{ "cells": [ { "cell_type": "markdown", "source": [ "# Reversing a Pulse Template\n", "\n", "The `TimeReversalPulseTemplate` allows to reverse arbitrary pulse templates. Let us start with a pulse that has a clear time ordering." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 1, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Simon\\Documents\\git\\qupulse\\qupulse\\utils\\sympy.py:26: UserWarning: scipy is not installed. This reduces the set of available functions to those present in numpy + manually vectorized functions in math.\n", " warnings.warn('scipy is not installed. This reduces the set of available functions to those present in numpy + '\n" ] }, { "data": { "text/plain": "
", "image/png": "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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from qupulse.pulses import TimeReversalPT, FunctionPT, TablePT, plotting\n", "\n", "forward_1 = FunctionPT('sin(2*pi*t / 10)', duration_expression='10', channel='A')\n", "wait = TablePT({'A': [(0, 0), (3, 0)]}, measurements=[('M', 0.5, 1)])\n", "forward_2 = FunctionPT('sin(2*pi*t / 5)', duration_expression='5', channel='A')\n", "\n", "forward_all = forward_1 @ wait @ forward_2\n", "\n", "_ = plotting.plot(forward_all, plot_measurements={'M'}, show=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "We can now easily create the same pulse backward" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 2, "outputs": [ { "data": { "text/plain": "
", "image/png": "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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "backward_all = TimeReversalPT(forward_all)\n", "_ = plotting.plot(backward_all, plot_measurements={'M'}, show=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "and use it in a composed template." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [ { "data": { "text/plain": "
", "image/png": "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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "intermediate = TablePT({'A': [(0, 0), (1, 0)]}, measurements=[('N', 0, 1)])\n", "composed = forward_all @ intermediate @ backward_all\n", "_ = plotting.plot(composed, plot_measurements={'M', 'N'}, show=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }