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plots clean-up, correct crop, prepare analysis

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Thibault Barnouin
2022-04-04 11:15:25 +02:00
parent 1ddff076b8
commit c4311bbb4b
399 changed files with 256 additions and 90 deletions
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src/lib/plots.py
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@@ -13,7 +13,7 @@ from copy import deepcopy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.widgets import RectangleSelector, Button
from matplotlib.widgets import RectangleSelector, Button, Slider
from matplotlib.transforms import Bbox
import matplotlib.font_manager as fm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
@@ -219,23 +219,19 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
stkQ = Stokes[np.argmax([Stokes[i].header['datatype']=='Q_stokes' for i in range(len(Stokes))])]
stkU = Stokes[np.argmax([Stokes[i].header['datatype']=='U_stokes' for i in range(len(Stokes))])]
stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
#pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
#pol_err_Poisson = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err_Poisson_noise' for i in range(len(Stokes))])]
pang = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])]
pang_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang_err' for i in range(len(Stokes))])]
if data_mask is None:
data_mask = Stokes[np.argmax([Stokes[i].header['datatype']=='Data_mask' for i in range(len(Stokes))])].data.astype(bool)
try:
if data_mask is None:
data_mask = Stokes[np.argmax([Stokes[i].header['datatype']=='Data_mask' for i in range(len(Stokes))])].data.astype(bool)
except KeyError:
data_mask = np.ones(stkI.shape).astype(bool)
pivot_wav = Stokes[0].header['photplam']
convert_flux = Stokes[0].header['photflam']
wcs = WCS(Stokes[0]).deepcopy()
#Get image mask
if data_mask is None:
data_mask = np.zeros(stkI.shape).astype(bool)
#Plot Stokes parameters map
if display is None or display.lower() == 'default':
plot_Stokes(Stokes, savename=savename, plots_folder=plots_folder)
@@ -252,7 +248,6 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
SNRi[maskI] = stkI.data[maskI]/np.sqrt(stk_cov.data[0,0][maskI])
pol.data[SNRi < SNRi_cut] = np.nan
data_mask = (1.-data_mask).astype(bool)
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut)
# Look for pixel of max polarization
@@ -339,13 +334,13 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
if step_vec == 0:
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[1],stkI.data.shape[1])-0.5, np.linspace(0,stkI.data.shape[0],stkI.data.shape[0])-0.5)
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', fontproperties=fontprops)
ax.add_artist(pol_sc)
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})
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=-Stokes[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)
# Display instrument FOV
@@ -383,6 +378,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
plt.show()
return fig, ax
class align_maps(object):
"""
Class to interactively align maps with different WCS.
@@ -430,7 +426,7 @@ class align_maps(object):
self.ax1.add_artist(px_sc)
try:
north_dir1 = AnchoredDirectionArrows(self.ax1.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.map[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
north_dir1 = AnchoredDirectionArrows(self.ax1.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=-self.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})
self.ax1.add_artist(north_dir1)
except KeyError:
pass
@@ -452,7 +448,7 @@ class align_maps(object):
self.ax2.add_artist(px_sc)
try:
north_dir2 = AnchoredDirectionArrows(self.ax2.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.other_map[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
north_dir2 = AnchoredDirectionArrows(self.ax2.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.other_map[0].header['orientat'], color='w', arrow_props={'ec': None, 'fc': 'w', 'alpha': 1,'lw': 2})
self.ax2.add_artist(north_dir2)
except KeyError:
pass
@@ -527,6 +523,8 @@ class overplot_radio(align_maps):
Inherit from class align_maps in order to get the same WCS on both maps.
"""
def overplot(self, other_levels, SNRp_cut=3., SNRi_cut=30., savename=None):
self.Stokes_UV = self.map
self.wcs_UV = self.wcs_map
#Get Data
obj = self.Stokes_UV[0].header['targname']
stkI = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='I_stokes' for i in range(len(self.Stokes_UV))])]
@@ -568,7 +566,7 @@ class overplot_radio(align_maps):
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0]), np.linspace(0,stkI.data.shape[1],stkI.data.shape[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 = self.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')
self.ax.autoscale(False)
@@ -584,7 +582,7 @@ class overplot_radio(align_maps):
px_size = self.wcs_UV.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(self.ax.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)
self.ax.add_artist(px_sc)
north_dir = AnchoredDirectionArrows(self.ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
north_dir = AnchoredDirectionArrows(self.ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color='w', arrow_props={'ec': None, 'fc': 'w', 'alpha': 1,'lw': 2})
self.ax.add_artist(north_dir)
@@ -599,6 +597,7 @@ class overplot_radio(align_maps):
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename)
plt.show(block=True)
class overplot_pol(align_maps):
"""
Class to overplot maps from different observations.
@@ -646,7 +645,7 @@ class overplot_pol(align_maps):
#Display full size polarization vectors
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0]), np.linspace(0,stkI.data.shape[1],stkI.data.shape[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 = self.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')
@@ -661,7 +660,7 @@ class overplot_pol(align_maps):
px_size = self.wcs_other.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(self.ax.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)
self.ax.add_artist(px_sc)
north_dir = AnchoredDirectionArrows(self.ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
north_dir = AnchoredDirectionArrows(self.ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color='w', arrow_props={'ec': None, 'fc': 'w', 'alpha': 1,'lw': 2})
self.ax.add_artist(north_dir)
@@ -750,7 +749,7 @@ class crop_map(object):
self.wcs_crop = self.wcs.deepcopy()
self.wcs_crop.array_shape = shape
if self.crpix_in_RS():
self.wcs_crop.wcs.crpix -= self.RSextent[::2]
self.wcs_crop.wcs.crpix = np.array(self.wcs_crop.wcs.crpix) - self.RSextent[::2]
else:
self.wcs_crop.wcs.crval = self.wcs.wcs_pix2world([self.RScenter],1)[0]
self.wcs_crop.wcs.crpix = self.RScenter-self.RSextent[::2]
@@ -793,6 +792,7 @@ class crop_map(object):
def writeto(self, filename):
self.hdul_crop.writeto(filename,overwrite=True)
class crop_Stokes(crop_map):
"""
Class to interactively crop a polarization map to desired Region of Interest.
@@ -810,7 +810,7 @@ class crop_Stokes(crop_map):
self.wcs_crop = self.wcs.deepcopy()
self.wcs_crop.array_shape = shape
if self.crpix_in_RS():
self.wcs_crop.wcs.crpix -= self.RSextent[::2]
self.wcs_crop.wcs.crpix = np.array(self.wcs_crop.wcs.crpix) - self.RSextent[::2]
else:
self.wcs_crop.wcs.crval = self.wcs.wcs_pix2world([self.RScenter],1)[0]
self.wcs_crop.wcs.crpix = self.RScenter-self.RSextent[::2]
@@ -849,4 +849,151 @@ class crop_Stokes(crop_map):
@property
def data_mask(self):
return self.hdul_crop[-1].data
return self.hdul_crop[-1].data
class pol_map(object):
"""
Class to interactively study polarization maps.
"""
def __init__(self,Stokes, SNRp_cut=3., SNRi_cut=30.):
self.Stokes = Stokes
self.wcs = deepcopy(WCS(Stokes[0].header))
self.SNRp_cut = SNRp_cut
self.SNRi_cut = SNRi_cut
self.SNRi = deepcopy(self.SNRi_cut)
self.SNRp = deepcopy(self.SNRp_cut)
self.region = None
#Get data
self.pivot_wav = self.Stokes[0].header['photplam']
self.convert_flux = self.Stokes[0].header['photflam']
self.I = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])].data
self.Q = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Q_stokes' for i in range(len(Stokes))])].data
self.U = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='U_stokes' for i in range(len(Stokes))])].data
self.IQU_cov = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])].data
self.P = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])].data
self.s_P = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])].data
self.PA = self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])].data
#Create figure
fontprops = fm.FontProperties(size=16)
self.fig = plt.figure(figsize=(15,15))
self.fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
self.ax = self.fig.add_subplot(111,projection=self.wcs)
self.ax.set_facecolor('black')
self.ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
self.ax.coords[0].set_axislabel('Right Ascension (J2000)')
self.ax.coords[0].set_axislabel_position('t')
self.ax.coords[0].set_ticklabel_position('t')
self.ax.coords[1].set_axislabel('Declination (J2000)')
self.ax.coords[1].set_axislabel_position('l')
self.ax.coords[1].set_ticklabel_position('l')
self.ax.axis('equal')
#Display total flux
im = self.ax.imshow(self.I*self.convert_flux, vmin=0., vmax=self.I[self.I > 0.].max()*self.convert_flux, aspect='auto', cmap='inferno')
cbar_ax = self.fig.add_axes([0.95, 0.12, 0.01, 0.75])
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
#Display polarization vectors in SNR_cut
self.pol_vector()
#Display scales and orientation
px_size = self.wcs.wcs.cdelt[0]*3600.
px_sc = AnchoredSizeBar(self.ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='white', fontproperties=fontprops)
self.ax.add_artist(px_sc)
pol_sc = AnchoredSizeBar(self.ax.transData, 2., r"$P$= 100%", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='white', fontproperties=fontprops)
self.ax.add_artist(pol_sc)
north_dir = AnchoredDirectionArrows(self.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=-self.Stokes[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})
self.ax.add_artist(north_dir)
#Display integrated values in ROI
self.pol_int
#Set axes for sliders (SNRp_cut, SNRi_cut)
ax_I_cut = self.fig.add_axes([0.125, 0.080, 0.35, 0.01])
ax_P_cut = self.fig.add_axes([0.125, 0.055, 0.35, 0.01])
ax_reset = self.fig.add_axes([0.125, 0.020, 0.05, 0.02])
SNRi_max = np.max(self.I[self.IQU_cov[0,0]>0.]/np.sqrt(self.IQU_cov[0,0][self.IQU_cov[0,0]>0.]))
SNRp_max = np.max(self.P[self.s_P>0.]/self.s_P[self.s_P > 0.])
s_I_cut = Slider(ax_I_cut,r"$SNR^{I}_{cut}$",1.,SNRi_max,valstep=1,valinit=self.SNRi_cut)
s_P_cut = Slider(ax_P_cut,r"$SNR^{P}_{cut}$",1.,SNRp_max,valstep=1,valinit=self.SNRp_cut)
b_reset = Button(ax_reset,"Reset")
def update_snri(val):
self.SNRi = val
self.quiver.remove()
self.pol_vector()
self.fig.canvas.draw_idle()
def update_snrp(val):
self.SNRp = val
self.quiver.remove()
self.pol_vector()
self.fig.canvas.draw_idle()
def reset(event):
s_I_cut.reset()
s_P_cut.reset()
s_I_cut.on_changed(update_snri)
s_P_cut.on_changed(update_snrp)
b_reset.on_clicked(reset)
plt.show(block=True)
@property
def cut(self):
s_I = np.sqrt(self.IQU_cov[0,0])
SNRp_mask, SNRi_mask = np.zeros(self.P.shape).astype(bool), np.zeros(self.I.shape).astype(bool)
SNRp_mask[self.s_P > 0.] = self.P[self.s_P > 0.] / self.s_P[self.s_P > 0.] > self.SNRp
SNRi_mask[s_I > 0.] = self.I[s_I > 0.] / s_I[s_I > 0.] > self.SNRi
return np.logical_and(SNRi_mask,SNRp_mask)
def pol_vector(self):
P_cut = np.ones(self.P.shape)*np.nan
P_cut[self.cut] = self.P[self.cut]
X, Y = np.meshgrid(np.arange(self.I.shape[1]),np.arange(self.I.shape[0]))
XY_U, XY_V = P_cut*np.cos(np.pi/2. + self.PA*np.pi/180.), P_cut*np.sin(np.pi/2. + self.PA*np.pi/180.)
self.quiver = self.ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=0.5, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, color='white')
return self.quiver
@property
def pol_int(self):
if self.region is None:
n_pix = self.I.size
s_I = np.sqrt(self.IQU_cov[0,0])
I_reg = self.I.sum()
I_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_I**2))
P_reg = self.Stokes[0].header['P_int']
P_reg_err = self.Stokes[0].header['P_int_err']
PA_reg = self.Stokes[0].header['PA_int']
PA_reg_err = self.Stokes[0].header['PA_int_err']
else:
n_pix = self.I[self.region].size
s_I = np.sqrt(self.IQU_cov[0,0])
s_Q = np.sqrt(self.IQU_cov[1,1])
s_U = np.sqrt(self.IQU_cov[2,2])
s_IQ = self.IQU_cov[0,1]
s_IU = self.IQU_cov[0,2]
s_QU = self.IQU_cov[1,2]
I_reg = self.I[self.region].sum()
Q_reg = self.Q[self.region].sum()
U_reg = self.U[self.region].sum()
I_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_I[self.region]**2))
Q_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_Q[self.region]**2))
U_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_U[self.region]**2))
IQ_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_IQ[self.region]**2))
IU_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_IU[self.region]**2))
QU_reg_err = np.sqrt(n_pix)*np.sqrt(np.sum(s_QU[self.region]**2))
P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) + ((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg
PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg))
PA_reg_err = (90./(np.pi*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)
return self.ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,P_reg_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,PA_reg_err), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction')