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02-introduction-pluto.jl
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### A Pluto.jl notebook ###
# v0.19.24
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
end
# ╔═╡ 2c1c69cb-69e6-486b-9e81-3d5c30999193
begin
# improve startup time on binder
import Pkg; Pkg.activate(Base.current_project())
import PlutoUI
import Plots
using PyCall
end
# ╔═╡ ad2f0ea1-ca4c-47ad-8c6d-fe39730eaf2c
md"""
[](https://mybinder.org/v2/gh/jolin-io/workshop-accelerate-Python-with-Julia/main?urlpath=pluto/open?path=./02-introduction-pluto.jl)
"""
# ╔═╡ c7d302a8-c0e4-11ec-24f9-5fd8c9a7e341
md"""
# Introduction to Julia II: Pluto, pure Julia
**Key message:** Julia solves the two language problem
- it is both easy to learn
- and high performant
"""
# ╔═╡ 04bed6e2-4514-4975-ba0e-991bd55853b7
md"""
## This is an interactive Pluto notebook
TLDR: everything is automatically re-executed for you
"""
# ╔═╡ 64443e2c-7b56-43ce-aafb-f80b807840ed
a = 2
# ╔═╡ 612af880-26e3-4ccd-9d26-20cc79162ac7
@bind b PlutoUI.Slider(1:10, default=5, show_value=true)
# ╔═╡ 0a79b260-889a-4d01-8170-47cf279d92ef
2b
# ╔═╡ 0099e358-0a4f-4b1c-84fa-16305b4e8dfe
c = [a b
a+b a*b]
# ╔═╡ bb6d2e79-3741-4053-a26d-8fb84ab102d5
Plots.heatmap(c)
# ╔═╡ 881026ba-cf61-4aff-9e84-786ac6e7af6f
md"""
## Two top ingredients for Julia's success
- structs with speed like C
- overloadable functions
"""
# ╔═╡ fb3fedb9-8ef3-4ac7-be9b-a1664bc327f3
md"""
### structs
"""
# ╔═╡ bf8d4242-b70c-4ad4-8ab9-9ce7ce1631fb
struct Cat
name
end
# ╔═╡ 649325fb-ce88-476c-894e-9d562c95aca8
struct Dog
name::String
age::Int
end
# ╔═╡ 22bdec44-2460-447b-9170-713f8e3aaee8
🐱 = Cat("kitty")
# ╔═╡ f793188f-6645-46a0-b9f4-2cd0a7578b9e
🐕 = Dog("dexter", 2)
# ╔═╡ 08081d91-d683-4a8b-a553-ae42b517580a
🐱.name, 🐕.name, 🐕.age
# ╔═╡ 56059199-0a9d-4695-8e54-5911722392e4
md"""
### functions
"""
# ╔═╡ 0afb7b78-1a53-462c-bbde-4eab7d4ba2be
describe(animal) = "This is $(animal.name)"
# ╔═╡ 1db44575-1eda-4f8a-94b1-d977788f4d50
# describe(dog::Dog) = "This is $(dog.name) and it is $(dog.age) years old"
# ╔═╡ b906d85c-3de0-4653-8c77-2a5a36a08d69
describe(🐱)
# ╔═╡ 8f608538-8a27-4b28-836c-23b7e2b06ebe
describe(🐕)
# ╔═╡ e6b62efb-657f-4413-940e-300f93e8e5e3
meet(animal1, animal2) = "$(animal1.name) meets $(animal2.name)"
# ╔═╡ 39577aeb-c21c-4ad6-a610-dd5dbf9c692a
# meet(cat::Cat, dog::Dog) = "The awesome cat $(cat.name) meets our $(dog.age) years old dog $(dog.name)."
# ╔═╡ 87da4727-cce6-4651-8e5d-2e33ed60a93e
meet(🐱, 🐕)
# ╔═╡ 0d323d27-2c2c-40d4-9c19-f8eba3b82556
meet(🐕, 🐕)
# ╔═╡ 82f7355e-628b-4665-b469-01962634d858
function our_little_story(animal1, animal2)
return [
describe(animal1)
describe(animal2)
meet(animal1, animal2)
]
end
# ╔═╡ 14886c87-77c8-4cc4-8dec-32984dea9bb5
our_little_story(🐱, 🐕)
# ╔═╡ cb951694-60f9-4fa6-94e7-c254ce9c156d
md"""
## Interact with Python, R, Fortran, C++, C, ...
The foreign function interfaces are extremely good. Also the other way around: You can easily include a Julia component into your C, Python or R package.
Here an example for `PyCall.jl`
"""
# ╔═╡ 1cc9b7b0-1ed4-405a-9597-e0de1c014dd4
array = [1,2,3,4,5,6]
# ╔═╡ 13c3e425-7948-4d22-893f-09539959e56f
begin
# multiline is like eval
# in vscode you even get code highlighting
# (note the string interpolation $array to interact with julia)
py"""
import numpy as np
reshaped = np.reshape($array, (2, 3))
"""
# single line is like exec
py"reshaped"
end
# ╔═╡ df3f0a84-8154-458b-90a1-c8d3a4d3dcef
md"""
In visual studio code, you even get syntax highlighting for the python strings
"""
# ╔═╡ 22b3a5f1-257b-4ee5-83b0-bdcd7c4dac34
md"""
Alterantively, use can use julia directly
"""
# ╔═╡ ca7ad18b-50b2-484a-86aa-98a90a6d03a0
begin
np = pyimport("numpy")
np.reshape(array, (2,3))
end
# ╔═╡ 30919fff-c123-4ce8-82a1-adad9e5ded0b
md"""
## Performance Test - Mandelbrot Example
Example is adapted from https://juliacomputing.com/assets/pdf/JuliaVsPython.pdf
**Julia is about 30x faster than Python here** (for larger images the performance impact is even higher, 1000x1000 has factor 50 already)
"""
# ╔═╡ 6777dc04-c5e4-4983-9ed8-87945969865b
md"""
width = $(
@bind width PlutoUI.Slider(10:10:250, default=50, show_value=true)
)
"""
# ╔═╡ 33fa73a0-87ef-4557-a2b2-80b60a20cb2d
md"""
### Julia version
"""
# ╔═╡ 1dfa459c-e611-4241-8e4f-9412c900b10b
begin
function mandelbrot(c, maxiter)
z = c
for n in 1:maxiter
if abs(z) > 2
return n
end
z = z*z + c
end
return 0
end
function mandelbrot_set(; xmin = -0.74877, xmax = -0.74872, ymin = 0.06505, ymax = 0.06510, width = 1000, height = 1000, maxiter = 2048)
r1 = range(xmin, xmax, length=width)
r2 = range(ymin, ymax, length=height)
n3 = zeros(Float32, width,height)
for i in 1:width
for j in 1:height
n3[i,j] = mandelbrot(r1[i] + r2[j]im, maxiter)
end
end
return (r1,r2,n3)
end
julia_performance = @timed mandelbrot_set(width=width, height=width)
end
# ╔═╡ 0853767e-24ab-4716-a19b-6c040d4f40bb
begin
(plotx, ploty, plotz) = julia_performance.value
Plots.heatmap(plotx, ploty, plotz)
end
# ╔═╡ 77ee3f66-65e2-457a-9dae-c26e126f0efb
md"""
### Python version
"""
# ╔═╡ 57d9e671-47d7-4718-b308-15a6997ac73b
python_performance = begin
py"""
import numpy as np
import time
import timeit
def mandelbrot(c,maxiter):
z = c
for n in range(maxiter):
if abs(z) > 2:
return n
z = z*z + c
return 0
def mandelbrot_set(xmin = -0.74877, xmax = -0.74872, ymin = 0.06505, ymax = 0.06510, width = 1000, height = 1000, maxiter = 2048):
r1 = np.linspace(xmin, xmax, width)
r2 = np.linspace(ymin, ymax, height)
n3 = np.empty((width,height))
for i in range(width):
for j in range(height):
n3[i,j] = mandelbrot(r1[i] + 1j*r2[j],maxiter)
return (r1, r2, n3)
"""
py"timeit.timeit(lambda: mandelbrot_set(width=$width, height=$width), number = 1)"
end
# ╔═╡ b3716732-8fd0-4b3c-863b-206becab2bd4
python_performance / julia_performance.time
# ╔═╡ 1860d1fb-1a33-4b00-a967-ad0f66e1b42f
md"""
## Further resources
- [learning Julia](https://julialang.org/learning/)
- [pyjulia / PyCall.jl](https://github.com/JuliaPy/PyCall.jl) ⚠️CAUTION this is not the same as [juliacall / PythonCall.jl](https://cjdoris.github.io/PythonCall.jl/stable/)⚠️
## Next
Next, we will look at one toy example and compare Cython, C++, and Julia
[Next Notebook](https://mybinder.org/v2/gh/jolin-io/workshop-accelerate-Python-with-Julia/main?filepath=03-example-cython-vs-cpp-vs-julia.ipynb)
"""
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