The center of drop and jump patterns is shifted from real value #291 (#292)

* fix centers in jumps and drops

* coeff

* add simple docstring

* fix utils

* fix spaces

* fix names

analytics/analytics/utils/common.py

@@ -7,6 +7,9 @@ from scipy.stats import gaussian_kde
 from typing import Union
 import utils
 
+SHIFT_FACTOR = 0.05
+CONFIDENCE_FACTOR = 0.2
+
 def exponential_smoothing(series, alpha):
     result = [series[0]]
     if np.isnan(result):
@@ -62,9 +65,9 @@ def segments_box(segments):
     max_time = pd.to_datetime(max_time, unit='ms')
     return min_time, max_time
 
-def intersection_segment(data, median):
+def find_intersections(data: pd.Series, median: float) -> list:
     """
-    Finds all intersections between flatten data and median
+        Finds all intersections between drop pattern data and median
     """
     cen_ind = []
     for i in range(1, len(data) - 1):
@@ -181,18 +184,21 @@ def find_drop_length(segment_data, min_line, max_line):
         print("retard alert!")
         return 0
 
-def drop_intersection(segment_data, median_line):
+def find_drop_intersections(segment_data: pd.Series, median_line: float) -> list:
-    x = np.arange(0, len(segment_data))
+    """
-    f = []
+        Finds all intersections between flatten data and median
-    for i in range(len(segment_data)):
+    """
-        f.append(median_line)
+    cen_ind = []
-    f = np.array(f)
+    for i in range(1, len(segment_data)-1):
-    g = []
+        if segment_data[i - 1] > median_line and segment_data[i + 1] < median_line:
-    for i in segment_data:
+            cen_ind.append(i)
-        g.append(i)
+    #   Delete close values except the last one
-    g = np.array(g)
+    del_ind = []
-    idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0
+    for i in range(1, len(cen_ind)):
-    return idx
+        if cen_ind[i] == cen_ind[i - 1] + 1:
+            del_ind.append(i - 1)
+
+    return [x for (idx, x) in enumerate(cen_ind) if idx not in del_ind]
 
 def find_drop(data, height, length):
     d_list = []
@@ -219,7 +225,6 @@ def peak_finder(data, size):
 def ar_mean(numbers):
     return float(sum(numbers)) / max(len(numbers), 1)
 
-
 def get_av_model(patterns_list):
     if len(patterns_list) == 0:
         return []
@@ -295,7 +300,7 @@ def find_confidence(segment: pd.Series) -> float:
     segment = utils.check_nan_values(segment)
     segment_min = min(segment)
     segment_max = max(segment)
-    return 0.2 * (segment_max - segment_min)
+    return CONFIDENCE_FACTOR * (segment_max - segment_min)
 
 def get_interval(data: pd.Series, center: int, window_size: int) -> pd.Series:
     left_bound = center - window_size
@@ -329,31 +334,28 @@ def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: i
         convolve_list.append(max(convolve_segment))
     return convolve_list
 
-
 def find_jump_parameters(segment_data: pd.Series, segment_from_index: int):
     flat_segment = segment_data.rolling(window=5).mean()
     flat_segment_dropna = flat_segment.dropna()
     segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(flat_segment_dropna)
-    jump_height = 0.95 * (segment_max_line - segment_min_line)
+    jump_height = (1 - SHIFT_FACTOR) * (segment_max_line - segment_min_line)
     jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line) # finds all interseprions with median
-    cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median)
+    cen_ind = utils.find_intersections(segment_data.tolist(), segment_median)
     jump_center = cen_ind[0]
-    segment_cent_index = jump_center - 5 + segment_from_index
+    segment_cent_index = jump_center + segment_from_index
     return segment_cent_index, jump_height, jump_length
 
-
 def find_drop_parameters(segment_data: pd.Series, segment_from_index: int):
     flat_segment = segment_data.rolling(window=5).mean()
     flat_segment_dropna = flat_segment.dropna()
     segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(flat_segment_dropna)
-    drop_height = 0.95 * (segment_max_line - segment_min_line)
+    drop_height = (1 - SHIFT_FACTOR) * (segment_max_line - segment_min_line)
     drop_length = utils.find_drop_length(segment_data, segment_min_line, segment_max_line)
-    cen_ind = utils.drop_intersection(flat_segment.tolist(), segment_median)
+    cen_ind = utils.find_drop_intersections(segment_data.tolist(), segment_median)
     drop_center = cen_ind[0]
-    segment_cent_index = drop_center - 5 + segment_from_index
+    segment_cent_index = drop_center + segment_from_index
     return segment_cent_index, drop_height, drop_length
 
-
 def get_distribution_density(segment: pd.Series) -> float:
     min_jump = min(segment)
     max_jump = max(segment)
@@ -371,7 +373,7 @@ def get_distribution_density(segment: pd.Series) -> float:
         segment_max_line = ax_list[max_peak_index, 0]
         segment_median = ax_list[antipeaks_kde[0], 0]
     except IndexError:
-        segment_max_line = max_jump
+        segment_max_line = max_jump * (1 - SHIFT_FACTOR)
-        segment_min_line = min_jump
+        segment_min_line = min_jump * (1 - SHIFT_FACTOR)
         segment_median = (max_jump - min_jump) / 2 + min_jump
     return segment_median, segment_max_line, segment_min_line