hastic
/
hastic-server
4
0
Fork 0
Code Issues Pull Requests Projects Releases 1 Wiki Activity
Browse Source

Fix models for choosing the "wrong" patterns #195 (#202) 

* jump and drop not crash

* fix logiс of try/except block in drops

left one block

* fix logic of try/except in jump

left one block
pull/1/head
Alexandr Velikiy 6 years ago committed by GitHub
parent
8e41b0f125
commit
4912026113
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 51 additions and 30 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 30
      analytics/models/drop_model.py
  2. 32
      analytics/models/jump_model.py
  3. 1
      analytics/models/peak_model.py

30
analytics/models/drop_model.py
Unescape View File

@ -47,19 +47,24 @@ class DropModel(Model):
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window = 5).mean()
pdf = gaussian_kde(flat_segment.dropna())
max_drop = max(flat_segment.dropna())
min_drop = min(flat_segment.dropna())
x = np.linspace(flat_segment.dropna().min() - 1, flat_segment.dropna().max() + 1, len(flat_segment.dropna()))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0]
try:
min_peak_index = peaks_kde[0]
max_peak_index = peaks_kde[1]
segment_median = ax_list[antipeaks_kde[0], 0]
segment_min_line = ax_list[min_peak_index, 0]
max_peak_index = peaks_kde[1]
segment_max_line = ax_list[max_peak_index, 0]
segment_median = ax_list[antipeaks_kde[0], 0]
except IndexError:
segment_max_line = max_drop
segment_min_line = min_drop
segment_median = (max_drop - min_drop) / 2 + min_drop
drop_height = 0.95 * (segment_max_line - segment_min_line)
drop_height_list.append(drop_height)
drop_length = utils.find_drop_length(segment_data, segment_min_line, segment_max_line)
@ -94,9 +99,7 @@ class DropModel(Model):
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
segment_median = ax_list[antipeaks_kde[0], 0]
@ -170,9 +173,16 @@ class DropModel(Model):
if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8:
upper_bound = self.state['convolve_max'] * 1.2
lower_bound = self.state['convolve_min'] * 0.8
delete_up_bound = self.state['conv_del_max'] * 1.02
delete_low_bound = self.state['conv_del_min'] * 0.98
try:
if max(conv) > upper_bound or max(conv) < lower_bound:
delete_list.append(segment)
elif max(conv) < delete_up_bound and max(conv) > delete_low_bound:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
except ValueError:
delete_list.append(segment)
else:
delete_list.append(segment)

32
analytics/models/jump_model.py
Unescape View File

@ -47,20 +47,25 @@ class JumpModel(Model):
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window = 5).mean()
flat_segment_dropna = flat_segment.dropna()
min_jump = min(flat_segment_dropna)
max_jump = max(flat_segment_dropna)
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0]
try:
min_peak_index = peaks_kde[0]
max_peak_index = peaks_kde[1]
segment_median = ax_list[antipeaks_kde[0], 0]
segment_min_line = ax_list[min_peak_index, 0]
max_peak_index = peaks_kde[1]
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_min_line = min_jump
segment_median = (max_jump - min_jump) / 2 + min_jump
jump_height = 0.95 * (segment_max_line - segment_min_line)
jump_height_list.append(jump_height)
jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
@ -95,9 +100,7 @@ class JumpModel(Model):
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
segment_median = ax_list[antipeaks_kde[0], 0]
@ -125,7 +128,7 @@ class JumpModel(Model):
self.state['convolve_min'] = self.state['WINDOW_SIZE']
if len(jump_height_list) > 0:
self.state['JUMP_HEIGHT'] = int(min(jump_height_list))
self.state['JUMP_HEIGHT'] = float(min(jump_height_list))
else:
self.state['JUMP_HEIGHT'] = 1
@ -167,13 +170,20 @@ class JumpModel(Model):
delete_list = []
pattern_data = self.model_jump
upper_bound = self.state['convolve_max'] * 1.2
lower_bound = self.state['convolve_min'] * 0.8
delete_up_bound = self.state['conv_del_max'] * 1.02
delete_low_bound = self.state['conv_del_min'] * 0.98
for segment in segments:
if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8:
try:
if max(conv) > upper_bound or max(conv) < lower_bound:
delete_list.append(segment)
elif max(conv) < delete_up_bound and max(conv) > delete_low_bound:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
except ValueError:
delete_list.append(segment)
else:
delete_list.append(segment)

1
analytics/models/peak_model.py
Unescape View File

@ -135,6 +135,7 @@ class PeakModel(Model):
if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
print("this must be deleted: {0}, index: {1}".format(max(conv), segment))
delete_list.append(segment)
else:
delete_list.append(segment)

Write Preview
Loading…
Cancel
Save