741 lines
26 KiB
Plaintext
741 lines
26 KiB
Plaintext
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"import pandas as pd\n",
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||
"<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>true_x</th>\n",
|
||
" <th>true_y</th>\n",
|
||
" <th>true_z</th>\n",
|
||
" <th>pred_x</th>\n",
|
||
" <th>pred_y</th>\n",
|
||
" <th>pred_z</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>-4.071363</td>\n",
|
||
" <td>-0.107142</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071313</td>\n",
|
||
" <td>-0.107099</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>-4.071363</td>\n",
|
||
" <td>-0.107142</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071314</td>\n",
|
||
" <td>-0.107100</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>-4.071362</td>\n",
|
||
" <td>-0.107142</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071313</td>\n",
|
||
" <td>-0.107100</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>-4.071362</td>\n",
|
||
" <td>-0.107142</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071314</td>\n",
|
||
" <td>-0.107098</td>\n",
|
||
" <td>0.009825</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>-4.071362</td>\n",
|
||
" <td>-0.107142</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071312</td>\n",
|
||
" <td>-0.107100</td>\n",
|
||
" <td>0.009825</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>476155</th>\n",
|
||
" <td>-4.070156</td>\n",
|
||
" <td>-0.107135</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071300</td>\n",
|
||
" <td>-0.107112</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>476156</th>\n",
|
||
" <td>-4.070156</td>\n",
|
||
" <td>-0.107135</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071300</td>\n",
|
||
" <td>-0.107112</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>476157</th>\n",
|
||
" <td>-4.070157</td>\n",
|
||
" <td>-0.107135</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071300</td>\n",
|
||
" <td>-0.107112</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>476158</th>\n",
|
||
" <td>-4.070158</td>\n",
|
||
" <td>-0.107135</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071300</td>\n",
|
||
" <td>-0.107112</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>476159</th>\n",
|
||
" <td>-4.070158</td>\n",
|
||
" <td>-0.107135</td>\n",
|
||
" <td>0.009901</td>\n",
|
||
" <td>-4.071300</td>\n",
|
||
" <td>-0.107112</td>\n",
|
||
" <td>0.009824</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>476160 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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",
|
||
"1 -4.071363 -0.107142 0.009901 -4.071314 -0.107100 0.009824\n",
|
||
"2 -4.071362 -0.107142 0.009901 -4.071313 -0.107100 0.009824\n",
|
||
"3 -4.071362 -0.107142 0.009901 -4.071314 -0.107098 0.009825\n",
|
||
"4 -4.071362 -0.107142 0.009901 -4.071312 -0.107100 0.009825\n",
|
||
"... ... ... ... ... ... ...\n",
|
||
"476155 -4.070156 -0.107135 0.009901 -4.071300 -0.107112 0.009824\n",
|
||
"476156 -4.070156 -0.107135 0.009901 -4.071300 -0.107112 0.009824\n",
|
||
"476157 -4.070157 -0.107135 0.009901 -4.071300 -0.107112 0.009824\n",
|
||
"476158 -4.070158 -0.107135 0.009901 -4.071300 -0.107112 0.009824\n",
|
||
"476159 -4.070158 -0.107135 0.009901 -4.071300 -0.107112 0.009824\n",
|
||
"\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": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "adsbpy",
|
||
"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": "3.13.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|