Tibeuleu/FOC_Reduction
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polarization map alignement and animated loop for polarization timing

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This commit is contained in:
Thibault Barnouin
2022-07-11 13:26:32 +02:00
parent 519cca13b0
commit 2c5d4212ac
188 changed files with 167 additions and 34 deletions
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146
src/lib/plots.py
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@@ -43,7 +43,6 @@ from matplotlib.patches import Rectangle, Circle
from matplotlib.path import Path
from matplotlib.widgets import RectangleSelector, LassoSelector, Button, Slider, TextBox
from matplotlib.colors import LogNorm
from matplotlib.ticker import LogFormatter
import matplotlib.font_manager as fm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
from astropy.wcs import WCS
@@ -327,7 +326,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
elif display.lower() in ['s_p','pol_err','pol_deg_err']:
# Display polarization degree error map
display='s_p'
vmin, vmax = 0., 10.
vmin, vmax = 0., np.max(pol_err.data[SNRp > SNRp_cut])*100.
p_err = deepcopy(pol_err.data)
p_err[p_err > vmax/100.] = np.nan
im = ax.imshow(p_err*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
@@ -470,11 +469,16 @@ class align_maps(object):
self.ax1.set_facecolor('k')
vmin, vmax = 0., np.max(data[data > 0.]*convert_flux)
im1 = self.ax1.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
for key, value in [["cmap",[["cmap","inferno"]]], ["norm",[["vmin",vmin],["vmax",vmax]]]]:
try:
test = kwargs[key]
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
im1 = self.ax1.imshow(data*convert_flux, aspect='equal', **kwargs)
fontprops = fm.FontProperties(size=16)
px_size = self.wcs_map.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(self.ax1.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
px_sc = AnchoredSizeBar(self.ax1.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
self.ax1.add_artist(px_sc)
try:
@@ -552,6 +556,7 @@ class align_maps(object):
def apply_align(self, event):
self.wcs_map.wcs.crpix = np.array(self.cr_map.get_data())
self.wcs_map.wcs.crval = np.array(self.wcs_map.pixel_to_world_values(*self.wcs_map.wcs.crpix))
self.wcs_other.wcs.crpix = np.array(self.cr_other.get_data())
self.wcs_other.wcs.crval = self.wcs_map.wcs.crval
self.fig.canvas.draw_idle()
@@ -650,10 +655,10 @@ class overplot_radio(align_maps):
self.fig2.canvas.draw()
def plot(self, levels, SNRp_cut=3., SNRi_cut=30., savename=None) -> None:
self.align()
if self.aligned:
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename)
plt.show(block=True)
while not self.aligned:
self.align()
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename)
plt.show(block=True)
class overplot_pol(align_maps):
@@ -738,10 +743,125 @@ class overplot_pol(align_maps):
self.fig2.canvas.draw()
def plot(self, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs) -> None:
self.align()
if self.aligned:
self.overplot(SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
plt.show(block=True)
while not self.aligned():
self.align()
self.overplot(SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
plt.show(block=True)
class align_pol(object):
def __init__(self, maps, **kwargs):
maps = np.array(maps)
self.ref_map, self.other_maps = maps[0], maps[1:]
self.wcs = WCS(self.ref_map[0].header)
self.wcs_other = np.array([WCS(map[0].header) for map in self.other_maps])
self.aligned = np.zeros(self.other_maps.shape[0], dtype=bool)
self.kwargs = kwargs
def single_plot(self, curr_map, wcs, v_lim=None, ax_lim=None, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs):
#Get data
stkI = curr_map[np.argmax([curr_map[i].header['datatype']=='I_stokes' for i in range(len(curr_map))])]
stkQ = curr_map[np.argmax([curr_map[i].header['datatype']=='Q_stokes' for i in range(len(curr_map))])]
stkU = curr_map[np.argmax([curr_map[i].header['datatype']=='U_stokes' for i in range(len(curr_map))])]
stk_cov = curr_map[np.argmax([curr_map[i].header['datatype']=='IQU_cov_matrix' for i in range(len(curr_map))])]
pol = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_deg_debiased' for i in range(len(curr_map))])]
pol_err = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_deg_err' for i in range(len(curr_map))])]
pang = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_ang' for i in range(len(curr_map))])]
try:
data_mask = curr_map[np.argmax([curr_map[i].header['datatype']=='Data_mask' for i in range(len(curr_map))])].data.astype(bool)
except KeyError:
data_mask = np.ones(stkI.shape).astype(bool)
pivot_wav = curr_map[0].header['photplam']
convert_flux = curr_map[0].header['photflam']
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
pol_err.data[pol_err.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
maskI = stk_cov.data[0,0] > 0
SNRi = np.zeros(stkI.data.shape)
SNRi[maskI] = stkI.data[maskI]/np.sqrt(stk_cov.data[0,0][maskI])
pol.data[SNRi < SNRi_cut] = np.nan
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut)
#Plot the map
plt.rcParams.update({'font.size': 10})
plt.rcdefaults()
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111, projection=wcs)
ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)", facecolor='k')
fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
cbar_ax = fig.add_axes([0.95, 0.12, 0.01, 0.75])
if not ax_lim is None:
lim = np.concatenate([wcs.world_to_pixel(ax_lim[i]) for i in range(len(ax_lim))])
x_lim, y_lim = lim[0::2], lim[1::2]
ax.set(xlim=x_lim,ylim=y_lim)
if v_lim is None:
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
else:
vmin, vmax = v_lim*convert_flux
for key, value in [["cmap",[["cmap","inferno"]]], ["norm",[["vmin",vmin],["vmax",vmax]]]]:
try:
test = kwargs[key]
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
im = ax.imshow(stkI.data*convert_flux, aspect='equal', **kwargs)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
px_size = wcs.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
ax.add_artist(px_sc)
north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.025, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, back_length=0., head_length=10., head_width=10., angle=curr_map[0].header['orientat'], color='white', text_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 0.4}, arrow_props={'ec': None,'fc':'w','alpha': 1,'lw': 1})
ax.add_artist(north_dir)
step_vec = 1
X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
ax.add_artist(pol_sc)
if not savename is None:
fig.savefig(savename+".png",bbox_inches='tight',dpi=300)
plt.show(block=True)
return fig, ax
def align(self):
for i, curr_map in enumerate(self.other_maps):
curr_align = align_maps(self.ref_map, curr_map, **self.kwargs)
self.wcs, self.wcs_other[i] = curr_align.align()
self.aligned[i] = curr_align.aligned
def plot(self, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs):
while not self.aligned.all():
self.align()
vmin = np.min([np.min(curr_map[0].data[curr_map[0].data > 0.]) for curr_map in self.other_maps])
vmax = np.max([np.max(curr_map[0].data[curr_map[0].data > 0.]) for curr_map in self.other_maps])
vmin, vmax = np.min([vmin, np.min(self.ref_map[0].data[self.ref_map[0].data > 0.])]), np.max([vmax, np.max(self.ref_map[0].data[self.ref_map[0].data > 0.])])
v_lim = np.array([vmin, vmax])
fig, ax = self.single_plot(self.ref_map, self.wcs, v_lim = v_lim, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
x_lim, y_lim = ax.get_xlim(), ax.get_ylim()
ax_lim = np.array([self.wcs.pixel_to_world(x_lim[i], y_lim[i]) for i in range(len(x_lim))])
for i, curr_map in enumerate(self.other_maps):
self.single_plot(curr_map, self.wcs_other[i], v_lim=v_lim, ax_lim=ax_lim, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename+'_'+str(i+1), **kwargs)
class crop_map(object):