5'_Prueba_temporal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imports import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**voy a intentar usar la variable flash_time y la variable flash_total_pe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys in optree: ['eventID', 'runID', 'subrunID', 'nuvX', 'nuvY', 'nuvZ', 'nuvT', 'nuvE', 'stepX', 'stepY', 'stepZ', 'stepT', 'dE', 'energydep', 'energydepX', 'energydepY', 'energydepZ', 'E', 'StartPx', 'StartPy', 'StartPz', 'EndPx', 'EndPy', 'EndPz', 'process', 'trackID', 'motherID', 'PDGcode', 'InTimeCosmics', 'InTimeCosmicsTime', 'dEtpc', 'dEpromx', 'dEpromy', 'dEpromz', 'dEspreadx', 'dEspready', 'dEspreadz', 'dElowedges', 'dEmaxedges', 'nopflash', 'flash_id', 'flash_time', 'flash_total_pe', 'flash_pe_v', 'flash_tpc', 'flash_y', 'flash_yerr', 'flash_z', 'flash_zerr', 'flash_x', 'flash_xerr', 'flash_ophit_time', 'flash_ophit_risetime', 'flash_ophit_starttime', 'flash_ophit_amp', 'flash_ophit_area', 'flash_ophit_width', 'flash_ophit_pe', 'flash_ophit_ch']\n"
     ]
    }
   ],
   "source": [
    "file_path = '/data/vidales/opana_tree_combined_v0411_both.root'\n",
    "file = uproot.open(file_path)\n",
    "optree = file['opanatree']['OpAnaTree'] # Tree con número de fotoelectrones\n",
    "print(\"Keys in optree:\", optree.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load variables to be predicted and associated data in a single step\n",
    "nuvZ, nuvT, flash_tpc, flash_time, flash_total_pe = (optree[key].array() for key in ['nuvZ', 'nuvT', 'flash_tpc', 'flash_time', 'flash_total_pe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nuvT: [[1.83e+03], [1.85e+03], [592, 1.47e+03], [520], ..., [889], [1.35e+03], [642]]\n",
      "nuvZ: [[108], [141], [450, 488], [-0.653], [182], ..., [332], [249], [262], [222]]\n",
      "flash_time: [[1.83, 1.89], [1.85, 1.9], ..., [1.35, ...], [0.942, 0.644, 0.924, 0.684]]\n",
      "flash_total_pe: [[4.25e+04, 2.39e+03], [7.32e+04, ...], ..., [8.01e+03, 7.11e+04, 392, 2.7e+03]]\n",
      "flash_tpc: [[0, 2], [0, 2], [0, 1, 3], [0, 2], ..., [0, ...], [1, 3], [0, 2], [0, 1, 2, 3]]\n"
     ]
    }
   ],
   "source": [
    "print(\"nuvT:\", nuvT)\n",
    "print(\"nuvZ:\", nuvZ)\n",
    "print(\"flash_time:\", flash_time)\n",
    "print(\"flash_total_pe:\", flash_total_pe)\n",
    "print(\"flash_tpc:\", flash_tpc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filter events with more than one neutrino**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming all variables are awkward arrays\n",
    "nuvT = nuvT[ak.num(nuvZ) == 1]\n",
    "nuvZ = nuvZ[ak.num(nuvZ) == 1]\n",
    "flash_time = flash_time[ak.num(nuvZ) == 1]\n",
    "flash_total_pe = flash_total_pe[ak.num(nuvZ) == 1]\n",
    "flash_tpc = flash_tpc[ak.num(nuvZ) == 1]\n",
    "\n",
    "# Now all the variables are filtered and contain only subarrays with length 1 in nuvT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filter events with no flash**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nuvT = nuvT[ak.num(flash_time) != 0]\n",
    "nuvZ = nuvZ[ak.num(flash_time) != 0]\n",
    "flash_time = flash_time[ak.num(flash_time) != 0]\n",
    "flash_total_pe = flash_total_pe[ak.num(flash_time) != 0]\n",
    "flash_tpc = flash_tpc[ak.num(flash_time) != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29007\n",
      "29007\n"
     ]
    }
   ],
   "source": [
    "print(ak.sum(ak.num(nuvT)))\n",
    "print(ak.sum(ak.num(nuvZ)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToF_inside: [3.61, 4.69, -0.0218, 6.06, 4.48, 15.9, ..., 0.267, 6.22, 11.1, 8.32, 8.73, 7.4] ns\n",
      "ToF_outside 367 ns\n",
      "bunch_separation: 19 ns\n"
     ]
    }
   ],
   "source": [
    "ToF_inside = ak.flatten(nuvZ) / 3E10 *1E9\n",
    "ToF_outside = 110 / 3E8 * 1E9\n",
    "bunch_separation = 19\n",
    "print(\"ToF_inside:\",ToF_inside, \"ns\")\n",
    "print(f\"ToF_outside {ToF_outside:.0f} ns\")\n",
    "print(f\"bunch_separation: {bunch_separation:.0f} ns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ToF outside solo añade un shift global. ToF inside no se le mete a hit_t porque debe intentar predecirlo. Hit_t no sé si tiene sentido hacer el módulo 19 pero creo que no.\n",
    "#hit_nuvT_c= np.mod(hit_nuvT - ToF_outside - ToF_inside, bunch_separation)\n",
    "nuvT_c= nuvT - ToF_inside - ToF_outside\n",
    "nuvT_c_bunch = np.mod(nuvT_c + 7, bunch_separation) # +7 is to center the distribution\n",
    "flash_time_c = flash_time*1000 - ToF_inside - ToF_outside\n",
    "flash_time_c_bunch  = flash_time*1000 - ToF_inside - ToF_outside - ak.flatten(nuvT_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.46e+03], [1.48e+03], [153], [643], ..., [173], [514], [971], [268]]\n"
     ]
    }
   ],
   "source": [
    "print(nuvT_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.46e+03, 1.52e+03], [1.48e+03, 1.52e+03], ..., [...], [127, 123, 206, 146]]\n",
      "[-234]\n"
     ]
    }
   ],
   "source": [
    "print(flash_time*1000  - ToF_outside - ToF_inside)\n",
    "print((flash_time*1000  - ToF_outside - ToF_inside - ak.flatten(nuvT_c))[28100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import awkward as ak\n",
    "\n",
    "# Assuming hit_nuvT, ToF_outside_set0, and ToF_inside are defined awkward arrays\n",
    "# Calculate the difference and flatten it\n",
    "#difference = np.mod(ak.flatten(nuvT - ToF_inside - ToF_outside), 19)\n",
    "\n",
    "\n",
    "# Calculate the standard deviation\n",
    "#std_dev = np.std(nuvT)\n",
    "#std_dev2 = np.std(nuvT_c + ToF_outside)\n",
    "\n",
    "# Set up the plot\n",
    "plt.figure(figsize=(18, 6))\n",
    "#plt.hist(nuvT, bins=600, color='#ff7f0e', edgecolor='black', alpha=0.8, label='nuvT')\n",
    "plt.hist(ak.flatten(flash_total_pe), bins=200, color='#17becf', edgecolor='black', alpha=0.5, label='nuvT - ToF_inside - ToF_outside')\n",
    "\n",
    "# Customize the title and labels\n",
    "plt.title('Neutrino Interaction Time distribution', fontsize=18, fontweight='bold', color='#333333')\n",
    "plt.xlabel('Time [ns]', fontsize=14, labelpad=10, fontweight='bold')\n",
    "plt.ylabel('# Events', fontsize=14, labelpad=10, fontweight='bold')\n",
    "\n",
    "# Set axis limits\n",
    "plt.xlim(10,50000)\n",
    "plt.ylim(0, None)\n",
    "\n",
    "# Enhance the grid and axis appearance\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6, color='gray')\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Add a light background color for the plot area to add contrast\n",
    "plt.gca().set_facecolor('#f9f9f9')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(fontsize=16)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sin ajustar ToF (ver posteriormente)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y = np.column_stack((nuvT_c, dEpromx_f, dEpromy_f, dEpromz_f))\n",
    "y = ak.to_numpy(nuvT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y: [[1832.7022871 ]\n",
      " [1851.59823681]\n",
      " [ 519.95393046]\n",
      " ...\n",
      " [ 889.15791959]\n",
      " [1346.44396473]\n",
      " [ 642.10002491]]\n",
      "y.shape: (29007, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"y:\", y)\n",
    "print(\"y.shape:\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.83, 1.89, None, None], [1.85, ..., None], ..., [0.501, 0.497, 0.58, 0.52]]\n",
      "[[4.25e+04, 2.39e+03, None, None], [...], ..., [2.54e+04, 2.87e+03, 952, 111]]\n",
      "[[0, 2, None, None], [0, 2, None, None], ..., [0, 1, ..., None], [0, 1, 2, 3]]\n"
     ]
    }
   ],
   "source": [
    "import awkward as ak\n",
    "import numpy as np\n",
    "\n",
    "# Determinar la longitud máxima\n",
    "target_length = max(len(arr) for arr in flash_time)\n",
    "\n",
    "# Rellenar las secuencias con `None` hasta la longitud máxima\n",
    "flash_time_padded = ak.pad_none(flash_time, target=target_length, axis=-1)\n",
    "flash_total_pe_padded = ak.pad_none(flash_total_pe, target=target_length, axis=-1)\n",
    "flash_tpc_padded = ak.pad_none(flash_tpc, target=target_length, axis=-1)\n",
    "\n",
    "# Ver los arrays rellenos\n",
    "print(flash_time_padded)\n",
    "print(flash_total_pe_padded)\n",
    "print(flash_tpc_padded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertir a arrays de NumPy\n",
    "flash_time_np = ak.to_numpy(flash_time_padded)\n",
    "flash_total_pe_np = ak.to_numpy(flash_total_pe_padded)\n",
    "flash_tpc_np = ak.to_numpy(flash_tpc_padded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Replace '--' with 0 in your NumPy array\n",
    "flash_time_array = np.array(flash_time_np, dtype=object)  # Assuming `flash_time_np` contains the data\n",
    "flash_time_array = flash_time_array.astype(float)  # Convert to float\n",
    "flash_time_array = np.nan_to_num(flash_time_array, nan=0.0)\n",
    "\n",
    "flash_total_pe_array = np.array(flash_total_pe_np, dtype=object)  # Assuming `flash_time_np` contains the data\n",
    "flash_total_pe_array = flash_total_pe_array.astype(float)  # Convert to float\n",
    "flash_total_pe_array = np.nan_to_num(flash_total_pe_array, nan=0.0)\n",
    "\n",
    "flash_tpc_array = np.array(flash_tpc_np, dtype=object)  # Assuming `flash_time_np` contains the data\n",
    "flash_tpc_array = flash_tpc_array.astype(float)  # Convert to float\n",
    "flash_tpc_array = np.nan_to_num(flash_tpc_array, nan=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29007, 4)\n",
      "(29007, 4)\n",
      "(29007, 4)\n"
     ]
    }
   ],
   "source": [
    "print(flash_time_array.shape)\n",
    "print(flash_total_pe_array.shape)\n",
    "print(flash_tpc_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29007, 4, 3)\n"
     ]
    }
   ],
   "source": [
    "X = np.stack((flash_time_array, flash_total_pe_array,flash_tpc_array), axis=2)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8702\n",
      "X_train dim.: (20305, 4, 3)\n",
      "X_test dim.: (8702, 4, 3)\n",
      "y_train dim.: (20305, 1)\n",
      "y_test dim.: (8702, 1)\n"
     ]
    }
   ],
   "source": [
    "test_size = int(np.floor(0.30*np.shape(y)[0]) )\n",
    "print(test_size)\n",
    "\n",
    "X_train, X_test = X[:-test_size], X[-test_size:]\n",
    "print('X_train dim.:',X_train.shape)\n",
    "print('X_test dim.:', X_test.shape)\n",
    "y_train, y_test = y[:-test_size], y[-test_size:]\n",
    "print('y_train dim.:',y_train.shape)\n",
    "print('y_test dim.:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model LSTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " masking_2 (Masking)         (None, 4, 3)              0         \n",
      "                                                                 \n",
      " lstm_4 (LSTM)               (None, 4, 10)             560       \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 10)                840       \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 10)               40        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 10)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 20)                220       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,681\n",
      "Trainable params: 1,661\n",
      "Non-trainable params: 20\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Masking, LSTM, Dense, Flatten, BatchNormalization, Dropout\n",
    "\n",
    "model = Sequential([\n",
    "    Masking(mask_value=0, input_shape=(4, 3)),\n",
    "    LSTM(10, return_sequences=True),  # LSTM con secuencias intermedias\n",
    "    LSTM(10, return_sequences=False),  # LSTM final\n",
    "    BatchNormalization(),  # Normalización para estabilizar el entrenamiento\n",
    "    Dropout(0.2),  # Dropout para prevenir overfitting\n",
    "    Dense(20, activation='relu'),  # Capa densa intermedia\n",
    "    Dense(1)  # Capa de salida escalar\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Fully Connected**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 12)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1664      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,033\n",
      "Trainable params: 12,033\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Input\n",
    "\n",
    "# Dimensiones de entrada\n",
    "n_samples = 29007  # Número de eventos\n",
    "n_timesteps = 4    # Número de elementos por evento\n",
    "n_features = 3     # Número de características por elemento\n",
    "\n",
    "# Arquitectura del modelo\n",
    "model = Sequential([\n",
    "    # Capa de entrada: Aplanado de (4, 3) a 12\n",
    "    Input(shape=(n_timesteps, n_features)),\n",
    "    Flatten(),\n",
    "    # Capas densas\n",
    "    Dense(128, activation='relu'),  # Primera capa oculta con 128 neuronas\n",
    "    Dense(64, activation='relu'),   # Segunda capa oculta con 64 neuronas\n",
    "    Dense(32, activation='relu'),   # Tercera capa oculta con 32 neuronas\n",
    "    \n",
    "    # Capa de salida\n",
    "    Dense(1, activation='linear')  # Salida escalar para regresión\n",
    "])\n",
    "\n",
    "# Compilación del modelo\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "\n",
    "# Resumen del modelo\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "635/635 [==============================] - 3s 4ms/step - loss: 231142976389120.0000 - mae: 1759036.8750 - val_loss: 2486942.5000 - val_mae: 1006.3854 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 3441102.0000 - mae: 1225.6584 - val_loss: 2079372.3750 - val_mae: 989.8456 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 3401015.5000 - mae: 1294.5447 - val_loss: 2422590.5000 - val_mae: 1137.1331 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 484632416.0000 - mae: 8115.1992 - val_loss: 208740704.0000 - val_mae: 5215.8433 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 5041298079744.0000 - mae: 673148.5625 - val_loss: 203182440448.0000 - val_mae: 143657.5156 - lr: 0.0010\n",
      "Epoch 6/30\n",
      "620/635 [============================>.] - ETA: 0s - loss: 33312137216.0000 - mae: 39637.6797\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 32549382144.0000 - mae: 38765.7539 - val_loss: 3576070.5000 - val_mae: 1438.6819 - lr: 0.0010\n",
      "Epoch 7/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 3136820.0000 - mae: 1267.0812 - val_loss: 2444003.5000 - val_mae: 1155.6960 - lr: 5.0000e-04\n",
      "Epoch 8/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 4377662.5000 - mae: 1468.1661 - val_loss: 2750105.2500 - val_mae: 1200.8053 - lr: 5.0000e-04\n",
      "Epoch 9/30\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 30079424.0000 - mae: 2921.5051 - val_loss: 29824632.0000 - val_mae: 3003.3601 - lr: 5.0000e-04\n",
      "Epoch 10/30\n",
      "625/635 [============================>.] - ETA: 0s - loss: 111515435008.0000 - mae: 77631.1328\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "635/635 [==============================] - 2s 3ms/step - loss: 145470930944.0000 - mae: 91366.6484 - val_loss: 1368499552256.0000 - val_mae: 717272.3750 - lr: 5.0000e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f20e64d44f0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "# Define the callbacks\n",
    "weights_file = \"/tmp/weights_nuvT.hdf5.keras\"\n",
    "\n",
    "checkpoint = ModelCheckpoint(\n",
    "weights_file, monitor='val_loss', verbose=0, save_best_only=True, mode='min'\n",
    ")\n",
    "early_stopping = EarlyStopping(\n",
    "monitor='val_loss', patience=8, restore_best_weights=True\n",
    ")\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "monitor='val_loss', factor=0.5, patience=4, min_lr=1e-6, verbose=1\n",
    ")\n",
    "\n",
    "# Add all callbacks to the list\n",
    "callbacks = [checkpoint, early_stopping, reduce_lr]\n",
    "\n",
    "\n",
    "model.fit(X_train, \n",
    "        y_train, \n",
    "        epochs=30, \n",
    "        batch_size=32, \n",
    "        callbacks=callbacks,\n",
    "        validation_data=(X_test, y_test),\n",
    "        verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[ 207.49306]\n",
      " [1035.993  ]\n",
      " [ 797.1255 ]\n",
      " ...\n",
      " [2477.9932 ]\n",
      " [2428.4932 ]\n",
      " [ 349.75092]]\n",
      "(8702, 1)\n"
     ]
    }
   ],
   "source": [
    "# Load the best weights\n",
    "model.load_weights(weights_file)\n",
    "\n",
    "# Predict on new data (X_test in this example)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Predictions:\", y_pred)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=(16,9))\n",
    "\n",
    "axs[0].scatter(y_test[:,0], y_pred[:,0], color=\"black\",s=5)\n",
    "axs[0].grid()\n",
    "\n",
    "#axs[0].plot([0,20],[0,20],color=\"r\")\n",
    "axs[0].title.set_text('nuvT')\n",
    "#axs[0].set_xlim([0,20])\n",
    "#axs[0].set_ylim([0,20])\n",
    "axs[0].set_ylabel('Predicted [ns]')\n",
    "axs[0].set_xlabel('Real [ns]')\n",
    "\n",
    "im0 = axs[1].hist2d(y_test[:,0], y_pred[:,0].flatten(), bins=(100, 300), cmap='viridis', cmax=30)\n",
    "fig.colorbar(im0[3], ax=axs[1])\n",
    "axs[1].grid()\n",
    "\n",
    "#axs[1].plot([0,20],[0,20],'r')\n",
    "#axs[1].set_xlim([0,20])\n",
    "#axs[1].set_ylim([0,20])\n",
    "axs[1].set_ylabel('Predicted [ns]')\n",
    "axs[1].set_xlabel('Real [ns]')\n",
    "axs[1].set_facecolor('#440154')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT_TF",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}