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validation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## get cal_stability.csv from\n",
"\n",
"```bash\n",
"export PGHOST=athena.mc.mwa128t.org\n",
"export PGPORT=5432\n",
"export PGDATABASE=mwa\n",
"export PGUSER=mwacode\n",
"export PGPASSWORD=...\n",
"cat > cal_stability.psql <<EOF\n",
"SELECT * FROM (\n",
" SELECT DISTINCT ON (fitid, obsid)\n",
" fitid,\n",
" obsid,\n",
" count(DISTINCT tileid) as count_tiles,\n",
" sum(x_phase_sigma_resid) as sum_x_phase_sigma_resid,\n",
" sum(x_phase_chi2dof) as sum_x_phase_chi2dof,\n",
" avg(x_phase_fit_quality) as avg_x_phase_fit_quality,\n",
" sum(y_phase_sigma_resid) as sum_y_phase_sigma_resid,\n",
" sum(y_phase_chi2dof) as sum_y_phase_chi2dof,\n",
" avg(y_phase_fit_quality) as avg_y_phase_fit_quality\n",
" FROM public.calibration_solutions\n",
" WHERE x_phase_fit_quality > 0.5\n",
" AND x_phase_chi2dof < 100\n",
" GROUP BY (fitid, obsid)\n",
") as foo\n",
"WHERE count_tiles > 100\n",
"EOF\n",
"\n",
"psql -f cal_stability.psql --csv > cal_stability.csv\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fitid</th>\n",
" <th>obsid</th>\n",
" <th>count_tiles</th>\n",
" <th>sum_x_phase_sigma_resid</th>\n",
" <th>sum_x_phase_chi2dof</th>\n",
" <th>avg_x_phase_fit_quality</th>\n",
" <th>sum_y_phase_sigma_resid</th>\n",
" <th>sum_y_phase_sigma_resid.1</th>\n",
" <th>sum_y_phase_chi2dof</th>\n",
" <th>avg_y_phase_fit_quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4.576000e+03</td>\n",
" <td>4.576000e+03</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" <td>4576.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.492289e+09</td>\n",
" <td>1.098908e+09</td>\n",
" <td>123.445367</td>\n",
" <td>529.456690</td>\n",
" <td>466.601977</td>\n",
" <td>0.724368</td>\n",
" <td>531.655059</td>\n",
" <td>531.655059</td>\n",
" <td>851.644966</td>\n",
" <td>0.721238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.084236e+07</td>\n",
" <td>2.215291e+07</td>\n",
" <td>4.694243</td>\n",
" <td>475.387947</td>\n",
" <td>378.507949</td>\n",
" <td>0.052761</td>\n",
" <td>415.005286</td>\n",
" <td>415.005286</td>\n",
" <td>1567.215204</td>\n",
" <td>0.054394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.370812e+09</td>\n",
" <td>1.054847e+09</td>\n",
" <td>101.000000</td>\n",
" <td>2.552566</td>\n",
" <td>0.055060</td>\n",
" <td>0.516095</td>\n",
" <td>2.373585</td>\n",
" <td>2.373585</td>\n",
" <td>0.047570</td>\n",
" <td>0.352601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.419351e+09</td>\n",
" <td>1.084625e+09</td>\n",
" <td>121.000000</td>\n",
" <td>227.326650</td>\n",
" <td>287.094978</td>\n",
" <td>0.707741</td>\n",
" <td>238.118690</td>\n",
" <td>238.118690</td>\n",
" <td>324.755940</td>\n",
" <td>0.705114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.542543e+09</td>\n",
" <td>1.099997e+09</td>\n",
" <td>125.000000</td>\n",
" <td>393.223420</td>\n",
" <td>410.043250</td>\n",
" <td>0.718620</td>\n",
" <td>424.276390</td>\n",
" <td>424.276390</td>\n",
" <td>465.647835</td>\n",
" <td>0.717451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.545130e+09</td>\n",
" <td>1.115069e+09</td>\n",
" <td>127.000000</td>\n",
" <td>659.869025</td>\n",
" <td>534.710260</td>\n",
" <td>0.744168</td>\n",
" <td>695.692975</td>\n",
" <td>695.692975</td>\n",
" <td>637.097330</td>\n",
" <td>0.740832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.717899e+09</td>\n",
" <td>1.139933e+09</td>\n",
" <td>128.000000</td>\n",
" <td>5902.604000</td>\n",
" <td>6537.203000</td>\n",
" <td>1.500000</td>\n",
" <td>4133.910000</td>\n",
" <td>4133.910000</td>\n",
" <td>25130.258000</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fitid obsid count_tiles sum_x_phase_sigma_resid \\\n",
"count 4.576000e+03 4.576000e+03 4576.000000 4576.000000 \n",
"mean 1.492289e+09 1.098908e+09 123.445367 529.456690 \n",
"std 7.084236e+07 2.215291e+07 4.694243 475.387947 \n",
"min 1.370812e+09 1.054847e+09 101.000000 2.552566 \n",
"25% 1.419351e+09 1.084625e+09 121.000000 227.326650 \n",
"50% 1.542543e+09 1.099997e+09 125.000000 393.223420 \n",
"75% 1.545130e+09 1.115069e+09 127.000000 659.869025 \n",
"max 1.717899e+09 1.139933e+09 128.000000 5902.604000 \n",
"\n",
" sum_x_phase_chi2dof avg_x_phase_fit_quality sum_y_phase_sigma_resid \\\n",
"count 4576.000000 4576.000000 4576.000000 \n",
"mean 466.601977 0.724368 531.655059 \n",
"std 378.507949 0.052761 415.005286 \n",
"min 0.055060 0.516095 2.373585 \n",
"25% 287.094978 0.707741 238.118690 \n",
"50% 410.043250 0.718620 424.276390 \n",
"75% 534.710260 0.744168 695.692975 \n",
"max 6537.203000 1.500000 4133.910000 \n",
"\n",
" sum_y_phase_sigma_resid.1 sum_y_phase_chi2dof avg_y_phase_fit_quality \n",
"count 4576.000000 4576.000000 4576.000000 \n",
"mean 531.655059 851.644966 0.721238 \n",
"std 415.005286 1567.215204 0.054394 \n",
"min 2.373585 0.047570 0.352601 \n",
"25% 238.118690 324.755940 0.705114 \n",
"50% 424.276390 465.647835 0.717451 \n",
"75% 695.692975 637.097330 0.740832 \n",
"max 4133.910000 25130.258000 1.500000 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" <th></th>\n",
" <th>fitid</th>\n",
" <th>obsid</th>\n",
" <th>count_tiles</th>\n",
" <th>sum_x_phase_sigma_resid</th>\n",
" <th>sum_x_phase_chi2dof</th>\n",
" <th>avg_x_phase_fit_quality</th>\n",
" <th>sum_y_phase_sigma_resid</th>\n",
" <th>sum_y_phase_sigma_resid.1</th>\n",
" <th>sum_y_phase_chi2dof</th>\n",
" <th>avg_y_phase_fit_quality</th>\n",
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" <th>12123</th>\n",
" <td>1717898679</td>\n",
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" <td>0.05506</td>\n",
" <td>0.834964</td>\n",
" <td>2.373585</td>\n",
" <td>2.373585</td>\n",
" <td>0.04757</td>\n",
" <td>0.833780</td>\n",
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" <th>3095</th>\n",
" <td>1542022813</td>\n",
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" <td>102</td>\n",
" <td>416.003330</td>\n",
" <td>139.81879</td>\n",
" <td>0.843800</td>\n",
" <td>268.161870</td>\n",
" <td>268.161870</td>\n",
" <td>197.27411</td>\n",
" <td>0.842676</td>\n",
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" <tr>\n",
" <th>3092</th>\n",
" <td>1542021718</td>\n",
" <td>1068293896</td>\n",
" <td>102</td>\n",
" <td>485.058260</td>\n",
" <td>140.50772</td>\n",
" <td>0.842749</td>\n",
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" <td>504.247740</td>\n",
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" <td>0.841833</td>\n",
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" <th>11622</th>\n",
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" <td>60.624004</td>\n",
" <td>149.48630</td>\n",
" <td>0.750000</td>\n",
" <td>66.539900</td>\n",
" <td>66.539900</td>\n",
" <td>151.01630</td>\n",
" <td>0.750000</td>\n",
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" <td>149.89522</td>\n",
" <td>0.843788</td>\n",
" <td>221.207440</td>\n",
" <td>221.207440</td>\n",
" <td>309.95154</td>\n",
" <td>0.838411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>1428841026</td>\n",
" <td>1112876240</td>\n",
" <td>122</td>\n",
" <td>302.387100</td>\n",
" <td>533.57324</td>\n",
" <td>0.732249</td>\n",
" <td>307.278470</td>\n",
" <td>307.278470</td>\n",
" <td>598.32434</td>\n",
" <td>0.729743</td>\n",
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" <th>1109</th>\n",
" <td>1419097650</td>\n",
" <td>1103144976</td>\n",
" <td>126</td>\n",
" <td>232.343580</td>\n",
" <td>533.69890</td>\n",
" <td>0.735639</td>\n",
" <td>198.096100</td>\n",
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" <td>483.05392</td>\n",
" <td>0.739968</td>\n",
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" <td>1117447824</td>\n",
" <td>124</td>\n",
" <td>308.286560</td>\n",
" <td>533.91705</td>\n",
" <td>0.725801</td>\n",
" <td>186.206190</td>\n",
" <td>186.206190</td>\n",
" <td>357.41248</td>\n",
" <td>0.742483</td>\n",
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" <th>1051</th>\n",
" <td>1416690746</td>\n",
" <td>1100726136</td>\n",
" <td>126</td>\n",
" <td>177.237950</td>\n",
" <td>534.42993</td>\n",
" <td>0.730832</td>\n",
" <td>177.080950</td>\n",
" <td>177.080950</td>\n",
" <td>521.73470</td>\n",
" <td>0.731497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1735</th>\n",
" <td>1431164274</td>\n",
" <td>1115203912</td>\n",
" <td>126</td>\n",
" <td>146.382350</td>\n",
" <td>534.63730</td>\n",
" <td>0.738911</td>\n",
" <td>139.233280</td>\n",
" <td>139.233280</td>\n",
" <td>514.28326</td>\n",
" <td>0.740090</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1844 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" fitid obsid count_tiles sum_x_phase_sigma_resid \\\n",
"12123 1717898679 1111842752 121 2.552566 \n",
"3095 1542022813 1068294248 102 416.003330 \n",
"3092 1542021718 1068293896 102 485.058260 \n",
"11622 1692959179 1104788616 128 60.624004 \n",
"3091 1542020157 1068207968 112 352.036650 \n",
"... ... ... ... ... \n",
"1458 1428841026 1112876240 122 302.387100 \n",
"1109 1419097650 1103144976 126 232.343580 \n",
"5030 1545144407 1117447824 124 308.286560 \n",
"1051 1416690746 1100726136 126 177.237950 \n",
"1735 1431164274 1115203912 126 146.382350 \n",
"\n",
" sum_x_phase_chi2dof avg_x_phase_fit_quality sum_y_phase_sigma_resid \\\n",
"12123 0.05506 0.834964 2.373585 \n",
"3095 139.81879 0.843800 268.161870 \n",
"3092 140.50772 0.842749 504.247740 \n",
"11622 149.48630 0.750000 66.539900 \n",
"3091 149.89522 0.843788 221.207440 \n",
"... ... ... ... \n",
"1458 533.57324 0.732249 307.278470 \n",
"1109 533.69890 0.735639 198.096100 \n",
"5030 533.91705 0.725801 186.206190 \n",
"1051 534.42993 0.730832 177.080950 \n",
"1735 534.63730 0.738911 139.233280 \n",
"\n",
" sum_y_phase_sigma_resid.1 sum_y_phase_chi2dof avg_y_phase_fit_quality \n",
"12123 2.373585 0.04757 0.833780 \n",
"3095 268.161870 197.27411 0.842676 \n",
"3092 504.247740 144.22452 0.841833 \n",
"11622 66.539900 151.01630 0.750000 \n",
"3091 221.207440 309.95154 0.838411 \n",
"... ... ... ... \n",
"1458 307.278470 598.32434 0.729743 \n",
"1109 198.096100 483.05392 0.739968 \n",
"5030 186.206190 357.41248 0.742483 \n",
"1051 177.080950 521.73470 0.731497 \n",
"1735 139.233280 514.28326 0.740090 \n",
"\n",
"[1844 rows x 10 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"stability = pd.read_csv('cal_stability.csv')\n",
"stability.drop(np.where(stability['obsid'] > 1_140_000_000)[0], inplace=True)\n",
"desc = stability.describe()\n",
"display(desc)\n",
"# drop outliers\n",
"for col in [ 'sum_x_phase_sigma_resid', 'sum_x_phase_chi2dof', 'sum_y_phase_sigma_resid', 'sum_y_phase_chi2dof']:\n",
" stability = stability[stability[col] < desc[col]['75%']] # + 1.5 * (desc[col]['75%'] - desc[col]['25%'])]\n",
"for col in [ 'avg_x_phase_fit_quality', 'avg_y_phase_fit_quality']:\n",
" stability = stability[stability[col] > desc[col]['25%']] # - 1.5 * (desc[col]['75%'] - desc[col]['25%'])]\n",
"\n",
"# find \"best\" solution\n",
"stability.sort_values(by='sum_x_phase_chi2dof', ascending=True, inplace=True)\n",
"stability"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"let's pick `1111842752`\n",
"\n",
"```bash\n",
"cd /data/dev/\n",
"giant-squid submit-vis -w 1111842752\n",
"giant-squid download 1111842752\n",
"tar -xvf /data/dev/1111842752_779814_vis.tar -C /data/incoming/\n",
"rm 1111842752.metafits 1111842752_flags.zip 1111842752_metafits_ppds.fits\n",
"mwax_calvin_processor --cfg=/home/dev/mwax_mover/mwax_mover_calvin.cfg\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fitid</th>\n",
" <th>obsid</th>\n",
" <th>count_tiles</th>\n",
" <th>sum_x_phase_sigma_resid</th>\n",
" <th>sum_x_phase_chi2dof</th>\n",
" <th>avg_x_phase_fit_quality</th>\n",
" <th>sum_y_phase_sigma_resid</th>\n",
" <th>sum_y_phase_sigma_resid.1</th>\n",
" <th>sum_y_phase_chi2dof</th>\n",
" <th>avg_y_phase_fit_quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12123</th>\n",
" <td>1717898679</td>\n",
" <td>1111842752</td>\n",
" <td>121</td>\n",
" <td>2.552566</td>\n",
" <td>0.05506</td>\n",
" <td>0.834964</td>\n",
" <td>2.373585</td>\n",
" <td>2.373585</td>\n",
" <td>0.04757</td>\n",
" <td>0.83378</td>\n",
" </tr>\n",
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],
"text/plain": [
" fitid obsid count_tiles sum_x_phase_sigma_resid \\\n",
"12123 1717898679 1111842752 121 2.552566 \n",
"\n",
" sum_x_phase_chi2dof avg_x_phase_fit_quality sum_y_phase_sigma_resid \\\n",
"12123 0.05506 0.834964 2.373585 \n",
"\n",
" sum_y_phase_sigma_resid.1 sum_y_phase_chi2dof avg_y_phase_fit_quality \n",
"12123 2.373585 0.04757 0.83378 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stability[stability['obsid'] == 1111842752]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"for fitid in 1427807058 1720774022; do\n",
" psql --csv -c 'SELECT * FROM public.calibration_solutions WHERE fitid='$fitid';' > calibration_solutions_fit$fitid.csv\n",
"done\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fitid</th>\n",
" <th>obsid</th>\n",
" <th>tileid</th>\n",
" <th>x_delay_m</th>\n",
" <th>y_delay_m</th>\n",
" <th>x_intercept</th>\n",
" <th>y_intercept</th>\n",
" <th>x_gains</th>\n",
" <th>y_gains</th>\n",
" <th>x_gains_pol1</th>\n",
" <th>...</th>\n",
" <th>x_phase_sigma_resid</th>\n",
" <th>y_phase_sigma_resid</th>\n",
" <th>x_phase_chi2dof</th>\n",
" <th>y_phase_chi2dof</th>\n",
" <th>x_phase_fit_quality</th>\n",
" <th>y_phase_fit_quality</th>\n",
" <th>x_gains_sigma_resid</th>\n",
" <th>y_gains_sigma_resid</th>\n",
" <th>x_gains_fit_quality</th>\n",
" <th>y_gains_fit_quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1427807058</td>\n",
" <td>1111842272</td>\n",
" <td>11</td>\n",
" <td>-0.3763</td>\n",
" <td>-0.1411</td>\n",
" <td>-58.1761</td>\n",
" <td>-69.5678</td>\n",
" <td>{0.63007,0.7068,0.79117,0.89212,0.9715,0.96785...</td>\n",
" <td>{0.59605,0.67415,0.75355,0.85173,0.92976,0.928...</td>\n",
" <td>{0.00127,0.00076,0.00208,0.00197,-0.00098,0.00...</td>\n",
" <td>...</td>\n",
" <td>2.0096</td>\n",
" <td>2.0901</td>\n",
" <td>3.0531</td>\n",
" <td>3.0568</td>\n",
" <td>0.7448</td>\n",
" <td>0.7474</td>\n",
" <td>{0.01423,0.01413,0.01638,0.01679,0.01939,0.019...</td>\n",
" <td>{0.0098,0.01727,0.01761,0.01314,0.01296,0.0150...</td>\n",
" <td>0.7487</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1427807058</td>\n",
" <td>1111842272</td>\n",
" <td>12</td>\n",
" <td>-0.0598</td>\n",
" <td>0.0909</td>\n",
" <td>-57.5521</td>\n",
" <td>-78.3174</td>\n",
" <td>{0.60164,0.71405,0.77236,0.82948,0.95686,0.978...</td>\n",
" <td>{0.60078,0.70498,0.76898,0.82718,0.94589,0.977...</td>\n",
" <td>{0.00429,0.00086,0.00167,0.00289,0.00515,-0.00...</td>\n",
" <td>...</td>\n",
" <td>2.2870</td>\n",
" <td>2.7929</td>\n",
" <td>3.3113</td>\n",
" <td>4.4548</td>\n",
" <td>0.7448</td>\n",
" <td>0.7422</td>\n",
" <td>{0.01271,0.0165,0.01407,0.02039,0.01253,0.0154...</td>\n",
" <td>{0.01401,0.01279,0.01745,0.02063,0.0142,0.0124...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1427807058</td>\n",
" <td>1111842272</td>\n",
" <td>13</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>{0.22249,0.25207,0.33078,0.36476,0.43037,0.480...</td>\n",
" <td>{0.22555,0.25498,0.33319,0.3793,0.44564,0.4893...</td>\n",
" <td>{0.00125,0.00206,0.00159,0.00147,0.00147,0.000...</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.0053,0.0051,0.00707,0.00615,0.0071,0.00899,...</td>\n",
" <td>{0.00461,0.00546,0.00735,0.01013,0.00844,0.010...</td>\n",
" <td>0.7474</td>\n",
" <td>0.7487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1427807058</td>\n",
" <td>1111842272</td>\n",
" <td>14</td>\n",
" <td>-0.4200</td>\n",
" <td>-0.4014</td>\n",
" <td>16.2008</td>\n",
" <td>-29.2370</td>\n",
" <td>{0.23934,0.27183,0.33112,0.41943,0.4497,0.4760...</td>\n",
" <td>{0.23222,0.26077,0.32102,0.40288,0.43657,0.464...</td>\n",
" <td>{-2e-05,0.00244,0.00174,-0.00034,0.00065,-0.00...</td>\n",
" <td>...</td>\n",
" <td>2.1901</td>\n",
" <td>2.2256</td>\n",
" <td>2.8576</td>\n",
" <td>3.3333</td>\n",
" <td>0.7422</td>\n",
" <td>0.7461</td>\n",
" <td>{0.00566,0.00831,0.00734,0.00748,0.0077,0.0098...</td>\n",
" <td>{0.00556,0.00779,0.00633,0.00633,0.0072,0.0095...</td>\n",
" <td>0.7474</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1427807058</td>\n",
" <td>1111842272</td>\n",
" <td>16</td>\n",
" <td>-0.0465</td>\n",
" <td>-0.2118</td>\n",
" <td>-68.5421</td>\n",
" <td>-106.2560</td>\n",
" <td>{0.60659,0.68845,0.74722,0.91654,0.96065,0.974...</td>\n",
" <td>{0.65754,0.74115,0.81359,0.97782,1.0178,1.0337...</td>\n",
" <td>{0.00576,0.00107,0.0022,0.00263,-0.00108,-0.00...</td>\n",
" <td>...</td>\n",
" <td>2.3714</td>\n",
" <td>2.3871</td>\n",
" <td>3.1206</td>\n",
" <td>3.6313</td>\n",
" <td>0.7448</td>\n",
" <td>0.7474</td>\n",
" <td>{0.01297,0.01418,0.02075,0.01574,0.01688,0.014...</td>\n",
" <td>{0.01371,0.01747,0.01444,0.01104,0.01694,0.014...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>609</th>\n",
" <td>1427807058</td>\n",
" <td>1111842752</td>\n",
" <td>164</td>\n",
" <td>-122.6140</td>\n",
" <td>-121.5710</td>\n",
" <td>29812.7000</td>\n",
" <td>30186.9000</td>\n",
" <td>{1.07626,1.05244,1.00086,0.99556,0.99472,0.969...</td>\n",
" <td>{1.03629,1.03407,0.99278,0.9846,0.9923,0.96763...</td>\n",
" <td>{-0.00261,-0.00237,-0.0002,3e-05,0.00052,0.001...</td>\n",
" <td>...</td>\n",
" <td>1.3025</td>\n",
" <td>1.1790</td>\n",
" <td>0.1119</td>\n",
" <td>0.0917</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.01674,0.01142,0.00938,0.01133,0.01323,0.012...</td>\n",
" <td>{0.01439,0.01585,0.01238,0.00852,0.00852,0.012...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>610</th>\n",
" <td>1427807058</td>\n",
" <td>1111842752</td>\n",
" <td>165</td>\n",
" <td>-122.8510</td>\n",
" <td>-121.7300</td>\n",
" <td>30197.3000</td>\n",
" <td>29465.1000</td>\n",
" <td>{0.60455,0.60846,0.58303,0.58598,0.57292,0.563...</td>\n",
" <td>{1.03966,1.02784,0.99235,0.99857,0.99328,0.966...</td>\n",
" <td>{0.00054,-0.00071,0.00113,-0.00026,0.00011,-0....</td>\n",
" <td>...</td>\n",
" <td>1.5937</td>\n",
" <td>1.0511</td>\n",
" <td>0.1676</td>\n",
" <td>0.0729</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.00662,0.00518,0.00689,0.00668,0.00533,0.004...</td>\n",
" <td>{0.00827,0.00749,0.00762,0.00723,0.00847,0.008...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>611</th>\n",
" <td>1427807058</td>\n",
" <td>1111842752</td>\n",
" <td>166</td>\n",
" <td>-122.1760</td>\n",
" <td>-122.3620</td>\n",
" <td>29842.4000</td>\n",
" <td>30564.4000</td>\n",
" <td>{0.68643,0.66975,0.64213,0.63505,0.63348,0.628...</td>\n",
" <td>{1.19306,1.16089,1.09496,1.07342,1.07218,1.069...</td>\n",
" <td>{-5e-05,-0.00087,-0.00028,-0.00092,0.0007,-0.0...</td>\n",
" <td>...</td>\n",
" <td>1.2339</td>\n",
" <td>1.4770</td>\n",
" <td>0.1004</td>\n",
" <td>0.1439</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.00602,0.00691,0.00654,0.00422,0.00607,0.005...</td>\n",
" <td>{0.0103,0.00607,0.00607,0.00607,0.00837,0.0066...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>612</th>\n",
" <td>1427807058</td>\n",
" <td>1111842752</td>\n",
" <td>167</td>\n",
" <td>-122.1050</td>\n",
" <td>-122.3920</td>\n",
" <td>29837.1000</td>\n",
" <td>30569.7000</td>\n",
" <td>{0.93347,0.87979,0.88227,0.84693,0.84071,0.821...</td>\n",
" <td>{1.17744,1.12009,1.12942,1.10352,1.09604,1.072...</td>\n",
" <td>{-0.00043,-0.00086,-0.00076,-0.00055,0.00011,-...</td>\n",
" <td>...</td>\n",
" <td>1.2674</td>\n",
" <td>1.1229</td>\n",
" <td>0.1060</td>\n",
" <td>0.0832</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.00657,0.0062,0.00649,0.00697,0.00685,0.0075...</td>\n",
" <td>{0.00825,0.00726,0.00781,0.00576,0.00703,0.007...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613</th>\n",
" <td>1427807058</td>\n",
" <td>1111842752</td>\n",
" <td>168</td>\n",
" <td>-123.2120</td>\n",
" <td>-121.7510</td>\n",
" <td>30581.4000</td>\n",
" <td>30237.0000</td>\n",
" <td>{1.1801,1.14841,1.12632,1.11389,1.08335,1.0654...</td>\n",
" <td>{1.21149,1.18995,1.17355,1.16385,1.14163,1.124...</td>\n",
" <td>{0.00041,-0.00218,-1e-05,-0.00189,-0.00055,-0....</td>\n",
" <td>...</td>\n",
" <td>1.2030</td>\n",
" <td>1.0290</td>\n",
" <td>0.0955</td>\n",
" <td>0.0699</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" <td>{0.00904,0.00982,0.01043,0.00776,0.0082,0.0075...</td>\n",
" <td>{0.00977,0.0078,0.00917,0.00858,0.00857,0.0078...</td>\n",
" <td>0.7500</td>\n",
" <td>0.7500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>614 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" fitid obsid tileid x_delay_m y_delay_m x_intercept \\\n",
"0 1427807058 1111842272 11 -0.3763 -0.1411 -58.1761 \n",
"1 1427807058 1111842272 12 -0.0598 0.0909 -57.5521 \n",
"2 1427807058 1111842272 13 0.0000 0.0000 0.0000 \n",
"3 1427807058 1111842272 14 -0.4200 -0.4014 16.2008 \n",
"4 1427807058 1111842272 16 -0.0465 -0.2118 -68.5421 \n",
".. ... ... ... ... ... ... \n",
"609 1427807058 1111842752 164 -122.6140 -121.5710 29812.7000 \n",
"610 1427807058 1111842752 165 -122.8510 -121.7300 30197.3000 \n",
"611 1427807058 1111842752 166 -122.1760 -122.3620 29842.4000 \n",
"612 1427807058 1111842752 167 -122.1050 -122.3920 29837.1000 \n",
"613 1427807058 1111842752 168 -123.2120 -121.7510 30581.4000 \n",
"\n",
" y_intercept x_gains \\\n",
"0 -69.5678 {0.63007,0.7068,0.79117,0.89212,0.9715,0.96785... \n",
"1 -78.3174 {0.60164,0.71405,0.77236,0.82948,0.95686,0.978... \n",
"2 0.0000 {0.22249,0.25207,0.33078,0.36476,0.43037,0.480... \n",
"3 -29.2370 {0.23934,0.27183,0.33112,0.41943,0.4497,0.4760... \n",
"4 -106.2560 {0.60659,0.68845,0.74722,0.91654,0.96065,0.974... \n",
".. ... ... \n",
"609 30186.9000 {1.07626,1.05244,1.00086,0.99556,0.99472,0.969... \n",
"610 29465.1000 {0.60455,0.60846,0.58303,0.58598,0.57292,0.563... \n",
"611 30564.4000 {0.68643,0.66975,0.64213,0.63505,0.63348,0.628... \n",
"612 30569.7000 {0.93347,0.87979,0.88227,0.84693,0.84071,0.821... \n",
"613 30237.0000 {1.1801,1.14841,1.12632,1.11389,1.08335,1.0654... \n",
"\n",
" y_gains \\\n",
"0 {0.59605,0.67415,0.75355,0.85173,0.92976,0.928... \n",
"1 {0.60078,0.70498,0.76898,0.82718,0.94589,0.977... \n",
"2 {0.22555,0.25498,0.33319,0.3793,0.44564,0.4893... \n",
"3 {0.23222,0.26077,0.32102,0.40288,0.43657,0.464... \n",
"4 {0.65754,0.74115,0.81359,0.97782,1.0178,1.0337... \n",
".. ... \n",
"609 {1.03629,1.03407,0.99278,0.9846,0.9923,0.96763... \n",
"610 {1.03966,1.02784,0.99235,0.99857,0.99328,0.966... \n",
"611 {1.19306,1.16089,1.09496,1.07342,1.07218,1.069... \n",
"612 {1.17744,1.12009,1.12942,1.10352,1.09604,1.072... \n",
"613 {1.21149,1.18995,1.17355,1.16385,1.14163,1.124... \n",
"\n",
" x_gains_pol1 ... \\\n",
"0 {0.00127,0.00076,0.00208,0.00197,-0.00098,0.00... ... \n",
"1 {0.00429,0.00086,0.00167,0.00289,0.00515,-0.00... ... \n",
"2 {0.00125,0.00206,0.00159,0.00147,0.00147,0.000... ... \n",
"3 {-2e-05,0.00244,0.00174,-0.00034,0.00065,-0.00... ... \n",
"4 {0.00576,0.00107,0.0022,0.00263,-0.00108,-0.00... ... \n",
".. ... ... \n",
"609 {-0.00261,-0.00237,-0.0002,3e-05,0.00052,0.001... ... \n",
"610 {0.00054,-0.00071,0.00113,-0.00026,0.00011,-0.... ... \n",
"611 {-5e-05,-0.00087,-0.00028,-0.00092,0.0007,-0.0... ... \n",
"612 {-0.00043,-0.00086,-0.00076,-0.00055,0.00011,-... ... \n",
"613 {0.00041,-0.00218,-1e-05,-0.00189,-0.00055,-0.... ... \n",
"\n",
" x_phase_sigma_resid y_phase_sigma_resid x_phase_chi2dof y_phase_chi2dof \\\n",
"0 2.0096 2.0901 3.0531 3.0568 \n",
"1 2.2870 2.7929 3.3113 4.4548 \n",
"2 0.0000 0.0000 0.0000 0.0000 \n",
"3 2.1901 2.2256 2.8576 3.3333 \n",
"4 2.3714 2.3871 3.1206 3.6313 \n",
".. ... ... ... ... \n",
"609 1.3025 1.1790 0.1119 0.0917 \n",
"610 1.5937 1.0511 0.1676 0.0729 \n",
"611 1.2339 1.4770 0.1004 0.1439 \n",
"612 1.2674 1.1229 0.1060 0.0832 \n",
"613 1.2030 1.0290 0.0955 0.0699 \n",
"\n",
" x_phase_fit_quality y_phase_fit_quality \\\n",
"0 0.7448 0.7474 \n",
"1 0.7448 0.7422 \n",
"2 0.7500 0.7500 \n",
"3 0.7422 0.7461 \n",
"4 0.7448 0.7474 \n",
".. ... ... \n",
"609 0.7500 0.7500 \n",
"610 0.7500 0.7500 \n",
"611 0.7500 0.7500 \n",
"612 0.7500 0.7500 \n",
"613 0.7500 0.7500 \n",
"\n",
" x_gains_sigma_resid \\\n",
"0 {0.01423,0.01413,0.01638,0.01679,0.01939,0.019... \n",
"1 {0.01271,0.0165,0.01407,0.02039,0.01253,0.0154... \n",
"2 {0.0053,0.0051,0.00707,0.00615,0.0071,0.00899,... \n",
"3 {0.00566,0.00831,0.00734,0.00748,0.0077,0.0098... \n",
"4 {0.01297,0.01418,0.02075,0.01574,0.01688,0.014... \n",
".. ... \n",
"609 {0.01674,0.01142,0.00938,0.01133,0.01323,0.012... \n",
"610 {0.00662,0.00518,0.00689,0.00668,0.00533,0.004... \n",
"611 {0.00602,0.00691,0.00654,0.00422,0.00607,0.005... \n",
"612 {0.00657,0.0062,0.00649,0.00697,0.00685,0.0075... \n",
"613 {0.00904,0.00982,0.01043,0.00776,0.0082,0.0075... \n",
"\n",
" y_gains_sigma_resid x_gains_fit_quality \\\n",
"0 {0.0098,0.01727,0.01761,0.01314,0.01296,0.0150... 0.7487 \n",
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"2 {0.00461,0.00546,0.00735,0.01013,0.00844,0.010... 0.7474 \n",
"3 {0.00556,0.00779,0.00633,0.00633,0.0072,0.0095... 0.7474 \n",
"4 {0.01371,0.01747,0.01444,0.01104,0.01694,0.014... 0.7500 \n",
".. ... ... \n",
"609 {0.01439,0.01585,0.01238,0.00852,0.00852,0.012... 0.7500 \n",
"610 {0.00827,0.00749,0.00762,0.00723,0.00847,0.008... 0.7500 \n",
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"612 {0.00825,0.00726,0.00781,0.00576,0.00703,0.007... 0.7500 \n",
"613 {0.00977,0.0078,0.00917,0.00858,0.00857,0.0078... 0.7500 \n",
"\n",
" y_gains_fit_quality \n",
"0 0.7500 \n",
"1 0.7487 \n",
"2 0.7487 \n",
"3 0.7500 \n",
"4 0.7487 \n",
".. ... \n",
"609 0.7500 \n",
"610 0.7500 \n",
"611 0.7500 \n",
"612 0.7500 \n",
"613 0.7500 \n",
"\n",
"[614 rows x 23 columns]"
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" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
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" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
" <td>1.0</td>\n",
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" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
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" <td>1720774022</td>\n",
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" <td>1.0</td>\n",
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" <th>126</th>\n",
" <td>1720774022</td>\n",
" <td>1111842752</td>\n",
" <td>167</td>\n",
" <td>-121.599860</td>\n",
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" <td>0.281431</td>\n",
" <td>0.242913</td>\n",
" <td>{0.94488156,0.95098704,0.98260796,0.99184835,0...</td>\n",
" <td>{0.74936527,0.7498623,0.7718289,0.77282333,0.7...</td>\n",
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" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
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" <td>-0.103501</td>\n",
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" <td>{0.731224,0.7450881,0.74102694,0.7468942,0.748...</td>\n",
" <td>{0.6641023,0.67164606,0.6670387,0.6729904,0.67...</td>\n",
" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
" <td>...</td>\n",
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" <td>0.000301</td>\n",
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" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
" <td>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}</td>\n",
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"text/plain": [
" fitid obsid tileid x_delay_m y_delay_m x_intercept \\\n",
"0 1720774022 1111842752 11 0.000000 0.000000 0.000000 \n",
"1 1720774022 1111842752 12 0.439999 0.250000 0.460334 \n",
"2 1720774022 1111842752 13 0.449999 0.370000 -0.759746 \n",
"3 1720774022 1111842752 14 0.190000 -0.320000 -0.669224 \n",
"4 1720774022 1111842752 15 NaN NaN NaN \n",
".. ... ... ... ... ... ... \n",
"123 1720774022 1111842752 164 -122.199860 -121.249860 0.634682 \n",
"124 1720774022 1111842752 165 -122.399860 -121.359860 0.237963 \n",
"125 1720774022 1111842752 166 -121.689860 -121.969864 0.135891 \n",
"126 1720774022 1111842752 167 -121.599860 -121.969864 0.281431 \n",
"127 1720774022 1111842752 168 -122.709860 -121.349860 -0.103501 \n",
"\n",
" y_intercept x_gains \\\n",
"0 0.000000 {0.7484061,0.76606566,0.7746018,0.78209025,0.7... \n",
"1 0.353340 {0.70867455,0.7312586,0.7299996,0.74329185,0.7... \n",
"2 -0.543437 {0.6837082,0.69188356,0.7031992,0.7059443,0.70... \n",
"3 -0.885811 {0.67427707,0.6813579,0.68938375,0.6881059,0.6... \n",
"4 NaN {NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,N... \n",
".. ... ... \n",
"123 0.525668 {0.8528507,0.8685218,0.87654287,0.87843096,0.8... \n",
"124 0.651534 {1.4738635,1.4616644,1.5008714,1.4929922,1.505... \n",
"125 0.236470 {1.2516271,1.2784814,1.2899439,1.3074113,1.304... \n",
"126 0.242913 {0.94488156,0.95098704,0.98260796,0.99184835,0... \n",
"127 -0.256632 {0.731224,0.7450881,0.74102694,0.7468942,0.748... \n",
"\n",
" y_gains \\\n",
"0 {0.71460205,0.7284375,0.738707,0.74988973,0.75... \n",
"1 {0.6967037,0.71711594,0.7157158,0.721745,0.727... \n",
"2 {0.6372388,0.6383224,0.6507942,0.6458605,0.645... \n",
"3 {0.66587925,0.6736478,0.67740786,0.6827722,0.6... \n",
"4 {NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,N... \n",
".. ... \n",
"123 {0.875041,0.8779086,0.8829902,0.8899064,0.9070... \n",
"124 {0.8669582,0.8686411,0.88614684,0.88983434,0.8... \n",
"125 {0.720418,0.7396842,0.76268166,0.7850155,0.787... \n",
"126 {0.74936527,0.7498623,0.7718289,0.77282333,0.7... \n",
"127 {0.6641023,0.67164606,0.6670387,0.6729904,0.67... \n",
"\n",
" x_gains_pol1 ... \\\n",
"0 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"1 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"2 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"3 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"4 {NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,N... ... \n",
".. ... ... \n",
"123 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"124 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"125 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"126 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"127 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ... \n",
"\n",
" x_phase_sigma_resid y_phase_sigma_resid x_phase_chi2dof y_phase_chi2dof \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.015123 0.016015 0.000229 0.000257 \n",
"2 0.018126 0.020670 0.000330 0.000429 \n",
"3 0.016357 0.023445 0.000268 0.000553 \n",
"4 NaN NaN NaN NaN \n",
".. ... ... ... ... \n",
"123 0.019162 0.022166 0.000369 0.000493 \n",
"124 0.022420 0.019628 0.000504 0.000387 \n",
"125 0.020378 0.028874 0.000417 0.000838 \n",
"126 0.018548 0.018909 0.000346 0.000360 \n",
"127 0.017299 0.018095 0.000301 0.000329 \n",
"\n",
" x_phase_fit_quality y_phase_fit_quality \\\n",
"0 0.000000 0.000000 \n",
"1 0.837240 0.829427 \n",
"2 0.834635 0.832031 \n",
"3 0.832031 0.843750 \n",
"4 NaN NaN \n",
".. ... ... \n",
"123 0.842448 0.832031 \n",
"124 0.824219 0.819010 \n",
"125 0.838542 0.841146 \n",
"126 0.828125 0.826823 \n",
"127 0.845052 0.826823 \n",
"\n",
" x_gains_sigma_resid \\\n",
"0 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"1 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"2 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"3 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"4 {NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,N... \n",
".. ... \n",
"123 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"124 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"125 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"126 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"127 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} \n",
"\n",
" y_gains_sigma_resid x_gains_fit_quality \\\n",
"0 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"1 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"2 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"3 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"4 {NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,N... 1.0 \n",
".. ... ... \n",
"123 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"124 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"125 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"126 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"127 {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} 1.0 \n",
"\n",
" y_gains_fit_quality \n",
"0 1.0 \n",
"1 1.0 \n",
"2 1.0 \n",
"3 1.0 \n",
"4 1.0 \n",
".. ... \n",
"123 1.0 \n",
"124 1.0 \n",
"125 1.0 \n",
"126 1.0 \n",
"127 1.0 \n",
"\n",
"[128 rows x 23 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fit1427807058 = pd.read_csv('calibration_solutions_fit1427807058.csv')\n",
"fit1720774022 = pd.read_csv('calibration_solutions_fit1720774022.csv')\n",
"display(fit1427807058)\n",
"display(fit1720774022)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fda90dc4980>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1600x1600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1600x1600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot x_delays for both fitids\n",
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(figsize=(16, 16))\n",
"cols = ['tileid', 'x_delay_m', 'y_delay_m', 'x_intercept', 'y_intercept']\n",
"fits = pd.merge(fit1720774022[cols], fit1427807058[cols], on='tileid', suffixes=('_1717898679', '_1427807058'))\n",
"\n",
"# display(fits.head(50))\n",
"\n",
"ax.scatter(fits['tileid'], fits['x_delay_m_1427807058'] - fits['x_delay_m_1717898679'], label='fitdiff')\n",
"\n",
"fig, ax = plt.subplots(figsize=(16, 16))\n",
"ax.scatter(fit1720774022['tileid'], fit1720774022['x_delay_m'], label='fit1720774022', s=1)\n",
"ax.scatter(fit1427807058['tileid'], fit1427807058['x_delay_m'], label='fit1427807058', s=1)\n",
"ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fda902eba40>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x1600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = ['tileid', 'x_gains', 'x_gains_pol1', 'y_gains', 'y_gains_pol1']\n",
"fits = pd.merge(fit1720774022[cols], fit1427807058[cols], on='tileid', suffixes=('_1717898679', '_1427807058'))\n",
"# display(fits)\n",
"\n",
"fig, ax = plt.subplots(figsize=(16, 16))\n",
"\n",
"gains1427807058 = [*map(float, fit1427807058['x_gains'][2][1:-1].split(','))]\n",
"gains1717898679 = [*map(float, fit1720774022['x_gains'][2][1:-1].split(','))]\n",
"ax.plot(range(24), gains1427807058, label='gains1427807058')\n",
"ax.plot(range(24), gains1717898679, label='gains1717898679')\n",
"ax.legend()\n",
"\n",
"# ax.scatter(fit1717898679['tileid'], -fit1717898679['x_delay_m'], label='fit1717898679', s=1)\n",
"# ax.scatter(fit1427807058['tileid'], fit1427807058['x_delay_m'], label='fit1427807058', s=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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