{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59093,"databundleVersionId":7469972,"sourceType":"competition"}],"dockerImageVersionId":30646,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd, numpy as np, os\nimport matplotlib.pyplot as plt, gc\n\ntrain = pd.read_csv('/kaggle/input/hms-harmful-brain-activity-classification/train.csv')\nprint('Train shape', train.shape )\ndisplay( train.head() )","metadata":{"execution":{"iopub.status.busy":"2024-02-02T10:24:18.809819Z","iopub.execute_input":"2024-02-02T10:24:18.810276Z","iopub.status.idle":"2024-02-02T10:24:20.815777Z","shell.execute_reply.started":"2024-02-02T10:24:18.810240Z","shell.execute_reply":"2024-02-02T10:24:20.814956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NAMES_ADD = ['FL','FR','PL','PR']\n\nFEATS_ADD = [['Fp1','F7','F3','C3','T3'],\n ['Fp2','F8','F4','C4','T4'],\n ['O1','T5','P3','C3','T3'],\n ['O2','T6','P4','C4','T4'],]\n\ndirectory_path_add_n = 'EEG_ADD_Spectrograms_N/'\nif not os.path.exists(directory_path_add_n):\n os.makedirs(directory_path_add_n) ","metadata":{"execution":{"iopub.status.busy":"2024-02-02T10:24:20.817505Z","iopub.execute_input":"2024-02-02T10:24:20.818321Z","iopub.status.idle":"2024-02-02T10:24:20.824159Z","shell.execute_reply.started":"2024-02-02T10:24:20.818291Z","shell.execute_reply":"2024-02-02T10:24:20.823360Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pywt\nprint(\"The wavelet functions we can use:\")\nprint(pywt.wavelist())\n\nUSE_WAVELET = None","metadata":{"execution":{"iopub.status.busy":"2024-02-02T10:24:20.826000Z","iopub.execute_input":"2024-02-02T10:24:20.826325Z","iopub.status.idle":"2024-02-02T10:24:21.409547Z","shell.execute_reply.started":"2024-02-02T10:24:20.826298Z","shell.execute_reply":"2024-02-02T10:24:21.407671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# DENOISE FUNCTION\ndef maddest(d, axis=None):\n return np.mean(np.absolute(d - np.mean(d, axis)), axis)\n\ndef denoise(x, wavelet='haar', level=1): \n coeff = pywt.wavedec(x, wavelet, mode=\"per\")\n sigma = (1/0.6745) * maddest(coeff[-level])\n\n uthresh = sigma * np.sqrt(2*np.log(len(x)))\n coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])\n\n ret=pywt.waverec(coeff, wavelet, mode='per')\n \n return ret","metadata":{"execution":{"iopub.status.busy":"2024-02-02T10:24:21.412851Z","iopub.execute_input":"2024-02-02T10:24:21.413453Z","iopub.status.idle":"2024-02-02T10:24:21.424782Z","shell.execute_reply.started":"2024-02-02T10:24:21.413412Z","shell.execute_reply":"2024-02-02T10:24:21.423078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import librosa\n\ndef spectrogram_from_eeg_add_n(parquet_path, display=False):\n \n # LOAD MIDDLE 50 SECONDS OF EEG SERIES\n eeg = pd.read_parquet(parquet_path)\n middle = (len(eeg)-10_000)//2\n eeg = eeg.iloc[middle:middle+10_000]\n \n # VARIABLE TO HOLD SPECTROGRAM\n img = np.zeros((128,256,4),dtype='float32')\n \n if display: plt.figure(figsize=(10,7))\n signals = []\n for k in range(4):\n COLS = FEATS_ADD[k]\n \n for kk in range(3):\n \n # COMPUTE PAIR DIFFERENCES\n x = eeg[COLS[kk]].values - eeg[COLS[kk+1]].values\n\n # FILL NANS\n m = np.nanmean(x)\n if np.isnan(x).mean()<1: x = np.nan_to_num(x,nan=m)\n else: x[:] = 0\n\n # DENOISE\n if USE_WAVELET:\n x = denoise(x, wavelet=USE_WAVELET)\n signals.append(x)\n\n # RAW SPECTROGRAM\n mel_spec = librosa.feature.melspectrogram(y=x, sr=200, hop_length=len(x)//256, \n n_fft=1024, n_mels=128, fmin=0, fmax=20, win_length=128)\n\n # LOG TRANSFORM\n width = (mel_spec.shape[1]//32)*32\n mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n # STANDARDIZE TO -1 TO 1\n mel_spec_db = (mel_spec_db+40)/40 \n img[:,:,k] += mel_spec_db\n \n # AVERAGE THE 4 MONTAGE DIFFERENCES\n img[:,:,k] /= 4.0\n \n if display:\n plt.subplot(3,2,k+1)\n plt.imshow(img[:,:,k],aspect='auto',origin='lower')\n plt.title(f'EEG {eeg_id} - Spectrogram_Add_N {NAMES_ADD[k]}')\n \n if display: \n plt.show()\n plt.figure(figsize=(10,5))\n offset = 0\n for k in range(4):\n if k>0: offset -= signals[3-k].min()\n plt.plot(range(10_000),signals[k]+offset,label=NAMES_ADD[3-k])\n offset += signals[3-k].max()\n plt.legend()\n plt.title(f'EEG {eeg_id} Signals')\n plt.show()\n print(); print('#'*25); print()\n \n return img ","metadata":{"execution":{"iopub.status.busy":"2024-02-02T10:24:21.426394Z","iopub.execute_input":"2024-02-02T10:24:21.426844Z","iopub.status.idle":"2024-02-02T10:24:21.461441Z","shell.execute_reply.started":"2024-02-02T10:24:21.426787Z","shell.execute_reply":"2024-02-02T10:24:21.460138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nPATH = '/kaggle/input/hms-harmful-brain-activity-classification/train_eegs/'\nDISPLAY = 4\nEEG_IDS = train.eeg_id.unique()\nall_eegs_add_n = {}\n\nfor i,eeg_id in enumerate(EEG_IDS):\n if (i%100==0)&(i!=0): print(i,', ',end='')\n \n # CREATE SPECTROGRAM FROM EEG PARQUET\n img = spectrogram_from_eeg_add_n(f'{PATH}{eeg_id}.parquet', i