docs/examples/DataSet/InMemoryDataSet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In memory dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks explains an alternative way of measuring where the raw data is not written directly to a sqlite database file but only kept in memory with the ability to export the data after the measurement is completed. This may significantly speed up measurements where a lot of data is acquired but there is no protection against any data lose that may happen during a measurement. (Power loss, computer crash etc.) However, there may be situations where this trade-off is worthwhile. Please do only use the in memory dataset for measurements if you understand the risks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy.random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.dataset import (\n",
    "    load_or_create_experiment,\n",
    "    load_by_guid,\n",
    "    load_by_run_spec,\n",
    "    initialise_or_create_database_at,\n",
    "    Measurement,\n",
    "    DataSetType,\n",
    ")\n",
    "from qcodes.tests.instrument_mocks import (\n",
    "    DummyInstrument,\n",
    "    DummyInstrumentWithMeasurement,\n",
    ")\n",
    "from qcodes.dataset.plotting import plot_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we set up two mock instruments and a database to measure into:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparatory mocking of physical setup\n",
    "\n",
    "dac = DummyInstrument(\"dac\", gates=[\"ch1\", \"ch2\"])\n",
    "dmm = DummyInstrumentWithMeasurement(name=\"dmm\", setter_instr=dac)\n",
    "\n",
    "station = qc.Station(dmm, dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise_or_create_database_at(\"./in_mem_example.db\")\n",
    "exp = load_or_create_experiment(experiment_name=\"in_mem_exp\", sample_name=\"no sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And run a standard experiment writing data to the database: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 18. \n"
     ]
    }
   ],
   "source": [
    "meas = Measurement(exp=exp)\n",
    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))  # now register the dependent oone\n",
    "\n",
    "meas.write_period = 0.5\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for set_v in np.linspace(0, 25, 10):\n",
    "        dac.ch1.set(set_v)\n",
    "        get_v = dmm.v1.get()\n",
    "        datasaver.add_result((dac.ch1, set_v), (dmm.v1, get_v))\n",
    "\n",
    "    dataset1D = datasaver.dataset  # convenient to have for data access and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The in memory measurement looks nearly identical with the only difference being that we explicitly pass in an Enum to select the dataset class that we want to use as a parameter to ``measurement.run``\n",
    "\n",
    "The ``DataSetType`` Enum currently has 2 members representing the two different types of dataset supported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 19. \n"
     ]
    }
   ],
   "source": [
    "with meas.run(dataset_class=DataSetType.DataSetInMem) as datasaver:\n",
    "    for set_v in np.linspace(0, 25, 10):\n",
    "        dac.ch1.set(set_v)\n",
    "        get_v = dmm.v1.get()\n",
    "        datasaver.add_result((dac.ch1, set_v), (dmm.v1, get_v))\n",
    "    datasetinmem = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(datasetinmem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetinmem.run_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the measurement is performed in this way the data is not written to the database but the metadata (run_id, timestamps, snapshot etc.) is.\n",
    "\n",
    "To preserve the raw data it must be exported it to another file format. See [Exporting QCoDes Datasets](./Exporting-data-to-other-file-formats.ipynb) for more information on exporting including how this can be done automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetinmem.export(\"netcdf\", path=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `export_info` attribute contains information about locations where the file was exported to. We will use this below to show how the data may be reloaded from a netcdf file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\jenielse\\\\source\\\\repos\\\\Qcodes\\\\docs\\\\examples\\\\DataSet\\\\qcodes_19.nc'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_netcdf = datasetinmem.export_info.export_paths[\"nc\"]\n",
    "path_to_netcdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected we can see this file in the current directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C has no label.\n",
      " Volume Serial Number is A6E9-9402\n",
      "\n",
      " Directory of C:\\Users\\jenielse\\source\\repos\\Qcodes\\docs\\examples\\DataSet\n",
      "\n",
      "10/11/2021  09:53    <DIR>          .\n",
      "27/09/2021  08:36    <DIR>          ..\n",
      "27/10/2021  11:01    <DIR>          .ipynb_checkpoints\n",
      "03/11/2021  12:57           885,940 Accessing-data-in-DataSet.ipynb\n",
      "27/09/2021  08:36            17,673 Benchmarking.ipynb\n",
      "27/09/2021  08:36    <DIR>          Cache\n",
      "03/11/2021  12:57            79,094 DataSet-class-walkthrough.ipynb\n",
      "27/09/2021  08:36            85,237 Dataset_Performance.ipynb\n",
      "10/11/2021  09:49           363,994 Exporting-data-to-other-file-formats.ipynb\n",
      "29/10/2021  09:28         2,838,528 export_example.db\n",
      "29/10/2021  09:28            32,768 export_example.db-shm\n",
      "29/10/2021  09:28         3,040,592 export_example.db-wal\n",
      "27/09/2021  08:36            16,283 Extracting-runs-from-one-DB-file-to-another.ipynb\n",
      "14/10/2021  15:10            78,918 import-data-from-legacy-dat-files.ipynb\n",
      "10/11/2021  09:49             9,246 InMemoryDataSet.ipynb\n",
      "10/11/2021  09:53           135,168 in_mem_example.db\n",
      "10/11/2021  09:53            32,768 in_mem_example.db-shm\n",
      "10/11/2021  09:53           164,832 in_mem_example.db-wal\n",
      "27/09/2021  08:36           107,861 Linking to parent datasets.ipynb\n",
      "27/09/2021  08:36            31,470 Measuring X as a function of time.ipynb\n",
      "27/09/2021  08:36           341,976 Offline Plotting Tutorial.ipynb\n",
      "27/09/2021  08:36           252,034 Offline plotting with categorical data.ipynb\n",
      "27/09/2021  08:36           295,746 Offline plotting with complex data.ipynb\n",
      "27/09/2021  08:36            29,073 Paramtypes explained.ipynb\n",
      "27/09/2021  08:36            13,370 Pedestrian example of subscribing to a DataSet.ipynb\n",
      "03/11/2021  12:57           522,174 Performing-measurements-using-qcodes-parameters-and-dataset.ipynb\n",
      "28/10/2021  13:01            16,384 qcodes_11.nc\n",
      "29/10/2021  09:25            16,384 qcodes_13.nc\n",
      "29/10/2021  09:26            16,384 qcodes_15.nc\n",
      "29/10/2021  09:31            16,384 qcodes_17.nc\n",
      "10/11/2021  09:53            16,384 qcodes_19.nc\n",
      "29/10/2021  09:27           339,144 qcodes_2.nc\n",
      "29/10/2021  09:28           339,144 qcodes_4.nc\n",
      "27/09/2021  08:36    <DIR>          Real_instruments\n",
      "29/10/2021  09:28            32,768 reimport_example.db\n",
      "27/09/2021  08:36            53,575 Saving_data_in_the_background.ipynb\n",
      "27/09/2021  08:36            12,010 subscriber json exporter.ipynb\n",
      "27/09/2021  08:36            19,815 The-Experiment-Container.ipynb\n",
      "27/09/2021  08:36           167,974 Threaded data acquisition.ipynb\n",
      "28/09/2021  10:48           447,548 Using_doNd_functions_in_comparison_to_Measurement_context_manager_for_performing_measurements.ipynb\n",
      "27/09/2021  08:36            36,354 Working with snapshots.ipynb\n",
      "25/10/2021  13:24           212,189 Working-With-Pandas-and-XArray.ipynb\n",
      "              37 File(s)     11,117,186 bytes\n",
      "               5 Dir(s)  305,320,095,744 bytes free\n"
     ]
    }
   ],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can interact with the dataset via the `cache` attribute of the dataset in the same way as you can with a regular dataset. However the in memory dataset does not implement methods that provide direct access to the data from the dataset object it self (get_parameter_data etc.) since these read data from the database. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reloading data from db and exported file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes import load_by_run_spec\n",
    "from qcodes.dataset.plotting import plot_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = load_by_run_spec(captured_run_id=datasetinmem.captured_run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<AxesSubplot:title={'center':'Run #19, Experiment in_mem_exp (no sample)'}, xlabel='Gate ch1 (V)', ylabel='Gate v1 (V)'>],\n",
       " [None])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dataset(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a dataset is loaded using ``load_by_run_spec`` and related functions QCoDeS will first check if the data is available in the database. If not if will check if the data has been exported to ``netcdf`` and then try to load the data from the last known export location. If this fails a warning will be raised and the dataset will be loaded without any raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dataset can also be loaded directly from the netcdf file. See [Exporting QCoDes Datasets](./Exporting-data-to-other-file-formats.ipynb) for more information on how this is done. Including information about how you can change the ``netcdf`` location."
   ]
  },
  {
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