full upload so not to lose anything important

tools/plot_scripts/data_spherical_projection.py

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+import argparse
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import colormaps
+from matplotlib.cm import ScalarMappable
+from matplotlib.colors import Colormap, ListedColormap
+from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
+from PIL import Image  # new import
+
+
+def get_colormap_with_special_missing_color(
+    colormap_name: str, missing_data_color: str = "black", reverse: bool = False
+) -> Colormap:
+    colormap = (
+        colormaps[colormap_name] if not reverse else colormaps[f"{colormap_name}_r"]
+    )
+    colormap.set_bad(missing_data_color)
+    colormap.set_over("white")
+    return colormap
+
+
+# --- Setup output folders (similar to data_missing_points.py) ---
+# Change the output path as needed
+output_path = Path("/home/fedex/mt/plots/data_2d_projections")
+datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+output_datetime_path = output_path / datetime_folder_name
+latest_folder_path = output_path / "latest"
+archive_folder_path = output_path / "archive"
+
+for folder in (
+    output_path,
+    output_datetime_path,
+    latest_folder_path,
+    archive_folder_path,
+):
+    folder.mkdir(exist_ok=True, parents=True)
+
+# --- Parse command-line arguments ---
+parser = argparse.ArgumentParser(
+    description="Plot two 2D projections (from .npy files) in vertical subplots"
+)
+parser.add_argument(
+    "--input1",
+    type=Path,
+    default=Path(
+        "/home/fedex/mt/data/subter/new_projection/1_loop_closure_illuminated_2023-01-23.npy"
+    ),
+    help="Path to first .npy file containing 2D projection data",
+)
+parser.add_argument(
+    "--input2",
+    type=Path,
+    default=Path(
+        "/home/fedex/mt/data/subter/new_projection/3_smoke_human_walking_2023-01-23.npy"
+    ),
+    help="Path to second .npy file containing 2D projection data",
+)
+parser.add_argument(
+    "--frame1",
+    type=int,
+    default=955,
+    help="Frame index to plot from first file (0-indexed)",
+)
+parser.add_argument(
+    "--frame2",
+    type=int,
+    default=242,
+    help="Frame index to plot from second file (0-indexed)",
+)
+parser.add_argument(
+    "--colormap",
+    default="viridis",
+    type=str,
+    help="Name of matplotlib colormap to use",
+)
+parser.add_argument(
+    "--missing-data-color",
+    default="black",
+    type=str,
+    help="Color to use for missing data in projection",
+)
+parser.add_argument(
+    "--reverse-colormap",
+    action="store_true",
+    help="Reverse the colormap if specified",
+)
+args = parser.parse_args()
+
+# --- Load the numpy projection data from the provided files ---
+# Each file is assumed to be a 3D array: (num_frames, height, width)
+proj_data1 = np.load(args.input1)
+proj_data2 = np.load(args.input2)
+
+# Choose the desired frames
+try:
+    frame1 = proj_data1[args.frame1]
+except IndexError:
+    raise ValueError(f"Frame index {args.frame1} out of range for file: {args.input1}")
+try:
+    frame2 = proj_data2[args.frame2]
+except IndexError:
+    raise ValueError(f"Frame index {args.frame2} out of range for file: {args.input2}")
+
+# Determine shared range across both frames (ignoring NaNs)
+global_vmin = 0  # min(np.nanmin(frame1), np.nanmin(frame2))
+global_vmax = 0.8  # max(np.nanmax(frame1), np.nanmax(frame2))
+
+# Create colormap using the utility (to mimic create_2d_projection)
+cmap = get_colormap_with_special_missing_color(
+    args.colormap, args.missing_data_color, args.reverse_colormap
+)
+
+# --- Create a figure with 2 vertical subplots ---
+fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))
+for ax, frame, title in zip(
+    (ax1, ax2),
+    (frame1, frame2),
+    (
+        "Projection of Lidar Frame without Degradation",
+        "Projection of Lidar Frame with Degradation (Artifical Smoke)",
+    ),
+):
+    im = ax.imshow(frame, cmap=cmap, aspect="auto", vmin=global_vmin, vmax=global_vmax)
+    ax.set_title(title)
+    ax.axis("off")
+
+# Adjust layout to fit margins for a paper
+plt.tight_layout(rect=[0, 0.05, 1, 1])
+# Add a colorbar with the colormap below the subplots
+cbar = fig.colorbar(im, ax=[ax1, ax2], orientation="vertical", fraction=0.05)
+cbar.set_label("Normalized Range")
+
+# Add a separate colorbar for NaN values
+sm = ScalarMappable(cmap=ListedColormap([cmap.get_bad(), cmap.get_over()]))
+divider = make_axes_locatable(cbar.ax)
+nan_ax = divider.append_axes(
+    "bottom", size="15%", pad="3%", aspect=3, anchor=cbar.ax.get_anchor()
+)
+nan_ax.grid(visible=False, which="both", axis="both")
+nan_cbar = fig.colorbar(sm, cax=nan_ax, orientation="vertical")
+nan_cbar.set_ticks([0.3, 0.7])
+nan_cbar.set_ticklabels(["NaN", f"> {global_vmax}"])
+nan_cbar.ax.tick_params(length=0)
+
+# Save the combined plot
+output_file = output_datetime_path / "data_2d_projections.png"
+plt.savefig(output_file, dpi=300, bbox_inches="tight", pad_inches=0.1)
+plt.close()
+
+print(f"Plot saved to: {output_file}")
+
+# --- Create grayscale images (high precision) from the numpy frames using Pillow ---
+# Convert NaN values to 0 and ensure the array is in float32 for 32-bit precision.
+for degradation_status, frame_number, frame in (
+    ("normal", args.frame1, frame1),
+    ("smoke", args.frame2, frame2),
+):
+    frame_gray = np.nan_to_num(frame, nan=0).astype(np.float32)
+    gray_image = Image.fromarray(frame_gray, mode="F")
+    gray_output_file = (
+        output_datetime_path
+        / f"frame_{frame_number}_grayscale_{degradation_status}.tiff"
+    )
+    gray_image.save(gray_output_file)
+    print(f"Grayscale image saved to: {gray_output_file}")
+
+# --- Handle folder structure: update latest folder and archive the output folder ---
+
+# Delete current latest folder and recreate it
+shutil.rmtree(latest_folder_path, ignore_errors=True)
+latest_folder_path.mkdir(exist_ok=True, parents=True)
+
+# Copy contents of the current output datetime folder to latest folder
+for file in output_datetime_path.iterdir():
+    shutil.copy2(file, latest_folder_path)
+
+# Copy this python script to both output datetime folder and latest folder for preservation
+script_path = Path(__file__)
+shutil.copy2(script_path, output_datetime_path)
+shutil.copy2(script_path, latest_folder_path)
+
+# Move the output datetime folder to the archive folder
+shutil.move(output_datetime_path, archive_folder_path)
+print(f"Output archived to: {archive_folder_path}")