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authorandreas128 <Andreas>2017-01-22 00:51:47 +0000
committerandreas128 <Andreas>2017-01-22 00:51:47 +0000
commita88e67cb485d6b4b7bc21aa3c9dedbab37190cb9 (patch)
tree2158dc19cd17b72b7a96a602f59bdfb5951cd814 /sync-measurement.ipynb
parent62d6affd9fd9c5d45305cd5880c16696c0744044 (diff)
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am-am am-pm diagram in syncmeasurement.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "from scipy import signal\n",
+ "import matplotlib.pyplot as plt\n",
+ "import src.dab_util as du"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import src.signal_gen as sg\n",
+ "reload(sg)\n",
+ "reload(du)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sg.gen_ramps(amplitudes=np.linspace(0.001, 0.95, num = 20))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a_in = np.fromfile(\"./input.dat\", dtype=np.complex64)\n",
+ "a_out = np.fromfile(\"./output.dat\", dtype=np.complex64)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def crop_signal(signal, n_window = 1000, n_zeros = 50000, debug = False):\n",
+ " #signal = signal[-10:-1]\n",
+ " mag = abs(signal)\n",
+ " window = np.ones(n_window) / float(n_window)\n",
+ " mag = scipy.signal.convolve(window, mag)\n",
+ " mag = scipy.signal.convolve(window, mag)\n",
+ " thr = 0.01 * np.max(mag)\n",
+ " idx_start = np.argmax(mag > thr)\n",
+ " idx_end = mag.shape[0] - np.argmax(np.flipud(mag > thr))\n",
+ " if debug:\n",
+ " plt.plot(mag < thr)\n",
+ " plt.plot((idx_start,idx_start), (0,0.1), color='g', linewidth=2)\n",
+ " plt.plot((idx_end,idx_end), (0,0.1), color='r', linewidth=2)\n",
+ " signal = signal[idx_start - n_zeros: idx_end + n_zeros]\n",
+ " return signal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a_in = du.crop_signal(a_in)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a_out = du.crop_signal(a_out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#plt.plot(a_in.real[780000 + 230000:2000000]+1, color='b');\n",
+ "#plt.plot(a_out.real[780000:2000000], color='g');\n",
+ "plt.plot(a_in.real+1, color='b');\n",
+ "plt.plot(a_out.real, color='g');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def lagcorr(x,y,lag=None,verbose=True):\n",
+ " '''Compute lead-lag correlations between 2 time series.\n",
+ "\n",
+ " <x>,<y>: 1-D time series.\n",
+ " <lag>: lag option, could take different forms of <lag>:\n",
+ " if 0 or None, compute ordinary correlation and p-value;\n",
+ " if positive integer, compute lagged correlation with lag\n",
+ " upto <lag>;\n",
+ " if negative integer, compute lead correlation with lead\n",
+ " upto <-lag>;\n",
+ " if pass in an list or tuple or array of integers, compute \n",
+ " lead/lag correlations at different leads/lags.\n",
+ "\n",
+ " Note: when talking about lead/lag, uses <y> as a reference.\n",
+ " Therefore positive lag means <x> lags <y> by <lag>, computation is\n",
+ " done by shifting <x> to the left hand side by <lag> with respect to\n",
+ " <y>.\n",
+ " Similarly negative lag means <x> leads <y> by <lag>, computation is\n",
+ " done by shifting <x> to the right hand side by <lag> with respect to\n",
+ " <y>.\n",
+ "\n",
+ " Return <result>: a (n*2) array, with 1st column the correlation \n",
+ " coefficients, 2nd column correpsonding p values.\n",
+ "\n",
+ " Currently only works for 1-D arrays.\n",
+ " '''\n",
+ "\n",
+ " import numpy\n",
+ " from scipy.stats import pearsonr\n",
+ "\n",
+ " if len(x)!=len(y):\n",
+ " raise Exception('Input variables of different lengths.')\n",
+ "\n",
+ " #--------Unify types of <lag>-------------\n",
+ " if numpy.isscalar(lag):\n",
+ " if abs(lag)>=len(x):\n",
+ " raise Exception('Maximum lag equal or larger than array.')\n",
+ " if lag<0:\n",
+ " lag=-numpy.arange(abs(lag)+1)\n",
+ " elif lag==0:\n",
+ " lag=[0,]\n",
+ " else:\n",
+ " lag=numpy.arange(lag+1) \n",
+ " elif lag is None:\n",
+ " lag=[0,]\n",
+ " else:\n",
+ " lag=numpy.asarray(lag)\n",
+ "\n",
+ " #-------Loop over lags---------------------\n",
+ " result=[]\n",
+ " if verbose:\n",
+ " print '\\n#<lagcorr>: Computing lagged-correlations at lags:',lag\n",
+ "\n",
+ " for ii in lag:\n",
+ " if ii<0:\n",
+ " result.append(pearsonr(x[:ii],y[-ii:]))\n",
+ " elif ii==0:\n",
+ " result.append(pearsonr(x,y))\n",
+ " elif ii>0:\n",
+ " result.append(pearsonr(x[ii:],y[:-ii]))\n",
+ "\n",
+ " result=numpy.asarray(result)\n",
+ "\n",
+ " return result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l = min(a_out.shape[0], a_in.shape[0])\n",
+ "a_out = a_out[0:l]\n",
+ "a_in = a_in[0:l]\n",
+ "\n",
+ "c = lagcorr(abs(a_out), abs(a_in), 1000)[:,0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#def corr(a_out, a_in):\n",
+ "# import scipy\n",
+ "# corr = np.correlate(\n",
+ "# a_out,\n",
+ "# a_in,\n",
+ "# 'full')\n",
+ "# delay = np.argmax(corr) - a_in.shape[0] + 1\n",
+ "# #plt.plot(range(-corr.shape[0]/2, corr.shape[0]/2),corr)\n",
+ "# return delay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#delay = corr(a_out, a_in)\n",
+ "delay = np.argmax(c)\n",
+ "a_out = a_out[delay - 1:]\n",
+ "delay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l = min(a_out.shape[0], a_in.shape[0])\n",
+ "a_out = a_out[0:l]\n",
+ "a_in = a_in[0:l]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(a_in, color='g');\n",
+ "plt.plot(a_out - 1, color='y');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_amp_ratio(ampl_1, ampl_2):\n",
+ " idxs = (a_in > ampl_1) & (a_in < ampl_2)\n",
+ " ratio = (a_out[idxs] / a_in[idxs])\n",
+ " return ratio.mean(), ratio.var()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_phase(ampl_1, ampl_2):\n",
+ " idxs = (a_in > ampl_1) & (a_in < ampl_2)\n",
+ " ratio = np.angle(a_out[idxs]) - np.angle(a_in[idxs])\n",
+ " return ratio.mean(), ratio.var()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bins = np.linspace(0.1,1,num=10)\n",
+ "res = []\n",
+ "for ampl_1, ampl_2 in zip(bins, bins[1:]):\n",
+ " res.append(get_amp_ratio(ampl_1, ampl_2))\n",
+ "mean, var = zip(*res)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(mean)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bins = np.linspace(0.1,1,num=10)\n",
+ "res = []\n",
+ "for ampl_1, ampl_2 in zip(bins, bins[1:]):\n",
+ " res.append(get_phase(ampl_1, ampl_2))\n",
+ "mean, var = zip(*res)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(mean)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}