132 lines
3.8 KiB
Python
132 lines
3.8 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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path_BS_raw_data = ""
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filename_BS_raw_data = ""
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BS_raw_data = np.array([]) # BS raw data : all measurement (go and back)
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r = np.array([])
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r_2D = np.array([])
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freq = np.array([])
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freq_text = list()
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time = np.array([])
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path_BS_noise_data = ""
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filename_BS_noise_data = ""
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BS_noise_data = np.array([]) # BS noise 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 = 0
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nb_profiles_per_sec = 0.0
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nb_cells = 0
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cell_size = 0.0
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pulse_length = 0.0
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nb_pings_per_sec = 0
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nb_pings_averaged_per_profile = 0.0
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kt = np.array([])
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gain_rx = np.array([])
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gain_tx = np.array([])
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snr = np.array([]) # snr is computed with BS_noise_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 = np.array([])
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r_reshape = np.array([])
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BS_raw_data_reshape = np.array([])
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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 = np.array([]) # minimum boundary of time (spin box tmin)
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tmax = np.array([]) # maximum boundary of time (spin box tmin)
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BS_data = np.array([]) # BS data limited with tmin and tmax values of spin box
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BS_data_section = np.array([]) # BS data in the section. Values NaN outside the bottom of the section are deleted
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Noise_data = np.array([]) # Noise_data = BS_noise_data[:, :, tmin:tmax]
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SNR_data = np.array([]) # SNR_data = snr[:, :, tmin:tmax]
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t = np.array([])
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r_bottom = np.array([])
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val_bottom = np.array([])
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ind_bottom = np.array([])
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freq_bottom_detection = 0
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# --- Processed data in Signal Processing Tab ---
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BS_data_section_SNR_filter = np.array([]) # BS data filtered with SNR values (remove point if SNR < value)
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BS_data_section_averaged = np.array([]) # BS data averaged
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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 = 0
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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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frequencies_to_compute_VBI = np.array([])
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VBI_cross_section = np.array([])
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frequency_to_compute_SSC = 0
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ks = 0
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SSC_fine = np.array(())
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SSC_sand = np.array([])
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