{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Minimum-maximum módszer, MinMaxMethod\n",
    "\n",
    "A minimum-maximum módszeres kiértékelésre két különböző út van, az interaktív matplotlib ablak, illetve a manuális minimum maximum megadás. Itt a már meglévő interferogramot értékelem ki, illetve szimulált interferogramokon is bemutatok funkciókat."
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\pyt\\pysprint\\pysprint\\core\\bases\\dataset.py:203: PySprintWarning: Extreme values encountered during normalization.\n",
      "Nan values: 105\n",
      "Inf values: 1\n",
      "  PySprintWarning\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"
    }
   ],
   "source": [
    "m = ps.MinMaxMethod.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",
    ")\n",
    "m.chdomain()\n",
    "m.slice(2, 3.9)\n",
    "\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 A manuális módszer\n",
    "Pontosan megtalálni a szélsőértékek helyét *csak kódot használva* különböző adatsorok esetén elég nehéz feladat. Manuális beállítás esetén `MinMaxMethod.xmin` és `MinMaxMethod.xmax` paramétereket kell megadni, melyek a minimumok és maximumok helyeire mutató `np.ndarray`-ok. A szélsőértékek megtalálásához a `MinMaxMethod.detect_peak` és a `MinMaxMethpd.detect_peak_cwt` két beépített segédfüggvény. Ez a két függvény a `scipy.signal.find_peaks` és a `scipy.signal.find_peaks_cwt` függvényeket használja a kódon belül."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x165d53e21c8>"
      ]
     },
     "execution_count": 3,
     "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": [
    "xmax, ymax, xmin, ymin = m.detect_peak(pmax=0.5, pmin=4, threshold=0.15)\n",
    "\n",
    "plt.figure()\n",
    "m.plot()\n",
    "plt.plot(xmax, ymax, \"ko\", label=\"maximumok\")\n",
    "plt.plot(xmin, ymin, \"bo\", label=\"minimumok\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Látható, hogy bármennyit is állítgatjuk a paraméterek értékeit, tökéletes eredményt a legtöbb esetben nem tudunk elérni. A `detect_peak` és a `detect_peak_cwt` rögzíti automatikusan a minimumok és a maximumok helyét. Ezután két lehetőségünk van. Az első, hogy meghívjuk a `build_phase(reference_point, SPP_callbacks=None)` függvényt, amivel a program az eddig megadott szélsőértékek, a referencia pont és az SPP-k helyzetéből meghatározza a fázist (`ps.core.phase.Phase`), majd az kerül visszatérítésre. Fontos megadni az `SPP_callbacks` argumentumot. Ez lehet szám, vagy list számokkal. A programnak fontos tudnia ezekről, hiszen ezek adják meg azokat a pozíciókat, ahol a fázis menete előjelet vált. Ha nem adtuk meg az `SPP_callbacks` argumentumot, akkor a program megnézi, hogy az interferogram objektumon állítottunk-e be állandó fázisú pontot, és amennyiben igen azt fogja használni. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "fazis = m.build_phase(reference_point=2.355, SPP_callbacks=2.77);\n",
    "fazis.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Látható, hogy ebben az esetben a referencia pont környékén letörés tapasztalható. Ez azért van, mert a program a referencia pontból kiindulva két irányba kezdi felépíteni a fázist, így ott legtöbbször a görbe nem folytonos. Itt két lehetőségünk van: használjuk a `flip_around(value, side=\"left\")` függvényt, és átfordítjuk a fázisgrafikon megfelelő részét, vagy egyszerűen a `slice(start, stop)` függvényt meghívva csak az egyik oldalt használjuk. Itt egyszerűen csak a referencia ponttól nagyobb körfrekvencia értékeket fogom használni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -109.90494 ± 5.11096 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 301.56948 ± 15.13862 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -113.76563 ± 18.35335 fs^3$"
      ],
      "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": [
    "fazis.slice(2.355, None)\n",
    "fazis.fit(reference_point=2.355, order=3);\n",
    "fazis.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Köszönhetően a szélsőértékek pontatlan meghatározásának az eredmény is eléggé pontatlan\n",
    "(körülbelül GD = $-83 fs$, GDD = $165 fs^2$, TOD = $115 fs^3$ lenne a valós), ráadásul az előjelek sem stimmelnek.\n",
    "\n",
    "### 9.2 Az interaktív módszer\n",
    "\n",
    "Vizsgáljuk meg, hogy pontos szélsőértékek esetén hogyan fest a fenti számolás. Ehhez használom az `init_edit_session` függvényt, ahol bejelölöm a szélsőértékeket. Pontot hozzáadni az `i`, törölni a `d` billentyűvel lehet. Ezután a `calculate` metódust fogom használni, amely felépíti a fázist és görbét is illeszt.\n",
    "\n",
    "\n",
    "**FONTOS:**\n",
    "Az állandó fázisú pontokat is be kell jelölni, mint szélsőérték, mivel a program megkeresi a megadott SPP helyzetekhez a legközelebbi szélsőértéket és azt kezeli állandó fázisú pontként."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69 extremal points were recorded.\n"
     ]
    }
   ],
   "source": [
    "# interaktív módba váltás\n",
    "with ps.interactive():\n",
    "    # az interaktív szélsőérték kereső elindítása\n",
    "    m.init_edit_session(threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 67.51616 ± 1.26439 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -137.80872 ± 3.58942 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -142.13762 ± 4.24521 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Itt nem adok meg SPP_callbacks értéket, hanem magára az objektumra állítom be\n",
    "# az állandó fázisú pont helyét. Ekkor ezt fogja a használni a program.\n",
    "# Ha mindkettő adott, akkor az SPP_callbacks argumentum értéke preferált.\n",
    "m.positions = 2.77\n",
    "\n",
    "m.calculate(reference_point=2.355, order=3, scan=True, show_graph=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fent a `calculate` metódusban az `scan` argumentumról: Az alapértelmezett értéke `False`, vagyis a metódus csak felépíti a fázist, majd nem végez semmilyen vizsgálatot rajta, hanem csak a megadott görbét illeszti rá, majd abból számolja a diszperziós együtthatókat. Ez akkor lehet jó, amikor a referencia pont és az SPP egybeesik, vagy a referencia pont az adatsor szélén van, mivel ezekben az esetekben a görbében nincs törés. Ha `True`, akkor felbontja a referencia pont mentén a fázisgrafikont, majd külön kiszámolja mindkét oldalra a diszperziós együtthatókat. Ha azok kevéssé térnek el, akkor az együtthatók átlagát adja vissza, természetesen az előjelek egyeztetésével. Ha az együtthatók egy előre meghatározott határnál jobban eltérnek a két oldalon, akkor csak az egyik oldalon számolt együtthatókat adja vissza. Ekkor mindig azt az oldalt használja, ahol több adatpontunk van. Látható, hogy a fenti példában a csak a jobb oldalt használta. A teljes output itt is el van rejtve a felhasználó elől, de elérhető: lefuttatom újra, úgy, hogy látható legyen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ _evaluate.py:157 -       min_max_method() ] refpoint set to 2.355094355366024 instead of 2.355094355366024.\n",
      "[ _evaluate.py:173 -       min_max_method() ] SPP_callbacks are now 0.41490564463397606, with ref_point 2.355094355366024.\n",
      "[ _evaluate.py:67 -        _split_on_SPP() ] 0.41490564463397606 is outside of array range, skipping.\n",
      "[ _evaluate.py:106 - _build_single_phase_data() ] x was split to 1 pieces (including the flip).\n",
      "[ _evaluate.py:64 -        _split_on_SPP() ] split value was set to 0.42069634211239215 instead of 0.41490564463397606.\n",
      "[ _evaluate.py:106 - _build_single_phase_data() ] x was split to 2 pieces (including the flip).\n",
      "[ minmax.py:188 -            calculate() ] left side evaluated to [   34.99186545  -737.69982878 -3030.40892732     0.\n",
      "     0.             0.        ], used 13 points.\n",
      "[ minmax.py:189 -            calculate() ] right side evaluated to [-67.51615927 137.80871673 142.13761854   0.           0.\n",
      "   0.        ], used 56 points.\n",
      "[ minmax.py:203 -            calculate() ] Max relative difference is too high, using right side.\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 67.51616 ± 1.26439 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -137.80872 ± 3.58942 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -142.13762 ± 4.24521 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "\n",
    "m.calculate(reference_point=2.355, order=3, scan=True, show_graph=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt láthatóvá válik pl., hogy a két oldalra milyen eredmény adódik (GD, GDD, TOD, FOD, QOD az adatok sorrendje) és hány adatot használtunk fel: \n",
    "> left side evaluated to [34.99186545 -737.69982878 -3030.40892732 0. 0.], used 13 points.\n",
    "\n",
    "> right side evaluated to [-74.99734682 155.78155789 122.85346734  0.   0.], used 56 points.\n",
    "\n",
    "     \n",
    "Az interaktív felületet használva a kapott együtthatók jóval pontosabbak lettek. Ha már egyszer lefuttattuk a `build_phase` vagy a `calculate` metódusokat, akkor az objektum eltárolja az eredeti fázist. Ezt természetesen módosíthatjuk és számolhatjuk belőle a diszperziós együtthatókat ahogyan egy előző munkafüzetben már bemutattam. A következőképpen érhetjuk el a tárolt fázist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eredeti_fazis = m.phase\n",
    "\n",
    "eredeti_fazis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.32446267, 2.28753955, 2.25787113, 2.22564403, 2.19683193,\n",
       "        2.16800742, 2.14458298, 2.1142057 , 2.0890698 , 2.06459245,\n",
       "        2.04001903, 2.01404055, 2.35981505, 2.44214592, 2.49196519,\n",
       "        2.54733396, 2.62171747, 2.95002751, 3.01799527, 3.07284106,\n",
       "        3.11615201, 3.15208013, 3.19068292, 3.21755217, 3.24857127,\n",
       "        3.274549  , 3.3010034 , 3.32594962, 3.34740469, 3.36913836,\n",
       "        3.39109505, 3.4093241 , 3.43187197, 3.45060647, 3.46737518,\n",
       "        3.48430767, 3.50355548, 3.52090986, 3.53617851, 3.5538584 ,\n",
       "        3.56948242, 3.58292577, 3.59880699, 3.61254184, 3.62631212,\n",
       "        3.64025813, 3.65431181, 3.66847443, 3.68267526, 3.694666  ,\n",
       "        3.70673508, 3.71888327, 3.73111135, 3.74342011, 3.75573547,\n",
       "        3.76572153, 3.77826009, 3.79088242, 3.80105652, 3.81383188,\n",
       "        3.8241297 , 3.83448328, 3.84489308, 3.85796532, 3.86580382,\n",
       "        3.8763846 , 3.88702346, 3.89772088]),\n",
       " array([ -0.7864983 ,  -3.92809095,  -7.06968361, -10.21127626,\n",
       "        -13.35286891, -16.49446157, -19.63605422, -22.77764687,\n",
       "        -25.91923953, -29.06083218, -32.20242483, -35.34401749,\n",
       "         -0.7864983 ,  -3.92809095,  -7.06968361, -10.21127626,\n",
       "        -13.35286891, -10.21127626,  -7.06968361,  -3.92809095,\n",
       "         -0.7864983 ,   2.35509436,   5.49668701,   8.63827966,\n",
       "         11.77987232,  14.92146497,  18.06305762,  21.20465028,\n",
       "         24.34624293,  27.48783558,  30.62942824,  33.77102089,\n",
       "         36.91261354,  40.0542062 ,  43.19579885,  46.33739151,\n",
       "         49.47898416,  52.62057681,  55.76216947,  58.90376212,\n",
       "         62.04535477,  65.18694743,  68.32854008,  71.47013273,\n",
       "         74.61172539,  77.75331804,  80.8949107 ,  84.03650335,\n",
       "         87.178096  ,  90.31968866,  93.46128131,  96.60287396,\n",
       "         99.74446662, 102.88605927, 106.02765192, 109.16924458,\n",
       "        112.31083723, 115.45242988, 118.59402254, 121.73561519,\n",
       "        124.87720785, 128.0188005 , 131.16039315, 134.30198581,\n",
       "        137.44357846, 140.58517111, 143.72676377, 146.86835642]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eredeti_fazis.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.3 Szimuláció több SPP-vel\n",
    "Bár a minimum-maximum módszer nem túl pontos magasabb rendű diszperzió esetén, a program képes tetszőleges számú SPP esetén is felépíteni a fázist. Erre mutatok itt egy példát."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visszaállítom a log szintet az alapbeállításra\n",
    "logger.setLevel(logging.CRITICAL)\n",
    "\n",
    "g = ps.Generator(1.9, 3, 2.355, delay=0, GD=100, GDD=3000, FOD=-500000, normalize=True, resolution=0.005)\n",
    "g.generate()\n",
    "\n",
    "myminmax = ps.MinMaxMethod(*g.data)\n",
    "myminmax.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ezen az interferogramon 2.186, 2.322 és 2.561 PHz körfrekvenciaértéknél van állandó fázisú pont. Az alábbi cellában ismét az interaktív panelt használva bejelölöm a szélsőértékek helyét (az állandó fázisú pontokat is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1166 extremal points were recorded.\n"
     ]
    }
   ],
   "source": [
    "with ps.interactive():\n",
    "    myminmax.init_edit_session(threshold=0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# az állandó fázisú pontok beállítása\n",
    "myminmax.positions = 2.186, 2.322, 2.561"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 73.00563 ± 1.75492 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = 3061.64527 ± 17.55223 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -180.21010 ± 101.24161 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle FOD = -501212.54874 ± 266.63603 fs^4$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myminmax.calculate(2.355, 4, scan=True, show_graph=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.4 Féloldalas kiértékelés\n",
    "\n",
    "A szoftver támogatja az ún. féloldalas kiértékelést is. Ekkor minden oszcillációs periódusban egyetlen karakterisztikus pontot kell bejelölnünk (minimum, maximum, nulla átmenet, stb..).\n",
    "Teljesen hasonlóan működik, csak a `side` argumentum értékét kell változtatnunk az éppen aktuális detektáló függvénynél. A bemutatáshoz a fenti valós interferogramot használom fel, aminek a minimumait fogom használni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 extremal points were recorded.\n"
     ]
    }
   ],
   "source": [
    "with ps.interactive():\n",
    "    m.init_edit_session(side=\"min\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fentebb az állandó fázisú pontot már rögzítettem ezen az objektumon, ezért itt nem teszem meg, azonban ez fontos lépés. Ellenőrizzük le, hogy valóban ismeri ezt az objektum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.77"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A számolásnál pedig a `onesided=True` argumentumot használjuk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -67.00275 ± 2.72451 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -143.61009 ± 8.14739 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -132.37321 ± 9.82490 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.calculate(reference_point=2.355, order=3, scan=True, show_graph=True, onesided=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha ezt elfelejtenénk, a program figyelmeztet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\pyt\\pysprint\\pysprint\\core\\methods\\minmax.py:263: PySprintWarning: Trying to build phase as two-sided, but the detection was one-sided. Use `onesided=True`.\n",
      "  PySprintWarning\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = -33.50137 ± 1.36226 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -71.80505 ± 4.07370 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -66.18660 ± 4.91245 fs^3$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.calculate(reference_point=2.355, order=3, scan=True, show_graph=True);"
   ]
  }
 ],
 "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
}