openpilot v0.9.6 release

date: 2024-01-12T10:13:37
master commit: ba792d576a49a0899b88a753fa1c52956bedf9e6

common/stat_live.py

@@ -0,0 +1,73 @@
+import numpy as np
+
+class RunningStat():
+  # tracks realtime mean and standard deviation without storing any data
+  def __init__(self, priors=None, max_trackable=-1):
+    self.max_trackable = max_trackable
+    if priors is not None:
+      # initialize from history
+      self.M = priors[0]
+      self.S = priors[1]
+      self.n = priors[2]
+      self.M_last = self.M
+      self.S_last = self.S
+
+    else:
+      self.reset()
+
+  def reset(self):
+    self.M = 0.
+    self.S = 0.
+    self.M_last = 0.
+    self.S_last = 0.
+    self.n = 0
+
+  def push_data(self, new_data):
+    # short term memory hack
+    if self.max_trackable < 0 or self.n < self.max_trackable:
+      self.n += 1
+    if self.n == 0:
+      self.M_last = new_data
+      self.M = self.M_last
+      self.S_last = 0.
+    else:
+      self.M = self.M_last + (new_data - self.M_last) / self.n
+      self.S = self.S_last + (new_data - self.M_last) * (new_data - self.M)
+      self.M_last = self.M
+      self.S_last = self.S
+
+  def mean(self):
+    return self.M
+
+  def variance(self):
+    if self.n >= 2:
+      return self.S / (self.n - 1.)
+    else:
+      return 0
+
+  def std(self):
+    return np.sqrt(self.variance())
+
+  def params_to_save(self):
+    return [self.M, self.S, self.n]
+
+class RunningStatFilter():
+  def __init__(self, raw_priors=None, filtered_priors=None, max_trackable=-1):
+    self.raw_stat = RunningStat(raw_priors, -1)
+    self.filtered_stat = RunningStat(filtered_priors, max_trackable)
+
+  def reset(self):
+    self.raw_stat.reset()
+    self.filtered_stat.reset()
+
+  def push_and_update(self, new_data):
+    _std_last = self.raw_stat.std()
+    self.raw_stat.push_data(new_data)
+    _delta_std = self.raw_stat.std() - _std_last
+    if _delta_std <= 0:
+      self.filtered_stat.push_data(new_data)
+    else:
+      pass
+      # self.filtered_stat.push_data(self.filtered_stat.mean())
+
+# class SequentialBayesian():