diff --git a/neos_dataset_preprocessing.ipynb b/neos_dataset_preprocessing.ipynb
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+++ b/neos_dataset_preprocessing.ipynb
@@ -0,0 +1,1044 @@
+{
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+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b97688d5-dbd8-451e-924b-902649ef3712",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import math"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "838b4878-cb24-4330-ac58-48de11ee1372",
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+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "folder='/home/unipi/v.vichi3/Desktop/'\n",
+ "df=pd.read_csv(folder+'sbdb_query_results.csv')\n",
+ "df.head()"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "7f165f80-3253-49b0-b43c-a8722f60e57b",
+ "metadata": {},
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+ "data": {
+ "text/plain": [
+ "(34901, 11)"
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+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "df.shape"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c41deece-a459-4ef3-8c83-0f87756dac92",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['spkid', 'pha', 'H', 'epoch_mjd', 'e', 'a', 'i', 'om', 'w', 'ma',\n",
+ " 'moid'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f91e7f7c-4d5a-44c8-b0ba-9c2b71a01839",
+ "metadata": {},
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 34901 entries, 0 to 34900\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 spkid 34901 non-null int64 \n",
+ " 1 pha 34875 non-null object \n",
+ " 2 H 34897 non-null float64\n",
+ " 3 epoch_mjd 34901 non-null int64 \n",
+ " 4 e 34901 non-null float64\n",
+ " 5 a 34901 non-null float64\n",
+ " 6 i 34901 non-null float64\n",
+ " 7 om 34901 non-null float64\n",
+ " 8 w 34901 non-null float64\n",
+ " 9 ma 34901 non-null float64\n",
+ " 10 moid 34876 non-null float64\n",
+ "dtypes: float64(8), int64(2), object(1)\n",
+ "memory usage: 2.9+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "eb543294-e46b-431e-862a-a0244cadd9fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(34876, 11)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Eliminate the null values\n",
+ "df.drop(df[df['moid'].isna()].index,inplace=True)\n",
+ "df.reset_index(drop=True,inplace=True)\n",
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "e4ad2c21-bd10-4714-9d9a-aeb4ed639b36",
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+ "min 3.001703e+06 9.260000 44221.000000 0.002800 0.461700 \n",
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+ "metadata": {},
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+ " spkid pha H epoch_mjd e a i om \\\n",
+ "3510 3024715 Y 17.69 60400 0.9480 17.800 19.67 48.69 \n",
+ "6483 3434371 N 24.11 54767 0.9060 7.315 32.39 213.81 \n",
+ "11522 3683246 N 20.00 60400 0.9407 21.440 93.63 338.57 \n",
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+ "20682 3843840 N 18.17 58729 0.9789 59.680 159.03 187.95 \n",
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+ "28090 54240416 N 18.61 60400 0.8883 9.983 4.72 77.69 \n",
+ "\n",
+ " w ma moid \n",
+ "3510 333.32 117.04 0.0111 \n",
+ "6483 263.82 357.31 0.1090 \n",
+ "11522 311.75 35.25 0.4590 \n",
+ "15455 77.94 360.00 0.5970 \n",
+ "17114 151.26 0.39 0.3330 \n",
+ "19835 193.09 1.90 0.1010 \n",
+ "20634 57.12 114.87 0.2340 \n",
+ "20682 176.27 0.09 0.2580 \n",
+ "20698 75.55 122.88 0.3770 \n",
+ "20742 57.16 114.83 0.2330 \n",
+ "22110 57.80 66.15 0.0817 \n",
+ "28090 298.05 26.08 0.1390 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[df['a']>5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "11f926f6-ee7c-47f1-82ae-1c5299e219f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(34864, 11)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#We eliminate the values with a>5\n",
+ "df.drop(df[df['a']>5].index,inplace=True)\n",
+ "df.reset_index(drop=True,inplace=True)\n",
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d1203169-ac0c-4d03-9dac-ebd136cd7680",
+ "metadata": {},
+ "outputs": [
+ {
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+ " w | \n",
+ " ma | \n",
+ " moid | \n",
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+ " 34864.000000 | \n",
+ " 34864.000000 | \n",
+ " 34864.000000 | \n",
+ " 34864.000000 | \n",
+ " 34864.000000 | \n",
+ " 3.486400e+04 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 2.360419e+07 | \n",
+ " 23.487377 | \n",
+ " 59701.525298 | \n",
+ " 0.437086 | \n",
+ " 1.744036 | \n",
+ " 11.983268 | \n",
+ " 171.907288 | \n",
+ " 182.575511 | \n",
+ " 171.776436 | \n",
+ " 8.546965e-02 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 2.362906e+07 | \n",
+ " 2.893991 | \n",
+ " 1572.100889 | \n",
+ " 0.176795 | \n",
+ " 0.556275 | \n",
+ " 10.567228 | \n",
+ " 103.655813 | \n",
+ " 104.304053 | \n",
+ " 122.267979 | \n",
+ " 9.826509e-02 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 3.001703e+06 | \n",
+ " 9.260000 | \n",
+ " 44221.000000 | \n",
+ " 0.002800 | \n",
+ " 0.461700 | \n",
+ " 0.010000 | \n",
+ " 0.010000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 4.540000e-07 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 3.701855e+06 | \n",
+ " 21.330000 | \n",
+ " 59976.000000 | \n",
+ " 0.304400 | \n",
+ " 1.294000 | \n",
+ " 4.420000 | \n",
+ " 80.377500 | \n",
+ " 93.367500 | \n",
+ " 49.525000 | \n",
+ " 1.280000e-02 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 3.843818e+06 | \n",
+ " 23.800000 | \n",
+ " 60400.000000 | \n",
+ " 0.451600 | \n",
+ " 1.692000 | \n",
+ " 8.490000 | \n",
+ " 171.650000 | \n",
+ " 184.545000 | \n",
+ " 164.730000 | \n",
+ " 4.520000e-02 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 5.416727e+07 | \n",
+ " 25.600000 | \n",
+ " 60400.000000 | \n",
+ " 0.564900 | \n",
+ " 2.171000 | \n",
+ " 16.802500 | \n",
+ " 252.400000 | \n",
+ " 272.662500 | \n",
+ " 291.915000 | \n",
+ " 1.280000e-01 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 5.443990e+07 | \n",
+ " 33.580000 | \n",
+ " 60435.000000 | \n",
+ " 0.970300 | \n",
+ " 4.816000 | \n",
+ " 154.350000 | \n",
+ " 359.980000 | \n",
+ " 359.960000 | \n",
+ " 360.000000 | \n",
+ " 7.080000e-01 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " spkid H epoch_mjd e a \\\n",
+ "count 3.486400e+04 34860.000000 34864.000000 34864.000000 34864.000000 \n",
+ "mean 2.360419e+07 23.487377 59701.525298 0.437086 1.744036 \n",
+ "std 2.362906e+07 2.893991 1572.100889 0.176795 0.556275 \n",
+ "min 3.001703e+06 9.260000 44221.000000 0.002800 0.461700 \n",
+ "25% 3.701855e+06 21.330000 59976.000000 0.304400 1.294000 \n",
+ "50% 3.843818e+06 23.800000 60400.000000 0.451600 1.692000 \n",
+ "75% 5.416727e+07 25.600000 60400.000000 0.564900 2.171000 \n",
+ "max 5.443990e+07 33.580000 60435.000000 0.970300 4.816000 \n",
+ "\n",
+ " i om w ma moid \n",
+ "count 34864.000000 34864.000000 34864.000000 34864.000000 3.486400e+04 \n",
+ "mean 11.983268 171.907288 182.575511 171.776436 8.546965e-02 \n",
+ "std 10.567228 103.655813 104.304053 122.267979 9.826509e-02 \n",
+ "min 0.010000 0.010000 0.000000 0.000000 4.540000e-07 \n",
+ "25% 4.420000 80.377500 93.367500 49.525000 1.280000e-02 \n",
+ "50% 8.490000 171.650000 184.545000 164.730000 4.520000e-02 \n",
+ "75% 16.802500 252.400000 272.662500 291.915000 1.280000e-01 \n",
+ "max 154.350000 359.980000 359.960000 360.000000 7.080000e-01 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "17be8338-6c23-4cc9-ae08-d1ab7f705a79",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of PHAs: 2421\n",
+ "Percentage of PHAs: 6.944125745754933\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Count the number of PHAs in the dataset\n",
+ "print(\"Number of PHAs:\", np.count_nonzero(df['pha']=='Y'))\n",
+ "print(\"Percentage of PHAs:\", 100*np.count_nonzero(df['pha']=='Y')/df.shape[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "cafcc865-5058-4a84-932b-8dcf95a5dcd4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of asteroids with MOID=0: 0\n",
+ "Number of asteroids with MOID < 0.05: 18275\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Number of asteroids with MOID=0 and MOID <0.05\n",
+ "print(\"Number of asteroids with MOID=0:\", len(df[df['moid']==0.0]))\n",
+ "print(\"Number of asteroids with MOID < 0.05:\", len(df[df['moid']<0.05]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "cb2545a2-9961-4118-a2e3-94647adc4d85",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "