def get_account_info(cls, name):
"""
读取账号
:return: dict
"""
account = read_data("账号.json") # 去读所有账号
if name is account.keys():
return account(name)
class TestLogin(unittest.TestCase):
# 初始化
def setUp(self):
# 获取apilogin对象
self.login = ApiLogin()
# 登录测试接口方法
@parameterized.expand(read_data("login.json"))
def test_login(self, mobile, password):
# 调用登录接口
result = self.login.api_login(mobile, password)
print(result.json())
# 断言
assert_common(self, result)
# 提取token值
token = result.json().get("data")
# 追加到api公共变量
api.headers["Authorization"] = "Bearer " + token
print(api.headers)
def build_data(key):
data1 = []
data1.append(tuple(read_data("address.yaml").get(key).values()))
return data1
import numpy as np
from tools.plot_state import plot_state
from tools.read_data import read_data
from tools.read_world import read_world
from prediction_step import prediction_step
from correction_step import correction_step
if __name__ == "__main__":
# Read world data, i.e. landmarks
landmarks = read_world('./data/world.dat')
# Read sensor readings, i.e. odometry and range-bearing sensor
data = read_data('./data/sensor_data.dat')
n_landmarks = len(landmarks)
# observed_landmarks is a vector that keeps track of which landmarks have been observed so far.
# observed_landmarks[i] will be true if the landmark with id = i has been observed at some point by the robot
observed_landmarks = [False] * n_landmarks
# Initialize belief:
# mu: 2N+3x1 vector representing the mean of the normal distribution
# The first 3 components of mu correspond to the pose of the robot,
# and the landmark poses (xi, yi) are stacked in ascending id order.
# sigma: (2N+3)x(2N+3) covariance matrix of the normal distribution
mu = np.zeros(2 * n_landmarks + 3)
rob_sigma = np.zeros((3, 3))
rob_map_sigma = np.zeros((3, 2 * n_landmarks))
map_sigma = 1e10 * np.eye(2 * n_landmarks)
# Construct a (3+3*n_landmarks, 3+3*n_landmarks) matrix
def read_data(cls):
return read_data("data.yaml")