Prettify fft comparisons
This commit is contained in:
jaseg
parent
0a042733dd
commit
99b9c0ecc1
3 changed files with 180 additions and 64 deletions
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@ -104,7 +104,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 221,
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"metadata": {},
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"outputs": [
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{
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@ -116,7 +116,7 @@
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}
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],
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"source": [
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"with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n",
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"with open('data/raw_freq.bin', 'rb') as f:\n",
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" mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
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" print('mean:', np.mean(mains_noise), 'len:', len(mains_noise))\n",
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" mains_noise -= np.mean(mains_noise)"
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@ -610,13 +610,21 @@
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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": 220,
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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-220-44ad44d9c4c6>: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",
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" fig, ax = plt.subplots(figsize=(12, 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": "3bcd7f07ef26458da90f748fd74addfb",
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"model_id": "f63ab2c387f948b29d992a3edd04f4fa",
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"version_major": 2,
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"version_minor": 0
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},
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@ -630,10 +638,10 @@
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x7f23d0aa37f0>"
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"<matplotlib.legend.Legend at 0x7f00ecacf3a0>"
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]
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},
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"execution_count": 7,
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"execution_count": 220,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -645,7 +653,7 @@
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"# ser, std = np.mean(sers), np.std(sers)\n",
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"# results = { nbits: [ res.get() for res in series ] for nbits, series in results.items() }\n",
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"\n",
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"with open(f'/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-19-19-30-05.json', 'r') as f:\n",
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"with open(f'data/dsss_experiments_res-2020-02-19-19-30-05.json', 'r') as f:\n",
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" results = json.load(f)\n",
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"\n",
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"for nbits, series in results.items():\n",
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@ -666,21 +674,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 218,
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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-36-8e813d331cd8>: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",
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"<ipython-input-218-eb5258414ca6>: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",
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" fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\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": "c05f590a7c0040628c5c5d4032bb7c8e",
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"model_id": "6651827b5eca4a0ba46effefd605f06c",
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"version_major": 2,
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"version_minor": 0
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},
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@ -795,9 +803,9 @@
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"\n",
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"results = []\n",
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"for fn in [\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-18-35.json',\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-26-07.json',\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-29-02.json'\n",
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" 'data/dsss_experiments_res-2020-02-20-12-18-35.json',\n",
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" 'data/dsss_experiments_res-2020-02-20-12-26-07.json',\n",
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" 'data/dsss_experiments_res-2020-02-20-12-29-02.json'\n",
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"]:\n",
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" with open(fn, 'r') as f:\n",
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" results += json.load(f)\n",
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@ -893,21 +901,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 268,
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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-35-cafaa6062c72>: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",
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" fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n"
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"<ipython-input-268-fd9510d5c128>: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",
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" fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\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": "fa93fb31355840148f941dab8b60f51c",
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"model_id": "be4678ec02394b81b8aee83c759e1009",
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"version_major": 2,
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"version_minor": 0
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},
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@ -922,21 +930,22 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-35-cafaa6062c72>:16: RuntimeWarning: divide by zero encountered in log10\n",
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"<ipython-input-268-fd9510d5c128>:17: RuntimeWarning: divide by zero encountered in log10\n",
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" cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n"
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]
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}
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],
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"source": [
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"fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n",
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"fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\n",
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"empty.axis('off')\n",
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"#fig.tight_layout()\n",
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"\n",
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"results = []\n",
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"\n",
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"for fn in [\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-14-10-13.json',\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-21-57.json',\n",
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" '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-23-47.json',\n",
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" 'data/dsss_experiments_res-2020-02-20-14-10-13.json',\n",
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" 'data/dsss_experiments_res-2020-02-20-13-21-57.json',\n",
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" 'data/dsss_experiments_res-2020-02-20-13-23-47.json',\n",
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"]:\n",
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" with open(fn, 'r') as f:\n",
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" results += json.load(f)\n",
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@ -953,16 +962,27 @@
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" stds = np.array([ np.std(values) for values in data ])\n",
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" decimation_sers[decimation] = list(zip(amps, sers, stds))\n",
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" \n",
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" amps = [ amp*1000 for amp in amps ]\n",
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" l, = ax.plot(amps, np.clip(sers, 0, 1), label=f'decimation={decimation}', color=cm_func(decimation))\n",
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" ax.fill_between(amps, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.2)\n",
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" ax.axhline(0.5, color='gray', ls=(0, (3, 4)), lw=0.8)\n",
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"ax.grid()\n",
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"ax.set_xlabel('Amplitude in mHz')\n",
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"ax.set_xlabel('Amplitude [mHz]')\n",
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"ax.set_ylabel('Symbol error rate')\n",
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"\n",
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"norm = matplotlib.colors.Normalize(vmin=np.log10(min(decimations)), vmax=np.log10(max(decimations)))\n",
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"cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', label=\"Decimation\", ticks=[np.log10(d) for d in decimations])\n",
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"cb1.ax.set_yticklabels([f'{d:.1f}' for d in decimations])\n",
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"yticks = [np.log10(d) for d in decimations]\n",
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"cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', ticks=yticks)\n",
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"cb1t = cbar_ax.twinx()\n",
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"cb1t.set_ylim(cbar_ax.get_ylim())\n",
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"cb1t.set_yticks(yticks)\n",
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"\n",
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"cbar_ax.set_yticklabels([f'{d/sampling_rate:.1f}' for d in decimations])\n",
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"cbar_ax.set_ylabel(\"chip duration [s]\", labelpad=-70)\n",
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"\n",
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"cb1t.set_yticklabels([f'{d/sampling_rate * 2**nbits:.1f}' for d in decimations])\n",
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"cb1t.set_ylabel(\"symbol duration [s]\")\n",
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"\n",
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"\n",
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"def plot_intercepts(ax, SER_TH = 0.5):\n",
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" intercepts = {}\n",
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@ -988,8 +1008,10 @@
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" std = data[:,1]\n",
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" \n",
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" ax.set_xlim([min(x), max(x)])\n",
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" l = ax.plot(x, y, label='Amplitude at SER=0.5', color='orange')\n",
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" ax.legend(loc=3)\n",
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" y = [ v*1000 if v is not None else v for v in y ]\n",
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" l = ax.plot(x, y, label='Amplitude at SER=0.5 [mHz]', color='orange')\n",
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" #ax.legend(loc=3)\n",
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" ax.set_ylabel('Amplitude at SER=0.5 [mHz]')\n",
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" ax.grid()\n",
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" \n",
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" x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n",
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@ -1002,12 +1024,43 @@
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" ax.set_ylim([min(y)*0.9, max(y)*1.1])\n",
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" ax.set_xscale('log')\n",
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" ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, _: '{:g}'.format(x)))\n",
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" ax.set_xticks([1, 2, 5, 10, 20, 50])\n",
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" xticks = [1, 2, 5, 10, 20, 50]\n",
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" ax.set_xticks(xticks)\n",
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" ax.set_xticklabels([ f'{x/sampling_rate:.1f}' for x in xticks ])\n",
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" ax.set_xlim([1, 60])\n",
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" ax.set_xlabel('chip duration [s]')\n",
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" \n",
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" axt = ax.twiny()\n",
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" axt.set_xlim(ax.get_xlim())\n",
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" axt.set_xscale('log')\n",
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" axt.set_xticks(xticks)\n",
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" axt.set_xticklabels([ f'{x/sampling_rate * 2**nbits:.1f}' for x in xticks ])\n",
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" axt.set_xlabel('symbol duration [s]')\n",
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" \n",
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" return l\n",
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"\n",
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"l1 = plot_intercepts(intercept_ax)\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": 227,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"41.6"
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]
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},
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"execution_count": 227,
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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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"13 * 2**5 / 10"
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]
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}
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],
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"metadata": {
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@ -141,13 +141,21 @@
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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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"execution_count": 72,
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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-72-51d3a7cc1678>:20: 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, ax = plt.subplots()\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": "fd94bc97a276400db0539b703c4eeeac",
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"model_id": "2991d932b113496a9135d569f9577abe",
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"version_major": 2,
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"version_minor": 0
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},
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@ -161,10 +169,10 @@
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{
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"data": {
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"text/plain": [
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"(1.6666666666666667e-05, 0.5)"
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"(5e-07, 0.02)"
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]
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},
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"execution_count": 23,
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"execution_count": 72,
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"metadata": {},
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"output_type": "execute_result"
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}
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
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"ax.set_xlabel('T in s')\n",
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"ax.set_ylabel('Amplitude Δf')\n",
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"\n",
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"for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n",
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" ax.axvline(1/t, color='red', alpha=0.5)\n",
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" ax.annotate(f'{t} s', xy=(1/t, 3e-3), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n",
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"#ax.text(1/60, 10,'60 s', ha='left')\n",
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"ax.grid()\n",
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"ax.set_xlim([1/60000, 0.5])"
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"ax.set_xlim([1/60000, 0.5])\n",
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"ax.set_ylim([5e-7, 2e-2])"
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]
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},
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 104,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"metadata": {},
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"outputs": [],
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"source": [
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"db = sqlite3.connect('/mnt/c/Users/jaseg/shared/waveform-raspi.sqlite3')"
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"db = sqlite3.connect('data/waveform-raspi.sqlite3')"
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]
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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": "f960454ab93244db97e039ae7ebd02ee",
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"model_id": "55690b4f59d54454b83deb5200c05f09",
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"version_major": 2,
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"version_minor": 0
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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": "264a9f8478e449289c1592c9595dc6cc",
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"model_id": "cf5c2e35008740379508eaaedfcf3b31",
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"version_major": 2,
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"version_minor": 0
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},
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@ -246,7 +246,7 @@
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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": "7c2382eb8e124ef9b29546ecaed3e155",
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"model_id": "efa9db02cdfc4f6c86d1eadd4769d097",
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"version_major": 2,
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"version_minor": 0
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},
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@ -279,7 +279,7 @@
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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": "d29e8ee5bf7f4e94a1749239aec24d6d",
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"model_id": "e4122530c1444f75aca410ad51d4a733",
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"version_major": 2,
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"version_minor": 0
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},
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@ -356,7 +356,7 @@
|
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{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "47c40f28b5a34a94b4a631fd62107521",
|
||||
"model_id": "a36a994f6f484a6cba81b6e680c9eee1",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -412,7 +412,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "4a4cb62296df496bad37d93547d3c26a",
|
||||
"model_id": "e4116959544f44f6a4b898419a5d51db",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -490,7 +490,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -509,21 +509,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 81,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-81-a751e13723ea>:17: 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(figsize=(9,5))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "3809a1a83b5844e3906f1d74bcd15b5d",
|
||||
"model_id": "09069e2c9cb740d5b58846371aeea0f2",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -548,7 +540,7 @@
|
|||
"5.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 81,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -585,21 +577,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 156,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-156-ddde6af5dee1>:20: 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": "d137ae59ed7947ce8e3f7295e102f2f0",
|
||||
"model_id": "b812e4b240a642398f09bb3cff8f5cee",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -616,7 +600,7 @@
|
|||
"(1.6666666666666667e-05, 0.5)"
|
||||
]
|
||||
},
|
||||
"execution_count": 156,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -649,6 +633,71 @@
|
|||
"ax.grid()\n",
|
||||
"ax.set_xlim([1/60000, 0.5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c835382368b6423ab5446835c5785afd",
|
||||
"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": [
|
||||
"(5e-07, 0.02)"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Number of samplepoints\n",
|
||||
"N = len(data)\n",
|
||||
"# sample spacing\n",
|
||||
"T = 1.0 / 10.0\n",
|
||||
"x = np.linspace(0.0, N*T, N)\n",
|
||||
"yf = scipy.fftpack.fft(data)\n",
|
||||
"xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n",
|
||||
"\n",
|
||||
"yf = 2.0/N * np.abs(yf[:N//2])\n",
|
||||
"\n",
|
||||
"average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n",
|
||||
"\n",
|
||||
"average_width = 6\n",
|
||||
"average_start = 20\n",
|
||||
"yf = average_from(yf, average_start, average_width)\n",
|
||||
"xf = average_from(xf, average_start, average_width)\n",
|
||||
"yf = average_from(yf, 70, 4)\n",
|
||||
"xf = average_from(xf, 70, 4)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.loglog(xf, yf)\n",
|
||||
"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
|
||||
"ax.set_xlabel('T in s')\n",
|
||||
"ax.set_ylabel('Amplitude Δf')\n",
|
||||
"\n",
|
||||
"for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n",
|
||||
" ax.axvline(1/t, color='red', alpha=0.5)\n",
|
||||
" ax.annotate(f'{t} s', xy=(1/t, 3e-5), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n",
|
||||
"#ax.text(1/60, 10,'60 s', ha='left')\n",
|
||||
"ax.grid()\n",
|
||||
"ax.set_xlim([1/60000, 0.5])\n",
|
||||
"ax.set_ylim([5e-7, 2e-2])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue