Ptrskay3/PySprint

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Bevezetés\n",
    "A PySprint csomag egy koherens és magas szintű API-t biztosít a spektrális interferometrián alapuló mérések kiértékelésére.\n",
    "\n",
    "__ÚJ__\n",
    "\n",
    "A programhoz egy parancssoros felhasználói felület is tartozik, amely képes kiértékelni/kódot generálni/figyelni nagy számú mérést. Ez a program [Rust](https://www.rust-lang.org/) nyelven íródott, és egy beágyazott Python interpretert használ, így gyorsabban működik, mintha kizárólag a PySprint csomagot használnánk. Bővebben erről a [GitHub oldalán.](https://github.com/Ptrskay3/pysprint-cli)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Telepítés\n",
    "\n",
    "A csomag telepítéséhez legalább **3.6.x** verziójú Python szükséges.\n",
    "\n",
    "**Szükséges csomagok:**\n",
    "\n",
    " <ul>\n",
    "  <li>numpy</li>\n",
    "  <li>scipy</li>\n",
    "  <li>pandas</li>\n",
    "  <li>matplotlib</li>\n",
    "  <li>Jinja2</li>\n",
    "</ul> \n",
    "\n",
    "\n",
    "**Opcionális csomagok:**\n",
    "\n",
    " <ul>\n",
    "  <li>lmfit - a részletes illesztési statisztikák miatt</li>\n",
    "  <li>numba - a non-uniform FFT gyorsításához</li>\n",
    "  <li>pytest - a tesztek futtatásáért</li>\n",
    "  <li>Dask - a WFT módszer parallel futtatásáért</li>\n",
    "  <li>scikit-learn - a RANSAC filter miatt</li>\n",
    "</ul> \n",
    "\n",
    "\n",
    "#### 1.1.1 Anaconda környezet\n",
    "\n",
    "\n",
    "Anaconda esetén a telepítés az Anaconda Prompt megnyitásával zajlik. Ahhoz, hogy az Anaconda ne kérdezzen rá minden alkalommal, hogy biztosan telepíteni szeretnénk-e, célszerű futtatni a\n",
    "```shell\n",
    "conda config --set always_yes true\n",
    "```\n",
    "sort. Ezután minden beírt parancs végre fog hajtódni rákérdezés nélkül.\n",
    "\n",
    "A telepítéshez a következő sort kell beírni:\n",
    "\n",
    "```shell\n",
    "conda install pip && pip install pysprint[optional]\n",
    "```\n",
    "\n",
    "\n",
    "Anaconda Promptban a következő paranccsal le is ellenőrizhető, hogy telepítve van-e a csomag.\n",
    "\n",
    "```shell\n",
    "pip list pysprint\n",
    "```\n",
    "\n",
    "Ez valami hasonlót ad vissza, ha telepítve van:\n",
    "```shell\n",
    " packages in environment at C:\\Users\\lp\\miniconda3:\n",
    "\n",
    " Name                    Version                   Build  Channel\n",
    "pysprint                  0.29.0                   py39_0    ptrskay\n",
    "```\n",
    "Ha valamilyen okból nem sikerült, akkor az előbbi parancs egy üres sort fog visszaadni.\n",
    "\n",
    "A csomag frissítése ugyancsak az Anaconda Prompttal történik. \n",
    "\n",
    "A frissíteni a köv. paranccsal lehetséges:\n",
    "```shell\n",
    "python -m pip install --upgrade pysprint\n",
    "```\n",
    "\n",
    "A frissítésen és telepítésen kívül az Anaconda Prompt nem szükséges.\n",
    "\n",
    "#### 1.1.2 Hagyományos Python környezet, PIP\n",
    "\n",
    "Hagyományos Python környezetet használva a telepítés a Parancssorban történik:\n",
    "```shell\n",
    "python -m pip install pysprint\n",
    "```\n",
    "A frissítéshez tartozó parancs pedig:\n",
    "```shell\n",
    "python -m pip install --upgrade pysprint\n",
    "```\n",
    "\n",
    "\n",
    "### 1.2 A development verzió telepítése, contributing\n",
    "\n",
    "Új felhasználók számára a Git verziókövető szoftver ismerete lehet a legnehezebben érthető. Nagyon gyorsan bonyolulttá válhat, de az itt leírtak segíthetnek a folyamat könnyű és gondmentes végrehajtásában.\n",
    "A kód a [GitHub](https://github.com/Ptrskay3/PySprint)-on tárolódik. Ahhoz, hogy a program fejlesztésében részt lehessen venni, szükség van egy [GitHub felhasználóra](https://github.com/signup/free), illetve a [Git](https://git-scm.com/) szoftver telepítésére. \n",
    "\n",
    "Néhány forrás, ahol részletesebben olvashatunk a Git-ről:\n",
    "* [GitHub instructions](https://docs.github.com/en/github/getting-started-with-github/set-up-git)\n",
    "* [NumPy documentation](https://numpy.org/doc/stable/dev/index.html)\n",
    "\n",
    "Az első lépés ezek után egy saját \"fork\" készítése. A [projekt oldalán](https://github.com/Ptrskay3/PySprint) a jobb felső sarokban a Fork gomb megnyomása után egy másolatot készít a saját felhasználónknak. Ezután a parancssort használva a következőket kell beírnunk:\n",
    "```shell\n",
    "git clone https://github.com/your-user-name/pysprint.git pysprint-yourname\n",
    "cd pysprint-yourname\n",
    "git remote add upstream https://github.com/Ptrskay3/PySprint\n",
    "```\n",
    "Itt természetesen a `your-user-name` helyére a saját Git felhasználónk nevét kell helyettesíteni. Ez a gépünkre letölti a megfelelő fájlokat a `pysprint-yourname` könyvtárba és összekapcsolja azt az \"upstream\" kóddal.\n",
    "\n",
    "Töltsük le a [Rust programozási nyelvet](https://www.rust-lang.org/tools/install), majd a terminálban váltsunk a nightly verziójára:\n",
    "```shell\n",
    "rustup toolchain install nightly\n",
    "rustup override set nightly\n",
    "```\n",
    "\n",
    "Ezután a megfelelő Python környezetet kell beállítanunk. Anaconda esetén ez egyszerű:\n",
    "\n",
    "A terminált használva először győződjünk meg, hogy az Anaconda legfrissebb verziója van-e telepítve (`conda update conda`). Ez követően navigáljunk el a `pysprint-yourname` könyvtárba, ahova a program letöltődött. Itt a parancssorban futtassuk a következőket:\n",
    "```shell\n",
    "# a környezet létrehozása és aktiválása\n",
    "conda env create -f environment.yml\n",
    "conda activate pysprint-dev\n",
    "\n",
    "# a program telepítése a letöltött fájlokból\n",
    "python -m pip install -e .[optional]\n",
    "```\n",
    "Ekkor a lokális telepítésből már tudjuk importálni a csomagot:\n",
    "```shell\n",
    "python  # interpreter elindítása\n",
    ">>> import pysprint as ps\n",
    ">>> print(ps.__version__)\n",
    "0.29.0\n",
    "```\n",
    "\n",
    "A Git segítségével különböző ágakkal (branch) lehet dolgozni. Ezek közül a fő ág a `master`. A `master` branch-en csakis a production-ready kódot szeretnénk tartani, így ott sose dolgozzunk. \n",
    "Először győződjünk meg, hogy a legfrissebb verzió van nálunk:\n",
    "```shell\n",
    "git checkout master\n",
    "git pull upstream master\n",
    "```\n",
    "Készítsünk egy új ágat:\n",
    "```shell\n",
    "git checkout -b my-new-feature\n",
    "```\n",
    "Ezzel létrehoztuk a `my-new-feature` ágat és át is váltottunk rá. Ezután elkezdhetjük a kódot írni.\n",
    "\n",
    "Ha megváltoztattunk bizonyos fájlokat, akkor a `git status` paranccsal ellenőrizhetjük a fájlok státuszát.\n",
    "Mielőtt a változtatásokat véglegesítenénk előbb mindenképpen futtassuk le az automatizált teszteket, hogy megbizonyosodjuk arról, hogy nem rontottunk el már meglévő funkciókat. Ezt két módon lehetséges a gyökérkönyvtárban:\n",
    "```\n",
    "cd pysprint/tests\n",
    "pytest -vv\n",
    "```\n",
    "vagy\n",
    "```\n",
    "tox\n",
    "```\n",
    "Amennyiben ezek nem adnak hibát akkor a változtatásokat a saját \"fork\"-ba fel is tölthetjük a következő módon:\n",
    "```shell\n",
    "git add -A\n",
    "git commit -m \"some description about what I've done\"\n",
    "git push origin my-new-feature\n",
    "```\n",
    "\n",
    "A manuális környezet beállítása helyett használható a Docker is. A projekt biztosít ehhez a főkönyvtárában egy `Dockerfile`-t."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Első lépések\n",
    "Szigorúan véve nem szükséges a `numpy` és `matplotlib` importálás ahhoz, hogy a csomag működjön, de a biztonság kedvéért ajánlott előtte azokat is meghívni. Ez után a csomag importálása szokásos módon az\n",
    "```python\n",
    "import pysprint as ps\n",
    "```\n",
    "sorral történik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pysprint as ps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A verzió számát a python csomagoknál megszokott \n",
    "```python\n",
    "ps.__version__\n",
    "```\n",
    "rövidítéssel érhetjük el. A későbbiekben fontos lesz, mivel néhány kisebb hiba javításra kerülhet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verzió:  0.29.0-13-g310f3c9-dirty\n",
      "szerző:  Leéh Péter\n"
     ]
    }
   ],
   "source": [
    "print('verzió: ', ps.__version__)\n",
    "print('szerző: ', ps.__author__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A saját telepítésemnél a verzió száma más logikán alapul, normál telepítésnél rendes verziószámot fog kiírni, pl. *0.29.0*.\n",
    "\n",
    "### 1.4 Hibák jelentése\n",
    "\n",
    "A programhoz tartozik a `print_info` függvény, amivel minden fontos információt kiír a környezetről, ami a problémák megoldásánál hasznos lehet. Hibát jelenteni a szoftver [GitHub oldalán](https://github.com/Ptrskay3/PySprint/issues) az *issue* fül alatt lehetséges, vagy a *leeh123peter@gmail.com* e-mail címen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PYSPRINT ANALYSIS TOOL\n",
      "\n",
      "        SYSTEM\n",
      "----------------------\n",
      "python     : 3.9.4.final.0\n",
      "python-bits: 64\n",
      "OS         : Windows\n",
      "OS-release : 10\n",
      "Version    : 10.0.19041\n",
      "machine    : AMD64\n",
      "processor  : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel\n",
      "byteorder  : little\n",
      "\n",
      "      DEPENDENCY\n",
      "----------------------\n",
      "pysprint   : 0.29.0-13-g310f3c9-dirty\n",
      "numpy      : 1.20.2\n",
      "scipy      : 1.6.2\n",
      "matplotlib : 3.4.1\n",
      "pandas     : 1.2.4\n",
      "pytest     : 6.2.3\n",
      "lmfit      : 1.0.2\n",
      "numba      : 0.53.1\n",
      "IPython    : 7.22.0\n",
      "jinja2     : 2.11.3\n",
      "dask       : 2021.04.0\n",
      "sklearn    : 0.24.1\n",
      "\n",
      "      ADDITIONAL\n",
      "----------------------\n",
      "Conda-env  : False\n",
      "IPython    : True\n",
      "Spyder     : False\n",
      "Rust-ext   : True\n"
     ]
    }
   ],
   "source": [
    "ps.print_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Mértékegységek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Először fontos kikötni a mértékegységeket: hullámhossztartományban *nm*, frekvenciatartományban *PHz* egységekben dolgozik a program. **Minden kiértékelés előtt frekvenciatartományba kell váltani az adatsort.** Az eredmények ezután *fs* (és hatványai) egységekben lesznek kiszámítva."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Szimulációk\n",
    "\n",
    "Ez egy kevésbé használt funkció, a példák során ezt is fogom használni, így illik ezt is bemutatni.\n",
    "A funkció a következőképpen néz ki (az alapértelmezett értékeivel):\n",
    "\n",
    "```\n",
    "ps.Generator(\n",
    "    start,   < -- a spektrum kezdőértéke, hullámhossztartomány esetén nm, frekvenciatartomány esetén PHz\n",
    "    stop,    < -- a spektrum végértéke, hullámhossztartomány esetén nm, frekvenciatartomány esetén PHz\n",
    "    center,  < -- a központi hullámhossz/frekvencia\n",
    "    delay=0, < -- a karok közti időbeli késleltetés fs egységekben\n",
    "    GD=0,     \n",
    "    GDD=0,\n",
    "    TOD=0,   < -- a diszperziós együtthatók\n",
    "    FOD=0,\n",
    "    QOD=0,\n",
    "    resolution=0.1, < -- az interferogram felbontása nm egységekben (függetlenül milyen tartományban dolgozunk)\n",
    "    delimiter=\",\",  < -- a mentésnél az alapértelmezett elválasztó\n",
    "    pulse_width=10, < -- az impulzus félértékszélességét szabályzó paraméter fs egységben (lent tau_p)\n",
    "    normalize=False, < -- normálja-e az interferogramot (ha igen, akkor a karok spektrumát is visszaadja)\n",
    "    chirp=0, < -- lineáris csörp paraméter (lent a)\n",
    ")\n",
    "```\n",
    "\n",
    "A spektrális intenzitás alakja:\n",
    "\n",
    "\n",
    "$\\LARGE{I(\\omega) = C\\cdot e^{\\frac{(\\omega-\\omega_0)^2\\cdot \\tau_p^2}{4 ln2\\sqrt{1 + a^2}}}}$\n",
    "\n",
    "A használatához először inicializálni kell a `Generator` objektumot, aztán annak függvényében, hogy milyen tartományban szeretnénk generálni az interferogramot két lehetőség van:\n",
    "* `ps.Generator.generate_freq()` - a frekvenciatartománybeli generálás\n",
    "* `ps.Generator.generate_wave()` - a hullámhossztartománybeli generálás\n",
    "* `ps.Generator.generate()` - automatikusan eldönti, hogy milyen tartományban generálja\n",
    "\n",
    "A következőkben két interferogramot generálok, az egyiket frekvenciatartományban (1 és 4 *PHz* között 2.355 *PHz* központi frekvenciával fog egy harmadrendű diszperziót tartalmazó interferogramot generálni 100 *fs* karok közti késleltetésnél), illetve a másikat hullámhossztartományban (hasonló paraméterekkel, csak 400 és 1000 *nm* között 800 *nm* központi hullámhosszal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = ps.Generator(1, 4, 2.355, 100, TOD=500, pulse_width=5) # <-- itt még nem számolja ki a spektrumot\n",
    "f.generate_freq() # < -- ezzel a sorral már kiszámolja a spektrumot\n",
    "\n",
    "g = ps.Generator(400, 1000, 800, 100, TOD=500, normalize=True) # <-- hasonlóan itt még nem számolja ki a spektrumot\n",
    "g.generate_wave() # < -- ezzel a sorral már kiszámolja a spektrumot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ezzel létrehoztuk a `g` és `f` generátor objektumokat és generáltuk is a hozzájuk tartozó spektrumokat. Néhány funkciója a `Generator` objektumoknak:\n",
    "* `ps.Generator.show()` <-- mutassa a generált interferogramot\n",
    "* `ps.Generator.phase_graph()` <-- mutassa a generált interferogramot együtt a spektrális fázisfüggvénnyel\n",
    "* `ps.Generator.save(name)` <-- elmenti a spektrumot *name* névvel egy txt fájlba\n",
    "* `ps.Generator.data` <-- adja vissza a generált adatokat np.ndarray-ként\n",
    "\n",
    "Példaként néhányat ezek közül meghívok:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.phase_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully saved as ifg.txt.\n"
     ]
    }
   ],
   "source": [
    "g.save(\"ifg\")"
   ]
  },
  {
   "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": [
    "f.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A generált adatok eléréséhez a `ps.Generator.data` függvényt használhatjuk. Ha normáljuk az interferogramot, akkor ez vissza fogja adni a karok spektrumait is. Minden visszaadott érték `np.ndarray` típusú."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gx, gy, gref, gsam = g.data # a g normált volt, ezért a referencia- és tárgynyaláb spektrumát is visszaadja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx, fy = f.data # az f pedig nem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(fy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Alapelemek és koszinusz-függvény illesztéses módszer, CosFitMethod\n",
    "\n",
    "\n",
    "Néhány művelet minden kiértékelési módszernél hasznos lehet, (ilyenek pl. a   hullámhossztartományból frekvenciatartományba váltás vagy fordítva, normálás, fontos részek kivágása, stb.) ezért ezt minden objektum eléri. Erről egy külön munkafüzetet készítettem, ahol részletesen leírok mindent. \n",
    "Ez a következő szekcióban érhető el.\n",
    "\n",
    "A támogatott kiértékelési eljárások:\n",
    "\n",
    "* `ps.MinMaxMethod` - Minimum-maximum módszer\n",
    "* `ps.CosFitMethod` - Fázismodulált koszinusz-függvény illesztéses módszer\n",
    "* `ps.FFTMethod` - Fourier-transzformációs módszer\n",
    "* `ps.WFTMethod` - Ablakolt Fourier-transzformációs módszer (a 0.12.1 verziótól elérhető)\n",
    "* `ps.SPPMethod` - Állandó fázisú pont módszere\n",
    "\n",
    "Az alábbi bevezető példában a `CosFitMethod`-ot fogom használni. Kétféleképpen tölthetjük be az interferogramot: fájlból töltjük be az adatokat, vagy `np.ndarray`-ban már Pythonban be van töltve nekünk (pl. a szimulációt használtuk) a szükséges spektrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\pyt\\pysprint\\pysprint\\core\\bases\\dataset.py:207: PySprintWarning: Extreme values encountered during normalization.\n",
      "Nan values: 105\n",
      "Inf values: 1\n",
      "  PySprintWarning\n"
     ]
    }
   ],
   "source": [
    "# 1. mód: np.ndarrayból töltjük be\n",
    "a = ps.CosFitMethod(gx, gy, gref, gsam) # < -- ezek a fenti szimulációból származnak\n",
    "\n",
    "# vagy röviden:\n",
    "# a = ps.CosFitMethod(*g.data)\n",
    "\n",
    "# 2. mód: fájlból töltjük be\n",
    "b = ps.CosFitMethod.parse_raw(\n",
    "    'datasets/ifg.trt',\n",
    "    'datasets/ref.trt',\n",
    "    'datasets/sam.trt',\n",
    "    skiprows=8,\n",
    "    meta_len=6,\n",
    "    delimiter=\";\",\n",
    "    decimal=\",\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Az `parse_raw` függvény alapbeállításait a `help(ps.Dataset.parse_raw)` függvénnyel könnyen átnézhetjük. Például egy tizedespontokat használó, vesszővel elválasztott adatokat tartalmazó fájlt a következőképpen kellene helyesen betölteni:\n",
    "```python\n",
    "mm2 = ps.MinMaxMethod.parse_raw('ifg.txt', 'ref.txt', 'sam.txt', decimal='.', sep=',', skiprows=0, meta_len=0) \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method parse_raw in module pysprint.core.bases.dataset:\n",
      "\n",
      "parse_raw(filename, ref=None, sam=None, skiprows=0, decimal='.', sep=None, delimiter=None, comment=None, usecols=None, names=None, swapaxes=False, na_values=None, skip_blank_lines=True, keep_default_na=False, meta_len=1, errors='raise', callback=None, parent=None, **kwargs) method of pysprint.core.bases._dataset_base._DatasetBase instance\n",
      "    Dataset object alternative constructor.\n",
      "    Helps to load in data just by giving the filenames in\n",
      "    the target directory.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filename: `str`\n",
      "        base interferogram\n",
      "        file generated by the spectrometer\n",
      "    ref: `str`, optional\n",
      "        reference arm's spectra\n",
      "        file generated by the spectrometer\n",
      "    sam: `str`, optional\n",
      "        sample arm's spectra\n",
      "        file generated by the spectrometer\n",
      "    skiprows: `int`, optional\n",
      "        Skip rows at the top of the file. Default is `0`.\n",
      "    decimal: `str`, optional\n",
      "        Character recognized as decimal separator in the original dataset.\n",
      "        Often `,` for European data.\n",
      "        Default is `.`.\n",
      "    sep: `str`, optional\n",
      "        The delimiter in the original interferogram file.\n",
      "        Default is `,`.\n",
      "    delimiter: `str`, optional\n",
      "        The delimiter in the original interferogram file.\n",
      "        This is preferred over the `sep` argument if both given.\n",
      "        Default is `,`.\n",
      "    comment: `str`, optional\n",
      "        Indicates remainder of line should not be parsed. If found at the beginning\n",
      "        of a line, the line will be ignored altogether. This parameter must be a\n",
      "        single character. Default is `'#'`.\n",
      "    usecols: list-like or callable, optional\n",
      "        If there a multiple columns in the file, use only a subset of columns.\n",
      "        Default is [0, 1], which will use the first two columns.\n",
      "    names: array-like, optional\n",
      "        List of column names to use. Default is ['x', 'y']. Column marked\n",
      "        with `x` (`y`) will be treated as the x (y) axis. Combined with the\n",
      "        usecols argument it's possible to select data from a large number of\n",
      "        columns.\n",
      "    swapaxes: bool, optional\n",
      "        Whether to swap x and y values in every parsed file. Default is False.\n",
      "    na_values: scalar, str, list-like, or dict, optional\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values. By default the following values are interpreted as\n",
      "        NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’,\n",
      "        ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’,\n",
      "        ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.\n",
      "    skip_blank_lines: bool\n",
      "        If True, skip over blank lines rather than interpreting as NaN values.\n",
      "        Default is True.\n",
      "    keep_default_na: bool\n",
      "        Whether or not to include the default NaN values when parsing the data.\n",
      "        Depending on whether na_values is passed in, the behavior changes. Default\n",
      "        is False. More information available at:\n",
      "        https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html\n",
      "    meta_len: `int`, optional\n",
      "        The first `n` lines in the original file containing the meta\n",
      "        information about the dataset. It is parsed to be dict-like.\n",
      "        If the parsing fails, a new entry will be created in the\n",
      "        dictionary with key `unparsed`.\n",
      "        Default is `1`.\n",
      "    errors: string, optional\n",
      "        Determines the way how mismatching sized datacolumns behave.\n",
      "        The default is `raise`, and it will raise on any error.\n",
      "        If set to `force`, it will truncate every array to have the\n",
      "        same shape as the shortest column. It truncates from\n",
      "        the top of the file.\n",
      "    callback : callable, optional\n",
      "        The function that notifies parent objects about SPP related\n",
      "        changes. In most cases the user should leave this empty. The\n",
      "        default callback is only initialized if this object is constructed\n",
      "        by the `pysprint.SPPMethod` object.\n",
      "    parent : any class, optional\n",
      "        The object which handles the callback function. In most cases\n",
      "        the user should leave this empty.\n",
      "    kwargs : dict, optional\n",
      "        The window class to use in WFTMethod. Has no effect while using other\n",
      "        methods. Must be a subclass of pysprint.core.windows.WindowBase.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ps.CosFitMethod.parse_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minden ilyen objektum különleges print funkcióval rendelkezik. Itt néhány alapvető információt jelenít meg magáról, illetve ha fájlból töltöttük be, akkor kiolvas adatokat egy saját dictionary-be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CosFitMethod\n",
      "----------\n",
      "Parameters\n",
      "----------\n",
      "Datapoints: 2633\n",
      "Predicted domain: wavelength\n",
      "Range: from 360.50000 to 1200.25000 nm\n",
      "Normalized: True\n",
      "Delay value: Not given\n",
      "SPP position(s): Not given\n",
      "----------------------------\n",
      "Metadata extracted from file\n",
      "----------------------------\n",
      "{\n",
      "    \"Average\": \"1 scans\",\n",
      "    \"Data measured with spectrometer name\": \"1107006U1\",\n",
      "    \"Integration time\": \"2,00 ms\",\n",
      "    \"Nr of pixels used for smoothing\": \"0\",\n",
      "    \"Wave\": \"Sample\",\n",
      "    \"comment\": \"m_ifg 8,740\",\n",
      "    \"reference_comment\": \"m_ref 8,740\",\n",
      "    \"sample_comment\": \"m_sam 8,740\",\n",
      "    \"unparsed\": [\n",
      "        [\n",
      "            \"Timestamp [10 microsec ticks]256373440\"\n",
      "        ]\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A saját dictionary a `meta` kulcsszóval érhető el."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comment': 'm_ifg 8,740',\n",
       " 'Integration time': '2,00 ms',\n",
       " 'Average': '1 scans',\n",
       " 'Nr of pixels used for smoothing': '0',\n",
       " 'Data measured with spectrometer name': '1107006U1',\n",
       " 'unparsed': [['Timestamp [10 microsec ticks]256373440']],\n",
       " 'Wave': 'Sample',\n",
       " 'reference_comment': 'm_ref 8,740',\n",
       " 'sample_comment': 'm_sam 8,740'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Akár saját információt is hozzáadhatunk a dictionary adattípusnál megszokott módon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.meta['fontos_adat_amit_be_szeretnek_allitani'] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comment': 'm_ifg 8,740',\n",
       " 'Integration time': '2,00 ms',\n",
       " 'Average': '1 scans',\n",
       " 'Nr of pixels used for smoothing': '0',\n",
       " 'Data measured with spectrometer name': '1107006U1',\n",
       " 'unparsed': [['Timestamp [10 microsec ticks]256373440']],\n",
       " 'Wave': 'Sample',\n",
       " 'reference_comment': 'm_ref 8,740',\n",
       " 'sample_comment': 'm_sam 8,740',\n",
       " 'fontos_adat_amit_be_szeretnek_allitani': 20}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A metaadatok közül talán a comment a legfontosabb, mivel ez az, amit a mérés során mi írunk be. Ezt kiírtani a köv. módon lehet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m_ifg 8,740\n"
     ]
    }
   ],
   "source": [
    "print(b.meta['comment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter Notebookban, illetve Jupyter Labon belül az ilyen objektumoknak - a `print` funkcióhoz hasonló - HTML reprezentációja van:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div id=\"header\" class=\"row\" style=\"height:10%;width:100%;\">\n",
       "        <div style='float:left' class=\"column\">\n",
       "        <table style=\"border:1px solid black;float:top;\">\n",
       "        <tbody>\n",
       "        <tr>\n",
       "        <td colspan=2 style=\"text-align:center\">\n",
       "        <font size=\"5\">CosFitMethod</font>\n",
       "        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td colspan=2 style=\"text-align:center\">\n",
       "        <font size=\"3.5\">Parameters</font>\n",
       "        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"><b>Datapoints<b></td>\n",
       "            <td style=\"text-align:center\"> 2633</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td style=\"text-align:center\"><b>Predicted domain<b> </td>\n",
       "            <td style=\"text-align:center\"> wavelength </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"> <b>Range min</b> </td>\n",
       "        <td style=\"text-align:center\">360.50000 nm</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"> <b>Range max</b> </td>\n",
       "        <td style=\"text-align:center\">1200.25000 nm</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"> <b>Normalized</b></td>\n",
       "        <td style=\"text-align:center\"> True </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"><b>Delay value</b></td>\n",
       "        <td style=\"text-align:center\">Not given</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td style=\"text-align:center\"><b>SPP position(s)</b></td>\n",
       "        <td style=\"text-align:center\">Not given</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "        <td colspan=2 style=\"text-align:center\">\n",
       "        <font size=\"3.5\">Metadata</font>\n",
       "        </td>\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>comment </b></th>\n",
       "        <td style=\"text-align:center\"> m_ifg 8,740 </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>Integration time </b></th>\n",
       "        <td style=\"text-align:center\"> 2,00 ms </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>Average </b></th>\n",
       "        <td style=\"text-align:center\"> 1 scans </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>Nr of pixels used for smoothing </b></th>\n",
       "        <td style=\"text-align:center\"> 0 </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>Data measured with spectrometer name </b></th>\n",
       "        <td style=\"text-align:center\"> 1107006U1 </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>unparsed </b></th>\n",
       "        <td style=\"text-align:center\"> [['Timestamp [10 microsec ticks]256373440']] </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>Wave </b></th>\n",
       "        <td style=\"text-align:center\"> Sample </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>reference_comment </b></th>\n",
       "        <td style=\"text-align:center\"> m_ref 8,740 </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>sample_comment </b></th>\n",
       "        <td style=\"text-align:center\"> m_sam 8,740 </td>\n",
       "           </tr>\n",
       "        \n",
       "           <tr>\n",
       "        <th style=\"text-align:center\"> <b>fontos_adat_amit_be_szeretnek_allitani </b></th>\n",
       "        <td style=\"text-align:center\"> 20 </td>\n",
       "           </tr>\n",
       "        \n",
       "            </tbody>\n",
       "        </table>\n",
       "        </div>\n",
       "        <div style='float:leftt' class=\"column\"><img 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'>"
      ],
      "text/plain": [
       "<pysprint.core.methods.cosfit.CosFitMethod at 0x2b707b38e08>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Láthattuk a kiíratás során, hogy *Predicted domain: wavelength*. Frekvenciatartományba váltani a `chdomain()` függvénnyel lehet. Ekkor újra kiprintelve már a domain frequency-re vált. Megjegyzés: ez csak akkor működik jól, ha `PHz` és `nm` egységekben dolgozunk, különben rossz lehet a predikció."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.chdomain()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `plot()` metódussal meg tudjuk nézni az adatsort. Jupyterben nem szükséges a `plt.show()` meghívása, de máshol az lehet. Itt látható, hogy a számunkra fontos rész kb. 2.0 és 3.75 *PHz* között van, a többit nem kívánjuk használni. A kivágás a `slice()` metódussal lehetséges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "b.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "b.slice(2, 3.75)\n",
    "b.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Egy ilyen objektumbeli adatokat elérni - a Generatorhoz hasonlóan - a `data` metódussal lehet. Ez egy `pandas.DataFrame` objektumot ad vissza, aminek a reprezentációja itt Jupyterben szintén HTML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.750800</td>\n",
       "      <td>-0.009973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.748337</td>\n",
       "      <td>0.198811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.745877</td>\n",
       "      <td>0.322196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.743420</td>\n",
       "      <td>0.393736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.740892</td>\n",
       "      <td>0.299712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>2.002649</td>\n",
       "      <td>0.240820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>2.001989</td>\n",
       "      <td>0.446164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>2.001330</td>\n",
       "      <td>0.380374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>2.000671</td>\n",
       "      <td>0.550828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>1.999991</td>\n",
       "      <td>0.589695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1363 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x         y\n",
       "0     3.750800 -0.009973\n",
       "1     3.748337  0.198811\n",
       "2     3.745877  0.322196\n",
       "3     3.743420  0.393736\n",
       "4     3.740892  0.299712\n",
       "...        ...       ...\n",
       "1358  2.002649  0.240820\n",
       "1359  2.001989  0.446164\n",
       "1360  2.001330  0.380374\n",
       "1361  2.000671  0.550828\n",
       "1362  1.999991  0.589695\n",
       "\n",
       "[1363 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha pl. szeretnénk csak az `y` értékeket kinyerni `np.ndarray` formában, akkor azt megtehetjük így:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "y_ertekei = b.data.y.values\n",
    "print(type(y_ertekei))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.009973    0.19881073  0.32219592 ...  0.3803739   0.55082753\n",
      "  0.58969469]\n"
     ]
    }
   ],
   "source": [
    "print(y_ertekei)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fázismodulált koszinusz-függvény illesztéses módszernél megjelenik néhány specifikus elem. Az első ilyen a `GD_lookup(reference_point, engine='cwt', silent=False, **kwargs)`. Ez a függvény a `reference_point` környezetében egymást követő minimumok/maximumok távolságát vizsgálja, majd a $2\\pi$-t osztja ezeknek a távolságoknak az átlagával. Ez jól becsüli a GD-t, amennyiben a szélsőértékeket relatíve pontosan találja meg a függvény. Ennek testreszabására való az `engine` argumentum, illetve a további `**kwargs` keyword argumentumok. Ebben a példában az alapbeállítás megfelelő, így nem módosítok rajta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted GD is ± 90.17543 fs based on reference point of 2.35500.\n"
     ]
    }
   ],
   "source": [
    "b.GD_lookup(reference_point=2.355)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ezt lefuttatva a program már be is állította az illesztésnél a GD kezdőértékét. Természetesen előfordulhat, hogy manuálisan szeretnénk értéket megadni, ezt a \n",
    "```python\n",
    "b.guess_GD(szám)\n",
    "```\n",
    "sorral lehetséges.\n",
    "A további együtthatók kezdőértékeit találgatni a GD-vel azonos séma alapján tudjuk: \n",
    "```python\n",
    "b.guess_GDD(másik szám)\n",
    "b.guess_TOD(harmadik szám)\n",
    "stb.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koszinusz-függvény illesztésénél két módon végezhetjük el a kiértékelést, a hagyományos illesztési eljárással, vagy a szakdolgozatomban leírt szukcesszív illesztést használva. Először a hagyományost mutatom be. Ekkor relatíve jó kezdeti tippeket kell adnunk a paramétereknek, hogy megfelelően jól közelítse az illesztés az eredeti adatpontokat. Ezt ennél a példánál - ismerve a valódi értékeket - megadom a kezdőértékeket, ezután a `set_max_order(order)` függvénnyel megadom, hogy maximum hányadrendű diszperziót keressen. Ezután futtatom a `calculate(reference_point)` metódust, ami a kiértékelést végzi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle R^2 = 0.95701\n",
       "$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 83.91576 ± 0.00000 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -169.23559 ± 0.00000 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -112.87929 ± 0.00000 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b.guess_GD(83) # az előbbi GD = 90.17543 nem elég jó tipp, azzal pontatlan az illesztés\n",
    "b.guess_GDD(-170)\n",
    "b.guess_TOD(-115)\n",
    "b.set_max_order(3)\n",
    "b.calculate(reference_point=2.355);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ezután a `plot_result()` függvényt hívásával az illesztést meg is tekinthetjük."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle R^2 = 0.95701$"
      ],
      "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": [
    "b.plot_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Az általam írt optimalizáló szukcesszív módszert az \n",
    "\n",
    "```python\n",
    "optimizer(reference_point,\n",
    "          order=3,\n",
    "          initial_region_ratio=0.1,\n",
    "          extend_by=0.1,\n",
    "          coef_threshold=0.3,\n",
    "          max_tries=5000,\n",
    "          show_endpoint=True)\n",
    "```\n",
    "függvény meghívásával érhetjük el. A helyes működéshez az `initial_region_ratio` argumentum, vagyis a kezdőrégió aránya és az `extend_by` argumentum, vagyis a tartománynövekedés megfelelő beállítása szükséges. Néhány gyakori hiba és azok megoldása a `help(ps.CosFitMethod.optimizer)` kiíratásával angol nyelven olvasható.  Mivel az előbbi hagyományos kiértékelésnél a kezdőértékeket jól megtippeltem, mielőtt futtatnám a saját optimalizáló algoritmust elrontom a kezdőértékeket, csak a GD-t hagyom meg. Ebben a példában az alapbeállítás elégséges, így nem változtatok meg semmilyen argumentumot, csak a referencia pontot és a diszperzió rendjét adom meg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working... \\"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 83.91564 \\ fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -169.16239 \\ fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -113.10974 \\ fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle with \\ R^2 = 0.95692.$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b.guess_GDD(1)\n",
    "b.guess_TOD(1)\n",
    "b.optimizer(reference_point=2.355, order=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kontrasztként bemutatom azt is, hogy mi történik, amikor valamilyen beállítás nem megfelelő. Az `extend_by` értékét jóval el fogom állítani, vagyis túlságosan gyorsan fog az illesztés régiója nőni, így az eljárás nem fog megfelelő illesztést találni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working... -"
     ]
    },
    {
     "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": [
      "Max tries (currently set to 5000) reached without finding a suitable fit.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python37\\lib\\site-packages\\lmfit\\printfuncs.py:179: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  spercent = '({:.2%})'.format(abs(par.stderr/par.value))\n"
     ]
    }
   ],
   "source": [
    "b.guess_GDD(1)\n",
    "b.guess_TOD(1)\n",
    "b.optimizer(reference_point=2.355, order=3, extend_by=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ekkor kékkel az eredeti adatsor, pirossal az illesztett görbe, feketével pedig az aktuális illesztési terület látszódik.\n"
   ]
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}