async def do_pcm_test_fft_correction(dut, pdm_fname, test_num):
'''Correct for fft innacuracies.'''
print('Starting pcm test with fft correction.')
x = np.load(pdm_fname, allow_pickle=True)
x = parse_mic_data.pad_pdm(x) # pad half-second signal to 1 second
x = pdm.pdm_to_pcm(x, 2)
y = aco.aco(x)
cocotb.fork(write_pcm_input(dut, x)) # change on falling edge of pdm clk
fft_out = await check_fft(dut, y[2], test_num)
wake_bad = await read_wake_no_assert(dut) # non-deterministic wake
print('Running through with known fft value.')
for i in range(8000):
await FallingEdge(dut.wb_clk_i)
y = aco.aco(x, fft_override=fft_out)
cocotb.fork(write_pcm_input(dut, x)) # change on falling edge of pdm clk
# fft_out = await check_fft(dut, y[2], test_num)
cocotb.fork(check_final(dut, y[8], test_num))
aco_out = y[-1]
aco_out = aco_out.reshape((int(aco_out.size / 13), 13))
wrd_out = na.get_numpy_pred(aco_out)[0]
wake_expected = (wrd_out[0] > wrd_out[1])
await read_wake(dut, wake_expected)
print('Finished test.')
return wake_expected # assert ensured expected is observed
def get_cosine_test_vector():
'''Get a max amplitude cosine test vector to try saturate the pipeline
with.'''
t = np.linspace(0, 1, 16000)
f = 20 # Hz
A = 2**15 - 1 # max 16b signed amplitude
x = A * np.cos(2 * np.pi * f * t)
x = aco.pdm_model(x, 'fast')
sigs = aco.aco(x)
return x, sigs
def test_on_set(foo, params):
global best_time, best_set, best_q
s, d, t, q = params[0], params[1], params[2], params[3]
st = time.clock()
res = aco(m, s[0], d, t, s[1], s[2], s[3], q)
en = time.clock() - st
if res in good_answers:
if best_time is None or en < best_time:
best_time = en
best_set = s
best_q = q
def get_multi_cosine_test_vector():
'''Get a max amplitude cosine test vector to try saturate the pipeline
with.'''
t = np.linspace(0, 1, 16000)
n_freqs = 100
freqs = np.logspace(0, np.log10(16000), 10)
A = (2**15 - 1) / n_freqs # max 16b signed amplitude
x = np.zeros(16000)
for f in freqs:
x += A * np.cos(2 * np.pi * f * t)
x = aco.pdm_model(x, 'fast')
sigs = aco.aco(x)
return x, sigs
def get_random_sample_test_vector(test_num):
top_dir = '../../../py/'
categories = ['yes/', 'noise/', 'no/', 'unknown/']
category = categories[test_num % 4]
sample_dir = top_dir + category
fnames = os.listdir(sample_dir)
idx = np.random.randint(len(fnames))
fname = sample_dir + fnames[idx]
print('Running test with', fname)
x = pdm.read_sample_file(fname)
x = aco.pdm_model(x, 'fast')
sigs = aco.aco(x)
return x, sigs
def process_pdm_wake(source='mic', method='cic2'):
if source[-4:] == '.wav': # 16b wavfile saved on computer
fs, x = wavfile.read(source)
x = x[:16000] / 2**8
dfe_out = x
elif source == 'mic': # pdm microphone
x = get_pdm(serial_port=TEENSY_PORT)
x = pad_pdm(x)
elif source[-4:] == '.npy': # saved pdm sample
x = np.load(source, allow_pickle=True)
x = pad_pdm(x)
else:
raise Exception('unkown source ' + source)
if source[-4:] != '.wav': # process pdm to pcm if not a wav file
if method == 'cic1':
dfe_out = pdm.pdm_to_pcm(x, 1).astype(np.int16)
dfe_out *= 8
dfe_out = np.clip(dfe_out, -128, 127).astype(np.int8)
elif method == 'cic2':
dfe_out = pdm.pdm_to_pcm(x, 2)
# dfe_out = dfe_out / 2**5 # scaling now in pdm.py
# dfe_out = np.clip(dfe_out, -128, 127).astype(np.int8)
elif method == 'ideal': # ideal decimation
dfe_out = signal.decimate(x, 10)
dfe_out = signal.decimate(dfe_out, 5)
dfe_out = signal.decimate(dfe_out, 5)
else:
raise Exception('unkown method ' + method)
detect_max(dfe_out, 'dfe_out')
if False: # rescaling
dfe_out = dfe_out.astype(np.float32)
dfe_out[:5] = dfe_out.mean()
dfe_out -= dfe_out.min()
dfe_out *= 2**8 / dfe_out.max()
dfe_out -= 2**7
dfe_out = dfe_out.astype(np.int8)
aco_out = aco.aco(dfe_out)[-1]
aco_out = aco_out.reshape((int(aco_out.size / 13), 13))
wrd_out = na.get_numpy_pred(aco_out)[0]
wake = (wrd_out[0] > wrd_out[1])
if wake:
print('WAKE!')
else:
print('sleep.')
return x, dfe_out, wake
def test_fix_p():
D = np.array([[0, 6, 3, 7, 1], [6, 0, 8, 10, 6], [3, 8, 0, 6, 7],
[7, 10, 6, 0, 5], [1, 6, 7, 5, 0]])
good_answers = ([0, 4, 1, 3, 2], 26), ([3, 4, 0, 1, 2], 26), \
([2, 0, 4, 1, 3], 26), ([2, 3, 4, 1, 0], 26), \
([1, 4, 0, 2, 3], 26), ([0, 4, 3, 2, 1], 26)
m = 5 # amount of ants and cities
Q = MAX_DIS * m // 2 # coefficient of the alleged best way
P = (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
) # coefficient of evaporation
a = 2 # coefficient of strengthen the sense of smell
b = 1 # coefficient of strengthen of desire
e = 2 # amount of elite ants
gen = 200
timing = [None for i in range(len(P))]
l = []
for j in (0, 1, 2):
for t in range(TIMES):
for p in P:
st = time.clock()
res = aco(m, e, D, gen, a, b, p, Q)
en = time.clock() - st
if res in good_answers:
if timing[P.index(p)] is None or en < timing[P.index(p)]:
timing[P.index(p)] = en
print(timing)
l.append(timing)
timing = [None for i in range(len(P))]
plt.plot(P, l[0], P, l[1], P, l[2])
plt.xlabel(r'$p$')
plt.ylabel(r'$Time(sec)$')
plt.title(r'Вариация параметра p')
plt.grid(True)
plt.show()
def test_fix_Q():
D = np.array([[0, 6, 3, 7, 1], [6, 0, 8, 10, 6], [3, 8, 0, 6, 7],
[7, 10, 6, 0, 5], [1, 6, 7, 5, 0]])
good_answers = ([0, 4, 1, 3, 2], 26), ([3, 4, 0, 1, 2], 26), \
([2, 0, 4, 1, 3], 26), ([2, 3, 4, 1, 0], 26), \
([1, 4, 0, 2, 3], 26), ([0, 4, 3, 2, 1], 26)
m = 5 # amount of ants and cities
Q = range(MIN_DIS, MAX_DIS * m) # coefficient of the alleged best way
timing = [None for i in range(len(Q))]
l = []
p = 0.2 # coefficient of evaporation
a = 1 # coefficient of strengthen the sense of smell
b = 1 # coefficient of strengthen of desire
e = 1 # amount of elite ants
gen = 200
for j in (0, 1, 2):
for t in range(TIMES):
for q in Q:
st = time.clock()
res = aco(m, e, D, gen, a, b, p, q)
en = time.clock() - st
if res in good_answers:
if timing[Q.index(q)] is None or en < timing[Q.index(q)]:
timing[Q.index(q)] = en
print(timing)
l.append(timing)
timing = [None for i in range(len(Q))]
plt.plot(Q, l[0], Q, l[1], Q, l[2])
plt.xlabel(r'$Q$')
plt.ylabel(r'$Time(sec)$')
plt.title(r'Вариация параметра Q')
plt.grid(True)
plt.show()
async def do_test(dut, test_num):
print('Beginning test #{}.'.format(test_num))
await reset(dut) # reset to clear out previous values in pipeline
dut.en_i <= 1
if test_num == 0:
await do_test_no_en(dut)
return
elif test_num == 1:
x, y = get_sample_test_vector()
elif test_num == 2:
x, y = get_cosine_test_vector()
elif test_num == 3:
x, y = get_multi_cosine_test_vector()
elif test_num == 4:
x, y = get_random_test_vector()
else:
x, y = get_random_sample_test_vector(test_num)
cocotb.fork(write_input(dut, x))
cocotb.fork(check_preemphasis(dut, y[0]))
cocotb.fork(check_framing(dut, y[1]))
fft_out = await check_fft(dut, y[2], test_num)
# Take RTL FFT output and feed it to the ACO model to obtain the rest of the
# expected signals. This is because we don't have a bit-accurate model of
# the FFT.
y = aco.aco(x, fft_override=fft_out)
await reset(dut) # reset to clear out previous values in pipeline
dut.en_i <= 1
cocotb.fork(write_input(dut, x)) # rewrite the input
cocotb.fork(check_preemphasis(dut, y[0]))
cocotb.fork(check_framing(dut, y[1]))
cocotb.fork(check_fft(dut, y[2], test_num))
cocotb.fork(check_power_spectrum(dut, y[3], test_num))
cocotb.fork(check_filterbank(dut, y[4], test_num))
cocotb.fork(check_log(dut, y[5], test_num))
cocotb.fork(check_dct(dut, y[6], test_num))
cocotb.fork(check_quant(dut, y[7], test_num))
await check_final(dut, y[8], test_num)
print()
def get_random_test_vector():
x = np.random.randint(-2**15, 2**15 - 1, size=16000)
x = aco.pdm_model(x, 'fast')
sigs = aco.aco(x)
return x, sigs
def get_sample_test_vector():
'''Get a real audio sample for input and calculate the expected outputs.'''
x = pdm.read_sample_file(pdm.SAMPLE_FNAME)
x_distorted = aco.pdm_model(x, 'fast')
sigs = aco.aco(x_distorted)
return x_distorted, sigs
contador = 1
nombre_instancia = "Instancia1.txt"
resultados = []
ejemploInstancias = InstanciaTXT(5, 10)
max_valor = 0
cont_max_valor = 0
sumatorio_tiempos = 0
#
while contador <= numero_de_iteraciones:
tiempo_inicial = time()
datosInstancia = ejemploInstancias.leeInstancia(nombre_instancia)
solucion = aco(datosInstancia, 100, 20, 5.0)
datos = solucion.solucion()
tiempo_final = time()
peso = 0
valor = 0
array = str(datos)
for i in datos:
peso = peso + i[1]
valor = valor + i[2]
if max_valor == valor:
cont_max_valor = cont_max_valor + 1
if max_valor < valor:
max_valor = valor