def getFreqBandOrValue(data, new_data, freq_value, global_max):
# delete first row
data = np.delete(data, 0, 0)
# add new_data as a row at the end of data. columns=electrodes rows=timestep
data = np.vstack([data, new_data])
# transpose the data numpy array
data = np.transpose(data)
# compute power spectrum of data
f, ps = sps.welch(data, fs=26)
# print the power spectrum
print("ps", ps)
# print the frequency
print("f", f)
extract_amplitude = []
# delta freq band
if freq_value == -1:
extract_amplitude = getAmplitudesByFrequencyBand(ps, 0)
# theta freq band
elif freq_value == -2:
extract_amplitude = getAmplitudesByFrequencyBand(ps, 1)
# alpha freq band
elif freq_value == -3:
extract_amplitude = getAmplitudesByFrequencyBand(ps, 2)
# specific freq value wanted
else:
interval = [freq_value - 0.5, freq_value + 0.5]
start_index = -1
end_index = -1
for i in range(len(f)):
if interval[0] <= f[i] <= interval[1]:
if start_index == -1:
start_index = i
else:
end_index = i
print("start ", start_index, f[start_index], "end ", end_index,
f[end_index])
extract_amplitude = ps[:, start_index:end_index]
# create a numpy array called temp
temp = np.asarray(extract_amplitude)
# temp holds mean of each row in extractAmplitude
temp = np.mean(temp, axis=1)
# calculate the maximum of the two values - called local_max
local_max = max(np.amax(temp), global_max)
# traverse through elements in the temp nuumpy array
for i in range(len(temp)):
# normalize all amplitudes by the global max
temp[i] = temp[i] / local_max
# return the temp, local_max, and data numpy arrays
return [temp, local_max, data]
def getCmapByFreqVal(data, newdata, freqValue, globalMax):
# delete first row
data = np.delete(data, 0, 0)
# add newdata as a row at the end of data. columns=electrodes rows=timestep
data = np.vstack([data, newdata])
data = np.transpose(data)
# compute power spectrum of data
f, ps = sps.welch(data, fs=26)
#print("ps", ps)
#print("f", f)
extractAmplitude = []
# delta freq band
if (freqValue == -1):
extractAmplitude = getAmplitudesByFrequencyBand(ps, 0)
# theta freq band
elif freqValue == -2:
extractAmplitude = getAmplitudesByFrequencyBand(ps, 1)
# alpha freq band
elif freqValue == -3:
extractAmplitude = getAmplitudesByFrequencyBand(ps, 2)
# specific freq value wanted
else:
interval = [freqValue - 0.5, freqValue + 0.5]
startIndex = -1
endIndex = -1
for i in range(len(f)):
if interval[0] <= f[i] <= interval[1]:
if startIndex == -1:
startIndex = i
else:
endIndex = i
#print("start ", startIndex, f[startIndex],
# "end ", endIndex, f[endIndex])
extractAmplitude = ps[:, startIndex:endIndex]
temp = np.asarray(extractAmplitude)
# temp holds mean of each row in extractAmplitude
temp = np.mean(temp, axis=1)
localMax = max(np.amax(temp), globalMax)
for i in range(len(temp)):
# normalize all amplitudes by the global max
temp[i] = temp[i] / localMax
return [temp, localMax, data]
def getCmapForZscores(data, newdata, freqValue):
# delete first row of data
data = np.delete(data, 0, 0)
# add newdata as a row at the end of data. columns=electrodes rows=timestep
data = np.vstack([data, newdata])
data = np.transpose(data)
# compute power spectrum of data
f, ps = sps.welch(data, fs=26)
#print("ps", ps)
extractAmplitude = []
# delta freq band
if (freqValue == -1):
extractAmplitude = getAmplitudesByFrequencyBand(ps, 0)
# theta freq band
elif freqValue == -2:
extractAmplitude = getAmplitudesByFrequencyBand(ps, 1)
# alpha freq band
elif freqValue == -3:
extractAmplitude = getAmplitudesByFrequencyBand(ps, 2)
# specific freq value wanted
else:
startIndex, endIndex = getInterval(f, freqValue)
extractAmplitude = ps[:, startIndex:endIndex]
temp = np.asarray(extractAmplitude)
# temp holds mean of each row in extractAmplitude
temp = np.mean(temp, axis=1)
#print("temp", temp)
# square all values to make them 0 <= x <= 1
temp = np.square(temp)
# calculate zscores for the array
zscoreArray = stats.zscore(temp)
# next line creates positive and negative zscores, so if the value was between 0 to 0.5, it is
# scaled to between -1 and 0, and if the value was between 0.5 and 1, it is scaled to between
# 0 and 1
zscoreArray = np.power(
(zscoreArray / np.amax(np.absolute(zscoreArray))), 3) / 2 + 0.5
return [zscoreArray, data]
def plotNodes(i):
global data
start_time = time.time()
inlet = StreamInlet(streams[0])
# get a new sample
sample = inlet.pull_sample()
newdata = np.asarray(sample[0][:n])
# print(newdata)
# delete first row of data
data = np.delete(data, 0, 0)
# add newdata as a row at the end of data. columns=electrodes rows=timestep
data = np.vstack([data, newdata])
data = np.transpose(data)
# compute power spectrum of data
f, ps = sps.welch(data, fs=26)
# get the amplitudes associated with the various bands of frequencies
extractAmplitudeDelta = getAmplitudesByFrequencyBand(ps, 0)
extractAmplitudeTheta = getAmplitudesByFrequencyBand(ps, 1)
extractAmplitudeAlpha = getAmplitudesByFrequencyBand(ps, 2)
tempDelta = np.asarray(extractAmplitudeDelta)
tempTheta = np.asarray(extractAmplitudeTheta)
tempAlpha = np.asarray(extractAmplitudeAlpha)
# temp holds mean of each row in extractAmplitude
tempDelta = np.mean(tempDelta, axis=1)
tempTheta = np.mean(tempTheta, axis=1)
tempAlpha = np.mean(tempAlpha, axis=1)
# square all values to make them 0 <= x <= 1
tempDelta = np.square(tempDelta)
tempTheta = np.square(tempTheta)
tempAlpha = np.square(tempAlpha)
# calculate zscores for the array
zscoreArrayDelta = stats.zscore(tempDelta)
zscoreArrayTheta = stats.zscore(tempTheta)
zscoreArrayAlpha = stats.zscore(tempAlpha)
sumOfZscores = zscoreArrayDelta + zscoreArrayTheta + zscoreArrayAlpha
zscoreArrayDelta = np.divide(zscoreArrayDelta, sumOfZscores)
zscoreArrayTheta = np.divide(zscoreArrayTheta, sumOfZscores)
zscoreArrayAlpha = np.divide(zscoreArrayAlpha, sumOfZscores)
# next line creates positive and negative zscores, so if the value was between 0 to 0.5, it is
# scaled to between -1 and 0, and if the value was between 0.5 and 1, it is scaled to between
# 0 and 1
zscoreArrayDelta = (
(zscoreArrayDelta / np.amax(zscoreArrayDelta)) / 2) + 0.5
zscoreArrayTheta = (
(zscoreArrayTheta / np.amax(zscoreArrayTheta)) / 2) + 0.5
zscoreArrayAlpha = (
(zscoreArrayAlpha / np.amax(zscoreArrayAlpha)) / 2) + 0.5
print(zscoreArrayAlpha)
# define vectors for plot colors and opacity
# altColors = freqs / 33
colorsDelta = cmap(zscoreArrayDelta)
colorsTheta = cmap(zscoreArrayTheta)
colorsAlpha = cmap(zscoreArrayAlpha)
# colors.astype(float)
# colors[:, -1] = maxes / maxes.max()
# print(altColors)
# print(colors)
ax1.set_xlim(-6, 6)
ax1.set_ylim(-6, 6)
ax2.set_xlim(-6, 6)
ax2.set_ylim(-6, 6)
ax3.set_xlim(-6, 6)
ax3.set_ylim(-6, 6)
# ax1.scatter(x, y, s = 100, c = altColors, cmap = plt.cm.jet_r)
ax1.scatter(x, y, s=100, c=colorsDelta)
ax2.scatter(x, y, s=100, c=colorsTheta)
ax3.scatter(x, y, s=100, c=colorsAlpha)
elapsed_time = time.time() - start_time