激光测量v2.2，添加测试版注释，删除正式版注释，修改部分数据格式

data_analyse_origin.py

@@ -46,11 +46,11 @@ def result_main():
     return csv_file_path
 
 
-def delete_data(names):
+def delete_data(name):
 
     """
     删除历史分析数据
-    :param names: 删除条件
+    :param name: 删除条件
     :return: csv文件路径
     """
 
@@ -63,14 +63,12 @@ def delete_data(names):
     csv_file_path = os.path.join(data_folder, 'history_data.csv')
     df = pd.read_csv(csv_file_path)
 
-    for name in names:
-        # 检查条件
-        condition = ((df['时间'].str.contains(name[0])) &
-                     (df['场站'].str.contains(name[1])) &
-                     (df['风机编号'].str.contains(name[2])))
+    condition = ((df['时间'].astype(str).str.contains(name[0])) &
+                 (df['场站'].astype(str).str.contains(name[1])) &
+                 (df['风机编号'].astype(str).str.contains(name[2])))
 
-        # 删除满足条件的行
-        df = df[~condition]
+    # 删除满足条件的行
+    df = df[~condition]
     # 如果需要，可以将修改后的 DataFrame 保存回 CSV 文件
     df.to_csv(csv_file_path, index=False)
 
@@ -85,7 +83,9 @@ def history_data(name):
     :return:
     """
 
-    wind_name, turbine_code, time_code = name[1], name[2], name[0]
+    time_code = name[0]
+    wind_name = name[1]
+    turbine_code = name[2]
     # 获取当前程序的绝对路径
     python_interpreter_path = sys.executable
     project_directory = os.path.dirname(python_interpreter_path)
@@ -193,13 +193,13 @@ def data_analyse(path: List[str]):
     max_keys = []
     mean_values = []
     for df in dist_distribute:
-        second_col_min = round(df[df.columns[1]].min(), 2)
-        second_col_max = round(df[df.columns[1]].max(), 2)
+        second_col_min = df[df.columns[1]].min()
+        second_col_max = df[df.columns[1]].max()
         min_row = df[df[df.columns[1]] == second_col_min]
         max_row = df[df[df.columns[1]] == second_col_max]
-        min_values.append(second_col_min)
+        min_values.append(round(second_col_min, 2))
         min_keys.append(round(min_row.iloc[0][df.columns[0]], 2))
-        max_values.append(second_col_max)
+        max_values.append(round(second_col_max, 2))
         max_keys.append(round(max_row.iloc[0][df.columns[0]], 2))
 
     for i in range(3):
@@ -230,7 +230,7 @@ def data_analyse(path: List[str]):
     twist_avg = round((twist_1 + twist_2 + twist_3) / 3, 2)
 
     # 降低给数据采样频率，降低接口负担
-    sampling_num = int(0.01 * sampling_fq_1)
+    sampling_num = int(0.015 * sampling_fq_1)
 
     # 将原始数据的时间列由计时时钟转换为实际时间
     data_tip.iloc[:, 0] = data_tip.iloc[:, 0] / 5000000
@@ -238,9 +238,9 @@ def data_analyse(path: List[str]):
     lowpass_data.iloc[:, 0] = lowpass_data.iloc[:, 0] / 5000000
 
     # 将需要保存到CSV的数据添加到return_list中
-    return_list.append(time_code)
-    return_list.append(wind_name)
-    return_list.append(turbine_code)
+    return_list.append(str(time_code))
+    return_list.append(str(wind_name))
+    return_list.append(str(turbine_code))
     return_list.append(sampling_fq_1)
     return_list.append(pitch_angle_root[0])
     return_list.append(pitch_angle_root[1])
@@ -282,16 +282,16 @@ def data_analyse(path: List[str]):
                 'ydata': lowpass_data['distance_filtered'].tolist()[::sampling_num],
                 'xmax': max(lowpass_data['time'].tolist()),
                 'xmin': min(lowpass_data['time'].tolist()),
-                'ymax': max(lowpass_data['distance_filtered'].tolist()),
-                'ymin': min(lowpass_data['distance_filtered'].tolist())
+                'ymax': max(lowpass_data['distance_filtered'].tolist()) + 0.02,
+                'ymin': min(lowpass_data['distance_filtered'].tolist()) - 0.02
             },
             'fft': {
                 'xdata': fft_x,
                 'ydata': fft_y,
                 'xmax': max(fft_x),
                 'xmin': min(fft_x),
-                'ymax': max(fft_y),
-                'ymin': min(fft_y)
+                'ymax': max(fft_y) + 0.02,
+                'ymin': 0
             }
         },
         'blade_tip': {
@@ -431,13 +431,13 @@ def data_analyse(path: List[str]):
     print('轮毂中心距离' + str(dist_cen))
     print('time_length:' + str(data_root.iloc[-1, 0]))
 
-    plot_data(result_line_tip, 'line', 'data1')
+    # plot_data(result_line_tip, 'line', 'data1')
     # plot_data(result_diff_tip, 'line', 'data_diff_1')
-    plot_data(result_scatter_tip, 'scatter', 'data1')
-    plot_data(result_line_root, 'line', 'data2')
+    # plot_data(result_scatter_tip, 'scatter', 'data1')
+    # plot_data(result_line_root, 'line', 'data2')
     # plot_data(result_diff_root, 'line', 'data_diff_2')
-    plot_data(result_scatter_root, 'scatter', 'data2')
-    plot_data(dist_distribute, 'scatter', 'dist_distribute')
+    # plot_data(result_scatter_root, 'scatter', 'data2')
+    # plot_data(dist_distribute, 'scatter', 'dist_distribute')
 
     return json_output
 
@@ -1041,7 +1041,7 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
     :param blade_cen_dist: 叶片内部距离。
     """
 
-    print('正在进行各周期净空距离计算......')
+    print('正在进行各周期净空距离分布计算......')
     time.sleep(1)
 
     combined_df_sorted = pd.concat([start_points, end_points]).sort_values(by='time')
@@ -1069,11 +1069,8 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
         segment = data_group[(data_group['time'] > start_time) & (data_group['time'] <= end_time)]
         min_distance = segment['distance'].min()
         clearance = np.abs(tower_dist - min_distance - blade_cen_dist[i % 3]) * np.cos(np.deg2rad(v_angle))
-        r_speed = (start_times[i + 2] - start_times[i]) * 3 / 5000000
+        r_speed = round(60 / ((start_times[i + 2] - start_times[i]) * 3 / 5000000), 2)
 
-        # 周期归一化
-        ratio = (end_time - start_time) / normalize_cycle
-        segment.loc[:, 'time'] = (segment['time'] - start_time) / ratio
 
         new_df = pd.DataFrame({
             'r_speed': [r_speed],
@@ -1083,6 +1080,9 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
         # 将结果添加到相应的 turbine 数据框中
         tower_clearance[i % 3] = pd.concat([tower_clearance[i % 3], new_df])
 
+
+    tower_clearance = [df.sort_values(by='r_speed') for df in tower_clearance]
+
     return tower_clearance
 
 

data_clean.py

@@ -36,8 +36,7 @@ def result_main():
     return csv_file_path
 
 
-def delete_data(names):
-
+def delete_data(name):
 
     python_interpreter_path = sys.executable
     project_directory = os.path.dirname(python_interpreter_path)
@@ -47,14 +46,11 @@ def delete_data(names):
     csv_file_path = os.path.join(data_folder, 'history_data.csv')
     df = pd.read_csv(csv_file_path)
 
-    for name in names:
-        
-        condition = ((df['时间'].str.contains(name[0])) &
-                     (df['场站'].str.contains(name[1])) &
-                     (df['风机编号'].str.contains(name[2])))
+    condition = ((df['时间'].astype(str).str.contains(name[0])) &
+                 (df['场站'].astype(str).str.contains(name[1])) &
+                 (df['风机编号'].astype(str).str.contains(name[2])))
 
-        
-        df = df[~condition]
+    df = df[~condition]
     
     df.to_csv(csv_file_path, index=False)
 
@@ -63,7 +59,10 @@ def delete_data(names):
 
 def history_data(name):
 
-    wind_name, turbine_code, time_code = name[1], name[2], name[0]
+
+    time_code = name[0]
+    wind_name = name[1]
+    turbine_code = name[2]
     
     python_interpreter_path = sys.executable
     project_directory = os.path.dirname(python_interpreter_path)
@@ -154,13 +153,13 @@ def data_analyse(path: List[str]):
     max_keys = []
     mean_values = []
     for df in dist_distribute:
-        second_col_min = round(df[df.columns[1]].min(), 2)
-        second_col_max = round(df[df.columns[1]].max(), 2)
+        second_col_min = df[df.columns[1]].min()
+        second_col_max = df[df.columns[1]].max()
         min_row = df[df[df.columns[1]] == second_col_min]
         max_row = df[df[df.columns[1]] == second_col_max]
-        min_values.append(second_col_min)
+        min_values.append(round(second_col_min, 2))
         min_keys.append(round(min_row.iloc[0][df.columns[0]], 2))
-        max_values.append(second_col_max)
+        max_values.append(round(second_col_max, 2))
         max_keys.append(round(max_row.iloc[0][df.columns[0]], 2))
 
     for i in range(3):
@@ -188,15 +187,15 @@ def data_analyse(path: List[str]):
     twist_3 = round(np.abs(pitch_angle_root[2] - pitch_angle_tip[2]), 2)
     twist_avg = round((twist_1 + twist_2 + twist_3) / 3, 2)
 
-    sampling_num = int(0.01 * sampling_fq_1)
+    sampling_num = int(0.015 * sampling_fq_1)
     data_tip.iloc[:, 0] = data_tip.iloc[:, 0] / 5000000
     data_root.iloc[:, 0] = data_root.iloc[:, 0] / 5000000
     lowpass_data.iloc[:, 0] = lowpass_data.iloc[:, 0] / 5000000
 
 
-    return_list.append(time_code)
-    return_list.append(wind_name)
-    return_list.append(turbine_code)
+    return_list.append(str(time_code))
+    return_list.append(str(wind_name))
+    return_list.append(str(turbine_code))
     return_list.append(sampling_fq_1)
     return_list.append(pitch_angle_root[0])
     return_list.append(pitch_angle_root[1])
@@ -238,16 +237,16 @@ def data_analyse(path: List[str]):
                 'ydata': lowpass_data['distance_filtered'].tolist()[::sampling_num],
                 'xmax': max(lowpass_data['time'].tolist()),
                 'xmin': min(lowpass_data['time'].tolist()),
-                'ymax': max(lowpass_data['distance_filtered'].tolist()),
-                'ymin': min(lowpass_data['distance_filtered'].tolist())
+                'ymax': max(lowpass_data['distance_filtered'].tolist()) + 0.02,
+                'ymin': min(lowpass_data['distance_filtered'].tolist()) - 0.02
             },
             'fft': {
                 'xdata': fft_x,
                 'ydata': fft_y,
                 'xmax': max(fft_x),
                 'xmin': min(fft_x),
-                'ymax': max(fft_y),
-                'ymin': min(fft_y)
+                'ymax': max(fft_y) + 0.02,
+                'ymin': 0
             }
         },
         'blade_tip': {
@@ -792,7 +791,7 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
         segment = data_group[(data_group['time'] > start_time) & (data_group['time'] <= end_time)]
         min_distance = segment['distance'].min()
         clearance = np.abs(tower_dist - min_distance - blade_cen_dist[i % 3]) * np.cos(np.deg2rad(v_angle))
-        r_speed = (start_times[i + 2] - start_times[i]) * 3 / 5000000
+        r_speed = round(60 / ((start_times[i + 2] - start_times[i]) * 3 / 5000000), 2)
 
         
         ratio = (end_time - start_time) / normalize_cycle
@@ -806,4 +805,6 @@ def blade_dist_distribute_cal(data_group: pd.DataFrame, start_points: pd.DataFra
         
         tower_clearance[i % 3] = pd.concat([tower_clearance[i % 3], new_df])
 
+    tower_clearance = [df.sort_values(by='r_speed') for df in tower_clearance]
+
     return tower_clearance