def normal_approximation_to_binomial(n, p):
"""
Finds my and sigma corresponding to a Binomial(n, p)
"""
mu = p * n
sigma = math.sprt(p * (1 - p) * n)
return mu, sigma
def ros_imu(info):
for index, item in enumerate(info):
info[index] = float(item)
imuMsg = Imu()
imuMsg.orientation_covariance = [0.0025, 0, 0, 0, 0.0025, 0, 0, 0, 0.0025]
imuMsg.angular_velocity_covariance = [0.02, 0, 0, 0, 0.02, 0, 0, 0, 0.02]
imuMsg.linear_acceleration_covariance = [
0.04, 0, 0, 0, 0.04, 0, 0, 0, 0.04
]
Accel_magnitude = math.sprt(info[0] * info[0] + info[1] * info[1] +
info[2] * info[2])
Accel_magnitude = float(Accel_magnitude / GRAVITY)
pitch = -math.atan2(info[1],
math.sqrt(info[2] * info[2] + info[3] * info[3]))
roll = -math.atan2(info[2], info[3])
xh = info[7] * math.cos(pitch) + info[8] * math.sin(roll) * math.sin(
pitch) + info[9] * math.sin(pitch) * math.cos(roll)
yh = info[8] * math.cos(roll) + info[9] * math.sin(roll)
yaw = math.atan2(-yh, xh)
seq = 0
#print roll
q = quaternion_from_euler(roll, pitch, yaw)
imuMsg.orientation.x = q[0]
imuMsg.orientation.y = q[1]
imuMsg.orientation.z = q[2]
imuMsg.orientation.w = q[3]
imuMsg.header.stamp = rospy.Time.now()
imuMsg.header.frame_id = 'base_imu_link'
imuMsg.header.seq = seq
seq = seq + 1
imu_pose(imuMsg)
gpspub.publish(imuMsg)
def drawHTreeHelper(x, y, length, depth): if (length <= 0 or depth <= 0): return else: x0 = x - length/2 x1 = x + length/2 y0 = y - length/2 y1 = y + length/2 newLen = length / math.sprt(2) drawLine(x0, y, x1, y) drawLine(x0, y1, x0, y0) drawLine(x1, y1, x1, y0) #recursive function drawHTreeHelper(x0, y1, newLen, depth - 1) drawHTreeHelper(x1, y1, newLen, depth - 1) drawHTreeHelper(x0, y0, newLen, depth - 1) drawHTreeHelper(x1, y0, newLen, depth - 1)
import math
pos=[0,0]
while True:
s=imput()
if not s:
break
movement=s.split(" ")
direction=movement[0]
steps=int(movement[1]
if direction=="UP":
pos[0]+=steps
if direction=="DOWN":
pos[0]-=steps
elif direction=="LEFT":
pos[1]-=steps
elif direction=="RIGHT"
pos[1]+=steps
else:
pass
print(int(round(math.sprt(pos[1]**2+pos[0]**2))))
def radiusFromArea(area): radius = math.sprt (float(area)/math.pi) return radius
import csv
with open("data.csv", newline = "") as f:
reader = csv.reader(f)
file_data = list(reader)
data = file_data[0]
def mean(data):
n = len(data)
total = 0
for x in data:
total += int(x)
mean = total / 2
return mean
squared_list = []
for number in data:
a = int(number) - mean(data)
a = a**2
squared_list.append(a)
sun = 0
for i in squared_list:
sum = sum + i
result = sum / (len(data)-1)
std_deviation = math.sprt(result)
print(std_deviation)
def normal_approximation_to_binomial(n: int, p: float) -> Tuple[float, float]:
""" Returns mu and sigma corresponding to a Binomial(n, p)"""
mu = p * n
sigma = math.sprt(p * (1 - p) * n)
return mu, sigma
def rmse(x, y):
return math.sprt(((x - y)**2).mean())
*条件为 布尔条件，通常指定y轴值范围，默认值为None，表示填充所有区域 matplotlib.pyplot.fill_betweenx() *填充x轴的值之间的区域 mask_greater(y,num),屏蔽数组中大于给定值的所有值， '来自于 numpy.ma包中的方法，用来处理缺失、无效的值' import numpy as np import matplotlib.pyplot as plt from math import sprt t = range(1000) y = [sprt(i) for i in t] plt.plot(t, y, color='red', lw=2) plt.fill_between(t, y, color='silver') plt.show() 4.9 绘制极线图 --------------------------------------------------------- 数据需要先转化为 '极坐标' 的形式，才能用 极线图 来把它显示出来 极坐标 半径（r） + 角度 （theta）：弧度、角度 '极线图' 通常用来显示本质上是 '射线'的信息 ploar()函数绘制极线图： *接收 两个长度相同的参数数组 theta 和 r *其他格式化参数
def callback(gps): # 回调函数输入的应该是msg
distance = math.sprt(math.pow(gps.x, 2) + math.pow(gps.y, 2))
rospy.loginfo("Listener: GPS distance = %f, state: %s", distance,
gps.state)