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Delete reproduction_effort/heartbeatanalyse.py

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David Rotermund 2024-02-14 22:44:02 +01:00 committed by GitHub
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reproduction_effort/heartbeatanalyse.py
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import torch
import matplotlib.pyplot as plt
from functions.preprocessing import preprocessing
from functions.bandpass import bandpass
if __name__ == "__main__":
if torch.cuda.is_available():
device_name: str = "cuda:0"
else:
device_name = "cpu"
print(f"Using device: {device_name}")
device: torch.device = torch.device(device_name)
filename_metadata: str = "raw/Exp001_Trial001_Part001_meta.txt"
filename_data: str = "Exp001_Trial001_Part001.mat"
filename_mask: str = "2020-12-08maskPixelraw2.mat"
first_none_ramp_frame: int = 100
spatial_width: float = 2
temporal_width: float = 0.1
lower_freqency_bandpass: float = 5.0
upper_freqency_bandpass: float = 14.0
target_camera: list[str] = ["acceptor", "donor"]
regressor_cameras: list[str] = ["oxygenation", "volume"]
ratio_sequence_a, ratio_sequence_b, mask = preprocessing(
filename_metadata=filename_metadata,
filename_data=filename_data,
filename_mask=filename_mask,
device=device,
first_none_ramp_frame=first_none_ramp_frame,
spatial_width=spatial_width,
temporal_width=temporal_width,
target_camera=target_camera,
regressor_cameras=regressor_cameras,
)
ratio_sequence_a = bandpass(
data=ratio_sequence_a,
device=ratio_sequence_a.device,
low_frequency=lower_freqency_bandpass,
high_frequency=upper_freqency_bandpass,
fs=100.0,
filtfilt_chuck_size=10,
)
ratio_sequence_b = bandpass(
data=ratio_sequence_b,
device=ratio_sequence_b.device,
low_frequency=lower_freqency_bandpass,
high_frequency=upper_freqency_bandpass,
fs=100.0,
filtfilt_chuck_size=10,
)
original_shape = ratio_sequence_a.shape
ratio_sequence_a = ratio_sequence_a.flatten(start_dim=0, end_dim=-2)
ratio_sequence_b = ratio_sequence_b.flatten(start_dim=0, end_dim=-2)
mask = mask.flatten(start_dim=0, end_dim=-1)
ratio_sequence_a = ratio_sequence_a[mask, :]
ratio_sequence_b = ratio_sequence_b[mask, :]
ratio_sequence_a = ratio_sequence_a.movedim(0, -1)
ratio_sequence_b = ratio_sequence_b.movedim(0, -1)
ratio_sequence_a -= ratio_sequence_a.mean(dim=0, keepdim=True)
ratio_sequence_b -= ratio_sequence_b.mean(dim=0, keepdim=True)
u_a, s_a, Vh_a = torch.linalg.svd(ratio_sequence_a, full_matrices=False)
u_a = u_a[:, 0]
s_a = s_a[0]
Vh_a = Vh_a[0, :]
heartbeatactivitmap_a = torch.zeros(
(original_shape[0], original_shape[1]), device=Vh_a.device, dtype=Vh_a.dtype
).flatten(start_dim=0, end_dim=-1)
heartbeatactivitmap_a *= torch.nan
heartbeatactivitmap_a[mask] = s_a * Vh_a
heartbeatactivitmap_a = heartbeatactivitmap_a.reshape(
(original_shape[0], original_shape[1])
)
u_b, s_b, Vh_b = torch.linalg.svd(ratio_sequence_b, full_matrices=False)
u_b = u_b[:, 0]
s_b = s_b[0]
Vh_b = Vh_b[0, :]
heartbeatactivitmap_b = torch.zeros(
(original_shape[0], original_shape[1]), device=Vh_b.device, dtype=Vh_b.dtype
).flatten(start_dim=0, end_dim=-1)
heartbeatactivitmap_b *= torch.nan
heartbeatactivitmap_b[mask] = s_b * Vh_b
heartbeatactivitmap_b = heartbeatactivitmap_b.reshape(
(original_shape[0], original_shape[1])
)
plt.subplot(2, 1, 1)
plt.plot(u_a.cpu(), label="aceptor")
plt.plot(u_b.cpu(), label="donor")
plt.legend()
plt.subplot(2, 1, 2)
plt.imshow(
torch.cat(
(
heartbeatactivitmap_a,
heartbeatactivitmap_b,
),
dim=1,
).cpu()
)
plt.colorbar()
plt.show()