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-rw-r--r--lab-windows/dsss_experiments-ber.ipynb198
1 files changed, 134 insertions, 64 deletions
diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb
index ce87548..a12b5a4 100644
--- a/lab-windows/dsss_experiments-ber.ipynb
+++ b/lab-windows/dsss_experiments-ber.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -29,7 +29,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -73,7 +73,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -90,7 +90,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -104,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -124,7 +124,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -139,7 +139,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -153,13 +153,13 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8a090f2d31104ec695a0c68bf39555d9",
+ "model_id": "c5fba7c7cec24f09a4f23f7c7d87eb90",
"version_major": 2,
"version_minor": 0
},
@@ -177,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -186,7 +186,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -366,13 +366,13 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b961e7d9571748fd8a58a5579f95743d",
+ "model_id": "5106e62521e246a0b0766cce3f9e556f",
"version_major": 2,
"version_minor": 0
},
@@ -386,10 +386,10 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7275b7807450>]"
+ "[<matplotlib.lines.Line2D at 0x7fc200d359d0>]"
]
},
- "execution_count": 26,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -397,6 +397,7 @@
"source": [
"fig, ax = plt.subplots(figsize=(12, 9))\n",
"\n",
+ "duration = 64\n",
"decimation = 100\n",
"extra_dec = 10\n",
"nbits = 5\n",
@@ -443,7 +444,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -453,13 +454,13 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "cebd805ecee34a05b229922af1abfe58",
+ "model_id": "15e67925b1bf47b5bbde289b353ed201",
"version_major": 2,
"version_minor": 0
},
@@ -473,12 +474,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "981c5c08524e4b56b72df4ca01e724da",
+ "model_id": "6432dbccc7db43d7a227384bca772716",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
+ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {},
@@ -490,22 +491,22 @@
"text": [
"nbits=5\n",
"nbits=6\n",
- "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
+ "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
"signal_amplitude=0.00015: ser=0.98958 ±0.00737, br=5.85938\n",
"signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
+ "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
"signal_amplitude=0.00032: ser=0.97396 ±0.00737, br=14.64844\n",
- "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
"signal_amplitude=0.00068: ser=0.94792 ±0.01949, br=29.29688\n",
"signal_amplitude=0.00100: ser=0.78125 ±0.02552, br=123.04688\n",
"signal_amplitude=0.00147: ser=0.28646 ±0.03683, br=401.36719\n",
"signal_amplitude=0.00215: ser=0.06250 ±0.03375, br=527.34375\n",
"signal_amplitude=0.00316: ser=0.00521 ±0.00737, br=559.57031\n",
- "signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
"signal_amplitude=0.00015: ser=0.97917 ±0.01473, br=7.03125\n",
+ "signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
"signal_amplitude=0.00022: ser=0.98438 ±0.00000, br=5.27344\n",
"signal_amplitude=0.00032: ser=0.98438 ±0.00000, br=5.27344\n",
- "signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
"signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
+ "signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
"signal_amplitude=0.00100: ser=0.15104 ±0.05156, br=286.52344\n",
"signal_amplitude=0.00147: ser=0.01562 ±0.00000, br=332.22656\n",
"signal_amplitude=0.00215: ser=0.01562 ±0.00000, br=332.22656\n",
@@ -518,10 +519,10 @@
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7275bfe3b490>"
+ "<matplotlib.legend.Legend at 0x7fc23a223550>"
]
},
- "execution_count": 28,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -594,13 +595,13 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3e654922b013420d83e47b27c43e18dc",
+ "model_id": "bda9f8070b0142928936a7752f318e97",
"version_major": 2,
"version_minor": 0
},
@@ -614,10 +615,10 @@
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7275bd43da50>"
+ "<matplotlib.legend.Legend at 0x7fc20046b730>"
]
},
- "execution_count": 29,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -650,13 +651,13 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "23d3975515a0462e94a01e7b3c5dd1a6",
+ "model_id": "33b1f777dda149eda3a2f031087372c9",
"version_major": 2,
"version_minor": 0
},
@@ -671,15 +672,95 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/usr/lib64/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
- " out=out, **kwargs)\n",
- "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n",
- "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
- " keepdims=keepdims)\n",
- "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n",
- " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
- "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+ " return _methods._mean(a, axis=axis, dtype=dtype,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " ret = ret.dtype.type(ret / rcount)\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+ " ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+ " arrmean = um.true_divide(\n",
+ "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
}
@@ -780,7 +861,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -789,13 +870,13 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "62259928c3a24739a4b03c9b9a83859a",
+ "model_id": "406505452bb84597a6c39bc12267dcc6",
"version_major": 2,
"version_minor": 0
},
@@ -810,7 +891,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log10\n"
+ "<ipython-input-25-50856f807a2a>:20: RuntimeWarning: divide by zero encountered in log10\n",
+ " cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n"
]
}
],
@@ -877,7 +959,7 @@
" for amp, ser, std in sorted(sers):\n",
" if last_ser > SER_TH and ser < SER_TH:\n",
" icp = last_amp + (SER_TH - last_ser) / (ser - last_ser) * (amp - last_amp)\n",
- " ic_std = abs(last_amp - amp) / 2# np.sqrt(np.mean(last_std**2 + std**2))\n",
+ " ic_std = (abs(last_amp - amp) / 2) + np.sqrt(np.mean(last_std**2 + std**2))\n",
" intercepts[dec] = (icp, ic_std)\n",
" break\n",
" last_amp, last_ser = amp, ser\n",
@@ -892,7 +974,6 @@
" data = np.array([ intercepts[dec] for dec in x ])\n",
" y = data[:,0]\n",
" std = data[:,1]\n",
- " \n",
" ax.set_xlim([min(x), max(x)])\n",
" y = [ v*1000 if v is not None else v for v in y ]\n",
" l = ax.plot(x, y, label='Amplitude at SER=0.5 [mHz]', color='orange')\n",
@@ -930,20 +1011,9 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "41.6"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"13 * 2**5 / 10"
]
@@ -951,9 +1021,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "winlabenv",
+ "display_name": "labenv",
"language": "python",
- "name": "winlabenv"
+ "name": "labenv"
},
"language_info": {
"codemirror_mode": {
@@ -965,7 +1035,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.8.1"
}
},
"nbformat": 4,
generated by cgit (git 2.34.1) at 2024-11-15 03:24:55 +0000