Lugovtsov/3.5.1-ne-plasma/processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d77ea758-4368-4ffc-86c7-2980c84ceaa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1c0385c-84ab-479f-a1ad-c2fc34e59e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def line(x, k, b):\n",
    "    return x*k + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "805a285a-8aaf-4e25-8c23-4503c8cca594",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"raw-data/discharge.txt\")\n",
    "U = df['U']*10\n",
    "I = df['I']*6/155"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "141d5962-2dcc-4795-ac71-5d497f89418b",
   "metadata": {},
   "outputs": [],
   "source": [
    "I_line = I[:8]\n",
    "U_line = U[:8]\n",
    "opt, cov = curve_fit(line, U_line, I_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e0b87229-443c-4642-b053-7ce974af2817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=300)\n",
    "\n",
    "# plt.minorticks_on()\n",
    "plt.grid(which='minor', linestyle=':', color='0.9')\n",
    "plt.grid(which='major', linestyle='-', color='0.8', linewidth=0.3)\n",
    "\n",
    "plt.errorbar(U, I, yerr=6/155, xerr=0.003*U, linestyle='', marker='.', markersize=0, label=\"Experiment\", color=\"tab:red\", alpha=0.5)\n",
    "plt.plot(U_line, line(U_line, opt[0], opt[1]), label=r\"Fitting: $R_{diff}=$ \" + f\"{-1/opt[0]:.1f}\" + r\"$\\pm 1.48$ kOhm\", color=\"tab:red\", linestyle='-')\n",
    "\n",
    "# plt.title(\"ВАХ разряда\", fontsize=16)\n",
    "plt.ylabel(\"Amperage, mA\")\n",
    "plt.xlabel(\"Voltage, V\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(r\"images\\vah-discharge.png\", facecolor=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c19433-6f3d-4376-a176-883abf2da51e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "**Кривая соответствует участку ГД** -- поднормальный тлеющий разряд"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "43c138ed-008c-46e0-b45c-8ab96cf134d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = []\n",
    "pr.append(pd.read_csv(r\"raw-data\\probe5.txt\"))\n",
    "pr.append(pd.read_csv(r\"raw-data\\probe3.txt\"))\n",
    "pr.append(pd.read_csv(r\"raw-data\\probe1-5.txt\"))\n",
    "\n",
    "I_name = [5, 3, 1.5]\n",
    "colors = [\"orange\", \"green\", \"blue\"]\n",
    "U_corr = [0.2, 0.3, 0.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "404f94e8-091e-4186-8d13-09452313493e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=300)\n",
    "\n",
    "# plt.minorticks_on()\n",
    "plt.grid(which='minor', linestyle=':', color='0.9')\n",
    "plt.grid(which='major', linestyle='-', color='0.8', linewidth=0.3)\n",
    "\n",
    "for index, probe in enumerate(pr):\n",
    "    U = probe['U'] - U_corr[index]\n",
    "    I = probe['I']\n",
    "    plt.errorbar(U, I, yerr=0.003*I, xerr=0.003*U, linestyle='-', marker='.', markersize=5, label=r\"Experiment $I_d = $\" + f\"{I_name[index]}\" +\" mA\", color=f\"tab:{colors[index]}\", alpha=1)\n",
    "# plt.plot(U_line, line(U_line, opt[0], opt[1]), label=r\"Fitting: $R_{diff}=$ \" + f\"{-1/opt[0]:.1f}\" + r\"$\\pm 1.48$ kOhm\", color=\"tab:red\", linestyle='-')\n",
    "\n",
    "# plt.title(\"ВАХ разряда\", fontsize=16)\n",
    "plt.ylabel(\"Amperage, $\\mu A$\")\n",
    "plt.xlabel(\"Voltage, V\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(r\"images\\vah-probe.png\", facecolor=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "bfaa33ac-4c2a-4f2e-9060-4a5cec648b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I_plus_5 = 90.4775\t I_minus_5 = -101.2500\n",
      "I_plus_3 = 48.5366\t I_minus_3 = -56.4262\n",
      "I_plus_1.5 = 23.7260\t I_minus_1.5 = -27.9161\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=300)\n",
    "\n",
    "# plt.minorticks_on()\n",
    "plt.grid(which='minor', linestyle=':', color='0.9')\n",
    "plt.grid(which='major', linestyle='-', color='0.8', linewidth=0.3)\n",
    "\n",
    "for index, probe in enumerate(pr):\n",
    "    U = probe['U'] - U_corr[index]\n",
    "    I = probe['I']\n",
    "    plt.errorbar(U, I, yerr=0.003*I, xerr=0.003*U, linestyle='-', marker='.', markersize=5, alpha=0.1)\n",
    "    \n",
    "    U_plus = np.concatenate([U[:5], [0]])\n",
    "    U_minus = np.concatenate([U[-5:], [0]])\n",
    "    \n",
    "    ptr_plus, _ = curve_fit(line, U[:5], I[:5])\n",
    "    ptr_minus, _ = curve_fit(line, U[-5:], I[-5:])\n",
    "    \n",
    "    plt.plot(U_plus, line(U_plus, ptr_plus[0], ptr_plus[1]), color=f\"tab:{colors[index]}\", linestyle='--', label=r\"Fitting $I_d = $\" + f\"{I_name[index]}\" +\" mA\")\n",
    "    plt.plot(U_minus, line(U_minus, ptr_minus[0], ptr_minus[1]), color=f\"tab:{colors[index]}\", linestyle='--')\n",
    "    \n",
    "    print(f\"I_plus_{I_name[index]} = {line(0, ptr_plus[0], ptr_plus[1]):.4f}\\t I_minus_{I_name[index]} = {line(0, ptr_minus[0], ptr_minus[1]):.4f}\")\n",
    "\n",
    "# plt.plot(U_line, line(U_line, opt[0], opt[1]), label=r\"Fitting: $R_{diff}=$ \" + f\"{-1/opt[0]:.1f}\" + r\"$\\pm 1.48$ kOhm\", color=\"tab:red\", linestyle='-')\n",
    "\n",
    "# plt.title(\"ВАХ разряда\", fontsize=16)\n",
    "plt.ylabel(\"Amperage, $\\mu A$\")\n",
    "plt.xlabel(\"Voltage, V\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(r\"images\\vah-probe.png\", facecolor=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "9c771922-a2ff-4712-9d9a-e630ed94970f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 2, 5, 6])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([2, 5, 6])\n",
    "np.concatenate([a, b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a41d068-44d5-4dc0-93d6-7c9853d338cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}