def cov(X, Y, biased=False):
assert (X.shape == Y.shape
and len(X.shape) == 1), "Expected data vectors of equal length"
assert len(X) > 1, "At least 2 data points are required"
X = X - np.mean(X)
Y = Y - np.mean(Y)
denom = len(X) if biased else len(X) - 1
return (np.sum(X * Y)) / denom
dtype=np.uint8)
b = np.array(
[range(2**56 - 3, 2**56),
range(2**16 - 3, 2**16),
range(2**8 - 3, 2**8)],
dtype=np.float)
print(np.sort(a, axis=None))
print(np.sort(b, axis=None))
print(np.sort(a, axis=0))
print(np.sort(b, axis=0))
print(np.sort(a, axis=1))
print(np.sort(b, axis=1))
print("Testing np.sum:")
a = np.array([253, 254, 255], dtype=np.uint8)
print(np.sum(a))
print(np.sum(a, axis=0))
a = np.array([range(255 - 3, 255),
range(240 - 3, 240),
range(250 - 3, 250)],
dtype=np.float)
print(np.sum(a))
print(np.sum(a, axis=0))
print(np.sum(a, axis=1))
print("Testing np.mean:")
a = np.array([253, 254, 255], dtype=np.uint8)
print(np.mean(a))
print(np.mean(a, axis=0))
a = np.array([range(255 - 3, 255),
range(240 - 3, 240),
def main():
sensor.enable_proximity = True
while True:
# Wait for user to put finger over sensor
while sensor.proximity < PROXIMITY_THRESHOLD_HI:
time.sleep(.01)
# After the finger is sensed, set up the color sensor
sensor.enable_color = True
# This sensor integration time is just a little bit shorter than 125ms,
# so we should always have a fresh value when we ask for it, without
# checking if a value is available.
sensor.integration_time = 220
# In my testing, 64X gain saturated the sensor, so this is the biggest
# gain value that works properly.
sensor.color_gain = APDS9660_AGAIN_4X
white_leds.value = True
# And our data structures
# The most recent data samples, equal in number to the filter taps
data = np.zeros(len(taps))
# The filtered value on the previous iteration
old_value = 1
# The times of the most recent pulses registered. Increasing this number
# makes the estimation more accurate, but at the expense of taking longer
# before a pulse number can be computed
pulse_times = []
# The estimated heart rate based on the recent pulse times
rate = None
# the number of samples taken
n = 0
# Rather than sleeping for a fixed duration, we compute a deadline
# in nanoseconds and wait for the new deadline time to arrive. This
# helps the long term frequency of measurements better match the desired
# frequency.
t0 = deadline = time.monotonic_ns()
# As long as their finger is over the sensor, capture data
while sensor.proximity >= PROXIMITY_THRESHOLD_LO:
deadline += dt
sleep_deadline(deadline)
value = sum(sensor.color_data) # Combination of all channels
data = np.roll(data, 1)
data[-1] = value
# Compute the new filtered variable by applying the filter to the
# recent data samples
filtered = np.sum(data * taps)
# We gathered enough data to fill the filters, and
# the light value crossed the zero line in the positive direction
# Therefore we need to record a pulse
if n > len(taps) and old_value < 0 <= filtered:
# This crossing time is estimated, but it increases the pulse
# estimate resolution quite a bit. If only the nearest 1/8s
# was used for pulse estimation, the smallest pulse increment
# that can be measured is 7.5bpm.
cross = estimated_cross_time(old_value, filtered, deadline)
# store this pulse time (in seconds since sensor-touch)
pulse_times.append((cross - t0) * 1e-9)
# and maybe delete an old pulse time
del pulse_times[:-10]
# And compute a rate based on the last recorded pulse times
if len(pulse_times) > 1:
rate = 60 / (pulse_times[-1] -
pulse_times[0]) * (len(pulse_times) - 1)
old_value = filtered
# We gathered enough data to fill the filters, so report the light
# value and possibly the estimated pulse rate
if n > len(taps):
print((filtered, rate))
n += 1
# Turn off the sensor and the LED and go back to the top for another run
sensor.enable_color = False
white_leds.value = False
print()
data = np.zeros(len(taps))
t0 = deadline = time.monotonic_ns()
n = 0
# Take an initial reading to subtract off later, so that the graph in mu
# accentuates the small short term changes in pressure rather than the large
# DC offset of around 980
offset = sensor.pressure
while True:
deadline += dt
sleep_deadline(deadline)
# Move the trace near the origin so small differences can be seen in the mu
# plot window .. you wouldn't do this subtraction step if you are really
# interested in absolute barometric pressure.
value = sensor.pressure - offset
if n == 0:
# The first time, fill the filter with the initial value
data = data + value
else:
# Otherwise, add it as the next sample
data = np.roll(data, 1)
data[-1] = value
filtered = np.sum(data * taps)
# Actually print every 10th value. This prints about 1.6 values per
# second. You can print values more quickly by removing the 'if' and
# making the print unconditional, or change the frequency of prints up
# or down by changing the number '10'.
if n % 10 == 0:
print((filtered, value))
n += 1
def normalized_rms_ulab(values):
# this function works with ndarrays only
minbuf = np.mean(values)
values = values - minbuf
samples_sum = np.sum(values * values)
return math.sqrt(samples_sum / len(values))