matplotlib study

发表于 更新于 分类于 阅读次数： 本文字数： 13k 阅读时长 ≈ 12 分钟

显示中文

import matplotlib.pyplot as plt

# 显示中文标签
plt.rcParams['font.sans-serif']=['SimHei']
# 显示负号
plt.rcParams['axes.unicode_minus']=False
#有中文使用 u'中文内容'

x,y轴等比例

import pylab as plt

b=plt.plot([1,2,3,4])
ax = plt.gca()
ax.set_aspect(1)

绘图

mpl.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文
mpl.rcParams['axes.unicode_minus'] = False  # 显示负号

df = pd.read_csv(r'小凸轮轴测试t02.csv')

# 2 row, 1 col
fig, axs = plt.subplots(2, 1)
# 两个图的 ax 分别画
ax_raw, ax_fitdiff = axs

x = df['t1']
y = df['t2']
ax_raw.grid(True, linestyle='-.')
ax_raw.set_title('原始数据和拟合数据')
ax_raw.plot(x, y, 'r', label='raw data')

df = df[df.t2 < -1000]
df = df.reset_index(drop=True)
x = df['t1']
df_yfit, df_diff = gen_fit(x, df['t2'], 32)

ax_raw.plot(x, df_yfit['t2'], 'g', label='fit data')

ax_fitdiff.grid(True, linestyle='-.')
ax_fitdiff.set_title('原始数据和拟合数据差值')
ax_fitdiff.plot(x, df_diff['t2'], 'b', label='diff data')

ax_raw.set_xlabel('X轴-脉冲数')
ax_raw.set_ylabel('Y轴-高度(um)')
ax_raw.legend(('原始数据', '拟合数据'), loc='best', shadow=True)

ax_fitdiff.set_xlabel('X轴-脉冲数')
ax_fitdiff.set_ylabel('Y轴-高度差值(um)')
ax_fitdiff.legend(('差值数据',), loc='best', shadow=True)
plt.show()

backend

website

There are three ways to configure your backend:

The rcParams[“backend”] (default: ‘agg’) parameter in your matplotlibrc file
The MPLBACKEND environment variable
The function matplotlib.use()

import matplotlib
matplotlib.use("TKAgg")

legend 显示不全

ax.legend(line1, ('原始数据',)) # 多加一个逗号就可以了

remove legend

ax.get_legend().remove()

光标值取消科学标记法

import matplotlib.pyplot as plt

def format_coord(x, y):
    return 'x:%1.4f, y:%1.4f' % (x, y)
#获取当前子图
ax=plt.gca()
ax.format_coord = format_coord
plt.show()
20210330_190910.png

remove line2d

it.pop(0).remove()

支持子图

# 两行1列，row,col,index
plt.subplot(211)
show_data(df_data, 'pulse', 'value', 'number1-value')

plt.subplot(212)
plt.plot(df_angle, value, label='measure center')

不等分子图

上面 2 个，下面 1 个

def test():
    import matplotlib.pyplot as plt
    import numpy as np

    def f(t):
        return np.exp(-t) * np.cos(2 * np.pi * t)

    t1 = np.arange(0, 5, 0.1)
    t2 = np.arange(0, 5, 0.02)

    plt.figure(12)
    plt.subplot(221)
    plt.plot(t1, f(t1), 'bo', t2, f(t2), 'r--')

    plt.subplot(222)
    plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')

    plt.subplot(212)
    plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

    plt.show()
20200525_173134.png

上面 1 个，下面 2 个

def test():
    import matplotlib.pyplot as plt
    import numpy as np

    def f(t):
        return np.exp(-t) * np.cos(2 * np.pi * t)

    t1 = np.arange(0, 5, 0.1)
    t2 = np.arange(0, 5, 0.02)

    grid = plt.GridSpec(2, 2, wspace=0.2, hspace=0.2)

    plt.subplot(grid[0,0:2])
    plt.plot(t1, f(t1), 'bo', t2, f(t2), 'r--')

    plt.subplot(grid[1,0])
    plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')

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

    plt.show()

pycharm 图形独立显示

File->Settings->Tools->Python Scientific
取消掉Show plots in toolwindow选项

color

# color=' #054E9F'
plt.plot(x, y, linewidth = '1', label = "test", color=' coral ', linestyle=':', marker='|')

极坐标系

在平面内取一个定点O，叫极点，引一条射线Ox，叫做极轴，再选定一个长度单位和角度的正方向（通常取逆时针方向）。对于平面内任何一点M，用ρ表示线段OM的长度（有时也用r表示），θ表示从Ox到OM的角度，ρ叫做点M的极径，θ叫做点M的极角，有序数对 (ρ,θ)就叫点M的极坐标，这样建立的坐标系叫做极坐标系。通常情况下，M的极径坐标单位为1（长度单位），极角坐标单位为rad（或°）

20200523_152818.png

绘制矩形

from matplotlib import patches

rect = patches.Rectangle((Coords1x,Coords1y),Coords2x-Coords1x,Coords2y-Coords1y,linewidth=1,edgecolor='r')
ax = plt.gca()
ax.add_patch(rect)
ax.figure.canvas.draw()

工具栏

pygarden

conda install kivy -c conda-forge
pip install kivy-garden
garden install matplotlib

动态交互

Rectangle Selector

def rect_select_callback(eclick, erelease):
    'eclick and erelease are the press and release events'
    x1, y1 = eclick.xdata, eclick.ydata
    x2, y2 = erelease.xdata, erelease.ydata
    print("(%3.2f, %3.2f) --> (%3.2f, %3.2f)" % (x1, y1, x2, y2))
    print(" The button you used were: %s %s" % (eclick.button, erelease.button))


def toggle_selector(event):
    print(' Key pressed.')
    if event.key in ['Q', 'q'] and event.toggle_selector.RS.active:
        print(' RectangleSelector deactivated.')
        event.toggle_selector.RS.set_active(False)
    if event.key in ['A', 'a'] and not event.toggle_selector.RS.active:
        print(' RectangleSelector activated.')
        event.toggle_selector.RS.set_active(True)

def onZoomCallback(event):
    if toggle_selector.RS.active:
        toggle_selector.RS.update()

# draw rectangle selector
toggle_selector.RS = RectangleSelector(self.ax, rect_select_callback,
                    drawtype='box', useblit=True,
                    button=[1],  # don't use middle button
                    minspanx=5, minspany=5,
                    spancoords='pixels',
                    interactive=True)

self.canvas.mpl_connect('key_press_event', toggle_selector)
# prevent disapper on zoom
self.canvas.mpl_connect('draw_event', onZoomCallback)

spanSelector

from itertools import cycle
import matplotlib.pyplot as plt
from matplotlib.widgets import SpanSelector
import numpy as np

if __name__ == '__main__':
    t = np.arange(180)
    value = (20 * np.sin((np.pi/2) * (t / 22.0)) +
    25 * np.random.random((len(t),)) + 50)
    fig, ax = plt.subplots(1, 1, figsize=(10, 5))
    ax.step(t, value)
    ax.set_ylabel("Stock Price (USD)")
    ax.set_xlabel("Time (days)")

    colors = cycle(list('rbymc'))
    def onselect(x0, x1):
        ax.axvspan(x0, x1, facecolor=next(colors), alpha=0.5)
        fig.canvas.draw_idle()
    ss = SpanSelector(ax, onselect, 'horizontal')
    plt.show()

# 绘制 span
self.ax.axvspan(it['points'][0], it['points'][2], facecolor='r', alpha=0.5)

激活，停止 使用

def on_chk_ProgMode(self):
    if self.chkProgMode.get():
        print('mpl_connect spanCID')
        self.span = SpanSelector(self.ax, self.onselect, 'horizontal', useblit=True, span_stays=True)
        self.spanCID = self.fig.canvas.mpl_connect('key_press_event', self.span)
    else:
        self.span.set_visible(False)

设置坐标轴


# 把x轴的刻度间隔设置为10
x_major_locator = MultipleLocator(10)
# 设置x轴的主刻度倍数
ax_raw.xaxis.set_major_locator(x_major_locator)


#设置坐标轴范围
plt.xlim((-5, 5))     #也可写成plt.xlim(-5, 5) 
plt.ylim((-2, 2))     #也可写成plt.ylim(-2, 2)

my_x_ticks = np.arange(datas.loc[:, "角度1"].min(), datas.loc[:, "角度1"].max(), 20)
my_y_ticks = np.arange(datas.loc[:, "数值"].min(), datas.loc[:, "数值"].max(), 500)
plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)

# 自动设置 坐标点
my_x_ticks = np.arange(datas.loc[:, "角度1"].min(), datas.loc[:, "角度1"].max())
my_y_ticks = np.arange(datas.loc[:, "数值"].min(), datas.loc[:, "数值"].max())

plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
ax = plt.axes()

ax.xaxis.set_major_locator(plt.MaxNLocator(15))
ax.yaxis.set_major_locator(plt.MaxNLocator(15))

# 把x轴的刻度间隔设置为1，并存在变量里
x_major_locator = MultipleLocator(1)
# 把y轴的刻度间隔设置为10，并存在变量里
y_major_locator = MultipleLocator(1)
# ax为两条坐标轴的实例
# ax = plt.gca()
# 把x轴的主刻度设置为1的倍数
ax1.xaxis.set_major_locator(x_major_locator)
# 把y轴的主刻度设置为10的倍数
ax1.yaxis.set_major_locator(y_major_locator)

# x,y 轴间距等比例
ax1.set_aspect(1)

散点图

N = 10
x = np.random.rand(N)  # 包含10个均匀分布的随机值的横坐标数组，大小[0, 1]
y = np.random.rand(N)  # 包含10个均匀分布的随机值的纵坐标数组
plt.scatter(x, y, alpha=0.6)  # 绘制散点图，透明度为0.6（这样颜色浅一点，比较好看）
plt.show()

柱状图

plt.subplot(312)
y = normalization_half_y[1:50]
bar_plot = plt.bar(half_x[1:50], normalization_half_y[1:50])
# plt.plot(half_x[1:], normalization_half_y[1:], 'blue')
plt.title('傅里叶变换', fontsize=9, color='blue')
plt.grid()

def autolabel(rects):
    for idx, rect in enumerate(bar_plot):
        height = rect.get_height()
        plt.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,
                '%.4f' % (y[idx]*100),
                ha='center', va='bottom', rotation=0)

autolabel(bar_plot)

茎叶图

stem(x,y, linefmt=None, markerfmt=None, basefmt=None)

x,y分别是横纵坐标。
linefmt:垂直线的颜色和类型。
linefmt=‘r-’，代表红色的实线。
basefmt指y=0那条直线。
markerfmt设置顶点的类型和颜色，比如C3.
C(大写字母C)是默认的，后面数字应该是0-9，改变颜色，最后的.或者o（小写字母o）分别可以设置顶点为小实点或者大实点。

plt.stem(freqs, a, linefmt='r-', markerfmt='ro',use_line_collection=True)

颜色表

20200916_130303.png 
def get_cmap(n, name='hsv'):
    '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
    RGB color; the keyword argument name must be a standard mpl colormap name.'''
    return plt.cm.get_cmap(name, n)

cmap = get_cmap(20)
plt.plot(x, row.T, cmap(colorIdex), label='raw data')

显示文字，标题，坐标说明

坐标轴说明：plt.xlabel()、plt.ylabel()
添加标题： plt.title()
插入文字： plt.text(60, .025, r’$number=5, sigma=0$’)
显示网格： plt.grid(True)

图例 legend

# 按照绘图顺序，写上名字
plt.legend(["dataAppend","measured_data"])

# 可以指定绘图对象和文字
for it in m_list:
    pt, = ax2.plot(x, it)
    pt_list.append(pt)
    df_m2 = pd.DataFrame(it, columns=['data'], dtype=float)
    m2max = df_m2.max()
    m2min = df_m2.min()
    print("m{}={:10.4f}".format(idx,m2max[0] - m2min[0]))
    nl_list.append("m{}={:10.4f}".format(idx,m2max[0] - m2min[0]))
    idx = idx + 1

ax2.legend(pt_list,nl_list, loc='upper right')

# set_clip_on(True) 可以避免文字超出坐标轴时还显示
it['text'] = self.ax.text(x1 + (x2 - x1) / 2, y2 - 10, it['name'], **style).set_clip_on(True)

注解

it['annotate'] = plt.annotate(it['name'], xy=(it['points'][0] + (it['points'][2] - it['points'][0]) / 2, it['points'][1]+(it['points'][3] - it['points'][1])/2), xytext=(+30, +30),
                                        textcoords='offset points', fontsize=16,arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=.2'))

# 删除注解
self.point_list.remove(it)


# Use the horizontalalignment (which can be shortened as ha) option to the annotate call:
# 居中显示
text_object = plt.annotate(label, xy=(x_values[i], y_values[i]), ha='center')

2d 3d 切换显示

if self.chk3D.get():
    self.ax.clear()
    self.ax = self.fig.add_subplot(111, projection='3d')
    // 如果不调用这个 工具栏图标功能会有问题
    self.toolbar.update()

    self.ax.plot_surface(self.draw3dlist[0],self.draw3dlist[1], self.draw3dlist[2], cmap=cm.coolwarm)
    self.fig.tight_layout()
else:
    self.ax.clear()
    self.ax = self.fig.add_subplot(111)
    self.toolbar.update()

    self.draw_contour()
// 调整绘图的左边距
self.fig.subplots_adjust(left=0.05)  # plot outside the normal area
self.canvas.draw()

plot

plt.plot() 传入二维的散点p1,p2（p1和p2的长度要一样）时，横坐标x绘制p1,纵坐标y绘制p2

scatter

plt.scatter()用于绘制散点图，传入参数必须是二维的：plt.scatter(p1,p2)，

一维数组画图

plt.plot() 可以用于绘制折线图。只传入一维的散点(n个)p1时，横坐标对应散点的次序，从0到n-1，纵坐标对应散点的值。示例：

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
 
p1=[0,1.1,1.8,3.1,4.0]  # 数据点
 
#创建绘图图表对象，可以不显式创建，跟cv2中的cv2.namedWindow()用法差不多
plt.figure('Draw')
 
plt.plot(p1)  # plot绘制折线图
plt.show()  # 显示绘图

同时显示多张图


def draw_1file1():
    file = "ori_data_20200709105813"
    data = load_data_standard(file + '.txt')
    count = len(data) - 10
    data = data[8:-8]

    plt.figure()
    plt.plot(-data['1']-data['10'], label='1+10')
    plt.plot(-data['2']-data['9'], label='2+9')
    plt.plot(-data['3']-data['12'], label='3+12')
    plt.plot(-data['4']-data['11'], label='4+11')
    plt.plot(-data['5']-data['14'], label='5+14')
    plt.plot(-data['6'] - data['13'], label='6+13')
    plt.plot(-data['7'] - data['16'], label='7+16')
    plt.plot(-data['8'] - data['15'], label='8+15')

    plt.legend()
    plt.grid(linestyle='-.')  # 生成网格
    plt.draw()

def draw_1file():
    # ori_data_20200606172221 打弯处理导致判断沟槽提前了
    file = "ori_data_20200709105813"
    data = load_data_standard(file + '.txt')
    count = len(data) - 10
    data = data[8:-8]

    # plt.plot(data, label='1')

    plt.figure()
    plt.plot(data['1'], label='1')
    plt.plot(data['2'], label='2')
    plt.plot(data['10'], label='10')
    plt.plot(data['3'], label='3')
    plt.plot(data['4'], label='4')
    plt.plot(data['12'], label='12')
    plt.plot(data['5'], label='5')
    plt.plot(data['14'], label='14')
    plt.plot(data['7'], label='7')
    plt.plot(data['8'], label='8')
    plt.plot(data['9'], label='9')
    plt.plot(data['11'], label='11')
    plt.plot(data['13'], label='13')
    plt.plot(data['15'], label='15')
    plt.plot(data['16'], label='16')

    plt.legend()
    plt.grid(linestyle='-.')  # 生成网格
    plt.draw()

    pass


if __name__ == "__main__":
    # repeatability()
    # l3_100()
    draw_1file1()
    draw_1file()
    plt.show()

鼠标控制

# button : {None, MouseButton.LEFT, MouseButton.MIDDLE, MouseButton.RIGHT, 'up', 'down'}
# up' and 'down' are used for scroll events.
import matplotlib.pyplot as plt
import numpy as np
 
fig = plt.figure()
def call_back(event):
    axtemp=event.inaxes
    x_min, x_max = axtemp.get_xlim()
    fanwei = (x_max - x_min) / 10
    if event.button == 'up':
        axtemp.set(xlim=(x_min + fanwei, x_max - fanwei))
        print('up')
    elif event.button == 'down':
        axtemp.set(xlim=(x_min - fanwei, x_max + fanwei))
        print('down')
    fig.canvas.draw_idle()  # 绘图动作实时反映在图像上
fig.canvas.mpl_connect('scroll_event', call_back)
fig.canvas.mpl_connect('button_press_event', call_back)
 
ax1 = plt.subplot(3,1,1)#截取幕布的一部分
ax1.xaxis.set_major_formatter(plt.NullFormatter())  # 取消x轴坐标
x = np.linspace(-5, 5, 10)
y = x ** 2 + 1
plt.ylabel('first')
plt.plot(x, y)
plt.grid()
ax2 = plt.subplot(3,1,2)
ax2.xaxis.set_major_formatter(plt.NullFormatter())  # 取消x轴坐标
y1=-x**2+1
plt.plot(x, y1)
 
ax3 = plt.subplot(3,1,3)
y3=-x*2+1
plt.plot(x, y3)
 
plt.show()

选择数据点事件

fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.set_title('click on points')

    line, = ax.plot(np.random.rand(100), 'o', picker=5)  # 5 points tolerance

    def onpick(event):
        thisline = event.artist
        xdata = thisline.get_xdata()
        ydata = thisline.get_ydata()
        ind = event.ind
        points = tuple(zip(xdata[ind], ydata[ind]))
        print('onpick points:', points)

    fig.canvas.mpl_connect('pick_event', onpick)

    plt.show()