def test_process_output_1ch():
plug = vh.load_plugin(plugin_key, rate, vh.ADAPT_NONE)
plug.initialise(1, 2, 2)
try:
# Too many channels
result = plug.process_block([[3, 4], [5, 6]], vh.RealTime(0, 0))
assert False
except TypeError:
pass
result = plug.process_block([[3, 3]], vh.RealTime(0, 0))
assert result[9] == [{"label": "", "values": np.array([5.0])}]
result = plug.process_block([[3, 0]], vh.RealTime(0, 0))
assert result[9] == [{"label": "", "values": np.array([4.0])}]
def get_feature_step_time(sample_rate, step_size, output_desc):
if output_desc["sampleType"] == vampyhost.ONE_SAMPLE_PER_STEP:
return vampyhost.frame_to_realtime(step_size, sample_rate)
elif output_desc["sampleType"] == vampyhost.FIXED_SAMPLE_RATE:
return vampyhost.RealTime('seconds', 1.0 / output_desc["sampleRate"])
else:
return 1
def test_process_without_initialise():
plug = vh.load_plugin(plugin_key, rate, vh.ADAPT_NONE)
try:
plug.process_block([[1, 2, 3, 4]], vh.RealTime(0, 0))
assert False
except Exception:
pass
def test_process_output_2ch():
plug = vh.load_plugin(plugin_key, rate, vh.ADAPT_NONE)
plug.initialise(2, 2, 2)
try:
# Too few channels
result = plug.process_block([[3, 4]], vh.RealTime(0, 0))
assert False
except TypeError:
pass
try:
# Too many channels
result = plug.process_block([[3, 4], [5, 6], [7, 8]],
vh.RealTime(0, 0))
assert False
except TypeError:
pass
result = plug.process_block([[3, 3], [4, 4]], vh.RealTime(0, 0))
assert (result[9][0]["values"] == np.array([5.0, 6.0])).all()
result = plug.process_block([[3, 0], [4, 0]], vh.RealTime(0, 0))
assert (result[9][0]["values"] == np.array([4.0, 5.0])).all()
def test_process_input_format():
plug = vh.load_plugin(plugin_key, rate, vh.ADAPT_NONE)
plug.initialise(2, 4, 4) # channels, stepsize, blocksize
result = plug.process_block([[1, 2, 3, 4], [5, 6, 7, 8]],
vh.RealTime(0, 0))
result = plug.process_block(
[np.array([1, 2, 3, 4]),
np.array([5, 6, 7, 8])], vh.RealTime(0, 0))
result = plug.process_block(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]),
vh.RealTime(0, 0))
try:
# Wrong number of channels
result = plug.process_block(np.array([[1, 2, 3, 4]]),
vh.RealTime(0, 0))
assert False
except TypeError:
pass
try:
# Wrong number of samples per channel
result = plug.process_block(np.array([[1, 2, 3], [4, 5, 6]]),
vh.RealTime(0, 0))
assert False
except TypeError:
pass
try:
# Differing numbers of samples per channel
result = plug.process_block(np.array([[1, 2, 3, 4], [5, 6, 7]]),
vh.RealTime(0, 0))
assert False
except TypeError:
pass
def vampy_decoder(dct):
if '__realtime__' in dct:
x = dct['__realtime__'][0]
y = dct['__realtime__'][1]
return vampyhost.RealTime(x, y)
if '__float32__' in dct:
# print('__float32__')
# print(type(dct['__float32__']))
return numpy.float32(dct['__float32__'])
if '__ndarray__' in dct:
# print('__ndarray__')
# print(dct['__ndarray__'])
x = numpy.array(dct['__ndarray__'])
return x
print(type(dct))
print(dct)
return dct
def timestamp_features(sample_rate, step_size, output_desc, features):
n = -1
if output_desc["sampleType"] == vampyhost.ONE_SAMPLE_PER_STEP:
for f in features:
n = n + 1
t = vampyhost.frame_to_realtime(n * step_size, sample_rate)
f["timestamp"] = t
yield f
elif output_desc["sampleType"] == vampyhost.FIXED_SAMPLE_RATE:
output_rate = output_desc["sampleRate"]
for f in features:
if "has_timestamp" in f:
n = int(f["timestamp"].to_float() * output_rate + 0.5)
else:
n = n + 1
f["timestamp"] = vampyhost.RealTime('seconds',
float(n) / output_rate)
yield f
else:
for f in features:
yield f