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Merged
rfievet merged 3 commits into from
Mar 11, 2026
Merged

adding predefined visualisation recipe #58

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993dfda
draft of predefined visualisation recipe
rfievet Mar 9, 2026
9020f26
fix: correct return type in _extract_band to match type signature
rfievet Mar 9, 2026
f46e98d
fix: update test mocks to return tuples matching _extract_band signature
rfievet Mar 9, 2026
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227 changes: 183 additions & 44 deletions copernicus_marimo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -835,7 +835,37 @@ def _(


@app.cell
def _(cached_images, file_metadata, mo, time_slider):
def _(cached_images, mo, satellite_type):
"""Create band recipe selector (independent of time slider to maintain state)."""
band_recipe_selector = None

if cached_images and len(cached_images) > 0:
from src.data.copernicus.band_recipes import get_available_recipes

_recipes = get_available_recipes()

# Filter recipes based on satellite type
if satellite_type.value == "S2":
# S2: Show optical recipes only
_recipe_names = [r.name for rid, r in _recipes.items() if not rid.startswith("sar_")]
elif satellite_type.value == "S1":
# S1: Show SAR recipes only
_recipe_names = [r.name for rid, r in _recipes.items() if rid.startswith("sar_")]
else: # S1+S2
# S1+S2: Show optical recipes (visualizing S2)
_recipe_names = [r.name for rid, r in _recipes.items() if not rid.startswith("sar_")]

if _recipe_names:
band_recipe_selector = mo.ui.dropdown(
options=_recipe_names,
label="Band Recipe",
)

return (band_recipe_selector,)


@app.cell
def _(band_recipe_selector, cached_images, file_metadata, mo, time_slider):
"""Display time slider and adjustment sliders in a compact side-by-side layout."""
slider_display = None
contrast_slider = None
Expand Down Expand Up @@ -877,6 +907,18 @@ def _(cached_images, file_metadata, mo, time_slider):
_current_idx = time_slider.value
_current_meta = file_metadata[_current_idx]

# Build adjustment controls
_adjustment_controls = [
mo.md("**Fine-tune image appearance**"),
contrast_slider,
brightness_slider,
gamma_slider,
]

# Add band recipe selector if available
if band_recipe_selector is not None:
_adjustment_controls.insert(1, band_recipe_selector)

# Create two-column layout: time slider on left, adjustments on right
slider_display = mo.vstack(
[
Expand All @@ -903,22 +945,25 @@ def _(cached_images, file_metadata, mo, time_slider):
],
align="start",
),
# Right column: Adjustment sliders
mo.vstack(
[
mo.md("**Fine-tune image appearance**"),
contrast_slider,
brightness_slider,
gamma_slider,
],
align="start",
),
# Right column: Adjustment sliders and band recipe
mo.vstack(_adjustment_controls, align="start"),
],
justify="start",
),
]
)
elif file_metadata and len(file_metadata) == 1:
# Build adjustment controls
_adjustment_controls = [
contrast_slider,
brightness_slider,
gamma_slider,
]

# Add band recipe selector if available
if band_recipe_selector is not None:
_adjustment_controls.insert(0, band_recipe_selector)

# Single image - just show adjustments compactly
slider_display = mo.vstack(
[
Expand All @@ -930,14 +975,7 @@ def _(cached_images, file_metadata, mo, time_slider):
**Date**: {file_metadata[0]['date_str']} | **File**: `{file_metadata[0]['filename'][:50]}...`
"""
),
mo.hstack(
[
contrast_slider,
brightness_slider,
gamma_slider,
],
justify="start",
),
mo.hstack(_adjustment_controls, justify="start"),
]
)

Expand Down Expand Up @@ -1009,9 +1047,11 @@ def apply_image_adjustments(image_array, contrast, brightness, gamma):
@app.cell
def _(
apply_image_adjustments,
band_recipe_selector,
brightness_slider,
cached_images,
contrast_slider,
crop_to_bbox,
file_metadata,
gamma_slider,
max_lat,
Expand All @@ -1023,13 +1063,13 @@ def _(
time_slider,
traceback,
):
"""Visualize the selected satellite image using cached data.
"""Visualize the selected satellite image using cached data with band recipe support.

This cell displays pre-processed images from cache, making slider
interactions nearly instant (no disk I/O or processing needed).

Visualization details:
- S2: RGB composite (natural color) with target bbox overlay
- S2: Supports multiple band recipes (True Color, False Color, Agriculture, NDVI, NDWI)
- S1: VV polarization (grayscale) with adaptive contrast
- Both: Already cropped to target bbox during pre-processing
- Image adjustments applied in real-time based on slider values
Expand Down Expand Up @@ -1063,12 +1103,46 @@ def _(
_brightness = brightness_slider.value if brightness_slider is not None else 50
_gamma = gamma_slider.value if gamma_slider is not None else 50

# Get selected band recipe
_selected_recipe = None
if band_recipe_selector is not None:
_selected_recipe = band_recipe_selector.value

# Apply band recipe if needed
_display_data = _image_data
_needs_recipe_processing = False

# Determine if we need to reprocess with recipe
if _selected_recipe is not None:
if satellite_type.value in ["S2", "S1+S2"]:
# S2: Reprocess if not True Color
_needs_recipe_processing = _selected_recipe != "True Color (RGB)"
elif satellite_type.value == "S1":
# S1: Reprocess if not default SAR VV
_needs_recipe_processing = _selected_recipe != "SAR VV (Surface)"

if _needs_recipe_processing:
# Need to reprocess with the selected recipe
from src.data.copernicus.band_recipes import apply_band_recipe

# Only pass bbox if crop_to_bbox is enabled
_recipe_bbox = _viz_bbox if crop_to_bbox.value else None

_recipe_result = apply_band_recipe(
_metadata["path"], _selected_recipe, bbox=_recipe_bbox
)
if _recipe_result is not None:
_display_data = _recipe_result
else:
# Fall back to cached data if recipe fails
print(f"Failed to apply recipe '{_selected_recipe}', using cached data")

# Create figure
fig, ax = plt.subplots(1, 1, figsize=(10, 8))

# Display the cached image
if _image_data is not None and _image_data.get("bounds_wgs84") is not None:
_bounds = _image_data["bounds_wgs84"]
# Display the image
if _display_data is not None and _display_data.get("bounds_wgs84") is not None:
_bounds = _display_data["bounds_wgs84"]
_extent = (
_bounds[0],
_bounds[2],
Expand All @@ -1078,7 +1152,7 @@ def _(

# PERFORMANCE FIX: If image is not cropped, crop it now for visualization
# This prevents matplotlib from rendering millions of pixels that won't be visible
_display_data = None
_display_array = None
_display_extent = _extent

# Calculate zoom area with padding
Expand All @@ -1100,18 +1174,22 @@ def _(
# Crop the image to the zoom area for faster rendering
from src.data.copernicus.image_processing import crop_to_bbox as _crop_fn

# Check if we have RGB or SAR data
if "rgb_array" in _image_data:
_display_data = _crop_fn(_image_data["rgb_array"], _bounds, _zoom_bbox)
elif "sar_array" in _image_data:
_sar_data = _image_data["sar_array"]
# Check if we have RGB, index, or SAR data
if "rgb_array" in _display_data:
_display_array = _crop_fn(_display_data["rgb_array"], _bounds, _zoom_bbox)
elif "index_array" in _display_data:
_display_array = _crop_fn(
_display_data["index_array"], _bounds, _zoom_bbox
)
elif "sar_array" in _display_data:
_sar_data = _display_data["sar_array"]
if _sar_data.ndim == 3:
_sar_data = _sar_data[:, :, 0] # Extract first polarization
_display_data = _crop_fn(_sar_data, _bounds, _zoom_bbox)
_display_array = _crop_fn(_sar_data, _bounds, _zoom_bbox)
else:
_display_data = None
_display_array = None

if _display_data is not None:
if _display_array is not None:
# Update extent to match cropped area
_display_extent = (
_zoom_bbox[0],
Expand All @@ -1124,28 +1202,85 @@ def _(
_needs_crop = False

# Display the image array (already normalized and ready)
# Check what type of data we have (RGB or SAR)
if "rgb_array" in _image_data:
# Check what type of data we have (RGB, index, or SAR)
if "rgb_array" in _display_data:
# RGB image (H, W, 3) - for S2 or S1+S2 mode
if _needs_crop and _display_data is not None:
if _needs_crop and _display_array is not None:
# Apply image adjustments
_adjusted_data = apply_image_adjustments(
_display_data, _contrast, _brightness, _gamma
_display_array, _contrast, _brightness, _gamma
)
ax.imshow(_adjusted_data, extent=_display_extent, aspect="auto")
else:
# Apply image adjustments
_adjusted_data = apply_image_adjustments(
_image_data["rgb_array"], _contrast, _brightness, _gamma
_display_data["rgb_array"], _contrast, _brightness, _gamma
)
ax.imshow(_adjusted_data, extent=_extent, aspect="auto")
elif "sar_array" in _image_data:
elif "index_array" in _display_data:
# Spectral index or SAR data (H, W) - for NDVI, NDWI, SAR VV/VH, etc.
_index_array = (
_display_array
if _needs_crop and _display_array is not None
else _display_data["index_array"]
)

# Check if this is SAR data (needs different normalization)
_is_sar = _display_data.get("metadata", {}).get("type") == "sar"

if _is_sar:
# SAR data: Use percentile-based normalization (dB values vary)
_vmin, _vmax = np.percentile(_index_array, [2, 98])
_normalized = np.clip(
(_index_array - _vmin) / (_vmax - _vmin + 1e-10), 0, 1
)
else:
# Spectral indices: Use fixed value range
_vmin, _vmax = _display_data.get("value_range", (-1, 1))
_normalized = np.clip(
(_index_array - _vmin) / (_vmax - _vmin + 1e-10), 0, 1
)

# Apply image adjustments
_adjusted_data = apply_image_adjustments(
_normalized, _contrast, _brightness, _gamma
)

# Display with appropriate colormap
_cmap = _display_data.get("colormap", "RdYlGn")
_extent_to_use = _display_extent if _needs_crop else _extent
im = ax.imshow(
_adjusted_data,
extent=_extent_to_use,
aspect="auto",
cmap=_cmap,
vmin=0,
vmax=1,
)
# Add colorbar for index visualization
cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)

# Label colorbar appropriately
if _is_sar:
_pol_name = _display_data.get("metadata", {}).get("polarization", "SAR")
cbar.set_label(
f"{_pol_name} Backscatter (dB)",
rotation=270,
labelpad=15,
)
else:
cbar.set_label(
_display_data.get("metadata", {}).get("index", "Index Value"),
rotation=270,
labelpad=15,
)
elif "sar_array" in _display_data:
# SAR grayscale image (H, W, 1) - squeeze to (H, W) - for S1 mode
if _needs_crop and _display_data is not None:
if _needs_crop and _display_array is not None:
# Normalize to 0-1 range for adjustments
_vmin, _vmax = np.percentile(_display_data, [2, 98])
_vmin, _vmax = np.percentile(_display_array, [2, 98])
_normalized = np.clip(
(_display_data - _vmin) / (_vmax - _vmin + 1e-10), 0, 1
(_display_array - _vmin) / (_vmax - _vmin + 1e-10), 0, 1
)

# Apply image adjustments
Expand All @@ -1162,7 +1297,7 @@ def _(
vmax=1,
)
else:
_sar_data = _image_data["sar_array"]
_sar_data = _display_data["sar_array"]
if _sar_data.ndim == 3:
_sar_data = _sar_data[:, :, 0] # Extract first polarization

Expand Down Expand Up @@ -1217,7 +1352,11 @@ def _(
ax.set_xlabel("Longitude (°E)", fontsize=12)
ax.set_ylabel("Latitude (°N)", fontsize=12)

_title = f"{satellite_type.value} Image - {_metadata['date_str']}\n{_metadata['filename'][:50]}..."
# Update title to show recipe name
_recipe_name = _selected_recipe if _selected_recipe else satellite_type.value
_title = (
f"{_recipe_name} - {_metadata['date_str']}\n{_metadata['filename'][:50]}..."
)
ax.set_title(_title, fontsize=11, fontweight="bold")

ax.grid(True, alpha=0.3, color="white")
Expand Down
11 changes: 11 additions & 0 deletions src/data/copernicus/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,11 @@
"""Copernicus Data Space Ecosystem client for fetching Sentinel-1 and Sentinel-2 data."""

from .band_recipes import (
apply_band_recipe,
get_available_recipes,
get_recipe_by_name,
get_recipe_names,
)
from .client import CopernicusClient
from .enums import S1AcquisitionMode, S1Polarization, S1ProductType, S2Band
from .image_processing import (
Expand Down Expand Up @@ -38,6 +44,11 @@
"S1ProductType",
"S1Polarization",
"S1AcquisitionMode",
# Band recipes
"get_available_recipes",
"get_recipe_names",
"get_recipe_by_name",
"apply_band_recipe",
# Image processing
"extract_rgb_composite",
"extract_sar_composite",
Expand Down
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