Commit 7b4c3f2d authored by Martin Bauer's avatar Martin Bauer
Browse files

Refactoring of plotting and stencil plotting

- stencil plotting & transformation now in ps.stencil
- additional documentation & notebooks
parent 0998f2e1
[flake8] [flake8]
max-line-length=120 max-line-length=120
exclude=pystencils/jupytersetup.py, exclude=pystencils/jupyter.py,
pystencils/plot2d.py pystencils/plot.py
pystencils/session.py pystencils/session.py
ignore = W293 W503 W291 ignore = W293 W503 W291
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from pystencils.session import * from pystencils.session import *
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Tutorial 01: Getting Started # Tutorial 01: Getting Started
## Overview ## Overview
*pystencils* is a package that can speed up computations on *numpy* arrays. All computations are carried out fully parallel on CPUs (single node with OpenMP, multiple nodes with MPI) or on GPUs. *pystencils* is a package that can speed up computations on *numpy* arrays. All computations are carried out fully parallel on CPUs (single node with OpenMP, multiple nodes with MPI) or on GPUs.
It is suited for applications that run the same operation on *numpy* arrays multiple times. It can be used to accelerate computations on images or voxel fields. Its main application, however, are numerical simulations using finite differences, finite volumes, or lattice Boltzmann methods. It is suited for applications that run the same operation on *numpy* arrays multiple times. It can be used to accelerate computations on images or voxel fields. Its main application, however, are numerical simulations using finite differences, finite volumes, or lattice Boltzmann methods.
There already exist a variety of packages to speed up numeric Python code. One could use pure numpy or solutions that compile your code, like *Cython* and *numba*. See [this page](demo_benchmark.ipynb) for a comparison of these tools. There already exist a variety of packages to speed up numeric Python code. One could use pure numpy or solutions that compile your code, like *Cython* and *numba*. See [this page](demo_benchmark.ipynb) for a comparison of these tools.
![Stencil](../img/pystencils_stencil_four_points_with_arrows.svg) ![Stencil](../img/pystencils_stencil_four_points_with_arrows.svg)
As the name suggests, *pystencils* was developed for **stencil codes**, i.e. operations that update array elements using only a local neighborhood. As the name suggests, *pystencils* was developed for **stencil codes**, i.e. operations that update array elements using only a local neighborhood.
It generates C code, compiles it behind the scenes, and lets you call the compiled C function as if it was a native Python function. It generates C code, compiles it behind the scenes, and lets you call the compiled C function as if it was a native Python function.
But lets not dive too deep into the concepts of *pystencils* here, they are covered in detail in the following tutorials. Let's instead look at a simple example, that computes the average neighbor values of a *numpy* array. Therefor we first create two rather large arrays for input and output: But lets not dive too deep into the concepts of *pystencils* here, they are covered in detail in the following tutorials. Let's instead look at a simple example, that computes the average neighbor values of a *numpy* array. Therefor we first create two rather large arrays for input and output:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
input_arr = np.random.rand(1024, 1024) input_arr = np.random.rand(1024, 1024)
output_arr = np.zeros_like(input_arr) output_arr = np.zeros_like(input_arr)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
We first implement a version of this algorithm using pure numpy and benchmark it. We first implement a version of this algorithm using pure numpy and benchmark it.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def numpy_kernel(): def numpy_kernel():
output_arr[1:-1, 1:-1] = input_arr[2:, 1:-1] + input_arr[:-2, 1:-1] + \ output_arr[1:-1, 1:-1] = input_arr[2:, 1:-1] + input_arr[:-2, 1:-1] + \
input_arr[1:-1, 2:] + input_arr[1:-1, :-2] input_arr[1:-1, 2:] + input_arr[1:-1, :-2]
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
%%timeit %%timeit
numpy_kernel() numpy_kernel()
``` ```
%% Output %% Output
3.84 ms ± 36.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 3.93 ms ± 40 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now lets see how to run the same algorithm with *pystencils*. Now lets see how to run the same algorithm with *pystencils*.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
src, dst = ps.fields(src=input_arr, dst=output_arr) src, dst = ps.fields(src=input_arr, dst=output_arr)
symbolic_description = ps.Assignment(dst[0,0], symbolic_description = ps.Assignment(dst[0,0],
(src[1, 0] + src[-1, 0] + src[0, 1] + src[0, -1]) / 4) (src[1, 0] + src[-1, 0] + src[0, 1] + src[0, -1]) / 4)
symbolic_description symbolic_description
``` ```
%% Output %% Output
$\displaystyle {{dst}_{(0,0)}} \leftarrow \frac{{{src}_{(-1,0)}}}{4} + \frac{{{src}_{(0,-1)}}}{4} + \frac{{{src}_{(0,1)}}}{4} + \frac{{{src}_{(1,0)}}}{4}$ $\displaystyle {{dst}_{(0,0)}} \leftarrow \frac{{{src}_{(-1,0)}}}{4} + \frac{{{src}_{(0,-1)}}}{4} + \frac{{{src}_{(0,1)}}}{4} + \frac{{{src}_{(1,0)}}}{4}$
src_W src_S src_N src_E src_W src_S src_N src_E
dst_C := ───── + ───── + ───── + ───── dst_C := ───── + ───── + ───── + ─────
4 4 4 4 4 4 4 4
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
plt.figure(figsize=(3,3)) plt.figure(figsize=(3,3))
ps.visualize_stencil_expression(symbolic_description.rhs) ps.stencil.plot_expression(symbolic_description.rhs)
``` ```
%% Output %% Output
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Here we first have created a symbolic notation of the stencil itself. This representation is built on top of *sympy* and is explained in detail in the next section. Here we first have created a symbolic notation of the stencil itself. This representation is built on top of *sympy* and is explained in detail in the next section.
This description is then compiled and loaded as a Python function. This description is then compiled and loaded as a Python function.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
kernel = ps.create_kernel(symbolic_description).compile() kernel = ps.create_kernel(symbolic_description).compile()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
This whole process might seem overly complicated. We have already spent more lines of code than we needed for the *numpy* implementation and don't have anything running yet! However, this multi-stage process of formulating the algorithm symbolically, and just in the end actually running it, is what makes *pystencils* faster and more flexible than other approaches. This whole process might seem overly complicated. We have already spent more lines of code than we needed for the *numpy* implementation and don't have anything running yet! However, this multi-stage process of formulating the algorithm symbolically, and just in the end actually running it, is what makes *pystencils* faster and more flexible than other approaches.
Now finally lets benchmark the *pystencils* kernel. Now finally lets benchmark the *pystencils* kernel.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def pystencils_kernel(): def pystencils_kernel():
kernel(src=input_arr, dst=output_arr) kernel(src=input_arr, dst=output_arr)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
%%timeit %%timeit
pystencils_kernel() pystencils_kernel()
``` ```
%% Output %% Output
639 µs ± 35 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 643 µs ± 8.66 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
This benchmark shows that *pystencils* is a lot faster than pure *numpy*, especially for large arrays. This benchmark shows that *pystencils* is a lot faster than pure *numpy*, especially for large arrays.
If you are interested in performance details and comparison to other packages like Cython, have a look at [this page](demo_benchmark.ipynb). If you are interested in performance details and comparison to other packages like Cython, have a look at [this page](demo_benchmark.ipynb).
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
## Short *sympy* introduction ## Short *sympy* introduction
In this tutorial we continue with a short *sympy* introduction, since the symbolic kernel definition is built on top of this package. If you already know *sympy* you can skip this section. In this tutorial we continue with a short *sympy* introduction, since the symbolic kernel definition is built on top of this package. If you already know *sympy* you can skip this section.
You can also read the full [sympy documentation here](http://docs.sympy.org/latest/index.html). You can also read the full [sympy documentation here](http://docs.sympy.org/latest/index.html).
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import sympy as sp import sympy as sp
sp.init_printing() # enable nice LaTeX output sp.init_printing() # enable nice LaTeX output
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
*sympy* is a package for symbolic calculation. So first we need some symbols: *sympy* is a package for symbolic calculation. So first we need some symbols:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
x = sp.Symbol("x") x = sp.Symbol("x")
y = sp.Symbol("y") y = sp.Symbol("y")
type(x) type(x)
``` ```
%% Output %% Output
sympy.core.symbol.Symbol sympy.core.symbol.Symbol
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
The usual mathematical operations are defined for symbols: The usual mathematical operations are defined for symbols:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr = x**2 * ( y + x + 5) + x**2 expr = x**2 * ( y + x + 5) + x**2
expr expr
``` ```
%% Output %% Output
$\displaystyle x^{2} \left(x + y + 5\right) + x^{2}$ $\displaystyle x^{2} \left(x + y + 5\right) + x^{2}$
2 2 2 2
x ⋅(x + y + 5) + x x ⋅(x + y + 5) + x
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now we can do all sorts of operations on these expressions: expand them, factor them, substitute variables: Now we can do all sorts of operations on these expressions: expand them, factor them, substitute variables:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr.expand() expr.expand()
``` ```
%% Output %% Output
$\displaystyle x^{3} + x^{2} y + 6 x^{2}$ $\displaystyle x^{3} + x^{2} y + 6 x^{2}$
3 2 2 3 2 2
x + x ⋅y + 6⋅x x + x ⋅y + 6⋅x
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr.factor() expr.factor()
``` ```
%% Output %% Output
$\displaystyle x^{2} \left(x + y + 6\right)$ $\displaystyle x^{2} \left(x + y + 6\right)$
2 2
x ⋅(x + y + 6) x ⋅(x + y + 6)
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr.subs(y, sp.cos(x)) expr.subs(y, sp.cos(x))
``` ```
%% Output %% Output
$\displaystyle x^{2} \left(x + \cos{\left(x \right)} + 5\right) + x^{2}$ $\displaystyle x^{2} \left(x + \cos{\left(x \right)} + 5\right) + x^{2}$
2 2 2 2
x ⋅(x + cos(x) + 5) + x x ⋅(x + cos(x) + 5) + x
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
We can also built equations and solve them We can also built equations and solve them
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
eq = sp.Eq(expr, 1) eq = sp.Eq(expr, 1)
eq eq
``` ```
%% Output %% Output
$\displaystyle x^{2} \left(x + y + 5\right) + x^{2} = 1$ $\displaystyle x^{2} \left(x + y + 5\right) + x^{2} = 1$
2 2 2 2
x ⋅(x + y + 5) + x = 1 x ⋅(x + y + 5) + x = 1
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
sp.solve(sp.Eq(expr, 1), y) sp.solve(sp.Eq(expr, 1), y)
``` ```
%% Output %% Output
$\displaystyle \left[ - x - 6 + \frac{1}{x^{2}}\right]$ $\displaystyle \left[ - x - 6 + \frac{1}{x^{2}}\right]$
⎡ 1 ⎤ ⎡ 1 ⎤
⎢-x - 6 + ──⎥ ⎢-x - 6 + ──⎥
⎢ 2⎥ ⎢ 2⎥
⎣ x ⎦ ⎣ x ⎦
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
A *sympy* expression is represented by an abstract syntax tree (AST), which can be inspected and modified. A *sympy* expression is represented by an abstract syntax tree (AST), which can be inspected and modified.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr expr
``` ```
%% Output %% Output
$\displaystyle x^{2} \left(x + y + 5\right) + x^{2}$ $\displaystyle x^{2} \left(x + y + 5\right) + x^{2}$
2 2 2 2
x ⋅(x + y + 5) + x x ⋅(x + y + 5) + x
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
ps.to_dot(expr, graph_style={'size': "9.5,12.5"} ) ps.to_dot(expr, graph_style={'size': "9.5,12.5"} )
``` ```
%% Output %% Output
<graphviz.files.Source at 0x7fc7dc51b2e8> <graphviz.files.Source at 0x7ff8a018e7f0>
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Programatically the children node type is acessible as ``expr.func`` and its children as ``expr.args``. Programatically the children node type is acessible as ``expr.func`` and its children as ``expr.args``.
With these members a tree can be traversed and modified. With these members a tree can be traversed and modified.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr.func expr.func
``` ```
%% Output %% Output
sympy.core.add.Add sympy.core.add.Add
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
expr.args expr.args
``` ```
%% Output %% Output
$\displaystyle \left( x^{2}, \ x^{2} \left(x + y + 5\right)\right)$ $\displaystyle \left( x^{2}, \ x^{2} \left(x + y + 5\right)\right)$
⎛ 2 2 ⎞ ⎛ 2 2 ⎞
⎝x , x ⋅(x + y + 5)⎠ ⎝x , x ⋅(x + y + 5)⎠
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
## Using *pystencils* ## Using *pystencils*
### Fields ### Fields
*pystencils* is a module to generate code for stencil operations. *pystencils* is a module to generate code for stencil operations.
One has to specify an update rule for each element of an array, with optional dependencies to neighbors. One has to specify an update rule for each element of an array, with optional dependencies to neighbors.
This is done use pure *sympy* with one addition: **Fields**. This is done use pure *sympy* with one addition: **Fields**.
Fields represent a multidimensional array, where some dimensions are considered *spatial*, and some as *index* dimensions. Spatial coordinates are given relative (i.e. one can specify "the current cell" and "the left neighbor") whereas index dimensions are used to index multiple values per cell. Fields represent a multidimensional array, where some dimensions are considered *spatial*, and some as *index* dimensions. Spatial coordinates are given relative (i.e. one can specify "the current cell" and "the left neighbor") whereas index dimensions are used to index multiple values per cell.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
my_field = ps.fields("f(3) : double[2D]") my_field = ps.fields("f(3) : double[2D]")
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Neighbors are labeled according to points on a compass where the first coordinate is west/east, second coordinate north/south and third coordinate top/bottom. Neighbors are labeled according to points on a compass where the first coordinate is west/east, second coordinate north/south and third coordinate top/bottom.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
field_access = my_field[1, 0](1) field_access = my_field[1, 0](1)
field_access field_access
``` ```
%% Output %% Output
$\displaystyle {{f}_{(1,0)}^{1}}$ $\displaystyle {{f}_{(1,0)}^{1}}$
f_E__1 f_E__1
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
The result of indexing a field is an instance of ``Field.Access``. This class is a subclass of a *sympy* Symbol and thus can be used whereever normal symbols can be used. It is just like a normal symbol with some additional information attached to it. The result of indexing a field is an instance of ``Field.Access``. This class is a subclass of a *sympy* Symbol and thus can be used whereever normal symbols can be used. It is just like a normal symbol with some additional information attached to it.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
isinstance(field_access, sp.Symbol) isinstance(field_access, sp.Symbol)
``` ```
%% Output %% Output
True True
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Building our first stencil kernel ### Building our first stencil kernel
Lets start by building a simple filter kernel. We create a field representing an image, then define a edge detection filter on the third pixel component which is blue for an RGB image. Lets start by building a simple filter kernel. We create a field representing an image, then define a edge detection filter on the third pixel component which is blue for an RGB image.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
img_field = ps.fields("img(4): [2D]") img_field = ps.fields("img(4): [2D]")
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
w1, w2 = sp.symbols("w_1 w_2") w1, w2 = sp.symbols("w_1 w_2")
color = 2 color = 2
sobel_x = (-w2 * img_field[-1,0](color) - w1 * img_field[-1,-1](color) - w1 * img_field[-1, +1](color) \ sobel_x = (-w2 * img_field[-1,0](color) - w1 * img_field[-1,-1](color) - w1 * img_field[-1, +1](color) \
+w2 * img_field[+1,0](color) + w1 * img_field[+1,-1](color) - w1 * img_field[+1, +1](color))**2 +w2 * img_field[+1,0](color) + w1 * img_field[+1,-1](color) - w1 * img_field[+1, +1](color))**2
sobel_x sobel_x
``` ```
%% Output %% Output
$\displaystyle \left(- {{img}_{(-1,-1)}^{2}} w_{1} - {{img}_{(-1,0)}^{2}} w_{2} - {{img}_{(-1,1)}^{2}} w_{1} + {{img}_{(1,-1)}^{2}} w_{1} + {{img}_{(1,0)}^{2}} w_{2} - {{img}_{(1,1)}^{2}} w_{1}\right)^{2}$ $\displaystyle \left(- {{img}_{(-1,-1)}^{2}} w_{1} - {{img}_{(-1,0)}^{2}} w_{2} - {{img}_{(-1,1)}^{2}} w_{1} + {{img}_{(1,-1)}^{2}} w_{1} + {{img}_{(1,0)}^{2}} w_{2} - {{img}_{(1,1)}^{2}} w_{1}\right)^{2}$
(-img_SW__2⋅w₁ - img_W__2⋅w₂ - img_NW__2⋅w₁ + img_SE__2⋅w₁ + img_E__2⋅w₂ - img (-img_SW__2⋅w₁ - img_W__2⋅w₂ - img_NW__2⋅w₁ + img_SE__2⋅w₁ + img_E__2⋅w₂ - img
2 2
_NE__2⋅w₁) _NE__2⋅w₁)
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
We have mixed some standard *sympy* symbols into this expression to possibly give the different directions different weights. The complete expression is still a valid *sympy* expression, so all features of *sympy* work on it. Lets for example now fix one weight by substituting it with a constant. We have mixed some standard *sympy* symbols into this expression to possibly give the different directions different weights. The complete expression is still a valid *sympy* expression, so all features of *sympy* work on it. Lets for example now fix one weight by substituting it with a constant.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
sobel_x = sobel_x.subs(w1, 0.5) sobel_x = sobel_x.subs(w1, 0.5)
sobel_x sobel_x
``` ```
%% Output %% Output
$\displaystyle \left(- 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} + {{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}}\right)^{2}$ $\displaystyle \left(- 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} + {{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}}\right)^{2}$
(-0.5⋅img_SW__2 - img_W__2⋅w₂ - 0.5⋅img_NW__2 + 0.5⋅img_SE__2 + img_E__2⋅w₂ - (-0.5⋅img_SW__2 - img_W__2⋅w₂ - 0.5⋅img_NW__2 + 0.5⋅img_SE__2 + img_E__2⋅w₂ -
2 2
0.5⋅img_NE__2) 0.5⋅img_NE__2)
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Now lets built an executable kernel out of it, which writes the result to a second field. Assignments are created using *pystencils* `Assignment` class, that gets the left- and right hand side of the assignment. Now lets built an executable kernel out of it, which writes the result to a second field. Assignments are created using *pystencils* `Assignment` class, that gets the left- and right hand side of the assignment.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
dst_field = ps.fields('dst: [2D]' ) dst_field = ps.fields('dst: [2D]' )
update_rule = ps.Assignment(dst_field[0,0], sobel_x) update_rule = ps.Assignment(dst_field[0,0], sobel_x)
update_rule update_rule
``` ```
%% Output %% Output
$\displaystyle {{dst}_{(0,0)}} \leftarrow \left(- 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} + {{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}}\right)^{2}$ $\displaystyle {{dst}_{(0,0)}} \leftarrow \left(- 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} + {{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}}\right)^{2}$
dst_C := (-0.5⋅img_SW__2 - img_W__2⋅w₂ - 0.5⋅img_NW__2 + 0.5⋅img_SE__2 + img_E dst_C := (-0.5⋅img_SW__2 - img_W__2⋅w₂ - 0.5⋅img_NW__2 + 0.5⋅img_SE__2 + img_E
2 2
__2⋅w₂ - 0.5⋅img_NE__2) __2⋅w₂ - 0.5⋅img_NE__2)
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Next we can see *pystencils* in action which creates a kernel for us. Next we can see *pystencils* in action which creates a kernel for us.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from pystencils import create_kernel from pystencils import create_kernel
ast = create_kernel(update_rule, cpu_openmp=False) ast = create_kernel(update_rule, cpu_openmp=False)
compiled_kernel = ast.compile() compiled_kernel = ast.compile()
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
This compiled kernel is now just an ordinary Python function. This compiled kernel is now just an ordinary Python function.
Now lets grab an image to apply this filter to: Now lets grab an image to apply this filter to:
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import requests import requests
import imageio import imageio
from io import BytesIO from io import BytesIO
response = requests.get("https://www.python.org/static/img/python-logo.png") response = requests.get("https://www.python.org/static/img/python-logo.png")
img = imageio.imread(BytesIO(response.content)).astype(np.double) img = imageio.imread(BytesIO(response.content)).astype(np.double)
img /= img.max() img /= img.max()
plt.imshow(img); plt.imshow(img);
``` ```
%% Output %% Output
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
filtered_image = np.zeros_like(img[..., 0]) filtered_image = np.zeros_like(img[..., 0])
# here we call the compiled stencil function # here we call the compiled stencil function
compiled_kernel(img=img, dst=filtered_image, w_2=0.5) compiled_kernel(img=img, dst=filtered_image, w_2=0.5)
plt.imshow(filtered_image, cmap='gray'); plt.imshow(filtered_image, cmap='gray');
``` ```
%% Output %% Output
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Digging into *pystencils* ### Digging into *pystencils*
On our way we have created an ``ast``-object. We can inspect this, to see what *pystencils* actually does. On our way we have created an ``ast``-object. We can inspect this, to see what *pystencils* actually does.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
ps.to_dot(ast, graph_style={'size': "9.5,12.5"}) ps.to_dot(ast, graph_style={'size': "9.5,12.5"})
``` ```
%% Output %% Output