1D-Loop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a 1D Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing that we want to do is to perform a simplet sweep experiment, meaning we want to change one parameter and want to measure at least one parameter. \n",
    "In order to do so we first have to import the required packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qcodes as qc\n",
    "\n",
    "from qcodes.tests.instrument_mocks import DummyInstrument\n",
    "from qcodes.instrument.parameter import Parameter\n",
    "from qcodes.instrument.specialized_parameters import ElapsedTimeParameter\n",
    "from qcodes.loops import Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have to define our instruments and parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "src1 = DummyInstrument(name='src1', gates=['v1', 'v2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = ElapsedTimeParameter('t')\n",
    "V_appl = Parameter('V', label='V_appl', unit='V', get_cmd=src1.v1.get, set_cmd=src1.v1.set)\n",
    "I_meas = Parameter('I', label='I_meas', unit='A', get_cmd=lambda: float(src1.v1()/1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define the experiment. This is done by using the loop function in QCoDeS. In this case we want to sweep the voltage applied and measure the current and the time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.reset_clock()\n",
    "loop = Loop(V_appl.sweep(-1,1,0.01), delay = 0.01).each(V_appl,I_meas,t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we run the measurement by simply executing the following command. We receive a set of information about start/end date of the experiment such as the shape of the data that has been aquired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started at 2020-05-29 16:00:28\n",
      "DataSet:\n",
      "   location = 'data/2020-05-29/#001_{name}_16-00-28'\n",
      "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
      "   Setpoint | V_set      | V            | (201,)\n",
      "   Measured | V          | V            | (201,)\n",
      "   Measured | I          | I            | (201,)\n",
      "   Measured | t          | t            | (201,)\n",
      "Finished at 2020-05-29 16:00:30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataSet:\n",
       "   location = 'data/2020-05-29/#001_{name}_16-00-28'\n",
       "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
       "   Setpoint | V_set      | V            | (201,)\n",
       "   Measured | V          | V            | (201,)\n",
       "   Measured | I          | I            | (201,)\n",
       "   Measured | t          | t            | (201,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loop.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Loop Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is always helpful to also plot the data. This is done by using the plot function that comes with the loop experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from qcodes.plots.pyqtgraph import QtPlot\n",
    "\n",
    "loop = Loop(V_appl.sweep(-1,1,0.01), delay = 0.01).each(V_appl,I_meas,t)\n",
    "data = loop.get_data_set(name='sweep')\n",
    "plot = QtPlot()\n",
    "plot.add(data.I)\n",
    "loop.run()\n",
    "plot.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to plot the data inside the jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<qcodes.plots.pyqtgraph.QtPlot at 0x1056c5510>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional note\n",
    "Because there have been some questions about this: It is very useful to store the plot that we just created for quick presentation of the raw data. This is done by as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.save('IV_sweep_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots give us a quick feedback about the measurement that we are running. The fastest way of getting this feedback is by live plotting the data aquired. The loop functions comes with a built-in live plotting function which is implemented as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started at 2020-05-12 17:27:26\n",
      "DataSet:\n",
      "   location = 'data/2020-05-12/#037_sweep_17-27-25'\n",
      "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
      "   Setpoint | V_set      | V            | (201,)\n",
      "   Measured | V          | V            | (201,)\n",
      "   Measured | I          | I            | (201,)\n",
      "   Measured | t          | t            | (201,)\n",
      "Finished at 2020-05-12 17:27:51\n"
     ]
    }
   ],
   "source": [
    "loop = Loop(V_appl.sweep(-1,1,0.01), delay = 0.1).each(V_appl,I_meas,t)\n",
    "data = loop.get_data_set(name='sweep')\n",
    "plot = QtPlot()\n",
    "plot.add(data.I)\n",
    "_ = loop.with_bg_task(plot.update).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Storing Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We all know the problem: Some time after the measurement you are searching for a particular file of data that you measured. You remeber exactly the setup you used and you also remember the time at which you did it, but where did the file go?\n",
    "In order to aviod this file-mess QCoDeS comes with a native database integration, which is implemented into a loop in just a few steps.\n",
    "It automatically formats/stores and manages the data for you in an sqlite database. You just have to tell QCoDeS the database location and everything else is taken care of. This means: You can not accidentally overwrite an experiment (every experiment has a uniquie ID). Some quick tips: Use a storage drive that is protected by some kind of backup system (like raid), use one db for each setup, since only one script can write into the db at a time. Storing the data per setup helps you on a long term to collect a lot of data in one place.\n",
    "You start by defining the database location such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes import initialise_database, load_or_create_experiment\n",
    "from qcodes.data.location import FormatLocation\n",
    "from qcodes.dataset.legacy_import import import_dat_file\n",
    "from qcodes.data.io import DiskIO\n",
    "\n",
    "exp_name = 'DC-IV-Sweep'\n",
    "sample_name = 'Test-Resistor'\n",
    "\n",
    "qc.config[\"core\"][\"db_location\"]='./experiments.db'\n",
    "initialise_database()\n",
    "exp=load_or_create_experiment(exp_name,sample_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop = Loop(V_appl.sweep(-1,1,0.01), delay = 0.001).each(V_appl,I_meas,t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how you store the data aquired by the loop in a .dat file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc_fmt='./data/{date}/#{counter}_{name}_{sample}'\n",
    "rcd={'name':exp_name, 'sample': sample_name}\n",
    "loc_provider = FormatLocation(fmt=loc_fmt)\n",
    "loc = loc_provider(DiskIO(\".\"),record=rcd)\n",
    "data = loop.get_data_set(location=loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started at 2020-05-12 17:48:00\n",
      "DataSet:\n",
      "   location = './data/2020-05-12/#003_DC-IV-Sweep_Test-Resistor'\n",
      "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
      "   Setpoint | V_set      | V            | (201,)\n",
      "   Measured | V          | V            | (201,)\n",
      "   Measured | I          | I            | (201,)\n",
      "   Measured | t          | t            | (201,)\n",
      "Finished at 2020-05-12 17:48:01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataSet:\n",
       "   location = './data/2020-05-12/#003_DC-IV-Sweep_Test-Resistor'\n",
       "   <Type>   | <array_id> | <array.name> | <array.shape>\n",
       "   Setpoint | V_set      | V            | (201,)\n",
       "   Measured | V          | V            | (201,)\n",
       "   Measured | I          | I            | (201,)\n",
       "   Measured | t          | t            | (201,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loop.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is how you import that .dat file's data into the database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 2. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2, 2, 2]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_dat_file(loc,exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Data form the DB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading data from the QCoDeS database is pretty simple. You just use the same command that has previously been used for storing the data. The data can automatically be exported in file formats like pandas dataframes. These are expremly usefull when it comes to analysing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DC-IV-Sweep#Test-Resistor#1@/Users/jonaskolzer/Documents/Quantacademy/Tutorial_Code/experiments.db\n",
       "--------------------------------------------------------------------------------------------------\n",
       "1-results-1-V_set,I,V,t-603\n",
       "2-results-2-V_set,I,V,t-603"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_or_create_experiment(exp_name,sample_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_or_create_experiment(exp_name,sample_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = data.data_set(2).get_data_as_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I':              I\n",
       " V_set         \n",
       " -1.00 -0.00100\n",
       " -0.99 -0.00099\n",
       " -0.98 -0.00098\n",
       " -0.97 -0.00097\n",
       " -0.96 -0.00096\n",
       " ...        ...\n",
       "  0.96  0.00096\n",
       "  0.97  0.00097\n",
       "  0.98  0.00098\n",
       "  0.99  0.00099\n",
       "  1.00  0.00100\n",
       " \n",
       " [201 rows x 1 columns],\n",
       " 'V':           V\n",
       " V_set      \n",
       " -1.00 -1.00\n",
       " -0.99 -0.99\n",
       " -0.98 -0.98\n",
       " -0.97 -0.97\n",
       " -0.96 -0.96\n",
       " ...     ...\n",
       "  0.96  0.96\n",
       "  0.97  0.97\n",
       "  0.98  0.98\n",
       "  0.99  0.99\n",
       "  1.00  1.00\n",
       " \n",
       " [201 rows x 1 columns],\n",
       " 't':                  t\n",
       " V_set             \n",
       " -1.00  1895.141532\n",
       " -0.99  1895.143006\n",
       " -0.98  1895.144407\n",
       " -0.97  1895.145641\n",
       " -0.96  1895.147224\n",
       " ...            ...\n",
       "  0.96  1895.424350\n",
       "  0.97  1895.425819\n",
       "  0.98  1895.427316\n",
       "  0.99  1895.428629\n",
       "  1.00  1895.429915\n",
       " \n",
       " [201 rows x 1 columns]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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