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44 lines
1.5 KiB
44 lines
1.5 KiB
from analytic_types import TimeSeries |
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from models import TriangleModel, ModelType |
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import utils |
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import scipy.signal |
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from scipy.signal import argrelextrema |
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from typing import Optional, List, Tuple |
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import numpy as np |
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import pandas as pd |
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class PeakModel(TriangleModel): |
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def get_model_type(self) -> ModelType: |
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return ModelType.PEAK |
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def find_segment_center(self, dataframe: pd.DataFrame, start: int, end: int) -> int: |
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data = dataframe['value'] |
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segment = data[start: end] |
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return segment.idxmax() |
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def get_best_pattern(self, close_patterns: TimeSeries, data: pd.Series) -> List[int]: |
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pattern_list = [] |
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for val in close_patterns: |
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max_val = data[val[0]] |
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ind = val[0] |
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for i in val: |
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if data[i] > max_val: |
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max_val = data[i] |
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ind = i |
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pattern_list.append(ind) |
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return pattern_list |
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def get_extremum_indexes(self, data: pd.Series) -> np.ndarray: |
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return argrelextrema(data.values, np.greater)[0] |
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def get_smoothed_data(self, data: pd.Series, confidence: float, alpha: float) -> pd.Series: |
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return utils.exponential_smoothing(data + self.state.confidence, alpha) |
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def get_possible_segments(self, data: pd.Series, smoothed_data: pd.Series, peak_indexes: List[int]) -> List[int]: |
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segments = [] |
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for idx in peak_indexes: |
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if data[idx] > smoothed_data[idx]: |
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segments.append(idx) |
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return segments
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