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")