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jbwhit/Catalogue_parse.ipynb

Created April 24, 2013 02:24
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Catalogue_parse.ipynb
{
"metadata": {
"name": "2013-04-24"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import asciitable\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rebecca's list\n",
"#L = asciitable.read('/Users/rebeccaallen/Zfourge/cosmos/v2.1/Clusters/cats/finalsampleall.cat')\n",
"final_sample = asciitable.read('finalsampleall.cat')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Messing around seeing what works\n",
"#for sample in final_sample:\n",
"# print sample['ra'], sample['dec'], sample['id']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#FI = asciitable.read('/Users/rebeccaallen/Zfourge/cosmos/v2.1/Clusters/cats/COS.dat')\n",
"big_catalogue = asciitable.read('COS.dat')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cat_ra = big_catalogue['col1']\n",
"cat_dec = big_catalogue['col2']\n",
"cat_id = big_catalogue['col3']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print cat_ra[0], cat_dec[0], cat_id[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"150.056808 2.173421 1\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rebecca\n",
"# for i in range(len(ra)):\n",
"# FI = asciitable.read('/Users/rebeccaallen/Zfourge/cosmos/v2.1/Clusters/cats/COS.dat')\n",
"# x = FI['col1']\n",
"# y = FI['col2']\n",
"# ide = FI['col3']\n",
"# dx = np.subtract(ra[i], x)\n",
"# for j in range(len(dec)):\n",
"# dy = np.subtract(dec[j], y)\n",
"# r = np.sqrt((dx)**2+(dy)**2)\n",
"# print np.argmin(r), r[np.argmin(r)]\n",
"# break\n",
"# table = {'r': r}\n",
"# asciitable.write(table, 'test.dat')\n",
"# break\n",
"# #Z = np.sort(table, order='r')\n",
"# for lineNumber, line in enumerate (table):\n",
"# # enumerate returns numbers starting with 0.\n",
"# if lineNumber == 0: continue\n",
"# if lineNumber > 1: break\n",
"# print line\n",
"# #asciitable.write(line, 'COSb.dat')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"for sample in final_sample:\n",
" dx = sample['ra'] - cat_ra\n",
" dy = sample['dec'] - cat_dec\n",
" r = np.sqrt(dx**2 + dy**2)\n",
" min_index = np.argmin(r)\n",
" print sample['ra'], sample['dec'], cat_ra[min_index], cat_dec[min_index], cat_id[min_index]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
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{
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],
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},
{
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"input": [],
"language": "python",
"metadata": {},
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{
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]
}
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