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Cocalibration Demonstrator Backend
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M4D
Cocalibration Demonstrator Backend
Commits
8bdd876e
Commit
8bdd876e
authored
1 year ago
by
Maximilian Gruber
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wip: first draft of plot showing compensated transfer function
parent
6bf3811d
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app/cocal_methods.py
+68
-21
68 additions, 21 deletions
app/cocal_methods.py
with
68 additions
and
21 deletions
app/cocal_methods.py
+
68
−
21
View file @
8bdd876e
...
@@ -25,6 +25,7 @@ import xmlschema
...
@@ -25,6 +25,7 @@ import xmlschema
from
pydub
import
AudioSegment
from
pydub
import
AudioSegment
from
PyDynamic.misc.tools
import
complex_2_real_imag
,
real_imag_2_complex
from
PyDynamic.misc.tools
import
complex_2_real_imag
,
real_imag_2_complex
from
PyDynamic.uncertainty.propagate_MonteCarlo
import
UMC_generic
from
PyDynamic.uncertainty.propagate_MonteCarlo
import
UMC_generic
from
PyDynamic.uncertainty.propagate_DFT
import
DFT_multiply
,
DFT_deconv
from
scipy.interpolate
import
interp1d
from
scipy.interpolate
import
interp1d
from
sounddevice
import
InputStream
,
play
,
query_devices
from
sounddevice
import
InputStream
,
play
,
query_devices
from
soundfile
import
SoundFile
from
soundfile
import
SoundFile
...
@@ -378,7 +379,6 @@ class CocalMethods:
...
@@ -378,7 +379,6 @@ class CocalMethods:
return
tree
return
tree
def
generate_spectral_domain_visualization
(
self
):
def
generate_spectral_domain_visualization
(
self
):
# provide shortcuts for plotting
# provide shortcuts for plotting
b
=
self
.
transfer_behavior
[
"
IIR
"
][
"
b
"
]
b
=
self
.
transfer_behavior
[
"
IIR
"
][
"
b
"
]
a
=
self
.
transfer_behavior
[
"
IIR
"
][
"
a
"
]
a
=
self
.
transfer_behavior
[
"
IIR
"
][
"
a
"
]
...
@@ -387,18 +387,25 @@ class CocalMethods:
...
@@ -387,18 +387,25 @@ class CocalMethods:
# further relevant parameters
# further relevant parameters
time_delta
=
np
.
mean
(
np
.
diff
(
self
.
ref_times
[
0
]))
time_delta
=
np
.
mean
(
np
.
diff
(
self
.
ref_times
[
0
]))
frame_rate
=
1.0
/
time_delta
frame_rate
=
1.0
/
time_delta
Nb
=
len
(
b
)
Nb
=
len
(
b
)
ba
=
np
.
concatenate
((
b
,
a
[
1
:]))
ba
=
np
.
concatenate
((
b
,
a
[
1
:]))
mask
=
np
.
logical_and
(
self
.
ref_frequency
<
5000.0
,
self
.
ref_frequency
>=
0.0
)
mask
=
np
.
logical_and
(
self
.
ref_frequency
<
5000.0
,
self
.
ref_frequency
>=
0.0
)
mask
=
np
.
logical_and
(
self
.
ref_frequency
<
20000.0
,
self
.
ref_frequency
>=
0.0
)
# visualize coefficient uncertainties by plotting the spectrum uncertainty via MC
# visualize coefficient uncertainties by plotting the spectrum uncertainty via MC
def
draw_samples
(
size
):
def
draw_samples
(
size
):
return
np
.
random
.
multivariate_normal
(
ba
,
ba_cov
,
size
=
size
)
return
np
.
random
.
multivariate_normal
(
ba
,
ba_cov
,
size
=
size
)
w
=
np
.
linspace
(
0
,
np
.
pi
,
50
)
## reduced frequency resolution
f
=
w
/
(
2
*
np
.
pi
)
*
frame_rate
# w = np.linspace(0, np.pi, 50)
w_exp
=
np
.
exp
(
-
1j
*
w
)
# ???
# f = w / (2 * np.pi) * frame_rate
# w_exp = np.exp(-1j * w) # ???
# full frequency resolution:
f
=
self
.
ref_frequency
w
=
self
.
ref_frequency
/
frame_rate
*
(
2
*
np
.
pi
)
w_exp
=
np
.
exp
(
-
1j
*
w
)
def
evaluate
(
sample
):
def
evaluate
(
sample
):
return
complex_2_real_imag
(
self
.
freqz_core
(
sample
,
Nb
,
w_exp
))
return
complex_2_real_imag
(
self
.
freqz_core
(
sample
,
Nb
,
w_exp
))
...
@@ -406,7 +413,7 @@ class CocalMethods:
...
@@ -406,7 +413,7 @@ class CocalMethods:
umc_kwargs
=
{
umc_kwargs
=
{
"
draw_samples
"
:
draw_samples
,
"
draw_samples
"
:
draw_samples
,
"
evaluate
"
:
evaluate
,
"
evaluate
"
:
evaluate
,
"
runs
"
:
20
,
"
runs
"
:
20
0
,
"
blocksize
"
:
8
,
"
blocksize
"
:
8
,
"
n_cpu
"
:
1
,
"
n_cpu
"
:
1
,
"
compute_full_covariance
"
:
True
,
"
compute_full_covariance
"
:
True
,
...
@@ -417,29 +424,69 @@ class CocalMethods:
...
@@ -417,29 +424,69 @@ class CocalMethods:
h
=
real_imag_2_complex
(
h_ri
)
h
=
real_imag_2_complex
(
h_ri
)
h_unc
=
real_imag_2_complex
(
np
.
sqrt
(
np
.
diag
(
h_cov
)))
h_unc
=
real_imag_2_complex
(
np
.
sqrt
(
np
.
diag
(
h_cov
)))
h_empirical
=
self
.
dut_spectrum
/
self
.
ref_spectrum
# compensate
h_comp_ri
,
h_comp_cov
=
DFT_deconv
(
complex_2_real_imag
(
h_empirical
),
h_ri
,
np
.
zeros
((
2
*
len
(
h_empirical
),
2
*
len
(
h_empirical
))),
h_cov
,
)
h_comp
=
real_imag_2_complex
(
h_comp_ri
)
h_comp_unc
=
real_imag_2_complex
(
np
.
sqrt
(
np
.
diag
(
h_comp_cov
)))
# regularize
#regularized_filter = sig.cheby2()
#h_regularized = self.freqz_core()
#h_compensated_and_regularized = DFT_multiply(
# h_compensated, h_regularized, h_regularized_co
#)
# visualize result
# visualize result
fig
,
ax
=
plt
.
subplots
(
2
,
1
,
sharex
=
True
)
fig
,
ax
=
plt
.
subplots
(
1
,
1
,
sharex
=
True
,
squeeze
=
False
)
ax
[
0
].
plot
(
f
,
np
.
abs
(
h
),
label
=
"
fitted TF
"
)
ax
[
0
,
0
].
plot
(
ax
[
0
].
fill_between
(
f
[
mask
],
f
,
np
.
abs
(
h
[
mask
]),
np
.
abs
(
h
)
-
np
.
abs
(
h_unc
),
label
=
"
fitted TF
"
,
np
.
abs
(
h
)
+
np
.
abs
(
h_unc
),
color
=
"
blue
"
,
)
ax
[
0
,
0
].
fill_between
(
f
[
mask
],
np
.
abs
(
h
[
mask
])
-
np
.
abs
(
h_unc
[
mask
]),
np
.
abs
(
h
[
mask
])
+
np
.
abs
(
h_unc
[
mask
]),
alpha
=
0.3
,
alpha
=
0.3
,
label
=
"
unc of fitted TF
"
,
label
=
"
unc of fitted TF
"
,
color
=
"
blue
"
,
)
)
ax
[
0
].
scatter
(
ax
[
0
,
0
].
scatter
(
self
.
ref_frequency
[
mask
],
self
.
ref_frequency
[
mask
],
np
.
abs
(
self
.
dut_spectrum
[
mask
]
/
self
.
ref_spectrum
[
mask
]),
np
.
abs
(
h_empirical
[
mask
]),
label
=
"
empirical TF
"
,
label
=
"
empirical TF
"
,
s
=
2
,
color
=
"
black
"
,
)
ax
[
0
,
0
].
scatter
(
self
.
ref_frequency
[
mask
],
np
.
abs
(
h_comp
[
mask
]),
label
=
"
compensated TF
"
,
s
=
1
,
s
=
1
,
color
=
"
red
"
,
)
)
ax
[
0
].
plot
(
f
,
np
.
ones_like
(
f
),
"
--r
"
,
label
=
"
ideal
"
)
ax
[
0
,
0
].
fill_between
(
ax
[
0
].
plot
(
f
,
np
.
abs
(
1.0
/
h
),
label
=
"
compensated empirical
"
)
f
[
mask
],
ax
[
0
].
legend
()
np
.
abs
(
h_comp
[
mask
])
-
np
.
abs
(
h_comp_unc
[
mask
]),
ax
[
0
].
set_xscale
(
"
log
"
)
np
.
abs
(
h_comp
[
mask
])
+
np
.
abs
(
h_comp_unc
[
mask
]),
ax
[
0
].
set_yscale
(
"
log
"
)
alpha
=
0.3
,
ax
[
1
].
plot
(
f
,
h_unc
)
label
=
"
unc of compensated TF
"
,
#plt.savefig(self.result_image_path)
color
=
"
red
"
,
)
ax
[
0
,
0
].
plot
(
f
,
np
.
ones_like
(
f
),
"
--r
"
,
label
=
"
ideal
"
)
ax
[
0
,
0
].
legend
()
ax
[
0
,
0
].
set_xscale
(
"
log
"
)
ax
[
0
,
0
].
set_yscale
(
"
log
"
)
# plt.savefig(self.result_image_path)
plt
.
show
()
plt
.
show
()
...
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