def test_beam_hatch():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
for hatch in ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*']:
f.beam.set_hatch(hatch)
f.close()
def test_beam_pad():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_pad(0.1)
f.beam.set_pad(0.3)
f.close()
def test_beam_frame():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_frame(True)
f.beam.set_frame(False)
f.close()
def test_beam_corner():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
for corner in ['top', 'bottom', 'left', 'right', 'top left', 'top right',
'bottom left', 'bottom right']:
f.beam.set_corner(corner)
f.close()
def test_beam_angle():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_angle(0.)
f.beam.set_angle(55.)
f.close()
def test_beam_linestyle():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_linestyle('solid')
f.beam.set_linestyle('dotted')
f.beam.set_linestyle('dashed')
f.close()
def test_beam_linewidth():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_linewidth(0)
f.beam.set_linewidth(1)
f.beam.set_linewidth(5)
f.close()
def test_beam_edgecolor():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.beam.set_edgecolor('black')
f.beam.set_edgecolor('#003344')
f.beam.set_edgecolor((1.0, 0.4, 0.3))
f.close()
def test_beam_addremove():
data = np.zeros((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.remove_beam()
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.remove_beam()
f.close()
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
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
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')
#title
gc.set_title(title, fontsize=title_size)
##Pol map
p = quality_cuts(stkI, stkIerr, p, perr, SNRp_cut, p_cut, SNRi_cut)
#2. polmap
gc.show_vectors(p,pa,scale=scalevec,\
step=2,color='black',linewidth=4.0)
gc.show_vectors(p,pa,scale=scalevec,\
step=2,color='grey',linewidth=2.0)
#show label
gc.add_label(0.1,0.95,'Total Flux',fontsize=label_fontsize,\
color='white',weight='bold',relative=True)
def test_numpy_beam():
data = np.arange(256).reshape((16, 16))
f = FITSFigure(data)
f.show_grayscale()
f.add_beam(major=0.1, minor=0.04, angle=10.)
f.close()
