demod prototype
This commit is contained in:
jaseg
parent
e9f7c87d38
commit
1fe557760d
1 changed files with 282 additions and 38 deletions
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"cells": [
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 1,
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7560730a2391425ab9dad7a1f22e5fb2",
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"model_id": "2450440508db4069b3df05fa08346b39",
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"version_major": 2,
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"version_minor": 0
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f0496c50b80>"
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"<matplotlib.image.AxesImage at 0x7fe4abaf7490>"
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]
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},
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"execution_count": 5,
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"def modulate(data, nbits=5, code=29):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = gold(nbits)[code]*2 - 1\n",
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" \n",
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" # same here\n",
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" data_centered = (data*2 - 1)\n",
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" return (mask[:, np.newaxis] @ data_centered[np.newaxis, :] + 1).T.flatten() //2"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def correlate(sequence, nbits=5, code=29, decimation=1):\n",
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"def correlate(sequence, nbits=5, code=29, decimation=1, mask_filter=lambda x: x):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = np.repeat(gold(nbits)[code]*2 -1, decimation)\n",
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" mask = mask_filter(np.repeat(gold(nbits)[code]*2 -1, decimation))\n",
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" # center\n",
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" sequence -= np.mean(sequence)\n",
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" return np.correlate(sequence, mask, mode='full')"
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@ -133,7 +134,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "eb7e7e5d7dfe4e00b18c4e5038c11182",
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"model_id": "750255b26cc74aa0974d288fda4c7142",
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"version_major": 2,
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"version_minor": 0
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f0494537dc0>]"
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"[<matplotlib.lines.Line2D at 0x7fe4aa733850>]"
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]
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},
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"execution_count": 8,
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c693349edfe843d6adab192d6a95c4dd",
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"model_id": "996e7034b52d47409ad4548c354b72bd",
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"version_major": 2,
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"version_minor": 0
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@ -218,10 +219,10 @@
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{
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"data": {
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"text/plain": [
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"(2.0, 1.0311014124075548)"
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"(2.0, 1.0490216904842018)"
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]
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},
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"execution_count": 9,
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -265,7 +266,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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@ -285,30 +286,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(63,) (63,)\n",
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"(63,) (63,)\n",
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"(63,) (63,)\n",
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"(63,) (63,)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<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",
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" fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f58125333c294cb1b426b735829c30c5",
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"model_id": "abdce9c2d307402f8578eb83d9ce9b79",
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"version_major": 2,
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"version_minor": 0
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},
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@ -325,7 +319,7 @@
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"(0.002, 0.012591236)"
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]
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},
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"execution_count": 54,
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -338,19 +332,21 @@
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"foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
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"noise = np.resize(mains_noise, len(foo))\n",
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"\n",
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"sosh = sig.butter(12, 0.05, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\n",
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"sosh = sig.butter(3, 0.01, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
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"#filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
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"filtered = sig.sosfilt(sosh, foo + noise)\n",
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"\n",
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"cor1 = correlate(foo + noise, nbits=nbits, decimation=decimation)\n",
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"cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
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"\n",
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"cor2_pe = correlate(filtered, nbits=nbits, decimation=decimation, mask_filter=lambda mask: sig.sosfilt(sosh, sig.sosfiltfilt(sosl, mask)))\n",
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"\n",
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"sosn = sig.butter(12, 4, btype='highpass', output='sos', fs=decimation)\n",
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"#cor1_flt = sig.sosfilt(sosn, cor1)\n",
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"#cor2_flt = sig.sosfilt(sosn, cor2)\n",
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"cor1_flt = cor1[1:] - cor1[:-1]\n",
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"cor2_flt = cor2[1:] - cor2[:-1]\n",
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"#cor1_flt = cor1[1:] - cor1[:-1]\n",
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"#cor2_flt = cor2[1:] - cor2[:-1]\n",
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"\n",
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"fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n",
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"fig.tight_layout()\n",
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@ -371,17 +367,265 @@
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"ax4.set_title('corr filtered')\n",
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"ax4.grid()\n",
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"\n",
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"ax5.plot(cor1_flt)\n",
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"ax5.set_title('corr raw (highpass)')\n",
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"ax5.grid()\n",
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"#ax5.plot(cor1_flt)\n",
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"#ax5.set_title('corr raw (highpass)')\n",
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"#ax5.grid()\n",
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"\n",
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"ax6.plot(cor2_flt)\n",
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"ax6.set_title('corr filtered (highpass)')\n",
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"#ax6.plot(cor2_flt)\n",
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"#ax6.set_title('corr filtered (highpass)')\n",
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"#ax6.grid()\n",
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"\n",
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"ax6.plot(cor2_pe)\n",
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"ax6.set_title('corr filtered w/ mask preemphasis')\n",
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"ax6.grid()\n",
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"\n",
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"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
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"rms(foo), rms(noise)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8feea8e305004d33a39f2541a59a0ffa",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(63,) (63,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fe4a9934c40>]"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig, ax = plt.subplots()\n",
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"\n",
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"seq = np.repeat(gold(6)[29]*2 -1, decimation)\n",
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"sosh = sig.butter(3, 0.01, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
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"seq_filtered = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, seq))\n",
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"#seq_filtered = sig.sosfilt(sosh, seq)\n",
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"\n",
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"ax.plot(seq)\n",
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"ax.plot(seq_filtered)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "01df90b27d57470d9216ae9c549413c8",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(63,) (63,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fe4a9596070>]"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig, axs = plt.subplots(3, 1, figsize=(9, 7), sharex=True)\n",
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"fig.tight_layout()\n",
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"axs = axs.flatten()\n",
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"for ax in axs:\n",
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" ax.grid()\n",
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"\n",
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"seq = np.repeat(gold(6)[29]*2 -1, decimation)\n",
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"sosh = sig.butter(3, 0.1, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
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"cor2_pe_flt = sig.sosfilt(sosh, cor2_pe)\n",
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"cor2_pe_flt2 = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, cor2_pe))\n",
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"\n",
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"axs[0].plot(cor2_pe)\n",
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"axs[1].plot(cor2_pe_flt)\n",
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"axs[2].plot(cor2_pe_flt2)\n",
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"\n",
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"#for idx in np.where(np.abs(cor2_pe_flt2) > 0.5)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<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",
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" fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n"
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]
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},
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{
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"data": {
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"model_id": "183f086844054252bc8ec77f7b99abfc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "30db7a9c75834d64a31bf8e0fb5f5911",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(FloatSlider(value=10.0, description='w', max=30.0, min=-10.0), Output()), _dom_classes=(…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fe482e19190>]"
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]
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},
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"execution_count": 49,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"threshold_factor = 5.0\n",
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"\n",
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"import ipywidgets\n",
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"\n",
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"cor_an = cor1\n",
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"\n",
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"fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n",
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"fig.tight_layout()\n",
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"\n",
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"ax1.matshow(sig.cwt(cor_an, sig.ricker, np.arange(1, 31)), aspect='auto')\n",
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"\n",
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"for i in np.linspace(1, 10, 19):\n",
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" offx = 5*i\n",
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" ax2.plot(sig.cwt(cor_an, sig.ricker, [i]).flatten() + offx, color='red')\n",
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"\n",
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" ax2.text(-50, offx, f'{i:.1f}',\n",
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" horizontalalignment='right',\n",
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" verticalalignment='center',\n",
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" color='black')\n",
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"ax2.grid()\n",
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"\n",
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"ax3.grid()\n",
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"\n",
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"cwt_res = sig.cwt(cor_an, sig.ricker, [7.3]).flatten()\n",
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"line, = ax3.plot(cwt_res)\n",
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"def update(w=10.0):\n",
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" line.set_ydata(sig.cwt(cor_an, sig.ricker, [w]).flatten())\n",
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" fig.canvas.draw_idle()\n",
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"ipywidgets.interact(update)\n",
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"\n",
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||||
"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": {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue