{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# QCoDeS Example with the Rigol DS 1074 Z oscilloscope" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook presents the implementation of QCoDeS driver for the Rigol DS1074Z oscilloscope." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing dependencies " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Qcodes import\n", "from qcodes.dataset import (\n", " Measurement,\n", " initialise_database,\n", " load_or_create_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.instrument_drivers.rigol import RigolDS1074Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create the instrument " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The instrument is created in the following way. The address can be found using the NI-MAX explorer." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Connected to: RIGOL TECHNOLOGIES DS1074Z (serial:DS1ZB161650342, firmware:00.04.00) in 5.44s\n" ] } ], "source": [ "rigol = RigolDS1074Z(\"rigol\", \"USB0::0x1AB1::0x04CE::DS1ZB161650342::INSTR\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trigger setup " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trigger source can be set to any channel (1 to 4). Here we use the input signal from channel 1 itself as a source for trigger. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rigol.trigger_edge_source(\"ch1\")\n", "rigol.trigger_edge_slope(\"negative\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The trigger-mode type supported by this oscilloscope are `edge`, `pulse`, `video` and `pattern`. Both the trigger mode and trigger level can be set in the following manner. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "rigol.trigger_mode(\"edge\")\n", "rigol.trigger_level(0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data acquisition and plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " This particular driver implements acquiring the trace as a `ParameterWithSetpoints`. Here, we show how the trace can be measured and plotted. For more information on `ParameterWithSetpoints` refer to [this notebook](http://microsoft.github.io/Qcodes/examples/Parameters/Simple-Example-of-ParameterWithSetpoints.html)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "initialise_database()\n", "exp = load_or_create_experiment(\n", " experiment_name=\"Oscilloscope trace\", sample_name=\"no_name\"\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 5\n" ] } ], "source": [ "meas_helper = Measurement(exp=exp)\n", "meas_helper.register_parameter(rigol.channels.ch1.trace)\n", "\n", "with meas_helper.run() as datasaver:\n", " datasaver.add_result((rigol.channels.ch1.trace, rigol.channels.ch1.trace()))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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