{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d5d625c",
   "metadata": {},
   "source": [
    "# Computing fourth-order shear statistics on a mock shape catalog\n",
    "\n",
    "In this notebook we compute the fourth-order shear correlation functions on a realistic shape catalog and compare it with the gaussian prediction. We then compute the fourth-order aperture mass statistics using the same setup as the one used in Fig 3 or [Porth+ (2025)](https://arxiv.org/abs/2509.07974)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90abdb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import orpheus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd66022-4b66-4c37-9cf3-0660255354c5",
   "metadata": {},
   "source": [
    "## Obtaining a realistic mock shape catalog\n",
    "\n",
    "We will run this notebook using a mocks shape catalog from the [SLICS](https://slics.roe.ac.uk/) ensemble. First, let us download this catalog and read in its contents.\n",
    "\n",
    "**Note:** If you want to run the notebook yourself, you need to update the `savepath_SLICS` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9caef8a-6509-4ff3-a8b8-488bde4a9386",
   "metadata": {},
   "outputs": [],
   "source": [
    "catname = \"GalCatalog_LOS11.fits\"\n",
    "path_to_SLICS = \"http://cuillin.roe.ac.uk/~jharno/SLICS/MockProducts/KiDS450/\" + catname\n",
    "savepath_SLICS = \"/vol/euclidraid4/data/lporth/HigherOrderLensing/Mocks/SLICS_KiDS450/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35da3114-2a5d-4667-85be-18519deae89d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget {path_to_SLICS} -P {savepath_SLICS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5b9a138",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x_arcmin', 'y_arcmin', 'z_spectroscopic', 'z_photometric', 'shear1', 'shear2', 'eps_obs1', 'eps_obs2']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'UNKNOWN' did not parse as fits unit: At col 0, Unit 'UNKNOWN' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'UNKNOWN' did not parse as fits unit: At col 0, Unit 'UNKNOWN' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "slicscat = Table.read(savepath_SLICS + catname)\n",
    "print(slicscat.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51a1b43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdownsample = 28 # Downsample mock to roughy match nbar of Porth+25 tests on SLICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09e9a4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "shapecat = orpheus.SpinTracerCatalog(spin=2,\n",
    "                                     pos1=slicscat[\"x_arcmin\"][::fdownsample],\n",
    "                                     pos2=slicscat[\"y_arcmin\"][::fdownsample],\n",
    "                                     tracer_1=slicscat[\"shear1\"][::fdownsample],\n",
    "                                     tracer_2=slicscat[\"shear2\"][::fdownsample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33171035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of galaxies:109672 --> effective nbar: 0.305/arcmin^2 on 100.00 deg^2\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of galaxies:%i --> effective nbar: %.3f/arcmin^2 on %.2f deg^2\"%(\n",
    "    shapecat.ngal, shapecat.ngal/(shapecat.len1*shapecat.len2), shapecat.len1*shapecat.len2/3600.))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419ebe18",
   "metadata": {},
   "source": [
    "## Computation of full 4PCF (few bins)\n",
    "\n",
    "Let us first showcase that in the case of a coarsely binned 4PCF the actual computation is not too expensive. As in this case also the memory footprint of the 4PCF is small, we can use the runtime-optimised implementation (i.e. we set `lowmem=False`) and we further accelerate the estimator using the `Tree`-approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f7142a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_sep = 1.\n",
    "max_sep = 128.\n",
    "nbinsr = 14\n",
    "nthreads = 48\n",
    "method=\"Tree\"\n",
    "lowmem = False\n",
    "tree_resos = [0.,1.,2.,4.]\n",
    "rmin_pixsize = 20\n",
    "nmax=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09da7c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fourpcf = orpheus.GGGGCorrelation_NoTomo(min_sep=min_sep, max_sep=max_sep,nbinsr=nbinsr,nmaxs=nmax,\n",
    "                                         method=method, nthreads=nthreads,verbosity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93e4b581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........|.........|.........|.........|.........|.........|.........|.........|.........|.........|CPU times: user 1h 46min, sys: 1min 11s, total: 1h 47min 12s\n",
      "Wall time: 2min 15s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "fourpcf.process(shapecat, lowmem=lowmem, statistics=['4pcf_multipole'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d4b81a",
   "metadata": {},
   "source": [
    "Let us now plot the result and compare it with the gaussian prediction (i.e. the _disconnected_ part of the 4PCF). To estimate the latter we compute the second-order shear correlation functions and rebin then to a fine linear binning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2aea81d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 28s, sys: 89.6 ms, total: 1min 28s\n",
      "Wall time: 2.28 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Compute shear 2pcf from data\n",
    "fac_minsep = 0.05\n",
    "fac_maxsep = 2.\n",
    "min_sep_disc = fac_minsep*fourpcf.min_sep\n",
    "max_sep_disc = fac_maxsep*fourpcf.max_sep\n",
    "binsize_disc = 0.1\n",
    "ggcorr = orpheus.GGCorrelation(min_sep=min_sep_disc, max_sep=max_sep_disc,binsize=binsize_disc, \n",
    "                               rmin_pixsize=fourpcf.rmin_pixsize, tree_resos=fourpcf.tree_resos, nthreads=fourpcf.nthreads)\n",
    "ggcorr.process(shapecat)\n",
    "\n",
    "# Map to a very fine linspace\n",
    "from scipy.interpolate import interp1d\n",
    "linarr = np.linspace(min_sep_disc,max_sep_disc,int(max_sep_disc/(binsize_disc*min_sep_disc)))\n",
    "xip_spl = interp1d(x=ggcorr.bin_centers_mean,y=ggcorr.xip[0].real,fill_value=0,bounds_error=False)\n",
    "xip_lin = xip_spl(linarr)\n",
    "xim_spl = interp1d(x=ggcorr.bin_centers_mean,y=ggcorr.xim[0].real,fill_value=0,bounds_error=False)\n",
    "xim_lin = xim_spl(linarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d8d361",
   "metadata": {},
   "source": [
    "With the 2PCF in hand we can use the orpheus-internal `gauss4pcf_analytic` method to convert the measured 2PCFs to the disconnected 4PCFs. To get the full 4PCF we transform the 4PCF multiples to the real-space basis. As we only want to consider a single radial bin combination we use the `multipoles2npcf_singlethetcombi` method for that matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d81c041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 195 ms, sys: 998 µs, total: 196 ms\n",
      "Wall time: 194 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Selection of bins for this example\n",
    "icomp = 0 # Natural component (0-7)\n",
    "itheta1 = 6 # Index of first radial bin\n",
    "itheta2 = 10 # Index of second radial bin\n",
    "itheta3  = 9 # Index of third radial bin\n",
    "\n",
    "# Get disconnected 4PCF from measured 2PCF\n",
    "fourpcf_disc = fourpcf.gauss4pcf_analytic(itheta1, itheta2, itheta3, nsubr=4, \n",
    "                                          xip_arr=xip_lin, xim_arr=xim_lin, \n",
    "                                          thetamin_xi=linarr[0], thetamax_xi=linarr[-1], dtheta_xi=linarr[1]-linarr[0])\n",
    "\n",
    "# Get full 4PCF by transforming the 4PCF multipoles to the real space basis\n",
    "fourpcf_slice, fourpcrnorm_slice = fourpcf.multipoles2npcf_singlethetcombi(elthet1=itheta1,elthet2=itheta2,elthet3=itheta3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85e5a61",
   "metadata": {},
   "source": [
    "Finally, let us plot the results. As in the [Porth+ (2025)](https://arxiv.org/pdf/2509.07974) paper (cf Fig 2) we can see how the overall shape between the disconnected and the full 4PCF is fairly similar, and we also see that there is a significant disconnected contribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50aae4df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot estimate of disconnected 4PCF\n",
    "plt.imshow(fourpcf_disc[icomp].real,origin='lower',extent=[0,2*np.pi,0,2*np.pi])\n",
    "plt.colorbar()\n",
    "plt.title(r\"Re$[\\Gamma_{%i,\\mathrm{disc.}}^\\times](%.2f,%.2f,%.2f,\\phi_{12},\\phi_{13})$\"%(icomp,\n",
    "    fourpcf.bin_centers[0,itheta1],fourpcf.bin_centers[0,itheta2],fourpcf.bin_centers[0,itheta3]))\n",
    "plt.show()\n",
    "\n",
    "# Plot full 4pcf --> Use same normalisation as for disconnected piece to emphasize the amplitude difference\n",
    "plt.imshow(fourpcf_slice[icomp].real,vmin=np.min(fourpcf_disc[icomp].real),vmax=np.max(fourpcf_disc[icomp].real),\n",
    "           origin='lower',extent=[0,2*np.pi,0,2*np.pi])\n",
    "plt.colorbar()\n",
    "plt.title(r\"Re$[\\Gamma_{%i}^\\times](%.2f,%.2f,%.2f,\\phi_{12},\\phi_{13})$\"%(icomp,\n",
    "    fourpcf.bin_centers[0,itheta1],fourpcf.bin_centers[0,itheta2],fourpcf.bin_centers[0,itheta3]))\n",
    "plt.show()\n",
    "\n",
    "# Plot overall normalization --> Shape makes sense given poisson sampling + square footprint of extent 10degx10deg.\n",
    "plt.imshow(fourpcrnorm_slice.real,origin='lower',extent=[0,2*np.pi,0,2*np.pi])\n",
    "plt.title(r\"Re$[\\mathcal{N}](%.2f,%.2f,%.2f,\\phi_{12},\\phi_{13})$\"%(\n",
    "    fourpcf.bin_centers[0,itheta1],fourpcf.bin_centers[0,itheta2],fourpcf.bin_centers[0,itheta3]))\n",
    "plt.colorbar()\n",
    "plt.xlabel(r\"$\\phi_{12}$ [rad]\")\n",
    "plt.ylabel(r\"$\\phi_{13}$ [rad]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05b82c8",
   "metadata": {},
   "source": [
    "## Computation of full 4PCF (many bins)\n",
    "\n",
    "_Warning: This example requires significant CPU (~200h) resources!_ \n",
    "\n",
    "We now proceed to compute the 4PCF in many radial bins s.t. we expect to get an unbiased estimate of the fourth-order aperture mass statistics. As in this case the length of the internal arrays exceeds the C-imposed limit for the runtime-optimised implemenation, we have to resort to the memory-optimised implementation which only uses about 10GB in total (as opposed to an estimated hypothetical 500GB for the runtime-optimised implementation). \n",
    "\n",
    "_We note that while the setup below reproduces the one in [Porth+ (2025)](https://arxiv.org/abs/2509.07974) the underlying ellipticity data does not include any noise._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13b4338d",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_sep = 0.25\n",
    "max_sep = 166.2854082610904\n",
    "nbinsr = 65\n",
    "nbinsphi = 128\n",
    "nthreads = 48\n",
    "method=\"Tree\"\n",
    "lowmem = True\n",
    "tree_resos = [0.,1.]\n",
    "rmin_pixsize = 40\n",
    "nmax = 15\n",
    "mapradii = np.geomspace(1,32,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0724f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fourpcf_fine = orpheus.GGGGCorrelation_NoTomo(min_sep=min_sep, max_sep=max_sep,nbinsr=nbinsr,nmaxs=nmax,nbinsphi=nbinsphi,\n",
    "                                              method=method, nthreads=nthreads,verbosity=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7025b532",
   "metadata": {},
   "source": [
    "Processing the catalog we see that there are printed fairly detailed updates that show how much time is spent in the allocation of the Gn and ind the updates of the Upsilonn. As a rough estimate the fraction of redundant computations done within the memory-optimized implementatoin is given as Time_per_Gn/Time_per_Upsnbatch. In the current setup this is about 25 per cent. For more details on the memory-optimised implementation see appendix A2 in [Porth+ (2025)](https://arxiv.org/abs/2509.07974)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08eaee03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using batchsize of 998 for radial bins\n",
      "Doing region 0/63496 for thetabatch 0/48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed Gn for first gal in region 0/63496 for thetabatch 0/48 in 0.0138 seconds\n",
      "Allocated Ups for first gal in region 0/63496 for thetabatch 0/48 in 1.4243 seconds for 998 theta-combis\n",
      "Done region 0/63496 for thetabatch 0/48\n",
      "Doing region 634/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 634/63496 for thetabatch 0/48 in 0.0065 seconds\n",
      "Allocated Ups for first gal in region 634/63496 for thetabatch 0/48 in 0.0736 seconds for 998 theta-combis\n",
      "Done region 634/63496 for thetabatch 0/48\n",
      "Doing region 1268/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 1268/63496 for thetabatch 0/48 in 0.0072 seconds\n",
      "Allocated Ups for first gal in region 1268/63496 for thetabatch 0/48 in 0.0728 seconds for 998 theta-combis\n",
      "Done region 1268/63496 for thetabatch 0/48\n",
      "Doing region 1902/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 1902/63496 for thetabatch 0/48 in 0.0059 seconds\n",
      "Allocated Ups for first gal in region 1902/63496 for thetabatch 0/48 in 0.0726 seconds for 998 theta-combis\n",
      "Done region 1902/63496 for thetabatch 0/48\n",
      "Doing region 2536/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 2536/63496 for thetabatch 0/48 in 0.0076 seconds\n",
      "Allocated Ups for first gal in region 2536/63496 for thetabatch 0/48 in 0.0729 seconds for 998 theta-combis\n",
      "Done region 2536/63496 for thetabatch 0/48\n",
      "Doing region 3170/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 3170/63496 for thetabatch 0/48 in 0.0070 seconds\n",
      "Allocated Ups for first gal in region 3170/63496 for thetabatch 0/48 in 0.0757 seconds for 998 theta-combis\n",
      "Done region 3170/63496 for thetabatch 0/48\n",
      "Doing region 3804/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 3804/63496 for thetabatch 0/48 in 0.0085 seconds\n",
      "Allocated Ups for first gal in region 3804/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 3804/63496 for thetabatch 0/48\n",
      "Doing region 4438/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 4438/63496 for thetabatch 0/48 in 0.0107 seconds\n",
      "Allocated Ups for first gal in region 4438/63496 for thetabatch 0/48 in 0.0730 seconds for 998 theta-combis\n",
      "Done region 4438/63496 for thetabatch 0/48\n",
      "Doing region 5072/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 5072/63496 for thetabatch 0/48 in 0.0143 seconds\n",
      "Allocated Ups for first gal in region 5072/63496 for thetabatch 0/48 in 0.0719 seconds for 998 theta-combis\n",
      "Done region 5072/63496 for thetabatch 0/48\n",
      "Doing region 5706/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 5706/63496 for thetabatch 0/48 in 0.0119 seconds\n",
      "Allocated Ups for first gal in region 5706/63496 for thetabatch 0/48 in 0.0729 seconds for 998 theta-combis\n",
      "Done region 5706/63496 for thetabatch 0/48\n",
      "Doing region 6340/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 6340/63496 for thetabatch 0/48 in 0.0129 seconds\n",
      "Allocated Ups for first gal in region 6340/63496 for thetabatch 0/48 in 0.0748 seconds for 998 theta-combis\n",
      "Done region 6340/63496 for thetabatch 0/48\n",
      "Doing region 6974/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 6974/63496 for thetabatch 0/48 in 0.0128 seconds\n",
      "Allocated Ups for first gal in region 6974/63496 for thetabatch 0/48 in 0.0742 seconds for 998 theta-combis\n",
      "Done region 6974/63496 for thetabatch 0/48\n",
      "Doing region 7608/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 7608/63496 for thetabatch 0/48 in 0.0133 seconds\n",
      "Allocated Ups for first gal in region 7608/63496 for thetabatch 0/48 in 0.0733 seconds for 998 theta-combis\n",
      "Done region 7608/63496 for thetabatch 0/48\n",
      "Doing region 8242/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 8242/63496 for thetabatch 0/48 in 0.0134 seconds\n",
      "Allocated Ups for first gal in region 8242/63496 for thetabatch 0/48 in 0.0721 seconds for 998 theta-combis\n",
      "Done region 8242/63496 for thetabatch 0/48\n",
      "Doing region 8876/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 8876/63496 for thetabatch 0/48 in 0.0145 seconds\n",
      "Allocated Ups for first gal in region 8876/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 8876/63496 for thetabatch 0/48\n",
      "Doing region 9510/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 9510/63496 for thetabatch 0/48 in 0.0144 seconds\n",
      "Allocated Ups for first gal in region 9510/63496 for thetabatch 0/48 in 0.0728 seconds for 998 theta-combis\n",
      "Done region 9510/63496 for thetabatch 0/48\n",
      "Doing region 10144/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 10144/63496 for thetabatch 0/48 in 0.0143 seconds\n",
      "Allocated Ups for first gal in region 10144/63496 for thetabatch 0/48 in 0.0742 seconds for 998 theta-combis\n",
      "Done region 10144/63496 for thetabatch 0/48\n",
      "Doing region 10778/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 10778/63496 for thetabatch 0/48 in 0.0150 seconds\n",
      "Allocated Ups for first gal in region 10778/63496 for thetabatch 0/48 in 0.0738 seconds for 998 theta-combis\n",
      "Done region 10778/63496 for thetabatch 0/48\n",
      "Doing region 11412/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 11412/63496 for thetabatch 0/48 in 0.0159 seconds\n",
      "Allocated Ups for first gal in region 11412/63496 for thetabatch 0/48 in 0.0723 seconds for 998 theta-combis\n",
      "Done region 11412/63496 for thetabatch 0/48\n",
      "Doing region 12046/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 12046/63496 for thetabatch 0/48 in 0.0152 seconds\n",
      "Allocated Ups for first gal in region 12046/63496 for thetabatch 0/48 in 0.0739 seconds for 998 theta-combis\n",
      "Done region 12046/63496 for thetabatch 0/48\n",
      "Doing region 12680/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 12680/63496 for thetabatch 0/48 in 0.0157 seconds\n",
      "Allocated Ups for first gal in region 12680/63496 for thetabatch 0/48 in 0.0737 seconds for 998 theta-combis\n",
      "Done region 12680/63496 for thetabatch 0/48\n",
      "Doing region 13314/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 13314/63496 for thetabatch 0/48 in 0.0164 seconds\n",
      "Allocated Ups for first gal in region 13314/63496 for thetabatch 0/48 in 0.0724 seconds for 998 theta-combis\n",
      "Done region 13314/63496 for thetabatch 0/48\n",
      "Doing region 13948/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 13948/63496 for thetabatch 0/48 in 0.0183 seconds\n",
      "Allocated Ups for first gal in region 13948/63496 for thetabatch 0/48 in 0.0738 seconds for 998 theta-combis\n",
      "Done region 13948/63496 for thetabatch 0/48\n",
      "Doing region 14582/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 14582/63496 for thetabatch 0/48 in 0.0185 seconds\n",
      "Allocated Ups for first gal in region 14582/63496 for thetabatch 0/48 in 0.0715 seconds for 998 theta-combis\n",
      "Done region 14582/63496 for thetabatch 0/48\n",
      "Doing region 15216/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 15216/63496 for thetabatch 0/48 in 0.0162 seconds\n",
      "Allocated Ups for first gal in region 15216/63496 for thetabatch 0/48 in 0.0747 seconds for 998 theta-combis\n",
      "Done region 15216/63496 for thetabatch 0/48\n",
      "Doing region 15850/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 15850/63496 for thetabatch 0/48 in 0.0175 seconds\n",
      "Allocated Ups for first gal in region 15850/63496 for thetabatch 0/48 in 0.0737 seconds for 998 theta-combis\n",
      "Done region 15850/63496 for thetabatch 0/48\n",
      "Doing region 16484/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 16484/63496 for thetabatch 0/48 in 0.0165 seconds\n",
      "Allocated Ups for first gal in region 16484/63496 for thetabatch 0/48 in 0.0742 seconds for 998 theta-combis\n",
      "Done region 16484/63496 for thetabatch 0/48\n",
      "Doing region 17118/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 17118/63496 for thetabatch 0/48 in 0.0174 seconds\n",
      "Allocated Ups for first gal in region 17118/63496 for thetabatch 0/48 in 0.0729 seconds for 998 theta-combis\n",
      "Done region 17118/63496 for thetabatch 0/48\n",
      "Doing region 17752/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 17752/63496 for thetabatch 0/48 in 0.0183 seconds\n",
      "Allocated Ups for first gal in region 17752/63496 for thetabatch 0/48 in 0.0730 seconds for 998 theta-combis\n",
      "Done region 17752/63496 for thetabatch 0/48\n",
      "Doing region 18386/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 18386/63496 for thetabatch 0/48 in 0.0162 seconds\n",
      "Allocated Ups for first gal in region 18386/63496 for thetabatch 0/48 in 0.0735 seconds for 998 theta-combis\n",
      "Done region 18386/63496 for thetabatch 0/48\n",
      "Doing region 19020/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 19020/63496 for thetabatch 0/48 in 0.0175 seconds\n",
      "Allocated Ups for first gal in region 19020/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 19020/63496 for thetabatch 0/48\n",
      "Doing region 19654/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 19654/63496 for thetabatch 0/48 in 0.0177 seconds\n",
      "Allocated Ups for first gal in region 19654/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 19654/63496 for thetabatch 0/48\n",
      "Doing region 20288/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 20288/63496 for thetabatch 0/48 in 0.0192 seconds\n",
      "Allocated Ups for first gal in region 20288/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 20288/63496 for thetabatch 0/48\n",
      "Doing region 20922/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 20922/63496 for thetabatch 0/48 in 0.0179 seconds\n",
      "Allocated Ups for first gal in region 20922/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 20922/63496 for thetabatch 0/48\n",
      "Doing region 21556/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 21556/63496 for thetabatch 0/48 in 0.0168 seconds\n",
      "Allocated Ups for first gal in region 21556/63496 for thetabatch 0/48 in 0.0735 seconds for 998 theta-combis\n",
      "Done region 21556/63496 for thetabatch 0/48\n",
      "Doing region 22190/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 22190/63496 for thetabatch 0/48 in 0.0168 seconds\n",
      "Allocated Ups for first gal in region 22190/63496 for thetabatch 0/48 in 0.0739 seconds for 998 theta-combis\n",
      "Done region 22190/63496 for thetabatch 0/48\n",
      "Doing region 22824/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 22824/63496 for thetabatch 0/48 in 0.0173 seconds\n",
      "Allocated Ups for first gal in region 22824/63496 for thetabatch 0/48 in 0.0739 seconds for 998 theta-combis\n",
      "Done region 22824/63496 for thetabatch 0/48\n",
      "Doing region 23458/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 23458/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 23458/63496 for thetabatch 0/48 in 0.0738 seconds for 998 theta-combis\n",
      "Done region 23458/63496 for thetabatch 0/48\n",
      "Doing region 24092/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 24092/63496 for thetabatch 0/48 in 0.0193 seconds\n",
      "Allocated Ups for first gal in region 24092/63496 for thetabatch 0/48 in 0.0722 seconds for 998 theta-combis\n",
      "Done region 24092/63496 for thetabatch 0/48\n",
      "Doing region 24726/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 24726/63496 for thetabatch 0/48 in 0.0174 seconds\n",
      "Allocated Ups for first gal in region 24726/63496 for thetabatch 0/48 in 0.0724 seconds for 998 theta-combis\n",
      "Done region 24726/63496 for thetabatch 0/48\n",
      "Doing region 25360/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 25360/63496 for thetabatch 0/48 in 0.0181 seconds\n",
      "Allocated Ups for first gal in region 25360/63496 for thetabatch 0/48 in 0.0731 seconds for 998 theta-combis\n",
      "Done region 25360/63496 for thetabatch 0/48\n",
      "Doing region 25994/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 25994/63496 for thetabatch 0/48 in 0.0180 seconds\n",
      "Allocated Ups for first gal in region 25994/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 25994/63496 for thetabatch 0/48\n",
      "Doing region 26628/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 26628/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 26628/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 26628/63496 for thetabatch 0/48\n",
      "Doing region 27262/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 27262/63496 for thetabatch 0/48 in 0.0153 seconds\n",
      "Allocated Ups for first gal in region 27262/63496 for thetabatch 0/48 in 0.0730 seconds for 998 theta-combis\n",
      "Done region 27262/63496 for thetabatch 0/48\n",
      "Doing region 27896/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 27896/63496 for thetabatch 0/48 in 0.0123 seconds\n",
      "Allocated Ups for first gal in region 27896/63496 for thetabatch 0/48 in 0.0731 seconds for 998 theta-combis\n",
      "Done region 27896/63496 for thetabatch 0/48\n",
      "Doing region 28530/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 28530/63496 for thetabatch 0/48 in 0.0139 seconds\n",
      "Allocated Ups for first gal in region 28530/63496 for thetabatch 0/48 in 0.0742 seconds for 998 theta-combis\n",
      "Done region 28530/63496 for thetabatch 0/48\n",
      "Doing region 29164/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 29164/63496 for thetabatch 0/48 in 0.0151 seconds\n",
      "Allocated Ups for first gal in region 29164/63496 for thetabatch 0/48 in 0.0731 seconds for 998 theta-combis\n",
      "Done region 29164/63496 for thetabatch 0/48\n",
      "Doing region 29798/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 29798/63496 for thetabatch 0/48 in 0.0177 seconds\n",
      "Allocated Ups for first gal in region 29798/63496 for thetabatch 0/48 in 0.0736 seconds for 998 theta-combis\n",
      "Done region 29798/63496 for thetabatch 0/48\n",
      "Doing region 30432/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 30432/63496 for thetabatch 0/48 in 0.0178 seconds\n",
      "Allocated Ups for first gal in region 30432/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 30432/63496 for thetabatch 0/48\n",
      "Doing region 31066/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 31066/63496 for thetabatch 0/48 in 0.0173 seconds\n",
      "Allocated Ups for first gal in region 31066/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 31066/63496 for thetabatch 0/48\n",
      "Doing region 31700/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 31700/63496 for thetabatch 0/48 in 0.0169 seconds\n",
      "Allocated Ups for first gal in region 31700/63496 for thetabatch 0/48 in 0.0746 seconds for 998 theta-combis\n",
      "Done region 31700/63496 for thetabatch 0/48\n",
      "Doing region 32334/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 32334/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 32334/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 32334/63496 for thetabatch 0/48\n",
      "Doing region 32968/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 32968/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 32968/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 32968/63496 for thetabatch 0/48\n",
      "Doing region 33602/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 33602/63496 for thetabatch 0/48 in 0.0154 seconds\n",
      "Allocated Ups for first gal in region 33602/63496 for thetabatch 0/48 in 0.0754 seconds for 998 theta-combis\n",
      "Done region 33602/63496 for thetabatch 0/48\n",
      "Doing region 34236/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 34236/63496 for thetabatch 0/48 in 0.0180 seconds\n",
      "Allocated Ups for first gal in region 34236/63496 for thetabatch 0/48 in 0.0733 seconds for 998 theta-combis\n",
      "Done region 34236/63496 for thetabatch 0/48\n",
      "Doing region 34870/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 34870/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 34870/63496 for thetabatch 0/48 in 0.0754 seconds for 998 theta-combis\n",
      "Done region 34870/63496 for thetabatch 0/48\n",
      "Doing region 35504/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 35504/63496 for thetabatch 0/48 in 0.0158 seconds\n",
      "Allocated Ups for first gal in region 35504/63496 for thetabatch 0/48 in 0.0720 seconds for 998 theta-combis\n",
      "Done region 35504/63496 for thetabatch 0/48\n",
      "Doing region 36138/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 36138/63496 for thetabatch 0/48 in 0.0131 seconds\n",
      "Allocated Ups for first gal in region 36138/63496 for thetabatch 0/48 in 0.0737 seconds for 998 theta-combis\n",
      "Done region 36138/63496 for thetabatch 0/48\n",
      "Doing region 36772/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 36772/63496 for thetabatch 0/48 in 0.0152 seconds\n",
      "Allocated Ups for first gal in region 36772/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 36772/63496 for thetabatch 0/48\n",
      "Doing region 37406/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 37406/63496 for thetabatch 0/48 in 0.0150 seconds\n",
      "Allocated Ups for first gal in region 37406/63496 for thetabatch 0/48 in 0.0720 seconds for 998 theta-combis\n",
      "Done region 37406/63496 for thetabatch 0/48\n",
      "Doing region 38040/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 38040/63496 for thetabatch 0/48 in 0.0156 seconds\n",
      "Allocated Ups for first gal in region 38040/63496 for thetabatch 0/48 in 0.0719 seconds for 998 theta-combis\n",
      "Done region 38040/63496 for thetabatch 0/48\n",
      "Doing region 38674/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 38674/63496 for thetabatch 0/48 in 0.0118 seconds\n",
      "Allocated Ups for first gal in region 38674/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 38674/63496 for thetabatch 0/48\n",
      "Doing region 39308/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 39308/63496 for thetabatch 0/48 in 0.0112 seconds\n",
      "Allocated Ups for first gal in region 39308/63496 for thetabatch 0/48 in 0.0733 seconds for 998 theta-combis\n",
      "Done region 39308/63496 for thetabatch 0/48\n",
      "Doing region 39942/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 39942/63496 for thetabatch 0/48 in 0.0120 seconds\n",
      "Allocated Ups for first gal in region 39942/63496 for thetabatch 0/48 in 0.0723 seconds for 998 theta-combis\n",
      "Done region 39942/63496 for thetabatch 0/48\n",
      "Doing region 40576/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 40576/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 40576/63496 for thetabatch 0/48 in 0.0722 seconds for 998 theta-combis\n",
      "Done region 40576/63496 for thetabatch 0/48\n",
      "Doing region 41210/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 41210/63496 for thetabatch 0/48 in 0.0179 seconds\n",
      "Allocated Ups for first gal in region 41210/63496 for thetabatch 0/48 in 0.0729 seconds for 998 theta-combis\n",
      "Done region 41210/63496 for thetabatch 0/48\n",
      "Doing region 41844/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 41844/63496 for thetabatch 0/48 in 0.0175 seconds\n",
      "Allocated Ups for first gal in region 41844/63496 for thetabatch 0/48 in 0.0739 seconds for 998 theta-combis\n",
      "Done region 41844/63496 for thetabatch 0/48\n",
      "Doing region 42478/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 42478/63496 for thetabatch 0/48 in 0.0148 seconds\n",
      "Allocated Ups for first gal in region 42478/63496 for thetabatch 0/48 in 0.0737 seconds for 998 theta-combis\n",
      "Done region 42478/63496 for thetabatch 0/48\n",
      "Doing region 43112/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 43112/63496 for thetabatch 0/48 in 0.0156 seconds\n",
      "Allocated Ups for first gal in region 43112/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 43112/63496 for thetabatch 0/48\n",
      "Doing region 43746/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 43746/63496 for thetabatch 0/48 in 0.0154 seconds\n",
      "Allocated Ups for first gal in region 43746/63496 for thetabatch 0/48 in 0.0724 seconds for 998 theta-combis\n",
      "Done region 43746/63496 for thetabatch 0/48\n",
      "Doing region 44380/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 44380/63496 for thetabatch 0/48 in 0.0167 seconds\n",
      "Allocated Ups for first gal in region 44380/63496 for thetabatch 0/48 in 0.0741 seconds for 998 theta-combis\n",
      "Done region 44380/63496 for thetabatch 0/48\n",
      "Doing region 45014/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 45014/63496 for thetabatch 0/48 in 0.0179 seconds\n",
      "Allocated Ups for first gal in region 45014/63496 for thetabatch 0/48 in 0.0720 seconds for 998 theta-combis\n",
      "Done region 45014/63496 for thetabatch 0/48\n",
      "Doing region 45648/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 45648/63496 for thetabatch 0/48 in 0.0181 seconds\n",
      "Allocated Ups for first gal in region 45648/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 45648/63496 for thetabatch 0/48\n",
      "Doing region 46282/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 46282/63496 for thetabatch 0/48 in 0.0177 seconds\n",
      "Allocated Ups for first gal in region 46282/63496 for thetabatch 0/48 in 0.0736 seconds for 998 theta-combis\n",
      "Done region 46282/63496 for thetabatch 0/48\n",
      "Doing region 46916/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 46916/63496 for thetabatch 0/48 in 0.0172 seconds\n",
      "Allocated Ups for first gal in region 46916/63496 for thetabatch 0/48 in 0.0724 seconds for 998 theta-combis\n",
      "Done region 46916/63496 for thetabatch 0/48\n",
      "Doing region 47550/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 47550/63496 for thetabatch 0/48 in 0.0177 seconds\n",
      "Allocated Ups for first gal in region 47550/63496 for thetabatch 0/48 in 0.0720 seconds for 998 theta-combis\n",
      "Done region 47550/63496 for thetabatch 0/48\n",
      "Doing region 48184/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 48184/63496 for thetabatch 0/48 in 0.0174 seconds\n",
      "Allocated Ups for first gal in region 48184/63496 for thetabatch 0/48 in 0.0728 seconds for 998 theta-combis\n",
      "Done region 48184/63496 for thetabatch 0/48\n",
      "Doing region 48818/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 48818/63496 for thetabatch 0/48 in 0.0177 seconds\n",
      "Allocated Ups for first gal in region 48818/63496 for thetabatch 0/48 in 0.0727 seconds for 998 theta-combis\n",
      "Done region 48818/63496 for thetabatch 0/48\n",
      "Doing region 49452/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 49452/63496 for thetabatch 0/48 in 0.0169 seconds\n",
      "Allocated Ups for first gal in region 49452/63496 for thetabatch 0/48 in 0.0722 seconds for 998 theta-combis\n",
      "Done region 49452/63496 for thetabatch 0/48\n",
      "Doing region 50086/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 50086/63496 for thetabatch 0/48 in 0.0169 seconds\n",
      "Allocated Ups for first gal in region 50086/63496 for thetabatch 0/48 in 0.0723 seconds for 998 theta-combis\n",
      "Done region 50086/63496 for thetabatch 0/48\n",
      "Doing region 50720/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 50720/63496 for thetabatch 0/48 in 0.0161 seconds\n",
      "Allocated Ups for first gal in region 50720/63496 for thetabatch 0/48 in 0.0731 seconds for 998 theta-combis\n",
      "Done region 50720/63496 for thetabatch 0/48\n",
      "Doing region 51354/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 51354/63496 for thetabatch 0/48 in 0.0168 seconds\n",
      "Allocated Ups for first gal in region 51354/63496 for thetabatch 0/48 in 0.0731 seconds for 998 theta-combis\n",
      "Done region 51354/63496 for thetabatch 0/48\n",
      "Doing region 51988/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 51988/63496 for thetabatch 0/48 in 0.0152 seconds\n",
      "Allocated Ups for first gal in region 51988/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 51988/63496 for thetabatch 0/48\n",
      "Doing region 52622/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 52622/63496 for thetabatch 0/48 in 0.0153 seconds\n",
      "Allocated Ups for first gal in region 52622/63496 for thetabatch 0/48 in 0.0755 seconds for 998 theta-combis\n",
      "Done region 52622/63496 for thetabatch 0/48\n",
      "Doing region 53256/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 53256/63496 for thetabatch 0/48 in 0.0143 seconds\n",
      "Allocated Ups for first gal in region 53256/63496 for thetabatch 0/48 in 0.0758 seconds for 998 theta-combis\n",
      "Done region 53256/63496 for thetabatch 0/48\n",
      "Doing region 53890/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 53890/63496 for thetabatch 0/48 in 0.0108 seconds\n",
      "Allocated Ups for first gal in region 53890/63496 for thetabatch 0/48 in 0.0767 seconds for 998 theta-combis\n",
      "Done region 53890/63496 for thetabatch 0/48\n",
      "Doing region 54524/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 54524/63496 for thetabatch 0/48 in 0.0117 seconds\n",
      "Allocated Ups for first gal in region 54524/63496 for thetabatch 0/48 in 0.0724 seconds for 998 theta-combis\n",
      "Done region 54524/63496 for thetabatch 0/48\n",
      "Doing region 55158/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 55158/63496 for thetabatch 0/48 in 0.0121 seconds\n",
      "Allocated Ups for first gal in region 55158/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 55158/63496 for thetabatch 0/48\n",
      "Doing region 55792/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 55792/63496 for thetabatch 0/48 in 0.0109 seconds\n",
      "Allocated Ups for first gal in region 55792/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 55792/63496 for thetabatch 0/48\n",
      "Doing region 56426/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 56426/63496 for thetabatch 0/48 in 0.0120 seconds\n",
      "Allocated Ups for first gal in region 56426/63496 for thetabatch 0/48 in 0.0726 seconds for 998 theta-combis\n",
      "Done region 56426/63496 for thetabatch 0/48\n",
      "Doing region 57060/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 57060/63496 for thetabatch 0/48 in 0.0109 seconds\n",
      "Allocated Ups for first gal in region 57060/63496 for thetabatch 0/48 in 0.0728 seconds for 998 theta-combis\n",
      "Done region 57060/63496 for thetabatch 0/48\n",
      "Doing region 57694/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 57694/63496 for thetabatch 0/48 in 0.0124 seconds\n",
      "Allocated Ups for first gal in region 57694/63496 for thetabatch 0/48 in 0.0722 seconds for 998 theta-combis\n",
      "Done region 57694/63496 for thetabatch 0/48\n",
      "Doing region 58328/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 58328/63496 for thetabatch 0/48 in 0.0128 seconds\n",
      "Allocated Ups for first gal in region 58328/63496 for thetabatch 0/48 in 0.0728 seconds for 998 theta-combis\n",
      "Done region 58328/63496 for thetabatch 0/48\n",
      "Doing region 58962/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 58962/63496 for thetabatch 0/48 in 0.0118 seconds\n",
      "Allocated Ups for first gal in region 58962/63496 for thetabatch 0/48 in 0.0743 seconds for 998 theta-combis\n",
      "Done region 58962/63496 for thetabatch 0/48\n",
      "Doing region 59596/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 59596/63496 for thetabatch 0/48 in 0.0121 seconds\n",
      "Allocated Ups for first gal in region 59596/63496 for thetabatch 0/48 in 0.0725 seconds for 998 theta-combis\n",
      "Done region 59596/63496 for thetabatch 0/48\n",
      "Doing region 60230/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 60230/63496 for thetabatch 0/48 in 0.0109 seconds\n",
      "Allocated Ups for first gal in region 60230/63496 for thetabatch 0/48 in 0.0734 seconds for 998 theta-combis\n",
      "Done region 60230/63496 for thetabatch 0/48\n",
      "Doing region 60864/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 60864/63496 for thetabatch 0/48 in 0.0106 seconds\n",
      "Allocated Ups for first gal in region 60864/63496 for thetabatch 0/48 in 0.0723 seconds for 998 theta-combis\n",
      "Done region 60864/63496 for thetabatch 0/48\n",
      "Doing region 61498/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 61498/63496 for thetabatch 0/48 in 0.0105 seconds\n",
      "Allocated Ups for first gal in region 61498/63496 for thetabatch 0/48 in 0.0736 seconds for 998 theta-combis\n",
      "Done region 61498/63496 for thetabatch 0/48\n",
      "Doing region 62132/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 62132/63496 for thetabatch 0/48 in 0.0097 seconds\n",
      "Allocated Ups for first gal in region 62132/63496 for thetabatch 0/48 in 0.0681 seconds for 998 theta-combis\n",
      "Done region 62132/63496 for thetabatch 0/48\n",
      "Doing region 62766/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 62766/63496 for thetabatch 0/48 in 0.0084 seconds\n",
      "Allocated Ups for first gal in region 62766/63496 for thetabatch 0/48 in 0.0678 seconds for 998 theta-combis\n",
      "Done region 62766/63496 for thetabatch 0/48\n",
      "Doing region 63400/63496 for thetabatch 0/48\n",
      "Computed Gn for first gal in region 63400/63496 for thetabatch 0/48 in 0.0077 seconds\n",
      "Allocated Ups for first gal in region 63400/63496 for thetabatch 0/48 in 0.0633 seconds for 998 theta-combis\n",
      "Done region 63400/63496 for thetabatch 0/48\n",
      "Done allocating 4pcfs for thetabatch 7/48\n",
      "Done allocating 4pcfs for thetabatch 3/48\n",
      "Done allocating 4pcfs for thetabatch 22/48\n",
      "Done allocating 4pcfs for thetabatch 19/48\n",
      "Done allocating 4pcfs for thetabatch 26/48\n",
      "Done allocating 4pcfs for thetabatch 2/48\n",
      "Done allocating 4pcfs for thetabatch 41/48\n",
      "Done allocating 4pcfs for thetabatch 5/48\n",
      "Done allocating 4pcfs for thetabatch 8/48\n",
      "Done allocating 4pcfs for thetabatch 39/48\n",
      "Done allocating 4pcfs for thetabatch 18/48\n",
      "Done allocating 4pcfs for thetabatch 27/48\n",
      "Done allocating 4pcfs for thetabatch 30/48\n",
      "Done allocating 4pcfs for thetabatch 25/48\n",
      "Done allocating 4pcfs for thetabatch 37/48\n",
      "Done allocating 4pcfs for thetabatch 35/48\n",
      "Done allocating 4pcfs for thetabatch 34/48\n",
      "Done allocating 4pcfs for thetabatch 6/48\n",
      "Done allocating 4pcfs for thetabatch 29/48\n",
      "Done allocating 4pcfs for thetabatch 10/48\n",
      "Done allocating 4pcfs for thetabatch 31/48\n",
      "Done allocating 4pcfs for thetabatch 11/48\n",
      "Done allocating 4pcfs for thetabatch 42/48\n",
      "Done allocating 4pcfs for thetabatch 12/48\n",
      "Done allocating 4pcfs for thetabatch 1/48\n",
      "Done allocating 4pcfs for thetabatch 14/48\n",
      "Done allocating 4pcfs for thetabatch 9/48\n",
      "Done allocating 4pcfs for thetabatch 16/48\n",
      "Done allocating 4pcfs for thetabatch 44/48\n",
      "Done allocating 4pcfs for thetabatch 17/48\n",
      "Done allocating 4pcfs for thetabatch 33/48\n",
      "Done allocating 4pcfs for thetabatch 24/48\n",
      "Done allocating 4pcfs for thetabatch 28/48\n",
      "Done allocating 4pcfs for thetabatch 36/48\n",
      "Done allocating 4pcfs for thetabatch 32/48\n",
      "Done allocating 4pcfs for thetabatch 23/48\n",
      "Done allocating 4pcfs for thetabatch 13/48\n",
      "Done allocating 4pcfs for thetabatch 15/48\n",
      "Done allocating 4pcfs for thetabatch 21/48\n",
      "Done allocating 4pcfs for thetabatch 40/48\n",
      "Done allocating 4pcfs for thetabatch 20/48\n",
      "Done allocating 4pcfs for thetabatch 4/48\n",
      "Done allocating 4pcfs for thetabatch 45/48\n",
      "Done allocating 4pcfs for thetabatch 38/48\n",
      "Done allocating 4pcfs for thetabatch 43/48\n",
      "Done allocating 4pcfs for thetabatch 46/48\n",
      "Done allocating 4pcfs for thetabatch 0/48\n",
      "Done allocating 4pcfs for thetabatch 47/48\n",
      "CPU times: user 6d 14h 30min 11s, sys: 2min 34s, total: 6d 14h 32min 45s\n",
      "Wall time: 3h 28min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "M4correlators = fourpcf_fine.process(shapecat, lowmem=lowmem, mapradii=mapradii, statistics=['M4'])\n",
    "\n",
    "mmstar = M4correlators[0][:,0]\n",
    "mapmx = orpheus.GGGGCorrelation_NoTomo.MMStar2MapMx_fourth(mmstar) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3727a4",
   "metadata": {},
   "source": [
    "As a comparison we also compute the fourth-order aperture statistics using the direct estimator. Note that as this estimators weighs the data intrinsically different as compared to the 4PCF estimator, we do not expect perfect agreement, but the two curves should nevertheless trace each other. In the plots we further plot an individual line for each coveage cutoff which should give a hint of the expected spread between the two estimators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db0339ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done 21/21 aperture radiiCPU times: user 11min 15s, sys: 831 ms, total: 11min 15s\n",
      "Wall time: 49.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "direct = orpheus.Direct_MapnEqual(order_max=4,Rmin=mapradii[0],Rmax=mapradii[-1],nbinsr=len(mapradii),accuracies=8,ap_weights='InvShot')\n",
    "shapecat.create_mask()\n",
    "mapstat_direct, _ = direct.process(shapecat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b14166",
   "metadata": {},
   "source": [
    "Similar to the case of coarse binning above we now compute the gaussian counterpart. This can be achieved twofold; either by directly transforming the shear 2PCFs to the second-order aperture statistics or by inferring the connected 4PCF from the 2PCFs and computing the full integral as it was done for the full 4PCF. To show that both methods converge we will do both.\n",
    "\n",
    "* For the first case we directly proceed with the measured 2PCF from above\n",
    "* For the second case we use the intermal method `estimateMap4disc` that automatically runs through the aforementioned pipeline. To ensure a good binning we further subdivide the original radial bins by a factor of three (in each dimension), hence emulating the continuously allocated bins used within the full 4PCF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fbe5f95a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27 ms, sys: 2 µs, total: 27 ms\n",
      "Wall time: 3.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "map4_disc_from_map2 = 3*ggcorr.computeMap2(mapradii)[0,0]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56e81a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using batchsize of 5721 for radial bins\n",
      "CPU times: user 11h 51min 39s, sys: 627 ms, total: 11h 51min 40s\n",
      "Wall time: 15min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mmstar_disc, mapmx_disc = fourpcf_fine.estimateMap4disc(cat=shapecat, radii=mapradii, nsubr=3, basis='both')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5945a972",
   "metadata": {},
   "source": [
    "Finally, let us plot the recovered aperture statistics. \n",
    "\n",
    "* When looking at the `MM*`-basis we find a significant disconnected part at small scales and that the real parts all trace each other -- this gives a first hint that the integration is somewhat converged. Secondly we confirm the imaginary parts (which contribute to the parity-violating aperture mass correlators) to be approcximately zero. Finally, when comparing the disconnected pieces obtained from the full 5D integral and the product of Map2 obtained via the 2pcf we again conclude a well-converged integration (as one would have expected given the results of [Silvestre-Rosello+ (2025)](https://arxiv.org/abs/2509.07973)).\n",
    "\n",
    "* When plotting the connected part in the `MapMx`-basis we find good agreement between the 4PCF pipeline and the measurement from the direct estimator and we find the B-modes, as well as the parity violating modes to be consistent with zero.\n",
    "\n",
    "* To assess the differences between the two estimators more clearly we further plot the ratio of the cuves in the above figure. \n",
    "\n",
    "    * The discrepancies on the large scales are mainly due to increasingly different area probed by the two estimators. \n",
    "\n",
    "    * Similarly, on small scales, we observe a decreased amplitude which can be traced back to the employed lower integration cutoff, but it also has a random contribution stemming from the different spatial averages performed by the direct and the 4PCF-based estimator. In particular, for the former we use an inverse-shot-noise weighting which is not optimal given that we do not have shape noise present in the mocks. We note that once sufficiently many tracers (either by a larger nbar or a larger area) are included, the latter contribution decreases (cf rhs of Fig 3 in [Porth+ (2025)](https://arxiv.org/abs/2509.07974), where also shape noise was included)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b8e9a6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot MM* basis\n",
    "for elcomp in range(8):\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mmstar[elcomp].real, 'b-',label=(elcomp==0)*r\"$Re\\left\\langle M^n (M^*)^{4-n}\\right\\rangle$\")\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mmstar[elcomp].imag, 'r-',label=(elcomp==0)*r\"$Im\\left\\langle M^n (M^*)^{4-n}\\right\\rangle$\")\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mmstar_disc[elcomp].real, 'k-',label=(elcomp==0)*r\"$Re\\left\\langle M^n (M^*)^{4-n}\\right\\rangle_c$\")\n",
    "plt.semilogx(mapradii,mapradii**1.5*map4_disc_from_map2, 'c--', label=r\"$3 \\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle^2$\")\n",
    "plt.axhline(0,color='k',ls='--')\n",
    "plt.xlabel('Aperture radius [arcmin]')\n",
    "plt.ylabel(r'$\\theta^{1.5} \\ \\times \\ \\left\\langle M^n (M^*)^{4-n}\\right\\rangle$')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# Plot MapMx basis\n",
    "for elcov in range(5):\n",
    "    plt.semilogx(mapradii,mapradii**1.5*(mapstat_direct[:,elcov,3]-3*mapstat_direct[:,elcov,1]**2),'b--',label=(elcov==0)*r\"$\\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle_c$  (Direct)\")\n",
    "plt.semilogx(mapradii,mapradii**1.5*(mapmx[0]-map4_disc_from_map2), 'b-', label=r\"$\\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle_c$\")\n",
    "for elcomp in range(1,4):\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mapmx[elcomp], 'r-', label=(elcomp==1)*r\"$\\left\\langle M_{\\rm{ap}}^{3}  M_{\\times}\\right\\rangle$\")\n",
    "for elcomp in range(5,11):\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mapmx[elcomp], 'g-', label=(elcomp==5)*r\"$\\left\\langle M_{\\rm{ap}}^{2}  M_{\\times}^2 \\right\\rangle$\")\n",
    "for elcomp in range(11,15):\n",
    "    plt.semilogx(mapradii,mapradii**1.5*mapmx[elcomp], 'c-', label=(elcomp==11)*r\"$\\left\\langle M_{\\rm{ap}}  M_{\\times}^3 \\right\\rangle$\")\n",
    "plt.semilogx(mapradii,mapradii**1.5*mapmx[15], 'orange', label=r\"$\\left\\langle M_{\\times}^4 \\right\\rangle$\")\n",
    "#plt.semilogx(mapradii,mapradii**1.5*mapmx_disc[0], 'k-', label=r\"$\\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle_{\\rm{disc.}}$\")\n",
    "#plt.semilogx(mapradii,mapradii**1.5*map4_disc_from_map2, 'c--', label=r\"$3 \\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle^2$\")\n",
    "#plt.semilogx(mapradii,mapradii**1.5*3*mapstat_direct[:,1,1]**2,'b--',label=r\"$3 \\left\\langle M_{\\rm{ap}}^{2}\\right\\rangle (Direct)$\")\n",
    "plt.axhline(0,color='k',ls='--')\n",
    "plt.xlabel('Aperture radius [arcmin]')\n",
    "plt.ylabel(r'$\\theta^{1.5} \\ \\times \\ \\left\\langle M_{\\rm{ap}}^{n}  M_{\\times}^{4-n} \\right\\rangle$')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# Plot Ratio between measurement\n",
    "for elcov in range(5):\n",
    "    plt.semilogx(mapradii,(mapmx[0]-map4_disc_from_map2)/(mapstat_direct[:,elcov,3]-3*mapstat_direct[:,elcov,1]**2),'b-',\n",
    "                 label=(elcov==0)*r\"$\\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle_{\\mathrm{c}}$: 4PCF/Direct\")\n",
    "for elcomp in range(8):\n",
    "    plt.semilogx(mapradii,mmstar_disc[elcomp]/map4_disc_from_map2,'r-',alpha=0.2)\n",
    "plt.semilogx(mapradii,np.mean(mmstar_disc,axis=0)/map4_disc_from_map2,'r-',lw=3,label=r\"$\\left\\langle M_{\\rm{ap}}^{4}\\right\\rangle_{\\mathrm{disc.}}$: 4PCF/2PCF\")\n",
    "plt.axhline(1,color='k',ls='--')\n",
    "plt.fill_between(x=mapradii,y1=0.98,y2=1.02,color='grey',alpha=0.3)\n",
    "plt.ylim(0.9,1.05)\n",
    "plt.xlabel('Aperture radius [arcmin]')\n",
    "plt.ylabel('Ratio ')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b540c2d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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