parouby
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acoused
mirror of https://forgemia.inra.fr/theophile.terraz/acoused
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settings is updated

dev-brahim
brahim 2023-09-12 16:44:38 +02:00
parent b3d820b99c
commit 420896ca42
1 changed files with 6 additions and 8 deletions
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14
settings.py
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@ -16,8 +16,7 @@ time = np.array([])
path_BS_noise_data = "" path_BS_noise_data = ""
filename_BS_noise_data = "" filename_BS_noise_data = ""
BS_noise_data = np.array([]) BS_noise_data = np.array([]) # BS noise data averaged (array has the same shape than BS_raw_data shape)
snr = np.array([])
date = [] date = []
date_noise = [] date_noise = []
@ -34,22 +33,21 @@ kt = np.array([])
gain_rx = np.array([]) gain_rx = np.array([])
gain_tx = np.array([]) gain_tx = np.array([])
snr = np.array([]) snr = np.array([]) # snr is computed with BS_noise_data
snr_reshape = np.array([])
time_snr = np.array([]) time_snr = np.array([])
# --- reshape raw data for table of values in Acoustic Data tab --- # --- reshape raw data for table of values in Acoustic Data tab ---
time_reshape = np.array([]) time_reshape = np.array([])
r_reshape = np.array([]) r_reshape = np.array([])
BS_raw_data_reshape = np.array([]) BS_raw_data_reshape = np.array([])
snr_reshape = np.array([]) snr_reshape = np.array([]) # snr is reshape to be included in table of values in acoustic data tab
DataFrame_acoustic = pd.DataFrame() DataFrame_acoustic = pd.DataFrame()
# --- Processed data in Acoustic Data Tab and used in Acoustic processing tab --- # --- Processed data in Acoustic Data Tab and used in Acoustic processing tab ---
BS_data = np.array([]) # BS data limited with tmin and tmax values of spin box BS_data = np.array([]) # BS data limited with tmin and tmax values of spin box
BS_data_section = np.array([]) # BS data in the section. Values NaN outside the bottom of the section are deleted BS_data_section = np.array([]) # BS data in the section. Values NaN outside the bottom of the section are deleted
Noise_data = np.array([]) Noise_data = np.array([]) # Noise_data = BS_noise_data[:, :, tmin:tmax]
SNR_data = np.array([]) SNR_data = np.array([]) # SNR_data = snr[:, :, tmin:tmax]
t = np.array([]) t = np.array([])
r_bottom = np.array([]) r_bottom = np.array([])
val_bottom = np.array([]) val_bottom = np.array([])
@ -60,7 +58,7 @@ freq_bottom_detection = 0
BS_data_section_SNR_filter = np.array([]) # BS data filtered with SNR values (remove point if SNR < value) BS_data_section_SNR_filter = np.array([]) # BS data filtered with SNR values (remove point if SNR < value)
BS_data_section_averaged = np.array([]) # BS data averaged BS_data_section_averaged = np.array([]) # BS data averaged
time_average = np.array([]) time_average = np.array([])
SNR_data_average = np.array([]) SNR_data_average = np.array([]) # SNR data computed with BS signal averaged (not with BS raw signal)
water_attenuation = 0 water_attenuation = 0
sediment_attenuation = 0 sediment_attenuation = 0