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finesse
pykat
Commits
72a5c650
Commit
72a5c650
authored
Dec 01, 2014
by
Andreas Freise
Browse files
adding fft propagation basics and an example
parent
978dbf2f
Changes
2
Show whitespace changes
Inline
Side-by-side
examples/fft/spatial_filter.py
0 → 100644
View file @
72a5c650
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
from
__future__
import
unicode_literals
import
matplotlib
BACKEND
=
'Qt4Agg'
matplotlib
.
use
(
BACKEND
)
import
pylab
as
pl
from
pykat.utilities.optics.gaussian_beams
import
HG_beam
,
beam_param
from
pykat.fft.fft
import
*
import
numpy
as
np
import
scipy
def
main
():
# wavelength
Lambda
=
1064.0E-9
# distance to propagate/focal length of lens
D
=
10
# mix coefficients
c1
=
0.7
c2
=
0.3
# mode indices
n1
=
2
m1
=
3
n2
=
1
m2
=
0
######## Generate Grid stucture required for FFT propagation ####
xpoints
=
512
ypoints
=
512
xsize
=
0.05
ysize
=
0.05
# Apply offset such that the center of the beam lies in the
# center of a grid tile
xoffset
=
-
0.5
*
xsize
/
xpoints
yoffset
=
-
0.5
*
ysize
/
ypoints
shape
=
grid
(
xpoints
,
ypoints
,
xsize
,
ysize
,
xoffset
,
yoffset
)
x
=
shape
.
xaxis
y
=
shape
.
yaxis
######## Generates a mixture of fields ################
gx
=
beam_param
(
w0
=
2e-3
,
z
=
0
)
gy
=
beam_param
(
w0
=
2e-3
,
z
=
0
)
beam
=
HG_beam
(
gx
,
gy
,
n1
,
m1
)
field1
=
beam
.
Unm
(
x
,
y
)
beam2
=
HG_beam
(
gx
,
gy
,
n2
,
m2
)
field2
=
beam2
.
Unm
(
x
,
y
)
global
field
,
laser1
,
laser2
field
=
np
.
sqrt
(
c1
)
*
field1
+
np
.
sqrt
(
c2
)
*
field2
####### Apply phase plate #######################################
laser1
=
field
*
(
np
.
conjugate
(
field1
))
laser2
=
field
*
(
np
.
conjugate
(
field2
))
####### Propagates the field by FFT ##############################
laser1
=
FFT_propagate
(
laser1
,
shape
,
Lambda
,
D
,
1
)
laser2
=
FFT_propagate
(
laser2
,
shape
,
Lambda
,
D
,
1
)
f
=
D
#laser1 = apply_lens(laser1, shape, Lambda, f)
#laser2 = apply_lens(laser2, shape, Lambda, f)
laser1
=
apply_thin_lens
(
laser1
,
shape
,
Lambda
,
f
)
laser2
=
apply_thin_lens
(
laser2
,
shape
,
Lambda
,
f
)
laser1
=
FFT_propagate
(
laser1
,
shape
,
Lambda
,
D
,
1
)
laser2
=
FFT_propagate
(
laser2
,
shape
,
Lambda
,
D
,
1
)
# midpoint computation for even number of points only!
midx
=
(
xpoints
)
//
2
midy
=
(
ypoints
)
//
2
coef1
=
np
.
abs
(
laser1
[
midx
,
midy
])
coef2
=
np
.
abs
(
laser2
[
midx
,
midy
])
ratio
=
(
coef1
/
coef2
)
**
2
pc2
=
1
/
(
1
+
ratio
)
pc1
=
pc2
*
ratio
print
(
"c1 {0}, coef1 {1}, error {3} (raw output {2})"
.
format
(
c1
,
pc1
,
coef1
,
np
.
abs
(
c1
-
pc1
)))
print
(
"c2 {0}, coef2 {1}, error {3} (raw output {2})"
.
format
(
c2
,
pc2
,
coef2
,
np
.
abs
(
c2
-
pc2
)))
# plot hand tuned for certain ranges and sizes, not automtically scaled
fig
=
pl
.
figure
(
110
)
fig
.
clear
()
off1
=
xpoints
/
10
off2
=
xpoints
/
6
pl
.
subplot
(
1
,
3
,
1
)
pl
.
imshow
(
abs
(
field
))
pl
.
xlim
(
midx
-
off1
,
midx
+
off1
)
pl
.
ylim
(
midy
-
off1
,
midy
+
off1
)
pl
.
draw
()
pl
.
subplot
(
1
,
3
,
2
)
pl
.
imshow
(
abs
(
laser1
))
pl
.
xlim
(
midx
-
off2
,
midx
+
off2
)
pl
.
ylim
(
midy
-
off2
,
midy
+
off2
)
pl
.
draw
()
pl
.
subplot
(
1
,
3
,
3
)
pl
.
imshow
(
abs
(
laser2
))
pl
.
xlim
(
midx
-
off2
,
midx
+
off2
)
pl
.
ylim
(
midy
-
off2
,
midy
+
off2
)
pl
.
draw
()
if
in_ipython
():
pl
.
show
(
block
=
0
)
else
:
pl
.
show
(
block
=
1
)
# testing if the script is run from within ipython
def
in_ipython
():
try
:
__IPYTHON__
except
NameError
:
return
False
else
:
return
True
if
__name__
==
'__main__'
:
main
()
pykat/fft/fft.py
0 → 100644
View file @
72a5c650
"""
------------------------------------------------------
Functions related to FFT propogation of beams.
Work in progress, currently these functions are
untested!
Andreas 30.11.2014
http://www.gwoptics.org/pykat/
------------------------------------------------------
"""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
from
__future__
import
unicode_literals
import
numpy
as
np
def
apply_lens
(
field
,
grid
,
Lambda
,
f
):
# apply a phase factor representing a lens
k
=
2.0
*
np
.
pi
/
Lambda
return
field
*
(
np
.
exp
(
2.0
*
1j
*
k
*
(
2
*
f
-
np
.
sign
(
f
)
*
np
.
sqrt
((
2.0
*
f
)
**
2
-
grid
.
r_squared
))))
def
apply_thin_lens
(
field
,
grid
,
Lambda
,
f
):
# apply a phase factor representing a thin lens
k
=
2.0
*
np
.
pi
/
Lambda
return
field
*
(
np
.
exp
(
1.0
*
1j
*
k
*
grid
.
r_squared
/
(
2.0
*
f
)))
def
FFT_propagate
(
field
,
grid
,
Lambda
,
distance
,
nr
):
# FFT propagation code in a fixed grid
k
=
2.0
*
np
.
pi
/
Lambda
*
nr
plD
=
np
.
pi
*
Lambda
*
distance
/
nr
field
=
np
.
fft
.
fft2
(
field
)
field
=
field
*
np
.
exp
(
-
1j
*
k
*
distance
)
*
np
.
exp
(
1j
*
plD
*
grid
.
fft_ir_squared
)
field
=
np
.
fft
.
ifft2
(
field
)
return
field
def
FFT_scale_propagate
(
field
,
grid0
,
grid1
,
Lambda
,
distance
,
w0
,
w1
,
nr
):
# FFT propagation code with an adaptive grid size.
# Propagates to a scaled coordinate system, see Virgo Book of
# Physics pages 179-184, the scaling factor is given
# as w1/w0 with w0 the beam size at the start of propagation
# and w1 the expected beam size at the end of propatation.
# NOT YET TESTED
k
=
2.0
*
np
.
pi
/
Lambda
*
nr
plD
=
np
.
pi
*
Lambda
*
distance
*
w0
/
w1
/
nr
z0
=
distance
/
(
w1
/
w0
-
1.0
)
# initial scaling
field
=
field
*
np
.
exp
(
-
1j
*
k
*
grid0
.
r_squared
/
(
2.0
*
z0
))
field
=
np
.
fft
.
fft2
(
field
)
# scaled propagator
field
=
field
*
np
.
exp
(
-
1j
*
k
*
distance
)
*
np
.
exp
(
1j
*
plD
*
grid0
.
fft_ir_squared
)
field
=
np
.
fft
.
ifft2
(
field
)
# final scaling
field
=
field
*
w0
/
w1
*
np
.
exp
(
1j
*
grid1
.
r_squared
*
(
z0
+
L
)
/
(
2.0
*
z0
*
z0
))
return
field
class
grid
():
# Data structure to describe the size and axes for a (x,y) data array
# of complex beam amplitudes. Also contain also data structures for
# FFT propagation
def
__init__
(
self
,
xpoints
,
ypoints
,
xsize
,
ysize
,
xoffset
,
yoffset
):
self
.
xpoints
=
xpoints
self
.
ypoints
=
ypoints
self
.
xsize
=
xsize
self
.
ysize
=
ysize
self
.
xoffset
=
xoffset
self
.
yoffset
=
yoffset
# compute x and y axis
self
.
xstep
=
self
.
xsize
/
self
.
xpoints
self
.
ystep
=
self
.
ysize
/
self
.
ypoints
xvector
=
np
.
arange
(
self
.
xpoints
)
yvector
=
np
.
arange
(
self
.
ypoints
)
self
.
xaxis
=-
self
.
xsize
/
2.0
+
self
.
xstep
/
2.0
+
xvector
*
self
.
xstep
+
self
.
xoffset
self
.
yaxis
=-
self
.
ysize
/
2.0
+
self
.
ystep
/
2.0
+
yvector
*
self
.
ystep
+
self
.
yoffset
# and some useful variables based on the axis
self
.
X
,
self
.
Y
=
np
.
meshgrid
(
self
.
xaxis
,
self
.
yaxis
)
self
.
r_squared
=
(
self
.
X
)
**
2
+
(
self
.
Y
)
**
2
self
.
r
=
np
.
sqrt
(
self
.
r_squared
)
self
.
angle
=
np
.
arctan2
(
self
.
Y
,
self
.
X
)
# compute frequency axis
self
.
xaxis_fft
=
np
.
fft
.
fftshift
(
np
.
fft
.
fftfreq
(
self
.
xpoints
))
/
self
.
xstep
self
.
yaxis_fft
=
np
.
fft
.
fftshift
(
np
.
fft
.
fftfreq
(
self
.
ypoints
))
/
self
.
ystep
# some useful variables based on the frequency axis
self
.
fft_X
,
self
.
fft_Y
=
np
.
meshgrid
(
self
.
xaxis_fft
,
self
.
yaxis_fft
)
self
.
fft_ir_squared
=
np
.
fft
.
ifftshift
((
self
.
fft_X
)
**
2
+
(
self
.
fft_Y
)
**
2
)
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