def Image_compare_micro(string='aml_vs_pml.png'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
c1 = color_own([1, 1, 0, 1])
c2 = color_own([1, 0, 1, 1])
c3 = color_own([0.6, 1.2, 0, 1])
c4 = color_own([0, 1, 1, 1])
else:
c1 = color_own([0, 0, 1, 1])
c2 = color_own([1, 0, 0, 1])
c3 = color_own([0.5, 1, 0, 1])
c4 = color_own([1, 0, 1, 1])
rc('xtick', labelsize=24)
rc('ytick', labelsize=24)
Separation_E1 = 20
Separation_E2 = 2
Separation_E3 = 20
xx1 = np.array(range(-1000, 1000, 1))
xx2 = xx1
xx3 = xx1
yy1 = xx1 * 0 + 10
yy2 = xx1 * 0 + 1
yy3 = xx1 * 0 + 200
uu1 = np.sqrt(xx1 * xx1 + yy1 * yy1) / Separation_E1
uu2 = np.sqrt(xx2 * xx2 + yy2 * yy2) / Separation_E2
uu3 = np.sqrt(xx3 * xx3 + yy3 * yy3) / Separation_E3
dSeparation1 = uu1 / (uu1 * uu1 + 2) * Separation_E1
dSeparation2 = uu2 / (uu2 * uu2 + 2) * Separation_E2
dSeparation3 = uu3 / (uu3 * uu3 + 2) * Separation_E3
A1 = (uu1 * uu1 + 2) / (uu1 * np.sqrt(uu1 * uu1 + 4))
A2 = (uu2 * uu2 + 2) / (uu2 * np.sqrt(uu2 * uu2 + 4))
A3 = (uu3 * uu3 + 2) / (uu3 * np.sqrt(uu3 * uu3 + 4))
dm1 = 2.5 * np.log10(A1)
dm2 = 2.5 * np.log10(A2)
dm3 = 2.5 * np.log10(A3)
xx1 = xx1 / 250
xx2 = xx2 / 250
xx3 = xx3 / 250
figure = plt.figure(figsize=(12, 12))
plt.subplots_adjust(hspace=0.1)
ax1 = plt.subplot2grid((2, 1), (0, 0), rowspan=1)
plt.plot(xx1, dSeparation1, color=c1, linewidth=4)
line1, = plt.plot(xx2, dSeparation2, color=c3, linewidth=4)
plt.plot(xx3, dSeparation3, linestyle='--', color=c4, linewidth=4)
line1.set_dashes([10, 2, 10, 2])
plt.yticks(
[1, 2, 3, 4, 5, 6, 7, 8],
['1.0 ', '2.0 ', '3.0 ', '4.0 ', '5.0 ', '6.0 ', '7.0 ', '8.0 '])
plt.ylim([0, 8])
plt.ylabel('Shift [mas]', fontsize=30)
plt.plot(xx1, xx1 * 0 + 0.5, color=c2, linewidth=3)
ax1.tick_params(length=6, width=2)
ax1.tick_params(which='minor', length=4, width=2)
xticklabels1 = ax1.get_xticklabels()
plt.setp(xticklabels1, visible=False)
ax2 = plt.subplot2grid((2, 1), (1, 0), rowspan=1, sharex=ax1)
plt.semilogy(xx1, dm1, color=c1, linewidth=4)
line1, = plt.semilogy(xx2, dm2, color=c3, linewidth=4)
plt.semilogy(xx3, dm3, linestyle='--', color=c4, linewidth=4)
line1.set_dashes([10, 2, 10, 2])
plt.semilogy(xx1, xx1 * 0 + 0.001, color=c2, linewidth=3)
plt.ylim([0.0001, 1])
plt.ylabel('Magnification [mag]', fontsize=30)
ax2.tick_params(length=6, width=2)
ax2.tick_params(which='minor', length=4, width=2)
plt.xlabel('$\Delta$ T [yr]', fontsize=30)
figure.savefig(imagepath + string, format='png')
print('Create Image: ' + imagepath + string + '.png')
plt.close(figure)
A = pos_vec[1][0]
A = A[np.where(A != 0)]
f_L = 10**(-0.4 * (gmag[0]))
f_S = 10**(-0.4 * (gmag[1]))
f_L2 = 10**(-0.4 * (gmag2[0]))
f_S2 = 10**(-0.4 * (gmag2[1]))
f_L / f_S
m1 = -2.5 * np.log10(f_L + f_S * A)
m2 = -2.5 * np.log10(f_L + f_S)
m12 = -2.5 * np.log10(f_L2 + f_S2 * A)
m22 = -2.5 * np.log10(f_L2 + f_S2)
d = 1
if d:
dark(1)
c1 = color_own([1, 0.5, 0, 1])
c2 = color_own([1, 1, 0, 1])
c3 = color_own([0, 1, 1, 1])
c4 = color_own([0.6, 1.2, 0, 1])
c5 = color_own([1, 1, 1, 1])
else:
c1 = color_own([1, 0.5, 0, 1])
c2 = color_own([1, 0, 0, 1])
c3 = color_own([0, 0, 1, 1])
c4 = color_own([0, 1, 0, 1])
c5 = color_own([0, 0, 0, 1])
ee = Time(epoch + t_0, format='jyear').jd
plt.plot(ee - 2450000,
m12 - m22 + np.median(obs['col2']),
color=c1,
def Image_Illustration2(string='Illustration'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0., 0., 0., 1])
grey = color_own([.5, .5, 0.5, 1])
cyan = color_own([0, 1, 1, 1])
blue = color_own([0, 0, 1, 1])
lime = color_own([0.6, 1.2, 0, 1])
green = color_own([0, 1, 0, 1])
red = color_own([1, 0, 0, 1])
orange = color_own([1, 1, 0, 1])
else:
black = color_own([0., 0., 0., 1])
grey = color_own([.5, .5, 0.5, 1])
cyan = color_own([0, 1, 1, 1])
blue = color_own([0, 0, 1, 1])
lime = color_own([0.6, 1.2, 0, 1])
green = color_own([0, 1, 0, 1])
red = color_own([1, 0, 0, 1])
orange = color_own([1, 1, 0, 1])
t = np.array([12, 35, 41, 61, 73, 89])
scandir = np.array([0.1, 0.7, 0.4, 0.8, 0.2, 0.1]) * np.pi
#Position_lens
x1 = np.linspace(
1, 13, 100) + 0.3 * np.sin(np.linspace(np.pi / 4, 12 * np.pi / 4, 100))
y1 = np.linspace(
1, 3, 100) + 0.3 * np.cos(np.linspace(np.pi / 4, 12 * np.pi / 4, 100))
#unlensed Position_source
x2 = np.linspace(
5, 9, 100) # + 0.03 * np.sin(np.linspace(np.pi/4,12*np.pi/4,100))
y2 = np.linspace(
7, 4.5, 100) # + 0.03* np.cos(np.linspace(np.pi/4,12*np.pi/4,100))
d = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
TE = 2
X2 = x2 + (x2 - x1) * TE / (d**2 + 2 * TE)
Y2 = y2 + (y2 - y1) * TE / (d**2 + 2 * TE)
dX2 = x1 - X2
dY2 = y1 - Y2
dx2 = x1 - x2
dy2 = y1 - y2
fig = plt.figure(figsize=(12, 8))
ax = plt.subplot(111)
ax.axis('equal')
ax.axis('off')
#---------------------------------------------------------------
#axis
plt.xlim([-0.5, 14])
xr = 12
yr = 7
plt.text(xr - 0.8,
0,
'RA $\cdot$ cos(Dec)',
verticalalignment='center',
fontsize=25)
plt.text(0,
yr + 0.25,
'Dec',
fontsize=25,
horizontalalignment='center',
rotation=90)
plt.arrow(-0.025,
0,
xr - 1,
0,
width=0.05,
overhang=0.5,
head_width=0.5,
head_length=0.5,
color=black,
zorder=100,
length_includes_head=True)
plt.arrow(0,
-0.025,
0,
yr - 0.5,
width=0.05,
overhang=0.5,
head_width=0.5,
head_length=0.5,
color=black,
zorder=100,
length_includes_head=True)
plt.text(2,
1.5,
'Lens',
color=grey,
fontsize=25,
horizontalalignment='center',
rotation=0,
weight='bold')
#---------------------------------------------------------------
# Motion source
plt.plot(x1, y1, color=grey, linewidth=7)
fig.savefig(imagepath + string + '_1.png', format='png')
plt.text(4,
7.5,
'Source',
color=blue,
fontsize=25,
horizontalalignment='center',
rotation=0,
weight='bold')
plt.plot(x2,
y2,
color=cyan,
linestyle='--',
dashes=(10, 5),
linewidth=3,
zorder=-1)
fig.savefig(imagepath + string + '_2.png', format='png')
plt.plot(X2, Y2, color=blue, linewidth=3)
for i in range(len(t)):
plt.plot([x2[t[i]], X2[t[i]]], [y2[t[i]], Y2[t[i]]], ':', color=black)
fig.savefig(imagepath + string + '_3.png', format='png')
delta = 0.05
for i in range(len(t)):
xm1 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + x1[t[i]]
ym1 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + y1[t[i]]
xm2 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + X2[t[i]]
ym2 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + Y2[t[i]]
plt.plot(xm2, ym2, color=black, linewidth=1, zorder=1)
plt.plot(xm1, ym1, color=black, linewidth=1, zorder=1)
fig.savefig(imagepath + string + '_4.png', format='png')
for i in range(len(t)):
xm1 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + x1[t[i]]
ym1 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + y1[t[i]]
xm2 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + X2[t[i]]
ym2 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + Y2[t[i]]
dsc = ((dx2[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dy2[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
dSC = ((dX2[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dY2[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
ttX2 = np.array([0, -dSC[1] / 2, dSC[1] / 2, 0]) * np.cos(
scandir[i]) + ([x1[t[i]], x1[t[i]], X2[t[i]], X2[t[i]]])
ttY2 = np.array([0, -dSC[1] / 2, dSC[1] / 2, 0]) * np.sin(
scandir[i]) + ([y1[t[i]], y1[t[i]], Y2[t[i]], Y2[t[i]]])
ttx2 = np.array([0, -dsc[1] / 2 - 0.2, dsc[1] / 2 - 0.2, 0]) * np.cos(
scandir[i]) + ([x1[t[i]], x1[t[i]], x2[t[i]], x2[t[i]]])
tty2 = np.array([0, -dsc[1] / 2 - 0.2, dsc[1] / 2 - 0.2, 0]) * np.sin(
scandir[i]) + ([y1[t[i]], y1[t[i]], y2[t[i]], y2[t[i]]])
if i % 2 == 0:
plt.arrow(ttx2[2],tty2[2], 0.0001*(ttx2[2]-ttx2[1]),0.0001*(tty2[2]-tty2[1]),color= red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttx2[1],tty2[1], 0.0001*(ttx2[1]-ttx2[2]),0.0001*(tty2[1]-tty2[2]),color= red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX2[2],ttY2[2], 0.0001*(ttX2[2]-ttX2[1]),0.0001*(ttY2[2]-ttY2[1]),color= orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX2[1],ttY2[1], 0.0001*(ttX2[1]-ttX2[2]),0.0001*(ttY2[1]-ttY2[2]),color= orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True, zorder = 10)
plt.plot(ttx2[0:2], tty2[0:2], color=black, linestyle=':')
plt.plot(ttX2[0:2], ttY2[0:2], color=black, linestyle=':')
plt.plot(ttx2[1:3],
tty2[1:3],
color=red,
linewidth=3,
linestyle='--',
dashes=(10, 10))
plt.plot(ttX2[1:3],
ttY2[1:3],
color=orange,
linewidth=3,
linestyle='-')
plt.plot(ttx2[2:], tty2[2:], color=black, linestyle=':')
plt.plot(ttX2[2:], ttY2[2:], color=black, linestyle=':')
#if i ==0 :
fig.savefig(imagepath + string + '_5.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '.png', format='png')
plt.close(fig)
def Image_Illustration_Multi(string='Illustration_Multi'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
grey = color_own([.5, .5, 0.5, 1])
cyan = color_own([0, 1, 1, 1])
blue = color_own([0, 0, 1, 1])
lime = color_own([0.6, 1.2, 0, 1])
green = color_own([0, 1, 0, 1])
red = color_own([1, 0, 0, 1])
orange = color_own([1, .5, 0, 1])
else:
black = color_own([0., 0., 0., 1])
grey = color_own([.5, .5, 0.5, 1])
cyan = color_own([0, 1, 1, 1])
blue = color_own([0, 0, 1, 1])
lime = color_own([0.6, 1.2, 0, 1])
green = color_own([0, 1, 0, 1])
red = color_own([1, 0, 0, 1])
orange = color_own([1, 1, 0, 1])
t = np.array([12, 35, 41, 61, 73, 89])
scandir = np.array([0.1, 0.7, 0.4, 0.8, 0.2, 0.1]) * np.pi
x1 = np.linspace(
1, 13, 100) + 0.3 * np.sin(np.linspace(np.pi / 4, 12 * np.pi / 4, 100))
y1 = np.linspace(
1, 3, 100) + 0.3 * np.cos(np.linspace(np.pi / 4, 12 * np.pi / 4, 100))
x2 = np.linspace(
3, 7, 100) # + 0.03 * np.sin(np.linspace(np.pi/4,12*np.pi/4,100))
y2 = np.linspace(
7, 4.5, 100) # + 0.03* np.cos(np.linspace(np.pi/4,12*np.pi/4,100))
x3 = np.linspace(
12, 10, 100) # + 0.03 * np.sin(np.linspace(np.pi/4,12*np.pi/4,100))
y3 = np.linspace(
8, 6, 100) # + 0.03* np.cos(np.linspace(np.pi/4,12*np.pi/4,100))
d2 = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
d3 = np.sqrt((x1 - x3)**2 + (y1 - y3)**2)
TE = 1.5
X2 = x2 + (x2 - x1) * TE / (d2**2 + 2 * TE)
Y2 = y2 + (y2 - y1) * TE / (d2**2 + 2 * TE)
X3 = x3 + (x3 - x1) * TE / (d3**2 + 2 * TE)
Y3 = y3 + (y3 - y1) * TE / (d3**2 + 2 * TE)
dX2 = x1 - X2
dY2 = y1 - Y2
dx2 = x1 - x2
dy2 = y1 - y2
dX3 = x1 - X3
dY3 = y1 - Y3
dx3 = x1 - x3
dy3 = y1 - y3
fig = plt.figure(figsize=(12, 8.8))
plt.subplots_adjust(top=0.95,
bottom=0.05,
left=0.05,
right=0.95,
hspace=0.0,
wspace=0.2)
ax = plt.subplot(111)
ax.axis('equal')
ax.axis('off')
for i in range(len(t)):
delta = 0.05
xm1 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + x1[t[i]]
ym1 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + y1[t[i]]
xm2 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + X2[t[i]]
ym2 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + Y2[t[i]]
xm3 = np.array([-1, 1, 1, -1, -1]) * np.cos(
scandir[i]) + delta * np.array([-1, -1, 1, 1, -1]) * np.sin(
scandir[i]) + X3[t[i]]
ym3 = np.array([-1, 1, 1, -1, -1]) * np.sin(
scandir[i]) - delta * np.array([-1, -1, 1, 1, -1]) * np.cos(
scandir[i]) + Y3[t[i]]
dSC3 = ((dX3[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dY3[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
dSC2 = ((dX2[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dY2[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
dsc3 = ((dx3[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dy3[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
dsc2 = ((dx2[t[i]]).reshape(-1,1)*[np.sin(scandir[i]),np.cos(scandir[i])] \
+ (dy2[t[i]]).reshape(-1,1) *[-np.cos(scandir[i]),np.sin(scandir[i])])[0]
ttX2 = np.array([0, -dSC2[1] / 2, dSC2[1] / 2, 0]) * np.cos(
scandir[i]) + ([x1[t[i]], x1[t[i]], X2[t[i]], X2[t[i]]])
ttY2 = np.array([0, -dSC2[1] / 2, dSC2[1] / 2, 0]) * np.sin(
scandir[i]) + ([y1[t[i]], y1[t[i]], Y2[t[i]], Y2[t[i]]])
ttX3 = np.array([0, -dSC3[1] / 2, dSC3[1] / 2, 0]) * np.cos(
scandir[i]) + ([x1[t[i]], x1[t[i]], X3[t[i]], X3[t[i]]])
ttY3 = np.array([0, -dSC3[1] / 2, dSC3[1] / 2, 0]) * np.sin(
scandir[i]) + ([y1[t[i]], y1[t[i]], Y3[t[i]], Y3[t[i]]])
ttx2 = np.array([0, -dsc2[1] / 2 - 0.3, dsc2[1] / 2 - 0.3, 0
]) * np.cos(scandir[i]) + (
[x1[t[i]], x1[t[i]], x2[t[i]], x2[t[i]]])
tty2 = np.array([0, -dsc2[1] / 2 - 0.3, dsc2[1] / 2 - 0.3, 0
]) * np.sin(scandir[i]) + (
[y1[t[i]], y1[t[i]], y2[t[i]], y2[t[i]]])
if i == 3: off = [-0.4, -0.2]
else: off = [0, -0.2]
ttx3 = np.array([0, -dsc3[1] / 2 + off[1], dsc3[1] / 2 + off[1], 0
]) * np.cos(scandir[i]) + (
[x1[t[i]], x1[t[i]], x3[t[i]], x3[t[i]]])
tty3 = np.array([0, -dsc3[1] / 2 + off[1], dsc3[1] / 2 + off[1], 0
]) * np.sin(scandir[i]) + (
[y1[t[i]], y1[t[i]], y3[t[i]], y3[t[i]]])
ttX3 = np.array([0, -dSC3[1] / 2 + off[0], dSC3[1] / 2 + off[0], 0
]) * np.cos(scandir[i]) + (
[x1[t[i]], x1[t[i]], X3[t[i]], X3[t[i]]])
ttY3 = np.array([0, -dSC3[1] / 2 + off[0], dSC3[1] / 2 + off[0], 0
]) * np.sin(scandir[i]) + (
[y1[t[i]], y1[t[i]], Y3[t[i]], Y3[t[i]]])
'''
if i % 2 == 0:
plt.arrow(ttX2[2],ttY2[2], 0.0001*(ttX2[2]-ttX2[1]),0.0001*(ttY2[2]-ttY2[1]),color = color_own([0,0,1,1]),head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX2[1],ttY2[1], 0.0001*(ttX2[1]-ttX2[2]),0.0001*(ttY2[1]-ttY2[2]),color = color_own([0,0,1,1]),head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.plot(ttX2[0:2],ttY2[0:2],color = black, linestyle= ':')
plt.plot(ttX2[1:3],ttY2[1:3],color = [158/200,1/200,66/200, 1], linewidth = 3,linestyle= '-')
plt.plot(ttX2[2:],ttY2[2:],color = black, linestyle= ':')
'''
if i % 2 == 0:
plt.plot(xm2, ym2, color=black, linewidth=1, zorder=1)
plt.plot(xm1, ym1, color=black, linewidth=1, zorder=1)
plt.arrow(ttX2[2],ttY2[2], 0.0001*(ttX2[2]-ttX2[1]),0.0001*(ttY2[2]-ttY2[1]),color=red ,head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX2[1],ttY2[1], 0.0001*(ttX2[1]-ttX2[2]),0.0001*(ttY2[1]-ttY2[2]),color=red ,head_width=0.2,\
overhang = 0.5, length_includes_head=True, zorder = 10)
plt.plot(ttX2[0:2], ttY2[0:2], color=black, linestyle=':')
plt.plot(ttX2[1:3], ttY2[1:3], color=red, linewidth=3)
plt.plot(ttX2[2:], ttY2[2:], color=black, linestyle=':')
plt.plot(ttx2[0:2], tty2[0:2], color=black, linestyle=':')
plt.plot(ttx2[1:3],
tty2[1:3],
color=orange,
linewidth=3,
linestyle='-',
dashes=(10, 2))
plt.plot(ttx2[2:], tty2[2:], color=black, linestyle=':')
plt.arrow(ttx2[2],tty2[2], 0.0001*(ttx2[2]-ttx2[1]),0.0001*(tty2[2]-tty2[1]),color = orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttx2[1],tty2[1], 0.0001*(ttx2[1]-ttx2[2]),0.0001*(tty2[1]-tty2[2]),color = orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True, zorder = 10)
if i >= 3:
plt.plot(ttX3[0:2], ttY3[0:2], color=black, linestyle=':')
plt.plot(ttX3[1:3], ttY3[1:3], color=red, linewidth=3)
plt.plot(ttX3[2:], ttY3[2:], color=black, linestyle=':')
plt.arrow(ttX3[2],ttY3[2], 0.0001*(ttX3[2]-ttX3[1]),0.0001*(ttY3[2]-ttY3[1]),color=red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX3[1],ttY3[1], 0.0001*(ttX3[1]-ttX3[2]),0.0001*(ttY3[1]-ttY3[2]),color= red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.plot(xm3, ym3, color=black, linewidth=1, zorder=1)
plt.plot(ttx3[0:2], tty3[0:2], color=black, linestyle=':')
plt.plot(ttx3[1:3],
tty3[1:3],
color=orange,
linewidth=3,
linestyle='-',
dashes=(10, 2))
plt.plot(ttx3[2:], tty3[2:], color=black, linestyle=':')
plt.arrow(ttx3[2],tty3[2], 0.0001*(ttx3[2]-ttx3[1]),0.0001*(tty3[2]-tty3[1]),color = orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttx3[1],tty3[1], 0.0001*(ttx3[1]-ttx3[2]),0.0001*(tty3[1]-tty3[2]),color = orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True, zorder = 10)
'''
else:
plt.plot(xm3,ym3, color = 'grey', linewidth = 2, zorder = -1)
'''
elif i >= 3:
plt.plot(xm3, ym3, color=black, linewidth=1, zorder=1)
plt.plot(xm1, ym1, color=black, linewidth=1, zorder=1)
#plt.plot(xm2,ym2, color = 'grey', linewidth = 2, zorder = -1)
plt.plot(ttX3[0:2], ttY3[0:2], color=black, linestyle=':')
plt.plot(ttX3[1:3], ttY3[1:3], color=red, linewidth=3)
plt.plot(ttX3[2:], ttY3[2:], color=black, linestyle=':')
plt.arrow(ttX3[2],ttY3[2], 0.0001*(ttX3[2]-ttX3[1]),0.0001*(ttY3[2]-ttY3[1]),color= red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttX3[1],ttY3[1], 0.0001*(ttX3[1]-ttX3[2]),0.0001*(ttY3[1]-ttY3[2]),color= red, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.plot(ttx3[0:2], tty3[0:2], color=black, linestyle=':')
plt.plot(ttx3[1:3],
tty3[1:3],
color=orange,
linewidth=3,
linestyle='-',
dashes=(10, 2))
plt.plot(ttx3[2:], tty3[2:], color=black, linestyle=':')
plt.arrow(ttx3[2],tty3[2], 0.0001*(ttx3[2]-ttx3[1]),0.0001*(tty3[2]-tty3[1]),color= orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True ,zorder = 10)
plt.arrow(ttx3[1],tty3[1], 0.0001*(ttx3[1]-ttx3[2]),0.0001*(tty3[1]-tty3[2]),color= orange, head_width=0.2,\
overhang = 0.5, length_includes_head=True, zorder = 10)
'''
else:
plt.plot(xm3,ym3, color = 'grey', linewidth = 2, zorder = -1)
plt.plot(xm2,ym2, color = 'grey', linewidth = 2, zorder = -1)
plt.plot(xm1,ym1, color = 'grey', linewidth = 2, zorder = -1)
'''
#if i ==0 :
plt.plot(x1, y1, color=grey, linewidth=7)
plt.plot(x2,
y2,
color=cyan,
linestyle='--',
dashes=(6, 2),
linewidth=3,
zorder=-1)
plt.plot(X2, Y2, color=blue, linewidth=3)
plt.plot(x3,
y3,
color=lime,
linestyle='--',
dashes=(6, 2),
linewidth=3,
zorder=-1)
plt.plot(X3, Y3, color=green, linewidth=3)
plt.xlim([-0.2, 13.5])
xr = 12
yr = 7
plt.text(xr - 0.8,
0,
'RA $\cdot$ cos(Dec)',
verticalalignment='center',
fontsize=25)
plt.text(0,
yr + 0.25,
'Dec',
fontsize=25,
horizontalalignment='center',
rotation=90)
plt.text(2,
1.5,
'Lens',
color=grey,
fontsize=25,
horizontalalignment='center',
rotation=0,
weight='bold')
plt.text(4,
7.5,
'Star 1',
color=blue,
fontsize=25,
horizontalalignment='center',
rotation=0,
weight='bold')
plt.text(11,
8,
'Star 2',
color=green,
fontsize=25,
horizontalalignment='center',
rotation=0,
weight='bold')
plt.arrow(-0.025,
0,
xr - 1,
0,
width=0.05,
overhang=0.5,
head_width=0.5,
head_length=0.5,
color=black,
zorder=100,
length_includes_head=True)
plt.arrow(0,
-0.025,
0,
yr - 0.5,
width=0.05,
overhang=0.5,
head_width=0.5,
head_length=0.5,
color=black,
zorder=100,
length_includes_head=True)
fig.savefig(imagepath + string + '.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '.png', format='png')
plt.close(fig)
def Image_precision(string='Sig_vs_Gmag',
Gaia_precision=path + 'InputTable/resolution_Gaia.png',
pres=False):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
color1 = color_own([0.85, 0, 0, 1])
color2 = color_own([0, 0, 1, 1])
color3 = color_own([0, 1, 1, 1])
color4 = color_own([0.5, 1, 0, 1])
color5 = color_own([1, 1, 0, 1])
else:
black = color_own([0., 0., 0., 1])
color1 = color_own([0.85, 0, 0, 1])
color2 = color_own([0, 0, 1, 1])
color3 = color_own([0, 1, 1, 1])
color4 = color_own([0, 1, 0, 1])
color5 = color_own([1, 1, 0, 1])
fig = plt.figure(figsize=[12, 10])
Gmag = np.arange(4, 22, 0.01)
datafile = cbook.get_sample_data(Gaia_precision)
img = imread(datafile)
z = 10**(0.4 * (np.maximum(Gmag, 14) - 15)) #(14-np.minimum(Gmag, 14))
z2 = 10**(0.4 * (np.maximum(Gmag, 12) - 15))
sig_pi = (-1.631 + 680.766 * z2 + 32.732 * z2**2)**0.5 / 1000
sig_fov2 = (-1.631 + 680.766 * z + 32.732 * z**2)**0.5 / 1000 * 7.75 + 0.1
sig_fov3 = sig_fov2 / np.sqrt(9)
plt.plot([0, 1], [-5, -5],
c=color1,
linewidth=3,
label='formal precision from Gaia DR2 (per CCD)')
plt.plot([0, 1], [-5, -5],
c=color2,
linewidth=3,
label='actual precision from Gaia DR2 (per CCD)')
plt.yticks([
np.log10(i)
for i in [20, 10, 5, 2, 1, 0.5, 0.2, 0.1, 0.05, 0.02, 0.01]
], [20, 10, 5, 2, 1, 0.5, 0.2, 0.1, 0.05, 0.02, 0.01],
fontsize=25)
plt.xticks(fontsize=25)
plt.ylabel('Standard deviation of AL field angle [mas]', fontsize=30)
plt.xlabel('G magnitude', fontsize=30)
plt.imshow(img,
zorder=0,
extent=[5, 21.04, np.log10(0.0195),
np.log10(10)])
plt.axis('auto')
plt.xlim([4, 22])
plt.ylim([np.log10(0.005), np.log10(40)])
if pres:
plt.legend(loc='upper left', fontsize=20)
fig.savefig(imagepath + string + '_1.png', format='png')
plt.plot(Gmag,
np.log10(sig_pi),
'--',
c=color3,
dashes=(5, 5),
linewidth=3,
label='predicted end-of-mission parallax error')
if pres:
plt.legend(loc='upper left', fontsize=20)
fig.savefig(imagepath + string + '_2.png', format='png')
plt.plot(Gmag,
np.log10(sig_fov2),
':',
c=color4,
linewidth=5,
label='used Standard deviation (per CCD)')
if pres:
plt.legend(loc='upper left', fontsize=20)
fig.savefig(imagepath + string + '_3.png', format='png')
plt.plot(Gmag,
np.log10(sig_fov3),
c=color5,
linewidth=7,
label='used Standard deviation for 9 CCD observations')
plt.plot([5, 21.04, 21.04, 5, 5], [
np.log10(0.0195),
np.log10(0.0195),
np.log10(10),
np.log10(10),
np.log10(0.0195)
],
linewidth=2,
color=[0.5, 0.5, 0.5, 1],
zorder=0.1)
plt.axis('auto')
plt.xlim([4, 22])
plt.ylim([np.log10(0.005), np.log10(40)])
plt.legend(loc='upper left', fontsize=20)
fig.savefig(imagepath + string + '.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '.png', format='png')
plt.close(fig)
def Image_Window(string='resolve_Window', pres=False):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
else:
black = color_own([0., 0., 0., 1])
c1 = color_own([0, 1, 1, 1])
c2 = color_own([1, 0, 0, 1])
c3 = color_own([1, 1, 0.2, 1])
c4 = color_own([0.4, 0.4, 0.4, 1])
c_star = [c1, c2, c3, c4]
c_grid1 = color_own([0, int(dark), 1, 1])
c_grid2 = color_own([0.5, 1, 0, 1])
star = np.array([[0, 0], [0.1, 0.9], [-1, -1.1], [-0.5, 0.1]])
fig = plt.figure(figsize=[12, 12])
x_width = 0.059
y_width = 0.177
#------------------------------------------------------------
# axis
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.grid(True)
plt.axis('equal')
plt.axis([-1.5, 1.5, -1.4, 1.6])
plt.ylabel('Across-scan direction (AC) [arcsec]', fontsize=30)
plt.xlabel('Along-scan direction (AL) [arcsec]', fontsize=30)
#------------------------------------------------------------
#------------------------------------------------------------
#Grid Major Star
for i in range(-6, 7):
plt.plot([-6 * x_width, 6 * x_width], [i * y_width, i * y_width],
c=c_grid1,
linewidth=3)
plt.plot([i * x_width, i * x_width], [-6 * y_width, 6 * y_width],
c=c_grid1,
linewidth=3)
plt.text(
0,
1.4,
"Along-scan direction\n $12\,\mathrm{pix} \\times 0.059 \mathrm{''/pix} = 0.708\mathrm{''}$",
fontsize=25,
verticalalignment='center',
horizontalalignment='center',
rotation=0)
plt.text(
0.7,
0,
"Across-scan direction\n $12\,\mathrm{pix} \\times 0.177 \mathrm{''/pix} = 2.124\mathrm{''}$",
fontsize=25,
verticalalignment='center',
horizontalalignment='center',
rotation=90)
plt.arrow(0,6*y_width+2*x_width, -6*x_width+0.02,0,color= black, head_width=0.1,\
overhang = 0.5, length_includes_head=True ,zorder = 10, linewidth = 3)
plt.arrow(0,6*y_width+2*x_width, 6*x_width-0.02,0,color= black, head_width=0.1,\
overhang = 0.5, length_includes_head=True ,zorder = 10, linewidth = 3)
plt.arrow(8*x_width,0,0, -6*y_width+0.02,color= black, head_width=0.1,\
overhang = 0.5, length_includes_head=True ,zorder = 10, linewidth = 3)
plt.arrow(8*x_width,0,0, 6*y_width-0.02,color= black, head_width=0.1,\
overhang = 0.5, length_includes_head=True ,zorder = 10, linewidth = 3)
plt.scatter(star[:1, 0],
star[:1, 1],
marker=(5, 1),
c=c_star[:1],
s=[3000],
zorder=1000)
if pres: fig.savefig(imagepath + string + '_1.png', format='png')
#------------------------------------------------------------
#------------------------------------------------------------
#Grid Minor Star
plt.scatter(star[1:3, 0],
star[1:3, 1],
marker=(5, 1),
c=c_star[1:3],
s=[2000, 2000],
zorder=1000)
if pres: fig.savefig(imagepath + string + '_2.png', format='png')
for i in range(-5, 8):
plt.plot([-15 * x_width, -6 * x_width], [i * y_width, i * y_width],
c=c_grid2,
linewidth=3,
zorder=-1)
for i in range(-15, -5):
plt.plot([i * x_width, i * x_width], [-5 * y_width, 7 * y_width],
c=c_grid2,
linewidth=3,
zorder=-1)
plt.scatter(star[3:, 0],
star[3:, 1],
marker=(5, 1),
c=c_star[3:],
s=[2000],
zorder=1000)
if pres: fig.savefig(imagepath + string + '_3.png', format='png')
#------------------------------------------------------------
fig.savefig(imagepath + string + '.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '.png', format='png')
plt.close(fig)
def PlotHistAll(Analysis_result, string = 'mass_accuracy_histogram', log = False, logbin = True, \
ylim = [0,60], xlim = [0.0,1.92], extended = True, future = False, use_error = 0, vary = None, ty = 'Mass'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
'''
plot an histogram of input masses with color coded accuracy
'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark: string = 'dark_' + string
try:
a = Analysis_result[1][0][1][1]
TCA = None
except IndexError:
stats = np.array([i[0][2:] for i in Analysis_result[0]])
if ty == 'Mass' or ty == 'm':
stats_plot = stats[:, 0]
elif ty == 'GL':
stats_plot = np.array([i[0].getMag() for i in Analysis_result[2]])
elif ty == 'GS':
stats_plot = np.array([i[1].getMag() for i in Analysis_result[2]])
elif ty == 'dG':
stats_plot = np.array(
[i[1].getMag() - i[0].getMag() for i in Analysis_result[2]])
elif ty == 'c':
stats_plot = np.array([
dist(i[0], i[1], unit='arcsec', T=i[1].getTca())
for i in Analysis_result[2]
])
stats_in = stats
stats_v = []
stats_in_v = []
stats_plot_v = []
vary_bool = vary is not None
TCA_v = []
if vary_bool:
Events_v = vary[2]
if ty == 'Mass' or ty == 'm':
stats_plot_v = np.array([i[0].getMass() for i in vary[2]])
elif ty == 'GL':
stats_plot_v = np.array([i[0].getMag() for i in vary[2]])
elif ty == 'GS':
stats_plot_v = np.array([i[1].getMag() for i in vary[2]])
elif ty == 'dG':
stats_plot_v = np.array(
[i[1].getMag() - i[0].getMag() for i in vary[2]])
elif ty == 'c':
stats_plot_v = np.array([
dist(i[0], i[1], unit='arcsec', T=i[1].getTca())
for i in vary[2]
])
for i in range(len(vary[0])):
stats_in_v.append(Events_v[i][0].getMass())
mm_v = np.array([
vary[0][i][x][0][6] for x in range(len(vary[0][i]))
if vary[0][i][x][0][4] != -999
])
mp_v = np.array([
vary[0][i][x][0][7] for x in range(len(vary[0][i]))
if vary[0][i][x][0][4] != -999
])
mmm_v = percentile(mm_v)
ppp_v = percentile(mp_v)
delta_M_v = max(ppp_v[1], -mmm_v[1]) / Events_v[i][0].getMass()
stats_v.append(delta_M_v)
TCA_v.append(Events_v[i][0].getTca())
stats_v = np.array(stats_v)
stats_in_v = np.array(stats_in_v)
stats_plot_v = np.array(stats_plot_v)
TCA_v = np.array(TCA_v)
gmag = np.array([i[0].getMag() for i in Events_v])
Good_ = np.array([name_good(i[0].getId()) for i in Events_v])
gg = np.where((gmag > 5) & (Good_))
stats_v = stats_v[gg]
stats_plot_v = stats_plot_v[gg]
TCA_v = TCA_v[gg]
if not extended:
ngm_v = np.where(TCA_v < 2019.5)
if future:
ngm_v = np.where(TCA_v > 2019.5)
stats_v = stats_v[ngm_v]
stats_plot_v = stats_plot_v[ngm_v]
Eventnames = Analysis_result[1]
Events = Analysis_result[2]
ID = np.array([i[0].getId() for i in Events])
gmag = np.array([i[0].getMag() for i in Events])
Good_ = np.array([name_good(i[0].getId()) for i in Events])
gg = np.where((gmag > 5) & (Good_))
stats = stats[gg]
stats_plot = stats_plot[gg]
stats_in = stats_in[gg]
ID = ID[gg]
TCA = np.array([ev[0].getTca() for ev in Events])
TCA = TCA[gg]
if not extended:
ngm = np.where(TCA < 2019.5)[0]
if future:
ngm = np.where(TCA > 2019.5)[0]
stats = stats[ngm]
stats_plot = stats_plot[ngm]
else:
stats = np.array([i[0][2:] for i in Analysis_result])
stats_plot = np.array([i[0][2:] for i in Analysis_result])
#--------------------------------------------
#limits for the colorcoding
lim1 = 0.15
lim2 = 0.000
lim3 = 0.3
lim4 = 0.5
lim = [lim1, lim2, lim3, lim4, 1]
if vary_bool:
p10 = np.where(stats_v < lim1)[0]
p20 = np.where(stats_v < lim2)[0]
p30 = np.where(stats_v < lim3)[0]
p50 = np.where(stats_v < lim4)[0]
p100 = np.where(stats_v < 1)[0]
elif use_error == -1:
p10 = np.where(-stats[:, 4] / stats[:, 0] < lim1)[0]
p20 = np.where(-stats[:, 4] / stats[:, 0] < lim2)[0]
p30 = np.where(-stats[:, 4] / stats[:, 0] < lim3)[0]
p50 = np.where(-stats[:, 4] / stats[:, 0] < lim4)[0]
p100 = np.where(-stats[:, 4] / stats[:, 0] < 1)[0]
elif use_error == 1:
p10 = np.where(stats[:, 5] / stats[:, 0] < lim1)[0]
p20 = np.where(stats[:, 5] / stats[:, 0] < lim2)[0]
p30 = np.where(stats[:, 5] / stats[:, 0] < lim3)[0]
p50 = np.where(stats[:, 5] / stats[:, 0] < lim4)[0]
p100 = np.where(stats[:, 5] / stats[:, 0] < 1)[0]
elif use_error == -999:
p10 = np.where(
np.minimum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim1)[0]
p20 = np.where(
np.minimum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim2)[0]
p30 = np.where(
np.minimum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim3)[0]
p50 = np.where(
np.minimum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim4)[0]
p100 = np.where(
np.minimum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < 1)[0]
else:
p10 = np.where(
np.maximum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim1)[0]
p20 = np.where(
np.maximum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim2)[0]
p30 = np.where(
np.maximum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim3)[0]
p50 = np.where(
np.maximum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < lim4)[0]
p100 = np.where(
np.maximum(stats[:, 5], -stats[:, 4]) / stats[:, 0] < 1)[0]
p = [p10, p20, p30, p50, p100]
#--------------------------------------------
if not extended and TCA is not None:
print(len(p10), len(p20), len(p30), len(p50), len(p100), len(stats),
len(ngm))
else:
print(len(p10), len(p20), len(p30), len(p50), len(p100), len(stats),
len(np.unique(ID)))
#--------------------------------------------
#setup figure
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
plt.subplots_adjust(top=0.9, bottom=0.16)
#--------------------------------------------
#--------------------------------------------
#defines a scale for the bins
if ty == 'Mass' or ty == 'm':
if log:
plt.xscale('log')
bins = 1.3**np.arange(0, 20) * 0.05
elif logbin:
d = 1.25**np.arange(0, 11) * 0.05
bins = [np.sum(d[:i]) + 0.05 for i in range(len(d))]
else:
bins = 0.05 + 0.05 * np.arange(0, 30)
elif ty == 'c':
bins = [0, 1 / 3, 2 / 3, 1, 4 / 3, 5 / 3, 2, 3, 4, 5, 6, 7]
#bins = 0.5*np.arange(14)
#bins = 0.35 * 20 ** (np.arange(-2,5)/4)
print(bins)
elif 'G' in ty:
bins = 8 + np.arange(0, 11)
else:
bins = 10
#--------------------------------------------
#--------------------------------------------
#setup colores and hatches
col_list = [[0.6, 0.6, 0.6, 0.6], [1, 0, 0, 1], [1, 1, 0, 1], [0, 0, 1, 1],
[0, 1, 0, 1]]
color = iter(color_own(col_list))
hatch = iter(['', '//', 'x', '\\', '|'])
if dark:
black = color_own([0.7, 0.7, 0.7, 1])
else:
black = color_own([0., 0., 0., 1])
#--------------------------------------------
#--------------------------------------------
# plot input distribution
if ty == 'Mass' or ty == 'm':
if not extended and TCA is not None:
plt.hist(stats_in[ngm, 0],
bins=bins,
ls='-',
histtype='step',
linewidth=2,
color=black,
label='5 years sample',
zorder=-2)
if future and TCA is not None:
q = plt.hist(stats_in[ngm, 0],
bins=bins,
histtype='step',
color=black,
linewidth=5,
label='future events',
zorder=55)
else:
q = plt.hist(stats_in[:, 0],
bins=bins,
histtype='step',
color=black,
linewidth=5,
label='10 years sample',
zorder=55)
else:
q = plt.hist(stats_plot,
bins=bins,
histtype='step',
color=black,
linewidth=5,
label='10 years sample',
zorder=55)
#--------------------------------------------
#--------------------------------------------
# plot distribution for each limit
for j in range(4, -1, -1):
c = next(color)
h = next(hatch)
if len(p[j] > 0):
if vary_bool:
print(len(p[j]))
plt.hist(stats_plot_v[p[j]], bins = bins, color = c, hatch = h, rwidth = 0.9, \
label = '$\sigma < %i\%s$'%(100*lim[j],'%'))
else:
plt.hist(stats_plot[p[j]], bins = bins, color = c, hatch = h, rwidth = 0.9, \
label = '$\sigma < %i\%s$'%(100*lim[j],'%'))
#--------------------------------------------
#--------------------------------------------
#scale y axis with labels
if max(q[0]) < ylim[1]:
ylim = plt.ylim()
n = np.where(q[0] > ylim[1])[0]
if ylim[1] > 20:
y_minor_ticks = np.arange(ylim[0], ylim[1] + 5, 1)
y_major_ticks = np.arange(ylim[0] + 10, ylim[1] + 10, 10)
else:
y_minor_ticks = np.arange(ylim[0], ylim[1] + 5, 1)
y_major_ticks = np.arange(ylim[0] + 2, ylim[1] + 5, 2)
#--------------------------------------------
#--------------------------------------------
#setup axis and labels
ax.tick_params(axis='both', which='major', labelsize=20)
ax.tick_params(axis='both', which='minor', labelsize=0)
ax.set_yticks(y_major_ticks)
ax.set_yticks(y_minor_ticks, minor=True)
if 'G' in ty:
plt.xticks([(round(b, 2)) for b in bins],
["%i" % (round(b, 2)) for b in bins],
fontsize=20)
elif not isinstance(bins, int):
plt.xticks([(round(b, 2)) for b in bins],
["%0.2f" % (round(b, 2)) for b in bins],
fontsize=20)
for tick in ax.get_xticklabels():
tick.set_rotation(90)
plt.yticks(fontsize=25)
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed', linewidth=3, zorder=-20)
for j in n:
plt.text((q[1][j]+q[1][j+1])/2, ylim[1]-2,str(int(q[0][j])), verticalalignment = 'top',\
horizontalalignment = 'center', fontsize = 16)
if ty == 'Mass' or ty == 'm': plt.xlim(xlim)
plt.ylim(ylim)
if 'c' in ty:
plt.xlabel('$\phi_{min}\,$["]', fontsize=30)
plt.legend(fontsize=25, loc=1)
plt.title('Impact parameter', fontsize=40)
elif 'G' in ty:
plt.xlabel('$G\,[mag]$', fontsize=30)
plt.legend(fontsize=25, loc=2)
plt.title('G magnitude (source)', fontsize=40)
elif ty == 'Mass' or ty == 'm':
plt.xlabel('$M\,[M_{\odot}]$', fontsize=30)
plt.legend(fontsize=25, loc=1)
if future: plt.title('Future events', fontsize=40)
elif extended: plt.title('Extended mission', fontsize=40)
else: plt.title('Nominal mission', fontsize=40)
else: plt.legend(fontsize=25, loc=0)
plt.ylabel('#', fontsize=30)
#--------------------------------------------
#--------------------------------------------
#save figure
if ty != 'Mass' and ty != 'm':
string = string + '_' + ty
fig.savefig(imagepath + string + '.png', format='png')
if paperpath is not None:
fig.savefig(paperpath + string + '.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
plt.close(fig)
def PlotMassInOut(stats_vary, string='mass_input_output'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
if string == '':
string = 'mass_input_output'
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
else:
black = color_own([0., 0., 0., 1])
gray = color_own([0.5, 0.5, 0.5, 1])
if isinstance(stats[0][0][0], int):
m_minus = np.array([i[0][3] for i in stats])
stats = [stats[j] for j in np.where(m_minus > 0)[0]]
print(len(stats))
fig = plt.figure(figsize=(12, 8))
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
color = iter(color_own(len(stats) + 1))
for i in range(len(stats)):
if isinstance(stats[i][0][0], int):
mass = stats[i][0][2]
out_mass = stats[i][0][4]
sig_p_mass = stats[i][0][7]
sig_m_mass = -stats[i][0][6]
if out_mass > mass: sig_mass = sig_m_mass
else: sig_mass = sig_p_mass
else:
mass = np.array([j[0][2] for j in stats[i]])
out_mass = np.array([j[0][4] for j in stats[i]])
sig_p_mass = np.array([j[0][7] for j in stats[i]])
sig_m_mass = np.array([-j[0][6] for j in stats[i]])
sig_mass = sig_p_mass
sig_mass[np.where((out_mass - mass) > 0)] = sig_m_mass[np.where(
(out_mass - mass) > 0)]
order = np.argsort(mass)
out_mass = out_mass[order]
mass = mass[order]
sig_mass = sig_mass[order]
#if k != 0: mass = mass/mass[-1]
c = next(color)
plt.errorbar(mass,
out_mass,
yerr=sig_mass / np.sqrt(499),
fmt='o',
c='k')
t = ax1.get_xlim()
plt.plot(t, t, '-', color=gray, linewidth=4, zorder=-50)
plt.yticks(fontsize=25)
ax2 = plt.subplot2grid((3, 1), (2, 0))
fig.subplots_adjust(hspace=0.1)
color = iter(color_own(len(stats) + 1))
q = []
for i in range(len(stats)):
if isinstance(stats[i][0][0], int):
mass = stats[i][0][2]
out_mass = stats[i][0][4]
sig_p_mass = stats[i][0][7]
sig_m_mass = -stats[i][0][6]
if out_mass > mass: sig_mass = sig_p_mass
else: sig_mass = sig_p_mass
else:
mass = np.array([j[0][2] for j in stats[i]])
out_mass = np.array([j[0][4] for j in stats[i]])
sig_p_mass = np.array([j[0][7] for j in stats[i]])
sig_m_mass = np.array([-j[0][6] for j in stats[i]])
sig_mass = sig_p_mass
sig_mass[np.where((out_mass - mass) > 0)] = sig_m_mass[np.where(
(out_mass - mass) > 0)]
order = np.argsort(mass)
out_mass = out_mass[order]
mass = mass[order]
c = next(color)
q.append((out_mass - mass) / sig_mass * np.sqrt(499))
plt.plot(mass, (out_mass - mass) / sig_mass * np.sqrt(499), '.', c='k')
plt.plot(t, [0, 0], '-', color=gray, linewidth=4, zorder=-50)
plt.plot(t, [-1, -1], '--', color=gray, linewidth=3, zorder=-50)
plt.plot(t, [1, 1], '--', color=gray, linewidth=3, zorder=-50)
q = np.hstack(q)
print(np.mean(q), np.median(q))
ax1.set_xticklabels([])
ax1.tick_params(
axis='y',
which='major',
labelsize=25,
top=False,
bottom=False,
)
ax1.tick_params(
axis='y',
which='minor',
labelsize=15,
top=False,
bottom=False,
)
ax2.tick_params(axis='both', which='major', labelsize=25)
ax2.tick_params(axis='both', which='minor', labelsize=15)
ax2.set_xlabel('$M_{in}\,[M_{\odot}]$', fontsize=30)
ax1.set_ylabel('$M_{out}\,[M_{\odot}]$', fontsize=30)
ax2.set_ylabel('$\Delta M/\sigma\,M$', fontsize=30)
t = ax1.get_xlim()
plt.ylim([-5, 5])
fig.savefig(imagepath + string + '.png', format='png')
print('Create Image: ' + imagepath + string + '.png')
plt.close(fig)
def PlotMassAccuracy(
stats_vary,
ty='mass',
string='',
percent=False,
):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
'''
creats an mass accuracy plot for good events with vary masses
'''
'''
stats_vary: (result from Analysis(vary =True)
0D == Data, Names
1D == events
2D == different inputs
3D == different parameters
4D == num, type, init, 16p, 50p,84p, sig_m,sig_p
'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
else:
black = color_own([0., 0., 0., 1])
if ty == 'mass' or ty == 'm':
if string == '':
string = 'mass_accuracy_plot_mass'
k = 0
if ty == 'ra':
if string == '':
string = 'mass_accuracy_plot_ra'
k = 1
if ty == 'dec':
if string == '':
string = 'mass_accuracy_plot_dec'
k = 2
if ty == 'pmra':
if string == '':
string = 'mass_accuracy_plot_pmra'
k = 3
if ty == 'pmdec':
if string == '':
string = 'mass_accuracy_plot_pmdec'
k = 4
if ty == 'px':
if string == '':
string = 'mass_accuracy_plot_px'
k = 5
if ty == 'c':
if string == '':
string = 'mass_accuracy_plot_closest_aproach'
k = 6
if len(stats_vary) > 1:
stats = stats_vary[0]
Eventnames = stats_vary[1]
Events = stats_vary[2]
else:
stats = stats_vary[0]
Eventnames = None
#pic = np.arange(len(Eventnames))
#pic = np.random.choice(pic,30,replace = False)
#print(pic)
pic = np.array([63, 6, 46, 23, 50, 54, 39, 2, 115, 104, 4, 24, 87, 91])
#pic = np.array([ 128, 38, 62, 86, 24, 72, 93, 70, 37, 84, 148, 149, 93,53, 147, 31, 90, 24, 18])
pic = np.unique(np.concatenate((pic, pic + 10), axis=0))
pic = pic[np.where(pic < len(Events))]
q = np.array([stats[i][0][0][7] for i in pic])
q2 = np.array([stats[i][0][0][2] for i in pic])
#pic = pic[np.where(q > 0.05)]
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
color = iter(color_own(len(pic) + 2))
for i in pic:
if k != 0:
if k == 6:
Obsdate, Scandir, n_ccd = RealData.getRealObs(Events[i][0],
extended=True)
dra = np.array([j[1][2] - j[6][2]
for j in stats[i]]).reshape([1, -1])
ddec = np.array([j[2][2] - j[7][2]
for j in stats[i]]).reshape([1, -1])
dpmra = np.array([j[3][2] - j[8][2]
for j in stats[i]]).reshape([1, -1])
dpmdec = np.array([j[4][2] - j[9][2]
for j in stats[i]]).reshape([1, -1])
dpx = np.array([j[5][2] - j[10][2]
for j in stats[i]]).reshape([1, -1])
cd = np.array([cosdeg(j[2][2])
for j in stats[i]]).reshape([1, -1])
tt = np.array(Obsdate).reshape([-1, 1])
#np.abs((dra**3600000*cd+tt*dpmra)*s_scandir + (dec).reshape(-1,1) * sc_scandir[::-1])
tmin = int(
np.mean(
np.argmin(np.sqrt(
np.square(dra * 3600000 * cd + tt * dpmra) +
np.square(ddec * 3600000 + tt * dpmdec)),
axis=0)))
tt = tt[max([0, tmin - 10]):min([len(tt) - 1, tmin +
10])].reshape([-1, 1])
Scandir = Scandir[max([0, tmin -
10]):min([len(Scandir) - 1, tmin +
10])].reshape([-1, 1])
s_scandir = np.sin(np.array(Scandir).reshape([-1, 1]))
c_scandir = np.sin(np.array(Scandir).reshape([-1, 1]))
mass = np.array([j[0][2] for j in stats[i]]).reshape([1, -1])
TE = Events[i][0].getMass(
) * dpx * const_Einsteinradius * const_Einsteinradius
in_par = np.min(np.sqrt(
np.square(dra * 3600000 * cd + tt * dpmra) +
np.square((ddec * 3600000 + tt * dpmdec))) / np.sqrt(TE),
axis=0)
#in_par = np.min(np.sqrt(np.square(dra*3600000*cd+tt*dpmra)+np.square((ddec*3600000+tt*dpmdec))), axis = 0)
#in_par = in_par-np.min(in_par)
#in_par = in_par/np.mean(in_par)
if np.max(in_par) > 2000: continue
else:
in_par = np.array([
j[k][2] - j[k + 5][2] - stats[i][0][k][2] +
stats[i][0][k + 5][2] for j in stats[i]
])
else:
in_par = np.array([j[k][2] for j in stats[i]])
mass = np.array([j[0][2] for j in stats[i]])
sig_m = np.array([j[0][6] for j in stats[i]])
sig_p = np.array([j[0][7] for j in stats[i]])
order = np.argsort(in_par)
sig_m = sig_m[order]
sig_p = sig_p[order]
in_par = in_par[order]
#if k != 0: mass = mass/mass[-1]
if min(in_par) > 0 or k != 0:
c = next(color)
a = np.where(sig_p > np.mean(sig_p))
b = np.where(sig_p <= np.mean(sig_p))
ta = np.argsort(in_par[a])
tb = np.argsort(-in_par[b])
a2 = np.where(sig_p > np.mean(sig_m))
b2 = np.where(sig_p <= np.mean(sig_m))
ta2 = np.argsort(in_par[a2])
tb2 = np.argsort(-in_par[b2])
#plt.text(mass[0],sig_p[0] , str(i), fontsize = 20,verticalalignment = 'center',horizontalalignment = 'center')
p1 = Polygon(
np.vstack([[np.hstack([in_par[a][ta], in_par[b][tb]])],
[np.hstack([sig_p[a][ta], sig_p[b][tb]])]]).T, True)
p = PatchCollection([
p1,
], color=c, alpha=0.7, linewidth=4)
ax.add_collection(p)
if k == 0:
if Eventnames is None:
if percent:
plt.loglog(in_par,
sig_p / mass,
'.',
c=c,
markersize=15)
else:
plt.loglog(in_par, sig_p, '.', c=c, markersize=15)
else:
if percent:
plt.loglog(in_par,
sig_p / mass,
'.',
c=c,
label=Eventnames[i],
markersize=15)
else:
plt.loglog(in_par,
sig_p,
'.',
c=c,
label=Eventnames[i],
markersize=15)
else:
if Eventnames is None:
if percent:
plt.plot(in_par, sig_p / mass, '.', c=c, markersize=15)
else:
plt.plot(in_par, sig_p, '.', c=c, markersize=15)
else:
if percent:
plt.plot(in_par,
sig_p / mass,
'.',
c=c,
label=Eventnames[i],
markersize=15)
else:
plt.plot(in_par,
sig_p,
'.',
c=c,
label=Eventnames[i],
markersize=15)
ax.grid(axis='x', linewidth=3, zorder=-50)
xlim = np.array(plt.xlim())
xlim = np.array([xlim[0], 2.0])
ylim = plt.ylim()
if k == 0:
for i in [0.15, 0.3, 0.5, 1]:
plt.plot(xlim,
xlim * i,
color=plt.rcParams['grid.color'],
linewidth=3,
zorder=-50)
if i != 1:
plt.text(2.3,
2.2 * i,
str(int(i * 100)) + '%',
fontsize=25,
verticalalignment='center',
horizontalalignment='center')
else:
plt.text(ylim[1] * 1.15,
ylim[1] * 1.15,
str(int(i * 100)) + '%',
fontsize=25,
verticalalignment='center',
horizontalalignment='center')
plt.xlim(xlim)
plt.ylim(ylim)
ax.tick_params(axis='both', which='major', labelsize=25)
ax.tick_params(axis='both', which='minor', labelsize=0)
plt.xticks([0.1, 0.2, 0.4, 1, 2], [0.1, 0.2, 0.4, 1.0, 2.0],
fontsize=25)
plt.xticks(fontsize=25)
plt.yticks([0.02, 0.05, 0.1, 0.2, 0.4, 1],
[0.02, 0.05, 0.1, 0.2, 0.4, 1.0],
fontsize=25)
plt.yticks(fontsize=25)
if k == 6:
plt.xlabel(r'$\delta\theta_{min}\,\, [\theta_{E}]$', fontsize=30)
#plt.xlim([50,100])
#plt.ylim([0,.5])
ax.grid(axis='y', linewidth=3, zorder=-50)
elif k != 0:
plt.xlabel(stats[0][0][k][1], fontsize=30)
else:
plt.xlabel('$M\,[M_{\odot}]$', fontsize=30)
if percent:
plt.ylabel('$\sigma_{M}\,[\%]$', fontsize=30)
fig.savefig(imagepath + string + '_percent_' + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '_percent_' + '.png')
print('Create Image: ' + imagepath + string + '_percent_' + '.png')
else:
plt.ylabel('$\sigma_{M}\,[M_{\odot}]$', fontsize=30)
fig.savefig(imagepath + string + '.png')
if paperpath is not None: fig.savefig(paperpath + string + '.png')
print('Create Image: ' + imagepath + string + '.png')
plt.close(fig)
def PlotHistSingle(dat,
dat_stat,
dat_init,
Folder='',
string='Event',
par_type='Mass'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
'''
Plot histogram for an given fit parameter
'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
color = color_own([0., 1, 1, 1])
else:
black = color_own([0., 0., 0., 1])
color = color_own([0., 1, 1, 1])
rng = dat_stat[1] + 4 * dat_stat[3], dat_stat[1] + 4 * dat_stat[4]
fig = plt.figure(figsize=(12, 10))
#fig = plt.figure(figsize = (7,6))
ax = fig.add_subplot(111)
plt.subplots_adjust(top=0.9,
bottom=0.15,
left=0.13,
right=0.92,
hspace=0.2,
wspace=0.2)
k = plt.hist(dat, bins=25, range=rng, color=color, rwidth=0.9)
y = [
max(k[0]) / 2,
] * 3
plt.plot(dat_stat[0:3],
y,
'x-',
color=color_own([1, 0.5, 0, 1]),
linewidth=5,
mew=6,
markersize=30)
plt.plot((dat_init, dat_init), (0, max(k[0])),
'-',
color=color_own([1, 0, 0, 1]),
linewidth=10)
u = plt.ylim()
plt.ylim([u[0], 1.1 * u[1]])
if par_type == 'Mass':
plt.text(dat_init,u[1]*1.05,'$M = %.2f\,M_{\odot}$'%dat_init, verticalalignment = 'top',\
horizontalalignment = 'center', fontsize = 40)
if par_type == 'Mass': plt.xlabel('$M\,[M_{\odot}]$', fontsize=50)
plt.ylabel('#', fontsize=50)
ax.tick_params(axis='both', which='major', labelsize=30)
ax.tick_params(axis='both', which='minor', labelsize=0)
plt.xticks(fontsize=40)
plt.yticks(fontsize=40)
# Save files
if len(Folder) != 0:
if Folder[-1] != '/': Folder = Folder + '/'
if not os.path.isdir(imagepath + Folder): os.mkdir(imagepath + Folder)
if not os.path.isdir(imagepath + Folder + 'Good_Events/'):
os.mkdir(imagepath + Folder + 'Good_Events/')
if not os.path.isdir(imagepath + Folder + 'All_Events/'):
os.mkdir(imagepath + Folder + 'All_Events/')
if dat_stat[0] > 0:
#fig.savefig(imagepath + 'Good_Events/' + string)
for acc in [15, 30, 50, 100]:
if (dat_init - dat_stat[0]) / dat_init < acc / 100.:
axrange = ax.get_xlim()
center = (axrange[0] + axrange[1]) / 2
#fac = 2 * (100-acc)/85+ 1 * (acc-15)/85
if acc == 15:
fac = 3
elif acc == 30:
fac = 2
elif acc == 50:
fac = 1.5
else:
fac = 1
axrange_new = [(axrange[0]-center)*fac + center, \
(axrange[1] - center) * fac + center]
plt.xlim(axrange_new)
if not os.path.isdir(imagepath + Folder +
'Good_Events/p%i/' % acc):
os.mkdir(imagepath + Folder + 'Good_Events/p%i/' % acc)
fig.savefig(imagepath + Folder + 'Good_Events/p%i/' % acc +
string)
break
if string == 'Event_2390377345808152832_0':
plt.xlim([-1, 2])
fig.savefig(paperpath + 'Event_10')
if string == 'Event_4293318823182081408_0':
fig.savefig(paperpath + 'Event_70')
if string == 'Event_4451575895403432064_0':
fig.savefig(paperpath + 'Event_30')
if string == 'Event_470826482635701376_3':
fig.savefig(paperpath + 'Event_400')
fig.savefig(imagepath + Folder + 'All_Events/' + string)
plt.close(fig)
def PlotDifferentIdeas(dat_single,dat_multi_all,dat_multi_fps,dat_multi_sig, \
string = 'Diff_ID', k = None, Folder = 'Multi_events/'):
'''------------------------------------------------------------
Description:
---------------------------------------------------------------
Input:
---------------------------------------------------------------
Output:
------------------------------------------------------------'''
dark = 'black' in plt.rcParams['savefig.facecolor']
if dark:
string = 'dark_' + string
black = color_own([0.7, 0.7, 0.7, 1])
cc = color_own([[0.6, 1.2, 0, 0.9], [0, 1, 1, 0.9], [1, 1, 0, 0.9],
[1, 0, 1, 0.9]])
else:
black = color_own([0., 0., 0., 1])
cc = color_own([[0, 1, 0, 0.9], [0, 1, 1, 0.9], [1, .5, 0, 0.9],
[1, 0, 0, 0.9]])
t = [0, 0, 0, 0, 0, 0]
#match_events betwenn differnt methods
ev = []
c = 0
try:
a = dat_single[0][0][0][0][0]
except:
error = 1
else:
error = 3
ti = time.time()
for i in range(len(dat_multi_all[1])):
c += 1
event_id = dat_multi_all[1][i]
m = dat_multi_all[2][i][0].getMass()
nstars = []
nstars.append(len(dat_multi_all[2][i]) - 1)
if error == 3:
ev1 = [
event_id,
[
dat_multi_all[0][i][x][0]
for x in range(len(dat_multi_all[0][i]))
]
]
else:
ev1 = [event_id, dat_multi_all[0][i][0]]
for j in range(len(dat_multi_fps[1])):
if dat_multi_fps[1][j] == event_id:
if error == 3:
ev1.append([
dat_multi_fps[0][j][x][0]
for x in range(len(dat_multi_fps[0][j]))
])
nstars.append(len(dat_multi_fps[2][j]) - 1)
else:
ev1.append(dat_multi_fps[0][j][0])
nstars.append(len(dat_multi_fps[2][j]) - 1)
if len(ev1) == 2:
ev1.append(None)
nstars.append(0)
for j in range(len(dat_multi_sig[1])):
if dat_multi_sig[1][j] == event_id:
if error == 3:
ev1.append([
dat_multi_sig[0][j][x][0]
for x in range(len(dat_multi_sig[0][j]))
])
nstars.append(len(dat_multi_sig[2][j]) - 1)
else:
ev1.append(dat_multi_sig[0][j][0])
nstars.append(len(dat_multi_sig[2][j]) - 1)
if len(ev1) == 3:
ev1.append(None)
nstars.append(0)
acc = -999
ind = -1
counter = 0
for j in range(len(dat_single[1])):
if dat_single[1][j] == event_id:
counter += 1
if ind == -1:
if error == 3:
acc = percentile(np.array([dat_single[0][j][x][0][6]/dat_single[0][j][x][0][2] \
for x in range(len(dat_single[0][j]))]))[1]
else:
acc = dat_single[0][j][0][3]
ind = j
else:
if error == 3:
pc = percentile(np.array([dat_single[0][j][x][0][6]/dat_single[0][j][x][0][2] \
for x in range(len(dat_single[0][j]))]))[1]
if pc > acc:
acc = pc
ind = j
else:
if dat_single[0][j][0][3] > acc:
acc = dat_single[0][j][0][3]
ind = j
if ind != -1:
nstars.append(1)
if error == 3:
ev1.append([
dat_single[0][ind][x][0]
for x in range(len(dat_single[0][ind]))
])
#if percentile(np.array([dat_multi_all[0][i][x][0][6]/dat_multi_all[0][i][x][0][3] \
# for x in range(len(dat_multi_all[0][i]))]))[1] > -1:
else:
ev1.append(dat_single[0][ind][0])
#if dat_multi_all[0][i][0][3] > 0:
else:
ev1.append(None)
nstars.append(0)
ev1.append(nstars)
ev1.append(m)
ev.append(ev1)
t[0] = time.time() - ti
t0 = time.time()
#select_events
#plot line
if len(Folder) != 0:
if Folder[-1] != '/': Folder + '/'
if not os.path.isdir(imagepath + Folder): os.mkdir(imagepath + Folder)
outstrings = []
if k is None:
k = np.arange(len(ev))
for kk in range(len(k)):
ti = time.time()
fig = plt.figure(figsize=(12, 11))
ax = fig.add_subplot(111)
plt.subplots_adjust(top=0.9,
bottom=0.1,
left=0.16,
right=0.9,
hspace=0.2,
wspace=0.2)
i = abs(k[kk])
ax.tick_params(axis='both', which='major', labelsize=25)
ax.tick_params(axis='both', which='minor', labelsize=0)
#if kk%2 == 0:
ax.set_ylabel('$\Delta M \,/\, M_{int} \,[\%]$', fontsize=30)
ax.set_xlabel('Number of used background stars', fontsize=30)
ax.set_title(name(ev[i][0]), fontsize=40)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True)
xx = []
num = []
if error == 3:
#for k in range(50):
ll = 0.2
color = iter(cc)
t[4] = time.time() - ti
print(name(ev[i][0]), ev[i][-1])
for j in range(1, 5):
ti = time.time()
sep = 0.5 * j
c = next(color)
good = True
if j < 3:
if ev[i][-2][j - 1] == ev[i][-2][j]: good = False
if good:
if ev[i][j] is not None:
mm = np.array([
ev[i][j][x][6] / ev[i][j][x][2] * 100
for x in range(len(ev[i][j]))
if ev[i][j][x][4] != -999
])
mp = np.array([
ev[i][j][x][7] / ev[i][j][x][2] * 100
for x in range(len(ev[i][j]))
if ev[i][j][x][4] != -999
])
mm1 = mm[np.where(mm > -200)]
mp1 = mp[np.where(mp < 200)]
mm1 = mm1[np.where(mm1 < 0)]
mp1 = mp1[np.where(mp1 > 0)]
parts = ax.violinplot([mp1],
positions=[sep],
showextrema=False)
for pc in parts['bodies']:
pc.set_facecolor(c)
pc.set_edgecolor(c)
pc.set_alpha(0.7)
pc.set_zorder(-0.1 * j)
rm = percentile(mm)
rp = percentile(mp)
plt.plot([sep, sep, sep],
rp[0:3],
'_-',
c=black,
markersize=30,
linewidth=8,
markeredgewidth=8)
print(j, np.array(rp[0:3]) * ev[i][-1] / 100, rp[0:3])
if j == 4:
xlim = ax.get_xlim()
plt.plot([0, sep * 5 / 4], [rp[1], rp[1]],
linestyle='--',
dashes=(10, 5),
linewidth=5,
color=black,
zorder=-20)
ylim = ax.get_xlim()
xx.append(sep)
num.append(str(ev[i][-2][j - 1]))
#plt.text(sep, ylim[0],str(ev[i][-1][j-1]), verticalalignment = 'bottom',\
# horizontalalignment = 'right', fontsize = 40)
t[2] += time.time() - ti
else:
xx.append(sep)
num.append(' ')
else:
xx.append(sep)
num.append(' ')
else:
alpha = 1
ll = 5
color=iter(([0, 0., 1., alpha ],\
[0, 1, 0., alpha ], \
[1, 0, 0.5, alpha ], \
[1., 0.5,0, alpha ]))
for j in range(1, len(ev[i])):
c = next(color)
if ev[i][j][0] is not None:
m_minus, m, mplus = ev[i][j][3:6]
m = ev[i][j][2]
plt.plot([sep, sep, sep], [m_minus / m, 1, mplus / m],
'.',
c=c,
linewidth=ll)
ax.grid(axis='both', linewidth=3, zorder=-50000)
plt.xlim([0, sep / 4 * 5])
ti = time.time()
plt.xticks(xx, num)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
nn = ''.join(('_'.join(name(ev[i][0]).split(' '))).split(':'))
fig.savefig(imagepath + Folder + string + '_' + nn + '.png',
format='png')
outstrings.append(imagepath + Folder + string + '_' + nn + '.png')
if paperpath is not None:
fig.savefig(paperpath + string + '_' + nn + '.png', format='png')
print('Create Image: ' + imagepath + Folder + string + '_' + nn +
'.png')
plt.close(fig)
t[5] += time.time() - ti
t[1] = time.time() - t0
return outstrings
