{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QCoDeS Example with Yokogawa GS200 and Keithley 7510 Multimeter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will show how to use the Yokogawa GS200 smu and keithley 7510 dmm to perform a sweep measurement. The GS200 smu will source current through a 10 Ohm resistor using the **program** feature, and **trigger** the the 7510 dmm, which will measure the voltage across the resistor by **digitize** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from qcodes.dataset.plotting import plot_dataset\n",
    "from qcodes.dataset.measurements import Measurement\n",
    "\n",
    "from qcodes.instrument_drivers.yokogawa.GS200 import GS200\n",
    "from qcodes.instrument_drivers.tektronix.keithley_7510 import Keithley7510"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to: YOKOGAWA GS210 (serial:91W434594, firmware:2.02) in 0.06s\n",
      "Connected to: KEITHLEY INSTRUMENTS DMM7510 (serial:04450961, firmware:1.6.7d) in 0.04s\n"
     ]
    }
   ],
   "source": [
    "gs = GS200(\"gs200\", 'USB0::0x0B21::0x0039::91W434594::INSTR')\n",
    "dmm = Keithley7510(\"dmm_7510\", 'USB0::0x05E6::0x7510::04450961::INSTR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.reset()\n",
    "dmm.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. GS200 setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set the source mode to be \"current\" (by default it's \"votage\"), and set the current range and votlage limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.source_mode('CURR')\n",
    "gs.current(0)\n",
    "gs.current_range(.01)\n",
    "gs.voltage_limit(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the output should be off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'off'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Trigger Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The BNC port will be use for triggering out. There are three different settings for trigger out signal:\n",
    "\n",
    "• **Trigger** (default)\n",
    "\n",
    "This pin transmits the TrigBusy signal. A low-level signal upon trigger generation and a\n",
    "high-level signal upon source operation completion.\n",
    "\n",
    "• **Output**\n",
    "\n",
    "This pin transmits the output state. A high-level signal if the output is off and a lowlevel\n",
    "signal if the output is on.\n",
    "\n",
    "• **Ready**\n",
    "\n",
    "This pin transmits the source change completion signal (Ready). This is transmitted\n",
    "10 ms after the source level changes as a low pulse with a width of 10 μs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "By default, the setting for BNC trigger out is \"TRIG\".\n"
     ]
    }
   ],
   "source": [
    "print(f'By default, the setting for BNC trigger out is \"{gs.BNC_out()}\".')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Program the sweep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GS200 does not have a build-in \"sweep\" function, but the \"program\" feature can generate a source data pattern that user specified as a program in advance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is a simple program, in which the current changes first to 0.01A, then -0.01A, and returns to 0A:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.start() # Starts program memory editing\n",
    "gs.current(0.01)\n",
    "gs.current(-0.01)\n",
    "gs.current(0.0)\n",
    "gs.program.end()  # Ends program memory editing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be save to the system memory (memory of the GS200):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.save('test1_up_and_down.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage of saving to the memory is that the user can have multiple patterns stored:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.start() # Starts program memory editing\n",
    "gs.current(0.01)\n",
    "gs.current(-0.01)\n",
    "gs.current(0.005)\n",
    "gs.current(0.0)\n",
    "gs.program.end()  # Ends program memory editing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.save('test2_up_down_up.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the first one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.load('test1_up_and_down.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interval time between each value is set as following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The interval time is 0.1 s\n"
     ]
    }
   ],
   "source": [
    "gs.program.interval(.1)\n",
    "print(f'The interval time is {float(gs.program.interval())} s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the change is instant, so the output would be like the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'source current(A)')"
      ]
     },
     "execution_count": 13,
     "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": [
    "t_axis = [0, 0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.4]\n",
    "curr_axis = [0, 0.01, 0.01, -0.01, -0.01, 0, 0, 0]\n",
    "plt.plot(t_axis, curr_axis)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('source current(A)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we want to introduce a \"slope\" between each source values: (see the user's manual for more examples of the \"slope time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope time is 0.1 s\n"
     ]
    }
   ],
   "source": [
    "gs.program.slope(.1)\n",
    "print(f'The slope time is {float(gs.program.slope())} s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result, the expected output current will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'source current(A)')"
      ]
     },
     "execution_count": 15,
     "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": [
    "t_axis = [0, 0.1, 0.2, 0.3, 0.4]\n",
    "curr_axis = [0, 0.01, -0.01, 0, 0]\n",
    "plt.plot(t_axis, curr_axis)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('source current(A)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the GS200 will keep repeating this pattern once it starts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ON'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.program.repeat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only want it to generate the pattern once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The program repetition mode is now OFF.\n"
     ]
    }
   ],
   "source": [
    "gs.program.repeat('OFF')\n",
    "print(f'The program repetition mode is now {gs.program.repeat()}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: at this moment, the output of the GS200 should still be off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'off'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Keithley 7510 Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Setup basic digitize mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DMM7510 digitize functions make fast, predictably spaced measurements. The speed, sensitivity, and bandwidth of the digitize functions allows you to make accurate voltage and current readings of fast signals, such as those associated with sensors, audio, medical devices, power line issues, and industrial processes. The digitize functions can provide 1,000,000 readings per second at 4½ digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set the digitize function to measure voltage, and the range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dmm.digi_sense_function('voltage')\n",
    "dmm.digi_sense.range(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The system will determines when the 10 MΩ input divider is enabled: (for voltage measurement only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmm.digi_sense.input_impedance('AUTO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define the precise acquisition rate at which the digitizing measurements are made: (this is for digitize mode only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acquisition rate is 10000 digitizing measurements per second.\n"
     ]
    }
   ],
   "source": [
    "readings_per_second = 10000\n",
    "dmm.digi_sense.acq_rate(readings_per_second)\n",
    "print(f'The acquisition rate is {dmm.digi_sense.acq_rate()} digitizing measurements per second.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will let the system to decide the aperture size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmm.digi_sense.aperture('AUTO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to tell the instrument how many readings will be recorded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 measurements will be made every time the digitize function is triggered.\n"
     ]
    }
   ],
   "source": [
    "number_of_readings = 4000\n",
    "dmm.digi_sense.count(number_of_readings)\n",
    "print(f'{dmm.digi_sense.count()} measurements will be made every time the digitize function is triggered.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Use an user buffer to store the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer_name = 'userbuff01'\n",
    "buffer_size = 100000\n",
    "buffer = dmm.buffer(buffer_name, buffer_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The user buffer \"userbuff01\" can store 100000 readings, which is more than enough for this example.\n"
     ]
    }
   ],
   "source": [
    "print(f'The user buffer \"{buffer.short_name}\" can store {buffer.size()} readings, which is more than enough for this example.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the benefits of using a larger size is: base on the settings above, the GS200 will send more than one trigger to the 7510. Technically, once a trigger is received, the 7510 unit would ignore any other trigger until it returns to idle. However, in reality it may still response to more than the first trigger. A large size will prevent the data in the buffer from being overwritten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 readings in the buffer at this moment.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {buffer.last_index() - buffer.first_index()} readings in the buffer at this moment.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Setup the tigger in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the falling edge will be used to trigger the measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FALL'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmm.trigger_in_ext_edge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NONE'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmm.digitize_trigger()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want an external trigger to trigger the measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EXT'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmm.digitize_trigger('external')\n",
    "dmm.digitize_trigger()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Check for errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    smu_error = gs.system_errors()\n",
    "    if 'No error' in smu_error:\n",
    "        break\n",
    "    print(smu_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    dmm_error = dmm.system_errors()\n",
    "    if 'No error' in dmm_error:\n",
    "        break\n",
    "    print(dmm_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Make the measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To clear the external trigger in, and clear the buffer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmm.trigger_in_ext_clear()\n",
    "buffer.clear_buffer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are total 0 readings in the buffer \"userbuff01\".\n"
     ]
    }
   ],
   "source": [
    "total_data_points = int(buffer.number_of_readings())\n",
    "print(f'There are total {total_data_points} readings in the buffer \"{buffer.short_name}\".')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the measurement by turning the GS200 on and run the program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleep_time = 1  # a sleep time is required, or the GS200 will turn off right away, and won't run the whole program\n",
    "with gs.output.set_to('on'):\n",
    "    gs.program.run()\n",
    "    time.sleep(sleep_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GS200 should be off after running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'off'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8000 readings in total, so the measurement was performed 2.0 times.\n"
     ]
    }
   ],
   "source": [
    "total_data_points = int(buffer.number_of_readings())\n",
    "print(f'There are {total_data_points} readings in total, so the measurement was performed {round(total_data_points/4000, 2)} times.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a time series as the setpoints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 1/readings_per_second\n",
    "t0 = 0\n",
    "t1 = (total_data_points-1)*dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer.set_setpoints(start=buffer.t_start, stop=buffer.t_stop, label='time') # \"label\" will be used for setpoints name\n",
    "buffer.t_start(t0)\n",
    "buffer.t_stop(t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set number of points by specifying the \"data_start\" point and \"data_end\" point for the buffer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer.data_start(1)\n",
    "buffer.data_end(total_data_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"n_pts\" is read only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.n_pts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The setpoints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000e+00, 1.000e-04, 2.000e-04, ..., 7.997e-01, 7.998e-01,\n",
       "       7.999e-01])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.setpoints()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are the available elements that saved to the buffer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date',\n",
       " 'fractional_seconds',\n",
       " 'measurement',\n",
       " 'measurement_formatted',\n",
       " 'measurement_status',\n",
       " 'measurement_unit',\n",
       " 'relative_time',\n",
       " 'seconds',\n",
       " 'time',\n",
       " 'timestamp'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.available_elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User can select multiple ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'relative_time']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.elements(['measurement', 'relative_time'])\n",
    "buffer.elements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 167. \n"
     ]
    }
   ],
   "source": [
    "meas = Measurement()\n",
    "meas.register_parameter(buffer.data)\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    data = buffer.data\n",
    "    datasaver.add_result((buffer.data, data()))\n",
    "    \n",
    "    dataid = datasaver.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axes._subplots.AxesSubplot at 0x21cbfeafac8>,\n",
       "  <matplotlib.axes._subplots.AxesSubplot at 0x21cbfeb0ac8>],\n",
       " [None, None])"
      ]
     },
     "execution_count": 45,
     "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"
    },
    {
     "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(datasaver.dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(The second plot is t-t plot so a straight line.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'measurement': {'measurement': array([ 3.030930e-06,  6.061867e-06,  2.273203e-04, ..., -7.226490e-12,\n",
       "         -1.515476e-06, -6.061881e-06]),\n",
       "  'time': array([0.000e+00, 1.000e-04, 2.000e-04, ..., 7.997e-01, 7.998e-01,\n",
       "         7.999e-01])},\n",
       " 'relative_time': {'relative_time': array([0.000000e+00, 1.000010e-04, 2.000020e-04, ..., 7.997029e-01,\n",
       "         7.998029e-01, 7.999029e-01]),\n",
       "  'time': array([0.000e+00, 1.000e-04, 2.000e-04, ..., 7.997e-01, 7.998e-01,\n",
       "         7.999e-01])}}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = datasaver.dataset\n",
    "dataset.get_parameter_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load previously saved pattern (GS200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember we had another pattern stored? Let's load that one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.program.load('test2_up_down_up.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alwasy clear the buffer, and external trigger in for the dmm, at the beginning of each measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmm.trigger_in_ext_clear()\n",
    "buffer.clear_buffer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 readings in the buffer \"userbuff01\".\n"
     ]
    }
   ],
   "source": [
    "total_data_points = int(buffer.number_of_readings())\n",
    "print(f'There are {total_data_points} readings in the buffer \"{buffer.short_name}\".')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleep_time = 1\n",
    "with gs.output.set_to('on'):\n",
    "    gs.program.run()\n",
    "    time.sleep(sleep_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8000 readings in total, so the measurement was performed 2.0 times.\n"
     ]
    }
   ],
   "source": [
    "total_data_points = int(buffer.number_of_readings())\n",
    "print(f'There are {total_data_points} readings in total, so the measurement was performed {round(total_data_points/4000, 2)} times.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'relative_time']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.elements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 168. \n"
     ]
    }
   ],
   "source": [
    "meas = Measurement()\n",
    "meas.register_parameter(buffer.data)\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    data = buffer.data\n",
    "    datasaver.add_result((buffer.data, data()))\n",
    "    \n",
    "    dataid = datasaver.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axes._subplots.AxesSubplot at 0x21cc01f0dc8>,\n",
       "  <matplotlib.axes._subplots.AxesSubplot at 0x21cc01ed388>],\n",
       " [None, None])"
      ]
     },
     "execution_count": 54,
     "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"
    },
    {
     "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(datasaver.dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the available elements are not numerical values, for example, \"timestamp\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['timestamp', 'measurement']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffer.elements(['timestamp', 'measurement'])\n",
    "buffer.elements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x21cc17c0288>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement()\n",
    "meas.register_parameter(buffer.data, paramtype=\"array\")  # remember to set paramtype=\"array\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 169. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "    data = buffer.data\n",
    "    datasaver.add_result((buffer.data, data()))\n",
    "    \n",
    "    dataid = datasaver.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'measurement': {'measurement': array([[-1.363910e-05,  1.363921e-05,  1.970109e-04, ...,  3.485577e-05,\n",
       "           4.394858e-05,  3.637124e-05]]),\n",
       "  'time': array([[0.000e+00, 1.000e-04, 2.000e-04, ..., 7.997e-01, 7.998e-01,\n",
       "          7.999e-01]])},\n",
       " 'timestamp': {'timestamp': array([['10/22/2020 07:02:50.876712500', '10/22/2020 07:02:50.876812501',\n",
       "          '10/22/2020 07:02:50.876912502', ...,\n",
       "          '10/22/2020 07:02:51.676415373', '10/22/2020 07:02:51.676515374',\n",
       "          '10/22/2020 07:02:51.676615375']], dtype='<U29'),\n",
       "  'time': array([[0.000e+00, 1.000e-04, 2.000e-04, ..., 7.997e-01, 7.998e-01,\n",
       "          7.999e-01]])}}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.get_parameter_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs.reset()\n",
    "dmm.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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