Ptrskay3/PySprint

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doc/hu_fft.ipynb

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Fourier-transzformációs módszer, FFTMethod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A kiértékelés a lépései:\n",
    "\n",
    "**betöltés → előfeldolgozás → IFFT → ablakolás → FFT → fázis**\n",
    "\n",
    "A programban is hasonló nevű a függvényeket kell meghívni. Az ajánlott sorrend a függvények hívásában a fenti folyamatábra, mivel nem garantált, hogy a tengelyek helyesen fognak transzformálódni tetszőleges sorrendű függvényhívások után.\n",
    "\n",
    "\n",
    "A bemutatást szimulált példákkal kezdem, majd rátérek egy mért interferogram kiértékelésére is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pysprint as ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = ps.Generator(1, 4, 2.5, 1500, GDD=400, TOD=400, FOD=1000, pulse_width=4, resolution=0.05)\n",
    "g.generate_freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1 Automatikus kiértékelés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interferogram received.\n",
      "Applying IFFT...Done\n",
      "Acquiring gaussian window parameters...Done\n",
      "A 12 order gaussian window centered at 2670.46925 fs with FWHM 4055.08976 fs was applied.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\pyt\\pysprint\\pysprint\\core\\_fft_tools.py:100: UserWarning: The peak is too close to the origin, manual control is advised.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying FFT...Done\n",
      "Calculating...\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 1499.99661 ± 0.00026 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 400.01231 ± 0.00093 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = 400.02410 ± 0.00105 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle FOD = 999.88506 ± 0.00554 fs^4$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "f = ps.FFTMethod(*g.data)\n",
    "f.autorun(reference_point=2.5, order=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Egy másik automatikus kiértékelés (ugyan azon az interferogramon), ezúttal csak a fázist kapjuk meg. Ennek a fázisgrafikonnak a széleit kivágjuk a `slice` függvénnyel, majd a `fit` metódust használva számolhatjuk a diszperziós együtthatókat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\pyt\\pysprint\\pysprint\\core\\_fft_tools.py:100: UserWarning: The peak is too close to the origin, manual control is advised.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pysprint.core.phase.Phase'>\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -1499.99867 ± 0.00002 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -400.00218 ± 0.00007 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -400.02816 ± 0.00008 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle FOD = -999.90775 ± 0.00048 fs^4$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2 = ps.FFTMethod(*g.data)\n",
    "phase = f.autorun(show_graph=False, enable_printing=False)\n",
    "\n",
    "print(type(phase))\n",
    "\n",
    "phase.slice(1.1, 3.9)\n",
    "\n",
    "phase.fit(reference_point=2.5, order=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bár látható volt, hogy a program jól határozta meg a Gauss ablakfüggvény paramétereit és ezáltal a diszperziós együtthatókat is, de jelzett, hogy a kivágandó csúcs túl közel van az origóhoz, így jobb ha azt manuálisan állítjuk be. Nézzük meg a fázist az illesztett görbével:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "phase.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majd az illesztési hiba:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "phase.errorplot(percent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2 Manuális kiértékelés\n",
    "\n",
    "Nézzünk meg egy manuális kiértékelést. Itt a nekem meglévő interferogramot fogom használni, ami enyhén szólva sem ideális a Fourier-transzformációs kiértékeléshez, de megpróbálom a legtöbb használható információt kihozni belőle. Mivel már előre tudom hogy hogyan érdemes az ablakfüggvényt beállítani, így itt az ún. `inplace=False` argumentumot fogom használni. Alapvetően minden függvény amit meghívunk `inplace=True` módon hajtódik végre, azaz megváltoztatja magát az objektumot. Így működik pl. a python listáknál az `append` függvény:\n",
    "```python\n",
    ">>> a = []\n",
    ">>> a.append(1)\n",
    ">>> print(a)\n",
    "[1]\n",
    "```\n",
    "A csomag során sok függvénynél lehetőség van megadni az `inplace=False` argumentumot, ami nem változtatja meg magát az objektumot, hanem visszaad egy új másolatot belőle, és kért függvényt azon a másolaton fogja végrehajtani. Ennek két előnye van: Az eredeti objektum (és így vele minden eredetileg betöltött adatsor) megmarad, és anélkül hogy újra és újra betöltenénk más objektumba az adatokat, ezért elég belőle egy. A második előny pedig abból adódik, hogy megengedi a műveletek láncolását, ahogy az alábbi példa mutatja. ([fluent interfacing](https://en.wikipedia.org/wiki/Fluent_interface) and [method cascading](https://en.wikipedia.org/wiki/Method_cascading)) Itt a szokásos kiértékelési lépéseket hajtottam végre. Az utolsó függvény amit meghívtam rajta, az a `build_phase`, ami egy fázist ad vissza, ezért a hosszú láncolat után az lesz a visszatérített érték (ezt elneveztem `phase3`-nak)."
   ]
  },
  {
   "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": [
    "f3 = ps.FFTMethod.parse_raw('datasets/ifg.trt', skiprows=8, meta_len=8, decimal=\",\", delimiter=\";\")\n",
    "\n",
    "phase3 = (\n",
    "    f3.chdomain(inplace=False)\n",
    "    .ifft(inplace=False)\n",
    "    .window(at=145, fwhm=240, window_order=16, inplace=False)\n",
    "    .apply_window(inplace=False)\n",
    "    .fft(inplace=False)\n",
    "    .build_phase()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt a jobb olvashatóság miatt minden új függvénynél új sort kezdtem és zárójelbe tettem. Ezek nélkül így festene:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase4 = f3.chdomain(inplace=False).ifft(inplace=False).window(at=145, fwhm=240, window_order=16, plot=False, inplace=False).apply_window(inplace=False).fft(inplace=False).build_phase()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mivel nem volt ideális az interferogram vizsgáljuk meg milyen fázist kaptunk vissza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "phase3.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt észrevehető, hogy vannak olyan részei a görbének, amely valóban tartalmazza a minta fázistulajdonságait. Vágjuk ki ezt a részt a `slice` függvénnyel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "phase3.slice(1.71, 2.72)\n",
    "phase3.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ezután végezzük el az illesztést a `fit` függvénnyel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -83.98003 ± 0.03976 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 167.48826 ± 0.28810 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = 119.83522 ± 1.75236 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phase3.fit(reference_point=2.355, order=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A kapott diszperziós együtthatók valóban jó közelítéssel tükrözik a mintára jellemző (már egyéb módszerekkel meghatározott) koefficienseket. Vizsgáljuk meg az illesztési hibát is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "phase3.errorplot(percent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ugyan ez a kiértékelés hagyományosan, az `inplace=False` paraméterek nélkül így néz ki:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -83.98003 ± 0.03976 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 167.48826 ± 0.28810 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = 119.83522 ± 1.75236 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f4 = ps.FFTMethod.parse_raw('datasets/ifg.trt', skiprows=8, meta_len=8, decimal=\",\", delimiter=\";\")\n",
    "\n",
    "f4.chdomain()\n",
    "\n",
    "f4.ifft()\n",
    "\n",
    "f4.window(at=145, fwhm=240, window_order=16, plot=False)\n",
    "\n",
    "f4.apply_window()\n",
    "\n",
    "f4.fft()\n",
    "\n",
    "phase4 = f4.build_phase()\n",
    "\n",
    "phase4.slice(1.71, 2.72)\n",
    "\n",
    "phase4.fit(2.355, 3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Próbáljuk meg az impulzus időbeli alakját kiszámolni. Ehhez a `get_pulse_shape_from_file` függvényt fogom használni, aminek a tárgykar spektrumát adom meg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_t, y_t = f4.get_pulse_shape_from_file(\"datasets/sam.trt\", truncate=True, chdomain=True, skiprows=8, decimal=\",\", sep=\";\")"
   ]
  },
  {
   "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": [
    "plt.plot(x_t, y_t)\n",
    "plt.grid()\n",
    "plt.xlabel(\"t [fs]\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mivel a használt interferogram nem volt ideális, így itt az impulzus alakját nem lehetett tökéletesen visszakapni.\n",
    "\n",
    "Alapértelmezetten néhány dolog el van rejtve a felhasználó elől. Az előző `get_pulse_shape_from_file` függvényt újra lefuttatom, ezúttal teljes logging outputtal. Ezt szinte soha nem kell használnunk, itt is csak a magyarázat miatt van létjogosultsága."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ fftmethod.py:399 - get_pulse_shape_from_array() ] Shapes were truncated from 2.3226283197396467 to 2.7198388115237 with length 287.\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)\n",
    "\n",
    "x_t, y_t = f4.get_pulse_shape_from_file(\"datasets/sam.trt\", truncate=True, chdomain=True, skiprows=8, decimal=\",\", sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Látható, hogy *2.322* és *2.719 PHz* között 287 adatpontnál sikerült kiszámítani a \n",
    "  $I(t) = |\\mathcal{F}^{-1}\\{\\sqrt{|I_{tárgy}(\\omega)|}\\cdot e^{-i\\Phi{(\\omega)}}\\}|^2$\n",
    "kifejezés értékét. Ez annak köszönhető, hogy a kiszámolt fázist elég nagy tartományban nem tudtuk felhasználni (eredetileg a *1.71 - 2.72* *PHz* tartományt vágtuk ki), illetve az transzformációk során behozott numerikus hiba is közrejátszott.\n",
    "\n",
    "\n",
    "#### 7.3 NUFFT\n",
    "\n",
    "Végül a Non-unifrom FFT használata. Itt teljesen ugyan azt hajtom végre, mint fentebb, csak `usenifft=True` argumentummal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -38.80607 ± 0.08482 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 194.88497 ± 0.68237 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = 111.52580 ± 2.04064 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "# csak visszaállítom a log szintet az alapértelmezettre, hogy ne árassza el a képernyőt\n",
    "logger.setLevel(logging.ERROR)\n",
    "\n",
    "f5 = ps.FFTMethod.parse_raw('datasets/ifg.trt', skiprows=8, meta_len=8, decimal=\",\", delimiter=\";\")\n",
    "\n",
    "f5.chdomain()\n",
    "\n",
    "f5.ifft(usenifft=True)\n",
    "\n",
    "f5.window(at=155, fwhm=260, window_order=16, plot=False)\n",
    "\n",
    "f5.apply_window()\n",
    "\n",
    "f5.fft()\n",
    "\n",
    "phase6 = f5.build_phase()\n",
    "\n",
    "phase6.slice(None, 2.49)\n",
    "phase6.fit(2.355, 3);\n",
    "phase6.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A szimulációk alapján a NUFFT valamivel pontatlanabb eredményt ad, mint az interpoláció + FFT."
   ]
  }
 ],
 "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
}