Effect of Noise to Sin fitting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of Noise to Sin fiitting\n",
    "Here, we test sin fitting to datasets which noise or artifacts are artificially added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit.models import SineModel, ConstantModel\n",
    "from matplotlib_extension.pyplot import adjust_locator\n",
    "plt.style.use(\"matplotlib_extension.article\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data(N):  \n",
    "    x = np.linspace(0, 2*np.pi, N)\n",
    "    y = np.sin(x)\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SN ratio to Sin fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_SN(r, N):\n",
    "    x = np.linspace(0, 2*np.pi, N)\n",
    "    y = np.sin(x) + r * np.random.randn(N)\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17e7ca6d0>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "x, y = create_data(N)\n",
    "ax.plot(x, y, \":\", label=\"ideal\")\n",
    "\n",
    "r = 0.3\n",
    "N = 24\n",
    "x, y = create_data_SN(r, N)\n",
    "ax.plot(x, y, \"o\", label=\"+noise\")\n",
    "\n",
    "adjust_locator(ax)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amplitude obtained from fitting\n",
    "SN get larger than 1 and the result is get far from expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(0, 2, 20):\n",
    "    tmp = []\n",
    "    for i in range(100):\n",
    "        x, y = create_data_SN(r, N)\n",
    "        \n",
    "        model = SineModel()\n",
    "        param = model.guess(y, x)\n",
    "        result = model.fit(y,x=x, params=param)\n",
    "        tmp.append(result.values[\"amplitude\"])\n",
    "    data.append([r, np.mean(tmp), np.std(tmp)])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(data[0], data[1], fmt=\"o\", yerr=data[2])\n",
    "adjust_locator(ax)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(data[0], data[1], fmt=\"o\", yerr=data[2])\n",
    "adjust_locator(ax)\n",
    "plt.ylim(0, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pulse like noise to Sin fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_pulse(r, N, pos=3):\n",
    "    x = np.linspace(0, 2*np.pi, N)\n",
    "    p = np.tile(0, N)\n",
    "    p[pos] = r\n",
    "    y = np.sin(x) + p\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17eb426d0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "x, y = create_data(N)\n",
    "ax.plot(x, y, \":\", label=\"ideal\")\n",
    "\n",
    "r = 5\n",
    "N = 24\n",
    "x, y = create_data_pulse(r, N)\n",
    "ax.plot(x, y, \"o\", label=\"+noise\")\n",
    "\n",
    "adjust_locator(ax)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amplitude obtained from fitting\n",
    "pulse signal monotonically increase the fitting result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-100, 100, 20):\n",
    "    x, y = create_data_pulse(r, N)\n",
    "    \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Amplitude obtaine from fitting in small area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-5, 5, 500):\n",
    "    x, y = create_data_pulse(r, N)\n",
    "    \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Amplitude obtained from fitting when data is filtered by 2$\\sigma$\n",
    "The effect of pulse signal can be ruled out by filtering data with 2$\\sigma$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-100, 100, 20):\n",
    "    x, y = create_data_pulse(r, N)\n",
    "\n",
    "    sigma2 =np.std(y) * 2\n",
    "    filter = np.abs(y - np.mean(y)) < sigma2\n",
    "    x, y = x[filter], y[filter]\n",
    "        \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax, units=(None, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Amplitude obtained from fitting when data is filtered by 2$\\sigma$ in small area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-5, 5, 500):\n",
    "    x, y = create_data_pulse(r, N)\n",
    "\n",
    "    sigma2 =np.std(y) * 2\n",
    "    filter = np.abs(y - np.mean(y)) < sigma2\n",
    "    x, y = x[filter], y[filter]\n",
    "        \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax, units=(None, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add pulse to another position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = 5\n",
    "N = 24\n",
    "x, y = create_data_pulse(r, N, pos=20)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, \"o\")\n",
    "adjust_locator(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-100, 100, 20):\n",
    "    x, y = create_data_pulse(r, N, pos=20)\n",
    "    \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax, units=(None, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "N = 24\n",
    "for r in np.linspace(-100, 100, 20):\n",
    "    x, y = create_data_pulse(r, N, pos=10)\n",
    "\n",
    "    sigma2 =np.std(y) * 2\n",
    "    filter = np.abs(y - np.mean(y)) < sigma2\n",
    "    x, y = x[filter], y[filter]\n",
    "        \n",
    "    model = SineModel()\n",
    "    param = model.guess(y, x)\n",
    "    result = model.fit(y,x=x, params=param)\n",
    "    data.append([r, result.values[\"amplitude\"]])\n",
    "\n",
    "data = list(zip(*data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W1tHRgdLSUthstm7b2Gw2n/oAsG/fvh7rE9EQ5e+McmFhoej1eikoKJDa2lr5+c9/LnFxceJ0OkVE5JlnnpFXXnlFq3/48GEZPny4vPHGG3Lq1CnJy8uTyMhI+eSTTwI0x01E4cDvOZv58+fj8uXLWLVqFZxOJ1JTU1FSUqJNAjc0NCAi4qsLpvT0dGzbtg0rV67Er371K0yaNAlFRUVITk4OXGISUcjTiQThHhcR0W0GZW1UfX09Fi9ejKSkJMTExOCee+5BXl4eWltbfep9/PHH+O53v4vo6GhYrVasXr26y7H8WQqh0u9//3ukp6cjNjYWcXFx3dZpaGjA3LlzERsbi/j4eLz44ou4efOmT52ysjLcf//90Ov1mDhxIgoKCoLf+QHydxlLqCgvL8e8efMwduxY6HS6Lr8BExGsWrUKFosFMTExyMjIwL/+9S+fOleuXMHTTz8Ng8GAuLg4LF68GC0tLQpH0bP8/Hw8+OCDGDlyJOLj45GZmYm6ujqfOjdu3EBOTg6+9rWvYcSIEfjxj3/c5cZOf96vvRqM72579+6VhQsXygcffCBnz56VXbt2SXx8vKxYsUKr43a7xWQyydNPPy01NTXyzjvvSExMjGzatEmrc/jwYRk2bJisXr1aamtrZeXKlSEzH7Rq1SpZu3atOBwOMRqNXR6/efOmJCcnS0ZGhlRVVUlxcbGMGTNGcnNztTqffvqpxMbGisPhkNraWlm3bp0MGzZMSkpKFI6kfwoLCyUqKkq2bNkiJ0+elCVLlkhcXJy4XK7B7lqfiouL5de//rX85S9/EQCyc+dOn8dff/11MRqNUlRUJB999JE88cQTkpSUJNevX9fqzJ49W1JSUuTo0aPy97//XSZOnChZWVmKR9I9u90uW7dulZqaGqmurpbvf//7kpCQIC0tLVqdpUuXitVqldLSUqmsrJSZM2dKenq69nh/3q99GZSw6c7q1aslKSlJ23/rrbdk1KhR4vV6tbKXX35Z7r33Xm3f36UQg2Hr1q3dhk1xcbFERERoE+siIhs2bBCDwaCN+aWXXpKpU6f6tJs/f77Y7fag9nkg/F3GEqpuD5uOjg4xm82yZs0aray5uVn0er288847IiJSW1srAOQf//iHVmfv3r2i0+nk4sWLyvreX5cuXRIAcvDgQRH5cjyRkZGyY8cOrc6pU6cEgFRUVIhI/96vfQmZ/2LC7XZj9OjR2n5FRQUefvhhREVFaWV2ux11dXX4/PPPtToZGRk+x7Hb7T0unQglFRUVmDZtms+vq+12OzweD06ePKnVCYfxdS5jubWvfS1jCRfnzp2D0+n0GZvRaMSMGTO0sVVUVCAuLg4PPPCAVicjIwMRERH48MMPlfe5L263GwC08+348eNoa2vzGePkyZORkJDgM8a+3q99CYmwOXPmDNatW4fnnntOK+tpmUPnY73V6WkpRCi5k/F5PB5cv35dTUf7obdlLOHwWvSms/+9jc3pdCI+Pt7n8eHDh2P06NEhN/6Ojg4sX74cDz30kHZH2Ol0Iioqqsvc4u1j7Ov92peAhs0rr7wCnU7X63b7GqqLFy9i9uzZ+MlPfoIlS5YEsjsBN5DxEYWSnJwc1NTUoLCwUPlzB3Rt1IoVK7Bw4cJe69y6FOHf//43Zs2ahfT0dGzevNmnXk/LHDof661OT0sh7pS/4+uN2Wzucremv+MzGAyIiYnpZ6+DbyDLWMJFZ/9dLhcsFotW7nK5kJqaqtW5/f9zunnzJq5cuRJS41+2bBl2796N8vJyjB8/Xis3m81obW1Fc3Ozz9XNra9ff96vfQrgvJNfLly4IJMmTZKnnnpKbt682eXxzgni1tZWrSw3N7fLBPEPfvADn3Y2my2sJohvvVuzadMmMRgMcuPGDRH5coI4OTnZp11WVlbIThAvW7ZM229vb5dx48YNmQniN954Qytzu93dThBXVlZqdT744IOQmSDu6OiQnJwcGTt2rPzzn//s8njnBPF7772nlZ0+fbrbCeLe3q99GZSwuXDhgkycOFEee+wxuXDhgjQ1NWlbp+bmZjGZTPLMM89ITU2NFBYWSmxsbJdb36G6FOL8+fNSVVUlr732mowYMUKqqqqkqqpKrl69KiJf3Up8/PHHpbq6WkpKSuTrX/96t7e+X3zxRTl16pSsX78+pG9997aMJZRdvXpVe30AyNq1a6WqqkrOnz8vIl/e+o6Li5Ndu3bJxx9/LE8++WS3t77vu+8++fDDD+XQoUMyadKkkLn1/fzzz4vRaJSysjKfc+2LL77Q6ixdulQSEhJk//79UllZKTabTWw2m/Z4f96vfRmUsNm6dasA6Ha71UcffSTf+c53RK/Xy7hx4+T111/vcqx3331XvvnNb0pUVJRMnTpV9uzZo2oYvcrOzu52fAcOHNDq1NfXy5w5cyQmJkbGjBkjK1askLa2Np/jHDhwQFJTUyUqKkomTJggW7duVTsQP6xbt04SEhIkKipK0tLS5OjRo4PdpX45cOBAt69Vdna2iHx5ZfDqq6+KyWQSvV4vjz32mNTV1fkc4z//+Y9kZWXJiBEjxGAwyKJFi7QPlsHW07l263vp+vXr8otf/EJGjRolsbGx8sMf/tDnw1+kf+/X3nC5AhEpERK3volo6GPYEJESDBsiUoJhQ0RKMGyISAmGDREpwbAhIiUYNkSkBMOGiJRg2BCREgwbIlLi/wBC7Q2x5KIvKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 320x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(data[0], data[1], \"o\")\n",
    "adjust_locator(ax, units=(None, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "2022 Fukami Lab",
   "language": "python",
   "name": "2022-fukami-lab"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}