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2
.gitignore vendored
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@ -174,5 +174,3 @@ poetry.toml
pyrightconfig.json pyrightconfig.json
# End of https://www.toptal.com/developers/gitignore/api/python # End of https://www.toptal.com/developers/gitignore/api/python
config.yaml

8
Dockerfile
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@ -1,8 +0,0 @@
FROM ghcr.io/astral-sh/uv:latest
ADD pyproject.toml uv.lock /app
WORKDIR /app
RUN uv sync --frozen
CMD ["uv", "run", "python", "-m", "where_fi", "heatmap"]

58
config.yaml.example
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@ -1,58 +0,0 @@
receive_hosts:
- ["10.0.12.90", 8008]
- ["10.0.12.91", 8008]
transmit_host: ["10.0.12.64", 8008]
check_hosts: True
antennas:
order:
- [['10.0.12.91', 8008], 0]
- [['10.0.12.91', 8008], 1]
- [['10.0.12.90', 8008], 0]
- [['10.0.12.90', 8008], 1]
# The spacing between antennas in the linear antenna array in meters
spacing: 0.0285
collection_sample_rate: 20 # Hz
processing_sample_rate: 2 # Hz
# The central Wi-Fi channel to be used for data collection
# Should match one of the channels in the standard
# https://en.wikipedia.org/wiki/List_of_WLAN_channels
central_freq: 2442 # MHz
# The channel width in MHz. The options are 20, 40, 80, and 160 MHz
channel_width: 20
# frame_format: Can be HT, VHT, HE for the frame format used by
# 802.11n, 802.11ac, 802.11ax respectively
frame_format: VHT
preprocessing:
subcarrier_step: 1 # Skip every xth subcarrier to improve performance
# The steps to be applied to the samples before denoising, in this order
steps:
- fill_pilots
- skip_subcarriers
- remove_agc
- remove_sfo
# Type of denoising to apply after preprocessing, when accessing
# the sample for the main application processing
denoising: median
# The duration of the window to use for the denoising step (in seconds)
denoising_window: 1
music:
# The threshold to use to split the noise and signal subspaces
eigval_threshold: 100
# The resolution of the heatmap generated
heatmap:
theta_resolution: 100
tof_resolution: 100
tof_max: 2e-8

12
docker-compose.yml
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@ -1,12 +0,0 @@
services:
envoy:
image: envoyproxy/envoy
ports:
- "8080:8080"
volumes:
- ./where_fi/visualise/envoy.yaml:/etc/envoy/envoy.yaml
backend:
build: .
volumes:
- ./where_fi:/app/where_fi
- ./config.yaml:/app/config

15
eval_speedtest.sh
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@ -1,15 +0,0 @@
#!/bin/bash
for i in {1..80}; do
not_running=$(iperf3 -c 10.0.0.2 -d -t 10 -V | grep -Po '[0-9.]*(?= Mbits/sec)' | tail -n 1)
echo $not_running >> speedtest_not_running.log
echo "not running $not_running"
ssh cfalas@10.0.0.2 "ssh root@10.0.12.64 'ping 192.168.55.3 -c 1100 -i 0.01'" > /dev/null &
sleep 1s;
running=$(iperf3 -c 10.0.0.2 -d -t 10 -V | grep -Po '[0-9.]*(?= Mbits/sec)' | tail -n 1)
echo $running >> speedtest_running.log
echo "running $running"
sleep 1s;
done
wait

110
examples/measurements.py
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@ -1,110 +0,0 @@
from itertools import product
from queue import Queue
from typing import cast
import numpy as np
from where_fi.application import CSIApplication
from where_fi.collection import CSIMatrix
from where_fi.collection.ingest import RealtimeCSIProducer
from where_fi.config import config
from where_fi.visualise import server as visualise
app = CSIApplication(visualise_raw=True)
visualise.figures.all_figures["median"] = visualise.figures.RandomVariable(
"Median Phase"
)
visualise.figures.all_figures["median_magn"] = visualise.figures.RandomVariable(
"Median Magnitude"
)
visualise.figures.all_figures["denoised"] = visualise.figures.PerAntennaFigure(
[
visualise.figures.SimpleLineChart("Denoised CSI Phase", "Subcarrier", "Phase"),
visualise.figures.SimpleLineChart(
"Denoised CSI Amplitude", "Subcarrier", "Amplitude"
),
],
[np.angle, np.abs],
)
measurements: list[np.complex64] = []
subcarriers = [0, 1]
rx_antenna = [0, 1]
tx_antenna = [0]
subcarrier_phase: dict[tuple[int, int, int], Queue[float]] = {
x: Queue(config.collection_sample_rate)
for x in product(subcarriers, rx_antenna, tx_antenna)
}
subcarrier_magn: dict[tuple[int, int, int], Queue[float]] = {
x: Queue(config.collection_sample_rate)
for x in product(subcarriers, rx_antenna, tx_antenna)
}
proc_phase: dict[tuple[int, int, int], Queue[float]] = {
x: Queue(config.collection_sample_rate)
for x in product(subcarriers, rx_antenna, tx_antenna)
}
proc_magn: dict[tuple[int, int, int], Queue[float]] = {
x: Queue(config.collection_sample_rate)
for x in product(subcarriers, rx_antenna, tx_antenna)
}
@app.on_sample
def _(sample: CSIMatrix) -> None:
for i in product(subcarriers, rx_antenna, tx_antenna):
phase = cast(float, np.angle(sample[i]))
magn = cast(float, np.abs(sample[i]))
if subcarrier_phase[i].full():
subcarrier_phase[i].get()
subcarrier_phase[i].put(phase)
if subcarrier_magn[i].full():
subcarrier_magn[i].get()
subcarrier_magn[i].put(magn)
cnt = 0
@app.on_process
def _(proc: CSIMatrix) -> None:
global cnt
global proc_phase
for i in product(subcarriers, rx_antenna, tx_antenna):
phase = cast(float, np.angle(proc[i]))
magn = cast(float, np.abs(proc[i]))
if proc_phase[i].full():
proc_phase[i].get()
proc_phase[i].put(phase)
if proc_magn[i].full():
proc_magn[i].get()
proc_magn[i].put(magn)
print(sum(proc_phase[0, 0, 0].queue))
app.visualise_data(
np.array([x.queue for x in proc_phase.values()]),
"median",
)
app.visualise_data(
np.array([x.queue for x in proc_magn.values()]),
"median_magn",
)
app.visualise_data(
np.array([x.queue for x in subcarrier_phase.values()]),
visualise.figures.Figure.PHASE_ANALYSIS,
)
app.visualise_data(
np.array([x.queue for x in subcarrier_magn.values()]),
visualise.figures.Figure.MAGN_ANALYSIS,
)
app.visualise_data(proc, "denoised")
cnt += 1
if __name__ == "__main__":
print("Starting app")
app.set_producer(RealtimeCSIProducer())
app.start()

86
examples/motion_detector.py
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@ -1,86 +0,0 @@
"""Motion Detection example
This example shows how to use the CSI framework to connect to a FeitCSI host and
detect changes in the environment
"""
import numpy as np
import numpy.typing as npt
from where_fi.application import CSIApplication
from where_fi.collection.ingest import RealtimeCSIProducer
from where_fi.config import config
from where_fi.visualise import server as visualise
# Connect to a FeitCSI host
app = CSIApplication(visualise_raw=True)
# Register visualisations
app.register_figure(
"magn-diff",
visualise.figures.PerAntennaFigure(
[
visualise.figures.SimpleLineChart(
"Magnitude diff", "Subcarrier", "Magnitude"
),
],
[lambda x: x],
),
)
app.register_figure(
"phase-diff",
visualise.figures.PerAntennaFigure(
[
visualise.figures.SimpleLineChart("Phase diff", "Subcarrier", "Phase"),
],
[lambda x: x],
),
)
MAGN_THRESHOLD = 10
PHASE_THRESHOLD = 1
QUEUE_SIZE = 200
# Stores historical data for each receiving antenna, for each subcarrier
historical = np.zeros(
(QUEUE_SIZE, config.subcarriers, config.antennas.count, 1), dtype=np.complex64
)
empty = np.load("data/empty.npy")
sample_position = 0
@app.on_process
def _(sample: npt.NDArray[np.complex64]) -> None:
"""
Process the CSI data and detect changes in the environment.
This function is called by the framework at a fixed interval, with the latest CSI
sample received.
It is used to detect changes in the environment caused by motion, by comparing each
entry in the matrix with a moving average
"""
global sample_position
historical[sample_position] = sample
sample_position = (sample_position + 1) % QUEUE_SIZE
mean = np.mean(historical, axis=0)
magn_diff = np.abs(mean - sample)
phase_diff = np.abs(np.angle(mean) - np.angle(sample))
phase_diff = np.min(np.array([phase_diff, np.pi - phase_diff]), axis=0)
app.visualise_data(magn_diff, "magn-diff")
app.visualise_data(phase_diff, "phase-diff")
if (magn_diff > MAGN_THRESHOLD).any() or (phase_diff > PHASE_THRESHOLD).any():
print("Motion detected!")
else:
print("No motion detected!")
if __name__ == "__main__":
# Start the application
print("Starting app")
app.set_producer(RealtimeCSIProducer())
app.start()

123
examples/motion_detector_home_assistant.py
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@ -1,123 +0,0 @@
"""Motion Detection example
This example shows how to use the CSI framework to connect to a FeitCSI host and
detect changes in the environment
"""
import numpy as np
import numpy.typing as npt
import requests
from where_fi.application import CSIApplication
from where_fi.collection.ingest import RealtimeCSIProducer
from where_fi.config import config
from where_fi.visualise import server as visualise
# Connect to a FeitCSI host
app = CSIApplication(visualise_raw=True)
# Register visualisations
app.register_figure(
"magn-diff",
visualise.figures.PerAntennaFigure(
[
visualise.figures.SimpleLineChart(
"Magnitude diff", "Subcarrier", "Magnitude"
),
],
[lambda x: x],
),
)
app.register_figure(
"phase-diff",
visualise.figures.PerAntennaFigure(
[
visualise.figures.SimpleLineChart("Phase diff", "Subcarrier", "Phase"),
],
[lambda x: x],
),
)
MAGN_THRESHOLD = 0.2
PHASE_THRESHOLD = 0.02
QUEUE_SIZE = 20
# Stores historical data for each receiving antenna, for each subcarrier
historical = np.zeros(
(QUEUE_SIZE, config.subcarriers, config.antennas.count, 1), dtype=np.complex64
)
sample_position = 0
class HomeAssistantBinarySensor:
"""
Represents a binary sensor in Home Assistant.
Uses the HTTP API [1] to update the state of the sensor.
[1] - https://www.home-assistant.io/integrations/http/#binary-sensor
"""
def __init__(self, id: str, name: str) -> None:
self.id = id
self.name = name
BASE_URL = os.getenv("HOME_ASSISTANT_URL")
API_KEY = os.getenv("HOME_ASSISTANT_API_KEY")
self.url = f"{BASE_URL}/api/states/binary_sensor.{self.id}"
self.headers = {"Authorization": f"Bearer {API_KEY}"}
self.state = False
def update(self, state: bool) -> None:
if self.state == state:
return
self.state = state
data = {
"state": "on" if state else "off",
"attributes": {"friendly_name": self.name, "device_class": "motion"},
}
print(f"Updating sensor {self.name} to {data}")
requests.post(self.url, json=data, headers=self.headers)
sensor = HomeAssistantBinarySensor("motion_detector", "Room Motion Detector")
@app.on_process
def _(sample: npt.NDArray[np.complex64]) -> None:
"""
Process the CSI data and detect changes in the environment.
This function is called by the framework at a fixed interval, with the latest CSI
sample received.
It is used to detect changes in the environment caused by motion, by comparing each
entry in the matrix with a moving average
"""
global sample_position
historical[sample_position] = sample
sample_position = (sample_position + 1) % QUEUE_SIZE
mean = np.mean(historical, axis=0)
magn_diff = np.abs(mean - sample)
phase_diff = np.abs(np.angle(mean) - np.angle(sample))
app.visualise_data(magn_diff, "magn-diff")
app.visualise_data(phase_diff, "phase-diff")
if (magn_diff > MAGN_THRESHOLD).any() or (phase_diff > PHASE_THRESHOLD).any():
print("Motion detected!")
sensor.update(True)
else:
print("No motion detected!")
sensor.update(False)
if __name__ == "__main__":
# Start the application
print("Starting app")
app.set_producer(RealtimeCSIProducer())
app.start()

118
examples/music.py
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@ -1,118 +0,0 @@
import logging
import numpy as np
import torch
from where_fi.application import CSIApplication
from where_fi.collection import CSIMatrix
from where_fi.collection.ingest import RealtimeCSIProducer
from where_fi.config import config
from where_fi.processing.aoa import AoA
from where_fi.visualise import server as visualise
app = CSIApplication(visualise_raw=True)
logging.basicConfig(level=logging.INFO)
T = 500
N_sub1 = config.antennas.count // 2
N_sub2 = config.subcarriers // 2
L2 = config.subcarriers - N_sub2 + 1
L1 = config.antennas.count - N_sub1 + 1
N_sensors = config.subcarriers * config.antennas.count
historical = torch.zeros(T, N_sensors, N_sensors, dtype=torch.complex64)
cnt = 0
@app.on_sample
def _(sample: CSIMatrix) -> None:
global cnt
sample_before_resize = sample[:, :, 0].T
sample_after = sample_before_resize.reshape(-1, 1)
sample_tensor = torch.tensor(sample_after)
historical[cnt] = sample_tensor @ torch.conj(sample_tensor).T
print(
f"{sample.shape} => {sample_before_resize.shape} => {sample_after.shape} "
f"=> {historical[cnt].shape}"
)
cnt = (cnt + 1) % T
def get_steering(theta: float, tau: float) -> torch.Tensor:
"""
Calculate the alpha value for the given angle and time delay.
"""
sub, ant = np.indices((N_sub1, N_sub2))
alpha = np.exp(
-1j
* (
2 * np.pi * (sub * config.delta_f * tau)
+ 2
* np.pi
* (
ant
* config.antennas.spacing
* np.sin(theta)
* 299_792_458
/ (config.central_freq_hz + (sub - 28) * config.delta_f)
)
)
)
alpha = sub + 1j * ant
alpha = alpha.reshape(-1, 1)
return torch.tensor(alpha, dtype=torch.complex64)
aoa = AoA()
@app.on_process
def _(_: CSIMatrix) -> None:
R: torch.Tensor = torch.mean(historical, axis=0)
# Rss = torch.zeros(N_sub1 * N_sub2, N_sub1 * N_sub2, dtype=torch.complex64)
# for i in range(L1):
# for j in range(L2):
# Rss += R[i : i + N_sub1 * N_sub2, j : j + N_sub1 * N_sub2]
# Rss /= L1 * L2
Rss = R
aoa.historical_autocorr = torch.unsqueeze(Rss, 0)
aoa.heatmap(app.visualise_data)
# eigvals, eigvecs = torch.linalg.eig(Rss)
# app.visualise_data(eigvals.numpy(), visualise.figures.Figure.MUSIC_EIGENVALUES)
# E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
# E_n_H = torch.conj(E_n.T)
# heatmap = np.zeros(
# (config.music.heatmap.tof_resolution, config.music.heatmap.theta_resolution)
# )
# for i_theta, theta in enumerate(
# np.linspace(0, np.pi, config.music.heatmap.theta_resolution)
# ):
# for i_tau, tau in enumerate(
# np.linspace(
# 0, config.music.heatmap.tof_max, config.music.heatmap.tof_resolution
# )
# ):
# steering = get_steering(theta, tau)
# steering_h = torch.conj(steering.T)
# c = 1 / (steering_h @ E_n @ E_n_H @ steering)
# heatmap[i_tau, i_theta] = torch.abs(c)
# print(steering)
# app.visualise_data(heatmap, visualise.figures.Figure.AOA_HEATMAP)
# eigvals, eigvecs = torch.linalg.eig(Rss)
# app.visualise_data(eigvals.numpy(), visualise.figures.Figure.MUSIC_EIGENVALUES)
# E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
# print(E_n)
# c: torch.Tensor = 1 / (steering_h @ E_n @ E_n_H @ steering)
# return torch.abs(c)[:, 0, 0]
if __name__ == "__main__":
producer = RealtimeCSIProducer()
app.set_producer(producer)
app.start()

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progress_report/progress.aux
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\relax
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\abx@aux@refcontext{nty/global//global/global}
\abx@aux@cite{0}{feitcsi:project}
\abx@aux@segm{0}{0}{feitcsi:project}
\@writefile{toc}{\contentsline {section}{\numberline {1}Summary}{1}{section.1}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {2}Multi-NIC setup}{1}{section.2}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Network/hardware setup for multi-NIC CSI collection}}{2}{figure.1}\protected@file@percent }
\newlabel{fig:multi-nic}{{1}{2}{Network/hardware setup for multi-NIC CSI collection}{figure.1}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Custom antenna mount}}{2}{figure.2}\protected@file@percent }
\newlabel{fig:antenna-mount}{{2}{2}{Custom antenna mount}{figure.2}{}}
\abx@aux@cite{0}{Hsu2024}
\abx@aux@segm{0}{0}{Hsu2024}
\@writefile{toc}{\contentsline {section}{\numberline {3}Data Ingestion}{3}{section.3}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {4}Data Preprocessing}{3}{section.4}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {5}Angle of Arrival Estimation}{3}{section.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {6}Dataset Collection}{3}{section.6}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {7}Difficulties}{4}{section.7}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {8}Next Steps}{4}{section.8}\protected@file@percent }
\abx@aux@read@bbl@mdfivesum{nohash}
\abx@aux@read@bblrerun
\gdef \@abspage@last{4}

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% $ biblatex auxiliary file $
% $ biblatex bbl format version 3.2 $
% Do not modify the above lines!
%
% This is an auxiliary file used by the 'biblatex' package.
% This file may safely be deleted. It will be recreated by
% biber as required.
%
\begingroup
\makeatletter
\@ifundefined{ver@biblatex.sty}
{\@latex@error
{Missing 'biblatex' package}
{The bibliography requires the 'biblatex' package.}
\aftergroup\endinput}
{}
\endgroup
\refsection{0}
\datalist[entry]{nty/global//global/global}
\entry{Hsu2024}{inproceedings}{}
\name{author}{2}{}{%
{{hash=d5c2fb414951d03e3ec15a764a03592c}{%
family={Hsu},
familyi={H\bibinitperiod},
given={Ting-Wei},
giveni={T\bibinithyphendelim W\bibinitperiod}}}%
{{hash=1957109c91b54744e81c1e75d0d1e719}{%
family={Hsieh},
familyi={H\bibinitperiod},
given={Hung-Yun},
giveni={H\bibinithyphendelim Y\bibinitperiod}}}%
}
\strng{namehash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\strng{fullhash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\strng{bibnamehash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\strng{authorbibnamehash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\strng{authornamehash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\strng{authorfullhash}{cf444c64db6e8a2ac5a3d0f08cea6519}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{booktitle}{ICC 2024 - IEEE International Conference on Communications}
\field{title}{Robust Multi-User Pose Estimation Based on Spatial and Temporal Features from WiFi CSI}
\field{year}{2024}
\field{pages}{1600\bibrangedash 1605}
\range{pages}{6}
\verb{doi}
\verb 10.1109/ICC51166.2024.10623053
\endverb
\keyw{Heating systems;Doppler shift;Time-frequency analysis;Pose estimation;Feature extraction;Robustness;Frequency estimation}
\endentry
\entry{feitcsi:project}{online}{}
\name{author}{3}{}{%
{{hash=f7ddbbc0fd5053139c0492b03c89ce6b}{%
family={Hutar},
familyi={H\bibinitperiod},
given={Miroslav},
giveni={M\bibinitperiod}}}%
{{hash=64e0fb04b292022aee82ce3ae6ec84f9}{%
family={Brida},
familyi={B\bibinitperiod},
given={Peter},
giveni={P\bibinitperiod}}}%
{{hash=c94ebc034cee4b9c81a21d40bf16345e}{%
family={Machaj},
familyi={M\bibinitperiod},
given={Juraj},
giveni={J\bibinitperiod}}}%
}
\strng{namehash}{6e810c4ddea2920152d10133a21f0f88}
\strng{fullhash}{6e810c4ddea2920152d10133a21f0f88}
\strng{bibnamehash}{6e810c4ddea2920152d10133a21f0f88}
\strng{authorbibnamehash}{6e810c4ddea2920152d10133a21f0f88}
\strng{authornamehash}{6e810c4ddea2920152d10133a21f0f88}
\strng{authorfullhash}{6e810c4ddea2920152d10133a21f0f88}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{title}{FeitCSI, the 802.11 CSI tool}
\field{year}{2023}
\verb{urlraw}
\verb https://feitcsi.kuskosoft.com
\endverb
\verb{url}
\verb https://feitcsi.kuskosoft.com
\endverb
\endentry
\enddatalist
\endrefsection
\endinput

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[0] Config.pm:307> INFO - This is Biber 2.19
[0] Config.pm:310> INFO - Logfile is 'progress.blg'
[34] biber:340> INFO - === Fri Feb 7, 2025, 00:59:00
[40] Biber.pm:419> INFO - Reading 'progress.bcf'
[67] Biber.pm:979> INFO - Found 2 citekeys in bib section 0
[74] Biber.pm:4419> INFO - Processing section 0
[78] Biber.pm:4610> INFO - Looking for bibtex file 'refs.bib' for section 0
[78] bibtex.pm:1713> INFO - LaTeX decoding ...
[80] bibtex.pm:1519> INFO - Found BibTeX data source 'refs.bib'
[96] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
[96] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
[96] Biber.pm:4239> INFO - Sorting list 'nty/global//global/global' of type 'entry' with template 'nty' and locale 'en-US'
[96] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
[99] bbl.pm:660> INFO - Writing 'progress.bbl' with encoding 'UTF-8'
[99] bbl.pm:763> INFO - Output to progress.bbl

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\documentclass{article}
\usepackage{titling}
\usepackage{graphicx} % Required for inserting images
\usepackage{forest}
\usepackage{hyperref}
\usepackage[backend=biber]{biblatex}
\addbibresource{refs.bib}
\begin{document}
% \maketitle
\noindent\textbf{Progress Report:} \textit{Human Presence Detection using Wi-Fi Channel State Information}
\noindent\textbf{Author:} Christos Falas (\href{mailto:cf575@cam.ac.uk}{cf575@cam.ac.uk})
\noindent\textbf{Supervisor:} Markus Kuhn
\noindent\textbf{Project Checkers:} Pietro Lio (pl219) and Jeremy Yallop (jdy22)
\noindent\textbf{Date:} \today
\vspace{1cm}
\section{Summary}
Overall, I believe that the project is going well, and a lot of progress has been made. However, due to easier logistics, I decided to re-arrange the work plan slightly, so that some tasks that I wasn't planning on doing until a bit later are already done, aand others that were supposed to be done by now are not.
In the next sections, I try to explain these decisions, as well s give some context on what I've done, and what is left to be done.
Due to this change in work plan, it is hard to estimate whether my project is behind or ahead of schedule. I would imagine I am roughtly on track (since the extra work I've done is slightly more than the work I skipped). In order to not have this problem, at the end I devised a new work plan.
\section{Multi-NIC setup}
Per my initial plan, I was supposed to currently only be working using a single Wi-Fi NIC on the receiving end. However, this only allows me to use CSI data from 2 antennas, which is quite limiting (and would lead to an under-constrained solution in most cases). In order to combat this, I re-arranged my work plan, and moved the work to set up the multi-NIC system to an earlier stage.
In order to easily work with multiple NICs, I had to use multiple computers, since I was limited by the number of PCIe slots on each computer (this would not be a limit on commercial access point hardware). Along with my main desktop computer, I am using two identical small-form-factor machines, each of which have 2 PCIe ports.
Per my original proposal, I am using FeitCSI \cite{feitcsi:project} to control the Wi-Fi NICs over the network. However, FeitCSI does not currently support multiple NICs on the same machine. Therefore, in order to use both PCIe slots on the machine for NICs, I had to set up different virtual machines, so that each VM would only detect a single NIC. For ease of management, I configured the VMs to boot from the network, off of a single disk image, such that I only have to make changes in one place. The overall setup is as shown in Figure \ref{fig:multi-nic}.
While the setup is not realistic for a commercial deployment, it makes working with multiple NICs much easier, is a rough approximation of the desired setup that allows me to collect the same data. This collection process should be a lot easier with newer Wi-Fi 7 hardware.
\begin{figure}[h]
\begin{forest}
for tree={draw, align=center, l sep=1cm}
[Network Switch, s sep=1cm
[Main Machine
[Hypervisor
[TFTP \& NFS Server, name=tftp, yshift=0.8cm]
[Development Machine, name=dev] % Assigning a name to reference later
[FeitCSI, name=feit0 [NIC 0, edge=green]]
]
]
[Machine 1, tikz={\node [draw, inner sep=2mm, fit=()(!111)(!lll), rounded corners, dashed]{};}
[Hypervisor, name=hyper1
[FeitCSI, name=feit11 [NIC 1, name=nic1, edge=green]]
[FeitCSI, name=feit12 [NIC 2, edge=green]]
]
]
[Machine 2, tikz={\node [draw, inner sep=2mm, fit=()(!111)(!lll), rounded corners, dashed]{};}
[Hypervisor, name=hyper2
[FeitCSI, name=feit21 [NIC 3, edge=green]]
[FeitCSI, name=feit22 [NIC 4, edge=green]]
]
]
]
\draw[blue, bend left] (tftp.north) to[out=20, in=160] (feit21.north);
\draw[blue, bend left] (tftp.north) to[out=20, in=160] (feit22.north);
\draw[blue, bend left] (tftp.north) to[out=20, in=160] (feit11.north);
\draw[blue, bend left] (tftp.north) to[out=20, in=160] (feit12.north);
\draw[blue, bend left] (tftp.north) to[out=20, in=160] (feit0.north);
\end{forest}
\caption{Network/hardware setup for multi-NIC CSI collection}
\label{fig:multi-nic}
\end{figure}
To ensure consistency in the spacing between antennas, I built a simple custom mount for the 8 antennas (2 per NIC) that I am using, out of a tin can. This mount is shown in Figure \ref{fig:antenna-mount}.
\begin{figure}[h]
\includegraphics[scale=0.5]{images/antenna_mount.png}
\centering
\caption{Custom antenna mount}
\label{fig:antenna-mount}
\end{figure}
\section{Data Ingestion}
I have implemented a simple data ingestion pipeline, which connects to each of the FeitCSI instances over the network, and collects the CSI data from each of the NICs in realtime (at around 100Hz). The data is then merged using the correct antenna order, and then streamed to downstream consumers (which could save the data to a file for later processing, or process it in realtime).
Additionally, I implemented a simple visualisation framework which streams data to the browser in realtime, which allows me to see the data as it is being collected and processed. This is useful for debugging, and for understanding the data better.
\section{Data Preprocessing}
Data preprocessing was one of my first milestones per my original work plan. Even though I completed it initially in schedule, I believe the implementation is not optimal. Some of the math I used from the original paper I am replicating \cite{Hsu2024} is not very clear, which makes the preprocessing method used not as effective. Therefore, I am currently working on implementing a few different methods for data preprocessing, to see what works best.
\section{Angle of Arrival Estimation}
One of the main techniques I was planning to use was angle of arrival estimation, using the MUSIC algorithm. I have implemented this per the original work plan.
This allows me to estimate a 2-dimensional probability density function, estimating the response of the signal at a specific angle and time of flight. In theory, plotting a heatmap of this on a polar plane should give a rough indication of where a person is in the room.
I am currently in the process of evaluating the results from this, to see how well it performs under different conditions.
\section{Dataset Collection}
Over the weekend 1st-2nd February, I collected data using multiple different configurations, with both the empty room, with one or more people walking around the room (after going through the necessary ethics review). Using this data instead of the realtime data allows for more repeatable experiments, which will allow me to improve my method more effectively, and hence was done earlier than scheduled in the original work plan.
\section{Difficulties}
One of the main problems that I had which caused me to lose a lot of time was some hardware issues. The CPU in the main computer I was using had a hardware issue, causing my program to crash within a few seconds of running (but did not show any significant problems with other software running). This was quite difficult to diagnose, since I immediately assumed that this was a bug in my code. After a lot of debugging, running the program on different machines and environments, I realised that this is a hardware issue.
Even after diagnosing this, I had to wait for a long time (~3 weeks) for Intel to send me a replacement CPU, which caused me to not be able to do as much work over the Christmas vacation as I would have liked.
\section{Next Steps}
Since there have been many changes to the original work plan, causing me to both be ahead and behind simultaneously, here is a new work plan which I am planning to follow until the end of my project:
\begin{enumerate}
\item Evaluate different data preprocessing methods, and see which one works best and when. This will lead to improved accuracy in the heatmaps, so that the hotspots are more obvious. I aim to complete this by 21st February.
\item Automatically detect peaks/hotspots in the heatmap, to get an automatic counter for the number of people in the room (and evaluate prediction accuracy against the collected dataset). This was originally planned for January, but I believe it can now be done by 7th March.
\item Train a neural network to detect the exact position and poses of the people in the scene. I already have everything I need for this, so I believe it can be done by 21st March. This will be done simultaneously with writing a draft of the dissertation.
\item Evaluate whether using MIMO (Multiple Input, Multiple Output) will improve the accuracy of the system, and/or allow us to use less receiving antenna. This will also be done simultaneously with writing the dissertation, and should be completed by 4th April.
\item Finalise dissertation. This should give sufficient amount of time for editing, as well as for any unexpected issues that may arise, or for any other extensions I would like to add. This will likely be continuous until the submission deadline.
\end{enumerate}
\printbibliography
\end{document}

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@ -1,18 +0,0 @@
@INPROCEEDINGS{Hsu2024,
author={Hsu, Ting-Wei and Hsieh, Hung-Yun},
booktitle={ICC 2024 - IEEE International Conference on Communications},
title={Robust Multi-User Pose Estimation Based on Spatial and Temporal Features from WiFi CSI},
year={2024},
volume={},
number={},
pages={1600-1605},
keywords={Heating systems;Doppler shift;Time-frequency analysis;Pose estimation;Feature extraction;Robustness;Frequency estimation},
doi={10.1109/ICC51166.2024.10623053}}
@electronic{feitcsi:project,
author = {Hutar, Miroslav and Brida, Peter and Machaj, Juraj},
title = {FeitCSI, the 802.11 CSI tool},
url = {https://feitcsi.kuskosoft.com},
year = {2023}
}

36
pyproject.toml
View File

@ -4,28 +4,16 @@ version = "0.1.0"
description = "Use Wi-Fi Channel State Information to locate human presence" description = "Use Wi-Fi Channel State Information to locate human presence"
requires-python = ">=3.11" requires-python = ">=3.11"
dependencies = [ dependencies = [
"numpy>=2.0.0", "flask",
"numpy",
"flask-sock",
"pytest", "pytest",
"matplotlib", "matplotlib",
"scipy", "scipy",
"scipy-stubs", "scipy-stubs",
"typer", "typer"
"h5py>=3.12.1",
"pyyaml>=6.0.2",
"pydantic>=2.10.6",
"torch",
"grpcio>=1.70.0",
"requests>=2.32.3",
] ]
[build-system]
requires = ["setuptools", "wheel", "grpcio-tools"]
build-backend = "setuptools.build_meta"
[project.scripts]
where-fi = "where_fi.cli:cli"
[tool.ruff] [tool.ruff]
line-length = 88 line-length = 88
@ -42,19 +30,3 @@ reportMissingTypeStubs = "warning"
[tool.pytest.ini_options] [tool.pytest.ini_options]
python_files = "*.py" python_files = "*.py"
[tool.uv]
package = true
[tool.setuptools]
packages = ["where_fi"]
include-package-data = true
[dependency-groups]
dev = [
"grpc-stubs>=1.53.0.5",
"grpcio-tools>=1.70.0",
"matplotlib-stubs>=0.1.0",
"protoletariat>=3.3.9",
"types-requests>=2.32.0.20250328",
]

0
where_fi/__init__.py → src/__init__.py
View File

5
where_fi/__main__.py → src/__main__.py
View File

@ -2,10 +2,11 @@ import logging
from . import cli from . import cli
logging.basicConfig( logging.basicConfig(
level=logging.INFO, level=logging.INFO,
format="%(asctime)s %(name)-50s %(levelname)-8s %(message)s", format="%(asctime)s %(name)-40s %(levelname)-8s %(message)s",
) )
if __name__ == "__main__": if __name__ == "__main__":
cli.cli() cli.app()

48
src/cli.py Normal file
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@ -0,0 +1,48 @@
import logging
import multiprocessing as mp
import numpy as np
import numpy.typing as npt
import typer
from . import config, visualise
from .collection import ingest
from .processing.aoa import AoA
from .processing.preprocess import Preprocessor
app = typer.Typer()
logger = logging.getLogger(__name__)
@app.command()
def antennas() -> None:
"""Utility to help determine the order in which antennas are plugged in
Once the script is running, unplug and replug antennas from left to right, to get
the correct order. Every time an antenna is unplugged and replugged, the script will
print the antenna identifier. When you are done, press Ctrl+C to stop the script and
get the final order.
"""
from .utils import antenna_order
antenna_order.main()
@app.command()
def heatmap() -> None:
preprocessor = Preprocessor()
aoa = AoA()
webapp_queue: "mp.Queue[AoA]" = mp.Queue(config.SAMPLE_RATE)
# Start webapp in background process
webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
webapp.start()
def callback(antenna_data: npt.NDArray[np.complex128]) -> 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)
if not webapp_queue.full():
webapp_queue.put(aoa)
ingest.start_processing(callback)

10
where_fi/collection/csi_frame.py → src/collection/csi_frame.py
View File

@ -43,8 +43,8 @@ class CSIHeader:
self.num_rx = data[46] self.num_rx = data[46]
self.num_tx = data[47] self.num_tx = data[47]
self.num_subcarriers = struct.unpack("I", data[52:56])[0] self.num_subcarriers = struct.unpack("I", data[52:56])[0]
self.rssi1: int = struct.unpack("I", data[60:64])[0] self.rssi1 = struct.unpack("I", data[60:64])[0]
self.rssi2: int = struct.unpack("I", data[64:68])[0] self.rssi2 = struct.unpack("I", data[64:68])[0]
self.source_mac = struct.unpack("BBBBBB", data[68:74]) self.source_mac = struct.unpack("BBBBBB", data[68:74])
self.source_mac_string = "%02x:%02x:%02x:%02x:%02x:%02x" % struct.unpack( self.source_mac_string = "%02x:%02x:%02x:%02x:%02x:%02x" % struct.unpack(
"BBBBBB", data[68:74] "BBBBBB", data[68:74]
@ -93,14 +93,14 @@ class CSIHeader:
class CSI: class CSI:
@staticmethod @staticmethod
def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex64]: def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex128]:
csi_matrix: npt.NDArray[np.complex64] = np.zeros( csi_matrix: npt.NDArray[np.complex128] = np.zeros(
( (
header.num_subcarriers, header.num_subcarriers,
header.num_rx, header.num_rx,
header.num_tx, header.num_tx,
), ),
dtype=np.complex64, dtype=np.complex128,
) )
pos = 0 pos = 0
for j in range(header.num_rx): for j in range(header.num_rx):

213
src/collection/ingest.py Normal file
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@ -0,0 +1,213 @@
import logging
import multiprocessing as mp
import socket
import struct
import subprocess
import threading
import time
from datetime import datetime
from typing import Callable, NamedTuple, NoReturn
import numpy as np
import numpy.typing as npt
from .. import config
from .csi_frame import CSI
Host = tuple[str, int]
CSICallback = Callable[[npt.NDArray[np.complex128]], None]
class FeitHost:
def __init__(self, host: Host, command: str) -> None:
self.command = command
self.host = host
self.logger = logging.getLogger(
f"{__name__}.{self.__class__.__name__}-{self.host[0]}"
)
self.checker = threading.Thread(target=self.check_continuous)
self.checker.start()
def check_connection(self) -> bool:
feitcsi_status = subprocess.run(
f"ssh root@{self.host[0]} pgrep feitcsi",
check=False,
stdout=subprocess.DEVNULL,
shell=True,
)
return feitcsi_status.returncode == 0
def connect(self) -> None:
self.server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.server.connect(self.host)
self.server.send(b"stop\n")
self.server.send(self.command.encode())
self.logger.info(f"Connected to {self.host}")
def check_continuous(self) -> NoReturn:
"""
Repeatedly check if the FeitCSI service is running
Because FeitCSI is using TCP, we will get no information if the service stops,
or the computer is not reachable. In order to make debugging easier, this checks
and logs continuously if the service is running.
"""
last_status = False
while True:
if not self.check_connection():
self.logger.error(f"FeitCSI is not running on {self.host[0]}")
last_status = False
else:
if not last_status:
self.connect()
last_status = True
time.sleep(1)
class FeitTransmitter(FeitHost):
def __init__(self) -> None:
command = (
f"feitcsi --frequency {config.CENTRAL_FREQUENCY_MHZ} "
f"--channel-width {config.CHANNEL_WIDTH} "
f"--format {config.FRAME_FORMAT} "
f"--mode inject -s 1 --verbose "
f"--inject-delay {1_000_000 // config.SAMPLE_RATE}"
)
super().__init__(config.INJECT_HOST, command)
class FeitReceiver(FeitHost):
def __init__(self, host: Host) -> None:
command = (
f"feitcsi --frequency {config.CENTRAL_FREQUENCY_MHZ} "
f"--channel-width {config.CHANNEL_WIDTH} "
f"--format {config.FRAME_FORMAT} "
f"--mode measure"
)
super().__init__(host, command)
def listen(self, queue: "mp.Queue[CSI]") -> NoReturn:
prev_time = datetime.now()
self.logger.info("Listening for CSI data")
while True:
while not hasattr(self, "server"):
time.sleep(0.1)
# This is the max size of a UDP packet. The size of the actual CSI
# packet will depend on the frame format and channel width, which
# changes the number of subcarriers
data = self.server.recv(65535)
try:
csidata = CSI(data)
self.logger.debug(
f"Received CSI data after {datetime.now() - prev_time}"
)
prev_time = datetime.now()
queue.put(csidata)
except struct.error:
self.logger.error("Failed to parse CSI data")
class CSIProcessor:
def __init__(
self,
receiver_connections: dict[Host, "mp.Queue[CSI]"],
) -> None:
self.pending_data: dict[Host, tuple[datetime, CSI]] = {}
self.pending_data_lock = mp.Lock()
self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
self.last_processed = datetime.now()
self.connections = receiver_connections
def add_data(self, host: Host, data: CSI) -> None:
if (
host in self.pending_data
and self.last_processed < self.pending_data[host][0]
):
self.logger.warning(
f"Skipping data from {host} at {self.pending_data[host][0]}"
)
with self.pending_data_lock:
self.pending_data[host] = (datetime.now(), data)
def is_ready(self) -> bool:
for host in config.RECEIVE_HOSTS:
if (
host not in self.pending_data
or self.pending_data[host][0] <= self.last_processed
):
return False
return True
def process_data(
self,
callback: CSICallback | None = None,
pre_merge_callback: Callable[[dict[Host, CSI]], None] | None = None,
) -> None:
self.last_processed = datetime.now()
if pre_merge_callback is not None:
pre_merge_callback(
{host: data[1] for host, data in self.pending_data.items()}
)
antenna_data = [
np.expand_dims(self.pending_data[ip][1].matrix[:, antenna], axis=2)
for ip, antenna in config.ANTENNA_ORDER
]
# We have data from all servers
all_data = np.concat(antenna_data, axis=1)
if callback:
callback(all_data)
def process_forever(
self,
callback: CSICallback | None = None,
pre_merge_callback: Callable[[dict[Host, CSI]], None] | None = None,
) -> NoReturn:
while True:
for ip, queue in self.connections.items():
while not queue.empty():
self.add_data(ip, queue.get())
if self.is_ready():
self.process_data(callback, pre_merge_callback=pre_merge_callback)
else:
self.logger.debug("Not all data is ready")
time.sleep(0.0005)
class Receiver(NamedTuple):
ip: Host
receiver: FeitReceiver
queue: "mp.Queue[CSI]"
def start_processing(
csi_callback: CSICallback | None = None,
pre_merge_callback: Callable[[dict[Host, CSI]], None] | None = None,
) -> None:
receivers = [
Receiver(ip, FeitReceiver(ip), mp.Queue(config.SAMPLE_RATE))
for ip in config.RECEIVE_HOSTS
]
FeitTransmitter()
# Start injecting CSI frames
processor = CSIProcessor({r.ip: r.queue for r in receivers})
receiver_processes = [
threading.Thread(target=r.receiver.listen, args=(r.queue,)) for r in receivers
]
for proc in receiver_processes:
proc.start()
processing_thread = mp.Process(
target=processor.process_forever, args=(csi_callback, pre_merge_callback)
)
processing_thread.start()
try:
processing_thread.join()
except KeyboardInterrupt:
return

36
src/config.py Normal file
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@ -0,0 +1,36 @@
PREPROCESSING_SHORT_TERM_WINDOW_SIZE = 5
PREPROCESSING_LONG_TERM_ALPHA = 0.01
PREPROCESSING_BANDPASS_LOW_CUTOFF = 2
PREPROCESSING_BANDPASS_HIGH_CUTOFF = 40
AOA_SLIDING_WINDOW_SIZE = 40
RECEIVE_HOSTS = [("cfalas.com", 10001), ("cfalas.com", 10002)]
INJECT_HOST = ("cfalas.com", 10003)
ANTENNA_ORDER = [
(("cfalas.com", 10001), 0),
(("cfalas.com", 10002), 1),
(("cfalas.com", 10002), 0),
(("cfalas.com", 10001), 1),
]
SAMPLE_RATE = 100 # Hz
EIGVAL_THRESHOLD = 10
# DELTA_F = 78_125 # Spacing between subcarriers in Hz
DELTA_F = 312_500 # Spacing between subcarriers in Hz
CENTRAL_FREQUENCY_MHZ = 6195
ANTENNA_SPACING = 0.0285 # 2.85 cm
CHANNEL_WIDTH = 20
FRAME_FORMAT = "HT"
CENTRAL_FREQUENCY_HZ = CENTRAL_FREQUENCY_MHZ * 1_000_000
C = 299_792_458 # m/s
VISUALISE_RAW = False
HEATMAP_FPS = 10

137
src/processing/aoa.py Normal file
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@ -0,0 +1,137 @@
import logging
from datetime import datetime
import numpy as np
import numpy.typing as npt
from .. import config
logger = logging.getLogger(__name__)
class AoA:
def __init__(self) -> None:
self.historical_autocorr = np.array([])
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]:
assert len(data.shape) == 3
M = data.shape[0] # Number of subcarriers
N = data.shape[1] # Number of RX antennas
T = data.shape[2] # Number of TX antennas
self.N_subcarriers = M
self.N_rx = N
logger.debug(f"Smoothing: Subcarriers: {M}, RX antennas: {N}, TX antennas: {T}")
# 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)
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)
logger.debug(f"Smoothed: {H_sm.shape}")
return H_sm
def update(self, data: npt.NDArray[np.complex128]) -> None:
self.timestamp = datetime.now()
H_sm = self.smooth(data)
auto_corr = np.matmul(H_sm, np.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)
else:
self.historical_autocorr = np.append(
self.historical_autocorr, np.expand_dims(auto_corr, 0), axis=0
)
WINDOW_SIZE = config.AOA_SLIDING_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)
# 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.EIGVAL_THRESHOLD]
omega_base = np.exp(-2j * np.pi * config.DELTA_F)
phi_base = np.exp(
2j * np.pi * config.CENTRAL_FREQUENCY_HZ * config.ANTENNA_SPACING / config.C
)
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(
2j
* np.pi
* config.CENTRAL_FREQUENCY_HZ
* config.ANTENNA_SPACING
* (1 - np.cos(theta))
/ config.C
)
omega_t = np.expand_dims(omega_t, axis=-1)
phi_theta = np.expand_dims(phi_theta, axis=-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)
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)
def test_smoothing() -> None:
row, col = np.indices((4, 2))
data = row + 1j * col
aoa = AoA()
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)
def test_steering_vector() -> None:
aoa = AoA()
aoa.N_subcarriers = 10
aoa.N_rx = 2
print(aoa.omega_base)
print(aoa.phi_base)
tau = 1
theta = 0
print(aoa.steering_vector(theta, tau))
assert False

72
src/processing/preprocess.py Normal file
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@ -0,0 +1,72 @@
import logging
from queue import Queue
import numpy as np
import numpy.typing as npt
from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
from .. import config
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
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.filter = butter(
5,
[
config.PREPROCESSING_BANDPASS_LOW_CUTOFF,
config.PREPROCESSING_BANDPASS_HIGH_CUTOFF,
],
fs=config.SAMPLE_RATE,
btype="band",
output="sos",
)
def preprocess(self, h: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
# CSI data is not available for pilot subcarriers.
h_hat = np.where(
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
h,
)
# Skip every other subcarrier
# h_hat = h_hat[::2, :, :]
# logger.info(f"CSI shape: {h_hat.shape}")
h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
h_hat = np.nan_to_num(h_hat)
# h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3)
h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid")
# 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 = (
self.long_term_avg * (1 - config.PREPROCESSING_LONG_TERM_ALPHA)
+ h_hat * config.PREPROCESSING_LONG_TERM_ALPHA
)
# Remove long term average, to remove static paths
h_hat -= self.long_term_avg
# Apply bandpass filter to remove low and high frequency noise
if not hasattr(self, "filter_zi"):
self.filter_zi = (
np.expand_dims(sosfilt_zi(self.filter), axis=(-1, -2, -3)) * h_hat
)
h_hat_filt, self.filter_zi = sosfilt(
self.filter, [h_hat], zi=self.filter_zi, axis=0
)
return h_hat_filt[0]

56
src/utils/antenna_order.py Normal file
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@ -0,0 +1,56 @@
import logging
from ..collection import ingest
logger = logging.getLogger(__name__)
AntennaIdentifier = tuple[ingest.Host, int]
order: list[AntennaIdentifier] = []
prev_unplugged: set[AntennaIdentifier] = set()
antenna_average: dict[AntennaIdentifier, float] = {}
RSSI_THRESHOLD = 8
def callback(antenna_data: dict[ingest.Host, ingest.CSI]) -> None:
global prev_unplugged
global antenna_average
global order
unplugged: set[tuple[ingest.Host, int]] = set()
logger.debug(
"Antenna RSSI: "
+ str(
{
host: (csi.header.rssi1, csi.header.rssi2)
for host, csi in antenna_data.items()
}
)
)
for host, csi in antenna_data.items():
antenna_average[(host, 0)] = (
antenna_average.get((host, 0), csi.header.rssi1) * 0.9
+ csi.header.rssi1 * 0.1
)
antenna_average[(host, 1)] = (
antenna_average.get((host, 1), csi.header.rssi2) * 0.9
+ csi.header.rssi2 * 0.1
)
if csi.header.rssi1 > antenna_average[(host, 0)] + RSSI_THRESHOLD:
unplugged.add((host, 0))
if csi.header.rssi2 > antenna_average[(host, 1)] + RSSI_THRESHOLD:
unplugged.add((host, 1))
if prev_unplugged != unplugged:
if len(prev_unplugged) > len(unplugged):
logger.info(f"Antenna plugged in: {prev_unplugged - unplugged}")
else:
logger.info(f"Antenna unplugged: {unplugged - prev_unplugged}")
diff = list(unplugged - prev_unplugged)
if len(diff) == 1 and diff[0] not in order:
host, antenna = diff.pop()
order.append((host, antenna))
logger.info(f"Current order: {order}")
prev_unplugged = unplugged
def main() -> None:
ingest.start_processing(pre_merge_callback=callback)

148
src/visualise/__init__.py Normal file
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@ -0,0 +1,148 @@
from flask import Flask, render_template, Response, request
from flask_sock import Sock
import numpy as np
import numpy.typing as npt
from simple_websocket import Server
import time
from datetime import datetime
import multiprocessing as mp
import matplotlib.pyplot as plt
import io
import logging
from ..processing.aoa import AoA
from .. import config
import matplotlib
matplotlib.use("agg")
app = Flask(__name__)
sock = Sock(app)
aoa_queue = None
logger = logging.getLogger(__name__)
data: npt.NDArray[np.complex128] = np.array([], dtype=complex)
aoa: AoA = AoA()
@app.route("/preprocessed")
def preprocessed():
return render_template("preprocessed.html")
Subscriber = Server
subscriber_settings: dict[Subscriber, tuple[int, int, int]] = {}
@sock.route("/data")
def get_data(sock: Subscriber):
while True:
msg = sock.receive()
if len(msg.split()) != 3:
break
subcarrier, rx, tx = map(int, msg.split())
subscriber_settings[sock] = (subcarrier, rx, tx)
def add_data(
raw_data: npt.NDArray[np.complex128], new_data: npt.NDArray[np.complex128]
):
if config.VISUALISE_RAW:
magn = np.abs(raw_data)
phase = np.angle(raw_data)
new_mag = np.abs(new_data)
new_phase = np.angle(new_data)
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(magn[:, 0, 0], c="b")
axs[0, 0].plot(magn[:, 1, 0], c="orange")
axs[1, 0].plot(new_mag[:, 0, 0], c="b")
axs[1, 0].plot(new_mag[:, 1, 0], c="orange")
axs[0, 1].plot(phase[:, 0, 0], c="b")
axs[0, 1].plot(phase[:, 1, 0], c="orange")
axs[1, 1].plot(new_phase[:, 0, 0], c="b")
axs[1, 1].plot(new_phase[:, 1, 0], c="orange")
fig.savefig("/tmp/plot.png")
plt.close(fig)
global data
if data.size == 0:
data = np.expand_dims(new_data, axis=0)
else:
data = np.concat([data, np.expand_dims(new_data, axis=0)], axis=0)
# Only keep latest 100 entries
if data.shape[0] > 100:
data = data[-100:]
to_remove: list[Subscriber] = []
for subscriber in subscriber_settings:
try:
subcarrier, rx, tx = subscriber_settings[subscriber]
subscriber.send(new_data.real[subcarrier, rx, tx])
except Exception as e:
to_remove.append(subscriber)
print(e)
for subscriber in to_remove:
del subscriber_settings[subscriber]
def make_heatmap(aoa: AoA, max_tof: float):
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
# Compute the function values
Z = np.log(np.vectorize(aoa.evaluate)(Theta, R))
ax.pcolormesh(Theta, R, Z, edgecolors="face")
buf = io.BytesIO()
fig.savefig(buf, format="jpeg")
plt.close(fig)
buf.seek(0)
return buf
def gather_aoa(max_tof: float):
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)
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():
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"
)
def start(conn: "mp.Queue[AoA]"):
global aoa_queue
aoa_queue = conn
app.run(debug=True, use_reloader=False, host="0.0.0.0")

42
src/visualise/static/plot.js Normal file
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const plotElement = document.getElementById("plot");
const layout = {
title: "Real-Time Complex Data Visualization",
xaxis: { title: "Time (ms)" },
yaxis: { title: "Value" },
};
Plotly.newPlot(
plotElement,
[
{
x: [],
y: [],
type: "scatter",
mode: "lines+markers", // Line + markers
},
],
layout,
);
const socket = new WebSocket("ws://" + location.host + "/data");
socket.onopen = function () {
console.log("Connected to the server");
socket.send("0 0 0");
};
socket.addEventListener("message", function (msg) {
Plotly.extendTraces(
plotElement,
{
x: [[msg.timeStamp]],
y: [[msg.data]],
},
[0],
300,
);
});
function changeSubcarrier() {
const subcarrier = document.getElementById("subcarrier").value;
socket.send(subcarrier + " 0 0");
}

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src/visualise/templates/preprocessed.html Normal file
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<!DOCTYPE html>
<html>
<head>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-QWTKZyjpPEjISv5WaRU9OFeRpok6YctnYmDr5pNlyT2bRjXh0JMhjY6hW+ALEwIH" crossorigin="anonymous">
<script src="https://cdn.plot.ly/plotly-2.35.2.min.js" charset="utf-8"></script>
</head>
<body>
<div class="container">
<h1>Preprocessing</h1>
<div id="plot"></div>
<script src="{{url_for('static', filename='plot.js')}}"></script>
<input type="number" id="subcarrier" class="form-control" placeholder="Choose subcarrier" onchange="changeSubcarrier()" value="0">
</div>
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js" integrity="sha384-YvpcrYf0tY3lHB60NNkmXc5s9fDVZLESaAA55NDzOxhy9GkcIdslK1eN7N6jIeHz" crossorigin="anonymous"></script>
</body>

1369
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10
visualisation/motion_antenna/index.html
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@ -1,10 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.2.0/p5.min.js"></script>
<meta charset="utf-8">
</head>
<body>
<script src="sketch.js"></script>
</body>
</html>

87
visualisation/motion_antenna/sketch.js
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@ -1,87 +0,0 @@
let target = [150, 30];
let Tx = [300, 350];
let Rx = [50, 300];
let tx_target_distance;
let rx_target_distance;
let v_target = [6, 5];
let v_target_vec;
let target_speed;
let framerate = 60;
function setup() {
createCanvas(400, 400);
frameRate(framerate);
target_speed = sqrt(sq(v_target[0]) + sq(v_target[1]));
v_target_vec = createVector(v_target[0], v_target[1]);
}
let toTarget = [];
let toRx = [];
let f = 2; // Hz
let c = 50; // pixels/sec
let lambda = c / f;
let dt = 1 / framerate;
let distance_per_tick = (c / f) * dt;
let time = 0;
function draw() {
time += dt;
target[0] += v_target[0] * dt;
target[1] += v_target[1] * dt;
tx_target_distance = dist(target[0], target[1], Tx[0], Tx[1]);
rx_target_distance = dist(target[0], target[1], Rx[0], Rx[1]);
background(220);
strokeWeight(10);
stroke("black");
noFill();
point(target[0], target[1]);
point(Tx[0], Tx[1]);
point(Rx[0], Rx[1]);
if (time - int(time) < dt) {
toTarget.push(0);
}
strokeWeight(1);
stroke("blue");
for (let x in toTarget) {
toTarget[x] += distance_per_tick;
circle(Tx[0], Tx[1], 2 * toTarget[x]);
if (toTarget[x] > tx_target_distance) {
toTarget.shift();
toRx.push([target[0], target[1], 0]);
}
}
stroke("green");
for (let x of toRx) {
circle(x[0], x[1], 2 * x[2]);
x[2] += distance_per_tick;
if (dist(x[0], x[1], Rx[0], Rx[1]) < x[2]) {
toRx.shift();
}
}
stroke("red");
line(
target[0],
target[1],
target[0] + 100 * v_target[0],
target[1] + 100 * v_target[1],
);
line(target[0], target[1], Rx[0], Rx[1]);
line(target[0], target[1], Tx[0], Tx[1]);
let phi_T = v_target_vec.angleBetween(
createVector(Tx[0] - target[0], Tx[1] - target[1]),
);
let phi_R = v_target_vec.angleBetween(
createVector(Rx[0] - target[0], Rx[1] - target[1]),
);
let psi = target_speed * (cos(phi_R) + cos(phi_T));
let apparent_freq = psi / lambda;
print(apparent_freq);
}

240
where_fi/application.py
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@ -1,240 +0,0 @@
import logging
import multiprocessing as mp
import threading
import time
from typing import Any, Callable, NamedTuple
import numpy.typing as npt
from where_fi.collection import CSIMatrix, NoopCSIProducer, ingest
from where_fi.collection.csi_frame import CSI
from where_fi.collection.protocols import CSIProducer, MergedCSI
from where_fi.config import config
from where_fi.processing.preprocess import Preprocessor
from where_fi.visualise import server as visualise
from where_fi.visualise.server import figures
class Receiver(NamedTuple):
ip: ingest.Host
receiver: ingest.FeitReceiver
thread: threading.Thread
class Scheduler:
"""
A simple scheduler that runs a function at a given rate.
"""
def __init__(self, rate: float, func: Callable[[], None]) -> None:
self.rate = rate
self.func = func
self.active = True
def run(self) -> None:
"""
Run the scheduler in a loop, calling the function at the given rate.
"""
while self.active:
self.func()
time.sleep(1 / self.rate)
def start(self) -> None:
"""
Start the scheduler in a separate thread.
"""
self.thread = threading.Thread(target=self.run)
self.thread.start()
def stop(self) -> None:
"""
Stop the scheduler.
"""
self.active = False
class CSIApplication:
"""
A high-level application that manages CSI data ingestion and processing.
This allows a simple interface to be used for the main CLI logic, without having to
deal with the threads and queues directly.
It can be used using decortors to register callbacks for different stages of the
processing pipeline:
- `on_pre_merge`: Called with the raw CSI data (including headers) from each
receiver, before it is merged into a single matrix.
- `on_sample`: Called once per sample with the merged CSI data.
- `on_process`: Called at the processing sample rate, without any data (this is
mostly used as a scheduler).
"""
def __init__(self, visualise_raw: bool = False) -> None:
self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
self.producer = NoopCSIProducer()
self.webapp_queue: "mp.Queue[visualise.VisualiserData]" = mp.Queue(
config.processing_sample_rate
)
self.webapp = visualise.Webapp()
self.webapp_thread = threading.Thread(
target=self.webapp.start, args=(self.webapp_queue,)
)
self.visualise_raw = visualise_raw
self.pre_merge_callback = None
self.raw_csi_callback = None
self.preprocessed_csi_callback = None
self.processing_callback = None
self.preprocessor = Preprocessor()
self.custom_figures: dict[
visualise.figures.FigureId, visualise.figures.SpecificFigure
] = {}
def set_producer(self, producer: CSIProducer) -> None:
"""
Set the producer for the application. This is used to change the data source
at runtime.
"""
self.producer = producer
def register_figure(
self,
figure_id: visualise.figures.FigureId,
figure: visualise.figures.SpecificFigure,
) -> None:
self.custom_figures[figure_id] = figure
figures.all_figures[figure_id] = figure
def visualise_data(
self, data: npt.NDArray[Any], dtype: visualise.figures.FigureId
) -> None:
"""
Update a visualisation with the given data. The available visualisations are as
per visualise.server.all_figures.
"""
self.webapp_queue.put(visualise.VisualiserData(data, dtype))
def on_raw(self, func: Callable[[CSIMatrix], None]) -> Callable[[CSIMatrix], None]:
"""
Decorator to register a callback for the sample preprocessing.
"""
def decorator(data: CSIMatrix) -> None:
func(data)
self.raw_csi_callback = decorator
return decorator
def on_sample(
self, func: Callable[[CSIMatrix], None]
) -> Callable[[CSIMatrix], None]:
"""
Decorator to register a callback for the sample preprocessing.
"""
def decorator(data: CSIMatrix) -> None:
func(data)
self.preprocessed_csi_callback = decorator
return decorator
def on_pre_merge(
self, func: Callable[[dict[ingest.Host, CSI]], None]
) -> Callable[[dict[ingest.Host, CSI]], None]:
"""
Decorator to register a callback for the samples before merging.
"""
self.pre_merge_callback = func
return func
def on_process(
self, func: Callable[[CSIMatrix], None]
) -> Callable[[CSIMatrix], None]:
"""
Decorator to register a callback for the sample processing.
"""
self.processing_callback = func
return func
def process_sample(self, sample: MergedCSI) -> None:
"""Data pipeline for processing a sample.
This is the entry point for a sample being received from the producer. Depending
on the callbacks which are registered, this will call the appropriate functions
to pre-process and visualise the sample.
"""
if self.visualise_raw:
self.visualise_data(sample.matrix, visualise.figures.Figure.RAW_CSI)
if self.raw_csi_callback is not None:
self.raw_csi_callback(sample.matrix)
if self.pre_merge_callback is not None:
self.pre_merge_callback(sample.frames)
processed = self.preprocessor.preprocess(
sample.matrix, sample.frames, visualiser=self.visualise_data
)
# if self.visualise_raw:
# self.visualise_data(processed, visualise.figures.Figure.PROCESSED_CSI)
if self.preprocessed_csi_callback is not None:
self.preprocessed_csi_callback(processed)
def listen(self) -> None:
"""
Listen for incoming data from the selected ingestor, and call the appropriate
callback.
"""
data_gen = self.producer()
while True:
try:
sample = next(data_gen)
self.process_sample(sample)
except StopIteration:
break
except Exception as e:
self.logger.error(f"Error processing sample: {e}", exc_info=True)
break
def start(self) -> None:
# for receiver in self.receivers:
# receiver.thread.start()
self.buffer_thread = threading.Thread(
target=self.listen,
)
self.buffer_thread.start()
self.webapp_thread.start()
def get_proc_sample() -> None:
sample = self.preprocessor.last_sample
if self.visualise_raw and sample is not None:
self.visualise_data(
sample,
visualise.figures.Figure.PROCESSED_CSI,
)
if sample is not None and self.processing_callback is not None:
self.processing_callback(sample)
else:
self.logger.warning("No samples to process")
self.scheduler = Scheduler(config.processing_sample_rate, get_proc_sample)
self.scheduler.start()
try:
self.webapp_thread.join()
except KeyboardInterrupt:
self.stop()
def stop(self) -> None:
self.logger.info("Stopping application")
self.producer.stop()
if hasattr(self, "scheduler"):
self.scheduler.stop()
self.scheduler.thread.join()
self.buffer_thread.join()
self.webapp.active = False
self.webapp_thread.join()
self.logger.info("Application stopped")

92
where_fi/cli/__init__.py
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@ -1,92 +0,0 @@
import importlib.util
import logging
from pathlib import Path
import numpy as np
import numpy.typing as npt
import torch
import typer
from where_fi.application import CSIApplication
from where_fi.processing.aoa import AoA
from . import file, globals
cli = typer.Typer(callback=globals.main)
logger = logging.getLogger(__name__)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@cli.command()
def antennas() -> None:
"""Utility to help determine the order in which antennas are plugged in
Once the script is running, unplug and replug antennas from left to right, to get
the correct order. Every time an antenna is unplugged and replugged, the script will
print the antenna identifier. When you are done, press Ctrl+C to stop the script and
get the final order.
"""
from ..utils import antenna_order
if not globals.csi_producer.is_live:
raise NotImplementedError("This command only works with live data")
antenna_order.main()
@cli.command()
def heatmap() -> None:
app = CSIApplication(globals.csi_producer, visualise_raw=True)
aoa = AoA()
@app.on_sample
def _(sample: npt.NDArray[np.complex64]) -> None:
processed_tensor = torch.tensor(sample, device=device)
aoa.update(processed_tensor)
aoa.heatmap(visualiser=app.visualise_data)
app.start()
logger.info("Finished processing CSI data")
@cli.command()
def run(file: Path, app_name: str = "app") -> None:
"""
Run the application with the given file.
Can be used for running arbitrary CSI applications with non-default data streams
(e.g. from file or environment simulation).
"""
print(file.absolute())
if not file.exists():
print(f"Error: File '{file}' does not exist.")
raise typer.Exit(1)
module_name = file.stem
spec = importlib.util.spec_from_file_location(module_name, str(file))
if spec is None:
print(f"Could not load spec from {file}")
raise typer.Exit(1)
module = importlib.util.module_from_spec(spec)
if spec.loader is None:
print(f"Could not load module from {file}")
raise typer.Exit(1)
try:
spec.loader.exec_module(module)
except Exception as e:
print(f"Failed to execute {file}: {e}")
raise typer.Exit(1) from e
if not hasattr(module, app_name):
print(f"Error: '{app_name}' not defined in the module.")
raise typer.Exit(1)
if not isinstance(module.app, CSIApplication):
print(f"Error: '{app_name}' is not a CSIApplication.")
raise typer.Exit(1)
module.app.producer = globals.csi_producer
module.app.start()
cli.add_typer(file.app, name="file", help="Commands for working with CSI files")

52
where_fi/cli/file.py
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@ -1,52 +0,0 @@
import logging
import multiprocessing as mp
import threading
from datetime import datetime
from pathlib import Path
from typing import Any
import numpy as np
import numpy.typing as npt
import typer
from . import globals
app = typer.Typer()
logger = logging.getLogger(__name__)
def write_to_file(path: Path, queue: "mp.Queue[Any]") -> None:
import h5py
with h5py.File(path, "w") as file:
logger.info("Starting writer")
while True:
data = queue.get()
if data is None:
break
logger.info("Writing data to file")
file.create_dataset(datetime.now().isoformat(), data=data)
logger.info("Writer stopped")
@app.command()
def capture(output_path: Path) -> None:
"""Capture CSI data to a file"""
logger.info(f"Capturing data to {output_path}")
queue: "mp.Queue[None | npt.NDArray[np.complex128]]" = mp.Queue()
writer = threading.Thread(
target=write_to_file,
args=(
output_path,
queue,
),
)
writer.start()
def callback(antenna_data: npt.NDArray[np.complex128]) -> None:
logger.info(f"Got final CSI data with shape {antenna_data.shape}")
queue.put(antenna_data)
globals.csi_producer(csi_callback=callback)
queue.put(None)

20
where_fi/cli/globals.py
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@ -1,20 +0,0 @@
from pathlib import Path
from .. import collection
from ..collection import file, ingest, raytracing
csi_producer: collection.CSIProducer = collection.NoopCSIProducer()
is_live = True
def main(from_file: Path | None = None, from_environment: Path | None = None) -> None:
global csi_producer, is_live
if from_file and from_environment:
raise ValueError("Cannot specify both a data file and an environment file")
if from_file:
csi_producer = file.FileCSIPRoducer(path=from_file)
elif from_environment:
environment = raytracing.Environment.from_config(from_environment)
csi_producer = raytracing.SimulatedCSIProducer(environment)
else:
csi_producer = ingest.RealtimeCSIProducer()

24
where_fi/collection/__init__.py
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@ -1,24 +0,0 @@
from typing import Iterator
import numpy as np
import numpy.typing as npt
from . import file, ingest, raytracing
from .protocols import CSIProducer, MergedCSI
class NoopCSIProducer:
"""A no-op CSI producer that does nothing"""
is_live = False
def __call__(self) -> Iterator[MergedCSI]:
return iter([])
def stop(self) -> None:
pass
CSIMatrix = npt.NDArray[np.complex64]
__all__ = ["file", "ingest", "raytracing", "NoopCSIProducer", "CSIProducer"]

60
where_fi/collection/file.py
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@ -1,60 +0,0 @@
import logging
import time
from datetime import datetime, timedelta
from pathlib import Path
from typing import Iterator
import h5py
from . import protocols
logger = logging.getLogger(__name__)
class FileCSIPRoducer:
is_live = False
def __init__(self, path: Path) -> None:
self.path = path
if not self.path.exists():
raise FileNotFoundError(f"File {self.path} does not exist")
def __call__(self) -> Iterator[protocols.MergedCSI]:
"""
Replay the CSI data from the file.
"""
logger.info(f"Replaying CSI data from {self.path}")
with h5py.File(self.path, "r") as file:
try:
for key in file:
datetime.fromisoformat(key)
except ValueError as e:
logger.exception(
"The file provided was not generated using this software", e
)
start_time = datetime.fromisoformat(list(file.keys())[0])
target_offset = datetime.now() - start_time
for key in file:
logger.debug(f"Sending data from {key} at {datetime.now().isoformat()}")
# TODO: add raw frames
yield protocols.MergedCSI(
frames={},
matrix=file[key][:],
)
curr_time_virtual = datetime.fromisoformat(key)
new_offset = datetime.now() - curr_time_virtual
logger.debug(f"New offset {new_offset}, target is {target_offset}")
if new_offset > target_offset + timedelta(seconds=1):
logger.warning(
f"Data is {new_offset - target_offset} behind, lagging behind..."
)
time.sleep(max(0, (target_offset - new_offset).total_seconds()))
def stop(self) -> None:
"""
Stop the producer.
"""
logger.info("Stopping file CSI producer")

228
where_fi/collection/ingest.py
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@ -1,228 +0,0 @@
import logging
import multiprocessing as mp
import selectors
import socket
import subprocess
import threading
import time
from datetime import datetime
from typing import Iterator
import numpy as np
from ..config import config
from .csi_frame import CSI
from .protocols import MergedCSI
Host = tuple[str, int]
class FeitHost:
def __init__(self, host: Host, command: str) -> None:
self.command = command
self.host = host
self.logger = logging.getLogger(
f"{__name__}.{self.__class__.__name__}-{self.host[0]}"
)
self.active = True
self.checker = threading.Thread(target=self.check_continuous)
self.checker.start()
self.selectors: list[selectors.BaseSelector] = []
def check_connection(self) -> bool:
try:
feitcsi_status = subprocess.run(
f"ssh root@{self.host[0]} pgrep feitcsi",
check=False,
stdout=subprocess.DEVNULL,
shell=True,
timeout=3,
)
return feitcsi_status.returncode == 0
except subprocess.TimeoutExpired:
return False
def connect(self) -> None:
self.server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.server.connect(self.host)
self.server.send(b"stop\n")
self.server.send(self.command.encode())
for selector in self.selectors:
selector.register(self.server, selectors.EVENT_READ, data=self)
self.logger.info(f"Connected to {self.host}")
def check_continuous(self) -> None:
"""
Repeatedly check if the FeitCSI service is running
Because FeitCSI is using TCP, we will get no information if the service stops,
or the computer is not reachable. In order to make debugging easier, this checks
and logs continuously if the service is running.
"""
last_status = False
while self.active:
self.logger.debug(f"Checking connection to {self.host[0]}")
if not self.check_connection():
self.logger.error(f"FeitCSI is not running on {self.host[0]}")
last_status = False
else:
if not last_status:
self.connect()
last_status = True
time.sleep(1)
self.logger.info("Stopping host checker")
class FeitTransmitter(FeitHost):
def __init__(self) -> None:
command = (
f"feitcsi --frequency {config.central_freq} "
f"--channel-width {config.channel_width} "
f"--format {config.frame_format} "
f"--mode inject -s 1 --verbose "
f"--inject-delay {1_000_000 // config.collection_sample_rate}"
)
super().__init__(config.transmit_host, command)
class FeitReceiver(FeitHost):
"""
A class used to connect to the host running FeitCSI and receive CSI data over a UDP
socket.
"""
def __init__(self, host: Host) -> None:
command = (
f"feitcsi --frequency {config.central_freq} "
f"--channel-width {config.channel_width} "
f"--format {config.frame_format} "
f"--mode measure"
)
super().__init__(host, command)
def recv(self) -> CSI:
"""
Block until a CSI frame is received and return it.
"""
# Receive the data from the socket
data = self.server.recv(65535)
# Decode the data using the CSI frame format
csi = CSI(data)
return csi
def register(self, selector: selectors.BaseSelector) -> None:
"""
Register the receiver as a selector. This will allow us to multiplex the I/O
operations on a single thread.
This allows us to block until any of the receivers receive data
"""
self.selectors.append(selector)
if hasattr(self, "server"):
selector.register(self.server, selectors.EVENT_READ, data=self)
class CSIAntennaArray:
"""
Represents a set of receiver hosts that are used to receive CSI data, assumed to be
part of a linear antenna array.
This class is used to buffer a set of CSI data from multiple receivers, and once a
set of readings is ready for each receiver, it will be merged into a single sample
using the antenna order in the configuration.
When the data is ready, the following callbacks are called:
- `pre_merge_callback`: Called with the data before merging. This is useful for
per-antenna analysis, e.g. finding the correct antenna order
- `sample_callback`: Called with the merged data
"""
def __init__(self, receivers: list[FeitReceiver]) -> None:
self.pending_data: dict[Host, tuple[datetime, CSI]] = {}
self.pending_data_lock = mp.Lock()
self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
self.last_processed = datetime.now()
self.receivers = receivers
self.active = True
def add_data(self, host: Host, data: CSI) -> None:
if (
host in self.pending_data
and self.last_processed < self.pending_data[host][0]
):
self.logger.warning(
f"Skipping data from {host} at {self.pending_data[host][0]}"
)
with self.pending_data_lock:
self.pending_data[host] = (datetime.now(), data)
def is_ready(self) -> bool:
for host in config.receive_hosts:
if (
host not in self.pending_data
or self.pending_data[host][0] <= self.last_processed
):
return False
return True
def process_data(self) -> None | MergedCSI:
if not self.is_ready():
self.logger.debug("Not all data is ready")
return None
self.last_processed = datetime.now()
frames = {host: data[1] for host, data in self.pending_data.items()}
antenna_data = [
np.expand_dims(self.pending_data[ip][1].matrix[:, antenna], axis=2)
for ip, antenna in config.antennas.order
]
# We have data from all servers
all_data = np.concat(antenna_data, axis=1)
return MergedCSI(frames=frames, matrix=all_data)
def process_forever(self) -> Iterator[MergedCSI]:
# Register the receivers with the selector
self.logger.info("Starting to process data from receivers")
selector = selectors.DefaultSelector()
for receiver in self.receivers:
receiver.register(selector)
while self.active:
events = selector.select(timeout=0.1)
if not events:
self.logger.warning("No events received in the last 0.1 seconds")
continue
for key, _ in events:
receiver = key.data
self.add_data(receiver.host, receiver.recv())
sample = self.process_data()
if sample is not None:
yield sample
class RealtimeCSIProducer:
def __init__(self) -> None:
self.receivers = [FeitReceiver(ip) for ip in config.receive_hosts]
self.transmitter = FeitTransmitter()
# Start injecting CSI frames
self.buffer = CSIAntennaArray(self.receivers)
self.is_live = True
def __call__(self) -> Iterator[MergedCSI]:
return self.buffer.process_forever()
def stop(self) -> None:
for r in self.receivers:
r.active = False
self.transmitter.active = False
self.buffer.active = False

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where_fi/collection/protocols.py
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from typing import Iterator, NamedTuple, Protocol
import numpy as np
import numpy.typing as npt
from .csi_frame import CSI
CSIHost = tuple[str, int]
class MergedCSI(NamedTuple):
"""Merged CSI data from all antennas"""
frames: dict[CSIHost, CSI]
matrix: npt.NDArray[np.complex64]
class CSIProducer(Protocol):
is_live: bool
def __call__(self) -> Iterator[MergedCSI]: ...
def stop(self) -> None: ...

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where_fi/collection/raytracing/__init__.py
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import logging
import time
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Iterator
import numpy as np
import numpy.typing as npt
from where_fi.collection.protocols import CSIProducer, MergedCSI
from where_fi.config import config
C = 299_792_458
MAX_DEPTH = 2
LOSS_EXPONENT = 0.8
logger = logging.getLogger(__name__)
@dataclass
class PathComponent:
delay: float
phase_offset: float
attenuation: float
class ChannelImpulseResponse:
def __init__(self, path_components: list[PathComponent]) -> None:
self.path_components = path_components
@staticmethod
def delayed(
other: "ChannelImpulseResponse", delay: float, reflect: bool = False
) -> "ChannelImpulseResponse":
new_path_components: list[PathComponent] = []
distance = delay * C
for component in other.path_components:
new_path_components.append(
PathComponent(
delay=component.delay + delay,
phase_offset=component.phase_offset + (np.pi if reflect else 0),
attenuation=component.attenuation
* (1 / (1 + distance) ** LOSS_EXPONENT),
# attenuation=component.attenuation
# * (np.exp(-distance * LOSS_EXPONENT)),
)
)
return ChannelImpulseResponse(new_path_components)
def __add__(self, other: "ChannelImpulseResponse") -> "ChannelImpulseResponse":
return ChannelImpulseResponse(self.path_components + other.path_components)
class PathObject:
def __init__(self, x: float, y: float, z: float) -> None:
self.x = x
self.y = y
self.z = z
self.cir = ChannelImpulseResponse([])
def distance(self, other: "PathObject") -> float:
return (
(self.x - other.x) ** 2 + (self.y - other.y) ** 2 + (self.z - other.z) ** 2
) ** 0.5
def reflect(
self,
incoming_cir: ChannelImpulseResponse,
target: list["PathObject"],
depth: int = 0,
) -> None:
self.cir = self.cir + incoming_cir
if depth > MAX_DEPTH:
return
for obj in target:
if id(obj) == id(self):
continue
distance = self.distance(obj)
assert distance > 0
delay = distance / C
obj.reflect(
ChannelImpulseResponse.delayed(incoming_cir, delay, reflect=True),
target,
depth + 1,
)
class Transmitter(PathObject):
def __init__(self, x: float, y: float, z: float) -> None:
super().__init__(x, y, z)
DELTA_T = 10
GAMMA = np.pi / 4
class Receiver(PathObject):
def __init__(self, x: float, y: float, z: float, ideal: bool = True) -> None:
super().__init__(x, y, z)
self.delta_t = 0 if ideal else DELTA_T
self.gamma = 0 if ideal else GAMMA
def get_cfr(self) -> npt.NDArray[np.complex64]:
"""
Calculate the CSI matrix for this rx-tx pair. This is computed by the Fourier
Transform of the Channel Impulse Response (CIR). The CIR is calculated as in
[1], [2].
To get the FT of the CIR, we use the sifting property of the Dirac delta
[1] - https://tns.thss.tsinghua.edu.cn/wst/docs/pre
[2] - https://dl.acm.org/doi/10.1145/2543581.2543592, Equation 4
"""
cfr = np.zeros(len(config.subcarrier_frequencies), dtype=np.complex64)
for i_sub, f_sub in enumerate(config.subcarrier_frequencies):
cfr[i_sub] = sum(
[
component.attenuation
* np.exp(1j * component.phase_offset)
* np.exp(-1j * 2 * np.pi * f_sub * component.delay)
for component in self.cir.path_components
]
) * np.exp(
1j
* (
2
* np.pi
* (i_sub / len(config.subcarrier_frequencies))
* self.delta_t
+ self.gamma
)
)
return cfr
class Environment:
def __init__(self, objects: list[PathObject]) -> None:
self.transmitters = [obj for obj in objects if isinstance(obj, Transmitter)]
self.receivers = [obj for obj in objects if isinstance(obj, Receiver)]
self.objects = [
obj for obj in objects if obj not in self.transmitters + self.receivers
]
self.count = 0
def move(self) -> None:
self.count += 1
if self.count > 4000:
print("Moving objects")
for obj in self.objects:
obj.x += np.sin(self.count / 1000 * 2 * np.pi) * 2
@staticmethod
def from_config(filename: Path) -> "Environment":
"""
Read a config file that includes a scene description and create an envionment
based on that
The config file should be a text file where each line corresponds to an object.
The first word of each line should be the type of object (TX/RX/OBJ), followed
by the x and y coordinates of the object.
"""
objects: list[PathObject] = []
with open(filename, "r") as f:
for line in f.readlines():
if line.startswith("#"):
continue
parts = line.split(" ")
x, y, z = map(float, parts[1:])
if parts[0] == "TX":
objects.append(Transmitter(x, y, z))
elif parts[0] == "RX":
objects.append(Receiver(x, y, z))
else:
objects.append(PathObject(x, y, z))
return Environment(objects)
def add_awgn(
self, signal: npt.NDArray[np.complex64], snr_dB: float
) -> npt.NDArray[np.complex64]:
signal_power = np.mean(np.abs(signal) ** 2)
snr_linear = 10 ** (snr_dB / 10)
noise_power = signal_power / snr_linear
noise = np.sqrt(noise_power / 2) * (
np.random.randn(*signal.shape) + 1j * np.random.randn(*signal.shape)
)
return signal + noise.astype(np.complex64)
def get_csi(self) -> npt.NDArray[np.complex64]:
"""
Calculate the Channel State Information (CSI) matrix for the simulated
environment.
The returned matrix is of shape (num_subcarriers, num_receivers,
num_transmitters)
This is calculated by finding all paths leading to each receiver, and
calculating the CFR evaluated at each subcarrier.
"""
csi = np.zeros(
(
len(config.subcarrier_frequencies),
len(self.receivers),
len(self.transmitters),
),
dtype=np.complex64,
)
for i_tx, transmitter in enumerate(self.transmitters):
for obj in self.objects + self.receivers + self.transmitters:
obj.cir = ChannelImpulseResponse([])
transmitter.reflect(
ChannelImpulseResponse(
[
PathComponent(
delay=0,
phase_offset=0,
# attenuation=100000000,
attenuation=100,
)
]
),
self.objects + self.receivers,
)
for i_rx, receiver in enumerate(self.receivers):
logger.debug(f"RX {i_rx} paths: {len(receiver.cir.path_components)}")
csi[:, i_rx, i_tx] = self.add_awgn(receiver.get_cfr(), snr_dB=30)
return csi
class SimulatedCSIProducer(CSIProducer):
pass
def __init__(self, environment: Environment) -> None:
self.environment = environment
self.active = True
def __call__(self) -> Iterator[MergedCSI]:
while self.active:
self.environment.move()
start = datetime.now()
csi = self.environment.get_csi()
yield MergedCSI(
frames={},
matrix=csi,
)
time.sleep(
max(
0,
1 / config.collection_sample_rate
- (datetime.now() - start).total_seconds(),
)
)
def stop(self) -> None:
self.active = False

24
where_fi/config/__init__.py
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import logging
import sys
import pydantic
import yaml
from . import models
logger = logging.getLogger(__name__)
try:
config = yaml.safe_load(open("config.yaml"))
except FileNotFoundError:
logger.error(
"No config.yaml file found. Make sure to copy the "
"config.example.yaml file to config.yaml"
)
sys.exit(1)
try:
config = models.Config(**config)
except pydantic.ValidationError as e:
logger.error(f"Invalid config.yaml file: {e}")
sys.exit(1)

168
where_fi/config/models.py
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from functools import cached_property
from typing import Literal, Self
from pydantic import BaseModel, model_validator
Host = tuple[str, int]
class Preprocessing(BaseModel):
class Bandpass(BaseModel):
lowcut: int
highcut: int
@property
def bounds(self) -> tuple[int, int]:
return (self.lowcut, self.highcut)
bandpass: Bandpass | None = None
subcarrier_step: int = 1
denoising: Literal["none", "median", "mean"] = "median"
denoising_period: float = 1
steps: list[
Literal[
"fill_pilots",
"skip_subcarriers",
"remove_agc",
"remove_sfo",
"remove_sto",
"bandpass",
]
] = ["fill_pilots", "skip_subcarriers", "remove_agc", "remove_sfo"]
@model_validator(mode="after")
def skip_in_steps(self) -> Self:
if "skip_subcarriers" not in self.steps and self.subcarrier_step != 1:
raise ValueError(
"subcarrier_step must be 1 if skip_subcarriers is not in steps"
)
return self
@model_validator(mode="after")
def bandpass_in_steps(self) -> Self:
if "bandpass" not in self.steps and self.bandpass is not None:
raise ValueError(
"bandpass must be in preprocessing steps if bandpass configuration is "
"provided"
)
if "bandpass" in self.steps and self.bandpass is None:
raise ValueError(
"bandpass configuration must be provided if bandpass is in "
"preprocessing steps"
)
return self
class MUSIC(BaseModel):
eigval_threshold: float
window_size: int
class Heatmap(BaseModel):
theta_resolution: int
tof_resolution: int
tof_max: float
heatmap: Heatmap
class Antennas(BaseModel):
spacing: float
order: list[tuple[Host, int]]
@property
def count(self) -> int:
return len(self.order)
class Config(BaseModel):
receive_hosts: list[Host]
transmit_host: Host
antennas: Antennas
collection_sample_rate: int
processing_sample_rate: int
central_freq: int
channel_width: Literal[20, 40, 80, 160]
frame_format: Literal["NOHT", "HT", "VHT", "HESU"]
preprocessing: Preprocessing
music: MUSIC
@cached_property
def central_freq_hz(self) -> int:
return self.central_freq * 1_000_000
@cached_property
def band(self) -> Literal["2.4", "5", "6"]:
if self.central_freq in range(2412, 2484):
return "2.4"
if self.central_freq in range(5180, 5320):
return "5"
if self.central_freq in range(5955, 7115):
return "6"
raise ValueError(f"{self.central_freq} is not a valid Wi-Fi channel")
@cached_property
def num_guards(self) -> int:
if self.channel_width == 20:
return 7
return 11
@cached_property
def subcarriers(self) -> int:
nulls = {
20: 1,
40: 3,
}
return len(self.subcarrier_frequencies) + nulls[self.channel_width]
@cached_property
def _delta_f_no_skipping(self) -> int:
if self.frame_format == "HESU":
return 78_125
return 312_500
@cached_property
def delta_f(self) -> int:
return self._delta_f_no_skipping * self.preprocessing.subcarrier_step
@cached_property
def _subcarriers_no_skipping(self) -> list[int]:
used = {
20: (1, 29),
40: (2, 59),
}
subcarrier_indices = list(
range(-used[self.channel_width][1] + 1, -used[self.channel_width][0] + 1)
) + list(range(used[self.channel_width][0], used[self.channel_width][1]))
print(subcarrier_indices)
return [
self.central_freq_hz + i * self._delta_f_no_skipping
for i in subcarrier_indices
]
@cached_property
def subcarrier_frequencies(self) -> list[int]:
return self._subcarriers_no_skipping[:: self.preprocessing.subcarrier_step]
@model_validator(mode="after")
def channels(self) -> Self:
band_start = 2412 if self.band == "2.4" else 5180 if self.band == "5" else 5955
if self.band == "2.4" and self.channel_width not in [20, 40]:
raise ValueError(
f"2.4GHz channel {self.central_freq} must "
"have a channel width of 20 or 40 MHz"
)
if (self.band == "2.4" and (self.central_freq - band_start) % 5 != 0) or (
self.band in ["5", "6"]
and ((self.central_freq - band_start) % self.channel_width != 0)
):
raise ValueError(
f"Central frequency {self.central_freq} must be a channel as in https://en.wikipedia.org/wiki/List_of_WLAN_channels"
)
return self

0
where_fi/processing/__init__.py
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201
where_fi/processing/aoa.py
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import logging
from datetime import datetime
from typing import Any, Callable
import numpy as np
import numpy.typing as npt
import torch
from ..config import config
from ..visualise import server as visualise
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 = torch.tensor([], dtype=torch.complex64)
self.N_subcarriers = config.subcarriers
self.N_rx = config.antennas.count
self.timestamp = datetime.now()
pass
def smooth(self, data: torch.Tensor) -> torch.Tensor:
assert len(data.shape) == 3
M = data.shape[0] # Number of subcarriers
N = data.shape[1] # Number of RX antennas
T = data.shape[2] # Number of TX antennas
self.N_subcarriers = M
self.N_rx = N
logger.debug(f"Smoothing: Subcarriers: {M}, RX antennas: {N}, TX antennas: {T}")
# 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 = 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 = [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: torch.Tensor) -> None:
self.timestamp = datetime.now()
H_sm = self.smooth(data)
logger.debug(f"Calculated smoothed CSI matrix: {H_sm.shape}")
auto_corr = H_sm @ torch.conj(H_sm).T
# This matrix is by definition Hermitian.
# Therefore, all of its eigenvectors are orthogonal.
if len(self.historical_autocorr.shape) <= 1:
self.historical_autocorr = torch.unsqueeze(auto_corr, 0)
else:
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:]
logger.debug("Finished updating autocorrelation matrix")
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]
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
* (torch.sin(theta))
/ 299_792_458
)
assert omega_t.shape == phi_theta.shape == (N,)
omega_t = torch.unsqueeze(omega_t, dim=-1)
phi_theta = torch.unsqueeze(phi_theta, dim=-1)
assert omega_t.shape == phi_theta.shape == (N, 1)
antenna_v = omega_t ** torch.arange(self.N_subcarriers, dtype=torch.float32)
phis = phi_theta ** torch.arange(self.N_rx, dtype=torch.float32)
assert antenna_v.shape == (N, self.N_subcarriers)
assert phis.shape == (N, self.N_rx)
antenna_v = torch.unsqueeze(antenna_v, dim=1)
phis = torch.unsqueeze(phis, dim=-1)
assert antenna_v.shape == (N, 1, self.N_subcarriers)
assert phis.shape == (N, self.N_rx, 1)
steering = torch.bmm(phis, antenna_v)
assert steering.shape == (N, self.N_rx, self.N_subcarriers)
return steering.reshape(N, -1)
def evaluate(
self,
theta: torch.Tensor,
tof: torch.Tensor,
visualiser: None
| Callable[[npt.NDArray[Any], visualise.figures.Figure], None] = None,
) -> torch.Tensor:
R = torch.mean(self.historical_autocorr, dim=0)
# 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.eig(R)
if visualiser:
visualiser(eigvals.numpy(), visualise.figures.Figure.MUSIC_EIGENVALUES)
assert isinstance(eigvals, torch.Tensor)
assert isinstance(eigvecs, torch.Tensor)
logger.debug(f"Eigenvalues: {eigvals}")
E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
logger.info(f"Signal subspace: {E_n.shape}")
steering = torch.unsqueeze(self.steering_vector(theta, tof), dim=-1)
print(steering.shape)
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,
visualiser: None | Callable[[npt.NDArray[Any], visualise.figures.Figure], None],
) -> 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)),
visualiser=visualiser,
)
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)
if visualiser:
visualiser(heatmap, visualise.figures.Figure.AOA_HEATMAP)
return heatmap
def test_smoothing() -> None:
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)
print(smoothed)
def test_steering_vector() -> None:
aoa = AoA()
aoa.N_subcarriers = 10
aoa.N_rx = 4
tau = torch.Tensor([1, 0])
theta = torch.Tensor([0, 1])
print(aoa.steering_vector(theta, tau))
assert False

233
where_fi/processing/preprocess.py
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import logging
from typing import Any, Callable
import numpy as np
import numpy.typing as npt
from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
from where_fi.collection import CSIMatrix
from where_fi.collection.csi_frame import CSI
from where_fi.collection.protocols import CSIHost
from where_fi.config import config
from ..visualise import server as visualise
logger = logging.getLogger(__name__)
np.seterr(invalid="ignore")
class Preprocessor:
def __init__(self) -> None:
self.short_term_avg = np.zeros((1,), dtype=np.complex64)
self.long_term_avg = np.zeros((1,), dtype=np.complex64)
self._last_sample = None
self.denoising_samples = int(
config.preprocessing.denoising_period * config.collection_sample_rate
)
self.circular_buffer = np.zeros(
(
self.denoising_samples, # Number of samples
config.subcarriers, # Number of subcarriers
config.antennas.count, # Number of RX antennas
1, # Number of TX antennas
),
dtype=np.complex64,
)
self.sample_index = 0
@property
def last_sample(self) -> None | CSIMatrix:
"""
The last sample of the preprocessor. This is used for low frequency processing
"""
match config.preprocessing.denoising:
case "none":
return self._last_sample
case "median":
# Return the median of the last samples
median_abs = np.median(np.abs(self.circular_buffer), axis=0)
median_angle = np.median(np.angle(self.circular_buffer), axis=0)
ans = median_abs * np.exp(1j * median_angle)
return ans
case "mean":
# Return the mean of the last samples
return np.mean(self.circular_buffer, axis=0)
case _:
raise ValueError(
f"Invalid denoising method: {config.preprocessing.denoising}"
)
def remove_sto(self, csi: CSIMatrix) -> CSIMatrix:
"""
Remove sampling time offsets caused by:
- Sampling frequency offset
- Packet detection delay
This is done by multiplying the CSI matrices of consecutive antennas in the
array.
According to [1]:
> Conjugate multiplication and division are the only two methods to
> eliminate the SFO and PDD.
No citation or explanation is provided, so not sure why/whether it works.
Something similar is also done in [2] without explanation.
[1] - https://tns.thss.tsinghua.edu.cn/wst/docs/sanitization
[2] - https://doi.org/10.1109/ICC51166.2024.10623053
"""
csi_remove_sto = np.zeros_like(csi)
for antenna in range(csi.shape[1]):
antenna_nxt = (antenna + 1) % csi.shape[1]
csi_remove_sto[:, antenna, :] = np.multiply(
csi[:, antenna, :], csi[:, antenna_nxt, :].conj()
)
return csi_remove_sto
def remove_sfo(
self,
csi: CSIMatrix,
visualiser: None
| Callable[[npt.NDArray[Any], visualise.figures.Figure], None] = None,
) -> CSIMatrix:
"""
Remove sampling frequency offsets by linear regression.
This is caused by the difference in sampling frequency between the transmitter
and receiver, and this is linear in frequency. We can estimate it using linear
regression and compensate for it.
"""
N_st, N_rx, _ = csi.shape
unwrapped = np.unwrap(np.angle(csi[:, :, 0]), axis=0).reshape(N_st, N_rx, 1)
if visualiser:
visualiser(unwrapped, visualise.figures.Figure.UNWRAPPED_PHASE)
X = np.vstack([np.arange(N_st), np.ones(N_st)]).T
for antenna in range(csi.shape[1]):
tau, rho = np.linalg.lstsq(X, unwrapped[:, antenna, 0])[0]
csi[:, antenna, 0] = np.abs(csi[:, antenna, 0]) * np.exp(
1j
* (np.angle(csi[:, antenna, 0]) - (tau * np.arange(csi.shape[0]) + rho))
)
return csi
def remove_agc(self, csi: CSIMatrix, frames: dict[CSIHost, CSI]) -> CSIMatrix:
"""
Normalise the magnitude of the CSI data to compensate for the effect of the
Automatic Gain Control (AGC) of the receiver
References:
[1] -
"""
rssi = [
frames[host].header.rssi1 if antenna == 0 else frames[host].header.rssi2
for host, antenna in config.antennas.order
]
rssi_linear = np.reshape(10 ** (np.array(rssi) / 10), (1, -1, 1))
csi_power = np.sum(np.abs(csi) ** 2)
return csi * np.sqrt(rssi_linear / csi_power)
def skip_subcarriers(self, csi: CSIMatrix) -> CSIMatrix:
"""
Sometimes beccause of the large amount of processing, we need to skip some
subcarriers for the processing to be able to run in real time.
The subcarriers to skip are defined in the config file, per subcarrier_step. If
subcarrier_step is set to 1 (default), no subcarriers are skipped.
"""
return csi[:: config.preprocessing.subcarrier_step, :, :]
def fill_pilots(self, csi: CSIMatrix) -> CSIMatrix:
"""
Fill the pilot subcarriers with the average of the surrounding subcarriers.
This is done by averaging the subcarriers before and after the pilot
subcarriers. Pilots are detected by checking that the value is exactly 0.
Also, it adds placeholders for the middle null subcarriers.
"""
num_middle = 1 if config.channel_width == 20 else 3
assert csi.shape[0] + num_middle == config.subcarriers
with_middle = np.zeros(
(csi.shape[0] + num_middle, csi.shape[1], csi.shape[2]), dtype=np.complex64
)
with_middle[: csi.shape[0] // 2, :, :] = csi[: csi.shape[0] // 2, :, :]
with_middle[csi.shape[0] // 2 + num_middle :, :, :] = csi[
csi.shape[0] // 2 :, :, :
]
if num_middle == 3:
with_middle[csi.shape[0] // 2 + 1, :, :] = (
csi[csi.shape[0] // 2 - 1, :, :] + csi[csi.shape[0] // 2, :, :]
) / 2
return np.where(
np.expand_dims(with_middle[:, 0, 0] == 0, axis=(1, 2)),
correlate(with_middle, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
with_middle,
)
def bandpass(self, csi: CSIMatrix) -> CSIMatrix:
"""
Apply a Butterworth bandpass filter to the CSI data.
Remove low-frequency noise (caused by static paths) and high-frequency noise
(measurement variance).
"""
if not hasattr(self, "filter"):
assert config.preprocessing.bandpass is not None
self.filter = butter(
5,
config.preprocessing.bandpass.bounds,
fs=config.collection_sample_rate,
btype="band",
output="sos",
)
if not hasattr(self, "filter_zi"):
self.filter_zi = (
np.expand_dims(sosfilt_zi(self.filter), axis=(-1, -2, -3)) * csi
)
h_hat_filt, self.filter_zi = sosfilt(
self.filter, [csi], zi=self.filter_zi, axis=0
)
return h_hat_filt[0]
def preprocess(
self,
h: CSIMatrix,
frames: dict[CSIHost, CSI],
visualiser: None
| Callable[[npt.NDArray[Any], visualise.figures.Figure], None] = None,
) -> CSIMatrix:
# CSI data is not available for pilot subcarriers.
h_hat = h
for step in config.preprocessing.steps:
match step:
case "skip_subcarriers":
h_hat = self.skip_subcarriers(h_hat)
case "remove_agc":
h_hat = self.remove_agc(h_hat, frames)
case "remove_sfo":
h_hat = self.remove_sfo(h_hat, visualiser=visualiser)
case "remove_sto":
h_hat = self.remove_sto(h_hat)
case "fill_pilots":
h_hat = self.fill_pilots(h_hat)
case "bandpass":
h_hat = self.bandpass(h_hat)
logger.debug(f"CSI shape: {h_hat.shape}")
self._last_sample = h_hat
if config.preprocessing.denoising != "none":
self.circular_buffer[self.sample_index, : h_hat.shape[0]] = h_hat
self.sample_index = (self.sample_index + 1) % self.denoising_samples
return h_hat

0
where_fi/utils/__init__.py
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82
where_fi/utils/antenna_order.py
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@ -1,82 +0,0 @@
import logging
from collections import deque
from typing import Any, Collection
from where_fi.application import CSIApplication
from ..collection import ingest
from ..config import config
logger = logging.getLogger(__name__)
AntennaIdentifier = tuple[ingest.Host, int]
order: list[AntennaIdentifier] = []
prev_unplugged: set[AntennaIdentifier] = set()
long_antenna_average: dict[AntennaIdentifier, deque[int]] = {
(host, 0): deque(maxlen=15 * config.collection_sample_rate)
for host in config.receive_hosts
} | {
(host, 1): deque(maxlen=15 * config.collection_sample_rate)
for host in config.receive_hosts
}
short_antenna_average: dict[AntennaIdentifier, deque[int]] = {
(host, 0): deque(maxlen=2 * config.collection_sample_rate)
for host in config.receive_hosts
} | {
(host, 1): deque(maxlen=2 * config.collection_sample_rate)
for host in config.receive_hosts
}
RSSI_THRESHOLD = 10
def average(data: Collection[Any]) -> float:
return sum(data) / len(data)
app = CSIApplication(ingest.RealtimeCSIProducer())
@app.on_pre_merge
def callback(antenna_data: dict[ingest.Host, ingest.CSI]) -> None:
global prev_unplugged
global antenna_average
global order
unplugged: set[tuple[ingest.Host, int]] = set()
logger.debug(
"Antenna RSSI: "
+ str(
{
host: (csi.header.rssi1, csi.header.rssi2)
for host, csi in antenna_data.items()
}
)
)
for host, csi in antenna_data.items():
long_antenna_average[(host, 0)].append(csi.header.rssi1)
long_antenna_average[(host, 1)].append(csi.header.rssi2)
short_antenna_average[(host, 0)].append(csi.header.rssi1)
short_antenna_average[(host, 1)].append(csi.header.rssi2)
for antenna in range(2):
if (
average(short_antenna_average[(host, antenna)])
> average(long_antenna_average[(host, antenna)]) + RSSI_THRESHOLD
):
unplugged.add((host, antenna))
if prev_unplugged != unplugged:
if len(prev_unplugged) > len(unplugged):
logger.info(f"Antenna plugged in: {prev_unplugged - unplugged}")
else:
logger.info(f"Antenna unplugged: {unplugged - prev_unplugged}")
diff = list(unplugged - prev_unplugged)
if len(diff) == 1 and diff[0] not in order:
host, antenna = diff.pop()
order.append((host, antenna))
logger.info(f"Current order: {order}")
prev_unplugged = unplugged
def main() -> None:
app.start()

2
where_fi/visualise/.gitignore vendored
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@ -1,2 +0,0 @@
generated
frontend/src/grpc

11
where_fi/visualise/Makefile
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@ -1,11 +0,0 @@
all: generated frontend/src/grpc
generated: protos/*.proto
mkdir -p generated
find protos/ -type f -name "*.proto" | xargs uv run python -m grpc_tools.protoc -Iprotos --python_out=generated --pyi_out=generated --grpc_python_out=generated --mypy_grpc_out=generated
find protos/ -type f -name "*.proto" | xargs uv run protol --create-package --in-place --python-out generated/ protoc --proto-path=protos/
frontend/src/grpc: protos/*.proto
mkdir -p frontend/src/grpc
cd frontend && find ../protos/ -name "*.proto" | xargs npx protoc --ts_out=src/grpc -I../protos/

0
where_fi/visualise/__init__.py
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66
where_fi/visualise/envoy.yaml
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@ -1,66 +0,0 @@
admin:
access_log_path: /tmp/admin_access.log
address:
socket_address: { address: 0.0.0.0, port_value: 9901 }
static_resources:
listeners:
- name: listener_0
address:
socket_address: { address: 0.0.0.0, port_value: 8080 }
filter_chains:
- filters:
- name: envoy.filters.network.http_connection_manager
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
codec_type: auto
stat_prefix: ingress_http
route_config:
name: local_route
virtual_hosts:
- name: local_service
domains: ["*"]
routes:
- match: { prefix: "/" }
route:
cluster: echo_service
timeout: 0s
max_stream_duration:
grpc_timeout_header_max: 0s
cors:
allow_origin_string_match:
- prefix: "*"
allow_methods: GET, PUT, DELETE, POST, OPTIONS
allow_headers: keep-alive,user-agent,cache-control,content-type,content-transfer-encoding,custom-header-1,x-accept-content-transfer-encoding,x-accept-response-streaming,x-user-agent,x-grpc-web,grpc-timeout
max_age: "1728000"
expose_headers: custom-header-1,grpc-status,grpc-message
http_filters:
- name: envoy.filters.http.grpc_web
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.grpc_web.v3.GrpcWeb
- name: envoy.filters.http.cors
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.cors.v3.Cors
- name: envoy.filters.http.router
typed_config:
"@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
clusters:
- name: echo_service
connect_timeout: 0.25s
type: logical_dns
# HTTP/2 support
typed_extension_protocol_options:
envoy.extensions.upstreams.http.v3.HttpProtocolOptions:
"@type": type.googleapis.com/envoy.extensions.upstreams.http.v3.HttpProtocolOptions
explicit_http_config:
http2_protocol_options: {}
lb_policy: round_robin
load_assignment:
cluster_name: cluster_0
endpoints:
- lb_endpoints:
- endpoint:
address:
socket_address:
address: 127.0.0.1
port_value: 50051

20
where_fi/visualise/extract/__init__.py
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@ -1,20 +0,0 @@
import logging
import grpc
from ..generated import figure_pb2, figure_pb2_grpc
def run() -> None:
# NOTE(gRPC Python Team): .close() is possible on a channel and should be
# used in circumstances in which the with statement does not fit the needs
# of the code.
with grpc.insecure_channel("localhost:50051") as channel:
stub = figure_pb2_grpc.FigureServiceStub(channel)
for figure in stub.GetFigure(figure_pb2.FigureRequest()):
print(f"Figure: {figure}")
if __name__ == "__main__":
logging.basicConfig()
run()

89
where_fi/visualise/extract/__main__.py
View File

@ -1,89 +0,0 @@
from pathlib import Path
from typing import cast
import grpc
import matplotlib.pyplot as plt
import numpy as np
import typer
from ..generated import figure_pb2, figure_pb2_grpc
app = typer.Typer()
@app.command()
def list() -> None:
"""
List all figures from the gRPC service.
"""
print("Connecting to the server...")
with grpc.insecure_channel("localhost:50051") as channel:
stub = figure_pb2_grpc.FigureServiceStub(channel)
print("Retrieving available figures...\n")
figures = [x for x in stub.GetFigure(figure_pb2.FigureRequest())]
if not figures:
print("No figures found.")
else:
for i, figure in enumerate(figures, 1):
print(f"{i}. Figure: {figure.title} (ID: {figure.uuid})")
choice = typer.prompt("Which figure to extract?", type=int)
assert isinstance(choice, int)
if 1 <= choice <= len(figures):
selected_figure = figures[choice - 1]
print(f"You selected: {selected_figure.title}")
extract(selected_figure.uuid)
else:
print("Invalid selection. Exiting.")
def get_figure(uuid: str) -> tuple[figure_pb2.Figure, figure_pb2.FigureData]:
"""
Get the figure data for a specific UUID.
"""
print(f"Connecting to the server to extract data for UUID: {uuid}...")
with grpc.insecure_channel("localhost:50051") as channel:
stub = figure_pb2_grpc.FigureServiceStub(channel)
all_figures = {x.uuid: x for x in stub.GetFigure(figure_pb2.FigureRequest())}
figure_data = stub.GetFigureUpdate(figure_pb2.FigureDataRequest(uuid=uuid))
for data in figure_data:
return (all_figures[uuid], data)
raise ValueError(f"Figure with UUID {uuid} not found.")
@app.command()
def extract(uuid: str, output: Path | None = None) -> None:
"""
Extract data for a specific figure identified by its UUID.
"""
_, figure = get_figure(uuid)
if not output:
output = cast(Path, typer.prompt("Enter output file path:", type=Path))
with open(output, "wb") as f:
f.write(figure.SerializeToString())
@app.command()
def plot(uuid: str) -> None:
"""
Plot the figure data for a specific UUID.
"""
figure, data = get_figure(uuid)
if data.line:
for line in data.line.lines:
plt.plot(
line.x if line.x else np.arange(len(line.y)), line.y, label=line.label
)
plt.xlabel(figure.x_label)
plt.ylabel(figure.y_label)
plt.title(figure.title)
elif figure.heatmap:
plt.imshow(figure.heatmap.data, cmap="hot", interpolation="nearest")
elif figure.histogram:
plt.hist(figure.histogram.data, bins=figure.histogram.bins)
plt.show()
if __name__ == "__main__":
app()

9
where_fi/visualise/frontend/.editorconfig
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@ -1,9 +0,0 @@
[*.{js,jsx,mjs,cjs,ts,tsx,mts,cts,vue}]
charset = utf-8
indent_size = 2
indent_style = space
insert_final_newline = true
trim_trailing_whitespace = true
end_of_line = lf
max_line_length = 100

1
where_fi/visualise/frontend/.gitattributes vendored
View File

@ -1 +0,0 @@
* text=auto eol=lf

31
where_fi/visualise/frontend/.gitignore vendored
View File

@ -1,31 +0,0 @@
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
.DS_Store
dist
dist-ssr
coverage
*.local
.vite/
/cypress/videos/
/cypress/screenshots/
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?
*.tsbuildinfo

7
where_fi/visualise/frontend/.prettierrc.json
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@ -1,7 +0,0 @@
{
"$schema": "https://json.schemastore.org/prettierrc",
"semi": false,
"singleQuote": true,
"printWidth": 100
}

1
where_fi/visualise/frontend/env.d.ts vendored
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@ -1 +0,0 @@
/// <reference types="vite/client" />

24
where_fi/visualise/frontend/eslint.config.ts
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@ -1,24 +0,0 @@
import pluginVue from 'eslint-plugin-vue'
import { defineConfigWithVueTs, vueTsConfigs } from '@vue/eslint-config-typescript'
import skipFormatting from '@vue/eslint-config-prettier/skip-formatting'
// To allow more languages other than `ts` in `.vue` files, uncomment the following lines:
// import { configureVueProject } from '@vue/eslint-config-typescript'
// configureVueProject({ scriptLangs: ['ts', 'tsx'] })
// More info at https://github.com/vuejs/eslint-config-typescript/#advanced-setup
export default defineConfigWithVueTs(
{
name: 'app/files-to-lint',
files: ['**/*.{ts,mts,tsx,vue}'],
},
{
name: 'app/files-to-ignore',
ignores: ['**/dist/**', '**/dist-ssr/**', '**/coverage/**'],
},
pluginVue.configs['flat/essential'],
vueTsConfigs.recommended,
skipFormatting,
)

12
where_fi/visualise/frontend/index.html
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@ -1,12 +0,0 @@
<!DOCTYPE html>
<html lang="">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Where-Fi Visualizer</title>
</head>
<body>
<div id="app"></div>
<script type="module" src="/src/main.ts"></script>
</body>
</html>

5326
where_fi/visualise/frontend/package-lock.json generated
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File diff suppressed because it is too large Load Diff

46
where_fi/visualise/frontend/package.json
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@ -1,46 +0,0 @@
{
"name": "visualise",
"version": "0.0.0",
"private": true,
"type": "module",
"scripts": {
"dev": "vite",
"build": "run-p type-check \"build-only {@}\" --",
"preview": "vite preview",
"build-only": "vite build",
"type-check": "vue-tsc --build",
"lint": "eslint . --fix",
"format": "prettier --write src/"
},
"dependencies": {
"@mdi/font": "^7.4.47",
"@protobuf-ts/grpcweb-transport": "^2.9.4",
"buffer": "^6.0.3",
"google-protobuf": "^3.21.4",
"grpc-web": "^1.5.0",
"plotly.js-dist": "^3.0.1",
"plotly.js-dist-min": "^3.0.1",
"vue": "^3.5.13",
"vuetify": "^3.7.12"
},
"devDependencies": {
"@protobuf-ts/plugin": "^2.9.4",
"@tsconfig/node22": "^22.0.0",
"@types/node": "^22.13.1",
"@types/plotly.js": "^2.35.2",
"@types/plotly.js-dist-min": "^2.3.4",
"@vitejs/plugin-vue": "^5.2.1",
"@vue/eslint-config-prettier": "^10.1.0",
"@vue/eslint-config-typescript": "^14.3.0",
"@vue/tsconfig": "^0.7.0",
"eslint": "^9.18.0",
"eslint-plugin-vue": "^9.32.0",
"jiti": "^2.4.2",
"npm-run-all2": "^7.0.2",
"prettier": "^3.4.2",
"typescript": "~5.7.3",
"vite": "^6.0.11",
"vite-plugin-vue-devtools": "^7.7.1",
"vue-tsc": "^2.2.0"
}
}

42
where_fi/visualise/frontend/src/App.vue
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@ -1,42 +0,0 @@
<script setup lang="ts">
import { ref } from 'vue'
import PlotList from './components/PlotList.vue'
import Figure from './components/Figure.vue'
import { Figure as FigureType } from './grpc/figure'
var plots = ref<FigureType[]>([])
var paused = ref(false)
function addPlot(fig: FigureType) {
console.log('Plot added', fig)
plots.value.push(fig)
console.log(plots)
}
function removePlot(fig: FigureType) {
console.log('Plot removed', fig)
plots.value = plots.value.filter((x) => x.uuid !== fig.uuid)
console.log(plots)
}
declare module '@vue/runtime-core' {
interface ComponentCustomProperties {
$props: {
onClick?: (e: MouseEvent) => void
}
}
}
</script>
<template>
<v-app>
<PlotList @figure-selected="addPlot" @figure-deselected="removePlot" />
<v-main>
<v-container fluid>
<Figure v-for="plot in plots" :key="plot.uuid" :figure="plot" :paused="paused" />
</v-container>
<v-fab location="right bottom" color="secondary" @click="paused = !paused" icon app>
<v-icon :icon="paused ? 'mdi-play' : 'mdi-pause'"></v-icon>
</v-fab>
</v-main>
</v-app>
</template>

86
where_fi/visualise/frontend/src/assets/main.css
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@ -1,86 +0,0 @@
/* color palette from <https://github.com/vuejs/theme> */
:root {
--vt-c-white: #ffffff;
--vt-c-white-soft: #f8f8f8;
--vt-c-white-mute: #f2f2f2;
--vt-c-black: #181818;
--vt-c-black-soft: #222222;
--vt-c-black-mute: #282828;
--vt-c-indigo: #2c3e50;
--vt-c-divider-light-1: rgba(60, 60, 60, 0.29);
--vt-c-divider-light-2: rgba(60, 60, 60, 0.12);
--vt-c-divider-dark-1: rgba(84, 84, 84, 0.65);
--vt-c-divider-dark-2: rgba(84, 84, 84, 0.48);
--vt-c-text-light-1: var(--vt-c-indigo);
--vt-c-text-light-2: rgba(60, 60, 60, 0.66);
--vt-c-text-dark-1: var(--vt-c-white);
--vt-c-text-dark-2: rgba(235, 235, 235, 0.64);
}
/* semantic color variables for this project */
:root {
--color-background: var(--vt-c-white);
--color-background-soft: var(--vt-c-white-soft);
--color-background-mute: var(--vt-c-white-mute);
--color-border: var(--vt-c-divider-light-2);
--color-border-hover: var(--vt-c-divider-light-1);
--color-heading: var(--vt-c-text-light-1);
--color-text: var(--vt-c-text-light-1);
--section-gap: 160px;
}
@media (prefers-color-scheme: dark) {
:root {
--color-background: var(--vt-c-black);
--color-background-soft: var(--vt-c-black-soft);
--color-background-mute: var(--vt-c-black-mute);
--color-border: var(--vt-c-divider-dark-2);
--color-border-hover: var(--vt-c-divider-dark-1);
--color-heading: var(--vt-c-text-dark-1);
--color-text: var(--vt-c-text-dark-2);
}
}
*,
*::before,
*::after {
box-sizing: border-box;
margin: 0;
font-weight: normal;
}
body {
min-height: 100vh;
color: var(--color-text);
background: var(--color-background);
transition:
color 0.5s,
background-color 0.5s;
line-height: 1.6;
font-family:
Inter,
-apple-system,
BlinkMacSystemFont,
'Segoe UI',
Roboto,
Oxygen,
Ubuntu,
Cantarell,
'Fira Sans',
'Droid Sans',
'Helvetica Neue',
sans-serif;
font-size: 15px;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}

133
where_fi/visualise/frontend/src/components/Figure.vue
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<script setup lang="ts">
import * as Plotly from 'plotly.js-dist-min'
import { Figure, FigureData } from '../grpc/figure'
import { FigureServiceClient } from '../grpc/figure.client'
import { GrpcWebFetchTransport } from '@protobuf-ts/grpcweb-transport'
import { host } from '../connect'
import { onMounted, ref } from 'vue'
import { onBeforeUnmount } from 'vue'
import { computed } from 'vue'
const { figure, paused = false } = defineProps<{ figure: Figure; paused?: boolean }>()
const cancel = ref<boolean>(false)
function reshape(data: number[], width: number) {
if (data.length % width != 0) {
throw new Error('Data length is not divisible by width')
}
const height = data.length / width
const result: number[][] = new Array(height)
for (let i = 0; i < height; i++) {
result[i] = data.slice(i * width, (i + 1) * width)
}
return result
}
const data = ref<FigureData | null>(null)
const plotData = computed(() => {
if (data.value) {
switch (data.value.figure.oneofKind) {
case 'line':
return data.value.figure.line.lines.map((line) => ({
x: line.x.length == 0 ? undefined : line.x,
y: line.y,
type: 'scatter' as const,
name: line.label,
color: line.color ? line.color : undefined,
}))
case 'heatmap':
const xmin = data.value.figure.heatmap.xMin ?? 0
const xmax = data.value.figure.heatmap.xMax ?? data.value.figure.heatmap.width
const ymin = data.value.figure.heatmap.yMin ?? 0
const ymax = data.value.figure.heatmap.yMax ?? data.value.figure.heatmap.height
const x = Array(data.value.figure.heatmap.width)
.fill(0)
.map((_, i) => xmin + ((xmax - xmin) * i) / data.value.figure.heatmap.width)
const y = Array(data.value.figure.heatmap.height)
.fill(0)
.map((_, i) => ymin + ((ymax - ymin) * i) / data.value.figure.heatmap.height)
return [
{
z: reshape(data.value.figure.heatmap.data, data.value.figure.heatmap.width),
x: x,
y: y,
type: 'heatmap' as const,
colorscale: 'Blues',
reversescale: true,
},
]
case 'histogram':
const bin_start = data.value.figure.histogram.bins.slice(0, -1)
const bin_end = data.value.figure.histogram.bins.slice(1)
const bin_center = bin_start.map((start, i) => (start + bin_end[i]) / 2)
const bin_width = bin_start.map((start, i) => bin_end[i] - start)
console.log('Histogram data:', bin_center, bin_width)
console.log(
'Sizes:',
bin_center.length,
bin_width.length,
data.value.figure.histogram.data.length,
)
return data.value.figure.histogram.data.map((series) => ({
x: bin_center,
y: series.data,
width: bin_width,
type: 'bar' as const,
}))
}
}
return []
})
const layout = {
title: { text: figure.title },
xaxis: { title: { text: figure.xLabel }, type: figure.logx ? 'log' : undefined },
yaxis: { title: { text: figure.yLabel }, type: figure.logy ? 'log' : undefined },
height: 700,
}
function updateGraph() {
console.log('Updating graph with', plotData.value)
Plotly.newPlot(figure.uuid, plotData.value, layout, { responsive: true })
}
onMounted(async () => {
console.log('Mounted')
updateGraph()
const transport = new GrpcWebFetchTransport({ baseUrl: host })
const figureService = new FigureServiceClient(transport)
const stream = figureService.getFigureUpdate({ uuid: figure.uuid })
for await (const response of stream.responses) {
if (cancel.value) {
console.log('Stopping stream...')
break
}
console.log(`Got new value for ${figure.uuid}:`, response)
if (!paused) {
data.value = response
updateGraph()
}
}
})
onBeforeUnmount(() => {
console.log('Component is about to unmount, stopping stream...')
cancel.value = true
})
</script>
<template>
<v-row class="w-100">
<v-col cols="12">
<v-card class="w-100">
<v-card-title>{{ figure.title }}</v-card-title>
<v-card-text>
<div :id="figure.uuid" class="w-100" />
</v-card-text>
</v-card>
</v-col>
</v-row>
</template>

83
where_fi/visualise/frontend/src/components/PlotList.vue
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@ -1,83 +0,0 @@
<script lang="ts">
import { defineComponent, ref } from 'vue'
import { Figure } from '../grpc/figure'
import { FigureServiceClient } from '../grpc/figure.client'
import { GrpcWebFetchTransport } from '@protobuf-ts/grpcweb-transport'
import { host } from '../connect'
const plots = ref<Figure[]>([])
const loading = ref(true)
const error = ref<string | null>(null)
var emit = (_: any, ...args: any[]) => {
console.log('No emit function set', args)
}
export default defineComponent({
emits: ['figure-selected', 'figure-deselected'],
data() {
return {
selected: [],
loading: loading,
plots: plots,
error,
}
},
setup(_, ctx) {
console.log('Setup')
emit = ctx.emit
},
async mounted() {
const transport = new GrpcWebFetchTransport({ baseUrl: host })
const figureService = new FigureServiceClient(transport)
const stream = figureService.getFigure({})
for await (const response of stream.responses) {
console.log('Got new figure', response)
plots.value.push(response)
}
console.log('Done fetching figures')
loading.value = false
},
watch: {
selected(newVal: string[], oldVal: string[]) {
const added = newVal.filter((x) => !oldVal.includes(x))
const removed = oldVal.filter((x) => !newVal.includes(x))
for (const uuid of added) {
console.log('Selected', uuid)
const plot = plots.value.find((x) => x.uuid === uuid)
emit('figure-selected', plot)
}
for (const uuid of removed) {
console.log('Deselected', uuid)
const plot = plots.value.find((x) => x.uuid === uuid)
emit('figure-deselected', plot)
}
},
},
})
</script>
<template>
<v-navigation-drawer permanent width="250">
<v-list>
<v-list-item title="Available figures" subtitle="Choose some of the figures below" />
</v-list>
<v-divider />
<v-list v-model:selected="selected" select-strategy="leaf" nav>
<v-list-item v-for="plot in plots" :key="plot.uuid" :value="plot.uuid">
<v-list-item-title>{{ plot.title }}</v-list-item-title>
<v-list-item-subtitle class="text-high-emphasis"
>{{ plot.xLabel }} w/ {{ plot.yLabel }}</v-list-item-subtitle
>
</v-list-item>
<v-skeleton-loader
type="list-item-two-line"
v-for="n in 3"
:key="n"
v-if="loading"
></v-skeleton-loader>
</v-list>
</v-navigation-drawer>
</template>
<style scoped></style>

1
where_fi/visualise/frontend/src/connect.ts
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@ -1 +0,0 @@
export const host = 'http://localhost:8080'

24
where_fi/visualise/frontend/src/main.ts
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@ -1,24 +0,0 @@
import App from './App.vue'
import { createApp } from 'vue'
import 'vuetify/styles'
import { createVuetify } from 'vuetify'
import * as components from 'vuetify/components'
import * as directives from 'vuetify/directives'
import { aliases, mdi } from 'vuetify/iconsets/mdi'
import '@mdi/font/css/materialdesignicons.css'
const vuetify = createVuetify({
components,
directives,
theme: { defaultTheme: 'dark' },
icons: {
defaultSet: 'mdi',
aliases,
sets: {
mdi,
},
},
})
createApp(App).use(vuetify).mount('#app')

16
where_fi/visualise/frontend/tsconfig.app.json
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@ -1,16 +0,0 @@
{
"extends": "@vue/tsconfig/tsconfig.dom.json",
"include": ["env.d.ts", "src/**/*", "src/**/*.vue"],
"exclude": ["src/**/__tests__/*"],
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
"paths": {
"@/*": ["./src/*"]
}
},
"vueCompilerOptions": {
"fallthroughAttributes": true,
"strictTemplates": true
}
}

11
where_fi/visualise/frontend/tsconfig.json
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@ -1,11 +0,0 @@
{
"files": [],
"references": [
{
"path": "./tsconfig.node.json"
},
{
"path": "./tsconfig.app.json"
}
]
}

19
where_fi/visualise/frontend/tsconfig.node.json
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@ -1,19 +0,0 @@
{
"extends": "@tsconfig/node22/tsconfig.json",
"include": [
"vite.config.*",
"vitest.config.*",
"cypress.config.*",
"nightwatch.conf.*",
"playwright.config.*",
"eslint.config.*"
],
"compilerOptions": {
"noEmit": true,
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
"module": "ESNext",
"moduleResolution": "Bundler",
"types": ["node"]
}
}

15
where_fi/visualise/frontend/vite.config.ts
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@ -1,15 +0,0 @@
import { fileURLToPath, URL } from 'node:url'
import { defineConfig } from 'vite'
import vue from '@vitejs/plugin-vue'
import vueDevTools from 'vite-plugin-vue-devtools'
// https://vite.dev/config/
export default defineConfig({
plugins: [vue(), vueDevTools()],
resolve: {
alias: {
'@': fileURLToPath(new URL('./src', import.meta.url)),
},
},
})

32
where_fi/visualise/protos/figure.proto
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@ -1,32 +0,0 @@
syntax = "proto3";
import "figure_type/line.proto";
import "figure_type/heatmap.proto";
import "figure_type/histogram.proto";
message Figure {
string uuid = 1;
string title = 2;
string x_label = 3;
string y_label = 4;
bool logx = 5;
bool logy = 6;
}
message FigureData {
string uuid = 1;
oneof figure {
LineChartData line = 2;
HeatmapData heatmap = 3;
HistogramData histogram = 4;
}
}
message FigureRequest {}
message FigureDataRequest { string uuid = 1; }
service FigureService {
rpc GetFigure(FigureRequest) returns (stream Figure) {}
rpc GetFigureUpdate(FigureDataRequest) returns (stream FigureData) {}
}

13
where_fi/visualise/protos/figure_type/heatmap.proto
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@ -1,13 +0,0 @@
syntax = "proto3";
message HeatmapData {
string uuid = 1;
repeated float data = 2;
uint32 width = 3;
uint32 height = 4;
string cmap = 5;
float x_min = 6;
float x_max = 7;
float y_min = 8;
float y_max = 9;
}

9
where_fi/visualise/protos/figure_type/histogram.proto
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@ -1,9 +0,0 @@
syntax = "proto3";
message HistogramSeries { repeated float data = 1; }
message HistogramData {
string uuid = 1;
repeated HistogramSeries data = 2;
repeated float bins = 3;
}

18
where_fi/visualise/protos/figure_type/line.proto
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@ -1,18 +0,0 @@
syntax = "proto3";
message LineChartData {
message Line {
string uuid = 1;
repeated float x = 2;
repeated float y = 3;
string label = 4;
string color = 5;
}
repeated Line lines = 1;
}
message LineChartPoint {
string uuid = 1;
float x = 2;
float y = 3;
}

109
where_fi/visualise/server/__init__.py
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@ -1,109 +0,0 @@
import logging
import multiprocessing as mp
import queue
import threading
import time
from concurrent import futures
from dataclasses import dataclass
from typing import Any, Generator
import grpc
import numpy as np
import numpy.typing as npt
from ..generated import figure_pb2, figure_pb2_grpc
from . import figures
@dataclass
class VisualiserData:
data: npt.NDArray[Any]
dtype: figures.FigureId
class FigureServer(figure_pb2_grpc.FigureServiceServicer):
def __init__(self) -> None:
self.logger = logging.getLogger(__name__)
self.clients: dict[str, list[queue.Queue[figure_pb2.FigureData]]] = {}
self.clients_lock = threading.Lock()
def GetFigure(
self, request: figure_pb2.FigureRequest, context: grpc.ServicerContext
) -> Generator[figure_pb2.Figure, None, None]:
for figure_group in figures.all_figures.values():
for figure in figure_group.figures:
yield figure
def GetFigureUpdate(
self, request: figure_pb2.FigureDataRequest, context: grpc.ServicerContext
) -> Generator[figure_pb2.FigureData, None, None]:
self.logger.info(
f"Received request for figure data stream for figure {request.uuid}"
)
with self.clients_lock:
q: queue.Queue[figure_pb2.FigureData] = queue.Queue()
if request.uuid not in self.clients:
self.clients[request.uuid] = []
self.clients[request.uuid].append(q)
try:
while context.is_active():
try:
yield q.get(timeout=1)
except queue.Empty:
pass
finally:
with self.clients_lock:
self.clients[request.uuid].remove(q)
class Webapp:
def __init__(self) -> None:
self.logger = logging.getLogger(__name__)
self.active = True
self.figure_server = FigureServer()
def add_data(
self, dtype: figures.FigureId, new_data: npt.NDArray[np.complex128]
) -> None:
self.logger.debug(f"Adding data to figure server {dtype}")
if dtype not in figures.all_figures:
self.logger.error(f"Figure {dtype} not found")
return
updates = figures.all_figures[dtype].update(new_data)
for fig_id, update in updates.items():
for client in self.figure_server.clients.get(fig_id, []):
client.put(update)
def listen_for_data(self, data_queue: "mp.Queue[VisualiserData]") -> None:
while self.active:
self.logger.debug("Listening for data")
try:
data = data_queue.get(timeout=0.1)
self.add_data(data.dtype, data.data)
except queue.Empty:
pass
while not data_queue.empty():
data = data_queue.get()
def start(self, data_queue: "mp.Queue[VisualiserData]") -> None:
grpc_server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
figure_pb2_grpc.add_FigureServiceServicer_to_server(
self.figure_server, grpc_server
)
grpc_server.add_insecure_port("[::]:50051")
grpc_server.add_insecure_port("0.0.0.0:50051")
self.logger.info("Starting server on port 50051")
grpc_server.start()
self.logger.info("Server started")
data_thread = threading.Thread(target=self.listen_for_data, args=(data_queue,))
data_thread.start()
while self.active:
time.sleep(1)
self.logger.debug("Server is running")
grpc_server.stop(0.5)
data_thread.join()

316
where_fi/visualise/server/figures.py
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@ -1,316 +0,0 @@
import uuid
from abc import ABC, abstractmethod
from enum import Enum
from functools import reduce
from typing import Any, Callable, Sequence, cast
import numpy as np
import numpy.typing as npt
from ...config import config
from .generated import figure_pb2
from .generated.figure_type import heatmap_pb2, histogram_pb2, line_pb2
FigureUpdate = dict[str, figure_pb2.FigureData]
class SpecificFigure(ABC):
"""
This is a base class for all figures that can be visualised through the
visualisation server.
"""
figures: Sequence[figure_pb2.Figure]
@abstractmethod
def __init__(self) -> None:
raise NotImplementedError
@abstractmethod
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
"""
Update the figure with new data.
The exact shape and format of the data passed as an argument will differ
depending on the exact figure being plotted.
The return value should be a dictionary with the UUID of the figure as the key
and the new data as the value.
This allows one class to update multiple figures at once (e.g. a figure plotting
the phase and amplitude of a signal).
"""
raise NotImplementedError
class SimpleLineChart:
"""Helper class to create a simple line chart with one or more lines.
This allows generalising the creation of line charts, e.g. as in PerAntennaFigure.
"""
def __init__(self, title: str, x_label: str, y_label: str) -> None:
self.figure = figure_pb2.Figure(
uuid=str(uuid.uuid4()),
title=title,
x_label=x_label,
y_label=y_label,
)
def update(
self,
new_data: npt.NDArray[np.complex128],
labels: Sequence[str],
x_values: None | npt.NDArray[np.float64] = None,
) -> FigureUpdate:
lines = [
line_pb2.LineChartData.Line(
y=new_data[i],
label=labels[i],
x=x_values[i] if x_values is not None else None,
)
for i in range(new_data.shape[0])
]
return {
self.figure.uuid: figure_pb2.FigureData(
uuid=self.figure.uuid,
line=line_pb2.LineChartData(lines=lines),
)
}
class PerSubcarrierFigure(SpecificFigure):
def __init__(
self,
figures: Sequence[SimpleLineChart],
funcs: Sequence[Callable[[npt.NDArray[Any]], npt.NDArray[Any]]],
) -> None:
self.charts = figures
self.figures = [figure.figure for figure in figures]
self.funcs = funcs
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
"""
Update the figure with new data.
The data is expected to be in the shape (subcarriers, data).
"""
subcarrier_labels = [f"Subcarrier {i}" for i in range(new_data.shape[0])]
updates = [
figure.update(func(new_data), labels=subcarrier_labels)
for func, figure in zip(self.funcs, self.charts, strict=True)
]
return reduce((lambda a, b: a | b), updates)
class LabelledMultiLineChart(SpecificFigure):
def __init__(
self,
figures: Sequence[SimpleLineChart],
funcs: list[Callable[[npt.NDArray[Any]], npt.NDArray[Any]]],
labels: Sequence[str],
) -> None:
self.charts = figures
self.figures = [figure.figure for figure in figures]
self.funcs = funcs
self.labels = labels
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
data_by_antenna = new_data[:, :, 0].T
updates = [
figure.update(func(data_by_antenna), labels=self.labels)
for func, figure in zip(self.funcs, self.charts, strict=True)
]
return reduce((lambda a, b: a | b), updates)
class PerAntennaFigure(LabelledMultiLineChart):
"""A figure that plots data for each antenna separately.
This allows creating multiple figures, each having one line per antenna.
Each figure can have a different function that is used to transform the data before
plotting. For example, can be used to generate plots for the phase and amplitude of
a signal.
"""
def __init__(
self,
figures: Sequence[SimpleLineChart],
funcs: list[Callable[[npt.NDArray[Any]], npt.NDArray[Any]]],
) -> None:
super().__init__(
figures, funcs, [f"Antenna {i + 1}" for i in range(config.antennas.count)]
)
class MusicEigenvalueHistogram(SpecificFigure):
def __init__(self) -> None:
self.figure = figure_pb2.Figure(
uuid=str(uuid.uuid4()),
title="AoA Eigenvalues",
x_label="Eigenvalue",
y_label="Frequency of occurrence",
logx=True,
)
self.figures = [self.figure]
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
magn = np.abs(new_data)
# The frontend library used for plotting doesn't support logarithmic
# binning[1], so we have to do it manually.
# Using base 10 log for the bins to make the plots easier to comprehend.
# [1] - https://github.com/plotly/plotly.js/issues/1844
bins = cast(
npt.NDArray[np.float32],
np.logspace(np.log10(magn.min()), np.log10(magn.max()), 10),
)
hist, _ = np.histogram(magn, bins=bins)
series = [histogram_pb2.HistogramSeries(data=hist)]
histogram = histogram_pb2.HistogramData(data=series, bins=bins)
return {self.figure.uuid: figure_pb2.FigureData(histogram=histogram)}
class HeatmapFigure(SpecificFigure):
def __init__(self) -> None:
self.figure = figure_pb2.Figure(
uuid=str(uuid.uuid4()),
title="Angle of arrival Heatmap",
x_label="Angle of arrival",
y_label="Time of Flight",
)
self.figures = [self.figure]
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
heatmap = heatmap_pb2.HeatmapData(
uuid=self.figure.uuid,
data=new_data.flatten(),
width=new_data.shape[1],
height=new_data.shape[0],
x_min=0,
x_max=np.pi,
y_min=0,
y_max=config.music.heatmap.tof_max,
)
return {self.figure.uuid: figure_pb2.FigureData(heatmap=heatmap)}
class EmpiricalCDF(SpecificFigure):
"""Helper class to create a graph of the empirical cumulative distribution function
and probability density functions of a set of observations.
"""
def __init__(self, title: str, x_label: str) -> None:
self.chart = SimpleLineChart(title, x_label, "Cumulative Probability")
self.figures = [self.chart.figure]
def update(self, new_data: npt.NDArray[np.float64]) -> FigureUpdate:
new_data = np.sort(new_data, axis=-1)
if new_data.ndim == 1:
new_data = np.expand_dims(new_data, 0)
y_values = np.repeat(
np.linspace(0, 1, new_data.shape[-1])[np.newaxis, :],
new_data.shape[0],
axis=0,
)
return self.chart.update(y_values, labels=["Frequency"], x_values=new_data)
class RandomVariable(SpecificFigure):
def get_cdf(
self, new_data: npt.NDArray[np.float64]
) -> tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]]:
"""Get the cumulative distribution function of the data.
The data is expected to be in the shape (lines, data) or (data,) for single-line
charts.
"""
new_data = np.sort(new_data, axis=-1)
if new_data.ndim == 1:
new_data = np.expand_dims(new_data, 0)
y_values = np.expand_dims(np.linspace(0, 1, new_data.size), 0)
else:
y_values = np.repeat(
np.linspace(0, 1, new_data.shape[-1])[np.newaxis, :],
new_data.shape[0],
axis=0,
)
return new_data, y_values
def __init__(self, x_label: str, line_type: str = "Subcarrier") -> None:
self.charts = [
# SimpleLineChart(
# f"{x_label} Probability Density", x_label, "Probability Density"
# ),
SimpleLineChart(
f"{x_label} Cumulative Distribution", x_label, "Cumulative Probability"
),
]
self.funcs = [
# self.get_pdf,
self.get_cdf
]
self.figures = [figure.figure for figure in self.charts]
def update(self, new_data: npt.NDArray[np.complex128]) -> FigureUpdate:
"""
Update the figure with new data.
The data is expected to be in the shape (subcarriers, data).
"""
subcarrier_labels = [f"Subcarrier {i}" for i in range(new_data.shape[0])]
updates: list[FigureUpdate] = []
for func, figure in zip(self.funcs, self.charts, strict=True):
x, y = func(new_data)
updates.append(
figure.update(
y,
labels=subcarrier_labels,
x_values=x,
)
)
return reduce((lambda a, b: a | b), updates)
class Figure(Enum):
RAW_CSI = 0
UNWRAPPED_PHASE = 1
PROCESSED_CSI = 2
MUSIC_EIGENVALUES = 3
AOA_HEATMAP = 4
PHASE_ANALYSIS = 5
MAGN_ANALYSIS = 6
def figure_class(self) -> SpecificFigure:
return all_figures[self]
FigureId = str | Figure
all_figures: dict[FigureId, SpecificFigure] = {
Figure.RAW_CSI: PerAntennaFigure(
[
SimpleLineChart("Raw CSI Phase", "Subcarrier", "Phase"),
SimpleLineChart("Raw CSI Amplitude", "Subcarrier", "Amplitude"),
],
[np.angle, np.abs],
),
Figure.UNWRAPPED_PHASE: PerAntennaFigure(
[SimpleLineChart("Unwrapped CSI Phase", "Subcarrier", "Phase")], [lambda x: x]
),
Figure.PROCESSED_CSI: PerAntennaFigure(
[
SimpleLineChart("Processed CSI Phase", "Subcarrier", "Phase"),
SimpleLineChart("Processed CSI Amplitude", "Subcarrier", "Amplitude"),
],
[np.angle, np.abs],
),
Figure.MUSIC_EIGENVALUES: MusicEigenvalueHistogram(),
Figure.AOA_HEATMAP: HeatmapFigure(),
Figure.PHASE_ANALYSIS: RandomVariable("Phase"),
Figure.MAGN_ANALYSIS: RandomVariable("Magnitude"),
}