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5 Commits
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638440feff
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638440feff | ||
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785021b557 | ||
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42ac105240 | ||
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c2963f4a9f | ||
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a1e5f2fec7 |
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uv.lock
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uv.lock
@ -1,5 +1,9 @@
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wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/35/e8b2daf02ce933e4518e6f5682c72fd0ed66c15910ea1fb4168f442b71c4/torch-2.5.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:de5b7d6740c4b636ef4db92be922f0edc425b65ed78c5076c43c42d362a45457", size = 906474467 },
|
||||
{ url = "https://files.pythonhosted.org/packages/40/04/bd91593a4ca178ece93ca55f27e2783aa524aaccbfda66831d59a054c31e/torch-2.5.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:340ce0432cad0d37f5a31be666896e16788f1adf8ad7be481196b503dad675b9", size = 91919450 },
|
||||
{ url = "https://files.pythonhosted.org/packages/0d/4a/e51420d46cfc90562e85af2fee912237c662ab31140ab179e49bd69401d6/torch-2.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:603c52d2fe06433c18b747d25f5c333f9c1d58615620578c326d66f258686f9a", size = 203098237 },
|
||||
{ url = "https://files.pythonhosted.org/packages/d0/db/5d9cbfbc7968d79c5c09a0bc0bc3735da079f2fd07cc10498a62b320a480/torch-2.5.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:31f8c39660962f9ae4eeec995e3049b5492eb7360dd4f07377658ef4d728fa4c", size = 63884466 },
|
||||
{ url = "https://files.pythonhosted.org/packages/8b/5c/36c114d120bfe10f9323ed35061bc5878cc74f3f594003854b0ea298942f/torch-2.5.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:ed231a4b3a5952177fafb661213d690a72caaad97d5824dd4fc17ab9e15cec03", size = 906389343 },
|
||||
{ url = "https://files.pythonhosted.org/packages/6d/69/d8ada8b6e0a4257556d5b4ddeb4345ea8eeaaef3c98b60d1cca197c7ad8e/torch-2.5.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:3f4b7f10a247e0dcd7ea97dc2d3bfbfc90302ed36d7f3952b0008d0df264e697", size = 91811673 },
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||||
{ url = "https://files.pythonhosted.org/packages/5f/ba/607d013b55b9fd805db2a5c2662ec7551f1910b4eef39653eeaba182c5b2/torch-2.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:73e58e78f7d220917c5dbfad1a40e09df9929d3b95d25e57d9f8558f84c9a11c", size = 203046841 },
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||||
{ url = "https://files.pythonhosted.org/packages/57/6c/bf52ff061da33deb9f94f4121fde7ff3058812cb7d2036c97bc167793bd1/torch-2.5.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:8c712df61101964eb11910a846514011f0b6f5920c55dbf567bff8a34163d5b1", size = 63858109 },
|
||||
{ url = "https://files.pythonhosted.org/packages/69/72/20cb30f3b39a9face296491a86adb6ff8f1a47a897e4d14667e6cf89d5c3/torch-2.5.1-cp313-cp313-manylinux1_x86_64.whl", hash = "sha256:9b61edf3b4f6e3b0e0adda8b3960266b9009d02b37555971f4d1c8f7a05afed7", size = 906393265 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "filelock" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/86/17/d9a5cf4fcf46291856d1e90762e36cbabd2a56c7265da0d1d9508c8e3943/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c", size = 209506424 },
|
||||
{ url = "https://files.pythonhosted.org/packages/78/eb/65f5ba83c2a123f6498a3097746607e5b2f16add29e36765305e4ac7fdd8/triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc", size = 209551444 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typer"
|
||||
version = "0.15.1"
|
||||
@ -827,6 +1048,7 @@ dependencies = [
|
||||
{ name = "pyyaml" },
|
||||
{ name = "scipy" },
|
||||
{ name = "scipy-stubs" },
|
||||
{ name = "torch" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
|
||||
@ -842,6 +1064,7 @@ requires-dist = [
|
||||
{ name = "pyyaml", specifier = ">=6.0.2" },
|
||||
{ name = "scipy" },
|
||||
{ name = "scipy-stubs" },
|
||||
{ name = "torch" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
|
||||
|
||||
@ -3,6 +3,7 @@ import multiprocessing as mp
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import torch
|
||||
import typer
|
||||
|
||||
from .. import visualise
|
||||
@ -13,6 +14,7 @@ from . import file, globals
|
||||
|
||||
app = typer.Typer(callback=globals.main)
|
||||
logger = logging.getLogger(__name__)
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
@app.command()
|
||||
@ -36,19 +38,25 @@ def antennas() -> None:
|
||||
def heatmap() -> None:
|
||||
preprocessor = Preprocessor()
|
||||
aoa = AoA()
|
||||
manager = mp.Manager()
|
||||
webapp_queue: "mp.Queue[AoA]" = manager.Queue(config.sample_rate)
|
||||
|
||||
# Start webapp in background process
|
||||
manager = mp.Manager()
|
||||
webapp_queue: "mp.Queue[npt.NDArray[np.float32]]" = manager.Queue(
|
||||
config.sample_rate
|
||||
)
|
||||
webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
|
||||
webapp.start()
|
||||
|
||||
def callback(antenna_data: npt.NDArray[np.complex128]) -> None:
|
||||
def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
|
||||
logger.info(f"Got final CSI data with shape {antenna_data.shape}")
|
||||
processed = preprocessor.preprocess(antenna_data)
|
||||
# visualise.add_data(all_data, processed)
|
||||
aoa.update(processed)
|
||||
logger.info(f"Processed CSI data with shape {processed.shape}")
|
||||
processed_tensor = torch.tensor(processed, device=device)
|
||||
aoa.update(processed_tensor)
|
||||
|
||||
if not webapp_queue.full():
|
||||
webapp_queue.put(aoa)
|
||||
heatmap = aoa.heatmap()
|
||||
webapp_queue.put(heatmap)
|
||||
|
||||
globals.csi_producer(csi_callback=callback)
|
||||
logger.info("Finished processing CSI data")
|
||||
|
||||
@ -93,14 +93,14 @@ class CSIHeader:
|
||||
|
||||
class CSI:
|
||||
@staticmethod
|
||||
def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex128]:
|
||||
csi_matrix: npt.NDArray[np.complex128] = np.zeros(
|
||||
def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex64]:
|
||||
csi_matrix: npt.NDArray[np.complex64] = np.zeros(
|
||||
(
|
||||
header.num_subcarriers,
|
||||
header.num_rx,
|
||||
header.num_tx,
|
||||
),
|
||||
dtype=np.complex128,
|
||||
dtype=np.complex64,
|
||||
)
|
||||
pos = 0
|
||||
for j in range(header.num_rx):
|
||||
|
||||
@ -3,7 +3,7 @@ from typing import Callable, Protocol
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
CSICallback = Callable[[npt.NDArray[np.complex128]], None]
|
||||
CSICallback = Callable[[npt.NDArray[np.complex64]], None]
|
||||
|
||||
|
||||
class CSIProducer(Protocol):
|
||||
|
||||
@ -23,6 +23,13 @@ class MUSIC(BaseModel):
|
||||
eigval_threshold: int
|
||||
window_size: int
|
||||
|
||||
class Heatmap(BaseModel):
|
||||
theta_resolution: int
|
||||
tof_resolution: int
|
||||
tof_max: float
|
||||
|
||||
heatmap: Heatmap
|
||||
|
||||
|
||||
class Antennas(BaseModel):
|
||||
spacing: float
|
||||
|
||||
0
where_fi/processing/__init__.py
Normal file
0
where_fi/processing/__init__.py
Normal file
@ -3,21 +3,25 @@ from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import torch
|
||||
|
||||
from ..config import config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
torch.set_default_device(device)
|
||||
|
||||
|
||||
class AoA:
|
||||
def __init__(self) -> None:
|
||||
self.historical_autocorr = np.array([])
|
||||
self.historical_autocorr = torch.tensor([], dtype=torch.complex64)
|
||||
self.N_subcarriers = -1
|
||||
self.N_rx = -1
|
||||
self.timestamp = datetime.now()
|
||||
pass
|
||||
|
||||
def smooth(self, data: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
|
||||
def smooth(self, data: torch.Tensor) -> torch.Tensor:
|
||||
assert len(data.shape) == 3
|
||||
|
||||
M = data.shape[0] # Number of subcarriers
|
||||
@ -32,99 +36,151 @@ class AoA:
|
||||
# This only works with 1 TX antenna (i.e. no MIMO) - see #4 for more details
|
||||
assert T == 1, "The current implementation only supports 1 TX antenna"
|
||||
|
||||
H_n = np.zeros((N, M // 2, M // 2 + 1), dtype=np.complex128)
|
||||
H_n = torch.zeros((N, M // 2, M // 2 + 1), dtype=torch.complex64)
|
||||
|
||||
for i in range(N):
|
||||
for j in range(M // 2):
|
||||
H_n[i, j] = data[j : j + M // 2 + 1, i, 0]
|
||||
|
||||
H_sm_rows = [np.hstack(H_n[i : i + N // 2 + 1]) for i in range(N // 2)]
|
||||
H_sm = np.vstack(H_sm_rows)
|
||||
H_sm_rows = [torch.hstack(list(H_n[i : i + N // 2 + 1])) for i in range(N // 2)]
|
||||
H_sm = torch.vstack(H_sm_rows)
|
||||
|
||||
logger.debug(f"Smoothed: {H_sm.shape}")
|
||||
|
||||
return H_sm
|
||||
|
||||
def update(self, data: npt.NDArray[np.complex128]) -> None:
|
||||
def update(self, data: torch.Tensor) -> None:
|
||||
self.timestamp = datetime.now()
|
||||
H_sm = self.smooth(data)
|
||||
logger.debug(f"Calculated smoothed CSI matrix: {H_sm.shape}")
|
||||
|
||||
auto_corr = np.matmul(H_sm, np.conj(H_sm).T)
|
||||
auto_corr = H_sm @ torch.conj(H_sm).T
|
||||
# This matrix is by definition Hermitian.
|
||||
# Therefore, all of its eigenvectors are orthogonal.
|
||||
|
||||
if self.historical_autocorr.size == 0:
|
||||
self.historical_autocorr = np.expand_dims(auto_corr, 0)
|
||||
if len(self.historical_autocorr.shape) <= 1:
|
||||
self.historical_autocorr = torch.unsqueeze(auto_corr, 0)
|
||||
else:
|
||||
self.historical_autocorr = np.append(
|
||||
self.historical_autocorr, np.expand_dims(auto_corr, 0), axis=0
|
||||
self.historical_autocorr = torch.cat(
|
||||
(self.historical_autocorr, torch.unsqueeze(auto_corr, 0))
|
||||
)
|
||||
|
||||
WINDOW_SIZE = config.music.window_size
|
||||
if self.historical_autocorr.shape[0] > WINDOW_SIZE:
|
||||
self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:]
|
||||
|
||||
# Is the moving average also Hermitian?
|
||||
R = np.mean(self.historical_autocorr, axis=0)
|
||||
logger.debug("Finished updating autocorrelation matrix")
|
||||
|
||||
# The smallest eigenvectors span the noise subspace,
|
||||
# and the largest span the signal subspace.
|
||||
eigvals, eigvecs = np.linalg.eigh(R)
|
||||
self.E_n = eigvecs[:, np.abs(eigvals) < config.music.eigval_threshold]
|
||||
def steering_vector(self, theta: torch.Tensor, tof: torch.Tensor) -> torch.Tensor:
|
||||
assert theta.shape == tof.shape
|
||||
assert len(theta.shape) == 1
|
||||
N = theta.shape[0]
|
||||
|
||||
def steering_vector(
|
||||
self, theta: float, tof: float
|
||||
) -> npt.NDArray[np.complexfloating]:
|
||||
omega_t: npt.NDArray[np.complex128] = np.exp(-2j * np.pi * config.delta_f * tof)
|
||||
phi_theta: npt.NDArray[np.complex128] = np.exp(
|
||||
omega_t: torch.Tensor = torch.exp(-2j * np.pi * config.delta_f * tof)
|
||||
phi_theta: torch.Tensor = torch.exp(
|
||||
2j
|
||||
* np.pi
|
||||
* config.central_freq_hz
|
||||
* config.antennas.spacing
|
||||
* (1 - np.cos(theta))
|
||||
* (1 - torch.cos(theta))
|
||||
/ 299_792_458
|
||||
)
|
||||
assert omega_t.shape == phi_theta.shape == (N,)
|
||||
|
||||
omega_t = np.expand_dims(omega_t, axis=-1)
|
||||
phi_theta = np.expand_dims(phi_theta, axis=-1)
|
||||
omega_t = torch.unsqueeze(omega_t, dim=-1)
|
||||
phi_theta = torch.unsqueeze(phi_theta, dim=-1)
|
||||
|
||||
antenna_v = omega_t ** np.arange(self.N_subcarriers // 2)
|
||||
phis = phi_theta ** np.arange(self.N_rx // 2)
|
||||
antenna_v = np.expand_dims(antenna_v, axis=-1)
|
||||
steering = antenna_v * phis
|
||||
return steering.T.reshape(-1)
|
||||
assert omega_t.shape == phi_theta.shape == (N, 1)
|
||||
|
||||
def evaluate(self, theta: float, tof: float) -> float:
|
||||
try:
|
||||
steering = self.steering_vector(theta, tof)
|
||||
steering_h = np.conj(steering).T
|
||||
except Exception as e:
|
||||
logger.exception(e)
|
||||
return 0
|
||||
E_n = self.E_n
|
||||
E_n_H = np.conj(E_n).T
|
||||
c = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering))
|
||||
return np.abs(c.real)
|
||||
antenna_v = omega_t ** torch.arange(
|
||||
self.N_subcarriers // 2, dtype=torch.float32
|
||||
)
|
||||
phis = phi_theta ** torch.arange(self.N_rx // 2, dtype=torch.float32)
|
||||
|
||||
assert antenna_v.shape == (N, self.N_subcarriers // 2)
|
||||
assert phis.shape == (N, self.N_rx // 2)
|
||||
|
||||
antenna_v = torch.unsqueeze(antenna_v, dim=1)
|
||||
phis = torch.unsqueeze(phis, dim=-1)
|
||||
|
||||
assert antenna_v.shape == (N, 1, self.N_subcarriers // 2)
|
||||
assert phis.shape == (N, self.N_rx // 2, 1)
|
||||
|
||||
steering = torch.bmm(phis, antenna_v)
|
||||
|
||||
assert steering.shape == (N, self.N_rx // 2, self.N_subcarriers // 2)
|
||||
return steering.reshape(N, -1)
|
||||
|
||||
def evaluate(self, theta: torch.Tensor, tof: torch.Tensor) -> torch.Tensor:
|
||||
R = torch.mean(self.historical_autocorr, dim=0)
|
||||
assert (R == torch.conj(R).T).all()
|
||||
|
||||
# The smallest eigenvectors span the noise subspace,
|
||||
# and the largest span the signal subspace.
|
||||
logger.debug(f"Calculating eigenvectors of R: {R.shape}")
|
||||
eigvals, eigvecs = torch.linalg.eigh(R)
|
||||
assert isinstance(eigvals, torch.Tensor)
|
||||
assert isinstance(eigvecs, torch.Tensor)
|
||||
logger.info(f"Eigenvalues: {eigvals}")
|
||||
E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
|
||||
|
||||
logger.debug(f"Signal subspace: {E_n.shape}")
|
||||
steering = torch.unsqueeze(self.steering_vector(theta, tof), dim=-1)
|
||||
steering_h = torch.conj(steering).permute(0, 2, 1)
|
||||
|
||||
E_n = E_n.unsqueeze(0)
|
||||
E_n_H = torch.conj(E_n).permute(0, 2, 1)
|
||||
logger.debug(
|
||||
f"Heatmap multiplication: {steering_h.shape}, {E_n.shape}, "
|
||||
f"{E_n_H.shape}, {steering.shape}"
|
||||
)
|
||||
c: torch.Tensor = 1 / (steering_h @ E_n @ E_n_H @ steering)
|
||||
return torch.abs(c)[:, 0, 0]
|
||||
|
||||
def heatmap(self) -> npt.NDArray[np.float32]:
|
||||
thetas = np.linspace(
|
||||
0, np.pi, config.music.heatmap.theta_resolution, dtype=np.float32
|
||||
)
|
||||
tofs = np.linspace(
|
||||
0,
|
||||
config.music.heatmap.tof_max,
|
||||
config.music.heatmap.tof_resolution,
|
||||
dtype=np.float32,
|
||||
)
|
||||
thetas_mesh, tofs_mesh = np.meshgrid(thetas, tofs)
|
||||
logger.debug(
|
||||
f"Calculating heatmap with {thetas_mesh.shape} and {tofs_mesh.shape}"
|
||||
)
|
||||
evaluated = self.evaluate(
|
||||
torch.tensor(thetas_mesh.reshape(-1)),
|
||||
torch.tensor(tofs_mesh.reshape(-1)),
|
||||
)
|
||||
logger.debug(f"Evaluated heatmap: {evaluated.shape}")
|
||||
heatmap: npt.NDArray[np.float32] = evaluated.reshape(
|
||||
config.music.heatmap.tof_resolution,
|
||||
config.music.heatmap.theta_resolution,
|
||||
).numpy(force=True)
|
||||
return heatmap
|
||||
|
||||
|
||||
def test_smoothing() -> None:
|
||||
row, col = np.indices((4, 2))
|
||||
row, col = np.indices((6, 4))
|
||||
data = row + 1j * col
|
||||
data = np.expand_dims(data, axis=2)
|
||||
np.set_printoptions(linewidth=200)
|
||||
print(data.shape)
|
||||
aoa = AoA()
|
||||
aoa.N_subcarriers = 6
|
||||
aoa.N_rx = 4
|
||||
smoothed = aoa.smooth(data)
|
||||
H_0 = np.array([[0 + 0j, 0 + 1j, 0 + 2j], [0 + 1j, 0 + 2j, 0 + 3j]])
|
||||
H_01 = np.vstack([H_0, H_0 + 1])
|
||||
H_12 = np.vstack([H_0 + 1, H_0 + 2])
|
||||
expected = np.hstack([H_01, H_12])
|
||||
print(expected)
|
||||
assert np.allclose(smoothed, expected)
|
||||
print(smoothed)
|
||||
|
||||
|
||||
def test_steering_vector() -> None:
|
||||
aoa = AoA()
|
||||
aoa.N_subcarriers = 10
|
||||
aoa.N_rx = 2
|
||||
tau = 1
|
||||
theta = 0
|
||||
aoa.N_rx = 4
|
||||
tau = torch.Tensor([1, 0])
|
||||
theta = torch.Tensor([0, 1])
|
||||
print(aoa.steering_vector(theta, tau))
|
||||
assert False
|
||||
|
||||
@ -15,9 +15,9 @@ np.seterr(invalid="ignore")
|
||||
|
||||
class Preprocessor:
|
||||
def __init__(self) -> None:
|
||||
self.prev_entries: Queue[npt.NDArray[np.complex128]] = Queue(maxsize=100)
|
||||
self.short_term_avg = np.zeros((1,), dtype=np.complex128)
|
||||
self.long_term_avg = np.zeros((1,), dtype=np.complex128)
|
||||
self.prev_entries: Queue[npt.NDArray[np.complex64]] = Queue(maxsize=100)
|
||||
self.short_term_avg = np.zeros((1,), dtype=np.complex64)
|
||||
self.long_term_avg = np.zeros((1,), dtype=np.complex64)
|
||||
self.filter = butter(
|
||||
5,
|
||||
config.preprocessing.bandpass.bounds,
|
||||
@ -26,7 +26,7 @@ class Preprocessor:
|
||||
output="sos",
|
||||
)
|
||||
|
||||
def preprocess(self, h: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
|
||||
def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]:
|
||||
# CSI data is not available for pilot subcarriers.
|
||||
h_hat = np.where(
|
||||
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
|
||||
@ -46,7 +46,7 @@ class Preprocessor:
|
||||
|
||||
# Assume that all csi matrices will have the same shape
|
||||
if self.long_term_avg.shape != h_hat.shape:
|
||||
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex128)
|
||||
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)
|
||||
|
||||
self.long_term_avg = (
|
||||
self.long_term_avg * (1 - config.preprocessing.moving_average_alpha)
|
||||
|
||||
0
where_fi/utils/__init__.py
Normal file
0
where_fi/utils/__init__.py
Normal file
@ -13,7 +13,7 @@ from flask import Flask, Response, render_template, request
|
||||
from flask_sock import Sock
|
||||
from simple_websocket import Server
|
||||
|
||||
from .. import config
|
||||
from ..config import config
|
||||
from ..processing.aoa import AoA
|
||||
|
||||
matplotlib.use("agg")
|
||||
@ -92,18 +92,18 @@ def add_data(
|
||||
del subscriber_settings[subscriber]
|
||||
|
||||
|
||||
def make_heatmap(aoa: AoA, max_tof: float) -> io.BytesIO:
|
||||
def plot_heatmap(heatmap: npt.NDArray[np.float32]) -> io.BytesIO:
|
||||
logger.info(f"Making heatmap with aoa of {aoa.timestamp}")
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1, 1], polar=True)
|
||||
r = np.linspace(0, max_tof, 100) # Radius values
|
||||
theta = np.linspace(0, np.pi, 50) # Angle values
|
||||
R, Theta = np.meshgrid(r, theta) # Create a 2D grid of r and theta
|
||||
ax = fig.add_axes([0.1, 0.1, 0.9, 0.9]) # , polar=True)
|
||||
r = np.linspace(
|
||||
0, config.music.heatmap.tof_max, config.music.heatmap.tof_resolution
|
||||
)
|
||||
theta = np.linspace(0, np.pi, config.music.heatmap.theta_resolution) # Angle values
|
||||
|
||||
# Compute the function values
|
||||
Z = np.log(np.vectorize(aoa.evaluate)(Theta, R))
|
||||
mesh = ax.pcolormesh(theta, r, heatmap, edgecolors="face", vmin=0, vmax=50)
|
||||
|
||||
ax.pcolormesh(Theta, R, Z, edgecolors="face")
|
||||
fig.colorbar(mesh, ax=ax)
|
||||
buf = io.BytesIO()
|
||||
fig.savefig(buf, format="jpeg")
|
||||
plt.close(fig)
|
||||
@ -112,36 +112,23 @@ def make_heatmap(aoa: AoA, max_tof: float) -> io.BytesIO:
|
||||
return buf
|
||||
|
||||
|
||||
def gather_aoa(max_tof: float) -> Generator[bytes, None, None]:
|
||||
def gather_aoa() -> Generator[bytes, None, None]:
|
||||
assert aoa_queue is not None
|
||||
|
||||
prev_frame = datetime.now()
|
||||
while True:
|
||||
while (datetime.now() - prev_frame).total_seconds() < 1 / config.HEATMAP_FPS:
|
||||
time.sleep(0.01)
|
||||
while not aoa_queue.empty():
|
||||
logger.debug("Receiving from aoa pipe")
|
||||
aoa = aoa_queue.get()
|
||||
prev_frame = datetime.now()
|
||||
logger.debug(f"Generating heatmap of time {aoa.timestamp}")
|
||||
buf = make_heatmap(aoa, max_tof)
|
||||
heatmap = aoa_queue.get()
|
||||
buf = plot_heatmap(heatmap)
|
||||
yield (b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + buf.read() + b"\r\n")
|
||||
buf.close()
|
||||
|
||||
|
||||
@app.route("/aoa_tof")
|
||||
def aoa_tof() -> Response:
|
||||
max_tof_str = request.args.get("max_tof")
|
||||
try:
|
||||
max_tof = float(max_tof_str)
|
||||
except Exception:
|
||||
max_tof = 5e-8
|
||||
return Response(
|
||||
gather_aoa(max_tof), mimetype="multipart/x-mixed-replace; boundary=frame"
|
||||
)
|
||||
return Response(gather_aoa(), mimetype="multipart/x-mixed-replace; boundary=frame")
|
||||
|
||||
|
||||
def start(conn: "mp.Queue[AoA]") -> None:
|
||||
def start(conn: "mp.Queue[npt.NDArray[np.float32]]") -> None:
|
||||
global app, aoa_queue
|
||||
|
||||
aoa_queue = conn
|
||||
|
||||
Loading…
Reference in New Issue
Block a user