X_test = X_test.iloc[-test_window:]
y_test = y_test.iloc[-test_window:]
test_extrema = y_test.get(EXTREMA_COLUMN)
- n_test_minima = sp.signal.find_peaks(-test_extrema)[0].size
- n_test_maxima = sp.signal.find_peaks(test_extrema)[0].size
- n_test_extrema = n_test_minima + n_test_maxima
+ n_test_minima: int = sp.signal.find_peaks(-test_extrema)[0].size
+ n_test_maxima: int = sp.signal.find_peaks(test_extrema)[0].size
+ n_test_extrema: int = n_test_minima + n_test_maxima
min_test_extrema: int = calculate_min_extrema(
test_window, fit_live_predictions_candles
)
X = X.iloc[-train_window:]
y = y.iloc[-train_window:]
train_extrema = y.get(EXTREMA_COLUMN)
- n_train_minima = sp.signal.find_peaks(-train_extrema)[0].size
- n_train_maxima = sp.signal.find_peaks(train_extrema)[0].size
- n_train_extrema = n_train_minima + n_train_maxima
+ n_train_minima: int = sp.signal.find_peaks(-train_extrema)[0].size
+ n_train_maxima: int = sp.signal.find_peaks(train_extrema)[0].size
+ n_train_extrema: int = n_train_minima + n_train_maxima
min_train_extrema: int = calculate_min_extrema(
train_window, fit_live_predictions_candles
)
)
if debug:
logger.info(f"{dataframe[EXTREMA_COLUMN].to_numpy()=}")
- n_minima = sp.signal.find_peaks(-dataframe[EXTREMA_COLUMN])[0].size
- n_maxima = sp.signal.find_peaks(dataframe[EXTREMA_COLUMN])[0].size
- n_extrema = n_minima + n_maxima
+ n_minima: int = sp.signal.find_peaks(-dataframe[EXTREMA_COLUMN])[0].size
+ n_maxima: int = sp.signal.find_peaks(dataframe[EXTREMA_COLUMN])[0].size
+ n_extrema: int = n_minima + n_maxima
logger.info(f"{n_extrema=}")
return dataframe