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test_average_calculate.py

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+import torch
+from DataContainer import DataContainer
+import numpy as np
+from tqdm import trange
+
+# path: str = "/data_1/robert/2021-05-05/M3852M/raw"
+path: str = "/data_1/robert/2021-05-21/M3852M/raw"
+initital_mask_name: str | None = "mask.npy"
+initital_mask_update: bool = True
+initital_mask_roi: bool = False  # default: True
+
+experiment_id: int = 2
+trial_id: int = 180
+start_position: int = 0
+start_position_coefficients: int = 100
+remove_heartbeat: bool = True  # i.e. use SVD
+bin_size: int = 4
+threshold: float | None = 0.05  # Between 0 and 1.0
+
+
+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
+
+# ------------------------
+
+
+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,
+)
+
+
+list_of_experiments = af.get_experiments()
+print(
+    f"The following experiments have been found:\n {list_of_experiments.cpu().numpy()}"
+)
+assert experiment_id in list_of_experiments
+print(f"Continue with experiment: {experiment_id}")
+list_of_trials = af.get_trials(experiment_id).cpu().numpy()
+print(f"The following trials have been found:\n {list_of_trials}")
+
+result: torch.Tensor | None = None
+n: float = 0
+for trial_id in trange(0, len(list_of_trials)):
+    result_temp, mask = af.automatic_load(
+        experiment_id=experiment_id,
+        trial_id=int(list_of_trials[trial_id]),
+        start_position=start_position,
+        remove_heartbeat=remove_heartbeat,  # i.e. use SVD
+        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=threshold,
+    )
+    n += 1.0
+    if result is None:
+        result = result_temp
+    else:
+        result += result_temp
+
+assert result is not None
+assert mask is not None
+
+result /= n
+
+np.savez("result.npz", result=result.cpu(), mask=mask.cpu())

test_average_show.py

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+from Anime import Anime
+import matplotlib.pyplot as plt
+import numpy as np
+
+show_example_timeseries: bool = True
+play_movie: bool = True
+
+example_position_x: int = 280
+example_position_y: int = 440
+bin_size: int = 4
+bin_size_post: int | None = None
+
+data = np.load("result.npz")
+result = data["result"]
+mask = data["mask"]
+
+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
+
+if show_example_timeseries:
+    plt.plot(result[:, example_position_x, example_position_y])
+    plt.show()
+
+if play_movie:
+    ani = Anime()
+    ani.show(
+        result - 1.0, mask=mask, vmin_scale=0.5, vmax_scale=0.5
+    )  # , vmin=0.98)  # , vmin=1.0, vmax_scale=1.0)