Conversion from Keplerian elements to attributable elements
Vanessa Vichi
7 months ago
parent
66775f772a
commit
2077014614
@ -0,0 +1,675 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c2e5ae80-979a-454d-b87b-667ad732d63b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from numpy import linalg as LA\n",
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"import pandas as pd\n",
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"import math\n",
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"import scipy.optimize as optimize\n",
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"import spiceypy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "19159865-290b-4ad6-ad47-3a34d79dbb85",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting poliastro\n",
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" Using cached poliastro-0.17.0-py3-none-any.whl.metadata (11 kB)\n",
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"Requirement already satisfied: astropy<6,>=5.0 in ./miniconda3/envs/py39/lib/python3.9/site-packages (from poliastro) (5.3.4)\n",
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"Collecting astroquery>=0.3.9 (from poliastro)\n",
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" Using cached astroquery-0.4.7-py3-none-any.whl.metadata (7.2 kB)\n",
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"Collecting keyring>=15.0 (from astroquery>=0.3.9->poliastro)\n",
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" Using cached keyring-25.2.0-py3-none-any.whl.metadata (20 kB)\n",
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"Collecting pyvo>=1.1 (from astroquery>=0.3.9->poliastro)\n",
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"Requirement already satisfied: cryptography>=2.0 in ./miniconda3/envs/py39/lib/python3.9/site-packages (from SecretStorage>=3.2->keyring>=15.0->astroquery>=0.3.9->poliastro) (42.0.5)\n",
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"Requirement already satisfied: backports.tarfile in ./miniconda3/envs/py39/lib/python3.9/site-packages (from jaraco.context->keyring>=15.0->astroquery>=0.3.9->poliastro) (1.1.1)\n",
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"Requirement already satisfied: cffi>=1.12 in ./miniconda3/envs/py39/lib/python3.9/site-packages (from cryptography>=2.0->SecretStorage>=3.2->keyring>=15.0->astroquery>=0.3.9->poliastro) (1.16.0)\n",
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"Requirement already satisfied: pycparser in ./miniconda3/envs/py39/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=2.0->SecretStorage>=3.2->keyring>=15.0->astroquery>=0.3.9->poliastro) (2.21)\n",
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"Using cached poliastro-0.17.0-py3-none-any.whl (162 kB)\n",
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"Using cached astroquery-0.4.7-py3-none-any.whl (5.3 MB)\n",
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"Using cached keyring-25.2.0-py3-none-any.whl (38 kB)\n",
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"Using cached pyvo-1.5.1-py3-none-any.whl (910 kB)\n",
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"Installing collected packages: pyvo, keyring, astroquery, poliastro\n",
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"Successfully installed astroquery-0.4.7 keyring-25.2.0 poliastro-0.17.0 pyvo-1.5.1\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install poliastro"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "eb4d9629-fbbe-42cc-bbfe-5fb11aff2302",
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"metadata": {},
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"outputs": [],
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"source": [
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"from astropy import units as u\n",
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"from astropy import constants as const\n",
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"from astropy.time import Time\n",
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"from astropy.coordinates import get_body_barycentric_posvel\n",
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"from poliastro.bodies import Sun, Earth\n",
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"from poliastro.twobody import Orbit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "c5a3cf10-9e10-4d54-8c08-f8b5cac2b0de",
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"metadata": {},
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"outputs": [],
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"source": [
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"folder='/home/unipi/v.vichi3/Desktop/'\n",
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"df=pd.read_csv(folder+'neos_dataframe.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "51bac7c4-0fea-4dd9-b152-6ab13b5ed0b5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Unnamed: 0</th>\n",
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" <th>spkid</th>\n",
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" <th>pha</th>\n",
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" <th>H</th>\n",
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" <th>epoch_mjd</th>\n",
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" <th>e</th>\n",
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" <th>a</th>\n",
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" <th>i</th>\n",
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" <th>om</th>\n",
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" <th>w</th>\n",
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" <th>ma</th>\n",
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" <th>moid</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>20000433</td>\n",
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" <td>N</td>\n",
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" <td>10.41</td>\n",
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" <td>60400</td>\n",
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" <td>0.2227</td>\n",
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" <td>1.458</td>\n",
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" <td>10.83</td>\n",
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" <td>304.28</td>\n",
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" <td>178.90</td>\n",
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" <td>334.73</td>\n",
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" <td>0.1500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>20000719</td>\n",
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" <td>N</td>\n",
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" <td>15.59</td>\n",
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" <td>60400</td>\n",
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" <td>0.5469</td>\n",
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" <td>2.636</td>\n",
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" <td>11.58</td>\n",
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" <td>183.85</td>\n",
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" <td>156.22</td>\n",
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" <td>102.37</td>\n",
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" <td>0.2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>20000887</td>\n",
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" <td>N</td>\n",
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" <td>13.88</td>\n",
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" <td>60400</td>\n",
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" <td>0.5710</td>\n",
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" <td>2.472</td>\n",
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" <td>9.40</td>\n",
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" <td>110.42</td>\n",
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" <td>350.48</td>\n",
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" <td>289.48</td>\n",
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" <td>0.0803</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>20001036</td>\n",
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" <td>N</td>\n",
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" <td>9.26</td>\n",
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" <td>60400</td>\n",
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" <td>0.5328</td>\n",
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" <td>2.665</td>\n",
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" <td>26.69</td>\n",
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" <td>215.50</td>\n",
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" <td>132.48</td>\n",
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" <td>321.69</td>\n",
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" <td>0.3450</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>20001221</td>\n",
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" <td>N</td>\n",
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" <td>17.38</td>\n",
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" <td>60400</td>\n",
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" <td>0.4352</td>\n",
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" <td>1.920</td>\n",
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" <td>11.88</td>\n",
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" <td>171.31</td>\n",
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" <td>26.68</td>\n",
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" <td>197.64</td>\n",
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" <td>0.1080</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Unnamed: 0 spkid pha H epoch_mjd e a i om \\\n",
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"0 0 20000433 N 10.41 60400 0.2227 1.458 10.83 304.28 \n",
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"1 1 20000719 N 15.59 60400 0.5469 2.636 11.58 183.85 \n",
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"2 2 20000887 N 13.88 60400 0.5710 2.472 9.40 110.42 \n",
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"3 3 20001036 N 9.26 60400 0.5328 2.665 26.69 215.50 \n",
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"4 4 20001221 N 17.38 60400 0.4352 1.920 11.88 171.31 \n",
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"\n",
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" w ma moid \n",
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"0 178.90 334.73 0.1500 \n",
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"1 156.22 102.37 0.2010 \n",
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"2 350.48 289.48 0.0803 \n",
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"3 132.48 321.69 0.3450 \n",
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"4 26.68 197.64 0.1080 "
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "c68024ad-7917-401b-9f72-3ba3641a7bff",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Mean anomaly to true anomaly\n",
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"def kepler_equation(M,E,e):\n",
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" #M=mean anomaly, E=#eccentric anomaly, e=eccentricity\n",
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" return E-e*np.sin(E)-M\n",
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"def mean_to_true_an(M,e):\n",
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" E=optimize.newton(kepler_equation, M, args=(M,e))\n",
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" nu=math.atan2(math.sin(E)*math.sqrt(1-e**2),math.cos(E)-e) #true anomaly in radians\n",
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" return math.degrees(nu)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "f8f1dbef-9764-4732-9cfd-fb5cbebb6e5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Perform the conversion for each element of the DataFrame\n",
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"N=df.shape[0]\n",
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"nu=np.zeros((N,))\n",
|
||||
"for el in range(N):\n",
|
||||
" nu[el]=mean_to_true_an(df['ma'][el],df['e'][el])\n",
|
||||
"df['nu']=nu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"id": "8188f5ba-609c-45f9-89f1-549d9b71fb0c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Keplerian to Cartesian elements\n",
|
||||
"def kep_to_car(a,e,i,om,w,nu):\n",
|
||||
" orb=Orbit.from_classical(Sun, a<<u.AU, e<<u.one, i<<u.deg, om<<u.deg, w<<u.deg, nu<<u.deg)\n",
|
||||
" r=orb.r.to(u.AU)\n",
|
||||
" v=orb.v.to(u.AU/u.d)\n",
|
||||
" return r,v"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "12f68b4e-3682-4050-866d-836cb32ae0ea",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 completed\n",
|
||||
"10000 completed\n",
|
||||
"20000 completed\n",
|
||||
"30000 completed\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Perform the conversion for each element of the DataFrame\n",
|
||||
"car=np.zeros((N,6))\n",
|
||||
"for el in range(N):\n",
|
||||
" car[el,:3],car[el,3:]=kep_to_car(df['a'][el],df['e'][el],df['i'][el],df['om'][el],df['w'][el],df['nu'][el])\n",
|
||||
" if (el%10000==0):\n",
|
||||
" print(f\"{el} completed\")\n",
|
||||
"car_df=pd.DataFrame(car, columns=['x','y','z','vx','vy','vz'])\n",
|
||||
"car_df['EPOCH']=df['epoch_mjd']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "5a5f5a81-4b07-4a8a-9d47-39508a3b8525",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<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>x</th>\n",
|
||||
" <th>y</th>\n",
|
||||
" <th>z</th>\n",
|
||||
" <th>vx</th>\n",
|
||||
" <th>vy</th>\n",
|
||||
" <th>vz</th>\n",
|
||||
" <th>EPOCH</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>-1.043768</td>\n",
|
||||
" <td>-0.948369</td>\n",
|
||||
" <td>-0.267177</td>\n",
|
||||
" <td>0.007095</td>\n",
|
||||
" <td>-0.012608</td>\n",
|
||||
" <td>-0.000237</td>\n",
|
||||
" <td>60400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.009877</td>\n",
|
||||
" <td>2.220705</td>\n",
|
||||
" <td>-0.453875</td>\n",
|
||||
" <td>-0.010286</td>\n",
|
||||
" <td>0.006398</td>\n",
|
||||
" <td>-0.001450</td>\n",
|
||||
" <td>60400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>-0.590315</td>\n",
|
||||
" <td>0.914974</td>\n",
|
||||
" <td>0.038736</td>\n",
|
||||
" <td>-0.018393</td>\n",
|
||||
" <td>-0.008600</td>\n",
|
||||
" <td>0.003350</td>\n",
|
||||
" <td>60400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.888349</td>\n",
|
||||
" <td>1.323366</td>\n",
|
||||
" <td>-0.282285</td>\n",
|
||||
" <td>-0.008231</td>\n",
|
||||
" <td>0.011640</td>\n",
|
||||
" <td>-0.007167</td>\n",
|
||||
" <td>60400</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2.682621</td>\n",
|
||||
" <td>0.150702</td>\n",
|
||||
" <td>-0.116604</td>\n",
|
||||
" <td>0.000993</td>\n",
|
||||
" <td>0.007884</td>\n",
|
||||
" <td>-0.001671</td>\n",
|
||||
" <td>60400</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" x y z vx vy vz EPOCH\n",
|
||||
"0 -1.043768 -0.948369 -0.267177 0.007095 -0.012608 -0.000237 60400\n",
|
||||
"1 0.009877 2.220705 -0.453875 -0.010286 0.006398 -0.001450 60400\n",
|
||||
"2 -0.590315 0.914974 0.038736 -0.018393 -0.008600 0.003350 60400\n",
|
||||
"3 0.888349 1.323366 -0.282285 -0.008231 0.011640 -0.007167 60400\n",
|
||||
"4 2.682621 0.150702 -0.116604 0.000993 0.007884 -0.001671 60400"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"car_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "cc6906b5-f2f6-4855-814e-c49c01beb2d8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Cartesian to Attributable elements\n",
|
||||
"def car_to_att(r_sun, v_sun, epoch):\n",
|
||||
" #r_sun, v_sun are the Cartesian heliocentric elements of the asteroid\n",
|
||||
" #epoch is in MJD\n",
|
||||
" earth_posvel=get_body_barycentric_posvel('earth',Time(epoch,format='mjd'))\n",
|
||||
" r_earth=earth_posvel[0].xyz\n",
|
||||
" v_earth=earth_posvel[1].xyz\n",
|
||||
"\n",
|
||||
" #Cartesian geocentric elements\n",
|
||||
" r=r_sun-r_earth\n",
|
||||
" v=v_sun-v_earth\n",
|
||||
"\n",
|
||||
" #Rotation to the equatorial reference frame\n",
|
||||
" roteceq=spiceypy.spiceypy.pxform(\"ECLIPJ2000\",\"J2000\",0)\n",
|
||||
" r=roteceq.dot(r)\n",
|
||||
" v=roteceq.dot(v)\n",
|
||||
"\n",
|
||||
" rho=LA.norm(r)\n",
|
||||
" dz=r[0]**2+r[1]**2\n",
|
||||
" epsilon=np.finfo(float).eps\n",
|
||||
" if (dz.value<100*epsilon):\n",
|
||||
" ra=0\n",
|
||||
" else: ra=np.arctan2(r[1],r[0]) #in radians\n",
|
||||
" dec=np.arcsin(r[2]/rho)\n",
|
||||
"\n",
|
||||
" #Computation of first derivatives\n",
|
||||
" dadx=np.zeros((3,1))<<u.one/u.AU\n",
|
||||
" dddx=np.zeros((3,1))<<u.one/u.AU\n",
|
||||
" dadx[0]=-r[1]/dz\n",
|
||||
" dadx[1]=r[0]/dz\n",
|
||||
" dadx=dadx.reshape(1,-1) #transform dadx into a row vector\n",
|
||||
" dddx[0]=-r[2]*(r[0]/(np.sqrt(dz)*rho**2))\n",
|
||||
" dddx[1]=-r[2]*(r[1]/(np.sqrt(dz)*rho**2))\n",
|
||||
" dddx[2]=np.sqrt(dz)/rho**2\n",
|
||||
" dddx=dddx.reshape(1,-1)\n",
|
||||
"\n",
|
||||
" rho_dot=np.dot(r,v)/rho\n",
|
||||
" ra_dot=np.dot(dadx,v) #in 1/day\n",
|
||||
" dec_dot=np.dot(dddx,v) #in 1/day\n",
|
||||
" cos_ra=np.cos(ra)\n",
|
||||
" sin_ra=np.sin(ra)\n",
|
||||
" sin_dec=np.sin(dec)\n",
|
||||
" return [cos_ra.value,sin_ra.value,sin_dec.value, ra_dot[0].value, dec_dot[0].value]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "5b28583a-5474-418d-abdd-52713c7c03b2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 completed\n",
|
||||
"10000 completed\n",
|
||||
"20000 completed\n",
|
||||
"30000 completed\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Perform the conversion for each element of the DataFrame\n",
|
||||
"attr=np.zeros((N,5))\n",
|
||||
"for el in range(N):\n",
|
||||
" attr[el,:]=car_to_att(car_df.iloc[el,0:3]<<u.AU, car_df.iloc[el,3:6]<<u.AU/u.d, car_df['EPOCH'][el])\n",
|
||||
" if (el%10000==0):\n",
|
||||
" print(f\"{el} completed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "fabd950c-cd23-4fb8-8250-ec16dd2acc53",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"attr_df=pd.DataFrame(attr,columns=['cos_ra','sin_ra','sin_dec','ra_dot','dec_dot'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "b4e531f2-46da-4470-854a-d49fad2675c7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<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>cos_ra</th>\n",
|
||||
" <th>sin_ra</th>\n",
|
||||
" <th>sin_dec</th>\n",
|
||||
" <th>ra_dot</th>\n",
|
||||
" <th>dec_dot</th>\n",
|
||||
" <th>MOID</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>-0.084129</td>\n",
|
||||
" <td>-0.996455</td>\n",
|
||||
" <td>-0.606347</td>\n",
|
||||
" <td>0.006616</td>\n",
|
||||
" <td>0.006707</td>\n",
|
||||
" <td>0.1500</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.392310</td>\n",
|
||||
" <td>0.919833</td>\n",
|
||||
" <td>0.229663</td>\n",
|
||||
" <td>0.007516</td>\n",
|
||||
" <td>0.004055</td>\n",
|
||||
" <td>0.2010</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0.387645</td>\n",
|
||||
" <td>0.921809</td>\n",
|
||||
" <td>0.459919</td>\n",
|
||||
" <td>0.019881</td>\n",
|
||||
" <td>0.011605</td>\n",
|
||||
" <td>0.0803</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.791026</td>\n",
|
||||
" <td>0.611783</td>\n",
|
||||
" <td>0.166871</td>\n",
|
||||
" <td>0.011227</td>\n",
|
||||
" <td>0.003821</td>\n",
|
||||
" <td>0.3450</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.996437</td>\n",
|
||||
" <td>0.084345</td>\n",
|
||||
" <td>0.023712</td>\n",
|
||||
" <td>0.005317</td>\n",
|
||||
" <td>0.003798</td>\n",
|
||||
" <td>0.1080</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" cos_ra sin_ra sin_dec ra_dot dec_dot MOID\n",
|
||||
"0 -0.084129 -0.996455 -0.606347 0.006616 0.006707 0.1500\n",
|
||||
"1 0.392310 0.919833 0.229663 0.007516 0.004055 0.2010\n",
|
||||
"2 0.387645 0.921809 0.459919 0.019881 0.011605 0.0803\n",
|
||||
"3 0.791026 0.611783 0.166871 0.011227 0.003821 0.3450\n",
|
||||
"4 0.996437 0.084345 0.023712 0.005317 0.003798 0.1080"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"attr_df['MOID']=df['moid']\n",
|
||||
"attr_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "f65e0163-c9d8-492a-9095-0d7d34ffad5b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"attr_df.to_csv(folder+'neos_attr.csv',index=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.19"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue