{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!wget https://phy-act1.princeton.edu/public/data/dr6_noise_v2/N_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_noise_v2/N_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_noise_v2/r_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_noise_v2/r_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcL2bacHMA9z",
        "outputId": "4a76b2de-47dc-4148-db53-cae80ba78c1b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2026-08-20 21:47:09--  https://phy-act1.princeton.edu/public/data/dr6_noise_v2/N_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy\n",
            "Resolving phy-act1.princeton.edu (phy-act1.princeton.edu)... 128.112.102.238\n",
            "Connecting to phy-act1.princeton.edu (phy-act1.princeton.edu)|128.112.102.238|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6221504 (5.9M)\n",
            "Saving to: ‘N_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy’\n",
            "\n",
            "N_ell_pa4_pa5_pa6_d 100%[===================>]   5.93M  20.0MB/s    in 0.3s    \n",
            "\n",
            "2026-08-20 21:47:09 (20.0 MB/s) - ‘N_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy’ saved [6221504/6221504]\n",
            "\n",
            "--2026-08-20 21:47:09--  https://phy-act1.princeton.edu/public/data/dr6_noise_v2/N_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy\n",
            "Resolving phy-act1.princeton.edu (phy-act1.princeton.edu)... 128.112.102.238\n",
            "Connecting to phy-act1.princeton.edu (phy-act1.princeton.edu)|128.112.102.238|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3110816 (3.0M)\n",
            "Saving to: ‘N_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy’\n",
            "\n",
            "N_ell_pa4_pa5_pa6_d 100%[===================>]   2.97M  13.9MB/s    in 0.2s    \n",
            "\n",
            "2026-08-20 21:47:10 (13.9 MB/s) - ‘N_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy’ saved [3110816/3110816]\n",
            "\n",
            "--2026-08-20 21:47:10--  https://phy-act1.princeton.edu/public/data/dr6_noise_v2/r_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy\n",
            "Resolving phy-act1.princeton.edu (phy-act1.princeton.edu)... 128.112.102.238\n",
            "Connecting to phy-act1.princeton.edu (phy-act1.princeton.edu)|128.112.102.238|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 37328384 (36M)\n",
            "Saving to: ‘r_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy’\n",
            "\n",
            "r_ell_pa4_pa5_pa6_d 100%[===================>]  35.60M  33.3MB/s    in 1.1s    \n",
            "\n",
            "2026-08-20 21:47:11 (33.3 MB/s) - ‘r_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy’ saved [37328384/37328384]\n",
            "\n",
            "--2026-08-20 21:47:11--  https://phy-act1.princeton.edu/public/data/dr6_noise_v2/r_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy\n",
            "Resolving phy-act1.princeton.edu (phy-act1.princeton.edu)... 128.112.102.238\n",
            "Connecting to phy-act1.princeton.edu (phy-act1.princeton.edu)|128.112.102.238|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 18664256 (18M)\n",
            "Saving to: ‘r_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy’\n",
            "\n",
            "r_ell_pa4_pa5_pa6_d 100%[===================>]  17.80M  33.7MB/s    in 0.5s    \n",
            "\n",
            "2026-08-20 21:47:12 (33.7 MB/s) - ‘r_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy’ saved [18664256/18664256]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this notebook plots the autospectrum and correlation noise pseudospectrum,\n",
        "# averaged over splits, for a given array (pair), frequency (pair),\n",
        "# and polarization (pair) of either act_dr6.01 data (dr6v3) or acr_dr6.02 data\n",
        "# (dr6v4). the latter maps were used in the dr6 power spectrum analysis. the\n",
        "# mask in which these spectra are measured is large and smooth, so the\n",
        "# shape of the power spectrum will be qualitatively very similar.\n",
        "\n",
        "# IMPORTANT NOTES -- PLEASE READ\n",
        "\n",
        "# IMPORTANT NOTE 1: these spectra are really only useful for forecasting\n",
        "# studies -- THEY CANNOT BE USED FOR COSMOLOGICAL ANALYSIS. as described in\n",
        "# https://arxiv.org/abs/2303.04180, isotropic power spectra are not a realistic\n",
        "# model of the ACT noise. these spectra provide a reference for the general\n",
        "# \"shape\" of noise spectra that may be expected for large aperture telescopes\n",
        "# in Chile\n",
        "\n",
        "# IMPORTANT NOTE 2: the spectra have all been normalized such that, after adding\n",
        "# in inverse-quadrature over their split axes, they are unity at high ell in\n",
        "# the noise TT power spectrum. this sum represents the noise power spectrum of\n",
        "# the map that has been formed by a simple average of the splits. in other words\n",
        "# the per-split noise power spectrum in TT has a value of NSPLITS at high ell\n",
        "# and approximately 2*NSPLITS for EE and BB noise.\n",
        "\n",
        "# IMPORTANT NOTE 3: this means that it is up to the user to normalize the\n",
        "# white-noise level of the spectra for their desired forecasting application.\n",
        "# for constant-depth white noise of X uK-arcmin, the power spectrum is simply\n",
        "# C = (X * pi / 10800)^2 (in uK^2-steradians). so users should multiply\n",
        "# the spectra by C, then take the mean over the split axis, and further divide\n",
        "# by NSPLITS to get a representative isotropic full-sky noise power spectrum\n",
        "# with the shape of the corresponding ACT DR6 array-band\n",
        "\n",
        "# IMPORTANT NOTE 4: the dr6.01 and dr6.02 maps have different pixel window\n",
        "# functions, see https://arxiv.org/abs/2503.14451. the choice to normalize at\n",
        "# the highest-ell means that comparing dr6.01 to dr6.02 directly at lower ells\n",
        "# will be a little not-apples-to-apples.\n",
        "\n",
        "# IMPORTANT NOTE 5: the correlation noise spectra are independent of any change\n",
        "# in the normalization."
      ],
      "metadata": {
        "id": "3gnswXPiRAcC"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "IVZHg7L4L6GD"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import ndimage\n",
        "\n",
        "from os.path import join as opj\n",
        "\n",
        "### inputs\n",
        "my_arr = 6 # array of the autospectrum i will plot (4 for pa4, etc.)\n",
        "           # we assume 0 correlation between arrays, so the data does\n",
        "           # not covary different arrays\n",
        "\n",
        "my_freq1 = 'f090' # frequency band of the autospectrum i will plot\n",
        "my_freq2 = 'f150' # second frequency band for the correlation spectrum\n",
        "\n",
        "lmin = 100 # the minimum ell to use\n",
        "lmax = 10800 # the maximum ell to use\n",
        "delta_ell = 25 # the width of a tophat smoothing kernel for plotting\n",
        "\n",
        "data_directory = '.' # where the data files live on my system"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "zm1vcQQGL6GF"
      },
      "outputs": [],
      "source": [
        "### load the noise curves. note the different number of splits in dr6.01 and\n",
        "# dr6.02 maps!\n",
        "\n",
        "# shape is (narr=3, nfreq=2, nsplit=8, npol=3, lmax+1=10801)\n",
        "N_ell_dr6_01 = np.load(opj(data_directory, 'N_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy'))\n",
        "\n",
        "# shape is (narr=3, nfreq=2, nsplit=4, npol=3, lmax+1=10801)\n",
        "N_ell_dr6_02 = np.load(opj(data_directory, 'N_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy'))\n",
        "\n",
        "# shape is (narr=3, nfreq=2, nfreq=2, npol=3, npol=3, nsplit=8, lmax+1=10801)\n",
        "r_ell_dr6_01 = np.load(opj(data_directory, 'r_ell_pa4_pa5_pa6_dr6v3_lmax10800.npy'))\n",
        "\n",
        "# shape is (narr=3, nfreq=2, nfreq=2, npol=3, npol=3, nsplit=4, lmax+1=10801)\n",
        "r_ell_dr6_02 = np.load(opj(data_directory, 'r_ell_pa4_pa5_pa6_dr6v4_lmax10800.npy'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9F4Kwk_cL6GG"
      },
      "outputs": [],
      "source": [
        "### cut ells and smooth curves\n",
        "lsel = np.s_[..., lmin:lmax+1]\n",
        "l = np.arange(lmax+1)[lsel]\n",
        "N_ell_dr6_01 = ndimage.uniform_filter1d(N_ell_dr6_01[lsel], delta_ell, axis=-1, mode='nearest')\n",
        "r_ell_dr6_01 = ndimage.uniform_filter1d(r_ell_dr6_01[lsel], delta_ell, axis=-1, mode='nearest')\n",
        "N_ell_dr6_02 = ndimage.uniform_filter1d(N_ell_dr6_02[lsel], delta_ell, axis=-1, mode='nearest')\n",
        "r_ell_dr6_02 = ndimage.uniform_filter1d(r_ell_dr6_02[lsel], delta_ell, axis=-1, mode='nearest')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "RrnBGfwqL6GG"
      },
      "outputs": [],
      "source": [
        "### convert inputs into indices that we will use to plot data subsets\n",
        "\n",
        "# get index of my array\n",
        "my_arr_index = [4, 5, 6].index(my_arr)\n",
        "\n",
        "# get index of my frequency bands\n",
        "arrs2freqs = {\n",
        "    4: ['f150', 'f220'],\n",
        "    5: ['f090', 'f150'],\n",
        "    6: ['f090', 'f150']\n",
        "}\n",
        "my_freq_index1 = arrs2freqs[my_arr].index(my_freq1)\n",
        "my_freq_index2 = arrs2freqs[my_arr].index(my_freq2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "0-_CK8_pL6GH",
        "outputId": "fed14874-086a-4429-fe49-b31bb28eaf1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:30: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:31: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:30: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:31: SyntaxWarning: invalid escape sequence '\\e'\n",
            "/tmp/ipykernel_2809/1074048144.py:30: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.xlabel('$\\ell$')\n",
            "/tmp/ipykernel_2809/1074048144.py:31: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.ylabel('$N_{\\ell} / N^{TT}_{10,800} \\ \\mathrm{[a.u.]}$')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "my_pol1 = 'T' # polarization of the autospectrum i will plot (T, E, B)\n",
        "my_pol2 = 'T' # second polarization for the correlation spectrum\n",
        "\n",
        "# get index of my pols\n",
        "my_pol_index1 = 'TEB'.index(my_pol1)\n",
        "my_pol_index2 = 'TEB'.index(my_pol2)\n",
        "\n",
        "### plot curves (average and scatter over splits). compare dr6.01 and dr6.02!\n",
        "\n",
        "# plot autospectrum, average and scatter over splits\n",
        "y_dr6_01 = N_ell_dr6_01[my_arr_index, my_freq_index1, :, my_pol_index1].mean(axis=0)\n",
        "y_err_dr6_01 = N_ell_dr6_01[my_arr_index, my_freq_index1, :, my_pol_index1].std(axis=0)\n",
        "\n",
        "y_dr6_01 /= N_ell_dr6_01.shape[-3] # divide by nsplits to get effective level for map\n",
        "y_err_dr6_01 /= N_ell_dr6_01.shape[-3] # divide by nsplits to get effective level for map\n",
        "\n",
        "y_dr6_02 = N_ell_dr6_02[my_arr_index, my_freq_index1, :, my_pol_index1].mean(axis=0)\n",
        "y_err_dr6_02 = N_ell_dr6_02[my_arr_index, my_freq_index1, :, my_pol_index1].std(axis=0)\n",
        "\n",
        "y_dr6_02 /= N_ell_dr6_02.shape[-3] # divide by nsplits to get effective level for map\n",
        "y_err_dr6_02 /= N_ell_dr6_02.shape[-3] # divide by nsplits to get effective level for map\n",
        "\n",
        "plt.plot(l, y_dr6_01, alpha=0.8, label='dr6.01')\n",
        "plt.fill_between(l, (y_dr6_01 - y_err_dr6_01), (y_dr6_01 + y_err_dr6_01), alpha=0.3)\n",
        "plt.plot(l, y_dr6_02, alpha=0.8, label='dr6.02')\n",
        "plt.fill_between(l, (y_dr6_02 - y_err_dr6_02), (y_dr6_02 + y_err_dr6_02), alpha=0.3)\n",
        "plt.loglog()\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.xlabel('$\\ell$')\n",
        "plt.ylabel('$N_{\\ell} / N^{TT}_{10,800} \\ \\mathrm{[a.u.]}$')\n",
        "plt.title(f'noise auto(pseudo)spectrum: act_dr6 pa{my_arr}_{my_freq1}_{my_pol1}')\n",
        "plt.show()\n",
        "\n",
        "# NOTE THE DIFFERENT PIXEL WINDOW FUNCTIONS!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# do the same plot but for polarization\n",
        "\n",
        "my_pol1 = 'E' # polarization of the autospectrum i will plot (T, E, B)\n",
        "my_pol2 = 'E' # second polarization for the correlation spectrum\n",
        "\n",
        "# get index of my pols\n",
        "my_pol_index1 = 'TEB'.index(my_pol1)\n",
        "my_pol_index2 = 'TEB'.index(my_pol2)\n",
        "\n",
        "### plot curves (average and scatter over splits). compare dr6.01 and dr6.02!\n",
        "\n",
        "# plot autospectrum, average and scatter over splits\n",
        "y_dr6_01 = N_ell_dr6_01[my_arr_index, my_freq_index1, :, my_pol_index1].mean(axis=0)\n",
        "y_err_dr6_01 = N_ell_dr6_01[my_arr_index, my_freq_index1, :, my_pol_index1].std(axis=0)\n",
        "\n",
        "y_dr6_01 /= N_ell_dr6_01.shape[-3] # divide by nsplits to get effective level for map\n",
        "y_err_dr6_01 /= N_ell_dr6_01.shape[-3] # divide by nsplits to get effective level for map\n",
        "\n",
        "y_dr6_02 = N_ell_dr6_02[my_arr_index, my_freq_index1, :, my_pol_index1].mean(axis=0)\n",
        "y_err_dr6_02 = N_ell_dr6_02[my_arr_index, my_freq_index1, :, my_pol_index1].std(axis=0)\n",
        "\n",
        "y_dr6_02 /= N_ell_dr6_02.shape[-3] # divide by nsplits to get effective level for map\n",
        "y_err_dr6_02 /= N_ell_dr6_02.shape[-3] # divide by nsplits to get effective level for map\n",
        "\n",
        "plt.plot(l, y_dr6_01, alpha=0.8, label='dr6.01')\n",
        "plt.fill_between(l, (y_dr6_01 - y_err_dr6_01), (y_dr6_01 + y_err_dr6_01), alpha=0.3)\n",
        "plt.plot(l, y_dr6_02, alpha=0.8, label='dr6.02')\n",
        "plt.fill_between(l, (y_dr6_02 - y_err_dr6_02), (y_dr6_02 + y_err_dr6_02), alpha=0.3)\n",
        "plt.ylim(ymin=0.8)\n",
        "plt.loglog()\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.xlabel('$\\ell$')\n",
        "plt.ylabel('$N_{\\ell} / N^{TT}_{10,800} \\ \\mathrm{[a.u.]}$')\n",
        "plt.title(f'noise auto(pseudo)spectrum: act_dr6 pa{my_arr}_{my_freq1}_{my_pol1}')\n",
        "plt.show()\n",
        "\n",
        "# NOTE THE DIFFERENT PIXEL WINDOW FUNCTIONS!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "ynKYzGdOWq4w",
        "outputId": "d98152b3-379e-4815-c051-f02e2a4ab060"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:33: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:34: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:33: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:34: SyntaxWarning: invalid escape sequence '\\e'\n",
            "/tmp/ipykernel_2809/2212703726.py:33: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.xlabel('$\\ell$')\n",
            "/tmp/ipykernel_2809/2212703726.py:34: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.ylabel('$N_{\\ell} / N^{TT}_{10,800} \\ \\mathrm{[a.u.]}$')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_pol1 = 'T' # polarization of the autospectrum i will plot (T, E, B)\n",
        "my_pol2 = 'T' # second polarization for the correlation spectrum\n",
        "\n",
        "# get index of my pols\n",
        "my_pol_index1 = 'TEB'.index(my_pol1)\n",
        "my_pol_index2 = 'TEB'.index(my_pol2)\n",
        "\n",
        "# plot correlation spectrum, average and scatter over splits\n",
        "y_dr6_01 = r_ell_dr6_01[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].mean(axis=0)\n",
        "y_err_dr6_01 = r_ell_dr6_01[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].std(axis=0)\n",
        "\n",
        "y_dr6_02 = r_ell_dr6_02[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].mean(axis=0)\n",
        "y_err_dr6_02 = r_ell_dr6_02[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].std(axis=0)\n",
        "\n",
        "plt.plot(l, y_dr6_01, alpha=0.8, label='dr6.01')\n",
        "plt.fill_between(l, y_dr6_01 - y_err_dr6_01, y_dr6_01 + y_err_dr6_01, alpha=0.3)\n",
        "plt.plot(l, y_dr6_02, alpha=0.8, label='dr6.02')\n",
        "plt.fill_between(l, y_dr6_02 - y_err_dr6_02, y_dr6_02 + y_err_dr6_02, alpha=0.3)\n",
        "plt.semilogx()\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.xlabel('$\\ell$')\n",
        "plt.ylabel('$r_{\\ell} \\ \\mathrm{[a.u.]}$')\n",
        "plt.title(f'noise correlation (pseudo)spectrum: act_dr pa{my_arr}_{my_freq1}_{my_pol1} x pa{my_arr}_{my_freq2}_{my_pol2}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "Rnl29QwIWmfA",
        "outputId": "a7261ed6-6e8a-4326-a08a-02236e47a6e1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:22: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:23: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:22: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:23: SyntaxWarning: invalid escape sequence '\\e'\n",
            "/tmp/ipykernel_2809/1955775943.py:22: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.xlabel('$\\ell$')\n",
            "/tmp/ipykernel_2809/1955775943.py:23: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.ylabel('$r_{\\ell} \\ \\mathrm{[a.u.]}$')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# do the same plot but for polarization\n",
        "\n",
        "my_pol1 = 'E' # polarization of the autospectrum i will plot (T, E, B)\n",
        "my_pol2 = 'E' # second polarization for the correlation spectrum\n",
        "\n",
        "# get index of my pols\n",
        "my_pol_index1 = 'TEB'.index(my_pol1)\n",
        "my_pol_index2 = 'TEB'.index(my_pol2)\n",
        "\n",
        "# plot correlation spectrum, average and scatter over splits\n",
        "y_dr6_01 = r_ell_dr6_01[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].mean(axis=0)\n",
        "y_err_dr6_01 = r_ell_dr6_01[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].std(axis=0)\n",
        "\n",
        "y_dr6_02 = r_ell_dr6_02[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].mean(axis=0)\n",
        "y_err_dr6_02 = r_ell_dr6_02[my_arr_index, my_freq_index1, my_freq_index2, my_pol_index1, my_pol_index2, :].std(axis=0)\n",
        "\n",
        "plt.plot(l, y_dr6_01, alpha=0.8, label='dr6.01')\n",
        "plt.fill_between(l, y_dr6_01 - y_err_dr6_01, y_dr6_01 + y_err_dr6_01, alpha=0.3)\n",
        "plt.plot(l, y_dr6_02, alpha=0.8, label='dr6.02')\n",
        "plt.fill_between(l, y_dr6_02 - y_err_dr6_02, y_dr6_02 + y_err_dr6_02, alpha=0.3)\n",
        "plt.semilogx()\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.xlabel('$\\ell$')\n",
        "plt.ylabel('$r_{\\ell} \\ \\mathrm{[a.u.]}$')\n",
        "plt.title(f'noise correlation (pseudo)spectrum: act_dr pa{my_arr}_{my_freq1}_{my_pol1} x pa{my_arr}_{my_freq2}_{my_pol2}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "Vwr1s8y7XamU",
        "outputId": "8c420918-7fd8-4d8a-fc76-3d3e0c725a51"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:24: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:25: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:24: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:25: SyntaxWarning: invalid escape sequence '\\e'\n",
            "/tmp/ipykernel_2809/3200586416.py:24: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.xlabel('$\\ell$')\n",
            "/tmp/ipykernel_2809/3200586416.py:25: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.ylabel('$r_{\\ell} \\ \\mathrm{[a.u.]}$')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "pspy-della8",
      "language": "python",
      "name": "pspy-della8"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}