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irp
Fresnel
Commits
bc06658c
Commit
bc06658c
authored
Feb 10, 2021
by
Leon Merten Lohse
Browse files
fix missing wavelength
parent
441a23a0
Changes
3
Hide whitespace changes
Inline
Side-by-side
examples/Test2d.ipynb
View file @
bc06658c
%% Cell type:code id: tags:
```
python
import
numpy
as
np
import
xraylib
as
xrl
import
matplotlib.pyplot
as
plt
import
fresnel.propagate
as
propagate
```
%% Cell type:code id: tags:
```
python
from
platform
import
python_version
print
(
python_version
())
```
%%%% Output: stream
3.8.7
%% Cell type:code id: tags:
```
python
%
matplotlib
inline
```
%% Cell type:code id: tags:
```
python
#xrl.GetCompoundDataNISTByName("Kapton Polyimide Film")
```
%% Cell type:code id: tags:
```
python
# simulation box
energy
=
12.
# keV
wavelength
=
1.2398
/
(
energy
*
1e3
)
*
1e-3
# mm
nx
=
1200
nz
=
10000
wg_radius
=
25e-6
xtot
=
10
*
wg_radius
# mm
ztot
=
0.4
# mm
# units
dx
=
xtot
/
nx
/
wavelength
dz
=
ztot
/
nz
/
wavelength
print
(
f
"dx:
{
dx
}
, dz:
{
dz
}
wavelengths"
)
```
%%%% Output: stream
dx: 2.0164542668172287, dz: 387.1592192289079 wavelengths
%% Cell type:code id: tags:
```
python
# slice thickness according to "Multislice does it all"
eps1
=
0.1
eps2
=
0.1
wl0
=
1
dz_max
=
eps2
/
eps1
**
2
*
dx
**
2
/
wl0
print
(
dz_max
)
```
%%%% Output: stream
40.66087810165406
%% Cell type:code id: tags:
```
python
# materials and geometry
density_Ge
=
xrl
.
ElementDensity
(
xrl
.
SymbolToAtomicNumber
(
'Ge'
))
density_Polyimide
=
1.42
density_Si
=
xrl
.
ElementDensity
(
xrl
.
SymbolToAtomicNumber
(
'Si'
))
n_Ge
=
xrl
.
Refractive_Index
(
'Ge'
,
energy
,
density_Ge
)
n_Si
=
xrl
.
Refractive_Index
(
'Si'
,
energy
,
density_Si
)
n_PI
=
xrl
.
Refractive_Index
(
'Kapton Polyimide Film'
,
energy
,
density_Polyimide
)
xx
=
np
.
linspace
(
-
xtot
/
2
,
xtot
/
2
,
nx
)
core
=
(
np
.
abs
(
xx
)
<
wg_radius
)
cladding
=
~
core
refractive_index
=
n_Ge
*
cladding
+
1
*
core
```
%% Cell type:code id: tags:
```
python
boundary
=
(
0
,
0
,)
u0
=
np
.
exp
(
-
xx
**
2
/
(
2
*
(
2
*
wg_radius
)
**
2
)).
astype
(
np
.
complex128
)
```
%% Cell type:code id: tags:
```
python
propagator
=
propagate
.
FDPropagator2d
(
refractive_index
,
u0
,
dz
,
dx
)
```
%% Cell type:code id: tags:
```
python
field
=
np
.
zeros
((
nz
,
nx
),
dtype
=
np
.
complex128
)
field
[
0
,...]
=
u0
for
iz
in
range
(
1
,
nz
):
boundary
=
(
0
,
0
)
field
[
iz
,
...]
=
propagator
.
step
(
refractive_index
,
boundary
)
```
%% Cell type:code id: tags:
```
python
result
=
field
.
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
[:,:])
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
3
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (mm)'
)
ax
.
set_ylabel
(
r
'$x$ (nm)'
)
```
%%%% Output: execute_result
Text(0, 0.5, '$x$ (nm)')
%%%% Output: display_data

%% Cell type:code id: tags:
```
python
fresnel
propagator
=
propagate
.
MultislicePropagator
(
u0
,
(
dz
,
dx
,))
multislice
propagator
=
propagate
.
MultislicePropagator
(
u0
,
(
dz
,
dx
,))
```
%% Cell type:code id: tags:
```
python
field
=
np
.
zeros
((
nz
,
nx
),
dtype
=
np
.
complex128
)
field
[
0
,...]
=
u0
for
iz
in
range
(
1
,
nz
):
field
[
iz
,
...]
=
fresnelpropagator
.
step
(
refractive_index
)
```
%% Cell type:code id: tags:
```
python
result
=
field
.
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
[:,:])
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
3
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (mm)'
)
ax
.
set_ylabel
(
r
'$x$ (nm)'
)
```
%%%% Output: execute_result
Text(0, 0.5, '$x$ (nm)')
%%%% Output: display_data

%% Cell type:code id: tags:
```
python
```
...
...
examples/Test3d.ipynb
View file @
bc06658c
%% Cell type:code id: tags:
```
python
import
numpy
as
np
import
xraylib
as
xrl
import
matplotlib.pyplot
as
plt
import
fresnel.propagate
as
propagate
```
%% Cell type:code id: tags:
```
python
%
matplotlib
inline
```
%% Cell type:code id: tags:
```
python
energy
=
12.
# keV
# geometry
wavelength
=
1.2398
/
(
energy
*
1e3
)
*
1e-3
# mm
nx
=
1024
ny
=
512
nz
=
800
ztot
=
0.4
# mm
wg_radius
=
25e-6
xtot
=
10
*
wg_radius
# mm
ytot
=
10
*
wg_radius
# units
dx
=
xtot
/
nx
/
wavelength
dy
=
ytot
/
ny
/
wavelength
dz
=
ztot
/
nz
/
wavelength
print
(
f
"dx:
{
dx
}
, dy:
{
dy
}
, dz:
{
dz
}
wavelengths"
)
```
%%%% Output: stream
dx: 2.36303234392644, dy: 4.72606468785288, dz: 4839.490240361349 wavelengths
%% Cell type:code id: tags:
```
python
density_Ge
=
xrl
.
ElementDensity
(
xrl
.
SymbolToAtomicNumber
(
'Ge'
))
density_Polyimide
=
1.42
density_Si
=
xrl
.
ElementDensity
(
xrl
.
SymbolToAtomicNumber
(
'Si'
))
n_Ge
=
xrl
.
Refractive_Index
(
'Ge'
,
energy
,
density_Ge
)
n_Si
=
xrl
.
Refractive_Index
(
'Si'
,
energy
,
density_Si
)
n_PI
=
xrl
.
Refractive_Index
(
'Kapton Polyimide Film'
,
energy
,
density_Polyimide
)
print
(
'Ge'
,
n_Ge
-
1
)
print
(
'Si'
,
n_Si
-
1
)
print
(
'PI'
,
n_PI
-
1
)
xx
=
np
.
linspace
(
-
xtot
/
2
,
xtot
/
2
,
nx
).
reshape
([
1
,
-
1
])
yy
=
np
.
linspace
(
-
ytot
/
2
,
ytot
/
2
,
ny
).
reshape
([
-
1
,
1
])
core
=
(
np
.
abs
(
xx
)
<
wg_radius
)
*
(
np
.
abs
(
yy
)
<
wg_radius
)
cladding
=
~
core
refractive_index
=
n_Ge
*
cladding
+
1
*
core
```
%%%% Output: stream
Ge (-6.426292114225518e-06+7.120128111534574e-07j)
Si (-3.3845180545943876e-06+3.810012288290064e-08j)
PI (-2.1023289580313076e-06+2.2059592416020462e-09j)
%% Cell type:code id: tags:
```
python
u0
=
np
.
exp
(
-
(
xx
**
2
+
yy
**
2
)
/
(
2
*
(
2
*
wg_radius
)
**
2
)).
astype
(
np
.
complex128
)
boundary
=
(
0
,
0
,
0
,
0
)
propagator
=
propagate
.
FDPropagator3d
(
refractive_index
,
u0
,
dz
,
dy
,
dx
)
```
%% Cell type:code id: tags:
```
python
field
=
np
.
zeros
((
nz
,
ny
,
nx
),
dtype
=
np
.
complex128
)
field
[
0
,...]
=
u0
for
iz
in
range
(
1
,
nz
):
boundary
=
(
0
,
0
,
0
,
0
)
field
[
iz
,
...]
=
propagator
.
step
(
refractive_index
,
boundary
)
if
iz
%
100
==
0
:
print
(
iz
)
```
%%%% Output: stream
100
200
300
400
500
600
700
%% Cell type:code id: tags:
```
python
result
=
field
[:,
ny
//
2
,
:].
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
)
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
4
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (mm)'
)
ax
.
set_ylabel
(
r
'$x$ (nm)'
)
result
=
field
[:,:,
nx
//
2
].
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
)
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
4
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (um)'
)
ax
.
set_ylabel
(
r
'$y$ (nm)'
)
```
%%%% Output: execute_result
Text(0, 0.5, '$y$ (nm)')
%%%% Output: display_data

%%%% Output: display_data

%% Cell type:code id: tags:
```
python
fresnel
propagator
=
propagate
.
FresnelPropagator
(
u0
,
(
dz
,
dy
,
dx
))
multislice
propagator
=
propagate
.
FresnelPropagator
(
u0
,
(
dz
,
dy
,
dx
))
```
%% Cell type:code id: tags:
```
python
field
=
np
.
zeros
((
nz
,
ny
,
nx
),
dtype
=
np
.
complex128
)
field
[
0
,...]
=
u0
for
iz
in
range
(
1
,
nz
):
field
[
iz
,
...]
=
fresnel
propagator
.
step
(
refractive_index
)
field
[
iz
,
...]
=
multislice
propagator
.
step
(
refractive_index
)
if
iz
%
100
==
0
:
print
(
iz
)
```
%%%% Output: stream
100
200
300
400
500
600
700
%% Cell type:code id: tags:
```
python
result
=
field
[:,
ny
//
2
,
:].
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
)
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
4
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (mm)'
)
ax
.
set_ylabel
(
r
'$x$ (nm)'
)
result
=
field
[:,:,
nx
//
2
].
transpose
()
fig
,
ax
=
plt
.
subplots
()
im
=
ax
.
imshow
(
np
.
abs
(
result
)
**
2
,
aspect
=
'auto'
,
clim
=
(
0
,
4
),
cmap
=
'cubehelix'
,
extent
=
[
0
,
ztot
,
-
xtot
/
2
*
1e6
,
xtot
/
2
*
1e6
])
ax
.
set_ylim
(
-
2e6
*
wg_radius
,
2e6
*
wg_radius
)
fig
.
colorbar
(
im
)
ax
.
set_xlabel
(
r
'$z$ (um)'
)
ax
.
set_ylabel
(
r
'$y$ (nm)'
)
```
%%%% Output: execute_result
Text(0, 0.5, '$y$ (nm)')
%%%% Output: display_data

%%%% Output: display_data
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