def main():
start = time.time()
# 样例数据
# 实际使用可以直接使用args = command_parse()从命令行获取
args = command_parse()
# temp = [
# # "-n",
# # "16",
# "-f",
# "tcp",
# "-ip",
# "127.0.0.1-127.0.0.5",
# "-v",
# "-m",
# "proc",
# "-w",
# "port_by_proc.json",
# "-p",
# "100-150"
# ]
# args = command_parse(temp)
host_ports = ip_port_parse(args.ip,
args.p,
is_port=(args.f==PORT_SCANNER))
print('程序开始运行...')
available_ip_ports = run_scanner(concurrent_mode=args.m,
concurrent_num=args.n,
scanner_type=args.f,
host_ports=host_ports,)
if args.w:
save_as_json(available_ip_ports, args.w)
if args.v:
print(f'程序消耗总时间：{time.time()-start}s')
def get_acc(vel):
sl = ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + vel +
"_low.h5")
sh = ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + vel +
"_high.h5")
acc_low = group_avarage_acc(sl, group_by_x_n_z)
acc_to_save = {}
for key in acc_low.keys():
acc_to_save[key] = [acc_low[key][0].tolist(), acc_low[key][1]]
tls.save_as_json(acc_to_save, "accel_by_x_and_z_" + vel + "_lower")
print("Lower done")
acc_high = group_avarage_acc(sh, group_by_x_n_z)
acc_to_save = {}
for key in acc_high.keys():
acc_to_save[key] = [acc_high[key][0].tolist(), acc_high[key][1]]
tls.save_as_json(acc_to_save, "accel_by_x_and_z_" + vel + "_higher")
print("Higher done")
m = tls.merge_dict(
tls.read_json("accel_by_x_and_z_" + vel + "_higher"),
tls.read_json("accel_by_x_and_z_" + vel + "_lower"),
lambda a, b: [((np.array(a[0]) * a[1] + np.array(b[0]) * b[1]) /
(a[1] + b[1])).tolist(), a[1] + b[1]])
tls.save_as_json(m, "accel_by_x_and_z_" + vel)
print("DONE")
def get_velocity_by_loc_w(speed, groups=1, prefix=""):
higher_avg_vel = group_avarage_velocity_w(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_high.h5"),
tls.group_by_location,
groups=groups)
print("Higher vel calculated")
higer_dic = {}
for key in higher_avg_vel.keys():
higer_dic[str(key).replace("-0.0", "0.0")] = higher_avg_vel[key]
tls.save_as_json(
higer_dic, "raupach_data/" + prefix + "avg_vel_by_loc_higher_" + speed)
lower_avg_vel = group_avarage_velocity_w(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_low.h5"),
tls.group_by_location,
groups=groups)
print("Lower vel calculated")
lower_dic = {}
for key in lower_avg_vel.keys():
lower_dic[str(key).replace("-0.0", "0.0")] = lower_avg_vel[key]
tls.save_as_json(
lower_dic, "raupach_data/" + prefix + "avg_vel_by_loc_lower_" + speed)
merged = tls.merge_dict(lower_dic, higer_dic, merge_long_dict)
tls.save_as_json(merged,
"raupach_data/" + prefix + "avg_vel_by_loc_" + speed)
def get_average_velocity(speed):
low_speed = group_avarage_velocity(
ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + speed +
"_low.h5"),
lambda t, i: tls.group_by_height(t, i, 0, 0.18, 0.01))
high_speed = group_avarage_velocity(
ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + speed +
"_high.h5"),
lambda t, i: tls.group_by_height(t, i, 0, 0.18, 0.01))
tls.save_as_json(low_speed,
"cd_data/avg_vel_by_height_" + speed + "_lower")
tls.save_as_json(high_speed,
"cd_data/avg_vel_by_height_" + speed + "_higher")
mrg = tls.merge_dict(
low_speed, high_speed,
lambda a, b: [((np.array(a[0]) * a[1] + np.array(b[0]) * b[1]) /
(a[1] + b[1])).tolist(), a[1] + b[1]])
tls.save_as_json(mrg, "cd_data/avg_vel_by_height_" + speed)
def auto_disp_stress_calculator(speed):
high_stress = get_dispersive_stress(
ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + speed +
"_high.h5"), "raupach_data/avg_vel_by_loc_" + speed,
"cd_data/avg_vel_by_height_" + speed)
print("Stress higher calculated")
print(high_stress)
low_stress = get_dispersive_stress(
ft.Scene("/home/ron/Desktop/Alexey/the_dataset/traj_" + speed +
"_low.h5"), "raupach_data/avg_vel_by_loc_" + speed,
"cd_data/avg_vel_by_height_" + speed)
print("Stress lower calculeted")
print(low_stress)
tls.save_as_json(high_stress, "raupach_data/disp_stress_higher_" + speed)
tls.save_as_json(low_stress, "raupach_data/disp_stress_lower_" + speed)
merged_stress = tls.merge_dict(
high_stress, low_stress, lambda a, b: [(a[0] * a[1] + b[0] * b[1]) /
(a[1] + b[1]), a[1] + b[1]])
tls.save_as_json(merged_stress, "raupach_data/disp_stress_" + speed)
from tools import save_as_json
start = time.time()
# node_list, edge_dict = analyse("zdemo/20210319112759.json")
node_list, edge_dict = analyse("zdemo/20210525170638.json")
# print(edge_dict)
attr_dict, attr_list = attribute(node_list)
forward_list, output_id = forward_analyse(attr_dict, edge_dict)
template = {
"MyModel": {
"Path": "./demo/model.py",
"Name": ["Model"],
"Init": [" "],
"Super": ["Model"],
"Attribute": attr_list,
"Input": ["x"],
"Forward": forward_list,
"Output": [output_id],
"Function": ["Model"]
}
}
end = time.time()
json_data = save_as_json(template, "./templates/pytorch/demo/config.json")
# print(json_data)
print(end - start)
def auto_rey_stress_err_calculator(speed, skip_vel=True):
if not skip_vel:
get_velocity_by_loc(speed)
high_stress = get_reynolds_stress_errors(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_high.h5"),
"raupach_data/goruped_u_avg_vel_by_loc_" + speed,
"raupach_data/goruped_w_avg_vel_by_loc_" + speed, True)
print("Stress higher calculated")
print(high_stress)
low_stress = get_reynolds_stress_errors(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_low.h5"),
"raupach_data/goruped_u_avg_vel_by_loc_" + speed,
"raupach_data/goruped_w_avg_vel_by_loc_" + speed, True)
print("Stress lower calculeted")
print(low_stress)
tls.save_as_json(high_stress,
"raupach_data/rey_stress_higher_lerr_" + speed)
tls.save_as_json(low_stress, "raupach_data/rey_stress_lower_lerr_" + speed)
merged_stress = tls.merge_dict(
high_stress, low_stress, lambda a, b: [(a[0] * a[1] + b[0] * b[1]) /
(a[1] + b[1]), a[1] + b[1]])
tls.save_as_json(merged_stress, "raupach_data/rey_stress_lerr_" + speed)
high_stress = get_reynolds_stress_errors(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_high.h5"),
"raupach_data/goruped_u_avg_vel_by_loc_" + speed,
"raupach_data/goruped_w_avg_vel_by_loc_" + speed, False)
print("Stress higher calculated")
print(high_stress)
low_stress = get_reynolds_stress_errors(
ft.Scene("C:/Users/theem/Desktop/Projects/alpha offline/Data/traj_" +
speed + "_low.h5"),
"raupach_data/goruped_u_avg_vel_by_loc_" + speed,
"raupach_data/goruped_w_avg_vel_by_loc_" + speed, False)
print("Stress lower calculeted")
print(low_stress)
tls.save_as_json(high_stress,
"raupach_data/rey_stress_higher_herr_" + speed)
tls.save_as_json(low_stress, "raupach_data/rey_stress_lower_herr_" + speed)
merged_stress = tls.merge_dict(
high_stress, low_stress, lambda a, b: [(a[0] * a[1] + b[0] * b[1]) /
(a[1] + b[1]), a[1] + b[1]])
tls.save_as_json(merged_stress, "raupach_data/rey_stress_herr_" + speed)
import torch
import time
import neural_genesis
from framework_analyse.class_info_analyse import get_attr_init_dict
from tools import save_as_json
if __name__ == '__main__':
# start = time.time_ns()
start = time.time()
info_dict = get_attr_init_dict(neural_genesis.nn)
# end = time.time_ns()
end = time.time()
save_as_json(info_dict, './torchModel.json')
print(end - start)