ACT_DR6_lensing_transfer_function.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ACTCollaboration/DR6_Notebooks/blob/main/ACT_DR6_lensing_transfer_function.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating the ACT Lensing Transfer Function\n",
        "\n",
        "*Written by Joshua Kim and the ACT collaboration*\n",
        "\n",
        "---\n",
        "\n",
        "This notebook will demonstrate how we generate the lensing transfer function for the ACT Data Release 6 (DR6) analyses. In order to extract cosmological information from CMB lensing maps we compute the lensing power spectrum. Once we have this spectrum, it's important to correctly calibrate it in order to account for any biases. This calibration is done by testing our pipeline on simulations and then generating a multiplicative transfer function which is then used in conjunction with the biased spectrum in order to recover the \"true\" power spectrum.\n",
        "\n",
        "In order to compute this transfer function we rely on realistic simulations of the ACT lensing maps. For the actual analysis we use 400 simulations, however, due to the limitations of working in a notebook we have simplified the analysis by using only a handful of simulations. For anyone who wishes to recreate the full transfer function we suggest moving this code to a cluster and running it with the full suite of sims.\n",
        "\n",
        "\n",
        "The data for this release is publicly available on [Lambda](https://lambda.gsfc.nasa.gov/product/act/actadv_prod_table.html) and a full discussion of the ACT DR6 lensing analysis can be found in [Qu et al. (2023) ](https://arxiv.org/abs/2304.05202), [Madhavacheril et al. (2023)](https://arxiv.org/abs/2304.05203), [MacCrann et al. (2023)](https://arxiv.org/abs/2304.05196) and [Farren et al. (2023)](https://arxiv.org/abs/2309.05659).\n",
        "\n",
        "The pipeline presented below relies on a number of other packages including [`healpy`](https://healpy.readthedocs.io/en/latest/), [`pixell`](https://pixell.readthedocs.io/en/latest/) and [`NaMaster`](https://arxiv.org/abs/1809.09603) and we encourage users to refer to the relevant documentation as needed.\n",
        "\n",
        "For anyone who would like a bit more background in CMB analysis we recommend a few other resources from ACT at the end of this notebook."
      ],
      "metadata": {
        "id": "F1y6vWxNgvRM"
      },
      "id": "F1y6vWxNgvRM"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run ONCE only"
      ],
      "metadata": {
        "id": "o3pczygM4TTE"
      },
      "id": "o3pczygM4TTE"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install() # expect a kernel restart"
      ],
      "metadata": {
        "id": "UzyJxFHq4EaT",
        "outputId": "1ceb0d98-15a3-4747-83db-0c3bf78a18c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "UzyJxFHq4EaT",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A kernel restart is *expected* if the condacolab cell runs successfully. Proceed by testing the install with the next cell!"
      ],
      "metadata": {
        "id": "dwJhdBMSjArN"
      },
      "id": "dwJhdBMSjArN"
    },
    {
      "cell_type": "code",
      "source": [
        "import condacolab\n",
        "condacolab.check()"
      ],
      "metadata": {
        "id": "ZJMUgpFl885u",
        "outputId": "cb0f597e-e03a-44ef-95f7-1326b1d3cc0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "ZJMUgpFl885u",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c546798",
      "metadata": {
        "id": "6c546798"
      },
      "source": [
        "# Data Products"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2c44bc7",
      "metadata": {
        "id": "b2c44bc7"
      },
      "source": [
        "We need the following:\n",
        "\n",
        "*   Some secondary mask (e.g. from a galaxy survey of choice)\n",
        "*   ACT DR6 lensing analysis mask\n",
        "*   Simulations of ACT DR6 lensing reconstructions\n",
        "*   Input lensing potentials used to create the simulations mentioned above\n",
        "\n",
        "\n",
        "The first two are easiest to use in `healpix` format (make sure they have the same `Nside` resolution!), but can be converted from `pixell` formats using the `pixell.reproject` module. The latter two are most commonly formatted as spherical harmonic transform coefficients, commonly known as `alms`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll start by downloading the neccesary data, this might take a bit of time.\n",
        "This specific example of the galaxy mask used here is discussed in [DES Collaboration (2021)](https://arxiv.org/abs/2107.04646)."
      ],
      "metadata": {
        "id": "iReAk_nD6qUB"
      },
      "id": "iReAk_nD6qUB"
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# sims\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/maps/baseline/simulations/kappa_alm_sim_act_dr6_lensing_v1_baseline_000{1..9}.fits\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/maps/baseline/simulations/kappa_alm_sim_act_dr6_lensing_v1_baseline_0010.fits\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/sim_inputs/kappa_alm/input_kappa_alm_sim_000{1..9}.fits\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/sim_inputs/kappa_alm/input_kappa_alm_sim_0010.fits\n",
        "\n",
        "# lensing masks\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/maps/baseline/mask_act_dr6_lensing_v1_healpix_nside_4096_baseline.fits\n",
        "\n",
        "# galaxy mask\n",
        "!wget https://desdr-server.ncsa.illinois.edu/despublic/y3a2_files/baosample/DESY3_LSSBAO_MASK_HPIX4096NEST.fits\n"
      ],
      "metadata": {
        "id": "kRRTRGAj5_fe"
      },
      "id": "kRRTRGAj5_fe",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FILLER = \"#\"\n",
        "RECON_SIM_FORMAT = \"kappa_alm_sim_act_dr6_lensing_v1_baseline_####.fits\"\n",
        "INPUT_KAPPA_FORMAT = \"input_kappa_alm_sim_####.fits\""
      ],
      "metadata": {
        "id": "K8FQOhlihKOE"
      },
      "id": "K8FQOhlihKOE",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "335c9633",
      "metadata": {
        "id": "335c9633"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d3aeaf9",
      "metadata": {
        "id": "8d3aeaf9"
      },
      "source": [
        "Requires:\n",
        "\n",
        "*   `numpy`\n",
        "*   `healpy`\n",
        "*   `pymaster`\n",
        "*   `pixell`\n",
        "\n",
        "This cell can take a few minutes to run as well."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install numpy healpy pixell pyfisher\n",
        "\n",
        "!mamba install -y -c conda-forge namaster"
      ],
      "metadata": {
        "id": "W_9AGYhbzhRG"
      },
      "id": "W_9AGYhbzhRG",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "a74a5a88",
      "metadata": {
        "id": "a74a5a88"
      },
      "outputs": [],
      "source": [
        "import healpy as hp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pixell import enmap, reproject, lensing as plensing, curvedsky as cs\n",
        "\n",
        "import os, time\n",
        "\n",
        "import pymaster as nmt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d43a0453",
      "metadata": {
        "id": "d43a0453"
      },
      "source": [
        "# Constants"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6b17838",
      "metadata": {
        "id": "c6b17838"
      },
      "source": [
        "These constants set the ell range that will be analyzed, the size of the bin (`LperBin`) and the `NSIDE` of the healpix maps. There are two `LMAX` values here, `LMAX` refers to the maximum ell that the calculation will be performed up to and `LMAX_CUT` is the desired upper bound (more on this later!). Feel free to change these as needed!\n",
        "\n",
        "Note the number of bandpowers will be `(LMAX_CUT - LMIN) / LperBin`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "1f56bbc1",
      "metadata": {
        "id": "1f56bbc1"
      },
      "outputs": [],
      "source": [
        "LMIN = 40\n",
        "LMAX = 3000\n",
        "LMAX_CUT = 1000\n",
        "LperBin = 30\n",
        "NSIDE = 1024"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mask setup / manipulation\n",
        "\n",
        "Not necessary, but making our masks a bit easier to use for the sake of this tutorial."
      ],
      "metadata": {
        "id": "L0AgDktACyTK"
      },
      "id": "L0AgDktACyTK"
    },
    {
      "cell_type": "code",
      "source": [
        "# read in the masks...\n",
        "GALAXY_MASK = hp.read_map(\"DESY3_LSSBAO_MASK_HPIX4096NEST.fits\")\n",
        "LENSING_MASK = hp.read_map(\"mask_act_dr6_lensing_v1_healpix_nside_4096_baseline.fits\")\n",
        "# downgrade it to a more sensible resolution for our purposes...\n",
        "GALAXY_MASK = hp.ud_grade(GALAXY_MASK, NSIDE)\n",
        "LENSING_MASK = hp.ud_grade(LENSING_MASK, NSIDE)\n",
        "# and then make it binary for a simplified calculation.\n",
        "GALAXY_MASK[GALAXY_MASK < 0.3] = 0.\n",
        "GALAXY_MASK[GALAXY_MASK > 0.3] = 1.\n",
        "\n",
        "# build enmap versions of these masks for future usage\n",
        "# don't need these for running the rest of the notebook's cells!\n",
        "\n",
        "#shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(1.0/60))\n",
        "#LENSING_MASK_ENMAP = reproject.healpix2map(LENSING_MASK, shape, wcs)\n",
        "#GALAXY_MASK_ENMAP = reproject.healpix2map(GALAXY_MASK, shape, wcs)\n",
        "\n",
        "# plot!\n",
        "hp.mollview(GALAXY_MASK)\n",
        "print(f\"fsky (gmask): {np.sum(GALAXY_MASK) / np.size(GALAXY_MASK) : 0.5f}\")\n",
        "hp.mollview(LENSING_MASK)\n",
        "print(f\"fsky (kmask): {np.sum(LENSING_MASK) / np.size(LENSING_MASK) : 0.5f}\")\n",
        "\n",
        "joint = GALAXY_MASK*LENSING_MASK\n",
        "print(f\"fsky (overlap): {np.sum(joint) / np.size(joint) : 0.5f}\")"
      ],
      "metadata": {
        "id": "Q5-ll5I2CGWP",
        "outputId": "119db904-1dbe-4899-d43e-2ed1870ca6a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "id": "Q5-ll5I2CGWP",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fsky (gmask):  0.11590\n",
            "fsky (kmask):  0.23388\n",
            "fsky (overlap):  0.10049\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 850x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 850x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f29e377b",
      "metadata": {
        "id": "f29e377b"
      },
      "source": [
        "# Binning (NaMaster)\n",
        "\n",
        "We'll set up a few functions that will help us bin the spectra later on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "9b48d402",
      "metadata": {
        "id": "9b48d402"
      },
      "outputs": [],
      "source": [
        "# bin cls according to some scheme encoded in the coupling matrix\n",
        "def bin_spectra(cls, coupling_matrix_name):\n",
        "    cls_nmt = np.array([cls])\n",
        "    try:\n",
        "        wsp = nmt.NmtWorkspace()\n",
        "        wsp.read_from(coupling_matrix_name)\n",
        "        return wsp.decouple_cell(wsp.couple_cell(cls_nmt))[0]\n",
        "    except RuntimeError:\n",
        "        return None\n",
        "\n",
        "def make_bins(nside=NSIDE,LperBin=50,lmin=LMIN,lmax=LMAX):\n",
        "    ells    = np.arange(lmax,dtype='int32')\n",
        "    weights = 1. / LperBin * np.ones_like(ells)\n",
        "    weights[ells<lmin] = 0\n",
        "\n",
        "    # Now generate the bandpower indices, here by brute force.\n",
        "    # A -1 means that ell value is not included in any bandpower.\n",
        "    bpws = np.zeros_like(ells) - 1\n",
        "    ibin = 0\n",
        "    while LperBin*(ibin+1)+lmin<lmax:\n",
        "        bpws[LperBin*ibin+lmin:LperBin*(ibin+1)+lmin] = ibin\n",
        "        ibin += 1\n",
        "    # return Nmt binning instance\n",
        "    return nmt.NmtBin(nside,bpws=bpws,ells=ells,weights=weights)\n",
        "\n",
        "# return index for max bin index within a cutoff\n",
        "def max_bin(bin_edges, cutoff):\n",
        "    return np.argmax(np.nonzero(bin_edges < cutoff)[0]) + 1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed68876b",
      "metadata": {
        "id": "ed68876b"
      },
      "source": [
        "# Transfer function computation (with NaMaster)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "581845ad",
      "metadata": {
        "id": "581845ad"
      },
      "source": [
        "The commonly defined transfer function, or $T(l)$, is computed here as the following:\n",
        "\n",
        "$$\n",
        "T(l) = \\dfrac{\\dfrac{1}{N} \\displaystyle\\sum_{\\text{sim } i}^{N} \\text{NmtBin}\\left(C^{\\hat{\\kappa} X}_{i, NMT} \\right)}{\\text{NmtBin}\\left(C^{\\kappa X}_{\\text{CAMB}}\\right)}\n",
        "$$\n",
        "\n",
        "where $X$ refers to either the input lensing convergence $\\kappa$ (used to lens the simulations) or a correlated galaxy field $g$ generated by populating a galaxy simulation so that it has the same auto and cross spectra as the provided theory (see `orphics.maps.generate_correlated_alm`).\n",
        "\n",
        "The denominator is computed according to cosmological parameters used to create the lensing simulations written in a CAMB .ini file to result in a theory spectrum that is binned by NaMaster (by coupling and then decoupling with a mode-coupling cell), and the numerator is computed as a mean over sims of a NaMaster-computed pseudo-Cl between the reconstructed convergence $\\hat{\\kappa}$ and $X$, ensuring that the binning of the denominator is done exactly the same way as the numerator.\n",
        "\n",
        "This is what we should *divide* our initial measurement by to correct the data products for any mode-coupling or fractional sky effects that we expect from the simulations.\n",
        "\n",
        "For this case, we will treat $X = \\kappa$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c91eb8be",
      "metadata": {
        "id": "c91eb8be"
      },
      "source": [
        "## Numerator"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f2647a9",
      "metadata": {
        "id": "3f2647a9"
      },
      "source": [
        "We have to now iterate over sims and compute the mean pseudo-Cls with the masks passed into NaMaster. In our actual analysis this is done for 400 simulations, however, for the sake of this notebook we have limited the number of sims to save on both disk space and compute time.\n",
        "\n",
        "Here is the iterating function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "1c37c2a8",
      "metadata": {
        "id": "1c37c2a8"
      },
      "outputs": [],
      "source": [
        "START_INDEX = 1\n",
        "END_INDEX = 10 # inclusive\n",
        "\n",
        "!rm -rf nmt_transfer_function_coupling_matrix.fits\n",
        "COUPLING_MATRIX_NAME = \"nmt_transfer_function_coupling_matrix.fits\"\n",
        "\n",
        "# iterate a function \"fn\" over all sims with some indexing format\n",
        "# return dict d where d[idx] = fn(sims[idx], idx)\n",
        "# if is_healpix_map is set to False, assume the sims are alm files\n",
        "def iterate_over_kappa_sims(fn, sim_start=START_INDEX, sim_end=END_INDEX,\n",
        "                            common_format=\"sims_####.fits\", formatter=\"#\"):\n",
        "    data_dict = {}\n",
        "\n",
        "    for i in range(sim_start, sim_end+1):\n",
        "        # find appropriate file name for a given file index\n",
        "        ndigits = common_format.count(formatter)\n",
        "        filename = common_format.replace(formatter * ndigits, str(i).zfill(ndigits))\n",
        "\n",
        "        try:\n",
        "            print(f\"Running index #{str(i)}.\")\n",
        "            print(f\"Filename: {filename}\")\n",
        "            filedata = np.nan_to_num(hp.read_alm(filename)).astype(np.complex128)\n",
        "            print(\"Iterating...\")\n",
        "\n",
        "            fn(filedata, data_dict, i)\n",
        "            # for memory purposes\n",
        "            del filedata\n",
        "\n",
        "        except FileNotFoundError:\n",
        "            print(f\"Could not find {filename}.\")\n",
        "            continue\n",
        "\n",
        "    return data_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "abd3f448",
      "metadata": {
        "id": "abd3f448"
      },
      "source": [
        "And the helper function it needs to iterate:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "1038e442",
      "metadata": {
        "id": "1038e442"
      },
      "outputs": [],
      "source": [
        "# now defining the \"fn\" to be iterated over in the definition above\n",
        "# \"kappa\" here refers to the reconstructed kappa sims\n",
        "def iterator_clkk(kappa, data_dict, idx):\n",
        "    # the index # \"idx\" also specifies the input potential filename\n",
        "    # assuming recon sim index # == input sim index #\n",
        "    input_kappa_filename = INPUT_KAPPA_FORMAT.replace(FILLER * INPUT_KAPPA_FORMAT.count(FILLER),\n",
        "                                                      str(idx).zfill(INPUT_KAPPA_FORMAT.count(FILLER)))\n",
        "    print(f\"Input kappa: {input_kappa_filename}\")\n",
        "\n",
        "    # the input alms are lensing convergence values stored as double complex values\n",
        "    input_kappa = np.nan_to_num(hp.read_alm(input_kappa_filename)).astype(np.complex128)\n",
        "\n",
        "    # run NaMaster!\n",
        "    kmask = LENSING_MASK\n",
        "    gmask = GALAXY_MASK\n",
        "\n",
        "    kmap = hp.alm2map(input_kappa, NSIDE)\n",
        "    khat_map = hp.alm2map(kappa, NSIDE)\n",
        "\n",
        "    # generate NaMaster binning object\n",
        "    b = make_bins(nside=NSIDE,LperBin=LperBin,lmin=LMIN,lmax=LMAX)\n",
        "\n",
        "    # The reconstructed lensing sims are made from pre-masked CMB temperature maps,\n",
        "    # so the quadratic estimator results in a lensing map effectively masked twice by the ACT mask\n",
        "    field_k_hat = nmt.NmtField(kmask**2, [khat_map], masked_on_input=True)\n",
        "    # No need to worry about the input lensing fields, since they are generated on the full sky\n",
        "    field_k = nmt.NmtField(gmask, [kmap], masked_on_input=False)\n",
        "\n",
        "    # only compute coupling matrix if not pre-computed / saved before\n",
        "    w = nmt.NmtWorkspace()\n",
        "    if os.path.isfile(COUPLING_MATRIX_NAME):\n",
        "        print(\"Reading coupling matrix from disk.\")\n",
        "        w.read_from(COUPLING_MATRIX_NAME)\n",
        "    else:\n",
        "        w.compute_coupling_matrix(field_k, field_k_hat, b)\n",
        "        print(\"Writing coupling matrix to disk.\")\n",
        "        w.write_to(COUPLING_MATRIX_NAME)\n",
        "\n",
        "    # use coupling matrix to generate pseudo-cls\n",
        "    clkk = w.decouple_cell(nmt.compute_coupled_cell(field_k, field_k_hat))[0]\n",
        "    nmtells = b.get_effective_ells()\n",
        "\n",
        "    # only run to desired lmax cut\n",
        "    data_dict[idx] = clkk[:max_bin(nmtells, LMAX_CUT)]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "557e625e",
      "metadata": {
        "id": "557e625e"
      },
      "source": [
        "Now we iterate over the desired number of sims (expect this cell to take a while, of O(30 min). It can take O(hours) for multiple hundreds of sims using much more RAM than this Colab environment offers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "30edaa1b",
      "metadata": {
        "id": "30edaa1b",
        "outputId": "875d864f-3c89-44f8-afbe-5e6ac6bc84ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running index #1.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0001.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0001.fits\n",
            "Writing coupling matrix to disk.\n",
            "Running index #2.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0002.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0002.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #3.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0003.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0003.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #4.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0004.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0004.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #5.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0005.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0005.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #6.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0006.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0006.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #7.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0007.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0007.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #8.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0008.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0008.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #9.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0009.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0009.fits\n",
            "Reading coupling matrix from disk.\n",
            "Running index #10.\n",
            "Filename: kappa_alm_sim_act_dr6_lensing_v1_baseline_0010.fits\n",
            "Iterating...\n",
            "Input kappa: input_kappa_alm_sim_0010.fits\n",
            "Reading coupling matrix from disk.\n",
            "--\n",
            "Time elapsed: 1328.40751 seconds\n"
          ]
        }
      ],
      "source": [
        "time1 = time.time()\n",
        "clkk_data = iterate_over_kappa_sims(iterator_clkk, sim_start=START_INDEX, sim_end=END_INDEX,\n",
        "                                    common_format=RECON_SIM_FORMAT, formatter=FILLER)\n",
        "print(\"--\")\n",
        "print(f\"Time elapsed: {time.time()-time1:0.5f} seconds\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be0ecaaa",
      "metadata": {
        "id": "be0ecaaa"
      },
      "source": [
        "## Denominator"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b31a4fab",
      "metadata": {
        "id": "b31a4fab"
      },
      "source": [
        "The denominator is simply the ACT DR6 lensing auto-spectrum theory prediction binned with `NaMaster`. Both the numerator and denominator will be binned up to a much higher ell (`LMAX`) than the desired limit (`LMAX_CUT`) to prevent power leakage at the smallest considered scales, and then cut at that limiting bin number after all binning is done.\n",
        "\n",
        "For this purpose, we will use a provided theory prediction of the lensing auto-spectrum instead of computing one ourselves."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/ACTCollaboration/actsims/master/data/cosmo2017_10K_acc3_lenspotentialCls.dat"
      ],
      "metadata": {
        "id": "QlAkbW-4g7eJ",
        "outputId": "53c43740-0217-418b-e4ec-05e10d6703fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "QlAkbW-4g7eJ",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-17 20:11:06--  https://raw.githubusercontent.com/ACTCollaboration/actsims/master/data/cosmo2017_10K_acc3_lenspotentialCls.dat\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1120001 (1.1M) [text/plain]\n",
            "Saving to: ‘cosmo2017_10K_acc3_lenspotentialCls.dat’\n",
            "\n",
            "cosmo2017_10K_acc3_ 100%[===================>]   1.07M  6.56MB/s    in 0.2s    \n",
            "\n",
            "2024-04-17 20:11:07 (6.56 MB/s) - ‘cosmo2017_10K_acc3_lenspotentialCls.dat’ saved [1120001/1120001]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "8a9b1836",
      "metadata": {
        "id": "8a9b1836"
      },
      "outputs": [],
      "source": [
        "ells, dr6_lensing_clkk = np.loadtxt(\"cosmo2017_10K_acc3_lenspotentialCls.dat\", usecols=[0,5], unpack=True)\n",
        "clkk_theory = dr6_lensing_clkk[ells < 3*NSIDE] * np.pi / 2\n",
        "\n",
        "# usually monopole and dipole of lensing auto-spectrum is set to zero\n",
        "# if included at all, but needs to be included for binning\n",
        "clkk_theory = np.append(np.array([0,0]), clkk_theory)\n",
        "\n",
        "# Now bin with NaMaster. We will reuse \"b\"!\n",
        "b = make_bins(nside=NSIDE,LperBin=LperBin,lmin=LMIN,lmax=LMAX)\n",
        "\n",
        "# cut to LMAX_CUT\n",
        "clkk_theory_binned = bin_spectra(clkk_theory,\n",
        "                                 COUPLING_MATRIX_NAME)[:max_bin(b.get_effective_ells(), LMAX_CUT)]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "357bb21a",
      "metadata": {
        "id": "357bb21a"
      },
      "source": [
        "Compute the mean of the sim-based spectrum computation over the sims for the transfer function as well as the scatter via covariance matrix from the sims to estimate error bars for our transfer function measurement:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "1775a376",
      "metadata": {
        "id": "1775a376"
      },
      "outputs": [],
      "source": [
        "clkk_sims = np.array([clkk_data[k] for k in clkk_data.keys()])\n",
        "clkk_mean_sims = np.mean(clkk_sims, axis=0)\n",
        "\n",
        "# computing error bars (on the mean) from sims covariance\n",
        "errs = 1/np.sqrt(END_INDEX - START_INDEX + 1) * np.std(clkk_sims / clkk_theory_binned, axis=0)\n",
        "xfn = clkk_mean_sims / clkk_theory_binned\n",
        "\n",
        "# Write to disk\n",
        "ells = b.get_effective_ells()\n",
        "ells = ells[:max_bin(ells, LMAX_CUT)]\n",
        "np.savetxt(\"act-dr6-transfer-function.txt\", np.column_stack((ells, xfn, errs)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transfer function calculation (without NaMaster)"
      ],
      "metadata": {
        "id": "qinjXm_mMHDC"
      },
      "id": "qinjXm_mMHDC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The commonly defined transfer function, or $T(l)$, is computed here as the following:\n",
        "\n",
        "$$\n",
        "T(l) = \\dfrac{\\dfrac{1}{N} \\displaystyle\\sum_{\\text{sim } i}^{N} C^{\\hat{\\kappa} X}_{i}}{C^{\\kappa X}_{\\text{CAMB}}}\n",
        "$$\n",
        "\n",
        "where $X$ refers to either the input lensing convergence $\\kappa$ (used to lens the simulations) or a correlated galaxy field $g$ generated by populating a galaxy simulation so that it has the same auto and cross spectra as the provided theory (see `orphics.maps.generate_correlated_alm`). Again, we will treat $X = \\kappa$.\n",
        "\n",
        "The denominator is computed in the exact same way as above, but we no longer use NaMaster to bin the spectra in both the denominator and numerator. To compute the numerator, we first compute the full-sky cross spectrum between the sets of alms:\n",
        "\n",
        "$$\n",
        "C^{XY}_l = \\dfrac{1}{2l+1} \\displaystyle\\sum_{m = -l}^l A_{lm} B^*_{lm}\n",
        "$$\n",
        "\n",
        "and then utilize an approximation using powers of the $w$-factor to account for the sky fraction excluded by the powers of the analysis mask.\n",
        "\n",
        "We will not compute this version in the notebook as a $w$-factor that uses multiple powers of different analysis masks is not trivial to compute. However, here are the appropriate functions to compute this $w$-factor for a given `pixell` enmap-formatted mask:"
      ],
      "metadata": {
        "id": "Ywav7bm-MKsP"
      },
      "id": "Ywav7bm-MKsP"
    },
    {
      "cell_type": "code",
      "source": [
        "def wfactor(n,mask,sht=True,pmap=None,equal_area=False):\n",
        "    \"\"\"\n",
        "    Approximate correction to an n-point function for the loss of power\n",
        "    due to the application of a mask.\n",
        "\n",
        "    from https://github.com/msyriac/orphics/blob/master/orphics/maps.py\n",
        "    \"\"\"\n",
        "    assert mask.ndim==1 or mask.ndim==2\n",
        "    if pmap is None:\n",
        "        if equal_area:\n",
        "            npix = mask.size\n",
        "            pmap = 4*np.pi / npix if sht else enmap.area(mask.shape,mask.wcs) / npix\n",
        "        else:\n",
        "            pmap = enmap.pixsizemap(mask.shape,mask.wcs)\n",
        "    return np.sum((mask**n)*pmap) /np.pi / 4. if sht else np.sum((mask**n)*pmap) / np.sum(pmap)\n",
        "\n",
        "def w_n(mask,n=3):\n",
        "    \"\"\"compute the wfactor for an n-point function\"\"\"\n",
        "    pmap = enmap.pixsizemap(mask.shape,mask.wcs)\n",
        "    return wfactor(n,mask,sht=True,pmap=pmap)"
      ],
      "metadata": {
        "id": "zDpWw3MKTfFv"
      },
      "id": "zDpWw3MKTfFv",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "4f7806be",
      "metadata": {
        "id": "4f7806be"
      },
      "source": [
        "# T(l) results and plots"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do a quick visualization of the numerator and denominator to see their agreement:"
      ],
      "metadata": {
        "id": "FUhSgTClyBe_"
      },
      "id": "FUhSgTClyBe_"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "3ec029aa",
      "metadata": {
        "id": "3ec029aa",
        "outputId": "a1efa49c-0c11-4526-eb7d-785c5c924e90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# cut x-axis ells to LMAX_CUT\n",
        "ells = b.get_effective_ells()\n",
        "ells = ells[:max_bin(ells, LMAX_CUT)]\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(ells, clkk_theory_binned, label=\"denominator (NaMaster)\")\n",
        "plt.plot(ells, clkk_mean_sims, label=\"numerator (NaMaster)\")\n",
        "plt.xlabel(r\"$L$\", size=24)\n",
        "plt.ylabel(r\"$C_L$\", size=24)\n",
        "plt.legend(prop={'size': 16})\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we can only show 10 cross-correlations of sims, the covariance is very likely to be unconverged and the error bars will blow up; the error on the mean is expected to be smaller and more sensible once a full suite of simulations that have at least $N_{sims} > N_{bandpowers}$ simulations are run."
      ],
      "metadata": {
        "id": "9TIpDk4evkC3"
      },
      "id": "9TIpDk4evkC3"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "232a1d24",
      "metadata": {
        "id": "232a1d24",
        "outputId": "f7483a02-06c7-4107-c95d-154be7175180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,9))\n",
        "plt.title(f\"Transfer function from ACT DR6 lensing sims #{START_INDEX} ~ #{END_INDEX}\", size=20)\n",
        "plt.errorbar(ells, xfn, errs, fmt='-o', label=\"NaMaster\")\n",
        "plt.plot(ells, ells * 0. + 1., '--', color='grey')\n",
        "plt.legend(prop={'size': 16})\n",
        "plt.xlabel(r\"$L$\", size=24)\n",
        "plt.ylabel(r\"$\\bar{C}_L^{\\hat{\\kappa} \\kappa} / C_{L,th}^{\\kappa \\kappa}$\", size=24)\n",
        "plt.savefig(\"act-dr6-transfer-function-plot.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It appears as this transfer function is on the order of a few percent and is quite noisy and fluctuating around 1 with no obviously explicit scale dependence. Once again, it is highly recommended to compute the transfer function with more simulations to gain more confidence in the scatter, and once computed, to divide a computed cross-correlation measurement by the bandpowers of this transfer function."
      ],
      "metadata": {
        "id": "Y6bO5IWjyOnh"
      },
      "id": "Y6bO5IWjyOnh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Related resources:\n",
        "\n",
        "For more examples of how to use ACT data consider using:\n",
        "- The DR6 Lensing notebooks available on github\n",
        "- The DR4 and DR5 ACT notebooks available on [github](https://github.com/ACTCollaboration)\n",
        "- The `pixell` notebooks which are available on the [`pixell` repo](https://github.com/simonsobs/pixell/tree/master) and describe how to work with maps from ACT"
      ],
      "metadata": {
        "id": "NBUKFhV2AlTc"
      },
      "id": "NBUKFhV2AlTc"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:.conda-myenv] *",
      "language": "python",
      "name": "conda-env-.conda-myenv-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.16"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}