Updated API. First work on impedance tube code
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@ -2,4 +2,4 @@ from .soundpressureweighting import *
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from .filterbank_design import *
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from .fir_design import *
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from .colorednoise import *
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from .biquad import *
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@ -107,11 +107,27 @@ class TwoMicImpedanceTube:
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# Microphone transfer function
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G_AB = self.K*C[:,1,0]/C[:,0,0]
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return self.getFreq(), self.cut_to_limits(G_AB)
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def k(self, freq):
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"""
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Wave number, or thermoviscous wave number
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"""
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if self.thermoviscous:
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D = self.D_imptube
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S = pi/4*D**2
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d = PrsDuct(0, S=S, rh=D/4, cs='circ')
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d.mat = self.mat
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omg = 2*pi*freq
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G, Z = d.GammaZc(omg)
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return G
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else:
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return 2*pi*freq/self.mat.c0
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def R(self, meas):
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freq, G_AB = self.G_AB(meas)
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s = self.s
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k = 2*pi*freq/self.mat.c0
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k = self.k(freq)
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d1 = self.d1
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RpA = (G_AB - exp(-1j*k*s))/(exp(1j*k*s)-G_AB)
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@ -152,9 +152,11 @@ class IterRawData:
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stop_offset = self.lastblock_stop_offset
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else:
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stop_offset = self.blocksize
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# print(f'block: {block}, starto: {start_offset}, stopo {stop_offset}')
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self.i +=1
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return self.fa[block,start_offset:stop_offset,self.channels]
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return self.fa[block,start_offset:stop_offset,:][:,self.channels]
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class IterData(IterRawData):
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"""
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@ -362,25 +364,38 @@ class Measurement:
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"""Returns the measurement time in seconds since the epoch."""
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return self._time
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@property
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def rms(self, channels=None):
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def rms(self, channels=None, substract_average=False):
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"""Returns the root mean square values for each channel
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Args:
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channels: list of channels
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substract_average: If set to true, computes the rms of only the
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oscillating component of the signal, which is in fact the signal
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variance.
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Returns:
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1D array with rms values for each channel
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"""
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#
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try:
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return self._rms
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except AttributeError:
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pass
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meansquare = 0.
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meansquare = 0. # Mean square of the signal, including its average
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sum_ = 0. # Sumf of the values of the signal, used to compute average
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N = 0
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with self.file() as f:
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for block in self.iterData(channels):
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Nblock = block.shape[0]
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sum_ += np.sum(block, axis=0)
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N += Nblock
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meansquare += np.sum(block**2, axis=0) / self.N
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self._rms = np.sqrt(meansquare)
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return self._rms
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avg = sum_/N
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# In fact, this is not the complete RMS, as in includes the DC
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# If p = p_dc + p_osc, then rms(p_osc) = sqrt(ms(p)-ms(p_dc))
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if substract_average:
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meansquare -= avg**2
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rms = np.sqrt(meansquare)
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return rms
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def variance(self, channels=None):
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return self.rms(substract_average=True)
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def rawData(self, channels=None, **kwargs):
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"""Returns the raw data as stored in the measurement file,
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@ -406,7 +421,6 @@ class Measurement:
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return np.concatenate(rawdata, axis=0)
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def iterData(self, channels, **kwargs):
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sensitivity = kwargs.pop('sensitivity', self.sensitivity)
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if channels is None:
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@ -484,21 +498,26 @@ class Measurement:
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blocks = [signal[i*N:(i+1)*N] for i in range(Nblocks)]
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if noiseCorrection:
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enp1 = [blocks[i+1] - blocks[i] for i in range(Nblocks-1)]
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noise_est_np1 = [np.average(enp1[i+1]*enp1[i]) for i in range(Nblocks-2)]
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# The difference between the measured signal in the previous block and
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# the current block
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en = [None] + [blocks[i] - blocks[i-1] for i in range(1,Nblocks)]
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noise_est = [None] + [-np.average(en[i]*en[i+1]) for i in range(1,len(en)-1)]
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# Create weighting coefficients
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sum_inverse_noise = sum([1/n for n in noise_est_np1])
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c_np1 = [1/(ni*sum_inverse_noise) for ni in noise_est_np1]
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sum_inverse_noise = sum([1/n for n in noise_est[1:]])
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c_n = [1/(ni*sum_inverse_noise) for ni in noise_est[1:]]
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else:
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c_np1 = [1/(Nblocks-2)]*(Nblocks-2)
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c_n = [1/(Nblocks-2)]*(Nblocks-2)
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assert np.isclose(sum(c_np1), 1.0)
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assert np.isclose(sum(c_n), 1.0)
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assert Nblocks-2 == len(c_n)
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# Average signal over blocks
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avg = np.zeros((blocks[0].shape), dtype=float)
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for i in range(0, Nblocks-2):
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avg += c_np1[i]*blocks[i+1]
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for n in range(0, Nblocks-2):
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avg += c_n[n]*blocks[n+1]
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return avg
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def periodicCPS(self, N, channels=None, **kwargs):
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@ -514,7 +533,7 @@ class Measurement:
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window = Window.rectangular
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ps = PowerSpectra(N, window)
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avg = self.periodicAverage(N, channels, **kwargs)
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avg = np.asfortranarray(self.periodicAverage(N, channels, **kwargs))
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CS = ps.compute(avg)
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freq = getFreq(self.samplerate, N)
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@ -554,24 +573,23 @@ class Measurement:
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f.attrs['sensitivity'] = sens
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self._sens = sens
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def checkOverflow(self):
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def checkOverflow(self, channels):
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"""Coarse check for overflow in measurement.
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Return:
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True if overflow is possible, else False
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"""
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with self.file() as f:
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for block in self.iterBlocks(f):
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dtype = block.dtype
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if dtype.kind == 'i':
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# minvalue = np.iinfo(dtype).min
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maxvalue = np.iinfo(dtype).max
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if np.max(np.abs(block)) >= 0.9*maxvalue:
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return True
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else:
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# Cannot check for floating point values.
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return False
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for block in self.iterData(channels):
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dtype = block.dtype
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if dtype.kind == 'i':
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# minvalue = np.iinfo(dtype).min
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maxvalue = np.iinfo(dtype).max
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if np.max(np.abs(block)) >= 0.9*maxvalue:
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return True
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else:
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# Cannot check for floating point values.
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return False
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return False
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