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diff --git a/examples/serialbox/02.ipynb b/examples/Fortran_porting/02.ipynb
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diff --git a/examples/NDSL/01_basics.ipynb b/examples/NDSL/01_basics.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **GT4Py Tutorial : Stencil Basics**\n",
+    "\n",
+    "## **Introduction**\n",
+    "\n",
+    "This notebook will show how to create a simple GT4Py stencil that copies data from one variable to another.\n",
+    "\n",
+    "### **Notebook Requirements**\n",
+    "\n",
+    "- Python v3.11.x to v3.12.x\n",
+    "- [NOAA/NASA Domain Specific Language Middleware](https://github.com/NOAA-GFDL/NDSL)\n",
+    "- `ipykernel==6.1.0`\n",
+    "- [`ipython_genutils`](https://pypi.org/project/ipython_genutils/)\n",
+    "\n",
+    "### **Quick GT4Py (Cartesian version) Overview**\n",
+    "\n",
+    "GT4Py is a Domain Specific Language (DSL) in Python that enables a developer to write stencil computations.  Compared to simply running under Python, GT4Py achieves performance when the Python code is translated and compiled into a lower level language such as C++ and CUDA, which enables the codebase to execute on a multitude of architectures.  In this notebook, we will cover the basics of creating GT4Py stencils and demonstrate several intracies of the DSL. Additional information about GT4Py can be found at the [GT4Py site](https://gridtools.github.io/gt4py/latest/index.html).  One small note is that this tutorial covers and uses the Cartesian version of GT4Py and not the unstructured version.\n",
+    "\n",
+    "### **GT4Py Parallel/Execution Model**\n",
+    "\n",
+    "Within a 3-dimensional domain, GT4Py considers computations in two parts.  If we assume an `(I,J,K)` coordinate system as a reference, GT4Py separates computations in the Horizontal (`IJ`) spatial plane and Vertical (`K`) spatial interval.  In the Horizontal spatial plane, computations are implicitly executed in parallel, which also means that there is no assumed calculation order within the plane.  In the Vertical spatial interval, comptuations are specified by an iteration policy that will be discussed later through examples.\n",
+    "\n",
+    "Another quick note is that the computations are executed sequentially in the order they appear in code.\n",
+    "\n",
+    "## **Tutorial**\n",
+    "\n",
+    "### **Copy Stencil example**\n",
+    "\n",
+    "To demonstrate how to implement a GT4Py stencil, we'll step through an example that copies the values of one array into another array.  First, we import several packages. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script src=\"https://spcl.github.io/dace-webclient/dist/sdfv.js\"></script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-09 16:09:06|INFO|rank 0|ndsl.logging:Constant selected: ConstantVersions.GFS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gt4py.cartesian.gtscript import PARALLEL, computation, interval, stencil\n",
+    "from ndsl.dsl.typing import FloatField\n",
+    "from ndsl.quantity import Quantity\n",
+    "import numpy as np\n",
+    "from boilerplate import plot_field_at_k0, plot_field_at_kN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we walk through the example, we'll highlight different terms and such from the imported packages.  Let's first define, in GT4Py terms, two arrays of size 5 by 5 by 2 (dimensionally `I` by `J` by `K`).  These arrays are defined using a `Quantity` object, an NDSL data container for physical quantities.  More detailed information about the `Quantity` object and its arguments can be found from the [`Quantity` docstring](https://github.com/NOAA-GFDL/NDSL/blob/develop/ndsl/quantity.py#L270).  To make debugging easier, the `numpy` backend will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backend = 'numpy'\n",
+    "\n",
+    "nx = 5\n",
+    "ny = 5\n",
+    "nz = 2\n",
+    "\n",
+    "shape = (nx, ny, nz)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "arr = np.indices(shape,dtype=float).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
+    "\n",
+    "qty_in = Quantity(data=arr,\n",
+    "                  dims=[\"I\", \"J\", \"K\"],\n",
+    "                  units=\"m\",\n",
+    "                  gt4py_backend=backend)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will next create a simple GT4Py stencil that copies values from one input to another.  A stencil will look like a Python subroutine or function except that it uses specific GT4Py functionalities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@stencil(backend=backend)\n",
+    "def copy_stencil(input_field: FloatField,\n",
+    "                 output_field: FloatField):\n",
+    "    with computation(PARALLEL), interval(...):\n",
+    "        output_field = input_field"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, GT4Py (cartesian version) was designed for stencil-based computation. Since stencil calculations generally are localized computations, GT4Py stencils are written using variables and the variable's relative location if it's an array.  If there are no indices in brackets next to a GT4Py type (such as `FloatField`), it's implied to be at the [0] (for 1-dimension), [0,0] (for 2-dimension), or [0,0,0] (for 3-dimension) location.  For the simple example `copy_stencil`, the value of `input_field` simply gets copied to `output_field` at every point in the domain of interest.\n",
+    "\n",
+    "We see that this stencil does not contain any explicit loops.  As mentioned above in the notebook, GT4Py has a particular computation policy that implicitly executes in parallel within an `IJ` plane and is user defined in the `K` interval.  This execution policy in the `K` interval is dictated by the `computation` and `interval` keywords.  \n",
+    "\n",
+    "- `with computation(PARALLEL)` means that there's no order preference to executing the `K` interval.  This also means that the `K` interval can be computed in parallel to potentially gain performace if computational resources are available.\n",
+    "\n",
+    "- `interval(...)` means that the entire `K` interval is executed.  Instead of `(...)`, more specific intervals can be specified using a tuple of two integers.  For example... \n",
+    "\n",
+    "    - `interval(0,2)` : The interval `K` = 0 to 1 is executed.\n",
+    "    - `interval(0,-1)` : The interval `K` = 0 to N-2 (where N is the size of `K`) is executed.\n",
+    "\n",
+    "The decorator `@stencil(backend=backend)` (Note: `stencil` comes from the package `gt4py.cartesian.gtscript`) converts `copy_stencil` to use the specified `backend` to \"compile\" the stencil.  `stencil` can also be a function call to create a stencil object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copy_stencil_numpy = stencil(backend=\"numpy\", definition=copy_stencil)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the input and output parameters to `copy_stencil` are of type `FloatField`, which can essentially be thought of as a 3-dimensional NumPy array of `float` types.\n",
+    "\n",
+    "`plot_field_at_kN` plots the values within the `IJ` plane at `K = 0` if no integer is specified or at `K` equal to the integer that is specified as an argument.  As we can see in the plots below, `copy_stencil` copies the values from `qty_in` into `qty_out`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil`\n",
+      "Plotting qty_out from `copy_stencil` at K = 0\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting qty_out from `copy_stencil` at K = 1\n",
+      "Min and max values: 9.0 1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Nr6rsE0kaAIDLiVh8C5aVuYmi/AQAwKGopAEAnkC7GwAAh4oqTVELDWQrcxNFuxsAAIeikgYAeELE8ClioWVtZW6iSNIAAE9gTRoAAIcyLL4Fy+CJYwAA4BwqaQCAJ0TkU8TCSzKszE0USRoA4AlRw9q6ctRIYjAm0e4GAMChqKQBAJ4QtbhxzMrcRFFJAwA8ISqf5RGPUaNGyefzXTCqqqpMn4NKGgCAXtDY2KhI5C+v4nzzzTc1e/Zsff3rXzd9DpI0AMATUv3EsaFDh3b7vHLlSo0ZM0YzZ840fQ6SNADAE+xck+7q6tK///u/q6amRj6f+WRPkgYAIA5tbW3dPvv9fvn9/kvOefHFF/Xxxx9r4cKFcV2LjWMAAE+Iyhd7fndC47ONY/n5+crOzo6N2tray177qaeeUllZmfLy8uKKmUoaAOAJRgI7tD8/X5JCoZCCwWDs+OWq6OPHj+uVV17RCy+8EPc1SdIAAE9I1luwgsFgtyR9OevXr1dubq7Ky8vjvibtbgAAekk0GtX69etVUVGhfv3ir4uppAEAnmDH7u5XXnlFf/zjH7Vo0aKErkmSBgB4QrLa3fGYM2eODCPxN3PQ7gYAwKGopAEAnpDI87c/Pz/VSNIAAE+wo91tFe1uAAAcikoaAOAJbqykSdIAAE9wY5Km3Q0AgENRSQMAPIFKOk51dXUqKiqKPQe1pKREO3bssDMkAEAfZegvt2ElMhJ/JEnibK2kR44cqZUrV+qLX/yiDMPQ008/rdtuu01vvPGGJkyYYGdoAIA+xo2VtK1Jeu7cud0+P/LII6qrq9P+/ftJ0gAAz3PMmnQkEtFzzz2nzs5OlZSU9PidcDiscDgc+9zW1paq8AAALkclnYDDhw+rpKREp06dUlZWlrZu3arx48f3+N3a2lo9+OCDKY4QANAXuDFJ234L1tixY9Xc3Kz/+q//0ne+8x1VVFTof/7nf3r87tKlS9Xa2hoboVAoxdECAJA6tlfSAwYM0FVXXSVJKi4uVmNjo1avXq1169Zd8F2/3y+/35/qEAEAfYAbK2nbk/TnRaPRbuvOAAAkg2H4ZFhItFbmJsrWJL106VKVlZWpoKBA7e3t2rRpkxoaGlRfX29nWAAAOIKtSbqlpUV33XWXPvjgA2VnZ6uoqEj19fWaPXu2nWEBAPog3icdp6eeesrOywMAPMSNa9K27+4GAAA9c9zGMQAAegMbxwAAcCg3trtJ0gAAT3BjJc2aNAAADkUlDQDwBMNiu5s1aQAAeokhyTCszU812t0AADgUlTQAwBOi8snHE8cAAHAedncDAICkoZIGAHhC1PDJx8NMAABwHsOwuLvbhu3dtLsBAHAoKmkAgCe4ceMYSRoA4AluTNK0uwEAnnDuLVhWRrzee+893XnnnRoyZIgyMzP15S9/WQcPHjQ9n0oaAIBe8NFHH2n69Om68cYbtWPHDg0dOlTvvPOOBg8ebPocJGkAgCekenf3o48+qvz8fK1fvz52bPTo0XGdg3Y3AMATziZpn4UR3/V+/etfa/Lkyfr617+u3NxcXXfddXryySfjOgdJGgCAOLS1tXUb4XC4x+/97//+r+rq6vTFL35R9fX1+s53vqMlS5bo6aefNn0tkjQAwBOsVdF/2Rmen5+v7Ozs2Kitre3xetFoVJMmTdKPf/xjXXfddfr7v/97ffvb39YTTzxhOmbWpAEAnmDI2juhz80NhUIKBoOx436/v8fvjxgxQuPHj+927Etf+pJ+9atfmb4mSRoAgDgEg8FuSfpipk+friNHjnQ79rvf/U6FhYWmr0WSBgB4QqofZnLfffdp2rRp+vGPf6zbb79dBw4c0M9//nP9/Oc/N30O1qQBAN5gJGHEYcqUKdq6dat++ctfauLEiXr44Ye1atUqLViwwPQ5qKQBAN5gsZJWAnNvvfVW3XrrrQlfkkoaAACHopIGAHiCG98nTZIGAHiCG9+C1SeSdOCPEfXrH7E7DIdLtzsAV+hQwO4QXOGACuwOAX1EV0eXpMN2h+FYfSJJAwBwWYYvoc1f3eanGEkaAOAJblyTZnc3AAAORSUNAPCGZD28O4VI0gAAT3Dj7m7a3QAAOBSVNADAO2xoWVtBkgYAeIIb290kaQCAN7hw4xhr0gAAOBSVNADAI3yfDSvzU4skDQDwBtrdAAAgWaikAQDe4MJKmiQNAPAGF74Fi3Y3AAAORSUNAPAEN76qkiQNAPAGF65J0+4GAMChqKQBAN7gwo1jJGkAgCf4jLPDyvxUI0kDALyBNWkAAJAsVNIAAG9gTRoAAIei3Q0AAJKFShoA4A0urKRJ0gAAb3BhkqbdDQCAQ1FJAwC8gd3dAAA4kxufOEa7GwAAh7I1SdfW1mrKlCkKBALKzc3VvHnzdOTIETtDAgD0VUYSRhxWrFghn8/XbYwbNy6uc9iapPfs2aOqqirt379fu3bt0unTpzVnzhx1dnbaGRYAAEkxYcIEffDBB7Gxb9++uObbuia9c+fObp83bNig3NxcNTU1acaMGTZFBQDoi3yyuCadwJx+/fpp+PDhCV/TVJL+m7/5G9OBzJ49W3Pnzk0omNbWVklSTk5Ojz8Ph8MKh8Oxz21tbQldBwCARH0+9/j9fvn9/h6/+8477ygvL08ZGRkqKSlRbW2tCgoKTF/LVLs7Ozv7siMzM1PvvPOO7rjjDi1btsx0AOdEo1FVV1dr+vTpmjhxYo/fqa2t7XbN/Pz8uK8DAPCoc7dgWRmS8vPzu+Wi2traHi83depUbdiwQTt37lRdXZ2OHTumr3zlK2pvbzcdsqlKev369aZPuH37dn33u9/VQw89ZHqOJFVVVenNN9+8ZL9+6dKlqqmpiX1ua2sjUQMAzEnSE8dCoZCCwWDs8MWq6LKysth/FxUVaerUqSosLNSzzz6rb37zm6YumfQ16RtuuEGTJ0+Oa87ixYu1fft27d27VyNHjrzo9y7VUgAAIBWCwWC3JG3WoEGDdPXVV+vo0aOm5yR9d/egQYP0wgsvmPquYRhavHixtm7dqldffVWjR49OdjgAAJyV4luwPq+jo0O///3vNWLECNNzbN3dXVVVpU2bNmnbtm0KBAI6ceKEJMXWuAEASJZUP3HsH//xHzV37lwVFhbq/fff1/Lly5Wenq758+ebPoetSbqurk6SNGvWrG7H169fr4ULF6Y+IAAAkuTdd9/V/Pnz9ec//1lDhw7VDTfcoP3792vo0KGmz2FrkjYMGx6ECgDwphS/qnLz5s0WLnYWL9gAAHgD75MGAADJQiUNAPAEN76qkiQNAPCG854alvD8FCNJAwC8gTVpAACQLFTSAABPYE0aAACnot0NAACShUoaAOANFtvddlTSJGkAgDfQ7gYAAMlCJQ0A8AYXVtIkaQCAJ7jxFiza3QAAOBRJGgAAh6LdDQDwBtakAQBwJtakAQBA0lBJAwC8w4Zq2AqSNADAG1y4Jk27GwAAh6KSBgB4ghs3jpGkAQDeQLsbAAAkC5U0AMATaHcDAOBUtLsBAECyUEkDALzBhZU0SRoA4AmsSdskcLRV/dJP2R2Gww2yOwCXSLc7AFfoUMDuEFzhgArsDsHxIp+EU3cxF1bSrEkDAOBQfaKSBgDgslxYSZOkAQCe4MY1adrdAAA4FEkaAOANRhJGglauXCmfz6fq6uq45tHuBgB4gl3t7sbGRq1bt05FRUVxz6WSBgCgl3R0dGjBggV68sknNXjw4Ljnk6QBAN6QpHZ3W1tbtxEOX/xe76qqKpWXl6u0tDShkEnSAABvSFKSzs/PV3Z2dmzU1tb2eLnNmzfr0KFDF/25GaxJAwAQh1AopGAwGPvs9/t7/M69996rXbt2KSMjI+FrkaQBAJ7g+2xYmS9JwWCwW5LuSVNTk1paWjRp0qTYsUgkor1792rNmjUKh8NKT7/8Y4hJ0gAAb0jhE8duvvlmHT58uNuxyspKjRs3Tvfff7+pBC2RpAEAHpHKW7ACgYAmTpzY7djAgQM1ZMiQC45fChvHAABwKCppAIA32PyCjYaGhrjnkKQBAN5hw0syrKDdDQCAQ1FJAwA8wY2vqiRJAwC8weY16UTQ7gYAwKGopAEAnkC7GwAAp6LdDQAAkoVKGgDgCbS7AQBwKhe2u0nSAABvcGGSZk0aAACHopIGAHgCa9IAADgV7W4AAJAsVNIAAE/wGYZ8RuLlsJW5iSJJAwC8gXZ3fPbu3au5c+cqLy9PPp9PL774op3hAADgKLYm6c7OTl1zzTVau3atnWEAADzg3O5uKyPVbG13l5WVqayszM4QAABe4cJ2t6vWpMPhsMLhcOxzW1ubjdEAANC7XHULVm1trbKzs2MjPz/f7pAAAC7hxna3q5L00qVL1draGhuhUMjukAAAbmEkYaSYq9rdfr9ffr/f7jAAAC7kxseCuqqSBgDAS2ytpDs6OnT06NHY52PHjqm5uVk5OTkqKCiwMTIAQJ/D7u74HDx4UDfeeGPsc01NjSSpoqJCGzZssCkqAEBfZUfL2gpbk/SsWbNk2PAsVAAA3MBVG8cAAEiYYZwdVuanGEkaAOAJ7O4GAABJQyUNAPAGdncDAOBMvujZYWV+qtHuBgDAoaikAQDe4MJ2N5U0AMATUv0WrLq6OhUVFSkYDCoYDKqkpEQ7duyI6xwkaQCAN5y7T9rKiMPIkSO1cuVKNTU16eDBg7rpppt022236a233jJ9DtrdAAD0grlz53b7/Mgjj6iurk779+/XhAkTTJ2DJA0A8IRkPcykra2t23Ezr1GORCJ67rnn1NnZqZKSEtPXpN0NAPAGIwlDUn5+vrKzs2Ojtrb2opc8fPiwsrKy5Pf79Q//8A/aunWrxo8fbzpkKmkAAOIQCoUUDAZjny9VRY8dO1bNzc1qbW3V888/r4qKCu3Zs8d0oiZJAwA8IVnt7nO7tc0YMGCArrrqKklScXGxGhsbtXr1aq1bt87UfJI0AMAbHPAWrGg0qnA4bPr7JGkAAHrB0qVLVVZWpoKCArW3t2vTpk1qaGhQfX296XOQpAEAnpDqV1W2tLTorrvu0gcffKDs7GwVFRWpvr5es2fPNn0OkjQAwBtS/FjQp556ysLFzuIWLAAAHIpKGgDgCaludycDSRoA4A1R4+ywMj/FSNIAAG/gVZUAACBZqKQBAJ7gk8U16aRFYh5JGgDgDQ544li8aHcDAOBQVNIAAE/gFiwAAJyK3d0AACBZqKQBAJ7gMwz5LGz+sjI3UX0iSUd++458vv52h+FoAY21OwSXGGR3AC6RbncArtChgN0hOF70VAr/7o5+NqzMTzHa3QAAOFSfqKQBALgc2t0AADiVC3d3k6QBAN7AE8cAAECyUEkDADyBJ44BAOBUtLsBAECyUEkDADzBFz07rMxPNZI0AMAbaHcDAIBkoZIGAHgDDzMBAMCZ3PhYUNrdAAA4FJU0AMAbXLhxjCQNAPAGQ9beCc2aNAAAvYM1aQAAkDRU0gAAbzBkcU06aZGYRpIGAHiDCzeO0e4GAMChqKQBAN4QleSzOD/FqKQBAJ5wbne3lRGP2tpaTZkyRYFAQLm5uZo3b56OHDkS1zlI0gAA9II9e/aoqqpK+/fv165du3T69GnNmTNHnZ2dps9BuxsA4A0p3ji2c+fObp83bNig3NxcNTU1acaMGabOQZIGAHiDzbu7W1tbJUk5OTmm55CkAQCIQ1tbW7fPfr9ffr//knOi0aiqq6s1ffp0TZw40fS1WJMGAHjDuUraypCUn5+v7Ozs2Kitrb3spauqqvTmm29q8+bNcYVMJQ0A8IYk3YIVCoUUDAZjhy9XRS9evFjbt2/X3r17NXLkyLguSZIGAHhCsl6wEQwGuyXpizEMQ/fcc4+2bt2qhoYGjR49Ou5rkqQBAOgFVVVV2rRpk7Zt26ZAIKATJ05IkrKzs5WZmWnqHI5Yk167dq1GjRqljIwMTZ06VQcOHLA7JABAX5OkNWmz6urq1NraqlmzZmnEiBGxsWXLFtPnsL2S3rJli2pqavTEE09o6tSpWrVqlW655RYdOXJEubm5docHAOgroobks3AbVTS+uUYSXshheyX9+OOP69vf/rYqKys1fvx4PfHEE7riiiv0i1/8wu7QAACwla1JuqurS01NTSotLY0dS0tLU2lpqV5//XUbIwMA9Dkpbncng63t7j/96U+KRCIaNmxYt+PDhg3T22+/fcH3w+GwwuFw7PPnbygHAODirCZa3id9SbW1td1uIM/Pz7c7JAAAeo2tSfrKK69Uenq6Tp482e34yZMnNXz48Au+v3TpUrW2tsZGKBRKVagAALdzYbvb1iQ9YMAAFRcXa/fu3bFj0WhUu3fvVklJyQXf9/v9sZvIzd5MDgCApLO7s62OFLP9FqyamhpVVFRo8uTJuv7667Vq1Sp1dnaqsrLS7tAAALCV7Un6jjvu0Icffqhly5bpxIkTuvbaa7Vz584LNpMBAGCJET07rMxPMduTtHT24eOLFy+2OwwAQF9m8/ukE+GIJA0AQK+LGrJ0G5UNa9KuugULAAAvoZIGAHgD7W4AABzKkMUknbRITKPdDQCAQ1FJAwC8gXY3AAAOFY1KsnCvczT190nT7gYAwKGopAEA3kC7GwAAh3JhkqbdDQCAQ1FJAwC8wYWPBSVJAwA8wTCiMiy8ycrK3ESRpAEA3mAY1qph1qQBAMA5VNIAAG8wLK5JcwsWAAC9JBqVfBbWlW1Yk6bdDQCAQ1FJAwC8gXY3AADOZESjMiy0u+24BYt2NwAADkUlDQDwBtrdAAA4VNSQfO5K0rS7AQBwKCppAIA3GIYkK/dJ0+4GAKBXGFFDhoV2t0G7GwCAXmJErY847N27V3PnzlVeXp58Pp9efPHFuEMmSQMA0As6Ozt1zTXXaO3atQmfg3Y3AMATUt3uLisrU1lZWcLXk0jSAACvMKKytnEs9U8cc3WSPvevmjM6ben+dC8wImG7Q3CFM6dP2R2CK0TC6XaH4ArRU6n/S91toqfO/j+Xik1ZVnPFGZ2WJLW1tXU77vf75ff7rYR2Ua5O0u3t7ZKkfXrZ5khc4Ld2B+AS/J4AW7S3tys7O7tXzj1gwAANHz5c+05YzxVZWVnKz8/vdmz58uVasWKF5XP3xNVJOi8vT6FQSIFAQD6fz+5wJJ39F1Z+fr5CoZCCwaDd4TgWvydz+D2Zw+/JHCf+ngzDUHt7u/Ly8nrtGhkZGTp27Ji6urosn8swjAvyTW9V0ZLLk3RaWppGjhxpdxg9CgaDjvmfwMn4PZnD78kcfk/mOO331FsV9PkyMjKUkZHR69dJNlcnaQAAnKqjo0NHjx6NfT527Jiam5uVk5OjgoICU+cgSQMA0AsOHjyoG2+8Mfa5pqZGklRRUaENGzaYOgdJOsn8fr+WL1/eq2sUfQG/J3P4PZnD78kcfk+pNWvWLMu71n2GHQ8jBQAAl8VjQQEAcCiSNAAADkWSBgDAoUjSAAA4FEk6ydauXatRo0YpIyNDU6dO1YEDB+wOyVGS8X5VL6itrdWUKVMUCASUm5urefPm6ciRI3aH5Th1dXUqKiqKPZyjpKREO3bssDssR1u5cqV8Pp+qq6vtDgUmkKSTaMuWLaqpqdHy5ct16NAhXXPNNbrlllvU0tJid2iOkYz3q3rBnj17VFVVpf3792vXrl06ffq05syZo87OTrtDc5SRI0dq5cqVampq0sGDB3XTTTfptttu01tvvWV3aI7U2NiodevWqaioyO5QYBK3YCXR1KlTNWXKFK1Zs0aSFI1GlZ+fr3vuuUcPPPCAzdE5j8/n09atWzVv3jy7Q3G8Dz/8ULm5udqzZ49mzJhhdziOlpOTo8cee0zf/OY37Q7FUTo6OjRp0iT97Gc/049+9CNde+21WrVqld1h4TKopJOkq6tLTU1NKi0tjR1LS0tTaWmpXn/9dRsjQ1/Q2toq6WwCQs8ikYg2b96szs5OlZSU2B2O41RVVam8vLzb31FwPp44liR/+tOfFIlENGzYsG7Hhw0bprffftumqNAXRKNRVVdXa/r06Zo4caLd4TjO4cOHVVJSolOnTikrK0tbt27V+PHj7Q7LUTZv3qxDhw6psbHR7lAQJ5I04HBVVVV68803tW/fPrtDcaSxY8equblZra2tev7551VRUaE9e/aQqD8TCoV07733ateuXa58C5TXkaST5Morr1R6erpOnjzZ7fjJkyc1fPhwm6KC2y1evFjbt2/X3r17HftaVrsNGDBAV111lSSpuLhYjY2NWr16tdatW2dzZM7Q1NSklpYWTZo0KXYsEolo7969WrNmjcLhsNLT022MEJfCmnSSDBgwQMXFxdq9e3fsWDQa1e7du1kfQ9wMw9DixYu1detWvfrqqxo9erTdIblGNBpVOBy2OwzHuPnmm3X48GE1NzfHxuTJk7VgwQI1NzeToB2OSjqJampqVFFRocmTJ+v666/XqlWr1NnZqcrKSrtDc4xkvF/VC6qqqrRp0yZt27ZNgUBAJ06ckCRlZ2crMzPT5uicY+nSpSorK1NBQYHa29u1adMmNTQ0qL6+3u7QHCMQCFywl2HgwIEaMmQIexxcgCSdRHfccYc+/PBDLVu2TCdOnNC1116rnTt3XrCZzMuS8X5VL6irq5N09lV351u/fr0WLlyY+oAcqqWlRXfddZc++OADZWdnq6ioSPX19Zo9e7bdoQFJwX3SAAA4FGvSAAA4FEkaAACHIkkDAOBQJGkAAByKJA0AgEORpAEAcCiSNAAADkWSBgDAoUjSgIMtXLhQ8+bNszsMADYhSQMA4FAkaQAAHIokDQCAQ5GkAQBwKJI0AAAORZIGAMChSNIAADgUSRoAAIciSQMA4FA+wzAMu4MAAAAXopIGAMChSNIAADgUSRoAAIciSQMA4FAkaQAAHIokDQCAQ5GkAQBwKJI0AAAORZIGAMChSNIAADgUSRoAAIciSQMA4FD/HwWTrr6jbFwEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil`\")\n",
+    "copy_stencil(qty_in.data, qty_out.data)\n",
+    "print(\"Plotting qty_out from `copy_stencil` at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Plotting qty_out from `copy_stencil` at K = 1\")\n",
+    "plot_field_at_kN(qty_out.data,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Choosing subsets (or offsets) to perform stencil calculations**\n",
+    "\n",
+    "GT4Py also allows a subset of the `IJ` plane to be specified and executed in a fashion similar to using `interval(...)` in the K interval.  This is done by setting `origin` and `domain` when executing the stencil.\n",
+    "\n",
+    "- `origin` : This specifies the \"starting\" coordinate to perform computations in the `IJ` plane.  \n",
+    "\n",
+    "- `domain` : This specifies the range of the stencil computation based on `origin` as the \"starting\" coordinate (Note: May need to check whether this affects `interval()`)\n",
+    "\n",
+    "If these two parameters are not set, the GT4py stencil by default will iterate over the entire input domain.  The following demonstrates the effect of specifying different `origin` and `domain`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil` with origin=(1,0,0)\n",
+      "Plotting qty_out at K = 0 based on `copy_stencil` with origin=(1,0,0)\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resetting qty_out to zero...\n",
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil` with origin=(0,1,0)\n",
+      "Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,1,0)\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resetting qty_out to zero...\n",
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil` with origin = (0,0,1)\n",
+      "Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,0,1)\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting qty_out at K = 1 based on `copy_stencil` with origin=(0,0,1)\n",
+      "Min and max values: 9.0 1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resetting qty_out to zero...\n",
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 8.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil` with domain=(2,2,nz)\n",
+      "Plotting qty_out at K = 0 based on `copy_stencil` with domain = (2,2,nz)\n",
+      "Min and max values: 2.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resetting qty_out to zero...\n",
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing `copy_stencil` with origin = (2,2,0), domain=(2,2,nz)\n",
+      "Plotting qty_out at K = 0 based on `copy_stencil` with origin = (2,2,0), domain = (2,2,nz)\n",
+      "Min and max values: 6.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil` with origin=(1,0,0)\")\n",
+    "copy_stencil(qty_in.data, qty_out.data,origin=(1,0,0))\n",
+    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(1,0,0)\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Resetting qty_out to zero...\")\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil` with origin=(0,1,0)\")\n",
+    "copy_stencil(qty_in.data, qty_out.data,origin=(0,1,0))\n",
+    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,1,0)\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Resetting qty_out to zero...\")\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil` with origin = (0,0,1)\")\n",
+    "copy_stencil(qty_in.data, qty_out.data,origin=(0,0,1))\n",
+    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,0,1)\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Plotting qty_out at K = 1 based on `copy_stencil` with origin=(0,0,1)\")\n",
+    "plot_field_at_kN(qty_out.data, 1)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "print(\"Resetting qty_out to zero...\")\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil` with domain=(2,2,nz)\")\n",
+    "copy_stencil(qty_in.data, qty_out.data, domain=(2,2,nz))\n",
+    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with domain = (2,2,nz)\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "print(\"Resetting qty_out to zero...\")\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing `copy_stencil` with origin = (2,2,0), domain=(2,2,nz)\")\n",
+    "copy_stencil(qty_in.data, qty_out.data, origin=(2,2,0), domain=(2,2,nz))\n",
+    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin = (2,2,0), domain = (2,2,nz)\")\n",
+    "plot_field_at_kN(qty_out.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **`FORWARD` and `BACKWARD` `computation` keywords**\n",
+    "\n",
+    "Besides `PARALLEL`, the developer can specify `FORWARD` or `BACKWARD` as the iteration policy in `K`.  Essentially, the `FORWARD` policy has `K` iterating consecutively starting from the lowest vertical index to the highest, while the `BACKWARD` policy performs the reverse.  The following examples demonstrate the use of these two iteration policies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 12.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing 'copy_stencil' with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Min and max values: 10.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Min and max values: 11.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing 'mult_upward' with origin=(nhalo,nhalo,0),domain=(nx,ny,2)\n",
+      "Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\n",
+      "Min and max values: 10.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\n",
+      "Min and max values: 20.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 2 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\n",
+      "Min and max values: 40.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 3 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\n",
+      "Min and max values: 13.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing 'copy_stencil' to copy qty_out to qty_in\n",
+      "***\n",
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 10.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 1\n",
+      "Min and max values: 20.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 2\n",
+      "Min and max values: 40.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 3\n",
+      "Min and max values: 13.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 4\n",
+      "Min and max values: 14.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fCwoKJEl5eXkqKirSjh07JElDhw4NOG7//v0aP358i69DkAMAHMFnuOQzcx95K48dP368jOs81vV637UGT3YDAMDG6JEDABzBZ3Jo3czDZMKJIAcAOIL5t59FZpBHZlUAAKBF6JEDABzBK5e8Jh4IY+bYcCLIAQCOwNA6AACIOPTIAQCO4JW54XFv6EoJKYIcAOAI0Tq0TpADABwh0l6aEiqRWRUAAGgReuQAAEcwTL6P3OD2MwAArMPQOgAAiDj0yAEAjtDWrzFtK5b2yIuKiuRyuQJav379rCwJABClvF+//cxMi0SW98gHDBig3/zmN/7P7dtbXhIAALZheWq2b99ePXr0sLoMAECUY2g9TM6cOaP09HR985vf1IwZM3Tu3Llr7tvY2CiPxxPQAABoCZ/amW6RyNKqRo0apbKyMu3evVulpaWqrKzUnXfeqbq6umb3Ly4uVlJSkr9lZGS0ccUAAEQWS4fWc3Nz/f8+ePBgjRo1SllZWXr11Vf1/e9/v8n+CxcuVEFBgf+zx+MhzAEALeI1XPKaGB43c2w4WT5HfrUuXbro1ltv1dmzZ5v93u12y+12t3FVAIBowBx5G6ivr9f777+vtLQ0q0sBAEQZ4+u3nwXbDJ7s1tRTTz2lgwcP6sMPP9Tvfvc7fec731FMTIymT59uZVkAANiGpUPrH3/8saZPn64///nP6tatm+644w4dOXJE3bp1s7IsAEAU8solr4kXn5g5NpwsDfLNmzdbeXkAgIP4DHPz3D4jhMWEUGQO+AMAgBaJqFXrAACEy5VFa2aOj0QEOQDAEXxyyWdintvMseEUmX+9AAAALUKPHADgCDzZDQAAG4vWOfLIrAoAALQIPXIAgCP4ZPJZ6xG62I0gBwA4gmFy1bpBkAMAYB3efgYAACIOPXIAgCNE66p1ghwA4AgMrQMAgIhDjxwA4Ag8ax0AABu7MrRuprXGoUOHNHXqVKWnp8vlcmnbtm0B3xuGoUWLFiktLU3x8fHKycnRmTNnWv27CHIAAMKgoaFBQ4YM0apVq5r9/mc/+5leeOEFrV69Wu+88446deqkSZMm6eLFi626DkPrAABHaOvFbrm5ucrNzW32O8MwVFJSop/+9Ke67777JEkbN25U9+7dtW3bNj300EMtvg49cgCAI7T10Pr1VFZWqrq6Wjk5Of5tSUlJGjVqlN5+++1WnYseOQAAreDxeAI+u91uud3uVp2jurpaktS9e/eA7d27d/d/11IEeQSq+ryL1SUAUeJjqwtABAnV0HpGRkbA9sLCQhUVFZkpzRSCHADgCIbM3UJmfP3PqqoqJSYm+re3tjcuST169JAkXbhwQWlpaf7tFy5c0NChQ1t1LubIAQCOEKo58sTExIAWTJD37t1bPXr00L59+/zbPB6P3nnnHY0ePbpV56JHDgBAGNTX1+vs2bP+z5WVlTpx4oRSUlKUmZmp+fPn65//+Z/Vp08f9e7dW88884zS09M1bdq0Vl2HIAcAOEJb335WXl6uCRMm+D8XFBRIkvLy8lRWVqann35aDQ0N+sd//Ed9/vnnuuOOO7R7927FxcW16joEOQDAEdo6yMePHy/DMK75vcvl0pIlS7RkyZKga5KYIwcAwNbokQMAHCFaX2NKkAMAHMEwXDJMhLGZY8OJoXUAAGyMHjkAwBGi9X3kBDkAwBGidY6coXUAAGyMHjkAwBGidbEbQQ4AcIRoHVonyAEAjhCtPXLmyAEAsDF65AAARzBMDq1Hao+cIAcAOIIh6TrvMGnR8ZGIoXUAAGyMHjkAwBF8csnFk90AALAnVq2H2bJly+RyuTR//nyrSwEAwDYiokd+7NgxrVmzRoMHD7a6FABAlPIZLrmi8IEwlvfI6+vrNWPGDK1bt07JyclWlwMAiFKGYb5FIsuDPD8/X5MnT1ZOTs4N921sbJTH4wloAAA4maVD65s3b9bx48d17NixFu1fXFysxYsXh7kqAEA0YrFbiFVVVWnevHl6+eWXFRcX16JjFi5cqNraWn+rqqoKc5UAgGhxJcjNtEhkWY+8oqJCNTU1uv322/3bvF6vDh06pJUrV6qxsVExMTEBx7jdbrnd7rYuFQAQBaJ1sZtlQT5x4kSdPHkyYNvs2bPVr18/LViwoEmIAwCApiwL8oSEBA0cODBgW6dOndS1a9cm2wEAMMvsyvNIXbUeEfeRAwAQbl8FuZnFbiEsJoQiKsgPHDhgdQkAANhKRAU5AADhEq23nxHkAABHMGTuneIROrJu/ZPdAABA8OiRAwAcgaF1AADsLErH1glyAIAzmH3MaoT2yJkjBwDAxuiRAwAcgSe7AQBgY9G62I2hdQAAbIweOQDAGQyXuQVrEdojJ8gBAI4QrXPkDK0DAGBj9MgBAM7AA2EAALAvVq0DAIAW83q9euaZZ9S7d2/Fx8fr5ptv1rPPPisjxJPt9MgBAM7RhsPjy5cvV2lpqTZs2KABAwaovLxcs2fPVlJSkubOnRuy6xDkAABHaOuh9d/97ne67777NHnyZElSr1699Morr+jo0aNB19AchtYBAM5ghKC1wpgxY7Rv3z699957kqT//u//1uHDh5WbmxuCH/P/0SMHAKAVPB5PwGe32y23291kv5/85CfyeDzq16+fYmJi5PV69dxzz2nGjBkhrYcgj0B1n3e0uoSQq7K6AACQ6+tm5ngpIyMjYGthYaGKioqa7P3qq6/q5Zdf1qZNmzRgwACdOHFC8+fPV3p6uvLy8kzUEYggBwA4Q4juI6+qqlJiYqJ/c3O9cUn68Y9/rJ/85Cd66KGHJEmDBg3SRx99pOLiYoIcAACrJCYmBgT5tXzxxRdq1y5wKVpMTIx8Pl9I6yHIAQDO0MZPdps6daqee+45ZWZmasCAAfr973+v559/Xo888oiJIpoiyAEAztDGbz/7+c9/rmeeeUY//OEPVVNTo/T0dD322GNatGhR8DU0gyAHACAMEhISVFJSopKSkrBehyAHADhCtL7GlCAHADhDlL79jCe7AQBgY/TIAQDO0MaL3doKQQ4AcASX8VUzc3wkIsgBAM7AHDkAAIg09MgBAM7AHDkAADbG0DoAAIg09MgBAM4QpT1yghwA4AxRGuQMrQMAYGP0yAEAzsCqdQAA7Ctan+zG0DoAADZmaZCXlpZq8ODBSkxMVGJiokaPHq1du3ZZWRIAIFoZIWgRyNIg79mzp5YtW6aKigqVl5fr7rvv1n333ad3333XyrIAALANS+fIp06dGvD5ueeeU2lpqY4cOaIBAwZYVBUAIBq5ZHKOPGSVhFaLgvzv/u7vbnyi9u3Vo0cP3XPPPU0CuiW8Xq9++ctfqqGhQaNHj252n8bGRjU2Nvo/ezyeVl8HAIBo0qIgT0pKuuE+Pp9PZ86c0S9+8Qs99dRTWrJkSYsKOHnypEaPHq2LFy+qc+fO2rp1q/r379/svsXFxVq8eHGLzgsAQAAn3362fv36Fp9w586d+uEPf9jiIO/bt69OnDih2tpavfbaa8rLy9PBgwebDfOFCxeqoKDA/9nj8SgjI6PFtQEAHCxKn+wW8jnyO+64Q9nZ2S3ePzY2Vrfccoskafjw4Tp27JhWrFihNWvWNNnX7XbL7XaHrFYAAOwu5EHepUsXvf7660Ef7/P5AubBAQAICXrkobdw4ULl5uYqMzNTdXV12rRpkw4cOKA9e/ZYWRYAIApF65PdLA3ympoafe9739Mf//hHJSUlafDgwdqzZ4/uueceK8sCAMA2LA3y//iP/7Dy8gAAJ2FoHQAAG4vSIOelKQAA2Bg9cgCAI7DYDQAAO3Pyk90AALA95sgBAECkoUcOAHAE5sgBALAzhtYBAECkoUcOAHAGk0PrkdojJ8gBAM7A0DoAAIg09MgBAM4QpT1yghwA4AjRevsZQ+sAANgYQQ4AQJicP39eM2fOVNeuXRUfH69BgwapvLw8pNdgaB0A4AxtPEf+2WefaezYsZowYYJ27dqlbt266cyZM0pOTjZRRFMEOQDAEdp6jnz58uXKyMjQ+vXr/dt69+4dfAHXwNA6AACt4PF4AlpjY2Oz++3YsUPZ2dm6//77lZqaqmHDhmndunUhr4cgBwA4h2GifS0jI0NJSUn+Vlxc3OylPvjgA5WWlqpPnz7as2ePHn/8cc2dO1cbNmwI6U9iaD0CuT7rYHUJIVenjlaXEFJVVhcAB4vQe6DsIERz5FVVVUpMTPRvdrvdze7u8/mUnZ2tpUuXSpKGDRumU6dOafXq1crLyzNRSCB65AAAtEJiYmJAu1aQp6WlqX///gHbbrvtNp07dy6k9dAjBwA4Qlsvdhs7dqxOnz4dsO29995TVlZW8EU0gx45AMAZzMyPBzEs/+STT+rIkSNaunSpzp49q02bNmnt2rXKz88Pze/5GkEOAEAYjBgxQlu3btUrr7yigQMH6tlnn1VJSYlmzJgR0uswtA4AcAQrnrU+ZcoUTZkyJfiLtgBBDgBwhih9+xlD6wAA2Bg9cgCAM0Rpj5wgBwA4QrS+j5wgBwA4Q5T2yJkjBwDAxuiRAwCcIUp75AQ5AMARonWOnKF1AABsjB45AMAZGFoHAMC+GFoHAAARhx45AMAZGFoHAMDGojTIGVoHAMDGLA3y4uJijRgxQgkJCUpNTdW0adN0+vRpK0sCAEQpVwhaJLI0yA8ePKj8/HwdOXJEe/fu1eXLl3XvvfeqoaHByrIAANHICEGLQJbOke/evTvgc1lZmVJTU1VRUaG77rrLoqoAANGI28/aQG1trSQpJSXF4koAALCHiFm17vP5NH/+fI0dO1YDBw5sdp/GxkY1Njb6P3s8nrYqDwBgd6xaD6/8/HydOnVKmzdvvuY+xcXFSkpK8reMjIw2rBAAYHtRNj8uRUiQz5kzRzt37tT+/fvVs2fPa+63cOFC1dbW+ltVVVUbVgkAQOSxdGjdMAw98cQT2rp1qw4cOKDevXtfd3+32y23291G1QEAokm0LnazNMjz8/O1adMmbd++XQkJCaqurpYkJSUlKT4+3srSAADRhjny0CstLVVtba3Gjx+vtLQ0f9uyZYuVZQEAYBuWD60DANAWGFoHAMDOGFoHAACRhh45AMARGFoHAMDOonRonSAHADhDlAY5c+QAANgYPXIAgCMwRw4AgJ0xtA4AACINPXIAgCO4DEMuE08UNXNsOBHkAABnYGgdAABEGnrkAABHYNU6AAB2xtA6AACINPTIAQCOEK1D6/TIAQDOYISgBWnZsmVyuVyaP39+8Ce5BnrkAABHsKpHfuzYMa1Zs0aDBw8O/uLXQY8cAIAwqa+v14wZM7Ru3TolJyeH5RoEOQDAGUI0tO7xeAJaY2PjNS+Zn5+vyZMnKycnJ0w/iqH1iOT+v+j7+1WjOlhdQkjVqaPVJYRcldUFoIX+aHUBthaKBWsZGRkBnwsLC1VUVNRkv82bN+v48eM6duyY+YteB0EOAEArVFVVKTEx0f/Z7XY3u8+8efO0d+9excXFhbUeghwA4AyG8VUzc7ykxMTEgCBvTkVFhWpqanT77bf7t3m9Xh06dEgrV65UY2OjYmJigq/lKgQ5AMAR2nLV+sSJE3Xy5MmAbbNnz1a/fv20YMGCkIW4RJADABByCQkJGjhwYMC2Tp06qWvXrk22m0WQAwCcIUqftU6QAwAcweX7qpk53owDBw6YO8E1RN99TgAAOAg9cgCAMzC0DgCAfUXr288IcgCAM4ToPvJIwxw5AAA2Ro8cAOAIDK0DAGBnUbrYjaF1AABsjB45AMARGFoHAMDOWLUOAAAiDT1yAIAjMLQOAICdsWodAABEGnrkAABHiNahdUt75IcOHdLUqVOVnp4ul8ulbdu2WVkOACCa+QzzLQJZGuQNDQ0aMmSIVq1aZWUZAAAnMELQIpClQ+u5ubnKzc21sgQAAGyNOXIAgCO4ZHKOPGSVhJatgryxsVGNjY3+zx6Px8JqAAC2wpPdrFdcXKykpCR/y8jIsLokAAAsZasgX7hwoWpra/2tqqrK6pIAADZx5fYzMy0S2Wpo3e12y+12W10GAMCOovTJbpYGeX19vc6ePev/XFlZqRMnTiglJUWZmZkWVgYAgD1YGuTl5eWaMGGC/3NBQYEkKS8vT2VlZRZVBQCIRi7DkMvEgjUzx4aTpUE+fvx4GRH6HwYAEGV8Xzczx0cgWy12AwAAgWy12A0AgGAxtA4AgJ2xah0AABvjyW4AACDS0CMHADiC2aez8WQ3AACsxNA6AACINPTIAQCO4PJ91cwcH4kIcgCAMzC0DgAAIg09cgCAM/BAGAAA7CtaH9HK0DoAADZGjxwA4AxRutiNIAcAOIMhc+8Uj8wcZ2gdAOAMV+bIzbTWKC4u1ogRI5SQkKDU1FRNmzZNp0+fDvnvIsgBAAiDgwcPKj8/X0eOHNHevXt1+fJl3XvvvWpoaAjpdRhaBwA4gyGTc+St23337t0Bn8vKypSamqqKigrdddddwdfxVwhyAIAzhGixm8fjCdjsdrvldrtveHhtba0kKSUlJfgamkGQR6CMZ39ndQlAVLhsdQGIShkZGQGfCwsLVVRUdN1jfD6f5s+fr7Fjx2rgwIEhrYcgBwA4g0+Sy+TxkqqqqpSYmOjf3JLeeH5+vk6dOqXDhw+bKKB5BDkAwBFC9WS3xMTEgCC/kTlz5mjnzp06dOiQevbsGfT1r4UgBwAgDAzD0BNPPKGtW7fqwIED6t27d1iuQ5ADAJyhjZ/slp+fr02bNmn79u1KSEhQdXW1JCkpKUnx8fHB1/FXuI8cAOAMV4LcTGuF0tJS1dbWavz48UpLS/O3LVu2hPRn0SMHACAMjDZ6NjtBDgBwBl6aAgCAjYXo9rNIQ5ADABwhVLefRRoWuwEAYGP0yAEAzsAcOQAANuYzJJeJMPZFZpAztA4AgI3RIwcAOAND6wAA2JnJIFdkBjlD6wAA2Bg9cgCAMzC0DgCAjfkMmRoeZ9U6AAAINXrkAABnMHxfNTPHRyCCHADgDFE6Rx4RQ+urVq1Sr169FBcXp1GjRuno0aNWlwQAiDY+w3yLQJYH+ZYtW1RQUKDCwkIdP35cQ4YM0aRJk1RTU2N1aQAARDzLg/z555/Xo48+qtmzZ6t///5avXq1OnbsqBdffNHq0gAA0eTK0LqZFoEsDfJLly6poqJCOTk5/m3t2rVTTk6O3n777Sb7NzY2yuPxBDQAAFrEkMkgt/oHNM/SIP/Tn/4kr9er7t27B2zv3r27qqurm+xfXFyspKQkf8vIyGirUgEAiEiWD623xsKFC1VbW+tvVVVVVpcEALCLKB1at/T2s5tuukkxMTG6cOFCwPYLFy6oR48eTfZ3u91yu91tVR4AIJr4fJJM3Avui8z7yC3tkcfGxmr48OHat2+ff5vP59O+ffs0evRoCysDAMAeLH8gTEFBgfLy8pSdna2RI0eqpKREDQ0Nmj17ttWlAQCiSZQ+EMbyIH/wwQf16aefatGiRaqurtbQoUO1e/fuJgvgAAAwhSAPnzlz5mjOnDlWlwEAgO1ERJADABB2UfoaU4IcAOAIhuGTYeINZmaODSeCHADgDIbJF59E6By5rR4IAwAAAtEjBwA4g2FyjjxCe+QEOQDAGXw+yWVinjtC58gZWgcAwMbokQMAnIGhdQAA7Mvw+WSYGFqP1NvPGFoHAMDG6JEDAJyBoXUAAGzMZ0iu6AtyhtYBALAxeuQAAGcwDElm7iOPzB45QQ4AcATDZ8gwMbRuRGiQM7QOAHAGw2e+BWHVqlXq1auX4uLiNGrUKB09ejSkP4sgBwAgTLZs2aKCggIVFhbq+PHjGjJkiCZNmqSampqQXYMgBwA4guEzTLfWev755/Xoo49q9uzZ6t+/v1avXq2OHTvqxRdfDNnvIsgBAM7QxkPrly5dUkVFhXJycvzb2rVrp5ycHL399tsh+1m2Xux2ZeHBl7ps6h5/AIA1vtRlSW2zkMxsVlyp1ePxBGx3u91yu91N9v/Tn/4kr9er7t27B2zv3r27/vCHPwRfyF+xdZDX1dVJkg7rTYsrAQCYUVdXp6SkpLCcOzY2Vj169NDhavNZ0blzZ2VkZARsKywsVFFRkelzB8vWQZ6enq6qqiolJCTI5XKF9Voej0cZGRmqqqpSYmJiWK/VFqLt90j8JrvgN0W+tvw9hmGorq5O6enpYbtGXFycKisrdenSJdPnMgyjSd401xuXpJtuukkxMTG6cOFCwPYLFy6oR48epmu5wtZB3q5dO/Xs2bNNr5mYmBgVf1CviLbfI/Gb7ILfFPna6veEqyd+tbi4OMXFxYX9OleLjY3V8OHDtW/fPk2bNk2S5PP5tG/fPs2ZMydk17F1kAMAEMkKCgqUl5en7OxsjRw5UiUlJWpoaNDs2bNDdg2CHACAMHnwwQf16aefatGiRaqurtbQoUO1e/fuJgvgzCDIW8jtdquwsPCacyF2E22/R+I32QW/KfJF2++x2pw5c0I6lP7XXEakPjwWAADcEA+EAQDAxghyAABsjCAHAMDGCHIAAGyMIG+BcL9Lti0dOnRIU6dOVXp6ulwul7Zt22Z1SaYVFxdrxIgRSkhIUGpqqqZNm6bTp09bXZYppaWlGjx4sP+BHKNHj9auXbusLitkli1bJpfLpfnz51tdStCKiorkcrkCWr9+/awuy7Tz589r5syZ6tq1q+Lj4zVo0CCVl5dbXRaugyC/gbZ4l2xbamho0JAhQ7Rq1SqrSwmZgwcPKj8/X0eOHNHevXt1+fJl3XvvvWpoaLC6tKD17NlTy5YtU0VFhcrLy3X33Xfrvvvu07vvvmt1aaYdO3ZMa9as0eDBg60uxbQBAwboj3/8o78dPnzY6pJM+eyzzzR27Fh16NBBu3bt0v/8z//oX//1X5WcnGx1abgeA9c1cuRIIz8/3//Z6/Ua6enpRnFxsYVVhYYkY+vWrVaXEXI1NTWGJOPgwYNWlxJSycnJxi9+8QuryzClrq7O6NOnj7F3715j3Lhxxrx586wuKWiFhYXGkCFDrC4jpBYsWGDccccdVpeBVqJHfh1t9S5ZhFZtba0kKSUlxeJKQsPr9Wrz5s1qaGjQ6NGjrS7HlPz8fE2ePDngz5SdnTlzRunp6frmN7+pGTNm6Ny5c1aXZMqOHTuUnZ2t+++/X6mpqRo2bJjWrVtndVm4AYL8Oq73Ltnq6mqLqsL1+Hw+zZ8/X2PHjtXAgQOtLseUkydPqnPnznK73frBD36grVu3qn///laXFbTNmzfr+PHjKi4utrqUkBg1apTKysq0e/dulZaWqrKyUnfeeaf/9cp29MEHH6i0tFR9+vTRnj179Pjjj2vu3LnasGGD1aXhOnhEK6JKfn6+Tp06Zfu5Sknq27evTpw4odraWr322mvKy8vTwYMHbRnmVVVVmjdvnvbu3dvmb6AKl9zcXP+/Dx48WKNGjVJWVpZeffVVff/737ewsuD5fD5lZ2dr6dKlkqRhw4bp1KlTWr16tfLy8iyuDtdCj/w62updsgiNOXPmaOfOndq/f3+bv942HGJjY3XLLbdo+PDhKi4u1pAhQ7RixQqrywpKRUWFampqdPvtt6t9+/Zq3769Dh48qBdeeEHt27eX1+u1ukTTunTpoltvvVVnz561upSgpaWlNfmL4m233Wb7KYNoR5Bfx9Xvkr3iyrtk7T5XGU0Mw9CcOXO0detWvfXWW+rdu7fVJYWFz+dTY2Oj1WUEZeLEiTp58qROnDjhb9nZ2ZoxY4ZOnDihmJgYq0s0rb6+Xu+//77S0tKsLiVoY8eObXLr5nvvvaesrCyLKkJLMLR+A23xLtm2VF9fH9BjqKys1IkTJ5SSkqLMzEwLKwtefn6+Nm3apO3btyshIcG/fiEpKUnx8fEWVxechQsXKjc3V5mZmaqrq9OmTZt04MAB7dmzx+rSgpKQkNBkzUKnTp3UtWtX265leOqppzR16lRlZWXpk08+UWFhoWJiYjR9+nSrSwvak08+qTFjxmjp0qV64IEHdPToUa1du1Zr1661ujRcj9XL5u3g5z//uZGZmWnExsYaI0eONI4cOWJ1SUHbv3+/IalJy8vLs7q0oDX3eyQZ69evt7q0oD3yyCNGVlaWERsba3Tr1s2YOHGi8Z//+Z9WlxVSdr/97MEHHzTS0tKM2NhY4xvf+Ibx4IMPGmfPnrW6LNN+/etfGwMHDjTcbrfRr18/Y+3atVaXhBvgNaYAANgYc+QAANgYQQ4AgI0R5AAA2BhBDgCAjRHkAADYGEEOAICNEeQAANgYQQ4AgI0R5EAEe/jhhzVt2jSrywAQwQhyAABsjCAHAMDGCHIAAGyMIAcAwMYIcgAAbIwgBwDAxghyAABsjCAHAMDGCHIAAGzMZRiGYXURAAAgOPTIAQCwMYIcAAAbI8gBALAxghwAABsjyAEAsDGCHAAAGyPIAQCwMYIcAAAbI8gBALAxghwAABsjyAEAsDGCHAAAG/t/AbrCZ2JdRCMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing 'copy_downward' on qty_in with origin=(1,1,0), domain=(nx,ny,nz-1)\n",
+      "***\n",
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 14.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 1\n",
+      "Min and max values: 14.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 2\n",
+      "Min and max values: 14.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from gt4py.cartesian.gtscript import FORWARD, BACKWARD\n",
+    "\n",
+    "nx = 5\n",
+    "ny = 5\n",
+    "nz = 5\n",
+    "nhalo = 1\n",
+    "backend=\"numpy\"\n",
+    "\n",
+    "shape = (nx + 2 * nhalo, ny + 2 * nhalo, nz)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros(shape),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "arr = np.indices(shape,dtype=float).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
+    "qty_in = Quantity(data=arr,\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "\n",
+    "print(\"Executing 'copy_stencil' with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "copy_stencil(qty_in.data,qty_out.data,origin=(nhalo,nhalo,0),domain=(nx,ny,5))\n",
+    "\n",
+    "print(\"Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "plot_field_at_kN(qty_out.data,1)\n",
+    "\n",
+    "@stencil(backend=backend)\n",
+    "def mult_upward(qty_in: FloatField):\n",
+    "    with computation(FORWARD), interval(...):\n",
+    "        qty_in = qty_in[0,0,-1] * 2.0\n",
+    "\n",
+    "print(\"Executing 'mult_upward' with origin=(nhalo,nhalo,0),domain=(nx,ny,2)\")\n",
+    "mult_upward(qty_out.data, origin=(nhalo,nhalo,1), domain=(nx,ny,2))\n",
+    "print(\"Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
+    "plot_field_at_kN(qty_out.data,1)\n",
+    "print(\"Plotting values of qty_out at K = 2 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
+    "plot_field_at_kN(qty_out.data,2)\n",
+    "print(\"Plotting values of qty_out at K = 3 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
+    "plot_field_at_kN(qty_out.data,3)\n",
+    "\n",
+    "@stencil(backend=backend)\n",
+    "def copy_downward(qty_in: FloatField):\n",
+    "    with computation(BACKWARD), interval(...):\n",
+    "        qty_in = qty_in[0,0,1]\n",
+    "\n",
+    "print(\"Executing 'copy_stencil' to copy qty_out to qty_in\")\n",
+    "copy_stencil(qty_out, qty_in)\n",
+    "\n",
+    "print(\"***\")\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data,0)\n",
+    "print(\"Plotting values of qty_in at K = 1\")\n",
+    "plot_field_at_kN(qty_in.data,1)\n",
+    "print(\"Plotting values of qty_in at K = 2\")\n",
+    "plot_field_at_kN(qty_in.data,2)\n",
+    "print(\"Plotting values of qty_in at K = 3\")\n",
+    "plot_field_at_kN(qty_in.data,3)\n",
+    "print(\"Plotting values of qty_in at K = 4\")\n",
+    "plot_field_at_kN(qty_in.data,4)\n",
+    "\n",
+    "print(\"Executing 'copy_downward' on qty_in with origin=(1,1,0), domain=(nx,ny,nz-1)\")\n",
+    "copy_downward(qty_in.data, origin=(1,1,0), domain=(nx,ny,nz-1))\n",
+    "print(\"***\")\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data,0)\n",
+    "print(\"Plotting values of qty_in at K = 1\")\n",
+    "plot_field_at_kN(qty_in.data,1)\n",
+    "print(\"Plotting values of qty_in at K = 2\")\n",
+    "plot_field_at_kN(qty_in.data,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **`if/else` statements**\n",
+    "\n",
+    "GT4Py allows for `if/else` statements to exist within a stencil.  The following simple example shows a stencil `stencil_if_zero` modifing values of `in_out_field` depending on its initial value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 12.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Plotting values of qty_out at K = 0 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Min and max values: 10.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 1 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\n",
+      "Min and max values: 11.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running 'stencil_if_zero' on qty_out\n",
+      "Plotting values of qty_out at K = 0 based on running stencil_if_zero\n",
+      "Min and max values: 30.0 10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ALIaZPwDAEvy/4Y+yPwAA3cqpNf+2l+796dvVUPYHAMBimPkDACzB5ee7/QPpbn9m/gAAS2hY8/en+SI3N1fjx49XeHi4oqOjNWvWLI+P2e3bt082m63Z9sorr5xx3Llz5zY5f9q0aT7FRvIHAFiCSz38br4oLi5WVlaWtm7dqo0bN+rkyZOaOnWqamtrJUnx8fE6dOiQR7vnnnsUFhamjIyMFseeNm2aR78///nPPsVG2R8AgA6wfv16j+38/HxFR0erpKREkydPVlBQkGJjYz3OKSgo0NVXX62wsLAWx7bb7U36+oKZPwDAEuoNm99NkqqqqjxaXV2dV9d3Op2SpKioqGaPl5SUqLS0VPPmzWt1rKKiIkVHR2v48OG6+eabdfToUS//LZxC8gcAWEL99zf8+dOkU+X6yMhId8vNzW312i6XSwsXLtTEiRM1cuTIZs957rnndO655+rCCy9scaxp06bphRdeUGFhoR588EEVFxcrIyND9fX1Xv+7oOwPAIAPysvLFRER4d622+2t9snKytLOnTu1efPmZo9/++23WrNmje6+++5Wx7r22mvd/zxq1CglJyfr7LPPVlFRkaZMmeLFL2DmDwCwCJfRw+8mSRERER6tteSfnZ2tdevW6YMPPtDAgQObPefVV1/VN998o+uvv97n3zVkyBD1799fu3fv9roPM38AgCWcXrpvW3/fnvM3DEMLFixQQUGBioqKlJiYeMZzn3vuOf3sZz/TgAEDfI7rwIEDOnr0qOLi4rzuw8wfAIAOkJWVpT/96U9as2aNwsPDVVFRoYqKCn377bce5+3evVubNm3SL3/5y2bHSUpKUkFBgSSppqZGv/3tb7V161bt27dPhYWFmjlzpoYOHar09HSvY2PmDwCwBJfkvmO/rf19kZeXJ0lKTU312L9q1SrNnTvXvf38889r4MCBmjp1arPjlJWVuZ8UCAoK0scff6zVq1fr2LFjcjgcmjp1qu69916v7j1oQPIHAFhCW17U88P+vjC8/Arg/fffr/vvv9+rcUJDQ7Vhwwaf4mgOyb8LSneMMTsEAF3UhoOlZoeAAEDyBwBYQlvez//D/oGC5A8AsASXbHLJnzX/tvftakj+AABLYObfKHB+CQAA8AozfwCAJfj/kp/AmS+T/AEAluAybHL585y/H327msD5MwYAAHiFmT8AwBJcfpb9/XlBUFdD8gcAWMLpX+Zra/9AETi/BAAAeIWZPwDAEuplU70fL+rxp29XQ/IHAFgCZf9GgfNLAACAV5j5AwAsoV7+le7r2y8U05H8AQCWQNm/EckfAGAJfNinUeD8EgAA4BVm/gAASzBkk8uPNX+DR/0AAOheKPs3CpxfAgAAvMLMHwBgCXzSt5GpM/9ly5bJZrN5tKSkJDNDAgAEqPrvv+rnTwsUps/8zzvvPL333nvu7eBg00MCACCgmZ5pg4ODFRsba3YYAIAAR9m/kek1jF27dsnhcGjIkCGaM2eO9u/ff8Zz6+rqVFVV5dEAAPCGSz38boHC1F+SkpKi/Px8rV+/Xnl5edq7d68uuugiVVdXN3t+bm6uIiMj3S0+Pr6TIwYAoPszteyfkZHh/ufk5GSlpKQoISFBL7/8subNm9fk/MWLFysnJ8e9XVVVxR8AAACv1Bs21ftRuvenb1dj+pr/6fr06aNhw4Zp9+7dzR632+2y2+2dHBUAIBCw5t+oSy1g1NTUaM+ePYqLizM7FABAgDG+/6pfW5vBG/7ax6JFi1RcXKx9+/bp73//uy6//HIFBQVp9uzZZoYFAEBAM7Xsf+DAAc2ePVtHjx7VgAEDNGnSJG3dulUDBgwwMywAQACql031fnycx5++XY2pyX/t2rVmXh4AYCEuw791e5fRjsGYLHAWMAAAgFe61N3+AAB0lIYb9/zpHygC55cAANACl2x+N1/k5uZq/PjxCg8PV3R0tGbNmqWysjKPc1JTU5t84O6mm25qcVzDMLRkyRLFxcUpNDRUaWlp2rVrl0+xkfwBAOgAxcXFysrK0tatW7Vx40adPHlSU6dOVW1trcd5v/rVr3To0CF3e+ihh1oc96GHHtITTzyhlStXatu2berdu7fS09N1/Phxr2Oj7A8AsITOfsPf+vXrPbbz8/MVHR2tkpISTZ482b3/rLPO8voDd4Zh6LHHHtNdd92lmTNnSpJeeOEFxcTE6PXXX9e1117r1TjM/AEAluDPC378vV9AkpxOpyQpKirKY/+LL76o/v37a+TIkVq8eLG++eabM46xd+9eVVRUKC0tzb0vMjJSKSkp2rJli9exMPMHAMAHP/yirDevnne5XFq4cKEmTpyokSNHuvf/4he/UEJCghwOhz7++GPdeeedKisr02uvvdbsOBUVFZKkmJgYj/0xMTHuY94g+QMALMElP9/t//0Nfz/8oNzSpUu1bNmyFvtmZWVp586d2rx5s8f++fPnu/951KhRiouL05QpU7Rnzx6dffbZbY61NSR/AIAlGG24Y/+H/SWpvLxcERER7v2tzfqzs7O1bt06bdq0SQMHDmzx3JSUFEnS7t27m03+DfcGVFZWenwHp7KyUmPGjPHqd0is+QMALKLhq37+NEmKiIjwaGdK/oZhKDs7WwUFBXr//feVmJjYaoylpaWSdMYP3CUmJio2NlaFhYXufVVVVdq2bZsmTJjg9b8Lkj8AAB0gKytLf/rTn7RmzRqFh4eroqJCFRUV+vbbbyVJe/bs0b333quSkhLt27dPb775pq6//npNnjxZycnJ7nGSkpJUUFAgSbLZbFq4cKHuu+8+vfnmm/rkk090/fXXy+FwaNasWV7HRtkfAGAJnf2Gv7y8PEmnXuRzulWrVmnu3Lnq1auX3nvvPT322GOqra1VfHy8rrzySt11110e55eVlbmfFJCkO+64Q7W1tZo/f76OHTumSZMmaf369QoJCfE6NpI/AMASTi/dt7W/Lwyj5S8BxcfHq7i42OdxbDabli9fruXLl/sUz+ko+wMAYDHM/AEAltCW9/P/sH+gIPkDACyhs8v+XRllfwAALIaZPwDAEpj5NyL5AwAsgeTfiLI/AAAWw8wfAGAJzPwbkfwBAJZgyL/H9Vp+ZU/3QvIHAFgCM/9GrPkDAGAxzPwBAJbAzL8RyR8AYAkk/0aU/QEAsBhm/gAAS2Dm34jkDwCwBMOwyfAjgfvTt6uh7A8AgMUw8wcAWIJLNr9e8uNP366G5A8AsATW/BtR9gcAwGKY+QMALIEb/hqR/AEAlkDZvxHJHwBgCcz8G7HmDwCAxTDzBwBYguFn2T+QZv4kfwCAJRiSDMO//oGCsj8AABbDzB8AYAku2WTjDX+SSP4AAIvgbv9GXabs/8ADD8hms2nhwoVmhwIAQEDrEjP/jz76SE8//bSSk5PNDgUAEKBchk02XvIjqQvM/GtqajRnzhw9++yz6tu3r9nhAAAClGH43wKF6ck/KytL06dPV1paWqvn1tXVqaqqyqMBAADfmFr2X7t2rXbs2KGPPvrIq/Nzc3N1zz33dHBUAIBAxA1/jUyb+ZeXl+vWW2/Viy++qJCQEK/6LF68WE6n093Ky8s7OEoAQKBoSP7+tEBhWvIvKSnR4cOH9eMf/1jBwcEKDg5WcXGxnnjiCQUHB6u+vr5JH7vdroiICI8GAIA3Gr7q50/zRW5ursaPH6/w8HBFR0dr1qxZKisrcx//97//rQULFmj48OEKDQ3VoEGDdMstt8jpdLY47ty5c2Wz2TzatGnTfIrNtLL/lClT9Mknn3jsu+GGG5SUlKQ777xTQUFBJkUGAID/iouLlZWVpfHjx+u7777T73//e02dOlX/+Mc/1Lt3bx08eFAHDx7Uf//3f2vEiBH64osvdNNNN+ngwYN69dVXWxx72rRpWrVqlXvbbrf7FJtpyT88PFwjR4702Ne7d2/169evyX4AAPzl7x37vvZdv369x3Z+fr6io6NVUlKiyZMna+TIkfrLX/7iPn722Wfrv/7rv3Tdddfpu+++U3DwmVO03W5XbGysbwGdxvS7/QEA6Aynkr8/a/7+Xb+hnB8VFdXiORERES0mfkkqKipSdHS0hg8frptvvllHjx71KZYu8ZKfBkVFRWaHAABAi374mLndbm+17O5yubRw4UJNnDjxjNXtI0eO6N5779X8+fNbHGvatGm64oorlJiYqD179uj3v/+9MjIytGXLFq+XzLtU8gcAoKO016N+8fHxHvuXLl2qZcuWtdg3KytLO3fu1ObNm5s9XlVVpenTp2vEiBGtjnXttde6/3nUqFFKTk7W2WefraKiIk2ZMqX1HyKSPwDAIozvmz/9pVOPqp/+tFlrs/7s7GytW7dOmzZt0sCBA5scr66u1rRp0xQeHq6CggL17NnTp7iGDBmi/v37a/fu3SR/AAA6grePmhuGoQULFqigoEBFRUVKTExsck5VVZXS09Nlt9v15ptvev3em9MdOHBAR48eVVxcnNd9uOEPAGAJnf2Sn6ysLP3pT3/SmjVrFB4eroqKClVUVOjbb7+VdCrxT506VbW1tXruuedUVVXlPuf0d90kJSWpoKBA0qnv4fz2t7/V1q1btW/fPhUWFmrmzJkaOnSo0tPTvY6NmT8AwBraq+7vpby8PElSamqqx/5Vq1Zp7ty52rFjh7Zt2yZJGjp0qMc5e/fu1eDBgyVJZWVl7icFgoKC9PHHH2v16tU6duyYHA6Hpk6dqnvvvdenZ/1J/gAAa/D3Fb0+9jVaeTYwNTW11XN+OE5oaKg2bNjgUxzNoewPAIDFMPMHAFhCZ7/hrysj+QMALIFP+jai7A8AgMUw8wcAWINh8/mmvSb9AwTJHwBgCaz5N6LsDwCAxTDzBwBYQye/5KcrI/kDACyBu/0bUfYHAMBimPkDAKwjgEr3/iD5AwAsgbJ/I5I/AMAauOHPjTV/AAAshpk/AMAibN83f/oHBpI/AMAaKPu7UfYHAMBimPkDAKyBmb8byR8AYA181c+Nsj8AABbDzB8AYAl80rcRyR8AYA2s+btR9gcAwGKY+QMArIEb/txI/gAAS7AZp5o//QMFyR8AYA2s+bux5g8AgMUw8wcAWANr/m4kfwCANVD2d6PsDwCAxTDzBwBYAzN/N5I/AMAaSP5ulP0BALAYZv4AAGvgbn83kj8AwBJ4w18jyv4AAFiMqck/Ly9PycnJioiIUEREhCZMmKB3333XzJAAAIHKaIfmg9zcXI0fP17h4eGKjo7WrFmzVFZW5nHO8ePHlZWVpX79+iksLExXXnmlKisrW/4ZhqElS5YoLi5OoaGhSktL065du3yKzdTkP3DgQD3wwAMqKSnR9u3bdemll2rmzJn69NNPzQwLAAC/FRcXKysrS1u3btXGjRt18uRJTZ06VbW1te5zbrvtNr311lt65ZVXVFxcrIMHD+qKK65ocdyHHnpITzzxhFauXKlt27apd+/eSk9P1/Hjx72OzWYYRpdaxYiKitLDDz+sefPmtXpuVVWVIiMj9fW/higiPHBWMNIdY8wOAUAXteFgqdkhtKuqapf6DvtcTqdTERERHXON73NFwoP3qUdISJvHcR0/ri/uvKvNsX711VeKjo5WcXGxJk+eLKfTqQEDBmjNmjW66qqrJEmfffaZzj33XG3ZskU/+clPmoxhGIYcDoduv/12LVq0SJLkdDoVExOj/Px8XXvttV7F4tUNf639FSJJwcHBio2N1U9/+lPNmDHDq4ufrr6+Xq+88opqa2s1YcKEZs+pq6tTXV2de7uqqsrn6wAA4I8f5h673S673d5qP6fTKenUJFeSSkpKdPLkSaWlpbnPSUpK0qBBg86Y/Pfu3auKigqPPpGRkUpJSdGWLVu8Tv5eTZcjIyNbbaGhodq1a5euueYaLVmyxKuLS9Inn3yisLAw2e123XTTTSooKNCIESOaPTc3N9fjmvHx8V5fBwBgcQ2P+vnTJMXHx3vkotzc3FYv7XK5tHDhQk2cOFEjR46UJFVUVKhXr17q06ePx7kxMTGqqKhodpyG/TExMV73aY5XM/9Vq1Z5PeC6dev0m9/8RsuXL/fq/OHDh6u0tFROp1OvvvqqMjMzVVxc3OwfAIsXL1ZOTo57u6qqij8AAADeaac3/JWXl3uU/b2Z9WdlZWnnzp3avHmzHwG0n3Z/zn/SpEkaN26c1+f36tVLQ4cOlSSNHTtWH330kR5//HE9/fTTTc71trQCAEBHaXhCzVvZ2dlat26dNm3apIEDB7r3x8bG6sSJEzp27JjH7L+yslKxsbHNjtWwv7KyUnFxcR59xowZ43VM7X6XXJ8+ffTaa6+1ub/L5fJY1wcAoF108qN+hmEoOztbBQUFev/995WYmOhxfOzYserZs6cKCwvd+8rKyrR///4z3vuWmJio2NhYjz5VVVXatm3bGfs0x9Q3/C1evFgZGRkaNGiQqqurtWbNGhUVFWnDhg1mhgUACECd/Ya/rKwsrVmzRm+88YbCw8Pda/IN98lFRkZq3rx5ysnJUVRUlCIiIrRgwQJNmDDB42a/pKQk5ebm6vLLL5fNZtPChQt133336ZxzzlFiYqLuvvtuORwOzZo1y+vYTE3+hw8f1vXXX69Dhw4pMjJSycnJ2rBhg37605+aGRYAAH7Ly8uTJKWmpnrsX7VqlebOnStJevTRR9WjRw9deeWVqqurU3p6up566imP88vKytxPCkjSHXfcodraWs2fP1/Hjh3TpEmTtH79eoX48Bhjl3vO3xc85w/AanjOvw3X+D5XDL7vv/x+zn/fXf/ZobF2Fj7sAwCwhna62z8QBM50GQAAeIWZPwDAEvikbyOSPwDAGk57S1+b+wcIkj8AwBpY83djzR8AAIth5g8AsATW/BuR/AEA1kDZ342yPwAAFsPMHwBgDX6W/QNp5k/yBwBYA2V/N8r+AABYDDN/AIA1MPN3I/kDACyBR/0aUfYHAMBiSP4AAFgMZX8AgDWw5u9G8gcAWAJr/o0o+wMAYDHM/AEA1hFAs3d/kPwBANbAmr8bZX8AACyGmT8AwBK44a8RyR8AYA2U/d0o+wMAYDHM/AEAlkDZvxHJHwBgDZT93Sj7AwBgMcz8AQDWwMzfjeQPALAE1vwbkfwBANbAzN+NNX8AACyGmT8AwBqY+buR/AEAlsCafyPK/gAAWAzJHwBgDUY7NB9s2rRJM2bMkMPhkM1m0+uvv+5x3GazNdsefvjhM465bNmyJucnJSX5Fpgo+wMALKKzy/61tbUaPXq0brzxRl1xxRVNjh86dMhj+91339W8efN05ZVXtjjueeedp/fee8+9HRzseyon+QMA0AEyMjKUkZFxxuOxsbEe22+88YYuueQSDRkypMVxg4ODm/T1FWV/AIA1tFPZv6qqyqPV1dX5HVplZaXefvttzZs3r9Vzd+3aJYfDoSFDhmjOnDnav3+/z9cj+QMArKGdkn98fLwiIyPdLTc31+/QVq9erfDw8GaXB06XkpKi/Px8rV+/Xnl5edq7d68uuugiVVdX+3Q9yv4AAPigvLxcERER7m273e73mM8//7zmzJmjkJCQFs87fRkhOTlZKSkpSkhI0Msvv+xV1aCBqTP/3NxcjR8/XuHh4YqOjtasWbNUVlZmZkgAgABla4cmSRERER7N3+T/f//3fyorK9Mvf/lLn/v26dNHw4YN0+7du33qZ2ryLy4uVlZWlrZu3aqNGzfq5MmTmjp1qmpra80MCwAQiDr5UT9vPffccxo7dqxGjx7tc9+amhrt2bNHcXFxPvUztey/fv16j+38/HxFR0erpKREkydPNikqAEAg6uxH/Wpqajxm5Hv37lVpaamioqI0aNAgSaduHnzllVf0hz/8odkxpkyZossvv1zZ2dmSpEWLFmnGjBlKSEjQwYMHtXTpUgUFBWn27Nk+xdal1vydTqckKSoqyuRIAADwz/bt23XJJZe4t3NyciRJmZmZys/PlyStXbtWhmGcMXnv2bNHR44ccW8fOHBAs2fP1tGjRzVgwABNmjRJW7du1YABA3yKrcskf5fLpYULF2rixIkaOXJks+fU1dV5PFJRVVXVWeEBALq7Tv6wT2pqqgyj5U7z58/X/Pnzz3h83759Httr1671LYgz6DKP+mVlZWnnzp0t/rDc3FyPxyvi4+M7MUIAQLfXxdb7zdIlkn92drbWrVunDz74QAMHDjzjeYsXL5bT6XS38vLyTowSAIDAYGrZ3zAMLViwQAUFBSoqKlJiYmKL59vt9nZ5nhIAYD180reRqck/KytLa9as0RtvvKHw8HBVVFRIkiIjIxUaGmpmaACAQNPJa/5dmall/7y8PDmdTqWmpiouLs7dXnrpJTPDAgAgoJle9gcAoDNQ9m/UZR71AwCgQ1H2d+sSd/sDAIDOw8wfAGAJlP0bkfwBANZA2d+N5A8AsAaSvxtr/gAAWAwzfwCAJbDm34jkDwCwBsr+bpT9AQCwGGb+AABLsBmGbH68Wdafvl0NyR8AYA2U/d0o+wMAYDHM/AEAlsDd/o1I/gAAa6Ds70bZHwAAi2HmDwCwBMr+jUj+AABroOzvRvIHAFgCM/9GrPkDAGAxzPwBANZA2d+N5A8AsIxAKt37g7I/AAAWw8wfAGANhnGq+dM/QJD8AQCWwN3+jSj7AwBgMcz8AQDWwN3+biR/AIAl2Fynmj/9AwVlfwAALIaZPwDAGij7uzHzBwBYQsPd/v40X2zatEkzZsyQw+GQzWbT66+/7nF87ty5stlsHm3atGmtjrtixQoNHjxYISEhSklJ0YcffuhbYCL5AwCsouE5f3+aD2prazV69GitWLHijOdMmzZNhw4dcrc///nPLY750ksvKScnR0uXLtWOHTs0evRopaen6/Dhwz7FRtkfAIAOkJGRoYyMjBbPsdvtio2N9XrMRx55RL/61a90ww03SJJWrlypt99+W88//7x+97vfeT0OM38AgCW0V9m/qqrKo9XV1bU5pqKiIkVHR2v48OG6+eabdfTo0TOee+LECZWUlCgtLc29r0ePHkpLS9OWLVt8ui7JHwBgDUY7NEnx8fGKjIx0t9zc3DaFM23aNL3wwgsqLCzUgw8+qOLiYmVkZKi+vr7Z848cOaL6+nrFxMR47I+JiVFFRYVP16bsDwCAD8rLyxUREeHettvtbRrn2muvdf/zqFGjlJycrLPPPltFRUWaMmWK33G2hJk/AMAS2qvsHxER4dHamvx/aMiQIerfv792797d7PH+/fsrKChIlZWVHvsrKyt9um9AIvkDAKyik+/299WBAwd09OhRxcXFNXu8V69eGjt2rAoLC937XC6XCgsLNWHCBJ+uRfIHAKAD1NTUqLS0VKWlpZKkvXv3qrS0VPv371dNTY1++9vfauvWrdq3b58KCws1c+ZMDR06VOnp6e4xpkyZoieffNK9nZOTo2effVarV6/WP//5T918882qra113/3vLdb8AQCW0Nmf9N2+fbsuueQS93ZOTo4kKTMzU3l5efr444+1evVqHTt2TA6HQ1OnTtW9997rsYywZ88eHTlyxL19zTXX6KuvvtKSJUtUUVGhMWPGaP369U1uAmwNyR8AYA2d/Hrf1NRUGS0sFWzYsKHVMfbt29dkX3Z2trKzs30L5gco+wMAYDHM/AEAltDZZf+uzNSZf2sfPQAAoN24DP9bgDA1+Xvz0QMAANpFO73hLxCYWvb35qMHAACgfbHmDwCwBJv8XPNvt0jM162Sf11dncfXk6qqqkyMBgDQrfj7lr4OfsNfZ+pWj/rl5uZ6fEkpPj7e7JAAAOh2ulXyX7x4sZxOp7uVl5ebHRIAoJtorw/7BIJuVfa32+3t9vUkAIDFdPIb/royU5N/TU2Nx6cLGz56EBUVpUGDBpkYGQAAgcvU5N/SRw/y8/NNigoAEIhshiGbHzft+dO3qzE1+bf20QMAANqN6/vmT/8A0a1u+AMAAP7rVjf8AQDQVpT9G5H8AQDWwN3+biR/AIA18IY/N9b8AQCwGGb+AABL8PctfbzhDwCA7oayvxtlfwAALIaZPwDAEmyuU82f/oGC5A8AsAbK/m6U/QEAsBhm/gAAa+AlP24kfwCAJfB630aU/QEAsBhm/gAAa+CGPzeSPwDAGgxJ/jyuFzi5n+QPALAG1vwbseYPAIDFMPMHAFiDIT/X/NstEtOR/AEA1sANf24k/y5ow8FSs0MAAAQwkj8AwBpckmx+9g8Q3PAHALCEhrv9/Wm+2LRpk2bMmCGHwyGbzabXX3/dfezkyZO68847NWrUKPXu3VsOh0PXX3+9Dh482OKYy5Ytk81m82hJSUk+/7sg+QMA0AFqa2s1evRorVixosmxb775Rjt27NDdd9+tHTt26LXXXlNZWZl+9rOftTrueeedp0OHDrnb5s2bfY6Nsj8AwBo6+Ya/jIwMZWRkNHssMjJSGzdu9Nj35JNP6oILLtD+/fs1aNCgM44bHBys2NhYn2L5IWb+AABraEj+/rQO5HQ6ZbPZ1KdPnxbP27VrlxwOh4YMGaI5c+Zo//79Pl+LmT8AAD6oqqry2Lbb7bLb7X6Nefz4cd15552aPXu2IiIiznheSkqK8vPzNXz4cB06dEj33HOPLrroIu3cuVPh4eFeX4+ZPwDAGtpp5h8fH6/IyEh3y83N9SuskydP6uqrr5ZhGMrLy2vx3IyMDP385z9XcnKy0tPT9c477+jYsWN6+eWXfbomM38AgDW006N+5eXlHrNzf2b9DYn/iy++0Pvvv9/irL85ffr00bBhw7R7926f+jHzBwBYQns96hcREeHR2pr8GxL/rl279N5776lfv34+j1FTU6M9e/YoLi7Op34kfwAAOkBNTY1KS0tVWloqSdq7d69KS0u1f/9+nTx5UldddZW2b9+uF198UfX19aqoqFBFRYVOnDjhHmPKlCl68skn3duLFi1ScXGx9u3bp7///e+6/PLLFRQUpNmzZ/sUG2V/AIA1dPKjftu3b9cll1zi3s7JyZEkZWZmatmyZXrzzTclSWPGjPHo98EHHyg1NVWStGfPHh05csR97MCBA5o9e7aOHj2qAQMGaNKkSdq6dasGDBjgU2wkfwCANbgMyeZH8nf51jc1NVVGC38wtHSswb59+zy2165d61MMZ0LZHwAAi2HmDwCwBj7p60byBwBYhL9v6Quc5E/ZHwAAi2HmDwCwBsr+biR/AIA1uAz5Vbr38W7/royyPwAAFsPMHwBgDYbrVPOnf4Ag+QMArIE1f7cuUfZfsWKFBg8erJCQEKWkpOjDDz80OyQAQKBxGf63AGF68n/ppZeUk5OjpUuXaseOHRo9erTS09N1+PBhs0MDACAgmZ78H3nkEf3qV7/SDTfcoBEjRmjlypU666yz9Pzzz5sdGgAgkDSU/f1pAcLU5H/ixAmVlJQoLS3Nva9Hjx5KS0vTli1bmpxfV1enqqoqjwYAgFcM+Zn8zf4B7cfU5H/kyBHV19crJibGY39MTIwqKiqanJ+bm6vIyEh3i4+P76xQAQAIGKaX/X2xePFiOZ1OdysvLzc7JABAd0HZ383UR/369++voKAgVVZWeuyvrKxUbGxsk/PtdrvsdntnhQcACCQulyQ/ntV3Bc5z/qbO/Hv16qWxY8eqsLDQvc/lcqmwsFATJkwwMTIAAAKX6S/5ycnJUWZmpsaNG6cLLrhAjz32mGpra3XDDTeYHRoAIJDwkh8305P/Nddco6+++kpLlixRRUWFxowZo/Xr1ze5CRAAAL+Q/N1MT/6SlJ2drezsbLPDAADAErpE8gcAoMPxSV83kj8AwBIMwyXDjy/z+dO3qyH5AwCswfDz4zwBtObfrV7yAwAA/MfMHwBgDYafa/4BNPMn+QMArMHlkmx+rNsH0Jo/ZX8AACyGmT8AwBoo+7uR/AEAlmC4XDL8KPsH0qN+lP0BALAYZv4AAGug7O9G8gcAWIPLkGwkf4myPwAAlsPMHwBgDYYhyZ/n/ANn5k/yBwBYguEyZPhR9jcCKPlT9gcAWIPh8r/5YNOmTZoxY4YcDodsNptef/11z3AMQ0uWLFFcXJxCQ0OVlpamXbt2tTruihUrNHjwYIWEhCglJUUffvihT3FJJH8AADpEbW2tRo8erRUrVjR7/KGHHtITTzyhlStXatu2berdu7fS09N1/PjxM4750ksvKScnR0uXLtWOHTs0evRopaen6/Dhwz7FZjO6cR2jqqpKkZGR+vpfQxQRzt8xANDdVFW71HfY53I6nYqIiOiYa3yfK1JtlyvY1rPN43xnnFSRUdCmWG02mwoKCjRr1ixJp2b9DodDt99+uxYtWiRJcjqdiomJUX5+vq699tpmx0lJSdH48eP15JNPSpJcLpfi4+O1YMEC/e53v/M6HjImAMAaOrns35K9e/eqoqJCaWlp7n2RkZFKSUnRli1bmu1z4sQJlZSUePTp0aOH0tLSztjnTLr1DX8NRYuqmsB55SIAWEnD/393RhH6O5306x0/3+mkpFOVhNPZ7XbZ7XafxqqoqJAkxcTEeOyPiYlxH/uhI0eOqL6+vtk+n332mU/X79bJv7q6WpKU8ON95gYCAPBLdXW1IiMjO2TsXr16KTY2Vpsr3vF7rLCwMMXHx3vsW7p0qZYtW+b32J2pWyd/h8Oh8vJyhYeHy2azdei1qqqqFB8fr/Ly8g5bl+pMgfZ7JH5Td8Fv6vo68/cYhqHq6mo5HI4Ou0ZISIj27t2rEydO+D2WYRhN8o2vs35Jio2NlSRVVlYqLi7Ovb+yslJjxoxptk///v0VFBSkyspKj/2VlZXu8bzVrZN/jx49NHDgwE69ZkRERED8x90g0H6PxG/qLvhNXV9n/Z6OmvGfLiQkRCEhIR1+HW8lJiYqNjZWhYWF7mRfVVWlbdu26eabb262T69evTR27FgVFha6bxx0uVwqLCxUdna2T9fv1skfAICuqqamRrt373Zv7927V6WlpYqKitKgQYO0cOFC3XfffTrnnHOUmJiou+++Ww6Hw53YJWnKlCm6/PLL3ck9JydHmZmZGjdunC644AI99thjqq2t1Q033OBTbCR/AAA6wPbt23XJJZe4t3NyciRJmZmZys/P1x133KHa2lrNnz9fx44d06RJk7R+/XqPCsWePXt05MgR9/Y111yjr776SkuWLFFFRYXGjBmj9evXN7kJsDUkfy/Z7XYtXbq0TWs7XVGg/R6J39Rd8Ju6vkD7PWZJTU1t8SkGm82m5cuXa/ny5Wc8Z9++fU32ZWdn+1zmb3Lt7vySHwAA4Dte8gMAgMWQ/AEAsBiSPwAAFkPyBwDAYkj+XmiPbyd3Fa19X7o7ys3N1fjx4xUeHq7o6GjNmjVLZWVlZofll7y8PCUnJ7tfsjJhwgS9++67ZofVbh544AHZbDYtXLjQ7FDabNmyZbLZbB4tKSnJ7LD89uWXX+q6665Tv379FBoaqlGjRmn79u1mh4V2RvJvRXt9O7mraO370t1RcXGxsrKytHXrVm3cuFEnT57U1KlTVVtba3ZobTZw4EA98MADKikp0fbt23XppZdq5syZ+vTTT80OzW8fffSRnn76aSUnJ5sdit/OO+88HTp0yN02b95sdkh++frrrzVx4kT17NlT7777rv7xj3/oD3/4g/r27Wt2aGhvBlp0wQUXGFlZWe7t+vp6w+FwGLm5uSZG1T4kGQUFBWaH0e4OHz5sSDKKi4vNDqVd9e3b1/jf//1fs8PwS3V1tXHOOecYGzduNC6++GLj1ltvNTukNlu6dKkxevRos8NoV3feeacxadIks8NAJ2Dm34L2/HYyOo/T6ZQkRUVFmRxJ+6ivr9fatWtVW1urCRMmmB2OX7KysjR9+nSP/6a6s127dsnhcGjIkCGaM2eO9u/fb3ZIfnnzzTc1btw4/fznP1d0dLTOP/98Pfvss2aHhQ5A8m9BS99OPtP3lmEul8ulhQsXauLEiRo5cqTZ4fjlk08+UVhYmOx2u2666SYVFBRoxIgRZofVZmvXrtWOHTuUm5trdijtIiUlRfn5+Vq/fr3y8vK0d+9eXXTRRe5PjXdHn3/+ufLy8nTOOedow4YNuvnmm3XLLbdo9erVZoeGdsbrfRFQsrKytHPnzm6/9ipJw4cPV2lpqZxOp1599VVlZmaquLi4W/4BUF5erltvvVUbN27sUl9W80dGRob7n5OTk5WSkqKEhAS9/PLLmjdvnomRtZ3L5dK4ceN0//33S5LOP/987dy5UytXrlRmZqbJ0aE9MfNvQXt+OxkdLzs7W+vWrdMHH3zQ6Z967gi9evXS0KFDNXbsWOXm5mr06NF6/PHHzQ6rTUpKSnT48GH9+Mc/VnBwsIKDg1VcXKwnnnhCwcHBqq+vNztEv/Xp00fDhg3z+IpbdxMXF9fkj8tzzz232y9noCmSfwtO/3Zyg4ZvJ3f3tddAYhiGsrOzVVBQoPfff1+JiYlmh9QhXC6X6urqzA6jTaZMmaJPPvlEpaWl7jZu3DjNmTNHpaWlCgoKMjtEv9XU1GjPnj2Ki4szO5Q2mzhxYpPHZP/1r38pISHBpIjQUSj7t6K9vp3cVbT2fenuKCsrS2vWrNEbb7yh8PBw9/0YkZGRCg0NNTm6tlm8eLEyMjI0aNAgVVdXa82aNSoqKtKGDRvMDq1NwsPDm9yD0bt3b/Xr16/b3puxaNEizZgxQwkJCTp48KCWLl2qoKAgzZ492+zQ2uy2227ThRdeqPvvv19XX321PvzwQz3zzDN65plnzA4N7c3sxw26g//5n/8xBg0aZPTq1cu44IILjK1bt5odUpt98MEHhqQmLTMz0+zQ2qy53yPJWLVqldmhtdmNN95oJCQkGL169TIGDBhgTJkyxfjrX/9qdljtqrs/6nfNNdcYcXFxRq9evYwf/ehHxjXXXGPs3r3b7LD89tZbbxkjR4407Ha7kZSUZDzzzDNmh4QOwCd9AQCwGNb8AQCwGJI/AAAWQ/IHAMBiSP4AAFgMyR8AAIsh+QMAYDEkfwAALIbkDwCAxZD8gS5s7ty5mjVrltlhAAgwJH8AACyG5A8AgMWQ/AEAsBiSPwAAFkPyBwDAYkj+AABYDMkfAACLIfkDAGAxJH8AACzGZhiGYXYQAACg8zDzBwDAYkj+AABYDMkfAACLIfkDAGAxJH8AACyG5A8AgMWQ/AEAsBiSPwAAFkPyBwDAYkj+AABYDMkfAACLIfkDAGAx/x9cn0E+9uZ1TQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 1 based on running stencil_if_zero\n",
+      "Min and max values: 30.0 10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qty_out = Quantity(data=np.zeros(shape),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "arr = np.indices(shape,dtype=float).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
+    "qty_in = Quantity(data=arr,\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "print(\"Running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "copy_stencil(qty_in.data,qty_out.data,origin=(nhalo,nhalo,0),domain=(nx,ny,5))\n",
+    "print(\"Plotting values of qty_out at K = 0 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 1 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
+    "plot_field_at_kN(qty_out.data,1)\n",
+    "\n",
+    "@stencil(backend=backend)\n",
+    "def stencil_if_zero(in_out_field: FloatField):\n",
+    "    with computation(PARALLEL), interval(...):\n",
+    "        if in_out_field == 0.0:\n",
+    "            in_out_field = 30\n",
+    "        else:\n",
+    "            in_out_field = 10\n",
+    "print(\"Running 'stencil_if_zero' on qty_out\")\n",
+    "stencil_if_zero(qty_out.data)\n",
+    "print(\"Plotting values of qty_out at K = 0 based on running stencil_if_zero\")\n",
+    "plot_field_at_kN(qty_out.data,0)\n",
+    "print(\"Plotting values of qty_out at K = 1 based on running stencil_if_zero\")\n",
+    "plot_field_at_kN(qty_out.data,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Function calls**\n",
+    "\n",
+    "GT4Py also has the capability to create functions in order to better organize code.  The main difference between a GT4Py function call and a GT4Py stencil is that a function does not (and cannot) contain the keywords `computation` and `interval`.  However, array index referencing within a GT4py function is the same as in a GT4Py stencil.\n",
+    "\n",
+    "GT4Py functions can be created by using the decorator `function` (Note: `function` originates from the package `gt4py.cartesian.gtscript`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_in at K = 0\n",
+      "Min and max values: 12.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting values of qty_out at K = 0\n",
+      "Min and max values: 0.0 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing 'field_plus_one'\n",
+      "Plotting values of qty_out at K = 0 after executing 'field_plus_one'\n",
+      "Min and max values: 9.0 1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from gt4py.cartesian.gtscript import function\n",
+    "\n",
+    "@function\n",
+    "def plus_one(field: FloatField):\n",
+    "   return field[0, 0, 0] + 1\n",
+    "\n",
+    "@stencil(backend=backend)\n",
+    "def field_plus_one(source: FloatField, target: FloatField):\n",
+    "   with computation(PARALLEL), interval(...):\n",
+    "       target = plus_one(source)\n",
+    "\n",
+    "nx = 5\n",
+    "ny = 5\n",
+    "nz = 5\n",
+    "nhalo = 1\n",
+    "backend=\"numpy\"\n",
+    "\n",
+    "shape = (nx + 2 * nhalo, ny + 2 * nhalo, nz)\n",
+    "\n",
+    "qty_out = Quantity(data=np.zeros([nx, ny, nz]),\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "arr = np.indices(shape, dtype=float).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
+    "qty_in = Quantity(data=arr,\n",
+    "                   dims=[\"I\", \"J\", \"K\"],\n",
+    "                   units=\"m\",\n",
+    "                   gt4py_backend=backend\n",
+    "                   )\n",
+    "\n",
+    "print(\"Plotting values of qty_in at K = 0\")\n",
+    "plot_field_at_kN(qty_in.data)\n",
+    "print(\"Plotting values of qty_out at K = 0\")\n",
+    "plot_field_at_kN(qty_out.data)\n",
+    "print(\"Executing 'field_plus_one'\")\n",
+    "field_plus_one(qty_in.data, qty_out.data)\n",
+    "print(\"Plotting values of qty_out at K = 0 after executing 'field_plus_one'\")\n",
+    "plot_field_at_kN(qty_out.data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/gt4py/02_oo_gt4py.ipynb b/examples/NDSL/NDSL_basics.ipynb
similarity index 99%
rename from examples/gt4py/02_oo_gt4py.ipynb
rename to examples/NDSL/NDSL_basics.ipynb
index 9854e801..4757ba68 100644
--- a/examples/gt4py/02_oo_gt4py.ipynb
+++ b/examples/NDSL/NDSL_basics.ipynb
@@ -293,7 +293,7 @@
    "source": [
     "### **Example demonstrating error when writing to offset outputs**\n",
     "\n",
-    "While offsets can be applied to all input `gt_storage` variables in `gt4py`, output `gt_storage` varaibles cannot have such offsets.  When an offset is applied to an output stencil calcuation, the error `GTScriptSyntaxError: Assignment to non-zero offsets is not supported.` will be displayed."
+    "While offsets can be applied to all input `gt_storage` variables in `gt4py`, output `gt_storage` variables cannot have such offsets.  When an offset is applied to an output stencil calcuation, the error `GTScriptSyntaxError: Assignment to non-zero offsets is not supported.` will be displayed."
    ]
   },
   {
diff --git a/examples/gt4py/boilerplate.py b/examples/NDSL/boilerplate.py
similarity index 100%
rename from examples/gt4py/boilerplate.py
rename to examples/NDSL/boilerplate.py
diff --git a/examples/gt4py/01_basics.ipynb b/examples/gt4py/01_basics.ipynb
deleted file mode 100755
index e4e6521c..00000000
--- a/examples/gt4py/01_basics.ipynb
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@@ -1,497 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# **GT4Py Tutorial : Stencil Basics**\n",
-    "\n",
-    "## **Introduction**\n",
-    "\n",
-    "This notebook will show how to create a simple GT4Py stencil that copies data from one variable to another.\n",
-    "\n",
-    "### **Notebook Requirements**\n",
-    "\n",
-    "- Python v3.11.x to v3.12.x\n",
-    "- [NOAA/NASA Domain Specific Language Middleware](https://github.com/NOAA-GFDL/NDSL)\n",
-    "- `ipykernel==6.1.0`\n",
-    "- [`ipython_genutils`](https://pypi.org/project/ipython_genutils/)\n",
-    "\n",
-    "### **Quick GT4Py (Cartesian version) Overview**\n",
-    "\n",
-    "GT4Py enables a developer to write code in a Domain Specific Language (DSL) implemented using Python syntax.  Performance is achieved when the GT4Py Python code is translated and compiled into a lower level language such as C++ and CUDA, which enables the codebase to execute on a multitude of architectures.  In this notebook, we will cover the basics of creating GT4Py stencils and demonstrate several intracies of the DSL. Additional information about GT4Py can be found at the [GT4Py site](https://gridtools.github.io/gt4py/latest/index.html).  One small note is that this tutorial covers and uses the Cartesian version of GT4Py and not the unstructured version.\n",
-    "\n",
-    "### **GT4Py Parallel/Execution Model**\n",
-    "\n",
-    "Within a 3-dimensional domain, GT4Py considers computations in two parts.  If we assume an `(I,J,K)` coordinate system as a reference, GT4Py separates computations in the Horizontal (`IJ`) spatial plane and Vertical (`K`) spatial interval.  In the Horizontal spatial plane, computations are implicitly executed in parallel, which also means that there is no assumed calculation order within the plane.  In the Vertical spatial interval, comptuations are specified by an iteration policy that will be discussed later through examples.\n",
-    "\n",
-    "Another quick note is that the computations are executed sequentially in the order they appear in code.\n",
-    "\n",
-    "## **Tutorial**\n",
-    "\n",
-    "### **Copy Stencil example**\n",
-    "\n",
-    "To demonstrate how to implement a GT4Py stencil, we'll step through an example that copies the values of one array into another array.  First, we import several packages. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gt4py.cartesian.gtscript import PARALLEL, computation, interval, stencil\n",
-    "from ndsl.dsl.typing import FloatField\n",
-    "import gt4py.storage as gt_storage\n",
-    "import numpy as np\n",
-    "from boilerplate import plot_field_at_k0, plot_field_at_kN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we walk through the example, we'll highlight different terms and such from the imported packages.  Let's first define, in GT4Py terms, two arrays of size 5 by 5 by 1 (dimensionally `I` by `J` by `K`).  These arrays are defined using the `gt_storage` allocator, which creates arrays similar to NumPy and provide similar functions like `ones`, `zeros` (as shown below), `full` and `empty`.  There also is a `.from_array` function that lets the user define a `numpy` array whose data can be passed into a `gt_storage` array.\n",
-    "\n",
-    "A `gt_storage` function can take several parameters:\n",
-    "\n",
-    "- `backend` tells GT4Py how to optimally lay out the array for a particular architecture.  In this example, we will use the `numpy` backend since it's the easiest for debugging and testing purposes.  \n",
-    "\n",
-    "- `data` contains the numpy array that gets passed into a `gt_storage`.\n",
-    "\n",
-    "- `dtype` is the data type stored in the array.\n",
-    "\n",
-    "- `shape` contains the array shape that is passed as a tuple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backend = 'numpy'\n",
-    "\n",
-    "nx = 5\n",
-    "ny = 5\n",
-    "nz = 2\n",
-    "\n",
-    "shape = (nx, ny, nz)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "arr = np.indices(shape).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
-    "qty_in = gt_storage.from_array(\n",
-    "    data=arr,\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will next create a simple stencil that copies values from one `gt_storage` to another.  A GT4Py stencil will look like a Python subroutine or function except that it uses specific GT4Py functionalities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@stencil(backend=backend)\n",
-    "def copy_stencil(input_field: FloatField,\n",
-    "                 output_field: FloatField):\n",
-    "    with computation(PARALLEL), interval(...):\n",
-    "        output_field = input_field"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since stencil calculations generally are based localized computations, `gt4py` stencils are written using variables and the variable's relative location if it's an array.  If there are no indices in brackets next to a `gt4py` type (such as `FloadField`), it's implied to be at the [0] (for 1-dimension), [0,0] (for 2-dimension), or [0,0,0] (for 3-dimension) location.  For the simple example `copy_field_stencil`, the value of `field_in` simply gets copied to `field_out` at every point in the domain of interest.\n",
-    "\n",
-    "We see that this stencil does not contain any explicit loops.  As mentioned above in the notebook, GT4Py has a particular computation policy that implicitly executes in parallel within an `IJ` plane and is user defined in the `K` interval.  This execution policy in the `K` interval is dictated by the `computation` and `interval` keywords.  \n",
-    "\n",
-    "- `with computation(PARALLEL)` means that there's no order preference to executing the `K` interval, which means that the `K` interval can be computed in parallel to potentially gain performace if computational resources are available.\n",
-    "\n",
-    "- `interval(...)` means that the entire `K` interval is executed.  Instead of `(...)`, more specific intervals can be specified using a tuple of two integers.  For example... \n",
-    "\n",
-    "    - `interval(0,2)` : The interval `K` = 0 to 1 is executed.\n",
-    "    - `interval(0,-1)` : The interval `K` = 0 to N-2 (where N is the size of `K`) is executed.\n",
-    "\n",
-    "The decorator `@stencil(backend=backend)` (Note: `stencil` comes from the package `gt4py.cartesian.gtscript`) converts `copy_stencil` to use the specified `backend` to \"compile\" the stencil.  `stencil` can also be a function call to create a stencil object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "copy_stencil_numpy = stencil(backend=\"numpy\", definition=copy_stencil)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the input and output parameters to `copy_stencil` are of type `FloatField`, which is essentially a 3-dimensional `gt_storage` array of `float` types.\n",
-    "\n",
-    "`plot_field_at_kN` plots the values of the `IJ` plane at `K = 0` if no integer is specified or at `K` equal to the integer  that is specified.  As we can see in the plots below, `copy_stencil` copies the values from `qty_in` into `qty_out`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil`\")\n",
-    "copy_stencil(qty_in, qty_out)\n",
-    "print(\"Plotting qty_out from `copy_stencil` at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Plotting qty_out from `copy_stencil` at K = 1\")\n",
-    "plot_field_at_kN(qty_out,1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Choosing subsets (or offsets) to perform stencil calculations**\n",
-    "\n",
-    "GT4Py also allows a subset of the `IJ` plane to be executed in a fashion similar to the effect of `interval(...)` in the K interval.  This is done by setting `origin` and `domain` when executing the stencil.\n",
-    "\n",
-    "- `origin` : This specifies the \"starting\" coordinate in the `IJ` plane.  \n",
-    "\n",
-    "- `domain` : This specifies the range of the stencil computation based on `origin` as the \"starting\" coordinate (Note: I may need to check whether this affects `interval()`)\n",
-    "\n",
-    "If these two parameters are not set, the GT4py stencil by default will iterate over the entire input domain.  The following demonstrate the effect of specifying different `origin` and `domain`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil` with origin=(1,0,0)\")\n",
-    "copy_stencil(qty_in, qty_out,origin=(1,0,0))\n",
-    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(1,0,0)\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "print(\"Resetting qty_out to zero...\")\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil` with origin=(0,1,0)\")\n",
-    "copy_stencil(qty_in, qty_out,origin=(0,1,0))\n",
-    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,1,0)\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "print(\"Resetting qty_out to zero...\")\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil` with origin = (0,0,1)\")\n",
-    "copy_stencil(qty_in, qty_out,origin=(0,0,1))\n",
-    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin=(0,0,1)\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Plotting qty_out at K = 1 based on `copy_stencil` with origin=(0,0,1)\")\n",
-    "plot_field_at_kN(qty_out, 1)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "print(\"Resetting qty_out to zero...\")\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil` with domain=(2,2,nz)\")\n",
-    "copy_stencil(qty_in, qty_out, domain=(2,2,nz))\n",
-    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with domain = (2,2,nz)\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "print(\"Resetting qty_out to zero...\")\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing `copy_stencil` with origin = (2,2,0), domain=(2,2,nz)\")\n",
-    "copy_stencil(qty_in, qty_out, origin=(2,2,0), domain=(2,2,nz))\n",
-    "print(\"Plotting qty_out at K = 0 based on `copy_stencil` with origin = (2,2,0), domain = (2,2,nz)\")\n",
-    "plot_field_at_kN(qty_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **`FORWARD` and `BACKWARD` `computation` keywords**\n",
-    "\n",
-    "Besides `PARALLEL`, the developer can specify `FORWARD` or `BACKWARD` as the iteration policy in `K`.  Essentially, the `FORWARD` policy has `K` iterating consecutively starting from the lowest vertical index to the highest, while the `BACKWARD` policy performs the reverse.  The following examples demonstrate the use of these two iteration policies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gt4py.cartesian.gtscript import FORWARD, BACKWARD\n",
-    "\n",
-    "nx = 5\n",
-    "ny = 5\n",
-    "nz = 5\n",
-    "nhalo = 1\n",
-    "backend=\"numpy\"\n",
-    "\n",
-    "shape = (nx + 2 * nhalo, ny + 2 * nhalo, nz)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "arr = np.indices(shape).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
-    "qty_in = gt_storage.from_array(\n",
-    "    data=arr,\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    ")\n",
-    "\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in,0)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "\n",
-    "print(\"Executing 'copy_stencil' with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "copy_stencil(qty_in,qty_out,origin=(nhalo,nhalo,0),domain=(nx,ny,5))\n",
-    "\n",
-    "print(\"Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "print(\"Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "plot_field_at_kN(qty_out,1)\n",
-    "\n",
-    "@stencil(backend=backend)\n",
-    "def mult_upward(qty_in: FloatField):\n",
-    "    with computation(FORWARD), interval(...):\n",
-    "        qty_in = qty_in[0,0,-1] * 2.0\n",
-    "\n",
-    "print(\"Executing 'mult_upward' with origin=(nhalo,nhalo,0),domain=(nx,ny,2)\")\n",
-    "mult_upward(qty_out, origin=(nhalo,nhalo,1), domain=(nx,ny,2))\n",
-    "print(\"Plotting values of qty_out at K = 0 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "print(\"Plotting values of qty_out at K = 1 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
-    "plot_field_at_kN(qty_out,1)\n",
-    "print(\"Plotting values of qty_out at K = 2 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
-    "plot_field_at_kN(qty_out,2)\n",
-    "print(\"Plotting values of qty_out at K = 3 with origin=(nhalo,nhalo,1),domain=(nx,ny,2)\")\n",
-    "plot_field_at_kN(qty_out,3)\n",
-    "\n",
-    "@stencil(backend=backend)\n",
-    "def copy_downward(qty_in: FloatField):\n",
-    "    with computation(BACKWARD), interval(...):\n",
-    "        qty_in = qty_in[0,0,1]\n",
-    "\n",
-    "print(\"Executing 'copy_stencil' to copy qty_out to qty_in\")\n",
-    "copy_stencil(qty_out, qty_in)\n",
-    "\n",
-    "print(\"***\")\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in,0)\n",
-    "print(\"Plotting values of qty_in at K = 1\")\n",
-    "plot_field_at_kN(qty_in,1)\n",
-    "print(\"Plotting values of qty_in at K = 2\")\n",
-    "plot_field_at_kN(qty_in,2)\n",
-    "print(\"Plotting values of qty_in at K = 3\")\n",
-    "plot_field_at_kN(qty_in,3)\n",
-    "print(\"Plotting values of qty_in at K = 4\")\n",
-    "plot_field_at_kN(qty_in,4)\n",
-    "\n",
-    "print(\"Executing 'copy_downward' on qty_in with origin=(1,1,0), domain=(nx,ny,nz-1)\")\n",
-    "copy_downward(qty_in, origin=(1,1,0), domain=(nx,ny,nz-1))\n",
-    "print(\"***\")\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in,0)\n",
-    "print(\"Plotting values of qty_in at K = 1\")\n",
-    "plot_field_at_kN(qty_in,1)\n",
-    "print(\"Plotting values of qty_in at K = 2\")\n",
-    "plot_field_at_kN(qty_in,2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **`if/else` statements**\n",
-    "\n",
-    "GT4Py allows for `if/else` statements to exist within a stencil.  The following simple example shows a stencil `stencil_if_zero` modifing values of `in_out_field` depending on its initial value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "arr = np.indices(shape).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
-    "qty_in = gt_storage.from_array(\n",
-    "    data=arr,\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    ")\n",
-    "\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in,0)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "print(\"Running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "copy_stencil(qty_in,qty_out,origin=(nhalo,nhalo,0),domain=(nx,ny,5))\n",
-    "print(\"Plotting values of qty_out at K = 0 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "print(\"Plotting values of qty_out at K = 1 based on running copy_stencil with origin=(nhalo,nhalo,0),domain=(nx,ny,5)\")\n",
-    "plot_field_at_kN(qty_out,1)\n",
-    "\n",
-    "@stencil(backend=backend)\n",
-    "def stencil_if_zero(in_out_field: FloatField):\n",
-    "    with computation(PARALLEL), interval(...):\n",
-    "        if in_out_field == 0.0:\n",
-    "            in_out_field = 30\n",
-    "        else:\n",
-    "            in_out_field = 10\n",
-    "print(\"Running 'stencil_if_zero' on qty_out\")\n",
-    "stencil_if_zero(qty_out)\n",
-    "print(\"Plotting values of qty_out at K = 0 based on running stencil_if_zero\")\n",
-    "plot_field_at_kN(qty_out,0)\n",
-    "print(\"Plotting values of qty_out at K = 1 based on running stencil_if_zero\")\n",
-    "plot_field_at_kN(qty_out,1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Function calls**\n",
-    "\n",
-    "GT4Py also has the capability to create functions in order to better organize code.  The main difference between a function and a GT4Py stencil is that a function cannot contain calls to `computation` and `interval`.  However, array index referencing is the same as in a GT4Py stencil.\n",
-    "\n",
-    "Functions in GT4Py can be created by using the decorator `function` (Note: `function` originates from the package `gt4py.cartesian.gtscript`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gt4py.cartesian.gtscript import function\n",
-    "\n",
-    "@function\n",
-    "def plus_one(field: FloatField):\n",
-    "   return field[0, 0, 0] + 1\n",
-    "\n",
-    "@stencil(backend=backend)\n",
-    "def field_plus_one(source: FloatField, target: FloatField):\n",
-    "   with computation(PARALLEL), interval(...):\n",
-    "       target = plus_one(source)\n",
-    "\n",
-    "nx = 5\n",
-    "ny = 5\n",
-    "nz = 5\n",
-    "nhalo = 1\n",
-    "backend=\"numpy\"\n",
-    "\n",
-    "shape = (nx + 2 * nhalo, ny + 2 * nhalo, nz)\n",
-    "\n",
-    "qty_out = gt_storage.zeros(\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    "    shape=shape,\n",
-    ")\n",
-    "\n",
-    "arr = np.indices(shape).sum(axis=0)  # Value of each entry is sum of the I and J index at each point\n",
-    "qty_in = gt_storage.from_array(\n",
-    "    data=arr,\n",
-    "    backend=backend,\n",
-    "    dtype=float,\n",
-    ")\n",
-    "\n",
-    "print(\"Plotting values of qty_in at K = 0\")\n",
-    "plot_field_at_kN(qty_in)\n",
-    "print(\"Plotting values of qty_out at K = 0\")\n",
-    "plot_field_at_kN(qty_out)\n",
-    "print(\"Executing 'field_plus_one'\")\n",
-    "field_plus_one(qty_in, qty_out)\n",
-    "print(\"Plotting values of qty_out at K = 0 after executing 'field_plus_one'\")\n",
-    "plot_field_at_kN(qty_out)"
-   ]
-  }
- ],
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