if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ] * win_size
+ results = [0.0, ]
median = in_data.rolling(window=win_size).median()
stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
m_vals = median.values
s_vals = stdev_t.values
d_vals = in_data.values
- for day in range(win_size, in_data.size):
- if np.isnan(m_vals[day - 1]) or np.isnan(s_vals[day - 1]):
+ for day in range(1, in_data.size):
+ if np.isnan(m_vals[day]) \
+ or np.isnan(s_vals[day]) \
+ or np.isnan(d_vals[day]):
results.append(0.0)
- elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]):
+ elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]):
results.append(0.33)
- elif (m_vals[day - 1] - 3 * s_vals[day - 1]) <= d_vals[day] <= \
- (m_vals[day - 1] + 3 * s_vals[day - 1]):
+ elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \
+ (m_vals[day] + 3 * s_vals[day]):
results.append(0.66)
else:
results.append(1.0)
data_y = [val for val in in_data.values()]
data_pd = pd.Series(data_y, index=data_x)
- t_data, outliers = find_outliers(data_pd)
+ t_data, outliers = find_outliers(data_pd, outlier_const=1.5)
results = _evaluate_results(data_pd, t_data, window=moving_win_size)