diff --git a/examples/demo_coordinate_system.ipynb b/examples/demo_coordinate_system.ipynb
index 0ba689190..49a675d38 100644
--- a/examples/demo_coordinate_system.ipynb
+++ b/examples/demo_coordinate_system.ipynb
@@ -1,5 +1,49 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0711213a",
+   "metadata": {},
+   "source": [
+    "# Tests of coordinate system effects on shear profiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a2b9d35",
+   "metadata": {},
+   "source": [
+    "Authors: Marina Ricci, Tomomi Sunayama\n",
+    "\n",
+    "Tested, modified, and documented by: Camille Avestruz, Caio Lima de Oliveira\n",
+    "\n",
+    "In this notebook we illustrate the importance of setting the correct coordinate system for shear catalogs. We start by showing these effects on generated mock data, then on HSC Y3 data.\n",
+    "\n",
+    "Throughout this notebook we also show how to correctly set the coordinate system and how to update it and convert data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d67ea10",
+   "metadata": {},
+   "source": [
+    "### Shear coordinate system definition and conversion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b03619a",
+   "metadata": {},
+   "source": [
+    "* Celestial coordinate system: the declination $\\delta$ and the right ascension $\\alpha$ take the role of the spherical angles $\\theta$ and $\\varphi$.\n",
+    "\n",
+    "* Euclidean coordinate system: a cartesian coordinate system defined on the plane tanget to the celestial sphere at the point of observation. Here, the $y$-axis is parallel to the declination $\\delta$ and the $x$-axis is antiparallel to the right ascension $\\alpha$.\n",
+    "\n",
+    "In a small angles, planar approximation of the Celestial coordinates, both coordinate systems are related by a parity transformation of the $x$-axis. The conversion between the Euclidean ellipticity $\\epsilon^E = \\epsilon_1^E + i \\epsilon_2^E$ and the Celestial ellipticity $\\epsilon^C = \\epsilon_1^C + i \\epsilon_2^C$ is then given by the transformation $\\varphi \\rightarrow \\pi - \\varphi$:\n",
+    "\n",
+    "$$\\epsilon^C_1 + i \\epsilon_2^C = |\\epsilon| e^{2 i \\varphi^\\prime} = |\\epsilon| e^{2 i (\\pi  - \\varphi)} = |\\epsilon| e^{- 2 i \\varphi} = \\epsilon^E_1 - i \\epsilon_2^E$$"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "28a917d8-04b1-4a4d-9f3d-3bf0be1b5dba",
@@ -10,23 +54,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "423b5f84-831a-4da7-af39-92e1b6dc3818",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.12.5'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import clmm\n",
+    "\n",
     "clmm.__version__"
    ]
   },
@@ -37,8 +71,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from astropy.io import fits\n",
+    "from scipy import spatial\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -51,6 +88,7 @@
    "outputs": [],
    "source": [
     "from clmm.support import mock_data as mock\n",
+    "from clmm.utils import convert_units\n",
     "from clmm import Cosmology"
    ]
   },
@@ -105,7 +143,8 @@
     "    \"cluster_ra\": cluster_ra,\n",
     "    \"cluster_dec\": cluster_dec,\n",
     "    \"cluster_c\": concentration,\n",
-    "    \"cosmo\": cosmo}"
+    "    \"cosmo\": cosmo,\n",
+    "}"
    ]
   },
   {
@@ -121,7 +160,16 @@
     "    \"zsrc_min\": cluster_z + 0.1,\n",
     "    \"photoz_sigma_unscaled\": 0.05,\n",
     "    \"ngals\": 1000,\n",
-    "    \"pz_bins\": np.linspace(0, 10, 1001)}"
+    "    \"pz_bins\": np.linspace(0, 10, 1001),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fd4f56e",
+   "metadata": {},
+   "source": [
+    "We must supply the coordinate system information when generating a mock galaxy catalog. If we don't, a warning is issued and problems may arise!"
    ]
   },
   {
@@ -134,9 +182,7 @@
     "np.random.seed(679)\n",
     "\n",
     "mock_sources_euclidean_coord = mock.generate_galaxy_catalog(\n",
-    "    **cluster_kwargs,\n",
-    "    **source_kwargs,\n",
-    "    coordinate_system = \"euclidean\"\n",
+    "    **cluster_kwargs, **source_kwargs, coordinate_system=\"euclidean\"\n",
     ")"
    ]
   },
@@ -150,88 +196,107 @@
     "np.random.seed(679)\n",
     "\n",
     "mock_sources_celestial_coord = mock.generate_galaxy_catalog(\n",
+    "    **cluster_kwargs, **source_kwargs, coordinate_system=\"celestial\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec9fab17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(679)\n",
+    "\n",
+    "# In this case, we are going to generate a mock catalog without setting\n",
+    "# the coordinate system, which will raise a warning and default to \"euclidean\".\n",
+    "mock_sources_default_coord = mock.generate_galaxy_catalog(\n",
     "    **cluster_kwargs,\n",
     "    **source_kwargs,\n",
-    "    coordinate_system = \"celestial\"\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1b1cfb30",
+   "metadata": {},
+   "source": [
+    "Note that $e_1$ remains the same for all catalogs, while $e_2$ changes its sign between coordinate systems. Also, notice the `coordinate_system` metadata!"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "bc2a6f85-d71e-497e-875e-358e0c68cfad",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<b>GCData</b>\n",
-       "<br> <b>defined by:</b> cosmo=None\n",
-       "<br> <b>with columns:</b> ra, dec, e1, e2, z, ztrue, pzpdf, id\n",
-       "<br> <b>and pzpdf:</b> shared_bins [ 0.    0.01  0.02  0.03  0.04 ...  9.96  9.97  9.98  9.99 10.  ]\n",
-       "<br> 5 objects\n",
-       "<br> \n",
-       "<table id=\"table140158865710992\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>ra</th><th>dec</th><th>e1</th><th>e2</th><th>z</th><th>ztrue</th><th>pzpdf</th><th>id</th></tr></thead>\n",
-       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64[1001]</th><th>int64</th></tr></thead>\n",
-       "<tr><td>47.723234904384796</td><td>87.0656774075493</td><td>-0.011529738473189206</td><td>-0.02040149427643137</td><td>3.1805837221695556</td><td>3.043830614065144</td><td>3.647766317906808e-54 .. 1.9074233381687797e-247</td><td>0</td></tr>\n",
-       "<tr><td>50.11980155700514</td><td>87.14872380985058</td><td>0.013958344929753234</td><td>0.0011203741510053653</td><td>0.7856618017433363</td><td>0.7400628671331151</td><td>8.99369409332192e-18 .. 0.0</td><td>1</td></tr>\n",
-       "<tr><td>50.215503632796754</td><td>87.14154284237922</td><td>0.019262690657235064</td><td>0.0029416444987064665</td><td>1.5389107660369346</td><td>1.4196555173326562</td><td>2.4191360060433974e-35 .. 0.0</td><td>2</td></tr>\n",
-       "<tr><td>51.55166808262403</td><td>87.18878322339597</td><td>0.009405867269504943</td><td>0.008985368631618624</td><td>1.6557865622055825</td><td>1.5926395218848042</td><td>1.1503818689219576e-35 .. 0.0</td><td>3</td></tr>\n",
-       "<tr><td>52.609866325677494</td><td>87.2011500545015</td><td>0.0028097533943904243</td><td>0.005749717868734056</td><td>0.5174117144571546</td><td>0.5827653595181967</td><td>2.631862057687664e-09 .. 0.0</td><td>4</td></tr>\n",
-       "</table></div>"
-      ],
-      "text/plain": [
-       "GCData(cosmo=None, columns: ra, dec, e1, e2, z, ztrue, pzpdf, id, pzpdf: shared_bins)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mock_sources_euclidean_coord[:5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "d5fbe561-c072-41cf-abdf-14845f28c0c2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<b>GCData</b>\n",
-       "<br> <b>defined by:</b> cosmo=None\n",
-       "<br> <b>with columns:</b> ra, dec, e1, e2, z, ztrue, pzpdf, id\n",
-       "<br> <b>and pzpdf:</b> shared_bins [ 0.    0.01  0.02  0.03  0.04 ...  9.96  9.97  9.98  9.99 10.  ]\n",
-       "<br> 5 objects\n",
-       "<br> \n",
-       "<table id=\"table140158865706768\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>ra</th><th>dec</th><th>e1</th><th>e2</th><th>z</th><th>ztrue</th><th>pzpdf</th><th>id</th></tr></thead>\n",
-       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64[1001]</th><th>int64</th></tr></thead>\n",
-       "<tr><td>47.723234904384796</td><td>87.0656774075493</td><td>-0.011529738473189206</td><td>0.02040149427643137</td><td>3.1805837221695556</td><td>3.043830614065144</td><td>3.647766317906808e-54 .. 1.9074233381687797e-247</td><td>0</td></tr>\n",
-       "<tr><td>50.11980155700514</td><td>87.14872380985058</td><td>0.013958344929753234</td><td>-0.0011203741510053653</td><td>0.7856618017433363</td><td>0.7400628671331151</td><td>8.99369409332192e-18 .. 0.0</td><td>1</td></tr>\n",
-       "<tr><td>50.215503632796754</td><td>87.14154284237922</td><td>0.019262690657235064</td><td>-0.0029416444987064665</td><td>1.5389107660369346</td><td>1.4196555173326562</td><td>2.4191360060433974e-35 .. 0.0</td><td>2</td></tr>\n",
-       "<tr><td>51.55166808262403</td><td>87.18878322339597</td><td>0.009405867269504943</td><td>-0.008985368631618624</td><td>1.6557865622055825</td><td>1.5926395218848042</td><td>1.1503818689219576e-35 .. 0.0</td><td>3</td></tr>\n",
-       "<tr><td>52.609866325677494</td><td>87.2011500545015</td><td>0.0028097533943904243</td><td>-0.005749717868734056</td><td>0.5174117144571546</td><td>0.5827653595181967</td><td>2.631862057687664e-09 .. 0.0</td><td>4</td></tr>\n",
-       "</table></div>"
-      ],
-      "text/plain": [
-       "GCData(cosmo=None, columns: ra, dec, e1, e2, z, ztrue, pzpdf, id, pzpdf: shared_bins)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mock_sources_celestial_coord[:5]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f04aa68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mock_sources_default_coord[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a352677d",
+   "metadata": {},
+   "source": [
+    "Now, we can easily convert between the diferente coordinates with the method `update_coordinate_system()`. However, pay attention to the fact we must supply which columns we want to update!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2969f85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mock_sources_default_coord.update_coordinate_system(\"celestial\", \"e2\")\n",
+    "\n",
+    "mock_sources_default_coord[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "588701b8",
+   "metadata": {},
+   "source": [
+    "We can also update the coordinate system but not convert the data. This is useful if the original coordinate system was incorrectly set but may also lead to errors (as we intentionally do now)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "935e75ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mock_sources_default_coord.update_coordinate_system(\"euclidean\")\n",
+    "\n",
+    "mock_sources_wrong_coord = mock_sources_default_coord\n",
+    "\n",
+    "mock_sources_wrong_coord[:5]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7e852ccb-a49a-441f-89b9-da314fecd5ce",
@@ -242,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "6d76bb85-2c93-4778-8135-1f1e6c139689",
    "metadata": {},
    "outputs": [],
@@ -254,88 +319,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "353b4dbb-6239-4763-9125-d5d53b2f7d4e",
    "metadata": {},
    "outputs": [],
    "source": [
     "cl_euclidean = GalaxyCluster(\n",
-    "        cluster_id, cluster_ra, cluster_dec, cluster_z, \n",
-    "        mock_sources_euclidean_coord, \n",
-    "        coordinate_system=\"euclidean\"\n",
+    "    cluster_id,\n",
+    "    cluster_ra,\n",
+    "    cluster_dec,\n",
+    "    cluster_z,\n",
+    "    mock_sources_euclidean_coord,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "20c559a0-0ce2-411c-8d12-dbcb3fc42026",
    "metadata": {},
    "outputs": [],
    "source": [
     "cl_celestial = GalaxyCluster(\n",
-    "        cluster_id, cluster_ra, cluster_dec, cluster_z, \n",
-    "        mock_sources_celestial_coord, \n",
-    "        coordinate_system=\"celestial\"\n",
+    "    cluster_id,\n",
+    "    cluster_ra,\n",
+    "    cluster_dec,\n",
+    "    cluster_z,\n",
+    "    mock_sources_celestial_coord,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "64890dbd-1e26-4594-8a7c-f5837f765e39",
+   "execution_count": 19,
+   "id": "a275df25",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#here the mock sources have been generated with \"celestial\" coordinate, \n",
-    "#but we don't pass this info to the GalaxyCluster object\n",
-    "\n",
-    "cl_wrong_coordinate = GalaxyCluster(\n",
-    "        cluster_id, cluster_ra, cluster_dec, cluster_z, \n",
-    "        mock_sources_celestial_coord,\n",
-    "        #this is equivalent to the default coordinate_system = \"euclidean\"\n",
+    "cl_wrong = GalaxyCluster(\n",
+    "    cluster_id,\n",
+    "    cluster_ra,\n",
+    "    cluster_dec,\n",
+    "    cluster_z,\n",
+    "    mock_sources_wrong_coord,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "efefdd51-eb7a-4e26-a2dd-da574f213932",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f7946ce5db0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax1 = plt.subplots(1, 1)\n",
     "\n",
-    "ax1.scatter(cl_euclidean.galcat[\"e1\"], cl_euclidean.galcat[\"e2\"], s=1, alpha=0.3, label='euclidean')\n",
-    "ax1.scatter(cl_celestial.galcat[\"e1\"], cl_celestial.galcat[\"e2\"], s=1, alpha=0.3, color='red',label='celestial')\n",
+    "ax1.scatter(cl_euclidean.galcat[\"e1\"], cl_euclidean.galcat[\"e2\"], s=2, alpha=0.5, label=\"euclidean\")\n",
+    "ax1.scatter(\n",
+    "    cl_celestial.galcat[\"e1\"],\n",
+    "    cl_celestial.galcat[\"e2\"],\n",
+    "    s=2,\n",
+    "    alpha=0.5,\n",
+    "    color=\"red\",\n",
+    "    label=\"celestial\",\n",
+    ")\n",
     "\n",
-    "ax1.set_xlabel(\"e1\")\n",
-    "ax1.set_ylabel(\"e2\")\n",
+    "ax1.set_xlabel(\"$\\\\epsilon_1$\")\n",
+    "ax1.set_ylabel(\"$\\\\epsilon_2$\")\n",
     "ax1.set_aspect(\"equal\", \"datalim\")\n",
+    "ax1.set_xlim(-0.125, 0.125)\n",
+    "ax1.set_ylim(-0.125, 0.125)\n",
     "ax1.axvline(0, linestyle=\"dotted\", color=\"black\")\n",
     "ax1.axhline(0, linestyle=\"dotted\", color=\"black\")\n",
-    "plt.legend()"
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -346,44 +404,20 @@
     "## Compute and plot shear profiles"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "789c6e25",
+   "metadata": {},
+   "source": [
+    "We will now compute the tangential and cross components of the ellipticity for all three clusters. We should get the same result for `cl_euclidean` and `cl_celestial`, but a completely different dataset for `cl_wrong`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "f8b46476-6ecc-4afe-a9a6-8aebaf077f0c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          et                     ex          \n",
-      "--------------------- -----------------------\n",
-      "  0.02343407429303324  -1.214306433183765e-17\n",
-      " 0.014003236462197187                     0.0\n",
-      "   0.0194860084089354                     0.0\n",
-      " 0.013007966349034745                     0.0\n",
-      "0.0063995288660438285                     0.0\n",
-      "  0.01814716906590478 -1.0842021724855044e-19\n",
-      " 0.013463206312051472  -6.071532165918825e-18\n",
-      " 0.017087888114434374   4.336808689942018e-19\n",
-      " 0.007014354293438141 -3.2526065174565133e-18\n",
-      " 0.007324384676924073                     0.0\n",
-      "                  ...                     ...\n",
-      " 0.012995723322966854 -6.5052130349130266e-18\n",
-      "  0.01143184708723896  -5.204170427930421e-18\n",
-      " 0.006569475360506179 -3.0357660829594124e-18\n",
-      " 0.017779892137189217  -6.938893903907228e-18\n",
-      "  0.01147380711462187  -5.421010862427522e-18\n",
-      "  0.01044444821229118                     0.0\n",
-      "  0.01719223310320743  -9.540979117872439e-18\n",
-      " 0.013823571306899037   4.336808689942018e-19\n",
-      " 0.008850172017011177   8.673617379884035e-19\n",
-      " 0.006628155676339087                     0.0\n",
-      " 0.017720597889557217  -9.540979117872439e-18\n",
-      "Length = 1000 rows\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cl_euclidean.compute_tangential_and_cross_components(add=True)\n",
     "cl_euclidean.galcat[\"et\", \"ex\"].pprint(max_width=-1)"
@@ -391,42 +425,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "43a27c24-be4a-4380-bc46-b49810af780d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          et                    ex          \n",
-      "--------------------- ----------------------\n",
-      " 0.023434074293033236 1.9081958235744878e-17\n",
-      " 0.014003236462197187  3.469446951953614e-18\n",
-      "   0.0194860084089354  4.336808689942018e-18\n",
-      " 0.013007966349034745  4.336808689942018e-18\n",
-      " 0.006399528866043828  1.734723475976807e-18\n",
-      "  0.01814716906590478  4.662069341687669e-18\n",
-      "  0.01346320631205147  8.673617379884035e-18\n",
-      "  0.01708788811443437  4.336808689942018e-18\n",
-      " 0.007014354293438142  5.204170427930421e-18\n",
-      " 0.007324384676924072  1.734723475976807e-18\n",
-      "                  ...                    ...\n",
-      " 0.012995723322966852  9.974659986866641e-18\n",
-      "  0.01143184708723896  8.673617379884035e-18\n",
-      "0.0065694753605061796  5.204170427930421e-18\n",
-      "  0.01777989213718922  1.214306433183765e-17\n",
-      " 0.011473807114621868  8.456776945386935e-18\n",
-      "  0.01044444821229118 2.6020852139652106e-18\n",
-      "  0.01719223310320743 1.3877787807814457e-17\n",
-      " 0.013823571306899035 3.0357660829594124e-18\n",
-      " 0.008850172017011175 2.6020852139652106e-18\n",
-      " 0.006628155676339087  1.624609192833748e-18\n",
-      " 0.017720597889557214  1.474514954580286e-17\n",
-      "Length = 1000 rows\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cl_celestial.compute_tangential_and_cross_components(add=True)\n",
     "cl_celestial.galcat[\"et\", \"ex\"].pprint(max_width=-1)"
@@ -434,124 +436,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "d9632531",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cl_wrong.compute_tangential_and_cross_components(add=True)\n",
+    "cl_wrong.galcat[\"et\", \"ex\"].pprint(max_width=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "137d6945-67cc-42ef-b93b-07f3c20cad5f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f7946bdc580>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "f, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "\n",
-    "ax[0].hist(cl_euclidean.galcat[\"et\"], bins=50)\n",
-    "ax[0].hist(cl_celestial.galcat[\"et\"], bins=50,color='red',alpha=0.3)\n",
+    "ax[0].hist(cl_euclidean.galcat[\"et\"], bins=50, color=\"tab:blue\", alpha=0.5)\n",
+    "ax[0].hist(cl_celestial.galcat[\"et\"], bins=50, color=\"tab:red\", alpha=0.5)\n",
+    "ax[0].hist(cl_wrong.galcat[\"et\"], bins=50, color=\"tab:orange\", alpha=0.5)\n",
     "ax[0].set_xlabel(\"$\\\\epsilon_t$\", fontsize=\"xx-large\")\n",
     "\n",
-    "ax[1].hist(cl_euclidean.galcat[\"ex\"], bins=50)\n",
-    "ax[1].hist(cl_celestial.galcat[\"ex\"], bins=50,color='red',alpha=0.3)\n",
+    "ax[1].hist(cl_euclidean.galcat[\"ex\"], bins=50, color=\"tab:blue\", alpha=0.5)\n",
+    "ax[1].hist(cl_celestial.galcat[\"ex\"], bins=50, color=\"tab:red\", alpha=0.5)\n",
+    "ax[1].hist(cl_wrong.galcat[\"ex\"], bins=50, color=\"tab:orange\", alpha=0.5)\n",
     "ax[1].set_xlabel(\"$\\\\epsilon_x$\", fontsize=\"xx-large\")\n",
     "ax[1].set_yscale(\"log\")\n",
     "\n",
-    "plt.legend()"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "8700b684-279f-485c-98e3-a25ca5d9bd2f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<i>GCData length=10</i>\n",
-       "<table id=\"table140158854044624-84682\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>idx</th><th>radius_min</th><th>radius</th><th>radius_max</th><th>gt</th><th>gt_err</th><th>gx</th><th>gx_err</th><th>z</th><th>z_err</th><th>n_src</th><th>W_l</th></tr></thead>\n",
-       "<tr><td>0</td><td>156.71960928359934</td><td>464.4671603593981</td><td>699.5921737891952</td><td>0.06413360677252622</td><td>0.004914333288851318</td><td>-1.715010709204343e-17</td><td>4.034498594037631e-18</td><td>1.0911552118175314</td><td>0.11083918127320175</td><td>22</td><td>22.0</td></tr>\n",
-       "<tr><td>1</td><td>699.5921737891952</td><td>1011.2963458867513</td><td>1242.464738294791</td><td>0.039642488178504826</td><td>0.0016015681719071773</td><td>-1.50965113609602e-17</td><td>1.4037968563596243e-18</td><td>1.340843180212603</td><td>0.09927515578213908</td><td>54</td><td>54.0</td></tr>\n",
-       "<tr><td>2</td><td>1242.464738294791</td><td>1546.8197998039764</td><td>1785.3373028003869</td><td>0.02498877958964601</td><td>0.0008174836500513263</td><td>-8.045864322014928e-18</td><td>7.330813994518638e-19</td><td>1.141074816459248</td><td>0.07695590512125049</td><td>75</td><td>75.0</td></tr>\n",
-       "<tr><td>3</td><td>1785.3373028003869</td><td>2063.99121745076</td><td>2328.2098673059827</td><td>0.01921493231328005</td><td>0.0003916691096211929</td><td>-7.364822727368162e-18</td><td>3.971381403743137e-19</td><td>1.1760684211114605</td><td>0.05471300278442932</td><td>125</td><td>125.0</td></tr>\n",
-       "<tr><td>4</td><td>2328.2098673059827</td><td>2584.348460286764</td><td>2871.0824318115783</td><td>0.014872649894901871</td><td>0.00028689758132636664</td><td>-5.324571864838686e-18</td><td>3.1142934756710057e-19</td><td>1.2143592720456875</td><td>0.054067348171772577</td><td>138</td><td>138.0</td></tr>\n",
-       "<tr><td>5</td><td>2871.0824318115783</td><td>3154.1249537829503</td><td>3413.9549963171744</td><td>0.011371593615157109</td><td>0.00020822193844039795</td><td>-4.413359292550124e-18</td><td>2.0335406513695774e-19</td><td>1.2083318981155682</td><td>0.05274388342400906</td><td>192</td><td>192.0</td></tr>\n",
-       "<tr><td>6</td><td>3413.9549963171744</td><td>3680.7344103618984</td><td>3956.8275608227705</td><td>0.009920196782261583</td><td>0.00015732778542169504</td><td>-3.97012477722782e-18</td><td>1.6341242745876745e-19</td><td>1.311099393646099</td><td>0.05334127819405377</td><td>177</td><td>177.0</td></tr>\n",
-       "<tr><td>7</td><td>3956.8275608227705</td><td>4185.790588727914</td><td>4499.700125328366</td><td>0.008084689997088943</td><td>0.00017398558743095897</td><td>-2.757239249030866e-18</td><td>1.9209518576841595e-19</td><td>1.2856917023015786</td><td>0.07887750338660866</td><td>121</td><td>121.0</td></tr>\n",
-       "<tr><td>8</td><td>4499.700125328366</td><td>4733.6762564943365</td><td>5042.572689833962</td><td>0.00711930821972751</td><td>0.00018520179246980147</td><td>-2.4475459490810818e-18</td><td>2.0029258865114398e-19</td><td>1.3173896415983801</td><td>0.08741388146537891</td><td>67</td><td>67.0</td></tr>\n",
-       "<tr><td>9</td><td>5042.572689833962</td><td>5250.313838563012</td><td>5585.445254339558</td><td>0.005897636633904643</td><td>0.00026238880980461676</td><td>-1.7660344869925525e-18</td><td>2.883192426744197e-19</td><td>1.195583526441637</td><td>0.1086754688799238</td><td>29</td><td>29.0</td></tr>\n",
-       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
-       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
-       "display: inline-block; margin-right: 1em; }\n",
-       ".paginate_button { margin-right: 5px; }\n",
-       "</style>\n",
-       "<script>\n",
-       "\n",
-       "var astropy_sort_num = function(a, b) {\n",
-       "    var a_num = parseFloat(a);\n",
-       "    var b_num = parseFloat(b);\n",
-       "\n",
-       "    if (isNaN(a_num) && isNaN(b_num))\n",
-       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
-       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
-       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
-       "    else\n",
-       "        return isNaN(a_num) ? -1 : 1;\n",
-       "}\n",
-       "\n",
-       "require.config({paths: {\n",
-       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
-       "}});\n",
-       "require([\"datatables\"], function(){\n",
-       "    console.log(\"$('#table140158854044624-84682').dataTable()\");\n",
-       "    \n",
-       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
-       "    \"optionalnum-asc\": astropy_sort_num,\n",
-       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
-       "});\n",
-       "\n",
-       "    $('#table140158854044624-84682').dataTable({\n",
-       "        order: [],\n",
-       "        pageLength: 50,\n",
-       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
-       "        pagingType: \"full_numbers\",\n",
-       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], type: \"optionalnum\"}]\n",
-       "    });\n",
-       "});\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cl_euclidean.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
     "cl_euclidean.profile.show_in_notebook()"
@@ -559,121 +481,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "f6774006-189b-4226-a4dd-f31722b7713d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<i>GCData length=10</i>\n",
-       "<table id=\"table140158850345632-858330\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>idx</th><th>radius_min</th><th>radius</th><th>radius_max</th><th>gt</th><th>gt_err</th><th>gx</th><th>gx_err</th><th>z</th><th>z_err</th><th>n_src</th><th>W_l</th></tr></thead>\n",
-       "<tr><td>0</td><td>156.71960928359934</td><td>464.4671603593981</td><td>699.5921737891952</td><td>0.06413360677252622</td><td>0.004914333288851318</td><td>3.236836304038542e-17</td><td>5.694604839281488e-18</td><td>1.0911552118175314</td><td>0.11083918127320175</td><td>22</td><td>22.0</td></tr>\n",
-       "<tr><td>1</td><td>699.5921737891952</td><td>1011.2963458867513</td><td>1242.464738294791</td><td>0.039642488178504826</td><td>0.0016015681719071773</td><td>2.3942797975721554e-17</td><td>1.7339256407665797e-18</td><td>1.340843180212603</td><td>0.09927515578213908</td><td>54</td><td>54.0</td></tr>\n",
-       "<tr><td>2</td><td>1242.464738294791</td><td>1546.8197998039764</td><td>1785.3373028003869</td><td>0.02498877958964601</td><td>0.0008174836500513263</td><td>1.4314947158778716e-17</td><td>8.226643047345753e-19</td><td>1.141074816459248</td><td>0.07695590512125049</td><td>75</td><td>75.0</td></tr>\n",
-       "<tr><td>3</td><td>1785.3373028003869</td><td>2063.99121745076</td><td>2328.2098673059827</td><td>0.019214932313280052</td><td>0.0003916691096211907</td><td>1.150143348616073e-17</td><td>5.485774815412368e-19</td><td>1.1760684211114605</td><td>0.05471300278442932</td><td>125</td><td>125.0</td></tr>\n",
-       "<tr><td>4</td><td>2328.2098673059827</td><td>2584.348460286764</td><td>2871.0824318115783</td><td>0.014872649894901871</td><td>0.00028689758132636594</td><td>9.229663820156945e-18</td><td>4.1130251561838557e-19</td><td>1.2143592720456875</td><td>0.054067348171772577</td><td>138</td><td>138.0</td></tr>\n",
-       "<tr><td>5</td><td>2871.0824318115783</td><td>3154.1249537829503</td><td>3413.9549963171744</td><td>0.011371593615157109</td><td>0.00020822193844039795</td><td>7.167239869271198e-18</td><td>2.495744946047253e-19</td><td>1.2083318981155682</td><td>0.05274388342400906</td><td>192</td><td>192.0</td></tr>\n",
-       "<tr><td>6</td><td>3413.9549963171744</td><td>3680.7344103618984</td><td>3956.8275608227705</td><td>0.009920196782261583</td><td>0.00015732778542169504</td><td>6.286305265765002e-18</td><td>2.389401104568704e-19</td><td>1.311099393646099</td><td>0.05334127819405377</td><td>177</td><td>177.0</td></tr>\n",
-       "<tr><td>7</td><td>3956.8275608227705</td><td>4185.790588727914</td><td>4499.700125328366</td><td>0.008084689997088943</td><td>0.00017398558743095897</td><td>4.540068596193885e-18</td><td>2.5057110197055964e-19</td><td>1.2856917023015786</td><td>0.07887750338660866</td><td>121</td><td>121.0</td></tr>\n",
-       "<tr><td>8</td><td>4499.700125328366</td><td>4733.6762564943365</td><td>5042.572689833962</td><td>0.00711930821972751</td><td>0.00018520179246980147</td><td>4.442397035844523e-18</td><td>2.558011256616628e-19</td><td>1.3173896415983801</td><td>0.08741388146537891</td><td>67</td><td>67.0</td></tr>\n",
-       "<tr><td>9</td><td>5042.572689833962</td><td>5250.313838563012</td><td>5585.445254339558</td><td>0.0058976366339046436</td><td>0.0002623888098046163</td><td>3.2037706868424034e-18</td><td>3.1456351012656695e-19</td><td>1.195583526441637</td><td>0.1086754688799238</td><td>29</td><td>29.0</td></tr>\n",
-       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
-       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
-       "display: inline-block; margin-right: 1em; }\n",
-       ".paginate_button { margin-right: 5px; }\n",
-       "</style>\n",
-       "<script>\n",
-       "\n",
-       "var astropy_sort_num = function(a, b) {\n",
-       "    var a_num = parseFloat(a);\n",
-       "    var b_num = parseFloat(b);\n",
-       "\n",
-       "    if (isNaN(a_num) && isNaN(b_num))\n",
-       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
-       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
-       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
-       "    else\n",
-       "        return isNaN(a_num) ? -1 : 1;\n",
-       "}\n",
-       "\n",
-       "require.config({paths: {\n",
-       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
-       "}});\n",
-       "require([\"datatables\"], function(){\n",
-       "    console.log(\"$('#table140158850345632-858330').dataTable()\");\n",
-       "    \n",
-       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
-       "    \"optionalnum-asc\": astropy_sort_num,\n",
-       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
-       "});\n",
-       "\n",
-       "    $('#table140158850345632-858330').dataTable({\n",
-       "        order: [],\n",
-       "        pageLength: 50,\n",
-       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
-       "        pagingType: \"full_numbers\",\n",
-       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], type: \"optionalnum\"}]\n",
-       "    });\n",
-       "});\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cl_celestial.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
-    "# cl.profile.pprint(max_width=-1)\n",
     "cl_celestial.profile.show_in_notebook()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "7ab100d0-589f-42bd-bb90-eaefedf331cb",
+   "execution_count": null,
+   "id": "3dbc425f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ErrorbarContainer object of 3 artists>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = cl_euclidean.plot_profiles(xscale=\"log\")\n",
-    "\n",
-    "ax.errorbar(cl_celestial.profile['radius']*1.02, cl_celestial.profile['gt'],\n",
-    "            yerr= cl_celestial.profile['gt_err'],alpha=0.3)\n",
-    "\n",
-    "ax.errorbar(cl_celestial.profile['radius']*1.02, cl_celestial.profile['gx'],\n",
-    "            yerr= cl_celestial.profile['gx_err'],alpha=0.3)"
+   "outputs": [],
+   "source": [
+    "cl_wrong.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "cl_wrong.profile.show_in_notebook()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "38011c62-5261-47fa-a00d-489acb879c96",
+   "id": "f52c7dc3",
    "metadata": {},
    "source": [
     "### => When the correct coordinate system is specified, the profiles coming from the two catalogs are identical."
@@ -681,30 +511,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "7b098be6-8364-45c3-aa5a-ce28a84beb03",
+   "execution_count": null,
+   "id": "7ab100d0-589f-42bd-bb90-eaefedf331cb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "cl_wrong_coordinate.compute_tangential_and_cross_components(add=True)\n",
-    "cl_wrong_coordinate.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
-    "fig, ax = cl_wrong_coordinate.plot_profiles(xscale=\"log\")"
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    cl_euclidean.profile[\"radius\"],\n",
+    "    cl_euclidean.profile[\"gt\"],\n",
+    "    yerr=cl_euclidean.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"euclidean\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    cl_euclidean.profile[\"radius\"],\n",
+    "    cl_euclidean.profile[\"gx\"],\n",
+    "    yerr=cl_euclidean.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    cl_celestial.profile[\"radius\"] * 1.02,\n",
+    "    cl_celestial.profile[\"gt\"],\n",
+    "    yerr=cl_celestial.profile[\"gt_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"celestial\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    cl_celestial.profile[\"radius\"] * 1.02,\n",
+    "    cl_celestial.profile[\"gx\"],\n",
+    "    yerr=cl_celestial.profile[\"gx_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    ")\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eca43249-3f31-478c-af40-ed79fbeeeea8",
+   "id": "43c7340f",
    "metadata": {},
    "source": [
     "### => However, when the coordinate system is not correctly specified, the profiles are incorrect."
@@ -712,535 +574,698 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "fc8a5cad-a73d-4615-ac32-240924e139b9",
+   "execution_count": null,
+   "id": "7b098be6-8364-45c3-aa5a-ce28a84beb03",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#to recover the correct profile we need to compute the quantities outside of \n",
-    "#the GalaxyCluster object and add them back as galcat column in the GalacyCluster object\n",
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    cl_wrong.profile[\"radius\"],\n",
+    "    cl_wrong.profile[\"gt\"],\n",
+    "    yerr=cl_wrong.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"incorrect coordinate system\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    cl_wrong.profile[\"radius\"],\n",
+    "    cl_wrong.profile[\"gx\"],\n",
+    "    yerr=cl_wrong.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
     "\n",
-    "#ideally, we would like to just do \n",
-    "#cl_wrong_coordinate.compute_tangential_and_cross_components(coordinate_system='celestial', add=True)\n",
-    "#but this does not work at the moment\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
     "\n",
-    "theta, et, ex = compute_tangential_and_cross_components(\n",
-    "    cl_wrong_coordinate.ra, cl_wrong_coordinate.dec, \n",
-    "    cl_wrong_coordinate.galcat['ra'],cl_wrong_coordinate.galcat['dec'], \n",
-    "    cl_wrong_coordinate.galcat['e1'],cl_wrong_coordinate.galcat['e2'],\n",
-    "    coordinate_system='celestial')"
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f735bc6a",
+   "metadata": {},
+   "source": [
+    "### To recover the correct profile we need to update the coordinate system, compute the tangential and cross components again and recalculate the profile."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "ef228536-57ca-4438-9bc2-55495336a2d5",
+   "execution_count": null,
+   "id": "acdfb0e0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cl_wrong_coordinate.galcat.add_column(et,name='et_celestial')\n",
-    "cl_wrong_coordinate.galcat.add_column(ex,name='ex_celestial')"
+    "cl_wrong.update_coordinate_system(\"celestial\")\n",
+    "cl_wrong.compute_tangential_and_cross_components(add=True)\n",
+    "\n",
+    "cl_wrong.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "cl_wrong.profile.show_in_notebook()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "bbc202ab-1a89-4583-b75c-8e45a2d8172f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/galaxycluster.py:630: UserWarning: overwriting profile table.\n",
-      "  warnings.warn(f\"overwriting {table_name} table.\")\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cl_wrong_coordinate.make_radial_profile(bin_units=\"kpc\",\n",
-    "    tan_component_in='et_celestial', cross_component_in='ex_celestial',\n",
-    "    cosmo=cosmo)\n",
-    "\n",
-    "fig, ax = cl_wrong_coordinate.plot_profiles(xscale=\"log\")"
+   "execution_count": null,
+   "id": "2d034194",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    cl_wrong.profile[\"radius\"],\n",
+    "    cl_wrong.profile[\"gt\"],\n",
+    "    yerr=cl_wrong.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"correct coordinate system\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    cl_wrong.profile[\"radius\"],\n",
+    "    cl_wrong.profile[\"gx\"],\n",
+    "    yerr=cl_wrong.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5335da24-c97a-4b5d-b729-684f37cec8ae",
+   "id": "8f7908a7",
    "metadata": {},
    "source": [
-    "### => Now the profiles are correct. "
+    "## Let's now do the same test on real data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a5a7d1c8-1f41-40a8-987e-1834e1ae4b28",
+   "id": "b1572733",
    "metadata": {},
    "source": [
-    "## Same thing for Delta Sigma profiles"
+    "Here we present three datasets, each with different coordinate systems:\n",
+    "1. CosmoDC2 source galaxies with shears extracted from `TXPipe` for a single galaxy cluster (data is in `euclidean` coordinates);\n",
+    "2. Example source galaxies for galaxy clusters from a [Summer School](https://github.com/oguri/wlcluster_tutorial) taught by Masamune Oguri (data is also in `euclidean` coordinates);\n",
+    "3. HSC Y3 source galaxies with shears post processed by Tomomi Sunayama (data is in `celestial` coordinates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Instructions to download text data\n",
+    "\n",
+    "First, create a directory where you want to put the example data, e.g. for a given `data_coords_dir`:\n",
+    "\n",
+    "```\n",
+    "mkdir -p <YOUR PATH TO DATA COORDS DIR>/data_coords\n",
+    "cd <YOUR PATH TO DATA COORDS DIR>/data_coords\n",
+    "```\n",
+    "\n",
+    "Download all files from this [dropbox link](https://www.dropbox.com/scl/fo/dwsccslr5iwb7lnkf8jvx/AJkjgFeemUEHpHaZaHHqpAg?rlkey=efbtsr15mdrs3y6xsm7l48o0r&st=xb58ap0g&dl=0).  This will be a zip file, `CLMM_data.zip` of size 242 Mb. `scp` or `mv` this to `data_coords_dir`. From the directory, you should be able to unzip:\n",
+    "\n",
+    "```\n",
+    "unzip data_CLMM.zip -d .\n",
+    "```\n",
+    "\n",
+    "You now have the necessary data files to run this notebook. **Make sure to change the `data_coords_dir` variable in the cell below to the appropriate location where you unzipped these files.**\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "84446929-dc27-4418-87c3-ecc119e3cc9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/cosmology/parent_class.py:110: UserWarning: \n",
-      "Some source redshifts are lower than the cluster redshift.\n",
-      "Sigma_crit = np.inf for those galaxies.\n",
-      "  return compute_for_good_redshifts(\n",
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/dataops/__init__.py:174: RuntimeWarning: invalid value encountered in multiply\n",
-      "  cross_comp *= _sigma_c_arr\n"
-     ]
-    }
-   ],
-   "source": [
-    "cl_euclidean.compute_tangential_and_cross_components(\n",
-    "    shape_component1=\"e1\",\n",
-    "    shape_component2=\"e2\",\n",
-    "    tan_component=\"DeltaSigma_tan\",\n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "    add=True,\n",
-    "    cosmo=cosmo,\n",
-    "    is_deltasigma=True,\n",
-    ");\n",
-    "\n",
-    "#cl_euclidean.profile.show_in_notebook()"
+   "execution_count": 32,
+   "id": "2f61c348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  CHANGE <YOUR PATH TO DATA COORDS DIR> TO YOUR LOCATION\n",
+    "data_coords_dir = \"/home/caio/Development/cl-cosmo/support/data_coord_dir/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1e6b530",
+   "metadata": {},
+   "source": [
+    "### Example galaxy cluster from CosmoDC2\n",
+    "\n",
+    "Here, we plot an example galaxy cluster shear profile using cluster and source galaxy files generated from CosmoDC2 and processed through TXPipe. These are originally in `euclidean` coordinates. Let's start by importing the data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "e69ba994-e45b-4255-b639-b11735ca7866",
+   "execution_count": 33,
+   "id": "62569cbd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cl_euclidean.make_radial_profile(\n",
-    "    \"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    tan_component_in=\"DeltaSigma_tan\",\n",
-    "    cross_component_in=\"DeltaSigma_cross\",\n",
-    "    tan_component_out=\"DeltaSigma_tan\",\n",
-    "    cross_component_out=\"DeltaSigma_cross\",\n",
-    "    table_name=\"DeltaSigma_profile\",\n",
-    "    use_weights=False,\n",
-    ");"
+    "dc2_cluster = pd.read_pickle(data_coords_dir + \"/test_cluster.pkl\")\n",
+    "\n",
+    "cluster_z = dc2_cluster[\"redshift\"]  # Cluster redshift\n",
+    "cluster_ra = dc2_cluster[\"ra\"]  # Cluster Ra in deg\n",
+    "cluster_dec = dc2_cluster[\"dec\"]  # Cluster Dec in deg\n",
+    "\n",
+    "source = np.genfromtxt(data_coords_dir + \"/test_source.txt\", names=True)\n",
+    "\n",
+    "gal_ra = source[\"ra\"]\n",
+    "gal_dec = source[\"dec\"]\n",
+    "gal_e1 = source[\"e1\"]\n",
+    "gal_e2 = source[\"e2\"]\n",
+    "gal_z = source[\"zmean\"]\n",
+    "gal_id = np.arange(len(source[\"ra\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8991a69",
+   "metadata": {},
+   "source": [
+    "Now we generate a `GCData` object from the imported data. We are going to create two catalogs, each with a different choice of coordinate system, which we specify with `meta={\"coordinate_system\": \"coord\"}`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "5d9307d8-88dc-43fe-8e85-aed47f574282",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig1, ax1 = cl_euclidean.plot_profiles(tangential_component=\"DeltaSigma_tan\",\n",
-    "                                       cross_component=\"DeltaSigma_cross\", \n",
-    "                                       tangential_component_error=\"DeltaSigma_tan_err\", \n",
-    "                                       cross_component_error=\"DeltaSigma_cross_err\",\n",
-    "                                       table_name=\"DeltaSigma_profile\")"
+   "execution_count": 34,
+   "id": "fcb2ae6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc2_galaxies_euclidean = clmm.GCData(\n",
+    "    [gal_ra, gal_dec, gal_e1, gal_e2, gal_z, gal_id],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"],\n",
+    "    meta={\"coordinate_system\": \"euclidean\"},\n",
+    ")\n",
+    "\n",
+    "dc2_galaxies_celestial = clmm.GCData(\n",
+    "    [gal_ra, gal_dec, gal_e1, gal_e2, gal_z, gal_id],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"],\n",
+    "    meta={\"coordinate_system\": \"celestial\"},\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "18834199-201d-4f17-8558-1c6236515211",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/cosmology/parent_class.py:110: UserWarning: \n",
-      "Some source redshifts are lower than the cluster redshift.\n",
-      "Sigma_crit = np.inf for those galaxies.\n",
-      "  return compute_for_good_redshifts(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cl_celestial.compute_tangential_and_cross_components(\n",
-    "    shape_component1=\"e1\",\n",
-    "    shape_component2=\"e2\",\n",
-    "    tan_component=\"DeltaSigma_tan\",\n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "    add=True,\n",
-    "    cosmo=cosmo,\n",
-    "    is_deltasigma=True,\n",
-    ");\n",
-    "\n",
-    "cl_celestial.make_radial_profile(\n",
-    "    \"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    tan_component_in=\"DeltaSigma_tan\",\n",
-    "    cross_component_in=\"DeltaSigma_cross\",\n",
-    "    tan_component_out=\"DeltaSigma_tan\",\n",
-    "    cross_component_out=\"DeltaSigma_cross\",\n",
-    "    table_name=\"DeltaSigma_profile\",\n",
-    "    use_weights=False,\n",
-    ");\n",
-    "\n",
-    "fig2, ax2 = cl_celestial.plot_profiles(\n",
-    "    tangential_component=\"DeltaSigma_tan\", \n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "        tangential_component_error=\"DeltaSigma_tan_err\", \n",
-    "    cross_component_error=\"DeltaSigma_cross_err\",\n",
-    "    table_name=\"DeltaSigma_profile\")"
+   "execution_count": null,
+   "id": "2245f754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a GalaxyCluster.\n",
+    "dc2_cluster_euclidean = clmm.GalaxyCluster(\n",
+    "    \"Euclidean cluster\",\n",
+    "    cluster_ra,\n",
+    "    cluster_dec,\n",
+    "    cluster_z,\n",
+    "    dc2_galaxies_euclidean,\n",
+    ")\n",
+    "\n",
+    "dc2_cluster_celestial = clmm.GalaxyCluster(\n",
+    "    \"Celestial cluster\",\n",
+    "    cluster_ra,\n",
+    "    cluster_dec,\n",
+    "    cluster_z,\n",
+    "    dc2_galaxies_celestial,\n",
+    ")\n",
+    "\n",
+    "# Convert elipticities into shears for the members.\n",
+    "dc2_cluster_euclidean.compute_tangential_and_cross_components(add=True)\n",
+    "dc2_cluster_celestial.compute_tangential_and_cross_components(add=True)\n",
+    "print(dc2_cluster_euclidean.galcat.colnames)\n",
+    "print(dc2_cluster_celestial.galcat.colnames)\n",
+    "\n",
+    "# Calculate the radial profile of the cluster.\n",
+    "dc2_cluster_euclidean.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "dc2_cluster_celestial.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "print(dc2_cluster_euclidean.profile.colnames)\n",
+    "print(dc2_cluster_celestial.profile.colnames)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "63ce64f4-b8ea-44a8-a34e-a2f875bdcc2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/galaxycluster.py:630: UserWarning: overwriting DeltaSigma_profile table.\n",
-      "  warnings.warn(f\"overwriting {table_name} table.\")\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cl_wrong_coordinate.compute_tangential_and_cross_components(\n",
-    "    shape_component1=\"e1\",\n",
-    "    shape_component2=\"e2\",\n",
-    "    tan_component=\"DeltaSigma_tan\",\n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "    add=True,\n",
-    "    cosmo=cosmo,\n",
-    "    is_deltasigma=True,\n",
-    ");\n",
-    "\n",
-    "cl_wrong_coordinate.make_radial_profile(\n",
-    "    \"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    tan_component_in=\"DeltaSigma_tan\",\n",
-    "    cross_component_in=\"DeltaSigma_cross\",\n",
-    "    tan_component_out=\"DeltaSigma_tan\",\n",
-    "    cross_component_out=\"DeltaSigma_cross\",\n",
-    "    table_name=\"DeltaSigma_profile\",\n",
-    "    use_weights=False,\n",
-    ");\n",
-    "\n",
-    "fig2, ax2 = cl_wrong_coordinate.plot_profiles(\n",
-    "    tangential_component=\"DeltaSigma_tan\", \n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "    tangential_component_error=\"DeltaSigma_tan_err\", \n",
-    "    cross_component_error=\"DeltaSigma_cross_err\",\n",
-    "    table_name=\"DeltaSigma_profile\")"
+   "execution_count": null,
+   "id": "1764ff31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    dc2_cluster_euclidean.profile[\"radius\"],\n",
+    "    dc2_cluster_euclidean.profile[\"gt\"],\n",
+    "    yerr=dc2_cluster_euclidean.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"euclidean\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    dc2_cluster_euclidean.profile[\"radius\"],\n",
+    "    dc2_cluster_euclidean.profile[\"gx\"],\n",
+    "    yerr=dc2_cluster_euclidean.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    dc2_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    dc2_cluster_celestial.profile[\"gt\"],\n",
+    "    yerr=dc2_cluster_celestial.profile[\"gt_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"celestial\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    dc2_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    dc2_cluster_celestial.profile[\"gx\"],\n",
+    "    yerr=dc2_cluster_celestial.profile[\"gx_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    ")\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "028ab5a1",
+   "metadata": {},
+   "source": [
+    "### => There's no signal in either cluster! Is there a problem in DC2?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8902db07",
+   "metadata": {},
+   "source": [
+    "### Example source galaxies from M. Oguri\n",
+    "\n",
+    "This dataset is a curated selection of cluster and source catalogs from Summer School lectures delivered by Masamune Oguri.  There are eight galaxy clusters in this selection.  \n",
+    "\n",
+    "More details on the corresponding tutorial can be found at this [GitHub link](https://github.com/oguri/wlcluster_tutorial). These are also in the `euclidean` coordinate system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "591d800d-8316-4c70-b415-09a73e6b2574",
+   "execution_count": 37,
+   "id": "8eb0581e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/cosmology/parent_class.py:110: UserWarning: \n",
-      "Some source redshifts are lower than the cluster redshift.\n",
-      "Sigma_crit = np.inf for those galaxies.\n",
-      "  return compute_for_good_redshifts(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sigma_c = cosmo.eval_sigma_crit(cl_wrong_coordinate.z, cl_wrong_coordinate.galcat['z'])"
+    "clusters = [\n",
+    "    \"a1703\",\n",
+    "    \"gho1320\",\n",
+    "    \"sdss0851\",\n",
+    "    \"sdss1050\",\n",
+    "    \"sdss1138\",\n",
+    "    \"sdss1226\",\n",
+    "    \"sdss1329\",\n",
+    "    \"sdss1531\",\n",
+    "]\n",
+    "\n",
+    "zl_all = {\n",
+    "    \"a1703\": 0.277,\n",
+    "    \"gho1320\": 0.308,\n",
+    "    \"sdss0851\": 0.370,\n",
+    "    \"sdss1050\": 0.60,\n",
+    "    \"sdss1138\": 0.451,\n",
+    "    \"sdss1226\": 0.435,\n",
+    "    \"sdss1329\": 0.443,\n",
+    "    \"sdss1531\": 0.335,\n",
+    "}\n",
+    "\n",
+    "ra_cl_all = {\n",
+    "    \"a1703\": 198.771833,\n",
+    "    \"gho1320\": 200.703208,\n",
+    "    \"sdss0851\": 132.911917,\n",
+    "    \"sdss1050\": 162.666250,\n",
+    "    \"sdss1138\": 174.537292,\n",
+    "    \"sdss1226\": 186.712958,\n",
+    "    \"sdss1329\": 202.393708,\n",
+    "    \"sdss1531\": 232.794167,\n",
+    "}\n",
+    "\n",
+    "dec_cl_all = {\n",
+    "    \"a1703\": 51.817389,\n",
+    "    \"gho1320\": 31.654944,\n",
+    "    \"sdss0851\": 33.518361,\n",
+    "    \"sdss1050\": 0.285306,\n",
+    "    \"sdss1138\": 27.908528,\n",
+    "    \"sdss1226\": 21.831194,\n",
+    "    \"sdss1329\": 22.721167,\n",
+    "    \"sdss1531\": 34.240278,\n",
+    "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "2fed7d27-b626-4a7e-bf24-317bad5a888c",
+   "execution_count": 38,
+   "id": "0f237ea0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#to recover the correct profile we need to compute the quantities outside of \n",
-    "#the GalaxyCluster object and add them back as galcat column in the GalacyCluster object\n",
+    "cname = \"a1703\"\n",
     "\n",
-    "#ideally, we would like to just do \n",
-    "#cl_wrong_coordinate.compute_tangential_and_cross_components(coordinate_system='celestial', add=True)\n",
-    "#but this does not work at the moment\n",
+    "# cluster redshift\n",
+    "zl = zl_all.get(cname)\n",
     "\n",
-    "theta, dstan, dscross = compute_tangential_and_cross_components(\n",
-    "    cl_wrong_coordinate.ra, cl_wrong_coordinate.dec, \n",
-    "    cl_wrong_coordinate.galcat['ra'],cl_wrong_coordinate.galcat['dec'], \n",
-    "    cl_wrong_coordinate.galcat['e1'],cl_wrong_coordinate.galcat['e2'],\n",
-    "    coordinate_system='celestial', \n",
-    "    is_deltasigma=True,\n",
-    "    sigma_c = sigma_c )"
+    "# coordinates of the cluster center\n",
+    "ra_cl = ra_cl_all.get(cname)\n",
+    "dec_cl = dec_cl_all.get(cname)\n",
+    "\n",
+    "# fix source redshift to 1.0\n",
+    "zs = 1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "920a2011",
+   "metadata": {},
+   "source": [
+    "We inspect the first galaxy cluster, Abell 1703."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "f8248bda-030a-47aa-8733-a43b37ef7a78",
+   "execution_count": 39,
+   "id": "2a8705d3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cl_wrong_coordinate.galcat.add_column(dstan,name='DeltaSigma_tan_celestial')\n",
-    "cl_wrong_coordinate.galcat.add_column(dscross,name='DeltaSigma_cross_celestial')"
+    "rfile = data_coords_dir + \"/data/shear_\" + cname + \".dat\"\n",
+    "data = np.loadtxt(rfile, comments=\"#\")\n",
+    "\n",
+    "ra = data[:, 0]\n",
+    "dec = data[:, 1]\n",
+    "e1 = data[:, 2]\n",
+    "e2 = data[:, 3]\n",
+    "wei = data[:, 4]\n",
+    "ids = np.arange(np.shape(data)[0])\n",
+    "redshifts = np.ones(np.shape(data)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "46c8bd61-5959-4c7b-b7b9-dd35511e495f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/pbs/home/m/mricci/.conda/envs/clmm/lib/python3.10/site-packages/clmm/galaxycluster.py:630: UserWarning: overwriting DeltaSigma_profile table.\n",
-      "  warnings.warn(f\"overwriting {table_name} table.\")\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<b>GCData</b>\n",
-       "<br> <b>defined by:</b> cosmo='CCLCosmology(H0=70.0, Omega_dm0=0.22500000000000003, Omega_b0=0.045, Omega_k0=0.0)', bin_units='Mpc'\n",
-       "<br> <b>with columns:</b> radius_min, radius, radius_max, DeltaSigma_tan, DeltaSigma_tan_err, DeltaSigma_cross, DeltaSigma_cross_err, z, z_err, n_src, W_l\n",
-       "<br> 10 objects\n",
-       "<br> \n",
-       "<table id=\"table140158846394832\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>radius_min</th><th>radius</th><th>radius_max</th><th>DeltaSigma_tan</th><th>DeltaSigma_tan_err</th><th>DeltaSigma_cross</th><th>DeltaSigma_cross_err</th><th>z</th><th>z_err</th><th>n_src</th><th>W_l</th></tr></thead>\n",
-       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th></tr></thead>\n",
-       "<tr><td>0.15671960928359935</td><td>0.46446716035939806</td><td>0.6995921737891952</td><td>190051501875241.06</td><td>9649983174328.586</td><td>0.09215345961649268</td><td>0.015270074502458262</td><td>1.0911552118175314</td><td>0.11083918127320175</td><td>22</td><td>22.0</td></tr>\n",
-       "<tr><td>0.6995921737891952</td><td>1.0112963458867514</td><td>1.242464738294791</td><td>114653094293426.97</td><td>2898979264141.766</td><td>0.06784158721185626</td><td>0.004167236144654282</td><td>1.340843180212603</td><td>0.09927515578213908</td><td>54</td><td>54.0</td></tr>\n",
-       "<tr><td>1.242464738294791</td><td>1.546819799803977</td><td>1.785337302800387</td><td>76228503835158.64</td><td>1289719458562.2153</td><td>0.04429997672191164</td><td>0.002370525789413681</td><td>1.141074816459248</td><td>0.07695590512125049</td><td>75</td><td>75.0</td></tr>\n",
-       "<tr><td>1.785337302800387</td><td>2.0639912174507593</td><td>2.328209867305983</td><td>58120008332907.984</td><td>1534873725602.767</td><td>0.03498729466831276</td><td>0.0016337497972184402</td><td>1.1760684211114605</td><td>0.05471300278442932</td><td>125</td><td>125.0</td></tr>\n",
-       "<tr><td>2.328209867305983</td><td>2.584348460286765</td><td>2.871082431811579</td><td>43811132825732.35</td><td>818536036351.0841</td><td>0.027372871133279072</td><td>0.001264376953028166</td><td>1.2143592720456875</td><td>0.054067348171772577</td><td>138</td><td>138.0</td></tr>\n",
-       "<tr><td>2.871082431811579</td><td>3.1541249537829494</td><td>3.4139549963171745</td><td>34918820509793.312</td><td>715700841252.0444</td><td>0.02161699742545974</td><td>0.0006777947504387136</td><td>1.2083318981155682</td><td>0.05274388342400906</td><td>192</td><td>192.0</td></tr>\n",
-       "<tr><td>3.4139549963171745</td><td>3.6807344103619006</td><td>3.95682756082277</td><td>27867318283193.94</td><td>288016404616.3745</td><td>0.01764751408471792</td><td>0.0006936147755028247</td><td>1.311099393646099</td><td>0.05334127819405377</td><td>177</td><td>177.0</td></tr>\n",
-       "<tr><td>3.95682756082277</td><td>4.1857905887279125</td><td>4.499700125328366</td><td>23676492252914.723</td><td>325892892395.3659</td><td>0.013206030331073563</td><td>0.0006652124116508021</td><td>1.2856917023015786</td><td>0.07887750338660866</td><td>121</td><td>121.0</td></tr>\n",
-       "<tr><td>4.499700125328366</td><td>4.733676256494335</td><td>5.042572689833962</td><td>20277822255721.855</td><td>348381428571.66223</td><td>0.01301421018866444</td><td>0.0009175728511681354</td><td>1.3173896415983801</td><td>0.08741388146537891</td><td>67</td><td>67.0</td></tr>\n",
-       "<tr><td>5.042572689833962</td><td>5.250313838563014</td><td>5.585445254339558</td><td>17285265196381.973</td><td>417833907597.3777</td><td>0.009340141033264028</td><td>0.0008101011774997349</td><td>1.195583526441637</td><td>0.1086754688799238</td><td>29</td><td>29.0</td></tr>\n",
-       "</table></div>"
-      ],
-      "text/plain": [
-       "GCData(cosmo='CCLCosmology(H0=70.0, Omega_dm0=0.22500000000000003, Omega_b0=0.045, Omega_k0=0.0)', bin_units='Mpc', columns: radius_min, radius, radius_max, DeltaSigma_tan, DeltaSigma_tan_err, DeltaSigma_cross, DeltaSigma_cross_err, z, z_err, n_src, W_l)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cl_wrong_coordinate.make_radial_profile(\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    tan_component_in=\"DeltaSigma_tan_celestial\",\n",
-    "    cross_component_in=\"DeltaSigma_cross_celestial\",\n",
-    "    tan_component_out=\"DeltaSigma_tan\",\n",
-    "    cross_component_out=\"DeltaSigma_cross\",\n",
-    "    table_name=\"DeltaSigma_profile\",\n",
-    "    use_weights=False,)"
+   "execution_count": 40,
+   "id": "d76c9464",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oguri_galaxies_euclidean = clmm.GCData(\n",
+    "    [ra, dec, e1, e2, redshifts, ids],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"],\n",
+    "    meta={\"coordinate_system\": \"euclidean\"},\n",
+    ")\n",
+    "\n",
+    "oguri_galaxies_celestial = clmm.GCData(\n",
+    "    [ra, dec, e1, e2, redshifts, ids],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"],\n",
+    "    meta={\"coordinate_system\": \"celestial\"},\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "e300fcc2-ed7d-4ed1-ac63-9586375d0ee8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<b>GCData</b>\n",
-       "<br> <b>defined by:</b> cosmo='CCLCosmology(H0=70.0, Omega_dm0=0.22500000000000003, Omega_b0=0.045, Omega_k0=0.0)', bin_units='Mpc'\n",
-       "<br> <b>with columns:</b> radius_min, radius, radius_max, DeltaSigma_tan, DeltaSigma_tan_err, DeltaSigma_cross, DeltaSigma_cross_err, z, z_err, n_src, W_l\n",
-       "<br> 10 objects\n",
-       "<br> \n",
-       "<table id=\"table140158846394832\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>radius_min</th><th>radius</th><th>radius_max</th><th>DeltaSigma_tan</th><th>DeltaSigma_tan_err</th><th>DeltaSigma_cross</th><th>DeltaSigma_cross_err</th><th>z</th><th>z_err</th><th>n_src</th><th>W_l</th></tr></thead>\n",
-       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th></tr></thead>\n",
-       "<tr><td>0.15671960928359935</td><td>0.46446716035939806</td><td>0.6995921737891952</td><td>190051501875241.06</td><td>9649983174328.586</td><td>0.09215345961649268</td><td>0.015270074502458262</td><td>1.0911552118175314</td><td>0.11083918127320175</td><td>22</td><td>22.0</td></tr>\n",
-       "<tr><td>0.6995921737891952</td><td>1.0112963458867514</td><td>1.242464738294791</td><td>114653094293426.97</td><td>2898979264141.766</td><td>0.06784158721185626</td><td>0.004167236144654282</td><td>1.340843180212603</td><td>0.09927515578213908</td><td>54</td><td>54.0</td></tr>\n",
-       "<tr><td>1.242464738294791</td><td>1.546819799803977</td><td>1.785337302800387</td><td>76228503835158.64</td><td>1289719458562.2153</td><td>0.04429997672191164</td><td>0.002370525789413681</td><td>1.141074816459248</td><td>0.07695590512125049</td><td>75</td><td>75.0</td></tr>\n",
-       "<tr><td>1.785337302800387</td><td>2.0639912174507593</td><td>2.328209867305983</td><td>58120008332907.984</td><td>1534873725602.767</td><td>0.03498729466831276</td><td>0.0016337497972184402</td><td>1.1760684211114605</td><td>0.05471300278442932</td><td>125</td><td>125.0</td></tr>\n",
-       "<tr><td>2.328209867305983</td><td>2.584348460286765</td><td>2.871082431811579</td><td>43811132825732.35</td><td>818536036351.0841</td><td>0.027372871133279072</td><td>0.001264376953028166</td><td>1.2143592720456875</td><td>0.054067348171772577</td><td>138</td><td>138.0</td></tr>\n",
-       "<tr><td>2.871082431811579</td><td>3.1541249537829494</td><td>3.4139549963171745</td><td>34918820509793.312</td><td>715700841252.0444</td><td>0.02161699742545974</td><td>0.0006777947504387136</td><td>1.2083318981155682</td><td>0.05274388342400906</td><td>192</td><td>192.0</td></tr>\n",
-       "<tr><td>3.4139549963171745</td><td>3.6807344103619006</td><td>3.95682756082277</td><td>27867318283193.94</td><td>288016404616.3745</td><td>0.01764751408471792</td><td>0.0006936147755028247</td><td>1.311099393646099</td><td>0.05334127819405377</td><td>177</td><td>177.0</td></tr>\n",
-       "<tr><td>3.95682756082277</td><td>4.1857905887279125</td><td>4.499700125328366</td><td>23676492252914.723</td><td>325892892395.3659</td><td>0.013206030331073563</td><td>0.0006652124116508021</td><td>1.2856917023015786</td><td>0.07887750338660866</td><td>121</td><td>121.0</td></tr>\n",
-       "<tr><td>4.499700125328366</td><td>4.733676256494335</td><td>5.042572689833962</td><td>20277822255721.855</td><td>348381428571.66223</td><td>0.01301421018866444</td><td>0.0009175728511681354</td><td>1.3173896415983801</td><td>0.08741388146537891</td><td>67</td><td>67.0</td></tr>\n",
-       "<tr><td>5.042572689833962</td><td>5.250313838563014</td><td>5.585445254339558</td><td>17285265196381.973</td><td>417833907597.3777</td><td>0.009340141033264028</td><td>0.0008101011774997349</td><td>1.195583526441637</td><td>0.1086754688799238</td><td>29</td><td>29.0</td></tr>\n",
-       "</table></div>"
-      ],
-      "text/plain": [
-       "GCData(cosmo='CCLCosmology(H0=70.0, Omega_dm0=0.22500000000000003, Omega_b0=0.045, Omega_k0=0.0)', bin_units='Mpc', columns: radius_min, radius, radius_max, DeltaSigma_tan, DeltaSigma_tan_err, DeltaSigma_cross, DeltaSigma_cross_err, z, z_err, n_src, W_l)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cl_wrong_coordinate.DeltaSigma_profile"
+   "execution_count": null,
+   "id": "04b71872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oguri_cluster_euclidean = clmm.GalaxyCluster(cname, ra_cl, dec_cl, zl, oguri_galaxies_euclidean)\n",
+    "\n",
+    "oguri_cluster_celestial = clmm.GalaxyCluster(cname, ra_cl, dec_cl, zl, oguri_galaxies_celestial)\n",
+    "\n",
+    "# Convert elipticities into shears for the members.\n",
+    "oguri_cluster_euclidean.compute_tangential_and_cross_components(add=True)\n",
+    "oguri_cluster_celestial.compute_tangential_and_cross_components(add=True)\n",
+    "print(oguri_cluster_euclidean.galcat.colnames)\n",
+    "print(oguri_cluster_celestial.galcat.colnames)\n",
+    "\n",
+    "# Calculate the radial profile of the cluster.\n",
+    "oguri_cluster_euclidean.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "oguri_cluster_celestial.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "print(oguri_cluster_euclidean.profile.colnames)\n",
+    "print(oguri_cluster_celestial.profile.colnames)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "03cb3671-63ab-4e11-939b-e8c2d0d6ee60",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ErrorbarContainer object of 3 artists>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig3, ax3 = cl_wrong_coordinate.plot_profiles(\n",
-    "    tangential_component=\"DeltaSigma_tan\", \n",
-    "    cross_component=\"DeltaSigma_cross\",\n",
-    "    tangential_component_error=\"DeltaSigma_tan_err\", \n",
-    "    cross_component_error=\"DeltaSigma_cross_err\",\n",
-    "    table_name=\"DeltaSigma_profile\")\n",
-    "\n",
-    "\n",
-    "ax3.errorbar(cl_celestial.DeltaSigma_profile['radius']*1.02, cl_celestial.DeltaSigma_profile['DeltaSigma_tan'],\n",
-    "            yerr= cl_celestial.DeltaSigma_profile['DeltaSigma_tan_err'],alpha=0.3)\n",
-    "\n",
-    "ax3.errorbar(cl_celestial.DeltaSigma_profile['radius']*1.02, cl_celestial.DeltaSigma_profile['DeltaSigma_cross'],\n",
-    "            yerr= cl_celestial.DeltaSigma_profile['DeltaSigma_cross_err'],alpha=0.3)"
+   "execution_count": null,
+   "id": "22bae756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    oguri_cluster_euclidean.profile[\"radius\"],\n",
+    "    oguri_cluster_euclidean.profile[\"gt\"],\n",
+    "    yerr=oguri_cluster_euclidean.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"euclidean\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    oguri_cluster_euclidean.profile[\"radius\"],\n",
+    "    oguri_cluster_euclidean.profile[\"gx\"],\n",
+    "    yerr=oguri_cluster_euclidean.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    oguri_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    oguri_cluster_celestial.profile[\"gt\"],\n",
+    "    yerr=oguri_cluster_celestial.profile[\"gt_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"celestial\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    oguri_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    oguri_cluster_celestial.profile[\"gx\"],\n",
+    "    yerr=oguri_cluster_celestial.profile[\"gx_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    ")\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "180b4eb2",
+   "metadata": {},
+   "source": [
+    "### => As expected, we only see a clear signal in the `euclidean` cluster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd826a37",
+   "metadata": {},
+   "source": [
+    "### Example source galaxies from HSC Y3\n",
+    "\n",
+    "This dataset is a simplified version of HSC Y3 data (GAMA15H), post-processed by Tomomi Sunayama for testing purposes.  The pre-processed data is already public. These catalogs assume a **celestial** coordinate system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "cba6862c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hsc_cluster_cat = np.genfromtxt(\n",
+    "    data_coords_dir + \"/GAMA15H/redmapper_dr8_GAMA15H.txt\",\n",
+    "    dtype=np.dtype(\n",
+    "        [(\"ra\", np.float64), (\"dec\", np.float64), (\"z\", np.float64), (\"richness\", np.float64)]\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "hsc_source_cat = fits.getdata(data_coords_dir + \"/GAMA15H/GAMA15H_tutorial.fits\")\n",
+    "\n",
+    "cl = hsc_cluster_cat[0]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bebded34-7b95-4df6-a3a9-b64b31e64f43",
+   "id": "bbc58a20",
    "metadata": {},
    "source": [
-    "### => Similarly, we recover correct profiles, but not sure this olds when accounting for weights. "
+    "Here, we use a KDTree implementation in scipy to extract the background source galaxies for the first galaxy cluster in the dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "c9ffcb49-b6fb-46f1-b29b-9cf8f2ccaf9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Column name='DeltaSigma_tan' dtype='float64' length=10>\n",
-       " 190051501875241.06\n",
-       " 114653094293426.97\n",
-       "  76228503835158.64\n",
-       " 58120008332907.984\n",
-       "  43811132825732.35\n",
-       " 34918820509793.312\n",
-       "  27867318283193.94\n",
-       " 23676492252914.723\n",
-       " 20277822255721.855\n",
-       " 17285265196381.973,\n",
-       " <Column name='DeltaSigma_tan' dtype='float64' length=10>\n",
-       " 190051501875241.06\n",
-       " 114653094293426.97\n",
-       "  76228503835158.64\n",
-       " 58120008332907.984\n",
-       "  43811132825732.35\n",
-       " 34918820509793.312\n",
-       "  27867318283193.94\n",
-       " 23676492252914.723\n",
-       " 20277822255721.855\n",
-       " 17285265196381.973)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cl_celestial.DeltaSigma_profile['DeltaSigma_tan'],cl_wrong_coordinate.DeltaSigma_profile['DeltaSigma_tan']"
+   "id": "2657871a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source1 = hsc_source_cat[hsc_source_cat[\"photoz\"] > (cl[\"z\"] + 0.3)]\n",
+    "tree = spatial.cKDTree(np.array((source1[\"ra\"], source1[\"dec\"])).T)\n",
+    "sel = tree.query_ball_point([cl[\"ra\"], cl[\"dec\"]], 3)\n",
+    "bg = source1[sel]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68670871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hsc_galaxies_euclidean = clmm.GCData(\n",
+    "    [bg[\"RA\"], bg[\"Dec\"], bg[\"e1\"], bg[\"e2\"], bg[\"photoz\"], bg[\"weight\"]],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"w_ls\"],\n",
+    "    meta={\"coordinate_system\": \"euclidean\"},\n",
+    ")\n",
+    "\n",
+    "hsc_galaxies_celestial = clmm.GCData(\n",
+    "    [bg[\"RA\"], bg[\"Dec\"], bg[\"e1\"], bg[\"e2\"], bg[\"photoz\"], bg[\"weight\"]],\n",
+    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"w_ls\"],\n",
+    "    meta={\"coordinate_system\": \"celestial\"},\n",
+    ")\n",
+    "\n",
+    "hsc_cluster_euclidean = clmm.GalaxyCluster(\n",
+    "    \"Eucliden HSC cluster\",\n",
+    "    cl[\"ra\"],\n",
+    "    cl[\"dec\"],\n",
+    "    cl[\"z\"],\n",
+    "    hsc_galaxies_euclidean,\n",
+    ")\n",
+    "\n",
+    "hsc_cluster_celestial = clmm.GalaxyCluster(\n",
+    "    \"Celestial HSC cluster\",\n",
+    "    cl[\"ra\"],\n",
+    "    cl[\"dec\"],\n",
+    "    cl[\"z\"],\n",
+    "    hsc_galaxies_celestial,\n",
+    ")\n",
+    "\n",
+    "# Convert elipticities into shears for the members.\n",
+    "hsc_cluster_euclidean.compute_tangential_and_cross_components(add=True)\n",
+    "hsc_cluster_celestial.compute_tangential_and_cross_components(add=True)\n",
+    "print(hsc_cluster_euclidean.galcat.colnames)\n",
+    "print(hsc_cluster_celestial.galcat.colnames)\n",
+    "\n",
+    "# Calculate the radial profile of the cluster.\n",
+    "hsc_cluster_euclidean.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "hsc_cluster_celestial.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "print(hsc_cluster_euclidean.profile.colnames)\n",
+    "print(hsc_cluster_celestial.profile.colnames)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c71893c8-dadf-40ce-85a3-f63a5c6a5edc",
+   "id": "9e2abb47",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "fig, ax = plt.subplots(2, 1, height_ratios=[3, 1], sharex=True)\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    hsc_cluster_euclidean.profile[\"radius\"],\n",
+    "    hsc_cluster_euclidean.profile[\"gt\"],\n",
+    "    yerr=hsc_cluster_euclidean.profile[\"gt_err\"],\n",
+    "    alpha=0.5,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"euclidean\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    hsc_cluster_euclidean.profile[\"radius\"],\n",
+    "    hsc_cluster_euclidean.profile[\"gx\"],\n",
+    "    yerr=hsc_cluster_euclidean.profile[\"gx_err\"],\n",
+    "    marker=\".\",\n",
+    "    alpha=0.5,\n",
+    "    color=\"tab:red\",\n",
+    ")\n",
+    "\n",
+    "ax[0].errorbar(\n",
+    "    hsc_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    hsc_cluster_celestial.profile[\"gt\"],\n",
+    "    yerr=hsc_cluster_celestial.profile[\"gt_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"celestial\",\n",
+    ")\n",
+    "ax[1].errorbar(\n",
+    "    hsc_cluster_celestial.profile[\"radius\"] * 1.02,\n",
+    "    hsc_cluster_celestial.profile[\"gx\"],\n",
+    "    yerr=hsc_cluster_celestial.profile[\"gx_err\"],\n",
+    "    alpha=0.3,\n",
+    "    marker=\".\",\n",
+    "    color=\"tab:blue\",\n",
+    ")\n",
+    "\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xscale(\"log\")\n",
+    "ax[1].set_xlabel(\"R [kpc]\")\n",
+    "ax[0].set_ylabel(\"$g_t$\")\n",
+    "ax[1].set_ylabel(\"$g_x$\")\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78fe5a8d",
+   "metadata": {},
+   "source": [
+    "### => As expected, we only see a clear signal in the `celestial` cluster"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "clmm",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "clmm"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/demo_coordinate_system_datasets.ipynb b/examples/demo_coordinate_system_datasets.ipynb
deleted file mode 100644
index acbedfb65..000000000
--- a/examples/demo_coordinate_system_datasets.ipynb
+++ /dev/null
@@ -1,1013 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tests of coordinate system effects on shear profiles\n",
-    "\n",
-    "Author:  Tomomi Sunayama, July 15, 2024\n",
-    "\n",
-    "Tested, modified, and documented by: Camille Avestruz, July 18, 2024\n",
-    "\n",
-    "Reviewed by: Caio Lima de Oliveira, July 19, 2024\n",
-    "\n",
-    "This notebook illustrates the impact of having an incorrect coordinate system when calculating shear profiles.  The default coordinate system for `CLMM` is the euclidean/pixel coordinate system.  This is consistent with DC2 catalogs.  However, if we input a catalog that assumes a celestial (sky) coordinate system and use the default euclidean (pixel) coordinate system (or vice versa), the signal of shear around a cluster disappears because the signal essentially looks random.\n",
-    "\n",
-    "We test:\n",
-    "- CosmoDC2 source galaxies with shears extracted from `TXPipe` for a single galaxy cluster (euclidean/pixel coordinate system)\n",
-    "- Example source galaxies for galaxy clusters from a [Summer School](https://github.com/oguri/wlcluster_tutorial) taught by Masamune Oguri (euclidean/pixel coordinate system)\n",
-    "- HSC Y3 source galaxies with shears post processed by Tomomi Sunayama (celestial/sky coordinate system)\n",
-    "\n",
-    "We also \n",
-    "- Compare the explicit calculation of a shear profile on the HSC Y3 source galaxies against a profile produced from `CLMM`. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions to download text data\n",
-    "\n",
-    "Before running this notebook, you will need to download some data.  The data is available through a [dropbox link](https://www.dropbox.com/scl/fo/dwsccslr5iwb7lnkf8jvx/AJkjgFeemUEHpHaZaHHqpAg?rlkey=efbtsr15mdrs3y6xsm7l48o0r&st=xb58ap0g&dl=0)\n",
-    "\n",
-    "First, create a directory where you want to put the example data, e.g. for a given `data_coords_dir`:\n",
-    "```\n",
-    "mkdir -p <YOUR PATH TO DATA COORDS DIR>/data_coords\n",
-    "cd <YOUR PATH TO DATA COORDS DIR>/data_coords\n",
-    "```\n",
-    "Download all files from [dropbox link](https://www.dropbox.com/scl/fo/dwsccslr5iwb7lnkf8jvx/AJkjgFeemUEHpHaZaHHqpAg?rlkey=efbtsr15mdrs3y6xsm7l48o0r&st=xb58ap0g&dl=0).  This will be a zip file, `CLMM_data.zip` of size 242 Mb. scp or move this to `data_coords_dir`.\n",
-    "\n",
-    "From the directory, you should be able to unzip:\n",
-    "```\n",
-    "unzip data_CLMM.zip -d .\n",
-    "```\n",
-    "You now have the necessary data files to run this notebook.  \n",
-    "\n",
-    "**Make sure to change the `data_coords_dir` variable in the cell below to the appropriate location where you unzipped these files.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#  CHANGE <YOUR PATH TO DATA COORDS DIR> TO YOUR LOCATION\n",
-    "data_coords_dir = \"<YOUR PATH TO DATA COORDS DIR>/data_coords/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# %matplotlib inline\n",
-    "from astropy.table import Table\n",
-    "import pickle as pkl\n",
-    "from pathlib import Path\n",
-    "import pandas\n",
-    "from astropy.io import fits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from clmm import Cosmology\n",
-    "\n",
-    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example galaxy cluster from CosmoDC2\n",
-    "\n",
-    "Here, we plot an example galaxy cluster shear profile using `clmm`.  The cluster and source galaxy files are generated from the CosmoDC2 processed through TXPipe.  We test the coordinate system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster = pandas.read_pickle(data_coords_dir + \"/test_cluster.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster_z = cluster[\"redshift\"]  # Cluster redshift\n",
-    "cluster_ra = cluster[\"ra\"]  # Cluster Ra in deg\n",
-    "cluster_dec = cluster[\"dec\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source = np.genfromtxt(data_coords_dir + \"/test_source.txt\", names=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gal_ra = source[\"ra\"]\n",
-    "gal_dec = source[\"dec\"]\n",
-    "gal_e1 = source[\"e1\"]\n",
-    "gal_e2 = source[\"e2\"]\n",
-    "gal_z = source[\"zmean\"]\n",
-    "gal_id = np.arange(len(source[\"ra\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import clmm\n",
-    "import clmm.dataops as da\n",
-    "from clmm.utils import convert_units\n",
-    "\n",
-    "# Create a GCData with the galaxies.\n",
-    "galaxies = clmm.GCData(\n",
-    "    [gal_ra, gal_dec, gal_e1, gal_e2, gal_z, gal_id], names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we create a `GalaxyCluster` object, specifying an *incorrect* coordinate system.  For source galaxies from CosmoDC2, these are in the **euclidean** coordinate system.  We use the implemented kwarg when defining the galaxy cluster object to specify the **celestial** coordinate system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a GalaxyCluster.\n",
-    "cluster = clmm.GalaxyCluster(\n",
-    "    \"Name of cluster\", cluster_ra, cluster_dec, cluster_z, galaxies, coordinate_system=\"celestial\"\n",
-    ")\n",
-    "\n",
-    "# Convert elipticities into shears for the members.\n",
-    "cluster.compute_tangential_and_cross_components()\n",
-    "print(cluster.galcat.colnames)\n",
-    "\n",
-    "# Measure profile and add profile table to the cluster.\n",
-    "seps = convert_units(cluster.galcat[\"theta\"], \"radians\", \"Mpc\", cluster.z, cosmo)\n",
-    "\n",
-    "cluster.make_radial_profile(\n",
-    "    bins=da.make_bins(0.1, 3.0, 15, method=\"evenlog10width\"),\n",
-    "    bin_units=\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    include_empty_bins=False,\n",
-    "    gal_ids_in_bins=True,\n",
-    ")\n",
-    "print(cluster.profile.colnames)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we plot the resulting profile when `clmm` uses assumes a coordinate system inconsistent with the catalogs provided.  You will note that the signal is virtually zero at almost all radii."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "ax = fig.add_axes((0, 0, 1, 1))\n",
-    "errorbar_kwargs = dict(linestyle=\"\", marker=\"o\", markersize=1, elinewidth=0.5, capthick=0.5)\n",
-    "ax.errorbar(\n",
-    "    cluster.profile[\"radius\"],\n",
-    "    cluster.profile[\"gt\"],\n",
-    "    cluster.profile[\"gt_err\"],\n",
-    "    c=\"k\",\n",
-    "    **errorbar_kwargs\n",
-    ")\n",
-    "ax.set_xlabel(\"r [Mpc]\", fontsize=10)\n",
-    "ax.set_ylabel(r\"$g_t$\", fontsize=10)\n",
-    "ax.grid(lw=0.3)\n",
-    "ax.minorticks_on()\n",
-    "ax.grid(which=\"minor\", lw=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we create a `GalaxyCluster` object, specifying the *correct* coordinate system.  For source galaxies from CosmoDC2, these are in the **euclidean** coordinate system.  We use the implemented kwarg when defining the galaxy cluster object to also specify the **euclidean** coordinate system.  However, with a single galaxy cluster, the signal is not significant enough to clearly see a difference.  There is a possible excess excess with the correct coordinate system at larger radii.  Note:  First, the lensing signal in CosmoDC2 clusters at the inner radii is known to be weak due to a limitation in the resolution when the ray tracing was performed in generating the source galaxy shears.  Second, this has been process through `TXPipe`, which is something else we will need to understand."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster2 = clmm.GalaxyCluster(\n",
-    "    \"Name of cluster\", cluster_ra, cluster_dec, cluster_z, galaxies, coordinate_system=\"euclidean\"\n",
-    ")\n",
-    "cluster2.compute_tangential_and_cross_components()\n",
-    "print(cluster.galcat.colnames)\n",
-    "\n",
-    "# Measure profile and add profile table to the cluster.\n",
-    "seps = convert_units(cluster2.galcat[\"theta\"], \"radians\", \"Mpc\", cluster2.z, cosmo)\n",
-    "\n",
-    "cluster2.make_radial_profile(\n",
-    "    bins=da.make_bins(0.1, 3.0, 15, method=\"evenlog10width\"),\n",
-    "    bin_units=\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    include_empty_bins=False,\n",
-    "    gal_ids_in_bins=True,\n",
-    ")\n",
-    "print(cluster.profile.colnames)\n",
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "ax = fig.add_axes((0, 0, 1, 1))\n",
-    "errorbar_kwargs = dict(linestyle=\"\", marker=\"o\", markersize=1, elinewidth=0.5, capthick=0.5)\n",
-    "ax.errorbar(\n",
-    "    cluster.profile[\"radius\"], cluster.profile[\"gt\"], cluster.profile[\"gt_err\"], label=\"celestial\"\n",
-    ")\n",
-    "ax.errorbar(\n",
-    "    cluster2.profile[\"radius\"],\n",
-    "    cluster2.profile[\"gt\"],\n",
-    "    cluster2.profile[\"gt_err\"],\n",
-    "    label=\"euclidean\",\n",
-    ")\n",
-    "ax.set_xlabel(\"r [Mpc]\", fontsize=20)\n",
-    "ax.set_ylabel(r\"$g_t$\", fontsize=20)\n",
-    "ax.grid(lw=0.3)\n",
-    "ax.minorticks_on()\n",
-    "ax.grid(which=\"minor\", lw=0.1)\n",
-    "plt.legend(fontsize=20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example source galaxies from M. Oguri\n",
-    "\n",
-    "This dataset is a curated selection of cluster and source catalogs from Summer School lectures delivered by Masamune Oguri.  There are eight galaxy clusters in this selection.  \n",
-    "\n",
-    "More details on the corresponding tutorial can be found at this [GitHub link](https://github.com/oguri/wlcluster_tutorial). These are also in the **euclidean** coordinate system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clusters = [\n",
-    "    \"a1703\",\n",
-    "    \"gho1320\",\n",
-    "    \"sdss0851\",\n",
-    "    \"sdss1050\",\n",
-    "    \"sdss1138\",\n",
-    "    \"sdss1226\",\n",
-    "    \"sdss1329\",\n",
-    "    \"sdss1531\",\n",
-    "]\n",
-    "\n",
-    "zl_all = {\n",
-    "    \"a1703\": 0.277,\n",
-    "    \"gho1320\": 0.308,\n",
-    "    \"sdss0851\": 0.370,\n",
-    "    \"sdss1050\": 0.60,\n",
-    "    \"sdss1138\": 0.451,\n",
-    "    \"sdss1226\": 0.435,\n",
-    "    \"sdss1329\": 0.443,\n",
-    "    \"sdss1531\": 0.335,\n",
-    "}\n",
-    "\n",
-    "ra_cl_all = {\n",
-    "    \"a1703\": 198.771833,\n",
-    "    \"gho1320\": 200.703208,\n",
-    "    \"sdss0851\": 132.911917,\n",
-    "    \"sdss1050\": 162.666250,\n",
-    "    \"sdss1138\": 174.537292,\n",
-    "    \"sdss1226\": 186.712958,\n",
-    "    \"sdss1329\": 202.393708,\n",
-    "    \"sdss1531\": 232.794167,\n",
-    "}\n",
-    "\n",
-    "dec_cl_all = {\n",
-    "    \"a1703\": 51.817389,\n",
-    "    \"gho1320\": 31.654944,\n",
-    "    \"sdss0851\": 33.518361,\n",
-    "    \"sdss1050\": 0.285306,\n",
-    "    \"sdss1138\": 27.908528,\n",
-    "    \"sdss1226\": 21.831194,\n",
-    "    \"sdss1329\": 22.721167,\n",
-    "    \"sdss1531\": 34.240278,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cname = \"a1703\"\n",
-    "\n",
-    "# cluster redshift\n",
-    "zl = zl_all.get(cname)\n",
-    "\n",
-    "# coordinates of the cluster center\n",
-    "ra_cl = ra_cl_all.get(cname)\n",
-    "dec_cl = dec_cl_all.get(cname)\n",
-    "\n",
-    "# fix source redshift to 1.0\n",
-    "zs = 1.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We inspect the first galaxy cluster, Abell 1703."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rfile = data_coords_dir + \"/data/shear_\" + cname + \".dat\"\n",
-    "data = np.loadtxt(rfile, comments=\"#\")\n",
-    "\n",
-    "ra = data[:, 0]\n",
-    "dec = data[:, 1]\n",
-    "e1 = data[:, 2]\n",
-    "e2 = data[:, 3]\n",
-    "wei = data[:, 4]\n",
-    "ids = np.arange(np.shape(data)[0])\n",
-    "redshifts = np.ones(np.shape(data)[0])\n",
-    "galaxies = clmm.GCData(\n",
-    "    [ra, dec, e1, e2, redshifts, ids], names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"id\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we create a GalaxyCluster object, specifying the correct coordinate system. For source galaxies from the Oguri curated dataset, these are in the euclidean coordinate system. We use the implemented kwarg when defining the galaxy cluster object to also **specify the euclidean coordinate system**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster = clmm.GalaxyCluster(\"A1703euc\", ra_cl, dec_cl, zl, galaxies, coordinate_system=\"euclidean\")\n",
-    "\n",
-    "# Convert elipticities into shears for the members.\n",
-    "cluster.compute_tangential_and_cross_components()\n",
-    "print(cluster.galcat.colnames)\n",
-    "\n",
-    "# Measure profile and add profile table to the cluster.\n",
-    "seps = convert_units(cluster.galcat[\"theta\"], \"radians\", \"Mpc\", cluster.z, cosmo)\n",
-    "\n",
-    "cluster.make_radial_profile(\n",
-    "    bins=da.make_bins(0.2, 3.0, 8, method=\"evenlog10width\"),\n",
-    "    bin_units=\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    include_empty_bins=False,\n",
-    "    gal_ids_in_bins=True,\n",
-    ")\n",
-    "print(cluster.profile.colnames)\n",
-    "\n",
-    "\n",
-    "fig = plt.figure(figsize=(6, 4))\n",
-    "ax = fig.add_axes((0, 0, 1, 1))\n",
-    "errorbar_kwargs = dict(linestyle=\"\", marker=\"o\", markersize=1, elinewidth=0.5, capthick=0.5)\n",
-    "ax.errorbar(\n",
-    "    cluster.profile[\"radius\"], cluster.profile[\"gt\"], cluster.profile[\"gt_err\"], label=\"euclidean\"\n",
-    ")\n",
-    "\n",
-    "# assume incorrect coordinates\n",
-    "cluster2 = clmm.GalaxyCluster(\n",
-    "    \"A1703cel\", ra_cl, dec_cl, zl, galaxies, coordinate_system=\"celestial\"\n",
-    ")\n",
-    "\n",
-    "cluster2.compute_tangential_and_cross_components()\n",
-    "print(cluster2.galcat.colnames)\n",
-    "\n",
-    "# Measure profile and add profile table to the cluster.\n",
-    "seps = convert_units(cluster2.galcat[\"theta\"], \"radians\", \"Mpc\", cluster.z, cosmo)\n",
-    "\n",
-    "cluster2.make_radial_profile(\n",
-    "    bins=da.make_bins(0.2, 3.0, 8, method=\"evenlog10width\"),\n",
-    "    bin_units=\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    include_empty_bins=False,\n",
-    "    gal_ids_in_bins=True,\n",
-    ")\n",
-    "print(cluster2.profile.colnames)\n",
-    "\n",
-    "ax.errorbar(\n",
-    "    cluster2.profile[\"radius\"],\n",
-    "    cluster2.profile[\"gt\"],\n",
-    "    cluster2.profile[\"gt_err\"],\n",
-    "    label=\"celestial\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "ax.set_xlabel(\"r [Mpc]\", fontsize=20)\n",
-    "ax.set_ylabel(r\"$g_t$\", fontsize=20)\n",
-    "ax.grid(lw=0.3)\n",
-    "ax.minorticks_on()\n",
-    "ax.grid(which=\"minor\", lw=0.1)\n",
-    "plt.legend(fontsize=20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example source galaxies from HSC Y3\n",
-    "\n",
-    "This dataset is a simplified version of HSC Y3 data (GAMA15H), post-processed by Tomomi Sunayama for testing purposes.  The pre-processed data is already public. These catalogs assume a **celestial** coordinate system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster_cat = np.genfromtxt(\n",
-    "    data_coords_dir + \"/GAMA15H/redmapper_dr8_GAMA15H.txt\",\n",
-    "    dtype=np.dtype(\n",
-    "        [(\"ra\", np.float64), (\"dec\", np.float64), (\"z\", np.float64), (\"richness\", np.float64)]\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_cat = fits.getdata(data_coords_dir + \"/GAMA15H/GAMA15H_tutorial.fits\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cl = cluster_cat[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8820c46a",
-   "metadata": {},
-   "source": [
-    "Here, we use a KDTree implementation in scipy to extract the background source galaxies for the first galaxy cluster in the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import spatial\n",
-    "\n",
-    "source1 = source_cat[source_cat[\"photoz\"] > (cl[\"z\"] + 0.3)]\n",
-    "tree = spatial.cKDTree(np.array((source1[\"ra\"], source1[\"dec\"])).T)\n",
-    "sel = tree.query_ball_point([cl[\"ra\"], cl[\"dec\"]], 3)\n",
-    "bg = source1[sel]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf2df4dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Inspect background source galaxy selection\n",
-    "bg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a77e1bfe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sources = clmm.GCData(\n",
-    "    [bg[\"RA\"], bg[\"Dec\"], bg[\"e1\"], bg[\"e2\"], bg[\"photoz\"], bg[\"weight\"]],\n",
-    "    names=[\"ra\", \"dec\", \"e1\", \"e2\", \"z\", \"w_ls\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45d76a13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster = clmm.GalaxyCluster(\n",
-    "    \"redmapper\", cl[\"ra\"], cl[\"dec\"], cl[\"z\"], sources, coordinate_system=\"celestial\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1f88b26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sigma_c = cosmo.eval_sigma_crit(cl[\"z\"], sources[\"z\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6de9c9de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster.compute_tangential_and_cross_components(\n",
-    "    shape_component1=\"e1\",\n",
-    "    shape_component2=\"e2\",\n",
-    "    tan_component=\"DS_t\",\n",
-    "    cross_component=\"DS_x\",\n",
-    "    cosmo=cosmo,\n",
-    "    is_deltasigma=True,\n",
-    "    use_pdz=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5fa13fb2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4dcd4a7",
-   "metadata": {},
-   "source": [
-    "Now we construct a radial profile of the tangential and cross terms for the galaxy cluster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seps = convert_units(cluster.galcat[\"theta\"], \"radians\", \"Mpc\", cluster.z, cosmo)\n",
-    "\n",
-    "cluster.make_radial_profile(\n",
-    "    tan_component_in=\"DS_t\",\n",
-    "    cross_component_in=\"DS_x\",\n",
-    "    tan_component_out=\"DS_t\",\n",
-    "    cross_component_out=\"DS_x\",\n",
-    "    weights_in=\"w_ls\",\n",
-    "    bins=da.make_bins(0.1, 20.0, 15, method=\"evenlog10width\"),\n",
-    "    bin_units=\"Mpc\",\n",
-    "    cosmo=cosmo,\n",
-    "    include_empty_bins=False,\n",
-    "    gal_ids_in_bins=False,\n",
-    ")\n",
-    "print(cluster.profile.colnames)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "286dc0f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(6, 6))\n",
-    "ax = fig.add_axes((0, 0, 1, 1))\n",
-    "errorbar_kwargs = dict(linestyle=\"\", marker=\"o\", markersize=1, elinewidth=0.5, capthick=0.5)\n",
-    "ax.errorbar(\n",
-    "    cluster.profile[\"radius\"],\n",
-    "    cluster.profile[\"DS_t\"] / 1e13,\n",
-    "    cluster.profile[\"DS_t_err\"] / 1e13,\n",
-    "    label=\"celestial\",\n",
-    ")\n",
-    "plt.loglog()\n",
-    "\n",
-    "ax.set_xlabel(\"r [Mpc]\", fontsize=20)\n",
-    "ax.set_ylabel(r\"$\\Delta\\Sigma(r)$\", fontsize=20)\n",
-    "ax.grid(lw=0.3)\n",
-    "ax.minorticks_on()\n",
-    "ax.grid(which=\"minor\", lw=0.1)\n",
-    "plt.legend(fontsize=20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05e4ee26",
-   "metadata": {},
-   "source": [
-    "## Example explicit lensing profile measurement comparison with CLMM profile\n",
-    "\n",
-    "Here, we use the example HSC Y3 dataset to explicitly measure the lensing signal without using CLMM for comparison.  Note, we need to still define a cosmology to calculate comoving distances."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "651e0048",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from astropy.io import fits\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import spatial\n",
-    "from astropy.cosmology import WMAP5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dbefe49d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in the data\n",
-    "cluster_cat = np.genfromtxt(\n",
-    "    data_coords_dir + \"GAMA15H/redmapper_dr8_GAMA15H.txt\",\n",
-    "    dtype=np.dtype(\n",
-    "        [(\"RA\", np.float64), (\"Dec\", np.float64), (\"z\", np.float64), (\"richness\", np.float64)]\n",
-    "    ),\n",
-    ")\n",
-    "source_cat = fits.getdata(data_coords_dir + \"GAMA15H/GAMA15H_tutorial.fits\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a826c6e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cosmo = WMAP5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca4bd885",
-   "metadata": {},
-   "source": [
-    "Below, we measure lensing signals with simplified assumptions.  We do not account for responsivity, multiplicative, nor additive biases.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fdca16e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define functions for the explicit calculation\n",
-    "\n",
-    "\n",
-    "def calcDistanceAngle(a1, d1, a2, d2):\n",
-    "    \"\"\"Compute the angle between lens and source galaxies from ra, dec in radians\n",
-    "    a1 (float, ndarray) : lens RA in radians\n",
-    "    d1 (float, ndarray) : lens DEC in radians\n",
-    "    a2 (float, ndarray) : src RA in radians\n",
-    "    d2 (float, ndarray) : src DEC in radians\n",
-    "    \"\"\"\n",
-    "    return np.arccos(np.cos(d1) * np.cos(d2) * np.cos(a1 - a2) + np.sin(d1) * np.sin(d2))\n",
-    "\n",
-    "\n",
-    "def cosPhi2(a1, a2, d1, d2, distanceAngle):\n",
-    "    \"\"\"Compute $\\cos(2\\phi)$ for a given distance angle between lens and source galaxies from ra, dec in radians\n",
-    "    a1 (float, ndarray) : lens RA in radians\n",
-    "    a2 (float, ndarray) : src RA in radians\n",
-    "    d1 (float, ndarray) : lens DEC in radians\n",
-    "    d2 (float, ndarray) : src DEC in radians\n",
-    "    distanceAngle (float, ndarray) : angular distance between lens and source in radians\n",
-    "    \"\"\"\n",
-    "    return np.sin(a2 - a1) * np.cos(d1) / np.sin(distanceAngle)\n",
-    "\n",
-    "\n",
-    "def sinPhi2(a1, a2, d1, d2, distanceAngle):\n",
-    "    \"\"\"Compute $\\sin(2\\phi)$ for a given distance angle between lens and source galaxies from ra, dec in radians\n",
-    "    a1 (float, ndarray) : lens RA in radians\n",
-    "    a2 (float, ndarray) : src RA in radians\n",
-    "    d1 (float, ndarray) : lens DEC in radians\n",
-    "    d2 (float, ndarray) : src DEC in radians\n",
-    "    distanceAngle (float, ndarray) : angular distance between lens and source in radians\n",
-    "    \"\"\"\n",
-    "    return (-np.cos(d2) * np.sin(d1) + np.sin(d2) * np.cos(d1) * np.cos(a1 - a2)) / np.sin(\n",
-    "        distanceAngle\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def compute_sin2phi_cos2phi(a1, a2, d1, d2, distanceAngle):\n",
-    "    \"\"\"Compute necessary coefficients for the et and ex components, sin2phi and cos2phi\n",
-    "    a1 (float, ndarray) : lens RA in radians\n",
-    "    a2 (float, ndarray) : src RA in radians\n",
-    "    d1 (float, ndarray) : lens DEC in radians\n",
-    "    d2 (float, ndarray) : src DEC in radians\n",
-    "    distanceAngle (float, ndarray) : angular distance between lens and source in radians\n",
-    "    \"\"\"\n",
-    "    cosp = cosPhi2(a1, a2, d1, d2, distanceAngle)\n",
-    "    sinp = sinPhi2(a1, a2, d1, d2, distanceAngle)\n",
-    "    cos2p = cosp**2 - sinp**2\n",
-    "    sin2p = 2.0 * sinp * cosp\n",
-    "    return cos2p, sin2p\n",
-    "\n",
-    "\n",
-    "def calc_et_ex(e1, e2, cos2p, sin2p):\n",
-    "    \"\"\"Calculate the et and ex from the e1 e2 values of all sources and their sin2phi, cos2phi\"\"\"\n",
-    "    et = -(e1 * cos2p + e2 * sin2p)\n",
-    "    ex = -(-e1 * sin2p + e2 * cos2p)\n",
-    "    return et, ex\n",
-    "\n",
-    "\n",
-    "def populate_profile_sums(ps, i_r, src_in_bin, Sigma_cr, sel, et, ex):\n",
-    "    \"\"\"Populate the profile sums at a given radian bin from the calculated selection, sigma_crit, et, and ex\"\"\"\n",
-    "    ps[\"n\"][i_r] += sel.sum()\n",
-    "    ps[\"e_sq\"][i_r] += np.sum(et**2 + ex**2)\n",
-    "\n",
-    "    wt = src_in_bin[\"weight\"] * Sigma_cr**-2  # multiply by the lens weights if it is not one\n",
-    "    ps[\"w\"][i_r] += np.sum(wt)\n",
-    "\n",
-    "    wetsigma = wt * Sigma_cr * et\n",
-    "    ps[\"wetsigma\"][i_r] += np.sum(wetsigma)\n",
-    "    ps[\"wetsigma_sq\"][i_r] += np.sum(wetsigma**2)\n",
-    "\n",
-    "    wexsigma = wt * Sigma_cr * ex\n",
-    "    ps[\"wexsigma\"][i_r] += np.sum(wexsigma)\n",
-    "    ps[\"wexsigma_sq\"][i_r] += np.sum(wexsigma**2)\n",
-    "\n",
-    "    wsigmainv = wt * 1.0 / Sigma_cr\n",
-    "    ps[\"wsigmainv\"][i_r] += np.sum(wsigmainv)\n",
-    "\n",
-    "    wzs = wt * src_in_bin[\"photoz\"]\n",
-    "    ps[\"wzs\"][i_r] += np.sum(wzs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f343ae7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Relevant constants, radial binning, source photoz range and lens photoz range\n",
-    "\n",
-    "d2r = np.pi / 180.0\n",
-    "r2d = 180.0 / np.pi\n",
-    "Mpc = 3.08568025 * 10**19  # 1Mpc = 3.08568025*10**19 km\n",
-    "M_sun = 1.9884 * 10**33  # solar mass [g]\n",
-    "c_light = 2.99792458 * 10**5  # speed of light [km/s]\n",
-    "G = 6.67384 * 10 ** (-20)  # gravitational constant [km^3s^-2kg^-1]\n",
-    "Sigma_cr_fact = c_light**2 / (4 * np.pi * G) * Mpc * 10**3 / M_sun\n",
-    "rbin_edges = np.logspace(-1, np.log10(20), 15)  # Define your radial bins\n",
-    "\n",
-    "# Named numpy arrays for relevant profile values to explicitly compute and sum at each radii\n",
-    "profile_names = [\n",
-    "    \"e_sq\",\n",
-    "    \"w\",\n",
-    "    \"wetsigma\",\n",
-    "    \"wetsigma_sq\",\n",
-    "    \"wexsigma\",\n",
-    "    \"wexsigma_sq\",\n",
-    "    \"wsigmainv\",\n",
-    "    \"wzs\",\n",
-    "    \"n\",\n",
-    "]\n",
-    "profile_sums = np.rec.fromarrays(\n",
-    "    [np.zeros(len(rbin_edges) - 1) for i in profile_names], names=profile_names\n",
-    ")\n",
-    "\n",
-    "source_pz = {\"min\": 0.5, \"max\": 10}\n",
-    "lens_pz = {\"min\": 0.1, \"max\": 0.2}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8da9ec37",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select lens clusters and source galaxies from catalogs using kdtree\n",
-    "\n",
-    "source_pz_criteria = (source_cat[\"photoz\"] < source_pz[\"max\"]) & (\n",
-    "    source_cat[\"photoz\"] > source_pz[\"min\"]\n",
-    ")\n",
-    "selected_sources = source_cat[source_pz_criteria]\n",
-    "\n",
-    "tree = spatial.cKDTree(np.array((selected_sources[\"RA\"], selected_sources[\"Dec\"])).T)\n",
-    "\n",
-    "# We only select one,selecting many will take much more time to compute.\n",
-    "lens_pz_criteria = (cluster_cat[\"z\"] > lens_pz[\"min\"]) & (cluster_cat[\"z\"] < lens_pz[\"max\"])\n",
-    "lens_clusters = cluster_cat[lens_pz_criteria][:1]\n",
-    "\n",
-    "# Set weights for the cluster lenses to one\n",
-    "lens_weights = np.ones(lens_clusters.size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "972bdfcb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Calculate lensing profiles for each cluster lens\n",
-    "\n",
-    "for ilens in np.arange(lens_clusters.size):\n",
-    "\n",
-    "    # Select source galaxies for this cluster lens\n",
-    "    sel = tree.query_ball_point([lens_clusters[\"RA\"][ilens], lens_clusters[\"Dec\"][ilens]], 3)\n",
-    "    sel_z = (\n",
-    "        source_cat[sel][\"photoz\"] > lens_clusters[\"z\"][ilens]\n",
-    "    )  # Try to change the source galaxy selection\n",
-    "    source_bg = source_cat[sel][sel_z]\n",
-    "\n",
-    "    # Compute an angle between the lens and the source\n",
-    "    theta_ls = calcDistanceAngle(\n",
-    "        lens_clusters[\"RA\"][ilens] * d2r,\n",
-    "        lens_clusters[\"Dec\"][ilens] * d2r,\n",
-    "        source_bg[\"RA\"] * d2r,\n",
-    "        source_bg[\"Dec\"] * d2r,\n",
-    "    )\n",
-    "\n",
-    "    # Compute the comoving distance of the lens\n",
-    "    l_chi = cosmo.comoving_distance((lens_clusters[\"z\"][ilens])).value\n",
-    "    r = theta_ls * l_chi\n",
-    "    assign_r = np.digitize(r, rbin_edges)\n",
-    "\n",
-    "    for i_r in range(len(rbin_edges) - 1):\n",
-    "        # Subselection mask of source galaxies in the radial bin\n",
-    "        sel = assign_r == i_r + 1\n",
-    "\n",
-    "        # Subselected source galaxies and their respective angle, theta, to lens\n",
-    "        source_bg_inbin = source_bg[sel]\n",
-    "        theta_sub = theta_ls[sel]\n",
-    "\n",
-    "        # Compute the cos(2*phi) and sin(2*phi) for a given distance angle between lens and source galaxies\n",
-    "        cos2p, sin2p = compute_sin2phi_cos2phi(\n",
-    "            lens_clusters[\"RA\"][ilens] * d2r,\n",
-    "            source_bg_inbin[\"RA\"] * d2r,\n",
-    "            lens_clusters[\"Dec\"][ilens] * d2r,\n",
-    "            source_bg_inbin[\"Dec\"] * d2r,\n",
-    "            theta_sub,\n",
-    "        )\n",
-    "\n",
-    "        # Calculate tangential and cross terms from e1, e2 of all source galaxies in the rbin\n",
-    "        et, ex = calc_et_ex(source_bg_inbin[\"e1\"], source_bg_inbin[\"e2\"], cos2p, sin2p)\n",
-    "\n",
-    "        # Calculate critical surface mass density [M_sun/ comoving Mpc^2]. (1+zl)**-2 is for comoving coordinates.\n",
-    "        comoving_distance = cosmo.comoving_distance((source_bg_inbin[\"photoz\"])).value\n",
-    "        Sigma_cr = (\n",
-    "            Sigma_cr_fact\n",
-    "            / (1.0 - l_chi / comoving_distance)\n",
-    "            / l_chi\n",
-    "            / (1.0 + lens_clusters[\"z\"][ilens])\n",
-    "            / 10**12\n",
-    "        )\n",
-    "\n",
-    "        # Populate the profile_sums at this radial bin for this cluster lens\n",
-    "        populate_profile_sums(profile_sums, i_r, source_bg_inbin, Sigma_cr, sel, et, ex)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8078ef52",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute the lensing signal and errors to plot\n",
-    "\n",
-    "radial_bin = (\n",
-    "    2.0\n",
-    "    * (rbin_edges[1:] ** 3 - rbin_edges[:-1] ** 3)\n",
-    "    / (3.0 * (rbin_edges[1:] ** 2 - rbin_edges[:-1] ** 2))\n",
-    ")\n",
-    "gt = 0.5 * profile_sums[\"wetsigma\"] / profile_sums[\"w\"]\n",
-    "gt_err = 0.5 * np.sqrt(profile_sums[\"wetsigma_sq\"]) / profile_sums[\"w\"]\n",
-    "gx = 0.5 * profile_sums[\"wexsigma\"] / profile_sums[\"w\"]\n",
-    "gx_err = 0.5 * np.sqrt(profile_sums[\"wexsigma_sq\"]) / profile_sums[\"w\"]\n",
-    "sigma_cr = 1.0 / (profile_sums[\"wsigmainv\"] / profile_sums[\"w\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f4756df",
-   "metadata": {},
-   "source": [
-    "Below, we compare the explicitly calculated lensing signal against the CLMM calculated signal.  You will notice that the `CLMM` calculated profile is systematically lower than the one calculated using Tomomi's code.  This is likely due to a combination of assumed weighting scheme and other factors that differ between HSC post-processing and what `CLMM` assumes or a \"little h\" problem, which we will need to understand and possibly address."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Figure for the lensing signal\n",
-    "plt.figure(figsize=(6, 6))\n",
-    "\n",
-    "# From explicit calculation\n",
-    "plt.errorbar(radial_bin, gt, yerr=gt_err, label=\"original\")\n",
-    "\n",
-    "# From CLMM\n",
-    "plt.errorbar(\n",
-    "    cluster.profile[\"radius\"],\n",
-    "    cluster.profile[\"DS_t\"] / 1e13,\n",
-    "    cluster.profile[\"DS_t_err\"] / 1e13,\n",
-    "    label=\"CLMM\",\n",
-    ")\n",
-    "plt.loglog()\n",
-    "plt.legend(fontsize=20)\n",
-    "plt.xlabel(r\"$R[h^{-1}{\\rm Mpc}]$\", fontsize=20)\n",
-    "plt.ylabel(r\"$\\Delta\\Sigma(R)$\", fontsize=20)"
-   ]
-  }
- ],
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-    "name": "ipython",
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