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David Rotermund 2024-02-23 10:38:30 +01:00 committed by GitHub
parent 6f6b1c00f0
commit 7d4cef92f6
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2 changed files with 157 additions and 89 deletions
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30
reproduction_effort/binning_aligned_process.py
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@ -178,12 +178,36 @@ for i in range(0, len(camera_sequence)):
)
del y
donor_power_factor = heartbeat_coefficients[channels.index("donor")].clone()
acceptor_power_factor = heartbeat_coefficients[channels.index("acceptor")].clone()
donor_correction_factor = heartbeat_coefficients[channels.index("donor")].clone()
acceptor_correction_factor = heartbeat_coefficients[channels.index("acceptor")].clone()
for i in range(0, len(camera_sequence)):
camera_sequence[i] -= heartbeat_coefficients[i] * volume_heartbeat
donor_factor: torch.Tensor = (donor_correction_factor + acceptor_correction_factor) / (
2 * donor_correction_factor
)
acceptor_factor: torch.Tensor = (
donor_correction_factor + acceptor_correction_factor
) / (2 * acceptor_correction_factor)
# mean_values: list = []
# for i in range(0, len(channels)):
# mean_values.append(
# camera_sequence[i][..., first_none_ramp_frame:].nanmean(dim=-1, keepdim=True)
# )
# camera_sequence[i] -= mean_values[i]
camera_sequence[channels.index("acceptor")] *= acceptor_factor * mask.unsqueeze(-1)
camera_sequence[channels.index("donor")] *= donor_factor * mask.unsqueeze(-1)
# for i in range(0, len(channels)):
# camera_sequence[i] -= mean_values[i]
# exit()
# <-
data_acceptor, data_donor, mask = preprocessing(
@ -196,8 +220,6 @@ data_acceptor, data_donor, mask = preprocessing(
temporal_width=temporal_width,
target_camera=target_camera,
regressor_cameras=regressor_cameras,
donor_correction_factor=donor_power_factor,
acceptor_correction_factor=acceptor_power_factor,
)
ratio_sequence: torch.Tensor = data_acceptor / data_donor

216
reproduction_effort/binning_aligned_process_classic.py
View file

@ -8,7 +8,7 @@ import scipy.io as sio # type: ignore
from functions.binning import binning
from functions.align_cameras import align_cameras
from functions.preprocessing_classic import preprocessing
from functions.preprocessing_classsic import preprocessing
from functions.bandpass import bandpass
@ -43,7 +43,7 @@ required_order: list[str] = ["acceptor", "donor", "oxygenation", "volume"]
test_overwrite_with_old_bining: bool = False
test_overwrite_with_old_aligned: bool = True
test_overwrite_with_old_aligned: bool = False
filename_data_binning_replace: str = "bin_old/Exp001_Trial001_Part001.mat"
filename_data_aligned_replace: str = "aligned_old/Exp001_Trial001_Part001.mat"
@ -143,104 +143,150 @@ old: np.ndarray = np.array(file_handle["ratioSequence"]) # type:ignore
old = np.moveaxis(old, 0, -1)
old = np.moveaxis(old, 0, -2)
pos_x = 25
pos_y = 75
# pos_x = 25
# pos_y = 75
plt.figure(1)
plt.subplot(2, 1, 1)
new_select = new[pos_x, pos_y, :]
old_select = old[pos_x, pos_y, :]
plt.plot(new_select, label="New")
plt.plot(old_select, "--", label="Old")
plt.plot(old_select - new_select + 1.0, label="Old - New + 1")
plt.title(f"Position: {pos_x}, {pos_y}")
plt.legend()
# plt.figure(1)
# new_select = new[pos_x, pos_y, :]
# old_select = old[pos_x, pos_y, :]
# plt.plot(new_select, label="New")
# plt.plot(old_select, "--", label="Old")
# # plt.plot(old_select - new_select + 1.0, label="Old - New + 1")
# plt.title(f"Position: {pos_x}, {pos_y}")
# plt.legend()
plt.subplot(2, 1, 2)
differences = (np.abs(new - old)).max(axis=-1)
plt.imshow(differences)
plt.title("Max of abs(new-old) along time")
# plt.show(block=False)
# plt.figure(2)
# new_select1 = new[pos_x + 1, pos_y, :]
# old_select1 = old[pos_x + 1, pos_y, :]
# plt.plot(new_select1, label="New")
# plt.plot(old_select1, "--", label="Old")
# # plt.plot(old_select - new_select + 1.0, label="Old - New + 1")
# plt.title(f"Position: {pos_x+1}, {pos_y}")
# plt.legend()
# plt.show(block=False)
# plt.figure(3)
# plt.plot(old_select, label="Old")
# plt.plot(old_select1, "--", label="Old")
# # plt.plot(old_select - new_select + 1.0, label="Old - New + 1")
# # plt.title(f"Position: {pos_x+1}, {pos_y}")
# plt.legend()
# s1 = old_select[np.newaxis, 100:]
# s2 = new_select[np.newaxis, 100:]
# s3 = old_select1[np.newaxis, 100:]
# print("old-new", np.corrcoef(np.concatenate((s1, s2))))
# print("old-oldshift", np.corrcoef(np.concatenate((s1, s3))))
plt.figure(4)
mask = mask.cpu().numpy()
mask_flatten = np.reshape(mask, (mask.shape[0] * mask.shape[1]))
data = np.reshape(old, (old.shape[0] * old.shape[1], old.shape[-1]))
data = data[mask_flatten == 1, 100:]
cc = np.corrcoef(data)
cc_back = np.zeros_like(mask, dtype=np.float32)
cc_back = np.reshape(cc_back, (mask.shape[0] * mask.shape[1]))
rng = np.random.default_rng()
cc_back[mask_flatten] = cc[:, 400]
cc_back = np.reshape(cc_back, (mask.shape[0], mask.shape[1]))
plt.subplot(1, 2, 1)
plt.imshow(cc_back, cmap="hot")
plt.colorbar()
plt.show(block=False)
plt.subplot(1, 2, 2)
plt.plot(cc[:, 400])
ratio_sequence_a = bandpass(
data=data_acceptor,
device=data_acceptor.device,
low_frequency=lower_freqency_bandpass,
high_frequency=upper_freqency_bandpass,
fs=100.0,
filtfilt_chuck_size=10,
)
plt.show(block=True)
ratio_sequence_b = bandpass(
data=data_donor,
device=data_donor.device,
low_frequency=lower_freqency_bandpass,
high_frequency=upper_freqency_bandpass,
fs=100.0,
filtfilt_chuck_size=10,
)
original_shape = ratio_sequence_a.shape
# block=False
# ratio_sequence_a = bandpass(
# data=data_acceptor,
# device=data_acceptor.device,
# low_frequency=lower_freqency_bandpass,
# high_frequency=upper_freqency_bandpass,
# fs=100.0,
# filtfilt_chuck_size=10,
# )
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)
# ratio_sequence_b = bandpass(
# data=data_donor,
# device=data_donor.device,
# low_frequency=lower_freqency_bandpass,
# high_frequency=upper_freqency_bandpass,
# fs=100.0,
# filtfilt_chuck_size=10,
# )
mask = mask.flatten(start_dim=0, end_dim=-1)
ratio_sequence_a = ratio_sequence_a[mask, :]
ratio_sequence_b = ratio_sequence_b[mask, :]
# original_shape = ratio_sequence_a.shape
ratio_sequence_a = ratio_sequence_a.movedim(0, -1)
ratio_sequence_b = ratio_sequence_b.movedim(0, -1)
# 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)
ratio_sequence_a -= ratio_sequence_a.mean(dim=0, keepdim=True)
ratio_sequence_b -= ratio_sequence_b.mean(dim=0, keepdim=True)
# mask = mask.flatten(start_dim=0, end_dim=-1)
# ratio_sequence_a = ratio_sequence_a[mask, :]
# ratio_sequence_b = ratio_sequence_b[mask, :]
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, :]
# ratio_sequence_a = ratio_sequence_a.movedim(0, -1)
# ratio_sequence_b = ratio_sequence_b.movedim(0, -1)
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)
# ratio_sequence_a -= ratio_sequence_a.mean(dim=0, keepdim=True)
# ratio_sequence_b -= ratio_sequence_b.mean(dim=0, keepdim=True)
heartbeatactivitmap_a *= torch.nan
heartbeatactivitmap_a[mask] = s_a * Vh_a
heartbeatactivitmap_a = heartbeatactivitmap_a.reshape(
(original_shape[0], original_shape[1])
)
# 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, :]
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_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_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_a *= torch.nan
# heartbeatactivitmap_a[mask] = s_a * Vh_a
# heartbeatactivitmap_a = heartbeatactivitmap_a.reshape(
# (original_shape[0], original_shape[1])
# )
heartbeatactivitmap_b *= torch.nan
heartbeatactivitmap_b[mask] = s_b * Vh_b
heartbeatactivitmap_b = heartbeatactivitmap_b.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, :]
plt.figure(2)
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()
# 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.figure(2)
# 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()