Tibeuleu/FOC_Reduction
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Change reduction to take into account transmittance, and try plotting FOC's FOV

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This commit is contained in:
Thibault Barnouin
2021-06-21 20:22:45 +02:00
parent d9c45870e6
commit 5493120a56
22 changed files with 124 additions and 49 deletions
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30
src/lib/plots.py
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@@ -73,16 +73,20 @@ def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
#plots
im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower')
if not(rectangle is None):
x, y, width, height, color = rectangle[i]
ax.add_patch(Rectangle((x, y), width, height, edgecolor=color, fill=False))
x, y, width, height, angle, color = rectangle[i]
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
edgecolor=color, fill=False))
#position of centroid
ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
color='black')
ax.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], lw=1,
color='black')
ax.annotate(instr+":"+rootname, color='white', fontsize=5, xy=(0.02, 0.95), xycoords='axes fraction')
ax.annotate(filt, color='white', fontsize=10, xy=(0.02, 0.02), xycoords='axes fraction')
ax.annotate(exptime, color='white', fontsize=5, xy=(0.80, 0.02), xycoords='axes fraction')
ax.annotate(instr+":"+rootname,color='white',fontsize=5,xy=(0.02, 0.95),
xycoords='axes fraction')
ax.annotate(filt,color='white',fontsize=10,xy=(0.02, 0.02),
xycoords='axes fraction')
ax.annotate(exptime,color='white',fontsize=5,xy=(0.80, 0.02),
xycoords='axes fraction')
fig.subplots_adjust(hspace=0, wspace=0, right=0.85)
cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.75])
@@ -144,7 +148,7 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
return 0
def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
def polarization_map(Stokes, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec=1,
savename=None, plots_folder="", display=None):
"""
Plots polarization map from Stokes HDUList.
@@ -153,6 +157,10 @@ def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
Stokes : astropy.io.fits.hdu.hdulist.HDUList
HDUList containing I, Q, U, P, s_P, PA, s_PA (in this particular order)
for one observation.
rectangle : numpy.ndarray, optional
Array of parameters for matplotlib.patches.Rectangle objects that will
be displayed on each output image. If None, no rectangle displayed.
Defaults to None.
SNRp_cut : float, optional
Cut that should be applied to the signal-to-noise ratio on P.
Any SNR < SNRp_cut won't be displayed.
@@ -279,6 +287,12 @@ def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-Stokes[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
ax.add_artist(north_dir)
# Display instrument FOV
if not(rectangle is None):
x, y, width, height, angle, color = rectangle
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
edgecolor=color, fill=False))
# Compute integrated parameters and associated errors for pixels in the cut
n_pix = mask.size
I_int = stkI.data[mask].sum()
@@ -294,7 +308,7 @@ def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
P_int = np.sqrt(Q_int**2+U_int**2)/I_int*100.
P_int_err = (100./I_int)*np.sqrt((Q_int**2*Q_int_err**2 + U_int**2*U_int_err**2 + 2.*Q_int*U_int*QU_int_err)/(Q_int**2 + U_int**2) + ((Q_int/I_int)**2 + (U_int/I_int)**2)*I_int_err**2 - 2.*(Q_int/I_int)*IQ_int_err - 2.*(U_int/I_int)*IU_int_err)
PA_int = (90./np.pi)*np.arctan2(U_int,Q_int)+90.
PA_int = (90./np.pi)*np.arctan2(U_int,Q_int)+90.*2
PA_int_err = (90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err)
# Compute integrated parameters and associated errors for all pixels
@@ -313,7 +327,7 @@ def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
P_diluted_err = (100./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
P_diluted_err = np.sqrt(2/n_pix)*100.
PA_diluted = (90./np.pi)*np.arctan2(U_diluted,Q_diluted)+90.
PA_diluted = (90./np.pi)*np.arctan2(U_diluted,Q_diluted)+90.*2
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
PA_diluted_err = P_diluted_err/(2.*P_diluted)*180./np.pi

78
src/lib/reduction.py
View File

@@ -215,7 +215,7 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
if null_val is None:
null_val = [1.00*error.mean() for error in error_array]
elif type(null_val) is float:
null_val = [null_val,]*len(error_array)
null_val = [null_val,]*error_array.shape[0]
vertex = np.zeros((data_array.shape[0],4),dtype=int)
for i,image in enumerate(data_array):
@@ -233,7 +233,7 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
v_array[3] = np.max(vertex[:,3]).astype(int)
new_shape = np.array([v_array[1]-v_array[0],v_array[3]-v_array[2]])
rectangle = [v_array[2], v_array[0], new_shape[1], new_shape[0], 'b']
rectangle = [v_array[2], v_array[0], new_shape[1], new_shape[0], 0., 'b']
if display:
fig, ax = plt.subplots()
data = data_array[0]
@@ -243,7 +243,7 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
filt = headers[0]['filtnam1']
#plots
im = ax.imshow(data, vmin=data.min(), vmax=data.max(), origin='lower')
x, y, width, height, color = rectangle
x, y, width, height, angle, color = rectangle
ax.add_patch(Rectangle((x, y),width,height,edgecolor=color,fill=False))
#position of centroid
ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
@@ -432,7 +432,7 @@ def get_error(data_array, sub_shape=(15,15), display=False, headers=None,
minima = np.unravel_index(np.argmin(temp.sum(axis=0)),temp.shape[1:])
for i, image in enumerate(data):
rectangle.append([minima[1], minima[0], sub_shape[1], sub_shape[0], 'r'])
rectangle.append([minima[1], minima[0], sub_shape[1], sub_shape[0], 0., 'r'])
# Compute error : root mean square of the background
sub_image = image[minima[0]:minima[0]+sub_shape[0],minima[1]:minima[1]+sub_shape[1]]
#error = np.std(sub_image) # Previously computed using standard deviation over the background
@@ -819,7 +819,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
for c in range(smoothed.shape[1]):
# Compute distance from current pixel
dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*len(data_array))
g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*data_array.shape[0])
# Apply weighted combination
smoothed[r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
@@ -1063,12 +1063,58 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
if (pol0 < 0.).any() or (pol60 < 0.).any() or (pol120 < 0.).any():
print("WARNING : Negative value in polarizer array.")
#Stokes parameters
I_stokes = (2./3.)*(pol0 + pol60 + pol120)
Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120)
U_stokes = (2./np.sqrt(3.))*(pol60 - pol120)
# Stokes parameters
#default
#I_stokes = (2./3.)*(pol0 + pol60 + pol120)
#Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120)
#U_stokes = (2./np.sqrt(3.))*(pol60 - pol120)
#Remove nan
#transmittance corrected
trans2 = {'f140w' : 0.21, 'f175w' : 0.24, 'f220w' : 0.39, 'f275w' : 0.40, 'f320w' : 0.89, 'f342w' : 0.81, 'f430w' : 0.74, 'f370lp' : 0.83, 'f486n' : 0.63, 'f501n' : 0.68, 'f480lp' : 0.82, 'clear2' : 1.0}
trans3 = {'f120m' : 0.10, 'f130m' : 0.10, 'f140m' : 0.08, 'f152m' : 0.08, 'f165w' : 0.28, 'f170m' : 0.18, 'f195w' : 0.42, 'f190m' : 0.15, 'f210m' : 0.18, 'f231m' : 0.18, 'clear3' : 1.0}
trans4 = {'f253m' : 0.18, 'f278m' : 0.26, 'f307m' : 0.26, 'f130lp' : 0.92, 'f346m' : 0.58, 'f372m' : 0.73, 'f410m' : 0.58, 'f437m' : 0.71, 'f470m' : 0.79, 'f502m' : 0.82, 'f550m' : 0.77, 'clear4' : 1.0}
transmit = np.ones((3,)) #will be filter dependant
filt2 = headers[0]['filtnam2']
filt3 = headers[0]['filtnam3']
filt4 = headers[0]['filtnam4']
same_filt2 = np.array([filt2 == header['filtnam2'] for header in headers]).all()
same_filt3 = np.array([filt3 == header['filtnam3'] for header in headers]).all()
same_filt4 = np.array([filt4 == header['filtnam4'] for header in headers]).all()
if not (same_filt2 and same_filt3 and same_filt4):
print("WARNING : All images in data_array are not from the same \
band filter, the limiting transmittance will be taken.")
transmit2 = np.min([trans2[header['filtnam2'].lower()] for header in headers])
transmit3 = np.min([trans3[header['filtnam3'].lower()] for header in headers])
transmit4 = np.min([trans4[header['filtnam4'].lower()] for header in headers])
else :
transmit2 = trans2[filt2.lower()]
transmit3 = trans3[filt3.lower()]
transmit4 = trans4[filt4.lower()]
transmit *= transmit2*transmit3*transmit4
pol_efficiency = {'pol0' : 0.92, 'pol60' : 0.92, 'pol120' : 0.91}
pol_eff = np.ones((3,))
pol_eff[0] = pol_efficiency['pol0']
pol_eff[1] = pol_efficiency['pol60']
pol_eff[2] = pol_efficiency['pol120']
theta = np.array([np.pi, np.pi/3., 2.*np.pi/3.])
flux = 2.*np.array([pol0/transmit[0], pol60/transmit[1], pol120/transmit[2]])
norm = pol_eff[1]*pol_eff[2]*np.sin(-2.*theta[1]+2.*theta[2]) \
+ pol_eff[2]*pol_eff[0]*np.sin(-2.*theta[2]+2.*theta[0]) \
+ pol_eff[0]*pol_eff[1]*np.sin(-2.*theta[0]+2.*theta[1])
coeff = np.zeros((3,3))
for i in range(3):
coeff[0,i] = pol_eff[(i+1)%3]*pol_eff[(i+2)%3]*np.sin(-2.*theta[(i+1)%3]+2.*theta[(i+2)%3])/norm
coeff[1,i] = (-pol_eff[(i+1)%3]*np.sin(2.*theta[(i+1)%3]) + pol_eff[(i+2)%3]*np.sin(2.*theta[(i+2)%3]))/norm
coeff[2,i] = (pol_eff[(i+1)%3]*np.cos(2.*theta[(i+1)%3]) - pol_eff[(i+2)%3]*np.cos(2.*theta[(i+2)%3]))/norm
I_stokes = np.sum([coeff[0,i]*flux[i] for i in range(3)], axis=0)
Q_stokes = np.sum([coeff[1,i]*flux[i] for i in range(3)], axis=0)
U_stokes = np.sum([coeff[2,i]*flux[i] for i in range(3)], axis=0)
# Remove nan
I_stokes[np.isnan(I_stokes)]=0.
Q_stokes[I_stokes == 0.]=0.
U_stokes[I_stokes == 0.]=0.
@@ -1077,7 +1123,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
mask = (Q_stokes**2 + U_stokes**2) > I_stokes**2
if mask.any():
print("WARNING : I_pol > I_stokes : ", len(I_stokes[mask]))
print("WARNING : I_pol > I_stokes : ", I_stokes[mask].size)
#plt.imshow(np.sqrt(Q_stokes**2+U_stokes**2)/I_stokes*mask, origin='lower')
#plt.colorbar()
@@ -1156,10 +1202,10 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
I_pol = np.sqrt(Q_stokes**2 + U_stokes**2)
P = I_pol/I_stokes*100.
P[I_stokes <= 0.] = 0.
PA = (90./np.pi)*np.arctan2(U_stokes,Q_stokes)+90
PA = (90./np.pi)*np.arctan2(U_stokes,Q_stokes)+90.*2.
if (P>100.).any():
print("WARNING : found pixels for which P > 100%", len(P[P>100.]))
print("WARNING : found pixels for which P > 100%", P[P>100.].size)
#Associated errors
s_P = (100./I_stokes)*np.sqrt((Q_stokes**2*Stokes_cov[1,1] + U_stokes**2*Stokes_cov[2,2] + 2.*Q_stokes*U_stokes*Stokes_cov[1,2])/(Q_stokes**2 + U_stokes**2) + ((Q_stokes/I_stokes)**2 + (U_stokes/I_stokes)**2)*Stokes_cov[0,0] - 2.*(Q_stokes/I_stokes)*Stokes_cov[0,1] - 2.*(U_stokes/I_stokes)*Stokes_cov[0,2])
@@ -1169,7 +1215,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
debiased_P = np.sqrt(P**2 - s_P**2)
if (debiased_P>100.).any():
print("WARNING : found pixels for which debiased_P > 100%", len(debiased_P[debiased_P>100.]))
print("WARNING : found pixels for which debiased_P > 100%", debiased_P[debiased_P>100.].size)
#Compute the total exposure time so that
#I_stokes*exp_tot = N_tot the total number of events
@@ -1226,7 +1272,7 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
#Rotate original images using scipy.ndimage.rotate
new_data_array = []
new_error_array = []
for i in range(len(data_array)):
for i in range(data_array.shape[0]):
new_data_array.append(sc_rotate(data_array[i], ang, reshape=False,
cval=0.))
new_error_array.append(sc_rotate(error_array[i], ang, reshape=False,
@@ -1235,7 +1281,7 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_data_mask = sc_rotate(data_mask, ang, reshape=False, cval=True)
new_error_array = np.array(new_error_array)
for i in range(len(new_data_array)):
for i in range(new_data_array.shape[0]):
new_data_array[i][new_data_array[i] < 0.] = 0.
#Update headers to new angle