175 lines
5.7 KiB
Python
175 lines
5.7 KiB
Python
""" this file includs global variables shared between tab """
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import numpy as np
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import pandas as pd
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import datetime
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# --- load raw data ---
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ABS_name = []
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path_BS_raw_data = []
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filename_BS_raw_data = []
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BS_raw_data = [] # BS raw data : all measurement (go and back)
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depth = []
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depth_2D = []
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freq = []
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freq_text = []
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time = []
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path_BS_noise_data = ""
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filename_BS_noise_data = ""
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BS_noise_raw_data = np.array([]) # BS noise raw data : BS signal listen
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BS_noise_averaged_data = np.array([]) # BS noise raw data averaged (array has the same shape than BS_raw_data shape)
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date = []
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date_noise = []
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hour = []
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hour_noise = []
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nb_profiles = []
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nb_profiles_per_sec = []
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nb_cells = []
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cell_size = []
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pulse_length = []
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nb_pings_per_sec = []
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nb_pings_averaged_per_profile = []
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kt_read = []
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kt_corrected = []
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gain_rx = []
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gain_tx = []
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SNR_data = np.array([]) # SNR is computed with BS_noise_averaged_data
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time_snr = np.array([])
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# --- reshape raw data for table of values in Acoustic Data tab ---
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time_reshape = []
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time_snr_reshape = np.array([])
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depth_reshape = []
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BS_raw_data_reshape = []
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SNR_reshape = np.array([]) # snr is reshape to be included in table of values in acoustic data tab
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DataFrame_acoustic = pd.DataFrame()
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# --- Processed data in Acoustic Data Tab and used in Acoustic processing tab ---
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tmin = [] # minimum boundary of time (spin box tmin)
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tmin_snr = np.array([])
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tmax = [] # maximum boundary of time (spin box tmin)
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tmax_snr = np.array([])
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rmin = []
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rmax = []
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BS_cross_section = [] # BS data limited with tmin and tmax values of spin box
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# BS_data = stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]
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BS_stream_bed = [] # BS_data_section = BS data in the section. Values NaN outside the bottom of the section are deleted
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BS_noise_cross_section = np.array([]) # BS_noise_cros_section = BS_noise_data[:, :, tmin:tmax] (former Noise_data)
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SNR_cross_section = np.array([]) # SNR_data = snr[:, :, tmin:tmax]
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SNR_stream_bed = np.array([])
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time_cross_section = []
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t_snr = np.array([])
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depth_cross_section = []
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depth_bottom = []
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val_bottom = []
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ind_bottom = []
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freq_bottom_detection = []
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depth_bottom_detection_1st_int_area = []
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# --- Processed data in Signal Processing Tab ---
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# BS_cross_section_SNR_filter = np.array([[[]]]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
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# BS_cross_section_averaged = np.array([[[]]]) # BS data averaged - bottom is not detected
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# BS_cross_section_averaged_SNR = np.array([[[]]]) # BS data averaged and filtered with SNR - bottom is not detected
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# BS_stream_bed_SNR_filter = np.array([]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
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# BS_stream_bed_averaged = np.array([]) # BS data averaged - bottom is detected
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# BS_stream_bed_averaged_SNR = np.array([]) # BS data averaged and filtered with SNR - bottom is detected
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BS_cross_section_pre_process_SNR = np.array([[[]]]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
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BS_cross_section_pre_process_average = np.array([[[]]]) # BS data averaged - bottom is not detected
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BS_cross_section_pre_process_SNR_average = np.array([[[]]]) # BS data averaged and filtered with SNR - bottom is not detected
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BS_stream_bed_pre_process_SNR = np.array([]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
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BS_stream_bed_pre_process_average = np.array([]) # BS data averaged - bottom is detected
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BS_stream_bed_pre_process_SNR_average = np.array([]) # BS data averaged and filtered with SNR - bottom is detected
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time_average = np.array([])
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SNR_data_average = np.array([]) # SNR data computed with BS signal averaged (not with BS raw signal)
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water_attenuation = 0
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sediment_attenuation = 0
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FCB = np.array([])
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lin_reg = tuple()
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# --- Sample Data ---
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samples = []
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fine_sediment_path = ""
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fine_sediment_filename = ""
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fine_sediment_columns = []
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sample_distance_from_bank = np.array([]) # distance from left bank (m)
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sample_depth = np.array([]) # depth (m)
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sample_time = np.array([])
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radius_grain = np.array([]) # grain radius (um)
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Ctot_fine = np.array([]) # Total concentration (g/L)
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D50_fine = np.array([]) # median diameter (um)
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frac_vol_fine = np.array([]) # Volume fraction (%)
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frac_vol_fine_cumul = np.array([]) # Cumulated volume fraction (%)
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sand_sediment_path = ""
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sand_sediment_filename = ""
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sand_sediment_columns = []
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Ctot_sand = np.array([]) # Total concentration (g/L)
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D50_sand = np.array([]) # median diameter (um)
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frac_vol_sand = np.array([]) # Volume fraction (%)
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frac_vol_sand_cumul = np.array([]) # Cumulated volume fraction (%)
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Ctot_fine_per_cent = np.array([])
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Ctot_sand_per_cent = np.array([])
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# --- Acoustic inversion method ---
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temperature = []
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water_attenuation = np.array([])
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water_velocity = 0
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X_exponent = np.array([])
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zeta = np.array([])
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# kt_corrected = np.array([])
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# kt_corrected_2D = np.array([])
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# kt_corrected_3D = np.array([])
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J_cross_section = np.array([])
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J_stream_bed = np.array([[[]]])
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frequencies_to_compute_VBI = np.array([])
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VBI_cross_section = np.array([])
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VBI_stream_bed = np.array([[[]]])
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frequency_to_compute_SSC = 0
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ks = 0
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sv = 0
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alpha_s = 0
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SSC_fine = np.array(())
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SSC_sand = np.array([])
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# --- Save study ---
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acoustic_data = []
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dirname_save_as = ""
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filename_save_as = ""
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dirname_open = ""
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filename_open = ""
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read_table_trigger = 0
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