150 lines
14 KiB
Plaintext
150 lines
14 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5dabef1c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.11293824972675794\n",
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"0.01226740102904371\n",
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"0.0938096382728547\n",
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"0.009262073610238137\n",
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"0.40123743304422665\n",
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"0.041224279574892234\n",
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"0.40493839466080334\n",
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"0.019640130500072135\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.collections.LineCollection at 0x1ff88333ac0>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcc0lEQVR4nO3df3DU953f8ecbReTWloPc2CeDoAeNqRyCFTAyTuv0KoWm4FxtiM8hxj176h5D3Anx5TqnGm5uMCF3NSl/kHjslDAu48lMG42uxRwkpGoHz9bX/GiAQxbgWDmV+IIk+xzHET7Z2yDg3T++q7BaVtJXq+/ud/e7r8cMI30/38/u9/3Rd3npq893v981d0dERKrfnLgLEBGRaCjQRUQSQoEuIpIQCnQRkYRQoIuIJMT74trwTTfd5IsXLy7qse+++y7XX399tAVVOI25NmjMtWE2Yz558uRb7n5zoXWxBfrixYs5ceJEUY9Np9O0t7dHW1CF05hrg8ZcG2YzZjP7m8nWacpFRCQhFOgiIgmhQBcRSQgFuohIQijQRUQSYtpAN7MDZvammZ2ZZL2Z2dNmNmBmfWZ2R/RlSi05fvgbvLHzVhju5Y2dt3L88DfiLkmkKoQ5Qn8eWDfF+nuApdl/W4D/OPuypFYdP/wNlp/8E27h52BwCz9n+ck/UaiLhDBtoLv7S8DbU3RZD3zTAz8EGs1sflQFSm1Z9Fd7SNnFCW0pu8iiv9oTU0Ui1cPC3A/dzBYD33b35QXWfRvY7e7/O7t8DHjC3a+5asjMthAcxdPU1LSqq6urqKJHR0dpaGgo6rHVqmbGPNwLFnw7+v4FNPxqOFhwYMGKmIoqn5rZzzk05pnp6Og46e5thdZFcaWoFWgr+FvC3fcD+wHa2tq82CuldGVZcr2xc3Mw3QKkW75Ee/+TQTs3c8tDA3GWVha1sp9zaczRieJdLoPAopzlhcBwBM8rNej8HZ1kfO6EtozP5fwdnTFVJFI9ogj0w8Aj2Xe7fAy44O6vR/C8UoPuvO9znFn1p7zBzeDBkfmZVX/Knfd9Lu7SRCretFMuZvYtoB24ycwGgSeBegB33wccBT4FDADvAY+WqlipDXfe9zm473O8mk5zy0MD3BJ3QSJVYtpAd/dN06x34PORVSQiIkWJ7fa5IrkOnRpiT08/wyMZFjSm6FzbApkx7t794oS2DSub4y5VpGIp0KV8+rrh2C64MAjzFsKaHQC8990d3PfeG7T5B/kPczZyeOTjdP75y/zB8ksMjdQBMDSSYfvB0wAKdZFJKNClPPq64cjjMJYJli+ch7/4PLhz3ZUxMFhob7G7/jkYg8NXPk7+NRKZscvs6elXoItMQjfnkvI4tutqmI+7fBGujE1ous4u8u/e1z3p0wyPZCZdJ1LrFOhSHhcGQ3ddYL+YfF1jKopqRBJJgS7lMW9h6K7D/kHq5xhmEy9CTtXXBSdLRaQgBbqUx5odUJ93dF03F+bUT2h6z+fy3NzfY89nPsrCG1M0N6YwoLkxxVP33675c5Ep6KSolEfrxuBrgXe55LZdt2YHO7N90+m/5nvb2uOpV6QKKdClfFo3Xg32/HYRmTVNuYiIJIQCXUQkIRToIiIJoUAXEUkIBbqISEIo0EVEEkKBLiKSEAp0EZGEUKCLiCSEAl1EJCFCBbqZrTOzfjMbMLNtBdbfaGYvmFmfmf3IzJZHX6pUhb5u2LscdjYGX/smv7d52VVybSIRmDbQzawOeBa4B1gGbDKzZXnd/hjodfdW4BHga1EXKlVg/FOJLpwHPPh65PHKCM5Krk0kImGO0FcDA+5+zt0vAl3A+rw+y4BjAO7+KrDYzJoirVQqX6FPJRrLBO1xq+TaRCJi+Z/beE0HsweAde6+Obv8MHCXu2/N6fPvgd9w939rZquB72f7nMx7ri3AFoCmpqZVXV1dRRU9OjpKQ0NDUY+tVlUx5td7J183f8WMny7SMUdcW6lUxX6OmMY8Mx0dHSfdva3QujC3z7UCbfm/BXYDXzOzXuA0cAq4dM2D3PcD+wHa2tq8vb09xOavlU6nKfax1aoqxrx3a3ZKI8+8RbDpzIyfLtIxR1xbqVTFfo6YxhydMFMug8CinOWFwHBuB3d/x90fdfcVBHPoNwM/japIqRKFPpWoPnX1gyziVMm1iUQkTKAfB5aa2RIzmws8CBzO7WBmjdl1AJuBl9z9nWhLlYrXuhHufTo46sWCr/c+XRkfYFHJtYlEZNopF3e/ZGZbgR6gDjjg7mfN7LHs+n3Ah4Fvmtll4BXg90tYs1SyyT6VqBJUcm0iEQj1EXTufhQ4mte2L+f7HwBLoy1NRERmQleKiogkhAJdRCQhFOgiIgmhQBcRSYhQJ0VFSuHQqSH29PQzPJJhQWOKzrUtbFjZHHdZIlVLgS6xOHRqiO0HT5MZuwzA0EiG7QdPAyjURYqkKReJxZ6e/l+H+bjM2GX29PTHVJFI9VOgSyyGRzIzaheR6SnQJRYLGlMzaheR6SnQJRada1tI1ddNaEvV19G5tiWmikSqn06KSizGT3zqXS4i0VGgS2w2rGxWgItESFMuIiIJoUAXEUkIBbqISEIo0EVEEkKBLiKSEHqXi1QN3cxLZGoKdKkKupmXyPRCTbmY2Toz6zezATPbVmD9PDM7YmYvm9lZM3s0+lIldn3dsHc57GwMvvZ1l23TsdzMK8bxihRj2iN0M6sDngU+CQwCx83ssLu/ktPt88Ar7n6vmd0M9JvZf3b3iyWpWsqvrxuOPA5j2ZtnXTgfLAO0biz55st+M6+YxytSjDBH6KuBAXc/lw3oLmB9Xh8HbjAzAxqAt4FLkVYq8Tq262q4jRvLBO1lUPabecU8XpFimLtP3cHsAWCdu2/OLj8M3OXuW3P63AAcBm4DbgA+6+7fKfBcW4AtAE1NTau6urqKKnp0dJSGhoaiHlutYh/z672Tr5u/oiSbzB3zSGaMoV9muJLzep1jRvONKRpT9dFvPIbxQgXs5xhozDPT0dFx0t3bCq0Lc1LUCrTl/xZYC/QCnwA+BPxPM/tLd39nwoPc9wP7Adra2ry9vT3E5q+VTqcp9rHVKvYx790aTDvkm7cINp0pySbzx1zWd7nEMF6ogP0cA405OmECfRBYlLO8EBjO6/MosNuDw/0BM/spwdH6jyKpUuK3ZsfEOWWA+lTQXiZlvZlXBYxXZKbCzKEfB5aa2RIzmws8SDC9kutnwBoAM2sCWoBzURYqMWvdCPc+HRyhYsHXe59O7gnCWhuvJMK0R+jufsnMtgI9QB1wwN3Pmtlj2fX7gC8Dz5vZaYIpmifc/a0S1i1xaN1YW4FWa+OVqhfqwiJ3PwoczWvbl/P9MPDPoy1NRERmQvdyERFJCAW6iEhCKNBFRBJCgS4ikhAKdBGRhFCgi4gkhAJdRCQhFOgiIgmhQBcRSQgFuohIQijQRUQSQoEuIpIQCnQRkYRQoIuIJIQCXUQkIRToIiIJoUAXEUkIBbqISEIo0EVEEiJUoJvZOjPrN7MBM9tWYH2nmfVm/50xs8tm9veiL1dKpq8b9i6HnY3B177uuCuqPvoZSszM3afuYFYH/AT4JDAIHAc2ufsrk/S/F/hDd//EVM/b1tbmJ06cKKro3q9+lRUjI0U9tlq99tprLF68uDRP/rdnof+7cGXsatucemi5B5o+UppthlDSMUctop9hVY05IrU45t7GRlZ88YtFPdbMTrp7W6F17wvx+NXAgLufyz5ZF7AeKBjowCbgW8UUGtbIihXQ3l7KTVSc19JpFpdqzHuXQ1MdUDexfd6P4Q//vDTbDGGqMR86NcSenn6GRzIsaEzRubaFDSuby1tgroh+hiXdzxWqFsc8kk6X5HnDHKE/AKxz983Z5YeBu9x9a4G+1xEcxd/q7m8XWL8F2ALQ1NS0qqurq6iiR0dHaWhoKOqx1aqkY369d/J181eUZpshTDbmkcwYQ7/McCXntTvHjOYbUzSm6stZ4lUR/Qz12q4NsxlzR0fHrI7QrUDbZL8F7gW+VyjMAdx9P7AfgimX9iJ/K6fTaYp9bLUq6Zj3boUL569tn7c
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"nu = [1588, 1581, 1571, 1561, 1552, 1541, 1531, 1522, 1601, 1596, 1612, 1620, 1630, 1640, 1651, 1663]\n",
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"v = [30, 26.5, 21, 17.5, 14, 11.5, 10, 9, 30, 31, 25, 21.5, 17.5, 14.5, 12.5, 10.5]\n",
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"\n",
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"for i in range(len(v)):\n",
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" nu[i] /= 1596\n",
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" v[i] /= 31\n",
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" \n",
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"fig, ax = plt.subplots()\n",
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"\n",
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"plt.scatter(nu, v)\n",
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"\n",
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"nu_100 = [1597, 1583, 1571, 1528, 1505, 1480, 1447, 1407, 1349, 1553, 1653, 1685, 1718, 1759, 1817, 1905, 2073]\n",
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"u_100 = [3, 2.9, 2.9, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 2.7, 2.7, 2.4, 2.1, 1.8, 1.5, 1.2, 0.9]\n",
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"\n",
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"for i in range(len(u_100)):\n",
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" nu_100[i] /= 1597\n",
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" u_100[i] /= 3\n",
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" \n",
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"plt.scatter(nu_100, u_100)\n",
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"\n",
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"n = [0] * 4\n",
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"u = [0] * 4\n",
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"\n",
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"n[0] = [0, 2, 10, 19, 12, 4, 7]\n",
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"u[0] = [1.12, 0.2, 0.72, 1, 0.8, 0.38, 0.58]\n",
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"\n",
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"n[1] = [0, 2, 4, 7, 12, 19, 27]\n",
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"u[1] = [1.12, 1, 0.78, 0.6, 0.38, 0.2, 0.1]\n",
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"\n",
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"n[2] = [0, 1, 2, 3, 4, 5, 6]\n",
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"u[2] = [280, 65, 135, 185, 215, 240, 250]\n",
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"\n",
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"n[3] = [0, 1, 2, 3, 4, 5, 6]\n",
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"u[3] = [280, 230, 150, 100, 70, 45, 30]\n",
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"\n",
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"for i in range(len(u[1])-1):\n",
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" u[1][i+1] = u[1][0] - u[1][i+1]\n",
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"for i in range(len(u[3])-1):\n",
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" u[3][i+1] = u[3][0] - u[3][i+1]\n",
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"\n",
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"theta = [[], [], [], []]\n",
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"avg_theta = [0] * 4\n",
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"sigma_theta = [0] * 4\n",
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"\n",
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"for k in range(4):\n",
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" for i in range(len(n[k]) - 1):\n",
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" for j in range(len(n[k]) - i - 2):\n",
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" theta[k].append(np.abs(np.log((u[k][0] - u[k][i+1]) / (u[k][0] - u[k][j+i+2])) / (n[k][j+i+2] - n[k][i+1])))\n",
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" avg_theta[k] += theta[k][-1] \n",
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" \n",
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" avg_theta[k] /= len(theta[k])\n",
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" print(avg_theta[k])\n",
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" for i in range(len(theta[k])):\n",
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" sigma_theta[k] += (avg_theta[k] - theta[k][i]) **2\n",
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" sigma_theta[k] /= (len(theta[k])-1) \n",
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" print(np.sqrt(sigma_theta[k]))\n",
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" \n",
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"ax.grid()\n",
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"ax.hlines(0.707, 0.8, 1.3, color = 'red', linewidth = 0.5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1b8c0678",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "13da0f9f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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