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David Rotermund 2024-02-28 16:11:22 +01:00 committed by GitHub
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analyse_raw_data.py
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import torch
from functions.Anime import Anime
from functions.DataContainer import DataContainer
import matplotlib.pyplot as plt
import numpy as np
import argh
import os
def main(
path: str = "/data_1/hendrik/2021-06-17/M3859M/raw",
experiment_id: int = 1,
trial_id: int = 1,
use_svd: bool = True, # i.e. use SVD
mask_threshold: float | None = 0.0025, # Between 0 and 1.0
show_example_timeseries: bool = True,
example_position_x: int = 280,
example_position_y: int = 440,
movie_play: bool = True,
movie_vmin_scale: float | None = 0.05,
movie_vmax_scale: float | None = 0.1,
movie_enable_mask: bool = True,
movie_export: bool = False,
export_results: bool = True,
export_path: str = "Export",
):
if use_svd:
print("SVD mode")
else:
print("Classic mode")
if movie_export is False:
movie_file: str | None = None
else:
if use_svd:
movie_file = f"SVD_Exp{experiment_id}_Trial{trial_id}.mp4"
else:
movie_file = f"Classic_Exp{experiment_id}_Trial{trial_id}.mp4"
if export_results:
os.makedirs(export_path, exist_ok=True)
initital_mask_name: str | None = None
initital_mask_update: bool = True
initital_mask_roi: bool = False # default: True
start_position: int = 0
start_position_coefficients: int = 100
svd_iterations: int = 1 # SVD iterations: Do not touch! Keep at 1
bin_size: int = 4
display_logging_messages: bool = False
save_logging_messages: bool = False
# Post data processing modifiations
gaussian_blur_kernel_size: int | None = 3
gaussian_blur_sigma: float = 1.0
bin_size_post: int | None = None
# ------------------------
example_position_x = example_position_x // bin_size
example_position_y = example_position_y // bin_size
if bin_size_post is not None:
example_position_x = example_position_x // bin_size_post
example_position_y = example_position_y // bin_size_post
torch_device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu"
)
af = DataContainer(
path=path,
device=torch_device,
display_logging_messages=display_logging_messages,
save_logging_messages=save_logging_messages,
)
result, mask = af.automatic_load(
experiment_id=experiment_id,
trial_id=trial_id,
start_position=start_position,
remove_heartbeat=use_svd, # i.e. use SVD
iterations=svd_iterations,
bin_size=bin_size,
initital_mask_name=initital_mask_name,
initital_mask_update=initital_mask_update,
initital_mask_roi=initital_mask_roi,
start_position_coefficients=start_position_coefficients,
gaussian_blur_kernel_size=gaussian_blur_kernel_size,
gaussian_blur_sigma=gaussian_blur_sigma,
bin_size_post=bin_size_post,
threshold=mask_threshold,
)
if show_example_timeseries:
plt.plot(result[:, example_position_x, example_position_y].cpu())
plt.show()
if export_results:
if use_svd:
np.save(
os.path.join(export_path, f"SVD_{experiment_id}_{trial_id}_data.npy"),
result.cpu().numpy(),
)
if mask is not None:
np.save(
os.path.join(
export_path, f"SVD_{experiment_id}_{trial_id}_mask.npy"
),
result.cpu().numpy(),
)
else:
np.save(
os.path.join(
export_path, f"Classic_{experiment_id}_{trial_id}_data.npy"
),
result.cpu().numpy(),
)
if mask is not None:
np.save(
os.path.join(
export_path, f"Classic_{experiment_id}_{trial_id}_mask.npy"
),
result.cpu().numpy(),
)
if movie_play:
ani = Anime()
if movie_enable_mask:
ani.show(
result - 1.0,
mask=mask,
vmin_scale=movie_vmin_scale,
vmax_scale=movie_vmax_scale,
movie_file=movie_file,
)
else:
ani.show(
result - 1.0,
vmin_scale=movie_vmin_scale,
vmax_scale=movie_vmax_scale,
movie_file=movie_file,
)
if __name__ == "__main__":
argh.dispatch_command(main)