aigc

src/keyframed/curve.py

@@ -6,6 +6,9 @@
 from sortedcontainers import SortedDict
 from typing import Tuple, Optional, Union, Dict, Callable
 
+import numpy as np
+import torch
+
 from .interpolation import (
     bisect_left_keyframe, 
     INTERPOLATORS,
@@ -97,7 +100,15 @@ def __init__(
         interpolator_arguments=None,
     ):
         self.t=t
-        self.value=value
+        #self.value=value
+        ### <chatgpt>
+        if isinstance(value, np.ndarray):
+            self.value = np.array(value)  # Ensure a copy of the array is stored
+        elif isinstance(value, torch.Tensor):
+            self.value = value.clone()    # Ensure a clone of the tensor is stored
+        else:
+            self.value = value
+        ### </chatgpt>
         self.interpolation_method=interpolation_method
         if interpolator_arguments is None:
             interpolator_arguments = {}
@@ -110,6 +121,13 @@ def interpolator_arguments(self):
         return {}
 
     def __eq__(self, other) -> bool:
+        ### <chatgpt>
+        if isinstance(self.value, (np.ndarray, torch.Tensor)) and isinstance(other, (np.ndarray, torch.Tensor)):
+            if isinstance(self.value, np.ndarray):
+                return np.array_equal(self.value, np.array(other))
+            else:
+                return torch.equal(self.value, torch.tensor(other))
+        ### </chatgpt>
         return self.value == other
     def __repr__(self) -> str:
         #d = f"Keyframe(t={self.t}, value={self.value}, interpolation_method='{self.interpolation_method}')"
@@ -119,6 +137,15 @@ def _to_dict(self, *args, **kwargs) -> dict:
         d = {'t':self.t, 'value':self.value, 'interpolation_method':self.interpolation_method}
         if self.interpolator_arguments:
             d['interpolator_arguments'] = self.interpolator_arguments
+        ### <chatgpt>
+        # Ensure the representation of numpy arrays and tensors are handled correctly
+        if isinstance(self.value, np.ndarray):
+            d['value'] = self.value.tolist()
+        elif isinstance(self.value, torch.Tensor):
+            d['value'] = self.value.numpy().tolist()
+        else:
+            d['value'] = self.value
+        ### </chatgpt>
         return d
     def _to_tuple(self, *args, **kwags):
         if not self.interpolator_arguments:

src/keyframed/interpolation.py

@@ -1,7 +1,8 @@
 import math
 from numbers import Number
 from typing import Callable
-
+import numpy as np
+import torch
 
 def bisect_left_keyframe(k: Number, curve:'Curve', *args, **kargs) -> 'Keyframe':
     """
@@ -65,6 +66,18 @@ def linear(k, curve, *args, **kargs):
     right = bisect_right_keyframe(k, curve)
     x0, x1 = left.t, right.t
     y0, y1 = left.value, right.value
+
+    ### <chatgpt>
+    # Handle both NumPy arrays and PyTorch tensors
+    if isinstance(y0, (np.ndarray, torch.Tensor)) and isinstance(y1, (np.ndarray, torch.Tensor)):
+        d = x1 - x0
+        t = (x1 - k) / d
+        if isinstance(y0, np.ndarray):
+            return t * y0 + (1 - t) * y1
+        else:
+            return t * y0 + (1 - t) * y1
+    ### </chatgpt>
+
     d = x1-x0
     t = (x1-k)/d
     outv =  t*y0 + (1-t)*y1