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add feature

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baol 2024-11-28 17:18:21 +08:00
parent 77b6e0c14f
commit 639ae0cd8e
4 changed files with 60 additions and 48 deletions
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4
.gitignore vendored
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@ -1 +1,3 @@
.venv .venv
__pycache__

20
dat_2_pg.py
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@ -1,4 +1,5 @@
import psycopg import psycopg
from datetime import datetime, timedelta
from read_dat import read_binary_file from read_dat import read_binary_file
@ -7,22 +8,23 @@ df = read_binary_file(file_path)
# PostgreSQL连接配置 # PostgreSQL连接配置
postgres_config = { postgres_config = {
"host": "192.168.3.12", "host": "111.231.71.81",
"user": "prepare", "user": "lzflood",
"password": "preparepassword", "password": "lzfloodpassword",
"dbname": "preparedb", "dbname": "lzflooddb",
} }
postgres_conn = psycopg.connect(**postgres_config) postgres_conn = psycopg.connect(**postgres_config)
postgres_cursor = postgres_conn.cursor() postgres_cursor = postgres_conn.cursor()
for index, row in df.iterrows(): for index, row in df.iterrows():
current_date = datetime(2010, 7, 20) + timedelta(hours=index)
for i in range(len(row)): for i in range(len(row)):
postgres_cursor.execute( postgres_cursor.execute(
"INSERT INTO river_flow (river_id, created_at, runoff) VALUES (%s, %s, %s)", "INSERT INTO river_runoff (river_id, created_at, value) VALUES (%s, %s, %s)",
(i + 1, index, row[i]), (i + 1, current_date, row[i] / 86400),
) )
print("index, i", index, i)
postgres_conn.commit() postgres_conn.commit()
postgres_cursor.close() postgres_cursor.close()
postgres_conn.close() postgres_conn.close()

82
read_dat.py
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@ -1,3 +1,4 @@
from datetime import datetime, timedelta
import struct import struct
import pandas as pd import pandas as pd
@ -19,23 +20,31 @@ def read_binary_file(file_path):
# 读取接下来的8个字节，表示日期 # 读取接下来的8个字节，表示日期
date_bytes = file.read(8) date_bytes = file.read(8)
date = struct.unpack("<d", date_bytes)[0] date_float = struct.unpack("<d", date_bytes)[0]
print(f"Date: {date}") date_str = str(int(date_float))
year = int(date_str[:4])
month = int(date_str[4:6])
day = int(date_str[6:8])
# start_date = datetime(year, month, day)
# print(f"Date: {datetime(year, month, day)}")
# 第一个时间戳所有数据 # 第一个时间戳所有数据
# time_bytes = file.read(8)
# time = struct.unpack("<d", time_bytes)[0]
# print(f"Time: {time}")
# hours = int(time / 60)
# first_date = start_date + timedelta(hours=hours)
time_bytes = file.read(8) # print(f"Minute: {minute}")
time = struct.unpack("<d", time_bytes)[0] # data_size = column_count_int * 8 # 每个数据点8个字节
print(f"Time: {time}") # file.seek(data_size, 1)
data_size = column_count_int * 8 # 每个数据点8个字节 # time1_bytes = file.read(8)
file.seek(data_size, 1) # time1 = struct.unpack("<d", time1_bytes)[0]
time1_bytes = file.read(8) # print(f"Time: {time1}")
time1 = struct.unpack("<d", time1_bytes)[0] # file.seek(data_size, 1)
print(f"Time: {time1}") # time2_bytes = file.read(8)
file.seek(data_size, 1) # time2 = struct.unpack("<d", time2_bytes)[0]
time2_bytes = file.read(8) # print(f"Time: {time2}")
time2 = struct.unpack("<d", time2_bytes)[0]
print(f"Time: {time2}")
# for i in range(column_count_int): # for i in range(column_count_int):
# print(i) # print(i)
# if i > 10: # if i > 10:
@ -43,29 +52,30 @@ def read_binary_file(file_path):
# data_bytes = file.read(8) # data_bytes = file.read(8)
# data = struct.unpack("<d", data_bytes)[0] # data = struct.unpack("<d", data_bytes)[0]
# print(f"Data: {data}") # print(f"Data: {data}")
# while True: while True:
# # print("index", index) # print("index", index)
# if index > 335: if index > 335:
# break break
# time_bytes = file.read(8) time_bytes = file.read(8)
# time = struct.unpack("<d", time_bytes)[0] time = struct.unpack("<d", time_bytes)[0]
# # print(f"time: {time}") # print(f"time: {time}")
# # 读取数据矩阵
# data_size = column_count_int * 8 # 每个数据点8个字节
# data_bytes = file.read(data_size)
# data_matrix = struct.unpack(f"<{column_count_int}d", data_bytes)
# data_matrix = [data_matrix]
# new_df = pd.DataFrame(data_matrix)
# df = pd.concat([df, new_df], ignore_index=True)
# # print(f"Data Matrix: {data_matrix}")
# index = index + 1
# 读取数据矩阵
data_size = column_count_int * 8 # 每个数据点8个字节
data_bytes = file.read(data_size)
data_matrix = struct.unpack(f"<{column_count_int}d", data_bytes)
data_matrix = [data_matrix]
new_df = pd.DataFrame(data_matrix)
df = pd.concat([df, new_df], ignore_index=True)
# print(f"Data Matrix: {data_matrix}")
index = index + 1
print("index", index)
return df return df
# 使用函数读取文件 if __name__ == "__main__":
file_path = "lz.out/lz.rivqdown.dat" # 使用函数读取文件
df = read_binary_file(file_path) file_path = "lz.out/lz.rivqdown.dat"
print(df.shape) df = read_binary_file(file_path)
print(df.head()) print(df.shape)
print(df.head())

2
requirements.txt
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@ -1,4 +1,2 @@
pandas pandas
gdal
geopandas
psycopg[binary] psycopg[binary]