例1
import numpy as np import matplotlib.pyplot as plt from scipy import stats rx1 = np.array([54.52, 55.14, 55.80, 56.43, 57.08, 57.71, 58.35, 58.97, 59.61, 60.25]) #纵坐标 t1 = np.linspace(20.5, 47.5, 10) #横坐标 a = stats.linregress(t1, rx1) #寻求线性回归方程 k = a[0] #斜率 b = a[1] #截距 plt.rcParams['font.sans-serif'] = ['SimHei'] #使中文能够正常显示 plt.rcParams['axes.unicode_minus'] = False #使符号能够正常显示 plt.rcParams['font.size'] = 16 #改变字体大小 plt.figure(figsize=(12, 6)) #改变图片大小 plt.grid(True) #显示网格 plt.plot(t1, rx1, 'k-o') #图片:穿过点的折线 plt.title(u铜电阻值和温度曲线图(惠斯通电桥) #标题 plt.xlabel('t(℃)') #横坐标说明 plt.ylabel('$R_X$(Ω)') #纵坐标说明(一对$之间是Tex表达式） for i in zip(t1, rx1): plt.text(i[0], i[1], str(i[1]), ha='right', va='bottom') #给点添加数据 plt.show() #显示图片
例2
import numpy as np from scipy import stats from scipy import interpolate import matplotlib.pyplot as plt def xxdz(): u1 = np.array([0.23, 0.5, 0.75, 1.01, 1.25, 1.51, 1.75]) i1 = np.array([2.2, 4.6, 6.8, 9.7, 12.0, 14.7, 17.0]) k1, b1, *_ = stats.linregress(u1, i1) x1 = np.linspace(0, max(u1), 1000) u2 = np.array([0.23, 0.5, 0.75, 1, 1.26, 1.51, 1.75]) i2 = np.array([2.2, 4.8, 7.8, 10.0, 12.8, 15.5, 18.0]) k2, b2, *_ = stats.linregress(u2, i2) x2 = np.linspace(0, max(u2), 1000) plt.scatter(u1, i1, c='k', marker='^', label=内接法 #画点 plt.scatter(u2, i2, c='k', marker='o', label=外接法 plt.plot(x1, k1 * x1 b1, 'k', label=内接法 plt.plot(x2, k2 * x2 b2, 'k--', label = 外接法 plt.title(测量线性电阻的伏安特性) plt.xticks(np.linspace(0, 1.8, 10)) #设置坐标轴的刻度 plt.yticks(np.linspace(0, 20, 11)) plt.xlabel('U/V') plt.ylabel('I/mA') plt.legend() def bdtejg(): u1 = np.array([0.238, 0.426, 0.670, 0.740, 0.782, 0.810, 0.830, 0.852, 0.874, 0.885]) i1 = np.array([0.0, 0.0, 0.1, 1.2, 4.5, 9.0, 13.2, 18.8, 24.8, 28.5]) u2 = -np.array([2.5, 4.02, 4.1, 4.22, 4.51, 4.7, 4.76, 4.8, 4.84, 4.87]) i2 = -np.array([0.00, 0.1, 0.18, 0.28, 0.85, 2.03, 3.5, 5.88, 8.33, 12.18]) u3 = np.concatenate((u2, u1)) #连接两组数据 i3 = np.concatenate((i2, i1)) f = interpolate.interp1d(u3, i3, kind='cubic') #获得三个方插值函数以平滑曲线 xnew = np.linspace(min(u3), max(u3), 1000); #用于平滑曲线的x坐标 plt.plot(xnew, f(xnew), 'k') plt.scatter(u3, i3, c='k', marker='o') plt.title(半导体二极管2CW正反伏安特性曲线52) plt.xticks(np.linspace(-5, 1, 11)) plt.yticks(np.linspace(-15, 30, 10)) plt.xlabel('U/V') plt.ylabel('I/mA') def jtsjg(): u1 = np.array([0.00, 0.1, 0.26, 0.52, 0.9, 1.5, 2.24, 2.76, 3.38, 4.00, 4.54, 5.0]) i1 = np.array([0.0, 5.09, 9.25, 9.4, 9.51, 9.6, 9.8, 9.91, 10.05, 10.09, 10.35, 10.4]) f1 = interpolate.interp1d(u1, i1, kind='linear') #获得线性(更高次会拟合)插值函数，使曲线平滑 u2 = np.array([0.0, 0.1, 0.15, 0.24, 0.4, 1.22, 1.9, 2.4, 3.2, 3.93, 4.51, 5.00]) i2 = np.array([0.0, 6.8, 11.6, 13.2, 13.8, 14.1, 14.4, 14.7, 15.0, 15.3, 15.6, 15.8]) f2 = interpolate.interp1d(u2, i2, kind='linear') u3 = np.array([0.0, 0.05, 0.1, 0.16, 0.55, 1.1, 1.85, 2.43, 2.87, 3.3, 3.8, 5]) i3 = np.array([0.0, 4.4, 10.3, 14.7, 18.3, 18.6, 19.3, 19.7, 20, 20.3, 20.6, 21]) f3 = interpolate.interp1d(u3, i3, kind='linear') xnew = np.linspace(0, 5, 1000) plt.plot(xnew, f1(xnew), 'k', label='40μA') plt.plot(xnew, f2(xnew), 'k--', label='60μA') plt.plot(xnew, f3(xnew), 'k-.', label='80μA') plt.scatter(u1, i1, c='k', marker='o') plt.scatter(u2, i2, c='k', marker='o') plt.scatter(u3, i3, c='k', marker='o') plt.title(‘晶体三极管输出特性曲线’) plt.xticks(np.linspace(0, 5, 11)) plt.yticks(np.linspace(0, 22, 12)) plt.xlabel('$U_{ce}$/V') plt.ylabel('$I_c$/mA') plt.legend() print(f1(3.5)) print(f2(3.5)) print(f3(3.5)) plt.rcParams['font.sans-serif'] = ['SimHei'] #使中文能够正常显示 plt.rcParams['axes.unicode_minus'] = False #使符号能够正常显示 plt.rcParams['font.size'] = 16 #改变字体大小 plt.figure(figsize=(12, 6)) #改变图片大小 plt.grid(True) #显示网格 ax = plt.gca() #获得坐标轴 ax.spines['right'].set_color('none') #隐藏右边框和上边框 ax.spines['top'].set_color('none') ax.spines['bottom'].set_position(('data', 0)) #将坐标轴移动到(0， 0) ax.spines['left'].set_position(('data', 0)) jtsjg() plt.show()
