From f8399f78a726d3a914ac155a26ec42f45c3a0a7c Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Wed, 28 Feb 2024 16:11:22 +0100
Subject: [PATCH] Delete analyse_raw_data.py

---
 analyse_raw_data.py | 150 --------------------------------------------
 1 file changed, 150 deletions(-)
 delete mode 100644 analyse_raw_data.py

diff --git a/analyse_raw_data.py b/analyse_raw_data.py
deleted file mode 100644
index b6208b6..0000000
--- a/analyse_raw_data.py
+++ /dev/null
@@ -1,150 +0,0 @@
-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)