163 lines
5.5 KiB
Python
163 lines
5.5 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 = []
|
|
r_2D = []
|
|
freq = []
|
|
freq_text = []
|
|
time = []
|
|
|
|
path_BS_noise_data = ""
|
|
filename_BS_noise_data = ""
|
|
BS_noise_raw_data = np.array([]) # BS noise raw data : BS signal listen
|
|
BS_noise_averaged_data = np.array([]) # BS noise raw data averaged (array has the same shape than BS_raw_data shape)
|
|
|
|
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 = []
|
|
gain_rx = []
|
|
gain_tx = []
|
|
|
|
SNR_data = np.array([]) # SNR is computed with BS_noise_averaged_data
|
|
time_snr = np.array([])
|
|
|
|
# --- reshape raw data for table of values in Acoustic Data tab ---
|
|
time_reshape = []
|
|
time_snr_reshape = np.array([])
|
|
r_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_data = stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]
|
|
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 = np.array([]) # BS_noise_cros_section = BS_noise_data[:, :, tmin:tmax] (former Noise_data)
|
|
SNR_cross_section = np.array([]) # SNR_data = snr[:, :, tmin:tmax]
|
|
SNR_stream_bed = np.array([])
|
|
t_cross_section = []
|
|
t_snr = np.array([])
|
|
depth_cross_section = []
|
|
depth_bottom = []
|
|
val_bottom = []
|
|
ind_bottom = []
|
|
freq_bottom_detection = []
|
|
|
|
# --- 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_cross_section_pre_process_SNR = np.array([[[]]]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
|
|
BS_cross_section_pre_process_average = np.array([[[]]]) # BS data averaged - bottom is not detected
|
|
BS_cross_section_pre_process_SNR_average = np.array([[[]]]) # BS data averaged and filtered with SNR - bottom is not detected
|
|
|
|
BS_stream_bed_pre_process_SNR = np.array([]) # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
|
|
BS_stream_bed_pre_process_average = np.array([]) # BS data averaged - bottom is detected
|
|
BS_stream_bed_pre_process_SNR_average = np.array([]) # 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)
|
|
|
|
water_attenuation = 0
|
|
sediment_attenuation = 0
|
|
|
|
FCB = np.array([])
|
|
lin_reg = tuple()
|
|
|
|
# --- Sample Data ---
|
|
|
|
samples = []
|
|
|
|
fine_sediment_path = ""
|
|
fine_sediment_filename = ""
|
|
fine_sediment_columns = []
|
|
|
|
sample_distance_from_bank = np.array([]) # distance from left bank (m)
|
|
sample_depth = np.array([]) # depth (m)
|
|
sample_time = np.array([])
|
|
|
|
radius_grain = np.array([]) # grain radius (um)
|
|
|
|
Ctot_fine = np.array([]) # Total concentration (g/L)
|
|
D50_fine = np.array([]) # median diameter (um)
|
|
frac_vol_fine = np.array([]) # Volume fraction (%)
|
|
|
|
frac_vol_fine_cumul = np.array([]) # Cumulated volume fraction (%)
|
|
|
|
sand_sediment_path = ""
|
|
sand_sediment_filename = ""
|
|
sand_sediment_columns = []
|
|
|
|
Ctot_sand = np.array([]) # Total concentration (g/L)
|
|
D50_sand = np.array([]) # median diameter (um)
|
|
frac_vol_sand = np.array([]) # Volume fraction (%)
|
|
|
|
frac_vol_sand_cumul = np.array([]) # Cumulated volume fraction (%)
|
|
|
|
Ctot_fine_per_cent = np.array([])
|
|
Ctot_sand_per_cent = np.array([])
|
|
|
|
# --- Acoustic inversion method ---
|
|
|
|
temperature = 0
|
|
water_attenuation = np.array([])
|
|
water_velocity = 0
|
|
|
|
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([])
|
|
|
|
|
|
|
|
|
|
|