diff --git a/applications/lofar2/model/pfb_bunton_annotated/Gen_filter12.m b/applications/lofar2/model/pfb_bunton_annotated/Gen_filter12.m
index 763902cc8e49faf79fe9cbca1637a228e09b9f42..50b22e3bd5a22032d5d45a5e4baab6def8b8795a 100644
--- a/applications/lofar2/model/pfb_bunton_annotated/Gen_filter12.m
+++ b/applications/lofar2/model/pfb_bunton_annotated/Gen_filter12.m
@@ -30,6 +30,8 @@ Norder = Kcoefs - 1;
 W0 = 1 / 15.3;
 %W0 = 1 / 15.19;
 c = fircls1(Norder, W0, .02, .0003);
+dc_gain_c = sum(c)
+% c = c / dc_gain_c;  % force DC gain = 1
 
 figure(1)
 subplot(3, 1, 1)
@@ -75,12 +77,13 @@ title ('Sum of correlator responses for Bin 0 and 1')
 % 2304, using Q = 12. The number of FFT blocks remains Ntaps = 12, because
 % the shape of cl is the same as for c. The FFT block size now becomes Nfft =
 % Kfft * Q = 16 * 12 = 192.
+% Using Q > 1 does cause the FIR filter to become slightly asymmetrical, so
+% the group delay is not exactly constant (Ncoefs - 1) / 2.
 Q = 12;
 % Y = interpft(X, N) returns a vector Y of length N obtained by interpolation
 % in the Fourier transform of X.
 cl = interpft(c, length(c) * Q) / Q;
-dc_gain_c = sum(c);
-dc_gain_cl = sum(cl);
+dc_gain_cl = sum(cl)
 Ncoefs = Kcoefs * Q;
 Nfft = Kfft * Q;
 
@@ -98,3 +101,6 @@ xlabel('Frequency (FFT bins)','FontSize',10)
 ylabel('Magnitude','FontSize',10)
 title ('Response of fircls1 filter c and interpolated cl')
 grid
+
+clT = cl';
+save('cl.txt', 'clT', '-ascii', '-double')
diff --git a/applications/lofar2/model/pfb_bunton_annotated/polyphase_analysis.m b/applications/lofar2/model/pfb_bunton_annotated/polyphase_analysis.m
index cb6fc0152d9abdb000c1c603111d5493b661d118..c9b3f3b7e2e6abc7a4493843d15009ecd4e60423 100644
--- a/applications/lofar2/model/pfb_bunton_annotated/polyphase_analysis.m
+++ b/applications/lofar2/model/pfb_bunton_annotated/polyphase_analysis.m
@@ -26,13 +26,19 @@ outBins = zeros(nof_blocks, Nfft);
 
 for k = 0:nof_blocks-1
     % Filter input using bfir as window, with downsampling rate Nstep
-    temp = bfir .* inData((1:Ncoefs) + Nstep*k);
+    tapData = bfir .* inData((1:Ncoefs) + Nstep*k);
     % Sum the FIR filter taps
-    temp2 = (1:Nfft) * 0;
+    pfsData = (1:Nfft) * 0;
     for m = 0:Ntaps-1
-        temp2 = temp2 + temp((1:Nfft) + Nfft*m);
+        pfsData = pfsData + tapData((1:Nfft) + Nfft*m);
     end
     % FFT
-    outBins(k+1, 1:Nfft) = fft(temp2);
+    outBins(k+1, 1:Nfft) = fft(pfsData);
+    if k < 3
+        %inData((1:Ncoefs) + Nstep*k)
+        %tapData
+        %pfsData
+        %outBins(k+1, :)
+    end
 end
-%plot(real(temp2))
+%plot(real(pfsData))
diff --git a/applications/lofar2/model/pfb_bunton_annotated/polyphase_synthesis_b.m b/applications/lofar2/model/pfb_bunton_annotated/polyphase_synthesis_b.m
index c13e920b95c837b9ba14ebc9d32ea9ed39c81284..815fd030b2da68c25e2bc9c02c2d32b345311078 100644
--- a/applications/lofar2/model/pfb_bunton_annotated/polyphase_synthesis_b.m
+++ b/applications/lofar2/model/pfb_bunton_annotated/polyphase_synthesis_b.m
@@ -20,24 +20,28 @@ Ncoefs = Ntaps * Nfft;
 bfir = (1:Ncoefs) * 0;
 bfir(1:length(filt)) = filt;
 
-% IFFT
-for n = 1:nof_blocks
-    temp(n, 1:Nfft) = real(ifft(inBins(n,:)));
-end
-
 outData = (1:nof_blocks*Nstep + Ncoefs) * 0;
 for n = 1:nof_blocks
+    % IFFT
+    inData = real(ifft(inBins(n,:)));
+
     % Copy the FFT data at each FIR tap
-    temp2 = (1:Ncoefs) * 0;
+    tapData = (1:Ncoefs) * 0;
     for m = 0:Ntaps-1
-        temp2((1:Nfft) + m*Nfft) = temp(n, 1:Nfft);
+        tapData((1:Nfft) + m*Nfft) = inData;
     end
     % Apply the FIR coefficients by using bfir as window
-    temp2 = temp2 .* bfir;
+    tapData = tapData .* bfir;
 
     % Sum the FIR taps by overlap add the weighted input to the output.
     % Progress with Nstep per block of Nfft to upsample by Nstep, to match
     % the downsample by Nstep of the analysis filterbank.
     range = (1:Ncoefs) + (n-1)*Nstep;
-    outData(range) = outData(range) + temp2;  % continuous time output
+    outData(range) = outData(range) + tapData;  % continuous time output
+    if n <= 3
+        %inBins(n,:)
+        %inData
+        %tapData
+        %outData(range)
+    end
 end
diff --git a/applications/lofar2/model/pfb_bunton_annotated/reconstruct.m b/applications/lofar2/model/pfb_bunton_annotated/reconstruct.m
index 2ee768eb6d80343485c1b6c4256c5d7479dc2e22..5e0524a1b0c6dd00afb750cd6b9e3f6841b036d7 100644
--- a/applications/lofar2/model/pfb_bunton_annotated/reconstruct.m
+++ b/applications/lofar2/model/pfb_bunton_annotated/reconstruct.m
@@ -11,14 +11,18 @@
 %
 % Usage from README.txt:
 % . first run Gen_filter12 to create prototype lowpass filter cl
-% . then run reconstruct with ImpulseResponse = 1 and Correct = 1 to calculate
-%   an impulse response and a correction filter for the analysis PFB and
-%   synthesis PFB transfer function error
-% . then rerun reconstruct with ImpulseResponse = 0 and Correct = 0 or 1
+% . then run reconstruct with ImpulseResponse = 0 and Correct = 0 to filter
+%   the inData
+% . to correct the overall response, rerun reconstruct with ImpulseResponse =
+%   1 and Correct = 1 to calculate an impulse response and a correction filter
+%   for the analysis PFB and synthesis PFB transfer function error
+% . then rerun reconstruct with ImpulseResponse = 0 and Correct = 1 to also
+%   apply the correction filtering
 % . use rng('default') to check SNR results, comment rng() to experiment with
-%   new noise input evry rerun
+%   new noise input every rerun
 
 close all
+format longE
 
 % Uncomment rng() line or run rng() line first in shell, to produce same random
 % numbers as with MATLAB restart, to verify the SNR results for Correct = 0, 1.
@@ -56,6 +60,8 @@ k = length(inData);  % = len * Nhold
 % 3) sine wave signal
 %inData = cos(((1:k).^2 ) * len / (k)^2);
 %inData = cos(2*pi*(1:k) * 0.031 / 16);
+%inData = cos(2*pi*(0:k-1) * 1.5 / Nfft);  % use integer for CW at bin
+%inData = (0:k-1);
 
 % 4) impulse signal
 if ImpulseResponse == 1
@@ -73,10 +79,10 @@ outBins = polyphase_analysis(inData, cl, Nfft, Nstep);
 outData = polyphase_synthesis_b(outBins, cl, Nstep);
 
 % adjust gain
-% . unit gain in prototype LPF Gen_filter12.m
-adjust = Nfft;  % normalize gain per bin for analysis LPF
-adjust = adjust / Ros;  % compensate for oversampling factor
-adjust = adjust * Nfft;  % normalize gain per bin for synthesis LPF
+% . assume unit gain in prototype LPF Gen_filter12.m
+% . adjust for 1/Nfft in synthesis IFFT and adjust for 1/Nup in synthesis
+%   interpolation, with analysis Ndown = synthesis Nup = Nstep.
+adjust = Nfft * Nstep;
 outData = adjust * outData;
 
 if ImpulseResponse == 1
@@ -85,19 +91,18 @@ if ImpulseResponse == 1
     correct = real(ifft(1./fft(outData)));
 end
 
-% Choose parameter k to suit oversampling ratio
-% oversampling ratio 32/23 then k=0
-% oversampling ratio 32/24 then k=39
-% oversampling ratio 32/25 then k=72
-% oversampling ratio 32/26 then k=3
-% oversampling ratio 32/27 then k=48
-k=39;
-corr = correct;
-% Correct for time shift for transfer function correction filter
-corr(1+k:length(corr)) = corr(1:length(corr)-k);
-
 % Correct for error in transfer function
 if Correct == 1
+    % Choose parameter k to suit oversampling ratio
+    % oversampling ratio 32/23 then k=0
+    % oversampling ratio 32/24 then k=39
+    % oversampling ratio 32/25 then k=72
+    % oversampling ratio 32/26 then k=3
+    % oversampling ratio 32/27 then k=48
+    k=39;
+    corr = correct;
+    % Correct for time shift for transfer function correction filter
+    corr(1+k:length(corr)) = corr(1:length(corr)-k);
     outData = ifft(fft(outData) .* fft(fftshift(corr)));
 end
 
@@ -120,7 +125,8 @@ diff = outData - inData(1:length(outData));
 %   With DEVP = 0.005 and W0 = 1 / 15.05 then db(0.005) = -46.0 dB and SNR =
 %   38.8 dB, so increased.
 SNR = 10 * log10(sum(abs(inData(10000:70000) .^ 2)) / sum(abs(diff(10000:70000) .^ 2)));
-sprintf('SNR = %6.2f [dB]', SNR)
+SNR = 20 * log10(std(inData(10000:70000)) / std(diff(10000:70000)));
+sprintf('SNR = %.5f [dB]', SNR)
 
 %plot(diff)
 %plot(db(fft(outData))) %to see frequency error
diff --git a/applications/lofar2/model/pfb_os/dsp_study_erko.txt b/applications/lofar2/model/pfb_os/dsp_study_erko.txt
index d0ef8e813f66e3f1cfe165daee50be5a4299557b..1c6939dca6b8ec38b4837d38348fedb75f90826a 100644
--- a/applications/lofar2/model/pfb_os/dsp_study_erko.txt
+++ b/applications/lofar2/model/pfb_os/dsp_study_erko.txt
@@ -68,6 +68,8 @@
 #   https://dss-kiel.de/images/teaching/lectures/advanced_digital_signal_processing/
 #   slides/adsp_06_multirate_processing.pdf
 # * [TUTHILL] Compensating for oversampling effects in polyphase channelizers, 2015
+# * [BUNTON] Multi-resolution FX Correlator, ALMA memo 447, 2003
+# * [FLIEGE] Multirate DSP
 #
 # https://ocw.mit.edu/courses/6-341-discrete-time-signal-processing-fall-2005/
 # Youtube: Guitars 4RL
@@ -92,8 +94,9 @@
     [PROAKIS 3.5]. Only unique for causal signals, because these are 0 for
     n < 0.
   * DFT: Every signal x(n) can be expressed as a linear combination of complex
-    sinusoids W_N^kn = exp(j w_k t_n). The coefficients of projecting x(n) on
-    W_N^kn for n = 0,1,...,N-1 yield the DFT of x is X(k) for k = 0,1,...,N-1.
+    sinusoids W_N^(kn) = exp(j w_k t_n). The coefficients of projecting x(n)
+    on W_N^(kn) for n = 0,1,...,N-1 yield the DFT of x is X(k) for k =
+    0,1,...,N-1.
   * DTFT: For N --> inf, linear combination of exp(j w t_n) = exp(j w T)^n
     [LYONS 3.14]
   * z-transform: sum n=0 --> inf, linear combination of z^n. Generalization
@@ -225,6 +228,18 @@ c) s-plane and z-plane
   . poles inside unit circle for stable system
   . real h[n] --> poles and zeros not on real axis are conjugate pairs
 
+- Complex numbers
+  . z = r exp(p) = r * (cos(p) + j sin(p))
+  . multiplying by a complex number yields a rotation, because z = r exp(p)
+  . negating z is like multiplying z by exp(j pi) = -1, so it preserves the
+    rotation, but yields a phasor that is 180 degrees offset of z.
+  . conjugate negates the imaginary part --> reverses phasor rotation, so
+    z * conj(z) = r^2 rotates z back to the positive real axis and yields its
+    length squared. Therefore z^(-1) = 1 / z = conj(z) / r^2.
+  . swap_re_im(z) = conj(z) * exp(j pi/2), so swapping the real and imaginary
+    part reverses the rotation and yields a phasor that is 90 degrees offset
+    behind the conj(z).
+
 
 3) Windows [JOS4 3]
 - Window types table [HARRIS 3.2, PROAKIS 8.2.2]
@@ -513,6 +528,9 @@ c) s-plane and z-plane
   . Type III, Ntaps odd --> HT(0) = 0, HT(fs/2) = 0 --> so BPF, advantages:
     - odd coefs are zero
     - group delay is (Ntaps - 1) / 2 is integer
+  . Linear phase implies symmetrical or antisymmetrical FIR coefficients,
+    because sin(wt - nT) + sin(wt + nT) = K * sin(wt), with K = 2 sin(nT)
+    only affecting the gain.
 - Design methods:
   . HT window design: ht[n] * w[n], best use Ntaps is odd for unit group delay.
   . Analytic signal generation using complex BPF [LYONS 9.5, HARRIS 8.5]:
@@ -760,9 +778,10 @@ c) s-plane and z-plane
 
 
 9) Discrete Fourier Transform (DFT)
-- The N roots of unity [JOS1 3.12, 5.1, PROAKIS 5.1.3, LYONS 4.3]. Note JOS
-  uses +j in W_N because inproduct is with conj(W_N), others use -j because
-  then W_N can be used directly in equation and matrix:
+- The N roots of unity [JOS1 3.12, 5.1, PROAKIS 5.1.3, LYONS 4.3, CROCHIERE Eq
+  7.8]. Note JOS uses +j in W_N because inproduct is with conj(W_N). CROCHIERE
+  also use +j. Wikipedia and others use -j because then W_N can be used directly
+  in equation and matrix:
 
   W_N = exp(-j 2pi/N) is primitive Nth root of unity
   W_N^k = exp(-j 2pi/N k)
@@ -815,7 +834,7 @@ c) s-plane and z-plane
   6.6, 7.1, PROAKIS 5.1.2, 5.1.3]:
 
                     N-1
-    X(w_k) = X(k) = sum x(n) W_N^kn
+    X(w_k) = X(k) = sum x(n) W_N^(kn)
                     n=0      exp(-j w_k t_n)
                              exp(-j 2pi/N k n)
       with:
@@ -877,22 +896,46 @@ c) s-plane and z-plane
     to an input sinusoidal and it is also the of a single DFT bin:
       X(m) = sin(pi * m) / sin(pi * m / K)
           ~= K * sinc(m) for K = N >~ 10
+- DFT and IDFT
+  . DFT uses exp(-j 2pi/N k n), IDFT uses exp(+j 2pi/N k n) for n = 0:N-1
+    folding the cos() and j sin() around n = 0 and then shifting by 1 yields
+      exp(-j phi) = fold(exp(+j phi)) and
+      exp(+j phi) = fold(exp(-j phi))
+    with
+      fold(x) = roll(flip(x), 1), so reverse order of elements in x and then
+      roll it one to the right, so element at N appears at 0 (= N % N).
+  . IDFT(x) = 1/N conj(DFT(conj(x))
+            = 1/N swap_re_im(DFT(swap_re_im(x)), [LYONS 13.7]
+            = 1/N * DFT(fold(x))
+    DFT(x) = N * IDFT(fold(x))
+  . for real x: IDFT(x) = 1/N conj(DFT(x))
+  . The DFT and IDFT form an equivalent pair. The order in which they are used
+    is often dont care.
 
 - DTFT properties [JOS4 B, PROAKIS 4.3]
   . Linearity: a1 x1[n] + a2 x2[n] <==> a1 X1(w) + a2 X2(w)
   . Scaling: x(t / a) <==> |a| X(a w)
   . Time shift: x(t - T) <==> X(w) exp(-j w T)
                 x[n - m] <==> X(w) exp(-j w m), t = n Ts, T = m Ts
+      If DFT(x[n]) = X(k), then DFT{x[(n - m) % N]) = X(k) exp(-j 2pi k m / N)
   . Frequency shift (complex modulation): x[n] exp(+j v n) <==> X(w - v), is
       dual of time shift
+      If DFT(x[n]) = X(k), then DFT(x[n] exp(j 2pi m n / N) = X((k - m) % N)
   . Real modulation: x[n] cos(v n) <==> 1/2 [X(w + v) + X(w - v)]
   . Conjugation: x*[n] <==> X*(-w)
+    if DFT(x[n]) = X(k), then DFT(x*[n]) = X*(N - k)
+  . Periodicity: If x (n) and x (k) are N point DFT pair then
+      x[n + N] = x[n] for all n
+      X(k + N) = X(k) for all k
   . Convolution:
       x * y <==> X Y
       x y <==> 1 / (2pi) X * Y
-  . flip(x) <==> flip(X), so when signal is folded (time reversed) about the
+  . fold(x) <==> fold(X), so when signal is folded (time reversed) about the
       origin in time, then its magnitude spectrum remains unchanged, and the
       phase spectrum changes sign (phase reversal).
+      Use flip() as in numpy for linear time reversal
+      Use fold() for modulo or circular time reversal = FLIP_n in [JOS1]
+      if DFT(x[n]) = X(k), then DFT(x[N - n]) = X(N - k)
   . d[n] <==> 1, dirac pulse with area 1 at n = 0
     d[n - k] <==> exp(-j w k), dirac pulse with area 1 at n = k
 
@@ -1315,7 +1358,7 @@ c) s-plane and z-plane
        where Hq(z^Q) is the z-transform of hq[n]:
 
            hq[n] = h(nQ + q),  +q for counter clockwise with delays z^(-1)
-                               [VAIDYANATHAN Eq 4.3.8]
+                               [VAIDYANATHAN Eq 4.3.8, CROCHIERE Eq 3.29]
 
                   +inf
            Hq(z) = sum hq[n] z^(-n),  0 <= q <= Q - 1
@@ -1359,11 +1402,20 @@ c) s-plane and z-plane
        where:
            p = Q - 1 - q
 
-           rp[n] = h(nQ - p),  -p for clockwise with advances z^(+1)
-
            Rp(z) = H{Q-1-p}(z),   so flipud Hq phases, but keep coefficient order
                                   per phase
 
+  . Type III polyphase [FLIEGE 1.1.2, CROCHIERE 3.3.3]
+    - p = Q - q = -q  (With Q % Q = 0)
+    - rp[n] = h(nQ - p)  [CROCHIERE Eq. 3.35],  -p for clockwise with advances
+      z^(+1)
+    - CROCHIERE does not mention type II and VAIDYANATHAN does not mention type
+      III. FLIEGE mentions all three types. The advantage of type I and III is
+      that they both begin with x[n=0] at branch p = q = 0 with H0, so they
+      yield same result except for a delay. The purpose of type III is then
+      that it yields a fold of the branches, so that DFT can be used instead of
+      IDFT, because DFT(x) = IDFT(fold(x)). The purpose of type II is ?.
+
   . Type I polyphase does not imply Direct Form FIR and type II polyphase is
     not the same as Transposed Direct Form FIR [CROCHIERE 3.3.3]:
     * Type I uses delay line z^(-1) and +q in hq[n] = h(nQ + q) and yields
@@ -1554,8 +1606,7 @@ c) s-plane and z-plane
 
 
 
-
-14) Polyphase filterbank (PFB) [HARRIS Fig 6.21, 9.21]
+13c) Polyphase DFT filterbank (PFB) [HARRIS Fig 6.21, 9.21]
   . The PFB implements M single channel down converters to output all k bins.
     The output rate per branch p in the single channel down converter is a
     factor M less, so all k = 0:M-1 bins of y[mM, k] can be calculated by using
@@ -1573,6 +1624,42 @@ c) s-plane and z-plane
     at bin 0, so bin k = M-1 is the newest and output last.
 
 
+14) WOLA DFT filterbank (PFB) [CROCHIERE 7.2.5]
+
+  * Mixer local oscillator (LO) and LPF and downsampler D [CROCHIERE Eq 7.65 =
+    Eq 7.9, HARRIS Eq 6.1] yields the short-time spectrum of signal x at time
+    n = mM, so M is the block size or downsample rate:
+
+              +inf
+      Xk(m) = sum h[mM - n] x[n] W_K^(kn),  W_K = exp(-jw_k) = exp(-j 2pi/K)
+             n=-inf
+
+    The filter h weigths x at n = nM.
+
+  * In Eq 7.9 = 7.65 the signal time frame is fixed to n = 0 and the window h
+    slides along at n = mM. For implementation is convenient to keep the filter
+    h invariant and slide the signal, therefor use r = n - mM:
+
+              +inf
+      Xk(m) = sum h[-r] x[r + mM] W_K^(k(r + mM)) = W_K^(kmM) Rk(m)
+             n=-inf
+
+      with:           +inf
+              Rk(m) = sum h[-r] x[r + mM] W_K^(kr)
+                     n=-inf
+
+    The term W_K^(kmM) converts the transform Rk(m) with sliding time frame r,
+    into Xk(m) with fixed time frame n.
+
+  * Define ym[r] = h[-r] x[r + mM]. Length h is Ncoef = Ntaps * K. The DFT of
+    ym[n] has Ncoef bins k', but for Rk only bins k' = Ntaps * k are needed.
+    This is equivalent to DFT of Ntaps blocks of stacked K time samples. Define
+    xm[r] = sum ym[r + lK], then:
+
+                     K-1
+             Rk(m) = sum xm[r] W_K^(kr) = DFT(xm[r])
+                    r = 0
+
 
 15) Quadrature Mirror Filter (QMF) [CROCHIERE 7.7, PROAKIS 10.9.6]
 
diff --git a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
index e1c04d54cf156f63479e9a628a91e058bc8859d1..8fe5820fd4df1663e83c85e08a5f599bcdf767dd 100644
--- a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
+++ b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
@@ -7,15 +7,42 @@
    "source": [
     "# Multirate mixer\n",
     "\n",
-    "Author: Eric Kooistra, May 2024\n",
+    "Author: Eric Kooistra, May - Dec 2024\n",
     "\n",
     "Purpose:\n",
     "* Practise DSP [1].\n",
-    "* Use multirate processing to implement a mixer \n",
+    "* Use multirate processing to implement a single channel mixer\n",
+    "* Extend single channel mixer to DFT filterbank\n",
+    "\n",
+    "Description:\n",
+    "\n",
+    "For a real input the mixer can either downconvert the positive or the negative frequeny band. Convention is to downconvert the positive frequency band [2, 3]. This leads to using IDFT in the analysis DFT filterbank and IDFT in the synthesis filterbank [2, 3]. By using IDFT(x) = DFT(fold(x)) it is possible to change to a DFT in the analysis filterbank, this corresponds to using a clockwise commutator [3] and type 3 polyphase filter structure [4].\n",
+    "\n",
+    "Sampling and anlogue signal yields the low pass spectrum and its replicas around multiples of fs. This endles replication is why the discrete samples can represent and anlogue signal. Downsampling of the digitized signal again causes aliasing of all replicas to baseband. Normally the interest is in the baseband replica. However using a BPF instead of an LPF any of the replicas can be selected for baseband. Similar for upsampling (synthesis) the baseband signal can be upconverted to the wanted replica by using a BPF instead of an LPF. The first step in upsampling is inserting zeros to increase the sample rate, without changing the digital signal. However this increased sample rate does make the replicas available in the digital domain.\n",
+    "\n",
+    "With a single channel mixer the downsampling by M implies that at the downsampled rate there is processing power to process M - 1 more channels. This fits a critically sampled DFT filterbank with K = M. Whereby the DFT should typically be an FFT.\n",
+    "\n",
+    "The single channel mixer and DFT filterbank can share a prototype LPF for all channels, because the downsampling and upsampling performs the demodulation and modulation, to and from baseband at 0 Hz. When Ros = K / M > 1 then there remains and offset frequency that is compensated by a phasor term W_K^(knM). With the DFT filterbank this phasor that can be implemented as a circular shift (modulo K) at the input of the DFT.  \n",
+    "\n",
+    "For perfect reconstruction by an analysis-synthesis filterbank the aliasing must cancel and distortion must be zero. Considering only adjancent channels is called pseudo QMF (two-channel), for the other channels the stop band attenuation suppresses the crosstalk. For zero distortion the transfer function of the channels must be power complementary. With an DFT filterbank (= complex modulated filterbank) the aliasing does not cancel. For critically sampled filterbanks this leads to the Modified DFT (MDFT) or Cosine modulated fiterbank [4]. By using oversampling the aliasing can be avoided. Hence using some oversampling and power complementary channel filters the oversampled DFT filterbank can achieve almost perfect reconstruction.\n",
+    "\n",
+    "Remarks:\n",
+    "* With oversampling the subband bandwidth can be increased to have a flat reponse up to fsub / 2. The transition band is then from fsub / 2 to Ros * fsub / 2 The subband filter is then not power complementary (around fsub / 2), so the subbands are not suitable for synthesis. It may be feasible achieve almost perfect reconstruction by first removing the transition band from the subbands, by separating the subbands into fine channels using a DFT, then zero the fine channels of the transistion band, and then synthesize the subband using IDFT.\n",
+    "\n",
+    "Features:\n",
+    "* Use full rate model of single channel down converter and up converter as expected exact golden reference result for the efficient polyphase implementation and WOLA implementation and refBunton.\n",
+    "* Use SNR of input and difference with time aligned reconstructed output, to show that perfect reconstruction depends on the pass band gain being one and the stopband gain approaching zero. Hence the SNR for center bin sine wave inputs improves, towards perfect reconstruction, when Ntaps is increased.\n",
+    "* Use analysis DFT filterbank and synthesis IDFT filterbank and verify that it yields the same result as with the single channel pipeline.\n",
+    "* Compare model with PFB reconstruction by reconstruct.m of John Bunton for Ntaps = 12 and Ndft = 192, using refBunton.\n",
+    "* Support Ncoefs <= Ndelays = Ntaps * Ndft, to verify correct implementation of coefs and delay line.\n",
+    "* Support WG with offset bin center frequency (e.g. wgSub = 1.5) or with noise (via SNR_WG_dB < 100), to verify that result is correct for any frequency, not only the center frequency.\n",
+    "* Support refBunton with asymmetrical hPrototype, to verify correct order implementation of coefs in analysis and synthesis. The refBunton has slightly asymmetrical hPrototype due to that it uses interpolation to increase the number of coefficient after designing the FIR filter with fircls1().\n",
     "\n",
     "References:\n",
     "1. dsp_study_erko, summary of DSP books\n",
-    "2. chapter 6 downconverter, 7 upconverter, 9 filterbank in [HARRIS]"
+    "2. chapter 7 in [CROCHIERE]\n",
+    "3. chapter 6 downconverter, 7 upconverter, 9 filterbank in [HARRIS]\n",
+    "4. chapter 1.1.2 (commutator), 7.3 (pseudo QMF), 7.3.3 (MDFT), 7.4 (Cos filterbank) 8.6 in [FLIEGE]"
    ]
   },
   {
@@ -34,6 +61,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "1c1ba454",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sys import maxsize\n",
+    "np.set_printoptions(threshold=maxsize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "fc530dbc",
    "metadata": {},
    "outputs": [],
@@ -50,15 +88,30 @@
     "    sys.path.insert(0, module_path)\n",
     "\n",
     "# Import rtdsp\n",
-    "from rtdsp.utilities import ceil_div, verify_result, is_integer_value, is_symmetrical, pow_db\n",
+    "from rtdsp.utilities import ceil_div, verify_result, is_integer_value, is_symmetrical, pow_db, snr_db, \\\n",
+    "                            read_coefficients_file\n",
     "from rtdsp.firfilter import filterbank_frequency_response\n",
     "from rtdsp.fourier import dtft\n",
-    "from rtdsp.multirate import down, up, unit_circle_loops_phasor_arr, \\\n",
-    "                            maximal_downsample_bpf, non_maximal_downsample_bpf, \\\n",
-    "                            maximal_upsample_bpf, non_maximal_upsample_bpf\n",
+    "from rtdsp.multirate import down, up\n",
+    "from rtdsp.singlechannel import unit_circle_loops_phasor_arr, \\\n",
+    "                                maximal_downsample_bpf, non_maximal_downsample_bpf, \\\n",
+    "                                maximal_upsample_bpf, non_maximal_upsample_bpf\n",
+    "from rtdsp.dftfilterbank import analysis_dft_filterbank, synthesis_dft_filterbank\n",
     "from rtdsp.plotting import plot_power_spectrum, plot_magnitude_spectrum, plot_phase_spectrum"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f527cbb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verification control\n",
+    "enExit = True  # Exit or continue when a verification fails\n",
+    "vb = 0  # Verbosity"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fbf4bf7b",
@@ -69,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "5b37a1dc",
    "metadata": {},
    "outputs": [
@@ -77,30 +130,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "wgSub        = 1.0\n",
-      "wgPhase      = 30.0\n",
-      "wgModulation = 0\n",
-      "kLo          = 1\n",
+      "Ntaps        = 12\n",
+      "Ndft         = 192\n",
+      "Ndelays      = 2304\n",
+      "Ncoefs       = 2304\n",
       "\n",
-      "Ros          = 1\n",
-      ". Ndown      = 16\n",
-      ". Ndft       = 16\n",
+      "wgSub        = 1.5\n",
+      "wgPhase      = 0.0\n",
+      "wgModulation = 0\n",
+      "kLo          = 2\n",
       "\n",
-      "Nsim         = 16\n"
+      "Ros          = 4/3\n",
+      ". Ndown      = 144\n",
+      ". Ndft       = 192\n"
      ]
     }
    ],
    "source": [
     "### Filterbank\n",
-    "Ntaps = 8  # number of taps per polyphase FIR filter\n",
-    "Ndft = 16  # DFT size\n",
-    "Ncoefs = Ndft * Ntaps\n",
-    "#Ncoefs = Ncoefs - 1   # try odd length\n",
+    "refBunton = True\n",
+    "#refBunton = False\n",
+    "if refBunton:\n",
+    "    Ntaps = 12\n",
+    "    Ndft = 192\n",
+    "    # To compare SNR results with reconstruct.m\n",
+    "    normalizeDcGain = False\n",
+    "    flipAPrototype = True\n",
+    "    # To compare SNR performance with generic hPrototype\n",
+    "    #normalizeDcGain = True\n",
+    "else:\n",
+    "    Ntaps = 8  # number of taps per polyphase FIR filter\n",
+    "    Ndft = 16  # DFT size\n",
+    "    #Ntaps = 4\n",
+    "    #Ndft = 8\n",
+    "    #Ntaps = 12\n",
+    "    #Ndft = 192\n",
+    "    Ntaps = 32\n",
+    "    # Generic hPrototype is symmetrical, so then coefs flip makes no difference\n",
+    "    flipAPrototype = False\n",
+    "Ndelays = Ndft * Ntaps\n",
+    "Ncoefs = Ndelays\n",
+    "#Ncoefs = Ndelays - 2  # try shorter length to verify impact of hPairGroupDelay\n",
+    "#Ncoefs = Ndelays - 1   # try odd length\n",
+    "print('Ntaps        =', Ntaps)\n",
+    "print('Ndft         =', Ndft)\n",
+    "print('Ndelays      =', Ndelays)\n",
+    "print('Ncoefs       =', Ncoefs)\n",
     "\n",
     "# Waveform generator\n",
-    "wgSub = 1.0 # in range(Nsub)\n",
-    "wgPhase = 30.0  # in degrees\n",
+    "wgSub = 1.5  # in range(Nsub)\n",
+    "wgPhase = 0.0  # in degrees\n",
     "wgModulation = 0  # for amplitude modulation (AM) frequency fsub / wgModulation, use 0 for no AM\n",
+    "                  # use >> 1 for fraction of fsub\n",
+    "print()\n",
     "print('wgSub        =', wgSub)\n",
     "print('wgPhase      =', wgPhase)\n",
     "print('wgModulation =', wgModulation)\n",
@@ -114,10 +196,14 @@
     "print('kLo          =', kLo)\n",
     "\n",
     "# Downsample rate for analysis\n",
-    "Ndown = Ndft * 3 // 4  # oversampled PFB\n",
-    "#Ndown = Ndft * 7 // 8\n",
-    "#Ndown = Ndft // 2\n",
-    "Ndown = Ndft  # Critically sampled PFB\n",
+    "if refBunton:\n",
+    "    Ndown = 144\n",
+    "else:\n",
+    "    Ndown = Ndft * 3 // 4  # oversampled PFB\n",
+    "    #Ndown = Ndft * 7 // 8\n",
+    "    #Ndown = Ndft * 15 // 16\n",
+    "    #Ndown = Ndft // 4\n",
+    "    #Ndown = Ndft  # Critically sampled PFB\n",
     "Ros = Fraction(Ndft, Ndown)\n",
     "print()\n",
     "print('Ros          =', Ros)\n",
@@ -125,20 +211,12 @@
     "print('. Ndft       =', Ndft)\n",
     "\n",
     "# Upsample rate (= downsample rate) for synthesis\n",
-    "Nup = Ndown\n",
-    "\n",
-    "# Time in number of subband periods to simulate, use small Nsim for more detail in point plots,\n",
-    "# use large Nsim for more accurracy in SNR estimates. Force Nsim as multiple of Ndown to avoid\n",
-    "# mismatch in array lengths.\n",
-    "Nsim = ceil_div(2 * Ntaps, Ndown) * Ndown\n",
-    "#Nsim = ceil_div(5 * Ntaps, Ndown) * Ndown\n",
-    "print()\n",
-    "print('Nsim         =', Nsim)"
+    "Nup = Ndown"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "e5680c7b",
    "metadata": {},
    "outputs": [],
@@ -150,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "74ca764f",
    "metadata": {},
    "outputs": [
@@ -158,7 +236,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tsub     = 16.0\n"
+      "Tsub     = 192.0\n",
+      "Tdown    = 144.0\n"
      ]
     }
    ],
@@ -174,30 +253,51 @@
     "# Two single side bands, one keeping only downconverted positive subband frequencies\n",
     "nofSsb = 1 if kLo == 0 else 2\n",
     "\n",
-    "print('Tsub     =', Tsub)"
+    "print('Tsub     =', Tsub)\n",
+    "print('Tdown    =', Tdown)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "786af296",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nsim     =    576 [Tsub]\n",
+      "Nsamples = 110592 [Ts]\n",
+      "Msamples =    768 [Tdown]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Time\n",
-    "# . number input samples to simulate \n",
+    "# Time in number of subband periods to simulate\n",
+    "# . use small Nsim for more detail in point plots,\n",
+    "# . use large Nsim for more accurracy in SNR estimates or when wgSub < 0.5,\n",
+    "# . force Nsim as multiple of Ndown to avoid mismatch in array lengths.\n",
+    "Nsim = ceil_div(3 * Ntaps, Ndown) * Ndown\n",
+    "Nsim *= 4\n",
+    "print('Nsim     = %6d [Tsub]' % Nsim)\n",
+    "\n",
+    "# Time in number input samples to simulate \n",
     "Nsamples = Nsim * Ndft\n",
     "# . input time index n for up rate\n",
     "n_i = np.arange(Nsamples)  # sample index, time in sample period units [Ts]\n",
     "n_s = n_i * Ts  # time in seconds\n",
     "n_sub = n_s / Ndft  # time in subband period units [Tsub]\n",
     "\n",
-    "# . number of downsampled samples\n",
+    "# Time in number of downsampled samples\n",
     "Msamples = Nsamples // Ndown\n",
     "# . downsampled time index m for down rate, n = m D, so m = n // D\n",
     "m_i = np.arange(Msamples)  # downsampled sample index\n",
     "m_s = down(n_s, Ndown)  # = m_i * Tdown, time in seconds\n",
-    "m_sub = m_s / Ndft  # time in subband period units [Tsub]"
+    "m_sub = m_s / Ndft  # time in subband period units [Tsub]\n",
+    "\n",
+    "print('Nsamples = %6d [Ts]' % Nsamples)\n",
+    "print('Msamples = %6d [Tdown]' % Msamples)"
    ]
   },
   {
@@ -210,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "1bb76ada",
    "metadata": {},
    "outputs": [
@@ -218,37 +318,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sum(hProtoype) =  1.0\n",
-      "PASSED\n"
+      "refBunton: is_symmetrical(hPrototype) = False\n",
+      "DC gain = 1.0051947660\n"
      ]
     }
    ],
    "source": [
-    "# Use windowed sync (= firwin) prototype FIR filter\n",
-    "# . For sinc() the ideal bandwidth is 2pi / Ndft = fs / Ndft = fsub, \n",
-    "# . Use half power bandwidth factor hpFactor to tune half power cutoff frequency of LPF.\n",
-    "# . Default hpFactor = 1.0 yields flat filterbank aggregate frequency response for\n",
-    "#   firwin hanning filter\n",
-    "hpFactor = 1.1\n",
-    "hpFactor = 1.0\n",
-    "BWbin = fs / Ndft  # bandwidth of one bin\n",
-    "BWpass = hpFactor * BWbin\n",
-    "fpass = BWpass / 2  # bin at DC: -fpass to +fpass\n",
-    "fcutoff = fpass\n",
-    "hPrototype = signal.firwin(Ncoefs, fcutoff, window='hann', fs=fs)\n",
-    "print('sum(hProtoype) = ', np.sum(hPrototype))\n",
-    "verify_result(is_symmetrical(hPrototype))"
+    "if refBunton:\n",
+    "    # Load LPF prototype FIR filter coefficients from GenPfilter12.m. These coefficients\n",
+    "    # are not symmetrical (so not exactly linear phase), which makes it possible to verify\n",
+    "    # whether the coefficients are applied in the correct order.\n",
+    "    hPrototype = read_coefficients_file('../pfb_bunton_annotated/cl.txt')\n",
+    "    if Ncoefs < Ndelays:\n",
+    "        dDiv = (Ndelays - Ncoefs) // 2\n",
+    "        dRem = (Ndelays - Ncoefs) % 2\n",
+    "        hPrototype = hPrototype[dDiv : -dDiv - dRem]\n",
+    "    if normalizeDcGain:\n",
+    "        hPrototype /= np.sum(hPrototype)\n",
+    "    print('refBunton: is_symmetrical(hPrototype) =', is_symmetrical(hPrototype))\n",
+    "else:\n",
+    "    # Use windowed sync (= firwin) prototype FIR filter\n",
+    "    # . For sinc() the ideal bandwidth is 2pi / Ndft = fs / Ndft = fsub, \n",
+    "    # . Use half power bandwidth factor hpFactor to tune half power cutoff frequency of LPF.\n",
+    "    # . Default hpFactor = 1.0 yields flat filterbank aggregate frequency response for\n",
+    "    #   HFbank firwin hanning filter, but for perfect reconstruction HFpowerbank needs to\n",
+    "    #   be flat.\n",
+    "    # Optimum flat HFpowerbank\n",
+    "    if Ndft == 16 and Ntaps == 8:\n",
+    "        hpFactor = 1.108\n",
+    "    elif Ndft == 16 and Ntaps == 32:\n",
+    "        hpFactor = 1.027\n",
+    "    elif Ndft == 192 and Ntaps == 12:\n",
+    "        hpFactor = 1.065\n",
+    "    else:\n",
+    "        # Default for optimum flat HFbank\n",
+    "        hpFactor = 1.0\n",
+    "    BWbin = fs / Ndft  # bandwidth of one bin\n",
+    "    BWpass = hpFactor * BWbin\n",
+    "    fpass = BWpass / 2  # bin at DC: -fpass to +fpass\n",
+    "    fcutoff = fpass\n",
+    "    hPrototype = signal.firwin(Ncoefs, fcutoff, window='hann', fs=fs)\n",
+    "    verify_result(is_symmetrical(hPrototype), ': is_symmetrical(hPrototype)', enExit)\n",
+    "print('DC gain = %.10f' % np.sum(hPrototype))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "6ebc94aa",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -273,13 +395,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
+   "id": "03da0b30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hGroupDelay     = 1151.5 samples\n",
+      "hPairGroupDelay = 2303 samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Single FIR filter group delay\n",
+    "hGroupDelay = (Ncoefs - 1) / 2\n",
+    "\n",
+    "# Group delay for LPF down and LPF up in series, is integer for Ncoefs is even and odd\n",
+    "hPairGroupDelay = Ncoefs - 1  # = 2 * hGroupDelay\n",
+    "\n",
+    "print('hGroupDelay     = %.1f samples' % hGroupDelay)\n",
+    "print('hPairGroupDelay = %d samples' % hPairGroupDelay)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "id": "3abeee86",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DC gain = 1.0051947660\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filterbank aggregate frequency response\n",
+    "h, f, HFprototype = dtft(hPrototype)\n",
+    "print('DC gain = %.10f' % np.abs(HFprototype[len(HFprototype) // 2]))\n",
+    "HFbank = filterbank_frequency_response(HFprototype, Ndft)\n",
+    "\n",
+    "# Magnitude squared response, to have correlator (power) response.\n",
+    "HFpowerbank = filterbank_frequency_response(HFprototype**2, Ndft)\n",
+    "\n",
+    "# Filterbank bin 1, 2 frequency responses, HFprototype is for bin 0\n",
+    "Lprototype = len(HFprototype)\n",
+    "Lbin = Lprototype // Ndft\n",
+    "HF1 = np.roll(HFprototype, 1*Lbin)\n",
+    "HF2 = np.roll(HFprototype, 2*Lbin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8008d6b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -289,7 +469,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -299,43 +479,39 @@
     }
    ],
    "source": [
-    "# Filterbank aggregate frequency response\n",
-    "h, f, HFprototype = dtft(hPrototype)\n",
-    "HFbank = filterbank_frequency_response(HFprototype, Ndft)\n",
-    "\n",
-    "# Filterbank bin 1, 2 frequency responses, HFprototype is for bin 0\n",
-    "Lprototype = len(HFprototype)\n",
-    "Lbin = Lprototype // Ndft\n",
-    "HF1 = np.roll(HFprototype, 1*Lbin)\n",
-    "HF2 = np.roll(HFprototype, 2*Lbin)\n",
-    "\n",
     "# Plot transfer function (use frequency in fsub units)\n",
     "fLim = None\n",
-    "fLim = (0, 2)\n",
     "dbLim = None\n",
-    "dbLim = (-100, 5)\n",
     "voltLim = None\n",
+    "\n",
     "plt.figure(1)\n",
+    "fLim = (0, 2)\n",
+    "if Ntaps <= 32:\n",
+    "    dbLim = (-120, 5)\n",
     "plot_power_spectrum(f, HFprototype, 'r--', fs / fsub, fLim, dbLim)  # bin 0\n",
     "plot_power_spectrum(f, HF1, 'b--', fs / fsub, fLim, dbLim)  # bin 1\n",
     "plot_power_spectrum(f, HF2, 'm--', fs / fsub, fLim, dbLim)  # bin 2\n",
     "plot_power_spectrum(f, HFbank, 'g', fs / fsub, fLim, dbLim)  # all bins\n",
+    "\n",
     "plt.figure(2)\n",
+    "#fLim = (0, 1)\n",
+    "voltLim = (0.9, 1.1)\n",
     "plot_magnitude_spectrum(f, HFprototype, 'r--', fs / fsub, fLim, voltLim)  # bin 0\n",
     "plot_magnitude_spectrum(f, HF1, 'b--', fs / fsub, fLim, voltLim)  # bin 1\n",
     "plot_magnitude_spectrum(f, HF2, 'm--', fs / fsub, fLim, voltLim)  # bin 2\n",
-    "plot_magnitude_spectrum(f, HFbank, 'g', fs / fsub, fLim, voltLim)  # all bins"
+    "plot_magnitude_spectrum(f, HFbank, 'g--', fs / fsub, fLim, voltLim)  # all bins\n",
+    "plot_magnitude_spectrum(f, HFpowerbank, 'g', fs / fsub, fLim, voltLim)  # all bins"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "372445f4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -346,11 +522,77 @@
    ],
    "source": [
     "fLim = None\n",
-    "#fLim = [-1.1, 1.1]\n",
+    "#fLim = [-2.1, 2.1]\n",
     "aLim = None\n",
     "plot_phase_spectrum(f, HFprototype, fmt='g', fs=fs / fsub, fLim=fLim, aLim=aLim)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2c7b858c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FIR filter group delay = 1151.500000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_27035/4263550011.py:2: UserWarning: The filter's denominator is extremely small at frequencies [0.270, 0.295, 0.319, 0.344, 0.368, 0.393, 0.417, 0.442, 0.466, 0.491, 0.515, 0.540, 0.565, 0.589, 0.614, 0.638, 0.663, 0.687, 0.712, 0.736, 0.761, 0.785, 0.810, 0.834, 0.859, 0.884, 0.908, 0.933, 0.957, 0.982, 1.006, 1.031, 1.055, 1.080, 1.104, 1.129, 1.154, 1.178, 1.203, 1.227, 1.252, 1.276, 1.301, 1.325, 1.350, 1.374, 1.399, 1.424, 1.448, 1.473, 1.497, 1.522, 1.546, 1.571, 1.595, 1.620, 1.644, 1.669, 1.694, 1.718, 1.743, 1.767, 1.792, 1.816, 1.841, 1.865, 1.890, 1.914, 1.939, 1.963, 1.988, 2.013, 2.037, 2.062, 2.086, 2.111, 2.135, 2.160, 2.184, 2.209, 2.233, 2.258, 2.283, 2.307, 2.332, 2.356, 2.381, 2.405, 2.430, 2.454, 2.479, 2.503, 2.528, 2.553, 2.577, 2.602, 2.626, 2.651, 2.675, 2.700, 2.724, 2.749, 2.773, 2.798, 2.823, 2.847, 2.872, 2.896, 2.921, 2.945, 2.970, 2.994, 3.019, 3.043, 3.068, 3.093, 3.117],             around which a singularity may be present\n",
+      "  w, gd = signal.group_delay((hPrototype, [1.0]), w=1024, fs=1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# FIR filter group delay\n",
+    "w, gd = signal.group_delay((hPrototype, [1.0]), w=1024, fs=1)\n",
+    "#w, gd = signal.group_delay((np.convolve(hPrototype, hPrototype), [1.0]), w=1024, fs=1)\n",
+    "#w, gd = signal.group_delay((np.convolve(hPrototype, np.flip(hPrototype)), [1.0]), w=1024, fs=1)\n",
+    "#plt.title('FIR filter group delay')\n",
+    "plt.plot(w, gd)\n",
+    "plt.ylabel('Group delay [samples]')\n",
+    "plt.xlabel('Frequency [rad/sample]')\n",
+    "if refBunton:\n",
+    "    plt.ylim([0, 5000])\n",
+    "print('FIR filter group delay = %f' % hGroupDelay)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fa4b54d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select impulse response for downconverter / analysys and for upconverter / synthesis\n",
+    "# . Use flipped aPrototype to make SNR results for refBunton with CW equal to reconstruct.m.\n",
+    "#   The SNR improves slightly for flipped aPrototype, because refBunton hPrototype is\n",
+    "#   asymmetrical. The combination of convolute(np.flip(hPrototype), hPrototype) yields an\n",
+    "#   overall linear phase, so constant group delay for the reconstructed signal.\n",
+    "if flipAPrototype:\n",
+    "    aPrototype = np.flip(hPrototype)\n",
+    "else:\n",
+    "    aPrototype = hPrototype\n",
+    "sPrototype = hPrototype\n",
+    "#hPrototype"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ce7e672e",
@@ -361,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "id": "e74340e4",
    "metadata": {},
    "outputs": [],
@@ -373,6 +615,7 @@
     "wgFreq = wgSub * fsub  # in Hz\n",
     "wgAmpl = 1\n",
     "xData = wgAmpl * np.cos(2 * np.pi * wgFreq * n_s + np.radians(wgPhase))\n",
+    "#xData = n_i\n",
     "# Apply some modulation\n",
     "if wgModulation:\n",
     "    xData *= 1 + 0.5 * np.cos(2 * np.pi * fsub / wgModulation * n_s)"
@@ -380,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "id": "bd2add56",
    "metadata": {},
    "outputs": [],
@@ -405,23 +648,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "id": "7106ad3f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fe4df738970>]"
+       "(0.0, 10.0)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -432,7 +675,7 @@
    ],
    "source": [
     "plt.plot(n_sub, xData, 'b.-')\n",
-    "#plt.xlim([0,5])"
+    "plt.xlim([0,10])"
    ]
   },
   {
@@ -455,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "da53b25e",
    "metadata": {},
    "outputs": [],
@@ -467,23 +710,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "9acf0ec2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fe4df669f10>"
+       "<matplotlib.legend.Legend at 0x7fb4e035dee0>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -495,7 +738,7 @@
    "source": [
     "# x[n] --> LO --> LPF --> D --> y[mD, k] [HARRIS Fig 6.2] \n",
     "xLoData = xData * LO\n",
-    "yData = signal.lfilter(hPrototype, [1.0], xLoData)  # = y[n, k], Eq. 6.1\n",
+    "yData = signal.lfilter(aPrototype, [1.0], xLoData)  # = y[n, k], Eq. 6.1\n",
     "yDown = down(yData, Ndown)  # = y[mD, k]\n",
     "\n",
     "plt.plot(n_sub, yData.real, 'g-')\n",
@@ -523,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "b8036250",
    "metadata": {},
    "outputs": [
@@ -531,12 +774,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ndft = 16\n",
-      "Ndown = 16\n",
-      "w_k = 0.39269908169872414\n",
-      "D_w_k = 6.283185307179586\n",
+      "Ndft = 192\n",
+      "Ndown = 144\n",
+      "w_k = 0.06544984694978735\n",
+      "D_w_k = 9.42477796076938\n",
       "\n",
-      "PASSED\n"
+      "PASSED: LOdown == LoD\n"
      ]
     }
    ],
@@ -563,23 +806,15 @@
     "    plt.plot(m_sub, LOdown.imag, 'r--')\n",
     "    plt.plot(m_sub, loD.real, 'g-')\n",
     "    plt.plot(m_sub, loD.imag, 'g--')\n",
-    "verify_result(result)"
+    "verify_result(result, ': LOdown == LoD', enExit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "0663df66",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASSED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Verify that LOdown == 1 when Ndown == Ndft\n",
     "if Ndown == Ndft:\n",
@@ -587,12 +822,12 @@
     "    if not result:\n",
     "        plt.plot(m_sub, LOdown.real, 'r-')\n",
     "        plt.plot(m_sub, LOdown.imag, 'r--')\n",
-    "    verify_result(result)\n"
+    "    verify_result(result, ': LOdown == 1, for Ros = 1', enExit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "id": "3a039428",
    "metadata": {},
    "outputs": [
@@ -600,20 +835,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PASSED\n"
+      "PASSED: yDown == yBpfDownLo, for Ros >= 1\n"
      ]
     }
    ],
    "source": [
     "# x[n] --> BPF --> D --> LOdown --> y[m D, k] [HARRIS Fig 6.7]\n",
-    "hBpf = hPrototype * np.exp(1j * w_k * np.arange(Ncoefs))\n",
-    "yBpfData = signal.lfilter(hBpf, [1.0], xData)\n",
+    "aBpf = aPrototype * np.exp(1j * w_k * np.arange(Ncoefs))\n",
+    "yBpfData = signal.lfilter(aBpf, [1.0], xData)\n",
     "yBpfDown = down(yBpfData, Ndown)\n",
     "yBpfDownLo = yBpfDown * LOdown  # = y[m D, k]\n",
     "\n",
     "# result is True for any Ndft, Ndown, because LOdown is in equation of yBpfDownLo\n",
     "result = np.all(np.isclose(yDown, yBpfDownLo))\n",
-    "verify_result(result)"
+    "verify_result(result, ': yDown == yBpfDownLo, for Ros >= 1', enExit)"
    ]
   },
   {
@@ -636,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "id": "327236c2",
    "metadata": {},
    "outputs": [
@@ -644,23 +879,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ndft = 16\n",
-      "> Log maximal_downsample_bpf():\n",
-      "  . len(x)  = 256\n",
-      "  . Nx      = 241\n",
-      "  . Nxp     = 16\n",
-      "  . len(yc) = 16\n",
-      "  . Ndown   = 16\n",
-      "  . k       = 1\n",
-      "\n",
-      "PASSED\n"
+      "Ros = 4/3\n"
      ]
     }
    ],
    "source": [
-    "print('Ndft =', Ndft)\n",
+    "print('Ros =', Ros)\n",
     "if Ndown == Ndft:\n",
-    "    yMaxDownBpf = maximal_downsample_bpf(xData, Ndown, kLo, hPrototype)\n",
+    "    yMaxDownBpf = maximal_downsample_bpf(xData, Ndown, kLo, aPrototype)\n",
     "    yMaxDownBpfLo = yMaxDownBpf  # = yMaxDownBpf * LOdown, because LOdown = 1 when Ndown == Ndft\n",
     "\n",
     "    result = np.all(np.isclose(yDown, yMaxDownBpfLo))\n",
@@ -669,7 +895,7 @@
     "        plt.plot(m_sub, yDown.imag, 'g.--')\n",
     "        plt.plot(m_sub, yMaxDownBpfLo.real, 'r-')\n",
     "        plt.plot(m_sub, yMaxDownBpfLo.imag, 'r--')\n",
-    "    verify_result(result)"
+    "    verify_result(result, ': yDown == yMaxDownBpfLo, for Ros = 1', enExit)"
    ]
   },
   {
@@ -682,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "id": "aefa8615",
    "metadata": {},
    "outputs": [
@@ -691,50 +917,43 @@
      "output_type": "stream",
      "text": [
       "> non_maximal_downsample_bpf():\n",
-      "  . len(x)   = 256\n",
-      "  . Nx       = 241\n",
-      "  . Nblocks  = 16\n",
-      "  . len(yc)  = 16\n",
-      "  . Ndown    = 16\n",
-      "  . Ndft     = 16\n",
-      "  . k        = 1\n",
+      "  . len(x)   = 110592\n",
+      "  . Nx       = 110449\n",
+      "  . Nblocks  = 768\n",
+      "  . len(yc)  = 768\n",
+      "  . Ros      = 1.3333333333333333\n",
+      "  . Ndown    = 144\n",
+      "  . Ndft     = 192\n",
+      "  . k        = 2\n",
       "\n",
-      "PASSED\n"
+      "PASSED: yDown == yDownBpfLo for any Ros\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fe4ddd08850>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 700x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "yDownBpf = non_maximal_downsample_bpf(xData, Ndown, kLo, Ndft, hPrototype)\n",
+    "yDownBpf = non_maximal_downsample_bpf(xData, Ndown, kLo, Ndft, aPrototype)\n",
     "yDownBpfLo = yDownBpf  # = yDownBpf * LOdown * LOshift, because LOdown = 1 when Ndown == Ndft\n",
     "                       # and LOshift phase shifts compensate for time shift due to Ndown < Ndft\n",
     "\n",
     "result = np.all(np.isclose(yDown, yDownBpfLo))\n",
-    "verify_result(result)\n",
-    "\n",
-    "plt.plot(m_sub, yDown.real, 'g.-')\n",
-    "plt.plot(m_sub, yDown.imag, 'g.--')\n",
-    "plt.plot(m_sub, yDownBpfLo.real, 'r-')\n",
-    "plt.plot(m_sub, yDownBpfLo.imag, 'r--')"
+    "if not result:\n",
+    "    plt.plot(m_sub, yDown.real, 'g.-')\n",
+    "    plt.plot(m_sub, yDown.imag, 'g.--')\n",
+    "    plt.plot(m_sub, yDownBpfLo.real, 'r-')\n",
+    "    plt.plot(m_sub, yDownBpfLo.imag, 'r--')\n",
+    "verify_result(result, ': yDown == yDownBpfLo for any Ros', enExit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "860a1f90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only check bin phase and bin ampl for CW at center of bin, and with averaging over \n",
+    "# least Ntaps number of subband periods\n",
+    "verifyBin = wgSubIsInt and not wgModulation and SNR_WG_dB >= 100 and Nsim >= 2 * Ntaps"
    ]
   },
   {
@@ -745,70 +964,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "id": "f814c9b9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wgSub = 1.0, SNR_WG_dB = 100.0 dB, Nsim = 16 [Tsub]\n",
-      "binPhase = 30.001, diffPhase = 0.001, phaseMargin = 1.000 [deg], snrPhase_dB = 57.9 dB\n",
-      "PASSED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Verify subband bin phase\n",
-    "print('wgSub = %.1f, SNR_WG_dB = %.1f dB, Nsim = %d [Tsub]' % (wgSub, SNR_WG_dB, Nsim))\n",
-    "if wgSubIsInt and not wgModulation: \n",
+    "if verifyBin: \n",
+    "    print('wgSub = %.1f, Nsim = %d [Tsub]' % (wgSub, Nsim))\n",
     "    # The phaseMargin >> c_atol, because it depends on the stop band attenuation of the\n",
-    "    # hPrototype LPF. This is because the LO downconverts the positive frequency band\n",
+    "    # aPrototype LPF. This is because the LO downconverts the positive frequency band\n",
     "    # of the WG cos() wave, so the negative frequency band will appear in the stop band.\n",
-    "    if SNR_WG_dB < 100: \n",
-    "        # Determine some appropriate phase margin dependent on SNR.\n",
-    "        phaseMargin = 360 / 10**(SNR_WG_dB / 20)\n",
-    "    else:\n",
-    "        # Use fixed approriate phase margin for carrier only, so with no AWGN.\n",
-    "        phaseMargin = 1  # 0.01\n",
-    "    # Only check phase margin when it can be averaged over at least Ntaps number of subband periods\n",
-    "    if Nsim >= 2 * Ntaps:\n",
-    "        binPhase = np.mean(np.angle(yDownBpfLo[Ntaps:], deg=True))\n",
-    "        diffPhase = np.abs(wgPhase - binPhase)\n",
-    "        snrPhase_dB = pow_db(1 / diffPhase)\n",
-    "        print('binPhase = %.3f, diffPhase = %.3f, phaseMargin = %.3f [deg], snrPhase_dB = %.1f dB' %\n",
-    "              (binPhase, diffPhase, phaseMargin, snrPhase_dB))\n",
-    "        result = np.isclose(wgPhase, binPhase, atol=phaseMargin)\n",
-    "        verify_result(result)\n",
-    "    else:\n",
-    "        print('No snrPhase_dB estimate feasible.')"
+    "    # Use fixed approriate phase margin for carrier only, so with no AWGN.\n",
+    "    phaseMargin = 1  # 0.01\n",
+    "    binPhase = np.mean(np.angle(yDownBpfLo[Ntaps:], deg=True))\n",
+    "    diffPhase = np.abs(wgPhase - binPhase)\n",
+    "    snrPhase_dB = pow_db(1 / diffPhase)\n",
+    "    print('binPhase = %.3f, diffPhase = %.3f, phaseMargin = %.3f [deg], snrPhase_dB = %.1f dB' %\n",
+    "           (binPhase, diffPhase, phaseMargin, snrPhase_dB))\n",
+    "    result = np.isclose(wgPhase, binPhase, atol=phaseMargin)\n",
+    "    verify_result(result, ': wgPhase ~= binPhase', enExit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "id": "dd7a9503",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wgAmpl = 1.000, binAmpl = 0.499999, amplMargin = 0.010\n",
-      "PASSED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Verify subband bin amplitude\n",
-    "amplMargin = wgAmpl * 0.01\n",
-    "if wgSubIsInt and Nsim >= 2 * Ntaps and not wgModulation:\n",
-    "    # Only when it can be averaged over at least Ntaps number of subband periods\n",
+    "if verifyBin:\n",
+    "    print('wgSub = %.1f, Nsim = %d [Tsub]' % (wgSub, Nsim))\n",
+    "    amplMargin = wgAmpl * 0.01\n",
     "    binAmpl = np.mean(np.abs(yDownBpfLo[Ntaps:]))\n",
     "    print('wgAmpl = %.3f, binAmpl = %f, amplMargin = %.3f' % (wgAmpl, binAmpl, amplMargin))\n",
     "    result = np.isclose(wgAmpl / nofSsb, binAmpl, atol=amplMargin)\n",
-    "    verify_result(result)"
+    "    verify_result(result, ': wgAmpl / nofSsb ~= binAmpl', enExit)"
    ]
   },
   {
@@ -831,39 +1023,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "id": "3cf6aa74",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Up mixer local oscillator (LO) for channel k\n",
-    "LOp = 1 / LO  # p = positive sign in LOp = np.exp(+1j * w_k * n_s)"
+    "# . LOp = 1 / LO with p to indicate positive sign in LOp = np.exp(+1j * w_k * n_s)\n",
+    "# . Adjust LOp by hPairGroupDelay to be able to align reconstructed output data with input data\n",
+    "LOadjust = np.exp(-1j * w_k * hPairGroupDelay * Ts)\n",
+    "LOp = np.exp(+1j * w_k * n_s)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "03da0b30",
+   "execution_count": 31,
+   "id": "373ab5bb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "groupDelay = 112.0 samples\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Group delay of single channel downconverter and upconverter in series\n",
-    "groupDelay = (Ncoefs - Ndft) / 2 + (Ncoefs - Ndft) / 2  # for LPF down and LPF up\n",
-    "print('groupDelay = ' + str(groupDelay) + ' samples')\n",
-    "intGroupDelay = int(groupDelay)"
+    "# Back to back, down converter up converter, reference output yrUpLpfLo\n",
+    "# With (inefficient) processing at full rate, because down sampling is done after LO and LPF and\n",
+    "# upsampling is done before LPF and LOp. \n",
+    "#   yDown         ycUp        ycUpLpf\n",
+    "# y[mD, k] --> U ------> LPF --------> LOp --> ycUpLpfLo\n",
+    "ycUp = up(yDown, Nup)  # insert Nup - 1 zeros\n",
+    "ycUpLpf = Nup * signal.lfilter(sPrototype, [1.0], ycUp)  # interpolate by Nup\n",
+    "ycUpLpfLo = ycUpLpf * LOp * LOadjust # upconvert to positive bin kLo\n",
+    "yrUpLpfLo = ycUpLpfLo.real * nofSsb  # = ycUpLpfLo + np.conj(ycUpLpfLo), add negative bin -kLo"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 32,
    "id": "a5c60be5",
    "metadata": {},
    "outputs": [
@@ -871,12 +1063,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.989816226637721\n"
+      "wgSub  = 1.500000\n",
+      "kLo    = 2\n",
+      "xLen   = 105985 input samples\n",
+      "yrSNR  = 5.37011 [dB], single bin\n",
+      "yrAmpl = 0.461804\n"
      ]
     },
     {
      "data": {
-      "image/png": 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SjWldsnjqvUkNx9esWWPztTt5GlizzUd+LVk2l419/wu8vWHaNAgLs/n6gsbYwzMtUFNaxcd75bb63ouX8DTat6t84PnB/PqhFADe+Nd0Ssf6OaTd6sUvV8IWz/a8F8XJmol0NVTyXyun2E+UwGGIIEzgUMqzi/g8zg+A//n1jUbZ8OfNm2eXOqY82o//nXUAgJ9e/xyjqZRpqesIC/paBGJ2xl6eqZ31vzjBBesQBrhf5dVg+42C3c7/2z2dMZ7nKWAgo7LC8TaV2L3d6sUvV6KjnkkWKx8u7wXAr+acoteQLvaUJXAQIggTOJQvX725vN+YQ9AHUxt9d/LkSbvVM7bgWwCseGDGyCmmEEQYYb85brc6BPb1TK3UVZr4aNsYAN5dkIOxq2O22O3c1Y1F3eRgq5RemPC2e7vVg1+uRkc9O/TBceJN0/DmBr/5z2Q7qxI4ChGECRxG1cUSPj16PwD//cuyJsv7+/XrZ7e6/nLxpw1ZoQEk3DBg4c8XFtmtDoF9PVMr37x9nGzLCHq7lfDzkHsdW1fJY+DAdqsHv1yNDnkmSfy/z+SRrzf8TtJvVDc7qxI4ChGECRzGitfrl/df4IW/Tm/yvfudu8faQHMbLEu439zAVmAv7OmZGrGaa/nL18MA+M2TGXTp6enQ+jKb2WDZnu3W1f1yRTri2bGPY4iqug9PzLy7crwDVAkchQjCBA7BVFTBsgPy68f3X7mCh2fTppafn2+3+sYOrW40EgZio1pHYE/P1EjYb6I4W7+8/9/THV6fo9utq/vlirTbM0niw4/kwO1n079j0OSeDlAlcBQiCBM4hFVvxFAo+TLEo4CffNb8K53777/fbvUt/bTHzVc5t7/aERvV2ht7eqY2pDoLH670BWDxo2l0H+Dt8DqbtlvJru3Wlf1yVdrrWdyXiRws98ODWt5bMdpBqgSOQgRhArtTU3aDj3dNAOC9Fy7g6d388PqePXvsVueCBbBtm7xBrZtB/kGbPyJF7JNmZ+zpmdrY+98nOFkzga5Usnjl1NZPsAP17XbiBAmQAAP/+u9Ldmu3ruyXq9Jezz78QN6y7ScTkhg+o68jJAkciAjCBHZn/S+jG5b3/+zL+1os99prr9m13gUL5A1qN70nry46dcEHyeL4TZL1hL09UwuSxcqHwd0BePPBVHoP6+q0uhcsgNNp7jzS5xQAlXFn7HZtV/XLlWmPZ8mrktlzzR83LPx++TAHqhI4ChGECexKXZWZj7bKQ+Lvzj+Ht0/L+5Z99dVXDtHw5LuTMFLNOctITn0tlujbE0d5pjSHP4whrrp+ef+k1k9wAEHfNwGwLXqA3a7pqn65Mu3x7P/+uxKA50clMnaur6MkCRyICMIEdiNsSSwjfYrJtgzHnToGtPJb4qjNhbv28mTeEHk0Ydvyqw6pQ6+42obQYUtimeadwfc+mAXAI/3T6D+2uyJa5v9+HAasxFVP4+KRLLtc09X80gNt8SxsSSzjPHMIvfIgADPvqXW0LIGDEEGYwC6ELYklaNksLlrlfcssuPFisD9hS2JbPMeRmwsHLZD/DP1uBEiSw+rRG660IXR9mz1lGoOEOyCx98r9d22zjsR3fHce6ClvSB/29xy7XNOV/NILrXlW324za0cABkDi16EPKtZuBbYhgjCBXfjTFz1vrvCqT8h6M+nkFy0vlw4KCnKYnqfem0QnakirHUt66GmH1aM3HOmZs6lvs7fyyxlabbOOJuh78uulbUftM8HalfzSC6151rSvVb7dCjqOCMIEdiHTNKz5ZKmmlieLnjhxwmF6evh689gAOfja9kWBw+rRG470zNl0pM06mgW/HwvA8arpXI7Ls/l6ruSXXmjNMzW2W0HHEUGYwC6M9cpDXmJ/CwMWxhlb/iEZNWqUQzUtfKYOgG3xg8UrSTvhaM+cyVhjXvOJUu/SZh3N0Om9mOlzFgk3dnycYfP1XMkvvdCaZ2ON7e9rBepFBGECu/CTR/Kpn58Acqcg4c7SxaUtnlNVVeVQTc/8fgLu1JFsnkROuO0/aALHe+ZMlr5d2mhEoS1t1hkEPSLXH3rY9tdLruSXXmjNs9+/WsStV5HqabeCjiGCMIFdMJvk4MuHCoyYmGo8R9iSWOZ/PKvFc8rLyx2qqffwbjzU5+Yqyc8uOLQuveBoz5zJ/L/60ZtiADpR06Y26wyClsgpXr4tv4drp2x7le5KfumF1jzr2ccDkNtsW/tagXoRQZjALoQn9AHgk5+coloyklI9rtVOYdw4x2+uHfSkvAffthP9HV6XHnCGZ84ibXsG1+iDFybKSqQ2tVlnMGp2P6Z1ycKCBzs/SrPpWq7kl15ozbPwMLlPWzQxvs19rUC9iCBMYDOlWcXEVE4GIPDttu9ddvToUUdJamD+++Nv5V76Ntvh9bk6zvDMWYT/R94o+aG+Z+jc00thNY0JCigCYFtEF5uu40p+6YW7eiZJhJ8dDkDgs0bnCBI4FBGECWzm4D/PYsWdiV7ZDJ3R9hGn559/3oGqZAaM78GDPeVXkmF/s0/uJT3jDM+cRXh0DwAC51YrK6QZFv5OXul2sOQ+yjI7nnDYlfzSC3fzLPtgDll1I/CglkffmuBEVQJHIYIwgc2E75PngwVOy2/XeatXr3aEnCbcyr3U2yn1uTLO8szRVFwsI+r6FAACf6G+pf0THh3EBGMOtXiy568dz3PnKn7pibt5Fv6VvALywZ5n8PG1bZRUoA5EECawCWudlf258hyGwBd6tOtcZ22p0pB7qXI6l+PFBH1bcJVtcI4Ep1GLJyM7XWDMI0OUltMsQf6XAdi2p+OvSl3FLz1xN8/Co+TAK/CBCmfJETgYEYQJbOLk5rNclvrThUoefL19w+PO2lJlyPTezOxmv9xLesZVtsEJ31UDQOCEPAyGVgorRNB/DQZgf9G9VF4o6dA1XMUvPdGSZ6ZrVUQW3xy9fW2QMyUJHIgIwgQ2Eb7mCgCP+qbh1bVTu8798Y9/7AhJzbLw0frcSz2cVqcr4kzPHIVkldifJSfEDAzqrLCalpn29FBGeV7AhDf7/praoWu4gl96oyXPjn55mmo6M8i9kMlPj3CyKoGjEEGYwCbC43sBEPhITbvP3b9/v73ltEjQEvlH99vr93DtdKHT6nU1nOmZo0jflUmeZQhemHj4rUlKy2kRgwGCZlwEYNsO9w5dwxX80hsteRa+7QYA88bkYHBT6fCtoN2IIEzQYcpySoipuJma4q32b48yffp0OytqmZGz+zO9S6Zdci/pGWd65ijCV8oLSOb2OUPnXupe5h/0li8AewvvofpK+xOvuoJfeqNZzySJ8LPyApLAZzydK0jgUEQQJugwBz9Pw4IHE7yyGTbLt93nX7582QGqWqYh99J+9b6CUjvO9swRhEf7ABA454bCSlpnxg9HMMSjgCq6cuDj5Haf7wp+6Y3mPMs5cp7M2pF4UMtjb4vUFK6ECMIEHSZ8r7z5ceCU9qWmqEdy8qbaC383HJBzL10/V+TUul0FZ3tmbyoLyjlWqt7UFHdicDMQdE8uANu2tf/fXut+6ZHmPAtffh4A/x5pdB/U1cmKBI5EBGGCDiFZrOzPlVM/BD7v06Fr+Pq2f/TMFsY/OoiJxmxq8WT3/51yat2ugrM9szeRwWeowYsRHhcZ+9hQpeW0iaCfy1uC7bownZrS9m3IrXW/9EhznoUfk0fvA2dfd7YcgYMRQZigQ5z8Jp1C6wC6UEnAzyd26BrJye1/vWIrQbPlSfnb9qprmxqtoIRn9iR8hxmAwAnnVZua4k78F41lgPtVrtODw39r37+/1v3SI3d6Ziqt5kjRzdHbVwcqIUngQEQQJugQ4avleQuPDEjDq1vHJoo++eST9pTUJoLeuZl76eq9VF4sdXr9WkcJz+yFZJUIzxwJQOACb4XVtB03dwPzJ58DYNvm2nadq2W/9Mqdnh37l5yaYqD7ZabOb/8CKIG6EUGYoEPcSk1h7vA1NmzYYC85bWbq08MZ7ZmHCW/C/3rS6fVrHSU8sxcZe7I4bxmKJ2ZVp6ZojoWvdgdgR+5U6qrafs9p2S+9cqdn4aHytmvzRp8TqSlcEBGECdpNWW4p0eVyaop5b3b8yUyJLVVuz70U2sHcS3pGy9vgNKSm6H2GLn20MxIGMOfnE+jtVsI1qTdH//Fdm8/Tsl965U7Pws/IcxcDfyBSU7giIggTtJtD/5RTU4zzzGHEAx2fo6DUlipBvxoAwN6Ce6i+KvZgaw9a3gYn/Hg3AAIDKhVW0n48PN14dnw6ANvWV7f5PC37pVdu9yw38jwZtaNwp06kpnBRRBAmaDfheywABE6+ZNN1Fi1aZAc17ef+50fRx62YKrrSvb8X07wzCFsSq4gWraGUZ7ZSdbmCo/WpKd5Q54bdrRH0Uzk1wb8zAjAaTG1qt1r1S8/c7llDaoruZ+gxpJsyggQORQRhgnYhWazszxkDQOAPbesUtm7dag9J7Wb77+MotsrL/mvx5JRpDEHLZolArA0o5Zmt1KemGO5xkXFPDFdaToeouCynp6ijE2aMbWq3WvVLz9zuWfi38mvzwNllCqkROBoRhAnaRWpoJgVWXzpTxZxfdCw1RT0PPvignVS1jz990RMD1ob/l3DDgIU/f9FTET1aQinPbCV8uwmAwPG5mp3c/JevegG3Enm2pd1q1S89U++ZuayaI0U3t4X7mUhN4aqIIEzQLsJXyXm2Hu6fhrG7bbm2srOz7SGp3WSahiHd0fQl3MkwqT+DutIo5ZktSFaJ8IybqSnma2tC/u1kmoYBjQPI1tqtFv3SO/WeRS0/zQ264Ot2mWlBoxVWJXAUIggTtIvwWPmpO/Ahk83X6tKli83X6AhjjXmNRsIADFgYZ8xTRI+WUMozW8jcn0PuzdQUj7ytrdQUt9ORdqtFv/ROvWfhW0VqCj0ggjBBm7meV8aJm6kpAt8cYfP1unVTZqLp0rdLb77Kqf9Bk5BwZ+likby1NZTyzBbCV1wAYE6vM3Tpq93N2+vbbf0rSQPWVtutFv3SO/WehZ+RF5AEPt1JSTkCByOCMEGbqU9NMdYzl5FzBtt8vaysLDuoaj8LPpnFtndjGd1J/nF2x0Lo72KY//EsRfRoCaU8s4XwqJupKR7UdjqS+nbbyyAHXX0N1whbEnvXdqtFv/ROVlYWeVEXOFszGnfqePzt8UpLEjgQEYQJ2kz47joAAiddtMv15syZY5frdIQFn8ziTPkQOlMl5zyb2UMxLVpCSc86wo2rlRwtuTl6q9HUFLez4JNZ/P31DABGdS5s9cFBa34JZM/Cv8wBYLZPGj2GdVdYkcCRiCBM0CYkq8T+bHlyaOBzXe1yTaWXz3cyujO7dyYAUVsvK6pFKyjtWXuJDD6DGSPD3C8xPtD2V+hqIOAleSJ+YtV4qovunnhWa34JZM/CvzUCEDhLTJFwdUQQJmgTp7Zlkm8diDc3mPumfSY3q2FLlYBp8iuqqFgx76ItqMGz9hC+Xc4uHzgux2UmN498cCC+7leoxZO4tel3Las1vwTw+k9e4/CV+tQUvgqrETgaEYQJ2kT4qgIAHu53xubUFPWoYUuVgKd7ABCVPwLJKt29sEAVnrUZSWJ/+nAAAucbldViRwwGmDM4F4CoPdfvWlZTfgkA+PinK6iiKwPcrjD9uTFKyxE4GBGECdpEeEwPAALntn3futb4+c9/brdrdZRZPx2LB7Vcsg4i79tcpeWoHjV41layDuSSXTdc86kpmiNgtjw/MyrV567ltOSXQKbskjwKNm9UlsuM3gpaxi5BmNls5r333mPgwIF4e3vj5+fHwYMHWz3v22+/xWAwNPuJjRVbyKiF8ovXOXH95vD4L4fb7bqrVq2y27U6SudeRu7vJk90jtponwUHrowaPGsr4SHy6teAXmfo2t+18mUFPC9nUI8pHU9dlbnFclrySyCzL3UQAIFPeSisROAM7OLyokWLCA0N5Z133mHMmDGsWbOGJ598ksjIyDZtm7F48WJmzJjR6Njo0SJDsFo49M806pjNmE65jHrYfpObAwMD7XYtWwiYWEJsHEQdk/iJ0mJUjlo8awvhx+TAK9C/XGEl9mfy0yPoYSijTOpByuaT3P/qtGbLackvAVw4cZGMunG4YeHxxROUliNwAjaPhMXHx7N582Y++ugjli1bxhtvvMGRI0cYNmwYS5YsadM1AgICeOmllxp9+vTpY6s0gR0IWxLL638bB0BJnY9dN7lOTk6227VsIWCe/GN97Lz2Uxg4GrV4djfClsQyxZjJ/mv3A+Dhgmsu3NwNPNDvHADHtl9rsZwW/BLIhC2JxX+OOwBGTET+66zCigTOwOYgLDQ0FHd3d954442GY0ajkVdffZWYmBguXmzbK56Kigrq6upslSOwI2FLYglaNosS5K2KSqSeBC2bZbdAzNdXHSt/HlgkT37NqB3F1VSRquJuqMWzlqhvs2fMo5H3WZR4Z/tDdn14UAsB98vzM6MSW94PU+1+CWTq222+VfbrBt527WsF6sXmICw5OZmxY8fi49N4gujMmTMBSElJafUar7zyCj4+PhiNRh5++GESExNbPcdsNlNeXt7wqay8e74cQfv50xc9b27tI08Olbf6sfDnL3oqK8zO9Bruw2SjnFn8+NpzCqsR2EJ9m721QbvBJdsswJwF8tuC41fHIFmsrZQWqJk7+1pctK8VNMXmIKywsLDZp636YwUFBS2e6+npSVBQEJ9//jk7d+7kww8/5NSpUwQEBLQ6jP7RRx/RvXv3hs/cuXMb9ISEhGA2mxuWZwcHB3PlyhW2bNlCamoqx44dIyIigqysLNasWUNFRUWjsmVlZaxfv5709HQOHTpEZGQkp0+fZuPGjVy7dq1R2erqalauXElOTg579+7lxIkTJCUlERoaSkFBQaOyFouF5cuXk5+fz/bt20lMTCQ2Npbdu3eTm5vLihUrmuguKipi8+bNpKamcvToUSIiIsjIyGDt2rVNdJeXl7Nu3ToyMjI4cOAAkZGRnDp1ik2bNlFcXNyorMlkYuXKleTm5rJnzx6io6NJTEwkLCyMgoICvvzySzJNw277MZORcCfDNIzo6Gj27NlDbm4uK1euxGQyNbp+cXExmzZt4tSpU0RGRnLgwAEyMjJYt24d5eXlBAcHU1hYSHBwMBUVFaxdu5aMjAwiIiI4evQoqampbN68maKiokbXNZvNrFixgtzcXHbv3k1sbCyJiYls376d/Px8li9fjsViaXROQUEBoaGhJCUlceLECfbu3UtOTg4rV66kurqa4OBgAsZcASAyopqNGzdy+vRpIiMjOXToEOnp6axfv56ysrJG162oqGDNmjVkZWURERHBsWPHSE1NZcuWLVy5cqWJ7pCQEPLy8ti1axexsbEkJCSwY8cOLl26xFdffUVdXV2jcwoLCwkNDSU5OZnjx4+zb98+srOzWbVqFVVVVY3KlpSUsHHjRtLS0jhy5AiHDx8mLS2NDRs2UFpa2qhsZWUlq1evJisri/379xMVFUVKSgpbt25toru2tpaQkBAuXLjAzp07OX78OPHx8ezcuZMLFy4QEhJCbW1tk3tt69atpKSkEBUVxf79+8nKymL16tVUVlY2KltaWsqGDRtIS0vj8OHDHDlyhLS0NDZu3EhJSUmjslVVVaxatYrs7Gz27dvH8ePHSU5OJjQ0tKEt3a3NxsbGsmvXLvLy8lyij7jvhTF4c4NiqQ8rlqxqto/IyclxaB9htVoJDg7GarXy5ZdfUlBQQFhYGImJiXbpI26/15TuI+rLXrt2ze59RMZd2q3W+oj4+HhV9xH1Zevq6vjqq6+4dOkSO3bsICEhod19RHR0NDYj2cjIkSOlwMDAJsezs7MlQPrss8/adb2srCzJ29tbeuKJJ+5azmQySdevX2/4HD16VAKkpKSkdtUnaJmpxnQJrBJIDR8DddI0Y7pdrp+Xl2eX69iDTe/ESCBJ93mfVlqKqlGTZ80x1ZguGbA4rM2qjYd6JksgSSEvHGn2e7X7JZCR+1r9tFtXISkpyea4w+aRMG9vb8zmpkukTSZTw/ftYfTo0TzzzDNERkZisVhaLOfl5YWPj0/Dp2tX+2ylI7jF0rdLuTU8DgYsSLizdLF9ttIIDw+3y3XsQcDLowBIrh5PxcUyZcWoGDV51hxL3y5tNKJg7zarNgKmyis/o2KaX+iudr8EMnJfq592K7iFzUGYr68vhYWFTY7XHxs4cGC7rzlkyBBqamqoqqqyVZ7ABvxfrE9HIeGFianGc4QtiW110+C28rOf/cwu17EHg6b3ZYTHRay4E706Q2k5qkVNnjXHgk9m8cKQEwC4YbF7m1UbAU/JmzsfuzRCHkC5A7X7JZCZ/1c/elICgCdml2+3glvYHIRNnz6dzMxMyssb5+KJi4tr+L695OTkYDQaxeiWwsRslDPITzFmYZKMpFSPs2unEBISYrdr2YOA4XJyz6j9IvhvCbV51hxl1Z4A/PO543Zvs2pj9stjcaeOC5bBXDhxocn3WvBLABdj8ymlF+7UseyjEJdvt4Jb2ByELVy4EIvFwooVKxqOmc1mVq9ejZ+fH0OGyLmXbty4QXp6OsXFxQ3lioqKmlzv5MmT7Nq1i+9973u4uYldlZQk+oj8Stl/pGPSNqhtc+GAm3mFo86IFUktoTbP7sRaZyWmeCwA/vP7K6zG8XTt6829XTIBiFqf1+R7tfslkIneJHt3T+dMFv9+scJqBM7E5ijHz8+P5557jvfff58lS5awYsUKHnnkEc6fP88nn3zSUC4+Pp4JEyY02lD2+eef5/vf/z5/+ctf+Pe//82vf/1r/P396dy5M3/9619tlSawkeiMXgD4P+CYYFhtmwsH/Fh+YIgrn4C5zH57ZLoSavPsTs7uzeE63elCJVPm62PXjYAJ8oNt1LGmaSrU7pdAJvqYnCPTf0yR8Exn2OXXdd26dbzzzjusX7+exYsXU1tby549e5gzZ85dz3v22WcpLi7m008/5c033+Sbb75hwYIFJCYmMmGC2LJBSczlZhIr5Ez5/s87JpP8c88955DrdpSxjw6hn1sRZowkfp2utBxVojbP7iR6m5wSZ2aPLDyM+th7L+B78uKnqJxBTb5Tu18CmZhzcs43/7mdhGc6wy5BmNFoZNmyZRQWFmIymYiPj+eJJ55oVOahhx5CkiQ++OCDhmOLFy8mLi6Oa9euUVtbS0FBAevXrxf7RqqA7zZnUoMXfQ1FjHp4qEPqOHbsmEOu21EMbgYCBmYDELWzRGE16kRtnt1JTIy8mtd/0nWFlTiPB1+Rd3xIqxlD8Zkrjb5Tu18CqLpaRfIN+YF39o+GC890hph0JWiW6N3yfnT+/XMwuBlaKd0xxowZ45Dr2kKAXw0Ax5K7KaxEnajRs9uJzpNHg/wf76KwEufRZ3QPJnjJDw/H12Y3+k7tfgkgcWMmFjwY5F7IEL+BwjOdIYIwQbPEfOcFwOx7HDc3qqKiwmHX7igBCwcAcOLaOCxmsZfpnajRs3qKM66RUTsSgFkv6Ws0fc4o+TVs1KHGORvV7JdAJjq8DAB/3/MYDMIzvSGCMEETJKvEicvyj5n/93s5rB415oGbtmAU3SinnO6cChX5wu5EjZ7VE7tB3vdzgmc2vUbpa4VrwCOdAIjK6NvouJr9EshEp8ijtv73y6PwwjN9IYIwQRPyTlzisrU/HtRy/48cNzQ+atQoh127o7h7uuPfV97MO2rbVYXVqA81elZP9KEbAPgPz1dYifMJ+KmcWPm7G+OpLLiVs1HNfgnkB97oInnU1v8H8uR84Zm+EEGYoAnRm+Wkj/d2ycC7V/u2nWoPx48fd9i1bSHgHvlJNCreU2El6kOtngFEp8mjX/7+CgtRgKEz+jPUIx8LHsSuvTWCq2a/BJAZkUuJ1Asj1Ux/Tn7gFZ7pCxGECZoQHXUzZ83Yaw6tR61Lsec8I/+YRxWORrI23QpGz6jVs9obtcRfl5O0zg5qmqpBDwQMlRN+Ru27NadIrX4JZGJC5VHbGT6ZeHaVH/qEZ/pCBGGCJkSf6weA/0OOHQlas2aNQ6/fUWa8NA5PzFy29ufcgRyl5agKtXp2MjSLajrT01DKuHkjWj/BBQl4QE7WeuxUj4ZjavVLIBN9Qn7I859wa6Nu4Zm+EEGYoBGVlys5WX1zROFHwx1al1q3VDH6eDLTR36lE7XpksJq1IVaPYvZJc/fm93nHG4e+uzW5vxoMACx1ydQUyGvklSrXwKZ6PO+AMx+tHPDMeGZvtBnbyVokfgNWVhxZ4h7PoNn+Dq0LjVvzxEwWX4yjTrhmBxpWkWtnkUnyKsD/afpd2XZ+CeG0cdQjAlvkjbIOz6o1S8BlOVd54xZngc2+8WRDceFZ/pCBGGCRsTU56wZ1HQzYHvz4osvOryOjhLwpJysNSrPMbsFaBW1ehadPwwA/8DuCitRDoObgQd963d8kOdzqtUvAcSul1dhj+50nn4T+zQcF57pCxGECRoRndoVAP/7ax1e1759+xxeR0fxf3kMBqxk1w2nMKlAaTmqQY2eXUoo5IJlMO7UMePH+s42HjBDfg0ZlSTnnlKjXwKZ6AOVAPgPaTzlQXimL0QQJmjAWmclpvhmzppn+rZS2nbuueceh9fRUboP7sY075v5wtaJyfn1qNGzmE3nAZjqnUXXAV2VFaMwAUHyoprjReOw1lpU6ZdAJuaMPNruP8va6LjwTF+IIEzQQMb+XEqlnnhzg2kLHT+iUFhY6PA6bGHOOHkz5GNHxPZF9ajRs+hv5Uzj/qNFct17fjiGLlRSRg9Ob89SpV8CsNRYiC2RF0D5LxjQ6Dvhmb4QQZiggehQ+bXbzO6ZdOrcyeH1GQzqnvQe8JicoiPqnGMXKGgJNXoWndkbAP8AD4WVKI+Hlzv+vW+u7N16WZV+CeD0jnNU0g0frjPx6cYZ8oVn+kIEYYIGoqPlP/0nljmlvgEDBrReSEECXpY7x1OmMZTllCisRh2ozbPqkmq+qxoHgP+PhimsRh0ETJfnGkXFdlKdXwKZ6O3yKLtf73O4e7o3+k54pi9EECZoIDpvIAD+j3VupaR9SElJcUo9HaX/5L6M6XQeCTdOrMlUWo4qUJtnSZsyqaMTvm6XGeavz0z5dxLwg5s7PhSMJPm7ZIXVCJojOk7+6fWfXNHkO7XdYwLHIoIwAQAl2aWk18gjP7NedM4GsvPmzXNKPbYQMEJeuRQVcUNhJepAbZ5F75XzufkPyMXgJl7jAPj9ZCydqKHA6sukrlOUliNohuiLQwDwf6Jbk+/Udo8JHIsIwgQAxH59DoCxnXLpM663U+rcuHGjU+qxhYC58g97VFqfVkrqA7V5Fp0sbzDvf59ZYSXqwbunkfu7yfPCNn0crbAawZ1cOXWVnLphGLDi9+LoJt+r7R4TOBYRhAkAiD4oZxr3H+a8bXq0sD3HnJfkeUYJleOpLtZvNvZ61OSZZJWIviJnGp/9lHMeHLRCwCR5DmPxtYkKKxHcScwGOeXNZOM5ug9tmlxYTfeYwPGIIEwAQPQZuTPwny05rU4tbM8x4sFB9KSUWjzp3rcT07wzCFsSq7QsxVCTZ9mRFyiS+uKJmXtfGKu0HFXh4Sbfx6uzHtR9m1Ub0UdMAPiPuNzs92q6xwSORwRhAupMdcSV3cxZEzTQafW+8sorTquro2z/fRylyBOda/HklGkMQctm6fZHTU2eRX9zEYD7u2Xg5eOlsBr1ELYklv+LfgiQkHDTfZtVG9HpvQDwf7D5n1813WMCxyOCMAGp27K4QRe6c50J3x/Z+gl24ptvvnFaXR3lT1/0xMCtjNYSbhiw8OcveiqoSjnU5Fn0cdkX/3Eifcjt3Gqz8nxGvbdZNWEuN5NYcfOB9/khzZZR0z0mcDwiCBMQvbMIgNl9MnHzcF6TmDt3rtPq6iiZpmFId9wmEu5kmPSZk0pNnsXk9gfA/xGjwkrUhWiz6iV5SxZmjPQxFDPq4aHNllHTPSZwPCIIExAdL2canz3FuRPPMzIynFpfRxhrzGs0EgZgwMI4Y55CipRFLZ6VXyrnlEneWmv2j0corEZdiDarXqJ3FwPg3z+7xZQqarnHBM5BBGECYvJv5qyZ5+PUen18nFtfR1j6dunNUQV5orMBKxLuLF1cqqwwhVCLZ3FfZyHhxgiPCwyY1l9pOaqivs3eCsQkXbdZNRGdJI/a+k+vbrGMWu4xgXMQQZjOKfjuMufrhuCGhZk/bpqzxpF06dLFqfV1hAWfzGLbu7H0M8hPsD0NZYQtiWX+x7MUVqYMavEsOkLONO4/+KLCStRHfZudasxCfngw8MmTkbpts2pBskrEFMqvhP2/3/L8PLXcYwLnIIIwnROzKReAKcZz+Ax27hNYdna2U+vrKAs+mcXyd+XcPoO9inT9Y6YWz6JPdQXA369OYSXqZMEns0ipHsdD3WIA6NxFdPVKcyEmnwKrLx7Ucv+PW06popZ7TOAcxJ2pc6Ij5Uzj/qOuOL3uBx54wOl1dhS/54cDcNo0msrLlcqKURA1eGapsRB7TZ4P5j9fbHZ8N/wmyW01LtG9lZICRxO9+QIA93bJwLuXd4vl1HCPCZyHCMJ0TnSGnGm8pZw1jmTbtm1Or7OjDLq3P4PcC7HiTtLmLKXlKIYaPEvbnU053elKBZOfcc4+p1rFs5fcVuMuOS//n6B5oo/WAuA/pviu5dRwjwmchwjCdIypzERS5Tig5Zw1jkRr23P4DZBXl8UfKFNWiIKowbPo7fKorV/PTDyMHgqrUTeL//FjADJrR1KaIybmK0n0uX4AzH7I867l1HCPCZyHCMJ0TNLmLGrxpL/bVUbMbT5njSPR2vYcftPkV7dxJ/Wbl0oNnsXEykv7/SeVK6xE/WyO2MCoTvLDQ8Kmcwqr0S9VV6s4WX3zFXorKVXUcI8JnIcIwnRM9B450/js/jkt5qxxJL/4xS+cXqct+D3RA4C4y/pNeqkGz6IvDAJg9uNdFVaifn7xi1/gN+gSAHGH9TuXUWkSNmZhwYMh7vkMnuF717JquMcEzkMEYTom5jt5vz3/e0yK1L9y5UpF6u0o9z0/GjcsXLIOpCCxQGk5iqC0Z0Vni8mqlUcSZv1kjKJatMDKlSvxu09eQRp3WqQ+UIrofWUA+A9sPWGu0veYwLmIIEynSFaJ6CvyPpH+T/VSRMNTTz2lSL0dpWv/Lkw2yq904rZeUFiNMijtWczX8vL9iV7n6Dmih6JatMBTTz2F35N9AIgrHolklRRWpE+iT3YGwH9Gbatllb7HBM5FBGE6JffYRa5Y+9GJGu77Ucs5axxJYmKiIvXagt/wqwDEHW0547Uro7Rn0Yflf3f/YfociWwviYmJTF84Gk/MFEt9yD2qz4cHJbHWWYkpkhNh+/+gT6vllb7HBM5FBGE6JfobOdP4fV0zMPZQZqL5oEGDFKnXFvxu5mmNS++hqA6lUNqz6LNypnH/B5w/h1GLDBo0CC8fL6Z3uTmCu+2Swor0R+aB85RIvfDmBtOea/2BV+l7TOBcRBCmQ8KWxPJOyAQAzlX5ErYkVhEdFotFkXptwe8ZeVJt4vXRWGq0p99WlPRsy6+jOV4+BYCPvx6kWLvVEvV++Y2Wc1PFnWj9dZjAfoQtieXxp+WHXDes7P4gqdVztNgvCjqOCMJ0RtiSWIKWzeKaJI8oXJN6EbRsliI/aFevXnV6nbYy4ckRdKWCSrqRtlt/24so5VnYklie/4f/zc3UIbN2uGLtVkvU+zXTvxMAced6KylHV9T3tZes8oNbFZ3b1Ga12C8KOo4IwnTGn77oiQErIL/OkXDDgIU/f9HyhrKOYtq0aU6v01bcPd25v4ccfMXtvKywGuejlGd/+qInYG34fyXbrZao98svaDAAyZVjqKkwKylJN9zZ19LGNqvFflHQcUQQpjMyTcMaRhPqkXAnw+T83Ff79+93ep32wG98GQBxccrqUAKlPMs0DePO7kqpdqsl6v0a/fAQehlKMGPk5DaRtNUZdLSv1Wq/KOgYIgjTGWONedw+ogBgwMI4Y+v5a+zNokWLnF6nPfCbK2++G5env82jlfJMbreN0yso1W61RL1fBjcDM3vnABC39+57Fwrsw1hj3s2RsFu0pc1qtV8UdAwRhOmMpW+XcrvtBixIuLN0sfP3lfv3v//t9Drtgd/zwwE4Yx5FZWGFsmKcjFKevfOjK8ivdeRATMl2qyVu98tvkpwxPz5JdPvOYOnbpY1GwtraZrXaLwo6hrgbdcb8v/rRA7kT8MTMVOM5wpbEMv/jWU7XotWNagfe05/B7gVYcSfpG3292lHKs76D5RVmXpgxYlK03WqJ2/3ye1Te5inukkiB4AwWfDKL+zufAcCD2ja3Wa32i4KOIYIwnXEpoZAyeuJOHaVFFlKqxyn2Q6bljWr9BshJL+MOlCkrxMko5Vlc5A0AXhiVSLVkVLTdaonb/Zr5o1EAZNaOpDRHjCA6GskqcaG6HwDHQtLb3Ga13C8K2o8IwnRG/XY7U7zP0blPZ0W1PPfcc4rWbwt+0+UVZnEnlUl0qxRKeRZ3thsAfjOsrZQU3M7tfvUe3ZPRnc4DEL9Jf+lVnE1edD5Xpb50ooZ7ftj2fU613C8K2o8IwnRG3DE5eKjffkdJjh49qrSEDuP3RA8A4i4PV1SHs1HCM2udlfhr8rYvfk/1dXr9WuZOv/wG5QMQd0hfcxmVIC5U3pVkWudz7dqVRMv9oqD9iCBMZ8Rl9ADAb5by276MHz9eaQkd5r7nR+NOHflWX/IT9LOPoRKeZR08z3W6Y6SaKfNHO71+LXOnX373y9nY4850UUKOrog/fvOBd1RRu87Tcr8oaD8iCNMRdaY6kspvjig866uwGigrK1NaQofp0q8Lk403k7Zu0U+aBCU8i9suB7n3dcuiU+dOTq9fy9zpl9+Tcsb8uOJRSFapmTME9iIusxcAfrPd23WelvtFQfsRQZiOOLMrmxt0wYfrjH9ypNJyqK6uVlqCTfgNvwJA3DGTwkqchxKexcXc3P9wbInT69Y6d/o1LWg0npi5JvUmJ1I/Dw/OpvZGLUkV8jywmc8ObNe5Wu8XBe1DBGE6Im6XHDTM6HkONw/lrR85UvlA0BZm3nylW/+KVw8o4VlcjjwPbOYDnk6vW+vc6ZeXjxfTu8hpVeLC8pWQpAtObT+HCW96GMoY8/jwdp2r9X5R0D6U/yUWOI24eDlomDleHZNyT5w4obQEm/B7Rn6lm3h9DBZzncJqnIOzPasuqebkDXlEwe+5oU6t2xVozi+/0XLG/LgT+mizShC3R54HNrNX+x94td4vCtqHXYIws9nMe++9x8CBA/H29sbPz4+DBw86/FxB+4i7IG+z4/eQt8JKZIKCgpSWYBMTnhxBVyqooitnducoLccpONuz5K3nqKMT/QxFDPMXSUbbS3N++T0gz6uLO9fb2XJ0Q1yC/NPqN7Gy3edqvV8UtA+7BGGLFi3i008/5cUXX+Tzzz/H3d2dJ598kuPHjzv0XEHbKc+vIM0sJ2v0e2GEwmpk1q5dq7QEm3D3dGdGj5uvdnZdVliNc3C2Z/H75Xlgfv1yMLgpv6JXazTnl98COZhNrhqDudzsbEm6IO6iPEru90j7V6FqvV8UtBPJRuLi4iRAWrZsWcOx6upqadSoUdLs2bMddu6dJCUlSYCUlJTUvr+ATjjy9+8kkKSh7heVluJS/H52pASS9NrYo0pLcUleGHpCAkn68LFIpaW4DFaLVeptKJZAkuJXn1ZajstRdr5UAkkCSbqaVqS0HIEDsUfcYfNIWGhoKO7u7rzxxhsNx4xGI6+++ioxMTFcvHjRIecK2kfcgesA+Pmq59/UFbbn8Jsrv9qNyxugsBLn4GzP4goGA+D3uI9T63UVmvPL4GZgZh/59Xnc3mJnS3J5EjbLqWtGeFyg74Q+7T7fFfpFQduxOQhLTk5m7Nix+Pg07iRnzpwJQEpKikPONZvNlJeXN3wqK9v/7l1PxKXKGZvrt9tRAy+99JLSEmzG7/nhAJwxj6KyUB0LHhyJMz0rOltMbp08GX/GC6OcVq8r0ZJffpOqAIhLbF8OK0Hr1O9G4DfwUofOd4V+UdB2bA7CCgsL8fVtmviz/lhBQcvZxG0596OPPqJ79+4Nn7lz5zZcMyQkBLPZ3PBEERwczJUrV9iyZQupqakcO3aMiIgIsrKyWLNmDRUVFY3KlpWVsX79etLT0zl06BCRkZGcPn2ajRs3cu3atUZlq6urWblyJTk5Oezdu5cTJ06QlJREaGgoBQUFjcpaLBaWL19Ofn4+27dvJzExkdjYWHbv3k1ubi4rVqxooruoqIjNmzeTmprK0aNHiYiIICMjg7Vr1zbRXV5ezrp168jIyODAgQNERkZy6tQpNm7YSNyV4QD4BfYiODgYk8nEypUryc3NZc+ePURHR5OYmEhYWBgFBQV8+eWXWK1WgoODsVqtfPnllxQUFBAWFkZiYiLR0dHs2bOH3NxcVq5ciclkaqSluLiYTZs2cerUKSIjIzlw4AAZGRmsW7eO8vJygoOD2b17N8HBwVRUVLB27VoyMjKIiIjg6NGjpKamsnnzZoqKihpd12w2s2LFCnJzc9m9ezexsbEkJiayfft28vPzWb58ORaLpdE5BQUFhIaGkpSUxIkTJ9i7dy85OTmsXLmS6urqRmWvXbvGxo0bOX36NJGRkRw6dIj09HTWr19PWVlZo7IVFRVEpIQzxC0fK+5s/ksEqampbNmyhStXrjTRHRISQl5eHrt27SI2NpaEhAR27NjBpUuX+Oqrr6irq2t0TmFhIaGhoSQnJ3P8+HH27dtHdnY2q1atoqqqqlHZkpISNm7cSFpaGkeOHOHw4cOkpaWxYcMGSktLG5WtrKxk9erVZGVlsX//fqKiokhJSWHr1q1NdNfW1hISEsKFCxfYuXMnX3zxBfHx8ezcuZMLFy4QEhJCbW1tk3tt69atpKSkEBUVxf79+8nKymL16tVUVlY2KltaWsqGDRtIS0vj8OHDHDlyhLS0NDZu3EjkqjMAjPfMZv2u9VRVVbFq1Sqys7PZt28fx48fJzk5mdDQUAoLCxtdt66ujq+++opLly6xY8cOEhISiI2NZdeuXeTl5emmj9i6dWuzfcTIaXJuu9iLAzl16hSbNm2iuLi4UVk19BG332ta6SMi4+QkuH16pFNRUcGaNWvIysoiIiKCY8eOtdpHrF27VtN9RHx8vNP6iJKSkkZlnd1HREdHYzO2vhMdOXKkFBgY2OR4dna2BEifffaZQ841mUzS9evXGz5Hjx4Vc8Ja4EJsvgSS5E6tVFVUpbScBs6ePau0BLuwcFC0BJL013lHlJbicJzp2R8elOfbvTwqyml1uhot+XXtXEnDvKVrWdecrMp1sVqsUj/DVQkkKToktUPXcJV+UQ+oYk6Yt7c3ZnPTV1wmk6nhe0ec6+XlhY+PT8Ona9eu7ZWuG+q31ZnifY7OfTorrOYWly51bLhebdS/4o07qY7UH47EmZ7Fn5Xvab8ZVqfV6Wq05FevUT0Z0ykXgPhN2c6U5NLkRedzVepLJ2q454djOnQNV+kXBW3D5iDM19eXwsLCJsfrjw0c2PKWDbacK2g78VE3N5IdflVhJY1xd3eN+Sh+83oCEHdlmMJKHI+zPLPWWYm/dnOf06f6OqVOV+Rufs0cJE/3iDtS5Sw5Lk/c1gsATOt8DmMPY4eu4Sr9oqBt2ByETZ8+nczMTMrLyxsdj4uLa/jeEecK2k5cZg8A/GapK89S376u8eN67/NjcKeOAqsvlxKaPlS4Es7yLOvgecrogZFqpswf7ZQ6XZG7+eV3v7wnZ9yZ9ueyEjRP/PEaAGaO7PiqU1fpFwVtw+YgbOHChVgsFlasWNFwzGw2s3r1avz8/BgyZAgAN27cID09neLi4nafK+g4daY6Eq/f3Pbl2aaLIJQkNTVVaQl2oUvfzkw2yq904racV1aMg3GWZ3Hb5VGa+7pl0alzJ6fU6YrczS+/78vpE+KLRyJZJWdJcmnisuRRcT//jo9muUq/KGgbNgdhfn5+PPfcc7z//vssWbKEFStW8Mgjj3D+/Hk++eSThnLx8fFMmDChUQ6Utp4r6DhndmVzgy50o5zxT6prY9gnnnhCaQl2w2+EvDl63FH1pABxBM7yLC5GHqXxG1vilPpclbv5NS1oNJ6YuSb1JjvyghNVuSa1N2pJqhgL2PbA60r9oqB17LJt0bp163jnnXdYv349ixcvpra2lj179jBnzhyHnitonbhdcnAwo2f7N5J1NJs2bVJagt3w85Nf9da/+nVVnOVZXI78Smbmg15Oqc9VuZtfXt08uadLFgBx28RkcFs5tT0LE970MJQx5vHhHb6OK/WLgtYxSJLkEuPQ3333Hffddx9JSUnce++9SstRDa+OjWJVVgDv+3/L/514SGk5LsuZneeY/OxoOlPF9WovPIweSkvSLKbSarr18qCOTuRGXWL4g4OVluSy/Nf0o/zz5Fzenir/Keg4y390jDc3z+F7vROJKL5faTkCJ2CPuENdQyMCuxN/oT8Afg+pJzVFPa60Pcf4wBF0o5wbdCFtt+su+XeGZ8lbs6ijE/3cihjmP8jh9bkyrfnl94D8sBCf3dsZclyauAT559Rvom27t7hSvyhoHRGEuTAV+eWcMd9c5n9zex018eqrryotwW64e7ozo+c54NYrYFfEGZ7F7ZPngfn1zcHgpq4VvVqjNb/8guRRxuSqMZjLXXs+o6OJuyjPA5v5sG2rTV2pXxS0jgjCXJjEzeeQcGOo+yUGTO2ntJwmbNiwQWkJdsVvvJxqJS7edQMHZ3gWlyyvhvSbUu3wulyd1vwa+dBQ+hiKqcGLk9vOOUmV63E9r4z0Gnl/U78XRth0LVfrFwV3RwRhLkzcQTko8PO9qLCS5nnkkUeUlmBX/B6SM+bH5Q1QWInjcIZncQVyahq/x30cXper05pfBjcDM/vImfPj9nY8t5XeSdgsT0EY4XGBvhP62HQtV+sXBXdHBGEuTFyqnLF55vQahZU0z9mzZ5WWYFfqn4DPmEdRUVChsBrH4GjPis4Wk1s3FID7nx/l0Lr0QFv88pssZ8yPSxKLSTpKwwPvQNtXmbpavyi4OyIIc1Ekq0TcleHArW111EaPHj2UlmBXBkztRx9DMRJu9B7kxTTvDMKWxCoty6442rP4zTkAjPfMpsew7g6tSw+0xa/aWnmB/Mbzs12yzTqDuNPywie/e2ttvpar9YuCuyOCMBclP7GQQusA3KnjvufVue2L0dixvdXUStiSWIol+VVELZ6cMo0haNksl/pRc7Rn9fsY+g1x7e2fnEVrfoUtieXD4w8DIOHmkm3W0UhWibirciJsv8BeNl/P1fpFwd0RQZiLErclD4Ap3ufo3Ed96SkAcnNzlZZgV/70RU/A2vD/Em4YsPDnL9Q5EtkRHO1Z3NluAPjNsLZSUtAWWvPrT1/0xODibdbR5J24xFWpLx7UMv25MTZfz9X6RcHdEUGYixJ3TF5u7jfiqsJKWsbf319pCXYl0zSMO28pCXcyTMOUEeQAHOmZtc5K/LWbKVWeVt9qXi3Sml+ZpmFILt5mHU1cqLzl07TOWXj3tH0Uy9X6RcHdEUGYixKX0QO4tZ2OGgkLC1Nagl0Za8xrNKoAYMDCOGOeQorsjyM9yzp4njJ6YKSaKc+KSfn2oDW/9NBmHU38cXkemN9I+6wudbV+UXB3RBDmgtSZ6kgsl4fFZz47UGE1LfPWW28pLcGuLH279OaogjzR2YAVCXeWLi5VVpgdcaRncdvzAbivWxadOndyWD16ojW/6tvsrUBMcrk262jisuRXt37+7na5nqv1i4K7I4IwF+TMrmxu0IVulDM+0LbEgY7E1bbnWPDJLLa9G0sfwzUAehlKCVsSy/yPZymszH440rP4GDkQmDlGBAD2ojW/6tvsFGMW8sODgU9/8K1LtVlHUnujlqSKsQD4Petrl2u6Wr8ouDsiCHNB4nfL2+bM6HkO907qtfiXv/yl0hLszoJPZrH811kADDEWudyPmSM9i8uRV5b6BXg6rA690Ra/Fnwyi5PV43i813cAGDurt89QG6e2Z2HCmx6GMsY8Ptwu13TFflHQMuJuc0Hi4uU//SaUKyukFVasWKG0BIfg/6I8+phaPcblkrY6yjNTaTUnb8iv0P0WDnFIHXqkPX7Nniy31eg48bPQVuJ2FwEwo1c2bh72+Xdz1X5R0DzibnNB6rfN8XtInakp6vnBD36gtASHMPDeAQxzv4QVdxI2Ziktx644yrPkrVnU4kk/tyKG+Q9ySB16pD1++X+vKwAxF0UQ3FbiEuWfUL8J9nvYctV+UdA8IghzMSryyzljvrnM38aNZB1NfHy80hIcxuxB8rL16PDrCiuxL47yLG5fCQB+fXMxuKl3Ra/WaI9ffi+NwYCV7LphXDld5EBVrkPcRXnhk98jXex2TVfuFwVNEUGYi5G4+RwSbgx1v8SAKX2VlnNXhgxx3Sdu/xnysvXoU10VVmJfHOVZXLK8GtJvyg2HXF+vtMevHsO6M8lL3og6ZkOOoyS5DNfzSkmvkVOp2POB15X7RUFTRBDmYsQdlEdeZvravpGso6mrq1NagsPwf1ZONhpTPAZrnetkf3eUZ/EFgwGY+bjYL9KetNcv/xHydlHRh6sdIcelSNgkB6wjPC7Qd0Ifu13XlftFQVNEEOZixKfKGZv9ppsVVtI6RUWu+8pj6oLRdKaKMqkH6ftcZ1TBEZ4VpRWRUydnaJ/x/Ei7X1/PtNev2Q/IPwnR6bbvgejqxB2S54HNHJhv1+u6cr8oaIoIwlwJSSLuynAA/Oapf++3yZMnKy3BYXTq3ImZPTIBiN7mOptRO8Kz+G/kvfLGe2bTY5gYCbMn7fXL/4fyiGRixVhqKmscIclliDstL3zyu8e+/06u3C8KmiKCMBfiUkIhBVZf3KnjvudHKy2nVQ4ePKi0BIcye6L8ajgmRmEhdsQRnsUdqQLAb4jrBKtqob1+jXlsGL0N1zBjJPmbTAep0j6SVSLuqjxq6/ekfUcNXb1fFDRGBGEuRNwWeb+3Kd7n6NxH3ekpAF5++WWlJTgU/0dlD6Lz1Lt1VHtxhGdxZ7sB4DfDdebOqYX2+mVwM+Df7xwA0bvtsxeiK5J34hJXpb54UMs9z42x67VdvV8UNEYEYS5C2JJYfvmp3BlcMPUjbEmswopa59///rfSEhzKrBfllVPpNaO4llWisBr7YG/PQn8bzaFr0wH4bNsQTbRbLdERv/yny5PyoxO97C3HJQhbEsvch+U0Kp2oJfyjFLte39X7RcEdSC5CUlKSBEhJSUlKS3E6296NkUCSwCqBJBmwSCAfFyjLOM9sCSRpz9J4paWojlvtVhLtVkV8+3mKBJI00K1AslqsSstRFbfarKVRnyvarD6xR9whRsJcgD990RMDVkB+OpNww4CFP3+h7sn5etio1n+YvHIq+mCVwkrsgz09u9VuZbTSbrVER/ya8eMxuFNHgdWXi7H2XfmndW612fqfToPd26we+kXBLUQQ5gJkmoYh3WGlhDsZpmEKKWobzz//vNISHI7/bPnPmDQfZYXYCXt6ptV2qyU64lfnPp25p/PNlb2b8uwtSdM4o83qoV8U3EIEYS7AWGMe0HhSswEL44zq7kAPHz6stASHMztInpQfVzaOuupahdXYjj09k9ut1OiYFtqtluioX/5j5FxV0cdE4tDbGWvMazR6C/Zvs3roFwW3EEGYC7D07VJut9KABQl3li4uVU5UG5g4caLSEhzOhCdH0J3r3KALqWHa38zbnp6988IV6l+hg3barZboqF/+c+VtpKLPqXvrM2ez9O3SRiNhjmizeugXBbcQQZgLMP+jmXSjHABPzEw1niNsSSzzP56lsLK7U1rq+j+2bh5uzO5z89XODu1nwranZ937eABgpBojJs20Wy3RUb9m/2g4ACk3xlJ11TXmM9qDBZ/MYpoxHQAPah3SZvXQLwpu4aG0AIHtpO/NpoIxGKnmeqUHnl3GKS2pTZhMJqUlOAX/qVXsPwLRCR68pbQYG7GnZ1GH5Ezjr0xO4F+n5gDaaLdaoqN+DfEbyCD3QvItviRuPMXcd+6xszJtUmeqI9s0CICEb3KZ/kP7t1m99IsCGTES5gIc31IAgF+PDDy7dFJYTdsZPny40hKcgv88eVJ+zKWhCiuxHXt6FpUhb3Ie8LB22qzW6KhfBgP4+54HIDq8zG56tM7JLRlU0g0fypky3zG7kuilXxTIiCDMBYg6IdsYMOW6wkraR2ysPhJzznxxDG5YOG8ZQkGStrfmsZdnFfnlJFfLowgBPx1hl2sKmmKLX/73yyOV0Sld7CVH80SFXQXggb6ZuHdyzM+nXvpFgYwIwlyAqIvy8uiA72srDcKCBQuUluAUug3sxhSjvBVMzKbzyoqxEXt5FrMmAyvuDPe4yOD7B9jlmoKm2OLX7Kf7ABBTNArJKrVSWh8cT5B3EQi413Hz5PTSLwpkRBCmcS7FXuK8ZShuWJj98lil5bSLtWvXKi3BafiPugJAdKRZYSW2YS/PosIrAQgYesEu1xM0jy1+3fPDMXhh4prUm6wDuXZUpU0kq0RUofwKMuAZ+27afTt66hcFIgjTPFHrzwNwT+dMug3Q1muDt97S+jT1tuMf4A5AdGZvhZXYhr08izrdA4CAB8UIiyOxxS/Prp7M8MkAIHqryJyfFZHDVakfXpiY8ZLjFpHoqV8UiCBM80R9KydTDBivvfQHetqew//5IQB8VzkWU5l2Vz/ZwzNzuZm46+MBCHhhkM3XE7SMrX75T5DTJUSfEMFy1KZLAMzsnoFXN0+H1aOnflEggjDNE5UtZ2QPeNyosJL289Of/lRpCU5jxJwh9HMrogYvvtucqbScDmMPz5I2pGPCm76GYsY9Mdx2UYIWsdUv/0e8AYg+72sPOZqmYQHU5DKH1qOnflEggjBNU5J1jdNmeR7Yg4scs1zakezYsUNpCU7D4GbAv38OANF7ShRW03Hs4VnUTvnv/6BvNgY3QyulBbZgq1+zXxwJwBnzGMrytLX62t5EXZBTzAQEdnVoPXrqFwUiCNM0J9bI2+CM88yh33jHTRR1FH5+fkpLcCr+98qvIaO/096oZT328CzqO3nuYsBMbS9S0AK2+tVvUl9GdZL3RYz7WvvbbnWUgoR8cuqG4YYF/0WOXQClt35R74ggTMNEHagGIGBkgcJKOkZenr42avZ/Sg6Uo6+M1OySf1s9s9ZaOFEk/4gFBPWzhyTBXbDHPeY/RJ4LFR1RYfO1tErUOnl16DTvLHwGdXNoXXrrF/WOCMI0TNRZOY9PwFxt2tipk74ypd/3whg6UcMVaz9yj11UWk6HsNWz02GZlNGDrlQw/bkxdlIlaAl73GP+sywARJ/RVh5CexIVeXMB1LirDq9Lb/2i3tHmr7eAG1crSay6ucLspWEKq+kYffr0UVqCUzH2MHJfV3nJf8w32syPZatnUaFyvrTZvbPw8HK3hyTBXbDHPea/QJ6UH1syFkuNxebraZGoc/K/QcBjXg6vS2/9ot4RQZhGiVuXQR2dGOReyPAHtLnM//Tp00pLcDqzx8qT0qOjtPljZqtnUbHyU/6D0yvtIUfQCva4xyY9PZJulFNJN87sPGcHVdqiLKeEU2Z51PbBl0c5vD499ot6RgRhGiVqj7xSKWDweQwaXWD2+OOPKy3B6fg/LD9JR2f3V1hJx7DFM8kqEVUgr7YLeLqHnRQJ7oY97jF3T3f8esnBV3TYZZuvpzVOrMlEwo3RnfIYMNnxo1R67Bf1jAjCNEpUqjw/I2B2ncJKOs7mzZuVluB0/H88HIBU0xgq8suVFdMBbPHs/LfnKbD60oka/H6irS22tIq97jH/yXJbjY7T3yvkqIgbAASMuOSU+vTYL+oZEYRpkLobNcSU3pwP9vxAhdV0HD1uzzHw3gEMc7+EFXfiN2hvyb8tnkVtlBcj3Nctk869tJumQ0vY6x7zf0JeERh9cYhdrqclotLkrcYC5jjnlYMe+0U9I4IwDZK8KZ0qutLTUMqkp0cqLafD6HV7Dv/B8hL0mAjtjYTZ4lnUMTktR8BE7Sar1Rr2usf8XhyNASvZdcO4etrxKwTVQnVxFQmVEwAIeHGoU+rUa7+oV0QQpkGithcD8EC/c7i5a3RCGPDaa68pLUERZs+4ueQ/1bGZtx2BLZ5FnR8MQMA8bW00r2XsdY/1GNadiV7ZAMRsyLHLNbVA/Lp0avFkgNsVRs1xzgIovfaLekUEYRokKlHez+3B+6oVVmIbX3/9tdISFMH/2b4AxBSPwVpnVVhN++ioZ1dPXSGjVl5Z9sAikR/MWdjzHvMfIU/Kjz6i7X6nPTQsgBqU67QttvTaL+oVEYRpDMli5fhV+UcsYEFfhdXYxqOPPqq0BEWYumAMnamijB6k79PWqEJHPTu+Rl5dN9mYRa/h+k366WzseY/5PyAHIdHp2tsiraNEnZTnwgXMqnVanXrtF/WKCMI0RvqecxRLfTBSzf0/0vaIwpkzZ5SWoAidvD2Y2SMTgOhQbW051VHPog7XABAwRn8pDpTEnveY/w/l18kJ5eOoqXD9fT/rqmuJLrm5AOqHvk6rV6/9ol6xOQgzm8289957DBw4EG9vb/z8/Dh48GCbzv32228xGAzNfmJjY22V5pIc35IPgF+PTDw7eyisxjZ69dLPE/Wd+E+UX3PEaKyZd9SzqEx5n8iAh8WWLM7EnvfYmMeG0dtwDTNGkrdob2Vvezm5JZ1KuuFDOVOecd4CKD33i3rE5l/xRYsWERoayjvvvMOYMWNYs2YNTz75JJGRkTz44INtusbixYuZMWNGo2OjR4+2VZpLEhUt5+kJmHpdYSW24+npqbQExZj9eFeIhug8be120BHPKi6WkVx9c0ThZe2u5tUi9rzHDG4GZvfLZs+V3sTsLsLvVbtdWpVEhRUB8EC/TNw73e+0evXcL+oRm0bC4uPj2bx5Mx999BHLli3jjTfe4MiRIwwbNowlS5a0+ToBAQG89NJLjT4d3T/r+dnnCVuiseGFtiJJRF0cDkDA97U/r+bCBW3un2gPZr0oT1JPrxmF0WBimneGJtptRzyLWZOBFXeGe1xk8L39HKBK0BL2vsd8jPJr5d/unKOZNttRohLkXHYB995war167hf1iE1BWGhoKO7u7rzxxhsNx4xGI6+++ioxMTFcvHixzdeqqKigrs727O/naoYStGyWS3YOl2Iuct4yFDcszH5Z+xnHZ82apbQExTj274yG/zZj5JRpjCbabUc8iwqX94kMGNb2/kBgH+x5j4UtiWVjnvx2w4q7ZtpsR5AsVqIu31wA9Wxvp9at535Rj9gUhCUnJzN27Fh8fBqPysycOROAlJSUNl3nlVdewcfHB6PRyMMPP0xiYmKr55jNZsrLyxs+lZX1GwK7YcDCn7/o2Z6/iiaIWn8egHu6ZNKtf2dlxdiB7du3Ky1BMf70RU9Aavh/SSPttiOeRZ2R/04BD0qtlBTYG3veY3/6oicGbqVU0Uqb7QiZ4dkUSX3xwsSMHzt3AZSe+0U9YlMQVlhYiK9v01Uj9ccKCu6+8svT05OgoCA+//xzdu7cyYcffsipU6cICAggOTn5rud+9NFHdO/eveEzd+7chu8k3MkwDWvIPBwcHMyVK1fYsmULqampHDt2jIiICLKyslizZg0VFRWNypaVlbF+/XrS09M5dOgQkZGRnD59mo0bN3Lt2rVGZaurq1m5ciU5OTns3buXEydOkJSURGhoKAUFBY3KWiwWli9fTn5+Ptu3bycxMZHY2Fh2795Nbm4uK1aswGw2NzqnqKiIzZs3k5qaSsS+CgBmDL/E2rVrm+guLy9n3bp1ZGRkcODAASIjIzl16hSbNm2iuLi4UVmTycTKlSvJzc1lz549REdHk5iYSFhYGAUFBXz55ZdYrVaCg4OxWq18+eWXFBQUEBYWRmJiItHR0ezZs4fc3FxWrlyJyWRqdP3i4mI2bdrEqVOniIyM5MCBA2RkZLBu3TrKy8sJDg7ml7/8JcHBwVRUVLB27VoyMjKIiIjg6NGjpKamsnnzZoqKihpd12w2s2LFCnJzc9m9ezexsbEkJiayfft28vPzWb58ORaLpdE5BQUFhIaGkpSUxIkTJ9i7dy85OTmsXLmS6urqRmWvXbvGxo0bOX36NJGRkRw6dIj09HTWr19PWVlZo7IVFRWsWbOGrKwsIiIiOHbsGKmpqWzZsoUrV6400R0SEkJeXh67du0iwzQMaJx3SMKddNPQhnMKCwsJDQ0lOTmZ48ePs2/fPrKzs1m1ahVVVVWNrl9SUsLGjRtJS0vjyJEjHD58mLS0NDZs2EBpaWmjspWVlaxevZqsrCz2799PVFQUKSkpbN26tYnu2tpaQkJCuHDhAjt37uS+++4jPj6enTt3cuHCBUJCQqitrW1yr23dupWUlBQi9x0mrlzOOH6jdxqVlZWNypaWlrJhwwbS0tI4fPgwR44cIS0tjY0bN1JSUtKobFVVFatWrSI7O5t9+/Zx/PhxkpOTCQ0NpbCwsFHZuro6vvrqKy5dusSOHTtISEggNjaWXbt2kZeXR0hISJN7zRX6iKNHjxIREUFGRgZr167lpZdeslsfkWEahnTHT0Z9m3VUH3H7vebMPmLT3+MBmO59GqtH47KO7iOefPJJdu3aRWxsLAkJCezYsYNLly7x1VdfUVdX1+gcNfYR8fHx7eojoqKi2L9/P1lZWaxevVpTfUR0dDQ2I93EYrFI1dXVbfpYrVZJkiRp5MiRUmBgoHQn2dnZEiB99tlnTb5rjaysLMnb21t64okn7lrOZDJJ169fb/gcPXpUAiRIkgzUSdOM6e2uW+1M9syQQJK2/T5eaSl24YsvvlBagmJMNaZLBiwSSA0fLbTb9np2Ivg7CSSpr1uRZLVYHaRK0BL2vMe02mY7wk9HHJVAkv77waNOr1vP/aLWSEpKkgApKSmpw9doeKw5duwY3t7ebfpkZMjzWby9vTGbm+aLMZlMDd+3l9GjR/PMM88QGRmJxWJpsZyXlxc+Pj4Nn65d67eAkZBwZ+ni0nbXrWZKMoo4XSPPA3twkWusHH3zzTeVlqAYS98uvTmqIL+iM2DVRLttr2dRO+V9Ih/0zXFaxnHBLex5j9W32VuvJF2zrwU4fmEYAAFPdnN63XruF/VIQ4qK8ePHs3r16jadVP+60dfXl/z8/CbfFxYWAjBw4MAOiRoyZAg1NTVUVVU1mW/WOhJf//I48z9uW3oMrXBiTRbQl3GeufQbN0JpOXZh+fLl/OpXv1JahiIs+GQW24jll38bxVWpL90pZ9WSdOZ/rO5Jue31LCpZfjgKmOn6yT3ViD3vsfo2+6cvepJqGgsY+PDRSOZ//LBdrq8WCuIvkWMZhhsW/Bc5fwGUnvtFPdIQhA0YMIBFixa16+Tp06cTGRlJeXl5o2ApLi6u4fuOkJOTg9FovG10qz240bmL6z1xRx2URxcDRuUDrhGEzZ8/X2kJirLgk1n0GZrK3Lf74mGo45kPZ7R+ksK0xzNrTR0niuUfsYCF/R0lSXAX7H2PLfhkFgs+gRdHRLPxvD9VVXa9vCqIWpcLDGZa5yx8fMc7vX6994t6w6aJ+QsXLsRisbBixYqGY2azmdWrV+Pn58eQIUMajt+4cYP09HSKi4sbjhUVFTW55smTJ9m1axff+973cHPrmLzwXbanulAbUely3rSAue4KK7EfYlcEmP3qRHy4TrHUh6T1aUrLaZX2eHY6LJMyetKVCqYHjXKgKkFLOOoemzdP/jM8xXnb+TiLqEj59yNgXNPfJ2cg+kV9YVPGfD8/P5577jnef/99rl69yujRo1m7di3nz5/nP//5T6Oy8fHxPPzwwyxdupQPPvgAgOeffx5vb2/8/f3p168faWlprFixgs6dO/PXv/61w7rCz41BskouMwflxpUKEqvkFWYBPxmurBg7MnToUKUlKE4nbw8eG3SWsPxZhK8rYsbPlFZ0d9rjWVToZWAis/tk4eF1r+NECVrEUffYE4vHwVeQYhpP4XeF+N7rOsFYVLb8dwl43EuR+kW/qC9s3jty3bp1vPPOO6xfv57FixdTW1vLnj17mDNnTqvnPvvssxQXF/Ppp5/y5ptv8s0337BgwQISExOZMGFCh/R4YuKSdSBp2zNaL6wR4tamU0cnBrkXMny263R2NTU1SktQBYGPyU/e4Yl9FVbSOu3xLCpO3n4lYHplKyUFjsJR91i/Cb25v4s8chsR7Dr7SJZlX+OU+eYrdIUWQIl+UV/YvHek0Whk2bJlLFu27K7lHnroISSpcbLGxYsXs3jxYlslNOK+HrnElPkT/p8CJgU5/32+I4jaWw5AwJDzGAyuE4SVlJQoLUEVzHtrNKyFuKpJXMsopve4jm3Z5Qza6plklYgqkF9BBvzA9ZJ5agVH3mPz7i0iMQrCD7qzyGG1OJcTqzORmM0Yz/P0nzBcEQ2iX9QXNo+EqY0H7qsGIDymu8JK7EdUqvx3CfBvOWWHFpk0aZLSElTB4PsHMNmYhYQbB79IV1rOXWmrZ7lHcimw+tKJGvx+ov0ttrSKI++xwJ/IDwsHL02krrrWYfU4k6gIeZ/IgBFNV/07C9Ev6gvXC8IWymkxosqmUHHpusJqbKeuykxMmTyiF/D8IIXV2JfDhw8rLUE1BE6Td5cI36furX3a6lnUBnkT4vt9MvHuoczcGoFj7zG/l8fT01BKKT2JX33GYfU4k6iz9QuglPtpFP2ivnC5IGzIjAGM7HSBWjyJDNZ2xxC2JJYJPQqpoitu1JFx7LLSkuzKSy+9pLQE1RD4Qg8A9p8fh7XOevfCCtIWz8KWxPLrNVMBOFfR3yU3eNYKjrzH3D3d+d4QeeQ2fIO2X6GFLYllijGT6Cq53d4oV26FvegX9YXLBWEGAwROzAMgfKd2JziGLYklaNksztXJK2WsuPPc32e71A/aypUrlZagGh54fSJdqeCq1I+UjepNVdGaZ/XtthR5Hlix1JugZbNcqt1qCUffY/OekB8YwpMHOLQeR1LfZs+YRyPv5yrxq80BirVZ0S/qC4N052x5jfLdd99x3333kZSUROEeiaeW3scw94vk1gzWZKqKad4ZnDKNabRhrgELU43nSKkep6AygaN4dmA8Owtn8uEjR/ifw48oLadDiHarLy6fLsZ3ivwK70rqFfpN0V5SXtFmBR3l9rjj3ns7lobH5UbCAB761SQ8MZNnGULGHm0un840DWvUKQBIuJNhGqaQIvtTvzu9QCbwUXnkNjyht8JKWqY1z/TQbrWEo++xAZP7cE9n+ZVkxD+1mRZIbW1W9Iv6wiWDsC69jcztI88HC//3JYXVdIyxxrzbNsqVMWBhnDFPIUX254UXXlBagqqY9ys5pUNMxWRKs9U5x6Y1z8Ya86jflLweV2u3WsIZ91jgPVcACI/Q5s+J2vpa0S/qC23eNW0gMEDe1Cz8RDeFlXSMpW+XNhkel3Bn6eJSBVXZl4MHDyotQVUMm+XLBK9srLhz6J/qnBfWmmdLfnoFeV6NjCu2Wy3hjHts3ovyyG3EpYlYzNrbMk5tfa3oF/WF6wZhv5CHko+WTqWqsFxhNe3nB0vvoSuy7k7UMNV4jrAlscz/eJbCyuzH5MmTlZagOgKnyPmJwveqc4Vka565u8mjYJ6YMWJyyXarJZxxj83+2QS6G65TIvUica32VqQv+GQWkz3lV6ke1CreZkW/qC9cNggb9/hQhnlcogYvvg0+rbScdnP0H99RiQ993K5xw+xBSvU4l/shu30zd4HMvB/6ALA/dyySRX2BWGuebdspbzD/mwcTqJaMLtlutYQz7jEPL3ceH3wWgPCvrzm8PntTlFpIWo28RVFmTInibVb0i/rCZYMwgwECx58HIHyHWVkxHWDbejnz/zPjM/HwdE2bamtdI8u2PZnzi4l0popC6wBSt6gve/7dPLtxuZx9hfcAEPSW62yvpWWcdY8FPi7v5hGe1M8p9dmTnX89ixV37umSwYhZyq/uFP2ivnDNX/ebBC7wBmB/5gjQUCYOq7mW7ZkTAQh6uavCahzHsGFixdydeHXz5JH+8nyw8NXqS857N88iPk7hBl0Y5pHPfc+NdKIqQUs46x574m15a6qEGxMpOn3FKXXai237uwAQNKdIYSUyol/UFy4dhD38q4l0oobsuuFkhZ9TWk6bif4ymcvSALobrvPo2xOVluMw4uLilJagSgIfkUduw+PUt/H13Tzbtk1+0Flwb64mc/O5Is66xwZN78tU70x5/9Ng7aSqKMsq4nDpzdHb345QWI2M6Bf1hUsHYd36eRPQ6+aowooLCqtpO9tWyxPynx51Fk9vd4XVOI5nn31WaQmqZN6b8o9BdPlkrp9X16rCljyrKa1i98XpAAT9vK/zBAnuijPvscDp8sht+H7tBOB7PjpFLZ5MNGYz/lF17M0r+kV94dJBGEBgQAUA4VHaSFUh1VkIS5OzNAe96NobH69bt05pCapk5IODGOuZSx2dOPyFulabteTZ4b8lU053fN2vMPvlsU5WJWgJZ95j834kj9xGXBiPtdbitHptYdteuY8Nml2osJJbiH5RX7h+EPaGvPfityVTqL5aobCa1kn6TwoXrEPoQiVP/Ma1lyq/9dZbSktQLYGTLwIQvltdP2YtebZtszyZeP7kc7i5a2ckxNVx5j32wGsT6EY5RVJfktap6+GhOSovlrL/qrzVTNA7gxVWcwvRL+oLlw/CJgYOY7B7ISa8Ofov9aeq2PZvOVP6k8PS8PbppLAaxyK252iZeQvlkdvw7DGqSlXRnGd1VWZ25E4FIOjVHk5WJLgbzrzHOnl78NggeUXv/vXqmOR+N8L/ehIT3ozyvMDUp4crLacB0S/qC5cPwgwGCByXA0D4tmqF1dwdyWJlW4q8dU3QD113Llg9L7/8stISVMvcX07ESDX51oGcCVPPROfmPDv2j++4JvWmt1sJc34+QQFVgpZw9j0W+Jg8IhqeqP55gdt2yD9/QfdfwKCiwVvRL+oLlw/CAAKfld/7708fpupUFac3nSLLMhIvTDz57iSl5TicsLAwpSWoFu8eXjzc7+b+p6sKFFZzi+Y82/b1DQCeGZfhsjnttIqz77F5b8lJT+OqJlGSod7RMFNRBXsL5FWRC94coLCaxoh+UV/oosd89O2JeFBLZt0ocg5mKy2nRbb9S86v872BZ+jW16iwGscza5bIpH435s01ARAe00NZIbdxp2dWcy3bM27mtPtpFyUkCe6Cs++xIff3Z5LxHFbcOfBP9SUbrufAx8lU0o3BHoXMeGGU0nIaIfpFfaGLIMxnQGce6HkzVcVXeQqraQFJYluinKQvaL565gA5kvPnzystQdUE/nI4AMevT6HiYpmiWuq507OY5ckUSr74GMp5dLHrj95qDSXuscBp8sjt/nD19mPbQmVtC6blqG4hiegX9YUugjCAwAfklZH7ozorrKR5Mnee5XTtODyo5Qe/d90ErbdjNLr+aJ8tjHl4MKM65VGLJ0dUkqriTs+2rbqV086rs+vPY9QaStxjgS/0AGD/eXWmqqgpu8GuvGkABP28j8JqmiL6RX2hmyBs3mvyEuQjxVMxXatSWE1Twv55CYBH+p2h52B9vNbp2VN9GeHVRuAkOclw+C517Cd3u2dSnYWwM3JOsKAfu3ZOO62ixD32wOsT6UIlV6T+pGxMc3r9rfHtZ8mU0ZN+bsU88Ir6ctqJflFf6CYIm/r0MAa6X+YGXYj61yml5TRhW4y84XHQ09rbbLyjpKWpr4NWG/OC5L1Dw8+NRrIqv6jkds++W32SPOtQOlPFE78RryLViBL3mFcXDx71PQvA/nVXnV5/a2zbKPex8ydn4u6hrleRIPpFvaGbIMzgZmDeGDlVxf5t6hoJyzuURaJpCm5YePZ9fbyKBHj00UeVlqB6Hn5zAl6YuGAZTPpO5VNV3O7ZthXXADmnXefurp3TTqsodY8FPloDQHh8b0XqbwnLDTM7suUk2EGvdFdYTfOIflFf6CYIAwh8xhOA8LShqkpVEfb3XAACeqXRb5Q2tleyB998843SElRP515G5va5uahkZb7Cam55Jue0GwlA0HO66kY0hVL32LxfySsOYyonU3auWBENzXH8i2SuSv3oaSjloV+qM6ed6Bf1ha56z8fenoAbFs7WjsHoVsM07wzClsQqLYttx+TEhkHz1DVC52jE9hxtY2BPOQ/Xu/seUrzN1nt25pvTZNaNwgsT318iXkWqFaXuseGzBjDIrRALHvQb46N4u61n27pKQM5p18lLnT9/ol/UF+pshQ7iyOensCKv4DLjxSnTGIKWzVK0cyiMziX6hrxSZ/7vxymmQwnE9hytE7YkljVZDwJgxV3xNlvv2bYvLwP6yWmnVZS6x8KWxJJvlee51uKpeLsFOadd2NmbOe1+os5V8iD6Rb2hqyDsT1/0BG7lrpFww4CFP3+h3GqU7Z9kIeGGn89ZBk/R16qY119/XWkJqudPX/TEoKI2+/rrr8s57RKGArDgWfXmghIod4+prd0CxIckky8NpKuhkscWq3furegX9YWugrBM0zDu/CtLuJNhGqaMIGDbEblTCnq0TDENSrF27VqlJaieTNMwJBW12bVr15K16yynasfrKqedVlHqHlNbuwXYtuo6AE+NSMPY1UMxHa0h+kV9oasgbKwxr9HTGYABC+OMymTRL065xNEKef+yoPdGK6JBSR5//HGlJaie5tusVbE2+/jjj7Ptczmn3cP9ztBriD5y2mkVpe4xtfW1Up2Fbadv5rR7Qd0reUW/qC90FYQtfbv05rB4fecgIeHO0sWliujZ9XEaFjyY3iWTkX59FdGgJKdPn1Zaguq51WZvZR6XcFOszZ4+fZqwGHnD46Dv6yennVZR6h5rvt0q19eeXHeSXMswvLlB4O/UvZBE9Iv6QldB2IJPZrHt3VimGrNuBmIGnul7gvkfK7Nh6rYIOR1F0Fz1LOF2Jn376i/wbC+32uw5PLkV9Iy9R5kRqE7nrSSYpmLAyrPvq3OJv+AWSt1jt7dbd+oAGO+Rxfy/+imiZ1tIEQDzhqTRpaenIhraiugX9YWugjCQO4eU6nHsWZYOwKGiaVxLdX7+peuZVzhYeh8AQb8d4fT61YCHh3rnZaiJ+jZrlrwIGpYAwEfvXlNES9wmORAM6HWG/mN8FNEgaDtK3mP17TYvtRxPzKTXjeHYMgVWR0oS2767mdNuofoy5N+J6Bf1he6CsHoCfzuRe7pmUUVXPn/N+dsY7fnoFLV4MsGYy4RHfJ1evxq4ePGi0hI0x/98Jj8lb7oUwLk96U6vf/938gND0BP6ymmnVdRwjw2a0otXpn0HwF/+z+D0RNlnt5zibN0YOlHDU0vUv5BEDZ4JnIdugzCDAf7ntyYA/pkwi+uZV5xa/7a98obHC/wLnVqvmpg5c6bSEjTHPfOHEzggGSvufPxfzh3BvRx7njizPHq74H195bTTKmq5x95bMRp36jhwfRYJyxOdWve2YLmPfdz3NN0HeDu17o6gFs8EzkG3QRjA/D9OZoJ3Ltfpwb9e+84pdYYtiWWKMZPtRXICzh7d1bN9krPZtWuX0hI0yR8+lucSrs0J4OK32U6pM2xJLPc/4ImEG97cIH698vtYClpHLffYiJl9eXFcEgB/WVrjtHrD3o3hL8fnAJB6dYAqsva3hlo8EzgJyUVISkqSACkpKald563/bbIEktSHIqkyr9hB6mS2vRsjgSQZsEjymLxVAvm4Hqmrq1NagmZ5qHeqBJL09vgDDq+rvt3Wt1dutl+9tlstoaZ77Ozh/Ia+L3VdssPru9VupUb9rtrbrZo8E9ydjsYdt6PrkTCAFz6axkjPixTTh3+/Hu/QuuqzSN9KYmhQPIu0kixfvlxpCZrlD3+Scx39O/1BriRccGhdt7Kf109qVj77uaBtqOkeG//IQBaOkEfD/u+96w6vT407pLQFNXkmcDy6D8I8Ohn4/StXAVh2cBrmq47rHNSYRVpJxEa1HeeRN8fj1/0sJrz59I2zDq0rQ7RbzaK2e+x/Pu8PwDeFAWRuP+PQutJNw1HbDiltQW2eCRyL7oMwgJ9+dg+DPQopkAay5o1oh9UzotMloPEcMCWzSCuN2Ki24xgM8Iffy4kw/5XiT8npAofV1dNQ1rR+HbdbLaG2e2za00N5atB3SLjx119fdlg9poISDDSdb6uFdqs2zwSORQRhgJe3G+/+SF5p9tfdE6kts//ye8lcgxcm5Fc6cudgwKJoFmmlWbBggdISNM3335vMtC5ZVNKNf76W6pA6zq5LoFjqBdCw04Te262WUOM99j/L5NeB6/PmkHcw0yF1/Pl7xzFjBGjI2q+VdqtGzwSOQwRhN3kteDr93Is5bx3Gpjej7H79NT/cR0rtZDyoYZxnLkZMTDWeI2xJrGIZ+5UmOtpxo456wGCA//6vGwD8M24m5dlFdr1+3fUqFr3RiTo8uadrJlONWXiJdqsp1HiPzfrRCB7tm0odnfjkV/YflUr4+zE+PvN9AJbMiWGq8Zym+ls1eiZwHCIIu0lnHw9+80wOAB9tGYWlymS3a1+KOMOvdz0EwP+9eJZ080iqJSMp1eNU3yE4khEj9LlTgD0J+tNUxhnPU0ovlr9m3/xLn33/EPHm6fgYytkV70tK9ThOJJ7RfbvVEmq9x/7wFzlf13+yAiiMzrXbdc2FJbzy+35YcedHE5L5+OhsUqrHaaq/VatnAscggrDb+GXIdHoayki3jCHsnWN2uaZkruHnz5VwnR749crkN2um2uW6roDJZL9AV6+4exh4/+fy65VPj97HjXz7vGpJX5/AH088AcBn71xg8AQ5N5nwTFuo1a+5r43Bv2caZoz8/ef2eyX55+8d50zdePq5F/PPA9rc21StngkcgwjCbsOnjyeL58kdwl/WDkaqqbX5mute2Me+igC8MLF6Ry/cPdS/d5mzKCsrU1qCS/Djj6cxvFM+V6V+rHw9zubrWcqreOV1D8wYeWLQaV75++SG74Rn2kKtfhkM8D//I/eFX51+gOKUSzZfM/HTY3x8+kkAln94jT6DjTZfUwnU6pnAMYgg7A4W/3sKXQ2VnKydyN4lR226Vv6BM/zXjocA+NOPMpgQ0McOCl2HCRO0+aSqNjp5ufHeT+WtWZbtn0LNtQqbrvePpw4Sa76HboYK/h0xFMNtzw3CM22hZr8CfzPh1v69r5+26Vrmy6W88l5fLHjwwvhkFvxeu9tqqdkzgf0RQdgd9BrkzZtz0wD4MKQPUp2lQ9eRamr5+cJrXKcHM3pm8du14jXknRw5ckRpCS7Dos/vYaD7FS5Jg1j38xMdvk7GhkT+ECW/hvx0cR5DJvk0+l54pi3U7JfBAP/zOzMAXyTatn/vh987xum6CfR1v8YXB8bbS6IiqNkzgf0RQVgz/OY/kzBSTZxpOkc+6NjcsK9f2MPeijl4Ymb19p54dBKvIe/kxRdfVFqCy2Ds4s7vfihnzv9o+3jqym+0+xqWihv87DUDJrz53qDTvPrZ5CZlhGfaQu1+zf/DpIb9e798tWP79373j2N8dEpeDbn8z8X0GaL+Tbrvhto9E9gXEYQ1Q/+RXXjd7xQAz/xlBt4GE9O8M9q8+WvhoTMs3v4QAB88n86kueI1ZHP85z//UVqCS/HGv6bTjXJyrMPp3N2jXW0W4POnDhBtuk9+Dbm/8WvIeoRn2kLtfrm5G/jvN+VdSv73+OMY29nX1lwpZdG7fbDgwQ/HphD039p9DVmP2j0T2BcRhLXA5Inya8gqumLCyCnTGIKWzWq1c5Bqavl5UDFl9OS+Hud4d714DdkSYnsO+xLxf0lUIL8+rMWzzW0WIHNjIv9zTH4N+fe3zzN0sk+z5YRn2kILfnlJ8mpAC/JikPa02788cZRTdRPp415C8EHtB2CgDc8E9sOmIKyyspKlS5cyb948evXqhcFgYM2aNe26htls5r333mPgwIF4e3vj5+fHwYMHbZFlF77c1IvbtxiSN3+18v5nfZGulbR43sYf72Z3+Vw6UcOa7T3Ea8i7ILbnsC+3NtqWkfd7lPivvw/h6q5YqKtr9jxLxQ1+9iqY8OaxgWd47R9TWqxDeKYttODXh/9qrt1aWfLpAGrPtZzMNfmfUfzfSfk15L8+KKLvUG2/hqxHC54J7IhkA7m5uRIgDR06VHrooYckQFq9enW7rvHCCy9IHh4e0u9+9zspJCREmj17tuTh4SFFRUW16zpJSUkSICUlJbXrvJYwUi2B1OxnBNnSO4O2SJG//EaqTT8nSZIkbXs3RprQKVMCqwSS9KNxiXbR4coUFxcrLcGluFubNWCR/D1ipY/v3Syl/zNCkioqpG3vxkhTjemSBzUSSJKRG9L5k2V3rUN4pi204Nfd2m0PSqQXe+6RtizcLF3/9jtJslqlbe/GSFO8MiQDFgkkaXaPNKX/CnZFC54JZOwRd9gUhJlMJqmwsFCSJElKSEhodxAWFxcnAdKyZcsajlVXV0ujRo2SZs+e3S4t9g7CphrTG27yWx9rk2M9uSbN8Yxu+P72P7e9G2MXLa7Khg0blJbgUjTXZg1YJG+qmvy4DeRiw/e3H2+tzQrPtIUW/Gq+r7VI7jcfDuo/npik6e4nG7531b5WC54JZOwRd9j0OtLLy4sBAwZ0+PzQ0FDc3d154403Go4ZjUZeffVVYmJiuHjxoi3ybGLp26U3X0He2vwVDHz9TiLb/nWFl2em0bvTdUrpxbGa2TfPMjT8acDCn7/oqYR0zTB1qpgvZ0+aa7MSbmxYksqFnDqC/yuLx4dl0IkaChgM1L/6oaF8a21WeKYttOBX832tG9/8Lomo3WW8++RpxnbNpwYvUiz1f5/6dut6fa0WPBPYD0Un5icnJzN27Fh8fBpPAp45cyYAKSkpLZ5rNpspLy9v+FRWVtpV24JPZrHt3dgmm7/++LOZLPhlf9bETeTyje4c3VOBO03n2ki4k2EaZldNrkZRkX03nNY7LbXZ+R/PYsgID371jzEcOD+OotJOdKKmyfltabPCM22hBb9aardBy2bx4FM9+GTvZDIqBnE2xYwHTXcxcbW+VgueCeyHokFYYWEhvr6+TY7XHysoKGjx3I8++oju3bs3fObOndtwzZCQEMxmc8MEx+DgYK5cucKWLVtITU3l2LFjREREkJWVxZo1a6ioqGhUtqysjPXr1zPxZz342+6L7DsSw9cJtVRPy+HatWsNZb/6KpgZj3gwziOz0cRSkJ/mxnqdb3Rdi8XC8uXLyc/PZ/v27SQmJhIbG8vu3bvJzc1lxYoVTXQXFRWxefNmUlNTOXr0KBEREWRkZLB27domusvLy1m3bh0ZGRkcOHCAyMhITp06xaZNmyguLm5U1mQysXLlSnJzc9mzZw/R0dEkJiYSFhZGQUEBX375JVarleDgYKxWK19++SUFBQWEhYWRmJhIdHQ0e/bsITc3l5UrV2IymRpdv7i4mE2bNnHq1CkiIyM5cOAAGRkZrFu3jvLy8oZ/j+DgYCoqKli7di0ZGRlERERw9OhRUlNT2bx5M0VFRY2uazabWbFiBbm5uezevZvY2FgSExPZvn07+fn5LF++vOG69ecUFBQQGhpKUlISJ06cYO/eveTk5LBy5Uqqq6sblb127RobN27k9OnTREZGcujQIdLT01m/fj1lZWWNylZUVLBmzRqysrKIiIjg2LFjpKamsmXLFq5cudJEd0hICHl5eezatYvY2FgSEhLYsWMHly5d4quvvqKurq7ROYWFhYSGhpKcnMzx48fZt28f2dnZrFq1iqqqqkZlS0pK2LhxI+MX+fDp3nz2HDrBxiQLN6ZmU1pa2qisu0cVYz3OtanN1tbWEhISwoULF9i5cydnz54lPj6enTt3cuHCBUJCQqitrW1yr23dupWUlBSioqLYv38/WVlZrF69msrKykZlS0tL2bBhA2lpaRw+fJgjR46QlpbGxo0bKSkpaVS2qqqKVatWkZ2dzb59+zh+/DjJycmEhoZSWFjYqGxdXR1fffUVly5dYseOHSQkJBAbG8uuXbvIy8uzWx+Rnp7OoUOHiIyM5PTp02zcuLFRHxEcHEx1dTUrV64kJyeHvXv3cuLECZKSkggNDaWgoMChfcSd/95q7SN6Blbzyc48UtLz+E1IHI/+z8Qm91pcymbGe2Y3225HeWS7TB9x+fJlh/YRaWlpHDlyhMOHD5OWlsaGDRua9BGVlZWsXr2arKws9u/fT1RUFCkpKWzdurWJ7jv7iPj4eN30EdHR0dhM/XtJi8UiVVdXt+ljtVqbvNfsyJywkSNHSoGBgU2OZ2dnS4D02WeftXiuyWSSrl+/3vA5evSoXeeEtYdt78bcnF9T1+jPsCWuM0/BEZw9e1ZpCbqlo21WeKYtXM0vPfS1ruaZK2PXOWHHjh3D29u7TZ+MjAzboz/A29sbs9nc5Hj9LvLe3i0vOfby8sLHx6fh07VrV7to6gh3ew0kaJmEhASlJeiWjrZZ4Zm2cDW/9NDXuppngrvjUf8f48ePZ/Xq1W06qblXiB3B19eX/Pz8JscLC+XNiAcOHGiXepzBgk9mseCT+v9zjaSBjubpp59WWoKu6UibFZ5pC1f0y9X7Wlf0TNAyDUHYgAEDWLRokVMrnz59OpGRkZSXlzeanB8XF9fwvcB1+frrr0V2aI0hPNMWwi/tITzTF06bmH/jxg3S09MpLi5uOLZw4UIsFgsrVqxoOGY2m1m9ejV+fn4MGTLEWfIECiA6Gu0hPNMWwi/tITzTFzYHYcHBwXz44YesWrUKgN27d/Phhx/y4Ycfcv369YZy8fHxTJgwodGWDH5+fjz33HO8//77LFmyhBUrVvDII49w/vx5PvnkkyZ1CVwLsT2H9hCeaQvhl/YQnukLj9aL3J2//e1v5OXd2t8rLCyMsLAwAF566SW6d+9+1/PXrVvHH//4R9avX09paSlTp05lz549zJkzx1ZpApXz8ssvKy1B0E6EZ9pC+KU9hGf6wuaRsPPnzyPJ2x81+QwfPryh3EMPPYQkSXzwwQeNzjcajSxbtozCwkJMJhPx8fE88cQTtsoSaIBt27YpLUHQToRn2kL4pT2EZ/pC0WStAn3zwAMPKC1B0E6EZ9pC+KU9hGf6QgRhAsXIyclRWoKgnQjPtIXwS3sIz/SFCMIEinG3ZLwCdSI80xbCL+0hPNMXIggTKEaPHj2UliBoJ8IzbSH80h7CM30hgjCBYqSnpystQdBOhGfaQvilPYRn+kIEYQLFmDt3rtISBO1EeKYthF/aQ3imL0QQJlCMrVu3Ki1B0E6EZ9pC+KU9hGf6wiBJkqS0CHvw3Xffcd9995GUlMS9996rtByBQCAQCAQujD3iDjESJlAMsT2H9hCeaQvhl/YQnukLEYQJFOP1119XWoKgnQjPtIXwS3sIz/SFCMIEirFmzRqlJQjaifBMWwi/tIfwTF/YvIG3Wqiurgbg7NmzCisRtJXBgwfz3XffKS1D0A6EZ9pC+KU9hGfaoT7eqI8/OoLLBGHnzp0D4KWXXlJYiUAgEAgEAr1w7ty5Du/56TJBWEBAAAARERH06dNHYTWC1qisrGTu3LkcPXqUrl27Ki1H0AaEZ9pC+KU9hGfaori4mCeeeKIh/ugILpOiory8nO7du3P9+nV8fHyUliNoBeGX9hCeaQvhl/YQnmkLe/glJuYLBAKBQCAQKIAIwgQCgUAgEAgUwGWCMC8vL5YuXYqXl5fSUgRtQPilPYRn2kL4pT2EZ9rCHn65zJwwgUAgEAgEAi3hMiNhAoFAIBAIBFpCBGECgUAgEAgECiCCMIFAIBAIBAIFEEGYQCAQCAQCgQKIIEwgEAgEAoFAAVQZhFVWVrJ06VLmzZtHr169MBgMLe4sbzabee+99xg4cCDe3t74+flx8OBBm8sK2k5b/WqPryD8ciRt9SIhIYG33nqLSZMm0aVLF4YOHcoPf/hDMjMzm72u8MwxtNWvM2fO8NxzzzFy5Eg6d+5Mnz59mDNnDrt37272usIvx9He/q6ev/zlLxgMBiZPntzs98Izx9BWv7799lsMBkOzn9jY2CblW/VLUiG5ubkSIA0dOlR66KGHJEBavXp1s2VfeOEFycPDQ/rd734nhYSESLNnz5Y8PDykqKgom8oK2k5b/WqPr5Ik/HIkbfUiKChIGjBggPT2229L//73v6X/9//+n9S/f3+pS5cu0qlTp5qUF545hrb6tXfvXumJJ56QPvjgA2nFihXSP/7xDykgIEACpJCQkCblhV+Oo739nSRJ0sWLF6XOnTtLXbp0kSZNmtRsGeGZY2irX5GRkRIgLV68WFq/fn2jT1FRUZPyrfmlyiDMZDJJhYWFkiRJUkJCQov/GHFxcRIgLVu2rOFYdXW1NGrUKGn27NkdLitoH231q63lJEn45Wja6sWJEycks9nc6FhmZqbk5eUlvfjii42OC88cR3vunTupq6uTpk2bJo0bN67RceGXY+mIZ88//7z0yCOPSHPnzm02CBOeOY62+lUfhG3durXVa7bFL1W+jvTy8mLAgAGtlgsNDcXd3Z033nij4ZjRaOTVV18lJiaGixcvdqisoH201a+2lgPhl6Npqxf+/v54eno2OjZmzBgmTZrE2bNnGx0XnjmO9tw7d+Lu7s6QIUMoKytrdFz45Vja69mxY8cIDQ3lH//4R4tlhGeOoyP3WEVFBXV1dS1+3xa/VBmEtZXk5GTGjh3bZPfymTNnApCSktKhsgLlEX6pF0mSuHLlCn369Gl0XHimHqqqqiguLiY7O5vPPvuM8PBwHn300UZlhF/qwWKx8Pbbb/Paa68xZcqUFssJz9TDK6+8go+PD0ajkYcffpjExMQmZdril4dT1DqIwsJCfH19mxyvP1ZQUNChsgLlEX6plw0bNpCfn8+f//znRseFZ+rht7/9LSEhIQC4ubmxYMECgoODG5URfqmHr776iry8PA4dOnTXcsIz5fH09CQoKIgnn3ySPn36kJaWxt/+9jcCAgKIjo7mnnvuaSjbFr80HYRVV1c3u3Gm0Whs+L4jZQXKI/xSJ+np6fzqV79i9uzZvPzyy42+E56ph3feeYeFCxdSUFDAli1bsFgs1NTUNCoj/FIH165d43//93/54x//SN++fe9aVnimPP7+/vj7+zf8/w9+8AMWLlzI1KlTef/999m/f3/Dd23xS9OvI729vTGbzU2Om0ymhu87UlagPMIv9XH58mW+//3v071794a5DrcjPFMP48eP57HHHuOnP/0pe/bsobKykqeffhpJkhrKCL/UwR/+8Ad69erF22+/3WpZ4Zk6GT16NM888wyRkZFYLJaG423xS9NBmK+vL4WFhU2O1x8bOHBgh8oKlEf4pS6uX79OYGAgZWVl7N+/v9l/f+GZelm4cCEJCQmN8rsJv5QnKyuLFStWsHjxYgoKCjh//jznz5/HZDJRW1vL+fPnKSkpaSgvPFMvQ4YMoaamhqqqqoZjbfFL00HY9OnTyczMpLy8vNHxuLi4hu87UlagPMIv9WAymXj66afJzMxkz549TJw4sdlywjP1Uv+a6vr16w3HhF/Kk5+fj9VqZfHixYwYMaLhExcXR2ZmJiNGjGg091J4pl5ycnIwGo107dq14Vhb/NJ0ELZw4UIsFgsrVqxoOGY2m1m9ejV+fn4MGTKkQ2UFyiP8UgcWi4Xnn3+emJgYtm7dyuzZs1ssKzxTnqtXrzY5Vltby7p16/D29m4UQAu/lGfy5Mls3769yWfSpEkMHTqU7du38+qrrzaUF54pT1FRUZNjJ0+eZNeuXXzve9/Dze1WWNUWv1Q7MT84OJiysrKG1R67d+/m0qVLALz99tt0794dPz8/nnvuOd5//32uXr3K6NGjWbt2LefPn+c///lPo+u1p6yg/bTFr/aUE345nrZ48dvf/pZdu3bx9NNPU1JSwtdff93oGi+99FLDfwvPHEtb/Pr5z39OeXk5c+bMYdCgQVy+fJkNGzaQnp7O3//+90ZP6cIvx9OaZ3369OHZZ59tcl59rrA7vxOeOZa23GPPP/883t7e+Pv7069fP9LS0lixYgWdO3fmr3/9a6Prtckve2SadQTDhg2TgGY/ubm5DeWqq6ul3/3ud9KAAQMkLy8vacaMGdL+/fubvWZ7ygraR1v9ams5SRJ+OZq2eDF37twWyzTXfQjPHEdb/Nq0aZP02GOPSf3795c8PDyknj17So899pi0c+fOZq8p/HIs7envbqeljPmSJDxzJG3x6/PPP5dmzpwp9erVS/Lw8JB8fX2ll156ScrKymr2mq35ZZCk25bLCAQCgUAgEAicgqbnhAkEAoFAIBBoFRGECQQCgUAgECjA/weLekpa14J6QAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -886,22 +1082,30 @@
     }
    ],
    "source": [
-    "#   yDown         ycUp        ycUpLpf\n",
-    "# y[mD, k] --> U ------> LPF --------> LOp --> ycUpLpfLo\n",
-    "ycUp = up(yDown, Nup)  # insert Nup - 1 zeros\n",
-    "ycUpLpf = Nup * signal.lfilter(hPrototype, [1.0], ycUp)  # interpolate by Nup\n",
-    "ycUpLpfLo = ycUpLpf * LOp  # upconvert to positive bin kLo\n",
-    "yrUpLpfLo = ycUpLpfLo.real * nofSsb  # = ycUpLpfLo + np.conj(ycUpLpfLo), add negative bin -kLo\n",
-    "\n",
     "# Plot original real xData recovered yrUpLpfLo\n",
-    "# TODO: Why is the intGroupDelay only correct for integer wgSub ???\n",
-    "plt.plot(xData[0:len(yrUpLpfLo) - intGroupDelay], 'r.-')\n",
-    "plt.plot(yrUpLpfLo[intGroupDelay:], 'b.-')\n",
-    "plt.xlim([100, 150])\n",
+    "xDelayed = xData[0:len(yrUpLpfLo) - hPairGroupDelay]\n",
+    "xyDiff = yrUpLpfLo[hPairGroupDelay:] - xDelayed\n",
+    "if 1:\n",
+    "    plt.plot(xDelayed, 'r.-')\n",
+    "    plt.plot(yrUpLpfLo[hPairGroupDelay:], 'b.-')\n",
+    "    plt.plot(xyDiff, 'g')\n",
+    "else:\n",
+    "    plt.plot(xyDiff, 'g')\n",
+    "plt.xlim([0, 1000])\n",
     "\n",
     "if not wgModulation:\n",
+    "    offset = Ncoefs\n",
+    "    xLen = len(xDelayed[offset:])\n",
+    "    if xLen < Ncoefs:\n",
+    "        print('Too low Nsim for proper yrSNR calculation %d < %d' % (xLen, Ncoefs))\n",
+    "        verify_result(False, '', enExit)\n",
+    "    yrSNR = snr_db(xDelayed[offset:], xyDiff[offset:])\n",
     "    yrAmpl = np.sqrt(np.mean(np.abs(yrUpLpfLo[Ncoefs:]**2)) * nofSsb)\n",
-    "    print(yrAmpl)"
+    "    print('wgSub  = %f' % wgSub)\n",
+    "    print('kLo    = %d' % kLo)\n",
+    "    print('xLen   = %d input samples' % xLen)\n",
+    "    print('yrSNR  = %.5f [dB], single bin' % yrSNR)\n",
+    "    print('yrAmpl = %f' % yrAmpl)"
    ]
   },
   {
@@ -918,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 33,
    "id": "7049249e",
    "metadata": {},
    "outputs": [
@@ -926,7 +1130,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PASSED\n"
+      "PASSED: LOpDown == LOpD\n"
      ]
     }
    ],
@@ -947,23 +1151,15 @@
     "    plt.plot(m_sub, LOpDown.imag, 'r--')\n",
     "    plt.plot(m_sub, LOpD.real, 'g-')\n",
     "    plt.plot(m_sub, LOpD.imag, 'g--')\n",
-    "verify_result(result)"
+    "verify_result(result, ': LOpDown == LOpD', enExit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 34,
    "id": "64cc34f3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PASSED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Verify that LOpDown == 1 when Nup == Ndft\n",
     "if Nup == Ndft:\n",
@@ -971,12 +1167,12 @@
     "    if not result:\n",
     "        plt.plot(m_sub, LOpDown.real, 'r-')\n",
     "        plt.plot(m_sub, LOpDown.imag, 'r--')\n",
-    "    verify_result(result)"
+    "    verify_result(result, ': LOpDown == 1, for Ros = 1', enExit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "id": "71a91beb",
    "metadata": {},
    "outputs": [
@@ -984,16 +1180,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PASSED\n"
+      "PASSED: yrUpLpfLo == yrLoUpBpf, for Ros >= 1\n"
      ]
     }
    ],
    "source": [
     "#                       ycDownLo     ycLoUp                   \n",
     "# y[mD, k] --> LOpDown ---------> U -------> BPF --> ycLoUpBpf\n",
-    "ycDownLo = yDown * LOpDown  # upconvert to positive bin kLo\n",
+    "sBpf = sPrototype * np.exp(1j * w_k * np.arange(Ncoefs))\n",
+    "ycDownLo = yDown * LOpDown * LOadjust  # upconvert to positive bin kLo\n",
     "ycLoUp = Nup * up(ycDownLo, Nup)  # insert Nup - 1 zeros\n",
-    "ycLoUpBpf = signal.lfilter(hBpf, [1.0], ycLoUp)  # interpolate by Nup with BPF at kLo\n",
+    "ycLoUpBpf = signal.lfilter(sBpf, [1.0], ycLoUp)  # interpolate by Nup with BPF at kLo\n",
     "yrLoUpBpf = ycLoUpBpf.real * nofSsb  # = ycLoUpBpf + np.conj(ycLoUpBpf), add negative bin -kLo\n",
     "\n",
     "# result is True for any Ndft, Ndown, because LOdown is in equation of yBpfDownLo\n",
@@ -1002,7 +1199,7 @@
     "    plt.plot(yrUpLpfLo, 'r')\n",
     "    plt.plot(yrLoUpBpf, 'b')\n",
     "    #plt.xlim([0, 850])\n",
-    "verify_result(result)"
+    "verify_result(result, ': yrUpLpfLo == yrLoUpBpf, for Ros >= 1', enExit)"
    ]
   },
   {
@@ -1025,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 36,
    "id": "a3aae48a",
    "metadata": {},
    "outputs": [
@@ -1033,16 +1230,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ndft = 16\n",
-      "> Log maximal_upsample_bpf():\n",
-      "  . len(xBase) = 16\n",
-      "  . Nx         = 16\n",
-      "  . Nxp        = 16\n",
-      "  . len(yc)    = 256\n",
-      "  . Nup        = 16\n",
-      "  . k          = 1\n",
-      "\n",
-      "PASSED\n"
+      "Ndft = 192\n"
      ]
     }
    ],
@@ -1050,15 +1238,15 @@
     "print('Ndft =', Ndft)\n",
     "if Nup == Ndft:\n",
     "    # ycDownLo = yDown * LOdown = yDown, because LOdown = 1 when Ndown == Ndft\n",
-    "    ycLoBpfMaxUp = maximal_upsample_bpf(yDown, Nup, kLo, hPrototype)\n",
+    "    ycLoBpfMaxUp = maximal_upsample_bpf(yDown, Nup, kLo, sPrototype)\n",
     "    yrLoBpfMaxUp = ycLoBpfMaxUp.real * nofSsb  # add negative bin -kLo to make real\n",
     "\n",
-    "    result = np.all(np.isclose(ycDownLo, yDown))\n",
-    "    result = result and np.all(np.isclose(yrUpLpfLo, yrLoBpfMaxUp))\n",
+    "    result = np.all(np.isclose(yrUpLpfLo, yrLoBpfMaxUp))\n",
     "    if not result:\n",
     "        plt.plot(n_sub, yrUpLpfLo, 'g.-')\n",
     "        plt.plot(n_sub, yrLoBpfMaxUp, 'r-')\n",
-    "    verify_result(result)"
+    "        plt.xlim([20, 30])\n",
+    "    verify_result(result, ': yrUpLpfLo ==yrLoBpfMaxUp, for Ros = 1', enExit)"
    ]
   },
   {
@@ -1071,42 +1259,351 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 37,
    "id": "3c2c8ec5",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "if 0:\n",
+    "    ycLoBpfUp = non_maximal_upsample_bpf(yDown, Nup, kLo, Ndft, sPrototype)\n",
+    "    yrLoBpfUp = ycLoBpfUp.real * nofSsb  # add negative bin -kLo to make real\n",
+    "\n",
+    "    result = np.all(np.isclose(yrUpLpfLo, yrLoBpfUp))\n",
+    "    if not result:\n",
+    "        plt.plot(n_sub, yrUpLpfLo, 'g.-')\n",
+    "        plt.plot(n_sub, yrLoBpfUp, 'r.-')\n",
+    "        plt.xlim([12,24])\n",
+    "        verify_result(result, ': yrUpLpfLo == yrLoBpfUp, for Ros >= 1', enExit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9daf9f",
+   "metadata": {},
+   "source": [
+    "# 5 Compare with DFT filterbank\n",
+    "\n",
+    "Can use 'cw' or 'ccw' independently for analysis and synthesis PFB, because with fold() the IDFT can be expressed as a DFT and vice versa. However to have back to back DFT - IDFT in pipeline, use analysis 'cw' and synthesis 'ccw' [CROCHIERE 7.2.3]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4b55557c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select analysis filterbank\n",
+    "aStructure = 'wola'\n",
+    "aStructure = 'pfs'\n",
+    "aCommutator = 'cw'\n",
+    "\n",
+    "# Select synthesis filterbank\n",
+    "sStructure = 'wola'\n",
+    "sCommutator = 'ccw'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "abb548f6",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "> non_maximal_upsample_bpf():\n",
-      "  . len(xBase) = 16\n",
-      "  . Nblocks    = 16\n",
-      "  . len(yc)    = 256\n",
-      "  . Nup        = 16\n",
-      "  . Ndft       = 16\n",
-      "  . k          = 1\n",
+      "wgSub  = 1.500000\n",
+      "kLo    = 2\n",
+      "Ndft   = 192\n",
+      "Ndown  = 144\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Parameters\n",
+    "print('wgSub  = %f' % wgSub)\n",
+    "print('kLo    = %d' % kLo)\n",
+    "print('Ndft   = %d' % Ndft)\n",
+    "print('Ndown  = %d' % Ndown)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8bb436d",
+   "metadata": {},
+   "source": [
+    "# 5.1 Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a6de2be1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Verify all bins:\n",
+      "PASSED: Ac0 == Ac1\n",
+      "PASSED: Ac0 == Ac2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify that different analysis_dft_filterbank() structures yield exactly the same\n",
+    "print('Verify all bins:')\n",
+    "Ac0 = analysis_dft_filterbank(xData, Ndown, Ndft, aPrototype, 'pfs', commutator='cw', verbosity=vb)\n",
+    "Ac1 = analysis_dft_filterbank(xData, Ndown, Ndft, aPrototype, 'pfs', commutator='ccw', verbosity=vb)\n",
+    "Ac2 = analysis_dft_filterbank(xData, Ndown, Ndft, aPrototype, 'wola', verbosity=vb)\n",
+    "verify_result(np.all(np.isclose(Ac0, Ac1)), ': Ac0 == Ac1', enExit)\n",
+    "verify_result(np.all(np.isclose(Ac0, Ac2)), ': Ac0 == Ac2', enExit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a9ca3483",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> analysis_dft_filterbank():\n",
+      "  . len(x)     = 110592\n",
+      "  . Nblocks    = 768\n",
+      "  . Ros        = 1.3333333333333333\n",
+      "  . Ndown      = 144\n",
+      "  . Ndft       = 192\n",
+      "  . structure  = pfs\n",
+      "  . commutator = cw\n",
+      "\n",
+      "PASSED: yDown == yDownBin\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify that analysis_dft_filterbank() bin kLo yields same as single channel reference\n",
+    "# . Only need to verify one Ac[kLo], when Ac0 = Ac1 = Ac2 = Ac3.\n",
+    "Ac = analysis_dft_filterbank(xData, Ndown, Ndft, aPrototype, aStructure, aCommutator)\n",
+    "yDownBin = Ac[kLo]\n",
+    "#yDownBin = yDownBin[:len(yDown)]\n",
+    "\n",
+    "result = np.all(np.isclose(yDown, yDownBin))\n",
+    "if not result:\n",
+    "    if 1:\n",
+    "        plt.plot(m_sub, yDown.real, 'g.-')\n",
+    "        plt.plot(m_sub, yDown.imag, 'g.--')\n",
+    "        plt.plot(m_sub, yDownBin.real, 'r.-')\n",
+    "        plt.plot(m_sub, yDownBin.imag, 'r.--')\n",
+    "    else:\n",
+    "        plt.plot(m_sub, yDown.real - yDownBin.real, 'b.-')\n",
+    "        plt.plot(m_sub, yDown.imag - yDownBin.imag, 'b.--')\n",
+    "    plt.xlim([0, 20])\n",
+    "verify_result(result, ': yDown == yDownBin', enExit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3f49b8e",
+   "metadata": {},
+   "source": [
+    "# 5.2 Synthesis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "caf25198",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PASSED: Sr0 == Sr1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify that different synthesis_dft_filterbank() structures yield exactly the same\n",
+    "Sr0 = synthesis_dft_filterbank(Ac, Nup, Ndft, sPrototype, 'wola', 'cw', verbosity=vb)\n",
+    "Sr1 = synthesis_dft_filterbank(Ac, Nup, Ndft, sPrototype, 'wola', 'ccw', verbosity=vb)\n",
+    "verify_result(np.all(np.isclose(Sr0, Sr1)), ': Sr0 == Sr1', enExit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "47d5bf5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> synthesis_dft_filterbank():\n",
+      "  . Nblocks    = 768\n",
+      "  . Ros        = 1.3333333333333333\n",
+      "  . Nup        = 144\n",
+      "  . Ndft       = 192\n",
+      "  . structure  = wola\n",
+      "  . commutator = ccw\n",
       "\n",
-      "PASSED\n"
+      "PASSED: yrUpLpfLo == yr\n"
      ]
     }
    ],
    "source": [
-    "ycLoBpfUp = non_maximal_upsample_bpf(yDown, Nup, kLo, Ndft, hPrototype)\n",
-    "yrLoBpfUp = ycLoBpfUp.real * nofSsb  # add negative bin -kLo to make real\n",
+    "# Keep bin kLo and force all other bins to zero, to have exact comparison with full rate\n",
+    "# single channel reference.\n",
+    "Yc = Ac * 0\n",
+    "if kLo == 0:\n",
+    "    # DC bin\n",
+    "    Yc[kLo] = yDownBin\n",
+    "else:\n",
+    "    Yc[kLo] = yDownBin\n",
+    "    Yc[Ndft - kLo] = np.conjugate(yDownBin)\n",
+    "\n",
+    "# Single bin synthesis from PFB\n",
+    "yr = synthesis_dft_filterbank(Yc, Nup, Ndft, sPrototype, sStructure, sCommutator)\n",
+    "yr = yr[0 : len(yrUpLpfLo)]\n",
     "\n",
-    "result = np.all(np.isclose(yrUpLpfLo, yrLoBpfUp))\n",
+    "result = np.all(np.isclose(yrUpLpfLo, yr))\n",
     "if not result:\n",
     "    plt.plot(n_sub, yrUpLpfLo, 'g.-')\n",
-    "    plt.plot(n_sub, yrLoBpfUp, 'r.-')\n",
-    "    plt.xlim([6, 12])\n",
-    "verify_result(result)"
+    "    plt.plot(n_sub, yr, 'r.-')\n",
+    "    plt.xlim((20, 21))\n",
+    "verify_result(result, ': yrUpLpfLo == yr', enExit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "4818ad5a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> synthesis_dft_filterbank():\n",
+      "  . Nblocks    = 768\n",
+      "  . Ros        = 1.3333333333333333\n",
+      "  . Nup        = 144\n",
+      "  . Ndft       = 192\n",
+      "  . structure  = wola\n",
+      "  . commutator = ccw\n",
+      "\n",
+      "wgSub  = 1.500000\n",
+      "kLo    = 2\n",
+      "xLen   = 67696 input samples\n",
+      "yrSNR  = 5.37011 [dB], single bin\n",
+      "SNR_yr = 5.37011 [dB], single bin\n",
+      "SNR_sr = 22.18392 [dB], all bins\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare output with input\n",
+    "# . For Ros >= 1 the SNR for center bin wgSub approaches perfect reconstruction when Ntaps\n",
+    "#   is increased, because then the accuracy of the interpolated samples improves.\n",
+    "#   In the time domain this is understood because then there are more weighted values per\n",
+    "#   polyphase. In the frequency domain this is understood by the improved bandstop\n",
+    "#   suppression, because inserting zeros causes replicas that all need to be suppressed\n",
+    "#   by the interpolating LPF.\n",
+    "# . For off-center wgSub the SNR reduces more due to that the gain of both LPF is then < 1.\n",
+    "\n",
+    "# All bins synthesis from PFB\n",
+    "# . Use yr for comparison between single channel pipeline and using PFB for single bin\n",
+    "# . Use sr to see impact of aliasing in the other bins, due to limited stopband attenuation\n",
+    "#   of both LPF.\n",
+    "sr = synthesis_dft_filterbank(Ac, Nup, Ndft, sPrototype, sStructure, sCommutator)\n",
+    "sr = sr[0 : len(yrUpLpfLo)]\n",
+    "\n",
+    "# Output time aligned with input\n",
+    "x_yr = yr[hPairGroupDelay:]\n",
+    "x_sr = sr[hPairGroupDelay:]\n",
+    "\n",
+    "# SNR\n",
+    "# . For single bin yr the SNR is sligthly better than for sr, when wgSub is at bin center, because\n",
+    "#   then the LPF has gain 1 and the aliasing from the other bins is not in.\n",
+    "# . For all bin sr the SNR is much better than for yr, when wgSub is off bin center or with input\n",
+    "#   noise is added, provided that the gain at the edge of the bin is sqrt(0.5).\n",
+    "# . The SNR for CW input calculated for refBunton and with construct.m are about equal within \n",
+    "#   +-0.0005 dB. The difference is probably due to that the selected ranges for calculating the\n",
+    "#   SNR are not exactly the same. Varying the offset shows a similar spread in SNR.\n",
+    "offset = Ncoefs\n",
+    "#offset = 10000\n",
+    "endset = 70000\n",
+    "xLen = len(xDelayed[offset:endset])\n",
+    "if xLen < Ncoefs:\n",
+    "    print('Too low Nsim for proper SNR_yr calculation %d < %d' % (xLen, Ncoefs))\n",
+    "    verify_result(False, '', enExit)\n",
+    "xDelayed_snapshot = xDelayed[offset:endset]\n",
+    "x_yr_snapshot = x_yr[offset:endset]\n",
+    "x_sr_snapshot = x_sr[offset:endset]\n",
+    "x_yr_diff_snapshot = xDelayed_snapshot - x_yr_snapshot\n",
+    "x_sr_diff_snapshot = xDelayed_snapshot - x_sr_snapshot\n",
+    "SNR_yr = snr_db(xDelayed_snapshot, x_yr_diff_snapshot)\n",
+    "SNR_sr = snr_db(xDelayed_snapshot, x_sr_diff_snapshot)\n",
+    "\n",
+    "print('wgSub  = %f' % wgSub)\n",
+    "print('kLo    = %d' % kLo)\n",
+    "print('xLen   = %d input samples' % xLen)\n",
+    "print('yrSNR  = %.5f [dB], single bin' % yrSNR)\n",
+    "print('SNR_yr = %.5f [dB], single bin' % SNR_yr)\n",
+    "print('SNR_sr = %.5f [dB], all bins' % SNR_sr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "2c1ec80d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 250.0)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1FS8s88G5XgSXIZpxgD1fCdehVtPutSWBSMDmJI6etNGA7SKE2lqy1IebpKamct0FgotwqpmdJTyoCCBWOmbTVL3xZM0ajKdEzom0E7TZ8BUvLPNBxpjwIJr5GDd8ZUoQ2enAp9h90iYBJBPALmtFO2R61G2eeOIJrrtAcBFONbMxZUFFAJscpH5588maOdIIKZSvKyF73sZOB/Z8xcOeQsaY8CCa+Rg7SwPtdFCw+/6j77v98SRgcxHmrtjaHTEdAKjmkKU+vMGXX37JdRcILsKpZlbK7Y3Ld9hYJ1sCiU+erFljUr7iYU8hY0x4EM18jA1fiXodUM21fgrjK49+71G3P54EbJNAGiHFD+b8wLq5SuxMjQJkWnSSzJ49m+suEFyEM81sjLGdMYDE1pq0kCByTiQnwRqDXV+BDV/Raj2WI0vGmPAgmvkQirJ5E1g7x/opnvYVErBNkriYOJtz1RJIrE9haLUkl22SBAYGct0FgotwohmTY6Ix215KIkHTnfobKmvQoBEXE+f9/jnApq/YmhqlaY/lyJIxJjyIZj6C8RWztRx3xujHpTVfYZbm8aSvkIBtktidwgAN1VyAijSbeyG5bJOmo6OD6y4QXIQTzazkmDBTFramQn1VZOAMk54a9YCvkDEmPIhmPsKGr9TO0Y9La0TOifS4r5CAzQ0cTmFIx7yedyIWfvSjH3HdBYKLcKKZWY6JccpituVTK2bc+rLIwBkmlXLhAV8hY0x4EM18hA1fsTbHJoEEUXOiJr0rij1IwOYmdqdGaRvVXQCZFnWRgoICrrtAcBGfa2ZlTNmasmByS/jyZM0cLnyFjDHhQTTzAVZy1xhfsbYajzfTKzwasGm1Wrz99tuYP38+wsLCEB0djc8++8zuOZs3b4ZEIrH5Mn3ke/bsWZvtFAqFJ/8Up7E7hSEBvpyjn45h3RGTXDaXefXVV7nuAsFFfKqZldw1e1MWIUEhXrkD9hSOfMXq1KibvkLGmPAgmnkZK7lr9nzF25XmHg3YNm/ejL1792LTpk3Yt28fAgMD8dRTT6G0tNTmOa+99hoyMjJYr/T0dEydOhUrVqzAd77zHYtztm7danHO0qVLPfmnuIS9KQxIgNrZZtMYJJfNZRISErjuAsFFfKqZWY6JoymL5Xcu913fJonLU6Nu+goZY8KDaOZlXPQVb1eae2zz94qKCkRHR2PPnj34wx/+AADQaDRYtWoVZs+ejfLycqevVVpaijVr1uD999/Hn//8Z+Pxs2fPYu3atThy5AjWr18/6b66svm7szCbxDOVIeZIJoDIa4DSdHyFhgKjox75fAJB1ISFsZ6uRb2uv1GylRDM16lQcxhfsYZVTwGIrxAInsKDvuKJuMNjT9hkMhkCAwOxZcsW47HQ0FC88sor+Pzzz3HlyhWnr5WVlQWJRIIXX3zRZpuhoSGMj4+71WdPYrrNjDUsKrwkEmA5/+/y+QLZ5Fh4+FSzZcuM/8nllIWncVQ12nin2UE3fIWMMeFBNPMyixezfm26k1tf8VjAVlNTg/DwcEyfPp11/P777wcAKJVKp64zNjaGnJwcrF69GovN/mcxvPzyy5g+fTpCQ0Oxdu1aVFVV2b2mVqvF4OCg8TU8POxUX1yF2WbG1t6ArMUvaRoYGCB5bE4SG2v9KQOBv/hMM4oC1Gr9f3I8ZeEN7E2NaoPMcmTd8BUyxoQH0cyLUBRw9eqtXyMMxUtm5uJLX/FYwNbZ2Yl58+ZZHGeOqQ2G6oiioiL09vZi06ZNFu9NmTIFsbGx2LdvH44fP4733nsPtbW1WLNmDWpqamxec/fu3ZgxY4bxFRMTY+xzYmIitFqt8U4lPj4eXV1dyMnJgUqlwrlz51BUVITm5makpqZiaGiI1XZgYAAZGRloaGjA6dOnIZfL8fL3XnZq8Uu6vR2IjUX5H/8ItVrNuq5Op8OBAwfQ0dGBo0ePoqqqCgqFAvn5+WhtbUVSUpJFv7u7u5GdnQ2VSoWSkhIUFRWhsbERaWlpFv0eHBxEeno6GhsbcerUKcjlctTW1uLQoUPo6elhtdVoNEhOTkZraysKCgpQXl6OqqoqUBQFtVqN/fv3Y2JiAvHx8ZiYmMD+/fuhVqtBURSqqqpQXl6OgoICtLa2Ijk5GRqNhnX9np4eHDp0CLW1tZDL5Th16hQaGxuRnp6OwcFBxMfHo6ysDPHx8RgaGkJaWhoaGxtRVFSEkpISqFQqZGdno7u7m3VdrVaLpKQktLa2Ij8/HwqFAlVVVTh69Cg6Ojpw4MAB6HQ61jlqtRoymQzV1dUoKyvDiRMn0NLSguTkZIyOjrLa9vb2IisrCxcvXoRcLsfp06fR0NCAjIwMDAwMsNoODQ0hNTUVzc3NKCoqwrlz56BSqZCTk4Ouri6LficmJqKtrQ15eXlQKBSorKzEsWPHcPXqVSQkJGB8fJx1TmdnJ2QyGWpqalBaWoqTJ0/i0qVLOHjwIEZGRlht+/r6kJWVhbq6OhQXF+PMmTOoq6tDZmYm+vv7WW2Hh4eRkpKC5uZmFBYW4vz581AqlThy5IhFv8fGxpCYmIj29nYcP34cGRkZqKiowPHjx9He3o7ExESMjY1ZjLUjR45AqVTi/PnzKCwsRHNzM1JSUjA8PMxq29/fj8zMTNTV1eHMmTMoLi7GlX37gNhY0L29ABxXb/1sys/Q2dnJuu74+DgSEhJw9epVHDt2DJWVlVAoFMjLy0NbW5tXPOLixYvIyspCb28vq+3o6CiSk5PR0tKCEydOoKysDNXV1XhsymOgQVsEbbRE/zTRNJ+N8ZVTr7/ukkecPn1a0B5hOtbE4hF5eXmC9oiKigqve0RdXR2ysrLQ19fHajsyMoKDBw/i0qVLOHnyJEpLS1FTUwOZTIb+Tz7RFxsYHu7kGm4EtYGW3kKDxo+1P3boEa6khdnCYzlsS5YswfLly3Hy5EnW8ZaWFixZsgQfffQRtm3b5vA6L774ImQyGTo7O3HnnebP+y35+uuvERkZiYcffhiFhYVW22i1WmhNqjyUSiViYmI8msNmDlVPYX3OequBW+gYMMrsAyuRAJGRgJNPIMVKTU0NfvjDH3LdDYIL+ESzqCj9Sv8GGwv7b+sL5EogQe7zuYJ7umYKVU9hV8kuqLpUFr5ikc82CV8hY0x4EM28RFQUoFLd+vV1Q5qFWbAWGhTq9DqOvMphCwsLYwVFDBpDwl5YWJjDawwPD+P48eN4/PHHnQrWAGDp0qX4+c9/DrlcDp1OZ7VNSEgIpk+fbnxNmzbNqWu7g81pDIPPsqYwyEK6DhkZGeG6CwQX8YlmJgta3hpT7CZCnQo1h0m5CAkMsXjPIp9tEr5CxpjwIJp5CZOxY8yJtbGtnS99xWMB27x589DZ2WlxnDk2f/58h9c4duwYbty4YXU61B4LFizAzZs3effltVj8kgYg0eedWJTkk1w2uwwODnLdBYKLeF0zigIMN2lM7pomCPopC8Ow88Z+flwTPjPc8Y0ggwu+QsaY8CCaeRiK0j9dMzx84tvyQB4L2O6++240NTVZfIEuXLhgfN8RmZmZmDZtGp599lmXPrulpQWhoaE+eXLmCkyFV2hQqP6AwWNp842cyUK6DllOKmoFh1c1Yxa0HBsDYCV3zfAzJChEUFWhzmB1FwRrN4Iu+goZY8KDaOZBGE+prTUe4mpHA1t4LGBbv349dDodkpKSjMe0Wi1SUlIQHR2NBQsWAABu3LiBhoYG9PT0sM7v7u7G6dOnsW7dOkydOtXqZ3R3d1sc+/LLL5GXl4fHHnsMAQH822lLGiG1Gp6zpjDIQroOKSkp4boLBBfxqmZmC1raKrcHfDtl4QssbgQNWNwIuugrZIwJD6KZB2E8xSStn+tlPMwJ8tSFoqOjsWHDBmzfvh3Xrl3D0qVLkZaWhsuXL+OTTz4xtquoqMDatWsRFxeHHTt2GI8fPnwY4+PjdqdDN27ciLCwMKxevRqzZ89GXV0dkpKSMHXqVHzwwQee+lM8TvjMcNR21VpOj0J/NyytB8llc8DGjRu57gLBRbyqma3cNZM7YaHsaDAZnLoRBFzyFTLGhAfRzINY2eCdBqz6Clc5sR59JJWeno5t27YhIyMDW7duxdjYGAoKCvDwww87PDczMxOzZ8/GT3/6U5ttnnvuOfT09GDv3r349a9/jcOHD0MqlaKqqgoREebJG/zBqSkMspCuXVJSUrjuAsFFvKqZYY1GMeWumeNULpsLvkLGmPAgmnmQ8HDjU3vGV7SMrxjg2lc8tqyHkPDG1lSOoOopbKI2QTOuYR1nleMvWgTs3QtIpT7pE4EgOChKP3Vx8SIwMWFzqxhXyu2Fiq1tqyS0fno097Dh6T3xFQLBPhQF/O53QFsbANtbULnjK7xa1oNgH6emMAwLXpLiA0vIFizCw+OamSYFT0wAEFfumjlO57I56StkjAkPopkHYHylvd14iK++QgI2H2JtCkMyASzvNfxCig9sYm9fWQI/8bhmVpKCl/bB6rpr/pq7Zo4ni5rIGBMeRDMPYMVXFg2Al75CAjYfwuSymQZtdAAwEEIW0nWErV0sCPzF45pZSQq+Oh28yjHhAqdy2ZzwFTLGhAfRzAOYFTBFva5/wsZHXyEBmw9hpjAWzljIOt4+gxQfOMKZdfwI/MLjmllJCh5gzwZi4YyFfrfumiM8VdRExpjwIJp5gPBwALc8pXYOezo0OCAYkXMieeErJGDzMdIIKWaEzrB4ymbMOaFpoK5Ov9oyyWUz8s0333DdBYKLeFQzigKuXzfeCe+MgdVy+9tDb+fcVH2NU7lsNA0MDNj1FDLGhAfRzE0oCujvB3BrkVzazFNWzFoB5etKXvgKCdg4oKmnyeKOmJVzMjamT6wmBQhGRFjMLHg8ppmVpODGmbBYfZwGjcZecaYTeKKoiYwx4UE0cwPGV65eBWC90IBvnkICNg5wOueEFCAYmTdvHtddILiIxzQzSQpmcky0gZbN+JAUzCUOfcWBp5AxJjyIZm5g5isAeFloYAoJ2DjA6b0ASQGCkZqaGq67QHARj2lmSAo25pjMhsXTNb4kBXOJU75ix1PIGBMeRDM3MPMVTSB4WWhgCgnYOMDp9ZNIAYKRp556iusuEFzEY5oZig2MOSZmrsVsFcOHpGAuccpX7HgKGWPCg2jmBoZiA+MG72a+EhIUwjtPIQEbRzi9flIcf6J7LsnMzOS6CwQX8Zhm774L0LTNxSxDgkJ4kxTMNQ59xY6nkDEmPIhmbvDmmwD4u0iuNUjAxiFWF9KlTRbSDQ4GduwghQcA3njjDa67QHARj2hGUcCf/gQACO/Vjw9T+JZjwgfsLtAdHAy8+KLVKnQyxoQH0WySUBTw5z8D0PsK33PXGEjAxiFWF9KVAHWz9InV1FJSLcpAtmARHm5rxlRxff01qAjgeohlyT3fckz4gN0FupeOARqNVV8hY0x4EM0mAeMrnZ2gIoD+UPA+d42BBGwcwuScRM6JRFBAkP4gDYwF6hOrYzcC1F2kWhQANm/ezHUXCC7itmaGKi4mKbj9dsNxw92wGBfJdQanFui2UjFKxpjwIJpNAjNfuTLDcFwAvkICNo6RRkihfF2JFbNW6A8YIn2LxXRFXi165MgRrrtAcBG3NTNUcVksaCkR7yK5zuJwgW7AwlfIGBMeRLNJYOYrEJCvkICNJzT1NFkcMyYKk2pRPPTQQ1x3geAibmtmqOISwoKWfMThAt1mvkLGmPAgmk2C738fgDB9hQRsPMFuorAT28r4O5cuXeK6CwQXcUszigKuXQMgrKRgPmHXUwALXyFjTHgQzVyEogC1GoAwfYUEbDzBIlGYNkkUjoDDbWX8ndtuu43rLhBcZNKaMUnB33wjuKRgPmGr+MBY1GTmK2SMCQ+imQswvjIwACoC6LoNel8xBG1C8BUSsPEEJlF4wfQF+gMGjzUmCou8+OBb3/oW110guMikNRNwUjCfYBU1SRwXNZExJjyIZi5g5itd09hvC8FXSMDGI6QRUtwedjvrGCk+0NPc3Mx1FwguMmnNBJwUzDdcKWoiY0x4EM1cwA98hQRsPIMUH1jn4Ycf5roLBBeZtGak2MDjNPU69hUyxoQH0cwFliwBIGxf8UjAptVq8fbbb2P+/PkICwtDdHQ0PvvsM4fnnT17FhKJxOpLoVB47HOEhMPiA5FuVUXK14XHpDX71a8ACDMpmK844ytkjAkPopkLxOjXs1naB8H6ikcCts2bN2Pv3r3YtGkT9u3bh8DAQDz11FMoLS116vytW7ciIyOD9Vq6dKnHP0cI2C0+WBkg2q2qyBYswmNSmlEU8Ne/gooAugWYFMxXGF8xYsVX3pg/n7sOEiYF8UUnoCj9VmwHDoCKANTTIVxfod3kwoULNAB6z549xmOjo6P0kiVL6AceeMDuuXK5nAZAHzlyxKufY051dTUNgK6urnbpPF+RW5dLL/poEY0dML4k7+p/5kaApgGazs3lups+5V//+hfXXSC4iMua5ebSNPTfcewAjTiwxsCijxbRVB3lnc6KgNy6XPq7e79LfMWPIL7oAIOn0BIJ577iibjD7SdsMpkMgYGB2LJli/FYaGgoXnnlFXz++ee4cuWKU9cZGhrC+Pi41z9HCDhcpVyE1aKvvfYa110guIjLmhmquCySgiGcpGA+I42Q4o6wO1jHTH2FFqGvCB3iiw4weIqx2ICGoH3F7YCtpqYG4eHhmD59Ouv4/fffDwBQKpUOr/Hyyy9j+vTpCA0Nxdq1a1FVVeWVzxESdlcpF2G16MGDB7nuAsFFXNbMUMUl5KRgvmOvqEkiQl8ROsQXHWDwFMBQbMBO4xScr7gdsHV2dmLevHkWx5ljasOqwtaYMmUKYmNjsW/fPhw/fhzvvfceamtrsWbNGtTU1Hjsc7RaLQYHB42v4eFhp/42LrGbJCzCatEnn3yS6y4QXMRlzQxVXKTYwHvY8xVahL4idIgvOiA8XP/vJYDv9UPwvuJ2wDY6OoqQkBCL46Ghocb3bbF69WrIZDL88pe/xLPPPos//elPUCgUkEgk2L59u8c+Z/fu3ZgxY4bxFWOoFuns7ERiYiK0Wi3i4+MBAPHx8ejq6kJOTg5UKhXOnTuHoqIiNDc3IzU1FUNDQ6y2AwMDyMjIQENDA06fPg25XI6LFy8iKysLvb29rLajo6NITk5GS0sLTpw4gbKyMlRXV0Mmk0GtVrPavrPmHaurlA+E6Be7PPXAAxb97u7uRnZ2NlQqFUpKSlBUVITGxkakpaVZ9HtwcBDp6elobGzEqVOnIJfLUVtbi0OHDqGnp4fVVqPRIDk5Ga2trSgoKEB5eTmqqqpAURTUajX279+PiYkJxMfHY2JiAvv374darQZFUaiqqkJ5eTkKCgrQ2tqK5ORkaDQa1vV7enpw6NAh1NbWQi6X49SpU2hsbER6ejoGBwcRHx+PmpoaxMfHY2hoCGlpaWhsbERRURFKSkqgUqmQnZ2N7u5u1nW1Wi2SkpLQ2tqK/Px8KBQKVFVV4ejRo+jo6MCBAweg0+lY56jVashkMlRXV6OsrAwnTpxAS0sLkpOTMTo6ymrb29uLrKwsXLx4EXK5HKdPn0ZDQwMyMjIwMDDAajs0NITU1FQ0NzejqKgI586dg0qlQk5ODrq6uiz6nZiYiLa2NuTl5UGhUKCyshLHjh3D1atXkZCQgPHxcdY5nZ2dkMlkqKmpQWlpKU6ePIlLly7h4MGDGBkZYbXt6+tDVlYW6urqUFxcjDNnzqCurg6ZmZno7+9ntR0eHkZKSgqam5tRWFiI8+fPQ6lU4siRIxb9HhsbQ2JiItrb23H8+HHIZDJUVFTg+PHjaG9vR2JiIsbGxizG2pEjR6BUKqFesgRUBPCNjWKD3//o98jMzERdXR3OnDmD4uJi1NXVISsrC319fazrjoyM4ODBg7h06RJOnjyJ0tJS1NTUQCaTobOzk9V2fHwcCQkJuHr1Ko4dO4bKykooFArk5eWhra2Ntx6h0+lw4MABdHR04OjRo6iqqoJCoUB+fj5aW1uRlJRk0e9td28z+ApjKrd85ehdNE7ed59gPcJ0rInFIz777DNBe0RFRYVLHnH+/HkUFhaiubkZKSkpGB4eZrXt7+9neUStVArqLhpRrzNL2Fj6ymvLX/OJR5SXl8NdJDRN046b2WbVqlWYM2cOzpw5wzpeV1eHlStXIiEhweV59hdeeAEUReHGjRsIDAx0+3O0Wi20Wq3xd6VSiZiYGFRXV+Oee+5xqW++hKqn8Lui36HtepvxmGRCb7C5ikWQ/movIJVy2EPfUVFRYZz+JggDlzSjKFC7XkDsupt6Q2WMVQIsmrEIHz3+kWDyTPgOVU/hzcI30T7YbjwmVl8ROsQX7UBRoBK3IXb1FeP3myE4IBgrZq1AXEycz3zliy++wL333utW3OH2E7Z58+ahs7PT4jhzbP4kSsUXLFiAmzdvYmRkxCOfExISgunTpxtf06ZNs9mWT9gtPljcJuq9RQl+hGGPv50P3BTsCuRCwu6OKsRXCP4A4ynftwzWJJBgxawVUL6uFJyvuB2w3X333WhqasLg4CDr+IULF4zvu0pLSwtCQ0NZgZU3PkcI2C0+EFFVl7VgncBvnNZs504Awl6BXGg43FFFJL4idIgv2sBQHepvnuJ2wLZ+/XrodDokJSUZj2m1WqSkpCA6OhoLFug3M79x4wYaGhrQ09NjbNfd3W1xvS+//BJ5eXl47LHHEBBwq3vOfo6/4XCFcpFUdf3whz/kugsEF3FaM8N3eBkpNvAZxFf8A+KLNjBUh4b3GpYIMkHInuJ2wBYdHY0NGzZg+/bteOutt5CUlIRHH30Uly9fxocffmhsV1FRgYiICGNSHgBs3LgRTz/9NN5//318/PHHePPNN7F69WpMnToVH3zwwaQ+x98w7nxgukh5ABB3FqKqFv33v//NdRcILuK0Zt/9LgDg6Wawk4JpCGcFcoFh4Su0OH1F6BBftIFhP+K4ErPpUIF7ike2pkpPT8e2bduQkZGBrVu3YmxsDAUFBQ43pn3uuefQ09ODvXv34te//jUOHz4MqVSKqqoqREREeOxzhIw0Qorc53MRGbbIeKcQMAHseERfLSqWvUV/+ctfct0Fgos4rZmhOnS/IXc6cAII1gGRYYtBPU8JLs9ECDC+8oPQRcbijkAR+orQIb5og3ffBRWh/z4zN4CBE8L3FLerRIWIJ6o1fA1VTyE2J9b4u9iquuLj48m+eQLDoWYUBezYAWq8FrEbYQwcJLR+gcvc53MhjfDv7zXXiN1XhA7xRStQFKj9byD24U6jlzDewqWn8KJKlOAbdpbsFHW1KDEl4eEwWIuNBS5exM4Y3FrKA3qDlUCCXSUk8d3biN1XhA7xRTOY6tDwTv3Nh1nFudA9hQRsAkHs1aKmuY8EYWBXM5M9/hpnAmb574Ku5BISYvcVoUN80Qw/rQ5lIAGbQBB7VdeGDRu47gLBRexqZrLH37why7eFXMklJMTuK0KH+KIZJtWh/lhxTgI2gWCrWnQgBKAi4PdVXefOneO6CwQXsatZeDioCCDqdaBthuGY2ZYxQq3kEhJi9xWhQ3zRDIOvDISA9dRe6NWhDCRgEwhMVdfCKbNYx9tnALEbAWrb4xz1zDcsW7aM6y4QXMSeZtS2xxG7EaidbTJ1IQGCEYjIOZGCruQSEmL3FaFDfJEN9cZPELsRuHK74YDhRmThlFl+4SkkYBMQ0ggpZtwxz3qScMUeICrKb5OEh4aszJsReI1NzSgKO1X/tL5lzJxVgtwyRsjc8pVbGH3ly3/6raf4A8QXTaAo7LwYb7XY4PY75vuFp5CATWDYTBK+gwZqa/22sst0X1mCMLCqmaGKq+k2rV8mBQsVva+woQOAxts0fusp/gDxRQOMr3xrzK99hQRsAsNhkrCfVnYtWbKE6y4QXMSqZoYqrvBesPKmAP9IChYqdn3FTz3FHyC+aMDEV/yx2ICBBGwCw5gkbDYtGneW+cU/K7tKS0u57gLBRaxqZqjiiisxmbaAYcFWP0gKFipGXzHZd9HoK37qKf4A8UUDBl95twTsYgM/8xUSsAkM41ZVcyIRZDDXIB3wYqy+4s5fK7tI+brwsKqZoYor7hEY74SDdEDkYKhfJAULFWmEFJ8++ykiB0MRYL4Fnp96ij9AfNGAwVfe/qnhd9qwvZ2f+QoJ2ASINEIK5etKpC78LwDAeCCgCdZX3PlrZVdqairXXSC4iDXNmOrQr2bDuNH7eCDwbtRWvzFVodJf1o93I3+LCcO/ChMB/u0p/gDxRT3Uaw8jdiNw6Y5bx8b80FdIwCZgPhyTs37358ousgWL8LDQjKKw88t91reM0RT5vH8ENm+88QZ2aotEWYUuVIgvQu8rDQn66Xw/9xUSsAmYpp4mi2P+WtlFtmARHizNmCquaTf9uopLyMTHx4u2Cl2oiN4XGV+ZPi4KXyEBm4ARU2XXpk2buO4CwUVYmomkikvIbNq0SbRV6EJF9L4oMl8hAZuAYSq7jF9U2n8ru06ePMl1FwguwtLMUMX1jp9XcQmZkydPirYKXaiI3hcNvvKbCojCV0jAJmCYitG7hkKMx4J0/lnZ9cMf/pDrLhBchKWZoYrrTz8z/O6nVVxC5oc//CGrCj1YRFXoQkX0vshUna/V/xow4d++QgI2gSONkOL9VVv1v0j0FXf+WNnV2dnJdRcILmKqGfVfj+mruL59631/rOISMoxeTBV6ioiq0IWK2H2RevVBxG4Evpmm/52W+LevkIDND9BXdt3CH6tFJRKJ40YEXmHUzGTvUH+v4hIy5mNMTFXoQkXUvkhR2Nn8sX7HFMP/BtrPfYUEbH6AGPYBnDt3LtddILjI3LlzSXWogDAfY2KqQhcqovVF0+pQs5jVn32FBGx+gBiqRZVKJdddILiIUqkUXRWXkDEfY2LwFaEjWl80+MqyPojKV0jA5gcYK7v8uFr0iSee4LoLBBd54oknjFVcWy9AFFVcQsZ8jNmqQh8IAai7/MNXhI5ofdHgK081wbhjCuD/vuKxgE2r1eLtt9/G/PnzERYWhujoaHz22Wd2z6msrMQbb7yBlStX4rbbbsPChQvx/PPPo6nJ8lH82bNnIZFIrL4UCoWn/gxBwlR2/eB6qP6LKwEC/WwfwKysLK67QHCRrKwsYxXXO4YqLomfV3EJGfMxxvjK/NEg1vH2GYbig7VzfNk9ghVE64sGXzlwn/7XQJH4SpDjJs6xefNmyGQybNu2DcuWLUNqaiqeeuopyOVyPPTQQ1bP+dvf/oaysjJs2LABkZGR+OabbxAfH4977rkHCoUCq1atsjhn69atuO+++1jHli5d6qk/Q7BII6RA5G8R274HAKAz2Qcwd9HjkHLcP3chW7AIjzfeeAPU1DbEXlHdekojAcYC/LeKS8hYG2PSCCl23vEdqEfbbiV2M8UHMRLB+4rQEasvUpujETt4y1cmAgCdxP99xSNP2CoqKpCdnY3du3djz5492LJlC4qLi7Fo0SK89dZbNs/73e9+h7a2Nvzzn//Eq6++ir/85S84f/48xsfH8cEHH1g9Z82aNXjppZdYr5kzZ3rizxA8VqtF/aRiRvRbsAiQ+Ph47Ow/KqoqLiFja4w1jXUB5ondAUDj+Dc+6BXBHmL1xZ1D+aL0FY8EbDKZDIGBgdiyZYvxWGhoKF555RV8/vnnuHLlitXzVq9ejSlTprCOLVu2DCtXrkR9fb3NzxsaGsL4+Lgnuu5XWK0WBY1GtUrwFV0vv/wy110guMird9yBpoFLoqriEjK2xpjN4oMeCN5XhI7ofJGigKgoNN38RpS+4pGAraamBuHh4Zg+fTrr+P333w/AtUoWmqbR1dVl86nZyy+/jOnTpyM0NBRr165FVVWVw2tqtVoMDg4aX8PDw073R0jYNNZuWvBl+IcPH+a6CwRXoCiEbtqEZT0QVRWXkLE1xozFBwxMUVMRWd6Da0Tli4alPFBbi0XXLd8Wg694JGDr7OzEvHnzLI4zx9RqtdPXyszMREdHBzZu3Mg6PmXKFMTGxmLfvn04fvw43nvvPdTW1mLNmjWoqamxe83du3djxowZxldMTIyx34mJidBqtcZHy/Hx8ejq6kJOTg5UKhXOnTuHoqIiNDc3IzU1FUNDQ6y2AwMDyMjIQENDA06fPg25XI6LFy8iKysLvb29rLajo6NITk5GS0sLTpw4gbKyMlRXV0Mmk0GtVrPa6nQ6HDhwAB0dHTh69CiqqqqgUCiQn5+P1tZWJCUlWfR7293b2NWiuFUtSksk6N661dh2cHAQ6enpaGxsxKlTpyCXy1FbW4tDhw6hp6eHdV2NRoPk5GS0traioKAA5eXlqKqqAkVRUKvV2L9/PyYmJhAfH4+JiQns378farUaFEWhqqoK5eXlKCgoQGtrK5KTk6HRaFjX7+npwaFDh1BbWwu5XI5Tp06hsbER6enpGBwcRHx8PGJiYhAfH4+hoSGkpaWhsbERRUVFKCkpgUqlQnZ2Nrq7u1nX1Wq1SEpKQmtrK/Lz86FQKFBVVYWjR4+io6MDBw4cgE6nY52jVqshk8lQXV2NsrIynDhxAi0tLUhOTsbo6CirbW9vL7KysnDx4kXI5XKcPn0aDQ0NyMjIwMDAAKvt0NAQUlNT0dzcjKKiIpw7dw4qlQo5OTno6uqy6HdiYiLa2tqQl5cHhUKByspKHDt2DFevXkVCQgLGx8dZ53R2dkImk6GmpgalpaU4efIkLl26hIMHD2JkZITVtq+vD1lZWairq0NxcTHOnDmDuro6ZGZmor+/n9V2eHgYKSkpaG5uRmFhIc6fPw+lUokjR45Y9HtsbAyJiYlob2/H9d/9DjSAZ8yruOhbVVzMWDty5AiUSiXOnz+PwsJCNDc3IyUlBcPDw6zr9/f3IzMzE3V1dThz5gyKi4tRV1eHrKws9PX1sdqOjIzg4MGDuHTpEk6ePInS0lLU1NRAJpOhs7OT1XZ8fBwJCQm4evUqjh07hsrKSigUCuTl5aGtrc2vPKK7uxvZ2dlQqVQoKSlBUVERGhsbkZaWhh/96EestoxHrAxYiXfC38HCoVspz8E6YEeMvqipe+tWXniE6VgTi0csW7ZMsB5x/PhxVFRUoKKiAsePH0d7ezsSExMxNjZmMdaOHDmC0T/9CbREAtA0VnYx/8AZfAUS0KDx+x/9nrceUV5eDneR0DRtPovmMkuWLMHy5cstNqJtaWnBkiVL8NFHH2Hbtm0Or9PQ0IDo6GisXLkS58+fR2BgoN32X3/9NSIjI/Hwww+jsLDQZjutVgutVmv8XalUIiYmBtXV1bjnnnsc9ktIUPUUdv1rPVSzaNAB+r3VVl0D4koAaWsoMDrKdRcnxcmTJ/HUU09x3Q2Cs4SFgfqeBr98FrgeBgTq9HeHK3okiPttrl8nBgsVR2Ms5+5gbFx3KxVFMqG/IcylgiH98qYvukgwQ1S+aPCUnTGAag4ACTBrGBgKBZZ/JwpxMXG89pUvvvgC9957r1txh0eqRMPCwlgBEYNGozG+74hvvvkGTz/9NGbMmGHMiXPE0qVL8fOf/xwURUGn09k8JyQkBCEhtzZInzZtmsNrCxVphBRoXYjYOW0A9NUzxmrRC3MEW9VlPt1O4DfUI3MQ++M2yyquywt5bapixtEYe39tEECPW1aLPhooWF8ROmLyRcZTjMUGNNA9Dci9sAjSvyo57p1v8MiU6Lx586xuQsscmz9/vt3zr1+/jieffBIDAwMoLCx02N6UBQsW4ObNmxgZGXGt037Mzkf0RspgWoYvVG677Tauu0BwgZ0/1lpWcQn8O+jvOBpjTd/WWa8W/bbOi70i2ENMvrjzEcNTXdP9iEXmKR4J2O6++240NTVhcHCQdfzChQvG922h0WjwzDPPoKmpCQUFBVixYoVLn93S0oLQ0FC/fmrmKk1jXZb7NgYAjSNtgk0QvnTpEtddIDgLRaFp3EoVF1kKgtc4GmPhsyOsFzX1BwrWV4SOaHyRotA00m793zUReYpHArb169dDp9MhKSnJeEyr1SIlJQXR0dFYsGABAODGjRtoaGhAT08PAECn02Hjxo34/PPPceTIETzwwAM2P6O7u9vi2Jdffom8vDw89thjCAggu2wx+GO16IMPPsh1FwjOYKjk+p7I9vjzBxyNMVItyj9E4YsGTwnvZhfUAeLzFI9EOdHR0diwYQO2b9+Ot956C0lJSXj00Udx+fJlfPjhh8Z2FRUViIiIMFZS/P73v0deXh6efPJJ9PX14dNPP2W9TNm4cSOefvppvP/++/j444/x5ptvYvXq1Zg6darNRXbFisXeojDZW1Sgmzbn5uZy3QWCMxg2ZY7ugM3qUAI/cTTGmK2q7hq8lQ+ccgxYVw/B+orQEYUvGjwlrgSsp/Zi9BSPVIkC+qnNd955B59++in6+/sRGRmJv/71r3j88ceNbc6ePYu1a9ciLi4OO3bswCOPPIKSkhKb1zTt2j//+U9kZmbi66+/xuDgIGbNmoWf/OQniIuLc3lrKk9Ua/Adplq0dhaNiQD9l/sHXcKvFiXwHEMl10tSYDQYCDKkPZHqUD8iLAxLf6XBpTv1+q7oJr5C8CIGT3n3EeCr2QAk+u/dSoF5iifiDo8FbEJCDAEbAFBPLtZX6hkwluFfWATpycvcdWwSxMfHi3bfPCHhT985seHsGCMa8wcx+CLzfZPQhidshmImoX3fPBF3kMQvP2bnI/5TLfr6669z3QWCE+xcPcbKXRPyd05sODvGdj7iP74idMTgizsfIdWhDCRg82NsVosKsKomOTmZ6y4QnKBpgmwULlScHWP+5CtCRwy+SL5vtyABmx/jT5s2/5//83+47gLBHoZNmb/ba7kml9gquYSKs2PMn3xF6Pi9L1IUwntpUnFugARsfoyxWtTEXIVahl9VVcV1Fwi2MNmU+a4ewzGzPf7EVMklVJwdY/7kK0LHr33R4CtxRVrWU3sxewoJ2PwYpgw/ciAEAYYHHxL61qbNQirD/853vsN1Fwi22LkTVAQQ+RqNgnD9odkjQIhOgsg5kaCepwRTySVmnB1jpr7C5LIFTAjTV4SOX/uiwVf+/KjhdxoI1gGRAyGi9RQSsPk50ggp3i3WYSIQ+oUuJbf2FqXoOq675zQ6Hdn+hq9QqEfsRuCiYUNm0MC1acCnxwKhfF0pSmMVIq6MMcZXmNwi0z2LheQrQseffZHxlcaZt46NBQLvFutE6ykkYBMBOx8N1N8JW9m0WShcu3aN6y4QbMB8v8yruHauJfYiJFwdY0ZfMSBEXxE6/uyL5v9uGatDRfz9CuK6AwTv0/RtHcxX2xPaps1RUVFcd4FgA1vfr+Y7JqyfQOAlro4xf/AVoePPvki+X5aQW2ARYHXTZhpYPnsFRz1yncLCQq67QLBB+OwIq3v8zQqay02HCJPC1THmD74idPzZF8PvXG69OlTE3y8SsIkAq1VdEiDu/30FREUJoqpr8+bNXHeBYA2KQlxuL3uPP0MV1/8+87/c9YvgMq6OMZu+cnxAEJ7iD/itL1IU3snpJNWhZpCATQQYq7rmRCIY+vn/QB3wwrPjiFqtAvUX/pfif/zxx1x3gWAORYH6SyzeXdZhvBMO0gGRoQtBPU/h2jn/za/xR1wdY0ZfCV2IAMPst4QGdixuE4Sn+AN+6YsGX3n77l7970x1qMFXxFpwAIh8L9H09KV44om/YdYsKddd8hnZTy3EC9FXjL+TfQAJk8Vf9vgjuAfZW5TgSYzfJ4Of+IuveGIvUVEXHYyOfo2vvoqFRBIMmp4w/Bxj/QR0mDJlPgDg5k01gEAAOkydGoHFi+MEF+ztXnz11kCASWXX4nbw+S8RwybHQmPn99qN/zgDuFXFZfguCUmz7m4Kly/vxI0b9QAC7Y5/c49wxjMmc46vfWayepl/D4TiKf6AkMaYszjyFSFgzU+am92vbhV1wMZA02OGnzqrP7XaNpPW+rYjIyqbwR6fA7qmmbC+1+NMq815w4YNG7juAsGMppmwvsef4bvEF80Y8xwdbUJw8BwA7EBKnxkyZnIG2w+sjX9bXuHpc6z5jLf8ZbJ6OfoeELwHX8aYJ+H798nRzZ1OdwPj490mZ7DHvzuQgM1NbAV7jNECgTbvqLkI6sJvW4jaG22sASGZAJbftshnfZgMJSUleP7557nuBsGE8NsWQjXaxk4MNvku+UozWwZqLRizFhQB/F8mwNxnbPmLO74yWb2E6in+gD/6YvjUBVBp2m36iq+w5iuu3dx5HhKweR2dzTtq07tnAD4J4OKe3YvYnFhWfgAdoD/OZ+666y6uu0AwI+7RXYg9+Qvj7xKz75I3NDN/Wub4bpb/wZh7WPrLZJ/KTVYvxlOMuYwQhqf4A/7oi3/8zvP4/1r+bvzd3Fc8if2bPVPv4MfNHQnYeABz9+yNu2ZzmMquP53+E5r7mgHoK3B2JL0IOvK3kL78oZt/jXcYGBjgugsEE6iUt/Bn1UfA7QBoIEgSiJVzVyEuJs5YxeWOZs7c3Xr7blboOHoqZ+4rk9WL8ZRdJbvw1bWLGKd1mDIOvJglRfhwKOJ47CtCx998kUp5C9u/+l/gW/pALUgSiBVmvuIOpr6iL7e0/bSMj5CAjZfYv2sG3HsaJ42Qgv78c6zv+zsg0e/PVjtdg9j2PchNAS/NdXR0lOsuEAxQKW8htn0PMOPWsXHo8G7IYyxTdUUzZ4yU67tb/0Fn1VcmJiZQWblyUr4ijZBCGiHFJx//Gq+qD+Cm4V8WvvuK0PEnXzT6yjT97zSAMSu+4izWb/qE7SEiX9ZjGRYuvAyJJBATE2MICAi2+EnTTPWWBDdvdgC4defKB4KCZiEwcCrGxroQFhbutNlGvRkG1QyNRZ5A5GAolB/xzwSam5uxbNkyrrtBgP67UztdY5GzZP7dsaUZ342UmUq0Nv7teYUtz5jMOQC3PjPZG0Oh+YrQ8SdfdNZXbGH/po87GD/5+utA/OpXN8myHpNl5crsSf2P038xduHGjTqbZuyrf4DGx7sxPq7/b1eewjVN01ivFp2m8XaXJ0VZWZnfGJPQaZqmsV7FZfbdYTRz7umZb9Df4NxmEUjRtA5Tp64wjBd+LMxpz2e87S+20jQcpWcIzVeEjj/5orO+wsCHAM3Wzd3Y2DcIC1vO8pPp078AcK9bn+eRgE2r1eLdd99FRkYG+vv7ERkZiffeew8/+9nPPHquO5/jSWbNkjq847RnthJJgFfvns3N1loAFz4cavVuZvlwqNf65Q6xsbFcd4FgIHw41OpTFOa7wxjp4sV1OHt2C3xppKYGyudgzBns+Yw1f/Gur1ifRgWE7StCx5980Z6vcPtUPpA3fuKRgG3z5s2QyWTYtm0bli1bhtTUVDz11FOQy+V46KGHPHauO5/jaxwFddYM11tfQPMALihoFvY+NQXjARpcHQXS2oDz3YZKnKitXumDu6SlpfndApFC5Z2I17Chcx8AYM1M4BeLgIVhQJBkDGfPBsFXRso8LbN2N+vv2PIXXz6Vs3Zj+I9ndWgZAdIuA+d7casKnae+InT8yRdfu+Nn+M1EPkADa2aZ+spNw1NeBu/clJg/LePjTZ7bOWwVFRWIjo7Gnj178Ic//AEAoNFosGrVKsyePRvl5eUeOdedzzHHE1tEeAPf3zUDNA1IJMCYYS/Aa0MSTNc8i2c2HvPaZxKES/7h5zAQehxzDYnBwYG3vkPeQghGKgRM/QXwXo4c830YnwB0NBAgIb5CsE/+4efQF3Yc82/T/+59Xwn0uZ/wYmsqmUyGwMBAbNmyxXgsNDQUr7zyCv785z/jypUrWLBggdvnuvM5QsGZu2bAs0bLDIggif6/50+nIZlxHHJ5IAICAnmzY4M/bsEiBEynIiZ0OnxrzgSmGYyUudXzrKn63kjFgrm/eMtXmO9GUAAQSPPbV4SOEH3RfHpzYkKLb82hcZshuPeGr9yavhe2p7gdsNXU1CA8PBzTp09nHb///vsBAEql0mYg5cq57nyOVquFVqs1/j48POzsn8cLbBnt6GijjcVDXYcZHLcGyQRoeoI1jTqZalRP8dJLL/nss8SKwzwRs++IJwzVX4xUiJj7yuXLGeju/l+PpGmYf0ds+YovFw33R4Tgi46KjpgAP4D4ikMCHDexT2dnJ+bNm2dxnDmmVqs9cq47n7N7927MmDHD+IqJiTFeMzExEVqtFvHx8QD0dyxdXV3IycmBSqXCuXPnUFRUhObmZqSmpmJoaIjVdmBgABkZGWhoaMDp06chl8tx8eJFZGVlobe3l9V2dHQUycnJaGlpwYkTJ1BWVobq6mrIZDKo1WpWW51OhwMHDqCjowNHjx5FVVUVFAoF8vPzMTz8Q9TU/BrR0QOorv4DHnroGtrbX0FIyErQdCCYOJy5U5nMpLf5oBkf74ZW24aJCY3RbOXyIJw9G4zCwu/g2jUZ4uPjMTExgf3790OtVoOiKFRVVaG8vBwFBQVobW1FcnIyNBoN62/t6enBoUOHUFtbC7lcjlOnTqGxsRHp6ekYHBxEfHw88vPzER8fj6GhIaSlpaGxsRFFRUUoKSmBSqVCdnY2uru7WdfVarVISkpCa2sr8vPzoVAoUFVVhaNHj6KjowMHDhyATqdjnaNWqyGTyVBdXY2ysjKcOHECLS0tSE5OxujoKKttb28vsrKycPHiRcjlcpw+fRoNDQ3IyMjAwMAAq+3Q0BBSU1PR3NyMoqIinDt3DiqVCjk5Oejq6rLod2JiItra2pCXlweFQoHKykocO3YMV69eRUJCAsbHx1nndHZ2QiaToaamBqWlpTh58iQuXbqEgwcPYmRkhNW2r68PWVlZ+OKLfZDLlxh1/OqrWIyMqEDTY6BpDcz/sXbFSG195/TTm4G47bYotLW9irvuuoJr1zIRHJyKhoaZKCwsRHNzM1JSUjA8PMzqd39/PzIzM1FXV4czZ86guLgYdXV1yMrKQl9fH6vtyMgIDh48iEuXLuHkyZMoLS1FTU0NZDIZOjs7WW3Hx8eRkJCAq1ev4tixY6isrIRCoUBeXh7a2toE6RGtra1ISkqy6Hd3dzeys7OhUqlQUlKCoqIiNDY2Ii0tDZ99psGFC68iJuYmVKo9uOeePnR3/xZBQXeBpgNB0573lYmJMdD0mNFTzp4NRHFxMEpKpuDf//4Oursppz2C+RvF5BFpaWle9Yi6ujoUFxfjzJkzqKurQ2ZmJvr7+1lth4eHkZKSgubmZhQWFuLcufdw7ly4VV+xlX82GW+59TMAAQGhoOlAhIauQm/vf2HWLCXGx/+NoaG96O5ezguPcCVtyxZu57AtWbIEy5cvx8mTJ1nHW1pasGTJEnz00UfYtm2b2+e68znmT9iUSiViYmJ4l8PmSXyVr2KKN++WGxoa/HIbFl/i+zL4AAQETCHTmwLBmTHmb74idLj2RS6qN4X69IwXOWxhYWGsYIhBo9EY3/fEue58TkhICEJCQoy/T5s2zWZbf8HZaVTaJB/J3UfRk127yRmuXr1KAjYX8IWRmn93zI30yy+/hZ/+9Kce/UyC93BmjNnLgzOdRvWmr5AA7ha+9kVvrqlo/p2x5StCCdC8gdsB27x589DR0WFxvLOzEwAwf/58j5zrzucQrBc0MGY7PFSLcXqCVZnjmQod59ZucobAwEB3O+PX+GJxWvPvhm4CCAwIwLRpP7BqpIGBco98LsE3TGaMceEr3rwxFBre9kVvP5Vnvg8Thhw2naGyONiOr4gZtwO2u+++G3K5HIODg6yCgAsXLhjf98S57nwOwTqmZkvVU/hHcSz+7yJgkeFhZXCgfj83TxYB2rpb1q8QDZtFDbNmzfJgL4SJ+VMzQL+ytieKThxhukRD2w39Oltv/iQX0gjb/zASzYSFp/Qy9ZXcL7Oxr/QFL/uK9RtDmp7w+0DOE5pZexqv///nxbVBodd/nNb/4qyniB23c9guXLiAH//4x6z10bRaLVatWoU777wTCoUCAHDjxg20t7dj5syZmDlzpkvnutrWEXxdh41Lov62GLWjbaBNXPThO4At3w/Ggm8Feqwa1RVMn8ZdufIopNKPfPbZfIDLrVdMpyH+WaNGZke35R5/UxdB+fZlm9fIysrCiy++6OWeEjyFN/SK+ttiqEbbWNEZ4yvfnQavLxpuDtfV7p5mMprxwVeuDANJLWM432fynhOeImR4kcMWHR2NDRs2YPv27bh27RqWLl2KtLQ0XL58GZ988omxXUVFBdauXYu4uDjs2LHDpXNdbUtwnaaRdtBmT9fP9QEV3eMY3XXTeMyXScemT+PuuEOFkpL9rLtmR0/l+Iytp2UAcPOmmtMAzXwaQpYfYPHdoAOAxpF2u9d8/PHHPd5Pgvfwhl5NI+36r7cJt3xlwnjMV75ia+9lxleYp0tCeSpnrpmtp2W+fBp/C9trKoa9GwDNJDxF7Hhka6r09HS88847rD0+CwoK8PDDD3v0XHc+h2Cf8B6gdhYs9wDsYbdzNunYGzAmzkx/aLVtxvf4Zr72pxkCYC3HzPTv8S6uLU4b3gOoZsNyj78em6cAAA4dOiS4RT3FjDf08oSvAL65MWR8xXx6lXkqd/OmGuZBkC99hfGU0dEmBAfPAQCMjl5BYOAUu75izS+9gSvFAc5+Lwhs3J4SFSJkStQS6snFiP1xGyQThkFkSDKgFIux7t+tLl2Li9J/Z7FlvrZ+mj/5cuYc325M7Bh3q6wOPPcd/PqHauN3gvmOTOa7QRAXRl+hoU+3mKSv+PLGcDKY5+J62l8sgzHuccdXmO+F8Voi8BReTIkS/APpr/Yi9y+x2BUDqObozfW/PgfWvbbX5WtxebfsCNMpEfM7UFs/2Xemzp3DJZ4ug+/70SpAp8a0m8B4ALC8F4g7C6z7H/vfDSFumyNmvKEX4yvvrAXqZgGQAIl5wLq/uOYr9qpR+eArzGdPxiucOYf74NSzW8ZJf7UXd5+KhXIeEKQDVnY75ylihzxhI0/YbkFRwK5deD78SxxZCQTQwKq5kYiLifNo5Y753TJNc5FfIWzYd/QSjI19g7Cw5R4vg8+ty8WmnI3QSnT47pAE+z4LglSyAoiLA9bZ/5zR0VG76yMS+IXX9KIoIC4Oq9ZcxFdzgCAEYMWcVV73lYmJMUgkAbx6ws93mJQSb+7nS9VTeKf4HdR11wESIP7fAfiN5gdOeYqQ8UTcQQI2ErCxoOopxObEGn9npjJyn/dNuTWf7pr5BBeLR7r7XUhOTsarr77qzS4SPIg39eLSV7yx97K/4GtfMX4PmHU9DD999e8Ll5CAbZKQgM02UX9bjNobbS4v4eAtGLMdGfkKAQFBmJgYM979+ctTOfOnZTdvdhifPHK5ure1JRlc+S60tLTg+9//vvc6SPAo3tSLr75i+jQuICDYr57KmT8t8/bTeGfg2/fAl5AcNoLHsba8B5fl1kzuyokTJ/D0009bbcNv8w009sX0J9fBmDM0WlmSwZXvQn19PQnYBIQ39eKrr9jC/Kmc6Y0UH3xFXzx1GzSaKwgMFI6v8O17IDRIwEZgwddy69tvv93me+6ar7WAyvxJnqvn8Nk0neU7gzRavs0+5sp3wZ5mBP7hTb346iu2cOQpgO1cXG/5izVfKSsrw4MPPuj9/yEeYrJLBBH0kICNwCKudSFi55gs7wH9z7jWRZz2KzQ0dNLnOmO+BEu+r5mKFtywWM7D2e+CO5oRfI839eKrr7gDH3xFaGPs3dYFWD/n1tM0Vz1F7AQ4bkIQE9Jf7UXuYSDyGhDAVJLTwI5HaFD1FGf9am31z7V5+AhVTyHyQCROz70BAJgzDISO6b8TVDawbotzpfdEM2HhTb1MfUVi2OAgYIJ7XxE6QhpjVD2F7Q8bppFpIFjnuqeIHRKwEdhIpZC+l4t32xZhIhDGpyu1o22IzYnlzFxXr17NyeeKDaaK62JXrbGKq+tbQGZ+MJSfR2Hd/1BOl94TzYSFV/Uy8RXmCdtEAPe+InSEMsYYX2nWdhqPjQUC77YtcslTxA4J2AiWSKXYGWO4EzbkGtCGKbFdeb/jpEsURQzdF+zM+51+moLJMWF0f3E+oFS6ZKxEM2Hhdb1MfcUA174idIQyxhhfgbmvxIAEay5AAjaCVZpG2lkJwgC31TxkxXzf4EndiWbCwhd68c1XhI5QxhjR3TOQgI1glfAe9p0wwG01T3x8PDcfLDLCe/SLmpoyWd2JZsLCF3rxzVeEjlDGWHgP9Ok1JhDdXYcEbASrxLUuBB1gNn3BYTXPf/7nf3LyuWIjrnXhrelQuFfFRTQTFr7Qi2++InSEMsa2X/6uxVIeRHfXIQEbwSqmVV3BhmrRgAnghQc6EJUQ5fMk4aSkJJ9+nhih6in8JUZnvBMOcrOKi2gmLHyhF6sK3RC0SXhQhS5UhDDGqHoKf3pIo/+FVIe6BQnYCNYxVHUpP49CJqW/NZoIALT0GGq7an1e2fXss8/67LPECFPFVa+9aqwOHXeziotoJix8opdpFTqzHpsEqNW0k2rRScD3Mcb4StvYre0DSXXo5CEBG8E2UimgVOK9l7/Pyj+gQUMCCXaV7PJZVyoqKnz2WWJkZ8lOSFhzFtBr/OztkzZVopmw8JleUil2PjuDlSvJhaf4A3wfY97wFTFDAjaCQ5pG2ln5B4DeYBuv1fmsDwsWLPDZZ4mRpmv1oM2ygt3VmGgmLHypV9O1elauJOB7T/EH+D7GvOErYoYEbASHhPcHWq/s6g+0foIXGB8f99lniZHw/kDrVVxuaEw0Exa+1IsPnuIP8H2MecNXxAwJ2AgOiSvW6dfQYQYebajwKdbZO82jdHd3O25EmDTvFo9br+JyQ2OimbDwpV6Mp1hUi/rQU/wBvo+x35Z73lfEDAnYCA6RIgK5h9lr5gTrgB0/CfRZkvCqVat88jlihKqnsP1nTAa4SRXXYWCdZMWkr0s0Exa+1IvxlMhr+mpkAJgyDrz483FOqtCFCp/HGFVP4d21+v+WTHjOV8SMRwI2rVaLt99+G/Pnz0dYWBiio6Px2WefOTyvsrISb7zxBlauXInbbrsNCxcuxPPPP4+mpiaLtmfPnoVEIrH6UigUnvgzCLaIi4O0HvifYsPvEn2lT+0Mrc8qu5z5PhFcx7jH37duGo+NBQLvlgDr6gHExU362kQzYeFTvQyeokyUIClff+hmEKAJpDmpQhcqfB1jjK90hhmmbCWe8xUxI6FpmnbczD4vvPACZDIZtm3bhmXLliE1NRWVlZWQy+V46KGHbJ63fv16lJWVYcOGDYiMjMQ333yD+Ph4DA8PQ6FQsO4ezp49i7Vr12Lr1q247777WNd54oknMHPmTKf7+8UXX+Dee+9FdXU17rnnHtf/YDFCUYgq+Q+ovj3GfsQNCSLnREL5utKrH6/VahESEuLVzxAjUQlRqO2qZSUGSyaAyMFQKB/OcquSi2gmLHyuF0UBu3YhavWXUM0GJ74idPg6xrzpK0LFE3GH20/YKioqkJ2djd27d2PPnj3YsmULiouLsWjRIrz11lt2z/3d736HtrY2/POf/8Srr76Kv/zlLzh//jzGx8fxwQcfWD1nzZo1eOmll1gvV4I1wiSRStE0K8B6tWhvo9c//uOPP/b6Z4iRpp4myyquAKBxJtw2VaKZsPC5XoZlg5rmTeHMV4QOX8eYN31FzLgdsMlkMgQGBmLLli3GY6GhoXjllVfw+eef48qVKzbPXb16NaZMmcI6tmzZMqxcuRL19fU2zxsaGuJ9dYw/Eh4813plV9Bcr3+2UDY5FhrhwXOsV3F5QFOimbDgSq/w4Hmc+YrQ4esYCw+a7TVfETNuB2w1NTUIDw/H9OnTWcfvv/9+AIBSqXTpejRNo6ury+ZTs5dffhnTp09HaGgo1q5di6qqqkn1m+A6cWf1d0kS82rRs97/bKFsciw03jo/Yb2K66z71yaaCQuu9IorgfUq9LOcdEdQ8HWMSb/QGndMATzrK2LG7YCts7MT8+bNszjOHFOr1S5dLzMzEx0dHdi4cSPr+JQpUxAbG4t9+/bh+PHjeO+991BbW4s1a9agpqbG7jW1Wi0GBweNr+HhYZf6RNAjPduF3MPAymvQD0SJvsJrx/favJ4gbP59ILgPVU/hT8v1T8Al5nv8nf3G7esTzYQFV3oxvrKk/9axYB/5itDh4xij6ins+34XACBwwvO+ImZYAdvExAQ0Go1TL6ZWYXR01GrSY2hoqPF9Z2loaMBvfvMbPPDAA/jFL37Bem/16tWQyWT45S9/iWeffRZ/+tOfoFAoIJFIsH37drvX3b17N2bMmGF8xcTEANAHm4mJidBqtcY7lfj4eHR1dSEnJwcqlQrnzp1DUVERmpubkZqaiqGhIVbbgYEBZGRkoKGhAadPn4ZcLsfFixeRlZWF3t5eVtvR0VEkJyejpaUFJ06cQFlZGaqrqyGTyaBWq1ltdTodDhw4gI6ODhw9ehRVVVVQKBTIz89Ha2srkpKSLPrd3d2N7OxsqFQqlJSUoKioCI2NjUhLS7Po9+DgINLT09HY2IhTp05BLpejtrYWhw4dQk9PD6utRqNBcnIytIsXY12DBDvPwvhUZjwQqJ1NIzYnFq/+76uYmJjA/v37oVarQVEUqqqqUF5ejoKCArS2tiI5ORkajYZ1/Z6eHhw6dAi1tbWQy+U4deoUGhsbkZ6ejsHBQcTHx+PMmTOIj4/H0NAQ0tLS0NjYiKKiIpSUlEClUiE7Oxvd3d2s62q1WiQlJaG1tRX5+flQKBSoqqrC0aNH0dHRgQMHDkCn07HOUavVkMlkqK6uRllZGU6cOIGWlhYkJydjdHSU1ba3txdZWVm4ePEi5HI5Tp8+jYaGBmRkZGBgYIDVdmhoCKmpqWhubkZRURHOnTsHlUqFnJwcdHV1WfQ7MTERbW1tyMvLg0KhQGVlJY4dO4arV68iISEB4+PjrHM6Ozshk8lQU1OD0tJSnDx5EpcuXcLBgwcxMjLCatvX14c3k95EbE4srn5LryONW1VcP2+UAMuXGwuAUlJS0NzcjMLCQpw/fx5KpRJHjhyx6PfY2BgSExPR3t6O48ePIzk5GRUVFTh+/Dja29uRmJiIsbExi7F25MgRKJVKnD9/HoWFhWhubkZKSgqGh4dZbfv7+5GZmYm6ujqcOXMGxcXFqKurQ1ZWFvr6+lhtR0ZGcPDgQVy6dAknT55EaWkpampqIJPJ0NnZyWo7Pj6OhIQEXL16FceOHUNlZSUUCgXy8vLQ1tYmGo84ceKE2x7R2tqKgoIClJeXo6qqChRFQa1WY//+/ZiYmEB8fLyFR9z47nexrkGCD5iCR6YK3cRXnPEI07EmFo/IycnxmkdkZWWhrq4OxcXFOHPmDOrq6pCZmYn+/n5WW1OPeCfrHcTmxKJf/88/JgLYvjIRHs7yiIqKCtF4RHl5OdyFVSXKVGI6Q319Pe666y6sWrUKc+bMwZkzZ1jv19XVYeXKlUhISMBrr73m8HrffPMNHnzwQYyNjUGhUGD+/PlO9eOFF17QD/obNxAYaH31ZK1WC61Wa/xdqVQiJiaGVIm6CkUBsbGIeh2onQPW1jLerupSqVSIjIz0yrXFiM0qrmuAMgF6rd1MDiaaCQvO9DLxFdUckGpRF+DbGPOFrwgVT1SJBpn+ctdddyElJcWpE5kpz3nz5qGjo8Pi/c7OTgBwKvC6fv06nnzySQwMDOD8+fNOB2uAfi+1mzdvYmRkxCKPjiEkJIT1FHDatGlOX59gglQK5OaiSbketMTK/nBerOrq7+933IjgNDaruGZJACrXI6ZKNBMWnOll4ivwsa8IHb6NMV/4iphhBWxz587F5s2bXbrA3XffDblcjsHBQVbAdOHCBeP79tBoNHjmmWfQ1NSE06dPY8UK11ZAbmlpQWhoKAnCfIVUivDmhai90aZPFDbg7QogjUbjtWuLkfCgOVCNt1kUHCy/bZHHTJVoJiw41UsqRXjTQtSO+tZXhA7fxtj3gmaifvyqV31FzLhddLB+/XrodDokJSUZj2m1WqSkpCA6OhoLFiwwHr9x4wYaGhrQ06Pf40in02Hjxo34/PPPceTIETzwwAM2P8fanmlffvkl8vLy8NhjjyEggOyy5SuM1aJMKb4PqroWL17svYuLkBeqNF6v4iKaCQuu9WKqRbmoQhcqXGtmzo+ahkl1qBcJctzEPtHR0diwYQO2b9+Oa9euYenSpUhLS8Ply5fxySefsNpWVFRg7dq1iIuLw44dO/D73/8eeXl5eOaZZ9DX14dPP/2U1f6ll14y/vfGjRsRFhaG1atXY/bs2airq0NSUhKmTp1qc5FdgneQnu1CbhuwKwb40pBzEjihr+qi6ylII6Qe/0yFQoHly5d7/LpihKqn8Pcl+iquAFqv3Ypuvamuu+y5Ki6imbDgWi/GV959BPjKsPMBU4XuLV8ROlxrxkDVU9h5didqFw4AAO4cBUaCgeW9nvcVMeN2wAYA6enpeOedd5CRkYH+/n5ERkaioKAADz/8sN3zmDXa8vPzkZ+fb/G+acD23HPPITMzE3v37sXg4CBmzZoFqVSKuLg4LF261BN/BsFZwsMhra0FQCPWUFWuC7hV1ZX7fK7HzVUqJWbtCZg9/hCm/52WAGMBhj3+GiVApOfMn2gmLDjXy4qvmFahe8NXhA7nmuGWp0gg0Rei0UDvVCD3MCCtByDxrK+IGY/MI4aGhmLPnj3o7OyERqNBRUUFHn/8cYt2jzzyCGiaxo4dOwDoq1Jpmrb5MmXr1q24cOECent7MTY2BrVajYyMDBKscUFcHEDT2BkD1grltERf1bWrZJfHPzItLc3j1xQjO0t2QgKJMceElug13BUDgKY9uikz0UxYcK6Xqa+Y2L83fUXocK4ZbnmKsdjA1FMkEo/7ipghiV8E12GqumZJWAnCgPequvi6BYvQsF/F5dmSe6KZsOBcL1NfIXuLOgXnmsHBvqGRkaJeysPTkICNMDmkUoTP/4F5FT4kkGD5nZ5//M3XLViExvfv+L7FMQkkWD4/0uOmSjQTFrzQy8e+InT4oFn4zHCYxdd6vb4TBSiVJFjzICRgI0yauJDHjVNqDDRoxIVYToe7y//9v//X49cUIw+MGbaRM63iIpoRwB+9LHyF9t53VOjwQbO4kMdZz9e86SlihwRshEkj/UcRcg/rV7EOMJirZALYUftPj+8BeOzYMY9eT4xQ9RSyeooBAEGme/wdBtbtK/L45xHNhAVf9DL1FWbP4kAv+YrQ4Vozqp7Cu7X/NN4ABnnZU8QOCdgIk6epCdJ6fYXhhOGbRAcAtd/SIDYn1qPmGh0d7bFriRGmkms0SO+sOsmtPf7W1QNo9Hx+ENFMWPBGLxNfYebadF7yFaHDpWaMp9RN1xrXXhv3sqeIHRKwESZPeDggkVhWiwZ4vqqrra3NY9cSIztLdrJ+ZxY+NlZyeWEtJ6KZsOCNXj70FaHDpWbG6lAmgc28OpQH68P5GyRgI0weQxl+053werVocHCwx64lRhp7LLWgA4DGO+G1snuimbDgjV4+9BWhw6VmNqtDvegpYocEbITJYyjDD78RyroTBjxf1TVz5kyPXUuMzJ1muR+jZAJYPhLqtbJ7opmw4I1ePvQVocOlZuEzwy2OedtTxA4J2AjuIZUi7ge/Ze8tCv2dcF3XRUQlRHkk5+TixYtuX0OMUPUUohKi0H7dMHVivsdf5FavGSvRTFjwSi87vjLQpyZ5bAa40oyqpzDQq2Yd84WniB0SsBHcxrSqK1BnOEgDY9ChtqvWI4nCP/vZz9zvqMhgkoJru2pvTVxIvF8dykA0ExZ804vxlYXX2cfbb3aT4gMDXGjG+MqVsR79AYO5LLxOqkO9DQnYCO5jqOpSJug3EWdK8QH9HbEnEoWzs7Pd7qbYsNgyBvq74BXdeq28XclFNBMWvNPL4CsztGSrKltwoZmtYoPbtaQ61NuQgI3gPoaqLgBovhMwX/baE4nCfNiCRWjYTQoGvF7JRTQTFrzTy+ArTXeCbFVlAy40s+srpDrUq5CAjeA+hqouSCQI74VXtpXhwxYsQkO/ZQz7XzrJBLC8Fz7ZlJloJix4p5fBV8J7QYoPbMCFZjaLDXpBqkO9DAnYCO5jqOpCZCTizgfo74aZ5HZmW5kY9wbxq6++6n4/RUZcTJzFdCgdAMSVB/tkU2aimbDgnV4GX4lrW2S1+MBdT/EHuNDsnYffYf1u9JXLi0l1qJchARvBM0ilgFIJ6X8lIPcwsGBQf5iGPsl9R96bbiUJf/rpp57pp0ig6im8e3yb5ZYx2cC6uGyfbMpMNBMWvNRLKoX01b3GoiYmaAuYcN9T/AFfa0bVU3j7mGEaljYpYMoG1v3qf0mw5mVIwEbwLPHxkNYDu08bfjdsgVQ72uZWZddPfvITz/XRzzFuGaO5YrllTKME2OWbZG2imbDgrV47d0LaIMG7JbcW0p0IcN9T/AFfasb4SsvNLuOxMQ58RcyQgI3gWZqaAAAfPgjAg5VdX331lQc6Jw7sbhlD0z6r4iKaCQve6tXUBNC05VZVpFrUp5oxvgKOfUXMkICN4FlMKrs8WS16xx13uN83kcCXKi6imbDgrV6m1aJkqyoWvtSML74iZkjARvAsXqrsmjJligc6Jw74UsVFNBMWvNWLVIvaxJeahd/JD18RMyRgI3gWprKrbpa+sou5ITNUiw5oBiaVc9Le3u7ZfvopVD2F/tF+1jGuqriIZsKCt3o5qBat667z2BZ4QsNXmlH1FLpGDLlr5tvbkepQn0ECNoLnkUoh/U08cg8D32W2lTFMj7Zfb59UovCPf/xjz/bRD2GSgq8OXtEfMN0yhoMqLqKZsOC1XmbVokGmW+BNjHlsCzyh4QvNGF8xBmwGuPIVMeN2wKbVavH2229j/vz5CAsLQ3R0ND777DOnzj179iwkEonVl0Kh8OhnEXzM++9DWg98WwN28cEkt6o6evSoZ/vnh9zaisqA6ZYxHFRxEc2EBe/1MlSLenMLPKHhC82MxQYMHPuKmAly9wKbN2+GTCbDtm3bsGzZMqSmpuKpp56CXC7HQw895NQ1tm7divvuu491bOnSpV75LIKPMFSLeqr44D//8z891DH/xW5SMAdVXEQzYcF7vQzVooDnfEXo+EIzvvmKmHHrCVtFRQWys7Oxe/du7NmzB1u2bEFxcTEWLVqEt956y+nrrFmzBi+99BLrNXPmTK98FsFHGCq7PJUo/P/+3//zYOf8E7vFBhxUcRHNhAXv9TLZs5gUIOjxhWbL7lxmcYxLXxEzbgVsMpkMgYGB2LJli/FYaGgoXnnlFXz++ee4cuWK09caGhrC+Pi4Tz6L4AMMlV1xhsUuJWbToq5uK/PrX//awx30P7ZFb2P9bkwKLgEnVVxEM2HBe71M9ixmfMU83UJs21X5QrNnlj/D+p1rXxEzbgVsNTU1CA8Px/Tp01nH77//fgCAUql06jovv/wypk+fjtDQUKxduxZVVVUe/SytVovBwUHja3h42Kl+EdzAUNklRQRyDwOrumA010AEYEfJDpcShA8cOOCdfvoJVD2F/y7+bwD64Ni4ZYwsEOumRHFSxUU0Exa818tkz2Lp18HIPQws6zO8RwPBCHTZV4SOtzWj6in868K/AACBE/zwFTHjVsDW2dmJefPmWRxnjqnVarvnT5kyBbGxsdi3bx+OHz+O9957D7W1tVizZg1qamo89lm7d+/GjBkzjK+YmBjjNRMTE6HVahEfHw8AiI+PR1dXF3JycqBSqXDu3DkUFRWhubkZqampGBoaYrUdGBhARkYGGhoacPr0acjlcly8eBFZWVno7e1ltR0dHUVycjJaWlpw4sQJlJWVobq6GjKZDGq1mtVWp9PhwIED6OjowNGjR1FVVQWFQoH8/Hy0trYiKSnJot/d3d3Izs6GSqVCSUkJioqK0NjYiLS0NIt+Dw4OIj09HY2NjTh16hTkcjlqa2tx6NAh9PT0sNpqNBokJyejtbUVBQUFKC8vR1VVFSiKglqtxv79+zExMYH4+HhMTExg//79UKvVoAB8vXkzpPXAjhIYc050mEDtNyrE5sTi1f99FT09PTh06BBqa2shl8tx6tQpNDY2Ij09HYODg4iPj8e6desQHx+PoaEhpKWlobGxEUVFRSgpKYFKpUJ2dja6u7tZ/dZqtUhKSkJrayvy8/OhUChQVVWFo0ePoqOjAwcOHIBOp2Odo1arIZPJUF1djbKyMpw4cQItLS1ITk7G6Ogoq21vby+ysrJw8eJFyOVynD59Gg0NDcjIyMDAwACr7dDQEFJTU9Hc3IyioiKcO3cOKpUKOTk56Orqsuh3YmIi2trakJeXB4VCgcrKShw7dgxXr15FQkICxsfHjee8+r+vIjYnFp1Dncbv/Fgg8E4JsO6iDpq33kJ8R4fx+n19fcjKykJdXR2Ki4tx5swZ1NXVITMzE/39/ay+DA8PIyUlBc3NzSgsLMT58+ehVCpx5MgRi36PjY0hMTER7e3tOH78OL73ve+hoqICx48fR3t7OxITEzE2NmYx1o4cOQKlUonz58+jsLAQzc3NSElJwfDwMKttf38/MjMzUVdXhzNnzqC4uBh1dXXIyspCX18fq+3IyAgOHjyIS5cu4eTJkygtLUVNTQ1kMhk6OztZbcfHx5GQkICrV6/i2LFjqKyshEKhQF5eHtra2kTjET/72c987xEUhaqqKpSXl6OgoACtra1ITk6GRqNhXd/oEcuWQf7RR/jy97+33AIPOqOv/Pb//VYUHnH//fc75RHx8fHo7OyETCZDTU0NSktLcfLkSVy6dAkHDx7EyMgIq21fXx/eTHoTsTmxGLo5BACYkLB9Be++i/iODrc8oqKiQjQeUV5eDneR0LQ+i3NiYgI3b9506qSQkBBIJBIsWbIEy5cvx8mTJ1nvt7S0YMmSJfjoo4+wbds2lzr09ddfIzIyEg8//DAKCwuNx935LK1WC61Wa/xdqVQiJiYG1dXVuOeee1zqH8FFoqKA2lpEvUajdg5ubZcEfc5J5JxIKF9XOrwMRVGQSqXe66eAiUqIQm1XLSsxWDKhvxNWJkqAyEj9Zu8+hmgmLASlV1QUoFIh6nW45StCx5uaRSVEQdWlYh3jg68IlS+++AL33nuvW3GH8QnbuXPnEBYW5tSr0VAVEhYWxgqEGDQajfF9V1m6dCl+/vOfQy6XQ6fTGY+781khISGYPn268TVt2jSX+0WYJIbKrqY72aYKuFbVtXDhQi90zj/gaxUX0UxYCEovkyp0d3xF6HhTs8Yey/+HfPAVMWNc1uOuu+5CSkqKUycx05Dz5s1Dh2GqxZTOTv3UzPz58yfVqQULFuDmzZsYGRkx5qx567MIXiY8HKitRXgvjdrZ7L0AXanqcvbprxhZePtCNPU2sY7xoYqLaCYsBKWXh3xF6HhTs1lTZ+Hq0FXWMT74ipgxBmxz587F5s2bXTr57rvvhlwux+DgIKsY4MKFC8b3J0NLSwtCQ0NZT8K89VkELxMXB8TGIq4EiN14q8IIYG9VJY2w/1i/r6/P7vtihaqnoB5i52/ypYqLaCYsBKWXh3xF6HhDM6qews6zO28Fa4YFivniK2LGraKD9evXQ6fTISkpyXhMq9UiJSUF0dHRWLBggfH4jRs30NDQgJ6eHuOx7u5ui2t++eWXyMvLw2OPPYaAgFvdc+WzCDyCqRadEoXcw/rtTExxdquqlStXerGTwoTZMmb4pqHqmdmKapAfVVxEM2EhKL0YX5GscMtXhI6nNWM8pfZa7a2DElIdyhfcCtiio6OxYcMGbN++HW+99RaSkpLw6KOP4vLly/jwww9ZbSsqKhAREWGsogCAjRs34umnn8b777+Pjz/+GG+++SZWr16NqVOn4oMPPpj0ZxF4hlQKvPsupPXADK3l5s3ObClz5swZL3dSeOws2ck+wGwZo7lVxcWlqRLNhIXg9JJKgb/+9ZaveGALPKHhac1ubW/HLmBa0a0vNOCDr4gZt/cSTU9Px7Zt25CRkYGtW7dibGwMBQUFePjhhx2e+9xzz6Gnpwd79+7Fr3/9axw+fBhSqRRVVVWIiIjw6GcROGbnTkAi0ScJm33rnEkSfumll7zYOWFiNylYwv0ef0QzYSFIvUx9RYTFB57WzGEBEw98Rcy4HbCFhoZiz5496OzshEajQUVFBR5//HGLdo888ghomsaOHTuMx7Zu3YoLFy6gt7cXY2NjUKvVyMjIsLqPqCufReAhhmrRyW4pk5yc7MXOCZM50+ZYHDMmBfOgiotoJiwEqZebviJ0PK2Z3e3tAF74iphxO2AjEJzCsA+gcasqs2lRJknYFm+88YYPOikMqHoKUQlRaL/erj9guCE2JgWfBS+quIhmwkKQernpK0LHk5pR9RT6R/tZx1ieAvDCV8QMCdgIvsGwD6C0QTKpJGHT3EcxY0wK7rKRFJwNrGuU8KKKi2gmLASpl5u+InQ8pRnjK1cGDXtyMwVM1w2e0gB9sMYDXxEzJGAj+AZmH8CFCydVfPAf//EfPuoov7GbFJxgMNaFC3lRxUU0ExaC1MtNXxE6ntKM8RUjTAGT1uApwcH6nQ144CtihgRsBN8hlQIzZkyq+OCzzz7zQQf5j92kYEB/F3z77bwwVaKZsBCsXm74itDxlGZ2fUUiAVas0G9DxQNfETMkYCP4FjtJwgCgHdciKiHKYgpj1apVPuogf6HqKUBieZyvScFEM2EhaL0c+AoAv5wW9YRmtv6/8KmAiaCHBGwE32InSRjQ3w3XdtVa5J2YLrgsRpgcE824hnVcQvM3KVjsmgkNQetl5isw8xXtuNYvc9nc1czoKzqDrzAFTDS/CpgIekjARvAtZknCkdcMC146WPRybGzM933lERY5JgBAAyHj/E0KFrtmQkPQepn5SqgODj3FH3BXM2u5ayxf4UkBE0EPCdgIvsUsSViZoDcHy1iEnXeyaNEi3/aTZ1jLMWH+n/E1KVjsmgkNQetl5isAHHqKP+CuZg59hScFTAQ9JGAj+B6TJGEATi16eeHCBV/2kHfYXdCSp0nBYtdMaAheLxNfEctCuu5q5tBXeFLARNBDAjYCNxiShAHcyjsxm8IwXfTyueee830feYLDBS15mhQsZs2EiF/oZfAVsSyk645mVD2F7pFu/S/WFt/mqa+IGRKwEbjBkCQMANJ6IPcwsGCQ3cR00cv09HQOOsk9TFLw1cGr+gO2FrTkYVKwWDUTKn6hl8FXGE/x94V0J6sZ4yudw52s40LwFTFDAjYCNxiShE2Dtm+PwmaisCC3zfEAFgvlmi9oybNCA1PEqplQ8Qu9THxFDAvpTlYzhwvl8thXxAwJ2AjcwCQJR0YCAfqvYdOdsJkoLMhtczyAw4VyeZwULFbNhIpf6GVSfADA7xfSnaxmQvYVMUMCNgJ3SKX6RPkVKwBYLz5g+Pbqb/uuXzyBqqcsK7ggnKTgX/ziF1x3geACfqOXg+IDwPYC3UJjMppR9RQmzBeqg3B8RcyQgI3APV9/DQB2F718Kf8lwZurKzA5JlqdVn/A2oKWPE8Kpijx6OUP+JVedooPANsLdAsNVzVjfOWm7qb+gAB9RcyQgI3APWaJwmJZ9NIeDhe0FEBS8I9//GOuu0BwAb/Sy8xTnF2gW2i4qpk/+IqYIQEbgXuYRGFAVIte2sPhgpYCSAq+fPky110guIBf6WVWfODsAt1Cw1XN/MFXxAwJ2AjcwyQKz50LwH4um5CnL5zFYe4aIIik4NDQUK67QHABv9LLrPgA8E9fcUUzqp6CjtZZHBear4gZErAR+IFUCsyeDcB+LpvQc04c4VTumkCSgr/9bfEViggZv9PLbEcVf/QVZzVjfGVswrD3qIB9RcyQgI3AH5qaAEDUuWxO5ZgIJCm4rq6O6y4QXMAv9TLZUcUffcVZzfzJV8SMxwI2rVaLt99+G/Pnz0dYWBiio6Px2WefOTxv8+bNkEgkNl8dHR3GtmfPnrXZTqFQeOpPIXCF2e4HAPwu58QRDnNMAMEkBf/kJz/hugsEF/BLvUw8BfA/X3FWs8aeRr/xFTHjsYBt8+bN2Lt3LzZt2oR9+/YhMDAQTz31FEpLS+2e99prryEjI4P1Sk9Px9SpU7FixQp85zvfsThn69atFucsXbrUU38KgSvMdj/wx5wTezhcHwkQVFLw4cOHue4CwQX8Ui8zTwH8y1ccaUbVU4hKiLqVYmGCUH1FzEhomrbMbnaRiooKREdHY8+ePfjDH/4AANBoNFi1ahVmz56N8vJyl65XWlqKNWvW4P3338ef//xn4/GzZ89i7dq1OHLkCNavXz/p/n7xxRe49957UV1djXvuuWfS1yF4AYoC3nwTaG8HFQHEboQ+58Tk1oLZqin3+VxII6Rc9dSjMDkm5khogJaYTFssWgR89BHJMyEQnIWigF27gK++AsbHReMrjKewtrYzwGzyTnzFd3gi7vDIEzaZTIbAwEBs2bLFeCw0NBSvvPIKPv/8c1y5csWl62VlZUEikeDFF1+02WZoaAjj4+OT7jOBp0il+sRX+GfOiS0sckwA6+sjCSgp2C+2OhIRfquX2Y4q/uQr9jSz2IcYAGj9TWDkNeH6ipjxSMBWU1OD8PBwTJ8+nXX8/vvvBwAolUqnrzU2NoacnBysXr0aixcvttrm5ZdfxvTp0xEaGoq1a9eiqqrK7jW1Wi0GBweNr+HhYaf7Q+AAQ/EBYD/nRNWlEtwUhjnMlIWqS+U4x0RgScG/+tWvuO4CwQX8Xi8nfUVIuWy2NKPqKdR21Vr1lJBx/bp0QvUVMeORgK2zsxPz5s2zOM4cU6vVTl+rqKgIvb292LRpk8V7U6ZMQWxsLPbt24fjx4/jvffeQ21tLdasWYOamhqb19y9ezdmzJhhfMXExBj7nZiYCK1Wa7xTiY+PR1dXF3JycqBSqXDu3DkUFRWhubkZqampGBoaYrUdGBhARkYGGhoacPr0acjlcly8eBFZWVno7e1ltR0dHUVycjJaWlpw4sQJlJWVobq6GjKZDGq1mtVWp9PhwIED6OjowNGjR1FVVQWFQoH8/Hy0trYiKSnJot/d3d3Izs6GSqVCSUkJioqK0NjYiLS0NIt+Dw4OIj09HY2NjTh16hTkcjlqa2tx6NAh9PT0sNpqNBokJyejtbUVBQUFKC8vR1VVFSiKglqtxv79+zExMYH4+HhMTExg//79UKvVoCgKVVVVKC8vR0FBAVpbW5GcnAyNRsO6fk9PDw4dOoTa2lrI5XIMzZsH2omcExo0YnNisfffe5GdnY3u7m7WdbVaLZKSktDa2or8/HwoFApUVVXh6NGj6OjowIEDB6DT6VjnqNVqyGQyVFdXo6ysDCdOnEBLSwuSk5MxOjrKatvb24usrCxcvHgRcrkcp0+fRkNDAzIyMjAwMMBqOzQ0hNTUVDQ3N6OoqAjnzp3D3n/vRWxOLGq7aq1+b1k5JgBoiQQ3v/99JCQkYHx8nHX9zs5OyGQy1NTUoLS0FCdPnsSlS5dw8OBBjIyMsNr29fUhKysLdXV1KC4uxpkzZ1BXV4fMzEz09/ez2g4PDyMlJQXNzc0oLCzE+fPnoVQqceTIEXR1dbHajo2NITExEe3t7cbxWVFRgePHj6O9vR2JiYkYGxuzGGtHjhyBUqnE+fPnUVhYiObmZqSkpGB4eJjVtr+/H5mZmairq8OZM2dQXFyMuro6ZGVloa+vj9V2ZGQEBw8exKVLl3Dy5EmUlpaipqYGMpkMnZ2drLbj4+NISEjA1atXcezYMVRWVkKhUCAvLw9tbW2i8YikpCTBeMSpU6fQ2NiI9PR0DA4OWoy1tLQ0NDY2oqioCCUlJVCpVOifPdspX5mYmMDbqW/zwiNUKhVycnIsxppWq0ViYiL+8Y9/IC8vDwqFApWVlTh27Bg+Lv0YsTmxjtdyZP5eiQS6Zcs48YiKigrReISrqWHWsMhhm5iYwM2bN506OSQkBBKJBEuWLMHy5ctx8uRJ1vstLS1YsmQJPvroI2zbts2pa7744ovG/2F33nmnw/Zff/01IiMj8fDDD6OwsNBqG61WC632VtKlUqlETEwMyWHjKxQFxN7K5zLmnNCwuCOWQILIOZFQvq70ZQ89QlRClPW7YFjJMWGSggW0qGVrayu+973vcd0NgpP4vV62fEXAuWzWNLPpKwb/NHoKIEhfESpeyWE7d+4cwsLCnHo1Gh6jhoWFsQIiBo1GY3zfGYaHh3H8+HE8/vjjTgVrALB06VL8/Oc/h1wuh05nuYozoA8sp0+fbnxNmzbNqWsTOIJZpdywijeTcyKx0lRoUximWF3CAwDMc0wAICREcKZ68eJFrrtAcAG/18uGrwg5l82aZrZ8RUKbeQogSF8RM0HmB+666y6kpKQ4dTIz5Tlv3jzWemkMnZ2dAID58+c7db1jx47hxo0bVqdD7bFgwQLcvHkTIyMjFnl0BIEiZd/ZSuuBH3QBtbMNq5WboB3XIiohCnExcby/I2ag6imrEahkQh+sKROsnCQwU501axbXXSC4gCj0suIrAOzmyPLZU8w1s5XTy/gKK1hjEJiviBmLgG3u3LnYvHmzSxe5++67IZfLMTg4yAqYLly4YHzfGTIzMzFt2jQ8++yzLn1+S0sLQkNDyZMzfyM8HKitNa5UHlein8JglrpgoEGjtqsWsTmxgpjGsLmEBySgA2j9NjGsN4S5oGVQkIW9EHiMaPQy85XwXus3gkyOLJ89xVQzsfiKmPFI0cH69euh0+mQlJRkPKbVapGSkoLo6GgsWLDAePzGjRtoaGhAT08P6xrd3d04ffo01q1bh6lTp1r9nO7ubotjX375JfLy8vDYY48hIIDstOVXmC16yUxhRPYFWzycEtI0htUlPACESIJA5U9l3wULeEFLV5fzIXCLaPQy8xXjHqNWshP47immmtn0lXGAOgy/8RUx45EIJzo6Ghs2bMD27dvx1ltvISkpCY8++iguX76MDz/8kNW2oqICERERFuvHHD58GOPj43anQzdu3Iinn34a77//Pj7++GO8+eabWL16NaZOnYoPPvjAE38KgU8wOScLF946VA8o48cRMmbZXAhLfdgstweAm2NYV32DfWzhQsHmmDDL+hCEgWj0MvMVRzmyfPYURjO7vkLTWFdv8ntwMBAZKVhfETMeeySVnp6Obdu2ISMjA1u3bsXY2BgKCgrw8MMPO3V+ZmYmZs+ejZ/+9Kc22zz33HPo6enB3r178etf/xqHDx+GVCpFVVUVIiIiPPWnEPiEVArMmMG2IZrWl+RbzdfXT2Pw0WCZKQtbCcHm5fZCX9AyLy+P6y4QXEBUehl8xfTp/Q+67C8fxEdPycvLs+8r5st4SCT6BYSVSsH6ipjxyNZUQoNsTSUwwsIAQ8Uxg7Ek3wp8XerDpXJ7htBQYHTUV130KDqdDoGBgVx3g+AkotPLzFeEuHyQTqfDPR/f45qvCNhThAxvtqYiELxKeDhrwUvAZBrDxlM2vk1j2JuysFpuDwg+KfjAgQNcd4HgAqLTKzyctSm8o6nRL7u+RFRCFK985bV/vOaarwjcU8QOCdgI/CcuDhIrD4KN0xg8nxp1NGVhtdzeD5KC33jjDa67QHAB0enFFB+YYG9qFICxGp0vvvLJ8Ceu+YrAPUXskICNwH+kUpx85RXW3TBDXAl7iQ9zNlGbODVXqp7CJspGIQ2tr06zKLcH/CIp2G83E/dTRKcXU3xg5itM1aitfDZAoL4ikQjeU8QOCdgIguDuXbuAH/zAwlztTY0CgGZcw9kdMfNkTTOusfq+BDamLKKi/CIpWCrl59pVBOuIUi+p1MJXjMsHddu+ExSkr0RGCt5TxA4J2AiCoLy83GL9JAZH0xiA7++I7d4BwzBl0eXfayN5YrNjgu8QrV42pkaVCUDkN7C6thmDL32FqqcQlRCF9TnrbbYRg6+IGRKwEQTB9773vVtTGJGRNqcxrC1DBPj2jtjRHbDNKQs/29fPrzcS90NEq5fZHqNGaFrvK7ZMBb7zFcZTbK61BojGV8QMCdgIgkDDlN9LpfrpwpAQ1vvGjZzH7V/Hm3fEztwBA/o+Wq0KBfzKVDUaGwErgZeIWi8b08FGX5EE2z3d277CPK23FzyKxVfEDAnYCIJgYGCAfcCsJB/Qm2umA8/01h2xU3fABrJy/W8JD2tYaEbgNaLXy4qnAAZfybaytYoJ3vYVm0/rcWvKViy+ImZIwEYQBBY7WVjJOwFM7oh1dkpHAazPWe+xNZWcvwOWWO7px+CHOSZk9xFhIXq9bHgKAEgbJMi9sAihQaFW32fw5JM2R3mwgGFB34EQUfmKmCEBG0EQFBcXsw/YKMkHDHfEMvtPuZjFdWNzYrH4H4snZbJUPYXF/1jswh2w2Z5+xgb+WW5voRmB14heLzueApqG9N9tyCydY/cSzJO2KX+dMukbQmd9BQDeHXgMyn1aUfmKmCFbU5GtqQTB6OgowsLCLN+IigJqa63eGVMRwKb/CIaGtj+dwbBoxiLsfXwvpBH2lzeg6in8ruh3aLve5vCaEkgQGboQcalt1u+AmXJ7pdKpPgoJm5oReAnRy4AdT4FEAuoumnNfCQ0KRdb8rVj3yw+tN/BjXxEqZGsqgmj45JNPrL/hYBojUzEfgP3SfIa26212n7iZ3vk6Y6oAkPt8LpRZM7Cu0cbn+/GUhU3NCLyE6GXAxvJBAPRP2kx8xRm84StZ0iys+0eh7QQMP/YVMUOesJEnbMKHooD1623fEX/yB+zSnoKqS+WwIMCU4AB9Zdj8b83HjbEb6L7R7fS5oUGhelOtp+32Dbm5ZMqCQOAbFAXs2gV8+aX19w2+skn9L4fTluYEBwRjgp5AcGAwxnRj0NE6p88lviJcyBM2gmiwu22OldXKjdA0pL/cA+XsdyF7XubSZ45NjGFsYgxt19tcCtYA3DLV2FjbpurnK4+LbqsjgUP0MoFZPsjKmo8AjL6SOf+3AJx7gs8wNqEP0jTjGpeCNYD4itghARtBELzwwgv2G9iZGoVEAuzaBWmEFLnP52LRjEWe76CBxTMWg3qewrqIdcDOndbNHhDFlIVDzQi8guhlBQe+It13CrnP5yJyTqTxibw3IL5CAEjARhAIRUVF9hs4qPCCSgVQFKQRUlzedtnjgRtjqK3bWvWmSlF2E5fFUL3lUDMCryB6WcEJX5HWA8rXlbj5zk3v+ko9rS+IUKlE7StihgRsBEEQGRnpuJGDqVHExuoNDfBY4GYRqAH6zyBTFs5pRuANRC8b8MFXGE+prbV+goh8RcyQgI0gCLq7ncwhszeFAQCbNhnNFZi8wdoM1KKi9MnAthDRlIXTmhF4AdHLDj7wleCAYETNibLuK5sMC+jaugkUka+IGRKwEQSBTudkcq69KQwA0Gj0d6pTpuiDKyt3xlFzohAcEIzQoFAEBwRj0YxFWDxjMUKDQm0b6uLF+uvamq4ARDdl4bRmBF5A9LKDs76yeLHNwM3UVwIlgUZ/YTzl5js3oXxdad1XbOzzSgP6J2si8hUxQ5b1IMt6CIKGhgbcddddzp9gb/FLBubONDfX5ubPDmGmKhwhwoUsXdaMwClELydwxlcA9zwFcM5XJBJoli9HaL21bQ4IfIPzZT2Gh4cRFxeHJ554AnfccQckEglSU1NduoZWq8Xbb7+N+fPnIywsDNHR0fjss8/cbkvwLyorK107wdEUBnDrfbPpDKcxnaqwh0inLFzWjMApRC8ncMZXAH1ahMkTfJdw1ldoGorHHnP9+gTB4lbA1tPTg127dqG+vh5RUVGTusbmzZuxd+9ebNq0Cfv27UNgYCCeeuoplJaWutWW4F8888wzrp3ATGGE2t+sGYDN6QybODFVYYR5sibCKQuXNSNwCtHLCZz1FaYy3Vu+EhoKUBTu3rnT2Z4T/AHaDTQaDd3Z2UnTNE1XVlbSAOiUlBSnz79w4QINgN6zZ4/x2OjoKL1kyRL6gQcemHRbR1RXV9MA6OrqapfOI3DHv/71r8mdmJtL0wBNSyT6n868goP1r8hI/fm5ufr/Zo47ex2ApinKs/8jBMSkNSNwAtHLBRhfceVl6it//KP+Z2goTS9aRNOzZk3KV4hmwsETcYdbAZspkwnY/vjHP9KBgYH09evXWcf/53/+hwZAt7e3T6qtI0jAJjJyc2k6Ksr1YMud1+LFog7WCAS/JzdXH2z5ylOIrwgaT8QdnFaJ1tTUIDw8HNOnT2cdv//++wEASpMEbVfaEvwPt7bNYbaZuXnT+WnSyWKYqkBrq+imQM0hWx0JC6KXi0ilwOXLek+JirJdQeoJbPgK0UxccBqwdXZ2Yt68eRbHmWNqtXpSbc3RarUYHBw0voaHh93tOsHH/OIXv/DMhaRSIDPTM9eyRlaW6AM1Bo9pRvAJRK9JwtwQylzbq9glbPgK0UxcGAO2iYkJaDQap160h1YCGR0dRUhIiMXxUMMTkNHR0Um1NWf37t2YMWOG8RUTEwNAHwQmJiZCq9Ua71Ti4+PR1dWFnJwcqFQqnDt3DkVFRWhubkZqaiqGhoZYbQcGBpCRkYGGhgacPn0acrkcFy9eRFZWFnp7e1ltR0dHkZycjJaWFpw4cQJlZWWorq6GTCaDWq1mtdXpdDhw4AA6Ojpw9OhRVFVVQaFQID8/H62trUhKSrLod3d3N7Kzs6FSqVBSUoKioiI0NjYiLS3Not+Dg4NIT09HY2MjTp06BblcjtraWhw6dAg9PT2sthqNBsnJyWhtbUVBQQHKy8tRVVUFiqKgVquxf/9+TExMID4+HhMTE9i/fz/UajUoikJVVRXKy8tRUFCA1tZWJCcnQ6PRsK7f09ODQ4cOoba2FnK5HKdOnUJjYyPS09MxODiI+Ph45ObmIj4+HkNDQ0hLS0NjYyOKiopQUlIClUqF7OxsdHd3s66r1WqRlJSE1tZW5OfnQ6FQoKqqCkclEvQmJWHwjjsAGNYymiTMuaNz5uDLuDg0REQgIyMDAwMDrL4MDQ0hNTUVzc3NKCoqwrlz56BSqZCTk4Ouri6LficmJqKtrQ15eXlQKBSorKzEsWPHcPXqVSQkJGB8fJx1TmdnJ2QyGWpqalBaWoqTJ0/i0qVLOHjwIEZGRlht+/r6kJWVhbq6OhQXF+PMmTOoq6tDZmYm+vv7WW2Hh4eRkpKC5uZmFBYW4vz581AqlThy5IhFv8fGxpCYmIj29nYcP34ce/fuRUVFBY4fP4729nYkJiZibGzMYqwdOXIESqUS58+fR2FhIZqbm5GSkoLh4WFW2/7+fmRmZqKurg5nzpxBcXEx6urqkJWVhb6+PlbbkZERHDx4EJcuXcLJkydRWlqKmpoayGQydHZ2stqOj48jISEBV69exbFjx1BZWQmFQoG8vDy0tbWJxiMyMzMF7RGmY81tjzh6FB0dHThw4AB0Oh3rHLVaDZlMhurqapSVleHEiRNoaWlBcl8ftFlZuO5BX9HOn4+y3/8eXatXW/WIjz/+WNAeUVFRIRqPKC8vd+MbwXwxDMjlctrwPXH4qq+vt5hbnUwO28qVK+lHH33U4vhXX31FA6ATEhIm1dYcjUZDX79+3fgqKSkhOWwCo6mpyTsXdjcPheSU2MRrmhG8AtHLg/jIV4hmwsETOWxBTOB21113ISUlxakgz9rU5GSYN28eOjo6LI53dnYCAObPnz+ptuaEhISwns5NmzZt0n0mcENLSwuWLVvm+QtLpfoXRQG7dgF1dfrjY2OWbQMDgeBgQKcDVqzQr8lEpj9t4jXNCF6B6OVBXPGVWbOA224DvvkGWL7cJV8hmokLY8A2d+5cbN682acffvfdd0Mul2NwcJBVTHDhwgXj+5NpS/A/wsLCvPsBjMEyMEbb2OiyiRL0eF0zgkchenkBL/sK0Uxc+Kzo4MaNG2hoaEBPT4/x2Pr166HT6ZCUlGQ8ptVqkZKSgujoaCxYsGBSbQn+x+233+7bD2QSiUdH9T9JsOYyPteM4BZELx/gYV8hmomLIMdN7MMk1TJVmvn5+bh69SoA4Le//S1mzJgBAKioqMDatWsRFxeHHTt2AACio6OxYcMGbN++HdeuXcPSpUuRlpaGy5cv45NPPmF9jittCf5HQ0MDIiMjue4GwQWIZsKC6CU8iGbiwu2A7e9//zva2tqMv1MUBcqwDcdLL71kDNhskZ6ejnfeeQcZGRno7+9HZGQkCgoK8PDDD7vVluBfMJW9BOFANBMWRC/hQTQTF25PiV6+fBm0fscEi9fixYuN7R555BHQNG18usYQGhqKPXv2oLOzExqNBhUVFXj88cetfpYrbQn+xZEjR7juAsFFiGbCguglPIhm4kJC0x5aVE1AfPHFF7j33ntRXV2Ne+65h+vuEAgEAoFA8GM8EXdwutMBgeAsZAsW4UE0ExZEL+FBNBMXJGAjCIJf/epXXHeB4CJEM2FB9BIeRDNxQQI2giBITU3lugsEFyGaCQuil/AgmokLt6tEhQiz72h9fT3HPSE4y3e/+1188cUXXHeD4AJEM2FB9BIeRDPhwMQb9vY9d4QoA7avv/4agH7ZEQKBQCAQCARf8PXXX+PBBx+c1LmiDNjWrFkDACgqKsLMmTM57g3BEcPDw4iJiUFJSQnZB1YgEM2EBdFLeBDNhEVPTw8ef/xxY/wxGUS5rMfg4CBmzJiB69evs/YlJfATopfwIJoJC6KX8CCaCQtP6EWKDggEAoFAIBB4DgnYCAQCgUAgEHiOKAO2kJAQxMXFISQkhOuuEJyA6CU8iGbCguglPIhmwsITeokyh41AIBAIBAJBSIjyCRuBQCAQCASCkCABG4FAIBAIBALPIQEbgUAgEAgEAs8hARuBQCAQCAQCzyEBG4FAIBAIBALPEVXAptVq8fbbb2P+/PkICwtDdHQ0PvvsM667JXrOnj0LiURi9aVQKFhtiYa+ZXh4GHFxcXjiiSdwxx13QCKRIDU11WpbV7QhOnoPZzVzZdwBRDNvUVlZiTfeeAMrV67EbbfdhoULF+L5559HU1OTRVsyxviBs5p5eoyJai/RzZs3QyaTYdu2bVi2bBlSU1Px1FNPQS6X46GHHuK6e6Jn69atuO+++1jHli5dyvqdaOhbenp6sGvXLixcuBBRUVE4e/aszbauaEN09B6uaAY4N+4Aopm3+Nvf/oaysjJs2LABkZGR+OabbxAfH4977rkHCoUCq1atMrYlY4wfuKIZ4MExRouECxcu0ADoPXv2GI+Njo7SS5YsoR944AEOe0aQy+U0APrIkSN22xENfY9Go6E7OztpmqbpyspKGgCdkpJi0c4VbYiO3sVZzZwddzRNNPMmZWVltFarZR1ramqiQ0JC6E2bNhmPkTHGH5zVzNNjTDRTojKZDIGBgdiyZYvxWGhoKF555RV8/vnnuHLlCoe9IzAMDQ1hfHzc6ntEQ98TEhKCuXPnOmznijZER+/irGam2Bt3ANHMm6xevRpTpkxhHVu2bBlWrlyJ+vp64zEyxviDs5qZ4okxJpqAraamBuHh4Zg+fTrr+P333w8AUCqVHPSKYMrLL7+M6dOnIzQ0FGvXrkVVVRXrfaIhf3FFG6Ijv3A07gCima+haRpdXV2YOXOm8RgZY/zGmmYMnhpjoslh6+zsxLx58yyOM8fUarWvu0QwMGXKFMTGxuKpp57CzJkzUVdXh7///e9Ys2YNysvL8cMf/hAA0ZDPuKIN0ZEfODvuAKKZr8nMzERHRwd27dplPEbGGL+xppmnx5hoArbR0VGrm66GhoYa3ydww+rVq7F69Wrj788++yzWr1+PyMhIbN++HYWFhQCIhnzGFW2IjvzA2XEHEM18SUNDA37zm9/ggQcewC9+8QvjcTLG+IstzTw9xkQzJRoWFgatVmtxXKPRGN8n8IelS5fi5z//OeRyOXQ6HQCiIZ9xRRuiI3+xNu4Aopmv+Oabb/D0009jxowZxpwmBjLG+Ik9zazhzhgTTcA2b948dHZ2Whxnjs2fP9/XXSI4YMGCBbh58yZGRkYAEA35jCvaEB35jfm4A4hmvuD69et48sknMTAwgMLCQov/p2SM8Q9HmtlismNMNAHb3XffjaamJgwODrKOX7hwwfg+gV+0tLQgNDQU06ZNA0A05DOuaEN05Dfm4w4gmnkbjUaDZ555Bk1NTSgoKMCKFSss2pAxxi+c0cwWkx5jHlmURAAoFAqLNU40Gg29dOlSOjo6msOeEa5du2ZxTKlU0sHBwfSzzz5rPEY05BZ7a3q5og3R0XfY08zZcUfTRDNvMj4+Tj/77LN0UFAQfeLECZvtyBjjD85q5ukxJpqig+joaGzYsAHbt2/HtWvXsHTpUqSlpeHy5cv45JNPuO6eqNm4cSPCwsKwevVqzJ49G3V1dUhKSsLUqVPxwQcfGNsRDbkhPj4eAwMDxsqy/Px8XL16FQDw29/+FjNmzHBJG6Kj93FGM2fHHUA08ya///3vkZeXh2eeeQZ9fX349NNPWe+/9NJLAFzTgOjlXZzVzONjzEMBpyAYHR2l//CHP9Bz586lQ0JC6Pvuu48uLCzkuluiZ9++ffT9999P33HHHXRQUBA9b948+qWXXqKbm5st2hINfc+iRYtoAFZfra2txnauaEN09C7OaObKuKNpopm3iImJsamV+T/RZIzxA2c18/QYk9A0TXsw8CQQCAQCgUAgeBjRFB0QCAQCgUAgCBUSsBEIBAKBQCDwnP8f7DDXGkCjRNgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot original real xData recovered with sr for all bins\n",
+    "if 1:\n",
+    "    plt.plot(xDelayed_snapshot, 'r.-')\n",
+    "    plt.plot(x_sr_snapshot, 'g.-')\n",
+    "    plt.plot(x_sr_diff_snapshot, 'y.-')\n",
+    "else:\n",
+    "    plt.plot(x_sr_diff_snapshot, 'y.-')\n",
+    "plt.xlim([0, 250])\n",
+    "#plt.ylim([-0.01, 0.01])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c2492045",
+   "id": "3a647a98",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/applications/lofar2/model/pfb_os/pfb_reconstruction.ipynb b/applications/lofar2/model/pfb_os/pfb_reconstruction.ipynb
index 8fbe59367c8c8f7e38722280600a54ad3bab9b3c..f48302e6f51cbd742716f2b4498e6eee1aa2648d 100644
--- a/applications/lofar2/model/pfb_os/pfb_reconstruction.ipynb
+++ b/applications/lofar2/model/pfb_os/pfb_reconstruction.ipynb
@@ -56,7 +56,7 @@
     "    sys.path.insert(0, module_path)\n",
     "\n",
     "# Import rtdsp\n",
-    "from rtdsp.utilities import pow_db\n",
+    "from rtdsp.utilities import pow_db, snr_db\n",
     "from rtdsp.firfilter import prototype_fir_low_pass_filter, design_fir_low_pass_filter, \\\n",
     "                            design_fir_low_pass_filter_adjust, \\\n",
     "                            raised_cosine_response, square_root_raised_cosine_response, \\\n",
@@ -547,7 +547,7 @@
     "    # Input signal\n",
     "    inData = wgData[b * Ndft : (b + 1) * Ndft]\n",
     "    # Filter to downsample time signal\n",
-    "    pfsDownData[b] = pfs.filter_block(inData, flipped=False)\n",
+    "    pfsDownData[b] = pfs.filter_block(np.flip(inData))\n",
     "    # Frequency domain data\n",
     "    # . The order of the pfs polyphases 0 : Ndft-1 fits the input order for FFT (and IFFT)  \n",
     "    if analysisFFT:\n",
@@ -650,7 +650,7 @@
     "        plt.plot(timeData.imag)\n",
     "        break;\n",
     "    # Filter to upsample time domain data\n",
-    "    pfsUpData[b] = Ndft * pfs.filter_block(timeData.real, flipped=False)\n",
+    "    pfsUpData[b] = Ndft * pfs.filter_block(np.flip(timeData.real))\n",
     "    # Reconstructed time signal is the pfs output from the Ndft polyphases.\n",
     "    # . For upsampling the commutator selects the pfs polyphases 0 : Ndft-1,\n",
     "    #   which fits the data output order in time\n",
@@ -724,7 +724,7 @@
     "outData = reconstructedData[intGroupDelay :]\n",
     "diffData = inpData - outData\n",
     "diffDataMax = np.max(np.abs(diffData))\n",
-    "SNRdb = pow_db(np.std(wgData) / np.std(diffData))\n",
+    "SNRdb = snr_db(wgData, diffData)\n",
     "# Expected SNR = 11.96 dB for Ntap = 8, Ndft = 16, 'firwin', hpFactor = 1.0, 'noise'\n",
     "print('SNRdb = %.2f [dB]' % SNRdb)\n",
     "if SNRdb < 100:\n",
diff --git a/applications/lofar2/model/rtdsp/__init__.py b/applications/lofar2/model/rtdsp/__init__.py
index 98a0c24244b3b768dace56da0b5608ae6d62f649..9de8a42e536ce8460b25446300e7ad1ce41bd27e 100644
--- a/applications/lofar2/model/rtdsp/__init__.py
+++ b/applications/lofar2/model/rtdsp/__init__.py
@@ -1,14 +1,18 @@
 """Init file for the Radio Telescope Digital Signal Processing (RTDSP) package.
 
 Modules:
-. utilities : Basic functions used in the other modules
-. windows   : Windows functions for filter design
-. fourier   : DFT based functions
-. hilbert   : Hilbert transform
-. firfilter : FIR filter design
-. iirfilter : IIR filter design
-. multirate : Multirate functions for resampling
-. plotting  : Plotting impulse responses and spectra
+. utilities     : Basic functions used in the other modules
+. windows       : Windows functions for filter design
+. fourier       : DFT based functions
+. hilbert       : Hilbert transform
+. firfilter     : FIR filter design
+. iirfilter     : IIR filter design
+. multirate     : Multirate functions for resampling
+. singlechannel : Single channel downconverter downsampler and single channel
+                  upconverter upsampler, using LO
+. dftfilterbank : Multi channel analysis filterbank and multi channel synthesis
+                  filterbank, using DFT and IDFT
+. plotting      : Plotting impulse responses and spectra
 
 References:
 [1] dsp_study_erko.txt
diff --git a/applications/lofar2/model/rtdsp/dftfilterbank.py b/applications/lofar2/model/rtdsp/dftfilterbank.py
new file mode 100644
index 0000000000000000000000000000000000000000..92de87958296231fa4300cc5758fd983773e77df
--- /dev/null
+++ b/applications/lofar2/model/rtdsp/dftfilterbank.py
@@ -0,0 +1,329 @@
+#! /usr/bin/env python3
+###############################################################################
+#
+# Copyright 2024
+# ASTRON (Netherlands Institute for Radio Astronomy) <http://www.astron.nl/>
+# P.O.Box 2, 7990 AA Dwingeloo, The Netherlands
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+###############################################################################
+
+# Author: Eric Kooistra
+# Purpose: Multi channel analysis filterbank and multi channel synthesis
+#          filterbank, using DFT and IDFT
+# Description:
+#
+# Usage:
+# . Use [2] to verify this code.
+#
+# References:
+# [1] dsp_study_erko.txt
+# [2] pfb_os/multirate_mixer.ipynb
+#
+# Books:
+# . LYONS
+# . HARRIS
+# . CROCHIERE
+
+import numpy as np
+from sys import exit
+from .multirate import fold, \
+                       PolyPhaseFirFilterStructure, \
+                       polyphase_data_for_downsampling_whole_x
+
+
+def analysis_dft_filterbank(x, Ndown, Ndft, coefs, structure, commutator=None, verbosity=1):
+    """Analysis DFT filterbank with Ros = Ndft / Ndown.
+
+    Implements WOLA structure for DFT filterbank [CROCHIERE Fig 7.19]. Key
+    steps:
+
+    - Signal x has time index n. Mixer local oscillator (LO) and LPF and
+      downsampler M, yields the short-time spectrum of signal x at time n = mM,
+      so M is the block size or downsample rate [CROCHIERE Eq 7.65 = Eq 7.9,
+      HARRIS Eq 6.1]
+    - Change from fixed signal time frame n and sliding filter, to sliding time
+      frame r with fixed filter. This yields factor W_K^(kmM) [CROCHIERE Eq
+      7.69], with K = Ndft.
+    - Assume filter h length is Ncoefs = Ntaps * K, for K frequency bins. The
+      h[-r] weights the signal at n = nM, so the DFT of the weighted signal has
+      Ncoefs bins k', but for the filterbank only bins k' = k Ntaps are needed.
+      This pruned DFT can be calculated by stacking the blocks of K samples of
+      the weighted signal and then calculating the K point DFT [CROCHIERE Eq
+      7.73, Fig 7.19, BUNTON]. The stacking sum is like polyphase FIR with K
+      branches, and data is shifted in per block of M samples from x.
+    - The time (m) and bin (k) dependend phase shift W_K^(kmM) =
+      exp(-j 2pi k m M / K) of the DFT output is equivalent to a circular time
+      shift by l = (m M) % K samples of the DFT input. Circular time shift DFT
+      theorem [CROCHIERE Fig 7.21]:
+        x[n] <= DFT => X(k), then
+        x[(n - l) % K] <= DFT => X(k) exp(-j 2pi k l / K)
+    - Note: fold = roll -1 then flip = flip then roll +1
+
+    Input:
+    . x: Input signal x[n]
+    . Ndown: downsample factor and input block size
+    . Ndft: DFT size, number of polyphases in PFS FIR filter
+    . coefs: prototype LPF FIR filter coefficients for anti aliasing BPF
+    . structure: 'wola' or 'pfs'
+    . commutator: only for 'pfs', counter clockwise 'ccw' to use IDFT or
+      clockwise 'cw' to use DFT. Both yield identical result Yc, because
+      IDFT(x) = 1/Ndft * DFT(fold(x)).
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . Yc: Downsampled and downconverted output signal Yc[k, m], m = n // M for
+          all Ndft bins k. Complex baseband signals.
+    """
+
+    # Oversampling analysis DFT filterbank
+    if structure == 'wola':
+        # >>> Use WOLA structure [CROCHIERE Fig 7.19]
+        # Apply the coefs as a window conform [CROCHIERE Eq 7.70].
+        # For the pfs the coefs are already in the correct order
+        pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
+        # For the delay line the coefs still need to be flipped. This flip is
+        # omitted in reconstruct.m
+        wCoefs = np.flip(coefs)
+
+        # Get parameters
+        Ncoefs = pfs.Ncoefs
+        Ntaps = pfs.Ntaps
+        Ndelays = pfs.Ndelays
+
+        # Determine Nblocks of Ndown samples in x
+        _, _, _, Nblocks = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros=0)
+
+        # Prepend x with Ncoefs - 1 zeros to have first down sampled sample at
+        # m = 0 start at n = 0, and to fit Nblocks for WOLA
+        Nzeros = pfs.Ncoefs - 1
+        xData = np.concatenate((np.zeros(Nzeros), x))
+
+        # Prepare output
+        Yc = np.zeros((Ndft, Nblocks), dtype='cfloat')
+
+        # Need time shift offset to align with LO in single channel reference.
+        # This tOffset = 1 together with the flip of pfsData makes a fold(),
+        # so that the use of DFT in WOLA analysis is equivalent to using
+        # IDFT as in LO downconverter and PFS analysis.
+        tOffset = 1
+        # PFB loop per Ndown input xData samples
+        for b in range(Nblocks):
+            # Time shift
+            tShift = b * Ndown
+            # Apply window
+            tRange = np.arange(Ncoefs) + tShift
+            tData = xData[tRange]  # data for whole window
+
+            # 1) Variant using PFS
+            # Shifting in the (old and new) data for the whole window is
+            # equivalent to shifting in only the new data
+            if 0:
+                # load whole window data
+                pfs.shift_in_data(np.flip(tData))
+            else:
+                # shift in new data block
+                tBlock = tData[Ncoefs - Ndown:]
+                pfs.shift_in_data(np.flip(tBlock))
+            zPoly = pfs.polyDelays * pfs.polyCoefs
+            # Sum Ntaps
+            pfsData = np.sum(zPoly, axis=1)
+            # Flip polyphases 0:Ndft to match time order for DFT input
+            pfsData = np.flip(pfsData)
+
+            # 2) Variant using a delay line like in reconstruct.m
+            dLine = np.zeros(Ndelays, dtype='float')
+            # Need to load data at end of delay line to sum the taps correctly
+            # in case Ncoefs < Ndelays
+            dLine[Ndelays - Ncoefs : Ndelays] = tData * wCoefs
+            # Sum Ntaps
+            sumLine = np.zeros(Ndft, dtype='float')
+            for t in range(Ntaps):
+                sumLine += dLine[np.arange(Ndft) + t * Ndft]
+
+            # Verify that 1) PFS and 2) delay line yield same result
+            # . The two variants differ in structure, but yield same result.
+            if not np.allclose(sumLine, pfsData):
+                exit('ERROR: wrong analysis WOLA')
+
+            # Fixed time reference, roll is modulo Ndft
+            pfsShifted = np.roll(pfsData, tOffset + tShift)
+            # DFT
+            Yc[:, b] = np.fft.fft(pfsShifted)
+
+    elif structure == 'pfs':
+        # PFS with Ndft polyphases and shift in xBlocks of Ndown samples
+        pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
+
+        # Prepend x with Ndown - 1 zeros, and represent x in Nblocks of Ndown
+        # samples
+        Nzeros = Ndown - 1
+        xBlocks, xData, Nx, Nblocks = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
+
+        # Prepare output
+        Yc = np.zeros((Ndft, Nblocks), dtype='cfloat')
+
+        # PFB loop per Ndown input xData samples
+        for b in range(Nblocks):
+            # Filter block, the inData blocks are already flipped in
+            # polyphase_data_for_downsampling_whole_x()
+            inData = xBlocks[:, b]
+            pfsData = pfs.filter_block(inData)
+
+            # Time (m) and bin (k) dependend phase shift W_K^(kmM) by circular
+            # time shift of DFT input
+            tShift = b * Ndown
+            pfsShifted = np.roll(pfsData, -tShift)  # roll is modulo Ndft
+
+            # For 'ccw' apply IDFT, for 'cw' apply DFT(fold()), due to fold()
+            # both yield identical result
+            if commutator == 'cw':  # DFT
+                # For 'cw' fold the pfsData to apply the DFT
+                pfsShifted = np.roll(pfsShifted, -1)
+                pfsShifted = np.flip(pfsShifted)
+                Yc[:, b] = np.fft.fft(pfsShifted)
+            elif commutator == 'ccw':  # IDFT
+                # With 'ccw' keep the pfsData to apply IDFT
+                Yc[:, b] = np.fft.ifft(pfsShifted) * Ndft
+            else:
+                exit('ERROR: invalid commutator ' + str(commutator))
+    else:
+        exit('ERROR: invalid structure ' + str(structure))
+
+    if verbosity:
+        Ros = Ndft / Ndown
+        print('> analysis_dft_filterbank():')
+        print('  . len(x)     =', str(len(x)))
+        print('  . Nblocks    =', str(Nblocks))
+        print('  . Ros        =', str(Ros))
+        print('  . Ndown      =', str(Ndown))
+        print('  . Ndft       =', str(Ndft))
+        print('  . structure  =', structure)
+        if structure == 'pfs':
+            print('  . commutator =', commutator)
+        print('')
+    return Yc
+
+
+def synthesis_dft_filterbank(Xbase, Nup, Ndft, coefs, structure, commutator=None, verbosity=1):
+    """Synthesis DFT filterbank with Ros = Ndft / Nup.
+
+    Implements WOLA structure for DFT filterbank [CROCHIERE Fig 7.20]. Key
+    steps:
+
+    - Signal Xbase has time index m and Ndft values.
+
+    Input:
+    . Xbase: Complex baseband signals for Ndft bins, and Nblocks in time m.
+    . Nup: upsample factor and output block size
+    . Ndft: DFT size, number of polyphases in PFS FIR filter
+    . coefs: prototype LPF FIR filter coefficients for anti aliasing and
+      interpolating BPF
+    . structure: 'wola'
+    . commutator: 'cw' to use DFT or 'ccw' to use IDFT
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . y: Upsampled and upconverted output signal y[n].
+    """
+    Ros = Ndft / Nup
+
+    # Xbase has Ndft rows and Nblocks columns
+    Nblocks = np.size(Xbase, axis=1)
+
+    # Prepare output
+    Noutput = Nup * Nblocks + len(coefs)
+    y = np.zeros(Noutput, dtype='float')
+
+    # Adjust LO phase for group delay of analysis LPF and synthesis LPF in
+    # series. This is not needed in practise, but is needed to be able to
+    # exactly compare reconstructed signal with original input time series
+    # signal. Assume analysis coefs and synthesis coefs have linear phase and
+    # same length (but not necessarily same coefs), then the total group
+    # delay is (len(coefs) - 1) / 2 + (len(coefs) - 1) / 2.
+    hPairGroupDelay = len(coefs) - 1
+
+    # Oversampling synthesis DFT filterbank
+    if structure == 'wola':
+        # >>> Use WOLA structure [CROCHIERE Fig 7.20]
+        # Apply the coefs as a window conform [CROCHIERE Eq 7.76].
+        # Use PFS with Ndft polyphases for delay line and coefs window
+        pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
+        # Use coefs as window
+        wCoefs = coefs
+
+        # Get parameters
+        Ncoefs = pfs.Ncoefs
+        Ntaps = pfs.Ntaps
+        Ndelays = pfs.Ndelays  # zero padded coefs
+
+        # PFB loop per Nup output y samples
+        for b in range(Nblocks):
+            # For 'ccw' apply IDFT, for 'cw' apply DFT(fold()), due to fold()
+            # both # yield identical result
+            if commutator == 'cw':  # DFT
+                # For 'cw' fold the Xbase to apply the DFT
+                xTime = np.real(np.fft.fft(fold(Xbase[:, b])))
+            else:  # 'ccw', IDFT
+                # With 'ccw' keep the Xbase to apply IDFT
+                xTime = Ndft * np.real(np.fft.ifft(Xbase[:, b]))
+
+            # Apply filter group delay (hPairGroupDelay), and time (m) and bin
+            # (k) dependend phase shift W_K^(kmM) by circular time shift of
+            # DFT output. To get from fixed time reference to sliding time
+            # reference.
+            tShift = b * Nup - hPairGroupDelay
+            xShifted = np.roll(xTime, -tShift)  # roll is modulo Ndft
+
+            # 1) Variant using PFS.
+            # Load xTime at Ntaps in polyDelays
+            pfs.parallel_load(xShifted)
+            # Apply FIR coefs per delay element
+            zPoly = Nup * pfs.polyDelays * pfs.polyCoefs
+            zLine = pfs.map_to_delay_line(zPoly)
+            zLine = zLine[0:Ncoefs]
+
+            # 2) Variant using a delay line like in reconstruct.m
+            # Copy data at Ntaps
+            dLine = np.zeros(Ndelays, dtype='float')
+            for t in range(Ntaps):
+                dLine[np.arange(Ndft) + t * Ndft] = xShifted
+            # Apply window
+            yLine = np.zeros(Ncoefs, dtype='float')
+            yLine = Nup * dLine[0:Ncoefs] * wCoefs
+
+            # Verify that 1) PFS and 2) delay line yield same result
+            # . Both structures are in fact identical, because both use
+            #   parallel load of the xShifted data.
+            if not np.allclose(zLine, yLine):
+                exit('ERROR: wrong sythesis WOLA')
+
+            # Overlap add weigthed input to the output
+            tRange = np.arange(pfs.Ncoefs) + b * Nup
+            y[tRange] += zLine
+
+    else:
+        # No 'pfs' for fractional oversampled synthesis, because the
+        # interpolator cannot be separated in independent branches
+        # then. Expect when Ros is integer [CROCHIERE 7.2.4].
+        exit('ERROR: invalid structure ' + str(structure))
+
+    if verbosity:
+        print('> synthesis_dft_filterbank():')
+        print('  . Nblocks    =', str(Nblocks))
+        print('  . Ros        =', str(Ros))
+        print('  . Nup        =', str(Nup))
+        print('  . Ndft       =', str(Ndft))
+        print('  . structure  =', structure)
+        print('  . commutator =', commutator)
+        print('')
+    return y
diff --git a/applications/lofar2/model/rtdsp/multirate.py b/applications/lofar2/model/rtdsp/multirate.py
index 3c0bd409e4df12827849eadcc803b86632c4229d..7e9f14fee39426734a542b4cd2ed983d3d5a0f62 100644
--- a/applications/lofar2/model/rtdsp/multirate.py
+++ b/applications/lofar2/model/rtdsp/multirate.py
@@ -28,10 +28,10 @@
 #
 # References:
 # [1] dsp_study_erko.txt
-# [2] http://localhost:8888/notebooks/pfb_os/up_downsampling.ipynb
+# [2] pfb_os/up_downsampling.ipynb, pfb_os/multirate_mixer.ipynb
 #
 # Books:
-# . HARRIS 6 title Figure
+# . HARRIS 6
 # . LYONS
 # . CROCHIERE
 
@@ -54,6 +54,17 @@ def ones(shape, cmplx=False):
     return zeros(shape, cmplx) + 1
 
 
+def fold(x):
+    """Circular reverse sequence around index 0.
+
+    x[n] --> flip(x) --> x[N - n - 1]
+    x[n] --> fold(x) -> x[-n] = x[N - n] = roll(flip(x), 1)
+
+    The index is modulo N, so x[N - 0] = x[N] = x[0].
+    """
+    return np.roll(np.flip(x), 1)
+
+
 def down(x, D, phase=0):
     """Downsample x[n] by factor D, xD[m] = x[m D], m = n // D
 
@@ -137,8 +148,10 @@ class PolyPhaseFirFilterStructure:
                      [3, 7,11]]    3
              column:  0, 1, 2
 
-      Shift x[n] samples into delayLine from left, show sample index n in (n)
-      and delayLine index k in [k]. Shift in per sample or per block.
+      Shift x[n] samples into delayLine from left, show sample index n as value
+      in round brackets (n) and delayLine index k in [k] with square brackets.
+      Shift in per sample or per block. The block can also be as large as the
+      entire delayLine when the coefs are used as window.
 
         shift in -->(15,14,13,12,11,10, 9, 8, 7, 6, 5, 4) --> shift out
         delayLine = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11]
@@ -169,6 +182,7 @@ class PolyPhaseFirFilterStructure:
         self.Ncoefs = len(coefs)
         self.Nphases = Nphases  # branches, rows
         self.Ntaps = ceil_div(self.Ncoefs, Nphases)  # taps, columns
+        self.Ndelays = self.Ntaps * self.Nphases  # zero padded coefs
         self.cmplx = cmplx
         self.init_poly_coeffs()
         self.reset_poly_delays()
@@ -194,21 +208,24 @@ class PolyPhaseFirFilterStructure:
                                        Nphases = 4 rows (axis=0)
                                        Ntaps = 3 columns (axis=1)
         """
-        polyCoefs = np.zeros(self.Nphases * self.Ntaps)  # real
-        polyCoefs[0 : self.Ncoefs] = self.coefs
-        self.polyCoefs = polyCoefs.reshape((self.Ntaps, self.Nphases)).T
+        coefsLine = np.zeros(self.Ndelays)  # real coefficients values
+        coefsLine[0 : self.Ncoefs] = self.coefs
+        self.polyCoefs = coefsLine.reshape((self.Ntaps, self.Nphases)).T
 
     def reset_poly_delays(self):
         self.polyDelays = zeros((self.Nphases, self.Ntaps), self.cmplx)
 
-    def map_to_delay_line(self):
-        delayLine = self.polyDelays.T.reshape(-1)
+    def map_to_delay_line(self, polyDelays=None):
+        if polyDelays is None:
+            delayLine = self.polyDelays.T.reshape(-1)
+        else:
+            delayLine = polyDelays.T.reshape(-1)
         return delayLine
 
     def map_to_poly_delays(self, delayLine):
         self.polyDelays = delayLine.reshape((self.Ntaps, self.Nphases)).T
 
-    def shift_in_data(self, inData, flipped):
+    def shift_in_data(self, inData):
         """Shift block of data into the polyDelays structure.
 
         View polyDelays as delay line if L < Nphases. Shift in from the left
@@ -220,62 +237,58 @@ class PolyPhaseFirFilterStructure:
         . inData: Block of one or more input samples with index as time index
             n in inData[n], so oldest sample at index 0 and newest sample at
             index -1.
-        . flipped: False then inData order is inData[n] and still needs to be
-            flipped. If True then the inData is already flipped.
         """
         L = len(inData)
-        xData = inData if flipped else np.flip(inData)
-        if L < self.Nphases:
+        if L != self.Nphases:
             delayLine = self.map_to_delay_line()
             # Equivalent code:
             # delayLine = np.concatenate((inData, delayLine[L:]))
             delayLine = np.roll(delayLine, L)
-            delayLine[:L] = xData
+            delayLine[:L] = inData
             self.map_to_poly_delays(delayLine)
         else:
             # Equivalent code for L == Nphases: Shift in inData block directly
             # at column 0
             self.polyDelays = np.roll(self.polyDelays, 1, axis=1)
-            self.polyDelays[:, 0] = xData
+            self.polyDelays[:, 0] = inData
 
-    def filter_block(self, inData, flipped):
-        """Filter block of inData per polyphase.
+    def parallel_load(self, inData):
+        """Load block of data into each tap of the polyDelays structure.
+
+        Length L of inData is L = Nphases, so load the data directly at
+        each column of polyDelays.
 
         Input:
-        . inData: block of input data with length <= Nphases, time index n in
-            inData[n] increments, so inData[0] is oldest data and inData[-1] is
-            newest data. The inData block size is = 1 for upsampling or > 1
-            and <= Nphases for downsampling.
-        . flipped: False then inData order is inData[n] with newest sample last
-            and still needs to be flipped. If True then the inData is already
-            flipped in time, so with newest sample first.
-        Return:
-        . pfsData: block of polyphase FIR filtered output data for Nphases, with
-            pfsData[p] and p = 0:Nphases-1 from top to bottom.
+        . inData: Block of input samples with index as time index n in
+            inData[n], so oldest sample at index 0 and newest sample at
+            index -1.
         """
-        # Shift in one block of input data (1 <= len(inData) <= Nphases)
-        self.shift_in_data(inData, flipped)
-        # Apply FIR coefs per delay element
-        zData = self.polyDelays * self.polyCoefs
-        # Sum FIR taps per polyphase
-        pfsData = np.sum(zData, axis=1)
-        # Output block of Nphases filtered output data
-        return pfsData
+        for tap in range(self.Ntaps):
+            self.polyDelays[:, tap] = inData
+
+    def filter_block(self, inData):
+        """Filter block of inData per polyphase.
 
-    def filter_up(self, inSample):
-        """Filter same input sample at each polyphase, to upsample it by factor
-        U = Nphases.
+        For upsampling the inData should contain Nphases = Nup copies of the
+        input time sample.
+        For downsampling the inData should contain Nphases = Ndown input time
+        samples.
 
         Input:
-        . inSample: input sample that is applied to each polyphase
+        . inData: block of input data with length L. The time index n in
+            inData[n] increments, so inData[0] is oldest data and inData[-1]
+            is newest data.
         Return:
         . pfsData: block of polyphase FIR filtered output data for Nphases,
-            with pfsData[p] and p = 0:Nphases-1 from top to bottom. The
-            pfsData[0] is the first and thus oldest and pfsData[-1] is the last
-            and thus newest interpolated data.
+            with pfsData[p] and p = 0:Nphases-1 from top to bottom.
         """
-        inWires = inSample * ones(self.Nphases, np.iscomplexobj(inSample))
-        pfsData = self.filter_block(inWires, flipped=True)
+        # Shift in one block of input data
+        self.shift_in_data(inData)
+        # Apply FIR coefs per delay element
+        zPoly = self.polyDelays * self.polyCoefs
+        # Sum FIR taps per polyphase
+        pfsData = np.sum(zPoly, axis=1)
+        # Output block of Nphases filtered output data
         return pfsData
 
 
@@ -301,22 +314,22 @@ def polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros):
     Return:
     . polyX: polyphase data structure with size (Ndown, Nxp) for Ndown branches
         and Nxp samples from x per branch.
+    . lineX: Nx samples from x prepended with Nzeros, same content as polyX
     . Nx: Total number of samples from x, including prepended Ndown - 1 zeros.
     . Nxp: Total number of samples used from x per polyphase branch, is the
         number of samples Ny, that will be in downsampled output y[m] = x[m D],
         for m = 0, 1, 2, ..., Nxp - 1.
     """
     Lx = len(x)
-    Nxp = (Nzeros - 1 + Lx) // Ndown  # Number of samples per polyphase
-    Nx = 1 + Ndown * (Nxp - 1)  # Used number of samples from x
+    Nxp = (Nzeros + Lx) // Ndown  # Number of samples per polyphase
+    Nx = Ndown * Nxp - Nzeros  # Used number of samples from x
 
     # Load x into polyX with Ndown rows = polyphases
-    # . prepend x with Ndown - 1 zeros
+    # . prepend x with Nzeros zeros
     # . skip any last remaining samples from x, that are not enough yield a new
     #   output FIR sum.
-    polyX = zeros(Ndown * Nxp, np.iscomplexobj(x))
-    polyX[Ndown - 1] = x[0]
-    polyX[Ndown:] = x[1 : Nx]
+    lineX = zeros(Ndown * Nxp, np.iscomplexobj(x))
+    lineX[Nzeros:] = x[0 : Nx]
     # . Store data in time order per branch, so with oldest data left, to match
     #   use with lfilter(). Note this differs from order of tap data in
     #   pfs.polyDelays where newest data is left, because the block data is
@@ -339,8 +352,8 @@ def polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros):
     #            [3, 7,11]]            (12, 8, 4))           (0, 4, 8,12))
     #                                          v              |
     #                                                       oldest
-    polyX = np.flipud(polyX.reshape(Nxp, Ndown).T)
-    return polyX, Nx, Nxp
+    polyX = np.flipud(lineX.reshape(Nxp, Ndown).T)
+    return polyX, lineX, Nx, Nxp
 
 
 def polyphase_frontend(x, Nphases, coefs, sampling):
@@ -393,7 +406,7 @@ def polyphase_frontend(x, Nphases, coefs, sampling):
         #   length of each branch.
         Ndown = Nphases
         Nzeros = Ndown - 1
-        polyX, Nx, Nxp = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
+        polyX, _, Nx, Nxp = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
         # print(polyX[:, 0])
         # Filter Ndown parts of x per polyphase, because the FIR filter output
         # y will sum. The commutator index order for downsampling is p =
@@ -641,293 +654,3 @@ def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate an
         print('  . len(y) =', str(len(y)))
         print('')
     return y
-
-
-###############################################################################
-# Single bandpass channel up and downsampling and up and downconversion
-###############################################################################
-
-def unit_circle_loops_phasor_arr(k, N, sign):
-    """Return array of N phasors on k loops along the unit circle.
-
-    Polyphase dependent phase offsets for bin k, when N = Ndown = Ndft [HARRIS
-    Eq 6.8]. For k = 1 this yields the roots of unity.
-
-    Input:
-    . k: bin in range(N)
-    . N: number of phasors
-    . sign: +1 or -1 term in exp()
-    Return:
-    . pArr: exp(sign 2j pi k / N) for k in 0 : N - 1
-    """
-    pArr = np.array([np.exp(sign * 2j * np.pi * p * k / N) for p in range(N)])
-    return pArr
-
-
-def time_shift_phasor_arr(k, M, Ndft, Msamples, sign):
-    """Return array of Msamples phasors in time to compensate for oversampling
-    time shift.
-
-    The time shift due to down or upsampling causes a frequency component of
-    k * M / Ndft. With oversampling M < Ndft, and then after down or upsampling
-    there remains a frequency offset that can be compensated by given by mArr
-    [HARRIS Eq 9.3].
-
-    Input:
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
-    . M: downsample or upsample factor
-    . Ndft: DFT size, number of bins and number of polyphases in PFS FIR filter
-    . Msamples: Requested number of output samples in time
-    . sign: +1 or -1 term in exp()
-    Return:
-    . mArr = exp(sign 2j pi k * M / Ndft * m),  for m in 0 : Msamples - 1
-    """
-    mArr = np.exp(sign * 2j * np.pi * k * M / Ndft * np.arange(Msamples))
-    return mArr
-
-
-def maximal_downsample_bpf(x, Ndown, k, coefs, verbosity=1):
-    """BPF x at bin k in range(Ndown) and downsample x by factor D = Ndown and
-    Ndown = Ndft.
-
-    Implement maximal downsampling downconverter for one bin (= critically
-    sampled) [HARRIS Fig 6.14].
-
-    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
-    frequency bins, is DFT size. The downsampling is maximal so Ndown = Ndft.
-    The polyphase structure has Nphases = Ndown branches, so the input x
-    data that shifts in remains in each branch. Therefore each branch can be
-    FIR filtered independently for the whole input x using
-    polyphase_frontend().
-
-    Input:
-    . x: Input signal x[n]
-    . Ndown: downsample factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndown
-    . coefs: prototype FIR filter coefficients for anti aliasing BPF
-    - verbosity: when > 0 print() status, else no print()
-    Return:
-    . yc: Downsampled and downconverted output signal yc[m], m = n // D for
-          bin k. Complex baseband signal.
-    """
-    # Polyphase FIR filter input x
-    polyY, Nx, Nxp = polyphase_frontend(x, Ndown, coefs, 'down')
-
-    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
-    # [HARRIS Eq 6.8]
-    kPhasors = unit_circle_loops_phasor_arr(k, Ndown, 1)
-    polyYc = np.zeros((Ndown, Nxp), dtype='cfloat')
-    for p in range(Ndown):
-        polyYc[p] = polyY[p] * kPhasors[p]  # row = row * scalar
-
-    # Sum the branch outputs to get single downsampled and downconverted output
-    # complex baseband value yc.
-    yc = np.sum(polyYc, axis=0)
-
-    if verbosity:
-        print('> Log maximal_downsample_bpf():')
-        print('  . len(x)  =', str(len(x)))
-        print('  . Nx      =', str(Nx))
-        print('  . Nxp     =', str(Nxp))
-        print('  . len(yc) =', str(len(yc)))  # = Nxp
-        print('  . Ndown   =', str(Ndown))
-        print('  . k       =', str(k))
-        print('')
-    return yc
-
-
-def maximal_upsample_bpf(xBase, Nup, k, coefs, verbosity=1):
-    """BPF xBase at bin k in range(Ndft), and upsample xBase by factor U = Nup =
-    Ndft.
-
-    Implement maximal upsampling upconverter for one bin (= critically
-    sampled) [HARRIS Fig. 7.16].
-
-    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
-    frequency bins, is DFT size. The upsampling is maximal so Nup = Ndft. The
-    polyphase structure has Nphases = Nup branches, so the output yc data
-    shifts out from the same branch for each block of Nup samples. Therefore
-    each branch can be FIR filtered independently for the whole input xBase
-    using polyphase_frontend().
-
-    Input:
-    . xBase: Input equivalent baseband signal
-    . Nup: upsample factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Nup
-    . coefs: prototype FIR filter coefficients for anti aliasing and
-        interpolating BPF
-    - verbosity: when > 0 print() status, else no print()
-    Return:
-    . yc: Upsampled and upconverted output signal yc[n], n = m U at
-          intermediate frequency (IF) of bin k. Complex positive frequencies
-          only signal.
-    """
-    # Polyphase FIR filter input xBase
-    polyY, Nx, Nxp = polyphase_frontend(xBase, Nup, coefs, 'up')
-
-    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
-    # [HARRIS Eq 7.8 = 6.8, Fig 7.16], can be applied after the FIR filter,
-    # because the kPhasors per polyphase are constants.
-    kPhasors = unit_circle_loops_phasor_arr(k, Nup, 1)
-    polyYc = np.zeros((Nup, Nxp), dtype='cfloat')
-    for p in range(Nup):
-        polyYc[p] = polyY[p] * kPhasors[p]  # row = row * scalar
-
-    # Output Nup samples yc[m] for every input sample xBase[n]
-    yc = polyYc.T.reshape(1, Nup * Nx)[0]
-
-    if verbosity:
-        print('> Log maximal_upsample_bpf():')
-        print('  . len(xBase) =', str(len(xBase)))
-        print('  . Nx         =', str(Nx))
-        print('  . Nxp        =', str(Nxp))
-        print('  . len(yc)    =', str(len(yc)))  # = Nxp
-        print('  . Nup        =', str(Nup))
-        print('  . k          =', str(k))
-        print('')
-    return yc
-
-
-def non_maximal_downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
-    """BPF x at bin k in range(Ndown), and downsample x by factor D = Ndown.
-
-    Implement nonmaximal downsampling downconverter for one bin. The maximal
-    downsampling downconverter for one bin has the kPhasors per polyphase
-    branch of [HARRIS Eq. 6.8 and Fig 6.14]. For nonmaximal this needs to be
-    extended with the tPhasors per polyphase branch of [HARRIS Eq. 9.3, to
-    compensate for the oversampling time shift.
-
-    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
-    frequency bins, is DFT size. The polyphase FIR structure has Nphases = Ndft
-    branches, to fit the requested number of bins. The polyphase FIR structure
-    is maximally downsampled (= critically sampled) for Ndown = Ndft, but it
-    can support any Ndown <= Ndft. The input data shifts in per Ndown samples,
-    so it appears in different branches when Ndown < Ndft and a new block is
-    shifted in. Therefore the input data cannot be FIR filtered per branch for
-    the whole input x. Instead it needs to be FIR filtered per block of Ndown
-    input samples from x, using pfs.polyDelays in pfs.filter_block().
-
-    Input:
-    . x: Input signal x[n]
-    . Ndown: downsample factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
-    . Ndft: DFT size, number of polyphases in PFS FIR filter
-    . coefs: prototype LPF FIR filter coefficients for anti aliasing BPF
-    - verbosity: when > 0 print() status, else no print()
-    Return:
-    . yc: Downsampled and downconverted output signal yc[m], m = n // D for
-          bin k. Complex baseband signal.
-    """
-    # Prepend x with Ndown - 1 zeros, and represent x in Nblocks of Ndown
-    # samples
-    Nzeros = Ndown - 1
-    xBlocks, Nx, Nblocks = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
-    # print(xBlocks[:, 0])
-
-    # Prepare output
-    yc = np.zeros(Nblocks, dtype='cfloat')
-
-    # PFS with Ndft polyphases
-    pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
-    kPhasors = unit_circle_loops_phasor_arr(k, Ndft, 1)  # [HARRIS Eq 6.8]
-
-    # Oversampling time shift compensation via frequency dependent phase shift
-    tPhasors = time_shift_phasor_arr(k, Ndown, Ndft, Nblocks, -1)  # [HARRIS Eq 9.3]
-
-    for b in range(Nblocks):
-        # Filter block
-        inData = xBlocks[:, b]
-        pfsData = pfs.filter_block(inData, flipped=True)
-        # Phase rotate polyphases for bin k
-        pfsBinData = pfsData * kPhasors  # [HARRIS Eq 6.8, 9.3]
-        # Sum the polyphases to get single downsampled and downconverted output
-        # value
-        yc[b] = np.sum(pfsBinData) * tPhasors[b]
-
-    if verbosity:
-        print('> non_maximal_downsample_bpf():')
-        print('  . len(x)   =', str(len(x)))
-        print('  . Nx       =', str(Nx))
-        print('  . Nblocks  =', str(Nblocks))
-        print('  . len(yc)  =', str(len(yc)))  # = Nblocks
-        print('  . Ndown    =', str(Ndown))
-        print('  . Ndft     =', str(Ndft))
-        print('  . k        =', str(k))
-        print('')
-    return yc
-
-
-def non_maximal_upsample_bpf(xBase, Nup, k, Ndft, coefs, verbosity=1):
-    """BPF xBase at bin k in range(Nup), and upsample xBase by factor U = Nup.
-
-    Implement nonmaximal upsampling upconverter for one bin. The maximal
-    upsampling upconverter for one bin has the kPhasors per polyphase
-    branch similar of [HARRIS Fig 7.16]. For nonmaximal this needs to be
-    extended with the tPhasors per polyphase branch similar as for down in
-    [HARRIS Eq. 9.3], to compensate for the oversampling time shift.
-
-    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
-    frequency bins, is DFT size. The polyphase FIR structure has Nphases = Ndft
-    branches, to fit the requested number of bins. The polyphase FIR structure
-    is maximally upsampled (= critically sampled) for Nup = Ndft, but it
-    can support any Nup <= Ndft. The output data shifts out per Nup samples,
-    so it appears from different branches when Nup < Ndft and a new block is
-    shifted out. Therefore the output data cannot be FIR filtered per branch
-    for the whole input xBase. Instead it needs to be FIR filtered per block of
-    Nup output samples, using pfs.polyDelays in pfs.filter_up().
-
-    TODO
-    . This code only runs ok for Ros = 1, 2 when Ndft = 16, but not for
-      Ros = 4, so need to fix that. According to [CROCHIERE 7.2.7] the
-      polyphase structure is only suitable for Ros is integer >= 1. For other
-      Ros > 1 the weighted overlap-add (WOLA) structure is  suitable, so
-      need to add WOLA.
-
-    Input:
-    . xBase: Input equivalent baseband signal xBase[m]
-    . Nup: upsample factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
-    . Ndft: DFT size, number of polyphases in PFS FIR filter
-    . coefs: prototype LPF FIR filter coefficients for anti aliasing and
-             interpolationBPF
-    - verbosity: when > 0 print() status, else no print()
-    Return:
-    . yc: Upsampled and upconverted output signal yc[n], n = m U at
-          intermediate frequency (IF) of bin k. Complex positive frequencies
-          only signal.
-    """
-    Nblocks = len(xBase)
-
-    # Prepare output
-    polyYc = np.zeros((Nup, Nblocks), dtype='cfloat')
-
-    # PFS with Ndft polyphases
-    pfsUp = PolyPhaseFirFilterStructure(Nup, coefs, cmplx=True)
-    kPhasors = unit_circle_loops_phasor_arr(k, Ndft, 1)  # [HARRIS Eq 7.8]
-
-    # Oversampling time shift compensation via frequency dependent phase shift
-    tPhasors = time_shift_phasor_arr(k, Nup, Ndft, Nblocks, -1)  # [HARRIS Eq 9.3]
-
-    # Polyphase FIR filter input xBase
-    for b in range(Nblocks):
-        # Filter block
-        inSample = xBase[b]
-        pfsData = Nup * pfsUp.filter_up(inSample)
-        # Phase rotate polyphases for bin k
-        pfsBinData = pfsData * kPhasors[0:Nup]
-        # Output Nup samples yc[n] for every input sample xBase[m]
-        outBinData = pfsBinData * tPhasors[b]
-        polyYc[:, b] = outBinData
-
-    yc = polyYc.T.reshape(1, Nup * Nblocks)[0]
-
-    if verbosity:
-        print('> non_maximal_upsample_bpf():')
-        print('  . len(xBase) =', str(len(xBase)))
-        print('  . Nblocks    =', str(Nblocks))
-        print('  . len(yc)    =', str(len(yc)))  # = Nblocks
-        print('  . Nup        =', str(Nup))
-        print('  . Ndft       =', str(Ndft))
-        print('  . k          =', str(k))
-        print('')
-    return yc
diff --git a/applications/lofar2/model/rtdsp/singlechannel.py b/applications/lofar2/model/rtdsp/singlechannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..e51cbac059c80b2cb62dd95ac1406c987bdd1919
--- /dev/null
+++ b/applications/lofar2/model/rtdsp/singlechannel.py
@@ -0,0 +1,340 @@
+#! /usr/bin/env python3
+###############################################################################
+#
+# Copyright 2024
+# ASTRON (Netherlands Institute for Radio Astronomy) <http://www.astron.nl/>
+# P.O.Box 2, 7990 AA Dwingeloo, The Netherlands
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+###############################################################################
+
+# Author: Eric Kooistra
+# Purpose: Single channel downconverter downsampler and single channel
+#          upconverter upsampler, using LO
+# Description:
+#
+# Usage:
+# . Use [2] to verify this code.
+#
+# References:
+# [1] dsp_study_erko.txt
+# [2] pfb_os/multirate_mixer.ipynb
+#
+# Books:
+# . LYONS
+# . HARRIS
+# . CROCHIERE
+
+import numpy as np
+from .multirate import PolyPhaseFirFilterStructure, \
+                       polyphase_frontend, \
+                       polyphase_data_for_downsampling_whole_x
+
+
+def unit_circle_loops_phasor_arr(k, N, sign):
+    """Return array of N phasors on k loops along the unit circle.
+
+    Polyphase dependent phase offsets for bin k, when N = Ndown = Ndft [HARRIS
+    Eq 6.8]. For k = 1 this yields the roots of unity.
+
+    Input:
+    . k: bin in range(N)
+    . N: number of phasors
+    . sign: +1 or -1 term in exp()
+    Return:
+    . pArr: exp(sign 2j pi k / N) for k in 0 : N - 1
+    """
+    pArr = np.array([np.exp(sign * 2j * np.pi * p * k / N) for p in range(N)])
+    return pArr
+
+
+def time_shift_phasor_arr(k, M, Ndft, Msamples, sign):
+    """Return array of Msamples phasors in time to compensate for oversampling
+    time shift.
+
+    The time shift due to down or upsampling causes a frequency component of
+    k * M / Ndft. With oversampling M < Ndft, and then after down or upsampling
+    there remains a frequency offset that can be compensated given by mArr
+    [HARRIS Eq 9.3].
+
+    Input:
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . M: downsample or upsample factor
+    . Ndft: DFT size, number of bins and number of polyphases in PFS FIR filter
+    . Msamples: Requested number of output samples in time
+    . sign: +1 or -1 term in exp()
+    Return:
+    . mArr = exp(sign 2j pi k * M / Ndft * m),  for m in 0 : Msamples - 1
+    """
+    mArr = np.exp(sign * 2j * np.pi * k * M / Ndft * np.arange(Msamples))
+    return mArr
+
+
+def maximal_downsample_bpf(x, Ndft, k, coefs, verbosity=1):
+    """BPF x at bin k in range(Ndft) and downsample x by factor Ndown = Ndft.
+
+    Implement maximal downsampling downconverter for one bin (= critically
+    sampled) [HARRIS Fig 6.14].
+
+    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
+    frequency bins, is DFT size. The downsampling is maximal so Ndown = Ndft.
+    The polyphase structure has Nphases = Ndft branches, so the input x
+    data that shifts in remains in each branch. Therefore each branch can be
+    FIR filtered independently for the whole input x using
+    polyphase_frontend().
+
+    Input:
+    . x: Input signal x[n]
+    . Ndft: downsample factor, number of frequency bins
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . coefs: prototype FIR filter coefficients for anti aliasing BPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Downsampled and downconverted output signal yc[m], m = n // Ndown
+          for bin k. Complex baseband signal.
+    """
+    # Polyphase FIR filter input x
+    polyY, Nx, Nxp = polyphase_frontend(x, Ndft, coefs, 'down')
+
+    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
+    # [HARRIS Eq 6.8]
+    kPhasors = unit_circle_loops_phasor_arr(k, Ndft, 1)
+    polyYc = np.zeros((Ndft, Nxp), dtype='cfloat')
+    for p in range(Ndft):
+        polyYc[p] = polyY[p] * kPhasors[p]  # row = row * scalar
+
+    # Sum the branch outputs to get single downsampled and downconverted output
+    # complex baseband value yc.
+    yc = np.sum(polyYc, axis=0)
+
+    if verbosity:
+        print('> Log maximal_downsample_bpf():')
+        print('  . len(x)       =', str(len(x)))
+        print('  . Nx           =', str(Nx))
+        print('  . Nxp          =', str(Nxp))
+        print('  . len(yc)      =', str(len(yc)))  # = Nxp
+        print('  . Ndown = Ndft =', str(Ndft))
+        print('  . k            =', str(k))
+        print('')
+    return yc
+
+
+def maximal_upsample_bpf(xBase, Ndft, k, coefs, verbosity=1):
+    """BPF xBase at bin k in range(Ndft), and upsample xBase by factor Nup =
+    Ndft.
+
+    Implement maximal upsampling upconverter for one bin (= critically
+    sampled) [HARRIS Fig. 7.16].
+
+    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
+    frequency bins, is DFT size. The upsampling is maximal so Nup = Ndft. The
+    polyphase structure has Nphases = Ndft branches, so the output yc data
+    shifts out from the same branch for each block of Ndft samples. Therefore
+    each branch can be FIR filtered independently for the whole input xBase
+    using polyphase_frontend().
+
+    Input:
+    . xBase: Input equivalent baseband signal
+    . Ndft: upsample factor, number of frequency bins
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . coefs: prototype FIR filter coefficients for anti aliasing and
+        interpolating BPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Upsampled and upconverted output signal yc[n], n = m U at
+          intermediate frequency (IF) of bin k. Complex positive frequencies
+          only signal.
+    """
+    # Polyphase FIR filter input xBase
+    polyY, Nx, Nxp = polyphase_frontend(xBase, Ndft, coefs, 'up')
+
+    # Adjust LO phase for group delay of analysis LPF and synthesis LPF in
+    # series. This is not needed in practise, but is needed to be able to
+    # exactly compare reconstructed signal with original input time series
+    # signal. Assume coefs has group delay (len(coefs) - 1) / 2.
+    hPairGroupDelay = len(coefs) - 1
+    hPhasor = np.exp(-2j * np.pi * k / Ndft * hPairGroupDelay)
+    polyY *= hPhasor
+
+    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
+    # [HARRIS Eq 7.8 = 6.8, Fig 7.16], can be applied after the FIR filter,
+    # because the kPhasors per polyphase are constants.
+    kPhasors = unit_circle_loops_phasor_arr(k, Ndft, 1)
+    polyYc = np.zeros((Ndft, Nxp), dtype='cfloat')
+    for p in range(Ndft):
+        polyYc[p] = polyY[p] * kPhasors[p]  # row = row * scalar
+
+    # Output Ndft samples yc[m] for every input sample xBase[n]
+    yc = polyYc.T.reshape(1, Ndft * Nx)[0]
+
+    if verbosity:
+        print('> Log maximal_upsample_bpf():')
+        print('  . len(xBase) =', str(len(xBase)))
+        print('  . Nx         =', str(Nx))
+        print('  . Nxp        =', str(Nxp))
+        print('  . len(yc)    =', str(len(yc)))  # = Nxp
+        print('  . Nup = Ndft =', str(Ndft))
+        print('  . k          =', str(k))
+        print('')
+    return yc
+
+
+def non_maximal_downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
+    """BPF x at bin k in range(Ndown), and downsample x by factor D = Ndown.
+
+    Implement nonmaximal downsampling downconverter for one bin. The maximal
+    downsampling downconverter for one bin has the kPhasors per polyphase
+    branch of [HARRIS Eq. 6.8 and Fig 6.14]. For nonmaximal this needs to be
+    extended with the tPhasors per polyphase branch of [HARRIS Eq. 9.3, to
+    compensate for the oversampling time shift.
+
+    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
+    frequency bins, is DFT size. The polyphase FIR structure has Nphases = Ndft
+    branches, to fit the requested number of bins. The polyphase FIR structure
+    is maximally downsampled (= critically sampled) for Ndown = Ndft, but it
+    can support any Ndown <= Ndft. The input data shifts in per Ndown samples,
+    so it appears in different branches when Ndown < Ndft and a new block is
+    shifted in. Therefore the input data cannot be FIR filtered per branch for
+    the whole input x. Instead it needs to be FIR filtered per block of Ndown
+    input samples from x, using pfs.polyDelays in pfs.filter_block().
+
+    Input:
+    . x: Input signal x[n]
+    . Ndown: downsample factor
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . Ndft: DFT size, number of polyphases in PFS FIR filter
+    . coefs: prototype LPF FIR filter coefficients for anti aliasing BPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Downsampled and downconverted output signal yc[m], m = n // D for
+          bin k. Complex baseband signal.
+    """
+    Ros = Ndft / Ndown
+
+    # Prepend x with Ndown - 1 zeros, and represent x in Nblocks of Ndown samples
+    Nzeros = Ndown - 1
+    xBlocks, _, Nx, Nblocks = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
+
+    # Prepare output
+    yc = np.zeros(Nblocks, dtype='cfloat')
+
+    # PFS with Ndft polyphases
+    pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
+    kPhasors = unit_circle_loops_phasor_arr(k, Ndft, 1)  # [HARRIS Eq 6.8]
+
+    # Oversampling time shift compensation via frequency dependent phase shift
+    # [HARRIS Eq 9.3]
+    tPhasors = time_shift_phasor_arr(k, Ndown, Ndft, Nblocks, -1)
+
+    for b in range(Nblocks):
+        # Filter block
+        inData = xBlocks[:, b]
+        pfsData = pfs.filter_block(inData)
+        # Phase rotate polyphases for bin k
+        pfsBinData = pfsData * kPhasors  # [HARRIS Eq 6.8, 9.3]
+        # Sum the polyphases to get single downsampled and downconverted output
+        # value
+        yc[b] = np.sum(pfsBinData) * tPhasors[b]
+
+    if verbosity:
+        print('> non_maximal_downsample_bpf():')
+        print('  . len(x)   =', str(len(x)))
+        print('  . Nx       =', str(Nx))
+        print('  . Nblocks  =', str(Nblocks))
+        print('  . len(yc)  =', str(len(yc)))  # = Nblocks
+        print('  . Ros      =', str(Ros))
+        print('  . Ndown    =', str(Ndown))
+        print('  . Ndft     =', str(Ndft))
+        print('  . k        =', str(k))
+        print('')
+    return yc
+
+
+def non_maximal_upsample_bpf(xBase, Nup, k, Ndft, coefs, verbosity=1):
+    """BPF xBase at bin k in range(Nup), and upsample xBase by factor U = Nup.
+
+    TODO
+    . According to [CROCHIERE 7.2.7] the polyphase structure is only suitable
+      for Ros is integer >= 1.
+    . For other Ros > 1 the weighted overlap-add (WOLA) structure is suitable,
+      so need to add WOLA.
+
+    Remark:
+    . It is difficult to transpose the WOLA approach to a PFS approach that
+      suits any Ros > 1. For example, with blocks a, b, c, d, f, ... of Ndft
+      samples and block a = a0 a1 for Ros = 2, and replicating the block Ntaps
+      = 2 times in time, WOLA using rows:
+
+        a0 a1 a0 a1
+           b0 b1 b0 b1
+              c0 c1 c0 c1
+                 d0 d1 d0 d1
+                    e0 e1 e0 e1
+                       f0 f1 f0 f1
+                          g0 g1 g0 g1  --> time
+
+      results in PFS using columns:
+
+                 d0 e0 f0 g0
+                 c1 d1 e1 f1
+                 b0 c0 d0 e0
+                 a1 b1 c1 d1
+
+      For fractional Ros, e.g. Ros = 4/3, it can be tried to find a pattern
+      processing per column, but that is much more complicated then processing
+      per row as with WOLA (Ntaps = 2):
+
+        a0 a1 a2 a3 a0 a1 a2 a3
+                 b0 b1 b2 b3 b0 b1 b2 b3
+                          c0 c1 c2 c3 c0 c1 c2 c3
+                                   d0 d1 d2 d3 d0 d1 d2 d3
+                                            e0 e1 e2 e3 e0 e1 e2 e3
+                                                     f0 f1 f2 f3 f0 f1 f2 f3
+      results in PFS using columns:
+
+                          c0 c1 c2 d0 d1 d2 e0 e1 e2 f0 f1
+                          b3 b0 b1 c3 c0 c1 d3 d0 d1 e3 e0
+                          a2 a3    b2 b3    c2 c3    d2 d3
+
+      Extra complication is for e.g. Ros = 32 / 27, because 32 does not divide
+      (32 - 27). Anyway, for fractional Ros PFS approach seems not useful,
+      because the PFS is then changing for every block in time.
+
+    Input:
+    . xBase: Input equivalent baseband signal xBase[m]
+    . Nup: upsample factor
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . Ndft: DFT size, number of polyphases in PFS FIR filter
+    . coefs: prototype LPF FIR filter coefficients for anti aliasing and
+             interpolationBPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Upsampled and upconverted output signal yc[n], n = m U at
+          intermediate frequency (IF) of bin k. Complex positive frequencies
+          only signal.
+    """
+    Ros = Ndft // Nup
+    # if Ndft % Nup:
+    #     print('WARNING: Only support integer Ros')
+    #     return None
+
+    if verbosity:
+        print('> non_maximal_upsample_bpf():')
+        print('  . len(xBase) =', str(len(xBase)))
+        print('  . Ros        =', str(Ros))
+        print('  . Nup        =', str(Nup))
+        print('  . Ndft       =', str(Ndft))
+        print('  . k          =', str(k))
+        print('')
+    return None
diff --git a/applications/lofar2/model/rtdsp/utilities.py b/applications/lofar2/model/rtdsp/utilities.py
index d8dae69e01eca0ec0a2ce0aa0583a4d80b1c1a9a..ed74455f142335285555eb070a2c766893d95378 100644
--- a/applications/lofar2/model/rtdsp/utilities.py
+++ b/applications/lofar2/model/rtdsp/utilities.py
@@ -37,11 +37,13 @@ c_rtol = 1e-8   # 1/2**32 = 2.3e-10
 # Utilities
 ###############################################################################
 
-def verify_result(result):
+def verify_result(result, msg='', enExit=True):
     if result:
-        print('PASSED')
+        print('PASSED' + msg)
     else:
-        exit('FAILED')
+        print('FAILED' + msg)
+        if enExit:
+            exit()
 
 
 def ceil_div(num, den):
@@ -64,6 +66,11 @@ def pow_db(volts):
     return 20 * np.log10(np.abs(volts) + c_atol)
 
 
+def snr_db(signalData, noiseData):
+    """Signal to noise ration in dB"""
+    return pow_db(np.std(signalData) / (np.std(noiseData) + c_atol))
+
+
 def is_integer_value(value):
     """Return true when value is sufficiently close to its integer value."""
     return np.isclose(value, np.round(value))
@@ -108,9 +115,9 @@ def read_coefficients_file(filepathname):
     with open(filepathname, 'r') as fp:
         for line in fp:
             if line.strip():  # skip empty line
-                s = int(line)   # one coef per line
+                s = float(line)   # one coef per line
                 coefs.append(s)
-    return coefs
+    return np.array(coefs)
 
 
 def one_bit_quantizer(x):