{ "cells": [ { "cell_type": "markdown", "id": "e772e83e", "metadata": {}, "source": [ "# Threaded data acquisition" ] }, { "cell_type": "markdown", "id": "39e4699a", "metadata": {}, "source": [ "In this notebook, we will explore how threading can be used with measurement context manager or dond functions for faster data acquisition. It is important to note that, the threading QCoDeS provideds happens per instrument. Meaning, per instrument one thread is created and all parameters from same instrument gets assigned to the same thread for data acquizition. It is generally not safe for more than one thread to communicate with the same instrument at the same time.\n", "\n", "Let us begin with some necessary imports." ] }, { "cell_type": "code", "execution_count": 1, "id": "5c4ddb13", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:42:55.906168Z", "start_time": "2021-06-24T15:42:52.538980Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logging hadn't been started.\n", "Activating auto-logging. Current session state plus future input saved.\n", "Filename : C:\\Users\\v-singak\\.qcodes\\logs\\command_history.log\n", "Mode : append\n", "Output logging : True\n", "Raw input log : False\n", "Timestamping : True\n", "State : active\n", "Qcodes Logfile : C:\\Users\\v-singak\\.qcodes\\logs\\210624-8444-qcodes.log\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import time\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "\n", "from qcodes.dataset import (\n", " Measurement,\n", " ThreadPoolParamsCaller,\n", " do1d,\n", " initialise_or_create_database_at,\n", " load_or_create_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.instrument_drivers.mock_instruments import (\n", " DummyInstrument,\n", " DummyInstrumentWithMeasurement,\n", ")\n", "from qcodes.parameters import Parameter\n", "from qcodes.validators import Numbers\n" ] }, { "cell_type": "markdown", "id": "61443494", "metadata": {}, "source": [ "Now, setup some instruments!" ] }, { "cell_type": "code", "execution_count": 2, "id": "2059fa0f", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:03.046647Z", "start_time": "2021-06-24T15:43:03.033632Z" } }, "outputs": [], "source": [ "dac = DummyInstrument('dac', gates=['ch1', 'ch2'])\n", "dmm1 = DummyInstrumentWithMeasurement(name='dmm1', setter_instr=dac)\n", "dmm2 = DummyInstrumentWithMeasurement(name='dmm2', setter_instr=dac)" ] }, { "cell_type": "code", "execution_count": 3, "id": "2add302b", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:06.991369Z", "start_time": "2021-06-24T15:43:06.971369Z" } }, "outputs": [], "source": [ "class SleepyDmmExponentialParameter(Parameter):\n", " def __init__(self, name, **kwargs):\n", " super().__init__(name, **kwargs)\n", " self._ed = self._exponential_decay(5, 0.2)\n", " next(self._ed)\n", "\n", " def get_raw(self):\n", " dac = self.root_instrument._setter_instr\n", " val = self._ed.send(dac.ch1())\n", " next(self._ed)\n", " time.sleep(0.1)\n", " return val\n", "\n", " @staticmethod\n", " def _exponential_decay(a: float, b: float):\n", " x = 0\n", " while True:\n", " x = yield\n", " yield a * np.exp(-b * x) + 0.02 * a * np.random.randn()" ] }, { "cell_type": "markdown", "id": "61a87e95", "metadata": {}, "source": [ "The above parameter class is made to return data with a delay on purpose with help of `time.sleep(0.1)` statement in the `get_raw` method to simulate slow communication with actual instruments. " ] }, { "cell_type": "code", "execution_count": 4, "id": "b73fe146", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:27.600146Z", "start_time": "2021-06-24T15:43:27.584538Z" } }, "outputs": [], "source": [ "dmm1.add_parameter('v3',\n", " parameter_class=SleepyDmmExponentialParameter,\n", " initial_value=0,\n", " label='Gate v3',\n", " unit=\"V\",\n", " vals=Numbers(-800, 400),\n", " get_cmd=None, set_cmd=None)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e3fa32b7", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:29.631589Z", "start_time": "2021-06-24T15:43:29.622587Z" } }, "outputs": [], "source": [ "dmm2.add_parameter('v3',\n", " parameter_class=SleepyDmmExponentialParameter,\n", " initial_value=0,\n", " label='Gate v3',\n", " unit=\"V\",\n", " vals=Numbers(-800, 400),\n", " get_cmd=None, set_cmd=None)" ] }, { "cell_type": "markdown", "id": "614dcd9a", "metadata": {}, "source": [ "Initialize the database and load or create an experiment." ] }, { "cell_type": "code", "execution_count": 6, "id": "b8234d8f", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:33.421231Z", "start_time": "2021-06-24T15:43:33.383233Z" } }, "outputs": [], "source": [ "initialise_or_create_database_at(Path.cwd() / \"data_acquisition_with_and_without_threads.db\")\n", "exp = load_or_create_experiment(\n", " experiment_name='data_acquisition_with_and_without_threads',\n", " sample_name=\"no sample\"\n", ")" ] }, { "cell_type": "markdown", "id": "9b1705cc", "metadata": {}, "source": [ "## Measurement 1: Non threaded data acquisition\n", "\n", "In the following measurment, we do not use threads and note down the time taken for the data acquisition. " ] }, { "cell_type": "code", "execution_count": 7, "id": "b6bd8849", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:40.969654Z", "start_time": "2021-06-24T15:43:40.952656Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meas1 = Measurement(exp=exp, name='exponential_decay_non_threaded_data_acquisition')\n", "meas1.register_parameter(dac.ch1)\n", "meas1.register_parameter(dmm1.v3, setpoints=(dac.ch1,))\n", "meas1.register_parameter(dmm2.v3, setpoints=(dac.ch1,))" ] }, { "cell_type": "code", "execution_count": 8, "id": "986741c0", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:54.335162Z", "start_time": "2021-06-24T15:43:52.123980Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 102. \n", "Report:\n", "Data acquisition time: 2.116617700000006 s\n" ] } ], "source": [ "data_acq_time = 0\n", "with meas1.run() as datasaver:\n", " for set_v in np.linspace(0, 25, 10):\n", " dac.ch1.set(set_v)\n", "\n", " t1 = time.perf_counter()\n", " datasaver.add_result((dac.ch1, set_v),\n", " (dmm1.v3, dmm1.v3.get()),\n", " (dmm2.v3, dmm1.v3.get()))\n", " t2 = time.perf_counter()\n", "\n", " data_acq_time += t2 - t1\n", "\n", " dataset1D1 = datasaver.dataset\n", "\n", "print('Report:')\n", "print(f'Data acquisition time: {data_acq_time} s')" ] }, { "cell_type": "code", "execution_count": 9, "id": "30528005", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:43:58.548261Z", "start_time": "2021-06-24T15:43:58.182766Z" } }, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cbax = plot_dataset(dataset1D1)" ] }, { "cell_type": "markdown", "id": "d4ba51ed", "metadata": {}, "source": [ "## Measurement 2: Threaded data acquisition\n", "\n", "In this measurement, we use `ThreadPoolParamsCaller` for threaded data acquisition. Here also we record the time taken for data acquisition.\n", "\n", "`ThreadPoolParamsCaller` will create a thread pool, and will call given parameters in those threads. Each group of parameters that have the same ``underlying_instrument`` protperty will be called in it's own separate thread, so that parameters that interact with the same instrument are always called sequentially (since communication within the single instrument is not thread-safe). Thanks to the fact that the pool of threads gets reuse for every new call of the parameters, the performance penalty of creating and shutting down threads is not significant in many cases.\n", "\n", "If there is a benefit in creating new threads for every new parameter call, then use ``call_params_threaded`` function instead." ] }, { "cell_type": "code", "execution_count": 10, "id": "eb8d918c", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:44:18.136341Z", "start_time": "2021-06-24T15:44:18.125342Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meas2 = Measurement(exp=exp, name='exponential_decay_threaded_data_acquisition')\n", "meas2.register_parameter(dac.ch1)\n", "meas2.register_parameter(dmm1.v3, setpoints=(dac.ch1,))\n", "meas2.register_parameter(dmm2.v3, setpoints=(dac.ch1,))" ] }, { "cell_type": "code", "execution_count": 11, "id": "fda3b476", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:44:27.672698Z", "start_time": "2021-06-24T15:44:26.414320Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 103. \n", "Report:\n", "Data acquisition time: 1.1658875999999907 s\n" ] } ], "source": [ "pool_caller = ThreadPoolParamsCaller(dac.ch1, dmm1.v3, dmm2.v3) # <----- This line is different\n", "\n", "data_acq_time = 0\n", "with meas2.run() as datasaver, pool_caller as call_params_in_pool: # <----- This line is different\n", " for set_v in np.linspace(0, 25, 10):\n", " dac.ch1.set(set_v)\n", "\n", " t1 = time.perf_counter()\n", " datasaver.add_result(*call_params_in_pool()) # <----- This line is different\n", " t2 = time.perf_counter()\n", "\n", " data_acq_time += t2 - t1\n", "\n", " # With ``call_params_threaded`` this line that measures parameters\n", " # and passes them to the datasaver would be:\n", " # datasaver.add_result(*call_params_threaded((dac.ch1, dmm1.v3, dmm2.v3)))\n", "\n", " dataset1D2 = datasaver.dataset\n", "\n", "print('Report:')\n", "print(f'Data acquisition time: {data_acq_time} s')" ] }, { "cell_type": "code", "execution_count": 12, "id": "a4b1e297", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:44:47.841670Z", "start_time": "2021-06-24T15:44:47.607706Z" } }, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cbax = plot_dataset(dataset1D2)" ] }, { "cell_type": "markdown", "id": "f7969796", "metadata": {}, "source": [ "## Non threaded and threaded data acquisition with do1d" ] }, { "cell_type": "markdown", "id": "4e4b2810", "metadata": {}, "source": [ "Lets now see how to do non threaded and threaded data acquisition with `do1d` function. For threaded data acquisition, `use_threads` argument will be set to `True`. Same argument is available on `do0d`, `do2d` and `dond` functions.\n", "\n", "### Measurement 3: Non threaded data acquisition with do1d" ] }, { "cell_type": "code", "execution_count": 13, "id": "13ca9e6e", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:45:10.334996Z", "start_time": "2021-06-24T15:45:07.095039Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 104. \n", "Report:\n", "Data acquisition time: 3.088559600000025 s\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t0 = time.perf_counter()\n", "do1d(dac.ch1, 0, 1, 10, 0, dmm1.v3, dmm2.v3, do_plot=True)\n", "t1 = time.perf_counter()\n", "\n", "print('Report:')\n", "print(f'Data acquisition time: {t1 - t0} s')" ] }, { "cell_type": "markdown", "id": "385aa662", "metadata": {}, "source": [ "### Measurement 4: Threaded data acquisition with do1d" ] }, { "cell_type": "code", "execution_count": 14, "id": "bb55a06b", "metadata": { "ExecuteTime": { "end_time": "2021-06-24T15:45:20.366979Z", "start_time": "2021-06-24T15:45:18.060200Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 105. \n", "Report:\n", "Data acquisition time: 2.1225642999999934 s\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t0 = time.perf_counter()\n", "do1d(dac.ch1, 0, 1, 10, 0, dmm1.v3, dmm2.v3, do_plot=True, use_threads=True) # <------- This line is different\n", "t1 = time.perf_counter()\n", "\n", "print('Report:')\n", "print(f'Data acquisition time: {t1 - t0} s')" ] }, { "cell_type": "code", "execution_count": null, "id": "e9249733", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }