{
 "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": [
    {
     "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": [
    "### 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 0x2bc721fb088>"
      ]
     },
     "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).\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": [
       "$\\displaystyle GD = 74.72850 ± 1.32604 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -154.78920 ± 3.88290 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -124.27194 ± 4.66781 fs^3$"
      ],
      "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, allow_parallel=True, show_graph=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fent a `calculate` metódusban az `allow_parallel` 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:143 -       min_max_method() ] refpoint set to 2.355094355366024 instead of 2.355094355366024.\n",
      "[ _evaluate.py:159 -       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:105 - _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:105 - _build_single_phase_data() ] x was split to 2 pieces (including the flip).\n",
      "[ minmax.py:164 -            calculate() ] left side evaluated to [   36.41177096  -692.71748017 -2694.08400292     0.\n",
      "     0.             0.        ], used 13 points.\n",
      "[ minmax.py:165 -            calculate() ] right side evaluated to [-74.72850092 154.78919995 124.27194356   0.           0.\n",
      "   0.        ], used 56 points.\n",
      "[ minmax.py:177 -            calculate() ] Max relative difference is too high, using right side.\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GD = 74.72850 ± 1.32604 fs^1$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle GDD = -154.78920 ± 3.88290 fs^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle TOD = -124.27194 ± 4.66781 fs^3$"
      ],
      "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, allow_parallel=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, 2.13767101, 2.1142057 , 2.0890698 , 2.06459245,\n",
       "        2.04001903, 2.01404055, 2.35981505, 2.39827298, 2.44214592,\n",
       "        2.49196519, 2.54733396, 2.62171747, 2.95002751, 3.01799527,\n",
       "        3.07284106, 3.11615201, 3.15208013, 3.19068292, 3.21755217,\n",
       "        3.24857127, 3.274549  , 3.3010034 , 3.32594962, 3.34740469,\n",
       "        3.36913836, 3.39109505, 3.4093241 , 3.43187197, 3.45060647,\n",
       "        3.46737518, 3.48430767, 3.50355548, 3.52090986, 3.53617851,\n",
       "        3.5538584 , 3.56948242, 3.58292577, 3.59880699, 3.61254184,\n",
       "        3.62631212, 3.64025813, 3.65431181, 3.66847443, 3.68267526,\n",
       "        3.694666  , 3.70914376, 3.71888327, 3.73111135, 3.74342011,\n",
       "        3.75573547, 3.76572153, 3.77826009, 3.79088242, 3.80105652,\n",
       "        3.81383188, 3.8241297 , 3.83448328, 3.84489308, 3.85796532,\n",
       "        3.86580382, 3.8763846 , 3.88702346]),\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, -16.49446157, -13.35286891, -10.21127626,\n",
       "         -7.06968361,  -3.92809095,  -0.7864983 ,   2.35509436,\n",
       "          5.49668701,   8.63827966,  11.77987232,  14.92146497,\n",
       "         18.06305762,  21.20465028,  24.34624293,  27.48783558,\n",
       "         30.62942824,  33.77102089,  36.91261354,  40.0542062 ,\n",
       "         43.19579885,  46.33739151,  49.47898416,  52.62057681,\n",
       "         55.76216947,  58.90376212,  62.04535477,  65.18694743,\n",
       "         68.32854008,  71.47013273,  74.61172539,  77.75331804,\n",
       "         80.8949107 ,  84.03650335,  87.178096  ,  90.31968866,\n",
       "         93.46128131,  96.60287396,  99.74446662, 102.88605927,\n",
       "        106.02765192, 109.16924458, 112.31083723, 115.45242988,\n",
       "        118.59402254, 121.73561519, 124.87720785, 128.0188005 ,\n",
       "        131.16039315, 134.30198581, 137.44357846, 140.58517111]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eredeti_fazis.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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, allow_parallel=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",
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