diff options
Diffstat (limited to 'notebooks')
| -rw-r--r-- | notebooks/FrameSynchronization.ipynb | 73 |
1 files changed, 35 insertions, 38 deletions
diff --git a/notebooks/FrameSynchronization.ipynb b/notebooks/FrameSynchronization.ipynb index a659a7b..e0187b2 100644 --- a/notebooks/FrameSynchronization.ipynb +++ b/notebooks/FrameSynchronization.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "5d5acf43", + "id": "57a55a8c", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ }, { "cell_type": "markdown", - "id": "4b294887", + "id": "dc4b9dfd", "metadata": {}, "source": [ "# Digital Frame Synchronization\n" @@ -24,22 +24,21 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "5bec50ff", - "metadata": {}, + "execution_count": 27, + "id": "025c6919", + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Header sequence (N=16): [1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1]\n", - "Cross correlation: [ 3. 4. 5. 5. 6. 4. 4. 6. 4. 4. 5. 8. 7. 10. 8. 7. 8. 10.\n", - " 7. 8. 8. 9. 14. 11. 9. 10. 12. 9. 13. 9. 9. 11. 13. 9. 12. 11.\n", - " 9. 9. 13. 10. 9. 11. 9. 14. 12. 9. 10. 11. 8. 11. 6. 9. 8. 11.\n", - " 6. 7. 9. 8. 7. 4. 4. 9. 9. 7. 6. 7. 9. 11. 8. 7. 10. 12.\n", - " 10. 11. 11. 10. 9. 10. 9. 7. 10. 7. 9. 6. 7. 6. 5. 4. 5. 2.\n", - " 4. 3.]\n", - "Correlation peak value: 14.0\n" + "Header (N=16): [1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1]\n", + "Stream (N=80): [0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1\n", + " 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 1 0\n", + " 0 1 1 1 0 1]\n", + "Correlation peak value: 16 at i=47\n" ] }, { @@ -48,13 +47,13 @@ "<StemContainer object of 3 artists>" ] }, - "execution_count": 2, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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"text/plain": [ "<Figure size 1800x360 with 1 Axes>" ] @@ -67,42 +66,40 @@ ], "source": [ "# Create test data\n", - "seq = np.array([(0xbeef >> i) & 0x1 for i in range(4 * 4)])\n", - "# seq = np.array([1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1])\n", - "\n", + "seq = np.unpackbits(np.array([0xbe, 0xef], dtype=np.dtype(\"uint8\")))\n", "stream = np.concatenate([\n", - " np.random.randint(low=0, high=2, size=30), seq, np.random.randint(low=0, high=2, size=30)\n", + " np.random.randint(low=0, high=2, size=32), seq, np.random.randint(low=0, high=2, size=32)\n", "])\n", "\n", - "print(f\"Header sequence (N={len(seq)}): {seq}\")\n", - "# print(f\"Data stream: {stream}\")\n", + "print(f\"Header (N={len(seq)}): {seq}\")\n", + "print(f\"Stream (N={len(stream)}): {stream}\")\n", "\n", - "# compute cross correlation\n", - "def distance(v):\n", - " return len(seq) - sum(np.logical_xor(v, seq))\n", - "\n", - "fifo = RingBuffer(len(seq))\n", - "xcorr = RingBuffer(len(stream) + len(seq))\n", + "# Create buffers for cross correlation\n", + "fifo = RingBuffer(len(seq), dtype=np.dtype(\"uint8\"))\n", + "xcorr = RingBuffer(len(stream) + len(seq), dtype=np.dtype(\"uint8\"))\n", "\n", "## fill FIFO with zeros\n", "fifo.extend(np.zeros(fifo.maxlen))\n", "\n", - "for i in range(len(stream) + len(seq)):\n", - " #print(np.array(fifo))\n", - " xcorr.append(distance(np.array(fifo)))\n", + "def correlation(v):\n", + " n = len(seq)\n", + " d = np.logical_xor(v, seq) # or bitwise_xor, no difference in this case\n", + " return n - sum(d)\n", + " \n", + "for i in range(len(stream) + len(seq) + 1):\n", + " xcorr.append(correlation(np.array(fifo)))\n", " \n", + " # append stream data\n", " # if the stream is finished use zeros\n", - " fifo.pop()\n", - " if i < len(stream):\n", - " fifo.appendleft(stream[i])\n", - " else:\n", - " fifo.appendleft(0)\n", + " fifo.append(stream[i] if i < len(stream) else 0)\n", "\n", - "print(f\"Cross correlation: {np.array(xcorr)}\")\n", - "print(f\"Correlation peak value: {max(np.array(xcorr))}\")\n", + "# unwrap values\n", + "xc = np.array(xcorr)\n", + "# print(f\"Cross correlation: {xc}\")\n", + "print(f\"Correlation peak value: {np.amax(xc)} at i={np.argmax(xc)}\")\n", "\n", "plt.figure(figsize = (25, 5))\n", - "plt.stem(np.array(xcorr))" + "plt.stem(xc)" ] } ], |