ACT_DR6_lensing_likelihood_with_cosmopower.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_likelihood_with_cosmopower.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SNmH8dH5bl8h",
      "metadata": {
        "id": "SNmH8dH5bl8h"
      },
      "source": [
        "# Cosmopower and the Lensing Likelihood\n",
        "\n",
        "*Written by Karen Perez, Boris Bolliet and the ACT collaboration*\n",
        "\n",
        "---\n",
        "\n",
        "This notebook will demonstrate how to work with the ACT Data Release 6 (DR6) lensing likelihood using Cosmopower. This is done by loading information about the ACT lensing data, including the covariance matrix and power spectrum, and running MCMC to estimate cosmological parameters. The MCMC library that we will use here is `Cobaya` ([arxiv](https://arxiv.org/abs/2005.05290), [ASCL](https://ascl.net/1910.019)), which is a general purpose statistical framework used in cosmological parameter estimation.\n",
        "\n",
        "In MCMC, we have to sample the parameter space and generate a theoretical power spectrum many times. Traditionally, this is done using Boltzmann theory codes such as CLASS ([code](https://github.com/lesgourg/class_public), [paper](https://arxiv.org/abs/1104.2932)) or CAMB ([code](https://github.com/cmbant/CAMB/), [paper](https://ui.adsabs.harvard.edu/abs/2000ApJ...538..473L/abstract)). In this notebook, we instead make use of emulators made with Cosmopower ([code](https://github.com/alessiospuriomancini/cosmopower), [paper](https://arxiv.org/abs/2106.03846)), a set of trained neural networks that significantly speeds up the computation of theory power spectra and parameter estimation. These specific emulators here are trained with Stage IV accuracy requirements, as reported [here](https://arxiv.org/abs/2303.01591).\n",
        "\n",
        "Currently, the cosmopower emulators are wrapped within the class_sz ([code](https://github.com/CLASS-SZ/class_sz), [paper](https://inspirehep.net/literature/2715411)) code, which easily allows for exact evaluation of background and thermodynamics quantities while calling the cosmopower emulators to predict the CMB spectra.\n",
        "\n",
        "### ACT DR6 data products\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",
        "For more information on `Cosmopower` readers can refer to [Spuria Mancini et al. (2021)](https://arxiv.org/abs/2106.03846) and the code can be found on [github](https://github.com/alessiospuriomancini/cosmopower).\n",
        "\n",
        "### Further background\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."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Lfqkq5hirGdW",
      "metadata": {
        "id": "Lfqkq5hirGdW"
      },
      "source": [
        "## Package instalation and setup\n",
        "First, we need to install `Cobaya`, `Cosmopower` and all the dependencies to make them interface with each other. We will also install the BAO likelihoods, available via `Cobaya`, as well as the ACT DR6 lensing likelihood. Since we are installing a lot of libraries, the following cell will take ~20 minutes to run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "L0wPUfQ5USpp",
      "metadata": {
        "id": "L0wPUfQ5USpp"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install tensorflow==2.13.0\n",
        "!pip install tensorflow_probability==0.21.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rTgk5B42Yisd",
      "metadata": {
        "id": "rTgk5B42Yisd"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pwd\n",
        "!pip install cosmopower\n",
        "!pip install mcfit\n",
        "!sudo apt install libgsl-dev\n",
        "!sudo apt install fftw3-dev\n",
        "!rm -rf class_sz\n",
        "!git clone https://github.com/CLASS-SZ/class_sz.git\n",
        "%cd class_sz\n",
        "!mv Makefile Makefile_os\n",
        "!mv Makefile_linux Makefile\n",
        "!make clean\n",
        "!make\n",
        "%cd python\n",
        "!pip install -e .\n",
        "%cd classy_szfast/classy_szfast/\n",
        "!scp config.py config_old.py\n",
        "!sed -i 's/cosmopower-organization\\//\\/content\\/cosmopower-organization\\//' config.py\n",
        "%cd /content/class_sz/python/classy_szfast/\n",
        "!pip install -e .\n",
        "%cd /content/class_sz/python/\n",
        "!pip install -e .\n",
        "%cd /content\n",
        "!rm -rf cosmopower-organization\n",
        "!mkdir cosmopower-organization\n",
        "%cd /content/cosmopower-organization\n",
        "!git clone https://github.com/cosmopower-organization/lcdm.git\n",
        "!git clone https://github.com/cosmopower-organization/mnu.git\n",
        "!git clone https://github.com/cosmopower-organization/neff.git\n",
        "!git clone https://github.com/cosmopower-organization/wcdm.git\n",
        "!git clone https://github.com/cosmopower-organization/reactemu-fr.git\n",
        "!git clone https://github.com/cosmopower-organization/ede.git\n",
        "!git clone https://github.com/cosmopower-organization/mnu-3states.git\n",
        "%cd /content/\n",
        "import sys\n",
        "sys.path.insert(1, \"/content/class_sz/python\")\n",
        "sys.path.insert(1, \"/content/class_sz/python/classy_szfast\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Q-b6ZC5qhpuf",
      "metadata": {
        "id": "Q-b6ZC5qhpuf"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%cd /content/\n",
        "!pip install cobaya\n",
        "!mkdir cosmo_pkgs\n",
        "!cobaya-install cosmo -p /content/cosmo_pkgs\n",
        "%cd /content/\n",
        "!pip install act_dr6_lenslike\n",
        "import act_dr6_lenslike\n",
        "import os\n",
        "actdr6path = act_dr6_lenslike.__file__\n",
        "actdr6path = actdr6path+\"/../\"\n",
        "actdr6path = os.path.normpath(actdr6path)\n",
        "%cd $actdr6path\n",
        "!mkdir -p data\n",
        "%cd data/\n",
        "!wget https://lambda.gsfc.nasa.gov/data/suborbital/ACT/ACT_dr6/likelihood/data/ACT_dr6_likelihood_v1.2.tgz\n",
        "!tar -zxvf ACT_dr6_likelihood_v1.2.tgz\n",
        "!rm ACT_dr6_likelihood_v1.2.tgz\n",
        "%cd /content/"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "txDfGzTprjGP",
      "metadata": {
        "id": "txDfGzTprjGP"
      },
      "source": [
        "## Import the relevant packages\n",
        "\n",
        "If everything in the cell above was installed correctly, then you should be able to import the following libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "115b109a",
      "metadata": {
        "id": "115b109a",
        "outputId": "29954f73-6e0b-497b-b87d-b09c3209bef1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/class_sz/python/classy_sz.cpython-310-x86_64-linux-gnu.so'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import numpy as np\n",
        "import cobaya\n",
        "import cosmopower\n",
        "import classy_sz\n",
        "import scipy\n",
        "import yaml\n",
        "import os\n",
        "\n",
        "from getdist import loadMCSamples, MCSamples\n",
        "from getdist import plots\n",
        "\n",
        "from cosmopower import cosmopower_NN\n",
        "from cosmopower import cosmopower_PCAplusNN\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pprint\n",
        "\n",
        "import subprocess\n",
        "\n",
        "import act_dr6_lenslike\n",
        "import time\n",
        "\n",
        "%matplotlib inline\n",
        "classy_sz.__file__"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06fc60fb",
      "metadata": {
        "id": "06fc60fb"
      },
      "source": [
        "# Loading and formatting the `Cobaya` input `yaml` file\n",
        "\n",
        "`Cobaya` requires 4 main components to do parameter estimation via MCMC:\n",
        "* Likelihood: An external code/library that computes the likelihoods to be explored.\n",
        "* Parameters: The list of parameters to be fixed, sampled or derived, along with their respective priors and values.\n",
        "* Theory: The theory code to compute the observables used in the likelihoods. Traditionally, this would be either CAMB or CLASS, but in this notebook we will use `Cosmopower`.\n",
        "* Sampler: The algorithm to sample the parameter space. In our case it will be MCMC.\n",
        "\n",
        "Setup instructions such as paths and other parameters for each of these components can be written to a yaml file that is then\n",
        "passed to Cobaya to perform parameter estimation. In this section, we will look at the contents of the yaml file, and edit some paths so that it works within this notebook and with all the packages we previously installed."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc7f7491",
      "metadata": {
        "id": "cc7f7491"
      },
      "source": [
        "First, let's download the setup yaml file and the initial covariance matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "H_KsAc0q5QD8",
      "metadata": {
        "id": "H_KsAc0q5QD8"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/misc/lcdm_actplanck_baseline.classy_szfast_mcmc.yaml\n",
        "\n",
        "!wget https://phy-act1.princeton.edu/public/data/dr6_lensing_v1/misc/lcdm_actplanck_baseline_covmat.covmat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DMsmQIjquYOC",
      "metadata": {
        "id": "DMsmQIjquYOC"
      },
      "outputs": [],
      "source": [
        "filename = \"lcdm_actplanck_baseline.classy_szfast_mcmc.yaml\"\n",
        "DR6_lensing_only_yaml = yaml.safe_load(open(filename))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iKSBNerUrm2x",
      "metadata": {
        "id": "iKSBNerUrm2x"
      },
      "source": [
        "The yaml file is now loaded into the notebook as python dictionary, and the setup instructions for the four different Cobaya components can be accessed as follows:\n",
        "\n",
        "```\n",
        "DR6_lensing_only_yaml[\"likelihood\"]\n",
        "DR6_lensing_only_yaml[\"theory\"]\n",
        "DR6_lensing_only_yaml[\"params\"]\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "444b6e92",
      "metadata": {
        "id": "444b6e92"
      },
      "source": [
        "Let's take a look at the likelihood block. We will have to change the paths to the likelihoods we  just installed (ACTDR6LensLike and BAO)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d5f77374",
      "metadata": {
        "id": "d5f77374"
      },
      "outputs": [],
      "source": [
        "path_to_bao_lkl = \"/content/cosmo_pkgs/data\"\n",
        "DR6_lensing_only_yaml[\"likelihood\"]['bao.sdss_dr12_lrg_bao_dmdh']['path'] = path_to_bao_lkl\n",
        "DR6_lensing_only_yaml[\"likelihood\"]['bao.sdss_dr16_lrg_bao_dmdh']['path'] = path_to_bao_lkl\n",
        "DR6_lensing_only_yaml [\"likelihood\"]['bao.sdss_dr7_mgs']['path'] = path_to_bao_lkl\n",
        "DR6_lensing_only_yaml[\"likelihood\"]['bao.sixdf_2011_bao']['path'] = path_to_bao_lkl"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed91d4a8",
      "metadata": {
        "id": "ed91d4a8"
      },
      "source": [
        "Let's check that the changes were implemented."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f247fa52",
      "metadata": {
        "id": "f247fa52",
        "outputId": "b7ea86ba-c866-42aa-d8d6-66ff95deccf2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{  'act_dr6_lenslike.ACTDR6LensLike': {  'apply_hartlap': True,\n",
            "                                         'input_params': [],\n",
            "                                         'kmax': 10,\n",
            "                                         'lens_only': True,\n",
            "                                         'limber': False,\n",
            "                                         'lmax': 4000,\n",
            "                                         'mock': False,\n",
            "                                         'no_like_corrections': True,\n",
            "                                         'nsims_act': 792.0,\n",
            "                                         'nsims_planck': 400.0,\n",
            "                                         'nz': 100,\n",
            "                                         'output_params': [],\n",
            "                                         'scale_cov': None,\n",
            "                                         'speed': -1,\n",
            "                                         'stop_at_error': True,\n",
            "                                         'trim_lmax': 2998,\n",
            "                                         'type': [],\n",
            "                                         'variant': 'actplanck_baseline',\n",
            "                                         'version': None},\n",
            "   'bao.sdss_dr12_lrg_bao_dmdh': {  'aliases': ['BAO'],\n",
            "                                    'cov_file': 'bao_data/sdss_DR12_LRG_BAO_DMDH_covtot.txt',\n",
            "                                    'grid_file': None,\n",
            "                                    'input_params': [],\n",
            "                                    'invcov_file': None,\n",
            "                                    'measurements_file': 'bao_data/sdss_DR12_LRG_BAO_DMDH.dat',\n",
            "                                    'observable_1': None,\n",
            "                                    'observable_2': None,\n",
            "                                    'observable_3': None,\n",
            "                                    'output_params': [],\n",
            "                                    'path': '/content/cosmo_pkgs/data',\n",
            "                                    'prob_dist': None,\n",
            "                                    'redshift': None,\n",
            "                                    'rs_fid': 1,\n",
            "                                    'rs_rescale': None,\n",
            "                                    'speed': 2000,\n",
            "                                    'stop_at_error': False,\n",
            "                                    'type': 'BAO',\n",
            "                                    'version': None},\n",
            "   'bao.sdss_dr16_lrg_bao_dmdh': {  'aliases': ['BAO'],\n",
            "                                    'cov_file': 'bao_data/sdss_DR16_LRG_BAO_DMDH_covtot.txt',\n",
            "                                    'grid_file': None,\n",
            "                                    'input_params': [],\n",
            "                                    'invcov_file': None,\n",
            "                                    'measurements_file': 'bao_data/sdss_DR16_LRG_BAO_DMDH.dat',\n",
            "                                    'observable_1': None,\n",
            "                                    'observable_2': None,\n",
            "                                    'observable_3': None,\n",
            "                                    'output_params': [],\n",
            "                                    'path': '/content/cosmo_pkgs/data',\n",
            "                                    'prob_dist': None,\n",
            "                                    'redshift': None,\n",
            "                                    'rs_fid': 1,\n",
            "                                    'rs_rescale': None,\n",
            "                                    'speed': 2000,\n",
            "                                    'stop_at_error': False,\n",
            "                                    'type': 'BAO',\n",
            "                                    'version': None},\n",
            "   'bao.sdss_dr7_mgs': {  'aliases': ['BAO'],\n",
            "                          'cov_file': None,\n",
            "                          'data': [  0.15,\n",
            "                                     4.465666824,\n",
            "                                     0.1681350461,\n",
            "                                     'DV_over_rs'],\n",
            "                          'grid_file': None,\n",
            "                          'input_params': [],\n",
            "                          'invcov_file': None,\n",
            "                          'measurements_file': None,\n",
            "                          'observable_1': None,\n",
            "                          'observable_2': None,\n",
            "                          'observable_3': None,\n",
            "                          'output_params': [],\n",
            "                          'path': '/content/cosmo_pkgs/data',\n",
            "                          'prob_dist': 'bao_data/sdss_MGS_prob.txt',\n",
            "                          'prob_dist_bounds': [0.8005, 1.1985],\n",
            "                          'redshift': None,\n",
            "                          'rs_fid': None,\n",
            "                          'rs_rescale': 4.29720761315,\n",
            "                          'speed': 5000,\n",
            "                          'stop_at_error': False,\n",
            "                          'type': 'BAO',\n",
            "                          'version': None},\n",
            "   'bao.sixdf_2011_bao': {  'aliases': ['BAO'],\n",
            "                            'cov_file': None,\n",
            "                            'data': [0.106, 0.336, 0.015, 'rs_over_DV'],\n",
            "                            'grid_file': None,\n",
            "                            'input_params': [],\n",
            "                            'invcov_file': None,\n",
            "                            'measurements_file': None,\n",
            "                            'observable_1': None,\n",
            "                            'observable_2': None,\n",
            "                            'observable_3': None,\n",
            "                            'output_params': [],\n",
            "                            'path': '/content/cosmo_pkgs/data',\n",
            "                            'prob_dist': None,\n",
            "                            'redshift': None,\n",
            "                            'rs_fid': None,\n",
            "                            'rs_rescale': 1.027369826,\n",
            "                            'speed': 5000,\n",
            "                            'stop_at_error': False,\n",
            "                            'type': 'BAO',\n",
            "                            'version': None}}\n"
          ]
        }
      ],
      "source": [
        "pp = pprint.PrettyPrinter(indent=3)\n",
        "pp.pprint(DR6_lensing_only_yaml[\"likelihood\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ecfdcb7",
      "metadata": {
        "id": "1ecfdcb7"
      },
      "source": [
        "Now, let's take a look at the parameters section of the yaml file. Nothing has to be changed here, simply take a look at the parameters, and their priors or fixed values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f9d5e6a",
      "metadata": {
        "id": "8f9d5e6a",
        "outputId": "86206783-4277-4604-8a06-f52b62ec0721",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{  'A': {'derived': 'lambda As: 1e9*As', 'latex': '10^9 A_\\\\mathrm{s}'},\n",
            "   'As': {'derived': True, 'latex': 'A_\\\\mathrm{s}'},\n",
            "   'H0': {  'latex': 'H_0',\n",
            "            'prior': {'max': 90.0, 'min': 50.0},\n",
            "            'proposal': 2.0,\n",
            "            'ref': {'dist': 'norm', 'loc': 69.351243, 'scale': 2.0}},\n",
            "   'S825': {  'derived': 'lambda sigma8, omegam: sigma8*(omegam/0.3)**0.25',\n",
            "              'latex': '\\\\sigma_8 (\\\\Omega_\\\\mathrm{m}/0.3)^{0.25}'},\n",
            "   'S85': {  'derived': 'lambda sigma8, omegam: sigma8*(omegam/0.3)**0.5',\n",
            "             'latex': '\\\\sigma_8 (\\\\Omega_\\\\mathrm{m}/0.3)^{0.5}'},\n",
            "   'YHe': {'derived': True, 'latex': 'Y_\\\\mathrm{P}'},\n",
            "   'age': {'derived': True, 'latex': '{\\\\rm{Age}}/\\\\mathrm{Gyr}'},\n",
            "   'chi2__BAO': {'derived': True, 'latex': '\\\\chi^2_\\\\mathrm{BAO}'},\n",
            "   'logA': {  'latex': 'ln10^{10}A_s',\n",
            "              'prior': {'max': 3.5, 'min': 2.5},\n",
            "              'proposal': 0.12892144,\n",
            "              'ref': {'dist': 'norm', 'loc': 2.9939341, 'scale': 0.12892144}},\n",
            "   'n_s': {  'latex': 'n_\\\\mathrm{s}',\n",
            "             'prior': {'dist': 'norm', 'loc': 0.96, 'scale': 0.02},\n",
            "             'proposal': 0.002,\n",
            "             'ref': {'dist': 'norm', 'loc': 0.965, 'scale': 0.004}},\n",
            "   'omega_b': {  'latex': '\\\\Omega_\\\\mathrm{b} h^2',\n",
            "                 'prior': {'dist': 'norm', 'loc': 0.02233, 'scale': 0.00036},\n",
            "                 'proposal': 0.0001,\n",
            "                 'ref': {'dist': 'norm', 'loc': 0.0224, 'scale': 0.0001},\n",
            "                 'renames': ['omegabh2']},\n",
            "   'omega_cdm': {  'latex': '\\\\Omega_\\\\mathrm{c} h^2',\n",
            "                   'prior': {'max': 0.99, 'min': 0.005},\n",
            "                   'proposal': 0.0005,\n",
            "                   'ref': {'dist': 'norm', 'loc': 0.12, 'scale': 0.001}},\n",
            "   'omegam': {'derived': True, 'latex': '\\\\Omega_\\\\mathrm{m}'},\n",
            "   'omegamh2': {  'derived': 'lambda omegam, H0: omegam*(H0/100)**2',\n",
            "                  'latex': '\\\\Omega_\\\\mathrm{m} h^2'},\n",
            "   'rdrag': {'derived': True, 'latex': 'r_\\\\mathrm{drag}'},\n",
            "   's8h5': {  'derived': 'lambda sigma8, H0: sigma8*(H0*1e-2)**(-0.5)',\n",
            "              'latex': '\\\\sigma_8/h^{0.5}'},\n",
            "   's8omegamp25': {  'derived': 'lambda sigma8, omegam: sigma8*omegam**0.25',\n",
            "                     'latex': '\\\\sigma_8 \\\\Omega_\\\\mathrm{m}^{0.25}'},\n",
            "   's8omegamp5': {  'derived': 'lambda sigma8, omegam: sigma8*omegam**0.5',\n",
            "                    'latex': '\\\\sigma_8 \\\\Omega_\\\\mathrm{m}^{0.5}'},\n",
            "   'sigma8': {'derived': True, 'latex': '\\\\sigma_8'},\n",
            "   'tau_reio': 0.055,\n",
            "   'zrei': {'derived': True, 'latex': 'z_\\\\mathrm{re}'}}\n"
          ]
        }
      ],
      "source": [
        "pp.pprint(DR6_lensing_only_yaml[\"params\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63f17a29",
      "metadata": {
        "id": "63f17a29"
      },
      "source": [
        "Next, we will look at the theory code instructions in the yaml file. Notice how we are using `classy_szfast` (`Cosmopower` emulator) as the theory code!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "158da586",
      "metadata": {
        "id": "158da586",
        "outputId": "9087ebf1-b669-4af4-bdbe-f83f85e29bde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{  'classy_szfast.classy_sz.classy_sz': {  'extra_args': {  'N_ncdm': 1,\n",
            "                                                            'N_ur': 2.0328,\n",
            "                                                            'k_pivot': 0.05,\n",
            "                                                            'modes': 's,t',\n",
            "                                                            'output': 'tCl,lCl,pCl',\n",
            "                                                            'skip_background_and_thermo': 0,\n",
            "                                                            'skip_pkl': 1,\n",
            "                                                            'skip_pknl': 1},\n",
            "                                           'lensing_lkl': 'ACT',\n",
            "                                           'path': None,\n",
            "                                           'speed': 0.2,\n",
            "                                           'stop_at_error': True,\n",
            "                                           'use_class_sz_fast_mode': 1}}\n"
          ]
        }
      ],
      "source": [
        "pp.pprint(DR6_lensing_only_yaml[\"theory\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cae30680",
      "metadata": {
        "id": "cae30680"
      },
      "source": [
        "As usual, we will use MCMC as the sampler. To make the chains converge faster, we will change the Rminus1_stop value from 0.01 to 0.1. However, if you want to reproduce the contours in [Madhavacheril et al. (2023)](https://arxiv.org/abs/2304.05203), you will have to change this parameter back to 0.01."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "t8u1WvooniH0",
      "metadata": {
        "id": "t8u1WvooniH0"
      },
      "outputs": [],
      "source": [
        "DR6_lensing_only_yaml[\"sampler\"]['mcmc']['Rminus1_stop']=0.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc954a21",
      "metadata": {
        "id": "dc954a21",
        "outputId": "1b37d636-304f-4fad-8cbf-b88d1335a5d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{  'mcmc': {  'Rminus1_cl_level': 0.95,\n",
            "              'Rminus1_cl_stop': 0.2,\n",
            "              'Rminus1_single_split': 4,\n",
            "              'Rminus1_stop': 0.1,\n",
            "              'burn_in': 0,\n",
            "              'callback_every': None,\n",
            "              'callback_function': None,\n",
            "              'check_every': None,\n",
            "              'covmat': 'auto',\n",
            "              'covmat_params': None,\n",
            "              'drag': False,\n",
            "              'drag_limits': None,\n",
            "              'fallback_covmat_scale': 4,\n",
            "              'learn_every': '40d',\n",
            "              'learn_proposal': True,\n",
            "              'learn_proposal_Rminus1_max': 2.0,\n",
            "              'learn_proposal_Rminus1_max_early': 30.0,\n",
            "              'learn_proposal_Rminus1_min': 0.0,\n",
            "              'max_samples': inf,\n",
            "              'max_tries': 10000,\n",
            "              'measure_speeds': True,\n",
            "              'output_every': '60s',\n",
            "              'oversample': None,\n",
            "              'oversample_power': 0.4,\n",
            "              'oversample_thin': True,\n",
            "              'proposal_scale': 1.9,\n",
            "              'seed': None,\n",
            "              'version': '3.2.2'}}\n"
          ]
        }
      ],
      "source": [
        "pp.pprint(DR6_lensing_only_yaml[\"sampler\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dfc22c89",
      "metadata": {
        "id": "dfc22c89"
      },
      "source": [
        "We also need to update the path to the covariance matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e0baaf78",
      "metadata": {
        "id": "e0baaf78",
        "outputId": "1c0287a5-0f78-4886-e23a-846f9bca7174",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'lcdm_actplanck_baseline_covmat.covmat'\n"
          ]
        }
      ],
      "source": [
        "path_covmat = \"lcdm_actplanck_baseline_covmat.covmat\"\n",
        "DR6_lensing_only_yaml[\"covmat\"] = path_covmat\n",
        "pp.pprint(DR6_lensing_only_yaml[\"covmat\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xacEsOm9fy3n",
      "metadata": {
        "id": "xacEsOm9fy3n"
      },
      "source": [
        "We can also change the location and the prefix to all the files that Cobaya will output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qiaKBpFUfhBt",
      "metadata": {
        "id": "qiaKBpFUfhBt",
        "outputId": "80798f5c-ae62-4468-d8be-dac85bf2e1ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "!mkdir cobaya_output\n",
        "DR6_lensing_only_yaml[\"output\"] = \"cobaya_output/lcdm_actplanck_baseline_notebook\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "MJ5-ppycWpNx",
      "metadata": {
        "id": "MJ5-ppycWpNx"
      },
      "source": [
        "Finally, we will save the dictionary into a new `yaml` file,  and then we will be ready to we are ready to run MCMC with `Cobaya` as the sampling framework and `Cosmopower` as the theory code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DvKM-NtrVa5W",
      "metadata": {
        "id": "DvKM-NtrVa5W"
      },
      "outputs": [],
      "source": [
        "file=open(\"/content/lcdm_actplanck_baseline.classy_szfast_mcmc.yaml\",\"w\")\n",
        "yaml.dump(DR6_lensing_only_yaml,file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39d735af",
      "metadata": {
        "id": "39d735af"
      },
      "source": [
        "# Running the chains"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iiY8jS8GGzrm",
      "metadata": {
        "id": "iiY8jS8GGzrm"
      },
      "source": [
        "We will now run MCMC with the `yaml` file we just edited and the command `cobaya-run`.\n",
        "\n",
        "**This process can take about an hour** in google colab with\n",
        "\n",
        "```\n",
        "Rminus1_stop = 0.01\n",
        "```\n",
        "\n",
        " and we generally recommend this type of analysis should not be done in a notebook.\n",
        " Here, we have chosen to run the code with\n",
        "\n",
        "```\n",
        "Rminus1_stop = 0.1\n",
        "```\n",
        "\n",
        "for speed and convenience.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1df5296",
      "metadata": {
        "id": "a1df5296",
        "outputId": "b5a2355d-921e-4ef0-94c6-2d4b6e5f8b88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[output] Output to be read-from/written-into folder 'cobaya_output', with prefix 'lcdm_actplanck_baseline_notebook'\n",
            "2024-04-17 20:07:30.645208: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "[tensorflow] *WARNING* Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n",
            "2024-04-17 20:07:35.807543: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 22525952 exceeds 10% of free system memory.\n",
            "2024-04-17 20:07:35.945172: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 22525952 exceeds 10% of free system memory.\n",
            "2024-04-17 20:07:35.976654: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 22525952 exceeds 10% of free system memory.\n",
            "2024-04-17 20:07:36.287411: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 22525952 exceeds 10% of free system memory.\n",
            "2024-04-17 20:07:36.374303: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 22525952 exceeds 10% of free system memory.\n",
            "[classy_szfast.classy_sz.classy_sz] Initialized!\n",
            "Loading ACT DR6 lensing likelihood v1.2...\n",
            "/usr/local/lib/python3.10/dist-packages/act_dr6_lenslike/act_dr6_lenslike.py:416: UserWarning: Hartlap correction to cinv: 0.949874686716792\n",
            "  warnings.warn(f\"Hartlap correction to cinv: {hartlap_correction}\")\n",
            "[bao.sdss_dr12_lrg_bao_dmdh] Initialized.\n",
            "[bao.sdss_dr16_lrg_bao_dmdh] Initialized.\n",
            "[bao.sixdf_2011_bao] Initialized.\n",
            "[mcmc] Getting initial point... (this may take a few seconds)\n",
            "[mcmc] Initial point: H0:67.58511, logA:3.311824, n_s:0.9652742, omega_b:0.02245882, omega_cdm:0.1191012\n",
            "[model] Measuring speeds... (this may take a few seconds)\n",
            "[model] Setting measured speeds (per sec): {act_dr6_lenslike.ACTDR6LensLike: 405.0, bao.sdss_dr12_lrg_bao_dmdh: 317.0, bao.sdss_dr16_lrg_bao_dmdh: 1910.0, bao.sdss_dr7_mgs: 2190.0, bao.sixdf_2011_bao: 1080.0, classy_szfast.classy_sz.classy_sz: 4.54}\n",
            "[autoselect_covmat] No cached covmat database present, not usable or not up-to-date. Will be re-created and cached.\n",
            "[autoselect_covmat] *WARNING* No covariance matrix found including at least one of the given parameters\n",
            "[mcmc] Could not automatically find a good covmat. Will generate from parameter info (proposal and prior).\n",
            "[mcmc] Covariance matrix not present. We will start learning the covariance of the proposal earlier: R-1 = 30 (would be 2 if all params loaded).\n",
            "[mcmc] Sampling!\n",
            "[mcmc] Progress @ 2024-04-17 20:07:46 : 1 steps taken, and 0 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:08:46 : 281 steps taken, and 49 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:09:46 : 590 steps taken, and 102 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:10:46 : 880 steps taken, and 151 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:11:46 : 1153 steps taken, and 199 accepted.\n",
            "[mcmc] Learn + convergence test @ 200 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00329146 s (1139 evaluations, 3.74897 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115015 s (1139 evaluations, 1.31003 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.00048828 s (1139 evaluations, 0.556151 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000515457 s (1139 evaluations, 0.587106 s total)\n",
            "   bao.sixdf_2011_bao : 0.000485383 s (1139 evaluations, 0.552852 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.205109 s (1139 evaluations, 233.62 s total)\n",
            "[mcmc]  - Acceptance rate: 0.173\n",
            "[mcmc]  - Convergence of means: R-1 = 5.972379 after 160 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:12:46 : 1420 steps taken, and 344 accepted.\n",
            "[mcmc] Learn + convergence test @ 400 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00323355 s (1495 evaluations, 4.83416 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115337 s (1495 evaluations, 1.72429 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000486507 s (1495 evaluations, 0.727328 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000513378 s (1495 evaluations, 0.7675 s total)\n",
            "   bao.sixdf_2011_bao : 0.000483804 s (1495 evaluations, 0.723287 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.207762 s (1495 evaluations, 310.604 s total)\n",
            "[mcmc]  - Acceptance rate: 0.310\n",
            "[mcmc]  - Convergence of means: R-1 = 10.681096 after 320 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:13:47 : 1694 steps taken, and 505 accepted.\n",
            "[mcmc] Learn + convergence test @ 600 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00320989 s (1873 evaluations, 6.01213 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115944 s (1873 evaluations, 2.17163 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000488576 s (1873 evaluations, 0.915103 s total)\n",
            "   bao.sdss_dr7_mgs : 0.00050774 s (1873 evaluations, 0.950997 s total)\n",
            "   bao.sixdf_2011_bao : 0.000489138 s (1873 evaluations, 0.916155 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.208793 s (1873 evaluations, 391.069 s total)\n",
            "[mcmc]  - Acceptance rate: 0.413\n",
            "[mcmc]  - Convergence of means: R-1 = 2.526085 after 480 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:14:47 : 1970 steps taken, and 632 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:15:47 : 2244 steps taken, and 760 accepted.\n",
            "[mcmc] Learn + convergence test @ 800 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.0031811 s (2320 evaluations, 7.38016 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115428 s (2320 evaluations, 2.67793 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000481985 s (2320 evaluations, 1.1182 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000505309 s (2320 evaluations, 1.17232 s total)\n",
            "   bao.sixdf_2011_bao : 0.000481125 s (2320 evaluations, 1.11621 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.209637 s (2320 evaluations, 486.358 s total)\n",
            "[mcmc]  - Acceptance rate: 0.461\n",
            "[mcmc]  - Convergence of means: R-1 = 0.792020 after 640 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:16:47 : 2521 steps taken, and 878 accepted.\n",
            "[mcmc] Learn + convergence test @ 1000 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00315153 s (2782 evaluations, 8.76754 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00114791 s (2782 evaluations, 3.19348 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000478858 s (2782 evaluations, 1.33218 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000501323 s (2782 evaluations, 1.39468 s total)\n",
            "   bao.sixdf_2011_bao : 0.000475934 s (2782 evaluations, 1.32405 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.209426 s (2782 evaluations, 582.624 s total)\n",
            "[mcmc]  - Acceptance rate: 0.487\n",
            "[mcmc]  - Convergence of means: R-1 = 0.972091 after 800 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:17:47 : 2799 steps taken, and 1001 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:18:47 : 3073 steps taken, and 1118 accepted.\n",
            "[mcmc] Learn + convergence test @ 1200 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00316434 s (3289 evaluations, 10.4075 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115463 s (3289 evaluations, 3.79756 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000482056 s (3289 evaluations, 1.58548 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000507473 s (3289 evaluations, 1.66908 s total)\n",
            "   bao.sixdf_2011_bao : 0.000476164 s (3289 evaluations, 1.5661 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.208765 s (3289 evaluations, 686.629 s total)\n",
            "[mcmc]  - Acceptance rate: 0.462\n",
            "[mcmc]  - Convergence of means: R-1 = 0.605380 after 960 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:19:47 : 3368 steps taken, and 1228 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:20:47 : 3682 steps taken, and 1348 accepted.\n",
            "[mcmc] Learn + convergence test @ 1400 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00315156 s (3799 evaluations, 11.9728 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115418 s (3799 evaluations, 4.38471 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000481281 s (3799 evaluations, 1.82839 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000507546 s (3799 evaluations, 1.92817 s total)\n",
            "   bao.sixdf_2011_bao : 0.000472642 s (3799 evaluations, 1.79557 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.205823 s (3799 evaluations, 781.923 s total)\n",
            "[mcmc]  - Acceptance rate: 0.447\n",
            "[mcmc]  - Convergence of means: R-1 = 0.526504 after 1120 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:21:48 : 3991 steps taken, and 1469 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:22:48 : 4293 steps taken, and 1595 accepted.\n",
            "[mcmc] Learn + convergence test @ 1600 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00313404 s (4301 evaluations, 13.4795 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00115318 s (4301 evaluations, 4.95983 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.00048216 s (4301 evaluations, 2.07377 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000505118 s (4301 evaluations, 2.17251 s total)\n",
            "   bao.sixdf_2011_bao : 0.000469949 s (4301 evaluations, 2.02125 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.204559 s (4301 evaluations, 879.808 s total)\n",
            "[mcmc]  - Acceptance rate: 0.434\n",
            "[mcmc]  - Convergence of means: R-1 = 0.316686 after 1280 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:23:48 : 4563 steps taken, and 1695 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:24:48 : 4845 steps taken, and 1797 accepted.\n",
            "[mcmc] Learn + convergence test @ 1800 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00311702 s (4835 evaluations, 15.0708 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00114931 s (4835 evaluations, 5.55693 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000482432 s (4835 evaluations, 2.33256 s total)\n",
            "   bao.sdss_dr7_mgs : 0.00050614 s (4835 evaluations, 2.44719 s total)\n",
            "   bao.sixdf_2011_bao : 0.00046722 s (4835 evaluations, 2.25901 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.205113 s (4835 evaluations, 991.722 s total)\n",
            "[mcmc]  - Acceptance rate: 0.423\n",
            "[mcmc]  - Convergence of means: R-1 = 0.353159 after 1440 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:25:48 : 5127 steps taken, and 1897 accepted.\n",
            "[mcmc] Learn + convergence test @ 2000 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00310554 s (5365 evaluations, 16.6612 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00114519 s (5365 evaluations, 6.14393 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000479011 s (5365 evaluations, 2.56989 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000504432 s (5365 evaluations, 2.70628 s total)\n",
            "   bao.sixdf_2011_bao : 0.000466944 s (5365 evaluations, 2.50515 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.205515 s (5365 evaluations, 1102.59 s total)\n",
            "[mcmc]  - Acceptance rate: 0.413\n",
            "[mcmc]  - Convergence of means: R-1 = 0.245745 after 1600 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:26:49 : 5406 steps taken, and 2008 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:27:49 : 5681 steps taken, and 2105 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:28:49 : 5957 steps taken, and 2186 accepted.\n",
            "[mcmc] Learn + convergence test @ 2200 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00310129 s (5995 evaluations, 18.5923 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.0011455 s (5995 evaluations, 6.86727 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000481032 s (5995 evaluations, 2.88379 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000509008 s (5995 evaluations, 3.0515 s total)\n",
            "   bao.sixdf_2011_bao : 0.000469551 s (5995 evaluations, 2.81496 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.205774 s (5995 evaluations, 1233.62 s total)\n",
            "[mcmc]  - Acceptance rate: 0.397\n",
            "[mcmc]  - Convergence of means: R-1 = 0.174603 after 1760 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:29:49 : 6281 steps taken, and 2304 accepted.\n",
            "[mcmc] Learn + convergence test @ 2400 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00308481 s (6540 evaluations, 20.1746 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.0011419 s (6540 evaluations, 7.46801 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000479067 s (6540 evaluations, 3.1331 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000507195 s (6540 evaluations, 3.31706 s total)\n",
            "   bao.sixdf_2011_bao : 0.000471983 s (6540 evaluations, 3.08677 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.20377 s (6540 evaluations, 1332.66 s total)\n",
            "[mcmc]  - Acceptance rate: 0.391\n",
            "[mcmc]  - Convergence of means: R-1 = 0.103430 after 1920 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:30:49 : 6598 steps taken, and 2418 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:31:49 : 6910 steps taken, and 2542 accepted.\n",
            "[mcmc] Learn + convergence test @ 2600 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00307034 s (7048 evaluations, 21.6398 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00114375 s (7048 evaluations, 8.06116 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.00047855 s (7048 evaluations, 3.37282 s total)\n",
            "   bao.sdss_dr7_mgs : 0.00050586 s (7048 evaluations, 3.5653 s total)\n",
            "   bao.sixdf_2011_bao : 0.000471589 s (7048 evaluations, 3.32376 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.202288 s (7048 evaluations, 1425.72 s total)\n",
            "[mcmc]  - Acceptance rate: 0.390\n",
            "[mcmc]  - Convergence of means: R-1 = 0.056721 after 2080 accepted steps\n",
            "[mcmc]  - Updated covariance matrix of proposal pdf.\n",
            "[mcmc] Progress @ 2024-04-17 20:32:49 : 7237 steps taken, and 2669 accepted.\n",
            "[mcmc] Progress @ 2024-04-17 20:33:49 : 7555 steps taken, and 2797 accepted.\n",
            "[mcmc] Learn + convergence test @ 2800 samples accepted.\n",
            "[likelihoodcollection] Average computation time:\n",
            "   act_dr6_lenslike.ACTDR6LensLike : 0.00305622 s (7556 evaluations, 23.0928 s total)\n",
            "   bao.sdss_dr12_lrg_bao_dmdh : 0.00114023 s (7556 evaluations, 8.61558 s total)\n",
            "   bao.sdss_dr16_lrg_bao_dmdh : 0.000477233 s (7556 evaluations, 3.60597 s total)\n",
            "   bao.sdss_dr7_mgs : 0.000505239 s (7556 evaluations, 3.81759 s total)\n",
            "   bao.sixdf_2011_bao : 0.000472343 s (7556 evaluations, 3.56902 s total)\n",
            "[theorycollection] Average computation time:\n",
            "   classy_szfast.classy_sz.classy_sz : 0.200779 s (7556 evaluations, 1517.09 s total)\n",
            "[mcmc]  - Acceptance rate: 0.388\n",
            "[mcmc]  - Convergence of means: R-1 = 0.038608 after 2240 accepted steps\n",
            "[mcmc]  - Convergence of bounds: R-1 = 0.159247 after 2800 accepted steps\n",
            "[mcmc] The run has converged!\n",
            "[mcmc] Sampling complete after 2800 accepted steps.\n",
            "[act_dr6_lenslike.actdr6lenslike] Average evaluation time for act_dr6_lenslike.ACTDR6LensLike: 0.00305622 s  (7556 evaluations)\n",
            "[bao.sdss_dr12_lrg_bao_dmdh] Average evaluation time for bao.sdss_dr12_lrg_bao_dmdh: 0.00114023 s  (7556 evaluations)\n",
            "[bao.sdss_dr16_lrg_bao_dmdh] Average evaluation time for bao.sdss_dr16_lrg_bao_dmdh: 0.000477233 s  (7556 evaluations)\n",
            "[bao.sdss_dr7_mgs] Average evaluation time for bao.sdss_dr7_mgs: 0.000505239 s  (7556 evaluations)\n",
            "[bao.sixdf_2011_bao] Average evaluation time for bao.sixdf_2011_bao: 0.000472343 s  (7556 evaluations)\n",
            "[classy_szfast.classy_sz.classy_sz] Average evaluation time for classy_szfast.classy_sz.classy_sz: 0.200779 s  (7556 evaluations)\n",
            "CPU times: user 14.6 s, sys: 1.79 s, total: 16.4 s\n",
            "Wall time: 26min 42s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "t1 = time.time()\n",
        "!cobaya-run /content/lcdm_actplanck_baseline.classy_szfast_mcmc.yaml -f\n",
        "\n",
        "t2 = time.time()\n",
        "\n",
        "delta_t = t2 - t1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4fca1377",
      "metadata": {
        "id": "4fca1377",
        "outputId": "93103202-a000-4bc0-e4c5-6fbc740ae974",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.70418249766032"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "minutes_run = delta_t/60\n",
        "minutes_run"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Zx4j7Io8Tqp6",
      "metadata": {
        "id": "Zx4j7Io8Tqp6"
      },
      "source": [
        "Once the above cells are done, you've succesfully run your MCMC chains using the `Cosmopower` emulator as the theory code. Although this might have taken a long time in a notebook, if you were to use this code on a few more cores it would be an incredibly fast way of deriving your parameter constraints."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "930f71f8",
      "metadata": {
        "id": "930f71f8"
      },
      "source": [
        "# Plotting\n",
        "\n",
        "Now that we've run our chains, we can plot the parameter contours and take a look at them!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91fe3c4b",
      "metadata": {
        "id": "91fe3c4b",
        "outputId": "3c1a3b30-bf99-42f0-947c-e356f943c411",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:outlier fraction 0.0066326530612244895 \n",
            "WARNING:root:outlier fraction 0.0066326530612244895 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 21 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "path_to_chains_dir = \"/content/cobaya_output\"\n",
        "final_chain_dir_list = []\n",
        "final_chain_dir_list.append(path_to_chains_dir +'/lcdm_actplanck_baseline_notebook')\n",
        "i=0\n",
        "readsamps = loadMCSamples(final_chain_dir_list[i],settings={'ignore_rows':0.3})\n",
        "p = readsamps.getParams()\n",
        "readsamps.addDerived(p.omega_b, name='ombh2', label='\\Omega_\\mathrm{b} h^2')\n",
        "readsamps.addDerived(p.omega_cdm, name='omch2', label='\\Omega_\\mathrm{c} h^2')\n",
        "#eadsamps.addDerived(p.n_s, name='n_s', label='n_\\mathrm{s}')\n",
        "\n",
        "g = plots.getSubplotPlotter()\n",
        "g.settings.fig_width_inch = 10\n",
        "\n",
        "g.settings.axes_fontsize = 10\n",
        "g.settings.lab_fontsize =13\n",
        "\n",
        "g.settings.legend_fontsize = 14\n",
        "g.settings.alpha_filled_add=0.1\n",
        "g.settings.colorbar_label_pad = 20.\n",
        "g.settings.figure_legend_frame = False\n",
        "\n",
        "g.settings.title_limit=0 #uncomment if you want to display marg stats.\n",
        "\n",
        "g.triangle_plot(readsamps,\n",
        "    ['ombh2','logA','n_s','H0','sigma8','S825'],\n",
        "    filled=[True,False],\n",
        "    legend_labels=[ 'This notebook (Cosmopower)'],\n",
        "    legend_loc='upper right',\n",
        "    colors = ['blue','red','green','red'],\n",
        "    line_args=[{'lw':'1','color':'blue'},{'lw':'1','color':'red'},{'lw':'1','color':'green'},{'lw':'1','color':'r'}])\n",
        "\n",
        "#g.export(path_to_chains_dir +'/DR6_lensing_only_contours_classy_szfast.pdf')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aV_PdhOQUlrQ",
      "metadata": {
        "id": "aV_PdhOQUlrQ"
      },
      "source": [
        "# Comparison to Madhavacheril et al. (2023).\n",
        "\n",
        "To compare the results we obtain in this notebook with the ones in [Madhavacheril et al. (2023)](https://arxiv.org/abs/2304.05203), we can download the chains and overplot them with this notebook's chains."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "LTlO5noyuBrQ",
      "metadata": {
        "id": "LTlO5noyuBrQ"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%cd /content/\n",
        "!mkdir dr6-lensing/\n",
        "%cd dr6-lensing\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.1.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.2.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.3.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.4.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.5.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.6.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.7.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.8.txt\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.checkpoint\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.covmat\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.input.yaml\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.progress\n",
        "!wget https://portal.nersc.gov/project/act/act_dr6_lensing/chains/dr6-lensing/lcdm_actplanck_baseline_167926925701.updated.yaml\n",
        "\n",
        "%cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "lLER37I-hyIC",
      "metadata": {
        "id": "lLER37I-hyIC",
        "outputId": "ba07b6d6-e124-4f54-bcb2-9ceb75c48b40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 28 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "path_to_dr6_chains_dir = \"/content/dr6-lensing\"\n",
        "final_chain_dr6_dir_list = []\n",
        "i=0\n",
        "final_chain_dr6_dir_list.append(path_to_dr6_chains_dir +'/lcdm_actplanck_baseline_167926925701')\n",
        "readsamps_dr6 = loadMCSamples(final_chain_dr6_dir_list[i],settings={'ignore_rows':0.3})\n",
        "all_samples_dr6 = []\n",
        "samples_dr6 = readsamps_dr6\n",
        "all_samples_dr6.append(samples_dr6)\n",
        "\n",
        "g = plots.getSubplotPlotter()\n",
        "g.settings.fig_width_inch = 10\n",
        "\n",
        "g.settings.axes_fontsize = 10\n",
        "g.settings.lab_fontsize =13\n",
        "\n",
        "g.settings.legend_fontsize = 14\n",
        "g.settings.alpha_filled_add=0.1\n",
        "g.settings.colorbar_label_pad = 20.\n",
        "g.settings.figure_legend_frame = False\n",
        "\n",
        "g.settings.title_limit=0 #uncomment if you want to display marg stats.\n",
        "\n",
        "g.triangle_plot([readsamps,readsamps_dr6],\n",
        "    ['omch2','ombh2','logA','ns','H0','sigma8','S825'],\n",
        "    filled=[True,False],\n",
        "    legend_labels=['This notebook','DR6 results (Madhavacheril et al 2023)'],\n",
        "    legend_loc='upper right',\n",
        "    colors = ['blue','red'],\n",
        "    line_args=[{'lw':'1','color':'blue'},{'lw':'2','color':'red'},{'lw':'1','color':'green'},{'lw':'1','color':'r'}])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5O0SVjWN_1SC",
      "metadata": {
        "id": "5O0SVjWN_1SC"
      },
      "source": [
        "# Related resources:\n",
        "\n",
        "For more examples of how to use ACT data consider using:\n",
        "- The DR6 Lensing notebooks available on [github](https://github.com/ACTCollaboration/DR6_Notebooks)\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": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
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