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PA
Operation
PythonControl
Commits
0b5a3917
Commit
0b5a3917
authored
2 months ago
by
Vadim Gubaidulin
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Added class from postmortem postprocessing.
parent
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growth_damp_utils.py
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0b5a3917
...
...
@@ -13,6 +13,184 @@ Q_X = 18.16
Q_Y
=
10.22
OMEGA_REV
=
2
*
pi
*
c
/
CIRCUMFERENCE
r_p
=
e
**
2
/
(
m_p
*
c
**
2
)
import
h5py
as
hp
import
numpy
as
np
import
pandas
as
pd
import
os
import
nafflib
import
argparse
import
matplotlib.pyplot
as
plt
from
scipy.signal
import
periodogram
,
lombscargle
,
hilbert
from
FITX
import
fit_risetime
class
FBTPostmortemViewer
():
def
__init__
(
self
,
filename
,
h_rf
):
self
.
filename
=
filename
self
.
filling_pattern_calibration
=
100
self
.
data_type
=
np
.
float32
self
.
h_rf
=
h_rf
def
read_data
(
self
):
_
,
extension
=
os
.
path
.
splitext
(
self
.
filename
)
# self.h_rf = h_rf
# print(filename, extension)
if
extension
==
"
.nxs
"
:
with
hp
.
File
(
filename
,
"
r
"
)
as
file
:
print
(
file
[
'
post-mortem/rcm/ans/rf/bbfdataviewer.1/
'
].
keys
())
data
=
np
.
array
(
file
[
'
post-mortem/rcm/ans/rf/bbfdataviewer.1/clean_data
'
][:])
print
(
'
Shape of nexus raw data
'
,
data
.
shape
)
len_data
=
len
(
data
)
len_common
=
int
(
len_data
//
self
.
h_rf
)
resize_data
=
int
(
len_common
*
self
.
h_rf
)
data_bunch_x
=
data
[:
resize_data
].
reshape
((
len_common
,
self
.
h_rf
)).
astype
(
np
.
float32
)
data_bunch_y
=
data_bunch_x
# data_bunch_x = data_bunch_x.T
# data_bunch_y = data_bunch_y.T
# data_bunch -= np.mean(data_bunch, axis=0, dtype=np.int64)
elif
extension
==
"
.txt
"
:
data_pd
=
pd
.
read_csv
(
self
.
filename
,
sep
=
"
\t
"
,
header
=
None
)
data_bunch_x
=
data_pd
.
to_numpy
()
data_bunch_y
=
data_bunch_x
# data_bunch -= np.mean(data_bunch, axis=0, dtype=np.int64)
elif
extension
==
"
.hdf5
"
:
with
hp
.
File
(
self
.
filename
)
as
f
:
# print(f['V'].shape)
data_bunch_x
=
np
.
array
(
f
[
'
H
'
],
dtype
=
np
.
int16
)[:,
:]
# data_bunch_x -= np.mean(data_bunch_x, axis=1, dtype=np.int64, keepdims=True)
data_bunch_y
=
np
.
array
(
f
[
'
V
'
],
dtype
=
np
.
int16
)[:,
:]
# data_bunch_y -= np.mean(data_bunch_y, axis=1, dtype=np.int64, keepdims=True)
data_bunch_x
=
data_bunch_x
.
T
data_bunch_y
=
data_bunch_y
.
T
else
:
raise
ValueError
()
# return data_bunch_x, data_bunch_y
self
.
data_bunch_x
=
data_bunch_x
.
astype
(
np
.
float32
)
self
.
data_bunch_y
=
data_bunch_y
.
astype
(
np
.
float32
)
print
(
f
'
Shape of the data is
'
,
data_bunch_x
.
shape
)
return
0
def
process_data
(
self
):
print
(
self
.
data_bunch_y
.
shape
)
self
.
filling_pattern
=
np
.
mean
(
self
.
data_bunch_y
,
axis
=
0
,
dtype
=
np
.
float32
)
self
.
data_bunch_x
-=
np
.
mean
(
self
.
data_bunch_x
,
axis
=
0
,
dtype
=
np
.
float32
,
keepdims
=
True
)
self
.
data_bunch_y
-=
np
.
mean
(
self
.
data_bunch_y
,
axis
=
0
,
dtype
=
np
.
float32
,
keepdims
=
True
)
self
.
spectrum_x
=
periodogram
(
self
.
data_bunch_x
.
flatten
(),
fs
=
self
.
h_rf
)
self
.
spectrum_y
=
periodogram
(
self
.
data_bunch_y
.
flatten
(),
fs
=
self
.
h_rf
)
self
.
spectrum_x1
=
periodogram
(
self
.
data_bunch_x
.
flatten
()[
0
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_x2
=
periodogram
(
self
.
data_bunch_x
.
flatten
()[
1
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_x3
=
periodogram
(
self
.
data_bunch_x
.
flatten
()[
2
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_x4
=
periodogram
(
self
.
data_bunch_x
.
flatten
()[
3
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_y1
=
periodogram
(
self
.
data_bunch_y
.
flatten
()[
0
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_y2
=
periodogram
(
self
.
data_bunch_y
.
flatten
()[
1
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_y3
=
periodogram
(
self
.
data_bunch_y
.
flatten
()[
2
::
4
],
fs
=
self
.
h_rf
//
4
)
self
.
spectrum_y4
=
periodogram
(
self
.
data_bunch_y
.
flatten
()[
3
::
4
],
fs
=
self
.
h_rf
//
4
)
# self.tunes_x = [nafflib.get_tune(self.data_bunch_x[:, i]) for i in range(self.h_rf)]
# self.tunes_y = [nafflib.get_tune(self.data_bunch_y[:, i]) for i in range(self.h_rf)]
self
.
fft_x
=
np
.
asarray
([
np
.
fft
.
rfft
(
self
.
data_bunch_x
[:,
i
])
for
i
in
range
(
self
.
h_rf
)]).
T
self
.
fft_y
=
np
.
asarray
([
np
.
fft
.
rfft
(
self
.
data_bunch_y
[:,
i
])
for
i
in
range
(
self
.
h_rf
)]).
T
self
.
fft_x
=
np
.
fft
.
rfft
(
self
.
data_bunch_x
,
axis
=
0
).
T
self
.
fft_y
=
np
.
fft
.
rfft
(
self
.
data_bunch_x
,
axis
=
1
).
T
self
.
fftfreq
=
np
.
fft
.
rfftfreq
(
self
.
data_bunch_x
.
shape
[
0
])
# self.bunch_phase = np.zeros((self.h_rf,))
# self.dominant_freqs = np.zeros((self.h_rf,))
# magnitudes = np.abs(self.fft_x)
# for i in range(self.h_rf):
# idx = np.argmax(magnitudes[1:]) + 1
# self.dominant_freqs[i] = self.fftfreq[idx]
# self.bunch_phase[i] = np.angle(fft_result[idx])
def
save_processed_data
(
self
):
pass
def
plot_spectrum
(
self
,
ax
=
None
,
plane
=
'
y
'
):
plane
=
list
(
plane
)
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
if
'
x
'
in
plane
:
ax
.
plot
(
self
.
spectrum_x
[
0
],
self
.
spectrum_x
[
1
],
label
=
'
horizontal
'
)
if
'
y
'
in
plane
:
ax
.
plot
(
self
.
spectrum_y
[
0
],
self
.
spectrum_y
[
1
],
label
=
'
vertical
'
)
ax
.
set_xlim
(
0
,
)
ax
.
set_ylim
(
0
,
)
ax
.
legend
()
def
plot_partial_spectrum
(
self
,
ax
=
None
,
plane
=
'
y
'
):
plane
=
list
(
plane
)
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
if
'
x
'
in
plane
:
ax
.
plot
(
self
.
spectrum_x1
[
0
],
self
.
spectrum_x1
[
1
],
label
=
'
horizontal, ADC1
'
)
ax
.
plot
(
self
.
spectrum_x2
[
0
],
self
.
spectrum_x2
[
1
],
label
=
'
horizontal, ADC2
'
)
ax
.
plot
(
self
.
spectrum_x3
[
0
],
self
.
spectrum_x3
[
1
],
label
=
'
horizontal, ADC3
'
)
ax
.
plot
(
self
.
spectrum_x4
[
0
],
self
.
spectrum_x4
[
1
],
label
=
'
horizontal, ADC4
'
)
if
'
y
'
in
plane
:
ax
.
plot
(
self
.
spectrum_y1
[
0
],
self
.
spectrum_y1
[
1
],
label
=
'
vertical, ADC1
'
)
ax
.
plot
(
self
.
spectrum_y2
[
0
],
self
.
spectrum_y2
[
1
],
label
=
'
vertical, ADC2
'
)
ax
.
plot
(
self
.
spectrum_y3
[
0
],
self
.
spectrum_y3
[
1
],
label
=
'
vertical, ADC3
'
)
ax
.
plot
(
self
.
spectrum_y4
[
0
],
self
.
spectrum_y4
[
1
],
label
=
'
vertical, ADC4
'
)
ax
.
legend
()
def
plot_offset
(
self
,
ax
=
None
,
plane
=
'
y
'
):
plane
=
list
(
plane
)
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
if
'
y
'
in
plane
:
ax
.
plot
(
self
.
data_bunch_y
.
flatten
())
def
plot_bunch_offset
(
self
,
bunch_numbers
,
axs
=
None
,
plane
=
'
y
'
):
plane
=
list
(
plane
)
if
axs
==
None
:
fig
,
axs
=
plt
.
subplots
(
len
(
bunch_numbers
),
1
)
for
i
in
range
(
len
(
bunch_numbers
)):
if
'
y
'
in
plane
:
axs
[
i
].
plot
(
self
.
data_bunch_y
[:,
bunch_numbers
[
i
]],
label
=
f
'
vertical, bunch
{
bunch_numbers
[
i
]
}
'
)
if
'
x
'
in
plane
:
axs
[
i
].
plot
(
self
.
data_bunch_x
[:,
bunch_numbers
[
i
]],
label
=
f
'
horizontal, bunch
{
bunch_numbers
[
i
]
}
'
)
fig
.
legend
()
def
plot_fitexp
(
self
,
ax
=
None
,
plane
=
'
y
'
):
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
if
plane
==
'
y
'
:
ax
.
plot
(
self
.
data_bunch_y
.
flatten
())
def
plot_filling_pattern
(
self
,
ax
=
None
):
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
ax
.
plot
(
np
.
abs
(
self
.
filling_pattern
)
/
self
.
filling_pattern_calibration
)
ax
.
set_xlabel
(
'
Bunch number
'
)
ax
.
set_ylabel
(
'
Bunch current (?mA?)
'
)
def
plot_tunes
(
self
,
ax
=
None
):
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
# ax.plot(self.fftfreq, self.fft_x)
ax
.
plot
(
self
.
fftfreq
,
np
.
abs
(
self
.
fft_y
))
def
plot_mean_fft
(
self
,
ax
=
None
):
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
ax
.
plot
(
self
.
fftfreq
,
np
.
mean
(
np
.
abs
(
self
.
fft_x
),
axis
=
1
))
def
plot_tune_vs_bunch_number
(
self
,
ax
=
None
):
if
ax
==
None
:
_
,
ax
=
plt
.
subplots
(
1
,
1
)
ax
.
plot
(
self
.
tunes_x
,
label
=
'
horizontal
'
,
alpha
=
0.5
)
ax
.
plot
(
self
.
tunes_y
,
label
=
'
vertical
'
,
alpha
=
0.5
)
ax
.
set_xlabel
(
'
Bunch number
'
)
ax
.
set_ylabel
(
'
Fractional tune
'
)
ax
.
legend
()
def
fit_growth_rate
(
self
):
pass
class
GrowthDampDataAnalyzer
():
def
__init__
(
self
,
folder
):
...
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