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make_fuv_wfc3_finder.py
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make_fuv_wfc3_finder.py
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from astropy.io import fits
from scipy.ndimage.interpolation import rotate
import numpy as np
from matplotlib import pyplot
from datetime import datetime
import math
import pdb
import scipy.interpolate
import scipy.ndimage
from helper_functions import add_date_to_plot
def congrid(a, newdims, method='linear', centre=False, minusone=False):
if not a.dtype in [np.float64, np.float32]:
a = np.cast[float](a)
m1 = np.cast[int](1)
print m1
ofs = np.cast[int](centre) * 0.5
old = np.array( a.shape )
ndims = len( a.shape)
#if len( newdims ) != ndims:
# print "[congrid] dimensions error. "\
# "This routine currently only supports "\
# "rebinning to the same number of dimensions."
# return None
newdims = np.asarray( newdims, dtype=float )
#newdims = newdims.reshape(newdims,1)
dimlist = []
if method == 'neighbor':
for i in range( ndims ):
base = np.indices(newdims)
dimlist.append( (old - m1) / (newdims - m1) * (base + ofs) - ofs )
cd = np.array( dimlist ).round().astype(int)
newa = a[list( cd )]
return newa
elif method in ['nearest','linear']:
for i in range( ndims ):
base = np.arange( newdims )
dimlist.append( (old - m1) / (newdims - m1) * (base + ofs) - ofs)
olddims = [np.arange(i, dtype = np.float) for i in list( a.shape )]
mint = scipy.interpolate.interp1d( olddims[-1], a, kind=method )
newa = mint
newa = mint( dimlist[-1] )
trorder = [ndims - 1] + range (ndims - 1 )
for i in range( ndims -2, -1, -1 ):
print 'here'
newa = newa.transpose( trorder)
mint = scipy.interpolate.interp1d( olddims[i], newa, kind=method )
newa = mint( dimlist[i] )
if ndims > 1:
newa = newa.transpose( trorder)
return newa
else:
print "Congrid error:"
return None
def make_fuv_finder_plot(stis_img, wfc3_cc_offset, sn_yloc_stis = 331):
'''
Plot a 3 panel pdf of the WFC3 image on the left, cropped to the slit location in the middle
and the STIS cross-dispersion profile on the right. The region plotted for WFPC2 was determined
by cross-correlating the cross-dispersion profiles of different x locations with the STIS data.
This is performed in the notebook currently.
'''
wfc3_img = fits.getdata('hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfc3_f275w_wf_drz.fits', 1)
orientat = 36.6-153.72 #+90
rot_wfc3_img = rotate(wfc3_img, orientat)
wfc3_y_start = 375+wfc3_cc_offset
fig = pyplot.figure(figsize = [20, 15])
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1, 3, 3)
wfc3_x_start = 717.0
wfc3_platescale = 0.0394 #arcsec/pix
stis_slit_height_pix = int(25.0/wfc3_platescale)
stis_slit_width_pix = 0.5/wfc3_platescale
wfc3_x_end = wfc3_x_start + stis_slit_width_pix
ax1.set_title('WFC3 Image of 2010JL')
im1 = ax1.imshow(rot_wfc3_img, interpolation = 'nearest', vmin = 0, vmax = 0.1)
ax1.set_ylim(50, 1200)
ax1.set_xlim(384, 884)
#ax1.plot([1220, 1220, 1250, 1250, 1220], [wfc3_y_start+wfc3_cc_offset, wfc3_y_start+wfc3_cc_offset+stis_slit_height_pix, wfc3_y_start+wfc3_cc_offset+stis_slit_height_pix, wfc3_y_start+wfc3_cc_offset, wfc3_y_start+wfc3_cc_offset], color = 'r')
#Make compass
#north_dx = -50.0*math.sin(orientat*math.pi/180.0)
#north_dy = 50.0*math.cos(orientat*math.pi/180.0)
#east_dx = 50.0*math.sin((90.0-orientat)*math.pi/180.0)
#east_dy = 50.0*math.cos((90.0-orientat)*math.pi/180.0)
#arrow_center_x = 1150
#arrow_center_y = 1050
#ax1.arrow(arrow_center_x, arrow_center_y, north_dx, north_dy, color = 'w', width = 0.5, head_length = 12*0.5)
#ax1.arrow(arrow_center_x, arrow_center_y, east_dx, east_dy, color = 'w', width = 0.5, head_length = 12*0.5)
#ax1.text(arrow_center_x + north_dx, arrow_center_y + north_dy +10, 'N', color = 'w' )
#ax1.text(arrow_center_x + east_dx, arrow_center_y + east_dy +10, 'E', color = 'w' )
ax2.set_title('STIS Slit position on WFC3 Image')
im2 = ax2.imshow(rot_wfc3_img, interpolation = 'nearest', vmin = 0, vmax = 0.1)
#ax2.set_ylim(wfc3_y_start+wfc3_cc_offset, wfc3_y_start+wfc3_cc_offset+stis_slit_height_pix)
ax2.set_ylim(wfc3_y_start+0, wfc3_y_start+0+stis_slit_height_pix)
ax2.set_xlim(684, 762)
ax2.axvspan(684, wfc3_x_start, color = 'k', alpha = 0.5)
ax2.axvspan(wfc3_x_end, 762, color = 'k', alpha = 0.5)
ax2.set_yticks(np.arange(ax2.get_ylim()[0], ax2.get_ylim()[1], 50))
ax2.grid(color = 'w')
ax3.set_title('Normalized XD profiles from STIS and WFC3')
wfc3_xd_lower_y = wfc3_y_start
wfc3_xd_upper_y = wfc3_y_start+stis_slit_height_pix
print stis_slit_height_pix
wfc3_xd_lower_x = wfc3_x_start
wfc3_xd_upper_x = wfc3_x_end+1
#Choose a small y region of background to normalize by
normalization = np.max(np.sum(rot_wfc3_img[wfc3_y_start:wfc3_y_start + 1*stis_slit_height_pix, wfc3_x_start:wfc3_x_end+1], axis = 1))
ax3.plot(np.sum(rot_wfc3_img[wfc3_xd_lower_y:wfc3_xd_upper_y, wfc3_xd_lower_x:wfc3_xd_upper_x], axis = 1)/normalization, np.arange(stis_slit_height_pix))
ax3.legend(['WFC3'], loc = 1)
#ax3.set_ylim(-25, 625)
ax3.grid()
# ax3.axhspan(300, 400, color = 'k', alpha = 0.5)
ax4 = ax3.twinx()
img = fits.getdata(stis_img, 1)
xd_profile = np.sum(img, axis = 1)
multfactor = 1.
xd_profile = congrid(xd_profile, multfactor*int(stis_slit_height_pix))
ax4.plot(xd_profile/np.max(xd_profile[550:650]), np.arange(len(xd_profile))+0, color = 'g')
#ax4.set_ylim(0, 1024)
ax4.legend(['STIS'], loc = 4)
#ax4.axhspan(319, 361, color = 'k', alpha = 0.5)
ax4.axhspan(195, 216, color = 'k', alpha = 0.5)
add_date_to_plot(ax3)
pdb.set_trace()
pyplot.savefig('2010jl_wfc3_finder_image_fuv.pdf')
#----------------------------
def make_nuv_finder_plot(stis_img, wfc3_cc_offset, sn_yloc_stis = 461):
'''
Plot a 3 panel pdf of the WFPC2 image on the left, cropped to the slit location in the middle
and the STIS cross-dispersion profile on the right. The region plotted for WFPC2 was determined
by cross-correlating the cross-dispersion profiles of different x locations with the STIS data.
This is performed in the notebook currently.
'''
wfc3_img = fits.getdata('hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfc3_f275w_wf_drz.fits', 1)
orientat = 36.6-153.72
rot_wfc3_img = rotate(wfc3_img, orientat)
wfc3_y_start = 375+wfc3_cc_offset
fig = pyplot.figure(figsize = [20, 15])
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1, 3, 3)
wfc3_x_start = 709.0
wfc3_platescale = 0.0394 #arcsec/pix
stis_slit_height_pix = int(25.0/wfc3_platescale)
stis_slit_width_pix = 0.5/wfc3_platescale
wfc3_x_end = wfc3_x_start + stis_slit_width_pix
ax1.set_title('WFC3 Image of 2010JL')
im1 = ax1.imshow(rot_wfc3_img, interpolation = 'nearest', vmin = 0, vmax = 0.1)
ax1.set_ylim(50, 1200)
ax1.set_xlim(384, 884)
ax2.set_title('STIS Slit position on WFC3 Image')
im2 = ax2.imshow(rot_wfc3_img, interpolation = 'nearest', vmin = 0, vmax = 0.1)
ax2.set_ylim(wfc3_y_start+0, wfc3_y_start+0+stis_slit_height_pix)
ax2.set_xlim(684, 762)
ax2.axvspan(684, wfc3_x_start, color = 'k', alpha = 0.5)
ax2.axvspan(wfc3_x_end, 762, color = 'k', alpha = 0.5)
ax2.set_yticks(np.arange(ax2.get_ylim()[0], ax2.get_ylim()[1], 50))
ax2.grid(color = 'w')
ax3.set_title('Normalized XD profiles from STIS (NUV) and WFC3')
wfc3_xd_lower_y = wfc3_y_start
wfc3_xd_upper_y = wfc3_y_start+stis_slit_height_pix
wfc3_xd_lower_x = wfc3_x_start
wfc3_xd_upper_x = wfc3_x_end+1
#Choose a small y region of background to normalize by
normalization = np.max(np.sum(rot_wfc3_img[wfc3_y_start:wfc3_y_start + 1*stis_slit_height_pix, wfc3_x_start:wfc3_x_end+1], axis = 1))
ax3.plot(np.sum(rot_wfc3_img[wfc3_xd_lower_y:wfc3_xd_upper_y, wfc3_xd_lower_x:wfc3_xd_upper_x], axis = 1)/normalization, np.arange(stis_slit_height_pix))
ax3.legend(['WFC3'], loc = 1)
#ax3.set_ylim(0, 634)
ax3.grid()
ax4 = ax3.twinx()
img = fits.getdata(stis_img, 1)
xd_profile = np.sum(img, axis = 1)
multfactor = 1.
xd_profile = congrid(xd_profile, multfactor*int(stis_slit_height_pix))
ax4.plot(xd_profile/np.max(xd_profile), np.arange(len(xd_profile)), color = 'g')
#ax4.set_ylim(, len(binned_xd_profile)+max_corr_indx)
ax4.legend(['STIS'], loc = 4)
ax4.axhspan(262, 285, color = 'k', alpha = 0.5)
add_date_to_plot(ax3)
pdb.set_trace()
pyplot.savefig('2010jl_wfc3_finder_image_fuv.pdf')
#----------------------------
#----------------------------
if __name__ == "__main__":
#make_fuv_finder_plot('2010jl_otfr/ocdd03010_flt.fits', 132) #CC program returned an offset of 132
make_nuv_finder_plot('2010jl_otfr/ocdd030d0_flt.fits', 61) #CC program returned an offset of 61
#highest correlation value = 16.938
#Best offset value = 138
#Best WFPC2 column = 1230