major updates
Esse commit está contido em:
Mahdad Jafarzadehesfahani
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import mne
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import numpy as np
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import matplotlib.pyplot as plt
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import yasa
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from mne.time_frequency import tfr_morlet, tfr_multitaper
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%matplotlib qt
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data_path = "P:\\3013102.01\\Data\\"
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participant_session = ['NL_DNDRS_0004__ses-2']
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for idx, c_subj in enumerate(participant_session):
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participant_number = c_subj.split('__')[0]
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session_number = c_subj.split('__')[1]
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# Reading EEG data
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path_EEG_L = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG L.edf'
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path_EEG_R = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG R.edf'
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# Load the EDF file
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raw_EEG_L = mne.io.read_raw_edf(path_EEG_L, preload=True)
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raw_EEG_R = mne.io.read_raw_edf(path_EEG_R, preload=True)
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raw_EEG_L.filter(l_freq=.3, h_freq=30)
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raw_EEG_R.filter(l_freq=.3, h_freq=30)
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raw_EEG_L_get_data = np.ravel(raw_EEG_L.get_data(units="uV"))
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raw_EEG_R_get_data = np.ravel(raw_EEG_R.get_data(units="uV"))
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loc = raw_EEG_L_get_data[7190*256:]
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roc = raw_EEG_R_get_data[7190*256:]
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plt.plot(loc)
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plt.plot(roc)
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REM_events = yasa.rem_detect(loc = loc, roc = roc, sf=256, hypno=None, include=4, amplitude=(30, 325), duration=(0.1, 1.2), freq_rem=(0.2, 8), remove_outliers=True, verbose=False)
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REM_events.summary()
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REM_events.plot_average(time_before=1, time_after=1);
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# Let's get a boolean mask of the REMs in data
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mask = REM_events.get_mask()
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loc_highlight = loc * mask[0, :]
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roc_highlight = roc * mask[1, :]
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loc_highlight[loc_highlight == 0] = np.nan
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roc_highlight[roc_highlight == 0] = np.nan
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plt.figure(figsize=(16, 4.5))
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plt.plot(loc, 'slategrey', label='LOC')
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plt.plot(roc, 'grey', label='ROC')
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plt.plot( loc_highlight, 'indianred')
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plt.plot( roc_highlight, 'indianred')
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plt.xlabel('Time (seconds)')
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plt.ylabel('Amplitude (uV)')
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plt.title('REM sleep EOG data')
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plt.legend()
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@@ -0,0 +1,77 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Aug 2 18:31:25 2023
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@author: mahjaf
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"""
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import mne
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import numpy as np
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import matplotlib.pyplot as plt
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from mne.time_frequency import tfr_morlet, tfr_multitaper
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%matplotlib qt
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data_path = "P:\\3013102.01\\Data\\"
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participant_session = ['NL_DNDRS_0004__ses-2']
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LRLR_events = {'NL_DNDRS_0004__ses-2': [7197, 7226, 7276, 7457, 7482, 7492]}
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for idx, c_subj in enumerate(participant_session):
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participant_number = c_subj.split('__')[0]
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session_number = c_subj.split('__')[1]
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# Reading EEG data
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path_EEG_L = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG L.edf'
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path_EEG_R = data_path + participant_number +'\\' + session_number + '\\eeg\\EEG R.edf'
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# Load the EDF file
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raw_EEG_L = mne.io.read_raw_edf(path_EEG_L, preload=True)
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raw_EEG_R = mne.io.read_raw_edf(path_EEG_R, preload=True)
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fs = int(raw_EEG_L.info['sfreq'])
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# Set up the events for creating epochs
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num_rows = len(LRLR_events[c_subj])
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events = np.zeros((num_rows, 3), dtype = 'int')
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# Fill the first column with the values from the Python list
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events[:, 0] = [element * fs for element in LRLR_events[c_subj]]
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events[:, 1] = 0
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events[:, 2] = 1
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# Create epochs for seconds 5-10 (event ID 1) and seconds 15-20 (event ID 2)
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epochs_EEG_L = mne.Epochs(raw_EEG_L, events, event_id=1, tmin=0, tmax=20, baseline=None)
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epochs_EEG_R = mne.Epochs(raw_EEG_R, events, event_id=1, tmin=0, tmax=20, baseline=None)
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epochs_EEG_L_get_data = np.transpose(np.squeeze(epochs_EEG_L.get_data()))
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epochs_EEG_R_get_data = np.transpose(np.squeeze(epochs_EEG_R.get_data()))
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for item in np.arange(len(LRLR_events[c_subj])):
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print(item)
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plt.figure()
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plt.plot(epochs_EEG_L_get_data[:, item])
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plt.plot(epochs_EEG_R_get_data[:, item])
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epochs.plot()
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epochs.plot_psd(fmin=.1, fmax=15, tmin=0, tmax=5, average=True)
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freqs = np.arange(5., 30., 1.)
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# You can trade time resolution or frequency resolution or both
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# in order to get a reduction in variance
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n_cycles = freqs / 2.
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power = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, return_itc=False)
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power.plot([0], baseline=(0., 0.1), mode='mean', vmin=, vmax=3.,
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title='Sim: Using Morlet wavelet')
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##
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n_cycles = freqs *5
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time_bandwidth = 2.0 # Least possible frequency-smoothing (1 taper)
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power = tfr_multitaper(epochs, freqs=freqs, n_cycles=n_cycles,
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time_bandwidth=time_bandwidth, return_itc=False)
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power.plot()
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# Plot results. Baseline correct based on first 100 ms.
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power.plot([0], baseline=(5., 5.1), mode='mean', vmin=-1., vmax=3.,
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title='Sim: Least smoothing, most variance')
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+65
-24
@@ -183,17 +183,23 @@ class OfflineDreamento():
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self.checkbox_plot_EMG_quality_evaluation.grid(row = 5, column = 4, sticky="w")
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#%% Checkbox for automatic eye movement detector
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self.automatic_REM_event_deetction = IntVar(value=1)
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self.checkbox_automatic_REM_event_deetction = Checkbutton(self.frame_import, text = "Automatic REM event detection",
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font = 'Calibri 11 ', variable = self.automatic_REM_event_deetction)
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self.checkbox_automatic_REM_event_deetction.grid(row = 6, column = 4, sticky="w")
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#%% Checkbox for plotting periodogram
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self.plot_psd = IntVar(value = 0)
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self.checkbox_plot_psd = Checkbutton(self.frame_import, text = "Plot peridogram",
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font = 'Calibri 11 ', variable = self.plot_psd)
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self.checkbox_plot_psd.grid(row = 6, column = 4, sticky="w", pady = 10)
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self.checkbox_plot_psd.grid(row = 7, column = 4, sticky="w", pady = 10)
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#%% Label to select the desired analysis
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#Label to read data and extract features
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self.label_analysis_data = Label(self.frame_import, text = "Select the data to analyze:",
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font = 'Calibri 13 ')
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self.label_analysis_data.grid(row = 7 , column = 4, sticky="w")
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self.label_analysis_data.grid(row = 8 , column = 4, sticky="w")
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#%% Checkbox for plotting spectrograms with markers
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self.analysis_signal_options = IntVar(value = 1)
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@@ -201,7 +207,7 @@ class OfflineDreamento():
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font = 'Calibri 11 ', variable = self.analysis_signal_options,\
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value = 1, command=self.analysis_signal_options_button_activator)
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self.checkbox_plot_additional_EMG.grid(row = 8, column = 4, sticky="w", pady = 10)
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self.checkbox_plot_additional_EMG.grid(row = 9, column = 4, sticky="w", pady = 10)
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#%% Checkbox for analyzing ZMax Hypndoyne only
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@@ -210,14 +216,14 @@ class OfflineDreamento():
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font = 'Calibri 11 ', variable = self.analysis_signal_options, value = 2,\
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command=self.analysis_signal_options_button_activator)
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self.checkbox_ZMax_Hypno_Dreamento.grid(row = 9, column = 4, sticky="w", pady = 10)
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self.checkbox_ZMax_Hypno_Dreamento.grid(row = 10, column = 4, sticky="w", pady = 10)
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#%% Checkbox for analyzing ZMax Hypndoyne only
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self.ZMax_Hypno_only = IntVar(value = 0)
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self.checkbox_ZMax_Hypno_only = Radiobutton(self.frame_import, text = "HDRecorder",
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font = 'Calibri 11 ', variable = self.analysis_signal_options, value = 3,\
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command=self.analysis_signal_options_button_activator)
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self.checkbox_ZMax_Hypno_only.grid(row = 10, column = 4, sticky="w", pady = 10)
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self.checkbox_ZMax_Hypno_only.grid(row = 11, column = 4, sticky="w", pady = 10)
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#%% EMG Y SCALE
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#Label to read data and extract features
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@@ -427,8 +433,14 @@ class OfflineDreamento():
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sigScript_org = sigScript_org[:, 1]
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# Read EMG
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print('Loading EEG file ... Please wait')
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EMG_data = mne.io.read_raw_brainvision(self.path_to_EMG , preload = True)
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print('Loading EEG file ... Please wait')
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if (self.path_to_EMG[-4:] == 'vhdr' or self.path_to_EMG[-4:] == 'VHDR'):
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EMG_data = mne.io.read_raw_brainvision(self.path_to_EMG , preload = True)
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elif (self.path_to_EMG[-3:] == 'edf' or self.path_to_EMG[-3:] == 'EDF'):
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EMG_data = mne.io.read_raw_edf(self.path_to_EMG, preload = True)
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print('EEG and EMG imported successfully')
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# Read annotations
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@@ -452,7 +464,7 @@ class OfflineDreamento():
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EMG_data_get1 = self.butter_bandpass_filter(data = EMG_data_get1, lowcut=10, highcut=100, fs = 256, order = 2)
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EMG_data_get2 = self.butter_bandpass_filter(data = EMG_data_get2, lowcut=10, highcut=100, fs = 256, order = 2)
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EMG_data_get3 = self.butter_bandpass_filter(data = EMG_data_get3, lowcut=10, highcut=100, fs = 256, order = 2)
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t_sync = np.arange(time_sync_event[0] - 256*5, time_sync_event[0] + 256*15)
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t_sync = np.arange(time_sync_event[0] - 256*5, time_sync_event[0] + 256*30)
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# notch filtering
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notch_freq = 50 # set the notch frequency to 50 Hz
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@@ -1079,8 +1091,8 @@ class OfflineDreamento():
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global EMG_files_list
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self.filenames = filedialog.askopenfilenames(title = 'select EMG file (.vhdr)',
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filetype = (("vhdr", "*.vhdr"),("vhdr", "*.vhdr"), ("All Files", "*.*")))
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self.filenames = filedialog.askopenfilenames(title = 'select EMG file (.vhdr or .edf)',
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filetype = (("vhdr", "*.vhdr"),("edf", "*.edf"), ("All Files", "*.*")))
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EMG_files_list = self.frame_import.tk.splitlist(self.filenames)
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self.n_label_files = len(EMG_files_list)
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@@ -1905,9 +1917,18 @@ class OfflineDreamento():
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# read EMG
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self.EMG_raw = mne.io.read_raw_brainvision(self.path_to_EMG)
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self.EMG_raw = self.EMG_raw.resample(int(256))
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if (self.path_to_EMG[-4:] == 'vhdr' or self.path_to_EMG[-4:] == 'VHDR'):
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self.EMG_raw = mne.io.read_raw_brainvision(self.path_to_EMG)
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self.EMG_raw = self.EMG_raw.resample(int(256))
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elif (self.path_to_EMG[-3:] == 'edf' or self.path_to_EMG[-3:] == 'EDF'):
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self.EMG_raw = mne.io.read_raw_edf(self.path_to_EMG, preload = True)
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self.EMG_filtered = self.EMG_raw.filter(l_freq=10, h_freq=100)
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self.EMG_filtered = self.EMG_filtered.resample(int(256))
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self.EMG_filtered_data1 = self.EMG_filtered.get_data()[0]
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self.EMG_filtered_data2 = self.EMG_filtered.get_data()[1]
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self.EMG_filtered_data1_minus_2 = self.EMG_filtered_data1 - self.EMG_filtered_data2
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@@ -1925,8 +1946,15 @@ class OfflineDreamento():
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self.EMG_filtered_data1_minus_2 = signal.filtfilt(b, a, self.EMG_filtered_data1_minus_2, axis=0)
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self.desired_EMG_scale_val = int(self.EMG_scale_options_val.get())
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self.desired_EMG_scale= [-1e-6* self.desired_EMG_scale_val, 1e-6* self.desired_EMG_scale_val]
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if (self.path_to_EMG[-4:] == 'vhdr' or self.path_to_EMG[-4:] == 'VHDR'):
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self.desired_EMG_scale= [-1e-6* self.desired_EMG_scale_val, 1e-6* self.desired_EMG_scale_val]
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elif (self.path_to_EMG[-3:] == 'edf' or self.path_to_EMG[-3:] == 'EDF'):
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self.desired_EMG_scale= [-1 * self.desired_EMG_scale_val, 1* self.desired_EMG_scale_val]
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# Check whether the user already synced EMG vs. EEG or not
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print(f'shape EMG signals = {np.shape(self.EMG_filtered_data1)}')
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@@ -2215,16 +2243,27 @@ class OfflineDreamento():
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ax_proba.set_xticks([])
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# read EMG
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self.EMG_raw = mne.io.read_raw_brainvision(self.path_to_EMG)
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self.EMG_raw = self.EMG_raw.resample(int(256))
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if (self.path_to_EMG[-4:] == 'vhdr' or self.path_to_EMG[-4:] == 'VHDR'):
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self.EMG_raw = mne.io.read_raw_brainvision(self.path_to_EMG)
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self.EMG_raw = self.EMG_raw.resample(int(256))
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elif (self.path_to_EMG[-3:] == 'edf' or self.path_to_EMG[-3:] == 'EDF'):
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self.EMG_raw = mne.io.read_raw_edf(self.path_to_EMG, preload = True)
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self.EMG_filtered = self.EMG_raw.filter(l_freq=10, h_freq=100)
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self.EMG_filtered_data1 = self.EMG_filtered.get_data()[0]
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self.EMG_filtered_data2 = self.EMG_filtered.get_data()[1]
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self.EMG_filtered_data1_minus_2 = self.EMG_filtered_data1 - self.EMG_filtered_data2
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self.desired_EMG_scale_val = int(self.EMG_scale_options_val.get())
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self.desired_EMG_scale= [-1e-6* self.desired_EMG_scale_val, 1e-6* self.desired_EMG_scale_val]
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if (self.path_to_EMG[-4:] == 'vhdr' or self.path_to_EMG[-4:] == 'VHDR'):
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self.desired_EMG_scale= [-1e-6* self.desired_EMG_scale_val, 1e-6* self.desired_EMG_scale_val]
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elif (self.path_to_EMG[-3:] == 'edf' or self.path_to_EMG[-3:] == 'EDF'):
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self.desired_EMG_scale= [-1 * self.desired_EMG_scale_val, 1* self.desired_EMG_scale_val]
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# Check whether the user already synced EMG vs. EEG or not
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print(f'shape EMG signals = {np.shape(self.EMG_filtered_data1)}')
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@@ -2882,9 +2921,9 @@ class OfflineDreamento():
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#%% Bulk autoscoring function
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def bulk_autoscoring(self, DreamentoScorer_path = ".\\DreamentoScorer\\",\
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model_path = "DreamentoScorer_model_beta_January2023.sav",\
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standard_scaler_path = "StandardScaler_td=3_bidirectional_Trainedon_500_estimator_3013097-06_1st_iter_121222.sav",\
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feat_selection_path = "Selected_Features_BoturaAfterTD=3_Bidirectional_500_estimator_3013097-06_121222.pickle",\
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model_path = "PooledData_Full_20percent_valid.sav",\
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standard_scaler_path = "StandardScaler_PooledDataset_Full_20percent_valid.sav",\
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feat_selection_path = "Selected_Features_BoturaAfterTD=3_Bidirectional_Donders2022_19-04-2023.pickle",\
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apply_post_scoring_N1_correction = True,\
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fs = 256):
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@@ -3073,7 +3112,7 @@ class OfflineDreamento():
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fig,AX = plt.subplots(nrows=4, figsize=(12, 6), gridspec_kw={'height_ratios': [3,3,1,1]})
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print('initiating the plot')
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# Increase font size while preserving original
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plt.legend(loc = 'right', prop={'size': 6})
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#plt.legend(loc = 'right', prop={'size': 6})
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old_fontsize = plt.rcParams["font.size"]
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plt.rcParams.update({"font.size": 9})
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ax0 = plt.subplot(4,1,1)
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@@ -3108,7 +3147,7 @@ class OfflineDreamento():
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x.append(i-1)
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y.append(p)
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x.append(i)
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ax2.step(x, y, where='post', color = 'black', linewidth = 1)
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ax2.step(x, y, where='post', color = 'black', linewidth = 2)
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rem = [i for i,j in enumerate(self.y_pred) if (self.y_pred[i]==4)]
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for i in np.arange(len(rem)) -1:
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ax2.plot([rem[i]-1, rem[i]], [-1,-1] , linewidth = 2, color = 'red')
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@@ -3124,7 +3163,7 @@ class OfflineDreamento():
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ax1.set_title(folder_autoscoring + 'EEG R')
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self.y_pred_proba.plot(ax = ax3, kind="area", alpha=0.8, stacked=True, lw=0, color = ['black', 'olive', 'deepskyblue', 'purple', 'red'])
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#ax3.legend().remove()
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#plt.tight_layout()
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# Save results?
|
||||
if int(self.checkbox_save_bulk_autoscoring_txt_results_val.get()) == 1:
|
||||
@@ -3167,6 +3206,7 @@ class OfflineDreamento():
|
||||
if int(self.checkbox_close_plots_val.get()) == 1:
|
||||
plt.close()
|
||||
# Store all stats in a single excel file
|
||||
|
||||
df = pd.DataFrame(data=self.all_stats)
|
||||
df = (df.T)
|
||||
path_to_save_all_stats = os.path.dirname(self.bulk_autoscoring_files_list[0]) + '\\Dreamento_all_sleep_stats.xlsx'
|
||||
@@ -4201,6 +4241,7 @@ class OfflineDreamento():
|
||||
|
||||
print('mouse cliked --> move plot')
|
||||
ax_tmp.set_xlim((np.floor(event.xdata)- int(7680/256/2), np.floor(event.xdata)+ int(7680/256/2)))
|
||||
|
||||
plt.draw()
|
||||
print(f'clicked sample{ {event.xdata}}')
|
||||
print(f'adjust xlm {(np.floor(event.xdata)- int(7680/2), np.floor(event.xdata)+ int(7680/2))}')
|
||||
|
||||
Diferenças do arquivo suprimidas por serem muito extensas
Carregar Diff
+2
-1
@@ -82,8 +82,9 @@ N.B. A complete **tutorial on how to run real-time Dreamento** can be found [HER
|
||||
```
|
||||
cd directory/to/Dreamento
|
||||
conda activate offlinedreamento
|
||||
python OfflineDreamento.py
|
||||
spyder
|
||||
```
|
||||
When spyder pops up, open ```offlinedreamento.py``` and proceed with the desired analysis. **If you are interested in autoscoring, we highly recommend this method. Otherwise, if you run offlinedreamento directly through command prompt you may get errors while autoscoring.
|
||||
You can post-process your recordings with Dreamento in three cases: (1) While having (Dreamento + Hypnodyne HDRecorder + data with other measurement modality, e.g., EMG), (2) Dreamento + Hypnodyne HDRecorder **WITHOUT** havign parallel recording with other measurement modality, and (3) recordings by ZMax only (e.g., online via HDRecorder or offline by pushing the record button on the headband).
|
||||
|
||||
1. Demo on post-processing (Dreamento + Hypnodyne HDRecorder + EMG data): [LINK](https://youtu.be/NzDdLlAd_F8)
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Aug 7 13:03:03 2023
|
||||
|
||||
@author: mahjaf
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import yasa
|
||||
import mne
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
path_to_BrainAmp_file = 'P:\\3013097.06\\BrainAmpEEG_DCM\\'
|
||||
subj = 'pilot_005.vhdr'
|
||||
|
||||
raw = mne.io.read_raw_brainvision(path_to_BrainAmp_file + subj, preload = True)
|
||||
fs = raw.info['sfreq']
|
||||
ch_names = raw.info['ch_names']
|
||||
|
||||
all_markers_with_timestamps = []
|
||||
for counter, marker in enumerate(markers.values()):
|
||||
|
||||
all_markers.append(marker)
|
||||
|
||||
for counter, marker in enumerate(markers.keys()):
|
||||
all_timestamp_markers.append(marker)
|
||||
|
||||
for i in np.arange(len(all_markers)):
|
||||
all_markers_with_timestamps.append(all_markers[i]+ '__'+ all_timestamp_markers[i])
|
||||
select_marker_for_sync()
|
||||
|
||||
# =============================================================================
|
||||
# # Get the data arrays for the specified channels
|
||||
# data_a, times = raw[raw.ch_names.index('C3')]
|
||||
# data_b, _ = raw[raw.ch_names.index('TP10')]
|
||||
#
|
||||
# # Calculate the difference between channel_a and channel_b
|
||||
# channel_diff = data_a - data_b
|
||||
#
|
||||
# ch_info = mne.create_info(ch_names=['a_minus_b'], sfreq=raw.info['sfreq'], ch_types=['eeg'])
|
||||
#
|
||||
# raw_diff = mne.io.RawArray(data=channel_diff, info=ch_info)
|
||||
#
|
||||
# raw.add_channels([raw_diff], force_update_info=True)
|
||||
#
|
||||
# raw_get = raw.get_data()
|
||||
#
|
||||
# =============================================================================
|
||||
# Concatenate the new channel with the original raw data
|
||||
desired_EEG_channels = ['C3', 'TP10']
|
||||
desired_EOG_channels = ['HEOG', 'TP10']
|
||||
desired_EMG_channels = ['EMG1', 'TP10']
|
||||
desired_channels = desired_EEG_channels + desired_EOG_channels + desired_EMG_channels
|
||||
ch_indices = [raw.ch_names.index(ch) for ch in desired_channels]
|
||||
|
||||
# Compute the derivation data
|
||||
derivation_data_eeg = raw.get_data(picks=ch_indices[0]) - raw.get_data(picks=ch_indices[1])
|
||||
derivation_data_eog = raw.get_data(picks=ch_indices[2]) - raw.get_data(picks=ch_indices[3])
|
||||
derivation_data_emg = raw.get_data(picks=ch_indices[4]) - raw.get_data(picks=ch_indices[5])
|
||||
|
||||
# Create an info object for the derivation channel
|
||||
derivation_info_EEG = mne.create_info(['EEG ' + desired_channels[0] + '-' + desired_channels[1]], raw.info['sfreq'], ch_types='eeg')
|
||||
derivation_info_EOG = mne.create_info(['EOG ' + desired_channels[2] + '-' + desired_channels[3]], raw.info['sfreq'], ch_types='eog')
|
||||
derivation_info_EMG = mne.create_info(['EMG ' + desired_channels[4] + '-' + desired_channels[5]], raw.info['sfreq'], ch_types='emg')
|
||||
|
||||
# Create an Evoked object for the derivation data
|
||||
derivation_evoked_EEG = mne.io.RawArray(data=derivation_data_eeg, info=derivation_info_EEG)
|
||||
derivation_evoked_EOG = mne.io.RawArray(data=derivation_data_eog, info=derivation_info_EOG)
|
||||
derivation_evoked_EMG = mne.io.RawArray(data=derivation_data_emg, info=derivation_info_EMG)
|
||||
|
||||
# Adding new derivations
|
||||
raw.add_channels([derivation_evoked_EEG], force_update_info=True)
|
||||
raw.add_channels([derivation_evoked_EOG], force_update_info=True)
|
||||
raw.add_channels([derivation_evoked_EMG], force_update_info=True)
|
||||
|
||||
|
||||
sls = yasa.SleepStaging(raw, eeg_name=derivation_info_EEG.ch_names[0],\
|
||||
eog_name = derivation_evoked_EOG.ch_names[0],\
|
||||
emg_name = derivation_evoked_EMG.ch_names[0])
|
||||
|
||||
hypno_pred = sls.predict() # Predict the sleep stages
|
||||
hypno_pred = yasa.hypno_str_to_int(hypno_pred) # Convert "W" to 0, "N1" to 1, etc
|
||||
yasa.plot_hypnogram(hypno_pred); # Plot
|
||||
|
||||
np.savetxt('output.txt', hypno_pred, fmt='%d')
|
||||
@@ -33775,3 +33775,178 @@
|
||||
2023-02-13 13:21:53 [INFO ] Initializing the model ...
|
||||
2023-02-13 13:21:53 [INFO ] Shape:
|
||||
2023-02-13 13:21:53 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 14:21:13 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 14:21:13 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 14:21:13 [INFO ] Initializing the model ...
|
||||
2023-06-30 14:21:13 [INFO ] Shape:
|
||||
2023-06-30 14:21:13 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 14:21:43 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 14:21:43 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 14:21:43 [INFO ] Initializing the model ...
|
||||
2023-06-30 14:21:43 [INFO ] Shape:
|
||||
2023-06-30 14:21:43 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:38:32 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:38:32 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:38:32 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:38:32 [INFO ] Shape:
|
||||
2023-06-30 15:38:32 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:39:02 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:39:02 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:39:02 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:39:02 [INFO ] Shape:
|
||||
2023-06-30 15:39:02 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:39:32 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:39:32 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:39:32 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:39:32 [INFO ] Shape:
|
||||
2023-06-30 15:39:32 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:40:02 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:40:02 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:40:02 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:40:02 [INFO ] Shape:
|
||||
2023-06-30 15:40:02 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:40:33 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:40:33 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:40:33 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:40:33 [INFO ] Shape:
|
||||
2023-06-30 15:40:33 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:41:03 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:41:03 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:41:03 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:41:03 [INFO ] Shape:
|
||||
2023-06-30 15:41:03 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:41:33 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:41:33 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:41:33 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:41:33 [INFO ] Shape:
|
||||
2023-06-30 15:41:33 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:42:03 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:42:03 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:42:03 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:42:03 [INFO ] Shape:
|
||||
2023-06-30 15:42:03 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:42:34 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:42:34 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:42:34 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:42:34 [INFO ] Shape:
|
||||
2023-06-30 15:42:34 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:43:04 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:43:04 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:43:04 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:43:04 [INFO ] Shape:
|
||||
2023-06-30 15:43:04 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:43:34 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:43:34 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:43:34 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:43:34 [INFO ] Shape:
|
||||
2023-06-30 15:43:34 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:44:05 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:44:05 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:44:05 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:44:05 [INFO ] Shape:
|
||||
2023-06-30 15:44:05 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:44:35 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:44:35 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:44:35 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:44:35 [INFO ] Shape:
|
||||
2023-06-30 15:44:35 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:45:05 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:45:05 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:45:05 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:45:05 [INFO ] Shape:
|
||||
2023-06-30 15:45:05 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:45:35 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:45:35 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:45:35 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:45:35 [INFO ] Shape:
|
||||
2023-06-30 15:45:35 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:46:06 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:46:06 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:46:06 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:46:06 [INFO ] Shape:
|
||||
2023-06-30 15:46:06 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:46:36 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:46:36 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:46:36 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:46:36 [INFO ] Shape:
|
||||
2023-06-30 15:46:36 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:47:06 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:47:06 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:47:06 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:47:06 [INFO ] Shape:
|
||||
2023-06-30 15:47:06 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:47:37 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:47:37 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:47:37 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:47:37 [INFO ] Shape:
|
||||
2023-06-30 15:47:37 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:48:07 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:48:07 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:48:07 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:48:07 [INFO ] Shape:
|
||||
2023-06-30 15:48:07 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:48:37 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:48:37 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:48:37 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:48:37 [INFO ] Shape:
|
||||
2023-06-30 15:48:37 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:49:08 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:49:08 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:49:08 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:49:08 [INFO ] Shape:
|
||||
2023-06-30 15:49:08 [INFO ] (1, 7680, 1, 1)
|
||||
2023-06-30 15:49:38 [INFO ] Loading new array for prediciton...
|
||||
2023-06-30 15:49:38 [INFO ]
|
||||
=======================================
|
||||
|
||||
2023-06-30 15:49:38 [INFO ] Initializing the model ...
|
||||
2023-06-30 15:49:38 [INFO ] Shape:
|
||||
2023-06-30 15:49:38 [INFO ] (1, 7680, 1, 1)
|
||||
|
||||
+208
@@ -0,0 +1,208 @@
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
2
|
||||
0
|
||||
2
|
||||
0
|
||||
0
|
||||
0
|
||||
2
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
2
|
||||
2
|
||||
2
|
||||
0
|
||||
0
|
||||
2
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
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|
||||
0
|
||||
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|
||||
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|
||||
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|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
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|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
4
|
||||
0
|
||||
0
|
||||
0
|
||||
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|
||||
4
|
||||
0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
4
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
1
|
||||
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