[CLN] Removed unused code

Author: Leguark

gempy_engine/modules/activator/activator_interface.py

@@ -6,7 +6,6 @@
 from ._soft_segment import soft_segment_unbounded
 
 import numpy as np
-import numbers
 
 
 def activate_formation_block(exported_fields: ExportedFields, ids: np.ndarray,
@@ -30,24 +29,6 @@ def activate_formation_block(exported_fields: ExportedFields, ids: np.ndarray,
             ids=ids,
             sigmoid_slope=sigmoid_slope
         )
-        return sigm
-
-        match BackendTensor.engine_backend:
-            case AvailableBackends.PYTORCH:
-                sigm = soft_segment_unbounded_torch(
-                    Z=Z_x,
-                    edges=scalar_value_at_sp,
-                    ids=ids,
-                    sigmoid_slope=sigmoid_slope
-                )
-            case AvailableBackends.numpy:
-                sigm = soft_segment_unbounded_np(
-                    Z=Z_x,
-                    edges=scalar_value_at_sp,
-                    ids=ids,
-                    sigmoid_slope=sigmoid_slope
-                )
-
     return sigm
 
 
@@ -89,140 +70,3 @@ def _compute_sigmoid(Z_x, scale_0, scale_1, drift_0, drift_1, drift_id, sigmoid_
     for i in range(len(ids)):
         sigm += _compute_sigmoid(Z_x, scalar_0_v[i], scalar_1_v[i], drift_0_v[i], drift_1_v[i], ids[i], sigmoid_slope)
     return sigm
-
-
-import torch
-
-
-def soft_segment_unbounded_torch(Z, edges, ids, sigmoid_slope):
-    """
-    Z:            (...,) tensor of scalar values
-    edges:   (K-1,) tensor of finite split points [e1, e2, ..., e_{K-1}]
-    ids:       (K,) tensor of the id for each of the K bins
-    tau:         scalar target peak slope m > 0
-    returns:      (...,) tensor of the soft‐assigned id
-    """
-    # --- ensure torch tensors on the same device/dtype ---
-    if not isinstance(Z, torch.Tensor):
-        Z = torch.tensor(Z, dtype=torch.float32, device=edges.device)
-    if not isinstance(edges, torch.Tensor):
-        edges = torch.tensor(edges, dtype=Z.dtype, device=Z.device)
-    if not isinstance(ids, torch.Tensor):
-        ids = torch.tensor(ids, dtype=Z.dtype, device=Z.device)
-
-    # --- 1) per-edge temp: tau_k = jump_k / (4 * m) ---
-    # jumps = ids[1:] - ids[:-1]  # shape (K-1,)
-
-    jumps = torch.abs(ids[1:] - ids[:-1])  # shape (K-1,)
-    tau_k = jumps / (4 * sigmoid_slope)  # shape (K-1,)
-
-    # --- 2) first bin (-∞, e1) ---
-    first = torch.sigmoid((edges[0] - Z) / tau_k[0])[..., None]  # (...,1)
-
-    # --- 3) last bin [e_{K-1}, ∞) ---
-    last = torch.sigmoid((Z - edges[-1]) / tau_k[-1])[..., None]  # (...,1)
-
-    # --- 4) middle bins [e_i, e_{i+1}) ---
-    left = torch.sigmoid((Z[..., None] - edges[:-1]) / tau_k[:-1])  # (...,K-2)
-    right = torch.sigmoid((Z[..., None] - edges[1:]) / tau_k[1:])  # (...,K-2)
-    middle = left - right  # (...,K-2)
-
-    # --- 5) assemble memberships and weight by ids ---
-    membership = torch.cat([first, middle, last], dim=-1)  # (...,K)
-    # return (membership * ids).sum(dim=-1)                  # (...,)
-
-    # weighted sum by the ids
-    ids__sum = (membership * ids).sum(dim=-1)
-
-    # make it at least 2d
-    ids__sum = ids__sum[None, :]
-
-    return ids__sum
-
-
-import numpy as np
-
-
-def soft_segment_unbounded_np(Z, edges, ids, sigmoid_slope):
-    """
-    Z:            array of shape (...,) of scalar values
-    edges:        array of shape (K-1,) of finite split points [e1, e2, ..., e_{K-1}]
-    ids:          array of shape (K,)   of the id for each of the K bins
-    sigmoid_slope: scalar target peak slope m > 0
-    returns:      array of shape (...,) of the soft-assigned id
-    """
-    Z = np.asarray(Z)
-    edges = np.asarray(edges)
-    ids = np.asarray(ids[::-1])
-
-    # Check if sigmoid function is num or array
-    match sigmoid_slope:
-        case np.ndarray():
-            membership = _final_faults_segmentation(Z, edges, sigmoid_slope)
-        case numbers.Number():
-            membership = _lith_segmentation(Z, edges, ids, sigmoid_slope)
-        case _:
-            raise ValueError("sigmoid_slope must be a float or an array")
-
-    ids__sum = np.sum(membership * ids, axis=-1)
-    return np.atleast_2d(ids__sum)
-
-
-def _final_faults_segmentation(Z, edges, sigmoid_slope):
-    first = _sigmoid(
-        scalar_field=Z,
-        edges=edges[0],
-        tau_k=1 / sigmoid_slope
-    )  # shape (...,)
-    last = _sigmoid(
-        scalar_field=Z,
-        edges=edges[-1],
-        tau_k=1 / sigmoid_slope
-    )
-    membership = np.concatenate(
-        [first[..., None], last[..., None]],
-        axis=-1
-    )  # shape (...,K)
-    return membership
-
-
-def _lith_segmentation(Z, edges, ids, sigmoid_slope):
-    # 1) per-edge temperatures τ_k = |Δ_k|/(4·m)
-    jumps = np.abs(ids[1:] - ids[:-1])  # shape (K-1,)
-    tau_k = jumps / float(sigmoid_slope)  # shape (K-1,)
-    # 2) first bin (-∞, e1) via σ((e1 - Z)/τ₁)
-    first = _sigmoid(
-        scalar_field=-Z,
-        edges=-edges[0],
-        tau_k=tau_k[0]
-    )  # shape (...,)
-    # 3) last  bin [e_{K-1}, ∞) via σ((Z - e_{K-1})/τ_{K-1})
-    # last = 1.0 / (1.0 + np.exp(-(Z - edges[-1]) / tau_k[-1]))  # shape (...,)
-    last = _sigmoid(
-        scalar_field=Z,
-        edges=edges[-1],
-        tau_k=tau_k[-1]
-    )
-    # 4) middle bins [e_i, e_{i+1}): σ((Z - e_i)/τ_i) - σ((Z - e_{i+1})/τ_{i+1})
-    # shape (...,1)
-    left = _sigmoid(
-        scalar_field=(Z[..., None]),
-        edges=edges[:-1],
-        tau_k=tau_k[:-1]
-    )
-    right = _sigmoid(
-        scalar_field=(Z[..., None]),
-        edges=edges[1:],
-        tau_k=tau_k[1:]
-    )
-    middle = left - right  # (...,K-2)
-    # 5) assemble memberships and weight by ids
-    membership = np.concatenate(
-        [first[..., None], middle, last[..., None]],
-        axis=-1
-    )  # shape (...,K)
-    return membership
-
-
-def _sigmoid(scalar_field, edges, tau_k):
-    return 1.0 / (1.0 + np.exp(-(scalar_field - edges) / tau_k))