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8058 lines
564 KiB
Markdown
8058 lines
564 KiB
Markdown
# Matplotlib 基础
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在使用**Numpy**之前，需要了解一些画图的基础。
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**Matplotlib**是一个类似**Matlab**的工具包，主页地址为
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[http://matplotlib.org](http://matplotlib.org)
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导入 `matplotlib` 和 `numpy`：
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In [1]:
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```py
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%pylab
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```
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```py
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Using matplotlib backend: Qt4Agg
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Populating the interactive namespace from numpy and matplotlib
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```
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## plot 二维图
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```py
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plot(y)
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plot(x, y)
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plot(x, y, format_string)
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```
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只给定 `y` 值，默认以下标为 `x` 轴：
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In [2]:
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```py
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%matplotlib inline
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x = linspace(0, 2 * pi, 50)
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plot(sin(x))
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```
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Out[2]:
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```py
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[<matplotlib.lines.Line2D at 0xa086fd0>]
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```
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给定 `x` 和 `y` 值：
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In [3]:
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```py
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plot(x, sin(x))
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```
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Out[3]:
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```py
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[<matplotlib.lines.Line2D at 0xa241898>]
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```
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多条数据线：
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In [4]:
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```py
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plot(x, sin(x),
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x, sin(2 * x))
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```
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Out[4]:
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```py
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[<matplotlib.lines.Line2D at 0xa508b00>,
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<matplotlib.lines.Line2D at 0xa508d30>]
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```
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使用字符串，给定线条参数：
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In [5]:
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```py
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plot(x, sin(x), 'r-^')
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```
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Out[5]:
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```py
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[<matplotlib.lines.Line2D at 0xba6ea20>]
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```
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多线条：
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In [9]:
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```py
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plot(x, sin(x), 'b-o',
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x, sin(2 * x), 'r-^')
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```
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Out[9]:
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```py
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[<matplotlib.lines.Line2D at 0xbcf1710>,
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<matplotlib.lines.Line2D at 0xbcf1940>]
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```
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更多参数设置，请查阅帮助。事实上，字符串使用的格式与**Matlab**相同。
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## scatter 散点图
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```py
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scatter(x, y)
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scatter(x, y, size)
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scatter(x, y, size, color)
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```
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假设我们想画二维散点图：
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In [10]:
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```py
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plot(x, sin(x), 'bo')
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```
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Out[10]:
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```py
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[<matplotlib.lines.Line2D at 0xbd6c0b8>]
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```
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可以使用 `scatter` 达到同样的效果：
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In [11]:
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```py
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scatter(x, sin(x))
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```
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Out[11]:
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```py
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<matplotlib.collections.PathCollection at 0xbd996d8>
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```
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事实上，scatter函数与**Matlab**的用法相同，还可以指定它的大小，颜色等参数：
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In [12]:
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```py
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x = rand(200)
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y = rand(200)
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size = rand(200) * 30
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color = rand(200)
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scatter(x, y, size, color)
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# 显示颜色条
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colorbar()
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```
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Out[12]:
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```py
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<matplotlib.colorbar.Colorbar instance at 0x000000000C31F448>
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```
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## 多图
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使用figure()命令产生新的图像：
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In [13]:
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```py
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t = linspace(0, 2*pi, 50)
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x = sin(t)
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y = cos(t)
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figure()
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plot(x)
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figure()
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plot(y)
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```
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Out[13]:
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```py
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[<matplotlib.lines.Line2D at 0xc680cf8>]
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```
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或者使用 `subplot` 在一幅图中画多幅子图：
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```py
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subplot(row, column, index)
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```
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In [15]:
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```py
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subplot(1, 2, 1)
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plot(x)
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subplot(1, 2, 2)
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plot(y)
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```
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Out[15]:
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```py
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[<matplotlib.lines.Line2D at 0xcd47518>]
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```
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## 向图中添加数据
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默认多次 `plot` 会叠加：
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In [16]:
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```py
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plot(x)
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plot(y)
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```
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Out[16]:
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```py
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[<matplotlib.lines.Line2D at 0xcbcfd30>]
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```
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可以跟**Matlab**类似用 hold(False)关掉，这样新图会将原图覆盖：
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In [17]:
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```py
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plot(x)
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hold(False)
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plot(y)
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# 恢复原来设定
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hold(True)
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```
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Out[17]:
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```py
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[<matplotlib.lines.Line2D at 0xcf4b9b0>]
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```
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## 标签
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可以在 `plot` 中加入 `label` ，使用 `legend` 加上图例：
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In [19]:
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```py
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plot(x, label='sin')
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plot(y, label='cos')
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legend()
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```
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Out[19]:
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```py
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<matplotlib.legend.Legend at 0xd2089b0>
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```
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或者直接在 `legend`中加入：
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In [21]:
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```py
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plot(x)
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plot(y)
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legend(['sin', 'cos'])
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```
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Out[21]:
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```py
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<matplotlib.legend.Legend at 0xd51fb00>
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```
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## 坐标轴，标题，网格
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可以设置坐标轴的标签和标题：
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In [22]:
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```py
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plot(x, sin(x))
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xlabel('radians')
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# 可以设置字体大小
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ylabel('amplitude', fontsize='large')
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title('Sin(x)')
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```
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Out[22]:
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```py
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<matplotlib.text.Text at 0xd727dd8>
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```
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用 'grid()' 来显示网格：
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In [23]:
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```py
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plot(x, sin(x))
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xlabel('radians')
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ylabel('amplitude', fontsize='large')
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title('Sin(x)')
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grid()
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```
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## 清除、关闭图像
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清除已有的图像使用：
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```py
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clf()
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```
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关闭当前图像：
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```py
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close()
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```
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关闭所有图像：
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```py
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close('all')
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```
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## imshow 显示图片
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灰度图片可以看成二维数组：
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In [25]:
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```py
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# 导入lena图片
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from scipy.misc import lena
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img = lena()
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img
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```
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Out[25]:
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```py
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array([[162, 162, 162, ..., 170, 155, 128],
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[162, 162, 162, ..., 170, 155, 128],
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[162, 162, 162, ..., 170, 155, 128],
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...,
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[ 43, 43, 50, ..., 104, 100, 98],
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[ 44, 44, 55, ..., 104, 105, 108],
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[ 44, 44, 55, ..., 104, 105, 108]])
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```
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我们可以用 `imshow()` 来显示图片数据：
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In [26]:
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```py
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imshow(img,
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# 设置坐标范围
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extent = [-25, 25, -25, 25],
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# 设置colormap
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cmap = cm.bone)
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colorbar()
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```
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Out[26]:
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```py
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<matplotlib.colorbar.Colorbar instance at 0x000000000DECFD88>
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```
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更多参数和用法可以参阅帮助。
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这里 `cm` 表示 `colormap`，可以看它的种类：
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In [28]:
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```py
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dir(cm)
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```
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Out[28]:
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```py
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[u'Accent',
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u'Accent_r',
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u'Blues',
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u'Blues_r',
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u'BrBG',
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u'BrBG_r',
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u'BuGn',
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u'BuGn_r',
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u'BuPu',
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u'BuPu_r',
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u'CMRmap',
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u'CMRmap_r',
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u'Dark2',
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u'Dark2_r',
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u'GnBu',
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u'GnBu_r',
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u'Greens',
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u'Greens_r',
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u'Greys',
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u'Greys_r',
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'LUTSIZE',
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u'OrRd',
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u'OrRd_r',
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u'Oranges',
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u'Oranges_r',
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u'PRGn',
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u'PRGn_r',
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u'Paired',
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u'Paired_r',
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u'Pastel1',
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u'Pastel1_r',
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u'Pastel2',
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u'Pastel2_r',
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u'PiYG',
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u'PiYG_r',
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u'PuBu',
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u'PuBuGn',
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u'PuBuGn_r',
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u'PuBu_r',
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u'PuOr',
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u'PuOr_r',
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u'PuRd',
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u'PuRd_r',
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u'Purples',
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u'Purples_r',
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u'RdBu',
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u'RdBu_r',
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u'RdGy',
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u'RdGy_r',
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u'RdPu',
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u'RdPu_r',
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u'RdYlBu',
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u'RdYlBu_r',
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u'RdYlGn',
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u'RdYlGn_r',
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u'Reds',
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u'Reds_r',
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'ScalarMappable',
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u'Set1',
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u'Set1_r',
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u'Set2',
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u'Set2_r',
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u'Set3',
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u'Set3_r',
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u'Spectral',
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u'Spectral_r',
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u'Wistia',
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u'Wistia_r',
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u'YlGn',
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u'YlGnBu',
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u'YlGnBu_r',
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u'YlGn_r',
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u'YlOrBr',
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u'YlOrBr_r',
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u'YlOrRd',
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u'YlOrRd_r',
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'__builtins__',
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'__doc__',
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'__file__',
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'__name__',
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'__package__',
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'_generate_cmap',
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'_reverse_cmap_spec',
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'_reverser',
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'absolute_import',
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u'afmhot',
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u'afmhot_r',
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u'autumn',
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u'autumn_r',
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u'binary',
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u'binary_r',
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u'bone',
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u'bone_r',
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u'brg',
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u'brg_r',
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u'bwr',
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u'bwr_r',
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'cbook',
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'cmap_d',
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'cmapname',
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'colors',
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u'cool',
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u'cool_r',
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u'coolwarm',
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u'coolwarm_r',
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u'copper',
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u'copper_r',
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'cubehelix',
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u'cubehelix_r',
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'datad',
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'division',
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u'flag',
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u'flag_r',
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'get_cmap',
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u'gist_earth',
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u'gist_earth_r',
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u'gist_gray',
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u'gist_gray_r',
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u'gist_heat',
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u'gist_heat_r',
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u'gist_ncar',
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u'gist_ncar_r',
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u'gist_rainbow',
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u'gist_rainbow_r',
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u'gist_stern',
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u'gist_stern_r',
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u'gist_yarg',
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u'gist_yarg_r',
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u'gnuplot',
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u'gnuplot2',
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u'gnuplot2_r',
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u'gnuplot_r',
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u'gray',
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u'gray_r',
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u'hot',
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u'hot_r',
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u'hsv',
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u'hsv_r',
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u'jet',
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u'jet_r',
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'ma',
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'mpl',
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u'nipy_spectral',
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u'nipy_spectral_r',
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'np',
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u'ocean',
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u'ocean_r',
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'os',
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u'pink',
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u'pink_r',
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'print_function',
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u'prism',
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u'prism_r',
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u'rainbow',
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u'rainbow_r',
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'register_cmap',
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'revcmap',
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u'seismic',
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u'seismic_r',
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'six',
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'spec',
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'spec_reversed',
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u'spectral',
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u'spectral_r',
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u'spring',
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u'spring_r',
|
||
u'summer',
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u'summer_r',
|
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u'terrain',
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u'terrain_r',
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'unicode_literals',
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u'winter',
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u'winter_r']
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||
```
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||
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使用不同的 `colormap` 会有不同的显示效果。
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In [29]:
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|
||
```py
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imshow(img, cmap=cm.RdGy_r)
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```
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Out[29]:
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||
|
||
```py
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<matplotlib.image.AxesImage at 0xe0883c8>
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```
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## 从脚本中运行
|
||
|
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在脚本中使用 `plot` 时，通常图像是不会直接显示的，需要增加 `show()` 选项，只有在遇到 `show()` 命令之后，图像才会显示。
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|
||
## 直方图
|
||
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从高斯分布随机生成1000个点得到的直方图：
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||
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||
In [30]:
|
||
|
||
```py
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||
hist(randn(1000))
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||
|
||
```
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||
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||
Out[30]:
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||
|
||
```py
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||
(array([ 2., 7., 37., 119., 216., 270., 223., 82., 31., 13.]),
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||
array([-3.65594649, -2.98847032, -2.32099415, -1.65351798, -0.98604181,
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||
-0.31856564, 0.34891053, 1.0163867 , 1.68386287, 2.35133904,
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||
3.01881521]),
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||
<a list of 10 Patch objects>)
|
||
```
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||
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|
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更多例子请参考下列网站：
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[http://matplotlib.org/gallery.html](http://matplotlib.org/gallery.html) |