MLE multibit decoding works
This commit is contained in:
jaseg
parent
e722744689
commit
61accdf087
1 changed files with 328 additions and 150 deletions
|
|
@ -72,7 +72,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "a4f8421f05544016854b22a49dbc3698",
|
||||
"model_id": "01394154cc52483e9d4483f1178d94c3",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -93,7 +93,7 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.image.AxesImage at 0x7f8fd0fdaac0>"
|
||||
"<matplotlib.image.AxesImage at 0x7fcfd7369250>"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
|
|
@ -112,33 +112,20 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def modulate(data, nbits=5, code=29):\n",
|
||||
"def modulate(data, nbits=5):\n",
|
||||
" # 0, 1 -> -1, 1\n",
|
||||
" mask = gold(nbits)[code]*2 - 1\n",
|
||||
" mask = np.array(gold(nbits))*2 - 1\n",
|
||||
" \n",
|
||||
" # same here\n",
|
||||
" data_centered = (data*2 - 1)\n",
|
||||
" return (mask[:, np.newaxis] @ data_centered[np.newaxis, :] + 1).T.flatten() //2"
|
||||
" sel = mask[data>>1]\n",
|
||||
" data_lsb_centered = ((data&1)*2 - 1)\n",
|
||||
"\n",
|
||||
" return (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def correlate(sequence, nbits=5, code=29, decimation=1, mask_filter=lambda x: x):\n",
|
||||
" # 0, 1 -> -1, 1\n",
|
||||
" mask = mask_filter(np.repeat(gold(nbits)[code]*2 -1, decimation))\n",
|
||||
" # center\n",
|
||||
" sequence -= np.mean(sequence)\n",
|
||||
" return np.correlate(sequence, mask, mode='full')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
|
|
@ -147,10 +134,73 @@
|
|||
"(31,) (31,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([-1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1,\n",
|
||||
" 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1,\n",
|
||||
" 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1,\n",
|
||||
" 1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1,\n",
|
||||
" -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1,\n",
|
||||
" 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1,\n",
|
||||
" 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1,\n",
|
||||
" -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1,\n",
|
||||
" -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data = np.array(list(range(16)))\n",
|
||||
"\n",
|
||||
"mask = np.array(gold(5))*2 - 1\n",
|
||||
" \n",
|
||||
"sel = mask[data>>1]\n",
|
||||
"data_lsb_centered = ((data&1)*2 - 1)\n",
|
||||
"mask.shape, data.shape, sel.shape\n",
|
||||
"\n",
|
||||
"#fig, ax = plt.subplots()\n",
|
||||
"#ax.plot(\n",
|
||||
"np.multiply(sel, np.tile(data_lsb_centered, (2**5-1, 1)).T).flatten()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x):\n",
|
||||
" mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
|
||||
"\n",
|
||||
" sequence -= np.mean(sequence)\n",
|
||||
" \n",
|
||||
" return np.array([np.correlate(sequence, row, mode='full') for row in mask])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(31,) (31,)\n",
|
||||
"(31,) (31,)\n",
|
||||
"shapes (1240,) (1240,)\n",
|
||||
"(31,) (31,)\n",
|
||||
"mask (33, 310)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c99513c0fb7f4b138367186127e379cf",
|
||||
"model_id": "182fd5ac86e74ad299a67e5f1d0b2b2b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -171,36 +221,43 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f8fce86df40>]"
|
||||
"<matplotlib.image.AxesImage at 0x7fcfd6c90070>"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"foo = modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0])).astype(float)\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.plot(correlate(foo))"
|
||||
"nbits = 5\n",
|
||||
"decimation = 10\n",
|
||||
"\n",
|
||||
"foo = np.repeat(modulate(np.array(list(range(4))), nbits).astype(float), decimation)\n",
|
||||
"bar = np.repeat(modulate(np.array(list(range(4))), nbits) * 2.0 - 1, decimation) * 1e-3\n",
|
||||
"print('shapes', foo.shape, bar.shape)\n",
|
||||
"\n",
|
||||
"mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
|
||||
"print('mask', mask.shape)\n",
|
||||
"\n",
|
||||
"fig, (ax1, ax2) = plt.subplots(2, figsize=(16, 5))\n",
|
||||
"fig.tight_layout()\n",
|
||||
"corr_m = np.array([np.correlate(foo, row, mode='full') for row in mask])\n",
|
||||
"#corr_m = np.array([row for row in mask])\n",
|
||||
"ax1.matshow(corr_m, aspect='auto')\n",
|
||||
"#ax.matshow(foo.reshape(32, 31)[::2,:])\n",
|
||||
"ax2.matshow(correlate(bar, decimation=decimation), aspect='auto')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(31,) (31,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "93658d824ced42e5b1107501398234b4",
|
||||
"model_id": "4cb2661eebb84478b06d285166ec13bc",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -222,10 +279,74 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(2.0, 0.944245383185962)"
|
||||
"<matplotlib.image.AxesImage at 0x7fcfd6c6cfa0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"decimation = 10\n",
|
||||
"\n",
|
||||
"fig, (ax1, ax2) = plt.subplots(2, figsize=(12, 5))\n",
|
||||
"fig.tight_layout()\n",
|
||||
"\n",
|
||||
"#mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
|
||||
"#mask_stretched = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, 1)).reshape((2**nbits + 1, (2**nbits-1) * 1))\n",
|
||||
"\n",
|
||||
"#ax1.matshow(mask)\n",
|
||||
"#ax2.matshow(mask_stretched, aspect='auto')\n",
|
||||
"\n",
|
||||
"foo = np.repeat(modulate(np.array(list(range(4)))).astype(float), 1).reshape((4, 31))\n",
|
||||
"foo_stretched = np.repeat(modulate(np.array(list(range(4)))).astype(float), 10).reshape(4, 310)\n",
|
||||
"\n",
|
||||
"ax1.matshow(foo)\n",
|
||||
"ax2.matshow(foo_stretched, aspect='auto')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(31,) (31,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "a2e2f747193b478bbfa792a0995ad4ed",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(31,) (31,)\n",
|
||||
"(31,) (31,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(2.0, 1.0234353995297893)"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -269,7 +390,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -289,15 +410,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(31,) (31,)\n",
|
||||
"(31,) (31,)\n",
|
||||
"(31,) (31,)\n",
|
||||
"(31,) (31,)\n"
|
||||
]
|
||||
|
|
@ -306,14 +425,14 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-76-c15a6a1f5988>:27: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n"
|
||||
"<ipython-input-96-b3aae757ccad>:33: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c24adc84c7294295a47a58aa7d5914f9",
|
||||
"model_id": "246c19a3c1424e7eb0b675ff060ea5b3",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -327,24 +446,30 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.001, 0.010294564)"
|
||||
"(0.002, 0.013899708)"
|
||||
]
|
||||
},
|
||||
"execution_count": 76,
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"decimation = 10\n",
|
||||
"signal_amplitude = 1.0e-3\n",
|
||||
"signal_amplitude = 2.0e-3\n",
|
||||
"nbits = 5\n",
|
||||
"\n",
|
||||
"#test_data = np.random.randint(0, 2, 100)\n",
|
||||
"test_data = np.array([0, 1, 0, 0, 1, 1, 1, 0])\n",
|
||||
"#test_data = np.array([0, 1, 0, 0, 1, 1, 1, 0])\n",
|
||||
"#test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**nbits), 64)\n",
|
||||
"#test_data = np.random.RandomState(seed=0).randint(0, 8, 64)\n",
|
||||
"#test_data = np.array(list(range(8)) * 8)\n",
|
||||
"#test_data = np.array([0, 1] * 32)\n",
|
||||
"test_data = np.array(list(range(64)))\n",
|
||||
"\n",
|
||||
"foo = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
|
||||
"noise = np.resize(mains_noise, len(foo))\n",
|
||||
"#noise = 0\n",
|
||||
"\n",
|
||||
"sosh = sig.butter(3, 0.01, btype='highpass', output='sos', fs=decimation)\n",
|
||||
"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
|
||||
|
|
@ -352,9 +477,9 @@
|
|||
"filtered = sig.sosfilt(sosh, foo + noise)\n",
|
||||
"\n",
|
||||
"cor1 = correlate(foo + noise, nbits=nbits, decimation=decimation)\n",
|
||||
"cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
|
||||
"#cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
|
||||
"\n",
|
||||
"cor2_pe = correlate(filtered, nbits=nbits, decimation=decimation, mask_filter=lambda mask: sig.sosfilt(sosh, sig.sosfiltfilt(sosl, mask)))\n",
|
||||
"#cor2_pe = correlate(filtered, nbits=nbits, decimation=decimation, mask_filter=lambda mask: sig.sosfilt(sosh, sig.sosfiltfilt(sosl, mask)))\n",
|
||||
"\n",
|
||||
"sosn = sig.butter(12, 4, btype='highpass', output='sos', fs=decimation)\n",
|
||||
"#cor1_flt = sig.sosfilt(sosn, cor1)\n",
|
||||
|
|
@ -362,7 +487,7 @@
|
|||
"#cor1_flt = cor1[1:] - cor1[:-1]\n",
|
||||
"#cor2_flt = cor2[1:] - cor2[:-1]\n",
|
||||
"\n",
|
||||
"fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n",
|
||||
"fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n",
|
||||
"fig.tight_layout()\n",
|
||||
"\n",
|
||||
"ax1.plot(foo + noise)\n",
|
||||
|
|
@ -373,13 +498,14 @@
|
|||
"ax2.plot(foo)\n",
|
||||
"ax2.set_title('filtered')\n",
|
||||
"\n",
|
||||
"ax3.plot(cor1)\n",
|
||||
"ax3.plot(cor1.T)\n",
|
||||
"ax3.set_title('corr raw')\n",
|
||||
"ax3.grid()\n",
|
||||
"\n",
|
||||
"ax4.plot(cor2)\n",
|
||||
"ax4.set_title('corr filtered')\n",
|
||||
"ax4.grid()\n",
|
||||
"#ax4.plot(cor2[:4].T)\n",
|
||||
"#ax4.set_title('corr filtered')\n",
|
||||
"#ax4.grid()\n",
|
||||
"ax4.matshow(cor1, aspect='auto')\n",
|
||||
"\n",
|
||||
"#ax5.plot(cor1_flt)\n",
|
||||
"#ax5.set_title('corr raw (highpass)')\n",
|
||||
|
|
@ -389,9 +515,9 @@
|
|||
"#ax6.set_title('corr filtered (highpass)')\n",
|
||||
"#ax6.grid()\n",
|
||||
"\n",
|
||||
"ax6.plot(cor2_pe)\n",
|
||||
"ax6.set_title('corr filtered w/ mask preemphasis')\n",
|
||||
"ax6.grid()\n",
|
||||
"#ax6.plot(cor2_pe[:4].T)\n",
|
||||
"#ax6.set_title('corr filtered w/ mask preemphasis')\n",
|
||||
"#ax6.grid()\n",
|
||||
"\n",
|
||||
"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
|
||||
"rms(foo), rms(noise)"
|
||||
|
|
@ -399,13 +525,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "1c8e27744cd0482782fa0d65ed550ba6",
|
||||
"model_id": "c08b2a1dbdef429eb22b598bd3dc0146",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -426,10 +552,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f8fc2cab1f0>]"
|
||||
"[<matplotlib.lines.Line2D at 0x7fcfd1635700>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -449,13 +575,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "483d7acbb9604c9c93533d342d6068ad",
|
||||
"model_id": "20117316e02548a99386c39e45e71ef1",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -474,14 +600,15 @@
|
|||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f8fc2679310>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'cor2_pe' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-15-f158dfc14cca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0msosh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbutter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'highpass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sos'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0msosl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbutter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lowpass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sos'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcor2_pe_flt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcor2_pe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mcor2_pe_flt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfiltfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcor2_pe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'cor2_pe' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
|
@ -506,21 +633,21 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-77-478546893e6f>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
"<ipython-input-57-3e7dc7c98d30>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "298c99e458364426829979eeaf01af6e",
|
||||
"model_id": "25fe274dea83415491fd7f86d38188d7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -534,38 +661,38 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f8f9968fa00>]"
|
||||
"[<matplotlib.lines.Line2D at 0x7fcf84cb4a60>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 77,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+7)**2\n",
|
||||
"nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2\n",
|
||||
"x = np.linspace(-1.5, 5.5, 10000)\n",
|
||||
"ax.plot(x, nonlinear_distance(x))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"execution_count": 97,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-78-ad8374b3e684>:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n"
|
||||
"<ipython-input-97-2d2c2f814215>:11: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, (ax1, ax3) = plt.subplots(2, figsize=(12, 5))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "126c195977aa4639a8026e0a857a8ab8",
|
||||
"model_id": "b083c661b5b441d6b7fc45201faa0576",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -577,127 +704,178 @@
|
|||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5a40fd3c41814b5f99e9f452c8923db4",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"interactive(children=(FloatSlider(value=10.0, description='w', max=30.0, min=-10.0), Output()), _dom_classes=(…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f8fa59086d0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 78,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cor_an (33, 20149)\n",
|
||||
"cwt_res (33, 20149)\n",
|
||||
"th (33, 20149)\n",
|
||||
"[((33,), (33,)), ((33,), (33,)), ((33,), (33,)), ((33,), (33,)), ((33,), (33,))]\n",
|
||||
"peaks: 180\n",
|
||||
"avg_peak 1.058897833824206\n",
|
||||
"skipped 3 symbols at 5889.0\n",
|
||||
"skipped 2 symbols at 8369.0\n",
|
||||
"skipped 2 symbols at 14568.5\n",
|
||||
"skipped 2 symbols at 16739.0\n",
|
||||
"decoding [ref|dec]:\n",
|
||||
" -1| -1 ✔ 1| 1 ✔ 2| 2 ✔ 3| 3 ✔ 4| 4 ✔ 5| 5 ✔ 6| 6 ✔ 7| 7 ✔ \n",
|
||||
" 8| 8 ✔ 9| 9 ✔ 10| 10 ✔ 11| 11 ✔ 12| 12 ✔ 13| 13 ✔ 14| 14 ✔ 15| 15 ✔ \n",
|
||||
" 16| -1 ✘ 17| -1 ✘ 18| 18 ✔ 19| 19 ✔ 20| 20 ✔ 21| 21 ✔ 22| 22 ✔ 23| 23 ✔ \n",
|
||||
" 24| 24 ✔ 25| -1 ✘ 26| 26 ✔ 27| 27 ✔ 28| 28 ✔ 29| 29 ✔ 30| 30 ✔ 31| 31 ✔ \n",
|
||||
" 32| 32 ✔ 33| 33 ✔ 34| 34 ✔ 35| 35 ✔ 36| 36 ✔ 37| 37 ✔ 38| 38 ✔ 39| 39 ✔ \n",
|
||||
" 40| 40 ✔ 41| 41 ✔ 42| 42 ✔ 43| 43 ✔ 44| 44 ✔ 45| -1 ✘ 46| 46 ✔ 47| 47 ✔ \n",
|
||||
" 48| 48 ✔ 49| 49 ✔ 50| 50 ✔ 51| 51 ✔ 52| -1 ✘ 53| 53 ✔ 54| 54 ✔ 55| 55 ✔ \n",
|
||||
" 56| 56 ✔ 57| 57 ✔ 58| 58 ✔ 59| 59 ✔ 60| 60 ✔ 61| 61 ✔ 62| 62 ✔ 63| 56 ✘ \n",
|
||||
"Symbol error rate r=0.09375\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"threshold_factor = 2.0\n",
|
||||
"threshold_factor = 4.0\n",
|
||||
"power_avg_width = 1024\n",
|
||||
"max_lookahead = 6.5\n",
|
||||
"\n",
|
||||
"bit_period = (2**nbits) * decimation\n",
|
||||
"peak_group_threshold = 0.1 * bit_period\n",
|
||||
"\n",
|
||||
"cor_an = cor1\n",
|
||||
"\n",
|
||||
"fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n",
|
||||
"#fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n",
|
||||
"fig, (ax1, ax3) = plt.subplots(2, figsize=(12, 5))\n",
|
||||
"fig.tight_layout()\n",
|
||||
"\n",
|
||||
"ax1.matshow(sig.cwt(cor_an, sig.ricker, np.arange(1, 31)), aspect='auto')\n",
|
||||
"#ax1.matshow(sig.cwt(cor_an, sig.ricker, np.arange(1, 31)), aspect='auto')\n",
|
||||
"\n",
|
||||
"for i in np.linspace(1, 10, 19):\n",
|
||||
" offx = 5*i\n",
|
||||
" ax2.plot(sig.cwt(cor_an, sig.ricker, [i]).flatten() + offx, color='red')\n",
|
||||
"\n",
|
||||
" ax2.text(-50, offx, f'{i:.1f}',\n",
|
||||
" horizontalalignment='right',\n",
|
||||
" verticalalignment='center',\n",
|
||||
" color='black')\n",
|
||||
"ax2.grid()\n",
|
||||
"#for i in np.linspace(1, 10, 19):\n",
|
||||
"# offx = 5*i\n",
|
||||
"# ax2.plot(sig.cwt(cor_an, sig.ricker, [i]).flatten() + offx, color='red')\n",
|
||||
"#\n",
|
||||
"# ax2.text(-50, offx, f'{i:.1f}',\n",
|
||||
"# horizontalalignment='right',\n",
|
||||
"# verticalalignment='center',\n",
|
||||
"# color='black')\n",
|
||||
"#ax2.grid()\n",
|
||||
"\n",
|
||||
"ax3.grid()\n",
|
||||
"print('cor_an', cor_an.shape)\n",
|
||||
"\n",
|
||||
"cwt_res = sig.cwt(cor_an, sig.ricker, [7.3]).flatten()\n",
|
||||
"line, = ax3.plot(cwt_res)\n",
|
||||
"def update(w=10.0):\n",
|
||||
" line.set_ydata(sig.cwt(cor_an, sig.ricker, [w]).flatten())\n",
|
||||
" fig.canvas.draw_idle()\n",
|
||||
"ipywidgets.interact(update)\n",
|
||||
"cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n",
|
||||
"ax3.plot(cwt_res.T)\n",
|
||||
"#def update(w = 1.0 * decimation):\n",
|
||||
"# line.set_ydata(sig.cwt(cor_an, sig.ricker, [w]).flatten())\n",
|
||||
"# fig.canvas.draw_idle()\n",
|
||||
"#ipywidgets.interact(update)\n",
|
||||
"\n",
|
||||
"print('cwt_res', cwt_res.shape)\n",
|
||||
"th = np.array([ np.convolve(np.abs(row), np.ones((power_avg_width,))/power_avg_width, mode='same') for row in cwt_res ])\n",
|
||||
"ax1.plot(th.T)\n",
|
||||
"print('th', th.shape)\n",
|
||||
"\n",
|
||||
"th = np.convolve(np.abs(cwt_res), np.ones((power_avg_width,))/power_avg_width, mode='same')\n",
|
||||
"peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th, cwt_res)), lambda elem: abs(elem[1][1]) > elem[1][0]*threshold_factor) if val ]\n",
|
||||
"def compare_th(elem):\n",
|
||||
" idx, (th, val) = elem\n",
|
||||
" #print('compare_th:', th.shape, val.shape)\n",
|
||||
" return np.any(np.abs(val) > th*threshold_factor)\n",
|
||||
"\n",
|
||||
"print([ (a.shape, b.shape) for a, b in zip(th.T, cwt_res.T) ][:5])\n",
|
||||
"\n",
|
||||
"peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th.T, cwt_res.T)), compare_th) if val ]\n",
|
||||
"print('peaks:', len(peaks))\n",
|
||||
"peak_group = []\n",
|
||||
"for group in peaks:\n",
|
||||
" pos = np.mean([idx for idx, _val in group])\n",
|
||||
" pol = np.mean([val for _idx, (_th, val) in group])\n",
|
||||
" pol = np.mean([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group])\n",
|
||||
" pol_idx = np.argmax(np.bincount([ np.argmax(np.abs(val)) for _idx, (_th, val) in group ]))\n",
|
||||
" #print(f'group', pos, pol, pol_idx)\n",
|
||||
" #for pol, (_idx, (_th, val)) in zip([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], group):\n",
|
||||
" # print(' ', pol, val)\n",
|
||||
" ax3.axvline(pos, color='cyan', alpha=0.3)\n",
|
||||
" \n",
|
||||
" if not peak_group or pos - peak_group[-1][1] > peak_group_threshold:\n",
|
||||
" if peak_group:\n",
|
||||
" peak_pos = peak_group[-1][3]\n",
|
||||
" ax3.axvline(peak_pos, color='red', alpha=0.3)\n",
|
||||
" ax3.axvline(peak_pos, color='red', alpha=0.6)\n",
|
||||
" #ax3.text(peak_pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
|
||||
" \n",
|
||||
" peak_group.append((pos, pos, pol, pos))\n",
|
||||
" peak_group.append((pos, pos, pol, pos, pol_idx))\n",
|
||||
" #ax3.axvline(pos, color='cyan', alpha=0.5)\n",
|
||||
" \n",
|
||||
" else:\n",
|
||||
" group_start, last_pos, last_pol, peak_pos = peak_group[-1]\n",
|
||||
" group_start, last_pos, last_pol, peak_pos, last_pol_idx = peak_group[-1]\n",
|
||||
" \n",
|
||||
" if abs(pol) > abs(last_pol):\n",
|
||||
" #ax3.axvline(pos, color='magenta', alpha=0.5)\n",
|
||||
" peak_group[-1] = (group_start, pos, pol, pos)\n",
|
||||
" peak_group[-1] = (group_start, pos, pol, pos, pol_idx)\n",
|
||||
" else:\n",
|
||||
" #ax3.axvline(pos, color='blue', alpha=0.5)\n",
|
||||
" peak_group[-1] = (group_start, pos, last_pol, peak_pos)\n",
|
||||
" peak_group[-1] = (group_start, pos, last_pol, peak_pos, last_pol_idx)\n",
|
||||
"\n",
|
||||
"avg_peak = np.mean(np.abs(np.array([last_pol for _1, _2, last_pol, _3, _4 in peak_group])))\n",
|
||||
"print('avg_peak', avg_peak)\n",
|
||||
"\n",
|
||||
"noprint = lambda *args, **kwargs: None\n",
|
||||
"def mle_decode(peak_groups, print=print):\n",
|
||||
" peak_groups = [ (pos, pol) for _1, _2, pol, pos in peak_groups ]\n",
|
||||
" candidates = [ [(pos, pol)] for pos, pol in peak_groups if pos < bit_period*1.5 ]\n",
|
||||
" peak_groups = [ (pos, pol, idx) for _1, _2, pol, pos, idx in peak_groups ]\n",
|
||||
" candidates = [ (0, [(pos, pol, idx)]) for pos, pol, idx in peak_groups if pos < bit_period*2.5 ]\n",
|
||||
" \n",
|
||||
" while candidates:\n",
|
||||
" chain_candidates = []\n",
|
||||
" for chain in candidates:\n",
|
||||
" pos, ampl = chain[-1]\n",
|
||||
" score_fun = lambda pos, npos, npol: abs(npol)/2 + nonlinear_distance((npos-pos)/bit_period)\n",
|
||||
" next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol) for npos, npol in peak_groups if pos < npos < pos + bit_period*3.5 ], reverse=True)\n",
|
||||
" for chain_score, chain in candidates:\n",
|
||||
" pos, ampl, _idx = chain[-1]\n",
|
||||
" score_fun = lambda pos, npos, npol: abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
|
||||
" next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
|
||||
" \n",
|
||||
" print(f' candidates for {pos}, {ampl}:')\n",
|
||||
" for score, npos, npol in next_candidates:\n",
|
||||
" print(f' {score:.4f} {npos:.2f} {npol:.2f}')\n",
|
||||
" for score, npos, npol, nidx in next_candidates:\n",
|
||||
" print(f' {score:.4f} {npos:.2f} {npol:.2f} {nidx:.2f}')\n",
|
||||
" \n",
|
||||
" if len(cor_an) - pos < 1.5*bit_period or not next_candidates:\n",
|
||||
" score = sum(score_fun(opos, npos, npol) for (opos, _opol), (npos, npol) in zip(chain[:-1], chain[1:])) / (len(chain)-1)\n",
|
||||
" nch, cor_len = cor_an.shape\n",
|
||||
" if cor_len - pos < 1.5*bit_period or not next_candidates:\n",
|
||||
" score = sum(score_fun(opos, npos, npol) for (opos, _opol, _oidx), (npos, npol, _nidx) in zip(chain[:-1], chain[1:])) / len(chain)\n",
|
||||
" yield score, chain\n",
|
||||
" \n",
|
||||
" else:\n",
|
||||
" for score, npos, npol in next_candidates[:3]:\n",
|
||||
" print('extending')\n",
|
||||
" for score, npos, npol, nidx in next_candidates[:3]:\n",
|
||||
" if score > 0.5:\n",
|
||||
" chain_candidates.append((score, chain + [(npos, npol)]))\n",
|
||||
" new_chain_score = chain_score * 0.9 + score * 0.1\n",
|
||||
" chain_candidates.append((new_chain_score, chain + [(npos, npol, nidx)]))\n",
|
||||
" print('chain candidates:')\n",
|
||||
" for score, chain in sorted(chain_candidates, reverse=True):\n",
|
||||
" print(' ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol in chain])])\n",
|
||||
" candidates = [ chain for _score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n",
|
||||
" print(' ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol, _idx in chain])])\n",
|
||||
" candidates = [ (chain_score, chain) for chain_score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n",
|
||||
"\n",
|
||||
"res = sorted(mle_decode(peak_group, print=lambda *args, **kwargs: None), reverse=True)\n",
|
||||
"res = sorted(mle_decode(peak_group, print=noprint), reverse=True)\n",
|
||||
"#for i, (score, chain) in enumerate(res):\n",
|
||||
"# print(f'Chain {i}@{score:.4f}: {chain}')\n",
|
||||
"(_score, chain), *_ = res\n",
|
||||
"for pos, pol in chain:\n",
|
||||
" ax3.axvline(pos, color='blue', alpha=0.5)\n",
|
||||
" ax3.text(pos-20, 0.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
|
||||
"\n",
|
||||
"ax3.plot(th)"
|
||||
"def viz(chain):\n",
|
||||
" last_pos = None\n",
|
||||
" for pos, pol, nidx in chain:\n",
|
||||
" if last_pos:\n",
|
||||
" delta = int(round((pos - last_pos) / bit_period))\n",
|
||||
" if delta > 1:\n",
|
||||
" print(f'skipped {delta} symbols at {pos}')\n",
|
||||
" for i in range(delta-1):\n",
|
||||
" yield None\n",
|
||||
" ax3.axvline(pos, color='blue', alpha=0.5)\n",
|
||||
" decoded = nidx*2 + (0 if pol < 0 else 1)\n",
|
||||
" yield decoded\n",
|
||||
" ax3.text(pos-20, 0.0, f'{decoded}', horizontalalignment='right', verticalalignment='center', color='black')\n",
|
||||
"\n",
|
||||
" last_pos = pos\n",
|
||||
"\n",
|
||||
"decoded = list(viz(chain))\n",
|
||||
"print('decoding [ref|dec]:')\n",
|
||||
"failures = 0\n",
|
||||
"for i, (ref, found) in enumerate(itertools.zip_longest(test_data, decoded)):\n",
|
||||
" print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\"}', end=' ')\n",
|
||||
" if ref != found:\n",
|
||||
" failures += 1\n",
|
||||
" if i%8 == 7:\n",
|
||||
" print()\n",
|
||||
"print(f'Symbol error rate r={failures/len(test_data)}')\n",
|
||||
"#ax3.plot(th)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue