0)
elif sumflag == "r":
data = image.sum(axis=1)
npts = len(data)
data_idx = num.arange(npts, dtype=float)
# data_err = data**(0.5)
### compute background
bgr = background(data, nbgr=nbgr, width=width, pow=pow, tangent=tangent, compress=compress)
return (data, data_idx, bgr)
def line_sum(image,sumflag='c',nbgr=0,width=0,pow=2.,tangent=False,compress=1):
"""
Sum down 'c'olumns or across 'r'ows
This returns the summed data and background
"""
if sumflag == 'c':
data = image.sum(axis=0)
elif sumflag == 'r':
data = image.sum(axis=1)
npts = len(data)
data_idx = num.arange(npts,dtype=float)
#data_err = data**(0.5)
### compute background
bgr = background(data,nbgr=nbgr,width=width,pow=pow,tangent=tangent,
compress=compress)
return (data, data_idx, bgr)
def image_bgr(image,lineflag='c',nbgr=3,width=100,pow=2.,tangent=False,
nline=1,filter=False,compress=1,plot=False):
"""
Calculate a 2D background for the image.
Parameters:
-----------
* image is the (hopefully clipped) image data
* lineflag ('c' or 'r') corresponds to to direction which is
used to generate the background
* nbgr = number of end points to use in linear background determination
(see background.background)
* width should correspond roughly to the actual peak
width in the lineflag direction. The background should fit
features that are in general broader than this value
Note that width = 0 corresponds to no polynomial bgr
* pow is the power of the polynomial used in background determination
(see background.background)
* tangent is a flag to indicate if local slope of the data should be fitted
(see background.background)
* nline = number of lines to average for each bgr line fit
* filter (True/False) flag indicates if a spline filter should be applied
before background subtraction. (see scipy.ndimage.filters)
* compress is a factor by which the number of points in each line is
reduced. This helps speed up the background fits.
* plot is a flag to indicate if a 'plot' should be made
"""
bgr_arr = num.zeros(image.shape)
# note this works poorly if the filter removes
# too much intensity. Use with caution!
if filter == True:
#image = ndimage.laplace(image)
#print 'spline filter'
image = ndimage.interpolation.spline_filter(image,order=3)
# fit to rows
if lineflag=='r':
if nline > 1:
ll = int(nline/2.)
n = image.shape[0]
line = num.zeros(len(image[0]))
for j in range(n):
idx = [k for k in range(j-ll,j+ll+1) if k>=0 and k<n]
line = line * 0.0
for k in idx:
line = line + image[k]
line = line/float(len(idx))
bgr_arr[j,:] = background(line,nbgr=nbgr,width=width,pow=pow,
tangent=tangent,compress=compress)
else:
for j in range(image.shape[0]):
bgr_arr[j,:] = background(image[j],nbgr=nbgr,width=width,pow=pow,
tangent=tangent,compress=compress)
# fit to cols
if lineflag=='c':
if nline > 1:
ll = int(nline/2.)
n = image.shape[1]
line = num.zeros(len(image[:,0]))
for j in range(n):
idx = [k for k in range(j-ll,j+ll+1) if k>=0 and k<n]
line = line * 0.0
for k in idx:
line = line + image[:,k]
line = line/float(len(idx))
bgr_arr[:,j] = background(line,nbgr=nbgr,width=width,pow=pow,
tangent=tangent,compress=compress)
else:
for j in range(image.shape[1]):
bgr_arr[:,j] = background(image[:,j],nbgr=nbgr,width=width,pow=pow,
tangent=tangent,compress=compress)
#show
if plot:
pyplot.figure(3)
pyplot.clf()
pyplot.subplot(3,1,1)
pyplot.imshow(image)
pyplot.title("image")
pyplot.colorbar()
pyplot.subplot(3,1,2)
pyplot.imshow(bgr)
pyplot.title("background")
pyplot.colorbar()
pyplot.subplot(3,1,3)
pyplot.imshow(image-bgr)
pyplot.title("image - background")
pyplot.colorbar()
return bgr_arr
