激光测量v2.5.1，叶片轮廓分组块大小计算取消写死，作为输入变量

data_clean.py

@@ -89,6 +89,7 @@ def data_analyse(path: List[str]):
     min_difference = 1.5  
     angle_cone = float(path[2])
     axial_inclination = float(path[3])
+    group_length = [10000, 30000, 5000]
     return_list = []
 
     wind_name, turbine_code, time_code, sampling_fq, angle_nan, angle_cen = find_param(locate_file)
@@ -121,11 +122,11 @@ def data_analyse(path: List[str]):
     lowpass_data, fft_x, fft_y, tower_freq, tower_max = ff.process_fft(filtered_data_cen, sampling_fq)
 
     result_line_tip, result_scatter_tip, border_rows_tip, cycle_len_tip, min_tip \
-        = data_normalize(filtered_data_tip, start_tip, end_tip)
+        = data_normalize(filtered_data_tip, start_tip, end_tip, group_length[0])
     result_line_root, result_scatter_root, border_rows_root, cycle_len_root, min_root \
-        = data_normalize(filtered_data_root, start_root, end_root)
+        = data_normalize(filtered_data_root, start_root, end_root, group_length[1])
     result_line_nan, result_scatter_nan, border_rows_nan, cycle_len_nan, min_nan \
-        = data_normalize(filtered_data_nan, start_nan, end_nan)
+        = data_normalize(filtered_data_nan, start_nan, end_nan, group_length[2])
 
     result_avg_tip, result_diff_tip = blade_shape(result_line_tip)
     result_avg_root, result_diff_root = blade_shape(result_line_root)
@@ -507,7 +508,7 @@ def cycle_calculate(data_group: pd.DataFrame, noise_threshold: float, min_distan
     return start_points, end_points, filtered_data
 
 
-def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_points: pd.DataFrame) \
+def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_points: pd.DataFrame, group_len: int) \
         -> Tuple[List[pd.DataFrame], List[pd.DataFrame], List[pd.DataFrame], int, list]:
 
 
@@ -551,7 +552,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
     turbines_scattered = []
     min_list = []
     sd_time = [-1, -1]
-    time_list = list(range(0, normalize_cycle, 9001))
+    time_list = list(range(0, normalize_cycle, group_len))
 
     for turbine in turbines:
         
@@ -561,7 +562,7 @@ def data_normalize(data_group: pd.DataFrame, start_points: pd.DataFrame, end_poi
         first_time = turbine_sorted['time'].iloc[0]
 
         
-        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), 9001))
+        bins = list(range(int(first_time), int(turbine_sorted['time'].max()), group_len))
         
         
         grouped = turbine_sorted.groupby(pd.cut(turbine_sorted['time'], bins=bins, right=False))