def plot_2005ip():
'''
Create a plot of the combined NUV and FUV spectrum of SN 2005ip, label the common SN
lines with different colors for different species and save
'''
redshift = 0.0072
tbdata = fits.getdata('2005ip_all_x1dsum.fits', 1)
sdqflags = fits.getval('2005ip_all_x1dsum.fits', 'sdqflags', 1)
good_indx = np.where(tbdata['dq'][0] & sdqflags == 0)
fig = pyplot.figure(figsize=[20, 10])
ax = fig.add_subplot(1, 1, 1)
deredshift_wl = deredshift_wavelength(tbdata['wavelength'][0][good_indx],
redshift)
smoothed_signal = convolve.convolve(
tbdata['flux'][0][good_indx],
make_kernel.make_kernel([15], 15, 'boxcar'))
ax.plot(deredshift_wl, smoothed_signal, 'b')
ax = label_spectra(ax)
ax.set_xlabel('Rest Wavelength ($\AA$)')
ax.set_ylabel('Flux (ergs/cm^2/s/$\AA$)')
ax.set_title(
'Preliminary HST/STIS Spectrum of SN 2005ip (smoothed by 15 pix)')
ax.set_ylim(-0.1E-15, 0.25E-15)
add_date_to_plot(ax)
pyplot.savefig('2005ip_combined_labeled.pdf')
def plot_2009ip():
'''
Create a plot of the combined NUV and FUV spectrum of SN 2009ip, label the common SN
lines with different colors for different species and save
'''
redshift = 0.0072
tbdata = fits.getdata('2009ip_all_x1dsum.fits', 1)
sdqflags = fits.getval('2009ip_all_x1dsum.fits', 'sdqflags', 1)
good_indx = np.where(tbdata['dq'][0]&sdqflags == 0)
fig = pyplot.figure(figsize = [20, 10])
ax = fig.add_subplot(1,1,1)
deredshift_wl = deredshift_wavelength(tbdata['wavelength'][0][good_indx], redshift)
smoothed_signal = convolve.convolve(tbdata['flux'][0][good_indx], make_kernel.make_kernel([15],15,'boxcar'))
ax.plot(deredshift_wl, smoothed_signal, 'b')
ax = label_spectra(ax)
ax.set_xlabel('Rest Wavelength ($\AA$)')
ax.set_ylabel('Flux (ergs/cm^2/s/$\AA$)')
ax.set_title('Preliminary HST/STIS Spectrum of SN 2009ip (smoothed by 15 pix)')
ax.set_ylim(-0.1E-15, 0.25E-15)
add_date_to_plot(ax)
pyplot.savefig('2009ip_combined_labeled.pdf')
def plot_indiv_spec_with_combined(indiv_filename, combined_filename):
'''
Plot each file individually with the combined spectrum to make sure one doesn't look
very different from the others
'''
tbdata_indiv = fits.getdata(indiv_filename, 1)
tbdata_combined = fits.getdata(combined_filename, 1)
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
sdqflags = fits.getval(indiv_filename, 'sdqflags', 1)
good_indx = np.where(tbdata_indiv['dq'][0]&sdqflags == 0)
ax.plot(tbdata_indiv['wavelength'][0][good_indx], tbdata_indiv['flux'][0][good_indx], label = 'indiv obs')
ax.plot(tbdata_combined['wavelength'][0], tbdata_combined['flux'][0], 'r', label = 'combined')
ax.legend(loc = 'best')
ax.set_xlabel('Wavelength')
ax.set_ylabel('Flux')
add_date_to_plot(ax)
ax.set_title('Compare Indiv Spec to Combine Spec: {}'.format(os.path.basename(indiv_filename)[0:9]))
pyplot.savefig(indiv_filename.replace('x1d.fits', 'comb_spec_comp.pdf'))
pyplot.close()
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 #Derived by visual inspection of image compares with WFPC2 image
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')
def make_fuv_finder_plot(stis_img, wfpc2_cc_offset, sn_yloc_stis=340):
'''
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.
'''
wfpc2_img = fits.getdata(
'hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfpc2_f300w_wf_drz.fits', 1)
orientat = 36.6144
rot_wfpc2_img = rotate(wfpc2_img, orientat)
wfpc2_y_start = 750
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)
wfpc2_x_start = 1229.5
wfpc2_x_end = 1234.5
wfpc2_platescale = 0.1 #arcsec/pix
stis_slit_height_pix = 25.0 / wfpc2_platescale
stis_slit_width_pix = 0.5 / wfpc2_platescale
ax1.set_title('WFPC2 Image of UGC 5189')
im1 = ax1.imshow(rot_wfpc2_img, interpolation='nearest', vmin=0, vmax=0.2)
ax1.set_ylim(700, 1150)
ax1.set_xlim(1100, 1300)
ax1.plot([1220, 1220, 1250, 1250, 1220], [
wfpc2_y_start + wfpc2_cc_offset,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix,
wfpc2_y_start + wfpc2_cc_offset, wfpc2_y_start + wfpc2_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 WFPC2 Image')
im2 = ax2.imshow(rot_wfpc2_img, interpolation='nearest', vmin=0, vmax=0.2)
ax2.set_ylim(wfpc2_y_start + wfpc2_cc_offset,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix)
ax2.set_xlim(1220, 1250)
ax2.axvspan(1220, wfpc2_x_start, color='k', alpha=0.5)
ax2.axvspan(wfpc2_x_end, 1250, 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 WFPC2')
ax3.plot(
np.sum(rot_wfpc2_img[wfpc2_y_start + wfpc2_cc_offset:wfpc2_y_start +
wfpc2_cc_offset + stis_slit_height_pix,
wfpc2_x_start:wfpc2_x_end + 1],
axis=1) / np.max(
np.sum(rot_wfpc2_img[wfpc2_y_start + wfpc2_cc_offset +
120:wfpc2_y_start + wfpc2_cc_offset +
175, wfpc2_x_start:wfpc2_x_end + 1],
axis=1)), np.arange(stis_slit_height_pix))
ax3.legend(['WFPC2'], loc=1)
ax3.grid()
ax4 = ax3.twinx()
img = fits.getdata(stis_img, 1)
xd_profile = np.sum(img, axis=1)
ax4.plot(xd_profile / np.max(xd_profile[550:650]),
np.arange(len(xd_profile)),
color='g')
ax4.set_ylim(0, 1024)
ax4.legend(['STIS'], loc=4)
add_date_to_plot(ax3)
pdb.set_trace()
pyplot.savefig('2010jl_finder_image_fuv.pdf')
def make_fuv_finder_plot(stis_img, wfpc2_cc_offset, sn_yloc_stis=340):
'''
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.
'''
wfpc2_img = fits.getdata(
'hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfpc2_f300w_wf_drz.fits', 1)
orientat = 36.6144
rot_wfpc2_img = rotate(wfpc2_img, orientat)
wfpc2_y_start = 750
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)
wfpc2_x_start = 1229.5
wfpc2_x_end = 1234.5
wfpc2_platescale = 0.1 #arcsec/pix
stis_slit_height_pix = 25.0 / wfpc2_platescale
stis_slit_width_pix = 0.5 / wfpc2_platescale
ax1.set_title('WFPC2 Image of UGC 5189')
im1 = ax1.imshow(rot_wfpc2_img, interpolation='nearest', vmin=0, vmax=0.2)
ax1.set_ylim(700, 1150)
ax1.set_xlim(1100, 1300)
ax1.plot([1220, 1220, 1250, 1250, 1220], [
wfpc2_y_start + wfpc2_cc_offset,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix,
wfpc2_y_start + wfpc2_cc_offset, wfpc2_y_start + wfpc2_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 WFPC2 Image')
im2 = ax2.imshow(rot_wfpc2_img, interpolation='nearest', vmin=0, vmax=0.2)
ax2.set_ylim(wfpc2_y_start + wfpc2_cc_offset,
wfpc2_y_start + wfpc2_cc_offset + stis_slit_height_pix)
ax2.set_xlim(1220, 1250)
ax2.axvspan(1220, wfpc2_x_start, color='k', alpha=0.5)
ax2.axvspan(wfpc2_x_end, 1250, 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 WFPC2')
ax3.plot(
np.sum(
rot_wfpc2_img[wfpc2_y_start + wfpc2_cc_offset:wfpc2_y_start +
wfpc2_cc_offset +
stis_slit_height_pix, wfpc2_x_start:wfpc2_x_end + 1],
axis=1) / np.max(
np.sum(
rot_wfpc2_img[wfpc2_y_start + wfpc2_cc_offset +
120:wfpc2_y_start + wfpc2_cc_offset +
175, wfpc2_x_start:wfpc2_x_end + 1],
axis=1)), np.arange(stis_slit_height_pix))
ax3.legend(['WFPC2'], loc=1)
ax3.grid()
ax4 = ax3.twinx()
img = fits.getdata(stis_img, 1)
xd_profile = np.sum(img, axis=1)
ax4.plot(
xd_profile / np.max(xd_profile[550:650]),
np.arange(len(xd_profile)),
color='g')
ax4.set_ylim(0, 1024)
ax4.legend(['STIS'], loc=4)
add_date_to_plot(ax3)
pdb.set_trace()
pyplot.savefig('2010jl_finder_image_fuv.pdf')
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 2006gy')
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('2006gy_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')
