add feature

.gitignore

@@ -1 +1,3 @@
 .venv
+
+__pycache__

dat_2_pg.py

@@ -1,4 +1,5 @@
 import psycopg
+from datetime import datetime, timedelta
 
 from read_dat import read_binary_file
 
@@ -7,22 +8,23 @@ df = read_binary_file(file_path)
 
 # PostgreSQL连接配置
 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_cursor = postgres_conn.cursor()
 
 for index, row in df.iterrows():
+    current_date = datetime(2010, 7, 20) + timedelta(hours=index)
     for i in range(len(row)):
         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_cursor.close()
 postgres_conn.close()

read_dat.py

@@ -1,3 +1,4 @@
+from datetime import datetime, timedelta
 import struct
 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
         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
+        # 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):
         #     print(i)
         #     if i > 10:
@@ -43,27 +52,28 @@ def read_binary_file(file_path):
         #     data_bytes = file.read(8)
         #     data = struct.unpack("<d", data_bytes)[0]
         #     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
 
 
+if __name__ == "__main__":
     # 使用函数读取文件
     file_path = "lz.out/lz.rivqdown.dat"
     df = read_binary_file(file_path)

requirements.txt

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