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kmap
xsocs
Commits
f933654f
Commit
f933654f
authored
Jun 09, 2016
by
Damien Naudet
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First guess improvements.
parent
f1d3b5dd
Changes
1
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1 changed file
with
45 additions
and
12 deletions
+45
-12
kmap/process/peak_fit.py
kmap/process/peak_fit.py
+45
-12
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kmap/process/peak_fit.py
View file @
f933654f
...
...
@@ -38,13 +38,13 @@ import h5py
import
numpy
as
np
from
scipy.optimize
import
leastsq
from
silx.math
import
curve_fit
#
from silx.math import curve_fit
disp_times
=
False
class
FitTypes
(
object
):
ALLOWED
=
range
(
2
)
GAUSSIAN
,
CENTROID
=
ALLOWED
LEASTSQ
,
CENTROID
=
ALLOWED
_const_inv_2_pi_
=
np
.
sqrt
(
2
*
np
.
pi
)
...
...
@@ -65,9 +65,9 @@ def _qspace_gauss_fit(x, y, v0):
if
result
[
4
]
not
in
[
1
,
2
,
3
,
4
]:
raise
ValueError
(
'Failed to fit : {0}.'
.
format
(
result
[
3
]))
result
=
result
[
0
]
result
[
0
]
=
_gauss_fn
(
result
,
result
[
1
])
return
result
#
result = result[0]
#
result[0] = _gauss_fn(result, result[1])
return
result
[
0
]
def
_qspace_centroid
(
x
,
y
,
v0
):
# TODO : throw exception if fit failed
...
...
@@ -76,7 +76,7 @@ def _qspace_centroid(x, y, v0):
return
[
y
[
idx
],
com
,
np
.
nan
]
def
get_peaks
(
qspace_f
,
fit_type
=
FitTypes
.
GAUSSIAN
,
fit_type
=
FitTypes
.
LEASTSQ
,
indices
=
None
,
n_proc
=
None
):
...
...
@@ -84,8 +84,8 @@ def get_peaks(qspace_f,
if
fit_type
not
in
FitTypes
.
ALLOWED
:
raise
ValueError
(
'Unknown fit type : {0}'
)
if
fit_type
==
FitTypes
.
GAUSSIAN
:
if
fit_type
==
FitTypes
.
LEASTSQ
:
fit_fn
=
_qspace_gauss_fit
if
fit_type
==
FitTypes
.
CENTROID
:
fit_fn
=
_qspace_centroid
...
...
@@ -174,7 +174,6 @@ def get_peaks(qspace_f,
fit_x
=
results
[...,
0
:
3
]
fit_y
=
results
[...,
3
:
6
]
fit_z
=
results
[...,
6
:
9
]
#success = np.frombuffer(shared_success)
success
=
np
.
ndarray
((
n_indices
,),
dtype
=
np
.
bool
)
t_total
=
time
.
time
()
-
t_total
...
...
@@ -209,6 +208,16 @@ def _init_thread(shared_res_,
qspace_f
=
qspace_f_
read_lock
=
read_lock_
def
_gauss_first_guess
(
x
,
y
):
i_max
=
y
.
argmax
()
y_max
=
y
[
i_max
]
p1
=
x
[
i_max
]
i_fwhm
=
np
.
where
(
y
>=
y_max
/
2.
)[
0
]
fwhm
=
(
x
[
1
]
-
x
[
0
])
*
len
(
i_fwhm
)
p2
=
fwhm
/
np
.
sqrt
(
2
*
np
.
log
(
2
))
#2.35482
p0
=
y_max
*
np
.
sqrt
(
2
*
np
.
pi
)
*
p2
return
[
p0
,
p1
,
p2
]
def
_fit_process
(
th_idx
):
...
...
@@ -235,9 +244,9 @@ def _fit_process(th_idx):
cube
=
np
.
ascontiguousarray
(
np
.
zeros
(
q_shape
[
1
:]),
dtype
=
q_dtype
)
x_0
=
[
1.0
,
q_x
.
mean
(),
1.0
]
y_0
=
[
1.0
,
q_y
.
mean
(),
1.0
]
z_0
=
[
1.0
,
q_z
.
mean
(),
1.0
]
x_0
=
None
y_0
=
None
z_0
=
None
while
True
:
# TODO : timeout
...
...
@@ -267,26 +276,50 @@ def _fit_process(th_idx):
success_z
=
True
z_sum
=
cube
.
sum
(
axis
=
0
).
sum
(
axis
=
0
)
if
z_0
is
None
:
z_0
=
_gauss_first_guess
(
q_z
,
z_sum
)
try
:
fit_z
=
fit_fn
(
q_z
,
z_sum
,
z_0
)
z_0
=
fit_z
except
Exception
as
ex
:
z_0
=
None
print
'failed z'
,
ex
success_z
=
False
z_sum
=
0
cube_sum_z
=
cube
.
sum
(
axis
=
2
)
y_sum
=
cube_sum_z
.
sum
(
axis
=
0
)
if
y_0
is
None
:
y_0
=
_gauss_first_guess
(
q_y
,
y_sum
)
try
:
fit_y
=
fit_fn
(
q_y
,
y_sum
,
y_0
)
y_0
=
fit_y
except
:
y_0
=
None
success_y
=
False
y_sum
=
0
x_sum
=
cube_sum_z
.
sum
(
axis
=
1
)
if
x_0
is
None
:
x_0
=
_gauss_first_guess
(
q_x
,
x_sum
)
try
:
fit_x
=
fit_fn
(
q_x
,
x_sum
,
x_0
)
x_0
=
fit_x
except
:
x_0
=
None
success_x
=
False
x_sum
=
0
t_fit
+=
time
.
time
()
-
t0
t0
=
time
.
time
()
...
...
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