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Explicitly set vos_baseurl
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‎tutorials/notebooks/vo/conesearch.ipynb‎

Lines changed: 40 additions & 62 deletions
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@@ -37,9 +37,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Python standard library\n",
@@ -64,6 +62,25 @@
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you are running an older version of `astroquery`, you might need to set `vos_baseurl` yourself, as follows."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from astropy.utils import minversion\n",
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"\n",
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"if not minversion(astroquery, '0.3.10'):\n",
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" conf.vos_baseurl = 'http://astropy.stsci.edu/aux/vo_databases/'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -143,9 +160,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"result_tab.write('my_result.tex', format='ascii.latex')"
@@ -173,9 +188,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Due to the unpredictability of external services,\n",
@@ -192,9 +205,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Don't run this cell if column names above are invalid.\n",
@@ -219,9 +230,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"my_db = vos_catalog.get_remote_catalog_db(conf.conesearch_dbname)\n",
@@ -239,9 +248,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"result = conesearch.conesearch(\n",
@@ -251,9 +258,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Number of rows is', result.nrows)"
@@ -276,9 +281,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"data_array = result.array.data\n",
@@ -288,9 +291,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"col_names = data_array.dtype.names\n",
@@ -300,9 +301,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"distance = data_array['_r']\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"distance_field = result.get_field_by_id('_r')\n",
@@ -334,9 +331,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"sorted_distance = distance[sorted_indices]\n",
@@ -357,9 +352,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with warnings.catch_warnings():\n",
@@ -370,9 +363,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"for url, tab in all_results.items():\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"i220keys = [k for k in all_results if 'I/220' in k] # pick out the first one with \"I/220\" in it\n",
@@ -410,9 +399,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"async_search = conesearch.AsyncConeSearch(\n",
@@ -431,7 +418,6 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"scrolled": true
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},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with warnings.catch_warnings():\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Predicted run time is', t_est, 'seconds')\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"t_real, tab = conesearch.conesearch_timer(\n",
@@ -515,9 +495,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Actual run time is', t_real, 'seconds')\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

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