Jupyter Snippet NP ch01-code-listing
Jupyter Snippet NP ch01-code-listing
Chapter 1: Computing with Python
Robert Johansson
Source code listings for Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib (ISBN 978-1-484242-45-2).
Interpreter
%%writefile hello.py
print("Hello from Python!")
Overwriting hello.py
!python hello.py
Hello from Python!
!python --version
Python 3.7.3
Input and output caching
3 * 3
9
In[1]
'3 * 3'
Out[1]
9
In
['', '3 * 3', 'In[1]', 'Out[1]', 'In']
Out
{1: 9, 2: '3 * 3', 3: 9, 4: ['', '3 * 3', 'In[1]', 'Out[1]', 'In', 'Out']}
1+2
3
1+2;
x = 1
x = 2; x
2
Documentation
import os
# try os.w<TAB>
import math
math.cos?
[0;31mDocstring:[0m
cos(x)
Return the cosine of x (measured in radians).
[0;31mType:[0m builtin_function_or_method
Interaction with System Shell
!touch file1.py file2.py file3.py
!ls file*
file1.py file2.py file3.py
files = !ls file*
len(files)
3
files
['file1.py', 'file2.py', 'file3.py']
file = "file1.py"
!ls -l $file
-rw-r--r-- 1 rob staff 0 May 6 12:30 file1.py
Running scripts from the IPython console
%%writefile fib.py
def fib(N):
"""
Return a list of the first N Fibonacci numbers.
"""
f0, f1 = 0, 1
f = [1] * N
for n in range(1, N):
f[n] = f0 + f1
f0, f1 = f1, f[n]
return f
print(fib(10))
Overwriting fib.py
!python fib.py
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
%run fib.py
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
fib(6)
[1, 1, 2, 3, 5, 8]
Debugger
fib(1.0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-25-da4664af34ee> in <module>
----> 1 fib(1.0)
~/Desktop/numerical-python-apress-revision/numerical-python-book-code/fib.py in fib(N)
5 """
6 f0, f1 = 0, 1
----> 7 f = [1] * N
8 for n in range(1, N):
9 f[n] = f0 + f1
TypeError: can't multiply sequence by non-int of type 'float'
%debug
> [0;32m/Users/rob/Desktop/numerical-python-apress-revision/numerical-python-book-code/fib.py[0m(7)[0;36mfib[0;34m()[0m
[0;32m 5 [0;31m """
[0m[0;32m 6 [0;31m [0mf0[0m[0;34m,[0m [0mf1[0m [0;34m=[0m [0;36m0[0m[0;34m,[0m [0;36m1[0m[0;34m[0m[0;34m[0m[0m
[0m[0;32m----> 7 [0;31m [0mf[0m [0;34m=[0m [0;34m[[0m[0;36m1[0m[0;34m][0m [0;34m*[0m [0mN[0m[0;34m[0m[0;34m[0m[0m
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[0m
ipdb> q
Timing and profiling code
%timeit fib(100)
12.5 µs ± 712 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
result = %time fib(100)
CPU times: user 21 µs, sys: 1 µs, total: 22 µs
Wall time: 27.2 µs
len(result)
100
import numpy as np
def random_walker_max_distance(M, N):
"""
Simulate N random walkers taking M steps, and return the largest distance
from the starting point achieved by any of the random walkers.
"""
trajectories = [np.random.randn(M).cumsum() for _ in range(N)]
return np.max(np.abs(trajectories))
%prun random_walker_max_distance(400, 10000)
20010 function calls in 0.353 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
10000 0.194 0.000 0.194 0.000 {method 'randn' of 'mtrand.RandomState' objects}
10000 0.069 0.000 0.069 0.000 {method 'cumsum' of 'numpy.ndarray' objects}
1 0.054 0.054 0.340 0.340 <ipython-input-30-a0ebffe020ed>:3(random_walker_max_distance)
1 0.019 0.019 0.282 0.282 <ipython-input-30-a0ebffe020ed>:8(<listcomp>)
1 0.013 0.013 0.353 0.353 <string>:1(<module>)
1 0.004 0.004 0.004 0.004 {method 'reduce' of 'numpy.ufunc' objects}
1 0.000 0.000 0.004 0.004 fromnumeric.py:69(_wrapreduction)
1 0.000 0.000 0.353 0.353 {built-in method builtins.exec}
1 0.000 0.000 0.004 0.004 fromnumeric.py:2397(amax)
1 0.000 0.000 0.000 0.000 fromnumeric.py:70(<dictcomp>)
1 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
Jupyter notebook
from IPython.display import display, Image, HTML, Math
Image(url='http://python.org/images/python-logo.gif')
import scipy, numpy, matplotlib
modules = [numpy, matplotlib, scipy]
row = "<tr> <td>%s</td> <td>%s</td> </tr>"
rows = "\n".join([row % (module.__name__, module.__version__) for module in modules])
s = "<table> <tr><th>Library</th><th>Version</th> </tr> %s</table>" % rows
s
'<table> <tr><th>Library</th><th>Version</th> </tr> <tr> <td>numpy</td> <td>1.13.3</td> </tr>\n<tr> <td>matplotlib</td> <td>3.0.0</td> </tr>\n<tr> <td>scipy</td> <td>1.1.0</td> </tr></table>'
HTML(s)
class HTMLDisplayer(object):
def __init__(self, code):
self.code = code
def _repr_html_(self):
return self.code
HTMLDisplayer(s)
Math(r'\hat{H} = -\frac{1}{2}\epsilon \hat{\sigma}_z-\frac{1}{2}\delta \hat{\sigma}_x')
$$\hat{H} = -\frac{1}{2}\epsilon \hat{\sigma}_z-\frac{1}{2}\delta \hat{\sigma}_x$$
class QubitHamiltonian(object):
def __init__(self, epsilon, delta):
self.epsilon = epsilon
self.delta = delta
def _repr_latex_(self):
return "$\hat{H} = -%.2f\hat{\sigma}_z-%.2f\hat{\sigma}_x$" % \
(self.epsilon/2, self.delta/2)
QubitHamiltonian(0.5, 0.25)
$\hat{H} = -0.25\hat{\sigma}_z-0.12\hat{\sigma}_x$
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
def f(mu):
X = stats.norm(loc=mu, scale=np.sqrt(mu))
N = stats.poisson(mu)
x = np.linspace(0, X.ppf(0.999))
n = np.arange(0, x[-1])
fig, ax = plt.subplots()
ax.plot(x, X.pdf(x), color='black', lw=2, label="Normal($\mu=%d, \sigma^2=%d$)" % (mu, mu))
ax.bar(n, N.pmf(n), align='edge', label=r"Poisson($\lambda=%d$)" % mu)
ax.set_ylim(0, X.pdf(x).max() * 1.25)
ax.legend(loc=2, ncol=2)
plt.close(fig)
return fig
from ipywidgets import interact
import ipywidgets as widgets
interact(f, mu=widgets.FloatSlider(min=1.0, max=20.0, step=1.0));
Jupyter nbconvert
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