Getting Started/02_power_supply.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ece723e4",
   "metadata": {},
   "source": [
    "# Power Supply\n",
    "\n",
    "With the PSLab's power supply, we can control programmable voltage and current sources to power many different kinds of experiments.\n",
    "\n",
    "## Requirements\n",
    "\n",
    "- Jumper wire x 3\n",
    "- Smallish resistor (~100R)\n",
    "- LED x 2\n",
    "\n",
    "As usual, start by importing the library and connecting to the device:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed7ae512",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pslab\n",
    "psl = pslab.ScienceLab()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424d681d",
   "metadata": {},
   "source": [
    "## Voltage sources\n",
    "\n",
    "The power supply is used by assigning the desired output value to the appropriate pin. We can read back the actual output value by connecting the voltage source pins (PV1-3) to the multimeter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c07ad276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3271794871794872"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psl.power_supply.pv1 = 1.7\n",
    "psl.multimeter.measure_voltage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1f08e0",
   "metadata": {},
   "source": [
    "### Blinking an LED\n",
    "\n",
    "Now that we've introducted the PSLab's programmable voltage source, we can do the \"Hello world!\" of hardware: Blinking an LED. Connect the positive leg of the LED to PV1, in series with a resistor, per this diagram: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4993520",
   "metadata": {},
   "source": [
    "<img src=\"images/blink_led.svg\" width=\"500\" height=\"340\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7917647f",
   "metadata": {},
   "source": [
    "**Do not connect the LED directly to the PV1 pin without a resistor in series!** If you connect the LED directly to the PV1 pin you may burn out the LED."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fa4697b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "for i in range(20):\n",
    "    psl.power_supply.pv1 = (5, 0)[i % 2]\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b32ff8e",
   "metadata": {},
   "source": [
    "### Changing the current direction\n",
    "\n",
    "The PV1 and PV2 pins can output negative voltages, i.e. they can drive the current in both directions. In order to measure negative voltages we must use one of CH1-3 as input to the multimeter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3a20eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.09808836996337"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psl.power_supply.pv1 = -1.7\n",
    "psl.multimeter.measure_voltage(channel=\"CH1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887d6437",
   "metadata": {},
   "source": [
    "We can visualize this using a second LED connected in parallel with the first, but with reversed polarity:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33cf1343",
   "metadata": {},
   "source": [
    "<img src=\"images/blink_led_reverse.svg\" width=\"500\" height=\"340\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78a2f3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(20):\n",
    "    psl.power_supply.pv1 = (5, -5)[i % 2]\n",
    "    time.sleep(0.5)\n",
    "psl.power_supply.pv1 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0747695",
   "metadata": {},
   "source": [
    "## Current source\n",
    "\n",
    "The PCS pin is a programmable current source. It can output a current up to 3.3 mA. In order to measure the generated current, we can connect a resistor between PCS and GND, and also connect the PCS pin to the multimeter.\n",
    "\n",
    "Before we turn on the current, the multimeter will read 0 V, since it's connected to ground via the resistor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60ceefd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psl.multimeter.measure_voltage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b101d912",
   "metadata": {},
   "source": [
    "Once we turn on the current, we can use the multimeter to read the voltage drop across the resistor. In this case we use a 330 Ω resistor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb244efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0021611721611721614"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psl.power_supply.pcs = 2e-3\n",
    "v_drop = psl.multimeter.measure_voltage()\n",
    "v_drop / 330  # Ohm's law, I = U / R"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3cf01b",
   "metadata": {},
   "source": [
    "Be aware that the full current range up to 3.3 mA is not always available; the actual maximum current depends on the load resistance:\n",
    "\n",
    "I_max = 3.3 V / (1 kΩ + R_load)\n",
    "\n",
    "In the case of the 330 Ω resistor we used here, the highest current we can get from the PCS pin is:\n",
    "\n",
    "3.3 V / 1330 Ω = 2.5 mA\n",
    "\n",
    "Let's take a look at what happens when we try to set a higher current than the source is able to provide:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e27bc98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f07461e6320>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "currents = np.arange(0, 3.3e-3, 1e-4)\n",
    "actual = []\n",
    "for i in currents:\n",
    "    psl.power_supply.pcs = i\n",
    "    v_drop = psl.multimeter.measure_voltage()\n",
    "    actual.append(v_drop / 330)\n",
    "plt.plot(currents, actual)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272ec5cf",
   "metadata": {},
   "source": [
    "We see that the actual current starts to drop sharply if a higher current than is available is requested. This highlights the importance of accounting for the load resistance when doing experiments with the current source.\n",
    "\n",
    "That's it for the power source! Next, we will see how we can use the PSLab's oscilloscope to capture large amounts of analog data very quickly."
   ]
  }
 ],
 "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}