add feature

color.py

@@ -77,6 +77,23 @@ def get_color_from_value(
     return tuple(current_color)
 
 
+def get_color_from_value(value: float, color_mapping: Dict[int, Tuple[int, int, int, int]]) -> Tuple[int, int, int, int]:
+    """
+    根据输入的值,从颜色映射字典中查找对应的颜色
+    
+    Parameters:
+        value: 输入值
+        color_mapping: 颜色映射字典
+        
+    Returns:
+        Tuple[int, int, int, int]: RGBA颜色元组
+    """
+    # 找到小于等于输入值的最大分界值
+    max_key = max([k for k in color_mapping if k <= value])
+    
+    return color_mapping[max_key]
+
+
 # 使用示例:
 if __name__ == "__main__":
     # 生成原始序列

read_dat.py

@@ -22,23 +22,33 @@ def read_binary_file(file_path):
         date = struct.unpack("<d", date_bytes)[0]
         print(f"Date: {date}")
 
-        while True:
-            # print("index", index)
-            if index > 335:
-                break
+        # 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}")
+        print(f"Time: {time}")
+        for i in range(column_count_int):
+            print(i)
+            data_bytes = file.read(8)
+            data = struct.unpack("<d", data_bytes)[0]
+            print(f"Data: {data}")
+        # while True:
+        #     # print("index", index)
+        #     if index > 335:
+        #         break
+        #     time_bytes = file.read(8)
+        #     time = struct.unpack("<d", time_bytes)[0]
+        #     # 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
 
     return df