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Added Y3 plot notebooks that were located at NERSC #90

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Added Y3 plot notebooks that were located at NERSC #90

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375 changes: 375 additions & 0 deletions notebooks/y3/Untitled.ipynb
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224 changes: 224 additions & 0 deletions notebooks/y3/add-star-det-col.ipynb
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569 changes: 569 additions & 0 deletions notebooks/y3/ambiguous-matches.ipynb
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185 changes: 185 additions & 0 deletions notebooks/y3/appendix-tables.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import fitsio\n",
"from astropy.table import Table\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"gal_mag_file = 'tables/galaxy-response-deredden.csv'\n",
"gal_col_file = 'tables/galaxy-color-response-deredden.csv'\n",
"str_mag_file = 'tables/star-mag-response-deredden.csv'\n",
"str_col_file = 'tables/star-color-response-deredden.csv'\n",
"\n",
"gal_mag = Table(np.round(pd.read_csv(gal_mag_file).values, 3))\n",
"gal_col = Table(np.round(pd.read_csv(gal_col_file).values, 3))\n",
"str_mag = Table(np.round(pd.read_csv(str_mag_file).values, 3))\n",
"str_col = Table(np.round(pd.read_csv(str_col_file).values, 3))\n",
"\n",
"gal_mag.write(gal_mag_file.replace('.csv', '-rounded.csv'), overwrite=True)\n",
"gal_col.write(gal_col_file.replace('.csv', '-rounded.csv'), overwrite=True)\n",
"str_mag.write(str_mag_file.replace('.csv', '-rounded.csv'), overwrite=True)\n",
"str_col.write(str_col_file.replace('.csv', '-rounded.csv'), overwrite=True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<i>Table length=22</i>\n",
"<table id=\"table140115370245200\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>col0</th><th>col1</th><th>col2</th><th>col3</th><th>col4</th><th>col5</th><th>col6</th><th>col7</th><th>col8</th><th>col9</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>-0.2</td><td>0.081</td><td>0.053</td><td>0.211</td><td>0.079</td><td>0.043</td><td>0.216</td><td>0.092</td><td>0.047</td><td>0.239</td></tr>\n",
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"<tr><td>0.4</td><td>-0.015</td><td>-0.008</td><td>0.161</td><td>-0.003</td><td>-0.001</td><td>0.139</td><td>0.004</td><td>-0.0</td><td>0.141</td></tr>\n",
"<tr><td>0.5</td><td>-0.019</td><td>-0.009</td><td>0.158</td><td>-0.007</td><td>-0.003</td><td>0.14</td><td>0.001</td><td>-0.001</td><td>0.16</td></tr>\n",
"<tr><td>0.6</td><td>-0.024</td><td>-0.01</td><td>0.157</td><td>-0.012</td><td>-0.005</td><td>0.146</td><td>-0.004</td><td>-0.003</td><td>0.161</td></tr>\n",
"<tr><td>0.7</td><td>-0.028</td><td>-0.011</td><td>0.158</td><td>-0.015</td><td>-0.007</td><td>0.147</td><td>-0.009</td><td>-0.005</td><td>0.159</td></tr>\n",
"<tr><td>0.8</td><td>-0.031</td><td>-0.011</td><td>0.159</td><td>-0.018</td><td>-0.007</td><td>0.146</td><td>-0.012</td><td>-0.007</td><td>0.161</td></tr>\n",
"<tr><td>0.9</td><td>-0.036</td><td>-0.011</td><td>0.162</td><td>-0.022</td><td>-0.008</td><td>0.152</td><td>-0.016</td><td>-0.009</td><td>0.171</td></tr>\n",
"<tr><td>1.0</td><td>-0.041</td><td>-0.011</td><td>0.167</td><td>-0.026</td><td>-0.01</td><td>0.161</td><td>-0.019</td><td>-0.011</td><td>0.176</td></tr>\n",
"<tr><td>1.1</td><td>-0.046</td><td>-0.011</td><td>0.173</td><td>-0.029</td><td>-0.012</td><td>0.17</td><td>-0.031</td><td>-0.016</td><td>0.193</td></tr>\n",
"<tr><td>1.2</td><td>-0.051</td><td>-0.01</td><td>0.184</td><td>-0.035</td><td>-0.013</td><td>0.178</td><td>-0.053</td><td>-0.024</td><td>0.21</td></tr>\n",
"<tr><td>1.3</td><td>-0.059</td><td>-0.011</td><td>0.194</td><td>-0.071</td><td>-0.03</td><td>0.221</td><td>-0.049</td><td>-0.024</td><td>0.215</td></tr>\n",
"<tr><td>1.4</td><td>-0.069</td><td>-0.013</td><td>0.21</td><td>-0.149</td><td>-0.091</td><td>0.276</td><td>-0.054</td><td>-0.018</td><td>0.223</td></tr>\n",
"<tr><td>1.5</td><td>-0.074</td><td>-0.015</td><td>0.222</td><td>-0.171</td><td>-0.105</td><td>0.288</td><td>-0.076</td><td>-0.028</td><td>0.236</td></tr>\n",
"<tr><td>1.6</td><td>-0.07</td><td>-0.016</td><td>0.224</td><td>-0.183</td><td>-0.112</td><td>0.3</td><td>-0.075</td><td>-0.015</td><td>0.22</td></tr>\n",
"<tr><td>1.7</td><td>-0.066</td><td>-0.016</td><td>0.224</td><td>-0.206</td><td>-0.126</td><td>0.314</td><td>-0.05</td><td>-0.007</td><td>0.24</td></tr>\n",
"<tr><td>1.8</td><td>-0.096</td><td>-0.028</td><td>0.265</td><td>-0.206</td><td>-0.127</td><td>0.334</td><td>-0.063</td><td>-0.017</td><td>0.255</td></tr>\n",
"<tr><td>1.9</td><td>-0.193</td><td>-0.092</td><td>0.358</td><td>-0.221</td><td>-0.112</td><td>0.363</td><td>-0.061</td><td>-0.003</td><td>0.22</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table length=22>\n",
" col0 col1 col2 col3 col4 col5 col6 col7 col8 col9 \n",
"float64 float64 float64 float64 float64 float64 float64 float64 float64 float64\n",
"------- ------- ------- ------- ------- ------- ------- ------- ------- -------\n",
" -0.2 0.081 0.053 0.211 0.079 0.043 0.216 0.092 0.047 0.239\n",
" -0.1 0.047 0.03 0.192 0.053 0.032 0.201 0.062 0.03 0.213\n",
" 0.0 0.026 0.016 0.182 0.028 0.013 0.182 0.03 0.009 0.177\n",
" 0.1 0.012 0.006 0.179 0.011 0.002 0.155 0.019 0.004 0.163\n",
" 0.2 0.002 -0.002 0.178 0.004 -0.0 0.14 0.011 0.001 0.145\n",
" 0.3 -0.009 -0.006 0.169 0.001 -0.001 0.14 0.007 0.0 0.134\n",
" 0.4 -0.015 -0.008 0.161 -0.003 -0.001 0.139 0.004 -0.0 0.141\n",
" 0.5 -0.019 -0.009 0.158 -0.007 -0.003 0.14 0.001 -0.001 0.16\n",
" 0.6 -0.024 -0.01 0.157 -0.012 -0.005 0.146 -0.004 -0.003 0.161\n",
" 0.7 -0.028 -0.011 0.158 -0.015 -0.007 0.147 -0.009 -0.005 0.159\n",
" 0.8 -0.031 -0.011 0.159 -0.018 -0.007 0.146 -0.012 -0.007 0.161\n",
" 0.9 -0.036 -0.011 0.162 -0.022 -0.008 0.152 -0.016 -0.009 0.171\n",
" 1.0 -0.041 -0.011 0.167 -0.026 -0.01 0.161 -0.019 -0.011 0.176\n",
" 1.1 -0.046 -0.011 0.173 -0.029 -0.012 0.17 -0.031 -0.016 0.193\n",
" 1.2 -0.051 -0.01 0.184 -0.035 -0.013 0.178 -0.053 -0.024 0.21\n",
" 1.3 -0.059 -0.011 0.194 -0.071 -0.03 0.221 -0.049 -0.024 0.215\n",
" 1.4 -0.069 -0.013 0.21 -0.149 -0.091 0.276 -0.054 -0.018 0.223\n",
" 1.5 -0.074 -0.015 0.222 -0.171 -0.105 0.288 -0.076 -0.028 0.236\n",
" 1.6 -0.07 -0.016 0.224 -0.183 -0.112 0.3 -0.075 -0.015 0.22\n",
" 1.7 -0.066 -0.016 0.224 -0.206 -0.126 0.314 -0.05 -0.007 0.24\n",
" 1.8 -0.096 -0.028 0.265 -0.206 -0.127 0.334 -0.063 -0.017 0.255\n",
" 1.9 -0.193 -0.092 0.358 -0.221 -0.112 0.363 -0.061 -0.003 0.22"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gal_col"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gal-scatter-color-stats-deredden.fits\n",
"gal-scatter-mag-stats-deredden-by-meas-mag.fits\n",
"gal-scatter-mag-stats-deredden-s2n.fits\n",
"gal-scatter-mag-stats-deredden.fits\n",
"gal-scatter-mag-stats-uncorrected.fits\n",
"gal-scatter-mag-stats.fits\n",
"galaxy-color-response-deredden-rounded.csv\n",
"galaxy-color-response-deredden.csv\n",
"galaxy-response-deredden-rounded.csv\n",
"galaxy-response-deredden.csv\n",
"grid-gal-scatter-color-stats-deredden.fits\n",
"grid-gal-scatter-mag-stats-deredden-s2n.fits\n",
"grid-gal-scatter-mag-stats-deredden.fits\n",
"grid-gal-scatter-mag-stats-deredden_dt.fits\n",
"star-color-response-deredden-rounded.csv\n",
"star-color-response-deredden.csv\n",
"star-mag-response-deredden-rounded.csv\n",
"star-mag-response-deredden.csv\n",
"star-scatter-color-stats-deredden.fits\n",
"star-scatter-mag-stats-deredden.fits\n",
"star-scatter-mag-stats-meas_cm_mag_deredden.fits\n",
"star-scatter-mag-stats-meas_cm_magderedden.csv\n",
"star-scatter-mag-stats-meas_cm_magderedden.fits\n",
"star-scatter-mag-stats-meas_psf_magderedden.fits\n",
"star-scatter-mag-stats-uncorrected.fits\n",
"stars-scatter-mag-stats-deredden-s2n.fits\n"
]
}
],
"source": [
"!ls tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "balrog-plots",
"language": "python",
"name": "balrog-plots"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
375 changes: 375 additions & 0 deletions notebooks/y3/balrog-paper-plots/Untitled.ipynb
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121 changes: 121 additions & 0 deletions notebooks/y3/balrog-paper-plots/check-df-cat.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import fitsio\n",
"import numpy as np\n",
"from astropy.table import Table\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sof_file = '/project/projectdirs/des/severett/Balrog/paper-plots/cats/balrog_sof_gold_phot_scatter.fits'\n",
"sof = Table.read(sof_file)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['true_id', 'true_bdf_T', 'true_bdf_flux_err', 'true_bdf_mag', 'true_bdf_g', 'true_bdf_fracdev', 'true_bdf_flux_deredden', 'true_bdf_mag_deredden', 'true_gap_flux_fwhm4asec', 'bal_id', 'meas_gapflux', 'meas_cm_T', 'meas_cm_flux_cov', 'meas_cm_flux_s2n', 'meas_cm_mag', 'meas_cm_s2n_r', 'meas_cm_fracdev', 'meas_cm_mag_deredden', 'flags_footprint', 'flags_foreground', 'flags_badregions', 'meas_FLAGS_GOLD_SOF_ONLY', 'meas_EXTENDED_CLASS_SOF', 'ext_mag', 'match_flag_1.5_asec', 'NearestNeighbors_class']\n"
]
}
],
"source": [
"print(sof.colnames)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_file = 'cats/ugriz-mof02-JHK-extcorr_27May20_kNN_class.fits'\n",
"df = Table.read(df_file)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['NearestNeighbors_class', 'id']\n"
]
}
],
"source": [
"print(df.colnames)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2826988\n",
"1567465\n",
"44.553531886233685\n"
]
}
],
"source": [
"print(len(df['NearestNeighbors_class']))\n",
"print(len(df[~np.isnan(df['NearestNeighbors_class'])]))\n",
"print(100. - (100. * len(df[~np.isnan(df['NearestNeighbors_class'])]) / len(df['NearestNeighbors_class'])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "balrog-plots",
"language": "python",
"name": "balrog-plots"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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