control_pid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:26:55.714304Z",
     "start_time": "2020-05-09T22:26:54.187965Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle, glob, matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import os, sys, serial, time, visdom\n",
    "from tqdm import tqdm, trange\n",
    "from tabulate import tabulate\n",
    "sys.path.append(r\"C:\\Users\\gkoolstra\\Documents\\Code\\qtrl\")\n",
    "sys.path.append(r\"C:\\Users\\gkoolstra\\Documents\\Arduino\\PID_control_sketch\")\n",
    "from pid import ArduinoPID\n",
    "from qtrl.fitting import common, fit\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Figures/PID_Schematic.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize the controller and turn off ramping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:26:59.478275Z",
     "start_time": "2020-05-09T22:26:57.292732Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PID Initialized!\n"
     ]
    }
   ],
   "source": [
    "Arnie = ArduinoPID()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:02.080575Z",
     "start_time": "2020-05-09T22:27:02.018648Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.dev.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:03.271226Z",
     "start_time": "2020-05-09T22:27:02.945400Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_ramping(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:14.170821Z",
     "start_time": "2020-05-09T22:27:09.005310Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════════════╤════════════════╕\n",
      "│ Function call      │ Result         │\n",
      "╞════════════════════╪════════════════╡\n",
      "│ get_display_mode   │ V              │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_manual_status  │ 0              │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_ramping_status │ 0              │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_cts_dac_value  │ 0.0            │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_input          │ 0.008056640625 │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_output         │ 0.0            │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_setpoint       │ 0.0            │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_Kp             │ 2.0            │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_Ki             │ 5.0            │\n",
      "├────────────────────┼────────────────┤\n",
      "│ get_Kd             │ 0.0            │\n",
      "╘════════════════════╧════════════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Arnie.test_sequence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monitor noise on the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:32:59.761558Z",
     "start_time": "2020-04-03T03:32:59.428209Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_ramping(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:33:05.960728Z",
     "start_time": "2020-04-03T03:33:05.884881Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_manual_output(0.5, output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:33:08.992878Z",
     "start_time": "2020-04-03T03:33:08.676720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arnie.get_manual_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:34:57.240123Z",
     "start_time": "2020-04-03T03:34:09.949031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input noise on the controller is 0.56mV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Input voltage (V)')"
      ]
     },
     "execution_count": 10,
     "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": [
    "inp = Arnie.monitor_controller(100)\n",
    "print(f\"Input noise on the controller is {np.std(inp)*1e3:.2f}mV\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(inp)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Input voltage (V)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the PID controller to a step response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:29.065864Z",
     "start_time": "2020-05-09T22:27:28.486003Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_ramping(False)\n",
    "Arnie.set_manual_output(None, output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:37.428166Z",
     "start_time": "2020-05-09T22:27:34.327720Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_tuning_parameters(0.01, 2.5, 0.00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:27:39.136191Z",
     "start_time": "2020-05-09T22:27:39.073693Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.dev.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:29:11.992800Z",
     "start_time": "2020-05-09T22:29:11.925978Z"
    }
   },
   "outputs": [],
   "source": [
    "Arnie.set_setpoint(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-09T22:30:00.614789Z",
     "start_time": "2020-05-09T22:29:12.068343Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input noise on the controller is 12.22mV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Input voltage (V)')"
      ]
     },
     "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": [
    "# target = 0.5\n",
    "# Arnie.set_setpoint(target)\n",
    "# time.sleep(0.25)\n",
    "inp = Arnie.monitor_controller(100)\n",
    "print(f\"Input noise on the controller is {np.std(inp)*1e3:.2f}mV\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(inp)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Input voltage (V)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test minimizer of the `cts_dac_output`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:36:42.089511Z",
     "start_time": "2020-04-03T03:36:41.766488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arnie.get_Kd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T03:36:58.968559Z",
     "start_time": "2020-04-03T03:36:53.877419Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════════════╤═══════════════════╕\n",
      "│ Function call      │ Result            │\n",
      "╞════════════════════╪═══════════════════╡\n",
      "│ get_display_mode   │ V                 │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_manual_status  │ 0                 │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_ramping_status │ 0                 │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_cts_dac_value  │ 0.0               │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_input          │ 0.49951171875     │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_output         │ 0.501018310546875 │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_setpoint       │ 0.49951171875     │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_Kp             │ 0.01              │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_Ki             │ 2.5               │\n",
      "├────────────────────┼───────────────────┤\n",
      "│ get_Kd             │ 0.0               │\n",
      "╘════════════════════╧═══════════════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Arnie.test_sequence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-28T22:35:50.738749Z",
     "start_time": "2020-03-28T22:35:50.661475Z"
    }
   },
   "outputs": [],
   "source": [
    "def minimize_cts_dac(cts_dac_sweep_pts, output):\n",
    "    Arnie.set_manual_output(output, output=True)\n",
    "    time.sleep(0.25)\n",
    "    \n",
    "    input_values = list()\n",
    "    for cts_dac_pt in tqdm(cts_dac_sweep_pts):\n",
    "        print(cts_dac_pt)\n",
    "#         Arnie.set_manual_output(cts_dac_pt)\n",
    "#         Arnie.set_cts_dac_output(cts_dac_pt)\n",
    "        time.sleep(0.25)\n",
    "        input_values.append(Arnie.get_input())\n",
    "        time.sleep(0.25)\n",
    "        \n",
    "    y_data = np.array(input_values)\n",
    "    x_data = cts_dac_sweep_pts\n",
    "    \n",
    "    g_offset = np.min(y_data)\n",
    "    g_deriv = 1.0\n",
    "    g_x0 = np.mean(x_data)\n",
    "    \n",
    "    fr = fit.fit1d(x_data, y_data, common.parabola, g_offset, g_deriv, g_x0, ret=True)\n",
    "    \n",
    "    if isinstance(fr, int):\n",
    "        print(\"Fitting failed\")\n",
    "    else:\n",
    "        cts_dac_setpoint = fr['params_dict']['x0']\n",
    "        print(f\"Setting 2nd DAC output to {cts_dac_setpoint:.3f} V\")\n",
    "#         Arnie.set_cts_dac_output(cts_dac_setpoint) # Set the cts dac value to the minimum\n",
    "        Arnie.set_manual_output(None, output=False) # Turn on the controller again\n",
    "        print(f\"Optimized controller!\")\n",
    "        \n",
    "    return x_data, y_data, fr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-28T22:45:54.406146Z",
     "start_time": "2020-03-28T22:45:45.404864Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/11 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  9%|███████▌                                                                           | 1/11 [00:00<00:07,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.275\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|███████████████                                                                    | 2/11 [00:01<00:06,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|██████████████████████▋                                                            | 3/11 [00:02<00:06,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|██████████████████████████████▏                                                    | 4/11 [00:03<00:05,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 45%|█████████████████████████████████████▋                                             | 5/11 [00:03<00:04,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 55%|█████████████████████████████████████████████▎                                     | 6/11 [00:04<00:03,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 64%|████████████████████████████████████████████████████▊                              | 7/11 [00:05<00:03,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42500000000000004\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 73%|████████████████████████████████████████████████████████████▎                      | 8/11 [00:06<00:02,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|███████████████████████████████████████████████████████████████████▉               | 9/11 [00:06<00:01,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.475\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 91%|██████████████████████████████████████████████████████████████████████████▌       | 10/11 [00:07<00:00,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 11/11 [00:08<00:00,  1.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting 2nd DAC output to 0.441 V\n",
      "Optimized controller!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Controller input (V)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QoKGhgUKhQGdn2dsHjTkDJY6vZbdpLXssRsnEvW3r+uuvGe+48cZ0+Or001PP5FOfgoceqnWEZlZDnZ2dtLa20t3dTUTQ3d1Na2vruEge/c6OK+nnpMRyJTAfWA5sBLwROBA4GPhqRFw/OqEO3ajNjnv//fCtb8EFF8CKFXDkkal38ra3gcrd1NDMxqtCoUB3d/c65U1NTSxevHj0AxqC/mbHHXBadUlzgRbgbcC2QC/wAPBr4BcRMSYO7I/6tOqPPw5nnQXf/z48/TTsu29KIEcfDetV86aLZlYvGhoaKPf9KolVY+SkmiFNqx4R90dEW0S8MyJ2ioi3RMSHIuLCsZI0amKbbdJ4x6OPpuTx1FPwgQ/AnDnw3e/Ciy/WOkIzq7KZM2fmKh9LfD+OampshE9+EhYsgMsvh+nT4XOfSxcU/vM/w9JKb8FuZmNNe3s7jY2Na5U1NjbS3t5eo4hGjhPHaJg0CY46Cn7/e7j1Vvjbv4X/+A8oFOAf/gH+/OdaR2hmI6ylpYWOjg6ampqQRFNTEx0dHbS0tNQ6tGHzrWNrZdEi+Pa34dxz4aWX4F3vSuMghxzigXQzqwtDvnWspBsqKbOcZs9O4x09PfCNb6Rex7vfDbvvDuedB6+9VusIzczKGujK8Y0kTQGmStpC0pRsKQDbjVaA496UKWm845FH4Cc/gYh0+GrWrHRdyBg5bc/MJo6Behz/SLp+Y2fStCPzs+VK4KzqhzbBbLhhShj33JNm5d1tN/i3f0s9k0MOgUsugVdfrXWUZmaDj3FI+kxEfG+U4qmKuhzjqERPD/z0p2kcpLs79U4+/GH42MdSYjEzq6IhXQCYVTyhXHlEnD9CsVXdmE0cfVatghtugB//OJ3W+9prsPfeKYEcfzxsummtIzSzcWjIg+PA3kXLAcAZwBEjGp0NrKEhHa66+OJ07ce3v51m4z3pJNh223SI63e/S+MjZmZVlvt0XEmbARdExJhJHmO+x1FOBHR1pSndf/YzeOEF2HHH1As54YR09bqZ2TAMp8dRqheYM/yQbFikdLjqhz+Exx5LZ2RttRV86Uuw/fZpXqyrroKVK2sdqZmNM5Vcx/FLSfOy5VfAg6Qzq6xeTJ685nDVAw/A5z8Pt9ySbjTV1ARtbfDww7WO0szGiUoGx99R9HQl0B0RS6oa1Qgbl4eqBrNiRepx/PjHcPXVaYD9ne+Ej38cjjkm3YDKzGwAQz5UFRG/IfUyNgOmkJKH1bv1119zuKqnJ10T0tOTTufdbjv49KfhrrtqHaWZjUGVHKr6OHA7cAzwfuA2SR+tdmA2gqZPT4erHnoo3a3w8MNTT2TPPdPy/e/Ds8/WOkozGyMqOVT1ILB/RDyVPd8SuCUidhqF+EbEhDxUNZhnnoGLLkpnZd19N2y0ERx7bDqU9Y53eKJFMxvWWVVLgBeKnr8APFrhix4q6UFJCyWdVmb9zpJulfSqpFMrqSvp7yTdJ2mVpHUaZBXaYos1h6vmz4cTT0yHtQ48MM2T9YUvpAH2MXKnMjMbPZX0OM4H3kw6kyqAI0mHrv4CEBHf6qfepGybQ0jJ5w7g+Ii4v2ibrYAm4CjgmYg4c7C6knYBVgE/BE6NiEG7Eu5xVKi3Fy67LF1oeP316Qr17bZLYyXHHgsHHOBb35pNIMPpcTwMXEFKGpASyGPAJtnSn32AhRGxKCJeAy4mJZ3VImJZRNwBrKi0bkQ8EBEPVhC35dXYmAbPr7oKli2DCy9M90s/91w46KCURFpb4dpr01lbZjYhDfrzMSK+NsR9T2ftQ1pLgH1HoS4AklqBVhgf9/gddZttBi0taXnppXRK76WXpqvUf/Qj2HxzOPLI1BM55JA0RmJmE0IlZ1XtKKlD0nWSbuxbKth3udHVSuc3GU7dtHFER0Q0R0TztGnT8lS1UpMnw/vfn5LG8uVw5ZVwxBFr/k6bliZb/MUvUpIxs3GtkgPWPwfOBs4BXs+x7yXAjKLn2wNLR6GuVdNGG6VkccQRaQzkpptST+SKK9LYyBveAIcdlnoi732vZ+41G4cqGeNYGRE/iIjbI2J+31JBvTuAOZJmSdoAOA6YV2Fcw6lro2WDDdLtbjs60qy9N94IH/0o3HprOsQ1bVpKHj/9KTz9dK2jNbMRUslZVWcAy4DLgdW3oIuIQb8JJB0OfBuYBJwbEe2STsrqny1pG6AL2JR0ptSLwNyIeL5c3WyfRwPfA6YBzwJ3R8S7B4rDZ1WNslWr4Lbb0qGrSy9NV6yvt1461ffYY+Goo2DrrWsdpZkNYjg3cnqkTHFExOyRCq7anDhqKCJdJ3LppWl56KF0f5EDDkhJ5Jhj0pXtZlZ3hpw4xgMnjjoRAffeu6Ynct99qfytb01J5Nhj08WHZlYXcicOSQdFxI2Sjim3PiIuG+EYq8aJo049+OCansidd6ayOXPg4IPTcuCBsOWWtY3RbAIbSuL4WkR8VdJPyqyOiBgzEx06cYwBjzwC8+bB//4v/OY36Y6GEuyxx5pEcsAB6dRgMxsVua8cj4ivZn9PLLOMmaQxVJ2dnRQKBRoaGigUCnR2dk6I166ZWbPgc5+DX/4SnnoqzZP1r/+aLkT87nfTKb5bbAFvfzt87Wvw+9+PyNXrE/G9nohtnoiq+jlHxLhf9tprr8jjwgsvjMbGxiBddBhANDY2xoUXXphrP0NRy9euWy+9FHHddRFf+lJEc3OEFAERkydHHHZYxJlnRtx1V8Trr+fa7UR8rydimyeikfqcga4o853qwfEyCoUC3d3d65Q3NTWxePHiEYysvl57zHjmGbj55nRY64Yb0lgJwNSpaVyk79DWDjsMOD38RHyvJ2KbJ6KR+pyHdFaVpAbgrRFxS8WvVIfyJo6GhgbKvS+SWFXlacZr+dpj1l//mhJI3/LXv6bypqY1SeSgg2CbbdaqNhHf64nY5olopD7nIc2OGxGrgP+q+FXGif4mRRyNyRJr+dpj1vTpcMIJcN558OijsGABnHUW7LUXXH55uop9221h113TOMq8efDccxPyvZ6IbZ6Iqv05VzLlyHWSjpUmzi3h2tvbaWxsXKussbGR9vb2cf3a44IEO+0En/pUOs13+XLo6oJ///eUYH70ozSr75Zb0rXeevz7eutxINA3t+94f6/972tiqPrnXG7go3gh3fFvFemeGc9nz58frF49LXkHxyPS4FJTU1NIiqamplEdPKzla497r7wScfPNEV/5SsR++8XrDQ0RECsgHlh//Vh4wAER3/1uxB/+kAblxyH/+5oYRuJzxoPjvo7Dynj+efjd7+CPf0xTo8yfD088kdY1NMAuu8Cee6bDXnvtla4r2Xjj2sZsNkqGM1eVgBZgVkR8XdIMYNuIuL06oY48Jw6rWESa6bcvifQtjz+e1kuw885rEklfMtlkoJthmo1Nw0kcPyAdqjooInaRtAVwXUTsXZ1QR54Thw3b0qVpWpTiZLI0u0WMBDvuuHYyectbfC8SG/P6SxyV3Mhp34jYU9JdABHxTHaPDLOJY7vt0vLe964pe/zxtRPJb34DF120Zn1fMuk71LXnnulKeLMxrpLEsULSJLJbt0qaRuqBmE1s22wD73lPWvo88cTaPZPf/z7dcrfPG9+47mGuKVNGP3azYagkcXyXdBOnrSS1A+8HvlzVqMzGqq23TvNqHXbYmrLly9ckkjvvTDe5uuSSNeunTElXuc+enf4WL9ttlwbpzepIRWdVSdoZOBgQcENEPFDtwEaSxzis7jz5ZEoi99wDDz+8ZunuhtdfX7PdhhumCSD7Eklxcpk1K90D3gzg5ZfTBbA9PWv+9vTAqaemswOHYCjTqg/Yf44Kbh1bL5w4bMxYsSL9py9OJosWrXn84otrtpXSRY3leiqzZ6eezMS5bnd8W7UqjakVJ4S+pa9s+fK160hpxoTzz0/T7gzBUAbH55PGNYr/5fU9D2DM3DrWbMxYf/30pT97NhxyyNrrItKXQ2kyefhhuOYaeOyxtbffbLPyPZUddoDtt4dJk0avXTawF14onwz6liVL1r2NwMYbp/nYZs6E5ub0d8aM9HfmzPSjYoPqnMfUb+KICN/D06yeSLDVVmnZb79117/0UrohVmlP5e674Yor1v7iWX/9tJ8tt0w9k+K//ZVtsUXVvojGrQh45RVYtqx8b6EvSTz77Nr1Jk1KyX3GjPRZ9yWDvmXGjPTDoEY9yn4Th6Q9B6oYEXeOfDhmNmSTJ6eJHHfddd11r7+efrUWJ5UnnoCnn043znrggfT3qadg5cr+X2OTTQZOMOXWbb752OvdrFiRegHPP79mKX6eZ13xmFWfKVNSApg1K92crDQxbLttXb9nAx2qGmhW3AAOGuFYzKxaJk1KhzWamtIU8/2JSOMoTz21JqkUPy4tW7w4/X366VS3HCklj75ksummKZ5Jk9IZY4P9rWSbSvbR0AC9vZV94b/ySmXv68Ybp/Zsskn6u+mmqSfX97hv3ZZbrt1bGOPT1gx0qOrA0QzEzOqAlL7oNtkECoXK661alQ639Jdgiv8+/3zaftWq9Gu8kr95th3Mhhuu+8U+ffq6CaD0eem6yZPruldQTYNexyFpfeCTwNuzopuBH0bE8G/4bGbjQ0ND6k1MmZIucqylvkRTLqk0NnqcZgRUcgHgD4D1ge9nz/8+K/t4tYIyMxuyvsNSVjWVJI69I2L3ouc3SvpTtQIyM7P6Vklafl3SDn1PJM0GypwmYGZmE0ElPY4vAjdJWkS6+K8JOLGqUZmZWd0aMHFIagBeBuYAO5ESx4KIeHUUYjMzszo0YOKIiFWS/isi9gPuGaWYzMysjlUyxnGdpGOzW8iamdkEV8kYx+eBycBKSa+QTXIYEb4vppnZBDRojyMiNomIhojYICI2zZ5XlDQkHSrpQUkLJZ1WZv3Okm6V9KqkUyupK2mKpOslPZT93aKSWMzMbGQMmjgk3VBJWZltJgFnAYcBc4HjJc0t2exp4LPAmTnqnka6mdQc4IbsuZmZjZJ+E4ekjbKbOU2VtEX2S3+KpAKwXQX73gdYGBGLIuI14GLgyOINImJZRNwBlE5fMlDdI4HzssfnAUdVEIuZmY2QgcY4/hE4hZQk5rPmhk7Pk3oDg5kOPFr0fAmwb4VxDVR364h4DCAiHpO0VbkdSGoFWgFmzpxZ4cuamdlg+u1xRMR3sps5nRoRsyNiVrbsHhH/XcG+y52FNfgNzodfN20c0RERzRHRPG3atDxVzWyc6ezspFAo0NDQQKFQoLOzs9YhjWmDnlUVEd+TtD9QKN4+Is4fpOoSYEbR8+2BpRXGNVDdJyRtm/U2tgWWVbhPM5uAOjs7aW1tpbe3F4Du7m5aW1sBaGlpqWVoY1Ylg+MXkAav/wbYO1vWuXl5GXcAcyTNkrQBcBwwr8K4Bqo7D/hI9vgjwJUV7tPMJqC2trbVSaNPb28vbW1tNYpo7KvkOo5mYG5Ef7f3Ki8iVko6GbgWmAScGxH3STopW3+2pG2ALmBTYJWkU7LXer5c3WzX3wT+R9LHgB7g7/LEZWYTS09PT65yG1wlieNeYBvgsbw7j4hfA78uKTu76PHjpMNQFdXNyp8CDs4bi5lNTDNnzqS7u7tsuQ1NJVOOTAXul3StpHl9S7UDMzMbCe3t7TQ2Nq5V1tjYSHt7e40iGvsq6XGcUe0gzMyqpW8AvK2tjZ6eHmbOnEl7e7sHxodBlQxdSNqaNCgOcHtEjKkzmZqbm6Orq6vWYZiZjSmS5kfEOidDVXJW1QeA20mD0B8A/ijp/SMfopmZjQWVHKpqI913fBmApGnA/wK/qGZgZmZWnyoZHG8oOTT1VIX1zMxsHKqkx3GNpGuBn2XPPwhcXb2QzMysnlUy5cgXJR1DunJcQEdEXF71yMzMrC71mzgkvZE0E+0fIuIy4LKs/O2SdoiIh0crSDMzqx8DjVV8G3ihTHlvts7MzCaggRJHISLuKS2MiC7STLlmZjYBDZQ4Nhpg3RtGOhAzMxsbBkocd0j6RGlhNivt/OqFZGZm9Wygs6pOAS6X1MKaRNEMbAAcXe3AzMysPvWbOCLiCWB/SQcCu2bFv4qIG0clMjMzq0uVXMdxE3DTKMRiZmZjgKcOMTOzXJw4zEIwaUcAAAt+SURBVMwsFycOMzPLxYnDzMxyceIwM7NcnDjMzCwXJw4zM8vFicPMzHJx4jAzs1ycOMzMLBcnDjMzy8WJw8zMcnHiMKuBzs5OCoUCDQ0NFAoFOjs7ax2SWcUGnR3XzEZWZ2cnra2t9Pb2AtDd3U1raysALS0ttQzNrCLucZiNsra2ttVJo09vby9tbW01isgsHycOs1HW09OTq9ys3lQ1cUg6VNKDkhZKOq3Mekn6brb+Hkl7Fq37nKR7Jd0n6ZSi8t0l3Srpz5J+KWnTarbBbKTNnDkzV7lZvala4pA0CTgLOAyYCxwvaW7JZocBc7KlFfhBVndX4BPAPsDuwHslzcnqnAOcFhFvBi4HvlitNphVQ3t7O42NjWuVNTY20t7eXqOIzPKpZo9jH2BhRCyKiNeAi4EjS7Y5Ejg/ktuAzSVtC+wC3BYRvRGxEvgNcHRWZyfgt9nj64Fjq9gGsxHX0tJCR0cHTU1NSKKpqYmOjg4PjNuYUc3EMR14tOj5kqyskm3uBd4uaUtJjcDhwIxsm3uBI7LHf1dUvhZJrZK6JHUtX758WA0xG2ktLS0sXryYVatWsXjxYicNG1OqmThUpiwq2SYiHgD+ndSjuAb4E7AyW/9R4NOS5gObAK+Ve/GI6IiI5ohonjZt2lDiNzOzMqqZOJawdm9ge2BppdtExI8jYs+IeDvwNPBQVr4gIt4VEXsBPwMerlL8ZmZWRjUTxx3AHEmzJG0AHAfMK9lmHnBCdnbVW4HnIuIxAElbZX9nAseQkkRxeQPwZeDsKrbBzMxKVO3K8YhYKelk4FpgEnBuRNwn6aRs/dnAr0njFwuBXuDEol1cKmlLYAXw6Yh4Jis/XtKns8eXAT+pVhvMzGxdiigddhh/mpubo6urq9ZhmJmNKZLmR0RzabmvHDczs1ycOMzMLBcnDjMzy8WJw8zMcnHiMDOzXJw4zMwsFycOMzPLxYnDzMxyceIwM7NcnDjMzCwXJw4zM8vFicPMzHJx4jAzs1ycOMzMLBcnDjMzy8WJw8zMcnHiMDOzXJw4zMwsFycOMzPLxYnDzMxyceIws1HR2dlJoVCgoaGBQqFAZ2dnrUOyIVqv1gGY2fjX2dlJa2srvb29AHR3d9Pa2gpAS0tLLUOzIXCPw8yqrq2tbXXS6NPb20tbW1uNIrLhcOIws6rr6enJVW71zYnDzKpu5syZucqtvjlxmFnVtbe309jYuFZZY2Mj7e3tNYrIhsOJw8yqrqWlhY6ODpqampBEU1MTHR0dHhgfoxQRtY6h6pqbm6Orq6vWYZiZjSmS5kdEc2m5exxmZpaLE4eZmeXixGFmZrk4cZiZWS5OHGZmlsuEOKtK0nKge4jVpwJPjmA4Y4HbPDG4zRPDcNrcFBHTSgsnROIYDkld5U5HG8/c5onBbZ4YqtFmH6oyM7NcnDjMzCwXJ47BddQ6gBpwmycGt3liGPE2e4zDzMxycY/DzMxyceIwM7NcJnTikHSopAclLZR0Wpn1LZLuyZZbJO1etG6xpD9LulvSmJh6t4L2Hpm19W5JXZL+ptK69WqYbR5znzFU/llJ2lvS65Len7duvRlmm8fl5yzpnZKey9p1t6TTK607qIiYkAswCXgYmA1sAPwJmFuyzf7AFtnjw4A/Fq1bDEytdTtGuL0bs2bcazdgQaV163EZTpvH4mec57PKtrsR+DXw/vH+OffX5vH8OQPvBK4a6vs10DKRexz7AAsjYlFEvAZcDBxZvEFE3BIRz2RPbwO2H+UYR1Il7X0xsn9ZwGQgKq1bp4bT5rGq0s/qM8ClwLIh1K03w2nzWDWcz2rYn/NEThzTgUeLni/JyvrzMeDqoucBXCdpvqTWKsQ30ipqr6SjJS0AfgV8NE/dOjScNsPY+4yhgjZLmg4cDZydt26dGk6bYZx+zpn9JP1J0tWS3pSzbr/Wy7PxOKMyZWV/bUo6kJQ4/qao+G0RsVTSVsD1khZExG+rEOdIqai9EXE5cLmktwNfB/620rp1aDhthrH3GUNlbf428KWIeF1aa/Px/Dn312YYv5/znaS5pl6UdDhwBTCnwroDmsiJYwkwo+j59sDS0o0k7QacAxwWEU/1lUfE0uzvMkmXk7p/9fyPraL29omI30raQdLUvHXryJDbHBFPjsHPGCprczNwcfYFOhU4XNLKCuvWoyG3OSKuGK+fc0Q8X/T415K+P2L/n2s9yFOrhZQ0FwGzWDNA9KaSbWYCC4H9S8onA5sUPb4FOLTWbRqB9r6RNQPFewJ/Jf06GbRuPS7DbPOY+4wrbXPJ9j9lzeD4uP2cB2jzuP2cgW2K/m3vA/SM1P/nCdvjiIiVkk4GriWdZXBuRNwn6aRs/dnA6cCWwPezXyorI80yuTXp0AakD+GiiLimBs2oWIXtPRY4QdIK4GXgg5H+1ZWtW5OG5DCcNksac58xVNzmXHVHI+7hGE6bGYP/l6HiNr8f+GTWm3wZOG6k/j97yhEzM8tlIp9VZWZmQ+DEYWZmuThxmJlZLk4cZmaWixOHmZnl4sRh45akNkn3Fc1+u2+tY8pD0jmS5g6yzVGDbVOmzluyfRckLZHUULL+bkn7SDpZ0olDid3GNycOG5ck7Qe8F9gzInYjTSPy6MC16ktEfDwi7h9ks6OAXIkD+BfgexGxmPSeHNC3QtLOpAvibgfOBT6bc982AThx2Hi1LfBkRLwKENkUItkv6ctg9b04Xpa0gaSNJC3KyneQdE026d3vsi9TJE2TdKmkO7LlbVn5GZIukHSjpIckfaI0mOzX/QJJ52U9oF9IaszWHSzpLqV7QpwracOs/GZJzdnjFyW1ZxPW3SZpa0n7A0cA/5n1EnaQ9FlJ92evcXGZODYBdouIP2VFPwOOK9rkuKyMiOgFFkvaZ5ifhY0zThw2Xl0HzJD0l2yOnndk5XcCb8keHwDcC+wN7Av8MSvvAD4TEXsBpwLfz8q/A/y/iNibdMX5OUWvtxvwHmA/4HRJ25WJaSegI+sBPQ98StJGpCkwPhgRbyZdvfzJMnUnA7dFxO6keZQ+ERG3APOAL0bEHhHxMHAa8JbsNU4qs5/mrM19/gc4SlLfLBIfJE2z3aeLoh6JGThx2DgVES8CewGtwHLgEkn/EBErgYWSdiHN3/Mt4O2kL8ffSdqYdAOvn0u6G/ghqfcC6XDXf2fl84BNs1/wAFdGxMsR8SRwU7bvUo9GxB+yxxeSZlveCXgkIv6SlZ+XxVPqNeCq7PF8oNBP0+8BOiV9GFhZZv222fsBQEQ8DtwHHCxpD2BFRBQnlmVAuSRoE9iEnavKxr+IeB24GbhZ0p+Bj5B+3f+OdEfHFcD/ZmWTSL2LBuDZiNijzC4bgP0i4uXiwmyeo9K5e8rN5VNum3JTXJezItbMD/Q6/f/ffQ8p8RwBfEXSm7Jk2edlYKOSOn2Hq57IHhfbKKtjtpp7HDYuSdpJ0pyioj2A7uzxb4FTgFsjYjlpIsudgfsiTUX9iKS/y/YjrbnX/HXAyUWvUZxcjszGSbYk3bLzjjJhzcwG7QGOB34PLAAKkt6Ylf898JscTX0B2CSLpwGYERE3Af8EbE66NW6xB0gzAhe7FDicdQ9TAezI2oe2zJw4bNzaGDivb6CYdObRGdm6P5JmRe2758I9wD1Fv+hbgI9J+hPpME7fbTU/CzRnA8/3s/YYwu2kOwjeBnw9sns8lHgA+EgWzxTgBxHxCnAi6dDYn4FVlL9LXX8uBr4o6S7STXouzPZzF2k85tnijSNiAbBZ0SE2sm1uA56IiEdK9v82Uq/MbDXPjms2TJLOAF6MiDMH2KYAXBURu45SWP2S9H+AFyLinEG2ewvw+Yj4+9GJzMYK9zjMJp4fAK9WsN1U4CtVjsXGIPc4zMwsF/c4zMwsFycOMzPLxYnDzMxyceIwM7NcnDjMzCyX/w/JY9TZwYq9RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "constant_pid_output = 0.10\n",
    "second_dac_sweep_points = np.linspace(0.25, 0.50, 11)\n",
    "\n",
    "x, y, fr = minimize_cts_dac(second_dac_sweep_points, constant_pid_output)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, y, 'ok')\n",
    "plt.plot(x, fr['params_dict']['a'] + fr['params_dict']['k'] * (x - fr['params_dict']['x0'])**2, '-r')\n",
    "plt.title(f\"Minimization for output = {constant_pid_output:.3f}V\")\n",
    "plt.xlabel(\"Sweep points (V)\")\n",
    "plt.ylabel(\"Controller input (V)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3.7 [measurement]",
   "language": "python",
   "name": "measurement"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  },
  "toc": {
   "base_numbering": 1,
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