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plot_for_lamda_and_gamma.py
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Created
Tue, Oct 8, 06:04
Size
4 KB
Mime Type
text/x-python
Expires
Thu, Oct 10, 06:04 (2 d)
Engine
blob
Format
Raw Data
Handle
21474301
Attached To
R6289 Motion correction paper
plot_for_lamda_and_gamma.py
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from
matplotlib
import
pyplot
as
plt
from
mpl_toolkits.mplot3d
import
Axes3D
import
numpy
as
np
#plt.rc('text', usetex=True)
#plt.rc('font', family='serif')
fig1
=
plt
.
figure
(
1
)
fig2
=
plt
.
figure
(
2
)
ax2
=
fig2
.
add_subplot
(
111
,
projection
=
'3d'
)
#folder = '/scratch/aymanns/Data_R57C10s/2_170608_fly1_007/results/'
#gs = [0,10,20,30,40,50,60,70,80,90,100]
#ls = [1000,2000,3000,4000,5000,6000,7000,8000,9000,10000]
#folder = '/scratch/aymanns/Data_R57C10s/4_170609_fly1_000/results/'
gs_coarse
=
[
0
,
10
,
20
,
30
,
40
,
50
,
60
,
70
,
80
,
90
,
100
]
ls_coarse
=
[
1000
,
2000
,
3000
,
4000
,
5000
,
6000
,
7000
,
8000
,
9000
,
10000
]
gs_fine
=
[
0
,
2
,
4
,
6
,
8
,
10
,
12
,
14
,
16
,
18
,
20
]
ls_fine
=
[
100
,
200
,
300
,
400
,
500
,
600
,
700
,
800
,
900
,
1000
]
#folder = '/scratch/aymanns/Data_R57C10s/1_170426_fly1_013/results/'
#gs = [0,10,20,30,40,50,100]
#ls = [1000,5000,10000]
#gs = [10]
#ls = [1000,2000,3000,4000,5000,10000]
experiments
=
[
'1_170426_fly1_013'
,
'2_170608_fly1_007'
,
'4_170609_fly1_000'
]
z
=
np
.
zeros
((
len
(
experiments
),
len
(
gs_coarse
)
+
len
(
gs_fine
),
len
(
ls_coarse
)))
for
experiment
,
eidx
in
zip
(
experiments
,
range
(
len
(
experiments
))):
folder
=
'/scratch/aymanns/Data_R57C10s/'
+
experiment
+
'/results/'
i
=
0
j
=
0
for
g
in
gs_coarse
:
for
l
in
ls_coarse
:
try
:
#plt.figure(1)
#cY = np.loadtxt(folder+'l{}g{}/cY_Out1.dat'.format(l,g))
#plt.plot(cY,label='l={}, g={}'.format(l,g))
#plt.figure(2)
ng
=
np
.
loadtxt
(
folder
+
'l{}g{}/ng_Out1.dat'
.
format
(
l
,
g
))
#ax2.scatter(g,l,ng)
#ax2.text(g,l,ng+13000, str(ng), 'y')
#ax2.plot([g,g],[l,l],[0,ng],color='gray',linestyle=':')
z
[
eidx
,
i
,
j
]
=
ng
except
FileNotFoundError
:
print
(
experiment
+
' l={} g={} does not exist'
.
format
(
l
,
g
))
#if g == 0:
# plt.figure(3)
# plt.scatter(l, ng)
j
+=
1
j
=
0
i
+=
1
i
=
0
j
=
0
for
g
in
gs_fine
:
for
l
in
ls_fine
:
try
:
ng
=
np
.
loadtxt
(
folder
+
'l{}g{}/ng_Out1.dat'
.
format
(
l
,
g
))
z
[
eidx
,
i
+
len
(
gs_coarse
),
j
]
=
ng
except
FileNotFoundError
:
print
(
experiment
+
' l={} g={} does not exist'
.
format
(
l
,
g
))
j
+=
1
j
=
0
i
+=
1
#plt.figure(1)
#plt.legend()
#plt.xlabel('frames')
#plt.ylabel('correlation with mean')
#plt.savefig('correlation.pdf')
#plt.show()
#
#plt.figure(2)
#plt.xlabel('gamma')
#plt.ylabel('lambda')
#ax2.set_zlabel('gradient of mean')
##ax2.set_zlim(bottom=0)
#plt.savefig('gradient.pdf')
#plt.show()
vmin
=
min
(
z
.
flatten
())
vmax
=
max
(
z
.
flatten
())
vmin
=
0
vmax
=
1
for
eidx
in
range
(
len
(
experiments
)):
mini
=
min
(
z
[
eidx
,:
len
(
gs_coarse
)]
.
flatten
())
z
[
eidx
,:
len
(
gs_coarse
)]
=
z
[
eidx
,:
len
(
gs_coarse
)]
-
mini
scaling_factor
=
max
(
z
[
eidx
,:
len
(
gs_coarse
)]
.
flatten
())
z
[
eidx
,:
len
(
gs_coarse
)]
=
z
[
eidx
,:
len
(
gs_coarse
)]
/
scaling_factor
mini
=
min
(
z
[
eidx
,
len
(
gs_coarse
):]
.
flatten
())
z
[
eidx
,
len
(
gs_coarse
):]
=
z
[
eidx
,
len
(
gs_coarse
):]
-
mini
scaling_factor
=
max
(
z
[
eidx
,
len
(
gs_coarse
):]
.
flatten
())
z
[
eidx
,
len
(
gs_coarse
):]
=
z
[
eidx
,
len
(
gs_coarse
):]
/
scaling_factor
for
experiment
,
eidx
in
zip
(
experiments
,
range
(
len
(
experiments
))):
plt
.
figure
()
fig3
,
ax3
=
plt
.
subplots
()
im
=
ax3
.
imshow
(
z
[
eidx
,:
len
(
gs_coarse
)],
vmin
=
vmin
,
vmax
=
vmax
)
cbar
=
ax3
.
figure
.
colorbar
(
im
,
ax
=
ax3
)
cbar
.
ax
.
set_ylabel
(
'Average gradient of mean image'
,
rotation
=-
90
,
va
=
"bottom"
)
idx
=
np
.
unravel_index
(
np
.
argmax
(
z
[
eidx
]),
z
[
eidx
]
.
shape
)
#print(ls[idx[0]],gs[idx[1]])
plt
.
plot
(
idx
[
1
],
idx
[
0
])
ax3
.
plot
(
idx
[
1
],
idx
[
0
],
'rx'
)
ax3
.
set_xticks
(
np
.
arange
(
len
(
ls_coarse
)))
ax3
.
set_yticks
(
np
.
arange
(
len
(
gs_coarse
)))
ax3
.
set_xticklabels
(
ls_coarse
)
ax3
.
set_yticklabels
(
gs_coarse
)
ax3
.
set_ylabel
(
'gamma'
)
ax3
.
set_xlabel
(
'lambda'
)
plt
.
setp
(
ax3
.
get_xticklabels
(),
rotation
=
45
,
ha
=
"right"
,
rotation_mode
=
"anchor"
)
fig3
.
tight_layout
()
plt
.
savefig
(
'coarse_colormap_'
+
experiment
+
'.eps'
)
for
experiment
,
eidx
in
zip
(
experiments
,
range
(
len
(
experiments
))):
plt
.
figure
()
fig3
,
ax3
=
plt
.
subplots
()
im
=
ax3
.
imshow
(
z
[
eidx
,
len
(
gs_coarse
):],
vmin
=
vmin
,
vmax
=
vmax
)
cbar
=
ax3
.
figure
.
colorbar
(
im
,
ax
=
ax3
)
cbar
.
ax
.
set_ylabel
(
'Average gradient of mean image'
,
rotation
=-
90
,
va
=
"bottom"
)
idx
=
np
.
unravel_index
(
np
.
argmax
(
z
[
eidx
,
len
(
gs_coarse
):]),
z
[
eidx
,
len
(
gs_coarse
):]
.
shape
)
#print(ls[idx[0]],gs[idx[1]])
plt
.
plot
(
idx
[
1
],
idx
[
0
])
ax3
.
plot
(
idx
[
1
],
idx
[
0
],
'rx'
)
ax3
.
set_xticks
(
np
.
arange
(
len
(
ls_coarse
)))
ax3
.
set_yticks
(
np
.
arange
(
len
(
gs_coarse
)))
ax3
.
set_xticklabels
(
ls_fine
)
ax3
.
set_yticklabels
(
gs_fine
)
ax3
.
set_ylabel
(
'gamma'
)
ax3
.
set_xlabel
(
'lambda'
)
plt
.
setp
(
ax3
.
get_xticklabels
(),
rotation
=
45
,
ha
=
"right"
,
rotation_mode
=
"anchor"
)
fig3
.
tight_layout
()
plt
.
savefig
(
'fine_colormap_'
+
experiment
+
'.eps'
)
#plt.figure(3)
#plt.xlabel('lambda')
#ax.set_zlabel('gradient of mean')
#plt.savefig('gradient_1D.pdf')
#plt.show()
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