diff --git a/notebooks/color_projection_clahe_demo.py b/notebooks/color_projection_clahe_demo.py
new file mode 100644
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+++ b/notebooks/color_projection_clahe_demo.py
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+import marimo as mo
+
+__generated_with__ = "0.6.15"
+
+app = mo.App()
+
+
+@app.cell
+def __():
+    import marimo as mo
+
+    mo.md(
+        """
+        # Color-coded projection and CLAHE demo
+
+        This notebook demonstrates how to use the
+        `color_coded_projection` and `_my_clahe_` utilities provided in this
+        repository. We load the sample `cells3d` dataset from scikit-image and
+        showcase both functions:
+
+        * **color_coded_projection** for creating a time/volume color projection
+        * **_my_clahe_** for applying Contrast Limited Adaptive Histogram Equalization (CLAHE)
+
+        Use the controls below to explore different color mappings for the
+        projection and adjust the CLAHE clip limit to see its effect on the
+        enhanced slice.
+        """
+    )
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from skimage import data
+
+    from clahe_equalize_adapthist import _my_clahe_
+    from color_coded_projection import color_coded_projection
+
+    return plt, np, data, _my_clahe_, color_coded_projection
+
+
+@app.cell
+def __():
+    import marimo as mo
+
+    colormap_dropdown = mo.ui.dropdown(
+        label="Projection colormap",
+        options=[
+            ("Plasma", "plasma"),
+            ("Viridis", "viridis"),
+            ("Inferno", "inferno"),
+            ("Magma", "magma"),
+            ("Cividis", "cividis"),
+        ],
+        value="plasma",
+    )
+
+    colormap_dropdown
+
+    return colormap_dropdown
+
+
+@app.cell
+def __():
+    import marimo as mo
+
+    clahe_clip_slider = mo.ui.slider(
+        label="CLAHE clip limit",
+        start=0.01,
+        stop=0.1,
+        step=0.005,
+        value=0.03,
+    )
+
+    clahe_clip_slider
+
+    return clahe_clip_slider
+
+
+@app.cell
+def __(data):
+    cells = data.cells3d()
+    # Select the membrane channel (index 1)
+    membrane_stack = cells[:, 1, :, :]
+    return membrane_stack
+
+
+@app.cell
+def __(membrane_stack, np):
+    # Normalize the stack to the range [0, 1]
+    stack_min = membrane_stack.min()
+    stack_max = membrane_stack.max()
+    normalized_stack = (membrane_stack - stack_min) / (stack_max - stack_min)
+    return normalized_stack
+
+
+@app.cell
+def __(color_coded_projection, colormap_dropdown, normalized_stack, np):
+    projection = color_coded_projection(
+        normalized_stack.astype(np.float32),
+        color_map=colormap_dropdown.value,
+    )
+    return projection
+
+
+@app.cell
+def __(colormap_dropdown, projection, plt):
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.imshow(projection)
+    ax.set_title(
+        f"Color-coded projection of membrane channel (cmap: {colormap_dropdown.value})"
+    )
+    ax.axis("off")
+    fig.tight_layout()
+    fig
+
+
+@app.cell
+def __(membrane_stack):
+    slice_index = 30
+    original_slice = membrane_stack[slice_index]
+    return original_slice, slice_index
+
+
+@app.cell
+def __(_my_clahe_, clahe_clip_slider, original_slice):
+    clahe_slice = _my_clahe_(
+        original_slice,
+        clip_limit=float(clahe_clip_slider.value),
+        nbins=256,
+    )
+    return clahe_slice
+
+
+@app.cell
+def __(clahe_clip_slider, clahe_slice, original_slice, plt, slice_index):
+    fig, axes = plt.subplots(1, 2, figsize=(10, 4))
+    axes[0].imshow(original_slice, cmap="gray")
+    axes[0].set_title(f"Original slice {slice_index}")
+    axes[0].axis("off")
+
+    axes[1].imshow(clahe_slice, cmap="gray")
+    axes[1].set_title(
+        f"CLAHE enhanced slice (clip_limit={float(clahe_clip_slider.value):.3f})"
+    )
+    axes[1].axis("off")
+
+    fig.tight_layout()
+    fig
+
+
+if __name__ == "__main__":
+    app.run()