python - Matplotlib polar 未按预期绘制

Question

我正在尝试插入笛卡尔图的极坐标“版本”。这是它的样子（根据在 Matlab 中完成的类似程序）：

这里有足够的代码来显示我得到的怪异：

from scipy import pi, sin, cos, log
from numpy import radians as rad
from numpy import log10, linspace
from matplotlib import pyplot as plt


############## Inputs ##############

#Beamwidth, in degrees
BW = 5

############## Constants for calculations ##############
# 0 = uniform/sin, 1 = cos, 2 = cos^2, etc


#Peak Pattern break points, from Table 3
p0, p1, p2, p3, p4 = -5.75, -14.4, -22.3, -31.5, -39.4


#Average pattern break points, from Table 3
a0, a1 ,a2, a3, a4 = -12.16, -20.6, -29, -37.6, -42.5


#Constant added to peak pattern to convert it to average, from Table 3
c0, c1, c2, c3, c4 = -3.72, -4.32, -4.6, -4.2, -2.61


#Mask floor levels, from Table 3
floor0, floor1, floor2, floor3, floor4 = -30, -50, -60, -70, -80


############## Calculations ##############

#Lists for plotting purposes

u_x = list(linspace(0,90,500))

u0_norm_y = list()
u0_peak_y = list()
u0_avg_y = list()

##Calculations start

for ang in u_x: 

########## Uniform

    u0 = pi * 50.8 * sin(rad(ang)) / BW

    def u0_norm(ang):
        if ang == 0:
            return 0
        else:
            return 20 * log10(abs(sin(u0) / u0))


    def u0_peak(ang, u0_norm):
        if ang == 0:
            return 0
        elif u0_norm(ang) > p0:
            return u0_norm(ang)
        elif -8.584 * log(2.876 * ang / BW) > floor0:
            return -8.584 * log(2.876 * ang / BW)
        else:
            return floor0

    def u0_avg(ang, u0_norm):
        if ang == 0:
            return 0
        elif u0_norm(ang) > a0:
            return u0_norm(ang)
        elif -8.584 * log(2.876 * ang / BW) + c0 > floor0:
            return -8.584 * log(2.876 * ang / BW) + c0
        else:
            return floor0 

    u0_peak_y.append(u0_peak(ang, u0_norm))
    u0_norm_y.append(u0_norm(ang))
    u0_avg_y.append(u0_avg(ang, u0_norm))

############## Plots ##############

#Uniform
fig1 = plt.figure()
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, polar = True)

ax1.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax1.plot(u_x, u0_peak_y, label= "Peak")
ax1.plot(u_x, u0_avg_y, label= "Average")

ax1.set_title("Uniform Pattern")
ax1.set_xlabel("Angle (degrees)")
ax1.set_ylabel("Normalized Antenna Pattern (dB)")

ax2.set_theta_zero_location("N")
ax2.set_theta_direction(-1)
ax2.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax2.plot(u_x, u0_peak_y, label= "Peak")
ax2.plot(u_x, u0_avg_y, label= "Average")
ax2.set_thetamin(0)
ax2.set_thetamax(90)



ax1.grid(True)
plt.tight_layout()
plt.subplots_adjust(wspace = 0.4)

plt.show()

显然，有些事情出了大问题。我的情节实际上是一团糟。我假设极坐标图与笛卡尔图的创建方式在代码方面非常不同，但我无法找到任何关于此的真实细节。

标签： pythonmatplotlibplot

Answer 1

您正在混淆弧度和度数（ImportanceOfBeingErnest 的直觉是正确的）。删除定义 u0 时对弧度的转换，直接将输入 u_x 转换为弧度。

from scipy import pi, sin, cos, log
from numpy import radians as rad
from numpy import log10, linspace
from matplotlib import pyplot as plt


############## Inputs ##############

#Beamwidth, in degrees
BW = 5

############## Constants for calculations ##############
# 0 = uniform/sin, 1 = cos, 2 = cos^2, etc


#Peak Pattern break points, from Table 3
p0, p1, p2, p3, p4 = -5.75, -14.4, -22.3, -31.5, -39.4


#Average pattern break points, from Table 3
a0, a1 ,a2, a3, a4 = -12.16, -20.6, -29, -37.6, -42.5


#Constant added to peak pattern to convert it to average, from Table 3
c0, c1, c2, c3, c4 = -3.72, -4.32, -4.6, -4.2, -2.61


#Mask floor levels, from Table 3
floor0, floor1, floor2, floor3, floor4 = -30, -50, -60, -70, -80

############## Calculations ##############

#Lists for plotting purposes

u_x = list(linspace(0,rad(90),500))


u0_norm_y = list()
u0_peak_y = list()
u0_avg_y = list()

##Calculations start

for ang in u_x:

########## Uniform

    u0 = pi * 50.8 * sin((ang)) / BW

    def u0_norm(ang):
        if ang == 0:
            return 0
        else:
            return 20 * log10(abs(sin(u0) / u0))


    def u0_peak(ang, u0_norm):
        if ang == 0:
            return 0
        elif u0_norm(ang) > p0:
            return u0_norm(ang)
        elif -8.584 * log(2.876 * ang / BW) > floor0:
            return -8.584 * log(2.876 * ang / BW)
        else:
            return floor0

    def u0_avg(ang, u0_norm):
        if ang == 0:
            return 0
        elif u0_norm(ang) > a0:
            return u0_norm(ang)
        elif -8.584 * log(2.876 * ang / BW) + c0 > floor0:
            return -8.584 * log(2.876 * ang / BW) + c0
        else:
            return floor0

    u0_peak_y.append(u0_peak(ang, u0_norm))
    u0_norm_y.append(u0_norm(ang))
    u0_avg_y.append(u0_avg(ang, u0_norm))

############## Plots ##############

#Uniform
fig1 = plt.figure()
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, polar = True)

ax1.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax1.plot(u_x, u0_peak_y, label= "Peak")
ax1.plot(u_x, u0_avg_y, label= "Average")

ax1.set_title("Uniform Pattern")
ax1.set_xlabel("Angle (degrees)")
ax1.set_ylabel("Normalized Antenna Pattern (dB)")

ax2.set_theta_zero_location("N")
ax2.set_theta_direction(-1)
ax2.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax2.plot(u_x, u0_peak_y, label= "Peak")
ax2.plot(u_x, u0_avg_y, label= "Average")
ax2.set_thetamin(0)
ax2.set_thetamax(90)



ax1.grid(True)
plt.tight_layout()
plt.subplots_adjust(wspace = 0.4)

plt.show()

这是我认为相同代码的更好版本，几乎没有编辑。

from scipy import pi, sin, cos, log
from numpy import radians as rad
from numpy import log10, linspace
from matplotlib import pyplot as plt


############## Inputs ##############

#Beamwidth, in degrees
BW = 5

############## Constants for calculations ##############
# 0 = uniform/sin, 1 = cos, 2 = cos^2, etc


#Peak Pattern break points, from Table 3
p0, p1, p2, p3, p4 = -5.75, -14.4, -22.3, -31.5, -39.4


#Average pattern break points, from Table 3
a0, a1 ,a2, a3, a4 = -12.16, -20.6, -29, -37.6, -42.5


#Constant added to peak pattern to convert it to average, from Table 3
c0, c1, c2, c3, c4 = -3.72, -4.32, -4.6, -4.2, -2.61


#Mask floor levels, from Table 3
floor0, floor1, floor2, floor3, floor4 = -30, -50, -60, -70, -80


############## Calculations ##############

#Lists for plotting purposes

u_x = list(linspace(0,rad(90),500))


u0_norm_y = list()
u0_peak_y = list()
u0_avg_y = list()

##Function definition
def u0_norm(ang, u0):
    if ang == 0:
        return 0
    else:
        return 20 * log10(abs(sin(u0) / u0))


def u0_peak(ang, u0):
    if ang == 0:
        return 0
    elif u0_norm(ang, u0) > p0:
        return u0_norm(ang, u0)
    elif -8.584 * log(2.876 * ang / BW) > floor0:
        return -8.584 * log(2.876 * ang / BW)
    else:
        return floor0


def u0_avg(ang, u0):
    if ang == 0:
        return 0
    elif u0_norm(ang, u0) > a0:
        return u0_norm(ang, u0)
    elif -8.584 * log(2.876 * ang / BW) + c0 > floor0:
        return -8.584 * log(2.876 * ang / BW) + c0
    else:
        return floor0

for ang in u_x:

    ########## Uniform

    u0 = pi * 50.8 * sin(ang) / BW

    u0_peak_y.append(u0_peak(ang, u0))
    u0_norm_y.append(u0_norm(ang, u0))
    u0_avg_y.append(u0_avg(ang, u0))

    ############## Plots ##############

#Uniform
fig1 = plt.figure()
ax1 = plt.subplot(121)
ax2 = plt.subplot(122, polar = True)

ax1.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax1.plot(u_x, u0_peak_y, label= "Peak")
ax1.plot(u_x, u0_avg_y, label= "Average")

ax1.set_title("Uniform Pattern")
ax1.set_xlabel("Angle (radians)")
ax1.set_ylabel("Normalized Antenna Pattern (dB)")

ax2.set_theta_zero_location("N")
ax2.set_theta_direction(-1)
ax2.plot(u_x, u0_norm_y, label= "Normalized Pattern")
ax2.plot(u_x, u0_peak_y, label= "Peak")
ax2.plot(u_x, u0_avg_y, label= "Average")
ax2.set_thetamin(0)
ax2.set_thetamax(90)



ax1.grid(True)
plt.tight_layout()
plt.subplots_adjust(wspace = 0.4)

plt.show()