def setFrequency(self, time=np.logspace(-7, -1, 256)):
self.Nch = self.time.size
wt = np.array([7.214369775966785e-20, 5.997984537445829e-20, 1.383536819510307e-20, 6.127201193993877e-20, 2.735622069700930e-20, 6.567948836420383e-20, 4.144963335850363e-20, 7.316414067200350e-20, 5.682375914662966e-20, 8.391977074915078e-20, 7.418756524583309e-20, 9.829637687190485e-20, 9.430643800653847e-20, 1.168146262188112e-19, 1.180370735968097e-19, 1.401723019040171e-19, 1.463726071463266e-19, 1.692722072070252e-19, 1.804796158499069e-19, 2.052560499147526e-19, 2.217507732438609e-19, 2.495469564846162e-19, 2.718603842873614e-19, 3.039069705922034e-19, 3.328334008394297e-19, 3.705052796297763e-19, 4.071277819975917e-19, 4.520053409594589e-19, 4.977334107366132e-19, 5.516707191291291e-19, 6.082931168675559e-19, 6.734956703766505e-19, 7.432489554623685e-19, 8.223651399147256e-19, 9.080210233648037e-19, 1.004250388267800e-18, 1.109225156214032e-18, 1.226448534750949e-18, 1.354938655056596e-18, 1.497875155579711e-18, 1.655024636692164e-18, 1.829422009902478e-18, 2.021527957180686e-18, 2.234394042862191e-18, 2.469158736824458e-18, 2.729043278909879e-18, 3.015882778812807e-18, 3.333221019045560e-18, 3.683642665131121e-18, 4.071174485366807e-18, 4.499238428427072e-18, 4.972519918024098e-18, 5.495403162992602e-18, 6.073431145514256e-18, 6.712116746365455e-18, 7.418091347704607e-18, 8.198210388921290e-18, 9.060466264497684e-18, 1.001332641867938e-17, 1.106647001686341e-17, 1.223031194783507e-17, 1.351661046246575e-17, 1.493814249254853e-17, 1.650922025025269e-17, 1.824549287949245e-17, 2.016440324953847e-17, 2.228509875325462e-17, 2.462885473506622e-17, 2.721908372832262e-17, 3.008174877960754e-17, 3.324546598231868e-17, 3.674192913569353e-17, 4.060610542324258e-17, 4.487669220181069e-17, 4.959641037849226e-17, 5.481251456381401e-17, 6.057719336989671e-17, 6.694815564512041e-17, 7.398915178848498e-17, 8.177066132132114e-17, 9.037055462918574e-17, 9.987491078055815e-17, 1.103788451159722e-16, 1.219874911140742e-16, 1.348170262066998e-16, 1.489958578076007e-16, 1.646658879212839e-16, 1.819839514458913e-16, 2.011233698894207e-16, 2.222757000537238e-16, 2.456526388749016e-16, 2.714881529754608e-16, 3.000408107960083e-16, 3.315963787425073e-16, 3.664706739627943e-16, 4.050127315080793e-16, 4.476082920363670e-16, 4.946836672898304e-16, 5.467100025245505e-16, 6.042079955957903e-16, 6.677531050397348e-16, 7.379813122861424e-16, 8.155954842977402e-16, 9.013724102689123e-16, 9.961705740887021e-16, 1.100938748010566e-15, 1.216725486808607e-15, 1.344689623369201e-15, 1.486111865526057e-15, 1.642407614840039e-15, 1.815141131499014e-15, 2.006041190779248e-15, 2.217018384471440e-15, 2.450184243392977e-15, 2.707872369692257e-15, 2.992661792874233e-15, 3.307402781094011e-15, 3.655245368051253e-15, 4.039670879180488e-15, 4.464526774284602e-15, 4.934065153895433e-15, 5.452985315986473e-15, 6.026480787914038e-15, 6.660291305149181e-15, 7.360760256360466e-15, 8.134898170257041e-15, 8.990452879276204e-15, 9.935987062502841e-15, 1.098096394385775e-14, 1.213584200318437e-14, 1.341217964828528e-14, 1.482275089528562e-14, 1.638167321535499e-14, 1.810454882702344e-14, 2.000862084851265e-14, 2.211294587257239e-14, 2.443858469135401e-14, 2.700881307980678e-14, 2.984935474755050e-14, 3.298863879030854e-14, 3.645808421795958e-14, 4.029241440643229e-14, 4.453000462105175e-14, 4.921326608894885e-14, 5.438907046503769e-14, 6.010921893911273e-14, 6.643096067976429e-14, 7.341756580308676e-14, 8.113895860149252e-14, 8.967241736929777e-14, 9.910334783010448e-14, 1.095261379057530e-13, 1.210451023825933e-13, 1.337755269287210e-13, 1.478448219118764e-13, 1.633937975650728e-13, 1.805780732628623e-13, 1.995696350122467e-13, 2.205585567465074e-13, 2.437549026489779e-13, 2.693908295460095e-13, 2.977229104105259e-13, 3.290347022305518e-13, 3.636395839428896e-13, 4.018838928348062e-13, 4.441503908040617e-13, 4.908620951685787e-13, 5.424865123659980e-13, 5.995403169151822e-13, 6.625945224685207e-13, 7.322801967084261e-13, 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2.387656249074371e-05, 2.638768394666778e-05, 2.916289862392297e-05, 3.222998971512441e-05, 3.561964367629314e-05, 3.936579782365431e-05, 4.350592904974602e-05, 4.808149299156779e-05, 5.313825827671661e-05, 5.872686606041739e-05, 6.490320915255368e-05, 7.172915206849267e-05, 7.927294798468421e-05, 8.761017620761336e-05, 9.682417843295337e-05, 1.070072955978771e-04, 1.182612851235724e-04, 1.306989769939818e-04, 1.444446003274482e-04, 1.596360362963627e-04, 1.764249271609239e-04, 1.949797924244976e-04, 2.154857030671910e-04, 2.381486646105023e-04, 2.631944925246626e-04, 2.908750792099106e-04, 3.214658697246949e-04, 3.552749625435381e-04, 3.926382043270680e-04, 4.339325952975191e-04, 4.795674127479124e-04, 5.300042093562213e-04, 5.857414026355948e-04, 6.473444397414629e-04, 7.154197401707392e-04, 7.906606243262904e-04, 8.738040302727717e-04, 9.657009935888906e-04, 1.067245638145834e-03, 1.179484028621435e-03, 1.303498707764836e-03, 1.440577691237741e-03, 1.592027938865682e-03, 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ab = 0.7866057737580476e0
#------- Compute Frequency components reqired for transform -------#
# This is for Digital filtering and here we evalute frequency domain responses
# ritght at this bases.
# a. Generate time base
n = np.ceil(-10*np.log(time.min()/time.max()))
tbase = time.max()*np.exp(-0.1*np.arange(0, n+1))
self.wt = wt
self.ab = ab
self.n = n
self.tbase = tbase
# b. Determine required frequencies
omega_int = (ab/tbase[0])*np.exp(0.1*(np.r_[1:786+tbase.size:(786+tbase.size)*1j]-425))
# Case1: Compute frequency domain reponses right at filter coefficient values
if self.switchInterp == False:
self.frequency = omega_int/(2*np.pi)
self.Nfreq = self.frequency.size
# Case2: Compute frequency domain reponses in logarithmic then intepolate
elif self.switchInterp == True:
# This is tested decision: works well 1e-4-1e0 S/m
self.frequency = np.logspace(-3, 8, 81)
self.omega_int = omega_int
self.Nfreq = self.frequency.size
else:
raise Exception('Not implemented!!')
if self.offset is not None and np.isscalar(self.offset):
self.offset = self.offset*np.ones(self.Nfreq)
elif self.offset is not None and not np.isscalar(self.offset):
self.offset = self.offset[0]*np.ones(self.Nfreq)
def SetOffset(self):
if np.isscalar(self.offset):
self.offset = self.offset*np.ones(self.Nfreq)