Delete test_average_calculate.py

test_average_calculate.py

@@ -1,113 +0,0 @@
-import torch
-from functions.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 = None
-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}")
-
-
-# mask
-_, mask = af.automatic_load(
-    experiment_id=experiment_id,
-    trial_id=int(list_of_trials[0]),
-    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,
-)
-
-if mask is not None:
-    np.save("mask.npy", mask.cpu())
-
-
-# data
-result: torch.Tensor | None = None
-count_not_nan: torch.Tensor | None = None
-
-n: float = 0
-for trial_id in trange(0, len(list_of_trials)):
-    result_temp, _ = af.automatic_load(  # type: ignore
-        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=None,
-    )
-    n += 1.0
-    if result is None:
-        result = (result_temp - 1.0).nan_to_num(nan=0.0)
-        count_not_nan = torch.isfinite(result_temp).type(torch.float32)
-    else:
-        result += (result_temp - 1.0).nan_to_num(nan=0.0)
-        count_not_nan += torch.isfinite(result_temp).type(torch.float32)
-
-    assert result is not None
-    if trial_id % 10 == 0:
-        np.save("result.npy", (result / count_not_nan).cpu())
-        np.save("count_not_nan.npy", (count_not_nan / n).cpu())
-
-assert result is not None
-assert count_not_nan is not None
-
-np.save("result.npy", (result / count_not_nan).cpu())
-np.save("count_not_nan.npy", (count_not_nan / n).cpu())