{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa52a710-d455-4e47-b0b0-514b9e5594a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b4dd36c-b166-4f05-aef7-097bf0bd6b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f7283f6-0c91-46c6-bbbb-bee497acff68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231ee36c6e0>"
      ]
     },
     "execution_count": 3,
     "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": [
    "x = np.array([-3,-2.5,-2,-1.5,-1,-0.5,0,0.5,1,1.5,2,2.5,3,-0.1,-0.2,-0.3,-0.4])\n",
    "y = np.array([-1.52, -1.49, -1.49, -1.47, -1.46, -1.26, 7.1, 19.4, 28.1, 37.4, 45.0, 50.6, 54.0, 4.13, 1.87, 0.06, -0.89])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(-1*x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dbcdb78-0c76-4f7f-96c0-fedd342294d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231ee4416d0>"
      ]
     },
     "execution_count": 4,
     "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": [
    "x = np.array([-2,-1.9,-1.8,-1.7,-1.6,-1.5,-1.4,-1,0,0.5])\n",
    "y = np.array([-1.5, -1.36, -1.04, -0.3, 1.26, 3.85, 8.0, 31.4, 123, 170])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(-1*x,y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc7b951b-1c4e-495b-8def-d6b94dcba4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231ee4e4410>"
      ]
     },
     "execution_count": 5,
     "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": [
    "x = np.array([-2,-1.9,-1.8,-1.7,-1.6,-1.5,-1.4,-1.3,-1,0,1])\n",
    "y = np.array([-1.33, -1.30, -1.26, -1.22, -1.12, -0.94, -0.53, 0.25, 5.8, 55, 112])\n",
    "\n",
    "def func_linear(x, m, b):\n",
    "    return m*x+b\n",
    "\n",
    "popt, pcov = curve_fit(func_linear, x, y)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(-1 * -x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "716ae141-649d-446c-b18f-7bef30d0aec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231f1696350>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHzNJREFUeJzt3QuMVNXhP/CzyMsqLILKYgGLb9GqhQpSbesDRWsMRK3aqFVja0vQiNiqJFVr2gStxmd91Vgf9YHaFg22ohYVUwUfoKlPotYKLSxoWxZEFyjcf87Nb/a/uy7iLjNnZ2Y/n+Rmd+49O3Pvnpm53zn3nDM1WZZlAQAgkW6pHggAQPgAAJLT8gEAJCV8AABJCR8AQFLCBwCQlPABACQlfAAASXUPZWbDhg1hyZIloU+fPqGmpqazdwcA+ALinKWrVq0KO+ywQ+jWrVtlhY8YPIYMGdLZuwEAdMDixYvD4MGDKyt8xBaPws737du3s3cHAPgCVq5cmTceFM7jFRU+CpdaYvAQPgCgsnyRLhM6nAIASQkfAEBSwgcAkJTwAQAkJXwAAEkJHwBAUsIHAJCU8AEAJFV2k4yVyvoNWXjx/f+E5asaw/Z9eodRw/qHLbr57hgASK1LhI9Zry8Nl818MyxtaGxaN6i2d7j0mOHhyL0Hdeq+AUBX060rBI+J9yxoETyi+obGfH3cDgCk063aL7XEFo+sjW2FdXF7LAcApFHV4SP28Wjd4tFcjBxxeywHAKRR1eEjdi4tZjkAYPNVdfiIo1qKWQ4A2HxVHT7icNo4qmVjA2rj+rg9lgMA0qjq8BHn8YjDaaPWAaRwO2433wcApFPV4SOK83jcfMqIUFfb8tJKvB3Xm+cDANLqEpOMxYBx+PA6M5wCQBnoEuEjipdWxuw8oLN3AwC6vKq/7AIAlBfhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AoHLCx+WXXx5qamrC5MmTm9Y1NjaGSZMmhQEDBoStt946HHfccWHZsmXF2FcAoCuHj5deeinceuutYZ999mmx/rzzzgszZ84MDz30UJgzZ05YsmRJOPbYY4uxrwBAVw0fH3/8cTj55JPDbbfdFrbZZpum9Q0NDeH2228PV199dTj00EPDyJEjwx133BGef/75MG/evGLuNwDQlcJHvKxy9NFHh7Fjx7ZYP3/+/LBu3boW6/fYY48wdOjQMHfu3M3fWwCg4nVv7x9Mnz49LFiwIL/s0lp9fX3o2bNn6NevX4v1AwcOzLe1Zc2aNflSsHLlyvbuEgBQrS0fixcvDueee2649957Q+/evYuyA9OmTQu1tbVNy5AhQ4pyvwBAFYSPeFll+fLlYcSIEaF79+75EjuVXn/99fnvsYVj7dq1YcWKFS3+Lo52qaura/M+p06dmvcVKSwx4AAA1atdl10OO+yw8Nprr7VYd8YZZ+T9Oi688MK81aJHjx5h9uzZ+RDbaOHChWHRokVhzJgxbd5nr1698gUA6BraFT769OkT9t577xbrttpqq3xOj8L6M888M0yZMiX0798/9O3bN5xzzjl58DjggAOKu+cAQNfocLop11xzTejWrVve8hE7ko4bNy7cdNNNxX4YAKBC1WRZloUyEke7xI6nsf9HbDkBAMpfe87fvtsFAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAyjd83HzzzWGfffYJffv2zZcxY8aExx57rGl7Y2NjmDRpUhgwYEDYeuutw3HHHReWLVtWiv0GALpC+Bg8eHC4/PLLw/z588PLL78cDj300DB+/Pjwxhtv5NvPO++8MHPmzPDQQw+FOXPmhCVLloRjjz22VPsOAFSgmizLss25g/79+4crr7wyHH/88WG77bYL9913X/579Pbbb4c999wzzJ07NxxwwAFf6P5WrlwZamtrQ0NDQ966AgCUv/acvzvc52P9+vVh+vTpYfXq1fnll9gasm7dujB27NimMnvssUcYOnRoHj42Zs2aNfkON18AgOrV7vDx2muv5f05evXqFX784x+HGTNmhOHDh4f6+vrQs2fP0K9fvxblBw4cmG/bmGnTpuVJqbAMGTKkY0cCAFRn+Nh9993Dq6++Gl544YUwceLEcNppp4U333yzwzswderUvImmsCxevLjD9wUAlL/u7f2D2Lqxyy675L+PHDkyvPTSS+G6664LJ554Yli7dm1YsWJFi9aPONqlrq5uo/cXW1DiAgB0DZs9z8eGDRvyfhsxiPTo0SPMnj27advChQvDokWL8j4hAADtbvmIl0iOOuqovBPpqlWr8pEtzzzzTHj88cfz/hpnnnlmmDJlSj4CJvZ0Peecc/Lg8UVHugAA1a9d4WP58uXh+9//fli6dGkeNuKEYzF4HH744fn2a665JnTr1i2fXCy2howbNy7cdNNNpdp3AKArzvNRbOb5AIDKk2SeDwCAjhA+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AQPgAAKqXlg8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwAQPgCA6qXlAwBISvgAAJISPgCApIQPACAp4QMASEr4AADKN3xMmzYt7L///qFPnz5h++23DxMmTAgLFy5sUaaxsTFMmjQpDBgwIGy99dbhuOOOC8uWLSv2fgMAXSF8zJkzJw8W8+bNC08++WRYt25dOOKII8Lq1aubypx33nlh5syZ4aGHHsrLL1myJBx77LGl2HcAoALVZFmWdfSPP/zww7wFJIaMb33rW6GhoSFst9124b777gvHH398Xubtt98Oe+65Z5g7d2444IADNnmfK1euDLW1tfl99e3bt6O7BgAk1J7z92b1+YgPEPXv3z//OX/+/Lw1ZOzYsU1l9thjjzB06NA8fLRlzZo1+Q43XwCA6tXh8LFhw4YwefLkcOCBB4a99947X1dfXx969uwZ+vXr16LswIED820b60cSk1JhGTJkSEd3CQCo5vAR+368/vrrYfr06Zu1A1OnTs1bUArL4sWLN+v+AIDy1r0jf3T22WeHRx99NDz77LNh8ODBTevr6urC2rVrw4oVK1q0fsTRLnFbW3r16pUvAEDX0K6Wj9g3NQaPGTNmhKeeeioMGzasxfaRI0eGHj16hNmzZzeti0NxFy1aFMaMGVO8vQYAKlb39l5qiSNZHnnkkXyuj0I/jthXY8stt8x/nnnmmWHKlCl5J9TY2/Wcc87Jg8cXGekCAFS/dg21rampaXP9HXfcEU4//fSmScbOP//8cP/99+cjWcaNGxduuummjV52ac1QWwCoPO05f2/WPB+lIHwAQOVJNs8HAEB7CR8AQFLCBwBQ/vN8QGdYvyELL77/n7B8VWPYvk/vMGpY/7BFt7Y7QQNQvoQPKsKs15eGy2a+GZY2NDatG1TbO1x6zPBw5N6DOnXfAGgfl12oiOAx8Z4FLYJHVN/QmK+P2wGoHMIHZX+pJbZ4tDUevLAubo/lAKgMwgdlLfbxaN3i0VyMHHF7LAdAZRA+KGuxc2kxywHQ+YQPyloc1VLMcgB0PuGDshaH08ZRLRsbUBvXx+2xHACVQfigrMV5POJw2qh1ACncjtvN9wFQOYQPyl6cx+PmU0aEutqWl1bi7bjePB8AlcUkY1SEGDAOH15nhlOAKiB8UDHipZUxOw/o7N0AYDO57AIAJCV8AABJCR8AQFLCBwCQlPABACQlfAAASQkfAEBSwgcAkJTwAQAkJXwAAEkJHwBAUsIHAJCU8AEAJCV8AABJCR8AQFLCBwCQlPABACQlfAAASQkfAEBSwgcAkJTwAQAkJXwAAEkJHwBAUsIHAJCU8AEAJCV8AABJCR8AQFLCBwCQlPABACQlfAAASQkfAEBSwgcAkJTwAQAkJXwAAEkJHwBAUsIHAJCU8AEAJCV8AABJCR8AQFLCBwCQlPABACQlfAAASQkfAEBSwgcAkJTwAQAkJXwAAOUdPp599tlwzDHHhB122CHU1NSEhx9+uMX2LMvCJZdcEgYNGhS23HLLMHbs2PDOO+8Uc58BgK4UPlavXh323XffcOONN7a5/Ve/+lW4/vrrwy233BJeeOGFsNVWW4Vx48aFxsbGYuwvAFDhurf3D4466qh8aUts9bj22mvDz372szB+/Ph83d133x0GDhyYt5CcdNJJm7/HAEBFK2qfj/fffz/U19fnl1oKamtrw+jRo8PcuXPb/Js1a9aElStXtlgAgOpV1PARg0cUWzqai7cL21qbNm1aHlAKy5AhQ4q5SwBAmen00S5Tp04NDQ0NTcvixYs7e5cAgEoJH3V1dfnPZcuWtVgfbxe2tdarV6/Qt2/fFgsAUL2KGj6GDRuWh4zZs2c3rYt9OOKolzFjxhTzoQCArjLa5eOPPw7vvvtui06mr776aujfv38YOnRomDx5cvjlL38Zdt111zyMXHzxxfmcIBMmTCj2vgMAXSF8vPzyy+GQQw5puj1lypT852mnnRbuvPPOcMEFF+RzgZx11llhxYoV4aCDDgqzZs0KvXv3Lu6eAwAVqSaLk3OUkXiZJo56iZ1P9f8AgMrQnvN3p492AQC6FuEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwBISvgAAJISPgCApIQPACAp4QMASKp72ocDyt36DVl48f3/hOWrGsP2fXqHUcP6hy261XT2bgFVRPgAmsx6fWm4bOabYWlDY9O6QbW9w6XHDA9H7j3IfwooCpddgKbgMfGeBS2CR1Tf0Jivj9sBikH4APJLLbHFI2vjf1FYF7fHcgCbS/gA8j4erVs8mouRI26P5QA2l/AB5J1Li1kO4PMIH0A+qqWY5QA+j/AB5MNp46iWjQ2ojevj9lgOYHMJH0A+j0ccThu1DiCF23G7+T6AYhA+gFycx+PmU0aEutqWl1bi7bjePB9AsZhkDGgSA8bhw+vMcAqUlPABtBAvrYzZeYD/ClAyLrsAAEkJHwBAUsIHAJCU8AEAJCV8AABJGe0CJBG/ETd+MV38fpg4TXucLdWkZdA1CR9Ayc16fWm4bOabLb45N07XHmdNNXkZdD0uuwAlDx4T71nQInhE9Q2N+fq4HehahA+gpJdaYotH1sa2wrq4PZYDug7hAyiZ2MejdYtHczFyxO2xHNB1CB9AycTOpcUsB1QH4QMomTiqpZjlgOogfAAlE4fTxlEtNRvZHtfH7bEc0HUIH0DJxHk84nDaqHUAKdyO2833AV2L8AGUVJzH4+ZTRoS62paXVuLtuN48H9D1lGySsRtvvDFceeWVob6+Puy7777hhhtuCKNGjSrVwwFlLAaMw4fXlWyGU7OnQmUpSfh44IEHwpQpU8Itt9wSRo8eHa699towbty4sHDhwrD99tuX4iGBMheDxpidBxT9fs2eCpWnJsuyos/uEwPH/vvvH37961/ntzds2BCGDBkSzjnnnHDRRRd97t+uXLky1NbWhoaGhtC3b99i7xpQhbOntn4TK7SnuKwD6bTn/F30Ph9r164N8+fPD2PHjv3/D9KtW3577ty5nym/Zs2afIebLwCbYvZUqFxFDx8fffRRWL9+fRg4cGCL9fF27P/R2rRp0/KkVFhiCwnAppg9FSpXp492mTp1at5EU1gWL17c2bsEVACzp0LlKnqH02233TZsscUWYdmyZS3Wx9t1dXWfKd+rV698ASi32VONooEKCR89e/YMI0eODLNnzw4TJkxo6nAab5999tnFfjigi8+eWt/Q2Oa35tb831wiHZ091SgaqLDLLnGY7W233Rbuuuuu8NZbb4WJEyeG1atXhzPOOKMUDwd0QaWcPbUwiqb1N/LGoBPXx+2bK7aqzH3v3+GRV/+V/4y3oasoyTwfJ554Yvjwww/DJZdckncy3W+//cKsWbM+0wkVoBizp142880WQSG2eMTg0ZHZUzc1iiZGmbg9TprW0UnSSt2q4nIRXXKej81hng+gM0+2sRXie7fN22S5+394QIcmTSv13CSlDDZCTeVbX8TXyuacv0s2vTpAJc6eWspRNKVuVdlYsClcLtqcYKMPTOWbVeIWt4oaagvQVUbRlHJuklJOupaiDwylVW51KHwAtDGKZmPtDnH9oA6Ooillq0qpgo2ZZCvf+hIG044SPgASjaIpZatKqYKNmWQr34slbHHrKOEDYCOjaOKomebi7c3pN1HKVpVSBRszyVa+5SVscesoHU4B2hADRuz4WcyRAYVWlXiNPd5LVsRWlVJNupZiJllKqxzrUMsHwCZG0Yzf78v5z2IMSSxVq0qpLheVsrWGNMqxDs3zAVBF8y2UYjhlYaRE2EhrzebOTULppajD9szzIXwAVJlSBJtymiOC8qxD4QOAojPDaeVbb4ZTALrqTLJ07TrU4RQASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AQPgAAKpX91BmsixrmiMeAKgMhfN24TxeUeFj1apV+c8hQ4Z09q4AAB04j8dvt62ob7XdsGFDWLJkSejTp0+oqdn8r5duncpiqFm8ePEmv+63ElX78XWFY3R8lU8dVrZqr79SHmOMEzF47LDDDqFbt26V1fIRd3jw4MElfYz4z67WJ1VXOL6ucIyOr/Kpw8pW7fVXqmPcVItHgdEuAEBSwgcAkFSXCh+9evUKl156af6zGlX78XWFY3R8lU8dVrZqr79yOcay63AKAFS3LtXyAQB0PuEDAEhK+AAAkhI+AICkKjp83HjjjeErX/lK6N27dxg9enR48cUXP7f8Qw89FPbYY4+8/Fe/+tXw5z//ucX22Pf2kksuCYMGDQpbbrllGDt2bHjnnXdCpRzjbbfdFr75zW+GbbbZJl/i/rcuf/rpp+czxzZfjjzyyFAJx3fnnXd+Zt/j31VTHR588MGfOca4HH300WVZh88++2w45phj8hkN4348/PDDm/ybZ555JowYMSLvab/LLrvk9bq5r+1yOb4//vGP4fDDDw/bbbddPnnTmDFjwuOPP96izM9//vPP1F98X6qE44t119bzs76+vizrryPH2NbrKy577bVX2dXhtGnTwv7775/PCL799tuHCRMmhIULF27y78rhXFix4eOBBx4IU6ZMyYcLLViwIOy7775h3LhxYfny5W2Wf/7558P3vve9cOaZZ4ZXXnklr6S4vP76601lfvWrX4Xrr78+3HLLLeGFF14IW221VX6fjY2NoRKOMb4xxGN8+umnw9y5c/Ppc4844ojwr3/9q0W5eKJaunRp03L//feHSji+KL6hN9/3Dz74oMX2Sq/DePJqfnzx+bnFFluE7373u2VZh6tXr86PKZ5svoj3338/D1KHHHJIePXVV8PkyZPDD37wgxYn6I48L8rl+OKJLoaP+GY+f/78/DjjiS++5zQXT2TN6++vf/1r6AztPb6CeIJrvv/xxFeO9deRY7zuuutaHFucgrx///6feQ2WQx3OmTMnTJo0KcybNy88+eSTYd26dfl7fjzmjSmbc2FWoUaNGpVNmjSp6fb69euzHXbYIZs2bVqb5U844YTs6KOPbrFu9OjR2Y9+9KP89w0bNmR1dXXZlVde2bR9xYoVWa9evbL7778/q4RjbO1///tf1qdPn+yuu+5qWnfaaadl48ePz8pBe4/vjjvuyGprazd6f9VYh9dcc01ehx9//HFZ1mFz8e1kxowZn1vmggsuyPbaa68W60488cRs3LhxRfufdebxtWX48OHZZZdd1nT70ksvzfbdd9+s3HyR43v66afzcv/97383WqZc66+jdRjL19TUZP/4xz/Kvg6XL1+eH+OcOXM2WqZczoUV2fKxdu3a/FNFbApq/p0w8Xb8xN+WuL55+SgmuUL5+IksNh02LxPnqI9Nhhu7z3I7xtY++eSTPAnH1N66hSR+Utl9993DxIkTw7///e9QKcf38ccfhx133DFv1Rk/fnx44403mrZVYx3efvvt4aSTTso/eZRbHXbEpl6HxfifldsXZcYv2mr9GoxN2PEywE477RROPvnksGjRolBJ9ttvv7xJPrbyPPfcc03rq63+Cq/BuP/xfafc67ChoSH/2fr5Vo7nwooMHx999FFYv359GDhwYIv18Xbra48Fcf3nlS/8bM99ltsxtnbhhRfmL47mT6LYXH/33XeH2bNnhyuuuCJvtjvqqKPyxyr344sn2t/+9rfhkUceCffcc0/+xv6Nb3wj/POf/6zKOozXyWNTaLws0Vy51GFHbOx1GL9l89NPPy3K876cXHXVVXlgPuGEE5rWxTfx2M9l1qxZ4eabb87f7GNfrRhSyl0MHLEp/g9/+EO+xA8BsZ9SvLwSVVv9xW9Yf+yxxz7zGizHOtywYUN+GfPAAw8Me++990bLlcu5sOy+1ZbiuPzyy8P06dPzT8jNO2XGT9EFsaPRPvvsE3beeee83GGHHVbW//7YeS8uBTF47LnnnuHWW28Nv/jFL0K1iZ+4Yh2NGjWqxfpKrsOu5L777guXXXZZHpab94mIQbEg1l08kcVP1Q8++GB+Hb6cxQ8AcWn+GnzvvffCNddcE373u9+FanPXXXeFfv365X0imivHOpw0aVL+YaWz+g91iZaPbbfdNu+Et2zZshbr4+26uro2/yau/7zyhZ/tuc9yO8bmn7Zi+HjiiSfyF8bniU2G8bHefffdUCnHV9CjR4/wta99rWnfq6kOY4exGB6/yBtZZ9VhR2zsdRg7Esde9cV4XpSDWHfx03I8GbVu4m4tntx22223iqi/tsRwXNj3aqm/KHYRiS2tp556aujZs2dZ1+HZZ58dHn300XywweDBgz+3bLmcCysyfMQnwsiRI/Nm5+ZNTvF280/GzcX1zctHsXdwofywYcPyf2zzMrEpOPb03dh9ltsxFnopx1aA2Bz49a9/fZOPEy9ZxP4CsTm1Eo6vudi8+9prrzXte7XUYWEo3Jo1a8Ipp5xStnXYEZt6HRbjedHZ4sijM844I//ZfIj0xsTLMrH1oBLqry1x1FJh36uh/gri5cwYJr7IB4DOqsMsy/LgMWPGjPDUU0/l74GbUjbnwqxCTZ8+Pe99e+edd2ZvvvlmdtZZZ2X9+vXL6uvr8+2nnnpqdtFFFzWVf+6557Lu3btnV111VfbWW2/lvZV79OiRvfbaa01lLr/88vw+Hnnkkexvf/tbPqJg2LBh2aeffloRxxj3v2fPntnvf//7bOnSpU3LqlWr8u3x509+8pNs7ty52fvvv5/95S9/yUaMGJHtuuuuWWNjY9kfXxwx8Pjjj2fvvfdeNn/+/Oykk07Kevfunb3xxhtVU4cFBx10UD4KpLVyq8O4P6+88kq+xLeTq6++Ov/9gw8+yLfHY4vHWPD3v/89+9KXvpT99Kc/zV+HN954Y7bFFltks2bN+sL/s3I+vnvvvTd/n4nH1fw1GEcLFJx//vnZM888k9dffF8aO3Zstu222+YjFcr9+OLoq4cffjh755138vfOc889N+vWrVv+PCzH+uvIMRaccsop+SiQtpRLHU6cODEfARj3pfnz7ZNPPmkqU67nwooNH9ENN9yQDR06ND/hxuFd8+bNa9r27W9/Ox+S2NyDDz6Y7bbbbnn5ONzvT3/6U4vtcYjRxRdfnA0cODB/8Rx22GHZwoULs0o5xh133DF/cbVe4pMrik/II444Ittuu+3yJ1ss/8Mf/rDT3hTae3yTJ09uKhvr6Dvf+U62YMGCqqrD6O23387r7YknnvjMfZVbHRaGXrZeCscUf8ZjbP03++23X/7/2GmnnfIh1O35n5Xz8cXfP698FEPloEGD8mP78pe/nN9+9913K+L4rrjiimznnXfOQ3///v2zgw8+OHvqqafKtv46+hyNYXHLLbfMfvOb37R5n+VSh6GN44pL89dUuZ4La/7vAAAAkqjIPh8AQOUSPgCApIQPACAp4QMASEr4AACSEj4AgKSEDwAgKeEDAEhK+AAAkhI+AICkhA8AICnhAwAIKf0/ezsSBWvyi1MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([-2, -1.5, -1.4, -1.3, -1.2, -1.1, -1, -0.9, -0.5, 0])\n",
    "y = np.array([-1.53, -1.44, -1.38, -1.24, -0.85, -0.05, 1.34, 3.7, 17.9, 45])\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(-1*x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8267a7c7-55a4-4dd1-a32b-c381762bfdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231f1740550>"
      ]
     },
     "execution_count": 7,
     "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": [
    "x = np.array([-2, -1.5, -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, 0, 1])\n",
    "y = np.array([-1.8, -1.79, -1.77, -1.76, -1.72, -1.66, -1.41, -0.78, 0.41, 1.92, 10.6, 43])\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(-1*x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6ed8e9e-d670-4891-b719-1bdb67225fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m=0.3826480973136403.\n",
      "Slope Uncertaintiy:0.004605946272412134\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x231f1e7b770>]"
      ]
     },
     "execution_count": 18,
     "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": [
    "x = np.array([5.2, 8.22, 7.41, 6.88, 5.49])\n",
    "y = np.array([0.4, 1.7, 1.4, 1.3, 0.7])\n",
    "yerr = np.array([0.02,0.01,0.01,0.005,0.01])\n",
    "def func_linear(x, m, b):\n",
    "    return m*x+b\n",
    "#popt, pcov = curve_fit(func_linear, x, y)\n",
    "\n",
    "print(f\"m={popt[0]}.\")\n",
    "fig, ax = plt.subplots()\n",
    "#ax.scatter(x, y)\n",
    "perr= np.sqrt(np.diag(pcov))\n",
    "m_error= perr[0]\n",
    "print(f'Slope Uncertaintiy:{m_error}')\n",
    "ax.errorbar(x, y, yerr, fmt='o', linewidth=2, capsize=6)\n",
    "popt, pcov = curve_fit(func_linear, x, y, sigma=yerr, absolute_sigma=True)\n",
    "\n",
    "ax.plot(x,func_linear(x, *popt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9f2e500-5354-4e51-9b38-cb0da4de0e72",
   "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}