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   "id": "280f1e1b-c050-4f4a-b45b-b4d488b55d99",
   "metadata": {
    "tags": []
   },
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   "source": [
    "# %load /home/glebi/git/experiment-automation/processing_tools.py\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import scienceplots\n",
    "\n",
    "plt.style.use(['science', 'russian-font'])\n",
    "\n",
    "mpl.rcParams.update({\n",
    "    'figure.figsize': [6, 4],\n",
    "    'savefig.facecolor': 'white',\n",
    "    'figure.dpi': 150.0,\n",
    "    'font.size': 12.0,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb054603-67f5-4418-8c09-e8e3210ffb17",
   "metadata": {},
   "source": [
    "U_1 = 12V -- первые данные Lugovcov\n",
    "\n",
    "U = 25V -- остальные данные\n",
    "\n",
    "Формат:\n",
    "t[us], U[V], U[V]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01617d5c-6498-42db-bbe0-b24c0bf40ea8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from random import random, randint, choice\n",
    "def plot_data(filename):\n",
    "    t, x, y = np.loadtxt(f\"data//{filename}\", skiprows=1, delimiter=\",\").T\n",
    "    r = 1 / (2**.5)\n",
    "    a = 150 # Э/А\n",
    "    B = -x / r * a\n",
    "    H = y / r * a\n",
    "    with mpl.rc_context({\n",
    "        'figure.figsize': [7, randint(3,5)],\n",
    "        'font.size': randint(10, 16),  \n",
    "        'axes.grid' : random() > .5,\n",
    "        'axes.facecolor': f\"'{1-random()*.07}'\",\n",
    "        'axes.grid.axis': choice(['both']),\n",
    "        'axes.grid.which': choice(['both', 'minor', 'major'])\n",
    "    }):\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.plot(B, H, lw=random()*.3, color=f\"C{randint(0, 7)}\", linestyle=choice(['-', '--', ':']))\n",
    "        ax.set_xlabel(\"Величина поля B, Э\")\n",
    "        ax.set_ylabel(\"Величина поля H, Э\")\n",
    "        fig.savefig(f\"output//{filename.split('.')[0]}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c5c0796-12f5-4433-a202-35ce633d9dfe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filenames = [\"Lugovcov.csv\", \"Kazikov.csv\", \"Smirnov.csv\", \"Zakharov.csv\"]\n",
    "\n",
    "for f in filenames:\n",
    "    plot_data(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa22ed90-4ccd-4957-b3a6-a9c983141716",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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