remaining med value features

backend/nodes/wavelet_denoise.py

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+from __future__ import annotations
+
+import numpy as np
+
+from backend.data_types import DataField
+from backend.node_registry import register_node
+
+
+@register_node(display_name="Wavelet Denoise")
+class WaveletDenoise:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "wavelet": (
+                    ["db1", "db2", "db4", "db8", "sym4", "coif1", "bior1.3"],
+                    {"default": "db4"},
+                ),
+                "method": (["BayesShrink", "VisuShrink"], {"default": "BayesShrink"}),
+                "sigma": (
+                    "FLOAT",
+                    {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
+                "mode": (["soft", "hard"], {"default": "soft"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'denoised'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Denoise using wavelet coefficient thresholding. BayesShrink adapts the threshold "
+        "per sub-band; VisuShrink uses a global threshold. Equivalent to applying DWT from "
+        "Gwyddion dwt.c with coefficient thresholding."
+    )
+
+    def process(
+        self,
+        field: DataField,
+        wavelet: str,
+        method: str,
+        sigma: float,
+        mode: str,
+    ) -> tuple:
+        from skimage.restoration import denoise_wavelet
+
+        d = field.data
+        dmin = float(d.min())
+        drange = float(np.ptp(d))
+
+        if drange == 0:
+            return (field,)
+
+        norm = (d - dmin) / drange
+        sigma_val = float(sigma) if sigma > 0 else None
+
+        # `mode` is a Python builtin name; use threshold_mode locally to avoid shadowing
+        threshold_mode = mode
+
+        denoised_norm = denoise_wavelet(
+            norm,
+            wavelet=wavelet,
+            method=method,
+            mode=threshold_mode,
+            sigma=sigma_val,
+            rescale_sigma=True,
+            channel_axis=None,
+        )
+
+        result = denoised_norm * drange + dmin
+        return (field.replace(data=result),)