def filtering2(data, sf, kind=1):
data = data - np.mean(data)
b = [0.2,0.2,0.2,0.2,0.2]
a=[1]
if kind == 1:
data = lfilter(b,a,data)
elif kind == 2:
data = filtfilt(b,a,data)
T = 1.0/sf
Fc = 1
c1 = 1.0/(1+np.tan(Fc*3.14*T))
c2 = (1-np.tan(Fc*3.14*T))/(1+np.tan(Fc*3.14*T))
b= [c1, -c1]
a = [1,-c2]
if kind == 1:
data = lfilter(b,a,data)
elif kind == 2:
data = filtfilt(b,a,data)
fh = sf/2.0
mb = 2
b,a = butter(mb,30/fh)
if kind == 1:
data = lfilter(b,a,data)
elif kind == 2:
data = filtfilt(b,a,data)
return data
def iir_noise_filter(name, df, current_col):
#extracting sampling rate from parameter file
parafile = name + '-parameters.txt'
try:
params = read_csv(parafile, delim_whitespace=True, engine='python', names=['0','1','2','3'], index_col=0)
except:
print('Cannot find parameter file')
samplerate = params['2']['Samplerate']
Samplingrates = {"2.5Hz":2.5, "5Hz":5.0, "10Hz":10.0, "15Hz":15.0,
"25Hz":25.0, "30Hz":30.0, "50Hz":50.0, "60Hz":60.0,
"100Hz":100.0, "500Hz":500.0, "1KHz":1000.0, "2KHz":2000.0,
"3.75KHz":3750.0, "7.5KHz":7500.0, "15KHz":1500.0, "30KHz":30000.0}
samplerate = Samplingrates[samplerate]
#constructing IIR notch filters
fs = samplerate # Sample frequency (Hz)
f0 = 60.0 # Frequency to be removed from signal (Hz)
Q = 2.0 # Quality factor
# Design notch filter
b, a = signal.iirnotch(f0, Q, fs)
c, d = signal.iirnotch(f0*2, Q, fs)
e, f = signal.iirnotch(f0*4, Q, fs)
g, h = signal.iirnotch(f0*5, Q, fs)
i, j = signal.iirnotch(f0*6, Q, fs)
#apply filters
yf1 = signal.filtfilt(b,a,df[current_col]) #60Hz filter
yf2 = signal.filtfilt(c,d,yf1) #120Hz filter
yf3 = signal.filtfilt(e,f,yf2) #240Hz filter
yf4 = signal.filtfilt(g,h,yf3) #300Hz filter
yf5 = signal.filtfilt(i,j,yf4) #360Hz filter
return yf5
def reconstruct(pitch,fs,coffs,syllable): gain = coffs[0] coffs[0] = 1; x = np.double(fs)/np.double(pitch) num = np.ceil((pitch*3)/10.0) #300ms thus if syllable != "s": ex_input = np.zeros(num*x) for i in range (0,int(num)): ex_input[(i*x)-1] = 1 # Filtering the signal out = signal.filtfilt([gain],coffs,ex_input) d_num = [1] d_den = [1,-0.9] out = signal.filtfilt(d_num,d_den,out) # De-emphasis out = np.int16(out/np.max(np.abs(out)) * 32767) else: ex_input = np.random.normal(0,1,num) out = signal.filtfilt([gain],coffs,ex_input) out = np.int16(out/np.max(np.abs(out)) * 32767) return out
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 _forward_data(self, data):
params = self.params
try:
mapped = filtfilt(self.__iir_num,
self.__iir_denom,
data,
axis=params.axis,
padtype=params.padtype,
padlen=params.padlen)
except TypeError:
# we have an ancient scipy, do manually
# but is will only support 2d arrays
if params.axis == 0:
data = data.T
if params.axis > 1:
raise ValueError("this version of scipy does not "
"support nd-arrays for filtfilt()")
if not (params['padlen'].is_default and params['padtype'].is_default):
warning("this version of scipy.signal.filtfilt() does not "
"support `padlen` and `padtype` arguments -- ignoring "
"them")
mapped = [filtfilt(self.__iir_num,
self.__iir_denom,
x)
for x in data]
mapped = np.array(mapped)
if params.axis == 0:
mapped = mapped.T
return mapped
def corr_func(drive, response, fsamp, fdrive, good_pts = [], filt = False, band_width = 1):
#gives the correlation over a cycle of drive between drive and response.
#First subtract of mean of signals to avoid correlating dc
drive = drive-np.median(drive)
response = response - np.median(response)
#bandpass filter around drive frequency if desired.
if filt:
b, a = sp.butter(3, [2.*(fdrive-band_width/2.)/fsamp, 2.*(fdrive+band_width/2.)/fsamp ], btype = 'bandpass')
drive = sp.filtfilt(b, a, drive)
response = sp.filtfilt(b, a, response)
#Compute the number of points and drive amplitude to normalize correlation
lentrace = len(drive)
drive_amp = np.sqrt(2)*np.std(drive)
#Throw out bad points if desired
if len(good_pts):
response[-good_pts] = 0.
lentrace = np.sum(good_pts)
corr_full = good_corr(drive, response, fsamp, fdrive)/(lentrace*drive_amp)
return corr_full
def ApplyFilter(self, data_in):
data_out = sp.filtfilt(self.coefb_low, self.coefa_low, data_in)
data_out = sp.filtfilt(self.coefb_high, self.coefa_high, data_out)
return data_out
def test_train_segments():
results = np.zeros((6, 12))
for i in range(6):
dat = io.loadmat('testset%02d.mat' % (i + 1))
eog_v = dat['eog_v_seg'][0]
eog_h = dat['eog_h_seg'][0]
fs = float(dat['fs'][0])
fir_len = 150
stop_limit = 500 / fs
wn = stop_limit / (fs/2)
b = sig.firwin(fir_len, wn)
eog_v_f = sig.filtfilt(b, 1, eog_v)
eog_h_f = sig.filtfilt(b, 1, eog_h)
p = PyGERT()
p.train(eog_h_f, eog_v_f)
results[i, 0] = p.mu_fix
results[i, 1] = p.sigma_fix
results[i, 2] = p.prior_fix
results[i, 3] = p.mu_sac
results[i, 4] = p.sigma_sac
results[i, 5] = p.prior_sac
results[i, 6] = p.mu_bli
results[i, 7] = p.sigma_bli
results[i, 8] = p.prior_bli
results[i, 9] = p.mu_bs
results[i, 10] = p.sigma_bs
results[i, 11] = p.prior_bs
print(results)
np.savetxt('segment_test_python.csv', results, delimiter=',')
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 lowhighpass_filter(data, cutperiod, pass_periods='low'):
"""Butterworth low- or high pass filter.
This function applies a linear filter twice, once forward and once
backwards. The combined filter has linear phase.
Args:
data (array, optional): Data array of shape (time, variables).
cutperiod (int): Period of cutoff.
pass_periods (str, optional): Either 'low' or 'high' to act as a low-
or high-pass filter
Returns:
Filtered data array.
"""
try:
from scipy.signal import butter, filtfilt
except:
print 'Could not import scipy.signal for butterworth filtering!'
fs = 1.
order = 3
ws = 1. / cutperiod / (0.5 * fs)
b, a = butter(order, ws, pass_periods)
if numpy.ndim(data) == 1:
data = filtfilt(b, a, data)
else:
for i in range(data.shape[1]):
data[:, i] = filtfilt(b, a, data[:, i])
return data
def plot_data(dataWindow):
print("Init plotting")
b, a = butter(2, 0.0001, 'high')
#b, a = butter(2, 0.5, 'high')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
line1, = ax1.plot(dataWindow.filt_data)
line2, = ax2.plot(eog2)
fig.show()
print("Start plotting")
#while True:
for t in range(40*12):
#print("Filtering")
eog1_filt = filtfilt(b, a, eog1)
eog2_filt = filtfilt(b, a, eog2)
#print("Plotting")
line1.set_ydata(dataWindow.filt_data)
#ax1.set_ylim((min(eog1_filt), max(eog1_filt)))
ax1.set_ylim(-50000,50000)
line2.set_ydata(eog2_filt)
#ax2.set_ylim((min(eog2_filt), max(eog2_filt)))
ax2.set_ylim(-50000,50000)
fig.canvas.draw()
fig.show()
sleep(.5)
print("Plotting ended")
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 test_sine(self):
rate = 2000
t = np.linspace(0, 1.0, rate + 1)
# A signal with low frequency and a high frequency.
xlow = np.sin(5 * 2 * np.pi * t)
xhigh = np.sin(250 * 2 * np.pi * t)
x = xlow + xhigh
b, a = butter(8, 0.125)
z, p, k = tf2zpk(b, a)
# r is the magnitude of the largest pole.
r = np.abs(p).max()
eps = 1e-5
# n estimates the number of steps for the
# transient to decay by a factor of eps.
n = int(np.ceil(np.log(eps) / np.log(r)))
# High order lowpass filter...
y = filtfilt(b, a, x, padlen=n)
# Result should be just xlow.
err = np.abs(y - xlow).max()
assert_(err < 1e-4)
# A 2D case.
x2d = np.vstack([xlow, xlow + xhigh])
y2d = filtfilt(b, a, x2d, padlen=n, axis=1)
assert_equal(y2d.shape, x2d.shape)
err = np.abs(y2d - xlow).max()
assert_(err < 1e-4)
# Use the previous result to check the use of the axis keyword.
# (Regression test for ticket #1620)
y2dt = filtfilt(b, a, x2d.T, padlen=n, axis=0)
assert_equal(y2d, y2dt.T)
def plot_comparison(f1, f2, data, xs=[-0.5, 7], ys=[-400, 1000], s=0):
'''Compare file 1 (red) with file 2 (blue), taking the mean and std
of the signal'''
d = handler.extract_data(f1)
d2 = handler.extract_data(f2)
# due to the 60Hz noise of the water heater, I have to filter it out with a notch filter
temp = np.array([56.0,64.0])/(d['fs']/2.0)
# 3 is the highest order I can go without it going crazy
b,a = butter(3, temp[0], btype='bandstop')
y = filtfilt(b,a,lowpass_filter((d['stim']*1e6, d['fs']))[0].T).T
y2 = filtfilt(b,a,lowpass_filter((d2['stim']*1e6, d2['fs']))[0].T).T
ystd = np.std(y, axis=1)
ymean = average((y, d['fs']))[0][:,0]
t = d['t']*1e3
y2std = np.std(y2, axis=1)
y2mean = average((y2, d2['fs']))[0][:,0]
t2 = d2['t']*1e3
plt.figure(figsize=(8,6))
plt.fill_between(t, (ymean-ystd), (ymean+ystd), alpha=0.5, facecolor=color_palette[0])
plt.plot(t, ymean, color=color_palette[0])
plt.fill_between(t2, y2mean - y2std, y2mean + y2std, alpha=0.5, facecolor=color_palette[5])
plt.plot(t2, y2mean, color=color_palette[5])
plt.xlim(xs)
plt.ylim(ys)
plt.ylabel('Voltage (µV)')
plt.xlabel('Time (ms)')
plt.title(descriptive_title(f1).replace('<br>', '\n'))
plt.legend([str(handler.get_current(data,f1))+'µA', str(handler.get_current(data,f2))+'µA'])
if s and type(s) == str:
plt.savefig(s + '.pdf')
elif s:
plt.savefig(f1[:-4]+ f2[:-4]+'-comaparison.pdf')
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 test_axis(self):
# Test the 'axis' keyword on a 3D array.
x = np.arange(10.0 * 11.0 * 12.0).reshape(10, 11, 12)
b, a = butter(3, 0.125)
y0 = filtfilt(b, a, x, padlen=0, axis=0)
y1 = filtfilt(b, a, np.swapaxes(x, 0, 1), padlen=0, axis=1)
assert_array_equal(y0, np.swapaxes(y1, 0, 1))
y2 = filtfilt(b, a, np.swapaxes(x, 0, 2), padlen=0, axis=2)
assert_array_equal(y0, np.swapaxes(y2, 0, 2))
def apply_filter(x, filter=None):
b, a = filter
try:
out_arr = signal.filtfilt(b, a, x, axis=0)
except TypeError:
out_arr = np.zeros_like(x)
for i_ch in range(x.shape[1]):
out_arr[:, i_ch] = signal.filtfilt(b, a, x[:, i_ch])
return out_arr
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 generate_peoples_results_files(self):
self.np_result = np.c_[self.results[0]['blue'], self.results[0]['green'], self.results[0]['red']]
list_number = len(self.results[0]['blue'])
# ICA
ica = FastICA(n_components=3, fun='logcosh', max_iter=2000)
ica_transformed = ica.fit_transform(self.np_result)
component_all = ica_transformed.ravel([1])
component_1 = component_all[:list_number]
component_2 = component_all[list_number:(2 * list_number)]
component_3 = component_all[(2 * list_number):(3 * list_number)]
# butter_smooth
N = 8
Wn = [1.6 / 30, 4.0 / 30]
t = np.linspace(1 / 30, list_number / 30, list_number)
b, a = signal.butter(N, Wn, 'bandpass', analog=False)
filter_1 = signal.filtfilt(b, a, component_1)
filter_2 = signal.filtfilt(b, a, component_2)
filter_3 = signal.filtfilt(b, a, component_3)
lowess_1 = sm.nonparametric.lowess(filter_1, t, frac=10.0 / list_number)
lowess_2 = sm.nonparametric.lowess(filter_2, t, frac=10.0 / list_number)
lowess_3 = sm.nonparametric.lowess(filter_3, t, frac=10.0 / list_number)
smooths = []
smooth_1 = lowess_1[:, 1]
smooth_2 = lowess_2[:, 1]
smooth_3 = lowess_3[:, 1]
smooths.append(smooth_1)
smooths.append(smooth_2)
smooths.append(smooth_3)
# FFT and spectrum
fft_1 = np.fft.fft(smooth_1, 256)
fft_2 = np.fft.fft(smooth_2, 256)
fft_3 = np.fft.fft(smooth_3, 256)
spectrum_1 = list(np.abs(fft_1) ** 2)
spectrum_2 = list(np.abs(fft_2) ** 2)
spectrum_3 = list(np.abs(fft_3) ** 2)
max1 = max(spectrum_1)
max2 = max(spectrum_2)
max3 = max(spectrum_3)
num_spec1 = spectrum_1.index(max(spectrum_1))
if num_spec1 > (list_number / 2):
num_spec1 = 256 - num_spec1
num_spec2 = spectrum_2.index(max(spectrum_2))
if num_spec2 > (list_number / 2):
num_spec2 = 256 - num_spec2
num_spec3 = spectrum_3.index(max(spectrum_3))
if num_spec3 > (list_number / 2):
num_spec3 = 256 - num_spec3
num_spec = [num_spec1, num_spec2, num_spec3]
max_all = [max1, max2, max3]
max_num = max_all.index(max(max_all))
self.heartRate = int(num_spec[max_num] * 1800 / 256) + 1
return smooths[max_num]
def sf_anal(infile, chunk_rate=80.0, n_steps=12, base_freq=440.0, min_level=0.001, cutoff=None):
min_sq_level = min_level**2
if cutoff is None: cutoff = chunk_rate
sr, wav = load_non_wav(infile)
chunk_size = int(round(float(sr) / chunk_rate))
wav = high_passed(sr, wav)
wav2 = wav * wav
freqs = 2**(np.linspace(0.0, n_steps, num=n_steps, endpoint=False)/n_steps) * base_freq
amp2 = wav2
rel_cutoff = 2.0/(float(sr)/cutoff) # relative to nyquist freq, not samplerate
b, a = RC(Wn=rel_cutoff)
for i in xrange(4):
amp2 = filtfilt(b, a, amp2)
mask = amp2>min_sq_level
little_amp2 = decimate(
amp2, chunk_size, ftype='fir'
)
little_mask = mask[np.arange(0,little_amp2.size,1)*chunk_size]
little_corrs = []
for freq in freqs:
# For now, offset is rounded to the nearest sample
offset = int(round(float(sr)/freq))
cov = np.zeros_like(wav)
cov[:-offset] = wav[offset:]*wav[:-offset]
# repeatedly filter; this is effectively an 8th-order lowpass now
smooth_cov = cov
for i in xrange(4):
smooth_cov = filtfilt(b, a, smooth_cov)
# technically the correlation should be taken wrt the harmonic mean of the variances at
# the two times, but we assume autocorrelation lag << smooth time
little_corrs.append(
decimate(
mask * smooth_cov/np.maximum(amp2, min_sq_level),
chunk_size,
ftype='fir' #FIR is needed to be stable at haptic rates
)
)
all_corrs = np.vstack(little_corrs)
sample_times = (np.arange(0,little_amp2.size,1)*chunk_size).astype(np.float)/sr
#trim "too quiet" stuff
all_corrs = all_corrs[:,np.where(little_mask)[0]]
sample_times = sample_times[np.where(little_mask)[0]]
little_amp2 = little_amp2[np.where(little_mask)[0]]
return dict(
all_corrs=all_corrs,
sample_times=sample_times,
amp=np.sqrt(little_amp2), #RMS amp is more usual
)
def filter_abr(self, x, duration = 0.00999424, nsamples = 244, Wn = 0.02, btype = 'high'): b, a = butter(10, Wn = Wn, btype = btype) if len(x.shape)==1: x_filt = filtfilt(b, a, x) elif len(x.shape)>1: x_filt = np.empty_like(x) for i in range(x.shape[1]): x_filt[:, i] = filtfilt(b, a, x[:, i]) return x_filt
def scatter(filename, thrs, nchannels=2, chnl=5):
b, a = iirfilter(1, (0.002, 0.05))
with trace_gen(filename, nchannels, chnl) as gen:
data = [(sum(event[thrs:thrs + 180]),
min(filtfilt(b, a, event)[thrs:thrs + 180]) - 200)
for event in gen if max(filtfilt(b, a, event)[:20]) < 400]
int_data = zip(*data)[0]
min_data = zip(*data)[1]
return int_data, min_data
def noise_filters(self,data): [b1, a1] = [self.high_pass_coefficients[0],self.high_pass_coefficients[1]] [b, a] = [self.low_pass_coefficients[0],self.low_pass_coefficients[1]] [bn, an] = [self.notch_coefficients[0],self.notch_coefficients[1]] for i,channel in enumerate(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 self.filtered_eeg[i] = y; return self.filtered_eeg
def filter(self):
"""
Filters this time sequence.
"""
eog_filt1 = np.zeros(len(self.vector))
b1, a1 = butter(1, 0.0003, 'lowpass')
eog_filt1 = filtfilt(b1, a1, self.vector)
matrix2 = (self.getVector() - eog_filt1)
b2, a2 = butter(1, 0.05, 'lowpass')
eogfilt2 = filtfilt(b2, a2, matrix2)
self.setVector(eogfilt2)
def profile(fname, ends = 100, stage_cal = 8.):
dat, attribs, f = bu.getdata(fname)
dat = dat[ends:-ends, :]
dat[:, stage_column]*=stage_cal
f.close()
b, a = sig.butter(3, 0.25)
int_filt = sig.filtfilt(b, a, dat[:, data_column])
proft = np.gradient(int_filt)
stage_filt = sig.filtfilt(b, a, dat[:, stage_column])
dir_sign = np.sign(np.gradient(stage_filt))
b, y, e = spatial_bin(dat[dir_sign<0, stage_column], proft[dir_sign<0])
return b, y, e
def Filter(data_in, f1, f2, sample_rate, filter_order):
nyq_rate = sample_rate / 2
w1, w2 = f1 / nyq_rate, f2 / nyq_rate
b, a = sp.butter(filter_order, [f2 / nyq_rate], btype='low')
data_out = sp.filtfilt(b, a, data_in)
b, a = sp.butter(filter_order, [f1 / nyq_rate], btype='high')
data_out = sp.filtfilt(b, a, data_out)
return data_out
def bandpass_filter(x, s_f, hi=400, lo=7000):
"""
:param x: one d numpy arrays
:param s_f: sampling frequency (in hz)
:param hi: hi-pass cutoff
:param lo: lo-pass cutoff
:return:
"""
hp_b, hp_a = sg.butter(4, hi / (s_f / 2.), btype='high')
lp_b, lp_a = sg.butter(4, lo / (s_f / 2.), btype='low')
x_hi = sg.filtfilt(hp_b, hp_a, x, axis=0)
x_lo = sg.filtfilt(lp_b, lp_a, x_hi, axis=0)
return x_lo
def motion_detected(self, data):
filtCutOff = 0.001;
[b, a] = butter(1, (2*filtCutOff*1.0)/(self.sr), btype='highpass');
acc_magFilt = filtfilt(b, a, data);
#acc_magFilt = lfilter(b, a, lfilter(b,a,data));
acc_magFilt = [abs(x) for x in acc_magFilt]
filtCutOff = 0.3;
[b, a] = butter(1, (2*filtCutOff)*1.0/(self.sr), btype='lowpass');
filtered = filtfilt(b, a, acc_magFilt);
#filtered = lfilter(b, a, lfilter(b,a,acc_magFilt));
stan = filtered < 0.05
return stan, filtered
def __amp_detect(self, x):
ref = np.floor(self.min_ref_per*self.sr/1000.0)
# HIGH-PASS FILTER OF THE DATA
(b,a) = signal.ellip(2, 0.1, 40, [self.fmin_detect*2.0/self.sr,self.fmax_detect*2.0/self.sr], btype='bandpass', analog=0, output='ba')
xf_detect = signal.filtfilt(b, a, x)
(b,a) = signal.ellip(2, 0.1, 40, [self.fmin_sort*2.0/self.sr,self.fmax_sort*2.0/self.sr], btype='bandpass', analog=0, output='ba')
xf = signal.filtfilt(b, a, x)
noise_std_detect = scipy.median(np.abs(xf_detect))/0.6745;
noise_std_sorted = scipy.median(np.abs(xf))/0.6745;
thr = self.stdmin * noise_std_detect #thr for detection is based on detected settings.
thrmax = self.stdmax * noise_std_sorted #thrmax for artifact removal is based on sorted settings.
# LOCATE SPIKE TIMES
nspk = 0;
xaux = np.argwhere(xf_detect[self.w_pre+1:len(xf_detect)-self.w_post-1-1] > thr) + self.w_pre + 1
xaux = np.resize(xaux,len(xaux))
xaux0 = 0;
index = []
for i in range(len(xaux)):
if xaux[i] >= (xaux0 + ref):
# after find a peak it begin search after ref over the last xaux
iaux = xf[xaux[i]:xaux[i]+np.floor(ref/2.0)].argmax(0) # introduces alignment
nspk = nspk + 1
index.append(iaux + xaux[i])
xaux0 = index[nspk-1];
# SPIKE STORING (with or without interpolation)
ls = self.w_pre + self.w_post
spikes = np.zeros([nspk,ls+4])
xf = np.concatenate((xf,np.zeros(self.w_post)),axis=0)
for i in range(nspk): # Eliminates artifacts
if np.max( np.abs( xf[index[i]-self.w_pre:index[i]+self.w_post] )) < thrmax :
spikes[i,:] = xf[index[i]-self.w_pre-1:index[i]+self.w_post+3]
aux = np.argwhere(spikes[:,self.w_pre] == 0) #erases indexes that were artifacts
if len(aux) != 0:
aux = aux.reshape((1,len(aux)))[0]
spikes = np.delete(spikes, aux, axis = 0)
index = np.delete(index,aux)
if self.interpolation == 'y':
# Does interpolation
spikes = self.__int_spikes(spikes)
return spikes, thr, index
def decimate(x, q=10, n=4, k=0.8, filterfun=ss.cheby1):
"""
scipy.signal.decimate like downsampling using filtfilt instead of lfilter,
and filter coeffs from butterworth or chebyshev type 1.
Parameters
----------
x : numpy.ndarray
Array to be downsampled along last axis.
q : int
Downsampling factor.
n : int
Filter order.
k : float
Aliasing filter critical frequency Wn will be set as Wn=k/q.
filterfun : function
`scipy.signal.filter_design.cheby1` or
`scipy.signal.filter_design.butter` function
Returns
-------
numpy.ndarray
Array of downsampled signal.
"""
if not isinstance(q, int):
raise TypeError("q must be an integer")
if n is None:
n = 1
if filterfun == ss.butter:
b, a = filterfun(n, k / q)
elif filterfun == ss.cheby1:
b, a = filterfun(n, 0.05, k / q)
else:
raise Exception('only ss.butter or ss.cheby1 supported')
try:
y = ss.filtfilt(b, a, x)
except: # Multidim array can only be processed at once for scipy >= 0.9.0
y = []
for data in x:
y.append(ss.filtfilt(b, a, data))
y = np.array(y)
try:
return y[:, ::q]
except:
return y[::q]
def bandpass_filter(self, data, cutoffs, fs, order):
low, high = cutoffs[0] / (fs / 2), cutoffs[1] / (fs / 2)
b, a = butter(order, [low, high], btype='band', output='ba')
y = filtfilt(b, a, data)
return np.asarray(y, dtype=np.int16)
def bandpass_filter(sample, band, sample_rate, filter_order):
nyq = sample_rate * 0.5
_band = [(f / nyq) for f in band]
b, a = signal.butter(filter_order, _band, btype='band')
return signal.filtfilt(b, a, sample)
def lowpass_filter(sample, cutoff, sample_rate, filter_order):
nyq = sample_rate * 0.5
_cutoff = cutoff / nyq
b, a = signal.butter(filter_order, _cutoff, btype='lowpass')
y = signal.filtfilt(b, a, sample)
return y
def filter_file(data_file_in, data_file_out, do_filtering,
do_remove_median):
try:
cut_off = params.getfloat('filtering', 'cut_off')
cut_off = [cut_off, 0.95 * (params.rate / 2.)]
except Exception:
cut_off = params.get('filtering', 'cut_off')
cut_off = cut_off.split(',')
try:
cut_off[0] = float(cut_off[0])
except Exception:
if comm.rank == 0:
print_and_log(
['First value of cut off must be a valid number'],
'error', logger)
sys.exit(1)
cut_off[1] = cut_off[1].replace(' ', '')
if cut_off[1] == 'auto':
cut_off[1] = 0.95 * (params.rate / 2.)
else:
try:
cut_off[1] = float(cut_off[1])
except Exception:
if comm.rank == 0:
print_and_log([
'Second value of cut off must either auto, or a valid a number'
], 'error', logger)
sys.exit(1)
chunk_size = params.getint('data', 'chunk_size')
nb_chunks, _ = data_file_in.analyze(chunk_size)
b, a = signal.butter(3, np.array(cut_off) / (params.rate / 2.), 'pass')
all_chunks = numpy.arange(nb_chunks, dtype=numpy.int64)
to_process = all_chunks[comm.rank::comm.size]
loc_nb_chunks = len(to_process)
N_total = params.nb_channels
if comm.rank == 0:
if do_filtering:
to_write = [
"Filtering the signal with a Butterworth filter in (%g, %g) Hz"
% (cut_off[0], cut_off[1])
]
if do_remove_median:
to_write += [
"Median over all channels is substracted to each channels"
]
print_and_log(to_write, 'default', logger)
pbar = get_progressbar(loc_nb_chunks)
for count, gidx in enumerate(to_process):
local_chunk, t_offset = data_file_in.get_data(gidx, chunk_size)
if do_filtering:
for i in nodes:
local_chunk[:,
i] = signal.filtfilt(b, a, local_chunk[:, i])
local_chunk[:, i] -= numpy.median(local_chunk[:, i])
if do_remove_median:
if not numpy.all(nodes == numpy.arange(N_total)):
global_median = numpy.median(
numpy.take(local_chunk, nodes, axis=1), 1)
else:
global_median = numpy.median(local_chunk, 1)
for i in nodes:
local_chunk[:, i] -= global_median
if data_file_in != data_file_out and data_file_in:
if data_file_in.is_stream:
t_offset -= data_file_in._times[
data_file_in._get_streams_index_by_time(t_offset)]
else:
t_offset -= data_file_in.t_start
data_file_out.set_data(t_offset, local_chunk)
if comm.rank == 0:
pbar.update(count)
if comm.rank == 0:
pbar.finish()
comm.Barrier()
### Read wav data to samples ###
fs, wav_data_BCM = wavfile.read(wavFullPathBCM)
wav_data_BCM = wav_data_BCM/32767
#wav_data_BCM = wav_data_BCM[0:20000]
fs, wav_data_REF = wavfile.read(wavFullPathREF)
wav_data_REF = wav_data_REF/32767
#wav_data_REF = wav_data_REF[0:20000]
### Filters audio data ###
low_cutoff = 60
high_cutoff = 6000
wn = [low_cutoff/(fs/2), high_cutoff/(fs/2)]
b, a = dsp.butter(4, wn, 'band')
wav_data_BCM = dsp.filtfilt(b,a,wav_data_BCM)
### Resamples audio to 12 kHz ###
wav_data_BCM = dsp.resample(wav_data_BCM,int(wav_data_BCM.shape[0]/4))
#plt.plot(wav_data_12kHz)
wav_data_BCM = utils.adjustSNR(wav_data_BCM,60)
low_cutoff = 20
high_cutoff = 6000
wn = [low_cutoff/(fs/2), high_cutoff/(fs/2)]
b, a = dsp.butter(4, wn, 'band')
wav_data_REF = dsp.filtfilt(b,a,wav_data_REF)
### Resamples audio to 12 kHz ###
wav_data_REF = dsp.resample(wav_data_REF,int(wav_data_REF.shape[0]/4))
#plt.plot(wav_data_12kHz)
wav_data_REF = utils.adjustSNR(wav_data_REF,60)
sig = np.zeros(nsamps)
n = (map(float, range(0, (nsamps))))
text_file = open("comb1.txt", "a")
# Synthesize bandlimited impulse train
limits = [24, 48]
for limit in limits:
for j in xrange(1, limit + 1):
for i in xrange(1, int(j + 1)):
harm = np.cos(map(lambda x: x * w0T * float(i), n))
sig = sig + harm
print(i)
sig = sig / np.max(sig)
speech = signal.filtfilt([1], A, sig)
# plt.plot(n[0:256], sig[0:256])
out = speech[4864:5121]
#plt.plot(out[0:257])
out = np.add(out, abs(np.min(out)))
out = np.multiply(out, 1 / (np.max(out)))
out = np.multiply(out, -1)
out = out + 1
out = np.multiply(out, 32767)
plt.plot(out[0:257])
out = np.int0(out)
# symmetric
text_file.write('static const uint16_t ')
text_file.write(name)
text_file.write(str(limit))
text_file.write('Atk')
# - f['SUMMED_OUTPUT'].value['isyn_e']
# - f['SUMMED_OUTPUT'].value['isyn_i']
] + [
f['SUMMED_OUTPUT'].value[name]
for name in f['SUMMED_OUTPUT'].dtype.names
],
[ # 'sum',
# r'$i_\mathrm{pas}$', r'$i_\mathrm{cap}$',
# r'$i_\mathrm{syn, E}$', r'$i_\mathrm{syn, I}$',
# r'$i_\mathrm{syn, E}+i_\mathrm{syn, I}$', 'residual'
] + [name for name in f['SUMMED_OUTPUT'].dtype.names],
[ # 'k',
# 'r', 'b', 'c', 'm', 'g', 'y'
] + ['k'] +
[colors[i] for i in range(PSET.populationParameters.size)]):
ax.semilogx(ss.filtfilt(b, a, data, axis=-1)[:, tind:].var(axis=1),
y,
lw=2,
label=label,
color=color)
f.close()
ax.set_yticks(y)
ax.set_yticklabels(yticklabels)
ax.axis(ax.axis('tight'))
ax.set_xlabel(r'variance (mV$^2$)')
fig.savefig(os.path.join(PSET.OUTPUTPATH,
'example_parallel_network_variance.pdf'),
bbox_inches='tight')
plt.close(fig)
def butter_highpass_filter(data, cutoff, fs, order=5):
b, a = butter_highpass(cutoff, fs, order=order)
y = signal.filtfilt(b, a, data)
return y
def process_file(audio_file):
#Read in the file upload
#FOR EXAMPLE: Folder: Bone Transducer, fileName = BoneTransducer_T02_2
#don't include .wav for obvious reasons
fileName = 'brp_h_t06'
sampFreq, snd = wav.read(fileName + '.wav')
# PREPROCESSING DATA
# ZERO-MEAN
snd = (snd - snd.mean()) / snd.std()
#snd = snd[50000:1450000] #used for dropouts
# 60 HZ INTERFERENCE NOTCH FILTER
f0 = 60.0 # Frequency to be removed from signal (Hz)
Q = 40.0 # Quality factor
w0 = f0 / (sampFreq / 2) # Normalized Frequency
# Design notch filter
b, a = signal.iirnotch(w0, Q)
snd = signal.filtfilt(b, a, snd)
# INITIALIZE
fig = plt.figure()
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,
ncols=2,
figsize=(15, 10))
file_len = len(snd)
# PLOT AUDIO WAVEFORM
timeArray = np.arange(0, len(snd), 1)
timeArray = timeArray / sampFreq
print("Length of file: " + str(len(snd) / sampFreq) + "s")
#snd_norm = np.amax(snd)
#ax1.plot(timeArray, snd/snd_norm, color='k')
ax1.plot(timeArray, snd, color='k')
ax1.set_title('Audio Waveform')
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Amplitude')
# CALCULATE FFT
dft = fft(snd[:490000]) # calculate fourier transform
N = len(dft) # length of discrete fourier transform
freqs = [i * sampFreq / N
for i in range(N)] # convert from dft frequencies to Hz
#dft_norm = np.amax(np.abs(dft))
#ax2.plot(freqs, np.abs(dft)/dft_norm, color='k') # change the indices to zoom in/out in frequency
ax2.plot(freqs, np.abs(dft),
color='k') # change the indices to zoom in/out in frequency
ax2.set_title('Freq Analysis')
ax2.set_xlabel('Frequencies (Hz)')
ax2.set_ylabel('DFT Coefficients')
ax2.set_xlim([0, 1000])
# CALCULATE SPECTROGRAM
Pxx, freqs, bins, im = ax3.specgram(snd, NFFT=1024, Fs=sampFreq)
ax3.set_title('Spectrogram')
ax3.set_xlabel('Time')
ax3.set_ylabel('Frequency')
ax3.set_ylim([0, 1000])
# CALCULATE PSD (based on Welch)
f, Pxx_den = signal.welch(snd[:file_len], sampFreq,
nperseg=1024) #PLACE INDEX HERE
#Pxx_den_norm = np.amax(Pxx_den)
#ax4.plot(f, Pxx_den/Pxx_den_norm, color='k')
ax4.plot(f, Pxx_den, color='k')
ax4.set_title('PSD (Welch)')
ax4.set_xlabel('Frequency [Hz]')
ax4.set_ylabel('PSD [V^2/Hz]')
ax4.set_xlim([0, 1000])
# POST-CALCULATION AESTHETIC
plt.suptitle('Results for ' + fileName, fontsize=20)
plt.tight_layout(rect=[0, 0.03, 1, 0.92])
figure_name = fileName + '.png'
fig.savefig(figure_name, bbox_inches='tight')
print("Saved!")
return figure_name
def filter_signal(self,
device_type="accelerometer",
filter_type="bandpass",
low_f=1,
high_f=10,
filter_order=1):
"""Filtering details: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.filtfilt.html
Arguments:
-device_type: "accelerometer" or "ECG"
-filter_type: filter type - "bandpass", "lowpass", or "highpass"
-low_f: low-end cutoff frequency, required for lowpass and bandpass filters
-high_f: high-end cutoff frequency, required for highpass and bandpass filters
-filter_order: integet for filter order
Adds columns to dataframe corresponding to each device. Filters all devices that are available.
"""
if device_type == "accelerometer":
self.accel_filter_low_f = low_f
self.accel_filter_low_h = high_f
for data_type in ["hip"]:
# DATA SET UP
if data_type == "hip" and self.hip_fname is not None:
data = np.array(
[self.df_hip["X"], self.df_hip["Y"], self.df_hip["Z"]])
original_df = self.df_hip
fs = self.hip_samplerate * .5
# FILTERING TYPES
if filter_type == "lowpass":
print("\nFiltering {} accelerometer data with {}Hz, "
"order {} lowpass filter.".format(
data_type, low_f, filter_order))
low = low_f / fs
b, a = butter(N=filter_order, Wn=low, btype="lowpass")
filtered_data = filtfilt(b, a, x=data)
self.filter_details = {
"Order": filter_order,
"Type": filter_type,
"F crit": [low_f]
}
if filter_type == "highpass":
print("\nFiltering {} accelerometer data with {}Hz, "
"order {} highpass filter.".format(
data_type, high_f, filter_order))
high = high_f / fs
b, a = butter(N=filter_order, Wn=high, btype="highpass")
filtered_data = filtfilt(b, a, x=data)
self.filter_details = {
"Order": filter_order,
"Type": filter_type,
"F crit": [high_f]
}
if filter_type == "bandpass":
print("\nFiltering {} accelerometer data with {}-{}Hz, "
"order {} bandpass filter.".format(
data_type, low_f, high_f, filter_order))
low = low_f / fs
high = high_f / fs
b, a = butter(N=filter_order,
Wn=[low, high],
btype="bandpass")
filtered_data = filtfilt(b, a, x=data)
self.filter_details = {
"Order": filter_order,
"Type": filter_type,
"F crit": [low_f, high_f]
}
original_df["X_filt"] = filtered_data[0]
original_df["Y_filt"] = filtered_data[1]
original_df["Z_filt"] = filtered_data[2]
print("\nFiltering complete.")
def __init__(
self,
inputfreq=40,
system="NTSC",
tape_format="VHS",
dod_threshold_p=vhs_formats.DEFAULT_THRESHOLD_P_DDD,
dod_threshold_a=None,
dod_hysteresis=vhs_formats.DEFAULT_HYSTERESIS,
track_phase=None,
):
# First init the rf decoder normally.
super(VHSRFDecode, self).__init__(
inputfreq, system, decode_analog_audio=False, has_analog_audio=False
)
self.dod_threshold_p = dod_threshold_p
self.dod_threshold_a = dod_threshold_a
self.dod_hysteresis = dod_hysteresis
if track_phase is None:
self.track_phase = 0
self.detect_track = True
self.needs_detect = True
elif track_phase == 0 or track_phase == 1:
self.track_phase = track_phase
self.detect_track = False
self.needs_detect = False
else:
raise Exception("Track phase can only be 0, 1 or None")
self.hsync_tolerance = 0.8
self.field_number = 0
self.last_raw_loc = None
# Then we override the laserdisc parameters with VHS ones.
if system == "PAL":
# Give the decoder it's separate own full copy to be on the safe side.
self.SysParams = copy.deepcopy(vhs_formats.SysParams_PAL_VHS)
self.DecoderParams = copy.deepcopy(vhs_formats.RFParams_PAL_VHS)
elif system == "NTSC":
if tape_format == "UMATIC":
self.SysParams = copy.deepcopy(vhs_formats.SysParams_NTSC_UMATIC)
self.DecoderParams = copy.deepcopy(vhs_formats.RFParams_NTSC_UMATIC)
else:
self.SysParams = copy.deepcopy(vhs_formats.SysParams_NTSC_VHS)
self.DecoderParams = copy.deepcopy(vhs_formats.RFParams_NTSC_VHS)
else:
raise Exception("Unknown video system! ", system)
# Lastly we re-create the filters with the new parameters.
self.computevideofilters()
cc = self.DecoderParams["color_under_carrier"] / 1000000
DP = self.DecoderParams
self.Filters["RFVideoRaw"] = lddu.filtfft(
sps.butter(
DP["video_bpf_order"],
[
DP["video_bpf_low"] / self.freq_hz_half,
DP["video_bpf_high"] / self.freq_hz_half,
],
btype="bandpass",
),
self.blocklen,
)
self.Filters["EnvLowPass"] = sps.butter(
8, [1.0 / self.freq_half], btype="lowpass"
)
# More advanced rf filter - only used for NTSC for now.
if system == "NTSC":
y_fm = sps.butter(
DP["video_bpf_order"],
[
DP["video_bpf_low"] / self.freq_hz_half,
DP["video_bpf_high"] / self.freq_hz_half,
],
btype="bandpass",
)
y_fm = lddu.filtfft(y_fm, self.blocklen)
y_fm_lowpass = lddu.filtfft(
sps.butter(
DP['video_lpf_extra_order'], [DP["video_lpf_extra"] / self.freq_hz_half], btype="lowpass"
),
self.blocklen,
)
y_fm_chroma_trap = lddu.filtfft(
sps.butter(
1,
[(cc * 0.9) / self.freq_half, (cc * 1.1) / self.freq_half],
btype="bandstop",
),
self.blocklen,
)
y_fm_filter = (
y_fm * y_fm_lowpass * y_fm_chroma_trap * self.Filters["hilbert"]
)
self.Filters["RFVideo"] = y_fm_filter
else:
y_fm_lowpass = lddu.filtfft(
sps.butter(
DP['video_lpf_extra_order'], [DP["video_lpf_extra"] / self.freq_hz_half], btype="lowpass"
),
self.blocklen,
)
y_fm_highpass = lddu.filtfft(
sps.butter(
DP['video_hpf_extra_order'], [DP["video_hpf_extra"] / self.freq_hz_half], btype="highpass"
),
self.blocklen,
)
self.Filters["RFVideo"] = self.Filters["RFVideo"] * y_fm_lowpass * y_fm_highpass
# Video (luma) de-emphasis
# Not sure about the math of this but, by using a high-shelf filter and then
# swapping b and a we get a low-shelf filter that goes from 0 to -14 dB rather
# than from 14 to 0 which the high shelf function gives.
da, db = gen_high_shelf(
DP["deemph_corner"] / 1.0e6, DP["deemph_gain"], 1 / 2, inputfreq
)
w, h = sps.freqz(db, da)
self.Filters["Fdeemp"] = lddu.filtfft((db, da), self.blocklen)
self.Filters["FVideo"] = self.Filters["Fvideo_lpf"] * self.Filters["Fdeemp"]
SF = self.Filters
SF["FVideo05"] = SF["Fvideo_lpf"] * SF["Fdeemp"] * SF["F05"]
# Filter to pick out color-under chroma component.
# filter at about twice the carrier. (This seems to be similar to what VCRs do)
chroma_lowpass = sps.butter(
4, [0.05 / self.freq_half, 1.4 / self.freq_half], btype="bandpass"
) # sps.butter(4, [1.2/self.freq_half], btype='lowpass')
self.Filters["FVideoBurst"] = chroma_lowpass
# The following filters are for post-TBC:
# The output sample rate is at approx 4fsc
fsc_mhz = self.SysParams["fsc_mhz"]
out_sample_rate_mhz = fsc_mhz * 4
out_frequency_half = out_sample_rate_mhz / 2
het_freq = fsc_mhz + cc
fieldlen = self.SysParams["outlinelen"] * max(self.SysParams["field_lines"])
# Final band-pass filter for chroma output.
# Mostly to filter out the higher-frequency wave that results from signal mixing.
# Needs tweaking.
chroma_bandpass_final = sps.butter(
2,
[
(fsc_mhz - 0.64) / out_frequency_half,
(fsc_mhz + 0.24) / out_frequency_half,
],
btype="bandpass",
)
self.Filters["FChromaFinal"] = chroma_bandpass_final
chroma_burst_check = sps.butter(
2,
[
(fsc_mhz - 0.14) / out_frequency_half,
(fsc_mhz + 0.04) / out_frequency_half,
],
btype="bandpass",
)
self.Filters["FChromaBurstCheck"] = chroma_burst_check
# Bandpass filter to select heterodyne frequency from the mixed fsc and color carrier signal
het_filter = sps.butter(
2,
[
(het_freq - 0.001) / out_frequency_half,
(het_freq + 0.001) / out_frequency_half,
],
btype="bandpass",
)
samples = np.arange(fieldlen)
# As this is done on the tbced signal, we need the sampling frequency of that,
# which is 4fsc for NTSC and approx. 4 fsc for PAL.
# TODO: Correct frequency for pal?
cc_wave_scale = cc / out_sample_rate_mhz
self.cc_ratio = cc_wave_scale
# 0 phase downconverted color under carrier wave
self.cc_wave = np.sin(2 * np.pi * cc_wave_scale * samples)
# +90 deg and so on phase wave for track2 phase rotation
cc_wave_90 = np.sin((2 * np.pi * cc_wave_scale * samples) + (np.pi / 2)) #
cc_wave_180 = np.sin((2 * np.pi * cc_wave_scale * samples) + np.pi)
cc_wave_270 = np.sin(
(2 * np.pi * cc_wave_scale * samples) + np.pi + (np.pi / 2)
)
# Standard frequency color carrier wave.
self.fsc_wave = utils.gen_wave_at_frequency(
fsc_mhz, out_sample_rate_mhz, fieldlen
)
self.fsc_cos_wave = utils.gen_wave_at_frequency(
fsc_mhz, out_sample_rate_mhz, fieldlen, np.cos
)
# Heterodyne wave
# We combine the color carrier with a wave with a frequency of the
# subcarrier + the downconverted chroma carrier to get the original
# color wave back.
self.chroma_heterodyne = {}
self.chroma_heterodyne[0] = sps.filtfilt(
het_filter[0], het_filter[1], self.cc_wave * self.fsc_wave
)
self.chroma_heterodyne[1] = sps.filtfilt(
het_filter[0], het_filter[1], cc_wave_90 * self.fsc_wave
)
self.chroma_heterodyne[2] = sps.filtfilt(
het_filter[0], het_filter[1], cc_wave_180 * self.fsc_wave
)
self.chroma_heterodyne[3] = sps.filtfilt(
het_filter[0], het_filter[1], cc_wave_270 * self.fsc_wave
)
def filter_simple(data, filter_coeffs):
fb, fa = filter_coeffs
return sps.filtfilt(fb, fa, data, padlen=150)
def log_envelope(x, fs, filt_len=100):
log_env = 10 * np.log10(10.**-4.5 + np.power(x.flatten()[:], 2.0))
w_n = np.hanning(filt_len)
w_n /= w_n.sum()
return sig.filtfilt(w_n, np.ones(1), log_env)
def butter_lowpass_filter(data,cutoff,fs,order):
normal_cutoff = cutoff / nyq
# Get the filter coefficients
b, a = butter(order, normal_cutoff, btype='low', analog=False)
y = filtfilt(b, a, data,axis=0)
return y
import soundfile as sf
from scipy import signal
import numpy as np
#read .wav file
input_signal,fs = sf.read('../data/Sound_Noise.wav')
#sampling frequency of input signal
sampl_freq = fs
#order of the filter
order = 4
#cutoff freq 4kHz
cutoff_freq = 4000
#digital frequency
Wn = 2*cutoff_freq/sampl_freq
#b and a are numerator and denominator polynomials respectively
b,a = signal.butter(order,Wn,'low')
print('a:',a)
print('b:',b)
#filter the input signal with butterworth filter
output_signal = signal.filtfilt(b,a,input_signal)
#write output signal into .wav file
sf.write('../data/Sound_With_ReducedNoise.wav',output_signal,fs)
def lowpass(s, f, order=2, fs=100.0):
b, a = signal.butter(order, f / (fs/2))
return signal.filtfilt(b, a, s)
def draw_lineplot(ax,
data,
dt=0.1,
T=(0, 200),
scaling_factor=1.,
vlimround=None,
label='local',
scalebar=True,
scalebarpos=0,
scalebarbasis='log2',
unit='mV',
ylabels=True,
color='r',
ztransform=True,
filter=False,
filterargs=dict(N=2, Wn=0.02, btype='lowpass')):
''' draw some nice lines'''
tvec = np.arange(data.shape[1]) * dt
try:
tinds = (tvec >= T[0]) & (tvec <= T[1])
except TypeError:
print(data.shape, T)
raise Exception
# apply temporal filter
if filter:
b, a = ss.butter(**filterargs)
data = ss.filtfilt(b, a, data, axis=-1)
# subtract mean in each channel
if ztransform:
dataT = data.T - data.mean(axis=1)
data = dataT.T
zvec = -np.arange(data.shape[0])
vlim = abs(data[:, tinds]).max()
if vlimround is None:
vlimround = 2.**np.round(np.log2(vlim)) / scaling_factor
else:
pass
yticklabels = []
yticks = []
for i, z in enumerate(zvec):
if i == 0:
ax.plot(tvec[tinds],
data[i][tinds] / vlimround + z,
lw=1,
rasterized=False,
label=label,
clip_on=False,
color=color)
else:
ax.plot(tvec[tinds],
data[i][tinds] / vlimround + z,
lw=1,
rasterized=False,
clip_on=False,
color=color)
yticklabels.append('ch. %i' % (i + 1))
yticks.append(z)
if scalebar:
if scalebarbasis == 'log2':
ax.plot([tvec[tinds][-1], tvec[tinds][-1]],
[-1 - scalebarpos, -2 - scalebarpos],
lw=2,
color=color,
clip_on=False)
ax.text(tvec[tinds][-1] + np.diff(T) * 0.03,
-1.5 - scalebarpos,
'$2^{' + '{}'.format(int(np.log2(vlimround))) + '}$ ' +
'{0}'.format(unit),
color=color,
rotation='vertical',
va='center')
elif scalebarbasis == 'log10':
# recompute scale bar size to show it on scientific format
vlimround10 = 10**np.round(np.log10(vlimround))
if vlimround10 >= 1:
vlimround10 = int(np.round(vlimround10))
rescale = vlimround10 / vlimround
ax.plot([tvec[tinds][-1], tvec[tinds][-1]],
np.array([0.5, -0.5]) * rescale - 1.5 - scalebarpos,
lw=2,
color=color,
clip_on=False)
ax.text(tvec[tinds][-1] + np.diff(T) * 0.03,
-1.5 - scalebarpos,
'{0} '.format(vlimround10) + '{0}'.format(unit),
color=color,
rotation='vertical',
va='center')
ax.axis(ax.axis('tight'))
ax.yaxis.set_ticks(yticks)
if ylabels:
ax.yaxis.set_ticklabels(yticklabels)
ax.set_ylabel('channel', labelpad=0.1)
else:
ax.yaxis.set_ticklabels([])
remove_axis_junk(ax, lines=['right', 'top'])
ax.set_xlabel(r'time (ms)', labelpad=0.1)
return vlimround
def hp_flt_applied(smp_data, fs=2000000, passfreq=500, flt_order=3):
b, a = butter_hp_flt(fs, passfreq, flt_order)
w, h = signal.freqz(b, a, worN=int(fs / 2))
p_paras = [passfreq, flt_order, w, abs(h)]
p_flt_data = signal.filtfilt(b, a, smp_data)
return p_flt_data
import pytest import numpy as np from numpy.testing import assert_array_almost_equal from scipy.signal import filtfilt from pylops.utils import dottest from pylops.utils.wavelets import ricker from pylops.avo.prestack import PrestackLinearModelling, PrestackWaveletModelling # Create medium parameters for multiple contrasts nt0 = 201 dt0 = 0.004 t0 = np.arange(nt0)*dt0 vp = 1200 + np.arange(nt0) + filtfilt(np.ones(5)/5., 1, np.random.normal(0, 80, nt0)) vs = 600 + vp/2 + filtfilt(np.ones(5)/5., 1, np.random.normal(0, 20, nt0)) rho = 1000 + vp + filtfilt(np.ones(5)/5., 1, np.random.normal(0, 30, nt0)) m = np.stack((np.log(vp), np.log(vs), np.log(rho)), axis=1) # Angles ntheta = 7 thetamin, thetamax = 0, 40 theta = np.linspace(thetamin, thetamax, ntheta) # Wavelet ntwav = 41 wav, twav, wavc = ricker(t0[:ntwav // 2 + 1], 20) # Shifted wavelet wavoff = 10
def low_pass_fliter(noisy_signal):
b, a = signal.butter(3, .9, btype='lowpass', analog=False)
low_passed = signal.filtfilt(b, a, noisy_signal)
return low_passed
def highpassfilter(self, data, cutoff, fs, order=5):
b, a = self.butter_highpass(cutoff, fs, order=order)
y = signal.filtfilt(b, a, data)
return y
def glove_filter(self, order=4, highcut=2):
nyq = 0.5 * self.sRate['glove']
high = highcut / nyq
b, a = butter(N=order, Wn=high, btype='lowpass')
self.glove = filtfilt(b=b, a=a, x=self.glove, axis=0)
def butter_lowpass_filtfilt(data, cutoff, fs, order=5):
b, a = butter_lowpass(cutoff, fs, order=order)
y = filtfilt(b, a, data)
return y
def lowpass_filter(self, data, cutoff, fs, order):
normal_cutoff = cutoff / (fs / 2)
b, a = butter(order, normal_cutoff, btype='low', output='ba')
y = filtfilt(b, a, data)
return np.asarray(y, dtype=np.int16)
def get_VTC_from_file(
subject,
run,
files_list,
cpt_blocs=[2, 3, 4, 5, 6, 7],
inout_bounds=[25, 75],
filt_cutoff=0.05,
filt_type="gaussian",
):
"""Short summary.
Parameters
----------
subject : type
Description of parameter `subject`.
run : type
Description of parameter `run`.
files_list : type
Description of parameter `files_list`.
cpt_blocs : type
Description of parameter `cpt_blocs`.
inout_bounds : type
Description of parameter `inout_bounds`.
Returns
-------
type
Description of returned object.
"""
# Find the logfiles belonging to a subject
subject_logfiles = []
for bloc in cpt_blocs:
subject_logfiles.append(
op.join(LOGS_DIR, find_logfile(subject, bloc, files_list)))
# Load and clean RT arrays
RT_arrays = []
RT_to_VTC = []
for idx_file, logfile in enumerate(subject_logfiles):
data = loadmat(logfile)
df_response = pd.DataFrame(data["response"])
# Replace commission errors by 0
df_clean, perf_dict = clean_comerr(df_response)
RT_raw = np.asarray(df_clean.loc[:, 4])
RT_raw = np.array([x if x != 0 else np.nan
for x in RT_raw]) # zeros to nans
# RT_interpolated = interpolate_RT(RT_raw)
RT_arrays.append(RT_raw)
if int(cpt_blocs[idx_file]) == int(run):
RT_to_VTC = RT_raw
performance_dict = perf_dict.copy()
df_response_out = df_response
# Obtain meand and std across runs
allruns_RT_array = np.concatenate(RT_arrays)
subj_mean = np.nanmean(allruns_RT_array)
subj_std = np.nanstd(allruns_RT_array)
# New VTC
VTC_raw = compute_VTC(RT_to_VTC, subj_mean, subj_std)
# VTC_thresholded = threshold_VTC(VTC_raw, thresh=3) # Compute VTC remove variability values above threshold
VTC_raw[np.isnan(VTC_raw)] = 0
VTC_interpolated = interpolate_RT(VTC_raw)
if filt_type == "gaussian":
filt = signal.gaussian(len(VTC_interpolated), fwhm2sigma(9))
VTC_filtered = np.convolve(VTC_interpolated, filt, "same") / sum(filt)
elif filt_type == "butterworth":
b, a = signal.butter(3, filt_cutoff) # (filt_order,filt_cutoff)
VTC_filtered = signal.filtfilt(b, a, VTC_interpolated)
IN_mask = np.ma.masked_where(
VTC_filtered >= np.quantile(VTC_filtered, inout_bounds[0] / 100),
VTC_filtered)
OUT_mask = np.ma.masked_where(
VTC_filtered < np.quantile(VTC_filtered, inout_bounds[1] / 100),
VTC_filtered)
IN_idx = np.where(IN_mask.mask == False)[0]
OUT_idx = np.where(OUT_mask.mask == False)[0]
return (
IN_idx,
OUT_idx,
VTC_raw,
VTC_filtered,
IN_mask,
OUT_mask,
performance_dict,
df_response_out,
RT_to_VTC,
)
def performPrep(eeg, refChan, srate, linenoise, referenceType='robust'):
dim = np.shape(eeg)
if refChan != 0:
eeg_chans = np.setdiff1d(
range(0, dim[0]),
refChan - 1) #remove the reference channel from the eeg channels
eeg = eeg[eeg_chans, :]
#finding bad channels
#finding channels with NaNs or constant values for long periods of time
org_dim = np.shape(eeg)
originalChannels = np.arange(org_dim[0])
channelsInterpolate = originalChannels
nanChannelMask = [False] * org_dim[0]
noSignalChannelMask = [False] * org_dim[0]
for i in range(0, org_dim[0]):
nanChannelMask[i] = np.sum(np.isnan(eeg[i, :])) > 0
for i in range(0, org_dim[0]):
noSignalChannelMask[i] = robust.mad(eeg[i, :]) < 10**(-10) or np.std(
eeg[i, :]) < 10**(-10)
badChannelsfromNans = channelsInterpolate[nanChannelMask]
badChannelsfromNoData = channelsInterpolate[noSignalChannelMask]
for i in range(0, org_dim[0]):
if nanChannelMask[i] == True or noSignalChannelMask[i] == True:
eeg = np.delete(eeg, i, axis=0)
channelsInterpolate = np.setdiff1d(
channelsInterpolate,
np.union1d(
badChannelsfromNans,
badChannelsfromNoData)) #channels to be used for interpolation
evaluationChannels = channelsInterpolate
new_dim = np.shape(eeg)
# find channels that have abnormally high or low amplitude
robustchanneldeviation = np.zeros(org_dim[0])
badChannelFromDeviationMask = [False] * (new_dim[0])
channeldeviation = np.zeros(new_dim[0])
for i in range(0, new_dim[0]):
channeldeviation[i] = 0.7413 * iqr(eeg[i, :])
channeldeviationSD = 0.7413 * iqr(channeldeviation)
channeldeviationMedian = np.nanmedian(channeldeviation)
robustchanneldeviation[evaluationChannels] = np.divide(
np.subtract(channeldeviation, channeldeviationMedian),
channeldeviationSD)
for i in range(0, new_dim[0]):
badChannelFromDeviationMask[i] = abs(
robustchanneldeviation[i]) > 5 or np.isnan(
robustchanneldeviation[i])
badChannelsfromDeviation = evaluationChannels[badChannelFromDeviationMask]
#finding channels with high frequency noise
if srate > 100:
eeg = np.transpose(eeg)
dim = np.shape(eeg)
X = np.zeros((dim[0], dim[1]))
B = filter_design(100,
A=np.array([1, 1, 0, 0]),
F=np.array([0, .36, 0.4, 1]),
srate=250)
for i in range(0, dim[1]):
X[:, i] = signal.filtfilt(B, 1, eeg[:, i])
noisiness = np.divide(robust.mad(np.subtract(eeg, X)), robust.mad(X))
noisinessmedian = np.nanmedian(noisiness)
noiseSD = np.median(
np.absolute(np.subtract(noisiness, np.median(noisiness)))) * 1.4826
zscoreHFNoise = np.divide(np.subtract(noisiness, noisinessmedian),
noiseSD)
HFnoisemask = [False] * new_dim[0]
for i in range(0, new_dim[0]):
HFnoisemask[i] = zscoreHFNoise[i] > 5 or np.isnan(zscoreHFNoise[i])
else:
X = eeg
noisinessmedian = 0
noisinessSD = 1
zscoreHFNoise = np.zeros(dim[1], 1)
badChannelsfromHFnoise = []
badChannelsfromHFnoise = evaluationChannels[HFnoisemask]
#finding channels by correlation
correlationSeconds = 1 # default value
correlationFrames = correlationSeconds * srate
correlationWindow = np.arange(correlationFrames)
correlationOffsets = np.arange(1, dim[0] - correlationFrames,
correlationFrames)
Wcorrelation = len(correlationOffsets)
maximumCorrelations = np.ones((org_dim[0], Wcorrelation))
drop_out = np.zeros((dim[1], Wcorrelation))
channelCorrelation = np.ones((Wcorrelation, dim[1]))
noiselevels = np.zeros((Wcorrelation, dim[1]))
channelDeviations = np.zeros((Wcorrelation, dim[1]))
drop = np.zeros((Wcorrelation, dim[1]))
n = len(correlationWindow)
XWin = np.reshape(np.transpose(X[0:n * Wcorrelation, :]),
(dim[1], n, Wcorrelation),
order='F')
dataWin = np.reshape(np.transpose(eeg[0:n * Wcorrelation, :]),
(dim[1], n, Wcorrelation),
order='F')
for k in range(0, Wcorrelation):
eegportion = np.transpose(np.squeeze(XWin[:, :, k]))
dataportion = np.transpose(np.squeeze(dataWin[:, :, k]))
windowCorrelation = np.corrcoef(np.transpose(eegportion))
abs_corr = np.abs(
np.subtract(windowCorrelation,
np.diag(np.diag(windowCorrelation))))
channelCorrelation[k, :] = np.quantile(
abs_corr, 0.98, axis=0) # problem is here is solved
noiselevels[k, :] = np.divide(
robust.mad(np.subtract(dataportion, eegportion)),
robust.mad(eegportion))
channelDeviations[k, :] = 0.7413 * iqr(dataportion, axis=0)
for i in range(0, Wcorrelation):
for j in range(0, dim[1]):
drop[i, j] = np.int(
np.isnan(channelCorrelation[i, j])
or np.isnan(noiselevels[i, j]))
if drop[i, j] == 1:
channelDeviations[i, j] = 0
noiselevels[i, j] = 0
maximumCorrelations[evaluationChannels, :] = np.transpose(
channelCorrelation)
drop_out[:] = np.transpose(drop)
noiselevels_out = np.transpose(noiselevels)
channelDeviations_out = np.transpose(channelDeviations)
thresholdedCorrelations = maximumCorrelations < 0.4
thresholdedCorrelations = thresholdedCorrelations.astype(int)
fractionBadCorrelationWindows = np.mean(thresholdedCorrelations, axis=1)
fractionBadDropOutWindows = np.mean(drop_out, axis=1)
badChannelsFromCorrelation = np.where(fractionBadCorrelationWindows > 0.01)
badChannelsFromCorrelation_out = badChannelsFromCorrelation[:]
badChannelsFromDropOuts = np.where(fractionBadDropOutWindows > 0.01)
badChannelsFromDropOuts_out = badChannelsFromDropOuts[:]
#medianMaxCorrelation = np.median(maximumCorrelations, 2);
badChannelsfromSNR = np.union1d(badChannelsFromCorrelation_out,
badChannelsfromHFnoise)
noisyChannels = np.union1d(
np.union1d(
np.union1d(
badChannelsfromDeviation,
np.union1d(badChannelsFromCorrelation_out,
badChannelsFromDropOuts_out)), badChannelsfromSNR),
np.union1d(badChannelsfromNans, badChannelsfromNoData))
print(noisyChannels)
def butter_lowpass_filter(_data, cutoff, fs, order=5):
_b, _a = butter_lowpass(cutoff, fs, order=order)
_y = filtfilt(_b, _a, _data)
return _y
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(np.array([x, y, z]), q_conj(q)))
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 _butter_bandpass_filter(self, signal, rate, low, hi, order=6):
"""butterwoth bandpass filter"""
b, a = self._butter_bandpass(low, hi, rate, order=order)
y = filtfilt(b, a, signal)
return y
def filter(dat, fc, del_t):
b, a = signal.butter(5, fc, 'low', analog=False, fs=1/del_t)
xd = signal.filtfilt(b, a, dat)
return xd
