acoused/settings.py

210 lines
6.0 KiB
Python

""" 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 = []
depth_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 = []
depth_real = []
lin_reg = []
# --- 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
sand_sample_target_indice = []
Ctot_fine_per_cent = []
Ctot_sand_per_cent = []
fine_sample_position = []
sand_sample_position = []
# --- Acoustic inversion method ---
temperature = 0
water_attenuation = []
water_velocity = 0
# kt_corrected = np.array([])
# kt_corrected_2D = np.array([])
# kt_corrected_3D = np.array([])
frequencies_to_compute_VBI = np.array([])
VBI_cross_section = []
# VBI_stream_bed = np.array([[[]]])
# --- Sediment Calibration
frequencies_for_calibration = []
range_lin_interp = []
M_profile_fine = []
calibration_file = []
ks = []
sv = []
X_exponent = []
alpha_s = []
zeta = []
J_cross_section = []
frequency_for_inversion = tuple()
SSC_fine = []
SSC_sand = []
# --- Save study ---
acoustic_data = []
dirname_save_as = ""
filename_save_as = ""
dirname_open = ""
filename_open = ""
read_table_trigger = 0