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data.py
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data.py
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import csv
import os
import random
from torch.utils.data import DataLoader, Dataset
from configs import parse_signal_args
import torch
import numpy as np
args = parse_signal_args()
# random seed
fix_seed = args.random_seed
random.seed(fix_seed)
def load_data():
all_data = []
bd_data = [] # bd所有的卫星数据
gal_data = []
gps_data = []
n = 4 # 四天
file_bd = os.listdir("data/BD/")
file_gps = os.listdir("data/GPS/")
file_gal = os.listdir("data/GAL/")
# 读取所有的BD数据
for i in range(4):
bd = os.listdir("data/BD/" + file_bd[i])
length1 = len(bd) - 1 # 每一个文件中的卫星个数文件个数，BD1数据文件中有几个csv存储数据 还有一个是readme文件
for j in range(length1):
data = []
local1 = "data/BD/" + file_bd[i] + "/" + bd[j] # 每一个卫星的位置
# # 打开CSV文件进行读取
with open(local1, 'r', newline='') as file:
csv_reader = csv.reader(file)
# 逐行读取数据并存储在data列表中
for row in csv_reader:
if len(row) == 0:
continue
row = stringTofloat(row)
for q in range(len(row)):
row[q] = round(row[q], 2)
data.append(row)
if len(data) != 0:
bd_data.append(data) # 存放所有的BD卫星的数据
all_data.append(data)
print(f"BD数据加载完成！一共{len(bd_data)}个卫星")
# 读取所有的GAL数据
for i in range(4):
gal = os.listdir("data/GAL/" + file_gal[i])
length2 = len(gal) - 1 # 每一个文件中的卫星个数文件个数，BD1数据文件中有几个csv存储数据 还有一个是readme文件
for j in range(length2):
data = []
local2 = "data/GAL/" + file_gal[i] + "/" + gal[j]
# # 打开CSV文件进行读取
with open(local2, 'r', newline='') as file:
csv_reader = csv.reader(file)
# 逐行读取数据并存储在data列表中
for row in csv_reader:
row = stringTofloat(row)
data.append(row)
if len(data) != 0:
gal_data.append(data)
all_data.append(data)
print(f"GAL数据加载完成！一共{len(gal_data)}个卫星")
# 读取GPS数据
for i in range(4):
gps = os.listdir("data/GPS/" + file_gps[i])
# print(len(bd))
length3 = len(gps) - 1 # 每一个文件中的卫星个数文件个数，BD1数据文件中有几个csv存储数据 还有一个是readme文件
for j in range(length3):
data = []
local3 = "data/GPS/" + file_gps[i] + "/" + gps[j]
# # 打开CSV文件进行读取
with open(local3, 'r', newline='') as file:
csv_reader = csv.reader(file)
# 逐行读取数据并存储在data列表中
for row in csv_reader:
row = stringTofloat(row)
data.append(row)
if len(data) != 0:
gps_data.append(data)
all_data.append(data)
print(f"GPS数据加载完成！一共{len(gps_data)}个卫星")
print(f"一共{len(all_data)}个卫星")
# bd_data = random.sample(bd_data, len(bd_data))
# gal_data = random.sample(gal_data, len(gal_data))
# gps_data = random.sample(gps_data, len(gps_data))
train_data = []
l = 1
train_data.append(bd_data[:int(l * len(bd_data))])
train_data.append(gal_data[:int(l * len(gal_data))])
train_data.append(gps_data[:int(l * len(gps_data))])
tr_data = []
for i in range(len(train_data)):
for l1 in train_data[i]:
tr_data.append(l1)
train_data = tr_data
print(f"训练：{len(train_data)}")
training = []
train = []
test = []
L = args.seq_len
for ls in train_data:
sig = []
for j in range(0, len(ls), L):
if (j + L) > len(ls):
break
patch = ls[j:j + L]
# if len(patch) != L:
# break
sig.append(patch)
for t in range(int(0.9*len(sig))):
training.append(sig[t])
for r in range(int(0.9*len(sig)),len(sig)):
test.append(sig[r])
train = training
# training = random.sample(training, len(training))
# print(len(training))
# train = training[:int(0.85*len(training))]
# test = training[int(0.85*len(training)):]
print(len(train))
print(len(test))
# for ls in test_data:
# for j in range(0, len(ls), L):
# if (j + L) > len(ls):
# break
# patch = ls[j:j + L]
# # if len(patch) != L:
# # break
# test.append(patch)
# print(len(test))
train_x, train_y = get_input(train)
test_x, test_y = get_input(test)
data_train = DataSet(train_x, train_y)
data_test = DataSet(test_x, test_y)
#
torch.save(data_train, 'data/dataset_train')
torch.save(data_test, 'data/dataset_test')
# train_loader = DataLoader(data_train, batch_size=args.batch_size)
# test_loader = DataLoader(data_test, batch_size=args.batch_size)
# for i, (x, label) in enumerate(train_loader):
# print(x.shape)
# print(label.shape)
# return data_train, data_test
def get_input(X_):
inputs = []
y = []
for x in X_: # L
i = []
v = []
for ss in x:
# print(len(ss))
j = []
j.append(ss[0])
j.append(ss[1])
j.append(ss[2])
j.append(ss[-2])
v = [ss[-1]]
i.append(j)
y.append(v)
inputs.append(i)
return torch.tensor(np.asarray(inputs).astype(np.float32)), torch.LongTensor(np.asarray(y))
# return inputs, y
class DataSet(Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
def __getitem__(self, item):
return self.x[item], self.y[item]
def __len__(self):
return len(self.x)
def stringTofloat(string_list):
s = []
s.append([float(i) for i in string_list])
s = s[0]
return s
load_data()