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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import sqlite3\n",
"import struct\n",
"import datetime\n",
"import scipy.fftpack\n",
"from scipy import signal as sig\n",
"from scipy import optimize as opt\n",
"\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import patches\n",
"import numpy as np\n",
"from scipy import signal, optimize\n",
"from tqdm.notebook import tnrange, tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"db = sqlite3.connect('data/waveform_1pps_debug.sqlite3')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Run 000: 2020-03-31 16:58:00 - 2020-03-31 16:58:36 ( 0:00:36.029, 36512sp)\n",
"Run 001: 2020-03-31 16:58:51 - 2020-03-31 17:05:19 ( 0:06:27.729, 392608sp)\n",
"Run 002: 2020-03-31 17:07:02 - 2020-03-31 17:41:34 ( 0:34:32.105, 37024sp)\n",
"Run 003: 2020-03-31 18:50:05 - 2020-03-31 18:50:43 ( 0:00:37.576, 38048sp)\n",
"Run 004: 2020-03-31 18:54:08 - 2020-03-31 19:14:32 ( 0:20:24.104, 1239424sp)\n"
]
}
],
"source": [
"for run_id, start, end, count in db.execute('SELECT run_id, MIN(rx_ts), MAX(rx_ts), COUNT(*) FROM measurements GROUP BY run_id'):\n",
" foo = lambda x: datetime.datetime.fromtimestamp(x/1000)\n",
" start, end = foo(start), foo(end)\n",
" print(f'Run {run_id:03d}: {start:%Y-%m-%d %H:%M:%S} - {end:%Y-%m-%d %H:%M:%S} ({str(end-start)[:-3]:>13}, {count*32:>9d}sp)')\n",
"last_run, n_records = run_id, count\n",
"sampling_rate = 1000.0"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2712791d61b549ecb531f886d88e1d54",
"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": "stderr",
"output_type": "stream",
"text": [
"The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
]
}
],
"source": [
"histogram = np.array(db.execute('SELECT gps_1pps, COUNT(*) FROM measurements WHERE gps_1pps != -1 AND run_id = ? GROUP BY gps_1pps', (last_run,)).fetchall())\n",
"hist_plot = histogram.astype(float)[1:-1]\n",
"hist_plot[:, 0] *= 2 / 5 * 2\n",
"hist_plot[:, 1] /= (1000 / 32)\n",
"\n",
"f_nom = 19.440e6\n",
"\n",
"font = {'family' : 'normal',\n",
" 'weight' : 'normal',\n",
" 'size' : 10}\n",
"matplotlib.rc('font', **font)\n",
"fig, ax = plt.subplots(figsize=(5, 4))\n",
"ax.grid()\n",
"# We have a bug that causes our measurements to occassionally be out by +/- 65534 counts. For now, fix this by simply throwing away these (very obviously invalid) bins.\n",
"ax.bar(hist_plot[:,0] - f_nom , hist_plot[:, 1])\n",
"\n",
"def gauss(x, *p):\n",
" A, mu, sigma = p\n",
" return A*np.exp(-(x-mu)**2/(2.*sigma**2))\n",
"\n",
"gauss_x = np.linspace(np.min(hist_plot[:,0]), np.max(hist_plot[:,0]), 10000)\n",
"coeff, var_matrix = opt.curve_fit(gauss, hist_plot[:,0], hist_plot[:,1], p0=[np.max(hist_plot[:,1]), np.mean(hist_plot[:,0]), 1])\n",
"hist_fit = gauss(gauss_x, *coeff)\n",
"ax.plot(gauss_x - f_nom, hist_fit, color='orange')\n",
"_A, mu, sigma = coeff\n",
"bbox_props = dict(fc='white', alpha=0.8, ec='none')\n",
"ax.annotate(f'σ² = {sigma**2 * 1e3:.1f} mHz ({sigma**2 / f_nom * 1e9:.2f} ppb)\\nμ = {mu-f_nom:+.1f} Hz ({(mu-f_nom)/f_nom * 1e6:+.2f} ppm)', xy=[0.6, 0.5], xycoords='figure fraction', bbox=bbox_props)\n",
"ax.set_xlabel('$f - f_{nom}$ [Hz]')\n",
"ax.set_ylabel('# observations')\n",
"\n",
"#ax.set_title('OCXO frequency derivation relative to GPS 1pps')\n",
"fig.savefig('fig_out/ocxo_freq_stability.eps', format='eps')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "labenv",
"language": "python",
"name": "labenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
