介紹ipython notebook¶
1.簡單介紹ipython notebook的安裝和使用，在ubuntu上:
sudo apt-get install ipython
但是並不是所有的版本都支持notebook功能，本人的系統安裝的是0.13的版本有
notebook，但是有個重要的功能沒有，什么功能等會再說，所以本人手動安裝的
ipython 1.1.0版本，你可以“ipython -V”查看版本號。
http://ipython.org/ 此網址可以下載最新的ipython版本¶
2.使用python的你也許對ipython有所耳聞或者使用過，簡單的介紹ipython：
ipython是一個強大而交互式運算架構：
(1).強大的交互式shell（終端運行）;
(2).一個基於瀏覽器的notbook，支持代碼、文本、數學運算、內嵌plots等;
(3).支持交互式的數據可視化和GUI工具包的使用;
(4).靈活、內嵌的解釋器加載到自己的項目;
(5).支持並行運算.¶
3.運行ipython notebook，在終端輸入：
ipython notebook
如果你使用matplotlib內嵌進網頁中,那么需要運行:
ipython notebook --matplotlib inline
OK，程序會自動在瀏覽器上新建一個標簽窗口。
所以ipython notebook就是一個后端服務和一個前端表現，服務默認端口8888，
前端也就是你在瀏覽器中看到的，如下圖:¶
In [5]:
from IPython.display import Image
Image(filename='/home/chaofan/Desktop/firstpage.png') #press shift+enter
Out[5]:
上圖即是控制窗口，我們可以按"New Notebook"新建一個，本人已經見了5個。
你現在所讀的頁面即是上圖 Advence打開后本人編輯 成現在的效果。¶
基本的操作¶
1.每次運行按shift-enter¶
In [7]:
i=0
print i #按shift+enter
可以看到輸出了0,我們可以直接對上面的程序做修改，再運行。¶
2.ipython提供個很多魔數，以%或者%%開始¶
In [1]:
%matplotlib inline
%matplotlib就是一個魔數，如果你在命令行加入--matplotlib inline
運行此命令一樣可以達到內嵌的效果。¶
下面是獲得連接信息¶
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%connect_info
3.可以直接運行bash¶
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ls -l
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pwd
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%%bash
echo 'Hello'
date
如果一個程序無線循環或者循環的時間太長想中斷可以
按ctr-m i,如下:¶
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import time
i=0
while True:
time.sleep(1)
print i
i+=1
4.載入圖片¶
上面已經使用過了載入圖片，下面載入notebook快捷鍵的圖片，
按ctr-m h也會彈出幫助窗口。¶
In [19]:
Image(filename='/home/chaofan/Desktop/help.png')
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載入url圖片：¶
In [22]:
Image(url='http://ww1.sinaimg.cn/mw600/6a77a719jw1dyx581xf1cj.jpg')
Out[22]:
高級處理¶
1.matplotlib使用¶
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import numpy as np
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import matplotlib.pyplot as plt
In [10]:
x = np.linspace(0, 3*np.pi, 500)
plt.plot(x, np.sin(x**2))
plt.title('A simple chirp');
plt.show()
url載入代碼：¶
In [28]:
%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py
In [29]:
#!/usr/bin/env python
# implement the example graphs/integral from pyx
from pylab import *
from matplotlib.patches import Polygon
def func(x):
return (x-3)*(x-5)*(x-7)+85
ax = subplot(111)
a, b = 2, 9 # integral area
x = arange(0, 10, 0.01)
y = func(x)
plot(x, y, linewidth=1)
# make the shaded region
ix = arange(a, b, 0.01)
iy = func(ix)
verts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]
poly = Polygon(verts, facecolor='0.8', edgecolor='k')
ax.add_patch(poly)
text(0.5 * (a + b), 30,
r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center',
fontsize=20)
axis([0,10, 0, 180])
figtext(0.9, 0.05, 'x')
figtext(0.1, 0.9, 'y')
ax.set_xticks((a,b))
ax.set_xticklabels(('a','b'))
ax.set_yticks([])
show()
3.在來些matplotlib的例子¶
In [1]:
from pylab import *
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x = linspace(0, 5, 10)
y = x ** 2
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figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('title')
show()
In [4]:
subplot(1,2,1)
plot(x, y, 'r--')
subplot(1,2,2)
plot(y, x, 'g*-');
In [5]:
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes
# main figure
axes1.plot(x, y, 'r')
axes1.set_xlabel('x')
axes1.set_ylabel('y')
axes1.set_title('title')
# insert
axes2.plot(y, x, 'g')
axes2.set_xlabel('y')
axes2.set_ylabel('x')
axes2.set_title('insert title');
In [6]:
fig, ax = plt.subplots()
ax.plot(x, x**2, label=r"$y = \alpha^2$")
ax.plot(x, x**3, label=r"$y = \alpha^3$")
ax.set_xlabel(r'$\alpha$', fontsize=18)
ax.set_ylabel(r'$y$', fontsize=18)
ax.set_title('title')
ax.legend(loc=2); # upper left corner
Out[6]:
In [7]:
fig, ax = plt.subplots(figsize=(12,6))
ax.plot(x, x+1, color="blue", linewidth=0.25)
ax.plot(x, x+2, color="blue", linewidth=0.50)
ax.plot(x, x+3, color="blue", linewidth=1.00)
ax.plot(x, x+4, color="blue", linewidth=2.00)
# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’
ax.plot(x, x+5, color="red", lw=2, linestyle='-')
ax.plot(x, x+6, color="red", lw=2, ls='-.')
ax.plot(x, x+7, color="red", lw=2, ls=':')
# custom dash
line, = ax.plot(x, x+8, color="black", lw=1.50)
line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...
# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...
ax.plot(x, x+ 9, color="green", lw=2, ls='*', marker='+')
ax.plot(x, x+10, color="green", lw=2, ls='*', marker='o')
ax.plot(x, x+11, color="green", lw=2, ls='*', marker='s')
ax.plot(x, x+12, color="green", lw=2, ls='*', marker='1')
# marker size and color
ax.plot(x, x+13, color="purple", lw=1, ls='-', marker='o', markersize=2)
ax.plot(x, x+14, color="purple", lw=1, ls='-', marker='o', markersize=4)
ax.plot(x, x+15, color="purple", lw=1, ls='-', marker='o', markersize=8, markerfacecolor="red")
ax.plot(x, x+16, color="purple", lw=1, ls='-', marker='s', markersize=8,
markerfacecolor="yellow", markeredgewidth=2, markeredgecolor="blue");
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fig, ax1 = plt.subplots()
ax1.plot(x, x**2, lw=2, color="blue")
ax1.set_ylabel(r"area $(m^2)$", fontsize=18, color="blue")
for label in ax1.get_yticklabels():
label.set_color("blue")
ax2 = ax1.twinx()
ax2.plot(x, x**3, lw=2, color="red")
ax2.set_ylabel(r"volume $(m^3)$", fontsize=18, color="red")
for label in ax2.get_yticklabels():
label.set_color("red")
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n = array([0,1,2,3,4,5])
xx = np.linspace(-0.75, 1., 100)
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fig, axes = plt.subplots(1, 4, figsize=(12,3))
axes[0].scatter(xx, xx + 0.25*randn(len(xx)))
axes[1].step(n, n**2, lw=2)
axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5)
axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5);
In [13]:
fig = plt.figure()
ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)
t = linspace(0, 2 * pi, 100)
ax.plot(t, t, color='blue', lw=3);
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import matplotlib.gridspec as gridspec
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fig = plt.figure()
gs = gridspec.GridSpec(2, 3, height_ratios=[2,1], width_ratios=[1,2,1])
for g in gs:
ax = fig.add_subplot(g)
fig.tight_layout()
In [33]:
alpha = 0.7
phi_ext = 2 * pi * 0.5
def flux_qubit_potential(phi_m, phi_p):
return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) \
- alpha * cos(phi_ext - 2*phi_p)
In [34]:
phi_m = linspace(0, 2*pi, 100)
phi_p = linspace(0, 2*pi, 100)
X,Y = meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T
In [35]:
fig, ax = plt.subplots()
p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, cmap=cm.RdBu,\
vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)
In [36]:
fig, ax = plt.subplots()
im = imshow(Z, cmap=cm.RdBu, vmin=abs(Z).min(),\
vmax=abs(Z).max(), extent=[0, 1, 0, 1])
im.set_interpolation('bilinear')
cb = fig.colorbar(im, ax=ax)
In [37]:
fig, ax = plt.subplots()
cnt = contour(Z, cmap=cm.RdBu, vmin=abs(Z).min(),\
vmax=abs(Z).max(), extent=[0, 1, 0, 1])
4.matplotlib 3D效果¶
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from mpl_toolkits.mplot3d.axes3d import Axes3D
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fig = plt.figure(figsize=(14,6))
# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)
# surface_plot with color grading and color bar
ax = fig.add_subplot(1, 2, 2, projection='3d')
p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, \
cmap=cm.coolwarm, linewidth=0, antialiased=False)
cb = fig.colorbar(p, shrink=0.5)
In [23]:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1, 1, 1, projection='3d')
p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)
In [24]:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1,1,1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)
cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)
ax.set_xlim3d(-pi, 2*pi);
ax.set_ylim3d(0, 3*pi);
ax.set_zlim3d(-pi, 2*pi);
就演示這么多吧，本文所有內容全是在notebook上編輯的,可以看出對於教育、科研、
開發具有很強的運算處理，同時很好的記錄，
也可以很好的演示給他人。寫代碼時也在寫博客。ipython已經非常流行了，再此介紹給
熱愛python的伙伴們.今年8月微軟捐贈10萬美元給ipython為支持其開發，足見其能量。¶
最后說一下為何ipython版本要高，因為在1.0+版本后有一個nbconvert功能，由於我們看到的
這個網頁本身並不是html的,默認是ipynb格式的文件，存儲的也都是json格式的內容，我們需要
把它轉成html頁面。
ipython nbconvert --to html Advance.ipynb¶
注：在博客園上顯示科學計算效果很不好（也許是本人不知怎么弄），所以給個完整鏈接：
http://wuchaofan.github.io/blogsource/python/Advance.html此鏈接是完整的，博客園上的
把這部分刪了。