diff --git a/package/lib/utils.py b/package/lib/utils.py
index e28bbff..7d38335 100755
--- a/package/lib/utils.py
+++ b/package/lib/utils.py
@@ -4,6 +4,14 @@ import numpy as np
 def rot2D(ang):
     """
     Return the 2D rotation matrix of given angle in degrees
+    ----------
+    Inputs:
+    ang : float
+        Angle in degrees
+    ----------
+    Returns:
+    rot_mat : numpy.ndarray
+        2D matrix of shape (2,2) to perform vector rotation at angle "ang".
     """
     alpha = np.pi * ang / 180
     return np.array([[np.cos(alpha), np.sin(alpha)], [-np.sin(alpha), np.cos(alpha)]])
@@ -13,6 +21,14 @@ def princ_angle(ang):
     """
     Return the principal angle in the 0° to 180° quadrant as PA is always
     defined at p/m 180°.
+    ----------
+    Inputs:
+    ang : float, numpy.ndarray
+        Angle in degrees. Can be an array of angles.
+    ----------
+    Returns:
+    princ_ang : float, numpy.ndarray
+        Principal angle in the 0°-180° quadrant in the same shape as input.
     """
     if not isinstance(ang, np.ndarray):
         A = np.array([ang])
@@ -32,6 +48,21 @@ def PCconf(QN, UN, QN_ERR, UN_ERR):
     """
     Compute the confidence level for 2 parameters polarisation degree and
     polarisation angle from the PCUBE analysis.
+    ----------
+    Inputs:
+    QN : float, numpy.ndarray
+        Normalized Q Stokes flux.
+    UN : float, numpy.ndarray
+        Normalized U Stokes flux.
+    QN_ERR : float, numpy.ndarray
+        Normalized error on Q Stokes flux.
+    UN_ERR : float, numpy.ndarray
+        Normalized error on U Stokes flux.
+    ----------
+    Returns:
+    conf : numpy.ndarray
+        2D matrix of same shape as input containing the confidence on the polarization
+        computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
     """
     mask = np.logical_and(QN_ERR > 0.0, UN_ERR > 0.0)
     conf = np.full(QN.shape, -1.0)
@@ -43,6 +74,19 @@ def PCconf(QN, UN, QN_ERR, UN_ERR):
 def CenterConf(mask, PA, sPA):
     """
     Compute the confidence map for the position of the center of emission.
+    ----------
+    Inputs:
+    mask : bool, numpy.ndarray
+        Mask of the polarization vectors from which the center of emission should be drawn.
+    PA : float, numpy.ndarray
+        2D matrix containing the computed polarization angle.
+    sPA : float, numpy.ndarray
+        2D matrix containing the total uncertainties on the polarization angle.
+    ----------
+    Returns:
+    conf : numpy.ndarray
+        2D matrix of same shape as input containing the confidence on the polarization
+        computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
     """
     chi2 = np.full(PA.shape, np.nan)
     conf = np.full(PA.shape, -1.0)
@@ -64,17 +108,36 @@ def CenterConf(mask, PA, sPA):
     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])])
+    c0 = np.unravel_index(np.argmax(conf), conf.shape)[::-1]
+    result = minimize(chisq, c0, 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")
+        print("Center of emission not found", result)
     return conf, result.x
 
 
 def sci_not(v, err, rnd=1, out=str):
     """
-    Return the scientifque error notation as a string.
+    Return the scientific error notation as a string.
+    ----------
+    Inputs:
+    v : float
+        Value to be transformed into scientific notation.
+    err : float
+        Error on the value to be transformed into scientific notation.
+    rnd : int
+        Number of significant numbers for the scientific notation.
+        Default to 1.
+    out : str or other
+        Format in which the notation should be returned. "str" means the notation
+        is returned as a single string, "other" means it is returned as a list of "str".
+        Default to str.
+    ----------
+    Returns:
+    conf : numpy.ndarray
+        2D matrix of same shape as input containing the confidence on the polarization
+        computation between 0 and 1 for 2 parameters of interest (Q and U Stokes fluxes).
     """
     power = -int(("%E" % v)[-3:]) + 1
     output = [r"({0}".format(round(v * 10**power, rnd)), round(v * 10**power, rnd)]
@@ -95,6 +158,16 @@ def wcs_PA(PC21, PC22):
     """
     Return the position angle in degrees to the North direction of a wcs
     from the values of coefficient of its transformation matrix.
+    ----------
+    Inputs:
+    PC21 : float
+        Value of the WCS matric PC[1,0]
+    PC22 : float
+        Value of the WCS matric PC[1,1]
+    ----------
+    Returns:
+    orient : float
+        Angle in degrees between the North direction and the Up direction of the WCS.
     """
     if (abs(PC21) > abs(PC22)) and (PC21 >= 0):
         orient = -np.arccos(PC22) * 180.0 / np.pi
diff --git a/package/test_center.py b/package/test_center.py
index 54c21d4..bedfda8 100644
--- a/package/test_center.py
+++ b/package/test_center.py
@@ -23,15 +23,20 @@ NGC1068snr[NGC1068["POL_DEG_ERR"].data > 0.0] = (
 )
 
 NGC1068centconf, NGC1068center = CenterConf(NGC1068conf > 0.99, NGC1068["POL_ANG"].data, NGC1068["POL_ANG_ERR"].data)
+NGC1068pos = WCS(NGC1068[0].header).pixel_to_world(*NGC1068center)
 
-figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(NGC1068[0].header)))
+figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(NGC1068[0].header)), sharex=True, sharey=True)
 
 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")
+vmin, vmax = (
+    0.5 * np.median(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
+    np.max(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
+)
+imngc = axngc[0].imshow(NGC1068["I_STOKES"].data * NGC1068["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), 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")
+ngcconfcenter = axngc[0].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
+ngcconfcentcont = axngc[0].contour(NGC1068centconf, [0.01], colors="gray")
 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]))
@@ -43,14 +48,14 @@ 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")
+ngccentcont = axngc[1].contour(NGC1068centconf, [0.01], colors="gray")
+ngccentcenter = axngc[1].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
 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")
+figngc.savefig("NGC1068_center.pdf", dpi=150, facecolor="None")
 
 ###################################################################################################
 
@@ -68,16 +73,21 @@ MRK463Esnr[MRK463E["POL_DEG_ERR"].data > 0.0] = (
 )
 
 MRK463Ecentconf, MRK463Ecenter = CenterConf(MRK463Econf > 0.99, MRK463E["POL_ANG"].data, MRK463E["POL_ANG_ERR"].data)
+MRK463Epos = WCS(MRK463E[0].header).pixel_to_world(*MRK463Ecenter)
 
-figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18,9), subplot_kw=dict(projection=WCS(MRK463E[0].header)))
+figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(MRK463E[0].header)), sharex=True, sharey=True)
 
 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")
+vmin, vmax = (
+    0.5 * np.median(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
+    np.max(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
+)
+immrk = axmrk[0].imshow(MRK463E["I_STOKES"].data * MRK463E["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), 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()
+mrkconfcenter = axmrk[0].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
+mrkconfcentcont = axmrk[0].contour(MRK463Ecentconf, [0.01], colors="gray")
+handles, labels = axmrk[0].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")
@@ -88,12 +98,12 @@ 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")
+mrkcentcont = axmrk[1].contour(MRK463Ecentconf, [0.01], colors="gray")
+mrkcentcenter = axmrk[1].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
 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")
+figmrk.savefig("MRK463E_center.pdf", dpi=150, facecolor="None")
 plt.show()