updated datasets

README.md

@@ -1,5 +1,5 @@
 ---
-title: waveOrder Phase Reconstruction Viewer
+title: WaveOrder
 emoji: 🔬
 python_version: 3.13
 colorFrom: blue
@@ -17,7 +17,7 @@ tags:
 - scientific-visualization
 ---
 
-# waveOrder: Phase Reconstruction Interactive Viewer
+# WaveOrder
 
 <div align="center">
 
@@ -29,13 +29,13 @@ tags:
 
 ## 📄 Paper
 
-**waveOrder: generalist framework for label-agnostic computational microscopy**
+**WaveOrder: generalist framework for label-agnostic computational microscopy**
 Chandler T., Ivanov I.E., Hirata-Miyasaki E., et al. "WaveOrder: Physics-informed ML for auto-tuned multi-contrast computational microscopy from cells to
  organisms." [arXiv:2412.09775](https://arxiv.org/abs/2412.09775) (2025)
 
 ## 🔬 About
 
-Interactive web interface for exploring phase reconstruction from quantitative label-free microscopy data. This demo showcases the waveOrder framework's capabilities for reconstructing phase contrast images with interactive parameter optimization.
+Interactive web interface for exploring phase reconstruction from quantitative label-free microscopy data. This demo showcases the WaveOrder framework's capabilities for reconstructing phase contrast images with interactive parameter optimization.
 
 ### Features
 
@@ -67,7 +67,7 @@ This demo uses concatenated 20x objective microscopy data from high-content scre
 
 - **Paper**: [arXiv:2412.09775](https://arxiv.org/abs/2412.09775)
 - **GitHub Repository**: [mehta-lab/waveorder](https://github.com/mehta-lab/waveorder)
-- **Documentation**: [waveOrder Docs](https://github.com/mehta-lab/waveorder/tree/main/docs)
+- **Documentation**: [WaveOrder Docs](https://github.com/mehta-lab/waveorder/tree/main/docs)
 
 ## 📝 Citation
 

data/20x.zarr/A/1/{002026 → 005028}/0/c/0/0/0/0/0

@@ -1,3 +1,3 @@
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-oid sha256:d195a11058e39269e8fe901a940ceac4dfdf2da2b529c7567fc5be994e4739e1
-size 10762964
+oid sha256:59b45f07dbdd34d19cb7a5be46263cc4a0c2e24724edd9b3de9003055a0308ba
+size 2767879

data/20x.zarr/A/1/{002027 → 005028}/0/zarr.json

@@ -3,8 +3,8 @@
     1,
     1,
     7,
-    1024,
-    1024
+    512,
+    512
   ],
   "data_type": "uint16",
   "chunk_grid": {
@@ -14,8 +14,8 @@
         1,
         1,
         7,
-        1024,
-        1024
+        512,
+        512
       ]
     }
   },

data/20x.zarr/A/1/{002028 → 005028}/zarr.json

@@ -53,7 +53,7 @@
       "omero": {
         "version": "0.5",
         "id": 0,
-        "name": "002028",
+        "name": "005028",
         "channels": [
           {
             "active": true,

data/20x.zarr/A/1/{002028 → 005029}/0/c/0/0/0/0/0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cd651a8f2de408b6c9f0827627a0428a34e9b79c0ec49d491f9c91e3ae45e4a7
-size 10796944
+oid sha256:ae859d743adcc5ca961730062f51e186b2b81c6a476b03cb026c91484773c48e
+size 2782178

data/20x.zarr/A/1/{002026 → 005029}/0/zarr.json

@@ -3,8 +3,8 @@
     1,
     1,
     7,
-    1024,
-    1024
+    512,
+    512
   ],
   "data_type": "uint16",
   "chunk_grid": {
@@ -14,8 +14,8 @@
         1,
         1,
         7,
-        1024,
-        1024
+        512,
+        512
       ]
     }
   },

data/20x.zarr/A/1/{002026 → 005029}/zarr.json

@@ -53,7 +53,7 @@
       "omero": {
         "version": "0.5",
         "id": 0,
-        "name": "002026",
+        "name": "005029",
         "channels": [
           {
             "active": true,

data/20x.zarr/A/1/{002027 → 005030}/0/c/0/0/0/0/0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b7193f55c6f1e53fef903483bbcb9b38c51624b8807b7179997147f5d4b1d7f3
-size 10768295
+oid sha256:7090092f581a783fcc89c31a2da439d4ce06e5f4c0ef9caa8ce1657ff12edf5d
+size 2786497

data/20x.zarr/A/1/{002028 → 005030}/0/zarr.json

@@ -3,8 +3,8 @@
     1,
     1,
     7,
-    1024,
-    1024
+    512,
+    512
   ],
   "data_type": "uint16",
   "chunk_grid": {
@@ -14,8 +14,8 @@
         1,
         1,
         7,
-        1024,
-        1024
+        512,
+        512
       ]
     }
   },

data/20x.zarr/A/1/{002027 → 005030}/zarr.json

@@ -53,7 +53,7 @@
       "omero": {
         "version": "0.5",
         "id": 0,
-        "name": "002027",
+        "name": "005030",
         "channels": [
           {
             "active": true,

data/20x.zarr/A/1/zarr.json

@@ -6,15 +6,15 @@
         "images": [
           {
             "acquisition": 0,
-            "path": "002026"
+            "path": "005028"
           },
           {
             "acquisition": 0,
-            "path": "002027"
+            "path": "005029"
           },
           {
             "acquisition": 0,
-            "path": "002028"
+            "path": "005030"
           }
         ]
       },

demo_utils.py

@@ -25,6 +25,9 @@ from xarray_ome import open_ome_dataset
 
 from waveorder import util
 from waveorder.models import isotropic_thin_3d
+from waveorder.cli.compute_transfer_function import (
+    _position_list_from_shape_scale_offset,
+)
 
 # Type alias for device specification
 Device = torch.device | str | None
@@ -467,8 +470,8 @@ def run_reconstruction(zyx_tile: torch.Tensor, recon_args: dict) -> torch.Tensor
     """
     Run phase reconstruction on a Z-stack.
 
-    Takes a 3D stack (Z, Y, X) and produces a 2D phase reconstruction (Y, X).
-    Device is inferred from the input tensor.
+    Uses waveorder's official _position_list_from_shape_scale_offset
+    to ensure proper z-position calculation and correct phase sign.
 
     Parameters
     ----------
@@ -491,18 +494,37 @@ def run_reconstruction(zyx_tile: torch.Tensor, recon_args: dict) -> torch.Tensor
     # Infer device from input tensor
     device = zyx_tile.device
 
-    # Prepare transfer function arguments - ensure all tensors are on the same device
-    tf_args = {}
-    for key, value in recon_args.items():
-        if isinstance(value, torch.Tensor):
-            tf_args[key] = value.to(device)
-        else:
-            tf_args[key] = value
-
+    # Prepare transfer function arguments
+    tf_args = recon_args.copy()
     Z, _, _ = zyx_tile.shape
-    tf_args["z_position_list"] = (
-        torch.arange(Z, device=device) - (Z // 2) + tf_args["z_offset"]
-    ) * tf_args["z_scale"]
+
+    # Extract z_offset value (keep as tensor if it is one, for gradient flow)
+    z_offset_value = recon_args["z_offset"]
+    if torch.is_tensor(z_offset_value):
+        # For optimization: extract scalar value for _position_list function
+        z_offset_scalar = z_offset_value.item()
+    else:
+        z_offset_scalar = z_offset_value
+
+    # Use waveorder's official function (returns torch.Tensor on CPU)
+    z_position_list_cpu = _position_list_from_shape_scale_offset(
+        shape=Z,
+        scale=recon_args["z_scale"],
+        offset=z_offset_scalar,
+    )
+
+    # Move to device and ensure gradient connection if z_offset is a parameter
+    if torch.is_tensor(z_offset_value) and z_offset_value.requires_grad:
+        # Recompute on device to maintain gradient connection
+        # Uses same formula as waveorder: -arange(Z) + (Z // 2) + offset
+        z_position_list = (
+            -torch.arange(Z, dtype=torch.float32, device=device) + (Z // 2) + z_offset_value
+        ) * recon_args["z_scale"]
+    else:
+        # No gradient needed, just move to device
+        z_position_list = z_position_list_cpu.to(device)
+
+    tf_args["z_position_list"] = z_position_list
     tf_args.pop("z_offset")
     tf_args.pop("z_scale")
 

optimize_demo.py

@@ -33,10 +33,10 @@ class Config:
     # Default FOV selection
     DEFAULT_ROW = "A"
     DEFAULT_COLUMN = "1"
-    DEFAULT_FIELD = "002026"
+    DEFAULT_FIELD = "005029"  # Center FOV
 
-    # Restrict to specific FOVs (filter large plate)
-    ALLOWED_FOVS = ['002026', '002027', '002028']
+    # Restrict to specific FOVs (center of well A/1 for better quality)
+    ALLOWED_FOVS = ['005028', '005029', '005030']
 
     # Channel selection (only BF channel in concatenated data)
     CHANNEL = 0  # BF is now channel 0 (GFP was filtered out during concatenation)
@@ -71,11 +71,11 @@ class Config:
 
     # UI slider ranges
     SLIDER_RANGES = {
-        "z_offset": (-0.5, 0.5, 0.01),
+        "z_offset": (-3.0, 3.0, 0.01),  # ±3 µm (1.5x Z-slice spacing for focus correction)
         "na_detection": (0.05, 0.65, 0.001),  # Max 0.65 to accommodate optimization
         "na_illumination": (0.05, 0.65, 0.001),  # Max 0.65 (but constrained <= NA_detection)
-        "tilt_zenith": (0.0, np.pi / 2, 0.005),
-        "tilt_azimuth": (0.0, 2 * np.pi, 0.001),
+        "tilt_zenith": (0.0, np.pi / 4, 0.005),
+        "tilt_azimuth": (0.0, np.pi / 4, 0.001),
     }
 
     # UI configuration
@@ -195,6 +195,23 @@ def get_slice_for_preview(z: int, data_xr_state):
     return (slice_img, slice_img)  # Preview mode: both sides show same image
 
 
+def update_original_slice_only(z: int, data_xr_state, current_reconstructed_state):
+    """
+    Update only the left (original) image when Z changes, keep reconstruction on right.
+
+    If no reconstruction exists yet, shows the original on both sides.
+    """
+    slice_img = extract_2d_slice(
+        data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
+    )
+
+    # If there's a reconstruction, keep it on the right; otherwise show original on both sides
+    if current_reconstructed_state is not None:
+        return (slice_img, current_reconstructed_state)
+    else:
+        return (slice_img, slice_img)
+
+
 # ============================================================================
 # RECONSTRUCTION CALLBACKS
 # ============================================================================
@@ -236,15 +253,25 @@ def run_reconstruction_ui(
         zyx_stack, pixel_scales_state, Config.RECON_CONFIG, param_values
     )
 
-    # Return updated image slider (no optimization results)
-    return (original_normalized, reconstructed_image)
+    # Return updated image slider AND reconstructed state
+    return (original_normalized, reconstructed_image), reconstructed_image
 
 
-def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
+def run_optimization_ui(
+    z: int,
+    num_iterations: int,
+    z_offset: float,
+    na_det: float,
+    na_ill: float,
+    tilt_zenith: float,
+    tilt_azimuth: float,
+    data_xr_state,
+    pixel_scales_state,
+):
     """
     Run OPTIMIZATION and stream updates to UI with iteration caching.
 
-    Uses OPTIMIZABLE_PARAMS as initial guesses, runs full optimization loop.
+    Uses current slider values as initial guesses, runs full optimization loop.
     Yields progressive updates for ImageSlider, loss plot, status,
     iteration history, iteration slider, and SLIDER UPDATES.
     """
@@ -256,6 +283,35 @@ def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
         data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
     )
 
+    # Build optimizable params with current slider values as initial values
+    optimizable_params_with_slider_values = {
+        "z_offset": (
+            Config.OPTIMIZABLE_PARAMS["z_offset"][0],  # enabled flag
+            z_offset,  # initial value from slider
+            Config.OPTIMIZABLE_PARAMS["z_offset"][2],  # learning rate
+        ),
+        "numerical_aperture_detection": (
+            Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][0],
+            na_det,
+            Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][2],
+        ),
+        "numerical_aperture_illumination": (
+            Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][0],
+            na_ill,
+            Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][2],
+        ),
+        "tilt_angle_zenith": (
+            Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][0],
+            tilt_zenith,
+            Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][2],
+        ),
+        "tilt_angle_azimuth": (
+            Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][0],
+            tilt_azimuth,
+            Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][2],
+        ),
+    }
+
     # Initialize tracking
     loss_history = []
     iteration_cache = []
@@ -273,15 +329,16 @@ def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
         gr.skip(),  # na_ill
         gr.skip(),  # tilt_zenith
         gr.skip(),  # tilt_azimuth
+        None,  # No reconstructed image yet
     )
 
-    # Run optimization with streaming
+    # Run optimization with streaming (using slider values as initial values)
     for result in run_optimization_streaming(
         zyx_stack,
         pixel_scales_state,
         Config.RECON_CONFIG,
-        Config.OPTIMIZABLE_PARAMS,
-        num_iterations=Config.RECON_CONFIG["num_iterations"],
+        optimizable_params_with_slider_values,
+        num_iterations=num_iterations,
     ):
         # Current iteration number
         n = result["iteration"]
@@ -301,9 +358,39 @@ def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
         loss_history.append({"iteration": int(n), "loss": result["loss"]})
 
         # Format iteration info
-        info_md = f"**Iteration {n}/{Config.RECON_CONFIG['num_iterations']}** | Loss: `{result['loss']:.2e}`"
-
-        # Yield updates - update ImageSlider AND sliders with latest params
+        info_md = f"**Iteration {n}/{num_iterations}** | Loss: `{result['loss']:.2e}`"
+
+        # Clip optimized parameters to slider ranges (avoid Gradio validation errors)
+        # Convert to float to ensure Gradio compatibility
+        clipped_params = {
+            "z_offset": float(np.clip(
+                result["params"].get("z_offset", 0.0),
+                Config.SLIDER_RANGES["z_offset"][0],
+                Config.SLIDER_RANGES["z_offset"][1],
+            )),
+            "numerical_aperture_detection": float(np.clip(
+                result["params"].get("numerical_aperture_detection", 0.55),
+                Config.SLIDER_RANGES["na_detection"][0],
+                Config.SLIDER_RANGES["na_detection"][1],
+            )),
+            "numerical_aperture_illumination": float(np.clip(
+                result["params"].get("numerical_aperture_illumination", 0.54),
+                Config.SLIDER_RANGES["na_illumination"][0],
+                Config.SLIDER_RANGES["na_illumination"][1],
+            )),
+            "tilt_angle_zenith": float(np.clip(
+                result["params"].get("tilt_angle_zenith", 0.0),
+                Config.SLIDER_RANGES["tilt_zenith"][0],
+                Config.SLIDER_RANGES["tilt_zenith"][1],
+            )),
+            "tilt_angle_azimuth": float(np.clip(
+                result["params"].get("tilt_angle_azimuth", 0.0),
+                Config.SLIDER_RANGES["tilt_azimuth"][0],
+                Config.SLIDER_RANGES["tilt_azimuth"][1],
+            )),
+        }
+
+        # Yield updates - update ImageSlider AND sliders with clipped params
         yield (
             (original_normalized, result["reconstructed_image"]),  # Update ImageSlider
             pd.DataFrame(loss_history),  # Loss plot
@@ -317,12 +404,13 @@ def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
                 interactive=True,
             ),
             gr.Markdown(value=info_md, visible=True),  # Show iteration info
-            # Update parameter sliders with optimized values:
-            result["params"].get("z_offset", gr.skip()),
-            result["params"].get("numerical_aperture_detection", gr.skip()),
-            result["params"].get("numerical_aperture_illumination", gr.skip()),
-            result["params"].get("tilt_angle_zenith", gr.skip()),
-            result["params"].get("tilt_angle_azimuth", gr.skip()),
+            # Update parameter sliders with clipped optimized values:
+            clipped_params["z_offset"],
+            clipped_params["numerical_aperture_detection"],
+            clipped_params["numerical_aperture_illumination"],
+            clipped_params["tilt_angle_zenith"],
+            clipped_params["tilt_angle_azimuth"],
+            result["reconstructed_image"],  # Update reconstructed image state
         )
 
     # Final yield (keep last state)
@@ -340,6 +428,7 @@ def run_optimization_ui(z: int, data_xr_state, pixel_scales_state):
         gr.skip(),  # Keep na_ill
         gr.skip(),  # Keep tilt_zenith
         gr.skip(),  # Keep tilt_azimuth
+        gr.skip(),  # Keep reconstructed image state
     )
 
 
@@ -361,13 +450,34 @@ def scrub_iterations(iteration_idx: int, history: list):
     # Update info display
     info_md = f"**Iteration {selected['iteration']}/{len(history)}** | Loss: `{selected['loss']:.2e}`"
 
-    # Extract parameter values at this iteration
+    # Extract parameter values at this iteration and clip to slider ranges
+    # Convert to float to ensure Gradio compatibility
     params = selected["params"]
-    z_offset = params.get("z_offset", 0.0)
-    na_det = params.get("numerical_aperture_detection", 0.55)
-    na_ill = params.get("numerical_aperture_illumination", 0.54)
-    tilt_zenith = params.get("tilt_angle_zenith", 0.0)
-    tilt_azimuth = params.get("tilt_angle_azimuth", 0.0)
+    z_offset = float(np.clip(
+        params.get("z_offset", 0.0),
+        Config.SLIDER_RANGES["z_offset"][0],
+        Config.SLIDER_RANGES["z_offset"][1],
+    ))
+    na_det = float(np.clip(
+        params.get("numerical_aperture_detection", 0.55),
+        Config.SLIDER_RANGES["na_detection"][0],
+        Config.SLIDER_RANGES["na_detection"][1],
+    ))
+    na_ill = float(np.clip(
+        params.get("numerical_aperture_illumination", 0.54),
+        Config.SLIDER_RANGES["na_illumination"][0],
+        Config.SLIDER_RANGES["na_illumination"][1],
+    ))
+    tilt_zenith = float(np.clip(
+        params.get("tilt_angle_zenith", 0.0),
+        Config.SLIDER_RANGES["tilt_zenith"][0],
+        Config.SLIDER_RANGES["tilt_zenith"][1],
+    ))
+    tilt_azimuth = float(np.clip(
+        params.get("tilt_angle_azimuth", 0.0),
+        Config.SLIDER_RANGES["tilt_azimuth"][0],
+        Config.SLIDER_RANGES["tilt_azimuth"][1],
+    ))
 
     return comparison, info_md, z_offset, na_det, na_ill, tilt_zenith, tilt_azimuth
 
@@ -389,7 +499,7 @@ def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scale
     """Build the Gradio interface with all components and event wiring."""
 
     with gr.Blocks() as demo:
-        gr.Markdown("# waveOrder Phase Reconstruction Demo")
+        gr.Markdown("# WaveOrder")
         gr.Markdown(
             "**Paper:** Chandler T., Ivanov I.E., Hirata-Miyasaki E., et al. \"WaveOrder: Physics-informed ML for auto-tuned multi-contrast computational microscopy from cells to organisms.\" "
             "[arXiv:2412.09775](https://arxiv.org/abs/2412.09775) (2025)\n\n"
@@ -500,6 +610,16 @@ def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scale
                 # Section 4: Reconstruction Actions
                 gr.Markdown("### 🔬 Phase Reconstruction")
 
+                # Number of optimization iterations control
+                num_iterations_slider = gr.Slider(
+                    minimum=1,
+                    maximum=50,
+                    value=Config.RECON_CONFIG["num_iterations"],
+                    step=1,
+                    label="Optimization Iterations",
+                    info="Number of gradient descent iterations (more = better quality, slower)",
+                )
+
                 with gr.Row():
                     optimize_btn = gr.Button(
                         "⚡ Optimize Parameters", variant="secondary", size="lg"
@@ -539,6 +659,7 @@ def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scale
         iteration_history = gr.State(value=[])
         current_data_xr = gr.State(value=data_xr)
         current_pixel_scales = gr.State(value=pixel_scales)
+        current_reconstructed = gr.State(value=None)  # Stores the current reconstructed image
 
         gr.Markdown("---")
 
@@ -554,6 +675,7 @@ def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scale
             na_ill_slider,
             tilt_zenith_slider,
             tilt_azimuth_slider,
+            num_iterations_slider,
             optimize_btn,
             reconstruct_btn,
             loss_plot,
@@ -562,6 +684,7 @@ def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scale
             iteration_history,
             current_data_xr,
             current_pixel_scales,
+            current_reconstructed,
             plate_metadata,
         )
 
@@ -579,6 +702,7 @@ def _wire_event_handlers(
     na_ill_slider,
     tilt_zenith_slider,
     tilt_azimuth_slider,
+    num_iterations_slider,
     optimize_btn,
     reconstruct_btn,
     loss_plot,
@@ -587,6 +711,7 @@ def _wire_event_handlers(
     iteration_history,
     current_data_xr,
     current_pixel_scales,
+    current_reconstructed,
     plate_metadata,
 ):
     """Wire all Gradio event handlers."""
@@ -610,22 +735,34 @@ def _wire_event_handlers(
         outputs=[na_ill_slider],
     )
 
-    # Image viewer for Z navigation (preview mode: same image twice)
+    # Image viewer for Z navigation
+    # On load: show preview mode (no reconstruction yet)
     demo.load(
         fn=get_slice_for_preview,
         inputs=[z_slider, current_data_xr],
         outputs=image_viewer,
     )
+    # On Z change: update only left (original) image, keep reconstruction on right
     z_slider.change(
-        fn=get_slice_for_preview,
-        inputs=[z_slider, current_data_xr],
+        fn=update_original_slice_only,
+        inputs=[z_slider, current_data_xr, current_reconstructed],
         outputs=image_viewer,
     )
 
     # Reconstruction buttons
     optimize_btn.click(
         fn=run_optimization_ui,
-        inputs=[z_slider, current_data_xr, current_pixel_scales],
+        inputs=[
+            z_slider,
+            num_iterations_slider,
+            z_offset_slider,
+            na_det_slider,
+            na_ill_slider,
+            tilt_zenith_slider,
+            tilt_azimuth_slider,
+            current_data_xr,
+            current_pixel_scales,
+        ],
         outputs=[
             image_viewer,
             loss_plot,
@@ -637,6 +774,7 @@ def _wire_event_handlers(
             na_ill_slider,
             tilt_zenith_slider,
             tilt_azimuth_slider,
+            current_reconstructed,  # Update reconstructed state
         ],
     )
 
@@ -652,7 +790,7 @@ def _wire_event_handlers(
             current_data_xr,
             current_pixel_scales,
         ],
-        outputs=[image_viewer],
+        outputs=[image_viewer, current_reconstructed],  # Update both viewer and state
     )
 
     # Iteration scrubbing - updates image AND all parameter sliders