{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Measuring X as a function of time\n", "\n", "Sometimes we'd like to measure something as a function of elapsed wall clock time. QCoDeS provides a convenient default way of doing such a measurement, namely by using the `ElapsedTimeParameter`.\n", "\n", "The main utility of having a default way of measuring time is the uniformity in data of different experiments." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "\n", "from qcodes.dataset import (\n", " Measurement,\n", " initialise_or_create_database_at,\n", " load_or_create_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.parameters import ElapsedTimeParameter, Parameter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepatory footwork: setup database and experiment" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tutorial#no_sample#1@C:\\Users\\wihpniel\\src\\qcodes\\docs\\examples\\DataSet\\x_as_a_function_of_time.db\n", "--------------------------------------------------------------------------------------------------\n", "1-results-1-time,noise-100" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initialise_or_create_database_at(os.path.join(os.getcwd(), 'x_as_a_function_of_time.db'))\n", "load_or_create_experiment('tutorial', 'no_sample')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The measurement itself\n", "\n", "We'll measure some Brownian motion. We set up a parameter for the noise." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "noise = Parameter('noise', \n", " label='Position',\n", " unit='m',\n", " get_cmd=lambda: np.random.randn())\n", "time = ElapsedTimeParameter('time')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meas = Measurement()\n", "meas.register_parameter(time)\n", "meas.register_parameter(noise, setpoints=[time])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 2\n" ] } ], "source": [ "with meas.run() as datasaver:\n", " pos = 0\n", " time.reset_clock()\n", " for _ in range(100):\n", " pos += noise()\n", " now = time()\n", " datasaver.add_result((noise, pos), (time, now))\n", " \n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axs, cbs = plot_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }