diff --git a/examples/anacal_dm/lsst_test/bps_slurm.yaml b/examples/anacal_dm/lsst_halo_highshear/bps_slurm.yaml
similarity index 100%
rename from examples/anacal_dm/lsst_test/bps_slurm.yaml
rename to examples/anacal_dm/lsst_halo_highshear/bps_slurm.yaml
diff --git a/examples/anacal_dm/lsst_test/bps_torque.yaml b/examples/anacal_dm/lsst_halo_highshear/bps_torque.yaml
similarity index 100%
rename from examples/anacal_dm/lsst_test/bps_torque.yaml
rename to examples/anacal_dm/lsst_halo_highshear/bps_torque.yaml
diff --git a/examples/anacal_dm/lsst_test/butler_clear.sh b/examples/anacal_dm/lsst_halo_highshear/butler_clear.sh
similarity index 100%
rename from examples/anacal_dm/lsst_test/butler_clear.sh
rename to examples/anacal_dm/lsst_halo_highshear/butler_clear.sh
diff --git a/examples/anacal_dm/lsst_test/butler_setup.sh b/examples/anacal_dm/lsst_halo_highshear/butler_setup.sh
similarity index 100%
rename from examples/anacal_dm/lsst_test/butler_setup.sh
rename to examples/anacal_dm/lsst_halo_highshear/butler_setup.sh
diff --git a/examples/anacal_dm/lsst_test/halo_config.yaml b/examples/anacal_dm/lsst_halo_highshear/halo_config.yaml
similarity index 100%
rename from examples/anacal_dm/lsst_test/halo_config.yaml
rename to examples/anacal_dm/lsst_halo_highshear/halo_config.yaml
diff --git a/examples/anacal_dm/lsst_test/shear_config.yaml b/examples/anacal_dm/lsst_halo_highshear/shear_config.yaml
similarity index 100%
rename from examples/anacal_dm/lsst_test/shear_config.yaml
rename to examples/anacal_dm/lsst_halo_highshear/shear_config.yaml
diff --git a/examples/anacal_dm/lsst_test/skymap.py b/examples/anacal_dm/lsst_halo_highshear/skymap.py
similarity index 100%
rename from examples/anacal_dm/lsst_test/skymap.py
rename to examples/anacal_dm/lsst_halo_highshear/skymap.py
diff --git a/examples/anacal_dm/lsst_test/bps_slurm_1_patch.yaml b/examples/anacal_dm/lsst_test/bps_slurm_1_patch.yaml
deleted file mode 100644
index 41be0bb..0000000
--- a/examples/anacal_dm/lsst_test/bps_slurm_1_patch.yaml
+++ /dev/null
@@ -1,38 +0,0 @@
-pipelineYaml: "halo_config.yaml"
-
-wmsServiceClass: lsst.ctrl.bps.parsl.ParslService
-project: shear
-campaign: test
-computeSite: Slurm
-
-# Make sure these values correspond to ones in the bin/run_demo.sh's
-# pipetask command line.
-payload:
-  payloadName: shear_test
-  butlerConfig: butler.yaml
-  inCollection: skymaps
-  outputRun: run
-  # dataQuery: skymap='lsst_sim' AND tract=0 AND patch in (0..999) AND band='i'
-  dataQuery: skymap='lsst_sim' AND tract=0 AND patch=0 AND band='i'
-  command: --register-dataset-types
-  
-parsl_config:
-  retries: 1
-  # monitoring: true
-  # checkpoint: false
-  # executor: WorkQueue
-  provider: Slurm
-  # nodes_per_block: 1
-  # worker_options: "--memory=90000
-
-.site.Slurm:
-    class: lsst.ctrl.bps.parsl.sites.Slurm
-    job_name: shear_test
-    nodes: 1
-    cores_per_node: 64
-    # walltime: '24:00:00'
-    walltime: '00:30:00'
-    # qos: regular
-    qos: debug
-    scheduler_options: |
-      #SBATCH --constraint=cpu
\ No newline at end of file
diff --git a/examples/anacal_dm/lsst_test/plot.ipynb b/examples/anacal_dm/lsst_test/plot.ipynb
deleted file mode 100644
index 6a2007d..0000000
--- a/examples/anacal_dm/lsst_test/plot.ipynb
+++ /dev/null
@@ -1,104 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.style.use(\"MNRAS\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "# Data\n",
-    "anacal_shear = np.array(\n",
-    "    [\n",
-    "        0.00397153,\n",
-    "        -0.02175738,\n",
-    "        -0.02148741,\n",
-    "        -0.02439542,\n",
-    "        -0.02667179,\n",
-    "        -0.02399448,\n",
-    "        -0.02572193,\n",
-    "        -0.02071802,\n",
-    "        -0.0189025,\n",
-    "        -0.01858847,\n",
-    "    ]\n",
-    ")\n",
-    "true_shear = np.array(\n",
-    "    [\n",
-    "        -0.03771292,\n",
-    "        -0.02892669,\n",
-    "        -0.02317557,\n",
-    "        -0.01906895,\n",
-    "        -0.01602246,\n",
-    "        -0.01369343,\n",
-    "        -0.01186802,\n",
-    "        -0.01040716,\n",
-    "        -0.00921723,\n",
-    "        -0.00823322,\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# Plot\n",
-    "plt.figure(figsize=(8, 8))\n",
-    "plt.plot(-true_shear, -anacal_shear, \"o-\", label=\"Anacal Shear vs True Shear\")\n",
-    "\n",
-    "# Labels and title\n",
-    "plt.xlabel(\"True Shear (gt)\")\n",
-    "plt.ylabel(\"Anacal Shear\")\n",
-    "plt.title(\"Anacal Shear vs True Shear\")\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.xlim(0.04, -0.01)\n",
-    "plt.ylim(0.04, -0.01)\n",
-    "plt.plot([0.04, -0.01], [0.04, -0.01], \"k--\")\n",
-    "\n",
-    "# Display plot\n",
-    "plt.show()"
-   ]
-  }
- ],
- "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": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/anacal_dm/lsst_test/plot_summary.ipynb b/examples/anacal_dm/lsst_test/plot_summary.ipynb
deleted file mode 100644
index bb349f4..0000000
--- a/examples/anacal_dm/lsst_test/plot_summary.ipynb
+++ /dev/null
@@ -1,1317 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.style.use(\"MNRAS\")\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# summary_path = \"/global/cfs/cdirs/des/zhou/swirl/forked_repos/xlens/examples/anacal_dm/lsst/summary/20241026T041912Z/deepsummary_stats_test_halo/deepsummary_stats_test_halo_lsst_sim_summary_20241026T041912Z.parq\"\n",
-    "summary_path = \"/global/cfs/cdirs/des/zhou/swirl/forked_repos/xlens/examples/anacal_dm/lsst_no_k/run/deepsummary_stats_test_halo/deepsummary_stats_test_halo_lsst_sim_run.parq\"\n",
-    "summary_table = pd.read_parquet(summary_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "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>angular_bin_left</th>\n",
-       "      <th>angular_bin_right</th>\n",
-       "      <th>ngal_in_bin</th>\n",
-       "      <th>eT</th>\n",
-       "      <th>eT_std</th>\n",
-       "      <th>eX</th>\n",
-       "      <th>eX_std</th>\n",
-       "      <th>rT</th>\n",
-       "      <th>rT_std</th>\n",
-       "      <th>rX</th>\n",
-       "      <th>rX_std</th>\n",
-       "      <th>gT_true</th>\n",
-       "      <th>gX_true</th>\n",
-       "      <th>kappa_true</th>\n",
-       "      <th>lensed_shift</th>\n",
-       "      <th>radial_lensed_shift</th>\n",
-       "      <th>r_weighted_gT</th>\n",
-       "      <th>r_weighted_gX</th>\n",
-       "      <th>median_match_dist</th>\n",
-       "      <th>match_failure_rate</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[208, 552, 976, 1290, 1736, 2158, 2492, 2954, ...</td>\n",
-       "      <td>[5.198809534183175, 3.0406736414653617, 4.1542...</td>\n",
-       "      <td>[0.007470293791367913, 0.0038864431450461078, ...</td>\n",
-       "      <td>[3.5419000837252845, -1.1331931593807107, 3.81...</td>\n",
-       "      <td>[0.008638611564086974, 0.004325451246010153, 0...</td>\n",
-       "      <td>[89.71259563502838, 76.39504201726825, 185.257...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[25.19052422485821, 105.25468479066421, 116.42...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[7.225517571326842, 18.26383273215864, 30.2423...</td>\n",
-       "      <td>[-0.00752035687272788, 0.005067127100681014, 0...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.5488072703765985, 10.919222583338604, 13.83...</td>\n",
-       "      <td>[6.548769677755795, 10.919214248243737, 13.837...</td>\n",
-       "      <td>[3.1493892314371674, 2.558497139901513, 5.7498...</td>\n",
-       "      <td>[-8.180027010857432e-05, 0.0024594143394312197...</td>\n",
-       "      <td>[0.43000175580223443, 0.42300000618197414, 0.4...</td>\n",
-       "      <td>[0.019230769230769232, 0.007246376811594203, 0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[198, 588, 938, 1336, 1820, 2198, 2472, 2828, ...</td>\n",
-       "      <td>[0.6662313838257743, 4.539501473263013, 8.5918...</td>\n",
-       "      <td>[0.005609725913625468, 0.004060991773016789, 0...</td>\n",
-       "      <td>[0.990151902622949, -1.54831400315603, -4.3503...</td>\n",
-       "      <td>[0.006485554003055403, 0.0041249767337139965, ...</td>\n",
-       "      <td>[39.73506053285262, 100.3133503891396, 182.136...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[37.556893644875366, 169.20195389749472, 131.8...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.975192881179395, 19.124544716842763, 28.884...</td>\n",
-       "      <td>[-0.14714300158848076, 0.0024171302191779986, ...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.73263487209572, 10.885067426731085, 13.8336...</td>\n",
-       "      <td>[6.567433247612955, 10.845045638493268, 13.815...</td>\n",
-       "      <td>[1.2113338758892804, 3.2909244757742737, 5.619...</td>\n",
-       "      <td>[-0.09615615556197016, 0.11941230796569324, 0....</td>\n",
-       "      <td>[0.41461516440910473, 0.4238130397024451, 0.42...</td>\n",
-       "      <td>[0.0, 0.0, 0.0021321961620469083, 0.0029940119...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[182, 504, 928, 1310, 1800, 2042, 2632, 2756, ...</td>\n",
-       "      <td>[0.016841201343422943, 3.8099603760161895, 5.2...</td>\n",
-       "      <td>[0.007891072960745246, 0.00404804241194834, 0....</td>\n",
-       "      <td>[-0.9052078052504157, -0.8623009311239875, 3.9...</td>\n",
-       "      <td>[0.007250211053246202, 0.004497851145142153, 0...</td>\n",
-       "      <td>[40.1488425016294, 114.12856903958433, 266.052...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[41.263181764582626, 116.63555545883275, 138.9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.276469440074452, 16.3527579501257, 28.53773...</td>\n",
-       "      <td>[-0.24753101709565195, -0.21797271904944848, -...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.376240750676421, 10.868438507230588, 13.880...</td>\n",
-       "      <td>[6.214727902444133, 10.82393177789738, 13.8616...</td>\n",
-       "      <td>[1.233020196148538, 3.706083989583944, 8.15724...</td>\n",
-       "      <td>[0.1861850071588648, 0.0034560247815812184, 0....</td>\n",
-       "      <td>[0.4050650050799802, 0.4300328583630993, 0.425...</td>\n",
-       "      <td>[0.0, 0.003968253968253968, 0.0043103448275862...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[190, 592, 952, 1268, 1710, 1972, 2494, 2890, ...</td>\n",
-       "      <td>[-0.024111934172243485, 5.223088991088742, 0.6...</td>\n",
-       "      <td>[0.006792124209619472, 0.0037991486425489594, ...</td>\n",
-       "      <td>[1.0202996325417908, -0.5515486334995419, 4.77...</td>\n",
-       "      <td>[0.007120614503725188, 0.003649350186774073, 0...</td>\n",
-       "      <td>[52.94127810030781, 130.8341935373365, 192.724...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[51.77645543372485, 135.31850099801517, 203.57...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.875527893913821, 19.22092733805622, 29.2689...</td>\n",
-       "      <td>[0.4457570941509337, -0.05657492855167967, 0.1...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.650300544857112, 10.943042072257182, 13.889...</td>\n",
-       "      <td>[6.523210449450341, 10.902253554665549, 13.870...</td>\n",
-       "      <td>[1.6498299632010953, 4.2510398775663205, 5.916...</td>\n",
-       "      <td>[0.1689986223677647, 0.07940359381975104, 0.12...</td>\n",
-       "      <td>[0.43759211871649956, 0.41683416078536073, 0.4...</td>\n",
-       "      <td>[0.0, 0.006756756756756757, 0.0, 0.0, 0.001169...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[190, 598, 936, 1356, 1638, 2102, 2406, 2850, ...</td>\n",
-       "      <td>[1.4058358726613513, 2.6789487842234108, -0.01...</td>\n",
-       "      <td>[0.00737543500869276, 0.004271049908922969, 0....</td>\n",
-       "      <td>[0.7921892574244538, -4.680978062148467, -0.64...</td>\n",
-       "      <td>[0.007147394271216624, 0.00396258822297712, 0....</td>\n",
-       "      <td>[17.217205687871523, 139.68438253015648, 200.9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[40.959181407459255, 125.08497866718496, 197.3...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.484670913437178, 19.45284825833544, 28.7972...</td>\n",
-       "      <td>[-0.21082624699507674, 0.1899980261730594, 0.2...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.624797652385868, 10.89088884234944, 13.8712...</td>\n",
-       "      <td>[6.4563566944380195, 10.851672100409964, 13.85...</td>\n",
-       "      <td>[0.3438137985808629, 4.540635868038737, 6.1750...</td>\n",
-       "      <td>[-0.6015560136043262, -0.02700934076479458, 0....</td>\n",
-       "      <td>[0.42047469044547797, 0.4225412692755799, 0.41...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0029498525073746312, 0.00244...</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[230, 614, 970, 1298, 1692, 1952, 2388, 2814, ...</td>\n",
-       "      <td>[-1.6717681492735075, 4.034213127730927, 5.957...</td>\n",
-       "      <td>[0.007322634839555932, 0.004034805895558108, 0...</td>\n",
-       "      <td>[-0.23491008382840778, -1.8827443669192017, 0....</td>\n",
-       "      <td>[0.006318718463048281, 0.003946534714646932, 0...</td>\n",
-       "      <td>[56.8610875285207, 133.50688772714938, 194.730...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[69.86857973530911, 134.40587202588205, 197.22...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.912574710463463, 19.638024667051, 29.607246...</td>\n",
-       "      <td>[-0.4277305356278238, -0.06006431952784774, -0...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.5382232027996725, 10.98911553990244, 13.884...</td>\n",
-       "      <td>[6.200166986992217, 10.914875566062578, 13.845...</td>\n",
-       "      <td>[1.5797139265142475, 4.2978789208270385, 5.947...</td>\n",
-       "      <td>[-1.0173769784584075, 0.052816299670211425, -0...</td>\n",
-       "      <td>[0.43301320869744425, 0.40793810076306386, 0.4...</td>\n",
-       "      <td>[0.0, 0.006514657980456026, 0.0041237113402061...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[206, 536, 1006, 1280, 1842, 2070, 2490, 2944,...</td>\n",
-       "      <td>[1.6203311141176364, 3.825506088623666, 5.2192...</td>\n",
-       "      <td>[0.007605297184275542, 0.004427448354272511, 0...</td>\n",
-       "      <td>[3.5049538109868097, 2.6149005549060904, -5.62...</td>\n",
-       "      <td>[0.007175306522460792, 0.0045066350611240105, ...</td>\n",
-       "      <td>[60.828842454883, 118.95726982122403, 190.6333...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[-3.4478533959166233, 91.87562235539434, 174.1...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.623418028829189, 17.048838938253258, 30.745...</td>\n",
-       "      <td>[0.2612167487572016, 0.306681816844551, -0.119...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.900904018890182, 10.994726705341487, 13.850...</td>\n",
-       "      <td>[6.632536406983318, 10.90862467429626, 13.8115...</td>\n",
-       "      <td>[1.4709713190960254, 3.7768741430974417, 5.831...</td>\n",
-       "      <td>[0.6146251932914356, 0.08100835064492833, -0.0...</td>\n",
-       "      <td>[0.41003381651770504, 0.4014026310662927, 0.40...</td>\n",
-       "      <td>[0.0, 0.0, 0.0019880715705765406, 0.0, 0.00542...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[184, 524, 946, 1350, 1756, 2084, 2450, 2944, ...</td>\n",
-       "      <td>[0.38791334870103755, 0.8769287067616945, 8.20...</td>\n",
-       "      <td>[0.007039841983687402, 0.004066845739254202, 0...</td>\n",
-       "      <td>[0.03617330050779935, 3.3090049531020376, -4.8...</td>\n",
-       "      <td>[0.007621150717727182, 0.003827607164156156, 0...</td>\n",
-       "      <td>[57.80076142386358, 110.91880209873426, 147.76...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[51.19383430069714, 119.61936722412918, 150.63...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[5.088257310240249, 16.758040534307984, 28.967...</td>\n",
-       "      <td>[0.23424330334713714, -0.021898046416860883, -...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.73583031379521, 10.834789125440922, 13.7997...</td>\n",
-       "      <td>[6.493509266248275, 10.75706637816148, 13.7618...</td>\n",
-       "      <td>[1.4868279319105033, 3.5469335060016673, 4.514...</td>\n",
-       "      <td>[1.7068091613664014, 0.027476112539658343, 0.2...</td>\n",
-       "      <td>[0.44851799159071776, 0.4258262141756871, 0.41...</td>\n",
-       "      <td>[0.0, 0.003816793893129771, 0.0021141649048625...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[176, 572, 946, 1314, 1680, 2008, 2572, 2828, ...</td>\n",
-       "      <td>[1.081050139611056, -0.17233920824743287, 7.94...</td>\n",
-       "      <td>[0.006939246031376988, 0.00406206315207079, 0....</td>\n",
-       "      <td>[-0.909725368632672, -0.07815721020313227, 1.5...</td>\n",
-       "      <td>[0.00683112586302168, 0.003918602057888591, 0....</td>\n",
-       "      <td>[38.34640659686405, 122.75736035382671, 183.24...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[39.278609784035595, 122.09723833529063, 176.8...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[4.435480650532712, 18.290919063793048, 28.901...</td>\n",
-       "      <td>[-0.43759018454652243, 0.04706509576362172, 0....</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.493316699559872, 10.868424501149429, 13.851...</td>\n",
-       "      <td>[6.191086287150966, 10.791755890732452, 13.810...</td>\n",
-       "      <td>[0.9682543910864427, 3.9340454829300526, 5.608...</td>\n",
-       "      <td>[-0.266859083075601, -0.02816591994701989, -0....</td>\n",
-       "      <td>[0.4461937580937082, 0.4306107422368509, 0.417...</td>\n",
-       "      <td>[0.0, 0.006993006993006993, 0.0021141649048625...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>[0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...</td>\n",
-       "      <td>[50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....</td>\n",
-       "      <td>[168, 584, 972, 1354, 1758, 2062, 2400, 2826, ...</td>\n",
-       "      <td>[1.6555114767031855, 0.37943708431910117, 6.75...</td>\n",
-       "      <td>[0.007404335670526299, 0.0041626001857503975, ...</td>\n",
-       "      <td>[-0.912546914961675, -2.935809800434458, -3.90...</td>\n",
-       "      <td>[0.0074066225987007035, 0.004082674559888755, ...</td>\n",
-       "      <td>[-20.108219725247665, 124.73419545474724, 191....</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[3.127331423382283, 150.32615656096146, 263.30...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[4.240519807447889, 18.62408798212504, 29.6209...</td>\n",
-       "      <td>[0.0874030873700297, -0.46811554906080416, -0....</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[6.489870951953579, 10.853398447071344, 13.961...</td>\n",
-       "      <td>[6.021851364002567, 10.771095713746117, 13.924...</td>\n",
-       "      <td>[0.1313529159833685, 3.9953128038323675, 5.817...</td>\n",
-       "      <td>[1.11205215159865, -0.2540530096569234, 0.1247...</td>\n",
-       "      <td>[0.4334838926242575, 0.4037171985881046, 0.422...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0014771048744460858, 0.00341...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000 rows × 20 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                      angular_bin_left  \\\n",
-       "0    [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "1    [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "2    [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "3    [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "4    [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "..                                                 ...   \n",
-       "995  [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "996  [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "997  [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "998  [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "999  [0.0, 50.6, 101.2, 151.8, 202.4, 253.0, 303.6,...   \n",
-       "\n",
-       "                                     angular_bin_right  \\\n",
-       "0    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "1    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "2    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "3    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "4    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "..                                                 ...   \n",
-       "995  [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "996  [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "997  [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "998  [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "999  [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....   \n",
-       "\n",
-       "                                           ngal_in_bin  \\\n",
-       "0    [208, 552, 976, 1290, 1736, 2158, 2492, 2954, ...   \n",
-       "1    [198, 588, 938, 1336, 1820, 2198, 2472, 2828, ...   \n",
-       "2    [182, 504, 928, 1310, 1800, 2042, 2632, 2756, ...   \n",
-       "3    [190, 592, 952, 1268, 1710, 1972, 2494, 2890, ...   \n",
-       "4    [190, 598, 936, 1356, 1638, 2102, 2406, 2850, ...   \n",
-       "..                                                 ...   \n",
-       "995  [230, 614, 970, 1298, 1692, 1952, 2388, 2814, ...   \n",
-       "996  [206, 536, 1006, 1280, 1842, 2070, 2490, 2944,...   \n",
-       "997  [184, 524, 946, 1350, 1756, 2084, 2450, 2944, ...   \n",
-       "998  [176, 572, 946, 1314, 1680, 2008, 2572, 2828, ...   \n",
-       "999  [168, 584, 972, 1354, 1758, 2062, 2400, 2826, ...   \n",
-       "\n",
-       "                                                    eT  \\\n",
-       "0    [5.198809534183175, 3.0406736414653617, 4.1542...   \n",
-       "1    [0.6662313838257743, 4.539501473263013, 8.5918...   \n",
-       "2    [0.016841201343422943, 3.8099603760161895, 5.2...   \n",
-       "3    [-0.024111934172243485, 5.223088991088742, 0.6...   \n",
-       "4    [1.4058358726613513, 2.6789487842234108, -0.01...   \n",
-       "..                                                 ...   \n",
-       "995  [-1.6717681492735075, 4.034213127730927, 5.957...   \n",
-       "996  [1.6203311141176364, 3.825506088623666, 5.2192...   \n",
-       "997  [0.38791334870103755, 0.8769287067616945, 8.20...   \n",
-       "998  [1.081050139611056, -0.17233920824743287, 7.94...   \n",
-       "999  [1.6555114767031855, 0.37943708431910117, 6.75...   \n",
-       "\n",
-       "                                                eT_std  \\\n",
-       "0    [0.007470293791367913, 0.0038864431450461078, ...   \n",
-       "1    [0.005609725913625468, 0.004060991773016789, 0...   \n",
-       "2    [0.007891072960745246, 0.00404804241194834, 0....   \n",
-       "3    [0.006792124209619472, 0.0037991486425489594, ...   \n",
-       "4    [0.00737543500869276, 0.004271049908922969, 0....   \n",
-       "..                                                 ...   \n",
-       "995  [0.007322634839555932, 0.004034805895558108, 0...   \n",
-       "996  [0.007605297184275542, 0.004427448354272511, 0...   \n",
-       "997  [0.007039841983687402, 0.004066845739254202, 0...   \n",
-       "998  [0.006939246031376988, 0.00406206315207079, 0....   \n",
-       "999  [0.007404335670526299, 0.0041626001857503975, ...   \n",
-       "\n",
-       "                                                    eX  \\\n",
-       "0    [3.5419000837252845, -1.1331931593807107, 3.81...   \n",
-       "1    [0.990151902622949, -1.54831400315603, -4.3503...   \n",
-       "2    [-0.9052078052504157, -0.8623009311239875, 3.9...   \n",
-       "3    [1.0202996325417908, -0.5515486334995419, 4.77...   \n",
-       "4    [0.7921892574244538, -4.680978062148467, -0.64...   \n",
-       "..                                                 ...   \n",
-       "995  [-0.23491008382840778, -1.8827443669192017, 0....   \n",
-       "996  [3.5049538109868097, 2.6149005549060904, -5.62...   \n",
-       "997  [0.03617330050779935, 3.3090049531020376, -4.8...   \n",
-       "998  [-0.909725368632672, -0.07815721020313227, 1.5...   \n",
-       "999  [-0.912546914961675, -2.935809800434458, -3.90...   \n",
-       "\n",
-       "                                                eX_std  \\\n",
-       "0    [0.008638611564086974, 0.004325451246010153, 0...   \n",
-       "1    [0.006485554003055403, 0.0041249767337139965, ...   \n",
-       "2    [0.007250211053246202, 0.004497851145142153, 0...   \n",
-       "3    [0.007120614503725188, 0.003649350186774073, 0...   \n",
-       "4    [0.007147394271216624, 0.00396258822297712, 0....   \n",
-       "..                                                 ...   \n",
-       "995  [0.006318718463048281, 0.003946534714646932, 0...   \n",
-       "996  [0.007175306522460792, 0.0045066350611240105, ...   \n",
-       "997  [0.007621150717727182, 0.003827607164156156, 0...   \n",
-       "998  [0.00683112586302168, 0.003918602057888591, 0....   \n",
-       "999  [0.0074066225987007035, 0.004082674559888755, ...   \n",
-       "\n",
-       "                                                    rT  \\\n",
-       "0    [89.71259563502838, 76.39504201726825, 185.257...   \n",
-       "1    [39.73506053285262, 100.3133503891396, 182.136...   \n",
-       "2    [40.1488425016294, 114.12856903958433, 266.052...   \n",
-       "3    [52.94127810030781, 130.8341935373365, 192.724...   \n",
-       "4    [17.217205687871523, 139.68438253015648, 200.9...   \n",
-       "..                                                 ...   \n",
-       "995  [56.8610875285207, 133.50688772714938, 194.730...   \n",
-       "996  [60.828842454883, 118.95726982122403, 190.6333...   \n",
-       "997  [57.80076142386358, 110.91880209873426, 147.76...   \n",
-       "998  [38.34640659686405, 122.75736035382671, 183.24...   \n",
-       "999  [-20.108219725247665, 124.73419545474724, 191....   \n",
-       "\n",
-       "                                                rT_std  \\\n",
-       "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, ...   \n",
-       "2    [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, ...   \n",
-       "4    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "..                                                 ...   \n",
-       "995  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "996  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "997  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "998  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "999  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "\n",
-       "                                                    rX  \\\n",
-       "0    [25.19052422485821, 105.25468479066421, 116.42...   \n",
-       "1    [37.556893644875366, 169.20195389749472, 131.8...   \n",
-       "2    [41.263181764582626, 116.63555545883275, 138.9...   \n",
-       "3    [51.77645543372485, 135.31850099801517, 203.57...   \n",
-       "4    [40.959181407459255, 125.08497866718496, 197.3...   \n",
-       "..                                                 ...   \n",
-       "995  [69.86857973530911, 134.40587202588205, 197.22...   \n",
-       "996  [-3.4478533959166233, 91.87562235539434, 174.1...   \n",
-       "997  [51.19383430069714, 119.61936722412918, 150.63...   \n",
-       "998  [39.278609784035595, 122.09723833529063, 176.8...   \n",
-       "999  [3.127331423382283, 150.32615656096146, 263.30...   \n",
-       "\n",
-       "                                                rX_std  \\\n",
-       "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, ...   \n",
-       "2    [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, ...   \n",
-       "4    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "..                                                 ...   \n",
-       "995  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "996  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "997  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "998  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "999  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "\n",
-       "                                               gT_true  \\\n",
-       "0    [7.225517571326842, 18.26383273215864, 30.2423...   \n",
-       "1    [5.975192881179395, 19.124544716842763, 28.884...   \n",
-       "2    [5.276469440074452, 16.3527579501257, 28.53773...   \n",
-       "3    [5.875527893913821, 19.22092733805622, 29.2689...   \n",
-       "4    [5.484670913437178, 19.45284825833544, 28.7972...   \n",
-       "..                                                 ...   \n",
-       "995  [5.912574710463463, 19.638024667051, 29.607246...   \n",
-       "996  [5.623418028829189, 17.048838938253258, 30.745...   \n",
-       "997  [5.088257310240249, 16.758040534307984, 28.967...   \n",
-       "998  [4.435480650532712, 18.290919063793048, 28.901...   \n",
-       "999  [4.240519807447889, 18.62408798212504, 29.6209...   \n",
-       "\n",
-       "                                               gX_true  \\\n",
-       "0    [-0.00752035687272788, 0.005067127100681014, 0...   \n",
-       "1    [-0.14714300158848076, 0.0024171302191779986, ...   \n",
-       "2    [-0.24753101709565195, -0.21797271904944848, -...   \n",
-       "3    [0.4457570941509337, -0.05657492855167967, 0.1...   \n",
-       "4    [-0.21082624699507674, 0.1899980261730594, 0.2...   \n",
-       "..                                                 ...   \n",
-       "995  [-0.4277305356278238, -0.06006431952784774, -0...   \n",
-       "996  [0.2612167487572016, 0.306681816844551, -0.119...   \n",
-       "997  [0.23424330334713714, -0.021898046416860883, -...   \n",
-       "998  [-0.43759018454652243, 0.04706509576362172, 0....   \n",
-       "999  [0.0874030873700297, -0.46811554906080416, -0....   \n",
-       "\n",
-       "                                            kappa_true  \\\n",
-       "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, ...   \n",
-       "2    [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, ...   \n",
-       "4    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "..                                                 ...   \n",
-       "995  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "996  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "997  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "998  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "999  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "\n",
-       "                                          lensed_shift  \\\n",
-       "0    [6.5488072703765985, 10.919222583338604, 13.83...   \n",
-       "1    [6.73263487209572, 10.885067426731085, 13.8336...   \n",
-       "2    [6.376240750676421, 10.868438507230588, 13.880...   \n",
-       "3    [6.650300544857112, 10.943042072257182, 13.889...   \n",
-       "4    [6.624797652385868, 10.89088884234944, 13.8712...   \n",
-       "..                                                 ...   \n",
-       "995  [6.5382232027996725, 10.98911553990244, 13.884...   \n",
-       "996  [6.900904018890182, 10.994726705341487, 13.850...   \n",
-       "997  [6.73583031379521, 10.834789125440922, 13.7997...   \n",
-       "998  [6.493316699559872, 10.868424501149429, 13.851...   \n",
-       "999  [6.489870951953579, 10.853398447071344, 13.961...   \n",
-       "\n",
-       "                                   radial_lensed_shift  \\\n",
-       "0    [6.548769677755795, 10.919214248243737, 13.837...   \n",
-       "1    [6.567433247612955, 10.845045638493268, 13.815...   \n",
-       "2    [6.214727902444133, 10.82393177789738, 13.8616...   \n",
-       "3    [6.523210449450341, 10.902253554665549, 13.870...   \n",
-       "4    [6.4563566944380195, 10.851672100409964, 13.85...   \n",
-       "..                                                 ...   \n",
-       "995  [6.200166986992217, 10.914875566062578, 13.845...   \n",
-       "996  [6.632536406983318, 10.90862467429626, 13.8115...   \n",
-       "997  [6.493509266248275, 10.75706637816148, 13.7618...   \n",
-       "998  [6.191086287150966, 10.791755890732452, 13.810...   \n",
-       "999  [6.021851364002567, 10.771095713746117, 13.924...   \n",
-       "\n",
-       "                                         r_weighted_gT  \\\n",
-       "0    [3.1493892314371674, 2.558497139901513, 5.7498...   \n",
-       "1    [1.2113338758892804, 3.2909244757742737, 5.619...   \n",
-       "2    [1.233020196148538, 3.706083989583944, 8.15724...   \n",
-       "3    [1.6498299632010953, 4.2510398775663205, 5.916...   \n",
-       "4    [0.3438137985808629, 4.540635868038737, 6.1750...   \n",
-       "..                                                 ...   \n",
-       "995  [1.5797139265142475, 4.2978789208270385, 5.947...   \n",
-       "996  [1.4709713190960254, 3.7768741430974417, 5.831...   \n",
-       "997  [1.4868279319105033, 3.5469335060016673, 4.514...   \n",
-       "998  [0.9682543910864427, 3.9340454829300526, 5.608...   \n",
-       "999  [0.1313529159833685, 3.9953128038323675, 5.817...   \n",
-       "\n",
-       "                                         r_weighted_gX  \\\n",
-       "0    [-8.180027010857432e-05, 0.0024594143394312197...   \n",
-       "1    [-0.09615615556197016, 0.11941230796569324, 0....   \n",
-       "2    [0.1861850071588648, 0.0034560247815812184, 0....   \n",
-       "3    [0.1689986223677647, 0.07940359381975104, 0.12...   \n",
-       "4    [-0.6015560136043262, -0.02700934076479458, 0....   \n",
-       "..                                                 ...   \n",
-       "995  [-1.0173769784584075, 0.052816299670211425, -0...   \n",
-       "996  [0.6146251932914356, 0.08100835064492833, -0.0...   \n",
-       "997  [1.7068091613664014, 0.027476112539658343, 0.2...   \n",
-       "998  [-0.266859083075601, -0.02816591994701989, -0....   \n",
-       "999  [1.11205215159865, -0.2540530096569234, 0.1247...   \n",
-       "\n",
-       "                                     median_match_dist  \\\n",
-       "0    [0.43000175580223443, 0.42300000618197414, 0.4...   \n",
-       "1    [0.41461516440910473, 0.4238130397024451, 0.42...   \n",
-       "2    [0.4050650050799802, 0.4300328583630993, 0.425...   \n",
-       "3    [0.43759211871649956, 0.41683416078536073, 0.4...   \n",
-       "4    [0.42047469044547797, 0.4225412692755799, 0.41...   \n",
-       "..                                                 ...   \n",
-       "995  [0.43301320869744425, 0.40793810076306386, 0.4...   \n",
-       "996  [0.41003381651770504, 0.4014026310662927, 0.40...   \n",
-       "997  [0.44851799159071776, 0.4258262141756871, 0.41...   \n",
-       "998  [0.4461937580937082, 0.4306107422368509, 0.417...   \n",
-       "999  [0.4334838926242575, 0.4037171985881046, 0.422...   \n",
-       "\n",
-       "                                    match_failure_rate  \n",
-       "0    [0.019230769230769232, 0.007246376811594203, 0...  \n",
-       "1    [0.0, 0.0, 0.0021321961620469083, 0.0029940119...  \n",
-       "2    [0.0, 0.003968253968253968, 0.0043103448275862...  \n",
-       "3    [0.0, 0.006756756756756757, 0.0, 0.0, 0.001169...  \n",
-       "4    [0.0, 0.0, 0.0, 0.0029498525073746312, 0.00244...  \n",
-       "..                                                 ...  \n",
-       "995  [0.0, 0.006514657980456026, 0.0041237113402061...  \n",
-       "996  [0.0, 0.0, 0.0019880715705765406, 0.0, 0.00542...  \n",
-       "997  [0.0, 0.003816793893129771, 0.0021141649048625...  \n",
-       "998  [0.0, 0.006993006993006993, 0.0021141649048625...  \n",
-       "999  [0.0, 0.0, 0.0, 0.0014771048744460858, 0.00341...  \n",
-       "\n",
-       "[1000 rows x 20 columns]"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [0.0805407258610811, 0.23907091677533607, 0.16...\n",
-       "1      [0.15037583436519883, 0.1906480507594857, 0.15...\n",
-       "2      [0.13142270390137178, 0.143283650077606, 0.107...\n",
-       "3      [0.11098198050265166, 0.14691058062410187, 0.1...\n",
-       "4      [0.31855755300064725, 0.13926287181128327, 0.1...\n",
-       "                             ...                        \n",
-       "995    [0.10398279328543975, 0.14709371929323686, 0.1...\n",
-       "996    [0.09244657307099179, 0.14331901668452254, 0.1...\n",
-       "997    [0.08803097372588443, 0.15108385789625486, 0.1...\n",
-       "998    [0.1156687430236408, 0.14900058954569126, 0.15...\n",
-       "999    [-0.2108848951020531, 0.14931020250081894, 0.1...\n",
-       "Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"gT_true\"] / summary_table[\"rT\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [0.035105318368555655, 0.033490355818158894, 0...\n",
-       "1      [0.03048526564814868, 0.032806445632689835, 0....\n",
-       "2      [0.030711226509170148, 0.03247288580564369, 0....\n",
-       "3      [0.03116339503695327, 0.03249181091450065, 0.0...\n",
-       "4      [0.019969198533944382, 0.032506396103791045, 0...\n",
-       "                             ...                        \n",
-       "995    [0.0277819858039452, 0.03219218868775293, 0.03...\n",
-       "996    [0.02418213564045791, 0.03174983881837192, 0.0...\n",
-       "997    [0.025723327777766137, 0.03197774803630108, 0....\n",
-       "998    [0.025250198832597423, 0.032047328743391446, 0...\n",
-       "999    [-0.006532299615686177, 0.03203061349188596, 0...\n",
-       "Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"r_weighted_gT\"] / summary_table[\"rT\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [208, 552, 976, 1290, 1736, 2158, 2492, 2954, ...\n",
-       "1      [198, 588, 938, 1336, 1820, 2198, 2472, 2828, ...\n",
-       "2      [182, 504, 928, 1310, 1800, 2042, 2632, 2756, ...\n",
-       "3      [190, 592, 952, 1268, 1710, 1972, 2494, 2890, ...\n",
-       "4      [190, 598, 936, 1356, 1638, 2102, 2406, 2850, ...\n",
-       "                             ...                        \n",
-       "995    [230, 614, 970, 1298, 1692, 1952, 2388, 2814, ...\n",
-       "996    [206, 536, 1006, 1280, 1842, 2070, 2490, 2944,...\n",
-       "997    [184, 524, 946, 1350, 1756, 2084, 2450, 2944, ...\n",
-       "998    [176, 572, 946, 1314, 1680, 2008, 2572, 2828, ...\n",
-       "999    [168, 584, 972, 1354, 1758, 2062, 2400, 2826, ...\n",
-       "Name: ngal_in_bin, Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"ngal_in_bin\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "1      [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "2      [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "3      [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "4      [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "                             ...                        \n",
-       "995    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "996    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "997    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "998    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "999    [50.6, 101.2, 151.8, 202.4, 253.0, 303.6, 354....\n",
-       "Name: angular_bin_right, Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"angular_bin_right\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 2.2343356   6.70300681 11.17167801 15.64034921 20.10902042 24.57769162\n",
-      " 29.04636282 33.51503403 37.98370523 42.45237643]\n"
-     ]
-    }
-   ],
-   "source": [
-    "area = np.mean(\n",
-    "    np.pi\n",
-    "    * (\n",
-    "        (summary_table[\"angular_bin_right\"] / 3600 * np.pi / 180.0) ** 2\n",
-    "        - (summary_table[\"angular_bin_left\"] / 3600 * np.pi / 180.0) ** 2\n",
-    "    )\n",
-    "    * (60 * 180 / np.pi) ** 2,\n",
-    "    axis=0,\n",
-    ")\n",
-    "\n",
-    "print(area)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def angular_area_in_arcminutes_from_pixel(pixel, pixel_scale):\n",
-    "    # Calculate the area in pixel space (in square pixels)\n",
-    "    pixel_area = np.pi * pixel**2\n",
-    "\n",
-    "    # Convert pixel area to angular area in square arcseconds\n",
-    "    angular_area_arcsec2 = pixel_area * (pixel_scale**2)\n",
-    "\n",
-    "    # Convert from square arcseconds to square arcminutes\n",
-    "    angular_area_arcmin2 = angular_area_arcsec2 / 3600\n",
-    "\n",
-    "    return angular_area_arcmin2\n",
-    "\n",
-    "\n",
-    "def angular_area_in_arcminutes(radius_arcmin):\n",
-    "    # Convert the radius from arcminutes to radians\n",
-    "    radius_rad = radius_arcmin * np.pi / (180 * 60)\n",
-    "\n",
-    "    # Calculate the angular area in steradians using the formula\n",
-    "    area_sr = 2 * np.pi * (1 - np.cos(radius_rad))\n",
-    "\n",
-    "    # Convert the angular area from steradians to square arcminutes\n",
-    "    area_arcmin2 = area_sr * (10800 / np.pi) ** 2  # (10800 = 180 * 60)\n",
-    "\n",
-    "    return area_arcmin2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "area_1 = angular_area_in_arcminutes(\n",
-    "    np.mean(summary_table[\"angular_bin_right\"], axis=0) / 60.0\n",
-    ") - angular_area_in_arcminutes(\n",
-    "    np.mean(summary_table[\"angular_bin_left\"], axis=0) / 60.0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 2.2343356   6.70300681 11.17167801 15.64034921 20.10902042 24.57769162\n",
-      " 29.04636282 33.51503403 37.98370523 42.45237643]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(area)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 2.23433559  6.70300664 11.17167728 15.64034725 20.10901628 24.5776841\n",
-      " 29.04635045 33.51501503 37.98367761 42.4523379 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(area_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11812969"
-      ]
-     },
-     "execution_count": 228,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(3437) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3437.746770784939"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 / np.pi * 180 * 60"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.64056080e+07, 7.92168239e+07, 1.32028040e+08, 1.84839256e+08,\n",
-       "       2.37650472e+08, 2.90461688e+08, 3.43272904e+08, 3.96084119e+08,\n",
-       "       4.48895335e+08, 5.01706551e+08])"
-      ]
-     },
-     "execution_count": 230,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# convert square radian to square arcmins\n",
-    "area * (60 * 180 / np.pi) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  738.359375 ,  2235.2734375,  3723.515625 ,  5226.4765625,\n",
-       "        6704.328125 ,  8179.3359375,  9694.21875  , 11161.203125 ,\n",
-       "       12634.296875 , 13319.140625 ])"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(summary_table[\"ngal_in_bin\"]) / (4800 * 0.2 / 60.0) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "129.96"
-      ]
-     },
-     "execution_count": 232,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(3420 * 0.2 / 60.0) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31.622776601683793"
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(len(summary_table))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.03381839,  0.05367405, -0.22227385,  0.21364404, -0.0950446 ,\n",
-       "       -0.01416464,  0.13551095,  0.12117743,  0.31612509,  0.06814178])"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(summary_table[\"eX\"], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.51933535, 2.70680507, 3.51854926, 3.98085687, 4.75611537,\n",
-       "       5.25374396, 5.91515149, 6.08491338, 6.5474241 , 6.13523526])"
-      ]
-     },
-     "execution_count": 235,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(summary_table[\"eX\"].to_numpy(), axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "vscode": {
-     "languageId": "latex"
-    }
-   },
-   "source": [
-    "$$ \\sigma_R=\\sqrt{\\frac{\\sigma_A^2}{B^2}+\\frac{A^2 \\sigma_B^2}{B^4}}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zzz//mLfeest8/PHH5sYbb7Srfd++fdZwq1WrVubtt982M2fONGPHjjWlS5e2m96ZK36np6fbnKyyYMECh9NlhOLZPV555RWbeWJjY02VKlVcPkjfqVMnl/e/3ITe7tx27777rmnWrJlp3ry5TZh6ZS1PP/20CQoKMjfddJPdHSRuvvnmHN9jdt57771s6zPmcjDet29fI10OgrPjzu+zxx9/3Nxwww0Ofx+qV69u1q1bZ4oWLWr8/f1Nly5dzOjRo80TTzyR5Uk9NWvWNOfPn8+ydm/eri+++KL1tbCwsCzfx6BBg6zT+fv7my1btmT5fo2xD2Kz289iYmLMfffdZ/fbkvFwNvSeM2eO9f02aNDAepJFZklJSWbgwIE2y2jevLnDOz388MMP5tNPPzWvv/663bps3769+fPPP03ZsmVN0aJFzdChQ81HH31kpk2bZu644w6Hn5Pc3KUiJ3kNvd35GXjqqadMy5YtHf5eSJf/fhswYIApUaKEadGihd1JdI5Ojpo5c6bx9/c3kszkyZMd1paenm4X/Hbs2NHmKu1btmwxbdq0MT179rQ72UWS2b9/v/VkkfLly5sHH3zQfPDBB2bGjBlmyJAhdtM/+OCDxhhjPvroI+Pn52dq1KhhRo8ebaZOnWo+//xz88QTT9jd2aFo0aIO7/5y/Phxc8cddzg8uUzKOvSePXu2+fzzz80zzzxj930wdOhQs2LFClOsWDETGhpqHn30UTN9+nQzZcoU07VrV7tluHKHjCu5+nsKAAAAeKOHHnrIWCwWExwcbC5evFhgy3388ceNxWIxDzzwQIEtEwAAIDfI7P9FZm+PzN4xMnsye2PI7LP7PiOz/xeZPZk9mT2ZvURmDwAA7NFRHQAAAHCjvIbezz33nN28LVu2tB4ETUtLM8ZcDl4yhxnVq1c3Tz31lLnlllusV3ieMWOG9aC3xWIx69evty4rPT3dGsh07Ngx27rmzJljsyyLxWI2b96c7Ty5Cb0zDlBfd9111qsAO/Lrr7/aradvvvkmy+nfeustm2nLli1rrrvuOnP99debnTt32k2/ePFiu/YnTZqUZftXBkQ5nRDw/PPPG0nZXil29+7dJjg42EgyM2fOzLa97t2729RZv35961XWHfn++++tB5q7d+9uc4DdkXnz5tmtC3eG3jExMTbBUY0aNbKc9uLFizYnJDRo0CDbtjdv3mxX+80332xKlSplvv/+e7vpDx8+bMqUKWMzff/+/XN8D5MmTbJOX69evSzXaWRkpF1w8cgjj5glS5ZYH/v27bObb/jw4S4fpJ8wYYLL+5+robe7t13G95sx/+4nVz4++OADU6tWLes62rp1q83VyP/zn//k+B6zk3FyQfHixW1qyezcuXMmJCTEVKhQIdv28uP7LD4+3rRt29Zm2pCQEFOyZEnTt29fh9vrl19+cXjV9qxOKvfm7Xro0CFrmCjJdOjQIcvadu/ebbOcvn37ZjmtMa6F3rGxsWb16tVm3759ZuXKlXZXuXYm9P7ll1+sJ0EFBwdneWJBhpSUFLsTJDp37pztZzXzXShq165typcvb26++WZz7Ngxu+mv/C7LeCxevDjH9+KqvITe7v4MXLn+HJ2YlHFXgYzgd+nSpaZkyZLW17/88kub9hYuXGj9jX3xxRezfS+vv/663fKWL1/ucNoVK1bYTduqVSsTHh5uPv30U4cn240fP95m+uDgYPPmm28aPz8/88orrzj87OzYscMuyH7nnXfspjt79qzZsGGD2bdvn5k/f77dPFmF3lfq0aOHzTyNGzc2xYsXN7fddpvDExmefvppu3Xw119/5biczFz9PQUAAAC8UceOHY3FYsn1yaC5tXjxYmOxWEyrVq0KdLkAAACuIrP/F5m9Y2T2tsjsyewzkNmT2RtDZp8VMvvLyOzJ7K98kNkDAIAr0VEdAAAAcKO8hN7ffPON3ZUqq1SpkmXo9N///tdm2qCgINO0aVOTkJBgM93evXvNO++8Y9atW2cz/sqD2zldgTo+Pt4UK1bMZnndunXLdh5XQ+8rg+bp06dnO60xxtSsWdOm/Tp16jjVdsajU6dO1pMDHMl8JdDevXtnOe2VV4CdMmVKtnVHRkaa4OBg87///c/h62lpaaZ58+ZGkqlWrVq2AbYxl68Cm/m9zZ492+G0586dM+Hh4Ua6fMXlXbt2Zdt2hho1ati0787Q+6effrI7KJ9diFO/fn2b6f/5558sp42JibFbN1WrVjV79uzJcp433njDZvqQkJBsTwxISEgwZcuWtU7v6Eq9V3ryySdt2u/Tp0+20xtjzEsvveTyQfrPP//cpf3PGNdD7/zcdo6C4HLlypmIiAib6c6fP28mTZpkZs+eneMJHNk5cOCAdTnh4eE5Tv/ss8+a0NDQLF/Pz++zd999127djBo1Ktv29+3bZ3dld4vF4vCkH2/erlOnTrWZ99prr832fV/5nosXL57ttK6E3pmNHj3aZt6cQu/o6GhTuXJl6/SPP/64U8uJiIiwnhCV8Rg/fnyW0zsKse+9994sr7ienp5u6tSpYzP9Y4895lRtrshL6J2fn4HMJ/hJl08UyHx3luPHj5v33nvPLFmyxGb8iRMnrIF4rVq1TGJiYrbL2759u93ysrpTTnp6ul2wXKVKFXPw4MEs209ISHB4B4F3330327pGjBhhM33nzp2znd4YY3d1f2dCb0ch9ujRo7P8Lo+Li7N7Pzm9F0dyu58DAAAA3qRevXrGz8/PPPnkkwW63EOHDhmLxZLtcQsAAABvQGb/LzJ7x8jsbZHZk9k7QmZPZk9mb4/Mnsz+ymnJ7MnsAQCALT8BAAAA8Ki4uDi9+OKLuueee2SMsY6vXbu2Vq1apRo1ajicr2zZsjbDycnJmjJliooUKWIz/rrrrtPo0aPVrl07m2nfeOMNSVKZMmXUqVOnbGssWrSomjdvbjNu5cqVOnXqVI7vz1kvv/yyJMnf31+33357jtNf+X4kaf/+/dqyZYvDaUuUKGE37scff1TJkiWzbL9nz542w7t27cpy2n/++cf6fOPGjVlOJ11e33369FFCQoLD15csWaKtW7dKkm6//Xb5+/tn21779u3txn3xxRcOp33hhRd07tw5SVLbtm11ww03ZNt2hqZNmzo1XW5kXg9JSUm6cOFCltOXLl3aZji7z6Cj7T5t2jTVrVs3y3kyb/dLly7p2LFjWU6/atUqRUZGWoerVq2a5bSSdM8999gML1++XMnJydnO4+fn+n/dLRaLy/O4Kj+3XebvN0l6+umnde2119q1+fjjj+uee+7J03u+ch8+d+6c9u/fn+309913X5b7sJS/32dhYWE2wyEhIXrnnXeybb9OnTp66623bMYZYzR9+nS7ab15u2au7fTp01kuS7JdV3FxcYqOjs52+tyqXr26S9NPmTJFJ0+etA4PHDjQqfmuvfZaDR8+3GbchAkTsnxfmb8DK1asqC+//FKBgYEOp7dYLLrllltsxmX32+cJ+fkZcPT5/M9//qMyZcrYjKtSpYqefvpp9e7d22b8888/r5iYGEnSww8/rODg4Gxra9Kkidq0aWMdtlgsatWqlcNpLRaLXR3jxo3TNddck2X7RYoUsWlfuvx7/swzz2RbV9euXW2Gd+/ene30kuv7gGT/+WzZsqXeeeedLL/LixUrpg4dOtiM87bPJwAAAFBQMo7vZXeMKz9UqVJFFovF+n8fAACAwoTM/jIy+8vI7MnsMyOzv4zM/jIy+3+R2ZPZk9mT2QMAAGSgozoAAABQADZt2qS9e/fq6NGjOnTokLZv3645c+bo0UcfVZUqVfTmm28qPT1d0uUDmw8++KC2bt2qOnXqZNlmQECAzXB4eLhatmzpVD3z58/X2bNnJV0OW5wJ1Zo0aWIznJ6ernXr1jm1vJxs2rRJf/75pySpQYMGdqGOM/VI0urVqx1O6+gAauaTAzKrVauWzfCVwWZmoaGh1ufffvttlmFVhj59+ig+Pt7ha1OmTLE+z3xw15EaNWrYhU7r169XWlqazbhz587pq6++sg7feuutObadwVF47C4dOnRQuXLlbIYdBQ8ZMm/L2NhYp6eVXN/uUvbbPvO2ztiPs9K8eXObz0tSUpJN+OVL8nPbZf5+ky7vN/nlym0iSc8++6zNSUiZ3XDDDapSpYrdfibl//dZ5u/r0qVLKygoKMdlDBs2TJUqVbIZt2DBArvpvHm79unTR0WLFrUO33HHHdlO70pteZFTuHml1NRUTZw40WZeV04suu+++2yGo6OjNW3aNIfTZn7/QUFBOf7eu/Lb5wn5+RnIy+fz+PHjmjNnjnW4f//+Ts03f/58PfTQQ+rXr58WLVpkF1JfKfNJcM587jKfUOLM73nmIP3MmTNKTEzMdh5X9oEMmbdNTr/Pkvd/PgEAAICCknFcsUKFCgW63MDAQIWGhtJRHQAA+Cwy++yR2f+LzJ7M3ld4c7brKjL7f3nzdiWzJ7Mns/8Xmb33fT4BAIBn2f81BwAAAMDtsjuIKV0+kHndddepW7duGj58uOrVq+fyMooVK+b0tFcGHZUrV9aJEydynCc8PNxu3O+//253tencuLKe6tWrO1WPoyDp999/z3MtGTIfGI6Li8ty2o4dO+rbb7+VdDnQ6N27t+bMmaMuXbo4nH7IkCEOx1+6dEmrVq2yDoeFhTm1LqpVq2Zz9eT4+Hjt3bvX5urrM2fOtDlo7Shk84SwsDBt3rxZ06dPV3h4uB5++GGX5k9NTXVrPY4Cgey2fcbJIxmioqKybd9isah+/fr65ZdfbNqoWbOmi5V6XkFvO1e+41xVt25dVaxY0Xql5yVLlmjw4MH65JNPVLx4cYfzHDx40OF4b/w+ky4Hnrfffrs+/PBD67jjx4/rzJkzNie2e/N2vfbaa7Vp0ybNmTNH1157rR544IF8rS0/bNu2zeZuANdcc43DsDUrTZo0Ua1atWw+fz///LNGjx7tlvpc+e3zhIL+DDj7+Zw7d671JJjSpUtne9X0K1WoUCHLkxbcIbu70LgyT2xsrFOhdH7z9s8nAAAAUFCSkpIk2d8xrSCEhoZmeydDAAAAb0Zm73w93pJxkdkXDDJ7MntnkdnnDZk9mb0zvD0TJbN3DZk9AAC4mtBRHQAAACgAW7ZsUVxcnJKSkpSWliZ/f38FBQWpZMmSKlu2rCpXrqzAwMACq+fXX3+1Pp88ebImT56cq3bcdVLipk2brM8XLlyohQsXerQeyf6qodldqXno0KEaP368Tp06Jeny1UK7deumIUOGaMKECQ5PGHBk8+bNNlf3bteuXS4qv+zYsWM2ofeKFStsXq9du3au23a3mjVr6s0338x2msOHD2vGjBnasWOHzfjstktuOLqie3ZXXM+83+7duzfHZZQvX95muFSpUs4V54W8advlhZ+fn8aMGaOnnnrKOu7rr7/W+vXr9cEHH+i2225zui1v/D7L0KNHD5vQW5L27dtndwc2b96ujRo1UqNGjbKdZteuXfr888915swZm/He8Jlbs2aNzXDmOwM4o1mzZjah96ZNm5ScnOzUVfpzkvk7MKc7TniCN34GVq5caX1epUqVfFlGbmS+ontu58nqjjIFzRc+nwAAAEBBSE9Pl8ViydWdkvKqII9hAwAAuBuZffa8MeMisy843pQPktm7xpu2XV6Q2ZPZFxQy+7zzxs8AmX3B8IXPJwAA8Bw6qgMAAAAFoEWLFp4uwSoyMlInT560Dj/11FPq1KlTrtpyNszNyZWhycCBA3X33Xfnqp2srqKc34oVK6Yvv/xSPXr0UEpKiqTLB9ZnzZqlxYsX67///a+GDx/uMFC90pXrwWKxaPHixbmuqUGDBtbnaWlpNic6SLkLWgpaWlqalixZoqlTp2r16tXq0qWLwsLCdPHiRU+XZnXttdfaDG/btk0JCQkqWrRolvNcGZQHBgaqatWq+Vafp/jCtsvsiSee0E8//aTly5dbxx0/flz9+/dXz549NXnyZKeuou/N32f169e3G5fTHQWu5M3bNSkpSfPmzdPUqVO1fft23XrrrSpWrJj1LnPeIvNV/XP6XXCkUaNG+uabb6zDiYmJOnv2bKH8LnGFJz8Df/zxh/V5SEhIvi+voBEuAwAAAN4pOTm5wJfJ/w8AAIAvI7PPnjdnXM4gs88f3pwPZiCzd8wXtl1mZPY58+btSmZPZk9mn384JgcAAHwBHdUBAACAq0xkZKTNcI0aNdS7d28PVXP5IHVcXJx1uFy5ch6tJ7c6deqk2bNna9CgQTYH2KOiovTwww9rxowZmjp1qho3bpxlG1duG2OM2rVrp5IlS+a5tjNnzig2NtZmXJEiRfLcbn6Ji4vTtGnT9MEHH+jChQsaMWKEPvvsM1WpUkUdOnTQ4cOHPV2i1S233GJzRe/4+HgtWrQo26DzyJEj1ucdO3b02Mka+cGXtl1mfn5++u6779S7d2+tW7fO5rWlS5fqhhtu0NixYzVmzJgsr4Lt7d9n1apVU1BQkM2J7M5cddmbt+v58+c1efJkffTRRzLG6IknntDixYtVtmxZ1ahRw6VQvyBk/g2+dOmSy21UqlTJYbtXa+jt6c9AXFyczp07Zx1OSEjI1+UBAAAAQIa9e/fq5ptvLrDlJSUl6fTp0wW2PAAAgMKMzD5/kNm7jzfng5mR2dvypW2XGZl91rx5u3o6r3UVmb37efozQGYPAADgHeioDgAAAFxlMh/89fTJhRcuXLAZ9nQ9eTFgwABVrlxZ99xzj44ePWrz2pYtW9S8eXM9/fTT+s9//qPg4GC7+R1tG3eE3ufPn7cbFxsb63VXaE9OTtaUKVP05ptv6sKFCxo+fLjeeOMNlS5d2tOlZem6667T3Xffrblz51rH/ec//9Htt99ucxX2DOfPn9euXbusw2PHji2QOvObL247R0qUKKHly5frqaee0kcffWTzWkJCgl566SXNnj1bM2bMUMuWLe3m94XvsxIlStjUmd33gDdv19jYWL399tuaOHGiUlJS9Oyzz2rs2LEqVqyYp0vL1pUnRUi5C70d/S4kJibmuiZf5S2fgejoaJvhzN8DAAAAAJBfXn31VQUGBqpGjRq5uvuXK6KiovTVV18pISEh35cFAABwNSCzzz9k9nnjzflgVsjsL/PFbecImb0tb96u3pLXuorM3n285TNAZg8AAOAd6KgOAAAAXGX8/Pxshq+8UrQneFs9edWqVSvt2LFDY8eO1ccff6z09HTra2lpaZowYYJWr16tJUuWqGLFijbzOloX1113XZ5rchSqXLx40atC74iICN12223avXu3QkND9eOPP6pHjx6eLsspn3zyiSIiIrR9+3ZJ0t9//61HH31Un3zyid02fe2116yh14svvqj27dsXeL3u5svbzpGgoCBNmTJFt956q0aOHKkDBw7YvL5nzx61bdtWEydO1KOPPmrzmi98nxUrVswmlMsqwPbm7bplyxYNGDBAJ06cUOXKlbVw4UI1a9bM02U5JSwszGb47NmzSk9Pt/vsZCckJMRuXJkyZfJcmy/x5s/AsWPHdOnSJYfbCQAAAADc6dSpU3rggQc8XQYAAABywdsyJW+rJ6/I7HPHm/PBnJDZ++62c4TM/jJv3q7enNfmhMzePbz5M0BmDwAA4BnO/0UNAAAAoFAoVaqUzfCePXs8U8j/y1zP3r17ZYzxTDFuEhoaqilTpmj79u3q1KmT3evbt29Xx44d7a7oml/bpkSJEnbj9u7d65a23WHTpk1q2bKldu/eLX9/fy1atMhrwjVnlCxZUmvXrtV9991nHffpp5+qU6dOWrJkiQ4cOKAtW7Zo2LBh+vDDD1W0aFFNmjRJ48eP91zRbuLr2y473bp1065du/T222/bnSCSkpKikSNHaurUqTbjfeH7LD4+3mb42muvtZvGm7frggUL1KFDB504cUIlSpTQihUrvCbsdEZ4eLjNcGJioiIiIlxqIyEhIcd2CzNv+wxkPnEkPT1dv/76q4eqAQAAAHC1McYU6AMAAADuQWaf/8jsXePN+aAzyOx9d9tlh8zeO7ert+W1riKzzztv+wyQ2QMAAHgHOqoDAAAAV5ly5crZDO/atUv//POPh6qRgoODVbJkSevwxYsX9ccff3isHndq1KiRVq1apSVLlqhGjRo2r+3bt0/vv/++zbjM22b16tVuqcPR1Ze9ZR2fPn1at99+u6KioiRJQ4YM8ckrlpcoUUIzZszQl19+aR23bt069e3bV7Vr19ZNN92kefPm6aGHHtLu3bv1+OOPe7Ba9ygs2y47wcHBGjNmjA4cOKDhw4fLYrHYvP7888/bnLzi7d9n6enpiomJsQ7XqVPHLiz15u26a9cuDRo0SImJiZKk0aNHq169eh6uyjUNGza0G/fXX3+51EbmO27UrFnT7qrvhZU3fgaKFClidyX2L774IldtXbx40Q0VAQAAALhaWCyWAn8AAADAPcjsCw6Zfc68OR90BZm972677JDZe9d29ca81lVk9nnjjZ8BMnsAAADvQEd1AAAA4CoTFhammjVrWofT0tL09ddf56qtr7/+WpMnT85zTU2bNrUZnjVrVq7aWbdunZ5//vk81+Oq3r17a+PGjdm+/tdff9ld3fjTTz+1Gc68Hn766adcnZBgjNGQIUN06NAhSVLlypXtri69aNEil9vND6+++qrOnDljHb7rrrs8WE3eREREaOzYserfv7/27t2r/fv3a/369fr555+1fft2nT9/XtOmTbPZ/3xZYdp227ZtU5cuXbJ8vWzZsvrkk0+0ZMkSBQcHW8fHxMRo3rx5NtN68/fZwYMHlZqaah3u2LGj3TTevF2feeYZm6vLe1NtznK0zleuXOlSGxnf7dm1WVh562egRYsWNsPz58+32045OXXqlBo1amR3UgMAAAAAAAAAoPAhs3c/Mvvc8+Z80FVk9r677cjs/+XN29Vb81pXkNnnjbd+BsjsAQAAPI+O6gAAAMBVqHXr1jbD7777rssHWQ8fPqyRI0eqfv36bq/ns88+0/Hjx11qIyoqSkOHDlXdunXzXE9urFq1KtvXQ0JC9N1336lKlSrWcadOnbI5eN+0aVMFBQVZh1NTUzV+/HiXa5k4caI2btxovSK8n5+fbrrpJptptmzZor///tvltt0pKSnJ7oSLzFexz8wYk48V5d7u3bvVrl07de/eXfPnz9d1112n2rVrq23bturevbuaNGmiwMBAtyzLmXWQkpLilmVlpTBtuwybNm2yXvE5K7169dKECRNsxh04cMBm2Ju/z37//Xeb4REjRtgMe/N2PX78uFasWGEzLj9rS0tLy/W82aldu7bdFdq///57l5a3a9cum2FvCX7zW0F/BlyR+Q4GSUlJGjFihNLT052aPykpSQMGDNDdd99td6V3AAAAAMiKMUbGGIWGhqpy5cqqVq2a2x9Vq1ZVmTJlrMsDAACA+5DZux+Zveu8OR90FZm97267DGT23r1dyez/RWb/L2/5fJLZAwAAeB4d1QEAAAA3Sk5Odjg+vw6e59bAgQNthk+dOqVx48Y5PX9sbKz69++vunXrqkOHDm6vJy4uTo8//rjT86empuree++VMUb33HNPnuvJjcxXaHakWLFiGjp0qM242NhY6/PixYurX79+Nq9/9NFH2rZtm9N1rF27VmPGjNHo0aPl5/fvf/nuuOMOm+mMMXrhhRecajNzgOquz/Phw4cVFxdnMy4yMjLbec6ePWszfOWVpj1l37596tixo4KDgzV58mRZLBa3tn/l1cAl6fTp0znOc+TIEbfWkFlh2XZXSkhI0JIlS3Kc7qGHHlJAQIB1OPMJQ978fbZw4ULr87Zt26px48Y2r3vzdt29e7ddgJldbenp6XavZ1fblSccSVJ0dLTTtWWuK6egdcyYMTbDkZGRWrp0qVPLSktLswl+GzdurG7dujlZqW/L789AXgwdOlT+/v4241auXKmHH344x89DcnKy7rrrLh04cED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-      "text/plain": [
-       "<Figure size 4800x5200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(6, 2, figsize=(24, 26))\n",
-    "angular_bin_mid = (\n",
-    "    (\n",
-    "        np.mean(summary_table[\"angular_bin_left\"], axis=0)\n",
-    "        + np.mean(summary_table[\"angular_bin_right\"], axis=0)\n",
-    "    )\n",
-    "    / 2\n",
-    "    / 60\n",
-    ")\n",
-    "\n",
-    "\n",
-    "mean_eX = np.mean(summary_table[\"eX\"], axis=0)\n",
-    "mean_rX = np.mean(summary_table[\"rX\"], axis=0)\n",
-    "sigma_eX = np.std(summary_table[\"eX\"].to_numpy(), axis=0) / np.sqrt(\n",
-    "    len(summary_table)\n",
-    ")\n",
-    "sigma_rX = np.std(summary_table[\"rX\"].to_numpy(), axis=0) / np.sqrt(\n",
-    "    len(summary_table)\n",
-    ")\n",
-    "sigma_gX = np.sqrt(\n",
-    "    (sigma_eX / mean_rX) ** 2 + (mean_eX * sigma_rX / mean_rX**2) ** 2\n",
-    ")\n",
-    "\n",
-    "true_gX = np.mean(summary_table[\"r_weighted_gX\"], axis=0) / np.mean(\n",
-    "    summary_table[\"rX\"], axis=0\n",
-    ")\n",
-    "\n",
-    "axes[0, 0].errorbar(\n",
-    "    angular_bin_mid, mean_eX / mean_rX, label=\"measured gX\", yerr=sigma_gX\n",
-    ")\n",
-    "axes[0, 0].plot(\n",
-    "    angular_bin_mid,\n",
-    "    true_gX,\n",
-    "    label=\"r weighted true gX\",\n",
-    ")\n",
-    "axes[0, 0].set_ylim(-0.01, 0.01)\n",
-    "axes[0, 0].set_ylabel(\"gX\")\n",
-    "axes[0, 0].set_xlabel(\"arcmin\")\n",
-    "axes[0, 0].legend()\n",
-    "\n",
-    "axes[1, 0].plot(angular_bin_mid, mean_eX / mean_rX - true_gX, label=\"true gX\")\n",
-    "axes[1, 0].set_ylabel(r\"gX_meas - gX_true\")\n",
-    "\n",
-    "mean_eT = np.mean(summary_table[\"eT\"], axis=0)\n",
-    "mean_rT = np.mean(summary_table[\"rT\"], axis=0)\n",
-    "sigma_eT = np.std(summary_table[\"eT\"].to_numpy(), axis=0) / np.sqrt(\n",
-    "    len(summary_table)\n",
-    ")\n",
-    "sigma_rT = np.std(summary_table[\"rT\"].to_numpy(), axis=0) / np.sqrt(\n",
-    "    len(summary_table)\n",
-    ")\n",
-    "sigma_gT = np.sqrt(\n",
-    "    (sigma_eT / mean_rT) ** 2 + (mean_eT * sigma_rT / mean_rT**2) ** 2\n",
-    ")\n",
-    "true_gT = np.mean(summary_table[\"r_weighted_gT\"], axis=0) / np.mean(\n",
-    "    summary_table[\"rT\"], axis=0\n",
-    ")\n",
-    "\n",
-    "axes[0, 1].errorbar(\n",
-    "    angular_bin_mid, mean_eT / mean_rT, label=\"measured gT\", yerr=sigma_gT\n",
-    ")\n",
-    "\n",
-    "axes[0, 1].set_ylim(0, 0.05)\n",
-    "# axes[0,1].plot(angular_bin_mid, summary_table[\"gT_true\"]/ summary_table[\"rT\"],label=\"true gT\")\n",
-    "axes[0, 1].plot(\n",
-    "    angular_bin_mid,\n",
-    "    true_gT,\n",
-    "    label=\"r weighted true gT\",\n",
-    ")\n",
-    "axes[0, 1].legend()\n",
-    "axes[0, 1].set_xlabel(\"arcmin\")\n",
-    "axes[0, 1].set_ylabel(\"gT\")\n",
-    "\n",
-    "axes[1, 1].plot(\n",
-    "    angular_bin_mid, mean_eT / mean_rT / true_gT - 1, label=\"true gT\"\n",
-    ")\n",
-    "axes[1, 1].set_ylabel(r\"gT_meas/gT_true - 1\")\n",
-    "axes[1, 1].set_ylim(-0.04, 0.04)\n",
-    "\n",
-    "# axes[1,0].errorbar()\n",
-    "\n",
-    "axes[3, 0].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"kappa_true\"].to_numpy(), axis=0)\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    "    yerr=np.std(summary_table[\"kappa_true\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table))\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    ")\n",
-    "axes[3, 0].set_ylabel(rf\"$<\\kappa$>\")\n",
-    "axes[3, 0].set_xlabel(\"arcmin\")\n",
-    "\n",
-    "axes[2, 0].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"rX\"].to_numpy(), axis=0)\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    "    yerr=np.std(summary_table[\"rX\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table))\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    ")\n",
-    "axes[2, 0].set_xlabel(\"arcmin\")\n",
-    "axes[2, 0].set_ylabel(rf\"<rX>\")\n",
-    "# axes[1, 0].set_ylim(0.16, 0.185)\n",
-    "\n",
-    "axes[2, 1].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"rT\"].to_numpy(), axis=0)\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    "    yerr=np.std(summary_table[\"rT\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table))\n",
-    "    / np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0),\n",
-    ")\n",
-    "axes[2, 1].set_xlabel(\"arcmin\")\n",
-    "axes[2, 1].set_ylabel(rf\"<rT>\")\n",
-    "# axes[1, 1].set_ylim(0.16, 0.185)\n",
-    "\n",
-    "\n",
-    "axes[3, 1].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0) / area,\n",
-    "    yerr=np.std(summary_table[\"ngal_in_bin\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table))\n",
-    "    / area,\n",
-    "    label=f\"n_halo={len(summary_table)}\",\n",
-    ")\n",
-    "axes[3, 1].set_xlabel(\"arcmin\")\n",
-    "axes[3, 1].set_ylabel(rf\"Detection Number density [arcmin$^{-2}$]\")\n",
-    "axes[3, 1].legend()\n",
-    "\n",
-    "axes[4, 0].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"lensed_shift\"], axis=0),\n",
-    "    yerr=np.std(summary_table[\"lensed_shift\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table)),\n",
-    "    label=\"shift\",\n",
-    ")\n",
-    "axes[4, 0].errorbar(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"radial_lensed_shift\"], axis=0),\n",
-    "    yerr=np.std(summary_table[\"radial_lensed_shift\"].to_numpy(), axis=0)\n",
-    "    / np.sqrt(len(summary_table)),\n",
-    "    label=\"radial_shift\",\n",
-    ")\n",
-    "axes[4, 0].set_ylabel(\"Lens shift [arcsec]\")\n",
-    "axes[4, 0].legend()\n",
-    "\n",
-    "axes[4, 1].plot(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"median_match_dist\"], axis=0),\n",
-    "    label=\"match failure rate\",\n",
-    ")\n",
-    "axes[4, 1].set_ylabel(\"Median match distance pixel\")\n",
-    "\n",
-    "axes[5, 0].plot(\n",
-    "    angular_bin_mid,\n",
-    "    np.mean(summary_table[\"match_failure_rate\"], axis=0),\n",
-    "    label=\"match failure rate\",\n",
-    ")\n",
-    "axes[5, 0].set_ylabel(\"Match failure rate (> 2 pixel)\")\n",
-    "\n",
-    "for ax in axes.flatten():\n",
-    "    ax.set_xlim(0, 8)\n",
-    "    ax.set_xlabel(\"Prelensed angular separation [arcmin]\")\n",
-    "\n",
-    "plt.subplots_adjust(hspace=0.2, wspace=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [0.019230769230769232, 0.007246376811594203, 0...\n",
-       "1      [0.0, 0.0, 0.0021321961620469083, 0.0029940119...\n",
-       "2      [0.0, 0.003968253968253968, 0.0043103448275862...\n",
-       "3      [0.0, 0.006756756756756757, 0.0, 0.0, 0.001169...\n",
-       "4      [0.0, 0.0, 0.0, 0.0029498525073746312, 0.00244...\n",
-       "                             ...                        \n",
-       "995    [0.0, 0.006514657980456026, 0.0041237113402061...\n",
-       "996    [0.0, 0.0, 0.0019880715705765406, 0.0, 0.00542...\n",
-       "997    [0.0, 0.003816793893129771, 0.0021141649048625...\n",
-       "998    [0.0, 0.006993006993006993, 0.0021141649048625...\n",
-       "999    [0.0, 0.0, 0.0, 0.0014771048744460858, 0.00341...\n",
-       "Name: match_failure_rate, Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"match_failure_rate\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [0.43000175580223443, 0.42300000618197414, 0.4...\n",
-       "1      [0.41461516440910473, 0.4238130397024451, 0.42...\n",
-       "2      [0.4050650050799802, 0.4300328583630993, 0.425...\n",
-       "3      [0.43759211871649956, 0.41683416078536073, 0.4...\n",
-       "4      [0.42047469044547797, 0.4225412692755799, 0.41...\n",
-       "                             ...                        \n",
-       "995    [0.43301320869744425, 0.40793810076306386, 0.4...\n",
-       "996    [0.41003381651770504, 0.4014026310662927, 0.40...\n",
-       "997    [0.44851799159071776, 0.4258262141756871, 0.41...\n",
-       "998    [0.4461937580937082, 0.4306107422368509, 0.417...\n",
-       "999    [0.4334838926242575, 0.4037171985881046, 0.422...\n",
-       "Name: median_match_dist, Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"median_match_dist\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      [6.548769677755795, 10.919214248243737, 13.837...\n",
-       "1      [6.567433247612955, 10.845045638493268, 13.815...\n",
-       "2      [6.214727902444133, 10.82393177789738, 13.8616...\n",
-       "3      [6.523210449450341, 10.902253554665549, 13.870...\n",
-       "4      [6.4563566944380195, 10.851672100409964, 13.85...\n",
-       "                             ...                        \n",
-       "995    [6.200166986992217, 10.914875566062578, 13.845...\n",
-       "996    [6.632536406983318, 10.90862467429626, 13.8115...\n",
-       "997    [6.493509266248275, 10.75706637816148, 13.7618...\n",
-       "998    [6.191086287150966, 10.791755890732452, 13.810...\n",
-       "999    [6.021851364002567, 10.771095713746117, 13.924...\n",
-       "Name: radial_lensed_shift, Length: 1000, dtype: object"
-      ]
-     },
-     "execution_count": 239,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary_table[\"radial_lensed_shift\"]"
-   ]
-  }
- ],
- "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": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}