acoused/settings.py

""" this file includs global variables shared between tab """

import numpy as np
import pandas as pd
import datetime

# --- load raw data ---

ABS_name = []

path_BS_raw_data = []
filename_BS_raw_data = []
BS_raw_data = []      # BS raw data : all measurement (go and back)
depth = []
depth_2D = []
freq = []
freq_text = []
time = []

path_BS_noise_data = []
filename_BS_noise_data = []
BS_noise_raw_data = []            # BS noise raw data : BS signal listen
BS_noise_averaged_data = []       # BS noise raw data averaged (array has the same shape than BS_raw_data shape)

noise_method = []
noise_value = []
SNR_filter_value = []
Nb_cells_to_average_BS_signal = []
data_preprocessed = []

date = []
date_noise = []
hour = []
hour_noise = []
nb_profiles = []
nb_profiles_per_sec = []
nb_cells = []
cell_size = []
pulse_length = []
nb_pings_per_sec = []
nb_pings_averaged_per_profile = []
kt_read = []
kt_corrected = []
gain_rx = []
gain_tx = []

SNR_raw_data = []              # SNR is computed with BS_noise_averaged_data
time_noise = []

# --- reshape raw data for table of values in Acoustic Data tab ---
time_reshape = []
time_snr_reshape = np.array([])
depth_reshape = []
BS_raw_data_reshape = []
SNR_reshape = np.array([])      # snr is reshape to be included in table of values in acoustic data tab
DataFrame_acoustic = pd.DataFrame()

# --- Processed data in Acoustic Data Tab and used in Acoustic processing tab ---
tmin = []                # minimum boundary of time (spin box tmin)
tmin_snr = np.array([])
tmax = []                 # maximum boundary of time (spin box tmin)
tmax_snr = np.array([])
rmin = []
rmax = []
BS_cross_section = []               # BS data limited with tmin and tmax values of spin box

BS_stream_bed = []                  # BS_data_section = BS data in the section. Values NaN outside the bottom of the section are deleted
# BS_noise_cross_section = []         # BS_noise_cross_section = BS_noise_data[:, :, tmin:tmax]  (former Noise_data)
SNR_cross_section = []              # SNR_data = snr[:, :, tmin:tmax]
SNR_stream_bed = []
time_cross_section = []
time_snr = []
depth_cross_section = []
depth_bottom = []
val_bottom = []
ind_bottom = []
freq_bottom_detection = []
depth_bottom_detection_1st_int_area = []

# --- Processed data in Signal Processing Tab ---
# BS_cross_section_SNR_filter = np.array([[[]]])      # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
# BS_cross_section_averaged = np.array([[[]]])        # BS data averaged - bottom is not detected
# BS_cross_section_averaged_SNR = np.array([[[]]])        # BS data averaged and filtered with SNR - bottom is not detected

# BS_stream_bed_SNR_filter = np.array([])             # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
# BS_stream_bed_averaged = np.array([])               # BS data averaged - bottom is detected
# BS_stream_bed_averaged_SNR = np.array([])               # BS data averaged and filtered with SNR - bottom is detected

BS_raw_data_pre_process_SNR = []
BS_raw_data_pre_process_average = []
BS_raw_data_pre_process_SNR_average = []

BS_cross_section_pre_process_SNR = []      # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
BS_cross_section_pre_process_average = []        # BS data averaged - bottom is not detected
BS_cross_section_pre_process_SNR_average = []        # BS data averaged and filtered with SNR - bottom is not detected

BS_stream_bed_pre_process_SNR = []             # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
BS_stream_bed_pre_process_average = []               # BS data averaged - bottom is detected
BS_stream_bed_pre_process_SNR_average = []               # BS data averaged and filtered with SNR - bottom is detected

time_average = np.array([])
SNR_data_average = np.array([])             # SNR data computed with BS signal averaged (not with BS raw signal)

sediment_attenuation = []

FCB = np.array([])
lin_reg = tuple()

# --- Sample Data ---

sample_fine = []

path_fine = ""
filename_fine = ""
columns_fine = []

distance_from_bank_fine = []            # distance from left bank (m)
depth_fine = []                         # depth (m)
time_fine = []

radius_grain_fine = []                         # grain radius (um)

Ctot_fine = []                            # Total concentration (g/L)
D50_fine = []                             # median diameter (um)
frac_vol_fine = []
# Volume fraction (%)

frac_vol_fine_cumul = []                  # Cumulated volume fraction (%)

fine_sample_profile = []                  # Fine sample choose for the profile in calibration

sample_sand = []

path_sand = ""
filename_sand = ""
columns_sand = []

distance_from_bank_sand = []            # distance from left bank (m)
depth_sand = []                         # depth (m)
time_sand = []

radius_grain_sand = []

Ctot_sand = []                            # Total concentration (g/L)
D50_sand = []                             # median diameter (um)
frac_vol_sand = []                        # Volume fraction (%)

frac_vol_sand_cumul = []                  # Cumulated volume fraction (%)

sand_sample_target = []                    # Sand sample target for calibration

Ctot_fine_per_cent = []
Ctot_sand_per_cent = []

# --- Acoustic inversion method ---

temperature = []
water_attenuation = []
water_velocity = []

X_exponent = np.array([])

zeta = np.array([])

# kt_corrected = np.array([])
# kt_corrected_2D = np.array([])
# kt_corrected_3D = np.array([])
J_cross_section = np.array([])
J_stream_bed = np.array([[[]]])

frequencies_to_compute_VBI = np.array([])
VBI_cross_section = np.array([])
VBI_stream_bed = np.array([[[]]])

frequency_to_compute_SSC = 0
ks = 0
sv = 0
alpha_s = 0

SSC_fine = np.array(())
SSC_sand = np.array([])

# --- Save study ---

acoustic_data = []
dirname_save_as = ""
filename_save_as = ""
dirname_open = ""
filename_open = ""

read_table_trigger = 0