phiflow 2 updates of forw NS sim

overview-ns-forw.ipynb

@@ -132,7 +132,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fb319d14f10>"
+       "<matplotlib.image.AxesImage at 0x7fa2f8d16eb0>"
       ]
      },
      "execution_count": 4,
@@ -198,31 +198,55 @@
      "text": [
       "Smoke: (xˢ=32, yˢ=40)\n",
       "Velocity: (xˢ=32, yˢ=40, vectorᵛ=2)\n",
-      "Inflow: (xˢ=32, yˢ=40)\n",
+      "Inflow: (xˢ=32, yˢ=40), spatial only: (xˢ=32, yˢ=40)\n"
-      "Inflow, spatial only: (xˢ=32, yˢ=40)\n"
      ]
     }
    ],
    "source": [
     "print(f\"Smoke: {smoke.shape}\")\n",
     "print(f\"Velocity: {velocity.shape}\")\n",
-    "print(f\"Inflow: {INFLOW.shape}\")\n",
+    "print(f\"Inflow: {INFLOW.shape}, spatial only: {INFLOW.shape.spatial}\")\n"
-    "print(f\"Inflow, spatial only: {INFLOW.shape.spatial}\")\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the phiflow output here indicates the type of a dimension, e.g., $^V$ for a vector dimension. Later on for learning, we'll also introduce batch dimensions.\n",
+    "Note that the phiflow output here indicates the type of a dimension, e.g., $^S$ for a spatial, and $^V$ for a vector dimension. Later on for learning, we'll also introduce batch dimensions.\n",
     "\n",
-    "The grid values can be accessed using the `values` property. This is an important difference to a phiflow tensor object, which does not have `values`, as illustrated in the code example below."
+    "The actual content of a shape object can be obtained via `.sizes`, or alternatively we can query the size of a specific dimension `dim` via `.get_size('dim')`. Here are two examples:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape content: (32, 40, 2)\n",
+      "Vector dimension: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Shape content: {velocity.shape.sizes}\")\n",
+    "print(f\"Vector dimension: {velocity.shape.get_size('vector')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The grid values can be accessed using the `values` property. This is an important difference to a phiflow tensor object, which does not have `values`, as illustrated in the code example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -231,7 +255,6 @@
       "Statistics of the different simulation grids:\n",
       "(xˢ=32, yˢ=40) float32  0.0 < ... < 0.20000000298023224\n",
       "(xˢ=(31, 32), yˢ=(40, 39), vectorᵛ=2) float32  -0.12352858483791351 < ... < 0.15530994534492493\n",
-      "(xˢ=32, yˢ=40) float32  0.0 < ... < 0.20000000298023224\n",
       "Reordered test tensor shape: (3, 5, 2)\n"
      ]
     }
@@ -240,7 +263,6 @@
     "print(\"Statistics of the different simulation grids:\")\n",
     "print(smoke.values)\n",
     "print(velocity.values)\n",
-    "print(INFLOW.values)\n",
     "\n",
     "# in contrast to a simple tensor:\n",
     "test_tensor = math.tensor(numpy.zeros([2, 5, 3]), spatial('x,y'), channel('vector'))\n",
@@ -267,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -310,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -323,10 +345,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fb31a2bc2b0>"
+       "<matplotlib.image.AxesImage at 0x7fa2f902cf40>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -358,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -415,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {