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CO_3-2_mirror_channels_gif.py
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CO_3-2_mirror_channels_gif.py
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#!/usr/bin/python
import matplotlib
from matplotlib.pylab import *
from astropy.io import fits
from matplotlib.ticker import *
import numpy as np
import matplotlib.font_manager
import matplotlib.cm as cm
from matplotlib import colors
from matplotlib.patches import Ellipse as ellipse
from scipy.optimize import *
from scipy.ndimage.interpolation import *
import seaborn as sns
from my_colormaps import *
# Constants and conversion factors
#---------------------------------
# Solar mass (g)
msol = 1.989e+33
# Gravitational constant (cm3 g-1 cm-2)
grav = 6.67259e-8
# Conversion factor: AU to cm
AU2cm = 1.49597871e+13
# Conversion factor: cm to km
cm2km = 1.0e-5
# Speed of light (km)
ckm = 2.99792458e+05
# Disk and star inputs
#---------------------------------
# Distance to source (parsec)
dist = 96.9
# Disk inclination (degrees --> radians)
inc = 36.0*np.pi/180.0
# Position angle (degrees --> radians)
dpa = (144.0 - 90.0)*np.pi/180.0
# Radius r0 (AU) for flaring relation: z = (h0/r0)**psi
r0 = 1.0
# Scale height at r0
h0 = 0
# Flaring index
psi = 1.00
# Mass of central star (g)
mstar = 2.5*msol
# Source velocity (km/s)
source = 5.775
#---------------------------------
# Compute first moment map
#---------------------------------
# Name of FITS image containing first moment map
fits_mom1 = 'CO_346GHz_line.shifted.robust.5.3sig_mom1.fixed.fits'
# Name of FITS image containing the channel maps
fits_chan = 'CO_346GHz_line.shifted.robust.5.fits'
# Read the header from the first moment map
head = fits.getheader(fits_mom1)
# Generate x and y axes: offset position in centimeters
nx = head['NAXIS1']
xpix = head['CRPIX1']
xdelt = head['CDELT1']
ny = head['NAXIS2']
ypix = head['CRPIX2']
ydelt = head['CDELT2']
# Convert from degrees --> arsec --> AU --> cm
xorig = ((arange(nx) - xpix + 1) * xdelt)*3600
yorig = ((arange(ny) - ypix + 1) * ydelt)*3600
# Source is contained within 4" x 4" - clip the x and y axes extent
xval = xorig[(xorig>=-4.0) & (xorig<=4.0)]*dist*AU2cm
yval = yorig[(yorig>=-4.0) & (yorig<=4.0)]*dist*AU2cm
# Make 2D array containing all (x,y) axis coordinates
xdisk,ydisk = np.meshgrid(xval,yval)
# Read the header from the channel map
head = fits.getheader(fits_chan)
# Create channel axis (Hz)
nv = head['NAXIS3']
vpix = head['CRPIX3']
vdelt = head['CDELT3']
vval = head['CRVAL3']
vel = ((arange(nv) - vpix + 1) * vdelt) + vval
# Extract rest frequency (Hz)
freq = head['RESTFRQ']
# Convert from frequency (Hz) to LRSK velocity (km/s) using
# rest frequency and source velocity
for i in range(nv):
vel[i] = (ckm*((freq-vel[i])/freq))-source
vel
# Rx(theta) = | cos(theta) -sin(theta) |
# | sin(theta) cos(theta) |
# (x',y') = Rx(theta)*(x,y)
# Note that z-coordinates remain unchanged
# Positive postion angle means clockwise rotation
x0 = np.cos(-dpa)*xdisk - np.sin(-dpa)*ydisk
y0 = np.sin(-dpa)*xdisk + np.cos(-dpa)*ydisk
xdisk = x0
ydisk = y0
# Create a sin(theta) and cos(theta) array where theta is measured from the x-axis
costheta = np.zeros((len(xval),len(yval)),dtype = float)
sintheta = np.zeros((len(xval),len(yval)),dtype = float)
for j in range(len(xval)):
for i in range(len(yval)):
if xval[j] == 0.0 and yval[i] == 0.0:
sintheta[i][j] = 0.0
costheta[i][j] = 0.0
else:
sintheta[i][j] = xdisk[i][j]/np.sqrt(xdisk[i][j]**2.0 + ydisk[i][j]**2.0)
costheta[i][j] = ydisk[i][j]/np.sqrt(xdisk[i][j]**2.0 + ydisk[i][j]**2.0)
#--------------------------------------------------------------------
# Step 2: calculate the z-coordinates for an inclined conical suface
#--------------------------------------------------------------------
# Conical surfaces are simply a series of circles swept out and
# offset and parallel to the plane of symmetry (a geometrically flat disk)
zflat = ydisk*np.tan(inc)
rflat = np.sqrt(xdisk**2.0+ydisk**2.0+zflat**2.0)
# Analytical solution for psi = 1.0
dtop = rflat*(h0/r0)/(1-(h0/r0)*np.tan(inc)*costheta)
dbot = rflat*(h0/r0)/(1+(h0/r0)*np.tan(inc)*costheta)
# Reshape the arrays
dtop = np.reshape(dtop, (len(xval),len(yval)))
dbot = np.reshape(dbot, (len(xval),len(yval)))
# Final scaling by cos(inc)
# dtop = dtop*AU2cm/np.cos(inc)
# dbot = dbot*AU2cm/np.cos(inc)
dtop = dtop/np.cos(inc)
dbot = dbot/np.cos(inc)
# Create the top and bottom cones
ztop = zflat + dtop
zbot = zflat - dbot
#-----------------------------------------------------------
# Step 3: calculate true disk radii projected on x-y plane
#-----------------------------------------------------------
# My prescription uses spherical radius
rtop = np.sqrt(xdisk**2.0 + ydisk**2.0 + ztop**2.0)
rbot = np.sqrt(xdisk**2.0 + ydisk**2.0 + zbot**2.0)
#------------------------------------------------
# Step 4: calculate projected keplerian velocity
#------------------------------------------------
vtop = np.zeros((len(xval),len(yval)),dtype=float)
vbot = np.zeros((len(xval),len(yval)),dtype=float)
vflat = np.zeros((len(xval),len(yval)),dtype=float)
for j in range(len(xval)):
for i in range(len(yval)):
if rtop[i][j]!=0.0:
vtop[i][j] = cm2km*np.sqrt(grav*mstar/rtop[i][j])*np.sin(inc)*sintheta[i][j]
if rbot[i][j]!=0.0:
vbot[i][j] = cm2km*np.sqrt(grav*mstar/rbot[i][j])*np.sin(inc)*sintheta[i][j]
if rflat[i][j]!=0.0:
vflat[i][j] = cm2km*np.sqrt(grav*mstar/rflat[i][j])*np.sin(inc)*sintheta[i][j]
#
#-----------------------------------------------------------------------
# Final step: make a contour map of upper and lower projected velocities
#-----------------------------------------------------------------------
# Reset x and y axes to offset in arcseconds
xval = xval/(dist*AU2cm)
yval = yval/(dist*AU2cm)
# Creat dictionaries containing axes and images
upper_vel = {'image':vtop, 'ra_offset': xval, 'dec_offset': yval}
lower_vel = {'image':vbot, 'ra_offset': xval, 'dec_offset': yval}
flat_vel = {'image':vflat, 'ra_offset': xval, 'dec_offset': yval}
# Use original x and y axes coordinates for the channel images
img_chan = {'image':squeeze(fits.getdata(fits_chan)), 'ra_offset':xorig, 'dec_offset':yorig}
# Set spacing between axes labels and tick direction
rcParams['ytick.major.pad'] = 12
rcParams['xtick.major.pad'] = 12
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
rcParams['xtick.major.size'] = 5
rcParams['xtick.minor.size'] = 2
rcParams['ytick.major.size'] = 5
rcParams['ytick.minor.size'] = 2
rcParams['ytick.labelsize'] = 14
rcParams['xtick.labelsize'] = 14
# Set seaborn plot styles
sns.set_style("white")
sns.set_style("ticks")
# sns.set_style({"xtick.direction": "in","ytick.direction": "in", "xtick.color" : "white", 'ytick.color' : 'white'})
# Set axes limits
xmin = -4.0
xmax = 4.0
ymin = -4.0
ymax = 4.0
# Select indices and velocities of corresponding channel maps
negchan = []
poschan = []
negvel = []
posvel = []
vlim = 8.40
# for i in range(nv-1):
for i in range(nv-1):
# for j in range(len(negchan)):
# if vel[i] < negcontours[j] and vel[i+1] > negcontours[j]: negchan[j] = int(i)
# if vel[i] < poscontours[j] and vel[i+1] > poscontours[j]: poschan[j] = int(i)
if vel[i] >= -1*vlim and vel[i] <= 0:
negchan.append(i);
negvel.append(vel[i]);
if np.abs(vel[i]) < 0.01:
poschan.append(i)
posvel.append(vel[i]);
if vel[i] >= 0 and vel[i] <= vlim:
poschan.append(i)
posvel.append(vel[i]);
# Set physical range of colour map
cxmin = img_chan['ra_offset'][-1]
cxmax = img_chan['ra_offset'][0]
cymin = img_chan['dec_offset'][0]
cymax = img_chan['dec_offset'][-1]
# Set limits and tics of colorbar
cbmin = -0.1
cbmax = 2.50001
cbtmj = 0.50
cbtmn = 0.10
cbnum = int((cbmax-cbmin)/cbtmn)
# Set colorpalette
#cpal = colors.ListedColormap(sns.cubehelix_palette(cbnum,start=0.5,rot=-0.8,light=0.05,dark=0.95,hue=0.75,reverse=True))
# cpal = cm.gist_heat
cpal = jesse_reds
# Adjust spacing between subplots
# Limit contour extent: within [0:67]
c1 = 12
c2 = 55
for i in range(len(negchan)):
# for i in range(5):
print(negvel[-i - 1], posvel[i])
# Set figure size and create image canvas (in cm)
fig, ax = plt.subplots(figsize=(10,10))
ax.grid(False)
ax.set_xlim(xmax,xmin)
ax.set_ylim(ymin,ymax)
ax.set_xlabel('Relative Right Ascension (arcsec)',x = 0.5, fontsize=14)
ax.set_ylabel('Relative Declination (arcsec)',y = 0.425, fontsize=14)
#Create Beam
beam_ellipse_color = 'w'
bmin = head['bmin']*3600.
bmaj = head['bmaj']*3600.
bpa = head['bpa']
el = ellipse(xy=[3,-3], width=bmin, height=bmaj, angle=-bpa, \
fc='k', ec=beam_ellipse_color, fill=True, zorder=10)
ax.add_artist(el)
velocity = '+/-%4.2f km/s' % (vel[poschan[i]])
text(3.5,3.0, velocity, fontsize=14, color = 'w')
plot([0.0],[0.0], '+', markersize=8, markeredgewidth=1.5, color='w')
plot([3.5, -3.5],[2.542898848, -2.542898848], ':', color='w')
# Create rotated images so that the disk major axis runs North to South (i.e. positive and negative velocity components lie on opposite sides of the y-axis).
angle = 144
negchan_rotated = rotate(img_chan['image'][negchan[-1*i]], angle, reshape = False)
poschan_rotated = rotate(img_chan['image'][poschan[i]], angle, reshape = False)
#Set the upper half of the negative channel image and the lower half of the positive channel image equal to zero. Add the two images so that contributions from the positive velocity channels are only shown in the upper half of the image, and vice versa. Rotate back to original position angle.
negchan_rotated[:][:128] = 0
poschan_rotated[:][128:] = 0
mirror = rotate(negchan_rotated + poschan_rotated, -1*angle)
cmap = imshow(mirror,
extent=[cxmax,cxmin,cymax,cymin],
vmin = cbmin,
vmax = cbmax,
interpolation='bilinear',
cmap=cpal)
# Generate continuum contour levels and plot contour maps
# contours = ([negcontours[i]])
# C1 = contour(-upper_vel['ra_offset'][c1:c2],upper_vel['dec_offset'][c1:c2],
# upper_vel['image'][c1:c2,c1:c2],
# extent=[cxmax,cxmin,cymax,cymin],
# levels=contours,
# colors='gray',
# linewidth=2.0)
# contours = ([poscontours[i]])
# C1 = contour(-upper_vel['ra_offset'][c1:c2],upper_vel['dec_offset'][c1:c2],
# upper_vel['image'][c1:c2,c1:c2],
# extent=[cxmax,cxmin,cymax,cymin],
# levels=contours,
# colors='gray',
# linewidth=2.0)
# contours = ([negcontours[i]])
# C2 = contour(-lower_vel['ra_offset'][c1:c2],upper_vel['dec_offset'][c1:c2],
# lower_vel['image'][c1:c2,c1:c2],
# extent=[cxmin,cxmax,cymax,cymin],
# levels=contours,
# colors='white',
# linewidth=2.0)
# contours = ([poscontours[i]])
# C2 = contour(-lower_vel['ra_offset'][c1:c2],upper_vel['dec_offset'][c1:c2],
# lower_vel['image'][c1:c2,c1:c2],
# extent=[cxmax,cxmin,cymax,cymin],
# levels=contours,
# colors='white',
# linewidth=2.0)
contours = ([negvel[-i-1]])
C2 = contour(-flat_vel['ra_offset'][8:59],flat_vel['dec_offset'][8:59],
flat_vel['image'][8:59,8:59],
extent=[cxmax,cxmin,cymax,cymin],
levels=contours,
colors='black',
linewidth=2.0)
contours = ([posvel[i]])
C2 = contour(-flat_vel['ra_offset'][8:59],flat_vel['dec_offset'][8:59],
flat_vel['image'][8:59,8:59],
extent=[cxmax,cxmin,cymax,cymin],
levels=contours,
colors='black',
linewidth=2.0)
# angle=np.array([30,35,40])
# angle= angle*np.pi/180.0
#
# y0 = 2.5
# x0 = 0.5*(y0*np.tan(angle))*cos(inc)
#
# x1 = np.cos(np.pi/2-dpa)*x0 - np.sin(np.pi/2-dpa)*y0
# y1 = np.sin(np.pi/2-dpa)*x0 + np.cos(np.pi/2-dpa)*y0
#
# x2 = np.cos(3*np.pi/2-dpa)*-x0 - np.sin(3*np.pi/2-dpa)*y0
# y2 = np.sin(3*np.pi/2-dpa)*-x0 + np.cos(3*np.pi/2-dpa)*y0
#
# for j in range(len(angle)):
#
# if i < 4:
# plot ([0,x1[j]],[0,y1[j]], ':', linewidth=1, color='k')
# plot ([0,x2[j]],[0,y2[j]], ':', linewidth=1, color='k')
#
# if i >= 4:
# plot ([0,x1[j]],[0,y1[j]], ':', linewidth=1, color='white')
# plot ([0,x2[j]],[0,y2[j]], ':', linewidth=1, color='white')
# Create and add a colour bar with label and tics
cax = fig.add_axes([0.891, 0.125, 0.03, 0.755])
cbar = colorbar(cmap, cax=cax)
cbar.set_label('Jy/beam', labelpad=-5, fontsize=20)
# cbar.set_ticks(np.arange(0, cbmax, cbtmaj))
# minorLocator = LinearLocator(cbnum + 1)
# cbar.ax.yaxis.set_minor_locator(minorLocator)
# cbar.update_ticks()
# Save the figure to pdf/eps
# fig.savefig('CO_346GHz_line.shifted.robust.5.mirror_channels_flat.png')
show(block=True)