def bus_from_polar(bus):
"""
Convert bus voltage from polar to rectangular.
"""
bus = bus.clone()
V = torch.polar(bus[:, 0, :], bus[:, 1, :])
bus[:, 0, :] = V.real
bus[:, 1, :] = V.imag
return bus
def tensor_creation_ops(self):
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
v = torch.tensor([3, 4, 5], dtype=torch.float32)
real = torch.tensor([1, 2], dtype=torch.float32)
imag = torch.tensor([3, 4], dtype=torch.float32)
inp = torch.tensor([-1.5, 0.0, 2.0])
values = torch.tensor([0.5])
quantized = torch.quantize_per_channel(
torch.tensor([[-1.0, 0.0], [1.0, 2.0]]),
torch.tensor([0.1, 0.01]),
torch.tensor([10, 0]),
0,
torch.quint8,
)
return (
torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]]),
# torch.sparse_coo_tensor(i, v, [2, 3]), # not work for iOS
torch.as_tensor([1, 2, 3]),
torch.as_strided(torch.randn(3, 3), (2, 2), (1, 2)),
torch.zeros(2, 3),
torch.zeros((2, 3)),
torch.zeros([2, 3], out=i),
torch.zeros(5),
torch.zeros_like(torch.empty(2, 3)),
torch.ones(2, 3),
torch.ones((2, 3)),
torch.ones([2, 3]),
torch.ones(5),
torch.ones_like(torch.empty(2, 3)),
torch.arange(5),
torch.arange(1, 4),
torch.arange(1, 2.5, 0.5),
torch.range(1, 4),
torch.range(1, 4, 0.5),
torch.linspace(3.0, 3.0, steps=1),
torch.logspace(start=2, end=2, steps=1, base=2.0),
torch.eye(3),
torch.empty(2, 3),
torch.empty_like(torch.empty(2, 3), dtype=torch.int64),
torch.empty_strided((2, 3), (1, 2)),
torch.full((2, 3), 3.141592),
torch.full_like(torch.full((2, 3), 3.141592), 2.71828),
torch.quantize_per_tensor(
torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8
),
torch.dequantize(quantized),
torch.complex(real, imag),
torch.polar(real, imag),
torch.heaviside(inp, values),
)
def enforce_constraints(self, V, S, Sd):
Sg = S + Sd
vm_constraint = self.powerflow_parameters.constraints[
"inequality/voltage_magnitude"]
active_constraint = self.powerflow_parameters.constraints[
"inequality/active_power"]
reactive_constraint = self.powerflow_parameters.constraints[
"inequality/reactive_power"]
vm = self.sigmoid_bound(V.abs(), vm_constraint.min, vm_constraint.max)
V = torch.polar(vm, V.angle()) # V * vm / V.abs()
Sg.real = self.sigmoid_bound(Sg.real, active_constraint.min,
active_constraint.max)
Sg.imag = self.sigmoid_bound(Sg.imag, reactive_constraint.min,
reactive_constraint.max)
S = Sg - Sd
return V, S
def phase_vocoder(
complex_specgrams: Tensor,
rate: float,
phase_advance: Tensor
) -> Tensor:
r"""Given a STFT tensor, speed up in time without modifying pitch by a
factor of ``rate``.
Args:
complex_specgrams (Tensor):
Either a real tensor of dimension of ``(..., freq, num_frame, complex=2)``
or a tensor of dimension ``(..., freq, num_frame)`` with complex dtype.
rate (float): Speed-up factor
phase_advance (Tensor): Expected phase advance in each bin. Dimension of (freq, 1)
Returns:
Tensor:
Stretched spectrogram. The resulting tensor is of the same dtype as the input
spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``.
Example - With Tensor of complex dtype
>>> freq, hop_length = 1025, 512
>>> # (channel, freq, time)
>>> complex_specgrams = torch.randn(2, freq, 300, dtype=torch.cfloat)
>>> rate = 1.3 # Speed up by 30%
>>> phase_advance = torch.linspace(
>>> 0, math.pi * hop_length, freq)[..., None]
>>> x = phase_vocoder(complex_specgrams, rate, phase_advance)
>>> x.shape # with 231 == ceil(300 / 1.3)
torch.Size([2, 1025, 231])
Example - With Tensor of real dtype and extra dimension for complex field
>>> freq, hop_length = 1025, 512
>>> # (channel, freq, time, complex=2)
>>> complex_specgrams = torch.randn(2, freq, 300, 2)
>>> rate = 1.3 # Speed up by 30%
>>> phase_advance = torch.linspace(
>>> 0, math.pi * hop_length, freq)[..., None]
>>> x = phase_vocoder(complex_specgrams, rate, phase_advance)
>>> x.shape # with 231 == ceil(300 / 1.3)
torch.Size([2, 1025, 231, 2])
"""
if rate == 1.0:
return complex_specgrams
if not complex_specgrams.is_complex() and complex_specgrams.size(-1) != 2:
raise ValueError(
"complex_specgrams must be either native complex tensors or "
"real valued tensors with shape (..., 2)")
is_complex = complex_specgrams.is_complex()
if not is_complex:
complex_specgrams = torch.view_as_complex(complex_specgrams)
# pack batch
shape = complex_specgrams.size()
complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-2:]))
# Figures out the corresponding real dtype, i.e. complex128 -> float64, complex64 -> float32
# Note torch.real is a view so it does not incur any memory copy.
real_dtype = torch.real(complex_specgrams).dtype
time_steps = torch.arange(
0,
complex_specgrams.size(-1),
rate,
device=complex_specgrams.device,
dtype=real_dtype)
alphas = time_steps % 1.0
phase_0 = complex_specgrams[..., :1].angle()
# Time Padding
complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 2])
# (new_bins, freq, 2)
complex_specgrams_0 = complex_specgrams.index_select(-1, time_steps.long())
complex_specgrams_1 = complex_specgrams.index_select(-1, (time_steps + 1).long())
angle_0 = complex_specgrams_0.angle()
angle_1 = complex_specgrams_1.angle()
norm_0 = complex_specgrams_0.abs()
norm_1 = complex_specgrams_1.abs()
phase = angle_1 - angle_0 - phase_advance
phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi))
# Compute Phase Accum
phase = phase + phase_advance
phase = torch.cat([phase_0, phase[..., :-1]], dim=-1)
phase_acc = torch.cumsum(phase, -1)
mag = alphas * norm_1 + (1 - alphas) * norm_0
complex_specgrams_stretch = torch.polar(mag, phase_acc)
# unpack batch
complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-2] + complex_specgrams_stretch.shape[1:])
if not is_complex:
return torch.view_as_real(complex_specgrams_stretch)
return complex_specgrams_stretch
# torch.quantize_per_tensor
reveal_type(
torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10,
torch.quint8)) # E: {Tensor}
# torch.quantize_per_channel
x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
quant = torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]),
torch.tensor([10, 0]), 0, torch.quint8)
reveal_type(x) # E: {Tensor}
# torch.dequantize
reveal_type(torch.dequantize(x)) # E: {Tensor}
# torch.complex
real = torch.tensor([1, 2], dtype=torch.float32)
imag = torch.tensor([3, 4], dtype=torch.float32)
reveal_type(torch.complex(real, imag)) # E: {Tensor}
# torch.polar
abs = torch.tensor([1, 2], dtype=torch.float64)
pi = torch.acos(torch.zeros(1)).item() * 2
angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
reveal_type(torch.polar(abs, angle)) # E: {Tensor}
# torch.heaviside
inp = torch.tensor([-1.5, 0, 2.0])
values = torch.tensor([0.5])
reveal_type(torch.heaviside(inp, values)) # E: {Tensor}
def phase(self, new_phase):
self.u = torch.polar(self.amplitude,
new_phase.broadcast_to(self.shape))
def amplitude(self, new_amplitude):
self.u = torch.polar(new_amplitude.broadcast_to(self.shape),
self.phase)
# torch.quantize_per_tensor
reveal_type(
torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10,
torch.quint8)) # E: torch.tensor.Tensor
# torch.quantize_per_channel
x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
quant = torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]),
torch.tensor([10, 0]), 0, torch.quint8)
reveal_type(x) # E: torch.tensor.Tensor
# torch.dequantize
reveal_type(torch.dequantize(x)) # E: torch.tensor.Tensor
# torch.complex
real = torch.tensor([1, 2], dtype=torch.float32)
imag = torch.tensor([3, 4], dtype=torch.float32)
reveal_type(torch.complex(real, imag)) # E: torch.tensor.Tensor
# torch.polar
abs = torch.tensor([1, 2], dtype=torch.float64)
pi = torch.acos(torch.zeros(1)).item() * 2
angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
reveal_type(torch.polar(abs, angle)) # E: torch.tensor.Tensor
# torch.heaviside
inp = torch.tensor([-1.5, 0, 2.0])
values = torch.tensor([0.5])
reveal_type(torch.heaviside(inp, values)) # E: torch.tensor.Tensor
torch.full((2, 3), 3.141592)
torch.full_like(torch.full((2, 3), 3.141592), 2.71828)
# torch.quantize_per_tensor
torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10,
torch.quint8)
# torch.quantize_per_channel
x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
quant = torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]),
torch.tensor([10, 0]), 0, torch.quint8)
# torch.dequantize
torch.dequantize(x)
# torch.complex
real = torch.tensor([1, 2], dtype=torch.float32)
imag = torch.tensor([3, 4], dtype=torch.float32)
torch.complex(real, imag)
# torch.polar
abs = torch.tensor([1, 2], dtype=torch.float64)
pi = torch.acos(torch.zeros(1)).item() * 2
angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
torch.polar(abs, angle)
# torch.heaviside
inp = torch.tensor([-1.5, 0, 2.0])
values = torch.tensor([0.5])
torch.heaviside(inp, values)
