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jupyter labextension install jupyterlab-plotly@1.2.0
jupyter lab build

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invalid_beat = [
    "[", "!", "]", "x", "(", ")", "p", "t", 
    "u", "`", "'", "^", "|", "~", "+", "s", 
    "T", "*", "D", "=", '"', "@"
]

abnormal_beats = [
    "L", "R", "B", "A", "a", "J", "S", "V", 
    "r", "F", "e", "j", "n", "E", "/", "f", "Q", "?"
]

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# Function to classify beats in normal (0) / abnormal (1)
def classify_beat(symbol):
    if symbol in abnormal_beats:
        return 1
    elif symbol == "N" or symbol == ".":
        return 0  
      
# Function to return a sequence surrounding a beat 
# with window_size for each side
def get_sequence(signal, beat_loc, window_sec, fs):

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all_sequences = []
all_labels = []
window_sec = 3
subject_map = []
for subject in records:
    record = wfdb.rdrecord(f'mit-bih-arrhythmia-database-1.0.0/{subject}')
    annotation = wfdb.rdann(f'mit-bih-arrhythmia-database-1.0.0/{subject}', 'atr')
    atr_symbol = annotation.symbol
    atr_sample = annotation.sample
    fs = record.fs

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from keras.models import Sequential
from keras.layers import Conv1D, Flatten, Dense, Dropout
from keras.optimizers import Adam

sequence_size = X_train.shape[1]
n_features = 1

model = Sequential([
    Conv1D(
        filters=8,

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model.fit(
    X_train, 
    y_train, 
    batch_size=128,
    epochs=15,
    validation_data=(X_val, y_val)
)

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sequence_size = X_train.shape[1]
n_features = 1 
n_subsequences = 4
subsequence_size = int(sequence_size / n_subsequences)

# Reshaping to be (samples, subsequences, sequence, feature)
X_train = X_train.reshape(-1, n_subsequences, subsequence_size, n_features)
X_val = X_val.reshape(-1, n_subsequences, subsequence_size, n_features)

cnn_lstm_model = Sequential([

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brew install pandoc
brew tap homebrew/cask
brew install basictex
eval "$(/usr/libexec/path_helper)"
# Update $PATH to include `/usr/local/texlive/2020basic/bin/x86_64-darwin`
sudo tlmgr update --self
sudo tlmgr install texliveonfly
sudo tlmgr install xelatex
sudo tlmgr install adjustbox
sudo tlmgr install tcolorbox

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CSV_URL = "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-red.csv"

def load_data() -> pd.DataFrame:
    try:
        data = pd.read_csv(CSV_URL, sep=";")
    except Exception as e:
        logger.exception(
            "Unable to download training & test CSV, "
            "check your internet connection. Error: %s", e
        )