python Plot 画图用法

以下内容全部转载粘贴复制于：CS青雀，重附连接：https://blog.csdn.net/ztf312/article/details/49933497

仅作自行查阅方便只用！！

图的存在，让数据变得形象化。无论多么复杂的东西，都是简单的组合。

plot画图时可以设定线条参数。包括：颜色、线型、标记风格。
1）控制颜色
颜色之间的对应关系为
b---blue c---cyan g---green k----black
m---magenta r---red w---white y----yellow
有三种表示颜色的方式:
a:用全名 b:16进制如：#FF00FF c：RGB或RGBA元组（1,0,1,1） d：灰度强度如：‘0.7’
2)控制线型
符号和线型之间的对应关系
- 实线
-- 短线
-. 短点相间线
：虚点线

>>>import matplotlib>>>from pylab import *
>>>help(plot)
Help on function plot in module matplotlib.pyplot:plot(*args, **kwargs)Plot lines and/or markers to the:class:`~matplotlib.axes.Axes`.  *args* is a variable lengthargument, allowing for multiple *x*, *y* pairs with anoptional format string.  For example, each of the following islegal::plot(x, y)         # plot x and y using default line style and colorplot(x, y, 'bo')   # plot x and y using blue circle markersplot(y)            # plot y using x as index array 0..N-1plot(y, 'r+')      # ditto, but with red plussesIf *x* and/or *y* is 2-dimensional, then the corresponding columnswill be plotted.An arbitrary number of *x*, *y*, *fmt* groups can bespecified, as in::a.plot(x1, y1, 'g^', x2, y2, 'g-')Return value is a list of lines that were added.The following format string characters are accepted to controlthe line style or marker:================    ===============================character           description================    ===============================``'-'``             solid line style``'--'``            dashed line style``'-.'``            dash-dot line style``':'``             dotted line style``'.'``             point marker``','``             pixel marker``'o'``             circle marker``'v'``             triangle_down marker``'^'``             triangle_up marker``'<'``             triangle_left marker``'>'``             triangle_right marker``'1'``             tri_down marker``'2'``             tri_up marker``'3'``             tri_left marker``'4'``             tri_right marker``'s'``             square marker``'p'``             pentagon marker``'*'``             star marker``'h'``             hexagon1 marker``'H'``             hexagon2 marker``'+'``             plus marker``'x'``             x marker``'D'``             diamond marker``'d'``             thin_diamond marker``'|'``             vline marker``'_'``             hline marker================    ===============================The following color abbreviations are supported:==========  ========character   color==========  ========'b'         blue'g'         green'r'         red'c'         cyan'm'         magenta'y'         yellow'k'         black'w'         white==========  ========In addition, you can specify colors in many weird andwonderful ways, including full names (``'green'``), hexstrings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) orgrayscale intensities as a string (``'0.8'``).  Of these, thestring specifications can be used in place of a ``fmt`` group,but the tuple forms can be used only as ``kwargs``.Line styles and colors are combined in a single format string, as in``'bo'`` for blue circles.The *kwargs* can be used to set line properties (any property that hasa ``set_*`` method).  You can use this to set a line label (for autolegends), linewidth, anitialising, marker face color, etc.  Here is anexample::plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)plot([1,2,3], [1,4,9], 'rs',  label='line 2')axis([0, 4, 0, 10])legend()If you make multiple lines with one plot command, the kwargsapply to all those lines, e.g.::plot(x1, y1, x2, y2, antialised=False)Neither line will be antialiased.You do not need to use format strings, which are justabbreviations.  All of the line properties can be controlledby keyword arguments.  For example, you can set the color,marker, linestyle, and markercolor with::plot(x, y, color='green', linestyle='dashed', marker='o',markerfacecolor='blue', markersize=12).  See:class:`~matplotlib.lines.Line2D` for details.The kwargs are :class:`~matplotlib.lines.Line2D` properties:agg_filter: unknownalpha: float (0.0 transparent through 1.0 opaque)         animated: [True | False]         antialiased or aa: [True | False]         axes: an :class:`~matplotlib.axes.Axes` instance         clip_box: a :class:`matplotlib.transforms.Bbox` instance         clip_on: [True | False]         clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         color or c: any matplotlib color         contains: a callable function         dash_capstyle: ['butt' | 'round' | 'projecting']         dash_joinstyle: ['miter' | 'round' | 'bevel']         dashes: sequence of on/off ink in points         data: 2D array (rows are x, y) or two 1D arrays         drawstyle: [ 'default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post' ]         figure: a :class:`matplotlib.figure.Figure` instance         fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top']         gid: an id string         label: any string         linestyle or ls: [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''`` ]         and any drawstyle in combination with a linestyle, e.g. ``'steps--'``.         linewidth or lw: float value in points         lod: [True | False]         marker: [ ``7`` | ``4`` | ``5`` | ``6`` | ``'o'`` | ``'D'`` | ``'h'`` | ``'H'`` | ``'_'`` | ``''`` | ``None`` | ``'None'`` | ``' '`` | ``'8'`` | ``'p'`` | ``','`` | ``'+'`` | ``'.'`` | ``'s'`` | ``'*'`` | ``'d'`` | ``3`` | ``0`` | ``1`` | ``2`` | ``'1'`` | ``'3'`` | ``'4'`` | ``'2'`` | ``'v'`` | ``'<'`` | ``'>'`` | ``'^'`` | ``'|'`` | ``'x'`` | ``'$...$'`` | *tuple* | *Nx2 array* ]markeredgecolor or mec: any matplotlib color         markeredgewidth or mew: float value in points         markerfacecolor or mfc: any matplotlib color         markerfacecoloralt or mfcalt: any matplotlib color         markersize or ms: float         markevery: None | integer | (startind, stride)picker: float distance in points or callable pick function         ``fn(artist, event)``         pickradius: float distance in points         rasterized: [True | False | None]         snap: unknownsolid_capstyle: ['butt' | 'round' |  'projecting']         solid_joinstyle: ['miter' | 'round' | 'bevel']         transform: a :class:`matplotlib.transforms.Transform` instance         url: a url string         visible: [True | False]         xdata: 1D array         ydata: 1D array         zorder: any number         kwargs *scalex* and *scaley*, if defined, are passed on to:meth:`~matplotlib.axes.Axes.autoscale_view` to determinewhether the *x* and *y* axes are autoscaled; the default is*True*.Additional kwargs: hold = [True|False] overrides default hold state

自用小代码：

from matplotlib import pyplot as pltfilename = './loss.txt'
step, v_loss, gan = [], [], []
j = 0
i = 1
# 相比open(),with open()不用手动调用close()方法
with open(filename, 'r') as f:# 将txt中的数据逐行存到列表lines里 lines的每一个元素对应于txt中的一行。然后将每个元素中的不同信息提取出来lines = f.readlines()# i变量，由于这个txt存储时有空行，所以增只读偶数行，主要看txt文件的格式，一般不需要# j用于判断读了多少条，step为画图的X轴for line in lines:if i > 1600:step.append(30 * i)v_loss.append(float(line))gan.append(float(0.2))i = i + 1else:i = i + 1# temp = line.split('loss ')# t = temp[1].split(',')# step.append(30*j)# j = j + 1# v_loss.append(float(line))# i = i + 1fig = plt.figure(figsize=(10, 5))  # 创建绘图窗口，并设置窗口大小
# 画第一张图
ax1 = fig.add_subplot(111)  # 将画面分割为2行1列选第一个
ax1.plot(step, v_loss, 'blue', label='Loss', linewidth=1)  # 画dis-loss的值，颜色红
ax1.legend(loc='upper right')  # 绘制图例，plot()中的label值
ax1.set_xlabel('step')  # 设置X轴名称
ax1.set_ylabel('Training loss')  # 设置Y轴名称
# ax1.set_title('Training Loss for U-Net')
# plt.show()# 画第二张图
# ax2 = fig.add_subplot(212)  # 将画面分割为2行1列选第二个
# ax2.plot(step, gan, 'blue', label='gan')  # 画gan-loss的值，颜色蓝
# ax2.legend(loc='upper right')  # loc为图例位置，设置在右上方，（右下方为lower right）
# ax2.set_xlabel('step')
# ax2.set_ylabel('Generator-loss')
# plt.show()  # 显示绘制的图# plt.figure()
# plt.plot(step, dis, 'red', label='dis')
plt.plot(step, gan, 'red', label='y=0.2', linewidth=3, linestyle="--")
plt.legend()
plt.show()

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