You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.1 KiB
71 lines
2.1 KiB
from scipy import signal |
|
import numpy as np |
|
import step_detect |
|
|
|
|
|
class PeaksDetector: |
|
def __init__(self): |
|
pass |
|
|
|
def fit(self, dataset, contamination=0.005): |
|
pass |
|
|
|
def predict(self, dataframe): |
|
array = dataframe['value'].as_matrix() |
|
window_size = 20 |
|
# window = np.ones(101) |
|
# mean_filtered = signal.fftconvolve( |
|
# np.concatenate([np.zeros(window_size), array, np.zeros(window_size)]), |
|
# window, |
|
# mode='valid' |
|
# ) |
|
# filtered = np.divide(array, mean_filtered / 101) |
|
|
|
window = signal.general_gaussian(2 * window_size + 1, p=0.5, sig=5) |
|
#print(window) |
|
filtered = signal.fftconvolve(array, window, mode='valid') |
|
|
|
# filtered = np.concatenate([ |
|
# np.zeros(window_size), |
|
# filtered, |
|
# np.zeros(window_size) |
|
# ]) |
|
filtered = filtered / np.sum(window) |
|
array = array[window_size:-window_size] |
|
filtered = np.subtract(array, filtered) |
|
|
|
import matplotlib.pyplot as plt |
|
|
|
# filtered = np.convolve(array, step, mode='valid') |
|
# print(len(array)) |
|
# print(len(filtered)) |
|
|
|
# step = np.hstack((np.ones(window_size), 0, -1*np.ones(window_size))) |
|
# |
|
# conv = np.convolve(array, step, mode='valid') |
|
# |
|
# conv = np.concatenate([ |
|
# np.zeros(window_size), |
|
# conv, |
|
# np.zeros(window_size)]) |
|
|
|
#data = step_detect.t_scan(array, window=window_size) |
|
data = filtered |
|
data /= data.max() |
|
|
|
#plt.plot(array[:1000]) |
|
plt.plot(data[:1000]) |
|
plt.show() |
|
|
|
result = step_detect.find_steps(data, 0.1) |
|
return [dataframe.index[x + window_size] for x in result] |
|
|
|
def save(self, model_filename): |
|
pass |
|
# with open(model_filename, 'wb') as file: |
|
# pickle.dump((self.clf, self.scaler), file) |
|
|
|
def load(self, model_filename): |
|
pass |
|
# with open(model_filename, 'rb') as file: |
|
# self.clf, self.scaler = pickle.load(file) |