{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b5ff8729", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:06.803781Z", "iopub.status.busy": "2024-10-11T06:16:06.803483Z", "iopub.status.idle": "2024-10-11T06:16:06.810297Z", "shell.execute_reply": "2024-10-11T06:16:06.809357Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# Copyright 2024 Keysight Technologies Inc." ] }, { "cell_type": "raw", "id": "289241d0", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Sweeping program variables\n", "==========================\n", "\n", "The :doc:`hello_hardware` tutorial covers the basic workflow for running experiments\n", "on hardware and extracting trace data and I/Q data. Here we expand on that and add\n", "sweeps to the programs.\n", "\n", "Setting up\n", "----------\n", "\n", "As before, we use two AWG channels and two digitizers." ] }, { "cell_type": "code", "execution_count": 2, "id": "38b48b8e", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:06.813477Z", "iopub.status.busy": "2024-10-11T06:16:06.813187Z", "iopub.status.idle": "2024-10-11T06:16:10.528305Z", "shell.execute_reply": "2024-10-11T06:16:10.527220Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "import keysight.qcs as qcs\n", "import numpy as np\n", "\n", "# set the following to True when connected to hardware:\n", "run_on_hw = False\n", "\n", "# instantiate channels representing two AWGs and two digitizers\n", "awgs = qcs.Channels(range(2), \"readoutawg\")\n", "digs = qcs.Channels(range(2), \"readoutreceiver\")\n", "\n", "# load our channel mapper to connect the virtual channes above to physical ones\n", "mapper = qcs.load(\"../../assets/channel_map.qcs\")" ] }, { "cell_type": "raw", "id": "0fac28a3", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Single variable sweeps\n", "----------------------\n", "\n", "We define a program that plays a single RF waveform on our AWG channels and performs\n", "an acquisition on the digitizer channels with an\n", ":py:class:`~keysight.qcs.channels.acquisitions.IntegrationFilter`\\ to perform I/Q\n", "demodulation of the trace data.\n", "\n", "We are sweeping the frequency of the RF pulse but keep the frequency of the\n", "integration filter constant." ] }, { "cell_type": "code", "execution_count": 3, "id": "e9243a28", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:10.534032Z", "iopub.status.busy": "2024-10-11T06:16:10.531835Z", "iopub.status.idle": "2024-10-11T06:16:10.731972Z", "shell.execute_reply": "2024-10-11T06:16:10.731016Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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Alternatively, we can retrieve them through the ``repetitions``\n", "attribute:" ] }, { "cell_type": "code", "execution_count": 4, "id": "9681e09d", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:10.735911Z", "iopub.status.busy": "2024-10-11T06:16:10.735327Z", "iopub.status.idle": "2024-10-11T06:16:10.741742Z", "shell.execute_reply": "2024-10-11T06:16:10.740918Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Sweep(rf=Array(name=frequencies, shape=(5,), dtype=float, unit=none)), Repeat(10)]\n" ] } ], "source": [ "print(program.repetitions.items)" ] }, { "cell_type": "raw", "id": "1c4305d8", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Next, we execute the program on hardware." ] }, { "cell_type": "code", "execution_count": 5, "id": "dbfd511f", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:10.745592Z", "iopub.status.busy": "2024-10-11T06:16:10.744988Z", "iopub.status.idle": "2024-10-11T06:16:10.931334Z", "shell.execute_reply": "2024-10-11T06:16:10.930491Z" } }, "outputs": [], "source": [ "if run_on_hw:\n", " # initialize the backend pass\n", " backend = qcs.HclBackend(channel_mapper=mapper)\n", " # the executor returns the program populated with results\n", " program = qcs.Executor(backend).execute(program)\n", " # (optional) export the data to an HDF5 file\n", " program.to_hdf5(\"program2.hdf5\")\n", "\n", "# we are loading a previously run program here for this example\n", "program = qcs.load(\"program2.hdf5\")" ] }, { "cell_type": "raw", "id": "5153c12c", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The results will now consist of five traces for each sweep point and for each channel." ] }, { "cell_type": "code", "execution_count": 6, "id": "c0211a80", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:10.936233Z", "iopub.status.busy": "2024-10-11T06:16:10.935873Z", "iopub.status.idle": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "program.plot_trace()" ] }, { "cell_type": "raw", "id": "3c11f724", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "We can retrieve the I/Q data by calling the\n", ":py:meth:`~keysight.qcs.programs.Program.get_iq` method, which returns a dataframe\n", "that has the sweep values in its column header." ] }, { "cell_type": "code", "execution_count": 7, "id": "24fbd7c2", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:11.534358Z", "iopub.status.busy": "2024-10-11T06:16:11.533946Z", "iopub.status.idle": "2024-10-11T06:16:11.659934Z", "shell.execute_reply": "2024-10-11T06:16:11.659104Z" } }, "outputs": [ { "data": { "text/html": [ "
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the frequency\n", "in the integration filter constant, we expect a peak in I/Q magnitude at the center\n", "frequency, which can be visually confirmed by plotting the data:" ] }, { "cell_type": "code", "execution_count": 8, "id": "da1d9674", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:11.663468Z", "iopub.status.busy": "2024-10-11T06:16:11.663150Z", "iopub.status.idle": "2024-10-11T06:16:11.813041Z", "shell.execute_reply": "2024-10-11T06:16:11.811338Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "(%{x}, %{y})", "legendgroup": "readoutreceiver, labels: (0,)", "legendgrouptitle": { "text": "readoutreceiver, labels: (0,)" }, "marker": { "color": "#ff005c", "symbol": "circle" }, "mode": "markers", "name": "I/Q for (rf, 4.05 GHz)", "showlegend": true, "type": "scatter", "x": [ 0.0047688759790845865 ], "xaxis": "x", "y": [ -0.003084332686597498 ], "yaxis": "y" }, { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "program.plot_classified(channel_subplots=False)" ] }, { "cell_type": "raw", "id": "c10c912d", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ " .. note::\n", "\n", " It is possible to perform variable sweeps both on hardware and software. To\n", " enable hardware sweeping, the program's\n", " :py:meth:`~keysight.qcs.programs.Program.sweep` method needs to be called before\n", " the :py:meth:`~keysight.qcs.programs.Program.n_shots` method. This will execute\n", " both the sweep as well as the repetitions on hardware. Any other sweeps defined\n", " after those will be performed in software.\n" ] }, { "cell_type": "raw", "id": "aa599c16", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Nested sweeps\n", "-------------\n", "Sweeps can be nested by calling the :py:meth:`~keysight.qcs.programs.Program.sweep`\n", "method multiple times. For example, we can sweep the frequency and amplitude in our\n", "program from above:" ] }, { "cell_type": "code", "execution_count": 12, "id": "2507fbad", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:12.999734Z", "iopub.status.busy": "2024-10-11T06:16:12.999353Z", "iopub.status.idle": "2024-10-11T06:16:13.010973Z", "shell.execute_reply": "2024-10-11T06:16:13.010172Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Sweep(amp=Array(name=amplitude, shape=(5,), dtype=float, unit=none)), Sweep(rf=Array(name=frequencies, shape=(5,), dtype=float, unit=none)), Repeat(10)]\n" ] } ], "source": [ "program = make_program()\n", "\n", "# specify the frequency values to be swept over\n", "frequencies = qcs.Array(\n", " \"frequencies\", value=[4.05e9, 4.1e9, 4.15e9, 4.2e9, 4.25e9], dtype=float\n", ")\n", "\n", "# specify the amplitude values to be swept over\n", "amplitudes = qcs.Array(\"amplitude\", value=[0.2, 0.4, 0.6, 0.8, 1], dtype=float)\n", "\n", "program.sweep(frequencies, frequency)\n", "program.sweep(amplitudes, amplitude)\n", "\n", "print(program.repetitions.items)" ] }, { "cell_type": "raw", "id": "a7bae23f", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "We execute this program as before and load in the results:" ] }, { "cell_type": "code", "execution_count": 13, "id": "b0a58295", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:13.014957Z", "iopub.status.busy": "2024-10-11T06:16:13.014270Z", "iopub.status.idle": "2024-10-11T06:16:13.242854Z", "shell.execute_reply": "2024-10-11T06:16:13.241775Z" } }, "outputs": [ { "data": { "text/html": [ "
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When plotting the I/Q data, we can easily switch between sweep axes by\n", "specifying the ``plot_axis`` argument. The default, ``plot_axis=0`` will plot\n", "the data over the first sweep (zeroth index of the dataframe columns). Note that\n", "this can also be used to plot the I/Q data as a function of shots, when the method\n", "is called with ``avg=False``\\." ] }, { "cell_type": "code", "execution_count": 14, "id": "0a914493", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:13.247352Z", "iopub.status.busy": "2024-10-11T06:16:13.246902Z", "iopub.status.idle": "2024-10-11T06:16:13.600174Z", "shell.execute_reply": "2024-10-11T06:16:13.599201Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "(rf, 4.05 GHz): (%{x}, %{y})", "legendgroup": "readoutreceiver, labels: (0,)", "legendgrouptitle": { "text": "readoutreceiver, labels: (0,)" }, "marker": { "color": "#ff005c", "symbol": "circle" }, "mode": "markers", "name": "I/Q for (amp, 0.2)", "showlegend": true, "type": "scatter", "x": [ 0.00010491074172007819 ], "xaxis": "x", "y": [ -3.634093349154561e-05 ], "yaxis": "y" }, { "hovertemplate": "(rf, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot over frequency for each amplitude\n", "program.plot_iq(channel_subplots=False, plot_axis=1)" ] }, { "cell_type": "raw", "id": "202ee62b", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Simultaneous sweeps\n", "-------------------\n", "\n", "Sweeps can also be performed simultaneously by passing the sweep values and targets\n", "as lists:" ] }, { "cell_type": "code", "execution_count": 16, "id": "ccb065f3", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:16:13.967494Z", "iopub.status.busy": "2024-10-11T06:16:13.967157Z", "iopub.status.idle": "2024-10-11T06:16:13.991140Z", "shell.execute_reply": "2024-10-11T06:16:13.990397Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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Instantaneous Phase0 rad
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Duration80 ns
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}, { "box": { "visible": true }, "line": { "color": "#00b287" }, "marker": { "color": "#00b287", "opacity": 0.5, "size": 8, "symbol": "diamond" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#c97a88" }, "marker": { "color": "#c97a88", "opacity": 0.5, "size": 8, "symbol": "triangle-down" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#ff005c" }, "marker": { "color": "#ff005c", "opacity": 0.5, "size": 8, "symbol": "hexagram" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#00d539" }, "marker": { "color": "#00d539", "opacity": 0.5, "size": 8, "symbol": "circle" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#ad6aff" }, "marker": { "color": "#ad6aff", "opacity": 0.5, "size": 8, "symbol": "square" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#ff9000" }, "marker": { "color": "#ff9000", "opacity": 0.5, "size": 8, "symbol": "diamond" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#00b287" }, "marker": { "color": "#00b287", "opacity": 0.5, "size": 8, "symbol": "triangle-down" }, "meanline": { "visible": true }, "type": "violin" }, { "box": { "visible": true }, "line": { "color": "#c97a88" }, "marker": { "color": "#c97a88", "opacity": 0.5, "size": 8, "symbol": "cross" }, "meanline": { "visible": true }, "type": "violin" } ] }, "layout": { "annotationdefaults": { "font": { "size": 12 } }, "autotypenumbers": "strict", "hoverlabel": { "bgcolor": "white", "font": { "family": "Rockwell", "size": 14 } }, "hovermode": "x unified", "legend": { "bgcolor": "white", "bordercolor": "Black", "borderwidth": 1, "font": { "family": "Rockwell" } }, "xaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true }, "yaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true } } }, "width": 900, "xaxis": { "anchor": "y", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "showticklabels": true, "tickangle": 0, "title": { "text": "I" } }, "yaxis": { "anchor": "x", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "title": { "text": "Q" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the results\n", "program.plot_iq(channel_subplots=False)" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "nbsphinx,raw_mimetype,-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "percent" } }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }