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