def heatmap(scaled_csi):
xax = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
Fs = 1 / np.mean(np.diff(xax))
print("Sampling Rate: " + str(Fs))
limits = [0, xax[-1], 1, no_subcarriers]
#x = subcarrier index
#y = time (s)
#z = amplitude (dBm)
for x in range(no_subcarriers):
hampelData = hampel(csi[x].flatten(), 5)
smoothedData = running_mean(hampelData, 5)
# b, a = signal.butter(9, [1/int(Fs/2), 2/int(Fs/2)], "bandpass")
# csi[x] = signal.lfilter(b, a, csi[x].flatten())
csi[x] = smoothedData
fig, ax = plt.subplots()
im = ax.imshow(csi, cmap="jet", extent=limits, aspect="auto")
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel("Amplitude (dBm)")
plt.xlabel("Time (s)")
plt.ylabel("Subcarrier Index")
plt.show()
def beatsfilter(scaled_csi):
xax = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
Fs = 1 / np.mean(np.diff(xax))
stabilities = []
for x in range(no_subcarriers):
finalEntry = csi[x].flatten()
variation = stats.variation(finalEntry)
if not np.isnan(variation) and variation < 0.5:
hampelData = hampel(finalEntry, 5)
smoothedData = running_mean(hampelData, 5)
filtData = bandpass(5, 1, 1.3, Fs, smoothedData)
wLength = 1 * Fs
N = np.mod(len(filtData), wLength)
windows = np.array_split(filtData, N)
psds = []
for window in windows:
f, Pxx_den = signal.welch(window, Fs)
fMax = f[np.argmax(Pxx_den)]
psds.append(fMax)
specStab = 1 / np.var(psds)
print("Sub: {} has spectral stability: {}".format(x, specStab))
# plt.plot(xax, filtData, alpha=0.5)
stabilities.append({
"sub": x,
"stability": specStab,
"data": filtData
})
stabilities.sort(key=lambda x: x["stability"], reverse=True)
bestStab = stabilities[0]["stability"]
news = []
for stab in stabilities:
if stab["stability"] >= (bestStab / 100) * 20:
news.append(stab)
print("Using {} subcarriers.".format(len(news)))
pxxs = []
for sub in news:
filtData = sub["data"]
f, Pxx_den = signal.welch(filtData, Fs)
pxxs.append(Pxx_den)
meanPsd = np.mean(pxxs, axis=0)
plt.plot(f * 60, meanPsd, alpha=0.5)
plt.xlabel("Estimated Heart Rate [bpm]")
plt.ylabel("PSD [V**2/Hz]")
plt.legend(loc="upper right")
plt.show()
def heatmap(scaled_csi):
timestamps = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
Fs = 1 / np.mean(np.diff(timestamps))
print("Sampling Rate: " + str(Fs))
limits = [0, timestamps[-1], 1, no_subcarriers]
for x in range(no_subcarriers):
hampelData = hampel(csi[x].flatten(), 5)
smoothedData = running_mean(hampelData, 5)
butteredData = bandpass(7, 0.2, 0.3, Fs, smoothedData)
csi[x] = butteredData
fig, ax = plt.subplots()
im = ax.imshow(csi, cmap="jet", extent=limits, aspect="auto")
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel("Amplitude (dBm)")
plt.xlabel("Time (s)")
plt.ylabel("Subcarrier Index")
plt.show()
def traceStats(scaled_csi):
xax = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
#Average sampling rate.
tdelta = (xax[-1] - xax[0]) / len(xax)
print("Frames: {}".format(no_frames))
print("Average sampling rate: {}Hz".format((1 / tdelta)))
print("CSI Shape: {}".format(csi.shape))
def statsgraph(scaled_csi):
xax = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
means = []
stds = []
for x in range(no_subcarriers):
variation = stats.variation(csi[x])
mean = np.mean(csi[x])
std = np.std(csi[x]) * 20
if variation <= 0.05:
print(x)
if np.isnan(variation):
variation = np.array(0)
csi[x] = variation
means.append(mean)
stds.append(std)
fig, ax1 = plt.subplots()
color = "tab:blue"
ax1.set_xlabel("Subcarrier Index")
ax1.plot(range(no_subcarriers),
means,
label="mean",
color=color,
linestyle="dashed")
ax1.plot(range(no_subcarriers), stds, label="std dev x 20", color=color)
ax1.tick_params(axis="y", labelcolor=color)
ax1.legend(loc="upper left")
ax2 = ax1.twinx()
color = "tab:red"
ax2.bar(range(no_subcarriers), csi, label="CV", color=color)
ax2.plot(range(no_subcarriers),
np.full((no_subcarriers), 0.05),
label="threshold",
color=color,
linestyle="dashed")
ax2.legend(loc="upper right")
fig.tight_layout()
plt.show()
def breathingfilter(scaled_csi):
xax = getTimestamps(scaled_csi)
csi, no_frames, no_subcarriers = getCSI(scaled_csi)
# Zeroing out invalid subcarriers.
# for i in []:
# for x in range(no_frames):
# csi[i][x] = 0
# for x in range(no_subcarriers):
for x in range(80, 100):
finalEntry = csi[x].flatten()
hampelData = hampel(finalEntry, 10)
smoothedData = running_mean(hampelData, 10)
# csi[x] = hampelData
csi[x] = smoothedData
plt.plot(xax, csi[x], alpha=0.5, label=x)
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.legend(loc="upper right")
plt.show()