def filter_data(self,eeg_data):
#FILTER CONSTANTS
fs = self.fs
fn = self.fn
filter_order = 2 #2nd order filter
f_high = 50
f_low = 5
wn = [59,61] #Nyquist filter window
[b,a] = signal.butter(filter_order,f_high/fn, 'low')
[b1,a1] = signal.butter(filter_order,f_low/fn, 'high')
[bn,an] = signal.butter(4,[x/fn for x in wn], 'stop')
filtered_eeg = []
spectogram = []
notched = []
high_passed = []
low_passed = []
print(eeg_data)
for i in range(len(eeg_data[0])):
channel = eeg_data[:,i]
high_passed = signal.filtfilt(b1,a1,channel); # high pass filter
low_passed = signal.filtfilt(b,a,high_passed); # low pass filter
y = signal.filtfilt(bn,an,low_passed); # notch filter
filtered_eeg.append(y);
self.filtered_eeg = filtered_eeg
return filtered_eeg
# if __name__ == '__main__':
# main()
def __init__(self, channels, sample_rate, leds=None):
self.leds = leds # Not needed if just plotting
self.channels = channels
self.sample_rate = sample_rate
self.nyquist = float(sample_rate) / 2
# Filter order - higher the order the sharper
# the curve
order = 3
# Cut off frequencies:
# Low pass filter
cutoff = 200 / self.nyquist
# Numerator (b) and denominator (a)
# polynomials of the filter.
b, a = butter(order, cutoff, btype='lowpass')
self.low_b = b
self.low_a = a
# High pass filter
cutoff = 4000 / self.nyquist
b, a = butter(order, cutoff, btype='highpass')
self.high_b = b
self.high_a = a
# Keep track of max brightness for each
# colour
self.max = [0.0, 0.0, 0.0]
# Make different frequencies fall faster
# bass needs to be punchy.
self.fall = [15.0, 2.5, 5.0]
def filterSignal(self, cutoff, order, type):
""" Filters the current signal with a specified filter.
:param cutoff:
The cut off frequency of the filter.
:type cutoff:
float
:param order:
The order of the filter to use.
:type order:
int
:param type:
The type of filter to use, either "low" or "high", for low pass
filter, or high pass filter.
:type type:
str
"""
self.logger.debug("Entering filterSignal (%s, %s, %s)" % (cutoff, order, type))
# Get signal parameters
sample_rate = float(self.parameters["sample rate"])
if type == "high":
[b, a] = butter(order, cutoff / (sample_rate / 2), btype=type)
self.signal = lfilter(b, a, self.signal)
# b = firwin(251, cutoff=[50, 200], nyq=(sample_rate / 2))
# a = 1
# self.signal = lfilter(b, a, self.signal)
# [b, a] = iirdesign(wp=cutoff / (sample_rate / 2), ws = 50 / (sample_rate / 2), gpass=1, gstop=12, ftype="butter")
else:
[b, a] = butter(order, cutoff / (sample_rate / 2), btype=type)
self.signal = lfilter(b, a, self.signal)
def __init__(self, fs, filter_type):
self.filter_type = filter_type # Of two types. Alpha and Bandpass
self.fs = fs # sample frequency: 220hz
if self.filter_type == 'alpha':
# butter = butterworth filter function
# An optimal (in one way -- smoothness) for designing a filter. Simple and popular.
# signal.butter is from scipy. Is there an equivalent in Java? Could be developed in Python and copied
# and pasted into Java. The values of these arrays is really only dependent on sampling frequency and thus
# likely to be the same for all uses of the app
# Returns two arrays of floats. Could b
self.b, self.a = signal.butter(5# order of filter. More coefficients = better filtration at cost of speed
, np.array([8.,12.])# boundaries of filter (8-12hz for Alpha
/(self.fs/2.) # /2 because this function needs normalized frequency
, 'bandpass')
elif self.filter_type == 'bandpass':
self.b, self.a = signal.butter(5, np.array([2., 36.] # bandpass filter has different freq cutoffs
)/(self.fs/2.), 'bandpass')
else:
print('Filter type ''%s'' not supported.'%self.filter_type)
return
self.nb = len(self.b)
self.na = len(self.a)
def decode_efm():
""" Decode EFM from STDIN, assuming it's a 28Mhz 8bit raw stream """
datao = np.fromstring(sys.stdin.read(SAMPLES), dtype=np.uint8).astype(np.int16)
datao = sps.detrend(datao, type='constant') # Remove DC
datao = auto_gain(datao, 10000, 'pre-filter') # Expand before filtering, since we'll lose much of signal otherwise
low_pass = sps.butter(4, 1.75 / FREQ_MHZ, btype='lowpass') # Low pass at 1.75 Mhz
datao = sps.lfilter(low_pass[0], low_pass[1], datao)
high_pass = sps.butter(4, 0.01333 / FREQ_MHZ, btype='highpass') # High pass at 13.333 khz
datao = sps.lfilter(high_pass[0], high_pass[1], datao)
# This is too slow, need to work out a way to do it in scipy
de_emphasis_filter = biquad_filter(-1.8617006585639506, 0.8706642683920058, 0.947680874725466, -1.8659578411373265, 0.9187262110931641)
datao = np.fromiter(run_filter(de_emphasis_filter, datao), np.int16) # De-emph - 26db below 500khz
# Could tie edge_pll and run_filter together as generators, but we want to see the filter output
bit_gen = edge_pll(datao, EFM_PIXEL_RATE) # This is a ultra-naive PLL that returns a bit-stream of 1 = edge, 0 = no-edge
try:
while 1:
run_until_start_code(bit_gen)
eat_three_bits(bit_gen)
process_efm_frame(bit_gen, 31) # 31 14 bit EFM codes in a frame
except StopIteration:
printerr('Hit the end of the bitstream')
datao = np.clip(datao, 0, 255).astype(np.uint8)
sys.stdout.write(datao.tostring())
def generate_noise(D,N):
"""Generate data for the changepoint detection. Data can either be of type 0
or type 1, but when it's a combination fo both, we define a target label
Input
- D,N Dimenstionality arguments D dimensions over N samples
Output
- Data in format
X is a matrix in R^{N x D}
y is a matrix in R^{N,} not to donfuse with {N,1}"""
#Check if we have even D, so we can split the array in future
assert D%2 == 0, 'We need even number of dimensions'
ratioP = 0.5 #balance of targets
X = np.random.randn(N,D)
y = np.zeros(N)
mark = np.zeros(N)
#Generate two filter cofficients
filters = {}
filters['b1'],filters['a1'] = signal.butter(4,2.0*cutoff1/fs,btype='lowpass')
filters['b0'],filters['a0'] = signal.butter(4,2.0*cutoff0/fs,btype='lowpass')
for i in xrange(N):
if np.random.rand() > 0.5: #Half of the samples will have changepoint, other half wont
Dcut = np.random.randint(pattern_len,D-pattern_len)
signalA = signal.filtfilt(filters['b1'],filters['a1'],X[i])
signalB = signal.filtfilt(filters['b0'],filters['a0'],X[i])
X[i] = np.concatenate((signalA[:Dcut],signalB[Dcut:]),axis=0) #Concatenate the two signals
if True: #Boolean: do you want to introduce a pattern at the changepoint?
Dstart = int(Dcut - pattern_len/2)
X[i,Dstart:Dstart+pattern_len] = pattern
y[i] = 1 #The target label
mark[i] = Dcut
else:
mode = int(np.random.rand()>ratioP)
X[i] = signal.filtfilt(filters['b'+str(mode)],filters['a'+str(mode)],X[i])
y[i] = 0 #The target label
return X,y,mark
def __init__(self, fn, channels, to_freq, montage_channels, montage, idx=1, csp_time=[0, 0.3], a_time=0.5):
self.data = sp.signalParser(fn)
self.tags = self.data.get_p300_tags(idx=idx, samples=False)
signal = self.data.prep_signal(to_freq, channels, montage_channels, montage)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
signal2 = np.zeros(signal.shape)
self.wrong_tags = self.data.get_p300_tags(idx=idx, rest=True, samples=False)
artifacts_data = np.zeros([len(channels), self.fs * a_time, len(self.tags)])
for i, p in enumerate(self.tags):
artifacts_data[..., i] = signal[
:, p * self.data.sampling_frequency : (p + a_time) * self.data.sampling_frequency
]
self.a_features, self.bands = artifactsCalibration(artifacts_data, self.data.sampling_frequency)
signal2 = np.zeros(signal.shape)
self.fs = to_freq
self.channels = channels
self.idx = idx
b, a = ss.butter(3, 2 * 1.0 / self.fs, btype="high")
b_l, a_l = ss.butter(3, 20.0 * 2 / self.fs, btype="low")
for e in xrange(len(self.channels)):
tmp = filtfilt(b, a, signal[e, :])
signal2[e, :] = filtfilt(b_l, a_l, tmp)
self.signal_original = signal2
self.t1, self.t2 = self.show_mean(csp_time, "Cz", dont_plot=False)
P, vals = self.train_csp(signal2, [self.t1, self.t2])
self.P = P
self.signal = np.dot(P[:, 0], signal2)
def test_fir():
print "Testing dsp-fir"
#ref = ones(512)
ref = (2.0 * random.rand(512)) - 1.0
#test short mono fir
writeaudio(ref)
h = signal.firwin(21, 0.4)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
writeaudio(expected, 'expected.wav')
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(expected, readaudio(), 1e-6)
#test long mono fir
writeaudio(ref)
h = signal.firwin(312, 0.4)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(expected, readaudio(), 1e-6)
#test short stereo fir, mono coeffs
writeaudio(transpose([ref,-ref]))
h = signal.firwin(21, 0.4)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(transpose([expected, -expected]), readaudio(), 1e-6)
#test long stereo fir, mono coeffs
writeaudio(transpose([ref,-ref]))
h = signal.firwin(312, 0.4)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(transpose([expected, -expected]), readaudio(), 1e-6)
#test asymmetric mono fir
writeaudio(ref)
impulse = concatenate(([1], zeros(499)))
b, a = signal.butter(2, 500.0/24000, 'low')
h = signal.lfilter(b, a, impulse)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(expected, readaudio(), 1e-6)
#test asymmetric stereo fir
writeaudio(transpose([ref,-ref]))
impulse = concatenate(([1], zeros(499)))
b, a = signal.butter(2, 500.0/24000, 'low')
h = signal.lfilter(b, a, impulse)
savetxt("test_coeffs.txt", h)
expected = signal.lfilter(h, 1, ref)
os.system("../file-qdsp -n 64 -i test_in.wav -o test_out.wav -p fir,h=test_coeffs.txt")
compareaudio(transpose([expected, -expected]), readaudio(), 1e-6)
os.remove('test_coeffs.txt')
def __init__(self,window_size, low, high): fs_Hz = 250; fn = fs_Hz/2 self.filtered_data = np.array((window_size,1)) ####################################### # Filter Creation # ------------------------------------- # # Create a filter using the scipy module, # based on specifications suggested by # Pan-Tompkins (bandpass from 5-15Hz) # # # 1) Establish constants: # a) filter_order = 2 # b) high pass cutoff = 15Hz # c) low pass cutoff = 5Hz # 2) Calculate the coefficients, store in variables filter_order = 2 f_high = high f_low = low self.high_pass_coefficients = signal.butter(filter_order,f_low/fn, 'high') self.low_pass_coefficients = signal.butter(filter_order,f_high/fn, 'low')
def makeFiltered(self):
filtered = np.zeros(len(self.data))
b1, a1 = butter(1, 0.0003, 'lowpass')
filtered = filtfilt(b1, a1, self.data)
filtered = self.data - filtered
b2, a2 = butter(1, 0.025, 'lowpass')
self.filt_data = filtfilt(b2, a2, filtered)
def filter_feepmt(feep):
"""
input: an instance of class FeePmt
output: buttersworth parameters of the equivalent FEE filter
"""
# high pass butterswoth filter ~1/RC
b1, a1 = signal.butter(1, feep.freq_LHPFd, 'high', analog=False)
b2, a2 = signal.butter(1, feep.freq_LHPFd, 'low', analog=False)
b0 = b2*(feep.Zin/(1+feep.C1/feep.C2))+b1*(feep.Zin*feep.R1/(feep.Zin+feep.R1))
a0 = a1
# LPF order 1
b1l, a1l = signal.butter(1, feep.freq_LPF1d, 'low', analog=False)
# LPF order 4
b2l, a2l = signal.butter(4, feep.freq_LPF2d, 'low', analog=False)
# convolve HPF, LPF1
a_aux = np.convolve(a0, a1l, mode='full')
b_aux = np.convolve(b0, b1l, mode='full')
# convolve HPF+LPF1, LPF2
a = np.convolve(a_aux, a2l, mode='full')
b_aux2 = np.convolve(b_aux, b2l, mode='full')
b = feep.A2*b_aux2
return b, a
def butter_bandpass(fs, lowcut=None, highcut=None, order=10):
"""
Parameters
----------
fs : float
Sample Rate
lowcut : float
Low pass frequency cutoff
highcut : float
High pass frequency cutoff
order : int
Butterworth filter order [Default = 10, i.e., 10th order Butterworth filter]
Reference
---------
http://scipy-cookbook.readthedocs.io/items/ButterworthBandpass.html
"""
from scipy.signal import butter
nyq = 0.5 * fs
if lowcut and highcut:
high = highcut / nyq
low = lowcut / nyq
sos = butter(order, [low, high], analog=False, btype='band', output='sos')
elif highcut:
high = highcut / nyq
sos = butter(order, high, btype='low', output='sos')
elif lowcut:
low = lowcut / nyq
sos = butter(order, low, btype='high', output='sos')
else:
print('Error -- must supply lowcut, highcut or both')
sos = None
return sos
def __init__(self, source, nchannels, order, fc, btype="low"):
Wn = fc.copy()
Wn = atleast_1d(Wn) # Scalar inputs are converted to 1-dimensional arrays
self.samplerate = source.samplerate
try:
Wn = Wn / self.samplerate * 2 + 0.0 # wn=1 corresponding to half the sample rate
except DimensionMismatchError:
raise DimensionMismatchError("Wn must be in Hz")
if btype == "low" or btype == "high":
self.filt_b = zeros((nchannels, order + 1))
self.filt_a = zeros((nchannels, order + 1))
if len(Wn) == 1: # if there is only one Wn value for all channel just repeat it
self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype)
self.filt_b = kron(ones((nchannels, 1)), self.filt_b)
self.filt_a = kron(ones((nchannels, 1)), self.filt_a)
else: # else make nchannels different filters
for i in xrange((nchannels)):
self.filt_b[i, :], self.filt_a[i, :] = signal.butter(order, Wn[i], btype=btype)
else:
self.filt_b = zeros((nchannels, 2 * order + 1))
self.filt_a = zeros((nchannels, 2 * order + 1))
if Wn.ndim == 1: # if there is only one Wn pair of values for all channel just repeat it
self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype)
self.filt_b = kron(ones((nchannels, 1)), self.filt_b)
self.filt_a = kron(ones((nchannels, 1)), self.filt_a)
else:
for i in xrange((nchannels)):
self.filt_b[i, :], self.filt_a[i, :] = signal.butter(order, Wn[:, i], btype=btype)
self.filt_a = self.filt_a.reshape(self.filt_a.shape[0], self.filt_a.shape[1], 1)
self.filt_b = self.filt_b.reshape(self.filt_b.shape[0], self.filt_b.shape[1], 1)
self.nchannels = nchannels
LinearFilterbank.__init__(self, source, self.filt_b, self.filt_a)
def bandfilter(data, fa=None, fb=None, Fs=1000.0, order=4, zerophase=True, bandstop=False):
N = len(data)
assert len(shape(data)) == 1
padded = zeros(2 * N, dtype=data.dtype)
padded[N / 2 : N / 2 + N] = data
padded[: N / 2] = data[N / 2 : 0 : -1]
padded[N / 2 + N :] = data[-1 : N / 2 - 1 : -1]
if not fa == None and not fb == None:
if bandstop:
b, a = butter(order, array([fa, fb]) / (0.5 * Fs), btype="bandstop")
else:
b, a = butter(order, array([fa, fb]) / (0.5 * Fs), btype="bandpass")
elif not fa == None:
# high pass
b, a = butter(order, fa / (0.5 * Fs), btype="high")
assert not bandstop
elif not fb == None:
# low pass
b, a = butter(order, fb / (0.5 * Fs), btype="low")
assert not bandstop
else:
assert 0
if zerophase:
return filtfilt(b, a, padded)[N / 2 : N / 2 + N]
else:
return lfilter(b, a, padded)[N / 2 : N / 2 + N]
assert 0
def filter(self):
path = os.getcwd()+'/trialGraspEventDetection_dataFiles'
self.Fgr = np.sum(self.values[:, 9:15], axis=1) # SAI
self.Fgl = np.sum(self.values[:, 0:7], axis=1) # SAI
# can use this to plot in matlab graspeventdetection_plot.m
np.savetxt(path+'/SAI_Fgr.txt', self.Fgr)
# can use this to plot in matlab
np.savetxt(path+'/SAI_Fgl.txt', self.Fgl)
# 0.55*pi rad/samples
b1, a1 = signal.butter(1, 0.55, 'high', analog=False)
self.f_acc_x = signal.lfilter(b1, a1, self.acc_x, axis=-1, zi=None)
self.f_acc_y = signal.lfilter(b1, a1, self.acc_y, axis=-1, zi=None)
self.f_acc_z = signal.lfilter(b1, a1, self.acc_z, axis=-1, zi=None)
# self.f_eff = signal.lfilter(b1, a1, self.eff, axis=-1, zi=None)
# type(eff)
self.FAII = np.sqrt(np.square(self.f_acc_x) +
np.square(self.f_acc_y) +
np.square(self.f_acc_z))
# can use this to plot in matlab
np.savetxt(path+'/FAII.txt', self.FAII)
# subtract base values from the values array
self.values1 = self.values - self.values.min(axis=0)
# pass the filter for each sensor
self.fvalues1 = np.zeros(self.values1.shape)
# 0.48*pi rad/samples
b, a = signal.butter(1, 0.48, 'high', analog=False)
for i in range(16):
self.fvalues1[:, i] = signal.lfilter(b, a, self.values1[:, i],
axis=-1, zi=None)
self.FAI = np.sum(self.fvalues1, axis=1)
# can use this to plot in matlab
np.savetxt(path+'/FAI.txt', self.FAI)
def butter_high_low_pass(lowcut, highcut, sampling_rate, order=5):
nyq_freq = sampling_rate*0.5
lower_bound = lowcut/nyq_freq
higher_bound = highcut/nyq_freq
b_high, a_high = butter(order, lower_bound, btype='high')
b_low, a_low = butter(order, higher_bound, btype='low')
return b_high, a_high, b_low, a_low
def prepare_audio_filters():
tf_rangel = 100000
tf_rangeh = 170000
# audio filters
tf = SysParams["audio_lfreq"]
N, Wn = sps.buttord(
[(tf - tf_rangel) / (freq_hz / 2.0), (tf + tf_rangel) / (freq_hz / 2.0)],
[(tf - tf_rangeh) / (freq_hz / 2.0), (tf + tf_rangeh) / (freq_hz / 2.0)],
5,
15,
)
Faudl = filtfft(sps.butter(N, Wn, btype="bandpass"))
tf = SysParams["audio_rfreq"]
N, Wn = sps.buttord(
[(tf - tf_rangel) / (freq_hz / 2.0), (tf + tf_rangel) / (freq_hz / 2.0)],
[(tf - tf_rangeh) / (freq_hz / 2.0), (tf + tf_rangeh) / (freq_hz / 2.0)],
5,
15,
)
Faudr = filtfft(sps.butter(N, Wn, btype="bandpass"))
N, Wn = sps.buttord(0.016 / (afreq / 2.0), 0.024 / (afreq / 2.0), 5, 15)
FiltAPost = filtfft(sps.butter(N, Wn))
N, Wn = sps.buttord(3.1 / (freq / 2.0), 3.5 / (freq / 2.0), 1, 20)
SysParams["fft_audiorf_lpf"] = Faudrf = filtfft(sps.butter(N, Wn, btype="lowpass"))
SysParams["fft_audiolpf"] = FiltAPost # * FiltAPost * FiltAPost
SysParams["fft_audio_left"] = Faudrf * Faudl * fft_hilbert
SysParams["fft_audio_right"] = Faudrf * Faudr * fft_hilbert
def filter_data(eeg_data, fs):
#FILTER CONSTANTS
fn = fs/2
filter_order = 2 #2nd order filter
f_high = 50
f_low = 5
wn = [59,61] #Nyquist filter window
[b,a] = signal.butter(filter_order,f_high/fn, 'low')
[b1,a1] = signal.butter(filter_order,f_low/fn, 'high')
[bn,an] = signal.butter(4,[x/fn for x in wn], 'stop')
filtered_eeg = []
spectogram = []
notched = []
high_passed = []
low_passed = []
channel = eeg_data
high_passed = signal.filtfilt(b1,a1,channel); # high pass filter
low_passed = signal.filtfilt(b,a,high_passed); # low pass filter
y = signal.filtfilt(bn,an,low_passed); # notch filter
return y
def temporally_filter(spatial, tfiltN, flash):
print("Temporally Filtering")
padlen=0
if tfiltN[2]==1: #%if only high pass temporal filter
[b,a]= butter(tfiltN[0],tfiltN[1],btype='highpass')
padlen=3
else: #%if band pass temporal filter
[b,a]=butter(tfiltN[0],[tfiltN[1],tfiltN[2]], btype='bandpass')
padlen=6
temporal=np.array(spatial, dtype = np.float32)
if len(flash) == 2:
temporal[flash[0]:flash[1]+1,:,:]=np.mean(spatial[4:flash[0]+1, :, :], 0)
bg=np.mean(temporal[:flash[0] + 1, :, :],0)
else:
bg = np.min(temporal, 0)
for t in np.arange(len(temporal)):
temporal[t, :, :] -= bg
#temporal[0, :, :] = 0
p = 0.0
for y in range(np.size(temporal, 1)):
for x in range(np.size(temporal, 2)):
temporal[:, y, x]=filtfilt(b,a, temporal[:, y, x], padlen=padlen)
if (100 * y / np.size(temporal, 1)) > p:
p = (100 * y / np.size(temporal, 1))
print(" %d%%\r" % (100 * y / np.size(temporal, 1))),
print('Temporally Filtered')
return temporal
def butter_bandpass(lowcut, highcut, samplingrate, order=4):
nyq = 0.5 * samplingrate
low = lowcut / nyq
high = highcut / nyq
print high, low
if high >=1. and low == 0.:
b = np.array([1.])
a = np.array([1.])
elif high < 0.95 and low > 0. :
wp = [1.05*low,high-0.05]
ws = [0.95*low,high+0.05]
print wp,ws
order,wn = buttord(wp,ws,0., 30.)
b, a = butter(order, wn, btype='band')
elif high>= 0.95:
print 'highpass',low,1.2*low,0.8*low
order,wn = buttord( 15*low,0.05*low,gpass=0.0, gstop=10.0)
print order,wn
b, a = butter(order, wn, btype='high')
elif low <= 0.05:
print 'lowpass',high
order,wn = buttord( high-0.05,high+0.05,gpass=0.0, gstop=10.0)
b, a = butter(order, wn, btype='low')
return b, a
def __init__(self,source, nchannels, order, fc, btype='low'):
Wn = np.asarray(np.atleast_1d(fc)).copy() #Scalar inputs are converted to 1-dimensional arrays
self.samplerate = source.samplerate
Wn= Wn/float(self.samplerate)*2 # wn=1 corresponding to half the sample rate
if btype=='low' or btype=='high':
self.filt_b=np.zeros((nchannels,order+1))
self.filt_a=np.zeros((nchannels,order+1))
if len(Wn)==1: #if there is only one Wn value for all channel just repeat it
self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype)
self.filt_b=np.kron(np.ones((nchannels,1)),self.filt_b)
self.filt_a=np.kron(np.ones((nchannels,1)),self.filt_a)
else: #else make nchannels different filters
for i in range((nchannels)):
self.filt_b[i,:], self.filt_a[i,:] = signal.butter(order, Wn[i], btype=btype)
else:
self.filt_b=np.zeros((nchannels,2*order+1))
self.filt_a=np.zeros((nchannels,2*order+1))
if Wn.ndim==1: #if there is only one Wn pair of values for all channel just repeat it
self.filt_b, self.filt_a = signal.butter(order, Wn, btype=btype)
self.filt_b=np.kron(np.ones((nchannels,1)),self.filt_b)
self.filt_a=np.kron(np.ones((nchannels,1)),self.filt_a)
else:
for i in range((nchannels)):
self.filt_b[i,:], self.filt_a[i,:] = signal.butter(order, Wn[:,i], btype=btype)
self.filt_a=self.filt_a.reshape(self.filt_a.shape[0],self.filt_a.shape[1],1)
self.filt_b=self.filt_b.reshape(self.filt_b.shape[0],self.filt_b.shape[1],1)
self.nchannels = nchannels
LinearFilterbank.__init__(self,source, self.filt_b, self.filt_a)
def bandpass_filter(series, low=200.0, high=4000.0):
"""
Filters given series with a 2nd order bandpass filter with default
cutoff frequencies of 200 Hz and 4 kHz.
Parameters
----------
series : pandas.Series
The data series to filter
Returns
-------
filtered_series : pandas.Series
"""
dt = series.index[1] - series.index[0]
fs_nyquist = (1.0/dt) / 2.0
if low < 0.1:
# Lowpass filter only.
bf, af = signal.butter(2, high/fs_nyquist, btype='lowpass')
elif high > 10000:
# Highpass filter only.
bf, af = signal.butter(2, low/fs_nyquist, btype='highpass')
else:
bf, af = signal.butter(2, (low/fs_nyquist, high/fs_nyquist),
btype='bandpass')
return pd.Series(signal.filtfilt(bf, af, series).astype(np.float32),
index=series.index)
def butterworth_plot(fig=None, ax=None):
"""
Plot of frequency response of the Butterworth filter with different orders.
"""
if fig is None:
fig, ax = plt.subplots(1, 2, figsize=(10, 4))
b1, a1 = signal.butter(1, 10, 'low', analog=True)
w, h1 = signal.freqs(b1, a1)
ang1 = np.rad2deg(np.unwrap(np.angle(h1)))
h1 = 20 * np.log10(abs(h1))
b2, a2 = signal.butter(2, 10, 'low', analog=True)
w, h2 = signal.freqs(b2, a2)
ang2 = np.rad2deg(np.unwrap(np.angle(h2)))
h2 = 20 * np.log10(abs(h2))
b4, a4 = signal.butter(4, 10, 'low', analog=True)
w, h4 = signal.freqs(b4, a4)
ang4 = np.rad2deg(np.unwrap(np.angle(h4)))
h4 = 20 * np.log10(abs(h4))
b6, a6 = signal.butter(6, 10, 'low', analog=True)
w, h6 = signal.freqs(b6, a6)
ang6 = np.rad2deg(np.unwrap(np.angle(h6)))
h6 = 20 * np.log10(abs(h6))
w = w/10
# PLOT
ax[0].plot(w, h1, 'b', w, h2, 'r', w, h4, 'g', w, h6, 'y', linewidth=2)
ax[0].axvline(1, color='black') # cutoff frequency
ax[0].scatter(1, -3, marker='s', edgecolor='0', facecolor='1', s=400)
#ax1.legend(('1', '2', '4', '6'), title='Filter order', loc='best')
ax[0].set_xscale('log')
fig.suptitle('Bode plot for low-pass Butterworth filter with different orders',
fontsize=16, y=1.05)
#ax1.set_title('Magnitude', fontsize=14)
ax[0].set_xlabel('Frequency / Critical frequency', fontsize=14)
ax[0].set_ylabel('Magnitude [dB]', fontsize=14)
ax[0].set_xlim(0.1, 10)
ax[0].set_ylim(-120, 10)
ax[0].grid(which='both', axis='both')
ax[1].plot(w, ang1, 'b', w, ang2, 'r', w, ang4, 'g', w, ang6, 'y', linewidth=2)
ax[1].axvline(1, color='black') # cutoff frequency
ax[1].legend(('1', '2', '4', '6'), title='Filter order', loc='best')
ax[1].set_xscale('log')
#ax2.set_title('Phase', fontsize=14)
ax[1].set_xlabel('Frequency / Critical frequency', fontsize=14)
ax[1].set_ylabel('Phase [degrees]', fontsize=14)
ax[1].set_yticks(np.arange(0, -300, -45))
ax[1].set_ylim(-300, 10)
ax[1].grid(which='both', axis='both')
plt.tight_layout(w_pad=1)
axi = plt.axes([.115, .4, .15, .35]) # inset plot
axi.plot(w, h1, 'b', w, h2, 'r', w, h4, 'g', w, h6, 'y', linewidth=2)
#ax11.set_yticks(np.arange(0, -7, -3))
axi.set_xticks((0.6, 1, 1.4))
axi.set_yticks((-6, -3, 0))
axi.set_ylim([-7, 1])
axi.set_xlim([.5, 1.5])
axi.grid(which='both', axis='both')
def filterBpass(self, fs, cutOffLow, cutOffHigh, bLineRemoved):
#fs / 2 je nyq frekvencia
B, A = butter(2, cutOffHigh / (fs / 2), btype='low') # 1st order Butterworth low-pass
C, D = butter(2, cutOffLow / (fs / 2), btype='high') # 1st order Butterworth high-pass
bLineRemoved_low = lfilter(B, A, bLineRemoved)
bLineRemoved_low_high = lfilter(C, D, bLineRemoved_low)
return bLineRemoved_low_high
def butter_bandpass(lowcut, highcut, fs, order=8,kword='lowpass'):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
if kword=='lower':
b, a = butter(order, high, btype='lowpass')
else:
b, a = butter(order, [low,high], btype='band')
return b, a
def _design(self):
if self.already_normalized_Wn:
self.Z, self.P, self.K = signal.butter(self.N, self.Wn,
self.filter_kind, analog=False,
output='zpk')
else:
self.Z, self.P, self.K = signal.butter(self.N, self.normalize_Wn(),
self.filter_kind, analog=False,
output='zpk')
def filtracja(kan,fs, czest): n=1 [bx,ax]=ss.butter(n, czest/(fs/2.),btype='highpass') y1a = ss.filtfilt(bx,ax,kan) [b,a]=ss.butter(n, [48./(fs/2.), 52./(fs/2.)],btype='bandstop') y1 = ss.filtfilt(b,a,y1a) return y1
def __init__(self):
print "Instantiating..."
self._joint_vec = np.zeros(6)
self._rate = rospy.Rate(10)
self._pc = None
self._pc_prev = np.array([0, 0, 0])
self._R_capsule_world = None
self._capsule_lin_vel = None
self._capsule_ang_vel = None
self._pa = None
self._pa_prev = np.array([0, 0, 0])
self._R_EPM_world = None
self._fm = np.zeros(3)
self._fm_prev = np.zeros(3)
self._tm = np.zeros(3)
self._tm_prev = np.zeros(3)
self._coupling_status_msg = Float64()
self._df_dx_msg = Vector3()
self._df_dx_raw_msg = Vector3()
self._dcapz_dEPMz_msg = Vector3()
self._dcapz_dEPMz_raw_msg = Vector3()
self._mag_wrench_msg = Wrench()
# print "To publish: " , self._to_publish
self._dipole = DipoleField(1.48, 1.48, SC.ACTUATOR_MAG_H, SC.CAPSULE_MAG_H)
# print "Mag info:", SC.ACTUATOR_MAG_H
# Initial conditions for Butterworth fulter
fs = 100.0
nyq = 0.5 * fs
cutoff = 0.5
order = 2
self._butter_zi_dfdx = np.zeros((3, order))
self._butter_params_dfdx = butter(order, cutoff / nyq, btype="lowpass", analog=False)
self._butter_zi_dz = np.zeros((3, order))
self._butter_params_dz = butter(order, cutoff / nyq, btype="lowpass", analog=False)
# Instantiate publishers and subscribers
self._coupling_status_pub = rospy.Publisher("/MAC/coupling_status_topic", Float64, queue_size=1000)
self._df_dx_pub = rospy.Publisher("/MAC/df_dx_topic", Vector3, queue_size=1000)
self._df_dx_raw_pub = rospy.Publisher("/MAC/df_dx_raw_topic", Vector3, queue_size=1000)
self._mag_wrench_pub = rospy.Publisher("/MAC/mag_wrench_topic", Wrench, queue_size=1000)
self._dcapz_dEPMz_pub = rospy.Publisher("/MAC/dcapz_dEPMz_topic", Vector3, queue_size=1000)
self._dcapz_dEPMz_raw_pub = rospy.Publisher("/MAC/dcapz_dEPMz_raw_topic", Vector3, queue_size=1000)
self._robot_sub = rospy.Subscriber("/mitsubishi_arm/joint_states", JointState, self._robot_pose_cb)
self._capsule_sub = rospy.Subscriber("/MAC/mac/odom", Odometry, self._capsule_pose_cb)
# Robot info
self._robot = self._wait_and_get_robot()
self._tree = kdl_tree_from_urdf_model(self._robot)
self._chain_to_magnet = self._tree.getChain("base_link", "magnet_center")
self._fksolver_magnet = KDL.ChainFkSolverPos_recursive(self._chain_to_magnet)
self._q_cur = KDL.JntArray(self._chain_to_magnet.getNrOfJoints())
# Initialize timer
pub_timer = rospy.Timer(rospy.Duration(0.01), self._determine_coupling_state)
def DesignFilter(self):
nyq_rate = self.fs / 2
self.coefb_low, self.coefa_low = sp.butter(self.filter_order,
[self.f_low / nyq_rate], btype='low')
self.coefb_high, self.coefa_high = sp.butter(self.filter_order,
[self.f_high / nyq_rate], btype='high')
def prepare_audio_filters():
forder = 256
forderd = 0
tf_rangel = 100000
tf_rangeh = 170000
# audio filters
tf = SP["audio_lfreq"]
N, Wn = sps.buttord(
[(tf - tf_rangel) / (freq_hz / 2.0), (tf + tf_rangel) / (freq_hz / 2.0)],
[(tf - tf_rangeh) / (freq_hz / 2.0), (tf + tf_rangeh) / (freq_hz / 2.0)],
1,
15,
)
Baudl, Aaudl = sps.butter(N, Wn, btype="bandpass")
tf = SP["audio_rfreq"]
N, Wn = sps.buttord(
[(tf - tf_rangel) / (freq_hz / 2.0), (tf + tf_rangel) / (freq_hz / 2.0)],
[(tf - tf_rangeh) / (freq_hz / 2.0), (tf + tf_rangeh) / (freq_hz / 2.0)],
1,
15,
)
N, Wn = sps.buttord(
[(tf - tf_rangel) / (freq_hz / 2.0), (tf + tf_rangel) / (freq_hz / 2.0)],
[(tf - tf_rangeh) / (freq_hz / 2.0), (tf + tf_rangeh) / (freq_hz / 2.0)],
5,
15,
)
Baudr, Aaudr = sps.butter(N, Wn, btype="bandpass")
N, Wn = sps.buttord(0.016 / (afreq / 2.0), 0.024 / (afreq / 2.0), 2, 15)
audiolp_filter_b, audiolp_filter_a = sps.butter(N, Wn)
# USE FIR
audiolp_filter_b = sps.firwin(257, 0.020 / (afreq / 2.0))
audiolp_filter_a = [1.0]
N, Wn = sps.buttord(3.1 / (freq / 2.0), 3.5 / (freq / 2.0), 1, 20)
audiorf_filter_b, audiorf_filter_a = sps.butter(N, Wn, btype="lowpass")
[Baudrf_FDLS, Aaudrf_FDLS] = fdls.FDLS_fromfilt(audiorf_filter_b, audiorf_filter_a, forder, forderd, 0)
SP["fft_audiorf_lpf"] = Faudrf = np.fft.fft(Baudrf_FDLS, blocklen)
[Baudiolp_FDLS, Aaudiolp_FDLS] = fdls.FDLS_fromfilt(audiolp_filter_b, audiolp_filter_a, forder, forderd, 0)
FiltAPost = np.fft.fft(Baudiolp_FDLS, blocklen)
SP["fft_audiolpf"] = FiltAPost * FiltAPost # * FiltAPost
[Baudl_FDLS, Aaudl_FDLS] = fdls.FDLS_fromfilt(Baudl, Aaudl, forder, forderd, 0)
[Baudr_FDLS, Aaudr_FDLS] = fdls.FDLS_fromfilt(Baudr, Aaudr, forder, forderd, 0)
Faudl = np.fft.fft(Baudl_FDLS, blocklen)
Faudr = np.fft.fft(Baudr_FDLS, blocklen)
SP["fft_audio_left"] = Faudrf * Faudl * fft_hilbert
SP["fft_audio_right"] = Faudrf * Faudr * fft_hilbert
def shave(signal, factor=4, wn=0.005, plot=False, clip=None):
"""
This function reduces the number of peaks in a signal. It does this by first
passing the signal through a Butterworth low-pass filter to get a trend. Then
the a standard deviation of the signal is calculated and a band is by
adding/substracting the trend with a standard deviation times a factor.
:param signal: The signal to be shaved
:param factor: The factor used to multiply the standard deviation when calculating
the allowed band
:param wn: Cutoff frequency of the Butterworth low-pass filter.
:param plot: True to plot the original signal, new signal and band
:param clip: List with the low and high clip values.
:return: Shaved signal.
"""
if clip is not None:
clip_signal = np.clip(signal, *clip)
bad_samples = (signal > clip[1]) | (signal < clip[0])
else:
clip_signal = signal
bad_samples = [False] * len(clip_signal)
butter_params = butter(5, wn)
mean_signal = np.mean(clip_signal)
clip_signal = np.hstack((mean_signal, clip_signal, mean_signal))
trend = filtfilt(*butter_params, clip_signal)[1:-1]
clip_signal = clip_signal[1:-1]
core_signal = clip_signal - trend
std_signal = np.std(core_signal)
upper_bound = trend + factor * std_signal
lower_bound = trend - factor * std_signal
bad_samples |= (signal > upper_bound) | (signal < lower_bound)
bad_samples_idx = np.where(bad_samples)[0]
for i, i_signal in enumerate(bad_samples_idx):
i_0 = prev_good(bad_samples_idx, i)
i_1 = next_good(bad_samples_idx, i)
if i_0 >= len(bad_samples):
i_0 = i_1
i_1 = next_good(bad_samples_idx, bad_samples_idx.index(i_1 - 1))
if i_1 < 0:
i_1 = i_0
i_0 = prev_good(bad_samples_idx, bad_samples_idx.index(i_0))
clip_signal[i_signal] = lerp(i_signal, i_0, i_1, clip_signal[i_0],
clip_signal[i_1])
if plot:
import matplotlib.pyplot as plt
plt.plot(signal, "b")
plt.plot(clip_signal, "y")
plt.plot(upper_bound, "g")
plt.plot(lower_bound, "g")
plt.plot(trend, "m")
plt.plot(bad_samples_idx, signal[bad_samples_idx], ".r")
plt.show()
return clip_signal
def calculate_eeg_area(epoch_df, sf=2048):
y = epoch_df.drop('time', axis=1).mean(axis=1)
b2, a2 = signal.butter(4, 200/(sf/2), btype='lowpass')
envelope = signal.filtfilt(b2, a2, np.abs(y))
area = np.trapz(envelope, epoch_df['time'].values)
return area
observations = np.loadtxt(file_observations)
observations = observations[:np.argmax(observations)]
observations = observations[(observations <= 0).nonzero()[0][-1] + 1:1000]
times = np.arange(len(observations)) * tstep_observation
fig = plt.figure(figsize=[myplot.figure_width, myplot.figure_width * 0.5])
ax = plt.gca()
ax.plot(times, observations, 'b', label='Experimental observations')
ax.set(xlabel=r'Time [s]',
ylabel=r'neutron count (per $\Delta t$)',
title=r"Estimation of the time of reactivity insertion.")
#ax.xlabel(r'$t' + myplot.unit('s') + r'$')
#plt.ylabel(r'neutron count (per $\Delta t$)')
b, a = signal.butter(5, 0.05)
observations_smoothed = signal.filtfilt(b, a, observations)
ax.plot(times,
observations_smoothed,
'y',
linewidth=3,
label='Smoothed signal')
max_resting = max(
observations_smoothed[:min(50, math.ceil(5 / tstep_observation))])
ind_start = np.asarray(
observations_smoothed >= 2 * max_resting).nonzero()[0][0]
ax.axvline(ind_start * tstep_observation,
color='k',
label='Approximate time of reactivity insertion',
import shlex
#Defining H(z) from numerator and denominator coefficents
def H(z,num,den):
Num = np.polyval(num,z**(-1))
Den = np.polyval(den,z**(-1))
return Num/Den
#Reading the soundfile
x,fs = sf.read('Sound_Noise.wav')
order = 4
fc = 4000.0
Wn = 2*fc/fs
#Passing butterworth filter
num,den = signal.butter(order,Wn,'low')
#Preparing own routine filter design
#Constructing H(z)
k = np.arange(len(x))
w = 2*np.pi*k/len(x)
z = np.exp(1j * w)
H_z = H(z,num,den)
#Computing DFT(FFT) of x(n)
X = np.fft.fft(x)
#Multiplying X and H to give Y and computig IDFT(IFFT) to give y(n)
Y = np.multiply(H_z,X)
y = np.fft.ifft(Y).real
#Kutter signalene
data_1, i1 = make_nice(data_1, N)
data_2 = make_nice_around(data_2, N, i1 +
100) #legger til 100 fordi indexen er etter klipp
data_3 = make_nice_around(data_3, N, i1 + 100)
#plotter kuttede signaler
plt.plot(data_1, 'blue')
plt.plot(data_2, 'green')
plt.plot(data_3, 'red')
plt.title('Cut signals')
plt.show()
#FILTRERING SKJER HER
soshp = signal.butter(10, 50, 'hp', fs=Fs, output='sos') #Høypassfilter
soslp = signal.butter(10, 1000, 'lp', fs=Fs, output='sos') #Lavpassfilter
data_1 = signal.sosfilt(soshp, data_1)
data_1 = signal.sosfilt(soslp, data_1)
data_2 = signal.sosfilt(soshp, data_2)
data_2 = signal.sosfilt(soslp, data_2)
data_3 = signal.sosfilt(soshp, data_3)
data_3 = signal.sosfilt(soslp, data_3)
#Plotter filtrert filtrert data
plt.plot(data_1, 'blue')
plt.plot(data_2, 'green')
plt.plot(data_3, 'red')
def butter_low_pass(low_cut, fs, order=5):
nyq = 0.5 * fs
low = low_cut / nyq
b, a = butter(order, low, btype='low')
return b, a
def butter_lowpass(cutoff, fs, order):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
return butter(order, normal_cutoff, btype='low', analog=False)
def butter_highpass(cutoff, fs, order=10):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq #[0.30, 0.95]
b, a = signal.butter(order, [0.2, 0.48], btype='bandpass', analog=False, output='ba')
return b, a
def load_MonitoringVI_file(self, filename, temp_list = None, process_therm = 1):
'''Reads in thermometer data text file created by MonitoringVI, and plots the temperature as a function of time.
temp_list is a list of tuples, [(heater_voltage,temperature), ...] which are plotted on top of the temperature
versus time points. This allows one to visually check the calibration, temp_list.
process_therm is the column number of the thermometer whose data is processed by several filtering algorithms and
plotted.
'''
pos = filename.rfind(os.sep)
try:
with io.open(filename[:pos+1]+ 'Make_ScanData.m',mode='r') as f:
while 1:
line = f.readline()
if line == '': # End of file is reached
break
elif line.find('ScanData.Heater_Voltage') >= 0:
Voltages = line[line.find('['):line.find(']')+1]
break
except:
print('Unable to find or read Make_ScanData.m for list of heater voltages')
Voltages = 'Unknown'
with io.open(filename,mode='r') as f:
temp_data_header = ''
while temp_data_header.strip() =='':
temp_data_header = f.readline()
therm_list = [t for t in temp_data_header.strip().split('\t')[1:] if (t.strip() != 'None') & (t.strip() != '')]
temp_data = np.loadtxt(filename, dtype=np.float, comments='#', delimiter=None, converters=None, skiprows=3, usecols=None, unpack=False, ndmin=0)
num_col = temp_data.shape[1]
start_col = 1 #index of first column in data that has thermometer data
if process_therm > num_col - start_col:
print('process_therm = {} exceeds number of thermometers in data. Choose an lower number. Aborting...'.format(process_therm))
return
# Gaussian Filter
num_pts_in_gaussian_window = 20
b = gaussian(num_pts_in_gaussian_window, 10)
ga = filters.convolve1d(temp_data[:,process_therm], b/b.sum())
# buterworth Filter
npts = temp_data[:,process_therm].size
end = temp_data[-1,0]
dt = end/float(npts)
nyf = 0.5/dt
b, a = butter(4, .1)#1.5/nyf)
fl = filtfilt(b, a, temp_data[:,process_therm])
#Spline Fit
sp = UnivariateSpline(temp_data[:,0], temp_data[:,process_therm])
#weiner filter
wi = wiener(temp_data[:,process_therm], mysize=40, noise=10)
fig1 = plt.figure( facecolor = 'w',figsize = (10,10))
ax = fig1.add_subplot(1,1,1)
if isinstance(temp_list, list):
for temp_tuple in temp_list:
hline = ax.axhline(y = temp_tuple[1],linewidth=1, color='g', alpha = 0.3 ,linestyle = ':', label = None)
color_incr = 1.0/(num_col-start_col)
for therm_num in range(start_col, num_col): # plot all thermometer data present
line = ax.plot(temp_data[:,0], temp_data[:,therm_num],color=(0,color_incr*therm_num,0), alpha = 0.4 if therm_num != 1 else 1, linewidth = 3,label = therm_list.pop(0) if therm_list[0] != None else 'Therm{0}'.format(therm_num))
#plot filter outputs for THE FIRST thermometer only
line2 = ax.plot(temp_data[:,0], ga, 'y', linewidth = 3, label = 'Gaussian Conv') # Gaussian Convolution
line3 = ax.plot(temp_data[:,0], fl, 'c', linewidth = 3, label = 'Butterworth') # butterworth
line4 = ax.plot(temp_data[:,0], sp(temp_data[:,0]), 'k', linewidth = 3, label = 'Spline') # bspline
line5 = ax.plot(temp_data[:,0], wi, 'r', linewidth = 3, label = 'Weiner') # weiner
ax.grid(b=True, which='major', color='b', alpha = 0.2, linestyle='-')
ax.grid(b=True, which='minor', color='b', alpha = 0.2,linestyle='--')
ax.set_title('Heater Voltages = {}'.format(Voltages), fontsize=12)
ax.set_ylabel('Temperature [Kelvin]')
ax.set_xlabel('Seconds')
ax.legend(loc = 'best', fontsize=10,scatterpoints =1, numpoints = 1, labelspacing = .1)
plt.show()
# integer filter
# sampling rate
fs = 1000
# cutoffs
f1 = 45
f2 = 55
# scaling factor in bits
q = 14
# scaling factor as facor...
scaling_factor = 2**q
# let's generate a sequence of 2nd order IIR filters
sos = signal.butter(2, [f1 / fs * 2, f2 / fs * 2], 'stop', output='sos')
sos = np.round(sos * scaling_factor)
# print coefficients
for biquad in sos:
for coeff in biquad:
print(int(coeff), ",", sep="", end="")
print(q)
# plot the frequency response
b, a = signal.sos2tf(sos)
w, h = signal.freqz(b, a)
pl.plot(w / np.pi / 2 * fs, 20 * np.log(np.abs(h)))
pl.xlabel('frequency/Hz')
pl.ylabel('gain/dB')
def butter_lowpass(curoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='lowpass', analog=False)
return b, a
def _filter(self, samples):
b, a = signal.butter(self.settings["filter"]["order"],
self.settings["filter"]["cutoff"],
self.settings["filter"]["response"],
fs=self.settings["sr"])
return signal.filtfilt(b, a, samples)
def bandpass(self, lowcut, highcut, order=4):
nyq = 0.5 * self.fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='bandpass')
self.filters.append((b, a))
def butter_bandpass(lowcut, highcut, fs, order):
nyq = 0.5 * fs # fs / 2
low = lowcut / nyq # lowcut * 2 / fs
high = highcut / nyq
b, a = signal.butter(order, [low, high], btype='band')
return b, a
def _filter_data(data: List[float]) -> List[float]:
b, a = signal.butter(3, 0.05)
return signal.lfilter(b, a, data)
import mne
import collections
from scipy.ndimage.filters import maximum_filter1d as maxfilter
from scipy.signal import butter, filtfilt
execfile("local_settings.py")
execfile("src/findpeak.py")
execfile("src/normalize.py")
name = "Jared_04_03_17"
data_dir = op.realpath(op.join("..", "..", "data"))
raw = mne.io.read_raw_fif(op.join(temp_dir, name + "_noblink.fif"))
events = raw.copy().load_data().pick_channels(['Erg1'])
events.filter(l_freq=40, h_freq=None, picks=[0], phase='zero')
x = events.get_data()
y = np.maximum(0, x)
sample_rate = 512
b, a = butter(2, 2.5 / (512.0 / 2), 'low')
y = filtfilt(b, a, y)
yd = np.hstack([[[0]], np.diff(y)])
peaks = findpeaks(np.squeeze(yd), 0.99, 512 * 3)
# plt.plot(np.hstack([x.T/np.max(x),yd.T/np.max(yd)]))
# for i in peaks:
# plt.axvline(x=i,color='red')
plt.show()
plt.figure()
#plt.plot(abs(fft(Out[35000:65000])))
#plt.plot(abs(Four))
# for Ind in range(frameLen,Ln - frameLen,frameLen):
# Out[Ind-Flen//2:Ind+Flen//2] = Out[Ind-Flen//2:Ind+Flen//2]*wnd
return real(Out)
dir = 'result'
cutOff = 1000 # Cutoff frequency
nyq = 0.5 * Fr1
# cutOff1 = 1450
# cutOff2 = 1550
# fc1 = cutOff1 / nyq
# fc2 = cutOff2 / nyq
# print(fc1, fc2)
cutOff = 150
fc = cutOff / nyq
# prin t(fc)
B, A = sgn.butter(9, 0.2)
U, V = sgn.freqz(B, A) # рисует передаточную функцию фильтра
plt.plot(U, abs(V))
out = restSign(np.array(Dat, dtype=np.float64), frameLen, result)
fout = fft(out[:1024])
plt.cla()
plt.figure()
plt.plot(fout)
plt.show()
out = sgn.lfilter(B, A, out)
write('result4.wav', Fr1, np.asarray(out, dtype=np.int16))
def put_audio():
"""
Getting Constants from Constants.py
Variables must be extracted from the Variables_dict who is managed by Disk_IO.
"""
""" Variables and constants
"""
f_hp = 35
Variables_dict = recall_pickled_dict() # Load Variables_dict
SampRate = Variables_dict["v.SamplingFreq"] # Sampling Frequency, Hz
Length = Variables_dict["v.Length"] # Pile length, m
Speed = Variables_dict["v.Speed"] # Wave speed
samples_acq = Variables_dict["v.samples_acq"] # N. of Samples acquired: c.n_blocks multiple
AveragesRequired = int(Variables_dict["v.AveragesRequired"]) # Required Averages number
#
FORMAT = aa.PCM_FORMAT_S32_LE # Int 4 Bytes signed
CHANNELS = int(2)
byte_width = 4
bytes_size = CHANNELS * byte_width * c.n_frames # Bytes in each period
""" Create the data_out alsaaudio object instance
In PCM_NORMAL mode, the call will block if the kernel buffer is full, and until enough sound has been
played to allow the sound data to be buffered. The call always returns the size of the data provided.
"""
#data_out = aa.PCM(aa.PCM_PLAYBACK, aa.PCM_NONBLOCK) # aa.PCM_NORMAL aa.PCM_NONBLOCK
data_out = aa.PCM(aa.PCM_PLAYBACK, aa.PCM_NORMAL)
data_out.setchannels(CHANNELS)
data_out.setrate(SampRate)
data_out.setformat(FORMAT)
data_out.setperiodsize(c.n_frames)
#
""" x1x2: vector of interleaved channels, Ch1, Ch2, output signal
"""
#
A = 0.5 * (2**31 - 1) # Amplitude referr. to 32 bit int f.s.
dt = 1.0 / 96000 # Sampling period
T = 1 # Total signal time length
N = int(T / dt) # Number of samples
L_echo_0 = Length # Toe echo
k_A_echo_0 = -1 * 0.1 # Echo_0 reflexion coefficient: 1 => free; -1 => vincolated
T_echo_0 = 2 * Length / Speed # Return time of echo_0
indx_echo_0 = round(T_echo_0 / dt) # Index of the 1.st echo_0 sample
T_effective = hammer_DT # Pulse equivalent length
T_pulse = T_effective * 3/2 # Length at the sine base, valid only for the half sine
N_pulse = round(T_pulse / dt) # Number of pulse samples
t = np.arange(N_pulse) * dt # Time length of the pulse sample
pulse = np.sin(np.pi * t / T_pulse) # Pulse as a positive half sine
pulse_echo_0 = k_A_echo_0 * pulse # Pulse echo_0
x1f = np.zeros(N) # Array of 0's, ch1
x2f = np.zeros(N) # Array of 0's, ch2
x1f[0:N_pulse] = pulse # Put pulse at index 0 for ch1
x2f[0:N_pulse] = pulse # Put pulse at index 0 for ch2
x2f[indx_echo_0:indx_echo_0+N_pulse] = pulse_echo_0 # Put echo_0 delayed pulse on ch2
#
# Insertion of a HP 1.st order filter
fnyquist = 0.5*SampRate
w_d = f_hp / fnyquist # Digital pulsation, 0..1, where 1 => fnyquist
b, a = butter(1, w_d, btype='high') # Digital HP Butterworth filter first order
y2f_hp_f = lfilter(b, a, x2f) # Filtering the signal
#
pk = np.max(x1f) # Normalize amplitude
x1f *= A / pk # Float type, original, ch1
y2f_hp_f *= A / pk # Float type, HP filtered, ch2
x1 = x1f.astype(np.int32) # Convert to int32, ch1
x2 = y2f_hp_f.astype(np.int32) # Convert to int32,
x1x2 = np.zeros(CHANNELS*c.N_of_samples, dtype=np.int32) # Array for interleaved Ch1, Ch2 data
#
x1x2[0::2] = x1 # Fill x1 at even indexes: ch1
x1x2[1::2] = x2 # Fill x1 at odd indexes: ch2
#
out_big_buffer = bytearray(bytes_size * c.n_blocks)
out_big_buffer[:] = pack('%ii' % int(2*c.N_of_samples), *x1x2) # Pack from numpy.int32 to bytes
out_short_buffer = bytearray(bytes_size) # Output array written one frame at time
#
if DEBUG:
tp = np.arange(0, N, 1) * dt
sel = int(1.2 * indx_echo_0)
fig = plt.figure(1)
fig.set_size_inches(w = 15, h = 9)
fig.subplots_adjust(hspace = 0.35)
plt.subplot(311)
plt.plot(tp[0:sel], x1[0:sel])
plt.title('Hammer')
plt.xlabel('Time [s]')
plt.ylabel('Force')
plt.grid(True)
#
plt.subplot(312)
plt.plot(tp[0:sel], x2f[0:sel])
plt.title('Acceleration')
plt.xlabel('Time [s]')
plt.ylabel('Acceleration')
plt.grid(True)
#
plt.subplot(313)
plt.plot(tp[0:sel], y2f_hp_f[0:sel])
plt.title('Acceleration, HP filtered')
plt.xlabel('Time [s]')
plt.ylabel('Acceleration')
plt.grid(True)
#
plt.show()
# #
time.sleep(3)
for n in range(AveragesRequired):
print("Started pulse N°", n + 1)
beg = 0
end = bytes_size
for i in range(c.n_blocks):
out_short_buffer[:] = out_big_buffer[beg:end]
size = data_out.write(out_short_buffer)
#print(size)
beg = end
end += bytes_size
# #
time.sleep(4.5)
# #
data_out.close()
print("put_audio.py terminated")
# ac_freq = globs['magnetom_ac_frequency']
ac_amps = globs['magnetom_rabi_spectrum_amps']
ac_freqs = globs['magnetom_rabi_spectrum_freqs']
fsweep_requested = (globs['magnetom_rabi_sweep_initial'],
globs['magnetom_rabi_sweep_final'])
# Compute actual bounds of sweep
amp_bounds = (bad_amp_calib(fsweep_requested[0]),
bad_amp_calib(fsweep_requested[1]))
# amp_bounds = (amp_calib(fsweep_requested[0], shot_id), amp_calib(fsweep_requested[1], shot_id))
fsweep_actual = (poly_freq_calib(amp_bounds[0]),
poly_freq_calib(amp_bounds[1]))
rabi_range = abs(fsweep_actual[1] - fsweep_actual[0])
# apply butterworth filter (same as rabi_sweep)
filter_b, filter_a = signal.butter(8, 2 * filter_cutoff / lockin_sample_rate)
filter_Q = signal.filtfilt(filter_b, filter_a, lockin_Q)
# Repeat Rabi sweep frequency calibrations for lockin trace:
amp_map = np.vectorize(amp_map)
rf_amps_lockin = amp_map(t_lockin, t0, rabiT, amp_bounds[0], amp_bounds[1])
rabi_freqs_lockin = poly_freq_calib(rf_amps_lockin)
ac_res_time_lockins = [
t_lockin[np.argmin(abs(rabi_freqs_lockin - ac_freq))]
for ac_freq in ac_freqs
]
ac_res_time_lockin = np.mean(ac_res_time_lockins)
ac_res_rabi_freqs = [
rabi_freqs_lockin[np.argmin(abs(rabi_freqs_lockin - ac_freq))]
for ac_freq in ac_freqs
]
camera.resolution = res
camera.framerate = 10
# Initialize the buffer and start capturing
rawCapture = PiYUVArray(camera, size=res)
stream = camera.capture_continuous(rawCapture,
format="yuv",
use_video_port=True)
# Measure the time needed to process 300 images to estimate the FPS
t = time.time()
# To filter the noise in the image we use a 3rd order Butterworth filter
# Wn = 0.02, the cut-off frequency, acceptable values are from 0 to 1
b, a = butter(3, 0.1)
line_pos = CAMERA_CENTER
first_frame = True
# start car
motor.duty_cycle = MOTOR_BRAKE + 120000
track_width = 0
# PID constants for sensitivity
_p = 1.0
i = 0.09
d = 0.08
# array to keep track of pid data
error_history = np.array([], dtype=int)
def butter_bandpass(highcut, fs, order=5):
nyq = 0.5 * fs
high = highcut / nyq
b, a = signal.butter(order, high, 'high', analog=False)
return b, a
from uk import pts, body
# generate 3D trajectory
N = 100
Np = 1
x0 = np.array([[0., 0., 0., 0., 0., 500.]]).T
xs = np.diag(
np.hstack((np.array([1, 1, 1]) * 1., np.array([1, 1, 1]) * 10.)))
x = np.cumsum(np.dot(xs, np.random.randn(6, N)), axis=1) + np.kron(
np.ones((1, N)), x0)
g = 10. * np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]],
dtype=float).T
# smooth trajectory
B, A = sig.butter(1, 0.05)
x = sig.filtfilt(B, A, x)
th = np.random.rand() * 2 * np.pi
om = np.array([[0, 0, 1], [0, 0, 0], [-1, 0, 0]])
R = sp.linalg.expm(th * om)
t = np.dot(R, -x0[3:6, :]) + x0[3:6, :]
A = np.array([[2000., 0, 0], [0, 2000., 0], [0, 0, 1.]])
d = np.array([0., 0., 0., 0.])
cams = [{
'R': np.identity(3),
't': np.zeros((3, 1)),
'A': A,
'd': d
}, {
def main():
xIMUdata = xIMU.xIMUdataClass(filePath, 'InertialMagneticSampleRate',
1 / samplePeriod)
time = xIMUdata.CalInertialAndMagneticData.Time
gyrX = xIMUdata.CalInertialAndMagneticData.gyroscope[:, 0]
gyrY = xIMUdata.CalInertialAndMagneticData.gyroscope[:, 1]
gyrZ = xIMUdata.CalInertialAndMagneticData.gyroscope[:, 2]
accX = xIMUdata.CalInertialAndMagneticData.accelerometer[:, 0]
accY = xIMUdata.CalInertialAndMagneticData.accelerometer[:, 1]
accZ = xIMUdata.CalInertialAndMagneticData.accelerometer[:, 2]
indexSel = np.all([time >= startTime, time <= stopTime], axis=0)
time = time[indexSel]
gyrX = gyrX[indexSel]
gyrY = gyrY[indexSel]
gyrZ = gyrZ[indexSel]
accX = accX[indexSel]
accY = accY[indexSel]
accZ = accZ[indexSel]
# Compute accelerometer magnitude
acc_mag = np.sqrt(accX * accX + accY * accY + accZ * accZ)
# HP filter accelerometer data
filtCutOff = 0.001
b, a = signal.butter(1, (2 * filtCutOff) / (1 / samplePeriod), 'highpass')
acc_magFilt = signal.filtfilt(b,
a,
acc_mag,
padtype='odd',
padlen=3 * (max(len(b), len(a)) - 1))
# Compute absolute value
acc_magFilt = np.abs(acc_magFilt)
# LP filter accelerometer data
filtCutOff = 5
b, a = signal.butter(1, (2 * filtCutOff) / (1 / samplePeriod), 'lowpass')
acc_magFilt = signal.filtfilt(b,
a,
acc_magFilt,
padtype='odd',
padlen=3 * (max(len(b), len(a)) - 1))
# Threshold detection
stationary = acc_magFilt < 0.05
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
ax1.plot(time, gyrX, c='r', linewidth=0.5)
ax1.plot(time, gyrY, c='g', linewidth=0.5)
ax1.plot(time, gyrZ, c='b', linewidth=0.5)
ax1.set_title("gyroscope")
ax1.set_xlabel("time (s)")
ax1.set_ylabel("angular velocity (degrees/s)")
ax1.legend(["x", "y", "z"])
ax2.plot(time, accX, c='r', linewidth=0.5)
ax2.plot(time, accY, c='g', linewidth=0.5)
ax2.plot(time, accZ, c='b', linewidth=0.5)
ax2.plot(time, acc_magFilt, c='k', linestyle=":", linewidth=1)
ax2.plot(time, stationary, c='k')
ax2.set_title("accelerometer")
ax2.set_xlabel("time (s)")
ax2.set_ylabel("acceleration (g)")
ax2.legend(["x", "y", "z"])
plt.show(block=False)
# Compute orientation
quat = np.zeros((time.size, 4), dtype=np.float64)
# initial convergence
initPeriod = 2
indexSel = time <= time[0] + initPeriod
gyr = np.zeros(3, dtype=np.float64)
acc = np.array([
np.mean(accX[indexSel]),
np.mean(accY[indexSel]),
np.mean(accZ[indexSel])
])
mahony = ahrs.filters.Mahony(Kp=1,
Ki=0,
KpInit=1,
frequency=1 / samplePeriod)
q = np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float64)
for i in range(0, 2000):
q = mahony.updateIMU(q, gyr=gyr, acc=acc)
# For all data
for t in range(0, time.size):
if (stationary[t]):
mahony.Kp = 0.5
else:
mahony.Kp = 0
gyr = np.array([gyrX[t], gyrY[t], gyrZ[t]]) * np.pi / 180
acc = np.array([accX[t], accY[t], accZ[t]])
quat[t, :] = mahony.updateIMU(q, gyr=gyr, acc=acc)
# -------------------------------------------------------------------------
# Compute translational accelerations
# Rotate body accelerations to Earth frame
acc = []
for x, y, z, q in zip(accX, accY, accZ, quat):
acc.append(q_rot(q_conj(q), np.array([x, y, z])))
acc = np.array(acc)
acc = acc - np.array([0, 0, 1])
acc = acc * 9.81
# Compute translational velocities
# acc[:,2] = acc[:,2] - 9.81
# acc_offset = np.zeros(3)
vel = np.zeros(acc.shape)
for t in range(1, vel.shape[0]):
vel[t, :] = vel[t - 1, :] + acc[t, :] * samplePeriod
if stationary[t] == True:
vel[t, :] = np.zeros(3)
# Compute integral drift during non-stationary periods
velDrift = np.zeros(vel.shape)
stationaryStart = np.where(np.diff(stationary.astype(int)) == -1)[0] + 1
stationaryEnd = np.where(np.diff(stationary.astype(int)) == 1)[0] + 1
for i in range(0, stationaryEnd.shape[0]):
driftRate = vel[stationaryEnd[i] - 1, :] / (stationaryEnd[i] -
stationaryStart[i])
enum = np.arange(0, stationaryEnd[i] - stationaryStart[i])
drift = np.array(
[enum * driftRate[0], enum * driftRate[1], enum * driftRate[2]]).T
velDrift[stationaryStart[i]:stationaryEnd[i], :] = drift
# Remove integral drift
vel = vel - velDrift
fig = plt.figure(figsize=(10, 5))
plt.plot(time, vel[:, 0], c='r', linewidth=0.5)
plt.plot(time, vel[:, 1], c='g', linewidth=0.5)
plt.plot(time, vel[:, 2], c='b', linewidth=0.5)
plt.legend(["x", "y", "z"])
plt.title("velocity")
plt.xlabel("time (s)")
plt.ylabel("velocity (m/s)")
plt.show(block=False)
# -------------------------------------------------------------------------
# Compute translational position
pos = np.zeros(vel.shape)
for t in range(1, pos.shape[0]):
pos[t, :] = pos[t - 1, :] + vel[t, :] * samplePeriod
fig = plt.figure(figsize=(10, 5))
plt.plot(time, pos[:, 0], c='r', linewidth=0.5)
plt.plot(time, pos[:, 1], c='g', linewidth=0.5)
plt.plot(time, pos[:, 2], c='b', linewidth=0.5)
plt.legend(["x", "y", "z"])
plt.title("position")
plt.xlabel("time (s)")
plt.ylabel("position (m)")
plt.show(block=False)
# -------------------------------------------------------------------------
# Plot 3D foot trajectory
posPlot = pos
quatPlot = quat
extraTime = 20
onesVector = np.ones(int(extraTime * (1 / samplePeriod)))
# Create 6 DOF animation
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, projection='3d') # Axe3D object
ax.plot(posPlot[:, 0], posPlot[:, 1], posPlot[:, 2])
min_, max_ = np.min(np.min(posPlot,
axis=0)), np.max(np.max(posPlot, axis=0))
ax.set_xlim(min_, max_)
ax.set_ylim(min_, max_)
ax.set_zlim(min_, max_)
ax.set_title("trajectory")
ax.set_xlabel("x position (m)")
ax.set_ylabel("y position (m)")
ax.set_zlabel("z position (m)")
plt.show(block=False)
plt.show()
def high_pass_filter(sub_data,cutoff):
nyq = 0.5 * 256
normal_cutoff = cutoff / nyq
b, a = signal.butter(2, normal_cutoff, btype='high', analog=False)
hpf = signal.filtfilt(b, a, sub_data)
return hpf
NewSound = GuassianNoise + array
#write("New-Sound-Added-With-Guassian-Noise.wav", Frequency, NewSound) # Saving it to the file.
b,a = signal.butter(5, 500/(Frequency/2), btype='highpass') # ButterWorth filter 4350
# In[20]:
filteredSignal = signal.lfilter(b,a,NewSound)
plt.plot(filteredSignal) # plotting the signal.
plt.title('Highpass Filter')
plt.xlabel('Frequency(Hz)')
plt.ylabel('Amplitude')
write("New-Filtered-Eagle-Sound.wav", Frequency, filteredSignal)
'''
def butter_lowpass_filter(dat, highcut, fs, order=5):
nyq = 0.5 * fs
high = highcut / nyq
b, a = butter(order, high, output='ba')
y = lfilter(b, a, dat)
return y
"""
start_sec = 100
end_sec = 300
start_fr = start_sec * frames_per_second
end_fr = end_sec * frames_per_second
#%%
#LFP DLC data (lstsq_dlc_norm)
#cutoff freq at 8 Hz (or maybe 10Hz, but use 8Hz for now)
order = 4
sampling_freq = 25
cutoff_freq = 4
normalized_cutoff_freq = 2 * cutoff_freq / sampling_freq
numerator_coeffs, denominator_coeffs = signal.butter(order,
normalized_cutoff_freq)
#lstsq_dlc_norm_aligned_offset_zeroed_filt_test = signal.filtfilt(numerator_coeffs, denominator_coeffs,lstsq_dlc_norm_aligned_offset_zeroed.loc[start_fr:end_fr,:],axis=0)
lstsq_dlc_norm_aligned_offset_zeroed_filt_test = signal.filtfilt(
numerator_coeffs,
denominator_coeffs,
lstsq_dlc_norm_aligned_offset_zeroed,
axis=0)
#lstsq_dlc_norm_aligned_offset_zeroed_filt_test = lstsq_dlc_norm_aligned_offset_zeroed
#z,p,k = butter(order,normalized_cutoff_freq,output='zpk')
#print(p)
#%% check the differences between filtered and unfiltered data
"""
filtered = lstsq_dlc_norm_aligned_offset_zeroed_filt_test
unfiltered = lstsq_dlc_norm_aligned_offset_zeroed.to_numpy()
for row in reader:
if j == 8:
j = 0
t = ((float(row[0]) / 1000) - timeOffest)
if t > 0:
time.append(t)
voltage.append(
(int(row[1]) * 5 / 1024) / (7500 / (37500))
) # equation to convert analog reading into voltage
current.append(((int(row[2])) - 510) * 5 / 1024 / 0.04 -
0.04) # convert analog current into amps
else:
j += 1
# Butterworth signal filtering to smooth out raw arduino readings
b, a = signal.butter(4, 0.01, btype='lowpass')
c, d = signal.butter(8, 0.08, btype='lowpass')
filtCurrent = signal.filtfilt(b, a, data['motorCurrentTotal'], padlen=0)
filtVoltage = signal.filtfilt(b, a, data['motorVoltsTotal'], padlen=0)
filtAccel = signal.filtfilt(c, d, data['accelComp'], padlen=0)
filtAccel2 = signal.filtfilt(c, d, data['accel2comp'], padlen=0)
filtLogCurrent = signal.filtfilt(c, d, current, padlen=0)
filtLogVoltage = signal.filtfilt(c, d, voltage, padlen=0)
# State: 0 for unknown, 1 for takeoff, 2 for hover, 3 for climb, 4 for descend. Change this to enum later
state = 0
peaks = [[0, 0]]
craters = [[0, 0]]
peaksfull = [[0, 0]]
cratersfull = [[0, 0]]
maxPeak = 0
def butter_bandpass(lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = signal.butter(order, [low, high], btype='band')
return b, a
def butter_lowpass(cutoff, fs, order=5):
nyq = 0.5 * fs #The Nyquist frequency is half the sampling rate.
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a