bit of doctring and fix on test_center
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@@ -4,6 +4,14 @@ import numpy as np
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def rot2D(ang):
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"""
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Return the 2D rotation matrix of given angle in degrees
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----------
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Inputs:
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ang : float
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Angle in degrees
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----------
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Returns:
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rot_mat : numpy.ndarray
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2D matrix of shape (2,2) to perform vector rotation at angle "ang".
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"""
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alpha = np.pi * ang / 180
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return np.array([[np.cos(alpha), np.sin(alpha)], [-np.sin(alpha), np.cos(alpha)]])
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@@ -13,6 +21,14 @@ def princ_angle(ang):
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"""
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Return the principal angle in the 0° to 180° quadrant as PA is always
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defined at p/m 180°.
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----------
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Inputs:
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ang : float, numpy.ndarray
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Angle in degrees. Can be an array of angles.
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----------
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Returns:
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princ_ang : float, numpy.ndarray
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Principal angle in the 0°-180° quadrant in the same shape as input.
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"""
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if not isinstance(ang, np.ndarray):
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A = np.array([ang])
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@@ -32,6 +48,21 @@ def PCconf(QN, UN, QN_ERR, UN_ERR):
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"""
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Compute the confidence level for 2 parameters polarisation degree and
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polarisation angle from the PCUBE analysis.
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----------
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Inputs:
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QN : float, numpy.ndarray
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Normalized Q Stokes flux.
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UN : float, numpy.ndarray
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Normalized U Stokes flux.
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QN_ERR : float, numpy.ndarray
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Normalized error on Q Stokes flux.
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UN_ERR : float, numpy.ndarray
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Normalized error on U Stokes flux.
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----------
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Returns:
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conf : numpy.ndarray
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2D matrix of same shape as input containing the confidence on the polarization
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computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
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"""
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mask = np.logical_and(QN_ERR > 0.0, UN_ERR > 0.0)
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conf = np.full(QN.shape, -1.0)
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@@ -43,6 +74,19 @@ def PCconf(QN, UN, QN_ERR, UN_ERR):
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def CenterConf(mask, PA, sPA):
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"""
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Compute the confidence map for the position of the center of emission.
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----------
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Inputs:
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mask : bool, numpy.ndarray
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Mask of the polarization vectors from which the center of emission should be drawn.
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PA : float, numpy.ndarray
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2D matrix containing the computed polarization angle.
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sPA : float, numpy.ndarray
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2D matrix containing the total uncertainties on the polarization angle.
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----------
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Returns:
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conf : numpy.ndarray
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2D matrix of same shape as input containing the confidence on the polarization
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computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
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"""
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chi2 = np.full(PA.shape, np.nan)
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conf = np.full(PA.shape, -1.0)
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@@ -64,17 +108,36 @@ def CenterConf(mask, PA, sPA):
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from scipy.special import gammainc
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conf[np.isfinite(PA)] = 1.0 - gammainc(0.5, 0.5 * chi2[np.isfinite(PA)])
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result = minimize(chisq, np.array(PA.shape) / 2.0, bounds=[(0, PA.shape[1]), (0.0, PA.shape[0])])
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c0 = np.unravel_index(np.argmax(conf), conf.shape)[::-1]
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result = minimize(chisq, c0, bounds=[(0, PA.shape[1]), (0.0, PA.shape[0])])
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if result.success:
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print("Center of emission found")
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else:
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print("Center of emission not found")
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print("Center of emission not found", result)
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return conf, result.x
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def sci_not(v, err, rnd=1, out=str):
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"""
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Return the scientifque error notation as a string.
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Return the scientific error notation as a string.
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----------
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Inputs:
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v : float
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Value to be transformed into scientific notation.
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err : float
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Error on the value to be transformed into scientific notation.
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rnd : int
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Number of significant numbers for the scientific notation.
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Default to 1.
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out : str or other
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Format in which the notation should be returned. "str" means the notation
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is returned as a single string, "other" means it is returned as a list of "str".
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Default to str.
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----------
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Returns:
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conf : numpy.ndarray
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2D matrix of same shape as input containing the confidence on the polarization
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computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
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"""
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power = -int(("%E" % v)[-3:]) + 1
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output = [r"({0}".format(round(v * 10**power, rnd)), round(v * 10**power, rnd)]
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@@ -95,6 +158,16 @@ def wcs_PA(PC21, PC22):
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"""
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Return the position angle in degrees to the North direction of a wcs
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from the values of coefficient of its transformation matrix.
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----------
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Inputs:
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PC21 : float
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Value of the WCS matric PC[1,0]
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PC22 : float
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Value of the WCS matric PC[1,1]
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----------
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Returns:
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orient : float
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Angle in degrees between the North direction and the Up direction of the WCS.
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"""
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if (abs(PC21) > abs(PC22)) and (PC21 >= 0):
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orient = -np.arccos(PC22) * 180.0 / np.pi
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@@ -23,15 +23,20 @@ NGC1068snr[NGC1068["POL_DEG_ERR"].data > 0.0] = (
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)
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NGC1068centconf, NGC1068center = CenterConf(NGC1068conf > 0.99, NGC1068["POL_ANG"].data, NGC1068["POL_ANG_ERR"].data)
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NGC1068pos = WCS(NGC1068[0].header).pixel_to_world(*NGC1068center)
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figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(NGC1068[0].header)))
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figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(NGC1068[0].header)), sharex=True, sharey=True)
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axngc[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
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imngc = axngc[0].imshow(NGC1068["I_STOKES"].data * NGC1068["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(), cmap="inferno")
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vmin, vmax = (
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0.5 * np.median(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
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np.max(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
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)
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imngc = axngc[0].imshow(NGC1068["I_STOKES"].data * NGC1068["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), cmap="inferno")
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ngcsnrcont = axngc[0].contour(NGC1068snr, levelssnr, colors="b")
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ngcconfcont = axngc[0].contour(NGC1068conf, levelsconf, colors="r")
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ngcconfcenter = axngc[0].plot(*np.unravel_index(np.argmax(NGC1068centconf), NGC1068centconf.shape)[::-1], "k+", label="Best confidence for center")
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ngcconfcentcont = axngc[0].contour(NGC1068centconf, 1.-levelsconf, colors="k")
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ngcconfcenter = axngc[0].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
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ngcconfcentcont = axngc[0].contour(NGC1068centconf, [0.01], colors="gray")
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handles, labels = axngc[0].get_legend_handles_labels()
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labels.append("SNR contours")
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handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcsnrcont.collections[0].get_edgecolor()[0]))
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@@ -43,14 +48,14 @@ axngc[0].legend(handles=handles, labels=labels)
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axngc[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
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ngccent = axngc[1].imshow(NGC1068centconf, vmin=0.0, cmap="inferno")
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ngccentcont = axngc[1].contour(NGC1068centconf, 1.-levelsconf, colors="grey")
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ngccentcenter = axngc[1].plot(*np.unravel_index(np.argmax(NGC1068centconf), NGC1068centconf.shape)[::-1], "k+", label="Best confidence for center")
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ngccentcont = axngc[1].contour(NGC1068centconf, [0.01], colors="gray")
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ngccentcenter = axngc[1].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
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handles, labels = axngc[1].get_legend_handles_labels()
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labels.append("CONF99 contour")
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handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngccentcont.collections[0].get_edgecolor()[0]))
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axngc[1].legend(handles=handles, labels=labels)
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figngc.savefig("NGC1068_center.pdf",dpi=150,facecolor="None")
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figngc.savefig("NGC1068_center.pdf", dpi=150, facecolor="None")
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###################################################################################################
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@@ -68,16 +73,21 @@ MRK463Esnr[MRK463E["POL_DEG_ERR"].data > 0.0] = (
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)
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MRK463Ecentconf, MRK463Ecenter = CenterConf(MRK463Econf > 0.99, MRK463E["POL_ANG"].data, MRK463E["POL_ANG_ERR"].data)
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MRK463Epos = WCS(MRK463E[0].header).pixel_to_world(*MRK463Ecenter)
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figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(MRK463E[0].header)))
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figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(MRK463E[0].header)), sharex=True, sharey=True)
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axmrk[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
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immrk = axmrk[0].imshow(MRK463E["I_STOKES"].data * MRK463E["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(), cmap="inferno")
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vmin, vmax = (
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0.5 * np.median(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
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np.max(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
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)
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immrk = axmrk[0].imshow(MRK463E["I_STOKES"].data * MRK463E["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), cmap="inferno")
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mrksnrcont = axmrk[0].contour(MRK463Esnr, levelssnr, colors="b")
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mrkconfcont = axmrk[0].contour(MRK463Econf, levelsconf, colors="r")
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mrkconfcenter = axmrk[0].plot(*np.unravel_index(np.argmax(MRK463Ecentconf), MRK463Ecentconf.shape)[::-1], "k+", label="Best confidence for center")
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mrkconfcentcont = axmrk[0].contour(MRK463Ecentconf, 1.-levelsconf, colors="k")
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handles, labels = axmrk[1].get_legend_handles_labels()
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mrkconfcenter = axmrk[0].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
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mrkconfcentcont = axmrk[0].contour(MRK463Ecentconf, [0.01], colors="gray")
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handles, labels = axmrk[0].get_legend_handles_labels()
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labels.append("SNR contours")
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handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrksnrcont.collections[0].get_edgecolor()[0]))
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labels.append("CONF99 contour")
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@@ -88,12 +98,12 @@ axmrk[0].legend(handles=handles, labels=labels)
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axmrk[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
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mrkcent = axmrk[1].imshow(MRK463Ecentconf, vmin=0.0, cmap="inferno")
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mrkcentcont = axmrk[1].contour(MRK463Ecentconf, 1.-levelsconf, colors="grey")
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mrkcentcenter = axmrk[1].plot(*np.unravel_index(np.argmax(MRK463Ecentconf), MRK463Ecentconf.shape)[::-1], "k+", label="Best confidence for center")
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mrkcentcont = axmrk[1].contour(MRK463Ecentconf, [0.01], colors="gray")
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mrkcentcenter = axmrk[1].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
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handles, labels = axmrk[1].get_legend_handles_labels()
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labels.append("CONF99 contour")
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handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkcentcont.collections[0].get_edgecolor()[0]))
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axmrk[1].legend(handles=handles, labels=labels)
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figmrk.savefig("MRK463E_center.pdf",dpi=150,facecolor="None")
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figmrk.savefig("MRK463E_center.pdf", dpi=150, facecolor="None")
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plt.show()
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