From b2e53bd2a5821af53bff335c9c8e8e36dfef49da Mon Sep 17 00:00:00 2001
From: Marina Ricci <rm003211@gmail.com>
Date: Tue, 30 Jul 2024 20:33:38 +0200
Subject: [PATCH] added notebook to highlight issues$

---
 examples/demo_coordinate_system.ipynb | 816 ++++++++++++++++++++++++++
 1 file changed, 816 insertions(+)
 create mode 100644 examples/demo_coordinate_system.ipynb

diff --git a/examples/demo_coordinate_system.ipynb b/examples/demo_coordinate_system.ipynb
new file mode 100644
index 000000000..e034fc9a2
--- /dev/null
+++ b/examples/demo_coordinate_system.ipynb
@@ -0,0 +1,816 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "28a917d8-04b1-4a4d-9f3d-3bf0be1b5dba",
+   "metadata": {},
+   "source": [
+    "### Here we generate mock source catalogs with different coordinate system and explore how that must be accounted to measure the correct shear profiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "423b5f84-831a-4da7-af39-92e1b6dc3818",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.12.5'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import clmm\n",
+    "clmm.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ed8f467-dbaa-413f-a2cf-f1ff7fd5513d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db760451-21e7-4c5b-8fdf-af066f2b8dd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from clmm.support import mock_data as mock\n",
+    "from clmm import Cosmology"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c31afe8f-062a-42e2-bc79-442ac06579e4",
+   "metadata": {},
+   "source": [
+    "## Generate the mock catalog with different source galaxy options\n",
+    "In this example, the mock data includes: shape noise, galaxies drawn from redshift distribution and photoz errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7c7b2a1a-e59e-4f8e-a415-d957af0b4b0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mock_cosmo = Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "11263d57-3e76-4fe1-adbc-9e1299e6900e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosmo = mock_cosmo\n",
+    "cluster_id = \"Awesome_cluster\"\n",
+    "\n",
+    "cluster_m = 1.0e15  # M200,m\n",
+    "cluster_z = 0.3\n",
+    "# Cluster centre coordinates\n",
+    "cluster_ra = 50.0\n",
+    "cluster_dec = 87.0\n",
+    "concentration = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "20841454-8b80-4b8b-adb9-c1f189f03c78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's put all these quantities in a single dictionary to facilitate clarity\n",
+    "cluster_kwargs = {\n",
+    "    \"cluster_m\": cluster_m,\n",
+    "    \"cluster_z\": cluster_z,\n",
+    "    \"cluster_ra\": cluster_ra,\n",
+    "    \"cluster_dec\": cluster_dec,\n",
+    "    \"cluster_c\": concentration,\n",
+    "    \"cosmo\": cosmo}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb8e1555-565e-40c3-8d76-b5039bf6043d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's put all these quantities in a single dictionary to facilitate clarity\n",
+    "source_kwargs = {\n",
+    "    \"zsrc\": \"chang13\",\n",
+    "    \"zsrc_min\": cluster_z + 0.1,\n",
+    "    \"photoz_sigma_unscaled\": 0.05,\n",
+    "    \"ngals\": 1000,\n",
+    "    \"pz_bins\": np.linspace(0, 10, 1001)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4db4311b-7947-4089-b092-d69de52bff69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e51cb813-c077-4d94-878e-0387e1b21801",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(679)\n",
+    "\n",
+    "mock_sources_celestial_coord = mock.generate_galaxy_catalog(\n",
+    "    **cluster_kwargs,\n",
+    "    **source_kwargs,\n",
+    "    coordinate_system = \"celestial\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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=\"table139982163318272\" 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"
+    }
+   ],
+   "source": [
+    "mock_sources_euclidean_coord[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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=\"table139982163313664\" 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"
+    }
+   ],
+   "source": [
+    "mock_sources_celestial_coord[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e852ccb-a49a-441f-89b9-da314fecd5ce",
+   "metadata": {},
+   "source": [
+    "## Generate cluster objects from mock data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6d76bb85-2c93-4778-8135-1f1e6c139689",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clmm.dataops\n",
+    "from clmm.dataops import compute_tangential_and_cross_components, make_radial_profile, make_bins\n",
+    "from clmm.galaxycluster import GalaxyCluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "64890dbd-1e26-4594-8a7c-f5837f765e39",
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "efefdd51-eb7a-4e26-a2dd-da574f213932",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f5022860730>"
+      ]
+     },
+     "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"
+    }
+   ],
+   "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",
+    "\n",
+    "ax1.set_xlabel(\"e1\")\n",
+    "ax1.set_ylabel(\"e2\")\n",
+    "ax1.set_aspect(\"equal\", \"datalim\")\n",
+    "ax1.axvline(0, linestyle=\"dotted\", color=\"black\")\n",
+    "ax1.axhline(0, linestyle=\"dotted\", color=\"black\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f134fc6f-fc61-462a-b283-51eb6cb0cfd2",
+   "metadata": {},
+   "source": [
+    "## Compute and plot shear profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "cl_euclidean.compute_tangential_and_cross_components(add=True)\n",
+    "cl_euclidean.galcat[\"et\", \"ex\"].pprint(max_width=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "cl_celestial.compute_tangential_and_cross_components(add=True)\n",
+    "cl_celestial.galcat[\"et\", \"ex\"].pprint(max_width=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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 0x7f502318fbe0>"
+      ]
+     },
+     "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"
+    }
+   ],
+   "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].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].set_xlabel(\"$\\\\epsilon_x$\", fontsize=\"xx-large\")\n",
+    "ax[1].set_yscale(\"log\")\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8700b684-279f-485c-98e3-a25ca5d9bd2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>GCData length=10</i>\n",
+       "<table id=\"table139982147778016-84682\" 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(\"$('#table139982147778016-84682').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",
+       "    $('#table139982147778016-84682').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"
+    }
+   ],
+   "source": [
+    "cl_euclidean.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "cl_euclidean.profile.show_in_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f6774006-189b-4226-a4dd-f31722b7713d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>GCData length=10</i>\n",
+       "<table id=\"table139982147772688-858330\" 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(\"$('#table139982147772688-858330').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",
+       "    $('#table139982147772688-858330').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"
+    }
+   ],
+   "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",
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38011c62-5261-47fa-a00d-489acb879c96",
+   "metadata": {},
+   "source": [
+    "### => When the correct coordinate system is specified, the profiles coming from the two catalogs are identical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7b098be6-8364-45c3-aa5a-ce28a84beb03",
+   "metadata": {},
+   "outputs": [
+    {
+     "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(add=True)\n",
+    "cl_wrong_coordinate.make_radial_profile(\"kpc\", cosmo=cosmo)\n",
+    "fig, ax = cl_wrong_coordinate.plot_profiles(xscale=\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eca43249-3f31-478c-af40-ed79fbeeeea8",
+   "metadata": {},
+   "source": [
+    "### => However, when the coordinate system is not correctly specified, the profiles are incorrect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fc8a5cad-a73d-4615-ac32-240924e139b9",
+   "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",
+    "\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",
+    "\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')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ef228536-57ca-4438-9bc2-55495336a2d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cl_wrong_coordinate.galcat.add_column(et,name='et_celestial')\n",
+    "cl_wrong_coordinate.galcat.add_column(ex,name='ex_celestial')"
+   ]
+  },
+  {
+   "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\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5335da24-c97a-4b5d-b729-684f37cec8ae",
+   "metadata": {},
+   "source": [
+    "### => Now the profiles are correct. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84446929-dc27-4418-87c3-ecc119e3cc9e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "clmm",
+   "language": "python",
+   "name": "clmm"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}