{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Devito domain specific language: an overview\n",
"\n",
"This notebook presents an overview of the Devito symbolic language, used to express and discretise operators, in particular partial differential equations (PDEs).\n",
"\n",
"For convenience, we import all Devito modules:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from devito import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## From equations to code in a few lines of Python\n",
"\n",
"The main objective of this tutorial is to demonstrate how Devito and its [SymPy](http://www.sympy.org/en/index.html)-powered symbolic API can be used to solve partial differential equations using the finite difference method with highly optimized stencils in a few lines of Python. We demonstrate how computational stencils can be derived directly from the equation in an automated fashion and how Devito can be used to generate and execute, at runtime, the desired numerical scheme in the form of optimized C code.\n",
"\n",
"\n",
"## Defining the physical domain\n",
"\n",
"Before we can begin creating finite-difference (FD) stencils we will need to give Devito a few details regarding the computational domain within which we wish to solve our problem. For this purpose we create a `Grid` object that stores the physical `extent` (the size) of our domain and knows how many points we want to use in each dimension to discretise our data.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Grid[extent=(1.0, 1.0), shape=(5, 6), dimensions=(x, y)]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid = Grid(shape=(5, 6), extent=(1., 1.))\n",
"grid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions and data\n",
"\n",
"To express our equation in symbolic form and discretise it using finite differences, Devito provides a set of `Function` types. A `Function` object:\n",
"\n",
"1. Behaves like a `sympy.Function` symbol\n",
"2. Manages data associated with the symbol\n",
"\n",
"To get more information on how to create and use a `Function` object, or any type provided by Devito, we can take a look at the documentation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Tensor symbol representing a discrete function in symbolic equations.\n",
"\n",
" A Function carries multi-dimensional data and provides operations to create\n",
" finite-differences approximations.\n",
"\n",
" A Function encapsulates space-varying data; for data that also varies in time,\n",
" use TimeFunction instead.\n",
"\n",
" Parameters\n",
" ----------\n",
" name : str\n",
" Name of the symbol.\n",
" grid : Grid, optional\n",
" Carries shape, dimensions, and dtype of the Function. When grid is not\n",
" provided, shape and dimensions must be given. For MPI execution, a\n",
" Grid is compulsory.\n",
" space_order : int or 3-tuple of ints, optional, default=1\n",
" Discretisation order for space derivatives.\n",
" `space_order` also impacts the number of points available around a\n",
" generic point of interest. By default, `space_order` points are\n",
" available on both sides of a generic point of interest, including those\n",
" nearby the grid boundary. Sometimes, fewer points suffice; in other\n",
" scenarios, more points are necessary. In such cases, instead of an\n",
" integer, one can pass:\n",
" * a 3-tuple `(o, lp, rp)` indicating the discretization order\n",
" (`o`) as well as the number of points on the left (`lp`) and\n",
" right (`rp`) sides of a generic point of interest;\n",
" * a 2-tuple `(o, ((lp0, rp0), (lp1, rp1), ...))` indicating the\n",
" discretization order (`o`) as well as the number of points on\n",
" the left/right sides of a generic point of interest for each\n",
" SpaceDimension.\n",
" shape : tuple of ints, optional\n",
" Shape of the domain region in grid points. Only necessary if `grid`\n",
" isn't given.\n",
" dimensions : tuple of Dimension, optional\n",
" Dimensions associated with the object. Only necessary if `grid` isn't\n",
" given.\n",
" dtype : data-type, optional, default=np.float32\n",
" Any object that can be interpreted as a numpy data type.\n",
" staggered : Dimension or tuple of Dimension or Stagger, optional, default=None\n",
" Define how the Function is staggered.\n",
" initializer : callable or any object exposing the buffer interface, default=None\n",
" Data initializer. If a callable is provided, data is allocated lazily.\n",
" allocator : MemoryAllocator, optional\n",
" Controller for memory allocation. To be used, for example, when one wants\n",
" to take advantage of the memory hierarchy in a NUMA architecture. Refer to\n",
" `default_allocator.__doc__` for more information.\n",
" padding : int or tuple of ints, optional\n",
" Allocate extra grid points to maximize data access alignment. When a tuple\n",
" of ints, one int per Dimension should be provided.\n",
"\n",
" Examples\n",
" --------\n",
" Creation\n",
"\n",
" >>> from devito import Grid, Function\n",
" >>> grid = Grid(shape=(4, 4))\n",
" >>> f = Function(name='f', grid=grid)\n",
" >>> f\n",
" f(x, y)\n",
" >>> g = Function(name='g', grid=grid, space_order=2)\n",
" >>> g\n",
" g(x, y)\n",
"\n",
" First-order derivatives through centered finite-difference approximations\n",
"\n",
" >>> f.dx\n",
" Derivative(f(x, y), x)\n",
" >>> f.dy\n",
" Derivative(f(x, y), y)\n",
" >>> g.dx\n",
" Derivative(g(x, y), x)\n",
" >>> (f + g).dx\n",
" Derivative(f(x, y) + g(x, y), x)\n",
"\n",
" First-order derivatives through left/right finite-difference approximations\n",
"\n",
" >>> f.dxl\n",
" Derivative(f(x, y), x)\n",
"\n",
" Note that the fact that it's a left-derivative isn't captured in the representation.\n",
" However, upon derivative expansion, this becomes clear\n",
"\n",
" >>> f.dxl.evaluate\n",
" f(x, y)/h_x - f(x - h_x, y)/h_x\n",
" >>> f.dxr\n",
" Derivative(f(x, y), x)\n",
"\n",
" Second-order derivative through centered finite-difference approximation\n",
"\n",
" >>> g.dx2\n",
" Derivative(g(x, y), (x, 2))\n",
"\n",
" Notes\n",
" -----\n",
" The parameters must always be given as keyword arguments, since SymPy\n",
" uses `*args` to (re-)create the dimension arguments of the symbolic object.\n",
" \n"
]
}
],
"source": [
"print(Function.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, let's create a function $f(x, y)$ and look at the data Devito has associated with it. Please note that it is important to use explicit keywords, such as `name` or `grid` when creating `Function` objects."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle f(x, y)$"
],
"text/plain": [
"f(x, y)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = Function(name='f', grid=grid)\n",
"f"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Data([[0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0.]], dtype=float32)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, Devito `Function` objects use the spatial dimensions `(x, y)` for 2D grids and `(x, y, z)` for 3D grids. To solve a PDE over several timesteps a time dimension is also required by our symbolic function. For this Devito provides an additional function type, the `TimeFunction`, which incorporates the correct dimension along with some other intricacies needed to create a time stepping scheme."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle g(t, x, y)$"
],
"text/plain": [
"g(t, x, y)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g = TimeFunction(name='g', grid=grid)\n",
"g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the default time order of a `TimeFunction` is `1`, the shape of `f` is `(2, 5, 6)`, i.e. Devito has allocated two buffers to represent `g(t, x, y)` and `g(t + dt, x, y)`:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 5, 6)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Derivatives of symbolic functions\n",
"\n",
"The functions we have created so far all act as `sympy.Function` objects, which means that we can form symbolic derivative expressions from them. Devito provides a set of shorthand expressions (implemented as Python properties) that allow us to generate finite differences in symbolic form. For example, the property `f.dx` denotes $\\frac{\\partial}{\\partial x} f(x, y)$ - only that Devito has already discretised it with a finite difference expression. There are also a set of shorthand expressions for left (backward) and right (forward) derivatives:\n",
"\n",
"| Derivative | Shorthand | Discretised | Stencil |\n",
"| ---------- |:---------:|:-----------:|:-------:|\n",
"| $\\frac{\\partial}{\\partial x}f(x, y)$ (right) | `f.dxr` | $\\frac{f(x+h_x,y)}{h_x} - \\frac{f(x,y)}{h_x}$ | |\n",
"| $\\frac{\\partial}{\\partial x}f(x, y)$ (left) | `f.dxl` | $\\frac{f(x,y)}{h_x} - \\frac{f(x-h_x,y)}{h_x}$ | |\n",
"\n",
"A similar set of expressions exist for each spatial dimension defined on our grid, for example `f.dy` and `f.dyl`. Obviously, one can also take derivatives in time of `TimeFunction` objects. For example, to take the first derivative in time of `g` you can simply write:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\frac{\\partial}{\\partial t} g(t, x, y)$"
],
"text/plain": [
"Derivative(g(t, x, y), t)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.dt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We may also want to take a look at the stencil Devito will generate based on the chosen discretisation:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle - \\frac{g(t, x, y)}{dt} + \\frac{g(t + dt, x, y)}{dt}$"
],
"text/plain": [
"-g(t, x, y)/dt + g(t + dt, x, y)/dt"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.dt.evaluate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There also exist convenient shortcuts to express the forward and backward stencil points, `g(t+dt, x, y)` and `g(t-dt, x, y)`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle g(t + dt, x, y)$"
],
"text/plain": [
"g(t + dt, x, y)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.forward"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle g(t - dt, x, y)$"
],
"text/plain": [
"g(t - dt, x, y)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.backward"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And of course, there's nothing to stop us taking derivatives on these objects:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\frac{\\partial}{\\partial t} g(t + dt, x, y)$"
],
"text/plain": [
"Derivative(g(t + dt, x, y), t)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.forward.dt"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\frac{\\partial}{\\partial y} g(t + dt, x, y)$"
],
"text/plain": [
"Derivative(g(t + dt, x, y), y)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.forward.dy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 45-Degree Rotated Finite Differences\n",
"\n",
"Rotated finite differences, when applied at a 45-degree angle, harness a unique isotropic discretization strategy for solving partial differential equations (PDEs) on a two-dimensional grid. This approach modifies the traditional finite difference stencil by rotating the coordinate system, specifically by 45 degrees, to better align with the problem's directional sensitivities. The mathematical transformations for this rotation are straightforward yet powerful, given by:\n",
"\n",
"$$x' = \\frac{x + y}{\\sqrt{2}}, \\quad y' = \\frac{y - x}{\\sqrt{2}}$$\n",
"\n",
"where $(x, y)$ and $(x', y')$ are the original and rotated coordinates, respectively.\n",
"\n",
"## Mathematical Definitions\n",
"\n",
"The partial derivatives in the rotated system become:\n",
"\n",
"$$\\frac{\\partial f}{\\partial x'} \\approx \\frac{f(x'+\\Delta x', y') - f(x'-\\Delta x', y')}{2\\Delta x'}$$\n",
"$$\\frac{\\partial f}{\\partial y'} \\approx \\frac{f(x', y'+\\Delta y') - f(x', y'-\\Delta y')}{2\\Delta y'}$$\n",
"\n",
"Here, $\\Delta x'$ and $\\Delta y'$ represent the grid spacing in the rotated coordinates, which effectively equals the original grid spacing scaled by $\\sqrt{2}$.\n",
"\n",
"## Use Case and Advantages\n",
"\n",
"The 45-degree rotated finite difference method shines in scenarios where PDEs exhibit significant diagonal features or anisotropic behaviors. Traditional finite difference stencils, aligned with the Cartesian grid, can introduce numerical errors or fail to capture the essence of such phenomena accurately. By rotating the stencil, this method ensures a more symmetric and isotropic treatment of the grid points, leading to enhanced accuracy and stability in numerical simulations.\n",
"\n",
"### Advantages Include:\n",
"\n",
"- **Reduced Numerical Dispersion:** By capturing diagonal relationships more naturally, this method mitigates the dispersion errors commonly seen in conventional finite difference schemes.\n",
"- **Enhanced Anisotropy Representation:** It offers a superior framework for simulating physical phenomena where directional dependencies are not aligned with the standard Cartesian axes.\n",
"- **Improved Accuracy:** The isotropic discretization helps in achieving higher accuracy, especially for problems with inherent diagonal or anisotropic features.\n",
"\n",
"The 45-degree rotated finite difference method is a powerful tool for numerical analysts and engineers, providing a nuanced approach to solving complex PDEs with improved fidelity to the physical and geometric intricacies of the underlying problem.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\frac{\\partial}{\\partial x} f(x, y)$"
],
"text/plain": [
"Derivative(f(x, y), x)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.dx45"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle \\left(\\frac{f(x, y)}{2 h_{x}} - \\frac{f(x - h_x, y - h_y)}{2 h_{x}}\\right) + \\left(\\frac{f(x, y)}{2 h_{x}} - \\frac{f(x - h_x, y + h_y)}{2 h_{x}}\\right)$"
],
"text/plain": [
"f(x, y)/(2*h_x) - f(x - h_x, y - h_y)/(2*h_x) + f(x, y)/(2*h_x) - f(x - h_x, y + h_y)/(2*h_x)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.dx45.evaluate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A linear convection operator\n",
"\n",
"**Note:** The following example is derived from [step 5](http://nbviewer.ipython.org/github/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb) in the excellent tutorial series [CFD Python: 12 steps to Navier-Stokes](http://lorenabarba.com/blog/cfd-python-12-steps-to-navier-stokes/).\n",
"\n",
"In this simple example we will show how to derive a very simple convection operator from a high-level description of the governing equation. We will go through the process of deriving a discretised finite difference formulation of the state update for the field variable $u$, before creating a callable `Operator` object. Luckily, the automation provided by SymPy makes the derivation very nice and easy.\n",
"\n",
"The governing equation we want to implement is the linear convection equation:\n",
"$$\\frac{\\partial u}{\\partial t}+c\\frac{\\partial u}{\\partial x} + c\\frac{\\partial u}{\\partial y} = 0.$$\n",
"\n",
"Before we begin, we must define some parameters including the grid, the number of timesteps and the timestep size. We will also initialize our velocity `u` with a smooth field:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": [
"nbval-ignore-output"
]
},
"outputs": [
{
"data": {
"image/png": 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",
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