def wrapup(self):
for name, readings in zip(('x', 'y', 'z'), self.counts):
self.output.write(name + ":\n")
for i, count in enumerate(readings):
if count:
self.output.write(" %d: %d\n" % (commands.signed(i), count))
self.output.write("%d: overruns\n" % self.num_overruns)
def process_time(self, time, delta, status, readings):
self.output.write("%f(%f)ms: 0x%x " % (time, delta, status))
sep = '('
for i, reading in enumerate(readings):
self.output.write("%s%d" % (sep, commands.signed(reading) - Offsets[i]))
sep = ', '
self.output.write(")\n")
def process_time(self, time, delta, status, readings):
self.time_since_output += delta
if self.samples_seen > 1:
dt = (self.last_delta + delta) / 2.0
a = []
adj = []
for i, reading in enumerate(self.last_readings):
a_n = in_per_sec2(commands.signed(reading) - Offsets[i])
a.append(a_n)
adj_n = -self.max_adj if self.v[i] >= 0 else self.max_adj
if abs(adj_n) * dt > abs(self.v[i]): adj_n = -self.v[i] / dt
adj.append(adj_n)
self.v[i] += (a_n + adj_n) * dt
self.s[i] += (self.v[i] + adj_n * dt) * dt
self.report(time, dt, a, adj)
self.last_delta = delta
self.last_readings = readings
def avg(histogram):
r'''
>>> avg([3,2,3])
1.0
>>> avg([0,2,0])
1.0
>>> avg([2,0,0])
0.0
>>> avg([0,0,2])
2.0
>>> avg([0] * 128 + [2])
-128.0
>>> avg([0] * 255 + [2])
-1.0
'''
total_count = sum = 0
for i, count in enumerate(histogram):
total_count += count
sum += commands.signed(i) * count
return sum / total_count
