Ptrskay3/PySprint

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doc/hu_phase.ipynb

Summary

Maintainability
Test Coverage
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Phase osztály\n",
    "\n",
    "A `Phase` osztály főként az FT kiértékelési módszer miatt jött létre, itt ugyanis nem feltétlen szükséges kikötni, hogy a fázis polinommal közelíthető. A koszinusz-függvény illesztéses módszeren kívül az összes kiértékelési eljárás ezt az osztályt használja. Miután a megfelelő számolásokat elvégeztük az adott módszer attribútumaként elérhetővé válik a `phase` vagy a `GD`. Mivel a WFT és SPP módszernél a csoportkésleltetés görbét kapjuk meg, így azokon a `GD`, míg a Fourier-transzformáción alapuló és a minimum-maximum módszernél a fázist, így ezeken a `phase` válik elérhetővé. A két görbe megkülönböztetését a `GD_mode` argumentum szabályozza. Nézzük meg egy generikus példán:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pysprint.core.phase import Phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ehhez itt egy tetszőleges polinomot használok:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(1, 30, 2000)\n",
    "y = 2 * x ** 3 - 3 * x ** 2 + 45 * x + 3\n",
    "\n",
    "fazis = Phase(x, y)\n",
    "fazis.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `fit` függvénnyel ráillesztek egy harmadfokú görbét:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 45.00000 ± 0.00000 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -6.00000 ± 0.00000 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = 12.00000 ± 0.00000 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fazis.fit(reference_point=0, order=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt látható, hogy a megadott együtthatókat adta vissza a megfelelő faktoriálissal szorozva.\n",
    "Ha ugyan ezt `GD_mode=True` mellett végzem el:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 47.00000 ± 0.00000 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 45.00000 ± 0.00000 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -6.00000 ± 0.00000 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle FOD = 12.00000 ± 0.00000 fs^4$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GD = Phase(x, y, GD_mode=True)\n",
    "GD.fit(reference_point=0, order=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt megjegyezném, hogy az `order` kulcsszó minden esetben a **keresett diszperzió rendjét** és nem feltétlenül az illesztés rendjét jelenti. Ennek köszönhető, hogy a harmadfokú polinomra `GD_mode=True` mellett az `order=4` volt a megfelelő, hiszen ekkor kerül harmadfokú görbe illeszésre. Ezeket könnyen le is kérdezhetjük az `order`, illetve a `dispersion_order` argumentumokkal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 3\n",
      "3 4\n"
     ]
    }
   ],
   "source": [
    "print(fazis.order, GD.order)\n",
    "print(fazis.dispersion_order, GD.dispersion_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A program illesztés után a kiszámolt együtthatókat elmenti, és a megfelelő kulcsszóval ezek elérhetőek:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.999999999999998"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fazis.TOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GD.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A hibák az `errorplot` függvénnyel nézhetők meg, a hibák mint `np.ndarray` az `errors` kulcsszóval érhetők el:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GD.errorplot(percent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.06581410e-13, -1.06581410e-13, -9.94759830e-14, ...,\n",
       "        7.27595761e-12,  0.00000000e+00,  0.00000000e+00])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GD.errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `slice`, vagyis a kivágás ugyan úgy működik, mint minden kiértékelés esetén:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18ab34a2f88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fazis3 = Phase(x, y)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "fazis3.plot(ax=ax, color=\"black\", label=\"kivágás előtt\")\n",
    "\n",
    "fazis3.slice(5, 25)\n",
    "\n",
    "fazis3.plot(ax=ax, color=\"red\", label=\"kivágás után\")\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Az objektumban tárolt adatok a `data` függvénnyel érhetők el:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.004002    5.01850925  5.03301651 ... 24.96598299 24.98049025\n",
      " 24.9949975 ] <class 'numpy.ndarray'>\n",
      "[  403.66076273   406.06328969   408.47722827 ... 30379.17802734\n",
      " 30431.94288131 30484.76956137] <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "x, y = fazis3.data\n",
    "print(x, type(x))\n",
    "print(y, type(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Természetesen a hibák nem léteznek addig, amíg nem illesztettünk görbét:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must fit a curve before requesting errors.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-2b3ede9219f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfazis3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\pyt\\pysprint\\pysprint\\core\\phase.py\u001b[0m in \u001b[0;36merrors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    383\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitted_curve\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitted_curve\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Must fit a curve before requesting errors.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Must fit a curve before requesting errors."
     ]
    }
   ],
   "source": [
    "fazis3.errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Phase` osztályban elérhető több simítási algoritmus is. Ebben a példában az alapértelmezett `gaussian_filter1d` függvényt mutatom be egy zajos adatsoron:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(1, 10, 1000)\n",
    "y = np.cos(x) + np.random.normal(0, 0.4, 1000)\n",
    "\n",
    "zajos = Phase(x, y)\n",
    "zajos.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18ab36036c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zajos.plot(color=\"red\", label=\"előtte\")\n",
    "zajos.smooth(method=\"gaussian_filter1d\", sigma=25)\n",
    "zajos.plot(color=\"blue\", label=\"utána\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha az adatsorban van egy olyan rész, amit ki szeretnénk vágni, ahhoz használhatjuk a `remove_range(start, stop)` függvényt. Az előbbi adatsorból töröljük ki a 4 és 6 közötti részt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zajos.remove_range(4, 6)\n",
    "zajos.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}