diff --git a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
index d35c21c8e611cb0017bbab2a46454613c7b89588..fd330d8f3e82985cc236c41914e9a059598470ae 100644
--- a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
+++ b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 1,
    "id": "02689e50",
    "metadata": {},
    "outputs": [],
@@ -33,19 +33,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 2,
    "id": "65235f50",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Auto reload module when it is changed\n",
     "%load_ext autoreload\n",
@@ -61,13 +52,13 @@
     "# Import rtdsp\n",
     "from rtdsp.firfilter import filterbank_frequency_response\n",
     "from rtdsp.fourier import dtft\n",
-    "from rtdsp.multirate import down, up, downsample_bpf\n",
+    "from rtdsp.multirate import down, up, maximal_downsample_bpf\n",
     "from rtdsp.plotting import plot_power_spectrum, plot_magnitude_spectrum"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 329,
+   "execution_count": 3,
    "id": "c49515de",
    "metadata": {},
    "outputs": [],
@@ -80,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 330,
+   "execution_count": 4,
    "id": "c5c90a6b",
    "metadata": {},
    "outputs": [],
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
+   "execution_count": 5,
    "id": "6d3a14bc",
    "metadata": {},
    "outputs": [],
@@ -113,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 6,
    "id": "9aa3a1ae",
    "metadata": {},
    "outputs": [],
@@ -134,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 7,
    "id": "0a69b385",
    "metadata": {},
    "outputs": [
@@ -165,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": 8,
    "id": "0b979a1f",
    "metadata": {},
    "outputs": [
@@ -229,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": 9,
    "id": "d76e42f5",
    "metadata": {},
    "outputs": [],
@@ -245,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 336,
+   "execution_count": 10,
    "id": "48d4a3b3",
    "metadata": {},
    "outputs": [],
@@ -253,7 +244,7 @@
     "# Carriers\n",
     "# . freq = center subband yields constant baseband I, Q signal\n",
     "# . phase = 0 yields Q = 0\n",
-    "subbands = np.array([1.1])  # in range(Nsub)\n",
+    "subbands = np.array([1.0])  # in range(Nsub)\n",
     "freqs = subbands * fsub  # in Hz\n",
     "phases = [0.0]  # in degrees\n",
     "ampl = 1\n",
@@ -264,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 337,
+   "execution_count": 11,
    "id": "0ae28649",
    "metadata": {},
    "outputs": [
@@ -272,9 +263,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "powCarriers = 0.500001\n",
+      "powCarriers = 0.500000\n",
       "powNoise = 0.000000\n",
-      "SNR = 100.959877 dB\n"
+      "SNR = 100.959873 dB\n"
      ]
     }
    ],
@@ -308,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 338,
+   "execution_count": 12,
    "id": "adc33e70",
    "metadata": {},
    "outputs": [],
@@ -333,12 +324,14 @@
    "id": "127b23a8",
    "metadata": {},
    "source": [
-    "# 3.1 LO --> LPF --> D "
+    "# 3.1 Full rate: LO --> LPF --> D\n",
+    "\n",
+    "Down convert bin kLo to baseband, then LPF still at sample rate and then downsample [HARRIS Fig 6.2]."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 339,
+   "execution_count": 13,
    "id": "9d3fb1c8",
    "metadata": {},
    "outputs": [],
@@ -351,23 +344,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 340,
+   "execution_count": 14,
    "id": "50334d52",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fcf63144040>"
+       "<matplotlib.legend.Legend at 0x7f202f761760>"
       ]
      },
-     "execution_count": 340,
+     "execution_count": 14,
      "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>"
       ]
@@ -398,12 +391,16 @@
    "id": "06436339",
    "metadata": {},
    "source": [
-    "## 3.2 BPF --> D --> LOdown"
+    "## 3.2 LO at downsampled rate: BPF --> D --> LOdown\n",
+    "\n",
+    "Use BPF centered at kLo (is LPF shifted by +kLo) still at sample rate, then downsample and do down conversion by from kLo to baseband at downsampled rate [HARRIS Fig 6.7].\n",
+    "\n",
+    "If Ndown = Ndft, then D * w_k = D * 2pi * k / Ndft is multiple of 2pi, so then LOdown = 1."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 341,
+   "execution_count": 15,
    "id": "25317d4e",
    "metadata": {},
    "outputs": [
@@ -436,7 +433,7 @@
     "print('D_w_k =', D_w_k)\n",
     "print('')\n",
     "\n",
-    "# Check that LO data rotates with w_k and LO down with D * w_k rad/s\n",
+    "# Verify that LO data rotates with w_k and LO down with D * w_k rad/s\n",
     "if np.all(np.isclose(LOdown, loD)):\n",
     "    print('PASSED')\n",
     "else:\n",
@@ -449,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 342,
+   "execution_count": 16,
    "id": "6f50284f",
    "metadata": {},
    "outputs": [
@@ -462,7 +459,7 @@
     }
    ],
    "source": [
-    "# If Ndown == Ndft then LOdown == 1\n",
+    "# Verify that LOdown == 1 when Ndown == Ndft\n",
     "if Ndown == Ndft:\n",
     "    if np.all(np.isclose(LOdown, 1.0)):\n",
     "        print('PASSED')\n",
@@ -475,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 343,
+   "execution_count": 17,
    "id": "3ae19d32",
    "metadata": {},
    "outputs": [
@@ -506,14 +503,22 @@
    "id": "f0642906",
    "metadata": {},
    "source": [
-    "## 3.3 D --> poly BPF --> LOdown\n",
+    "## 3.3 BPF and LO at downsampled rate: D --> poly BPF --> LOdown\n",
     "\n",
-    ". LOdown = 1 if D = Ndft"
+    "Partition the BPF FIR filter H(z) in Ndown polyphases to have Hp(z^Ndown) per polyphase branch p, so that the down sampling can be done before the BPF by using the Noble identity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe3e5fb8",
+   "metadata": {},
+   "source": [
+    "### 3.3.1 Maximally downsampled (= critically sampled)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 18,
    "id": "7a8f3e37",
    "metadata": {},
    "outputs": [
@@ -521,13 +526,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Ndft  = 16\n",
       "> downsample_bpf():\n",
       ". len(x) = 160\n",
       ". Ndown  = 16\n",
       ". Nx     = 145\n",
       ". Nxp    = 10\n",
       ". len(y) = 10\n",
-      ". Ndft   = 16\n",
       ". k      = 1.0\n",
       "\n",
       "PASSED\n"
@@ -535,24 +540,33 @@
     }
    ],
    "source": [
-    "yDownBpf = downsample_bpf(xData, Ndown, kLo, Ndft, hPrototype)\n",
-    "yDownBpfLo = yDownBpf * LOdown\n",
+    "print('Ndft  =', Ndft)\n",
+    "if Ndown == Ndft:\n",
+    "    yDownBpf = maximal_downsample_bpf(xData, Ndown, kLo, hPrototype)\n",
+    "    yDownBpfLo = yDownBpf  # = yDownBpf * LOdown, because LOdown = 1 when Ndown == Ndft\n",
     "\n",
-    "if np.all(np.isclose(yDown, yDownBpfLo)):\n",
-    "    # True for any Ndft, Ndown, because LOdown is in equation of yBpfDownLo\n",
-    "    print('PASSED')\n",
-    "else:\n",
-    "    print('FAILED')\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 np.all(np.isclose(yDown, yDownBpfLo)):\n",
+    "        print('PASSED')\n",
+    "    else:\n",
+    "        print('FAILED')\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--')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73a63d82",
+   "metadata": {},
+   "source": [
+    "### 3.3.2 Fractionally downsampled (= oversampled)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d005a8e8",
+   "id": "6dfb2975",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/applications/lofar2/model/rtdsp/multirate.py b/applications/lofar2/model/rtdsp/multirate.py
index 6c86e08de661eef80e8198238250bda5dc4171ac..378d53a54277974b8c764b7a80073706251f1c1b 100644
--- a/applications/lofar2/model/rtdsp/multirate.py
+++ b/applications/lofar2/model/rtdsp/multirate.py
@@ -184,7 +184,7 @@ def poly_structure_size_for_downsampling_whole_x(Lx, Ndown):
 
     Input:
     . Lx: len(x)
-    . Ndown: downsample rate
+    . Ndown: downsample rate and number of polyphase branches
     Return:
     . Nx: Total number of samples from x, including prepended Ndown - 1 zeros.
     . Nxp: Total number of samples used from x per polyphase branch, is Ny the number of samples that will be in
@@ -210,8 +210,11 @@ def poly_structure_data_for_downsampling_whole_x(x, Ndown):
     Lx = len(x)
     Nx, Nxp = poly_structure_size_for_downsampling_whole_x(Lx, Ndown)
 
+    # Load x into polyX
+    # . prepend x with Ndown - 1 zeros
+    # . skip any last remaining samples from x, that are not enough yield a new output FIR sum.
     polyX = np.zeros(Ndown * Nxp)
-    polyX[Ndown - 1] = x[0]  # prepend x with Ndown - 1 zeros
+    polyX[Ndown - 1] = x[0]
     polyX[Ndown:] = x[1 : Nx]
     polyX = polyX.reshape(Nxp, Ndown).T
     return polyX, Nx, Nxp
@@ -460,18 +463,19 @@ def downsample(x, Ndown, coefs, verify=False, verbosity=1):  # decimate
     return y
 
 
-def downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
-    """BPF at bin k of Ndft and downsample x by factor D = Ndown [HARRIS Fig 6.14]
+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 [HARRIS Fig 6.14]
 
-    Implement downsampling down converter for one bin [HARRIS Fig 6.14].
+    Implement maximal downsampling down converter for one bin [HARRIS Fig 6.14].
+
+    The downsampling is maximal so Ndown = Ndft is number of frequency bins, is DFT size.
 
     . see downsample()
 
     Input:
     . x: Input signal x[n]
-    . Ndown: downsample (decimation) factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
-    . Ndft: number of frequency bins, DFT size
+    . 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:
@@ -498,9 +502,13 @@ def downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
         polyY[p] = signal.lfilter(polyCoefs[p], a, polyX[p])
 
     # Phase rotate per polyphase, due to delay line at branch inputs [HARRIS Eq 6.8]
+    # . polyY can use index p, because order in polyY accounts for commutator,
+    # . phase rotator needs to use pCommutator to account for commutator, to fit
+    #   order in polyY and polyCoefs
     polyYC = np.zeros((Ndown, Nxp), dtype='cfloat')
     for p in range(Ndown):
-        polyYC[p] = polyY[p] * np.exp(1j * 2 * np.pi * (Ndown - 1 - p) * k / Ndown)
+        pCommutator = Ndown - 1 - p
+        polyYC[p] = polyY[p] * np.exp(1j * 2 * np.pi * pCommutator * k / Ndown)
 
     # Sum the branch outputs to get single downsampled and downconverted output value
     y = np.sum(polyYC, axis=0)
@@ -512,7 +520,6 @@ def downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
         print('. Nx     =', str(Nx))
         print('. Nxp    =', str(Nxp))
         print('. len(y) =', str(len(y)))  # = Nxp
-        print('. Ndft   =', str(Ndft))
         print('. k      =', str(k))
         print('')
     return y