def main():
max_all = 10
while True:
mic.record(samples_bit, len(samples_bit))
samples = np.array(samples_bit[3:])
spectrogram1 = spectrogram(samples)
# spectrum() is always nonnegative, but add a tiny value
# to change any zeros to nonzero numbers
spectrogram1 = np.log(spectrogram1 + 1e-7)
spectrogram1 = spectrogram1[1:(fft_size // 2) - 1]
min_curr = np.min(spectrogram1)
max_curr = np.max(spectrogram1)
if max_curr > max_all:
max_all = max_curr
else:
max_curr = max_curr - 1
print(min_curr, max_all)
min_curr = max(min_curr, 3)
# Plot FFT
data = (spectrogram1 - min_curr) * (51. / (max_all - min_curr))
# This clamps any negative numbers to zero
data = data * np.array((data > 0))
graph.show(data)
def compute_corr(self, X_test):
result = {}
Cxx = np.dot(
X_test,
X_test.transpose()) # precompute data auto correlation matrix
for f in self.stim_freqs:
Y = harmonic_reference(f,
self.fs,
np.max(X_test.shape),
Nh=self.Nh,
standardise_out=False)
rho = self.cca_eig(
X_test, Y,
Cxx=Cxx) # canonical variable matrices. Xc = X^T.W_x
result[f] = rho
return result
def main():
# value for audio samples
max_all = 10
# variable to move data along the matrix
scroll_offset = 0
# setting the y axis value to equal the scroll_offset
y = scroll_offset
while True:
# record the audio sample
mic.record(samples_bit, len(samples_bit))
# send the sample to the ulab array
samples = np.array(samples_bit[3:])
# creates a spectogram of the data
spectrogram1 = spectrogram(samples)
# spectrum() is always nonnegative, but add a tiny value
# to change any zeros to nonzero numbers
spectrogram1 = np.log(spectrogram1 + 1e-7)
spectrogram1 = spectrogram1[1:(fft_size//2)-1]
# sets range of the spectrogram
min_curr = np.min(spectrogram1)
max_curr = np.max(spectrogram1)
# resets values
if max_curr > max_all:
max_all = max_curr
else:
max_curr = max_curr-1
min_curr = max(min_curr, 3)
# stores spectrogram in data
data = (spectrogram1 - min_curr) * (51. / (max_all - min_curr))
# sets negative numbers to zero
data = data * np.array((data > 0))
# resets y
y = scroll_offset
# runs waves to write data to the LED's
waves(data, y)
# updates scroll_offset to move data along matrix
scroll_offset = (y + 1) % 9
# writes data to the RGB matrix
is31.show()
range(250 - 5, 250)],
dtype=np.int16)
print(np.min(a))
print(np.min(a, axis=0))
print(np.min(a, axis=1))
a = 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.min(a))
print(np.min(a, axis=0))
print(np.min(a, axis=1))
print("Testing np.max:")
print(np.max([1]))
print(np.max(np.array([1], dtype=np.float)))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.uint8)
print(np.max(a))
print(np.max(a, axis=0))
print(np.max(a, axis=1))
a = np.array([range(255 - 5, 255),
range(240 - 5, 240),
range(250 - 5, 250)],
dtype=np.uint8)
print(np.max(a))
print(np.max(a, axis=0))
print(np.max(a, axis=1))
a = np.array([range(255 - 5, 255),
range(240 - 5, 240),
range(250 - 5, 250)],
# around the wires or sometimes an I2C device just gets wedged. To more
# robustly handle the latter, the code will restart if that happens.
try:
mic.record(rec_buf, fft_size) # Record batch of 16-bit samples
samples = np.array(rec_buf) # Convert to ndarray
# Compute spectrogram and trim results. Only the left half is
# normally needed (right half is mirrored), but we trim further as
# only the low_bin to high_bin elements are interesting to graph.
spectrum = spectrogram(samples)[low_bin:high_bin + 1]
# Linearize spectrum output. spectrogram() is always nonnegative,
# but add a tiny value to change any zeros to nonzero numbers
# (avoids rare 'inf' error)
spectrum = np.log(spectrum + 1e-7)
# Determine minimum & maximum across all spectrum bins, with limits
lower = max(np.min(spectrum), 4)
upper = min(max(np.max(spectrum), lower + 6), 20)
# Adjust dynamic level to current spectrum output, keeps the graph
# 'lively' as ambient volume changes. Sparkle but don't saturate.
if upper > dynamic_level:
# Got louder. Move level up quickly but allow initial "bump."
dynamic_level = upper * 0.7 + dynamic_level * 0.3
else:
# Got quieter. Ease level down, else too many bumps.
dynamic_level = dynamic_level * 0.5 + lower * 0.5
# Apply vertical scale to spectrum data. Results may exceed
# matrix height...that's OK, adds impact!
data = (spectrum - lower) * (7 / (dynamic_level - lower))
for column, element in enumerate(column_table):
import time, gc, os
from ulab import numpy as np
from machine import PIN
digi = Pin(33, Pin.IN)
pot = ADC(Pin(32))
pot.atten(ADC.ATTN_11DB)
pot.width(ADC.WIDTH_11BIT)
TIMING = [0] * 50
vals = [0] * 50
ticks = 0
while True:
ticks = ticks + 1
TIMING[ticks % len(TIMING)] = time.ticks_us()
vals[ticks % len(TIMING)] = pot.read()
if time.ticks_us() - ticks >= 0:
pass
if ticks > 100000:
break
a = 1000 * 1000 * len(TIMING) / ((np.max(TIMING) - np.min(TIMING)))