def part_8():
object_list = ['ROXs12','ROXs42B','ROXs42B']
filename_list = ['ROXs12','ROXs42Bb','ROXs42Bc']
for i in range(len(filename_list)):
outfile = open('position_'+filename_list[i]+'.dat','w')
outfile.write('# '+'%25s'%'file name\t'\
+'%12s'%'x planet\t'\
+'%12s'%'y planet\t'\
+'%12s'%'position angle\t'\
+'%12s'%'projected separation (pixels)\n')
if filename_list[i] == 'ROXs12': int_x,int_y = 562,685
elif filename_list[i] == 'ROXs42Bb': int_x,int_y = 629,495
elif filename_list[i] == 'ROXs42Bc': int_x,int_y = 546,465
parts = ['','_CMsub','_ADIsub','_bPSFsub']
for j in parts:
filename = object_list[i]+j+'_aligned_median.fits'
fit_radius = 16
f = fits.open(filename)
scidata = f[0].data
x_data = scidata[int_y,int_x-fit_radius:int_x+fit_radius+1]
x_data -= min(x_data)
fit_x = np.linspace(-fit_radius,fit_radius,1+2*fit_radius)
xfitParam, xfitCovar = curve_fit(gaussian_pdf,fit_x,x_data)
y_data = scidata[int_y-fit_radius:int_y+fit_radius+1,int_x]
y_data -= min(y_data)
fit_y = np.linspace(-fit_radius,fit_radius,1+2*fit_radius)
yfitParam, yfitCovar = curve_fit(gaussian_pdf,fit_y,y_data)
thisPARANG = f[0].header['PARANG']
thisROTPPOSN = f[0].header['ROTPPOSN']
thisEL = f[0].header['EL']
thisINSTANGL = f[0].header['INSTANGL']
PA_yaxis = thisPARANG +thisROTPPOSN -thisEL -thisINSTANGL
PA_planet = PA_yaxis -rad_to_deg(arctan2(float(xfitParam[0]+int_x-512),float(yfitParam[0]+int_y-512)))
if PA_planet < 0: PA_planet += 360
outfile.write('%25s'%str(object_list[i]+j+'_aligned_median.fits')+'\t'\
+'%8s'%'%.3f'%(float(xfitParam[0]+int_x))+'\t'\
+'%8s'%'%.3f'%(float(yfitParam[0]+int_y))+'\t'\
+'%8s'%'%.3f'%(float(PA_planet))+'\t'\
+'%8s'%'%.3f'%(float(sqrt((xfitParam[0]+int_x-512)**2+(yfitParam[0]+int_y-512)**2)))+'\n')
tf = FITSFigure(filename)
tf.show_colorscale(cmap='gray')
tf.add_colorbar()
tf.colorbar.show(location='top',box_orientation='horizontal')
plt.savefig(object_list[i]+j+'_aligned_median.pdf',dpi=200)
plt.close()
outfile.close()
def test_colorbar_addremove():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.remove_colorbar()
f.add_colorbar()
f.close()
def test_colorbar_showhide():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.hide()
f.colorbar.show()
f.close()
def test_colorbar_addremove():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.remove_colorbar()
f.add_colorbar()
f.close()
def test_colorbar_showhide():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.hide()
f.colorbar.show()
f.close()
def test_colorbar_font():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_font(size='small', weight='bold', stretch='normal',
family='serif', style='normal', variant='normal')
f.close()
def test_colorbar_pad():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_pad(0.1)
f.colorbar.set_pad(0.2)
f.colorbar.set_pad(0.5)
f.close()
def test_colorbar_pad():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_pad(0.1)
f.colorbar.set_pad(0.2)
f.colorbar.set_pad(0.5)
f.close()
def test_colorbar_location():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_location('top')
f.colorbar.set_location('bottom')
f.colorbar.set_location('left')
f.colorbar.set_location('right')
f.close()
def test_colorbar_location():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_location('top')
f.colorbar.set_location('bottom')
f.colorbar.set_location('left')
f.colorbar.set_location('right')
f.close()
def test_colorbar_font():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_colorbar()
f.colorbar.set_font(size='small',
weight='bold',
stretch='normal',
family='serif',
style='normal',
variant='normal')
f.close()
def plot_exposure_image(filename):
"""Plot FOV image of exposure for one given energy slice"""
from astropy.io import fits
from aplpy import FITSFigure
fig = FITSFigure(filename, dimensions=(0, 1), slices=[10], figsize=(5, 5))
header = fits.getheader(filename)
fig.show_grayscale()
fig.add_colorbar()
ra, dec = header['CRVAL1'], header['CRVAL2']
# Bug: Marker doesn't show up at the center of the run
# Bug: aplpy show_circles doesn't show a circle in degress.
fig.show_markers(ra, dec)
fig.show_circles(ra, dec, 1.)
fig.tick_labels.set_xformat('dd')
fig.tick_labels.set_yformat('dd')
fig.ticks.set_xspacing(1)
fig.ticks.set_yspacing(1)
fig.colorbar.set_axis_label_text('Effective Area (cm^2)')
fig.save('exposure_image.png')
def plot_exposure_image(filename):
"""Plot FOV image of exposure for one given energy slice"""
from astropy.io import fits
from aplpy import FITSFigure
fig = FITSFigure(filename, dimensions=(0, 1), slices=[10], figsize=(5, 5))
header = fits.getheader(filename)
fig.show_grayscale()
fig.add_colorbar()
ra, dec = header['CRVAL1'], header['CRVAL2']
# Bug: Marker doesn't show up at the center of the run
# Bug: aplpy show_circles doesn't show a circle in degress.
fig.show_markers(ra, dec)
fig.show_circles(ra, dec, 1.)
fig.tick_labels.set_xformat('dd')
fig.tick_labels.set_yformat('dd')
fig.ticks.set_xspacing(1)
fig.ticks.set_yspacing(1)
fig.colorbar.set_axis_label_text('Effective Area (cm^2)')
fig.save('exposure_image.png')
def test_colorbar_invalid():
data = np.zeros((16, 16))
f = FITSFigure(data)
with pytest.raises(Exception):
f.add_colorbar() # no grayscale/colorscale was shown
model, gtmodel, ratio, counts, header = prepare_images()
# Plotting
fig = plt.figure(figsize=(15, 5))
image1 = fits.ImageHDU(data=model, header=header)
f1 = FITSFigure(image1, figure=fig, subplot=(1, 3, 1), convention='wells')
f1.show_colorscale(vmin=0, vmax=0.3, cmap='afmhot')
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('dd')
image2 = fits.ImageHDU(data=gtmodel, header=header)
f2 = FITSFigure(image2, figure=fig, subplot=(1, 3, 2), convention='wells')
f2.show_colorscale(vmin=0, vmax=0.3, cmap='afmhot')
f2.tick_labels.set_xformat('ddd')
f2.tick_labels.set_yformat('dd')
image3 = fits.ImageHDU(data=ratio, header=header)
f3 = FITSFigure(image3, figure=fig, subplot=(1, 3, 3), convention='wells')
f3.show_colorscale(vmin=0.9, vmax=1.1, cmap='RdBu')
f3.tick_labels.set_xformat('ddd')
f3.tick_labels.set_yformat('dd')
f3.add_colorbar(ticks=[0.9, 0.95, 1, 1.05, 1.1])
fig.text(0.12, 0.95, "Gammapy Background")
fig.text(0.45, 0.95, "Fermi Tools Background")
fig.text(0.75, 0.95, "Ratio: Gammapy/Fermi Tools")
plt.tight_layout()
plt.show()
def test_colorbar_invalid():
data = np.zeros((16, 16))
f = FITSFigure(data)
with pytest.raises(Exception):
f.add_colorbar() # no grayscale/colorscale was shown
"""Produces an image from 1FHL catalog point sources.
"""
import numpy as np
import matplotlib.pyplot as plt
from aplpy import FITSFigure
from gammapy.datasets import FermiGalacticCenter
from gammapy.image import catalog_image, SkyImage
from gammapy.irf import EnergyDependentTablePSF
# Create image of defined size
reference = SkyImage.empty(nxpix=300, nypix=100, binsz=1).to_image_hdu()
psf_file = FermiGalacticCenter.filenames()['psf']
psf = EnergyDependentTablePSF.read(psf_file)
# Create image
image = catalog_image(reference, psf, catalog='1FHL', source_type='point',
total_flux='True')
# Plot
fig = FITSFigure(image.to_fits()[0], figsize=(15, 5))
fig.show_colorscale(interpolation='bicubic', cmap='afmhot', stretch='log', vmin=1E-12, vmax=1E-8)
fig.tick_labels.set_xformat('ddd')
fig.tick_labels.set_yformat('dd')
ticks = np.logspace(-12, -8, 5)
fig.add_colorbar(ticks=ticks, axis_label_text='Flux (ph s^-1 cm^-2 TeV^-1)')
fig.colorbar._colorbar_axes.set_yticklabels(['{:.0e}'.format(_) for _ in ticks])
plt.tight_layout()
plt.show()
def make_polmap(filename, title=None, figure=None, subplot=(1, 1, 1)):
hawc = fits.open(filename)
p = hawc['DEBIASED PERCENT POL'] # %
theta = hawc['ROTATED POL ANGLE'] # deg
stokes_i = hawc['STOKES I'] # I
error_i = hawc['ERROR I'] # error I
# 1. plot Stokes I
# convert from Jy/pix to Jy/sq. arcsec
pxscale = stokes_i.header['CDELT2'] * 3600 # map scale in arcsec/pix
stokes_i.data /= pxscale**2
error_i.data /= pxscale**2
fig = FITSFigure(stokes_i, figure=figure, subplot=subplot)
# 2. perform S/N cut on I/\sigma_I
err_lim = 100
mask = np.where(stokes_i.data / error_i.data < err_lim)
# 3. mask out low S/N vectors by setting masked indices to NaN
p.data[mask] = np.nan
# 4. plot vectors
scalevec = 0.4 # 1pix = scalevec * 1% pol # scale vectors to make it easier to see
fig.show_vectors(p, theta, scale=scalevec,
step=2) # step size = display every 'step' vectors
# step size of 2 is effectively Nyquist sampling
# --close to the beam size
# 5. plot contours
ncontours = 30
fig.show_contour(stokes_i,
cmap=cmap,
levels=ncontours,
filled=True,
smooth=1,
kernel='box')
fig.show_contour(stokes_i,
colors='gray',
levels=ncontours,
smooth=1,
kernel='box',
linewidths=0.3)
# Show image
fig.show_colorscale(cmap=cmap)
# If title, set it
if title:
fig.set_title(title)
# Add colorbar
fig.add_colorbar()
fig.colorbar.set_axis_label_text('Flux (Jy/arcsec$^2$)')
# Add beam indicator
fig.add_beam(facecolor='red',
edgecolor='black',
linewidth=2,
pad=1,
corner='bottom left')
fig.add_label(0.02,
0.02,
'Beam FWHM',
horizontalalignment='left',
weight='bold',
relative=True,
size='small')
# Add vector scale
# polarization vectors are displayed such that 'scalevec' * 1% pol is 1 pix long
# must translate pixel size to angular degrees since the 'add_scalebar' function assumes a physical scale
vectscale = scalevec * pxscale / 3600
fig.add_scalebar(5 * vectscale, "p = 5%", corner='top right', frame=True)
return stokes_i, p, mask, fig
from gammapy.image import catalog_image, SkyImage
from gammapy.irf import EnergyDependentTablePSF
# Create image of defined size
reference = SkyImage.empty(nxpix=300, nypix=100, binsz=1).to_image_hdu()
psf_file = FermiGalacticCenter.filenames()['psf']
psf = EnergyDependentTablePSF.read(psf_file)
# Create image
image = catalog_image(reference,
psf,
catalog='1FHL',
source_type='point',
total_flux='True')
# Plot
fig = FITSFigure(image.to_fits(format='fermi-background')[0], figsize=(15, 5))
fig.show_colorscale(interpolation='bicubic',
cmap='afmhot',
stretch='log',
vmin=1E-12,
vmax=1E-8)
fig.tick_labels.set_xformat('ddd')
fig.tick_labels.set_yformat('dd')
ticks = np.logspace(-12, -8, 5)
fig.add_colorbar(ticks=ticks, axis_label_text='Flux (cm^-2 s^-1 TeV^-1)')
fig.colorbar._colorbar_axes.set_yticklabels(
['{:.0e}'.format(_) for _ in ticks])
plt.tight_layout()
plt.show()
stkI.data *= 1000.
stkIerr.data *= 1000.
#central coordinates
RA = (stkI.header['OBSRA'] * u.hourangle).to(u.deg)
DEC = stkI.header['OBSDEC'] * u.deg
###Figure
gc = FITSFigure(stkI, figure=fig)
#gc = FITSFigure(stkI,figure=fig)
##STOKES I
#colorscale
gc.show_colorscale(cmap=cmap, vmin=vmin, vmax=vmax, smooth=1, kernel='gauss')
#colorbae
gc.add_colorbar()
gc.colorbar.set_axis_label_text('I (mJy/sqarcsec)')
gc.colorbar.set_axis_label_font(size=label_colorbar)
gc.colorbar.set_font(size=tick_colorbar)
#recenter
gc.recenter(RA, DEC, width=width / 3600, height=height / 3600)
#contours
sigmaI = np.nanstd(stkIerr.data)
levelsI = sigmaI * 1.8**(np.arange(2, 12, 0.5))
#levelsI = np.arange(3,155,3)*sigmaI
gc.show_contour(colors='black',levels=levelsI,linewidth=0.1,\
filled=False,smooth=1,kernel='box',alpha=0.4)
#gc.show_contour(levels=levelsI,\
# filled=True,smooth=1,kernel='box',cmap='viridis')
#beam
gc.add_beam(color='red')
fermi_significance = np.nan_to_num(significance(correlated_counts, correlated_gtmodel, method="lima"))
# Gammapy significance
significance = np.nan_to_num(significance(correlated_counts, correlated_model, method="lima"))
titles = ["Gammapy Significance", "Fermi Tools Significance"]
# Plot
fig = plt.figure(figsize=(10, 5))
hdu1 = fits.ImageHDU(significance, header)
f1 = FITSFigure(hdu1, figure=fig, convention="wells", subplot=(1, 2, 1))
f1.set_tick_labels_font(size="x-small")
f1.tick_labels.set_xformat("ddd")
f1.tick_labels.set_yformat("ddd")
f1.show_colorscale(vmin=0, vmax=20, cmap="afmhot", stretch="sqrt")
f1.add_colorbar(axis_label_text="Significance")
f1.colorbar.set_width(0.1)
f1.colorbar.set_location("right")
hdu2 = fits.ImageHDU(fermi_significance, header)
f2 = FITSFigure(hdu2, figure=fig, convention="wells", subplot=(1, 2, 2))
f2.set_tick_labels_font(size="x-small")
f2.tick_labels.set_xformat("ddd")
f2.hide_ytick_labels()
f2.hide_yaxis_label()
f2.show_colorscale(vmin=0, vmax=20, cmap="afmhot", stretch="sqrt")
f2.add_colorbar(axis_label_text="Significance")
f2.colorbar.set_width(0.1)
f2.colorbar.set_location("right")
fig.text(0.15, 0.92, "Gammapy Significance")
fig.text(0.63, 0.92, "Fermi Tools Significance")
stokes_i = hawc['STOKES I'] # or hawc[0]. Note the extension is from the hawc.info() table above
fig = plt.figure(figsize=(7,7))
pxscale = stokes_i.header['CDELT2']*3600 # map scale in arcsec/pix
disp_i = stokes_i.copy()
disp_i.data /= pxscale**2
axs = FITSFigure(disp_i, figure=fig) # load HDU into aplpy figure
axs.show_colorscale(cmap=cmap) # display the data with WCS projection and chosen colormap
# FORMATTING
axs.set_tick_labels_font(size='small')
axs.set_axis_labels_font(size='small')
# Add colorbar
axs.add_colorbar()
axs.colorbar.set_axis_label_text('Flux (Jy/arcsec$^2$)')
plt.savefig('figs/Stokes_I.pdf',dpi=300)
plt.savefig('figs/Stokes_I.png',dpi=300)
# ### Stokes Q and U
# Similarly, we can plot the Stokes Q and Stokes U images:
# In[3]:
stokes_q = hawc['STOKES Q']
stokes_u = hawc['STOKES U']
fig = plt.figure(figsize=(12.8,9.6))
model, gtmodel, ratio, counts, header = prepare_images()
# Plotting
fig = plt.figure(figsize=(15, 5))
image1 = fits.ImageHDU(data=model, header=header)
f1 = FITSFigure(image1, figure=fig, subplot=(1, 3, 1), convention='wells')
f1.show_colorscale(vmin=0, vmax=0.3, cmap='afmhot')
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('dd')
image2 = fits.ImageHDU(data=gtmodel, header=header)
f2 = FITSFigure(image2, figure=fig, subplot=(1, 3, 2), convention='wells')
f2.show_colorscale(vmin=0, vmax=0.3, cmap='afmhot')
f2.tick_labels.set_xformat('ddd')
f2.tick_labels.set_yformat('dd')
image3 = fits.ImageHDU(data=ratio, header=header)
f3 = FITSFigure(image3, figure=fig, subplot=(1, 3, 3), convention='wells')
f3.show_colorscale(vmin=0.95, vmax=1.05, cmap='RdBu')
f3.tick_labels.set_xformat('ddd')
f3.tick_labels.set_yformat('dd')
f3.add_colorbar(ticks=[0.95, 0.975, 1, 1.025, 1.05])
fig.text(0.12, 0.95, "Gammapy Background")
fig.text(0.45, 0.95, "Fermi Tools Background")
fig.text(0.75, 0.95, "Ratio: Gammapy/Fermi Tools")
plt.tight_layout()
plt.show()
n_sources=n_sources, rad_dis=rad_dis, vel_dis=vel_dis, max_age=1e6, spiralarms=spiralarms, random_state=0
)
# Minimum source luminosity (ph s^-1)
luminosity_min = 4e34
# Maximum source luminosity (ph s^-1)
luminosity_max = 4e37
# Luminosity function differential power-law index
luminosity_index = 1.5
# Assigns luminosities to sources
luminosity = sample_powerlaw(luminosity_min, luminosity_max, luminosity_index, n_sources, random_state=0)
table["luminosity"] = luminosity
# Adds parameters to table: distance, glon, glat, flux, angular_extension
table = population.add_observed_parameters(table)
table.meta["Energy Bins"] = np.array([10, 500]) * u.GeV
# Create image
image = catalog_image(reference, psf, catalog="simulation", source_type="point", total_flux=True, sim_table=table)
# Plot
fig = FITSFigure(image.to_fits(format="fermi-background")[0], figsize=(15, 5))
fig.show_colorscale(interpolation="bicubic", cmap="afmhot", stretch="log", vmin=1e30, vmax=1e35)
fig.tick_labels.set_xformat("ddd")
fig.tick_labels.set_yformat("dd")
ticks = np.logspace(30, 35, 6)
fig.add_colorbar(ticks=ticks, axis_label_text="Flux (ph s^-1)")
fig.colorbar._colorbar_axes.set_yticklabels(["{:.0e}".format(_) for _ in ticks])
plt.tight_layout()
plt.show()
"""Produces an image from 1FHL catalog point sources.
"""
from aplpy import FITSFigure
from gammapy.datasets import FermiGalacticCenter
from gammapy.image import make_empty_image, catalog_image
from gammapy.irf import EnergyDependentTablePSF
# Create image of defined size
reference = make_empty_image(nxpix=300, nypix=100, binsz=1)
psf_file = FermiGalacticCenter.filenames()['psf']
psf = EnergyDependentTablePSF.read(psf_file)
# Create image
image = catalog_image(reference, psf, catalog='1FHL', source_type='point',
total_flux='True')
# Plot
fig = FITSFigure(image.to_fits()[0])
fig.show_grayscale(stretch='linear', interpolation='none')
fig.add_colorbar()
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('ddd')
f1.axis_labels.hide_x()
f1.show_colorscale(vmin=0, vmax=0.3)
hdu2 = fits.ImageHDU(gtmodel, header)
f2 = FITSFigure(hdu2,
figure=fig,
convention='wells',
subplot=[0.38, 0.25, 0.2, 0.26])
f2.tick_labels.set_font(size='x-small')
f2.tick_labels.set_xformat('ddd')
f2.tick_labels.hide_y()
f2.axis_labels.hide_y()
f2.show_colorscale(vmin=0, vmax=0.3)
f2.add_colorbar()
f2.colorbar.set_width(0.1)
f2.colorbar.set_location('right')
hdu3 = fits.ImageHDU(ratio, header)
f3 = FITSFigure(hdu3,
figure=fig,
convention='wells',
subplot=[0.67, 0.25, 0.2, 0.26])
f3.tick_labels.set_font(size='x-small')
f3.tick_labels.set_xformat('ddd')
f3.tick_labels.hide_y()
f3.axis_labels.hide()
f3.show_colorscale(vmin=0.9, vmax=1.1)
f3.add_colorbar()
f3.colorbar.set_width(0.1)
n_sources,
random_state=0)
table['luminosity'] = luminosity
# Adds parameters to table: distance, glon, glat, flux, angular_extension
table = population.add_observed_parameters(table)
table.meta['Energy Bins'] = np.array([10, 500]) * u.GeV
# Create image
image = catalog_image(reference,
psf,
catalog='simulation',
source_type='point',
total_flux=True,
sim_table=table)
# Plot
fig = FITSFigure(image.to_fits()[0], figsize=(15, 5))
fig.show_colorscale(interpolation='bicubic',
cmap='afmhot',
stretch='log',
vmin=1E30,
vmax=1E35)
fig.tick_labels.set_xformat('ddd')
fig.tick_labels.set_yformat('dd')
ticks = np.logspace(30, 35, 6)
fig.add_colorbar(ticks=ticks, axis_label_text='Flux (ph s^-1)')
fig.colorbar._colorbar_axes.set_yticklabels(
['{:.0e}'.format(_) for _ in ticks])
plt.tight_layout()
plt.show()
def calc_physics(ra=None,
dec=None,
r=0.17 * u.pc,
dr=0.05 * u.pc,
vexp=4 * u.km / u.s,
v0=14 * u.km / u.s,
cube_12co=None,
cube_13co=None,
dist=414 * u.pc,
snr_cutoff=5.,
shell_snr_cutoff=3.,
shell=True,
plot=False,
linewidth_mode='fwhm',
average_Tex=True):
"""
shell_snr_cutoff is the sigma cutoff for extracting the shell
voxels.
"""
#print(cube_12co.header['CDELT3'])
from spectral_cube.lower_dimensional_structures import Projection
pix_size = (cube_12co.header['CDELT2'] * u.deg).to(u.arcsec)
vstep = (cube_12co.header['CDELT3'] * (u.m / u.s)).to(u.km / u.s)
if shell:
model_pars = {
'dist': dist, # pc
'pix_size': pix_size, # arcsec
'vstep': vstep, # km/s
'acen': ra.to(u.deg), # deg
'dcen': dec.to(u.deg), # deg
'thickness': 0.0, # pc
'fwhm': 0.0, # km/s
'beta': 0.0, # spectral index
'R': r, # pc
'dr': dr, # pc
'vexp': vexp, # km/s
'depth_offset': 0.0, # pc
'vel_offset': 0.0, # km/s
'v0': v0, # km/s
'ignore_cloud': 1, #Ignore cloud.
'method': 'sample',
'write_fits': False,
'samples_per_voxel': 27
}
#Extract 12co shell voxels using model.
model_cube = SpectralCube.read(shell_model.ppv_model(**model_pars))
#shell_masked, shell_mask = extract_shell(
# cube_file=cube_12co, model_pars=model_pars, return_mask=True)
#Extract subcubes with same ra/dec range as shell voxel cube, but
#full velocity range.
subcube_shell_12co = cube_12co.subcube(model_cube.longitude_extrema[1],
model_cube.longitude_extrema[0],
model_cube.latitude_extrema[0],
model_cube.latitude_extrema[1])
subcube_shell_13co = cube_13co.subcube(model_cube.longitude_extrema[1],
model_cube.longitude_extrema[0],
model_cube.latitude_extrema[0],
model_cube.latitude_extrema[1])
#print(subcube_shell_12co, subcube_shell_13co, model_cube)
if plot:
plt.figure()
plt.subplot(131)
plt.imshow(subcube_shell_12co.moment0().data)
plt.subplot(132)
plt.imshow(subcube_shell_13co.moment0().data)
plt.subplot(133)
plt.imshow(model_cube.moment0().data)
plt.show()
else:
subcube_shell_12co = cube_12co
subcube_shell_13co = cube_13co
rms_12co = rms_map(cube=subcube_shell_12co)
rms_13co = rms_map(cube=subcube_shell_13co)
###
### Use 13co if 3sigma detected, otherwise use corrected 12co if 3sigma detected.
mask_use_13co = (subcube_shell_13co >= shell_snr_cutoff * rms_13co)
mask_use_12co = (~mask_use_13co) & (subcube_shell_12co >=
shell_snr_cutoff * rms_12co)
### Excitation Temperature
Tex = cube_Tex(subcube_shell_12co,
average_first=True,
plot=False,
average=average_Tex)
print("Tex is {}".format(Tex))
### Correct 12co for opacity.
subcube_shell_12co_correct = opacity_correct(subcube_shell_12co,
cube_thin=subcube_shell_13co,
snr_cutoff=snr_cutoff,
plot_ratio='ratio.png')
#print(subcube_shell_12co_correct)
subcube_shell_12co_correct = subcube_shell_12co_correct.with_mask(
mask_use_12co)
subcube_shell_13co = subcube_shell_13co.with_mask(mask_use_13co)
# if plot:
# plt.figure()
# plt.subplot(121)
# plt.imshow(subcube_shell_12co_correct.moment0().data)
# plt.subplot(122)
# plt.imshow(subcube_shell_13co.moment0().data)
# plt.show()
if shell:
### Extract shell voxels from opacity-corrected 12co
shell_12co_correct = extract_shell(subcube_shell_12co_correct,
keep_latlon=True,
model_cube=model_cube)
shell_13co = extract_shell(subcube_shell_13co,
keep_latlon=True,
model_cube=model_cube)
else:
shell_12co_correct = subcube_shell_12co_correct
shell_13co = subcube_shell_13co
# if plot:
# plt.figure()
# plt.subplot(121)
# plt.imshow(shell_12co_correct.moment0().data)
# plt.subplot(122)
# plt.imshow(shell_13co.moment0().data)
# plt.show()
###
### Use 13co if 3sigma detected, otherwise use corrected 12co if 3sigma detected.
# mask_use_13co = (shell_13co >= shell_snr_cutoff * rms_13co)
# mask_use_12co = (~mask_use_13co) & (subcube_shell_12co >= shell_snr_cutoff * rms_12co)
### Calculate column density of H2 from 13co where 13co is 3sig,
### otherwise use opacity-corrected 12co.
shell_nH2_12co = column_density_H2(shell_12co_correct,
Tex=Tex,
molecule="12co")
shell_nH2_13co = column_density_H2(shell_13co, Tex=Tex, molecule="13co")
#print(shell_nH2_12co.shape, shell_nH2_13co.shape)
print(np.nansum([shell_nH2_12co, shell_nH2_13co], axis=0),
shell_nH2_12co.unit)
shell_nH2 = Projection(np.nansum([shell_nH2_12co, shell_nH2_13co], axis=0),
header=shell_nH2_12co.header,
wcs=shell_nH2_12co.wcs,
unit=shell_nH2_12co.unit,
dtype=shell_nH2_12co.dtype,
meta=shell_nH2_12co.meta,
mask=shell_nH2_12co.mask)
#print(shell_nH2.shape)
# if plot:
# plt.figure()
# plt.subplot(131)
# plt.title("H2 from 12co")
# plt.imshow(shell_nH2_12co.data)
# plt.subplot(132)
# plt.title("H2 from 13co")
# plt.imshow(shell_nH2_13co.data)
# plt.subplot(133)
# plt.title("Total H2")
# plt.imshow(shell_nH2)
### Calculate Mass, Momentum, and Energy of Shell!
if shell:
shell_mass = mass(shell_nH2,
distance=414 * u.pc,
molecule='H2',
mass_unit=u.Msun)
shell_momentum = momentum(shell_mass, vexp)
shell_energy = energy(shell_mass, vexp)
shell_luminosity = (shell_energy / (r / vexp)).to(u.erg / u.s)
else:
mass_map = mass(shell_nH2,
distance=414 * u.pc,
molecule='H2',
mass_unit=u.Msun,
return_map=True)
if linewidth_mode == "fwhm":
vel_map = subcube_shell_13co.linewidth_fwhm()
elif linewidth_mode == "sigma":
vel_map = subcube_shell_13co.linewidth_sigma()
elif linewidth_mode == "sigma3D":
vel_map = 3.**0.5 * subcube_shell_13co.linewidth_sigma()
# plt.figure()
# plt.imshow(vel_map)
# plt.show()
#vel_average = np.nanmean(vel_map.value)
shell_mass = u.Quantity(np.nansum(mass_map))
shell_momentum = u.Quantity(np.nansum(mass_map * vel_map))
#shell_momentum = vel
shell_energy = 0.5 * u.Quantity(np.nansum(
mass_map * vel_map * vel_map))
#shell_energy = 0.5 * shell_mass
#print(u.Quantity(0.5*shell_mass*u.Quantity(np.nanmean(vel_map))**2.).to(u.erg))
if plot:
from aplpy import FITSFigure
from astropy.io import fits
hdu = shell_nH2.hdu
hdu.data = np.log10(shell_nH2.data)
fig = FITSFigure(hdu.data)
fig.show_colorscale(vmin=21.5, vmax=23.65, interpolation='none')
fig.add_colorbar()
# plt.imshow(np.log10(shell_nH2.value), interpolation='none',
# vmin=21.5, vmax=23.648)
# plt.colorbar()
# plt.title("log(n(H2) [cm^-2])")
#plt.show()
plt.savefig("../subregions/berneregion_lognH2.png")
hdu = fits.open("../berne_Nh_2.fits")[0]
hdu.data = np.log10(hdu.data)
fig = FITSFigure(hdu.data)
fig.show_colorscale(vmin=21.5, vmax=23.65, interpolation='none')
fig.add_colorbar()
#plt.show()
plt.savefig("../subregions/berne_lognH2.png")
# plt.figure()
# plt.imshow(vel_map.to(u.km/u.s).value, interpolation='none')
# #energy_map = (0.5*mass_map*vel_map*vel_map).to(u.erg).value
# vels = vel_map.to(u.km/u.s).value.flatten()
# plt.figure()
# plt.hist(vels[vels>0], normed=True, log=True, bins=40)
# plt.xlabel("Velocity FWHM (km/s)")
# plt.ylabel("PDF")
# plt.title('Velocity FWHM PDF')
# plt.show()
# plt.figure()
# plt.imshow(vel_map.data)
# plt.colorbar()
# plt.show()
return shell_mass, shell_momentum, shell_energy
hdu1 = fits.ImageHDU(model, header)
f1 = FITSFigure(hdu1, figure=fig, convention='wells', subplot=[0.18, 0.264, 0.18, 0.234])
f1.tick_labels.set_font(size='x-small')
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('ddd')
f1.axis_labels.hide_x()
f1.show_colorscale(vmin=0, vmax=0.3)
hdu2 = fits.ImageHDU(gtmodel, header)
f2 = FITSFigure(hdu2, figure=fig, convention='wells', subplot=[0.38, 0.25, 0.2, 0.26])
f2.tick_labels.set_font(size='x-small')
f2.tick_labels.set_xformat('ddd')
f2.tick_labels.hide_y()
f2.axis_labels.hide_y()
f2.show_colorscale(vmin=0, vmax=0.3)
f2.add_colorbar()
f2.colorbar.set_width(0.1)
f2.colorbar.set_location('right')
hdu3 = fits.ImageHDU(ratio, header)
f3 = FITSFigure(hdu3, figure=fig, convention='wells', subplot=[0.67, 0.25, 0.2, 0.26])
f3.tick_labels.set_font(size='x-small')
f3.tick_labels.set_xformat('ddd')
f3.tick_labels.hide_y()
f3.axis_labels.hide()
f3.show_colorscale(vmin=0.9, vmax=1.1)
f3.add_colorbar()
f3.colorbar.set_width(0.1)
f3.colorbar.set_location('right')
fig.text(0.19, 0.53, "Gammapy Background", color='black', size='9')
def Bmap(self,
idi=50.0,
pdp=3.0,
pmax=30.0,
color='rainbow',
scalevec=1.0,
clevels=100,
imin=0.0,
imax=None,
size_x=9.0,
size_y=9.0,
dpi=100.0):
# Initialization
# idi : Signal-to-noise ratio for Stokes I total intensity. Default is
# idi = 50.0, which leads to an upper limit of at least dP = 5.0%
# for the error on the polarization fraction P.
# pdp : Signal-to-noise ratio for the de-biased polarization fraction P.
# Default is pdp = 3.0, which is the commonly accepted detection
# threshold in the litterature.
# pmax : Maximum polarization fraction allowed.
# color : Color scheme used for the plots. Default is rainbow.
# scalevec : Length of the vectors plotted on the map.
# clevels : Number of contours plotted on the contour plot.
# imin : Minimum value plotted in the Stokes I intensity map.
# Default is 0.0.
# imax : Maximum value plotted in the Stokes I intensity map.
# If no value is provided, maximum is automatically detected.
print()
print('=====================================')
print('Plotting a lovable magnetic field map')
print('=====================================')
print()
# Creating a new fits object for APLpy's FITSFigure method to recognize
# for the Stokes I total intensity
plot_hdu = fits.PrimaryHDU(data=self.I, header=self.header)
# Loading the data in an APLpy figure
Bmap = FITSFigure(plot_hdu,
dpi=dpi,
figsize=(size_x, size_y),
slices=[0, 1])
print('Plotting the Stokes I total intensity map')
print()
# Showing the pixelated image and setting the aspect ratio
Bmap.show_colorscale(cmap=color, vmin=imin, vmax=imax)
# Plotting a filled contour plot of the data
Bmap.show_contour(cmap=color,
levels=clevels,
filled=True,
extend='both',
vmin=imin,
vmax=imax)
# Inverting ticks
#Bmap.ticks.set_tick_direction('in') # Not working in APLpy
# Adding a colorbar to the plot
Bmap.add_colorbar(pad=0.125)
# Adding the units to the colorbar
Bmap.colorbar.set_axis_label_text(self.units)
# Moving the colorbar to be on top of the figure
Bmap.colorbar.set_location('top')
# Creating a new fits object for APLpy's FITSFigure method to recognize
# for the polarization fraction P
pref = fits.PrimaryHDU(data=self.P, header=self.header)
pmap = pref.copy()
# for the magnetic field angle B
oref = fits.PrimaryHDU(data=self.B, header=self.header)
omap = oref.copy()
# A copy of the HDU is created before any masking is done.
# This fixes an issue where modifying pmap was carried to other
# instances of this method. Future fix should make sure pref is
# closed/deleted at the end of this method.
# Muting the numpy alerts triggered by nan values
np.warnings.filterwarnings('ignore')
print()
print('Applying happy selection criteria on polarization vectors')
print()
# Creating a mask to hide polarization vectors with low signal-to-noise ratios
imask_01 = np.where(
self.I / self.dI < idi) # Total intensity SNR threshold
imask_02 = np.where(self.I < 0) # Total intensity positive threshold
pmask = np.where(self.P / self.dP < pdp) # Polarization SNR threshold
pmaxmask = np.where(self.P > pmax) # Polarization SNR threshold
# Forcing the polarization vectors to share the same amplitude
pmap.data[np.where(self.P > 0.0)] = 1.0
# Masking all the indices for which the selection criteria failed
pmap.data[imask_01] = np.nan
pmap.data[imask_02] = np.nan
pmap.data[pmask] = np.nan
pmap.data[pmaxmask] = np.nan
print('Plotting the magnetic field segments')
print()
# Plotting the polarization vectors
Bmap.show_vectors(pmap, omap, scale=scalevec)
# Adding the beam size
Bmap.add_beam(facecolor='white',
edgecolor='black',
linewidth=2,
pad=1,
corner='bottom left')
# Removing the pixelated structure under the figure
Bmap.hide_colorscale(
) # Warning: You need to reopen it to create a new
# colorbar, APLpy deals poorly with
# contour maps by themselves
print('Returning the APLpy figure as output, please feel free to' +
' improve it (see online APLpy.FITSFigure documentation)')
print()
print('Don\'t forget to save the results using the' +
' .savefig(\'name.png\') function')
print()
print('Have fun!')
return Bmap
# Gammapy significance
significance = np.nan_to_num(significance(correlated_counts, correlated_model,
method='lima'))
titles = ['Gammapy Significance', 'Fermi Tools Significance']
# Plot
fig = plt.figure(figsize=(10, 5))
hdu1 = fits.ImageHDU(significance, header)
f1 = FITSFigure(hdu1, figure=fig, convention='wells', subplot=(1,2,1))
f1.set_tick_labels_font(size='x-small')
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('ddd')
f1.show_colorscale(vmin=0, vmax=10, cmap='afmhot')
f1.add_colorbar(axis_label_text='Significance')
f1.colorbar.set_width(0.1)
f1.colorbar.set_location('right')
hdu2 = fits.ImageHDU(fermi_significance, header)
f2 = FITSFigure(hdu2, figure=fig, convention='wells', subplot=(1,2,2))
f2.set_tick_labels_font(size='x-small')
f2.tick_labels.set_xformat('ddd')
f2.hide_ytick_labels()
f2.hide_yaxis_label()
f2.show_colorscale(vmin=0, vmax=10, cmap='afmhot')
f2.add_colorbar(axis_label_text='Significance')
f2.colorbar.set_width(0.1)
f2.colorbar.set_location('right')
fig.text(0.15, 0.92,"Gammapy Significance")
"""Produces an image from 1FHL catalog point sources.
"""
from aplpy import FITSFigure
from gammapy.datasets import FermiGalacticCenter
from gammapy.image import make_empty_image, catalog_image
from gammapy.irf import EnergyDependentTablePSF
# Create image of defined size
reference = make_empty_image(nxpix=300, nypix=100, binsz=1)
psf_file = FermiGalacticCenter.filenames()['psf']
psf = EnergyDependentTablePSF.read(psf_file)
# Create image
image = catalog_image(reference,
psf,
catalog='1FHL',
source_type='point',
total_flux='True')
# Plot
fig = FITSFigure(image.to_fits()[0])
fig.show_grayscale(stretch='linear', interpolation='none')
fig.add_colorbar()
# Gammapy significance
significance = np.nan_to_num(significance(correlated_counts, correlated_model,
method='lima'))
titles = ['Gammapy Significance', 'Fermi Tools Significance']
# Plot
fig = plt.figure(figsize=(10, 5))
hdu1 = fits.ImageHDU(significance, header)
f1 = FITSFigure(hdu1, figure=fig, convention='wells', subplot=(1, 2, 1))
f1.set_tick_labels_font(size='x-small')
f1.tick_labels.set_xformat('ddd')
f1.tick_labels.set_yformat('ddd')
f1.show_colorscale(vmin=0, vmax=10, cmap='afmhot')
f1.add_colorbar(axis_label_text='Significance')
f1.colorbar.set_width(0.1)
f1.colorbar.set_location('right')
hdu2 = fits.ImageHDU(fermi_significance, header)
f2 = FITSFigure(hdu2, figure=fig, convention='wells', subplot=(1, 2, 2))
f2.set_tick_labels_font(size='x-small')
f2.tick_labels.set_xformat('ddd')
f2.hide_ytick_labels()
f2.hide_yaxis_label()
f2.show_colorscale(vmin=0, vmax=10, cmap='afmhot')
f2.add_colorbar(axis_label_text='Significance')
f2.colorbar.set_width(0.1)
f2.colorbar.set_location('right')
fig.text(0.15, 0.92, "Gammapy Significance")
fig.text(0.63, 0.92, "Fermi Tools Significance")
stokes_i = hawc[
'STOKES I'] # or hawc[0]. Note the extension is from the hawc.info() table above
fig = plt.figure(figsize=(7, 7))
axs = FITSFigure(stokes_i, figure=fig) # load HDU into aplpy figure
axs.show_colorscale(
cmap=cmap) # display the data with WCS projection and chosen colormap
# FORMATTING
axs.set_tick_labels_font(size='small')
axs.set_axis_labels_font(size='small')
# Add colorbar
axs.add_colorbar()
axs.colorbar.set_axis_label_text('Flux (Jy/pix)')
# ## Stokes Q and U
# Similarly, we can plot the Stokes Q and Stokes U images:
# In[3]:
stokes_q = hawc['STOKES Q']
stokes_u = hawc['STOKES U']
axq = FITSFigure(
stokes_q,
subplot=(1, 2,
1)) # generate FITSFigure as subplot to have two axes together
axq.show_colorscale(cmap=cmap) # show Q
