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correction for center confidence and test script

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This commit is contained in:
Thibault Barnouin
2024-09-09 14:50:04 +02:00
parent bf5373d4e0
commit 08cb65200a
2 changed files with 122 additions and 8 deletions
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31
package/lib/utils.py
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@@ -39,23 +39,38 @@ def PCconf(QN, UN, QN_ERR, UN_ERR):
conf[mask] = 1.0 - np.exp(-0.5 * chi2[mask])
return conf
def Centerconf(mask, PA, sPA):
def CenterConf(mask, PA, sPA):
"""
Compute the confidence map for the position of the center of emission.
"""
chi2 = np.full(PA.shape, np.nan)
conf = np.full(PA.shape, -1.0)
yy, xx = np.indices(PA.shape)
def ideal(c):
itheta = np.degrees(np.arctan((yy+0.5-c[1])/(xx+0.5-c[0])))
itheta[np.isnan(itheta)] = PA[np.isnan(itheta)]
itheta = np.full(PA.shape, np.nan)
itheta[(xx + 0.5) != c[0]] = np.degrees(np.arctan((yy[(xx + 0.5) != c[0]] + 0.5 - c[1]) / (xx[(xx + 0.5) != c[0]] + 0.5 - c[0])))
itheta[(xx + 0.5) == c[0]] = PA[(xx + 0.5) == c[0]]
return princ_angle(itheta)
def chisq(c):
return np.sum((princ_angle(PA[mask])-ideal((x,y))[mask])**2/sPA[mask]**2)/np.sum(mask)
for x,y in zip(xx[np.isfinite(PA)],yy[np.isfinite(PA)]):
chi2[y,x] = chisq((x,y))
conf[mask] = 1.0 - np.exp(-0.5*chi2[mask])
return conf
return np.sum((princ_angle(PA[mask]) - ideal((c[0], c[1]))[mask]) ** 2 / sPA[mask] ** 2) / np.sum(mask)
for x, y in zip(xx[np.isfinite(PA)], yy[np.isfinite(PA)]):
chi2[y, x] = chisq((x, y))
from scipy.optimize import minimize
from scipy.special import gammainc
conf[np.isfinite(PA)] = 1.0 - gammainc(0.5, 0.5 * chi2[np.isfinite(PA)])
result = minimize(chisq, np.array(PA.shape) / 2.0, bounds=[(0, PA.shape[1]), (0.0, PA.shape[0])])
if result.success:
print("Center of emission found")
else:
print("Center of emission not found")
return conf, result.x
def sci_not(v, err, rnd=1, out=str):
"""

99
package/test_center.py Normal file
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@@ -0,0 +1,99 @@
import matplotlib.pyplot as plt
import numpy as np
from astropy.io.fits import open as fits_open
from astropy.wcs import WCS
from lib.utils import CenterConf, PCconf
from matplotlib.colors import LogNorm
from matplotlib.patches import Rectangle
levelssnr = np.array([3.0, 4.0])
levelsconf = np.array([0.99])
NGC1068 = fits_open("./data/NGC1068/5144/NGC1068_FOC_b0.05arcsec_c0.07arcsec.fits")
NGC1068conf = PCconf(
NGC1068["Q_STOKES"].data / NGC1068["I_STOKES"].data,
NGC1068["U_STOKES"].data / NGC1068["I_STOKES"].data,
np.sqrt(NGC1068["IQU_COV_MATRIX"].data[1, 1]) / NGC1068["I_STOKES"].data,
np.sqrt(NGC1068["IQU_COV_MATRIX"].data[2, 2]) / NGC1068["I_STOKES"].data,
)
NGC1068mask = NGC1068["DATA_MASK"].data.astype(bool)
NGC1068snr = np.full(NGC1068mask.shape, np.nan)
NGC1068snr[NGC1068["POL_DEG_ERR"].data > 0.0] = (
NGC1068["POL_DEG_DEBIASED"].data[NGC1068["POL_DEG_ERR"].data > 0.0] / NGC1068["POL_DEG_ERR"].data[NGC1068["POL_DEG_ERR"].data > 0.0]
)
NGC1068centconf, NGC1068center = CenterConf(NGC1068conf > 0.99, NGC1068["POL_ANG"].data, NGC1068["POL_ANG_ERR"].data)
figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(NGC1068[0].header)))
axngc[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
imngc = axngc[0].imshow(NGC1068["I_STOKES"].data * NGC1068["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(), cmap="inferno")
ngcsnrcont = axngc[0].contour(NGC1068snr, levelssnr, colors="b")
ngcconfcont = axngc[0].contour(NGC1068conf, levelsconf, colors="r")
ngcconfcenter = axngc[0].plot(*np.unravel_index(np.argmax(NGC1068centconf), NGC1068centconf.shape)[::-1], "k+", label="Best confidence for center")
ngcconfcentcont = axngc[0].contour(NGC1068centconf, 1.-levelsconf, colors="k")
handles, labels = axngc[0].get_legend_handles_labels()
labels.append("SNR contours")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcsnrcont.collections[0].get_edgecolor()[0]))
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcconfcont.collections[0].get_edgecolor()[0]))
labels.append("Center CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcconfcentcont.collections[0].get_edgecolor()[0]))
axngc[0].legend(handles=handles, labels=labels)
axngc[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
ngccent = axngc[1].imshow(NGC1068centconf, vmin=0.0, cmap="inferno")
ngccentcont = axngc[1].contour(NGC1068centconf, 1.-levelsconf, colors="grey")
ngccentcenter = axngc[1].plot(*np.unravel_index(np.argmax(NGC1068centconf), NGC1068centconf.shape)[::-1], "k+", label="Best confidence for center")
handles, labels = axngc[1].get_legend_handles_labels()
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngccentcont.collections[0].get_edgecolor()[0]))
axngc[1].legend(handles=handles, labels=labels)
figngc.savefig("NGC1068_center.pdf",dpi=150,facecolor="None")
###################################################################################################
MRK463E = fits_open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.07arcsec.fits")
MRK463Econf = PCconf(
MRK463E["Q_STOKES"].data / MRK463E["I_STOKES"].data,
MRK463E["U_STOKES"].data / MRK463E["I_STOKES"].data,
np.sqrt(MRK463E["IQU_COV_MATRIX"].data[1, 1]) / MRK463E["I_STOKES"].data,
np.sqrt(MRK463E["IQU_COV_MATRIX"].data[2, 2]) / MRK463E["I_STOKES"].data,
)
MRK463Emask = MRK463E["DATA_MASK"].data.astype(bool)
MRK463Esnr = np.full(MRK463Emask.shape, np.nan)
MRK463Esnr[MRK463E["POL_DEG_ERR"].data > 0.0] = (
MRK463E["POL_DEG_DEBIASED"].data[MRK463E["POL_DEG_ERR"].data > 0.0] / MRK463E["POL_DEG_ERR"].data[MRK463E["POL_DEG_ERR"].data > 0.0]
)
MRK463Ecentconf, MRK463Ecenter = CenterConf(MRK463Econf > 0.99, MRK463E["POL_ANG"].data, MRK463E["POL_ANG_ERR"].data)
figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(MRK463E[0].header)))
axmrk[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
immrk = axmrk[0].imshow(MRK463E["I_STOKES"].data * MRK463E["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(), cmap="inferno")
mrksnrcont = axmrk[0].contour(MRK463Esnr, levelssnr, colors="b")
mrkconfcont = axmrk[0].contour(MRK463Econf, levelsconf, colors="r")
mrkconfcenter = axmrk[0].plot(*np.unravel_index(np.argmax(MRK463Ecentconf), MRK463Ecentconf.shape)[::-1], "k+", label="Best confidence for center")
mrkconfcentcont = axmrk[0].contour(MRK463Ecentconf, 1.-levelsconf, colors="k")
handles, labels = axmrk[1].get_legend_handles_labels()
labels.append("SNR contours")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrksnrcont.collections[0].get_edgecolor()[0]))
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkconfcont.collections[0].get_edgecolor()[0]))
labels.append("Center CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkconfcentcont.collections[0].get_edgecolor()[0]))
axmrk[0].legend(handles=handles, labels=labels)
axmrk[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
mrkcent = axmrk[1].imshow(MRK463Ecentconf, vmin=0.0, cmap="inferno")
mrkcentcont = axmrk[1].contour(MRK463Ecentconf, 1.-levelsconf, colors="grey")
mrkcentcenter = axmrk[1].plot(*np.unravel_index(np.argmax(MRK463Ecentconf), MRK463Ecentconf.shape)[::-1], "k+", label="Best confidence for center")
handles, labels = axmrk[1].get_legend_handles_labels()
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkcentcont.collections[0].get_edgecolor()[0]))
axmrk[1].legend(handles=handles, labels=labels)
figmrk.savefig("MRK463E_center.pdf",dpi=150,facecolor="None")
plt.show()