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Niclas Esser
PAF Backend Simulator
Commits
aa3e455d
Commit
aa3e455d
authored
3 months ago
by
Weiwei Chen
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[mod]: create ShortTimeFFTFactory class to unit the forward and backward fft in the same instance.
parent
c7f7bb0f
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Pipeline
#246141
passed
3 months ago
Stage: build
Stage: test
Stage: stats
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pafsim/signal_generator/signal_generator.py
+42
-59
42 additions, 59 deletions
pafsim/signal_generator/signal_generator.py
with
42 additions
and
59 deletions
pafsim/signal_generator/signal_generator.py
+
42
−
59
View file @
aa3e455d
...
...
@@ -11,6 +11,25 @@ from scipy.signal import ShortTimeFFT
import
h5reader
class
ShortTimeFFTFactory
(
object
):
def
__init__
(
self
,
segment_len
,
sample_rate
,
fft_mode
=
'
twosided
'
):
hop_len
=
int
(
segment_len
/
2
)
dual_win
=
hann
(
segment_len
,
sym
=
True
)
# print(f" NOLA: {check_NOLA(dual_win, segment_len, hop_len)}")
self
.
ShortTimeFFT_factory
=
ShortTimeFFT
.
from_dual
(
dual_win
,
hop_len
,
sample_rate
,
fft_mode
=
fft_mode
)
def
forward_fft
(
self
,
samples
):
ffted_samples
=
self
.
ShortTimeFFT_factory
.
stft
(
samples
)
return
ffted_samples
def
backward_fft
(
self
,
samples
):
ffted_samples
=
self
.
ShortTimeFFT_factory
.
istft
(
samples
)
return
ffted_samples
def
generate_complex_samples
(
shape
):
'''
Generate complex samples in given shape
...
...
@@ -70,38 +89,7 @@ def create_interpolater(iv, matrix, k=3):
interpolater
=
interpolate
.
make_interp_spline
(
iv
,
matrix
,
k
=
k
)
return
interpolater
def
perform_short_time_fft
(
samples
,
segment_len
,
sample_rate
,
direction
=
1
,
fft_mode
=
'
twosided
'
):
'''
perfrom windowed and segmented fft.
arguments:
samples: time series to be ffted
segment_len: length of each segment
sample_rate: sample rate
direction: 1 for forward, -1 for backward
fft_mode:
'
twosided
'
or ...
returns:
ffted samples in dimension: [-1, channel, time]
'''
hop_len
=
int
(
segment_len
/
2
)
dual_win
=
hann
(
segment_len
,
sym
=
True
)
# print(f" NOLA: {check_NOLA(dual_win, segment_len, hop_len)}")
ShortTimeFFT_generator
=
ShortTimeFFT
.
from_dual
(
dual_win
,
hop_len
,
sample_rate
,
fft_mode
=
fft_mode
)
# print(f"invertible : {ShortTimeFFT_generator.invertible}")
if
direction
==
1
:
ffted_samples
=
ShortTimeFFT_generator
.
stft
(
samples
)
else
:
ffted_samples
=
ShortTimeFFT_generator
.
istft
(
samples
)
return
ffted_samples
def
generate_interpolated_acm
(
dataset
,
acm_path
,
frequencies
,
fft_frequencies
):
def
generate_interpolated_acm
(
dataset
,
acm_path
,
frequencies
,
fft_frequencies
):
'''
Interpolate the acm through frequencies.
...
...
@@ -127,8 +115,7 @@ def generate_interpolated_acm(dataset, acm_path,
return
interpolated_acm
def
generate_channelized_samples
(
length
,
element_num
,
segment_len
,
sample_rate
,
origin
=
'
same
'
):
def
generate_channelized_samples
(
length
,
element_num
,
fft_function
,
origin
=
'
same
'
):
'''
Generate channelized samples
...
...
@@ -136,9 +123,8 @@ def generate_channelized_samples(length, element_num, segment_len,
arguments:
length: length of the samples
element_num: number of elements
segment_len: length of each segment for short time FFT
sample_rate: sample rate
same_origin: weather use the same samples for all element
fft_function: function for the forward fft
origin: weather use the same samples for all element
returns:
channelized samples
'''
...
...
@@ -147,16 +133,13 @@ def generate_channelized_samples(length, element_num, segment_len,
if
origin
==
'
same
'
:
random_complex
=
generate_complex_samples
([
length
,])
# shape: (segment_len, -1)
fourier_samples
=
perform_short_time_fft
(
random_complex
,
segment_len
,
sample_rate
,
fft_mode
=
'
twosided
'
)
fourier_samples
=
fft_function
(
random_complex
)
fourier_samples_matrix
=
np
.
tile
(
fourier_samples
,
[
element_num
,
1
,
1
])
elif
origin
==
'
different
'
:
random_complex
=
generate_complex_samples
([
element_num
,
length
,])
fourier_samples_matrix
=
perform_short_time_fft
(
random_complex
,
segment_len
,
sample_rate
,
fft_mode
=
'
twosided
'
)
fourier_samples_matrix
=
fft_function
(
random_complex
)
elif
hasattr
(
origin
,
'
__iter__
'
):
fourier_samples_matrix
=
perform_short_time_fft
(
origin
,
segment_len
,
sample_rate
,
fft_mode
=
'
twosided
'
)
fourier_samples_matrix
=
fft_function
(
origin
)
return
fourier_samples_matrix
...
...
@@ -186,21 +169,18 @@ def weight_samples_using_acm(acm, channel_len, input_fourier_samples_matrix):
return
fourier_samples_matrix
def
convert_channelized_data_to_time_series
(
fourier_samples_matrix
,
full_length
,
segment_len
,
element_num
,
sample_rate
):
fft_function
):
'''
Convert channelized data to time series
arguments:
fourier_samples_matrix: fourier samples input
full_length: full length of the samples
segment_len: length of each segment for short time FFT
element_num: number of elements
sample_rate: sample rate
fft_function: function for the backward fft
returns:
time series
'''
time_series
=
perform_short_time_fft
(
fourier_samples_matrix
,
segment_len
,
sample_rate
,
fft_mode
=
'
twosided
'
,
direction
=
-
1
)
time_series
=
fft_function
(
fourier_samples_matrix
)
return
time_series
[:,
:
full_length
]
def
main
():
...
...
@@ -240,16 +220,16 @@ def main():
# signal
channel_len
=
len
(
fine_frequencies
)
segment_len
=
fft_size
short_time_fft_factory
=
ShortTimeFFTFactory
(
segment_len
,
sample_rate
)
interpolated_acm
=
generate_interpolated_acm
(
acm_matrix
,
signal_path
,
frequencies
,
fine_frequencies
)
channelized_samples_signal
=
generate_channelized_samples
(
output_length
,
element_num
,
s
egment_len
,
sample_rate
,
origin
=
'
same
'
)
element_num
,
s
hort_time_fft_factory
.
forward_fft
,
origin
=
'
same
'
)
channelized_correlated_signal
=
weight_samples_using_acm
(
interpolated_acm
,
channel_len
,
channelized_samples_signal
)
correlated_signal
=
convert_channelized_data_to_time_series
(
channelized_correlated_signal
,
output_length
,
segment_len
,
element_num
,
sample_rate
)
channelized_correlated_signal
,
output_length
,
short_time_fft_factory
.
backward_fft
)
# verification
acm_0
=
(
channelized_correlated_signal
[:,
0
,
:]
...
...
@@ -265,15 +245,16 @@ def main():
# noise
channel_len
=
len
(
fine_frequencies
)
segment_len
=
fft_size
short_time_fft_factory
=
ShortTimeFFTFactory
(
segment_len
,
sample_rate
)
interpolated_acm
=
generate_interpolated_acm
(
acm_matrix
,
t_noise_path
,
frequencies
,
fine_frequencies
)
channelized_samples_noise
=
generate_channelized_samples
(
output_length
,
element_num
,
s
egment_len
,
sample_rate
,
origin
=
'
different
'
)
element_num
,
s
hort_time_fft_factory
.
forward_fft
,
origin
=
'
different
'
)
channelized_correlated_noise
=
weight_samples_using_acm
(
interpolated_acm
,
channel_len
,
channelized_samples_noise
)
correlated_noise
=
convert_channelized_data_to_time_series
(
channelized_correlated_noise
,
output_length
,
segment_len
,
element_num
,
sample_rate
)
channelized_correlated_noise
,
output_length
,
short_time_fft_factory
.
backward_fft
)
# verification
acm_1
=
(
channelized_correlated_noise
[:,
0
,
:]
...
...
@@ -298,6 +279,7 @@ def main():
rfi_channel_len
=
fft_size
segment_len
=
fft_size
short_time_fft_factory
=
ShortTimeFFTFactory
(
segment_len
,
sample_rate
)
sinusoidal_freq
=
frequencies_rfi
[
0
]
complex_sinusoidal
=
generate_complex_sinusoidal
(
[
element_num
,
output_length
],
sinusoidal_freq
,
sample_rate
)
...
...
@@ -307,12 +289,13 @@ def main():
rfi_path
,
None
,
np
.
tile
(
frequencies_rfi
,
fft_size
))
channelized_samples_rfi
=
generate_channelized_samples
(
output_length
,
element_num
,
segment_len
,
sample_rate
,
origin
=
complex_sinusoidal
)
element_num
,
short_time_fft_factory
.
forward_fft
,
origin
=
complex_sinusoidal
)
channelized_correlated_rfi
=
weight_samples_using_acm
(
interpolated_acm_rfi
,
rfi_channel_len
,
channelized_samples_rfi
)
correlated_rfi
=
convert_channelized_data_to_time_series
(
channelized_correlated_rfi
,
output_length
,
segment_len
,
element_num
,
sample_rate
)
channelized_correlated_rfi
,
output_length
,
short_time_fft_factory
.
backward_fft
)
acm_rfi_reconstructed
=
(
channelized_correlated_rfi
[:,
5
,
:]
@
channelized_correlated_rfi
[:,
5
,
:].
conj
().
T
/
output_length
)
...
...
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