/gr40c

Primary LanguagePython

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pip install -U msgpack matplotlib pyvisa`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/feiyang/gr40/gr40c/server_comms.py:86: MarServerWarning: no RX data received\n",
      "  warnings.warn(k, MarServerWarning)\n"
     ]
    }
   ],
   "source": [
    "# 初始化梯度DAC,此时功放关闭。每次启动前先执行,再开梯度功放\n",
    "import numpy as np\n",
    "from experiment import Experiment\n",
    "\n",
    "console = Experiment(init_gpa=True)\n",
    "console.add_flodict({\n",
    "    f'ocra40_v{ch}': (np.array([0, 100]), np.array([0, 0])) for ch in range(40)\n",
    "})\n",
    "console.add_flodict({\n",
    "    'tx_gate': (np.array([0]), np.array([1]))  # 门控低有效\n",
    "})\n",
    "console.run()\n",
    "console.__del__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "from time import sleep\n",
    "import numpy as np, csv\n",
    "from matplotlib import pyplot as plt\n",
    "from experiment import Experiment\n",
    "from pyvisa import ResourceManager\n",
    "\n",
    "def read(typ: Literal['HIGH', 'MEAN', 'LOW']):\n",
    "    # 读取脉冲幅度的测量值\n",
    "    scope.write('MEASUrement:IMMed:SOUrce CH1')  # 设置测量源为通道 1\n",
    "    scope.write('MEASUrement:IMMed:TYPe ' + typ)  # 设置测量类型为脉冲幅度\n",
    "    return float(scope.query('MEASUrement:IMMed:VALUE?'))  # 读取脉冲幅度\n",
    "\n",
    "def write(ch: int, amp: float):\n",
    "    console = Experiment(init_gpa=False)\n",
    "    console.add_flodict({\n",
    "        f'ocra40_v{ch}': (np.array([10, 1000, 2000]), np.array([0, amp, 0])),\n",
    "    })\n",
    "    msg = console.run()\n",
    "    console.__del__()\n",
    "    return msg[1]\n",
    "\n",
    "def sweep(ch: int):\n",
    "    results = []\n",
    "    def expt(amp: float):\n",
    "        write(ch, amp)\n",
    "        sleep(.2)\n",
    "        high = read('HIGH')\n",
    "        low = read('LOW')\n",
    "        results.append([amp, high, low])\n",
    "        if np.abs(high - low) > 1.2:  # 1.5V/30A\n",
    "            return True\n",
    "        return False\n",
    "    \n",
    "    scope.write('TRIGger:A:LEVel 0.08')\n",
    "    for amp in np.linspace(0.02, 0.36, 18):\n",
    "        if expt(amp):\n",
    "            break\n",
    "    scope.write('TRIGger:A:LEVel -0.08')\n",
    "    for amp in np.linspace(-0.02, -0.36, 18):\n",
    "        if expt(amp):\n",
    "            break\n",
    "    try:\n",
    "        with open(f'./data/{ch}.csv', 'w') as f:\n",
    "            writer = csv.writer(f)\n",
    "            writer.writerows(results)\n",
    "    except FileNotFoundError:\n",
    "        print('未保存')\n",
    "    return np.array(results)\n",
    "\n",
    "try:\n",
    "    scope.close()\n",
    "except NameError:\n",
    "    pass\n",
    "# rm = ResourceManager()\n",
    "# scope = rm.open_resource(rm.list_resources()[0])\n",
    "# scope.query('*IDN?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f59d1335d0>]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qykQLtHbHfpnK0JBLG+muiygToWiEFwABlXbQHnUpFROl+Jgop7sDFxr/62b9v0NlokoJpkzUSu3qneF0t+BihBcAAcVKIxzPwZx8K7R8Mn+L1e7YoIJeuqGlKlAmwkkQXgAEFCuNUJTfUvdZZxOtSbXLRIMvaai7O9WnTIRiIbwACKjDk3VZaYRDPluwRY9MWKaDufmqmBCnV25spfZnUiZC8UUqCEaPHq06deooPj5e7dq109y5c4/73LFjx1qngh55M68D4E17MzkaAH+UiR78ZLEGf7zYCi4X1K+grwd2JLjAfSMvH330kQYPHqzXXnvNCi4jR45U165dtXr1alWqVKnI1yQmJlrfL8QR54B3cSgjDLOKyJxNtDp1n1UmGtSloe65uL61AR3gupGXF198Ubfddpv69euns846ywoxpUuX1jvvvHPc15iwUqVKlcO3ypUrB7qbAAI+YZeRl3D1+cKtuvLVGVZwMZNx3+/fTgM7NyC4wJ3hJScnR/Pnz1eXLl3++AUjI6327Nmzj/u6/fv3q3bt2qpZs6auuuoqLV++/LjPzc7OVkZGxlE3AO7BhN3wlZWbr2GfLdGgjxbpQE6+Opx5hr6+7wJ1qF/B6a7B4wIaXnbt2qX8/Pw/jZyYdkpKSpGvadSokTUq88UXX+j9999XQUGBOnTooC1b7KV0xxoxYoSSkpIO30zgAeDCowEoG4WVdTv3q+fomfrf3M0ylf/7OjfQf/u3U6UE5jDCIxN2S6J9+/a65ZZb1LJlS1100UX67LPPVLFiRb3++utFPn/YsGFKT08/fNu8eXPQ+wzg5CMvlI3CxxeLturKUTO0KsWUiWKtMtH9lzSkTARvTNitUKGCoqKilJqaetTjpm3mshRHTEyMWrVqpbVr1xb5/bi4OOsGwN0jL0mMvIRFmeiJL1fof3M3We3z6pW3lkFXSmS0BR4aeYmNjVXr1q01ffr0w4+ZMpBpmxGW4jBlp6VLl6pq1aoB7CmAQOFQxvCwflemrv73LCu4mDLRwL/U1we3nkdwgTeXSptl0n369FGbNm3Utm1ba6l0ZmamtfrIMCWi6tWrW3NXjCeffFLnnXee6tevr7S0NP3rX//Sxo0bdeuttwa6qwD8LCevQJk5+dZ95ryEri8Xb9PQT5dYP+szysRaW/ybE6EBz4aXG264QTt37tSjjz5qTdI1c1mmTJlyeBLvpk2brBVIhfbu3WstrTbPLVeunDVyM2vWLGuZNQBvjrqYqQ6J8YSXUJOdl68nv1yhD+bYZaK2dctbhypWZrQFARbh8/kOHVYfGsxSabPqyEzeNZvdAXDO6pR96jryJ2vUZeGjlzrdHfh5fsud78/XD6t3Wu0BF9fXoC4NFB3lunUgCMHPb842AhAwrDQK/eASHxOp/9zcWhc3LnrHdCAQCC8AAl42Sma+S0iViu46IriM6duWs4kQdIzvAQgYjgYIveBy53/n6/tDweWdvucSXOAIwguAgOFogNAOLh3OZJt/OIPwAiBgOBoglEpFC/4ILn0ILnAW4QVAwOzNPDRhtwwjL14OLne/v0DfrdphBZe3TXDhYEU4jPACIOBzXpJKMfLi1eByzwcLNH3VDsVF28HlfIILXIDwAiBg0g+yVNrrwWXaSju4mDkuBBe4BeEFQBBWGzHy4rVjHY4MLoy4wG0ILwCCsM8LIy9eCi53HxFc3urTRhc0ILjAXQgvAALCnDxyeLVRGUZevBVcUg8Hl44NOGAR7sMOuwACYl92nvIK7KPTmPPifhlZubr/w0XW5NzY6Ei9eQvBBe5FeAEQEGmZ9qiLWV4bHxPldHdwAsu2puuecQu0cfcBK7i8dUsbXdiQ4AL3IrwACAgOZfRGae/9Xzbqqa9WKie/QNWTS+nVm1qpVa1yTncNOCHCC4CA4GgAd9uXlathny3VV0u2W+0uTSrr+V7N+XnBEwgvAAKCowHca/m2dGsp9IbdBxQdGaGh3Rur/wV1FRER4XTXgGIhvAAICMpG7iwTjZu7SU98ucJaWWTKRKNuaqVzKBPBYwgvAAJ7NAAjL66wPztPD3+2VBMXb7PaXZpU0vO9WlAmgicRXgAERPrhkRfCi9NWbs+wykS/78pUVGSEHurWSLd1rEeZCJ5FeAEQ4KMB+Je9k2WiD+dt1uMTlys7r0DVkuKtMlHr2uWd7hpwWggvAAKC1UbOSj+Yq8e+WKbPF9lloosbVdSL17dUuTL8POB9hBcAAcFqI+dCyzsz1uudmeu1LyvPKhM92NUuE0VGUiZCaCC8AAgIRl6CH1rGzFyvt2fYocVoVDlBT1/dVG3qUCZCaCG8AAgIRl6CdybRmBkb9PaM35VxRGi5r0sDdTu7CqMtCEmEFwB+Z/YQMUtzDSbsBi+0NKxcVvd1bqjuTQktCG2EFwB+l3bQLhmZlbiJpRh58XdoGTtzg976+Y/Q0qBSWWuk5bKmVQktCAuEFwABKxkllYqxJozCP2cRWaFlxnprfotBaEG4IrwA8Lu9mRwN4E9rd+zXzW/9otSMbKtd34SWzg10WbOqhEOEJcILAL9LO/jHyAtOP7j0fvMX7dyXrdpnlNaQSxupB6EFYY7wAsDv0jgawC/W7fwjuDSukqBxt52n8mwyByjS6Q4ACD0cDeCn4PIGwQUoCuEFgN+xQd3p+f1QcNlBcAGKRHgB4HdpmWxQdzrB5cYjgssHt7YjuADHILwACNzICx+6JbJ+V6Y1x8UEF7NLrgkuZ5SNc7pbgOsQXgD4HUcDnFpwufGN2dZyaCu43EZwAY6H8AIgYCMvTNgtng1mxOUNex8Xs8W/CS4VCC7AcRFeAARstVEyIy/FCi5mjktKRpYVXMzkXIILcGKEFwB+5fP5jtjnhZGX4gYXs9U/wQUoHsILAL8yp0nnFfis+4SX49u4256cS3ABSo7wAiAgk3VjoyMVH8NbTFGWbU23Rly2p2dZ5xSZ4FIxgeACFBfHAwAI2EqjiAjO3znS8m3pennab/p2RarVtoNLO4ILUEKEFwB+xUqjP1uxLUMvT1+jb5bbocVkuitbVNPwHmcRXIBTQHgBEKCjAVhpZELLK9N/05TlKUeFlnv/0sAadQFwaggvAAJUNgrfkZeV2zOs8tCRoeWK5tU0sHN91a+U4HT3AM8jvADwq3A+lNGEFjPSMnnZH6Hl8ubVdB+hBfArwgsAvwrHowF+S92nF6eu+VNoGfiX+mpQmdAC+BvhBYBfhduE3W1pB9Vz9Exl5uRboaVHs6oa2LmBGhJagIAhvADwq3A7GuDdWRus4NKkaqJG3tBSjaoQWoBAYwcpAH4VTkcDZGbnadzcTdb9IZc0JLgAQUJ4ARCYslGZ0B95Gf/rZu3LylPdCmX0l8aVnO4OEDYILwACMmE3qVRoj7zkF/g0ZtYG6/7fzq+jyEh2EwaChfACwG/y8guskYhwWG00fWWqNu4+oKRSMbq2dQ2nuwOEFcILAL9JO2iPuhjmQz2UvTVjvfX1pna1VDqWtQ9AMBFeAPh9sm5ifLSio0L37WXplnTNXb9H0ZER6tO+jtPdAcJO6L67AHBsmXS5MqE93+XtGb9bXy9vXlVVkuKd7g4QdggvAPxmb2boHw2Qkp6lr5Zst+73v6Ce090BwhLhBYDfhMPRAO/O3qC8Ap/a1imvZjWSnO4OEJYILwD8JtSPBjiQk6dxc+xN6fp3rOt0d4CwRXgB4DehfjTAp/O3KP1grmqVL60uTSo73R0gbBFeAPhNKB8NUFDg0zsz/9iULopN6QDHEF4ABKBsFHojL9+v3qH1uzKVEB+tXm1qOt0dIKwRXgD4/2iAEBx5eevnQ5vSta2lMnFsSgc4ifACwG9CdbXR8m3pmv37bqtU1KcDm9IBTiO8APCbUF1t9PahowC6N62iasmlnO4OEPYILwD8wufzHR55CaXVRjsysvTl4m3W/Vs7sikd4AaEFwB+cSAnXzn5BSE38vLe7I3Kzfepde1yalkz2enuACC8APB3ySg2KlKlY6MUCg7m5OuDORut+7dewKZ0gFsQXgD4xZElo4iI0NgD5bOFW6yN92qUK6VLz67idHcABDO8jB49WnXq1FF8fLzatWunuXPnnvD548ePV+PGja3nN2vWTF9//XUwugngNITaZF1rU7pDE3X7nV+XTemAcAovH330kQYPHqzHHntMCxYsUIsWLdS1a1ft2LGjyOfPmjVLvXv3Vv/+/bVw4UL17NnTui1btizQXQVwGkLtaIAff9updTszVTYuWte3qeF0dwAcIcJnlggEkBlpOffcc/Xqq69a7YKCAtWsWVP33nuvhg4d+qfn33DDDcrMzNRXX311+LHzzjtPLVu21GuvvXbSXy8jI0NJSUlKT09XYmKin68GwPG8N3uDHv1iubqdXUWv/bV18V50ME06uFdudP/Hi/Xrxj26oU1NDbi4vn//574CKWOrVJDv3/8vEAyR0VLt86VI/45/lOTzO6DbRObk5Gj+/PkaNmzY4cciIyPVpUsXzZ49u8jXmMfNSM2RzEjN559/XuTzs7OzrduRFw8g+PZmHtqgrkwxR15SlkpvXyrlHpAbvWT+Eydp6aEbgKM9ni6nBDS87Nq1S/n5+apc+ejTV0171apVRb4mJSWlyOebx4syYsQIPfHEE37sNYBTkXbQnvOSVKoYc17yc6XP77KDS1ScFOWuUlNWXr7y8n2KjoxQfEzUqY2suDSUAX4R4eyKQs8f0GFGdY4cqTEjL6YsBcDFRwP8/KI98lKqnHTPXKlsJbnFzn3ZOv/Z76w9az69q4O1v8tJFRRIO1dJG2ZIG36WNs78c3iJKSPVOk+qc4FUp6NUraXrQhvgFQENLxUqVFBUVJRSU1OPety0q1Qpetmhebwkz4+Li7Nugfb7zv3q/OKPAf91AK8qnD130tVGJrT89Jx9/7LnAxpc1qTuU9eRP53SdZgN6YoVXPJypDc6STuWH/14TOlDYaUjYQXwUniJjY1V69atNX36dGvFUOGEXdMeMGBAka9p37699f1BgwYdfmzq1KnW404L7NRmwPtKxUTpnNrJJykX3S0V5EmNL5eaXuvKv7dmVfR9nRsU78l719vBJSJSqtfpiJGVVoQVwKtlI1PS6dOnj9q0aaO2bdtq5MiR1mqifv36Wd+/5ZZbVL16dWvuinHffffpoosu0gsvvKAePXroww8/1K+//qo33nhDTqpVvrTmPdLF0T4AbmeWFZc60e66M0ZKKUvsclGPF6UAb2ZXr0KZU/p7a67BXEuxHNhjf02uLf11Qol/LQAuDC9m6fPOnTv16KOPWpNuzZLnKVOmHJ6Uu2nTJmsFUqEOHTpo3LhxGj58uB5++GE1aNDAWmnUtGlTOSk6KlIVEwJfngJCVupy6cd/2ve7/0tKOHpivmf/3h7YbX8tXT6wvw6A4O3zEmzs8wK4kCkXvdVZ2r5YatRDuvGDgI+6BM2C96SJ90oNLpVuHu90b4Cw+PzmbCMAgTdzpB1c4pOlywNfLgqqwrJRKUZegGAhvAAIrNQV0g+F5aLnpIQQO+Dw4KHwQtkICBrCC4DAyc+zN6MryJUaXSY1v14hhzkvQNARXgAEzqyXpe2LpPgk6fKXQqtcVOjAobOZKBsBQUN4ARAYO1ZKPzwbuuWiQpSNgKAjvAAIXLkoP0dq2E1qfoNCVmHZiJEXIGgILwD8b9Yr0raFh8pFI0OzXHTsaqPSZzjdEyBsEF4A+NeOVdIP9o7Z6vaslFhVIctsk3Xw0JwXykZA0BBeAPi3XPTF3Xa5qEFXqUVvhbSsdMmXb9+nbAQEDeEFgP/MflXaOl+KS5KuCPFy0ZHzXcwJ0jHxTvcGCBuEFwD+sXO19P0z9v1uI6TEagp5h0tGzHcBgonwAuD0bZoj/fdqKT9bqn+J1PImhYXDRwOUc7onQFgJ+KnSAEJYQYE0e5Q07Ql77scZ9aUrXwn9clEh9ngBHEF4AXDqow5mL5c1U+x202ulK16W4hIUNtjjBXAE4QVAyW2eJ43vK2VskaLipO7/lFr3DZ8Rl0Ls8QI4gvACoGT7mpgVRdMelwrypPL1pF7vSlWbKyxRNgIcQXgBUIIy0d3Smsl2++yrpStekeITFbYOT9glvADBRHgBcHJbfpXG95PSN0lRsfZS6Db9w69MdLw5L4y8AEFFeAFw4jLRL/+Rpj4qFeRK5epK15syUQune+YOHA0AOILwAuD4H8yf3yOtnmS3z+ppL4M2hy3CRtkIcAThBcCfmS3+zWqitENloq7PSOfeSpnoT4cyMmEXcALhBcDRH8hzXpe+HW6XiZJr22Wiaq2c7pn75B6Q8rLs+yyVBoKK8ALAdjBNmjhAWvml3W5ypXTlKKlUstM9c3fJKDJGii3rdG+AsEJ4ASBtXXCoTLTR/jDu+rTU9nbKRCdyZMmI3ycgqAgvQLiXiea+KX37iJSfY5eJeo2Rqrd2umfux9EAgGMIL0C4ykqXJt4rrfjCbje+XLpqNGWi4uJoAMAxhBcgHG1bZJeJ9q63y0SXPiW1u5Pyxynt8VLO6Z4AYYfwAoRbmWjeW9I3D9tloqRaUq+xUg3KRCXGHi+AYwgvQLjIypC+HCgtn2C3G/WQepoyESMHp4SjAQDHEF6AcLB9iTS+j7TndykyWrrkSem8uykT+WW1EXNegGAjvAChXiaaP0aaPFTKz5aSakrXjZFqnut0z7yPshHgGMILEKqy90lfDpKWfWK3G3aTev6HMoe/cDQA4BjCCxCKUpZKH5sy0TopIkrq8rjU4V7KRP7EPi+AYwgvQKiViRa8K01+yD53J7GGdN07Uq12Tvcs9BwoXCrNnBcg2AgvQKjI3i99db+09GO73eBS6erXKWsEQl6OlLPPvs/vLxB0hBcgFKQut8tEu3+zy0SdH5U6DJQiI53uWWhvUKcIKT7J4c4A4YfwAni9TLTwv9LXf7fLRAnV7LOJap3ndM/CZL5LOSkyyuneAGGH8AJ4uUw0abC05CO7Xb+LdPUbUhnmYAQcK40ARxFeAC9KXWFvOrdrjV0m+stw6fxBlImChT1eAEcRXgCvWfiBNGmIlHdQSqhqryaq3cHpXoUXjgYAHEV4AbwiJ1Oa9IC0eJzdPvMvdpmobEWnexZ+OBoAcBThBfCCHavsMtHOVVJEpHTxw9IFQygTOV424lBLwAmEF8DtFo2zy0S5B6SyVaTr3pbqXOB0r8Jb4VJpykaAIwgvgFvlHLCXQC96327Xu1i65k3KRG7A0QCAowgvgBvtXG1vOrdzpV0m6vSw1HEwe4q4rWzEnBfAEYQXwG0Wf2hv82+ViSpL174l1b3Q6V7hSOzzAjiK8AK4qUw0+UF7x1yj7kV2cClbyeme4Vjs8wI4ivACuMHONfZqoh0r7PNyOg2TLnyAMpEbFeQzYRdwGOEFcNqS8dKX90m5mVKZSvZoS72LnO4Vjicr3RwqZd9n5AVwBOEFcEruQWnyQ9KCd+12nY7StW9LCZWd7hmKUzKKTZCiY53uDRCWCC+AE3attctEqcvsMtFFD0oXPUSZyFOTddmgDnAK4QUItqWf2GWinP1SmYr23i1nXux0r1Dic41YJg04hfACBEtuljRlqDR/zBFlorekhCpO9wwlwUojwHGEFyAYdq+zN51LXWqXiS78u10miuKvoOewxwvgON45gUBb9qk00ZSJ9kmlK0jXvCHV7+x0r3CqOBoAcBzhBQhkmeibh6Vf37bbtc+3VxMlVnW6ZzgdHA0AOI7wAgTCnt/tMlHKErvdcYh9PhFlIu+jbAQ4jndSwN+WT5AmDpSyM+x/nV/9htSgi9O9gr8cOLS7bimWSgNOIbwA/pKXLX07XJr7ht2u1d4uEyVVd7pnCMhSaUZeAKcQXgB/2LNeGt9X2r7Ibp8/SPrL/6NMFNJlI+a8AE7hnRU4XSu+kL4YYJeJzAqUq1+XGl7qdK8QCD4f+7wALkB4AU6nTDT1UWnOa3a7ZjvpunekpBpO9wyBYnZFLsi171M2AhxDeAFOxd4N0vh+0rYFdrvDQKnzo1JUjNM9QzDmu0TFSTGlne4NELYIL0BJrfxK+uJuKSvdXnHS8zWpUTene4VgOHKPl4gIp3sDhC3CC1BceTnStMelX0bb7Rpt7TJRck2ne4ZgYY8XwBUIL0Bx7N0ofdJP2jrfbrcfIHV5nDJRuGGPF8AVCC/Ayaz6Wvr8TrtMFJ8s9fyP1Pgyp3sFR/d4YZk04KTIQP7P9+zZo5tvvlmJiYlKTk5W//79tX///hO+plOnToqIiDjqdueddwaym0DR8nOlbx6RPuxtB5fqraU7fya4hDPKRkDoj7yY4LJ9+3ZNnTpVubm56tevn26//XaNGzfuhK+77bbb9OSTTx5uly7NrH4EWdpmu0y0Zd4fZaLOj0nRsU73DE5ijxcgtMPLypUrNWXKFM2bN09t2rSxHhs1apQuu+wyPf/886pWrdpxX2vCSpUqVQLVNeDEVk+RJtwhZaVJ8UnSVf+WmlzudK/gBhwNAIR22Wj27NlWqagwuBhdunRRZGSk5syZc8LXfvDBB6pQoYKaNm2qYcOG6cCBA8d9bnZ2tjIyMo66AadcJjJnE/3vBju4VDtHuuMnggv+wNEAQGiPvKSkpKhSpUpH/2LR0Spfvrz1veO56aabVLt2bWtkZsmSJXrooYe0evVqffbZZ0U+f8SIEXriiSf83n+EmfQt9qZzW+ba7XZ3SZc8SZkIR6NsBHgzvAwdOlT//Oc/T1oyOlVmTkyhZs2aqWrVqurcubPWrVunM88880/PNyMzgwcPPtw2Iy81a7LvBkpgzbfShNulg3uluCSp52ipyRVO9wpuZP6MGJSNAG+FlyFDhqhv374nfE69evWsOSs7duw46vG8vDxrBVJJ5rO0a9fO+rp27doiw0tcXJx1A06pTPTdP6SZI+12tVbSdWOk8nWd7hncPueFfV4Ab4WXihUrWreTad++vdLS0jR//ny1bt3aeuy7775TQUHB4UBSHIsWLbK+mhEYwG/St0qf/E3a/IvdbnuHdOlTUjRBGMeRmyXlHpp/x5wXIDQn7DZp0kTdunWzlj3PnTtXM2fO1IABA3TjjTceXmm0detWNW7c2Pq+YUpDTz31lBV4NmzYoIkTJ+qWW27RhRdeqObNmweqqwg3v02VXrvADi5xiVKvd6XLniO4oHiTdSOi7FVoAEJznxezasgEFjNnxawyuvbaa/XKK68c/r7Z+8VMxi1cTRQbG6tp06Zp5MiRyszMtOaumNcMHz48kN1EuMjPk77/hzTjJbtdtYXUa6xUvp7TPYOnJuuW41BGIJTDi1lZdKIN6erUqSOfz3e4bcLKjz/+GMguIVxlbLPLRJtm2+1zb5O6Ps1oC4qPPV4A1+BsI4S+tdOkz263P3xiE6QrX5GaXuN0r+A17PECuAbhBaFdJvrhGennF+x2lWb2/JYz/rxqDTgp9ngBXIPwgtCUsV369FZp4wy73aa/1PUZKSbe6Z7B8yMvLJMGnEZ4QehZ95306W3SgV1SbFnpipelZtc53SuEysgLZSPAcYQXhI6CfOmHZ6Wf/iXJJ1U2ZaKxUoX6TvcMoYCyEeAahBeEhn0pdplow892u3VfqduzUkwpp3uGkCsbEV4ApxFe4H2//2CXiTJ3SDFl7DJR815O9wohezQA4QVwGuEF3i4T/fic9KM5KNQnVTpbuv5dqUIDp3uGUMScF8A1CC/wpv077DLR+kObGp5zi9T9OcpECBzKRoBrEF7gPet/soPL/lQpprR0+UipxQ1O9wqhvmdQVrp9n7IR4DjCC7xVJvrpeenHZyVfgVSxiV0mqtjI6Z4h1B3c+8d9c7YRAEcRXuAN+3dKn91qT841Wv2f1P1fUmxpp3uGcCoZmdOko3jbBJzG30K43/qfD5WJUuwyUY8XpZa9ne4Vwgl7vACuQniBexUU2OcSmfOJrDJRY/tsokqNne4Zwg2TdQFXIbzAnTJ3SZ/dZm/1b7S4SerxvBRbxumeIRyxxwvgKoQXuM+GmdKn/aV926XoUlKPF6RWNzvdK4Qz9ngBXIXwAneViWa+JH33D7tMVKGhXSaqfJbTPUO4o2wEuArhBe6QuVuacLu0dprdbn6jPeISV9bpngFM2AVchvAC522cLX3yN2nfNik6XrrseXspdESE0z0DjikbEV4ANyC8wNky0ayXpelPSb586YwG9qZzlc92umfA0SgbAa5CeIFzZaLP75R++9ZuN7teuvwlykRwJ8pGgKsQXhB8m36xy0QZW+0ykTlQ0RysSJkIbl8qzcgL4AqEFwS3TDR7lDTtiUNlovr2aqIqTZ3uGXB8Pt8fZxuxVBpwBcILgjfsPsGUib6x202vk64YKcUlON0z4MTMadImbBuUjQBXILwg8DbPk8b3lTK2SFFxUvd/Sq37UiaCtybrmnO1YuKd7g0AwgsCPtw++1Vp2uNSQZ5Uvp5dJqra3OmeAcXHZF3AdQgvCNwb/ud3S2sm2+2zr5GueFmKT3S6Z0DJsMcL4DqEF/jfll+l8f2k9E1SVKzUbYTUpj9lIngTe7wArkN4gX/LRL/8W5r6qF0mKlfX3nSuagunewacOspGgOsQXuAfZinp5/dIqyfZ7bN6SleOokwE72OPF8B1CC84fVvn26uJ0g6Vibo+I517K2UihFjZiD1eALcgvOD0ykRzXpe+HS4V5Erl6ki9xkrVWjndM8B/KBsBrkN4wak5mCZNHCCt/NJuN7lSuupVKT7J6Z4B/sWEXcB1CC8oua0LDpWJNkqRMXaZqO1tlIkQmlgqDbgO4QUlKxPNfVP69hEpP0dKrm2Xiaqf43TPgMChbAS4DuEFxT/fZeK90oov7Hbjy6WrRkulkp3uGRDgQxkZeQHchvCCk9u2yC4T7V1vl4kufUpqdydlIoS+3ANSXpZ9n5EXwDUILzjxvzrnvSV987BdJkqqZZeJarR2umdAcEtGJrRzAjrgGoQXFC0rQ/pyoLR8gt1u1EPqacpE5ZzuGRA8R5aMGGkEXIPwgj/bvkQa30fa87sUGS1d8qR03t28eSP8MFkXcCXCC44uE80fI00eKuVnS0k1pevGSDXPdbpngDM4GgBwJcILbNn7pC/vk5Z9arcbdpd6/ps3bYQ3c2aXwd8DwFUIL5BSlkofmzLROikiSuryuNThXspEAGUjwJUIL+FeJlrwrjT5IXs5aGIN6bp3pFrtnO4Z4A7s8QK4EuElXGXvl74aJC0db7cbXCpd/Tpv0kBRc14YeQFchfASjlKX22Wi3b/ZZaLOj0odBkqRkU73DHDpuUZnON0TAEcgvIRbmWjhf6Wv/26XiRKqSb3GSLXOc7pngDtRNgJcifASTmWiSYOlJR/Z7fpdpKvfkMrwL0rguJiwC7gS4SUcpK6wN53btcYuE/1luHT+IMpEwMlQNgJcifAS6ha+L016QMo7KCVUtVcT1e7gdK8A98vLkXL22fcpGwGuQngJVTmZdmhZPM5un9lZusaUiSo43TPAWxvUKUKKT3K4MwCORHgJRTtW2quJdq2WIiKlix+RLhhMmQg4pWXSyVJklNO9AXAEwkuoWTROmjREyj0gla0iXfe2VOcCp3sFeHilEfNdALchvISKnAP2EuhF79vtehdL17wpla3odM8Ab2KlEeBahJdQsHO1XSbaudIuE3V6WOo4hDIRcDrY4wVwLcKL1y3+UPrq/kNlosrStW9LdTs63SvA+zgaAHAtwouXy0ST/24vhTbqdTpUJqrkdM+AENvjhfACuA3hxYt2rrE3nduxwl7G2WmYdOEDrIgAArFUmvACuA7hxWuWfCx9OUjKzZTKVJKufUuqd5HTvQJCDxN2AdcivHhF7kFp8kPSgnftdp2O9vyWhMpO9wwI7TkvjLwArkN48YJda+0yUeoyu0x00UPSRQ9SJgICiX1eANcivLjd0k+kL++TcvZLZSrak3LPvNjpXgGhj7IR4FqEF7fKzZKmDJXmjzmiTPSWlFDF6Z4Boa8gX8pKs+9TNgJch/DiRrvX2ZvOpS61y0RmJdFFQ6UoflxAUGSlS74C+z4jL4Dr8GnoNss+lSYOtMtEpSvYJ0HX7+x0r4DwLBnFJkjRsU73BsAxCC9uKhN987D069t2u/b59mqixKpO9wwI48m65ZzuCYAiEF7cUiYa31dKWWK3LxgsXfwIZSLAKRwNALgan45OWz5B+uJeKWefvSTz6jekBl2c7hUQ3g4fDcAyacCNAnbs8NNPP60OHTqodOnSSk5OLtZrfD6fHn30UVWtWlWlSpVSly5d9Ntvvykk5WVLkx6wR1xMcKnVXrrjZ4IL4AacKA2EZ3jJyclRr169dNdddxX7Nc8995xeeeUVvfbaa5ozZ47KlCmjrl27KisrSyFlz3rp7UuleW/a7Qvul/p8JSVVd7pnAAz2eAHCs2z0xBNPWF/Hjh1b7FGXkSNHavjw4brqqqusx9577z1VrlxZn3/+uW688UaFhBVfSF8MkLIz7DfGq1+XGl7qdK8AHImjAYDwHHkpqfXr1yslJcUqFRVKSkpSu3btNHv27OO+Ljs7WxkZGUfdXFsmMmcTfXyLHVxqtpPu/JngArgRRwMAruaa8GKCi2FGWo5k2oXfK8qIESOskFN4q1mzplxn7wbpnW7SnNfs9vn3SX0nSUk1nO4ZgKIc2Gt/LcVSacDz4WXo0KGKiIg44W3VqlUKpmHDhik9Pf3wbfPmzXKVlV9Jr18obVtgvxH2/ki65EkpKsbpngE4HibsAqEz52XIkCHq27fvCZ9Tr169U+pIlSr2mT2pqanWaqNCpt2yZcvjvi4uLs66uU5ejjTtcemX0Xa7RlvpunekZBeODAE4Gvu8AKETXipWrGjdAqFu3bpWgJk+ffrhsGLmr5hVRyVZseQKezdKn/STts632+0HSF0eZ7QF8AKfj31egHCd87Jp0yYtWrTI+pqfn2/dN7f9+/cffk7jxo01YcIE674pOQ0aNEj/+Mc/NHHiRC1dulS33HKLqlWrpp49e8ozVn0tvd7RDi7xyVLvD6WuTxNcAK8w54oV5Nr3KRsB4bVU2mw29+677x5ut2rVyvr6/fffq1OnTtb91atXW/NUCj344IPKzMzU7bffrrS0NF1wwQWaMmWK4uPj5Xr5uXaZaPardrt6a6nXWCm5ltM9A1AShaMuUXFSTGmnewOgCBE+s8FKCDGlJrPqyISixMTE4PyiaZukT/4mbZn3R5mo82OcRgt40dYF0psXSwlVpSHBXYAAhLOMEnx+c7bR6Vo9WZpwp5SVJsUnST3/IzXu4XSvAJwq9ngBXI/wcjploulPSLNG2e1q50i9xkjl6jjdMwCngz1eANcjvJyKtM2HykRz7Xa7u+y9WygTAd7H0QCA6xFeSmrNN9KEO6SDe6U4UyYaLTW5wuleAfAXykaA6xFeSlIm+u4paebLdrtqS3s1Ufm6TvcMwPHsS5FmjCzZazbOsL+yQR3gWoSXkkzMLQwubW+XLv2HFO3CnX0B/MGMkM75z6m9Nqm6v3sDwE8IL8VlSkNt/ibVvUg620Ob5gHhzJR+Og4p+evMZN3mNwSiRwD8gH1eAACApz6/A3Y8AAAAQCAQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKcQXgAAgKdEK8QUHpJtTqcEAADeUPi5Xfg5HlbhZd++fdbXmjVrOt0VAABwCp/jSUlJJ3xOhK84EcdDCgoKtG3bNiUkJCgiIkLhkFRNUNu8ebMSExMVTrj28Lv2cL1ug2sPv2sPt+v2+XxWcKlWrZoiIyPDa+TFXHCNGjUUbswf7HD4w10Urj38rj1cr9vg2sPv2sPpupNOMuJSiAm7AADAUwgvAADAUwgvHhcXF6fHHnvM+hpuuPbwu/ZwvW6Daw+/aw/X6y6OkJuwCwAAQhsjLwAAwFMILwAAwFMILwAAwFMILwAAwFMILx60Z88e3XzzzdamRcnJyerfv7/2799/wtfccccdOvPMM1WqVClVrFhRV111lVatWqVQv3bz/HvvvVeNGjWyrr1WrVoaOHCg0tPTFeo/8zfeeEOdOnWyXmN2m05LS5MXjB49WnXq1FF8fLzatWunuXPnnvD548ePV+PGja3nN2vWTF9//bW8qiTXvnz5cl177bXW883Pd+TIkfKyklz7m2++qY4dO6pcuXLWrUuXLif9cxIK1/3ZZ5+pTZs21ntAmTJl1LJlS/33v/9VOCK8eJD5EDNvXFOnTtVXX32ln376SbfffvsJX9O6dWuNGTNGK1eu1DfffGNtw3zppZcqPz9foXzt5qgIc3v++ee1bNkyjR07VlOmTLE+/EP9Z37gwAF169ZNDz/8sLzio48+0uDBg63loQsWLFCLFi3UtWtX7dixo8jnz5o1S71797Z+ngsXLlTPnj2tm/lZe01Jr938fOvVq6dnn31WVapUkZeV9Np/+OEH6+f+/fffa/bs2dYW+ub9bOvWrQrl6y5fvrweeeQR65qXLFmifv36WTfznh52zFJpeMeKFSvM0nbfvHnzDj82efJkX0REhG/r1q3F/v8sXrzY+v+sXbvWF27X/vHHH/tiY2N9ubm5vnC47u+//956/d69e31u17ZtW98999xzuJ2fn++rVq2ab8SIEUU+//rrr/f16NHjqMfatWvnu+OOO3xeU9JrP1Lt2rV9L730ks+rTufajby8PF9CQoLv3Xff9YXTdRutWrXyDR8+3BduGHnxGJO4zZChGTosZIZMzZlOc+bMKdb/IzMz0xqFqVu3rqdO3/bHtRumZGRKKdHR0WF13W6Xk5Oj+fPnW9dWyFyjaZvfg6KYx498vmH+5Xq854fStYcKf1y7GYXKzc21RibC5bp9Pp+mT5+u1atX68ILL1S4Ibx4TEpKiipVqnTUY+ZD2PylNd87kX//+98qW7asdZs8ebJVgoiNjVU4XHuhXbt26amnnjppySXUrtsLzM/GlDErV6581OOmfbzrNI+X5PmhdO2hwh/X/tBDD1knER8bZEPxus0/vsqWLWu9d/fo0UOjRo3SJZdconBDeHGJoUOHWpPuTnQ73Qm2Zt6EmRfw448/qmHDhrr++uuVlZWlcLj2wuPlzV/2s846S48//ricFqzrBkKZmfPz4YcfasKECdak11CXkJCgRYsWad68eXr66aetOTNmDlC48ca4eRgYMmSI+vbte8LnmMl5ZmLesZO58vLyrNUoJ5u0Z44aN7cGDRrovPPOs2bpm7/wZuJbqF/7vn37rMmr5i++ueaYmBg5LRjX7SUVKlRQVFSUUlNTj3rctI93nebxkjw/lK49VJzOtZuJ+Ca8TJs2Tc2bN1c4XLcpLdWvX9+6b1YbmUUYI0aMsFYWhhPCi0uY5cvmdjLt27e3lryaWqlZQWR89913KigosJbZFZepl5pbdna2Qv3azYiLmQdhDjebOHGia/51FuyfuduZYXBzfaaOb1YMGeYaTXvAgAHH/b0x3x80aNDhx0w51Dwe6tceKk712p977jlr5MGstDlyPli4/cwLCgpc8T4edE7PGEbJdevWzZphPmfOHN+MGTN8DRo08PXu3fvw97ds2eJr1KiR9X1j3bp1vmeeecb366+/+jZu3OibOXOm74orrvCVL1/el5qa6gvla09PT7dWnzRr1sxaWbV9+/bDN7NCIVSv2zDXuHDhQt+bb75prTb66aefrPbu3bt9bvXhhx/64uLifGPHjrVWWd1+++2+5ORkX0pKivX9v/71r76hQ4cefr75sxwdHe17/vnnfStXrvQ99thjvpiYGN/SpUt9XlPSa8/OzrZ+nuZWtWpV3wMPPGDd/+2333yhfu3PPvustWLwk08+Oerv9L59+3yhfN3mffzbb7+13tPN882fe/Pn3/wdDzeEFw8yHz7mg6ts2bK+xMREX79+/Y76S7t+/Xrrw8oskTXMctru3bv7KlWqZL2x16hRw3fTTTf5Vq1a5Qv1ay9cJlzUzTw3VK/bMB/kRV33mDFjfG42atQoX61atawPJ7OU9Jdffjn8vYsuusjXp0+fPy19b9iwofX8s88+2zdp0iSfV5Xk2gt/5sfezPNC/drN0vCirt38mQ/l637kkUd89evX98XHx/vKlSvna9++vRWAwlGE+Y/Toz8AAADFxWojAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAADgKYQXAAAgL/n/55cRJR0oPgcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rst = np.array(sweep(0))\n",
    "plt.plot(rst[:, 0], rst[:, 1])\n",
    "plt.plot(rst[:, 0], rst[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scope.query('MEASUrement:IMMed:UNIT?')\n",
    "scope.write('TRIGger:A:LEVel -0.3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'READY\\n'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope.query('TRIGger:STATe?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.94"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read('LOW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3181205662.py, line 1)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mimport4numpy as np\u001b[39m\n                 ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "write(4, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0376"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "console = Experiment(init_gpa=False)\n",
    "console.add_flodict({\n",
    "    'ocra40_v4': (np.array([10, 1000, 2000]), np.array([0, -0.3, 0])),\n",
    "})\n",
    "msg = console.run()\n",
    "console.__del__()\n",
    "msg[1]\n",
    "sleep(.1)\n",
    "read('LOW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv, numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def import_ch(ch: int):\n",
    "    with open(f'./data/{ch}.csv', 'r') as f:\n",
    "        reader = csv.reader(f)\n",
    "        return np.sort([\n",
    "            [float(i) for i in row] for row in reader if len(row) == 3\n",
    "        ], axis=0)\n",
    "\n",
    "def 划分高低电平(data: np.ndarray):\n",
    "    low = np.hstack((data[:17, 1], data[19:, 2]))\n",
    "    high = np.hstack((data[:17, 2], data[19:, 1]))\n",
    "    x = np.hstack((data[:17, 0], data[19:, 0]))\n",
    "    return low, high, x\n",
    "\n",
    "def import_all():\n",
    "    coef = []\n",
    "    series = []\n",
    "    for ch in range(40):\n",
    "        try:\n",
    "            raw = import_ch(ch)\n",
    "        except FileNotFoundError:\n",
    "            print(f'未找到通道 {ch} 的数据文件')\n",
    "            continue\n",
    "        low, high, x = 划分高低电平(raw)\n",
    "        series.append([x, low, high])\n",
    "        k, b = np.polyfit(x, high, 1)\n",
    "        coef.append([ch, k, b])\n",
    "    return np.array(coef), np.array(series)\n",
    "\n",
    "def plot_raw(series: np.ndarray):\n",
    "    plt.plot(series[:, 0], series[:, 1])\n",
    "    plt.plot(series[:, 0], series[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "for ch in range(39):\n",
    "    plt.plot(series[ch][0], series[ch][2], alpha=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未找到通道 32 的数据文件\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x71ee65699880>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef, series = import_all()\n",
    "plt.plot(coef[:, 0], coef[:, 1])\n",
    "plt.plot(coef[:, 0], coef[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12和32待补测,暂时用平均值代替"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_coef = coef[:, 1:3]\n",
    "fit_coef[12, 0] = np.mean(fit_coef[:, 0])\n",
    "fit_coef = np.insert(fit_coef, 32, [np.mean(fit_coef[:, 0]), np.mean(fit_coef[:, 1])], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 同步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def 三角脉冲(峰值: float, 压摆率: float, 采样率: float):\n",
    "    num_points = 2*round(np.abs(峰值) * 采样率 / 压摆率)\n",
    "    print('波形点数:', num_points)\n",
    "    x = 1e6 * np.arange(num_points) / 采样率\n",
    "    lhs = np.linspace(0, 峰值, num_points//2)\n",
    "    rhs = np.linspace(峰值, 0, num_points//2)\n",
    "    y = np.hstack((lhs, rhs))\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "波形点数: 40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x, y = 三角脉冲(-0.2, 100, 10000)\n",
    "plt.plot(x, y)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n",
      "波形点数: 100\n"
     ]
    }
   ],
   "source": [
    "from experiment import Experiment\n",
    "\n",
    "console = Experiment(init_gpa=False)\n",
    "console.add_flodict({\n",
    "    f'ocra40_v{ch}': 三角脉冲(0.1, 20, 10000) for ch in range(40)\n",
    "    # 'ocra40_v12': 三角脉冲(0.1, 10, 10000)\n",
    "    # 'ocra40_v39': (np.array([0, 1000, 5000]), np.array([0, 0.2, 0.6]))\n",
    "})\n",
    "console.run()\n",
    "console.__del__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测场图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_ch(ch: int, amp: float = 0.1):\n",
    "    console = Experiment(init_gpa=False)\n",
    "    console.add_flodict({\n",
    "        f'ocra40_v{c}': (\n",
    "            np.array([0, 1000]), \n",
    "            np.array([0, amp if c == ch else 0])\n",
    "        ) for c in range(40)\n",
    "    })\n",
    "    msg = console.run()\n",
    "    console.__del__()\n",
    "    return msg[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第一组\n",
    "set_ch(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第二组\n",
    "set_ch(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第三组\n",
    "set_ch(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性场拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "电流监视比例 = 20  # A/V\n",
    "import numpy as np, csv\n",
    "with open('polyfit.csv', 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    fit_coef = np.array(list(reader)).astype(float)\n",
    "\n",
    "def current_to_au(ch: int, current: float, scale_factor:float = 30.0):\n",
    "    assert 0 <= ch < 40, '通道号必须在 0 到 39 之间'\n",
    "    k, b = fit_coef[ch, :2]  # Volt = kx + b\n",
    "    current = current/scale_factor\n",
    "    x = (current/电流监视比例 - b) / k\n",
    "    return x\n",
    "\n",
    "def au_to_current(ch: int, au: float):\n",
    "    assert 0 <= ch < 40, '通道号必须在 0 到 39 之间'\n",
    "    k, b = fit_coef[ch, :2]  # Volt = kx + b\n",
    "    current = (k * au + b) * 电流监视比例\n",
    "    return current\n",
    "\n",
    "def xyz_to_40(xyz: int):\n",
    "    assert 0 <= xyz < 3, '0, 1, 2 代表 x, y, z 三个方向'\n",
    "    coef = fit_coef[:, xyz + 2]  # 前两列是k和b,后面三列是x, y, z\n",
    "    return np.array([current_to_au(ch, coef[ch]) for ch in range(40)])\n",
    "\n",
    "def apply_gradient(grad_arr: np.ndarray = np.zeros(40)):\n",
    "    grad_mat = grad_arr.reshape((5, 8))\n",
    "    for i in range(5):  # 五个功放\n",
    "        sum_curr = np.sum(\n",
    "            np.abs(au_to_current(i*8 + j, grad_mat[i, j])) for j in range(8)\n",
    "        )\n",
    "        if sum_curr > 9:\n",
    "            print(f'功放 {i} 的总电流超过 9A,当前值为 {sum_curr:.2f}A')\n",
    "            return False\n",
    "    console = Experiment(init_gpa=False)\n",
    "    console.add_flodict({\n",
    "        f'ocra40_v{ch}': (\n",
    "            np.array([100]), \n",
    "            np.array([grad_arr[ch]])\n",
    "        ) for ch in range(40)\n",
    "    })\n",
    "    console.run()\n",
    "    console.__del__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_x = xyz_to_40(0) \n",
    "grad_y = xyz_to_40(1)\n",
    "grad_z = xyz_to_40(2) \n",
    "grad_xy = (grad_x + grad_y) / 2\n",
    "grad_xyz = (grad_x + grad_y + grad_z) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_43522/3222022864.py:28: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  sum_curr = np.sum(\n"
     ]
    }
   ],
   "source": [
    "apply_gradient()  # 清零"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_43522/3222022864.py:28: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  sum_curr = np.sum(\n"
     ]
    }
   ],
   "source": [
    "apply_gradient(grad_x)  # 开启x方向线性梯度"
   ]
  }
 ],
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