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71 lines
2.1 KiB
71 lines
2.1 KiB
7 years ago
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from scipy import signal
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import numpy as np
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import step_detect
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class PeaksDetector:
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def __init__(self):
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pass
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def fit(self, dataset, contamination=0.005):
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pass
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def predict(self, dataframe):
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array = dataframe['value'].as_matrix()
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window_size = 20
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# window = np.ones(101)
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# mean_filtered = signal.fftconvolve(
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# np.concatenate([np.zeros(window_size), array, np.zeros(window_size)]),
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# window,
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# mode='valid'
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# )
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# filtered = np.divide(array, mean_filtered / 101)
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window = signal.general_gaussian(2 * window_size + 1, p=0.5, sig=5)
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#print(window)
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filtered = signal.fftconvolve(array, window, mode='valid')
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# filtered = np.concatenate([
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# np.zeros(window_size),
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# filtered,
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# np.zeros(window_size)
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# ])
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filtered = filtered / np.sum(window)
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array = array[window_size:-window_size]
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filtered = np.subtract(array, filtered)
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import matplotlib.pyplot as plt
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# filtered = np.convolve(array, step, mode='valid')
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# print(len(array))
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# print(len(filtered))
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# step = np.hstack((np.ones(window_size), 0, -1*np.ones(window_size)))
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#
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# conv = np.convolve(array, step, mode='valid')
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#
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# conv = np.concatenate([
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# np.zeros(window_size),
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# conv,
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# np.zeros(window_size)])
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#data = step_detect.t_scan(array, window=window_size)
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data = filtered
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data /= data.max()
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#plt.plot(array[:1000])
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plt.plot(data[:1000])
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plt.show()
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result = step_detect.find_steps(data, 0.1)
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return [dataframe.index[x + window_size] for x in result]
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def save(self, model_filename):
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pass
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# with open(model_filename, 'wb') as file:
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# pickle.dump((self.clf, self.scaler), file)
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def load(self, model_filename):
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pass
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# with open(model_filename, 'rb') as file:
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# self.clf, self.scaler = pickle.load(file)
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