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plot_spectra.py
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import numpy as np
from astropy import log
import astropy.units as u
from astropy.io import fits
from pyspeckit.spectrum.units import SpectroscopicAxis
from pyspeckit.spectrum.models.ammonia_constants import freq_dict
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
plt.rc('xtick', direction='in')
plt.rc('ytick', direction='in')
plt.rc('text', usetex=True)
plt.rc('font', **{'family' : "sans-serif"})
params = {'text.latex.preamble' : [r'\usepackage{amsmath}']}
plt.rcParams.update(params)
from opencube import make_cube_shh, update_model
from config import file_mle_x1, file_mle_x2, file_Ks
# the guys below are used to annotate npeaks map in make_presentable_Ks.py
from spectra_xy_list import xlist, ylist, labels
# will either make two subfigures (to be put on the sides of M-map)
# or makes a joint figure for spectra only for `split = False`
split = True
# colors for spectral components and their total
cbrews = ['#377eb8', '#4daf4a', '#984ea3', '#ff7f00'][::-1]
comp_sum_color = '#e41a1c'
spc = make_cube_shh()
update_model(spc, "cold_ammonia")
Kcut = 5 # the heuristical ln(Z1/Z2) cut for model selection
x1_mle = fits.getdata(file_mle_x1)
x2_mle = fits.getdata(file_mle_x2)
Ks = fits.getdata(file_Ks)
parcube = np.full_like(x2_mle, np.nan)
parcube[:6, :, :] = x1_mle[:6, :, :]
for i in range(1, 12):
parcube[i, :, :][Ks[1]>5] = x2_mle[i, :, :][Ks[1]>5]
gridspec_kw={}
do_ave = False
if not split:
plt.rc('font', size=10) # cosmetic tinkering
fig, axarr = plt.subplots(xlist.size, 2, sharex=False,
sharey=False, figsize=(11.5, 4.6),
gridspec_kw=gridspec_kw)
else:
plt.rc('font', size=14) # cosmetic tinkering
f11, axarr11 = plt.subplots(xlist.size, 1, sharex=True,
sharey=False, figsize=(7.75, 7.6),
gridspec_kw=gridspec_kw)
f22, axarr22 = plt.subplots(xlist.size, 1, sharex=True,
sharey=False, figsize=(3, 7.6),
gridspec_kw=gridspec_kw)
axarr = np.array([axarr11, axarr22]).T
# make the high-res xarr for models
def highres_xarr(xarr, N_res, refX):
# make sure the 11 and 22 cubes are in GHz
xarr.convert_to_unit(u.GHz)
xnparr = np.linspace(xarr.min(), xarr.max(), N_res)
xarr_hires = SpectroscopicAxis(xarr=xnparr, refX=refX,
velocity_convention="radio")
return xarr_hires
N = 1000 # resolution for modelled spectra
spc.cubelist[0].xarr.velocity_convention = "radio"
spc.cubelist[1].xarr.velocity_convention = "radio"
xarr11, xarr22 = spc.cubelist[0].xarr, spc.cubelist[1].xarr
xarr11.refX = freq_dict["oneone"]*u.Hz
xarr22.refX = freq_dict["twotwo"]*u.Hz
h_xarr11 = highres_xarr(xarr11, N, freq_dict["oneone"]*u.Hz)
h_xarr22 = highres_xarr(xarr22, N, freq_dict["twotwo"]*u.Hz)
h_xarr11.convert_to_unit("km/s")
h_xarr22.convert_to_unit("km/s")
lab_fmt = r"$\sigma={}~\mathrm{{km~s^{{-1}}}}$"
for x, y, (ax11, ax22), lab in zip(xlist, ylist, axarr, labels):
sp = spc.get_spectrum(x, y)
sp11 = spc.cubelist[0].get_spectrum(x, y)
sp22 = spc.cubelist[1].get_spectrum(x, y)
sp11.xarr.convert_to_unit("km/s")
sp22.xarr.convert_to_unit("km/s")
pars = parcube[:, y, x]
strK10 = r'$\ln K^{{\mathrm{{1}}}}_{{\mathrm{{0}}}} = {:.0f}$'.format(Ks[0, y, x])
strK21 = r'$\ln K^{{\mathrm{{2}}}}_{{\mathrm{{1}}}} = {:.0f}$'.format(Ks[1, y, x])
# plot the loc's of the spectra (cf. subsonic_analyses.py script)
ax22.annotate(int(lab), xy=(0.85, 0.8), xycoords='axes fraction',
size='medium', bbox=dict(boxstyle="circle", fc="0.9"))
ax11.text(x=0.85, y=0.80, s=strK10, transform=ax11.transAxes)
ax11.text(x=0.85, y=0.58, s=strK21, transform=ax11.transAxes)
ax11.text(x=0.5, y=0.80, s=r'$\mathrm{{(x, y) = ({}, {})}}$'.format(x, y),
transform=ax11.transAxes, size='small')
ax11.plot(sp11.xarr.value, sp11.data, drawstyle="steps-mid",
color='black', lw=0.5, zorder=0.1)
ax22.plot(sp22.xarr.value, sp22.data, drawstyle="steps-mid",
color='black', lw=0.5, zorder=0.1)
if np.isfinite(pars[6:18]).any():
log.warn("Spectrum at (x,y)=({},{}) contains"
" an additional velocity component!".format(x, y))
h_msum_11 = 0
mcomps, h_11s, h_22s = [], [], []
for p_ext, col in zip([pars[6:12], pars[0:6]], cbrews[2:]):
try:
h_11 = sp.specfit.get_model(h_xarr11, pars=p_ext)
h_22 = sp.specfit.get_model(h_xarr22, pars=p_ext)
# plot the individual LoS components
ax11.plot(h_xarr11.value, h_11, lw=1.2, zorder=1, color=col)
ax22.plot(h_xarr22.value, h_22, lw=1.2, zorder=1, color=col)
h_11s.append(h_11)
h_22s.append(h_22)
except ValueError:
pass
# plot the total fit
ax11.plot(h_xarr11.value, np.sum(h_11s, axis=0), lw=1.3,
color=comp_sum_color, ls=':', zorder=1)
ax22.plot(h_xarr22.value, np.sum(h_22s, axis=0), lw=1.3,
color=comp_sum_color, ls=':', zorder=1)
ax11.set_xlim(-15, 35)
ax22.set_xlim(-2, 17)
#ax22.set_ylim(-0.35, None)
ax11.set_ylim(-0.3, None)
if ax11.get_ylim()[1] < 0.5:
ax11.set_ylim(None, 0.5)
if not split and id(ax11) != id(axarr[-1, 0]):
ax11.set_xticklabels([])
ax22.set_xticklabels([])
# don't just skip integer T_MB values
ax11.yaxis.set_minor_locator(MultipleLocator(1))
ax22.yaxis.set_minor_locator(MultipleLocator(0.5))
# set the minor ticks equal to velocity reolution
ax11.xaxis.set_minor_locator(MultipleLocator(1))
ax22.xaxis.set_minor_locator(MultipleLocator(1))
ax11.set_xlabel(r"$\mathrm{Velocity~(km~s^{-1})}$")
ax22.set_xlabel(r"$\mathrm{Velocity~(km~s^{-1})}$")
if not split:
top_ax11, top_ax22 = axarr[0]
else:
top_ax11, top_ax22 = axarr11[0], axarr22[0]
top_ax11.text(0.04, 0.8, r'$\mathrm{NH_3~(1,1)}$',# size='large',
transform=top_ax11.transAxes)
top_ax22.text(0.04, 0.8, r'$\mathrm{NH_3~(2,2)}$',# size='large',
transform=top_ax22.transAxes)
# top left only
ax11.set_ylabel(r"$T_{\mathrm{MB}}~\mathrm{(K)}$")
if not split:
fig.tight_layout()
fig.savefig("figs/spectra.pdf", dpi=160)
else:
f11.tight_layout()
f22.tight_layout()
f11.savefig("figs/spectra-11.pdf", dpi=160)
f22.savefig("figs/spectra-22.pdf", dpi=160)
plt.show()