{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08d348cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from aiRNN import preprocessors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb069134",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          true_x    true_y    true_z    pred_x    pred_y    pred_z\n",
       "0      -4.071363 -0.107142  0.009901 -4.071313 -0.107099  0.009824\n",
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       "...          ...       ...       ...       ...       ...       ...\n",
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       "\n",
       "[476160 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.read_csv(\"predictions_LSTM_wu900_ps1600_aw0.001.csv\")\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42444041",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must have equal len keys and value when setting with an ndarray",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mpredictions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrue_lat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrue_lon\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrue_alt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m = np.array(preprocessors.denorm_coords(\n\u001b[32m      2\u001b[39m     predictions[\u001b[33m\"\u001b[39m\u001b[33mtrue_x\u001b[39m\u001b[33m\"\u001b[39m], predictions[\u001b[33m\"\u001b[39m\u001b[33mtrue_y\u001b[39m\u001b[33m\"\u001b[39m], predictions[\u001b[33m\"\u001b[39m\u001b[33mtrue_z\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m      3\u001b[39m ))\n\u001b[32m      4\u001b[39m predictions.loc[:, [\u001b[33m\"\u001b[39m\u001b[33mpred_lat\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpred_lon\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpred_alt\u001b[39m\u001b[33m\"\u001b[39m]] = np.array(preprocessors.denorm_coords(\n\u001b[32m      5\u001b[39m     predictions[\u001b[33m\"\u001b[39m\u001b[33mpred_x\u001b[39m\u001b[33m\"\u001b[39m], predictions[\u001b[33m\"\u001b[39m\u001b[33mpred_y\u001b[39m\u001b[33m\"\u001b[39m], predictions[\u001b[33m\"\u001b[39m\u001b[33mpred_z\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m      6\u001b[39m ))\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/pandas/core/indexing.py:912\u001b[39m, in \u001b[36m_LocationIndexer.__setitem__\u001b[39m\u001b[34m(self, key, value)\u001b[39m\n\u001b[32m    909\u001b[39m \u001b[38;5;28mself\u001b[39m._has_valid_setitem_indexer(key)\n\u001b[32m    911\u001b[39m iloc = \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.name == \u001b[33m\"\u001b[39m\u001b[33miloc\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj.iloc\n\u001b[32m--> \u001b[39m\u001b[32m912\u001b[39m \u001b[43miloc\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_setitem_with_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/pandas/core/indexing.py:1943\u001b[39m, in \u001b[36m_iLocIndexer._setitem_with_indexer\u001b[39m\u001b[34m(self, indexer, value, name)\u001b[39m\n\u001b[32m   1940\u001b[39m \u001b[38;5;66;03m# align and set the values\u001b[39;00m\n\u001b[32m   1941\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m take_split_path:\n\u001b[32m   1942\u001b[39m     \u001b[38;5;66;03m# We have to operate column-wise\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1943\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setitem_with_indexer_split_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1944\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1945\u001b[39m     \u001b[38;5;28mself\u001b[39m._setitem_single_block(indexer, value, name)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/pandas/core/indexing.py:1983\u001b[39m, in \u001b[36m_iLocIndexer._setitem_with_indexer_split_path\u001b[39m\u001b[34m(self, indexer, value, name)\u001b[39m\n\u001b[32m   1978\u001b[39m     \u001b[38;5;28mself\u001b[39m._setitem_with_indexer_frame_value(indexer, value, name)\n\u001b[32m   1980\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m np.ndim(value) == \u001b[32m2\u001b[39m:\n\u001b[32m   1981\u001b[39m     \u001b[38;5;66;03m# TODO: avoid np.ndim call in case it isn't an ndarray, since\u001b[39;00m\n\u001b[32m   1982\u001b[39m     \u001b[38;5;66;03m#  that will construct an ndarray, which will be wasteful\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1983\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setitem_with_indexer_2d_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1985\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ilocs) == \u001b[32m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m lplane_indexer == \u001b[38;5;28mlen\u001b[39m(value) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(pi):\n\u001b[32m   1986\u001b[39m     \u001b[38;5;66;03m# We are setting multiple rows in a single column.\u001b[39;00m\n\u001b[32m   1987\u001b[39m     \u001b[38;5;28mself\u001b[39m._setitem_single_column(ilocs[\u001b[32m0\u001b[39m], value, pi)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/pandas/core/indexing.py:2049\u001b[39m, in \u001b[36m_iLocIndexer._setitem_with_indexer_2d_value\u001b[39m\u001b[34m(self, indexer, value)\u001b[39m\n\u001b[32m   2047\u001b[39m     value = np.array(value, dtype=\u001b[38;5;28mobject\u001b[39m)\n\u001b[32m   2048\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ilocs) != value.shape[\u001b[32m1\u001b[39m]:\n\u001b[32m-> \u001b[39m\u001b[32m2049\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   2050\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mMust have equal len keys and value when setting with an ndarray\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   2051\u001b[39m     )\n\u001b[32m   2053\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, loc \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(ilocs):\n\u001b[32m   2054\u001b[39m     value_col = value[:, i]\n",
      "\u001b[31mValueError\u001b[39m: Must have equal len keys and value when setting with an ndarray"
     ]
    }
   ],
   "source": [
    "predictions.loc[:, [\"true_lat\", \"true_lon\", \"true_alt\"]] = np.array(preprocessors.denorm_coords(\n",
    "    predictions[\"true_x\"], predictions[\"true_y\"], predictions[\"true_z\"]\n",
    "))\n",
    "predictions.loc[:, [\"pred_lat\", \"pred_lon\", \"pred_alt\"]] = np.array(preprocessors.denorm_coords(\n",
    "    predictions[\"pred_x\"], predictions[\"pred_y\"], predictions[\"pred_z\"]\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb83932e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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