docs: Add cache examples to processor notebook 📚

demo/cache_tests.ipynb → demo/extras/cache_tests.ipynb

@@ -168,7 +168,7 @@
    "source": [
     "As you can see the preview run resolves the references to the data layers as they are provided by looking up the entities' references in the mapping.json. Note, that in the current case the result is not that interesting, though, since four different data layers are to be loaded. Therefore, there is nothing to be cached during recipe execution. Therefore the QueryProcessor will load all data layers from the referenced sources without storing any of them in the cache. \n",
     "\n",
-    "As a user, however, you can directly initiate the entire caching workflow (preview & full resolution recipe execution) by setting the context parameter when calling `recipe.execute(..., caching=True)`. "
+    "As a user, however, you can directly initiate the entire caching workflow (preview & full resolution recipe execution) by setting the context parameter when calling `recipe.execute(..., cache_data = True)`. "
    ]
   },
   {
@@ -178,7 +178,7 @@
    "outputs": [],
    "source": [
     "# same as above in a single step \n",
-    "result = recipe.execute(**{**context, \"caching\": True})"
+    "result = recipe.execute(**{**context, \"cache_data\": True})"
    ]
   },
   {
@@ -537,9 +537,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  },
-  "orig_nbformat": 4
+   "version": "3.9.2"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

demo/files/logs.txt

@@ -60,6 +60,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],
@@ -166,6 +169,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],
@@ -272,6 +278,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],
@@ -373,6 +382,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],
@@ -474,6 +486,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],
@@ -574,6 +589,9 @@ Attributes:
     _FillValue:     1.7976931348623157e+308
     value_type:     ordinal
     value_labels:   {1: 'SVHNIR', 2: 'SVLNIR', 3: 'AVHNIR', 4: 'AVLNIR', 5: '...
+Cache updated
+Sequence of layers: []
+Currently cached layers: []
 Applied verb evaluate:
 <xarray.DataArray 'colortype' (time: 3, y: 4, x: 4)>
 array([[[0., 0., 0., 0.],