Pyplot tutorial，Pyplot官方教程自翻譯

 

matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that 「axes」 here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis).

 

matplotlib.pyplot是一個命令行風格的函數集合，使matplotlib像MATLAB同樣工做。每一個pyplot 函數會對圖形窗口(figure)作一些改變，例如：建立一個圖形窗口、在圖形窗口上建立一個繪圖區(plotting area)、在繪圖區上畫一些線條、在線條上標註說明文字等等。在matplotlib.pyplot中，經過函數調用保留不一樣的狀態，這樣就能夠對當前圖形(figure)和繪圖區(plotting area)保持跟蹤，而且當前繪製函數(plotting functions)被導向到當前座標系（請注意這裏的「座標」，在文檔中的大多數地方，指的是圖形窗口的座標部分，而非嚴格意義上的數學術語）

import matplotlib.pyplot as plt plt.plot([1,2,3,4]) plt.ylabel('some numbers') plt.show()

 

(Source code, png, pdf)

 

 

You may be wondering why the x-axis ranges from 0-3 and the y-axis from 1-4. If you provide a single list or array to the plot() command, matplotlib assumes it is a sequence of y values, and automatically generates the x values for you. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are [0,1,2,3].

你可能感到奇怪，爲何x軸的範圍是多0到3而y軸是從1到4。若是你只給plot() 命令提供了一個列表或數組參數，matplotlib認爲它是一個y值的序列，而後自動生成x值。由於Python的序列範圍從0開始，因此默認的x向量與y向量有相同的長度，可是x從0開始。所以，x的值是[0,1,2,3]。

 

plot() is a versatile command, and will take an arbitrary number of arguments. For example, to plot x versus y, you can issue the command:

plot()是個通用【或萬能的】(versatile command)的命令，它有一個可變數量的參數。例如，繪製x和y，你能夠發出如下命令：

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

 

For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue

對於每一對x和y參數，有一個第三個參數能夠設置圖的顏色和線型。字母和符號的字符串格式來自MATLAB，顏色字母與線型字符緊貼。默認的字符串格式爲「b-」，這是一條實心藍色線。例如，要用紅色圓點繪製上圖，你要使用如下命令：

import matplotlib.pyplot as plt

plt.plot([1,2,3,4], [1,4,9,16], 'ro')

plt.axis([0, 6, 0, 20])

plt.show()

(Source code, png, pdf)

 

 

See the plot() documentation for a complete list of line styles and format strings. The axis() command in the example above takes a list of [xmin, xmax, ymin, ymax] and specifies the viewport of the axes.

If matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use numpy arrays. In fact, all sequences are converted to numpy arrays internally. The example below illustrates a plotting several lines with different format styles in one command using arrays.

查看 plot()文檔以得到完整的線型和格式化字符串。 axis() 命令在上例中接受了一個形如 [xmin, xmax, ymin, ymax]的列表而且說明了座標的視口(viewport)【什麼是視口？】

若是matplotlib只限於使用list工做，那它對於數據處理就沒什麼價值了。通常來說，你會使用numpy數組。事實上，全部序列(sequence)都會在內部轉爲numpy數組。下面的例子展現了在一條命令中使用數組用不一樣的格式繪製多條線條。

import numpy as np import matplotlib.pyplot as plt # evenly sampled time at 200ms intervals
 t = np.arange(0., 5., 0.2) # red dashes, blue squares and green triangles
 plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^') plt.show()

 

(Source code, png, pdf)

 

 

Controlling line properties 控制線條屬性

Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties

對於線圖，有不少能夠控制的屬性：線條寬度、線條樣式、抗鋸齒等等。點擊matplotlib.lines.Line2D查看詳細。有不少方法能夠設置線的屬性：

• Use keyword args:
• 使用關鍵字參數：

plt.plot(x, y, linewidth=2.0)

Use the setter methods of a Line2D instance. plot returns a list of Line2D objects; e.g., line1, line2 = plot(x1, y1, x2, y2). In the code below we will suppose that we have only one line so that the list returned is of length 1. We use tuple unpacking with line, to get the first element of that list:

• 使用Line2D實例的設置方法。plot返回一個Line2D對象的列表，例如：line1,line2=plot(x1, y1, x2, y2)，在下面的代碼中，假設只有一條線，這樣返回的列表長度爲1。咱們把線條組成的元組拆包到變量line，獲得列表的第1個元素。

line, = plt.plot(x, y, '-') line.set_antialiased(False) # turn off antialising

• Use the setp() command. The example below uses a MATLAB-style command to set multiple properties on a list of lines. setp works transparently with a list of objects or a single object. You can either use python keyword arguments or MATLAB-style string/value pairs:
• 使用setp() 命令。下面的例子使用了MATLAB樣式的命令在一個線條列表上設置多個屬性。setp透明地與單個對象或多個對象的列表一塊兒工做。既能夠用python的關鍵字參數，也能夠用MATLAB風格的「字符串/值」對。

• lines = plt.plot(x1, y1, x2, y2) # use keyword args
  plt.setp(lines, color='r', linewidth=2.0) # or MATLAB style string value pairs
  plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

   

 

Here are the available Line2D properties.

下面是Line2D的有效屬性

Property	Value Type
alpha	float
animated	[True | False]
antialiased or aa	[True | False]
clip_box	a matplotlib.transform.Bbox instance
clip_on	[True | False]
clip_path	a Path instance and a Transform instance, a Patch
color or c	any matplotlib color
contains	the hit testing function
dash_capstyle	['butt' | 'round' | 'projecting']
dash_joinstyle	['miter' | 'round' | 'bevel']
dashes	sequence of on/off ink in points
data	(np.array xdata, np.array ydata)
figure	a matplotlib.figure.Figure instance
label	any string
linestyle or ls	[ '-' | '--' | '-.' | ':' | 'steps' | ...]
linewidth or lw	float value in points
lod	[True | False]
marker	[ '+' | ',' | '.' | '1' | '2' | '3' | '4' ]
markeredgecolor or mec	any matplotlib color
markeredgewidth or mew	float value in points
markerfacecolor or mfc	any matplotlib color
markersize or ms	float
markevery	[ None | integer | (startind, stride) ]
picker	used in interactive line selection
pickradius	the line pick selection radius
solid_capstyle	['butt' | 'round' | 'projecting']
solid_joinstyle	['miter' | 'round' | 'bevel']
transform	a matplotlib.transforms.Transform instance
visible	[True | False]
xdata	np.array
ydata	np.array
zorder	any number

To get a list of settable line properties, call the setp() function with a line or lines as argument

調用setp() 函數，以一條或多條線圖做爲參數傳入，便可得到一個可設置的線圖屬性列表：

In [69]: lines = plt.plot([1, 2, 3]) In [70]: plt.setp(lines) alpha: float animated: [True | False] antialiased or aa: [True | False] ...snip

Working with multiple figures and axes

工做在多個圖形和座標上

MATLAB, and pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca() returns the current axes (a matplotlib.axes.Axes instance), and gcf() returns the current figure (matplotlib.figure.Figure instance). Normally, you don’t have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots.

MATLAB和pyplot，有當前圖形和座標的概念。全部繪製命令都是對當前座標進行操做。gca()函數返回當前座標系（一個matplotlib.axes.Axes實例），gcf() 返回當前圖形。一般你沒必要擔憂，由於這些都是幕後工做。下面是建立兩個子圖的腳本：

import numpy as np import matplotlib.pyplot as plt def f(t): return np.exp(-t) * np.cos(2*np.pi*t) t1 = np.arange(0.0, 5.0, 0.1) t2 = np.arange(0.0, 5.0, 0.02) plt.figure(1) plt.subplot(211) plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k') plt.subplot(212) plt.plot(t2, np.cos(2*np.pi*t2), 'r--') plt.show()

(Source code, png, pdf)

 

 

The figure() command here is optional because figure(1) will be created by default, just as a subplot(111) will be created by default if you don’t manually specify any axes. The subplot() command specifies numrows, numcols, fignum where fignum ranges from 1 to numrows*numcols. The commas in the subplot command are optional if numrows*numcols<10. So subplot(211) is identical to subplot(2, 1, 1). You can create an arbitrary number of subplots and axes. If you want to place an axes manually, i.e., not on a rectangular grid, use the axes() command, which allows you to specify the location as axes([left, bottom, width, height]) where all values are in fractional (0 to 1) coordinates. See pylab_examples example code: axes_demo.py for an example of placing axes manually and pylab_examples example code: subplots_demo.py for an example with lots of subplots.

　　figure() 命令在這裏是可選項，由於 figure(1) 是默認建立的，就像若是你不去手動建立任何座標系，那麼subplot(111)也會自動建立一個同樣。subplot()命令接受numrows、numcols、fignum 參數，fignum 範圍是從1到 numrows*numcols的乘積。若是numrows*numcols的乘積小於10，那麼逗號是可選項，可加可不加。因此subplot(211)與 subplot(2, 1, 1)徹底相同。你能夠在子圖和座標系中建立任意數。若是你要手動放置一個座標系，而不是在一個矩形的網格上，使用axes() 命令，它能夠經過函數axes([left, bottom, width, height])來指定座標系的位置，這個座標系的值在0~1之間。查看pylab_examples example code: axes_demo.py得到手動設置軸線示例代碼，查看pylab_examples example code: subplots_demo.py得到多子圖示例代碼。

 

You can create multiple figures by using multiple figure() calls with an increasing figure number. Of course, each figure can contain as many axes and subplots as your heart desires:

　　隨着圖形編號的增長，你能夠調用屢次figure() 函數來建立多個圖形。固然，每一個圖形均可以包含你指望的圖形和座標。

 

import matplotlib.pyplot as plt plt.figure(1)                # the first figure
 plt.subplot(211)             # the first subplot in the first figure
 plt.plot([1, 2, 3]) plt.subplot(212)             # the second subplot in the first figure
 plt.plot([4, 5, 6]) plt.figure(2)                # a second figure
 plt.plot([4, 5, 6])          # creates a subplot(111) by default
 plt.figure(1)                # figure 1 current; subplot(212) still current
 plt.subplot(211)             # make subplot(211) in figure1 current
 plt.title('Easy as 1, 2, 3') # subplot 211 title

 

You can clear the current figure with clf() and the current axes with cla(). If you find it annoying that states (specifically the current image, figure and axes) are being maintained for you behind the scenes, don’t despair: this is just a thin stateful wrapper around an object oriented API, which you can use instead (see Artist tutorial)

If you are making lots of figures, you need to be aware of one more thing: the memory required for a figure is not completely released until the figure is explicitly closed with close(). Deleting all references to the figure, and/or using the window manager to kill the window in which the figure appears on the screen, is not enough, because pyplot maintains internal references until close() is called.

　　你可使用clf() 函數清除當圖形，使用cla()清除當前座標。若是你以爲後臺保留狀態打擾了你，不要絕望：這只是圍繞着面向對象API的一個瘦狀態包，你可使用。【這句沒明白】

 　　若是你正在製做多個圖形，你要意識到一件事情：若是不明確調用close()函數來關閉圖形，那麼圖形所佔內存就不會被徹底釋放。刪除全部對圖形的引用，或者使用windows的任務管理器殺掉顯示在屏幕上的圖形窗口，這些都不夠，由於pyplot保持了內部的引用，直到調用close()顯式關閉。

 

Working with text

操做文本

 

The text() command can be used to add text in an arbitrary location, and the xlabel(), ylabel() and title() are used to add text in the indicated locations (see Text introduction for a more detailed example)

　　能夠在任意位置使用 text()命令，xlabel()、ylabel()、 title()用來在指定位置添加文本。(查看Text introduction 獲得更加詳細的示例)

import numpy as np import matplotlib.pyplot as plt # Fixing random state for reproducibility
 np.random.seed(19680801) mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) # the histogram of the data
 n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75) plt.xlabel('Smarts') plt.ylabel('Probability') plt.title('Histogram of IQ') plt.text(60, .025, r'$\mu=100,\ \sigma=15$') plt.axis([40, 160, 0, 0.03]) plt.grid(True) plt.show()

 (Source code, png, pdf)

 

 

All of the text() commands return an matplotlib.text.Text instance. Just as with with lines above, you can customize the properties by passing keyword arguments into the text functions or using setp():

全部的 text()命令會返回一個matplotlib.text.Text實例。正像上圖所示，你能夠經過向文本函數傳入參數或使用 setp()函數，來定製屬性。

t = plt.xlabel('my data', fontsize=14, color='red')

These properties are covered in more detail in Text properties and layout.

這些屬性在Text properties and layout中有詳細描述。

Using mathematical expressions in text

在文本中使用數學表達式

matplotlib accepts TeX equation expressions in any text expression. For example to write the expressionin the title, you can write a TeX expression surrounded by dollar signs:

plt.title(r'$\sigma_i=15$')

Matplotlib能夠在任何文本表達式中接受TeX等式。例如，在標題中寫這個被$符號的TeX表達式：

The r preceding the title string is important – it signifies that the string is a raw string and not to treat backslashes as python escapes. matplotlib has a built-in TeX expression parser and layout engine, and ships its own math fonts – for details see Writing mathematical expressions. Thus you can use mathematical text across platforms without requiring a TeX installation. For those who have LaTeX and dvipng installed, you can also use LaTeX to format your text and incorporate the output directly into your display figures or saved postscript – see Text rendering With LaTeX.

　　標題中的前導字母r很重要，它標誌着這個字符串是原始字符串，不要進行python的轉碼。Matplotlib有個內建的TeX表達式分析器和佈局引擎，承載它本身的數學字體，查看詳細Writing mathematical expressions。這樣你就能夠跨平臺使用數學文本而不須要安裝一個TeX軟件。對於那些安裝了LaTeX和dvipng的人，你也可使用LaTeX來格式化你的文本，合併輸出目錄到你的顯示圖形或保存腳本，查看Text rendering With LaTeX。

Annotating text

The uses of the basic text() command above place text at an arbitrary position on the Axes. A common use for text is to annotate some feature of the plot, and the annotate() method provides helper functionality to make annotations easy. In an annotation, there are two points to consider: the location being annotated represented by the argument xy and the location of the text xytext. Both of these arguments are (x,y) tuples.

import numpy as np import matplotlib.pyplot as plt ax = plt.subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = plt.plot(t, s, lw=2) plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05), ) plt.ylim(-2,2) plt.show()

 

(Source code, png, pdf)

 

 

In this basic example, both the xy (arrow tip) and xytext locations (text location) are in data coordinates. There are a variety of other coordinate systems one can choose – see Basic annotation and Advanced Annotation for details. More examples can be found in pylab_examples example code: annotation_demo.py.

　

在這個基礎的例子裏，xy兩個座標（箭頭）和xytext位置(文本位置)都在數據座標裏。也有其它形式的座標系統能夠選擇，查看Basic annotation 和 Advanced Annotation查看詳細信息。在pylab_examples example code: annotation_demo.py可查看更多示例。

 

Logarithmic and other nonlinear axes

對數和其它非線性座標

 

matplotlib.pyplot supports not only linear axis scales, but also logarithmic and logit scales. This is commonly used if data spans many orders of magnitude. Changing the scale of an axis is easy:

matplotlib.pyplot不只支持線性座標尺度，也支持對數和分對數尺度（logarithmic and logit scales）。若是數據跨越了多個大小的順序，就會用到這個功能【這句話的意思是多是同一個座標上有不一樣的度量尺度】。改變一個座標的尺度很容易：

plt.xscale(‘log’)

 

An example of four plots with the same data and different scales for the y axis is shown below.

下面是對於y軸的相同數據不一樣尺度（scales）的四個繪圖(plot)

import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import NullFormatter  # useful for `logit` scale

# Fixing random state for reproducibility
 np.random.seed(19680801) # make up some data in the interval ]0, 1[
 y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x = np.arange(len(y)) # plot with various axes scales
 plt.figure(1) # linear
 plt.subplot(221) plt.plot(x, y) plt.yscale('linear') plt.title('linear') plt.grid(True) # log
 plt.subplot(222) plt.plot(x, y) plt.yscale('log') plt.title('log') plt.grid(True) # symmetric log
 plt.subplot(223) plt.plot(x, y - y.mean()) plt.yscale('symlog', linthreshy=0.01) plt.title('symlog') plt.grid(True) # logit
 plt.subplot(224) plt.plot(x, y) plt.yscale('logit') plt.title('logit') plt.grid(True) # Format the minor tick labels of the y-axis into empty strings with

# `NullFormatter`, to avoid cumbering the axis with too many labels.

　# 用「NullFormatter」把Y軸的子刻度清空，以免太多顯示標籤

plt.gca().yaxis.set_minor_formatter(NullFormatter()) # Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"

　# 適應子圖佈局，由於logit圖形會比普通圖形打敗更多的空間，由於y軸刻度從1到10^{-3}

plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,wspace=0.35) plt.show()

 (Source code, png, pdf)

 

 

It is also possible to add your own scale, see Developer’s guide for creating scales and transformations for details.

 你也能夠添加你本身的尺度，查看Developer’s guide for creating scales and transformations得到更詳細的信息