Enso example development

notebooks/tutorial/CNN-Model-Training.ipynb

@@ -8364,7 +8364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

notebooks/tutorial/MLX-Demo-Custom-Arch.ipynb

@@ -899,7 +899,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

packages/pipeline/src/pyearthtools/pipeline/modifications/idx_modification.py

@@ -434,6 +434,8 @@ def __getitem__(self, idx: Any):
         return super().__getitem__(idx)
 
 
+
+
 class TemporalRetrieval(SequenceRetrieval):
     """
     Retrieve a sequence of samples from `SequenceRetrieval`,
@@ -453,6 +455,12 @@ def __init__(
         merge_kwargs: Optional[dict[str, Any]] = None,
         delta_unit: Optional[str] = None,
     ):
+        '''
+        Args:
+            samples: number of samples to fetch (negative for n-back, positive for n-forward)
+            delta_unit: e.g. "month" or "hour"
+            concat: whether to contact or merge
+        '''
         super().__init__(samples, merge_function=merge_function, concat=concat, merge_kwargs=merge_kwargs)
 
         def map_to_tuple(mod):

packages/pipeline/src/pyearthtools/pipeline/operations/xarray/normalisation.py

@@ -156,11 +156,32 @@ def unnormalise(self, sample):
 class Deviation(xarrayNormalisation):
     """Deviation Normalisation"""
 
-    def __init__(self, mean: FILE, deviation: FILE):
+    def __init__(self, mean: FILE | xr.Dataset | xr.DataArray| float, deviation: FILE | xr.Dataset | xr.DataArray | float):
+        '''
+        Each argument take take a Dataset, DataArray, float or file object. 
+
+        Args:
+            mean: mean values to subtract
+            deviation: deviation value to divide by
+        '''
         super().__init__()
         self.record_initialisation()
-        self.mean = self.open_file(mean)
-        self.deviation = self.open_file(deviation)
+
+        if isinstance(mean, xr.Dataset):
+            self.mean = mean
+        if isinstance(mean, xr.DataArray):
+            self.mean = mean            
+        elif isinstance(mean, float):
+            self.mean = mean
+        else:
+            self.mean = self.open_file(mean)
+
+        if isinstance(deviation, xr.Dataset):
+            self.deviation = deviation
+        elif isinstance(deviation, float):
+            self.deviation = deviation
+        else:
+            self.deviation = self.open_file(deviation)
 
     def normalise(self, sample):
         return (sample - self.mean) / self.deviation
@@ -169,6 +190,7 @@ def denormalise(self, sample):
         return (sample * self.deviation) + self.mean
 
 
+
 class Division(xarrayNormalisation):
     """Division based Normalisation"""
 
@@ -185,6 +207,8 @@ def denormalise(self, sample):
         return sample * self.division_factor
 
 
+
+
 @BackwardsCompatibility(Division)
 def TemporalDifference(*a, **k): ...