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authorjaseg <git-bigdata-wsl-arch@jaseg.de>2020-02-16 18:27:43 +0000
committerjaseg <git-bigdata-wsl-arch@jaseg.de>2020-02-16 18:28:10 +0000
commit1fe557760df0d3ec1fb9702cdac2c8cd284959b6 (patch)
treea664adb74e722f7dadd472fbb4c25528af9f5d6d
parente9f7c87d38b214183b87fa7846339c320282b36c (diff)
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demod prototype
-rw-r--r--lab-windows/dsss_experiments.ipynb320
1 files changed, 282 insertions, 38 deletions
diff --git a/lab-windows/dsss_experiments.ipynb b/lab-windows/dsss_experiments.ipynb
index bb56362..9566edf 100644
--- a/lab-windows/dsss_experiments.ipynb
+++ b/lab-windows/dsss_experiments.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -63,13 +63,13 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7560730a2391425ab9dad7a1f22e5fb2",
+ "model_id": "2450440508db4069b3df05fa08346b39",
"version_major": 2,
"version_minor": 0
},
@@ -90,10 +90,10 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f0496c50b80>"
+ "<matplotlib.image.AxesImage at 0x7fe4abaf7490>"
]
},
- "execution_count": 5,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -105,13 +105,14 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def modulate(data, nbits=5, code=29):\n",
" # 0, 1 -> -1, 1\n",
" mask = gold(nbits)[code]*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"
@@ -119,13 +120,13 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
- "def correlate(sequence, nbits=5, code=29, decimation=1):\n",
+ "def correlate(sequence, nbits=5, code=29, decimation=1, mask_filter=lambda x: x):\n",
" # 0, 1 -> -1, 1\n",
- " mask = np.repeat(gold(nbits)[code]*2 -1, decimation)\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')"
@@ -133,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -146,7 +147,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "eb7e7e5d7dfe4e00b18c4e5038c11182",
+ "model_id": "750255b26cc74aa0974d288fda4c7142",
"version_major": 2,
"version_minor": 0
},
@@ -167,10 +168,10 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f0494537dc0>]"
+ "[<matplotlib.lines.Line2D at 0x7fe4aa733850>]"
]
},
- "execution_count": 8,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -183,7 +184,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -196,7 +197,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c693349edfe843d6adab192d6a95c4dd",
+ "model_id": "996e7034b52d47409ad4548c354b72bd",
"version_major": 2,
"version_minor": 0
},
@@ -218,10 +219,10 @@
{
"data": {
"text/plain": [
- "(2.0, 1.0311014124075548)"
+ "(2.0, 1.0490216904842018)"
]
},
- "execution_count": 9,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -265,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -285,7 +286,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -294,21 +295,14 @@
"text": [
"(63,) (63,)\n",
"(63,) (63,)\n",
+ "(63,) (63,)\n",
"(63,) (63,)\n"
]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<ipython-input-54-34e6ee3f3fc5>:22: 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"
- ]
- },
- {
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f58125333c294cb1b426b735829c30c5",
+ "model_id": "abdce9c2d307402f8578eb83d9ce9b79",
"version_major": 2,
"version_minor": 0
},
@@ -325,7 +319,7 @@
"(0.002, 0.012591236)"
]
},
- "execution_count": 54,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -338,19 +332,21 @@
"foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
"noise = np.resize(mains_noise, len(foo))\n",
"\n",
- "sosh = sig.butter(12, 0.05, btype='highpass', output='sos', fs=decimation)\n",
- "sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\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",
"#filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
"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",
"\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",
"#cor2_flt = sig.sosfilt(sosn, cor2)\n",
- "cor1_flt = cor1[1:] - cor1[:-1]\n",
- "cor2_flt = cor2[1:] - cor2[:-1]\n",
+ "#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.tight_layout()\n",
@@ -371,17 +367,265 @@
"ax4.set_title('corr filtered')\n",
"ax4.grid()\n",
"\n",
- "ax5.plot(cor1_flt)\n",
- "ax5.set_title('corr raw (highpass)')\n",
- "ax5.grid()\n",
+ "#ax5.plot(cor1_flt)\n",
+ "#ax5.set_title('corr raw (highpass)')\n",
+ "#ax5.grid()\n",
+ "\n",
+ "#ax6.plot(cor2_flt)\n",
+ "#ax6.set_title('corr filtered (highpass)')\n",
+ "#ax6.grid()\n",
"\n",
- "ax6.plot(cor2_flt)\n",
- "ax6.set_title('corr filtered (highpass)')\n",
+ "ax6.plot(cor2_pe)\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)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8feea8e305004d33a39f2541a59a0ffa",
+ "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": [
+ "(63,) (63,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fe4a9934c40>]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "seq = np.repeat(gold(6)[29]*2 -1, decimation)\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",
+ "seq_filtered = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, seq))\n",
+ "#seq_filtered = sig.sosfilt(sosh, seq)\n",
+ "\n",
+ "ax.plot(seq)\n",
+ "ax.plot(seq_filtered)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "01df90b27d57470d9216ae9c549413c8",
+ "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": [
+ "(63,) (63,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fe4a9596070>]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(3, 1, figsize=(9, 7), sharex=True)\n",
+ "fig.tight_layout()\n",
+ "axs = axs.flatten()\n",
+ "for ax in axs:\n",
+ " ax.grid()\n",
+ "\n",
+ "seq = np.repeat(gold(6)[29]*2 -1, decimation)\n",
+ "sosh = sig.butter(3, 0.1, btype='highpass', output='sos', fs=decimation)\n",
+ "sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
+ "cor2_pe_flt = sig.sosfilt(sosh, cor2_pe)\n",
+ "cor2_pe_flt2 = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, cor2_pe))\n",
+ "\n",
+ "axs[0].plot(cor2_pe)\n",
+ "axs[1].plot(cor2_pe_flt)\n",
+ "axs[2].plot(cor2_pe_flt2)\n",
+ "\n",
+ "#for idx in np.where(np.abs(cor2_pe_flt2) > 0.5)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-49-9776d553457e>:5: 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"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "183f086844054252bc8ec77f7b99abfc",
+ "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"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "30db7a9c75834d64a31bf8e0fb5f5911",
+ "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 0x7fe482e19190>]"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "threshold_factor = 5.0\n",
+ "\n",
+ "import ipywidgets\n",
+ "\n",
+ "cor_an = cor1\n",
+ "\n",
+ "fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n",
+ "fig.tight_layout()\n",
+ "\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",
+ "\n",
+ "ax3.grid()\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",
+ "\n",
+ "import itertools\n",
+ "th = np.convolve(np.abs(cwt_res), np.ones((500,))/500, 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",
+ "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",
+ " ax3.axvline(pos, color='red', alpha=0.5)\n",
+ " ax3.text(pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
+ " \n",
+ "ax3.plot(th)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "43b48925433b4464928805812cfebc24",
+ "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"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fe4a120ba60>]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(2, 1, figsize=(9, 7))\n",
+ "fig.tight_layout()\n",
+ "axs = axs.flatten()\n",
+ "for ax in axs:\n",
+ " ax.grid()\n",
+ " \n",
+ "axs[0].plot(cor2_pe_flt2[1::10] - cor2_pe_flt2[:-1:10])\n",
+ "a, b = cor2_pe_flt2[1::10] - cor2_pe_flt2[:-1:10], np.array([0.0, -0.5, 1.0, -0.5, 0.0])\n",
+ "axs[1].plot(np.convolve(a, b, mode='full'))"
+ ]
}
],
"metadata": {