def test_copy(self):
g1 = Autoincrement()
g2 = g1.copy()
g2.get_single()
assert g1.get_single() == g2.get_single() - 1
def test_stream_single(self):
g = Autoincrement()
c = 0
for i in range(1000):
for val in g.stream_single():
assert val == c
c += 1
if c > 100000:
break
def test_values_batch(self):
ai = Autoincrement(start=-4.2, step=+4.2, dtype=np.float64)
count = 0
for i in range(10):
nb = np.random.randint(2, 10000)
values = ai.get_batch(nb)
for j, val in enumerate(values):
d = (Decimal(-4.2) + ((Decimal(count + j)) * Decimal(4.2)))
assert round(Decimal(val), 1) == round(d, 1)
count += nb
def test_stream_batch(self):
g = Autoincrement()
c = 0
for i in range(10):
nb = np.random.randint(2, 1000)
for values in g.stream_batch(nb):
for val in values:
assert val == c
c += 1
if c > 100000:
break
def test_values_batch(self):
gen = History(Autoincrement(), 42)
for i, v in enumerate(gen.get_batch(10)):
assert i == v
for i in range(10):
assert gen.get_prev(-10+i) == i
def __init__(self, gen_params: dict):
super().__init__(gen_params)
self.is_thread_safe = True
self.sequenceBegin = gen_params['sequenceBegin']
self.gen = Autoincrement(start=self.sequenceBegin,
step=1,
dtype=np.int32)
def test_values_single(self):
triangular_fun = BoundingOperator(
ApplyFunctionOperator(function=lambda x: abs((x % 4) - 2) - 1, generator=Autoincrement()), lb=-1, ub=1)
n = ScalingOperator(generator=triangular_fun, lb=-10, ub=10, dtype=np.float32)
for _ in range(10000):
assert n.get_single() == 10
assert n.get_single() == 0
assert n.get_single() == -10
assert n.get_single() == 0
def test_values_single(self):
triangular_fun = BoundingOperator(
ApplyFunctionOperator(function=lambda x: abs((x % 4) - 2) - 1, generator=Autoincrement()), lb=-1, ub=1)
rd = RandomDatetime(generator=triangular_fun, start=np.datetime64("1950"), end=np.datetime64("2042"), unit="Y")
for i in range(10000):
assert rd.get_single() == np.datetime64('2042')
assert rd.get_single() == np.datetime64('1996')
assert rd.get_single() == np.datetime64('1950')
assert rd.get_single() == np.datetime64('1996')
def test_values_single(self):
gen = History(Autoincrement(), 42)
for i in range(10):
assert i == gen.get_single()
for i in range(10):
assert gen.get_prev(-10+i) == i
for i in range(10):
assert i + 10 == gen.get_single()
for i in range(10):
assert gen.get_prev(-10+i) == i + 10
def test_values_batch(self):
triangular_fun = BoundingOperator(
ApplyFunctionOperator(function=lambda x: abs((x % 4) - 2) - 1, generator=Autoincrement()), lb=-1, ub=1)
rd = RandomDatetime(generator=triangular_fun, start=np.datetime64("1950"), end=np.datetime64("2042"), unit="Y")
tmp = [np.datetime64('2042'), np.datetime64('1996'), np.datetime64('1950'), np.datetime64('1996')]
count = 0
for i in range(10):
nb = np.random.randint(2, 1000)
values = rd.get_batch(nb)
for j, val in enumerate(values):
assert val == tmp[(count + j) % 4]
count += nb
def test_values_batch(self):
triangular_fun = BoundingOperator(
ApplyFunctionOperator(function=lambda x: abs((x % 4) - 2) - 1, generator=Autoincrement()), lb=-1, ub=1)
n = ScalingOperator(generator=triangular_fun, lb=-10, ub=10, dtype=np.float32)
tmp = [10, 0, -10, 0]
count = 0
for i in range(10):
nb = np.random.randint(2, 1000)
values = n.get_batch(nb)
for j, val in enumerate(values):
assert val == tmp[(count + j) % 4]
count += nb
def _get_ts(self):
time = Autoincrement()
data = ConstantValueGenerator(value=42, dtype=np.uint16)
return TimeSeries(time_gen=time, data_gen=data)
def test_values_batch(self):
gen = MeanHistory(Autoincrement(start=4), 4, initial_values=[0,1,2,3])
for i in range(42):
for j, v in enumerate(gen.get_batch(10)):
assert v == ((i * 10 + j) * 4.0 + 6.0) / 4.0
def test_values_single(self):
gen = MeanHistory(Autoincrement(start=4), 4, initial_values=[0,1,2,3])
for i in range(42):
assert gen.get_single() == (i * 4.0 + 6.0) / 4.0
def _get_afo(self, fun):
generator = Autoincrement()
afo = ApplyFunctionOperator(function=fun, generator=generator)
return afo
def test_type(self):
ai = Autoincrement(start=-42, step=+42, dtype=np.int32)
assert ai.get_single() == -42
assert isinstance(ai.get_batch(42), np.ndarray)
def test_shape(self):
ai = Autoincrement(start=-42, step=+42, dtype=np.int32)
nb = np.random.randint(2, 10000)
assert ai.get_batch(nb).shape == (nb,)
def test_values_single(self):
ai = Autoincrement(start=-42, step=+42, dtype=np.int32)
for i in range(np.random.randint(10, 10000)):
val = ai.get_single()
assert val == -42 + i * 42