Refactoring of Peak and Trough models #424 (#680)

analytics/analytics/analytic_types/learning_info.py

@@ -0,0 +1,17 @@
+import utils.meta
+
+@utils.meta.JSONClass
+class LearningInfo:
+
+    def __init__(self):
+        super().__init__()
+        self.confidence = []
+        self.patterns_list = []
+        self.pattern_width = []
+        self.pattern_height = []
+        self.pattern_timestamp = []
+        self.segment_center_list = []
+        self.patterns_value = []
+
+    def __str__(self):
+        return str(self.to_json())

analytics/analytics/models/__init__.py

@@ -1,7 +1,8 @@
 from models.model import Model, ModelState, AnalyticSegment
+from models.triangle_model import TriangleModel, TriangleModelState
 from models.drop_model import DropModel, DropModelState
-from models.peak_model import PeakModel, PeakModelState
+from models.peak_model import PeakModel
 from models.jump_model import JumpModel, JumpModelState
 from models.custom_model import CustomModel
-from models.trough_model import TroughModel, TroughModelState
+from models.trough_model import TroughModel
 from models.general_model import GeneralModel, GeneralModelState

analytics/analytics/models/custom_model.py

@@ -1,7 +1,10 @@
-from models import Model, AnalyticSegment
+from models import Model, AnalyticSegment, ModelState
+from analytic_types import AnalyticUnitId, ModelCache
+from analytic_types.learning_info import LearningInfo
 import utils
+
 import pandas as pd
-from typing import List
+from typing import List, Optional
 
 
 class CustomModel(Model):
@@ -10,9 +13,18 @@ class CustomModel(Model):
         dataframe: pd.DataFrame,
         labeled_segments: List[AnalyticSegment],
         deleted_segments: List[AnalyticSegment],
-        learning_info: dict
+        learning_info: LearningInfo
     ) -> None:
         pass
 
     def do_detect(self, dataframe: pd.DataFrame) -> list:
         return []
+
+    def find_segment_center(self, dataframe: pd.DataFrame, start: int, end: int) -> int:
+        pass
+
+    def get_model_type(self) -> (str, bool):
+        pass
+
+    def get_state(self, cache: Optional[ModelCache] = None) -> ModelState:
+        pass

analytics/analytics/models/drop_model.py

@@ -9,7 +9,8 @@ import utils
 import utils.meta
 import numpy as np
 import pandas as pd
-from analytic_types import AnalyticUnitId
+from analytic_types import AnalyticUnitId, TimeSeries
+from analytic_types.learning_info import LearningInfo
 
 @utils.meta.JSONClass
 class DropModelState(ModelState):
@@ -48,17 +49,17 @@ class DropModel(Model):
         dataframe: pd.DataFrame,
         labeled_segments: List[AnalyticSegment],
         deleted_segments: List[AnalyticSegment],
-        learning_info: dict
+        learning_info: LearningInfo
     ) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         window_size = self.state.window_size
         last_pattern_center = self.state.pattern_center
-        self.state.pattern_center = list(set(last_pattern_center + learning_info['segment_center_list']))
-        self.state.pattern_model = utils.get_av_model(learning_info['patterns_list'])
+        self.state.pattern_center = list(set(last_pattern_center + learning_info.segment_center_list))
+        self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
         convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, window_size)
         correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        height_list = learning_info['patterns_value']
+        height_list = learning_info.patterns_value
 
         del_conv_list = []
         delete_pattern_timestamp = []
@@ -70,18 +71,18 @@ class DropModel(Model):
             del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.state.pattern_model)
             if len(del_conv_drop): del_conv_list.append(max(del_conv_drop))
 
-        self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list)
-        self.state.drop_height = int(min(learning_info['pattern_height'], default = 1))
-        self.state.drop_length = int(max(learning_info['pattern_width'], default = 1))
+        self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list)
+        self.state.drop_height = int(min(learning_info.pattern_height, default = 1))
+        self.state.drop_length = int(max(learning_info.pattern_width, default = 1))
 
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
+    def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         possible_drops = utils.find_drop(data, self.state.drop_height, self.state.drop_length + 1)
         result = self.__filter_detection(possible_drops, data)
         return [(val - 1, val + 1) for val in result]
 
-    def __filter_detection(self, segments: list, data: list):
+    def __filter_detection(self, segments: List[int], data: list):
         delete_list = []
         variance_error = self.state.window_size
         close_patterns = utils.close_filtering(segments, variance_error)

analytics/analytics/models/general_model.py

@@ -16,7 +16,8 @@ import logging
 
 from typing import Optional, List, Tuple
 import math
-from analytic_types import AnalyticUnitId
+from analytic_types import AnalyticUnitId, TimeSeries
+from analytic_types.learning_info import LearningInfo
 
 PEARSON_FACTOR = 0.7
 
@@ -48,13 +49,13 @@ class GeneralModel(Model):
         dataframe: pd.DataFrame,
         labeled_segments: List[AnalyticSegment],
         deleted_segments: List[AnalyticSegment],
-        learning_info: dict
+        learning_info: LearningInfo
     ) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         last_pattern_center = self.state.pattern_center
-        self.state.pattern_center = list(set(last_pattern_center + learning_info['segment_center_list']))
-        self.state.pattern_model = utils.get_av_model(learning_info['patterns_list'])
+        self.state.pattern_center = list(set(last_pattern_center + learning_info.segment_center_list))
+        self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
         convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
         correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
 
@@ -71,7 +72,7 @@ class GeneralModel(Model):
         self.state.convolve_min, self.state.convolve_max = utils.get_min_max(convolve_list, self.state.window_size / 3)
         self.state.conv_del_min, self.state.conv_del_max = utils.get_min_max(del_conv_list, self.state.window_size)
 
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
+    def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         pat_data = self.state.pattern_model

analytics/analytics/models/jump_model.py

@@ -10,7 +10,8 @@ from typing import Optional, List, Tuple
 import math
 from scipy.signal import argrelextrema
 from scipy.stats import gaussian_kde
-from analytic_types import AnalyticUnitId
+from analytic_types import AnalyticUnitId, TimeSeries
+from analytic_types.learning_info import LearningInfo
 
 
 @utils.meta.JSONClass
@@ -49,17 +50,17 @@ class JumpModel(Model):
         dataframe: pd.DataFrame,
         labeled_segments: List[AnalyticSegment],
         deleted_segments: List[AnalyticSegment],
-        learning_info: dict
+        learning_info: LearningInfo
     ) -> None:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         window_size = self.state.window_size
         last_pattern_center = self.state.pattern_center
-        self.state.pattern_center = list(set(last_pattern_center + learning_info['segment_center_list']))
-        self.state.pattern_model = utils.get_av_model(learning_info['patterns_list'])
+        self.state.pattern_center = list(set(last_pattern_center + learning_info.segment_center_list))
+        self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
         convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, window_size)
         correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        height_list = learning_info['patterns_value']
+        height_list = learning_info.patterns_value
 
         del_conv_list = []
         delete_pattern_timestamp = []
@@ -71,18 +72,18 @@ class JumpModel(Model):
             del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.state.pattern_model)
             if len(del_conv_jump): del_conv_list.append(max(del_conv_jump))
 
-        self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list)
-        self.state.jump_height = float(min(learning_info['pattern_height'], default = 1))
-        self.state.jump_length = int(max(learning_info['pattern_width'], default = 1))
+        self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list)
+        self.state.jump_height = float(min(learning_info.pattern_height, default = 1))
+        self.state.jump_length = int(max(learning_info.pattern_width, default = 1))
 
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
+    def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         possible_jumps = utils.find_jump(data, self.state.jump_height, self.state.jump_length + 1)
         result = self.__filter_detection(possible_jumps, data)
         return [(val - 1, val + 1) for val in result]
 
-    def __filter_detection(self, segments, data):
+    def __filter_detection(self, segments: List[int], data: pd.Series):
         delete_list = []
         variance_error = self.state.window_size
         close_patterns = utils.close_filtering(segments, variance_error)

analytics/analytics/models/model.py

@@ -6,8 +6,9 @@ from typing import Optional, List, Tuple
 import pandas as pd
 import math
 import logging
-from analytic_types import AnalyticUnitId, ModelCache
+from analytic_types import AnalyticUnitId, ModelCache, TimeSeries
 from analytic_types.segment import Segment
+from analytic_types.learning_info import LearningInfo
 
 import utils.meta
 
@@ -96,12 +97,12 @@ class Model(ABC):
         dataframe: pd.DataFrame,
         labeled_segments: List[AnalyticSegment],
         deleted_segments: List[AnalyticSegment],
-        learning_info: dict
+        learning_info: LearningInfo
     ) -> None:
         pass
 
     @abstractmethod
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
+    def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
         pass
 
     @abstractmethod
@@ -146,7 +147,6 @@ class Model(ABC):
         if self.state.window_size == 0:
             self.state.window_size = math.ceil(max_length / 2) if max_length else 0
         model, model_type = self.get_model_type()
-        # TODO: learning_info: dict -> class
         learning_info = self.get_parameters_from_segments(dataframe, labeled, deleted, model, model_type)
         self.do_fit(dataframe, labeled, deleted, learning_info)
         logging.debug('fit complete successful with self.state: {} for analytic unit: {}'.format(self.state, id))
@@ -176,37 +176,29 @@ class Model(ABC):
 
     def get_parameters_from_segments(self, dataframe: pd.DataFrame, labeled: List[dict], deleted: List[dict], model: str, model_type: bool) -> dict:
         logging.debug('Start parsing segments')
-        learning_info = {
-            'confidence': [],
-            'patterns_list': [],
-            'pattern_width': [],
-            'pattern_height': [],
-            'pattern_timestamp': [],
-            'segment_center_list': [],
-            'patterns_value': [],
-        }
+        learning_info = LearningInfo()
         data = dataframe['value']
         for segment in labeled:
             confidence = utils.find_confidence(segment.data)[0]
-            learning_info['confidence'].append(confidence)
+            learning_info.confidence.append(confidence)
             segment_center = segment.center_index
-            learning_info['segment_center_list'].append(segment_center)
-            learning_info['pattern_timestamp'].append(segment.pattern_timestamp)
+            learning_info.segment_center_list.append(segment_center)
+            learning_info.pattern_timestamp.append(segment.pattern_timestamp)
             aligned_segment = utils.get_interval(data, segment_center, self.state.window_size)
             aligned_segment = utils.subtract_min_without_nan(aligned_segment)
             if len(aligned_segment) == 0:
                 logging.warning('cant add segment to learning because segment is empty where segments center is: {}, window_size: {}, and len_data: {}'.format(
                     segment_center, self.state.window_size, len(data)))
                 continue
-            learning_info['patterns_list'].append(aligned_segment)
+            learning_info.patterns_list.append(aligned_segment)
             if model == 'peak' or model == 'trough':
-                learning_info['pattern_height'].append(utils.find_confidence(aligned_segment)[1])
-                learning_info['patterns_value'].append(aligned_segment.values.max())
+                learning_info.pattern_height.append(utils.find_confidence(aligned_segment)[1])
+                learning_info.patterns_value.append(aligned_segment.values.max())
             if model == 'jump' or model == 'drop':
                 pattern_height, pattern_length = utils.find_parameters(segment.data, segment.from_index, model)
-                learning_info['pattern_height'].append(pattern_height)
-                learning_info['pattern_width'].append(pattern_length)
-                learning_info['patterns_value'].append(aligned_segment.values[self.state.window_size])
+                learning_info.pattern_height.append(pattern_height)
+                learning_info.pattern_width.append(pattern_length)
+                learning_info.patterns_value.append(aligned_segment.values[self.state.window_size])
         logging.debug('Parsing segments ended correctly with learning_info: {}'.format(learning_info))
         return learning_info
 

analytics/analytics/models/peak_model.py

@@ -1,36 +1,14 @@
-from models import Model, ModelState, AnalyticSegment
+from analytic_types import TimeSeries
+from models import TriangleModel
+import utils
 
 import scipy.signal
-from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 from typing import Optional, List, Tuple
-import utils
-import utils.meta
 import numpy as np
 import pandas as pd
-from analytic_types import AnalyticUnitId
-
-SMOOTHING_COEFF = 2400
-EXP_SMOOTHING_FACTOR = 0.01
-
 
-@utils.meta.JSONClass
-class PeakModelState(ModelState):
-
-    def __init__(
-        self,
-        confidence: float = 0,
-        height_max: float = 0,
-        height_min: float = 0,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.confidence = confidence
-        self.height_max = height_max
-        self.height_min = height_min
-
-
-class PeakModel(Model):
+class PeakModel(TriangleModel):
 
     def get_model_type(self) -> (str, bool):
         model = 'peak'
@@ -42,98 +20,27 @@ class PeakModel(Model):
         segment = data[start: end]
         return segment.idxmax()
 
-    def get_state(self, cache: Optional[dict] = None) -> PeakModelState:
-        return PeakModelState.from_json(cache)
-
-    def do_fit(
-        self,
-        dataframe: pd.DataFrame,
-        labeled_segments: List[AnalyticSegment],
-        deleted_segments: List[AnalyticSegment],
-        learning_info: dict
-    ) -> None:
-        data = utils.cut_dataframe(dataframe)
-        data = data['value']
-        window_size = self.state.window_size
-        last_pattern_center = self.state.pattern_center
-        self.state.pattern_center = list(set(last_pattern_center + learning_info['segment_center_list']))
-        self.state.pattern_model = utils.get_av_model(learning_info['patterns_list'])
-        convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        height_list = learning_info['patterns_value']
-
-        del_conv_list = []
-        delete_pattern_width = []
-        delete_pattern_height = []
-        delete_pattern_timestamp = []
-        for segment in deleted_segments:
-            del_max_index = segment.center_index
-            delete_pattern_timestamp.append(segment.pattern_timestamp)
-            deleted = utils.get_interval(data, del_max_index, window_size)
-            deleted = utils.subtract_min_without_nan(deleted)
-            del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model)
-            if len(del_conv): del_conv_list.append(max(del_conv))
-            delete_pattern_height.append(utils.find_confidence(deleted)[1])
-
-        self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list, height_list)
-
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
-        data = utils.cut_dataframe(dataframe)
-        data = data['value']
-        window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
-        all_maxs = argrelextrema(np.array(data), np.greater)[0]
-
-        extrema_list = []
-        for i in utils.exponential_smoothing(data + self.state.confidence, EXP_SMOOTHING_FACTOR):
-            extrema_list.append(i)
-
+    def get_best_pattern(self, close_patterns: TimeSeries, data: pd.Series) -> List[int]:
+        pattern_list = []
+        for val in close_patterns:
+            max_val = data[val[0]]
+            ind = val[0]
+            for i in val:
+                if data[i] > max_val:
+                    max_val = data[i]
+                    ind = i
+            pattern_list.append(ind)
+        return pattern_list
+
+    def get_extremum_indexes(self, data: pd.Series) -> np.ndarray:
+        return argrelextrema(data.values, np.greater)[0]
+
+    def get_smoothed_data(self, data: pd.Series, confidence: float, alpha: float) -> pd.Series:
+        return utils.exponential_smoothing(data + self.state.confidence, alpha)
+
+    def get_possible_segments(self, data: pd.Series, smoothed_data: pd.Series, peak_indexes: List[int]) -> List[int]:
         segments = []
-        for i in all_maxs:
-            if data[i] > extrema_list[i]:
-                segments.append(i)
-        result = self.__filter_detection(segments, data)
-        result = utils.get_borders_of_peaks(result, data, self.state.window_size, self.state.confidence)
-        return result
-
-    def __filter_detection(self, segments: list, data: list) -> list:
-        delete_list = []
-        variance_error = self.state.window_size
-        close_patterns = utils.close_filtering(segments, variance_error)
-        segments = utils.best_pattern(close_patterns, data, 'max')
-
-        if len(segments) == 0 or len(self.state.pattern_model) == 0:
-            return []
-        pattern_data = self.state.pattern_model
-        up_height = self.state.height_max * (1 + self.HEIGHT_ERROR)
-        low_height = self.state.height_min * (1 - self.HEIGHT_ERROR)
-        up_conv = self.state.convolve_max * (1 + 1.5 * self.CONV_ERROR)
-        low_conv = self.state.convolve_min * (1 - self.CONV_ERROR)
-        up_del_conv = self.state.conv_del_max * (1 + self.DEL_CONV_ERROR)
-        low_del_conv = self.state.conv_del_min * (1 - self.DEL_CONV_ERROR)
-        for segment in segments:
-            if segment > self.state.window_size:
-                convol_data = utils.get_interval(data, segment, self.state.window_size)
-                convol_data = utils.subtract_min_without_nan(convol_data)
-                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
-                if percent_of_nans > 0.5:
-                    delete_list.append(segment)
-                    continue
-                elif 0 < percent_of_nans <= 0.5:
-                    nan_list = utils.find_nan_indexes(convol_data)
-                    convol_data = utils.nan_to_zero(convol_data, nan_list)
-                    pattern_data = utils.nan_to_zero(pattern_data, nan_list)
-                conv = scipy.signal.fftconvolve(convol_data, pattern_data)
-                pattern_height = convol_data.values[self.state.window_size]
-                if pattern_height > up_height or pattern_height < low_height:
-                    delete_list.append(segment)
-                    continue
-                if max(conv) > up_conv or max(conv) < low_conv:
-                    delete_list.append(segment)
-                    continue
-                if max(conv) < up_del_conv and max(conv) > low_del_conv:
-                    delete_list.append(segment)
-            else:
-                delete_list.append(segment)
-        for item in delete_list:
-            segments.remove(item)
-        return set(segments)
+        for idx in peak_indexes:
+            if data[idx] > smoothed_data[idx]:
+                segments.append(idx)
+        return segments

analytics/analytics/models/triangle_model.py

@@ -0,0 +1,119 @@
+from analytic_types import AnalyticUnitId, TimeSeries
+from analytic_types.learning_info import LearningInfo
+from models import Model, ModelState, AnalyticSegment
+import utils
+import utils.meta
+
+import scipy.signal
+from scipy.fftpack import fft
+from typing import Optional, List, Tuple
+import numpy as np
+import pandas as pd
+
+
+EXP_SMOOTHING_FACTOR = 0.01
+
+
+@utils.meta.JSONClass
+class TriangleModelState(ModelState):
+
+    def __init__(
+        self,
+        confidence: float = 0,
+        height_max: float = 0,
+        height_min: float = 0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.confidence = confidence
+        self.height_max = height_max
+        self.height_min = height_min
+
+class TriangleModel(Model):
+
+    def get_state(self, cache: Optional[dict] = None) -> TriangleModelState:
+        return TriangleModelState.from_json(cache)
+
+    def do_fit(
+        self,
+        dataframe: pd.DataFrame,
+        labeled_segments: List[AnalyticSegment],
+        deleted_segments: List[AnalyticSegment],
+        learning_info: LearningInfo
+    ) -> None:
+        data = utils.cut_dataframe(dataframe)
+        data = data['value']
+        self.state.pattern_center = list(set(self.state.pattern_center + learning_info.segment_center_list))
+        self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
+        convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
+        correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
+        height_list = learning_info.patterns_value
+
+        del_conv_list = []
+        delete_pattern_width = []
+        delete_pattern_height = []
+        delete_pattern_timestamp = []
+        for segment in deleted_segments:
+            delete_pattern_timestamp.append(segment.pattern_timestamp)
+            deleted = utils.get_interval(data, segment.center_index, self.state.window_size)
+            deleted = utils.subtract_min_without_nan(deleted)
+            del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model)
+            if len(del_conv):
+                del_conv_list.append(max(del_conv))
+            delete_pattern_height.append(utils.find_confidence(deleted)[1])
+
+        self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list, height_list)
+
+    def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
+        data = utils.cut_dataframe(dataframe)
+        data = data['value']
+
+        all_extremum_indexes = self.get_extremum_indexes(data)
+        smoothed_data = self.get_smoothed_data(data, self.state.confidence, EXP_SMOOTHING_FACTOR)
+        segments = self.get_possible_segments(data, smoothed_data, all_extremum_indexes)
+        result = self.__filter_detection(segments, data)
+        result = utils.get_borders_of_peaks(result, data, self.state.window_size, self.state.confidence)
+        return result
+
+    def __filter_detection(self, segments: List[int], data: pd.Series) -> list:
+        delete_list = []
+        variance_error = self.state.window_size
+        close_patterns = utils.close_filtering(segments, variance_error)
+        segments = self.get_best_pattern(close_patterns, data)
+
+        if len(segments) == 0 or len(self.state.pattern_model) == 0:
+            return []
+        pattern_data = self.state.pattern_model
+        up_height = self.state.height_max * (1 + self.HEIGHT_ERROR)
+        low_height = self.state.height_min * (1 - self.HEIGHT_ERROR)
+        up_conv = self.state.convolve_max * (1 + 1.5 * self.CONV_ERROR)
+        low_conv = self.state.convolve_min * (1 - self.CONV_ERROR)
+        up_del_conv = self.state.conv_del_max * (1 + self.DEL_CONV_ERROR)
+        low_del_conv = self.state.conv_del_min * (1 - self.DEL_CONV_ERROR)
+        for segment in segments:
+            if segment > self.state.window_size:
+                convol_data = utils.get_interval(data, segment, self.state.window_size)
+                convol_data = utils.subtract_min_without_nan(convol_data)
+                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
+                if percent_of_nans > 0.5:
+                    delete_list.append(segment)
+                    continue
+                elif 0 < percent_of_nans <= 0.5:
+                    nan_list = utils.find_nan_indexes(convol_data)
+                    convol_data = utils.nan_to_zero(convol_data, nan_list)
+                    pattern_data = utils.nan_to_zero(pattern_data, nan_list)
+                conv = scipy.signal.fftconvolve(convol_data, pattern_data)
+                pattern_height = convol_data.values.max()
+                if pattern_height > up_height or pattern_height < low_height:
+                    delete_list.append(segment)
+                    continue
+                if max(conv) > up_conv or max(conv) < low_conv:
+                    delete_list.append(segment)
+                    continue
+                if max(conv) < up_del_conv and max(conv) > low_del_conv:
+                    delete_list.append(segment)
+            else:
+                delete_list.append(segment)
+        for item in delete_list:
+            segments.remove(item)
+        return set(segments)

analytics/analytics/models/trough_model.py

@@ -1,36 +1,14 @@
-from models import Model, ModelState, AnalyticSegment
+from analytic_types import TimeSeries
+from models import TriangleModel
+import utils
 
 import scipy.signal
-from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 from typing import Optional, List, Tuple
-import utils
-import utils.meta
 import numpy as np
 import pandas as pd
-from analytic_types import AnalyticUnitId
-
-SMOOTHING_COEFF = 2400
-EXP_SMOOTHING_FACTOR = 0.01
-
 
-@utils.meta.JSONClass
-class TroughModelState(ModelState):
-
-    def __init__(
-        self,
-        confidence: float = 0,
-        height_max: float = 0,
-        height_min: float = 0,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.confidence = confidence
-        self.height_max = height_max
-        self.height_min = height_min
-
-
-class TroughModel(Model):
+class TroughModel(TriangleModel):
     
     def get_model_type(self) -> (str, bool):
         model = 'trough'
@@ -42,99 +20,27 @@ class TroughModel(Model):
         segment = data[start: end]
         return segment.idxmin()
 
-    def get_state(self, cache: Optional[dict] = None) -> TroughModelState:
-        return TroughModelState.from_json(cache)
-
-    def do_fit(
-        self,
-        dataframe: pd.DataFrame,
-        labeled_segments: List[AnalyticSegment],
-        deleted_segments: List[AnalyticSegment],
-        learning_info: dict
-    ) -> None:
-        data = utils.cut_dataframe(dataframe)
-        data = data['value']
-        window_size = self.state.window_size
-        last_pattern_center = self.state.pattern_center
-        self.state.pattern_center = list(set(last_pattern_center + learning_info['segment_center_list']))
-        self.state.pattern_model = utils.get_av_model(learning_info['patterns_list'])
-        convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, window_size)
-        height_list = learning_info['patterns_value']
-
-        del_conv_list = []
-        delete_pattern_width = []
-        delete_pattern_height = []
-        delete_pattern_timestamp = []
-        for segment in deleted_segments:
-            del_min_index = segment.center_index
-            delete_pattern_timestamp.append(segment.pattern_timestamp)
-            deleted = utils.get_interval(data, del_min_index, window_size)
-            deleted = utils.subtract_min_without_nan(deleted)
-            del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model)
-            if len(del_conv): del_conv_list.append(max(del_conv))
-            delete_pattern_height.append(utils.find_confidence(deleted)[1])
-
-        self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list, height_list)
-
-    def do_detect(self, dataframe: pd.DataFrame) -> List[Tuple[int, int]]:
-        data = utils.cut_dataframe(dataframe)
-        data = data['value']
-        window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
-        all_mins = argrelextrema(np.array(data), np.less)[0]
-
-        extrema_list = []
-        for i in utils.exponential_smoothing(data - self.state.confidence, EXP_SMOOTHING_FACTOR):
-            extrema_list.append(i)
-
+    def get_best_pattern(self, close_patterns: TimeSeries, data: pd.Series) -> List[int]:
+        pattern_list = []
+        for val in close_patterns:
+            min_val = data[val[0]]
+            ind = val[0]
+            for i in val:
+                if data[i] < min_val:
+                    min_val = data[i]
+                    ind = i
+            pattern_list.append(ind)
+        return pattern_list
+
+    def get_extremum_indexes(self, data: pd.Series) -> np.ndarray:
+        return argrelextrema(data.values, np.less)[0]
+
+    def get_smoothed_data(self, data: pd.Series, confidence: float, alpha: float) -> pd.Series:
+        return utils.exponential_smoothing(data - self.state.confidence, alpha)
+
+    def get_possible_segments(self, data: pd.Series, smoothed_data: pd.Series, trough_indexes: List[int]) -> List[int]:
         segments = []
-        for i in all_mins:
-            if data[i] < extrema_list[i]:
-                segments.append(i)
-        result = self.__filter_detection(segments, data)
-        result = utils.get_borders_of_peaks(result, data, self.state.window_size, self.state.confidence, inverse = True)
-        return result
-
-    def __filter_detection(self, segments: list, data: list) -> list:
-        delete_list = []
-        variance_error = self.state.window_size
-        close_patterns = utils.close_filtering(segments, variance_error)
-        segments = utils.best_pattern(close_patterns, data, 'min')
-        if len(segments) == 0 or len(self.state.pattern_center) == 0:
-            segments = []
-            return segments
-        pattern_data = self.state.pattern_model
-        up_height = self.state.height_max * (1 + self.HEIGHT_ERROR)
-        low_height = self.state.height_min * (1 - self.HEIGHT_ERROR)
-        up_conv = self.state.convolve_max * (1 + 1.5 * self.CONV_ERROR)
-        low_conv = self.state.convolve_min * (1 - self.CONV_ERROR)
-        up_del_conv = self.state.conv_del_max * (1 + self.DEL_CONV_ERROR)
-        low_del_conv = self.state.conv_del_min * (1 - self.DEL_CONV_ERROR)
-        for segment in segments:
-            if segment > self.state.window_size:
-                convol_data = utils.get_interval(data, segment, self.state.window_size)
-                convol_data = utils.subtract_min_without_nan(convol_data)
-                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
-                if percent_of_nans > 0.5:
-                    delete_list.append(segment)
-                    continue
-                elif 0 < percent_of_nans <= 0.5:
-                    nan_list = utils.find_nan_indexes(convol_data)
-                    convol_data = utils.nan_to_zero(convol_data, nan_list)
-                    pattern_data = utils.nan_to_zero(pattern_data, nan_list)
-                conv = scipy.signal.fftconvolve(convol_data, pattern_data)
-                pattern_height = convol_data.values.max()
-                if pattern_height > up_height or pattern_height < low_height:
-                    delete_list.append(segment)
-                    continue
-                if max(conv) > up_conv or max(conv) < low_conv:
-                    delete_list.append(segment)
-                    continue
-                if max(conv) < up_del_conv and max(conv) > low_del_conv:
-                    delete_list.append(segment)
-            else:
-                delete_list.append(segment)
-        for item in delete_list:
-            segments.remove(item)
-
-        return set(segments)
+        for idx in trough_indexes:
+            if data[idx] < smoothed_data[idx]:
+                segments.append(idx)
+        return segments

analytics/analytics/utils/common.py

@@ -59,7 +59,10 @@ def find_pattern(data: pd.Series, height: float, length: int, pattern_type: str)
                     pattern_list.append(i)
     return pattern_list
 
-def find_jump(data, height, lenght):
+def find_jump(data, height, lenght) -> List[int]:
+    '''
+    Find jump indexes
+    '''
     j_list = []
     for i in range(len(data)-lenght-1):
         for x in range(1, lenght):
@@ -67,7 +70,10 @@ def find_jump(data, height, lenght):
                 j_list.append(i)
     return(j_list)
 
-def find_drop(data, height, length):
+def find_drop(data, height, length) -> List[int]:
+    '''
+    Find drop indexes
+    '''
     d_list = []
     for i in range(len(data)-length-1):
         for x in range(1, length):
@@ -116,7 +122,7 @@ def get_same_length(patterns_list):
             pat.extend(added_values)
     return patterns_list
 
-def close_filtering(pattern_list: List[int], win_size: int) -> List[Tuple[int, int]]:
+def close_filtering(pattern_list: List[int], win_size: int) -> TimeSeries:
     if len(pattern_list) == 0:
         return []
     s = [[pattern_list[0]]]
@@ -152,7 +158,7 @@ def find_interval(dataframe: pd.DataFrame) -> int:
     delta = utils.convert_pd_timestamp_to_ms(dataframe.timestamp[1]) - utils.convert_pd_timestamp_to_ms(dataframe.timestamp[0])
     return delta
 
-def get_start_and_end_of_segments(segments: List[List[int]]) -> List[Tuple[int, int]]:
+def get_start_and_end_of_segments(segments: List[List[int]]) -> TimeSeries:
     '''
     find start and end of segment: [1, 2, 3, 4] -> [1, 4]
     if segment is 1 index - it will be doubled: [7] -> [7, 7]
@@ -168,7 +174,6 @@ def get_start_and_end_of_segments(segments: List[List[int]]) -> List[Tuple[int,
         result.append(segment)
     return result
 
-
 def best_pattern(pattern_list: list, data: pd.Series, dir: str) -> list:
     new_pattern_list = []
     for val in pattern_list:
@@ -261,7 +266,7 @@ def get_interval(data: pd.Series, center: int, window_size: int, normalization =
         result_interval = subtract_min_without_nan(result_interval)
     return result_interval
 
-def get_borders_of_peaks(pattern_centers: List[int], data: pd.Series, window_size: int, confidence: float, max_border_factor = 1.0, inverse = False) -> List[Tuple[int, int]]:
+def get_borders_of_peaks(pattern_centers: List[int], data: pd.Series, window_size: int, confidence: float, max_border_factor = 1.0, inverse = False) -> TimeSeries:
     """
     Find start and end of patterns for peak
     max_border_factor - final border of pattern

analytics/tests/test_manager.py

@@ -1,5 +1,5 @@
 from models import PeakModel, DropModel, TroughModel, JumpModel, GeneralModel
-from models import PeakModelState, DropModelState, TroughModelState, JumpModelState, GeneralModelState
+from models import DropModelState, JumpModelState, GeneralModelState
 import utils.meta
 import aiounittest
 from analytic_unit_manager import AnalyticUnitManager