# south galaxy west region
se_blue = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/se_blue.dat', dtype=None, names=['wav','flux'])
se_red = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/se_red.dat', dtype=None, names=['wav','flux'])
# bridge west region
bw_blue = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/bw_new_blue.dat', dtype=None, names=['wav','flux'])
bw_red = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/bw_new_red.dat', dtype=None, names=['wav','flux'])
# bridge east region
be_blue = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/be_new_blue.dat', dtype=None, names=['wav','flux'])
be_red = np.genfromtxt(taffy_extdir + 'rawspectra_for_paperplot/be_new_red.dat', dtype=None, names=['wav','flux'])
# Make figure
# read in i band SDSS image
sdss_i, wcs_sdss = vcm.get_sdss('i')
# read in lzifu output file
lzifu_hdulist, wcs_lzifu = vcm.get_lzifu_products()
# create figure and plot
# the sdss image is plotted according the the function in the
# vel_channel_map code it is slightly modified here to work with gridspec.
fig = plt.figure(figsize=(9,9)) # figsize=(width, height)
# modify rc Params
mpl.rcParams["font.family"] = "serif"
mpl.rcParams["font.sans-serif"] = ["Computer Modern Sans"]
#mpl.rcParams["text.usetex"] = True
# I've kept this above commented out line here as a reminder.
# This line will stop any bold text from appearing anywhere
def main():
"""
The purpose of this code is to compare the Vs+V1+V1n map
(this is currently the top middle panel in Fig 7) with the
Vs+V2+V1n map. This is one of the plots the referee wanted
to see. I think they think that this will display the rotation
better. I'm not entirely sure.
This code is completely based on the codes --
1. stitche_vel_vdisp_cube.py and 2. contours_from_linefits.py.
"""
# Generate the Vs + V2 + V1n map
#genmap()
# Read in the new map and make contours
map_vel_comp2_hdu = fits.open(gaussfits_dir + 'vel_cube_comp2.fits')
map_vel_comp2_new_hdu = fits.open(gaussfits_dir + 'vel_cube_vs_v2_v1n.fits')
map_vel_comp2 = map_vel_comp2_hdu[0].data
map_vel_comp2_new = map_vel_comp2_new_hdu[0].data
# Diagnostic figures
# Do not delete this code block. Useful for checking.
"""
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.imshow(map_vel_comp2, origin='lower', vmin=-350, vmax=350)
ax2.imshow(map_vel_comp2_new, origin='lower', vmin=-350, vmax=350)
plt.show()
"""
# ------- Contours --------- #
# Plotting
# Change MPL RC params first to stop using TeX for all text
# becasue I can't use bold text with TeX.
mpl.rcParams['text.usetex'] = False
# Bold text
f = FontProperties()
f.set_weight('bold')
# read in i band SDSS image
sdss_i, wcs_sdss = vcm.get_sdss('i')
# read in lzifu output file
lzifu_hdulist, wcs_lzifu = vcm.get_lzifu_products()
# plot sdss image
fig, ax = vcm.plot_sdss_image(sdss_i, wcs_sdss)
# draw contours
x = np.arange(58)
y = np.arange(58)
X, Y = np.meshgrid(x,y)
# get colormap
colorbrewer_cm = vcm.get_colorbrewer_cm('blue2yellow')
# select contour map to plot and set the variables
# set the variables, levels, and limits below
con_map = map_vel_comp2_new
# apply min and max limits
minlim = -500
maxlim = 500
minidx = np.where(con_map < minlim)
maxidx = np.where(con_map > maxlim)
con_map[minidx] = np.nan
con_map[maxidx] = np.nan
levels = np.array([-350, -250, -200, -150, -100, 0, 100, 150, 200, 250, 350]) # vel both comp
# try smoothing the map to get smoother contours
# define kernel
kernel = Gaussian2DKernel(stddev=0.9)
con_map = convolve(con_map, kernel, boundary='extend')
c = ax.contour(X, Y, con_map, transform=ax.get_transform(wcs_lzifu),\
levels=levels, cmap=colorbrewer_cm, linewidths=2.0, interpolation='None')
# add colorbar inside figure
cbaxes = inset_axes(ax, width='30%', height='3%', loc=8, bbox_to_anchor=[0.02, 0.08, 1, 1], bbox_transform=ax.transAxes)
cb = plt.colorbar(c, cax=cbaxes, ticks=[min(levels), max(levels)], orientation='horizontal')
cb.ax.get_children()[0].set_linewidths(16.0)
cb.ax.set_xlabel(r'$\mathrm{Radial\ Velocity\ [km\, s^{-1}]}$', fontsize=15)
plt.show()
return None
def make_diff_map():
# read in indices file
h = fits.open(savedir + 'all_cases_indices.fits')
comp1_inv_idx = h['COMP1_INV'].data
comp2_inv_idx = h['COMP2_INV'].data
single_idx = h['SINGLE_IDX'].data
diffmean_idx = h['DIFFMEAN_IDX'].data
diffstd_idx = h['DIFFSTD_IDX'].data
diffboth_idx = h['DIFFBOTH_IDX'].data
intg_flux_comp1_hdu = fits.open(savedir + 'intg_flux_cube_comp1.fits')
intg_flux_comp2_hdu = fits.open(savedir + 'intg_flux_cube_comp2.fits')
intg_flux_comp1 = intg_flux_comp1_hdu[0].data
intg_flux_comp2 = intg_flux_comp2_hdu[0].data
diffmap = intg_flux_comp2 - intg_flux_comp1
# NaN out some spaxels that don't seem right
# ds9 coords [x,y]
nan_list = [[20, 47], [20, 48], [20, 49]]
for coord_to_nan in nan_list:
i = coord_to_nan[1] - 1
j = coord_to_nan[0] - 1
print "DS9 coords:", coord_to_nan, " Array coords:", i, j
diffmap[i, j] = np.nan
# Plotting
# read in i band SDSS image
sdss_i, wcs_sdss = vcm.get_sdss('i')
# read in lzifu output file
lzifu_hdulist, wcs_lzifu = vcm.get_lzifu_products()
# Save as fits file
hdr = lzifu_hdulist['B_LINE'].header
# Create file
hdu = fits.PrimaryHDU(diffmap, header=hdr)
# write
hdu.writeto(taffy_extdir + 'intg_flux_diff_map.fits', overwrite=True)
# ------------ also save indices file with proper header ------------ #
indices_hdu = fits.PrimaryHDU()
indices_hdul = fits.HDUList(indices_hdu)
hdr['EXTNAME'] = 'COMP1_INV'
indices_hdul.append(fits.ImageHDU(data=comp1_inv_idx, header=hdr))
hdr['EXTNAME'] = 'COMP2_INV'
indices_hdul.append(fits.ImageHDU(data=comp2_inv_idx, header=hdr))
hdr['EXTNAME'] = 'SINGLE_IDX'
indices_hdul.append(fits.ImageHDU(data=single_idx, header=hdr))
hdr['EXTNAME'] = 'DIFFMEAN_IDX'
indices_hdul.append(fits.ImageHDU(data=diffmean_idx, header=hdr))
hdr['EXTNAME'] = 'DIFFSTD_IDX'
indices_hdul.append(fits.ImageHDU(data=diffstd_idx, header=hdr))
hdr['EXTNAME'] = 'DIFFBOTH_IDX'
indices_hdul.append(fits.ImageHDU(data=diffboth_idx, header=hdr))
indices_hdul.writeto(taffy_extdir + 'all_cases_indices_with_wcs.fits',
overwrite=True)
sys.exit(0)
# plot sdss image
fig, ax = vcm.plot_sdss_image(sdss_i, wcs_sdss)
# draw contours
x = np.arange(58)
y = np.arange(58)
X, Y = np.meshgrid(x, y)
# get colormap
colorbrewer_cm = vcm.get_colorbrewer_cm('coolwarm')
kernel = Gaussian2DKernel(stddev=0.9)
diffmap = convolve(diffmap, kernel, boundary='extend')
c = ax.contour(X, Y, diffmap, transform=ax.get_transform(wcs_lzifu),\
cmap=colorbrewer_cm, linewidths=2.0, interpolation='None')
ax.clabel(c,
inline=True,
inline_spacing=2,
fontsize=8,
fmt='%1.1f',
lw=4,
ls='-')
# add colorbar inside figure
cbaxes = inset_axes(ax,
width='30%',
height='3%',
loc=8,
bbox_to_anchor=[0.02, 0.08, 1, 1],
bbox_transform=ax.transAxes)
#cb = plt.colorbar(c, cax=cbaxes, ticks=[min(levels), max(levels)], orientation='horizontal')
#cb.ax.get_children()[0].set_linewidths(10.0)
#cb.ax.set_xlabel(r'$\mathrm{Integrated\ flux [erg\, s^{-1}\, cm^{-2}\, \AA^{-1} * km\, s^{-1}]}$', fontsize=12)
plt.show()
return None
def main():
# ------------------------- Read in data ------------------------- #
# Read in HI data from Condon et al. 1993
hi_cube_hdu = fits.open(home +
'/Dropbox/Taffy/HI_Condon_et_al/taffy_HIcube.fits')
hi_cube = hi_cube_hdu[0].data
# Now read in optical IFU data
# Only need red channel for Halpha
obs_r_hdu = fits.open(taffy_data + 'Taffy_R.fits')
obs_r = obs_r_hdu[0].data
# Using my own line fits instead of lzifu
r_line_total = np.load(taffy_extdir + 'halpha_profile_mylinefits.npy')
# ------------------------- Other preliminaries ------------------------- #
# create wavelength array
# I read these data from the corresponding headers
# red wav arr
delt_r = 0.3 # i.e. the wav axis is sampled at 0.3A
red_wav_start = 6165.0
total_red_res_elem = 2350
red_wav_arr = [
red_wav_start + delt_r * i for i in range(total_red_res_elem)
]
red_wav_arr = np.asarray(red_wav_arr)
# get mask of all possible not NaN pixels
all_mask = vcm.get_region_mask('all_possibly_notnan_pixels_new')
# Make sure that rest wavelength is Halpha wavelength in air!! the IFU data was taken on the ground
# find line index in wavelength array
redshift = 0.0145 # average z
sys_vel = redshift * speed_of_light # avg systemic velocity is z*c = 4350 km/s
halpha_air_wav = 6562.80
halpha_wav = halpha_air_wav * (1 + redshift)
halpha_idx = np.argmin(abs(red_wav_arr - halpha_wav))
# ------------------------- Plotting ------------------------- #
# read in i band SDSS image
sdss_i, wcs_sdss = vcm.get_sdss('i')
# read in lzifu output file
lzifu_hdulist, wcs_lzifu = vcm.get_lzifu_products()
# plot sdss image
fig, ax = vcm.plot_sdss_image(sdss_i, wcs_sdss)
# Now read in regions file and convert to polygons
#with fl as open():
regstr = 'polygon(52.75971,52.679517,55.559628,52.679492,55.559611,55.479493,52.759775,55.479517) # color=red width=2'
regstr = regstr.split('(')[1].split(')')[0]
regstr = regstr.split(',')
reglist = list(
regstr) # this is now a list of floats represented as strings
reg_pn_list = []
for k in range(0, 8, 2):
reg_pn_list.append([float(reglist[k]), float(reglist[k + 1])])
reg_pn = pg.Polygon(reg_pn_list)
extract_spec(reg_pn, hi_cube)
#extract_spec(reg_pn, obs_r)
#plt.show()
return None