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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j_OYjrski6wJ"
+ },
+ "source": [
+ "# MIT_8.S50 Project 3: CMB analysis\n",
+ "\n",
+ "Author: Juan Mena-Parra, Kiyoshi Masui\n",
+ "\n",
+ "Date: January 12, 2020\n",
+ "\n",
+ "In this project, you will perform an end-to-end analysis for a cosmic microwave background (CMB) survey. Input data products are provided with this notebook. \n",
+ "\n",
+ "A reference for this project is Chapter 14 of Modern Cosmology,\n",
+ "2nd Ed. by Scott Dodelson and\n",
+ "Fabian Schmidt (hereafter D&S).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JhczFWFNi6kr"
+ },
+ "source": [
+ "## Preliminaries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KZDITo7i2i9Y"
+ },
+ "source": [
+ "Install required modules:\n",
+ "\n",
+ "- [PICO](https://github.com/marius311/pypico) for fast cosmological simulations\n",
+ "- [emcee](https://emcee.readthedocs.io/en/stable/) for MCMC\n",
+ "- [corner](https://corner.readthedocs.io/en/latest/) to make corner plots\n",
+ "- [pyFFTW](https://pypi.org/project/pyFFTW/) to perform FFTs faster\n",
+ "\n",
+ "NOTE: If you are running a Jupyter notebook, you do not need this step, but make sure these modules are installed in your python environment\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Nl2QrxNGUMWy",
+ "outputId": "5d12b5e4-de65-423b-9efc-6a949f2da1a0"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/marius311/pypico\n",
+ " Cloning https://github.com/marius311/pypico to /tmp/pip-req-build-4pniz7no\n",
+ " Running command git clone -q https://github.com/marius311/pypico /tmp/pip-req-build-4pniz7no\n",
+ "Building wheels for collected packages: pypico\n",
+ " Building wheel for pypico (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for pypico: filename=pypico-4.0.0-cp37-cp37m-linux_x86_64.whl size=207448 sha256=d11c354988c67bcc5d342d8c626f7c656a460ac0e42ebea7bfde2c3619743ff5\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-quwphwo3/wheels/ba/12/25/9a14a920251e686cf59dd8880f461312a4d62478703937e141\n",
+ "Successfully built pypico\n",
+ "Installing collected packages: pypico\n",
+ "Successfully installed pypico-4.0.0\n",
+ "--2022-01-25 02:47:48-- https://github.com/marius311/pypico-trainer/releases/download/jcset_py3/jcset_py3.dat\n",
+ "Resolving github.com (github.com)... 52.69.186.44\n",
+ "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n",
+ "HTTP request sent, awaiting response... 302 Found\n",
+ "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/6898955/d9becb80-7899-11e9-893e-98e149190ff5?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220125%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220125T024749Z&X-Amz-Expires=300&X-Amz-Signature=2dbb118adf914a7f1c56c0536e858b005cad11557cf253f3225da731b8cf0666&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=6898955&response-content-disposition=attachment%3B%20filename%3Djcset_py3.dat&response-content-type=application%2Foctet-stream [following]\n",
+ "--2022-01-25 02:47:49-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/6898955/d9becb80-7899-11e9-893e-98e149190ff5?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220125%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220125T024749Z&X-Amz-Expires=300&X-Amz-Signature=2dbb118adf914a7f1c56c0536e858b005cad11557cf253f3225da731b8cf0666&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=6898955&response-content-disposition=attachment%3B%20filename%3Djcset_py3.dat&response-content-type=application%2Foctet-stream\n",
+ "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 99120769 (95M) [application/octet-stream]\n",
+ "Saving to: ‘jcset_py3.dat’\n",
+ "\n",
+ "jcset_py3.dat 100%[===================>] 94.53M 10.9MB/s in 7.4s \n",
+ "\n",
+ "2022-01-25 02:47:57 (12.8 MB/s) - ‘jcset_py3.dat’ saved [99120769/99120769]\n",
+ "\n",
+ "Collecting emcee\n",
+ " Downloading emcee-3.1.1-py2.py3-none-any.whl (45 kB)\n",
+ "\u001b[K |████████████████████████████████| 45 kB 3.3 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from emcee) (1.19.5)\n",
+ "Installing collected packages: emcee\n",
+ "Successfully installed emcee-3.1.1\n",
+ "Collecting corner\n",
+ " Downloading corner-2.2.1-py3-none-any.whl (15 kB)\n",
+ "Requirement already satisfied: matplotlib>=2.1 in /usr/local/lib/python3.7/dist-packages (from corner) (3.2.2)\n",
+ "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1->corner) (2.8.2)\n",
+ "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1->corner) (3.0.6)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1->corner) (0.11.0)\n",
+ "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1->corner) (1.19.5)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1->corner) (1.3.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1->corner) (1.15.0)\n",
+ "Installing collected packages: corner\n",
+ "Successfully installed corner-2.2.1\n",
+ "Collecting pyfftw\n",
+ " Downloading pyFFTW-0.13.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.7 MB)\n",
+ "\u001b[K |████████████████████████████████| 1.7 MB 4.1 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16 in /usr/local/lib/python3.7/dist-packages (from pyfftw) (1.19.5)\n",
+ "Installing collected packages: pyfftw\n",
+ "Successfully installed pyfftw-0.13.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install modules. \n",
+ "!pip install git+https://github.com/marius311/pypico\n",
+ "!wget https://github.com/marius311/pypico-trainer/releases/download/jcset_py3/jcset_py3.dat\n",
+ "!pip install emcee\n",
+ "!pip install corner\n",
+ "!pip install pyfftw"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IWO57p7s4aFg"
+ },
+ "source": [
+ "Import modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "N0CplWX_UQRc"
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "from matplotlib.pyplot import *\n",
+ "import datetime\n",
+ "import scipy\n",
+ "from scipy import linalg as LA\n",
+ "import timeit\n",
+ "import pypico\n",
+ "import emcee\n",
+ "import corner\n",
+ "# These imports speed up FFTs, which makes a big difference to the map-maker runtime\n",
+ "# compared to just using the numpy or scipy fft.\n",
+ "import pyfftw\n",
+ "import pyfftw.interfaces.numpy_fft as fft\n",
+ "import multiprocessing\n",
+ "pyfftw.interfaces.cache.enable()\n",
+ "pyfftw.config.NUM_THREADS = multiprocessing.cpu_count()\n",
+ "\n",
+ "from IPython.core.display import Image \n",
+ "\n",
+ "rcParams['figure.figsize'] = (20.0, 8.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RUpdzndEs2mc"
+ },
+ "source": [
+ "## Colab Notebook\n",
+ "\n",
+ "In the first part of the project, you will use the TOD to create a CMB map that contains more than 16K pixels, which requires operating with $\\sim$16K x 16K matrices. Inverting such a large matrix is doable on a laptop CPU, but may take some time. It turns out that graphics processing units (GPUs) are very efficient for this task, and you can create python notebooks and connect to a GPU to perform computations using [google Colab](https://colab.research.google.com/notebooks/intro.ipynb). This is a Colab notebook. However, you should be able to run it on your laptop as a Jupyter notebook with minor modifications.\n",
+ "\n",
+ "## Colab Setup\n",
+ "\n",
+ "Mounting your Google drive. \n",
+ "\n",
+ "If you are using a Colab notebook and saved the provided dataset in your Google drive, you need to mount the Drive on your runtime. First, click on the \"file browser\" button (left menu) and then click on the 'Mount Drive' button.\n",
+ "\n",
+ "As an alternative, just run"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KEi4u01GLuE-",
+ "outputId": "337013ff-382a-4de4-a5e6-54ab68cd73c1"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ],
+ "source": [
+ "# You need this line if you cannot mount your Drive from the file browser, eg, if it is a shared notebook\n",
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yOPYSGtl6bzx"
+ },
+ "source": [
+ "To enable the GPU first:\n",
+ "\n",
+ "- Navigate to Edit→Notebook Settings\n",
+ "- select GPU from the Hardware Accelerator drop-down\n",
+ "\n",
+ "Next, confirm that you can connect to the GPU with tensorflow:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zVKxHwN839vn",
+ "outputId": "8e8dc2a1-f63f-48f0-d3cf-75aab3b6d1a7"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Found GPU at: /device:GPU:0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Enabling GPU\n",
+ "%tensorflow_version 2.x\n",
+ "import tensorflow as tf\n",
+ "device_name = tf.test.gpu_device_name()\n",
+ "if device_name != '/device:GPU:0':\n",
+ " raise SystemError('GPU device not found')\n",
+ "print('Found GPU at: {}'.format(device_name))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YGOtYw2p7LB6"
+ },
+ "source": [
+ "NOTE: In this notebook we use `tensorflow` for operations like matrix multiplications and matrix inversion. If you are using a Jupyter notebook just replace `tensorflow` functions by the respective `numpy` or `scipy` versions\n",
+ "\n",
+ "NOTE: [google Colab](https://colab.research.google.com/notebooks/intro.ipynb) has GPU usage limits in order to provide access to computational resources for free. Usage limits fluctuate, but if you use the GPU too much your access to this resource may be temporarily restricted (check [google Colab FAQ](https://research.google.com/colaboratory/faq.html)). Thus, it is recommended to enable the GPU only when you plan to use it and disable it otherwise."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YaPUIT8PP0NM"
+ },
+ "source": [
+ "## Load data\n",
+ "\n",
+ "Input data products include the time-ordered data (TOD) $d_t$ in units of $\\mu K$, and time-dependent telescope pointing locations\n",
+ "$(x_t, y_t)$ in radians. The time difference between each sample is 1 second."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "CixjhjYhdP5Q",
+ "outputId": "d28f718b-ef8e-4105-ea64-d73d29c03aad"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[('test_signal', (65536,)),\n",
+ " ('test_white_noise', (65536,)),\n",
+ " ('test_red_noise', (65536,)),\n",
+ " ('test_x', (65536,)),\n",
+ " ('test_y', (65536,)),\n",
+ " ('data_small_1', (262144,)),\n",
+ " ('x_small_1', (262144,)),\n",
+ " ('y_small_1', (262144,)),\n",
+ " ('data_small_2', (262144,)),\n",
+ " ('x_small_2', (262144,)),\n",
+ " ('y_small_2', (262144,)),\n",
+ " ('data_large_1', (1048576,)),\n",
+ " ('x_large_1', (1048576,)),\n",
+ " ('y_large_1', (1048576,)),\n",
+ " ('data_large_2', (1048576,)),\n",
+ " ('x_large_2', (1048576,)),\n",
+ " ('y_large_2', (1048576,))]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ],
+ "source": [
+ "# You can add a shortcut to the data to your drive using the following link\n",
+ "# https://drive.google.com/file/d/1-5UeZU3RK_3kjNbwXwHl7Qkjk1Rixsnx/\n",
+ "# Or you can download the data from the link and upload it to drive manually\n",
+ "cmb_data_dict = np.load('drive/MyDrive/Colab Notebooks/cmb_analysis_pset_data.npz')\n",
+ "[(k, cmb_data_dict[k].shape) for k in cmb_data_dict.keys()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cJN_wI6YRFQf"
+ },
+ "source": [
+ "You were provided with three versions of the input data. The first is a smaller test dataset, covering a smaller area of the sky, and with the data split into contributions from the signal, and two types of noise (which can be summed to produce realistic noisy data). The test dataset can be used to test and debug your code quickly. The other datasets are the \"real\" data---signal and noise are not separated. One of these is 4 times larger than the other (and 16 times larger than the testing dataset) and processing it will be a computational challenge. However, it also has more statistical power owing to its larger sky coverage. For the larger datasets we were given two separate observations (different seasons) of the same patch of the sky, so we can generate two separate maps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ib6Aw4V78oWZ"
+ },
+ "source": [
+ "## 1. Map-making\n",
+ "\n",
+ "In the map-making, step you convert time-order data, $d_t$ to an estimate from\n",
+ "the signal map $\\hat s_i$.\n",
+ "You will implement the algorithm derived in D&S Equation 14.29. The algorithm\n",
+ "is actually just linear least-squares, but you are solving for thousands of\n",
+ "parameters (the map pixel values) with millions of input data points.\n",
+ "You are provided with the time-ordered data and the time-dependent pointing locations\n",
+ "$(x_t, y_t)$ in radians. From the later you will construct the pointing\n",
+ "operator $P_{ti}$. Your map $\\hat s_i$ will be on a Cartesian grid with pixel\n",
+ "widths of 0.0015707 radians. For the `test` dataset the map will be $32\\times 32$ pixels, for the `small` dataset the map will be $128\\times 32$ pixels, and for the `large` dataset the map will be $256\\times 64$ pixels.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TzB8RmA6RAYV"
+ },
+ "outputs": [],
+ "source": [
+ "pixel_width = 0.0015707 # radians\n",
+ "Nx_test, Ny_test = 32, 32 # Test map size in x and y direction\n",
+ "Nx_small, Ny_small = 128, 32\n",
+ "Nx_large, Ny_large = 256, 64\n",
+ "Ts = 1. # Time difference between samples in seconds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "o9U97OCVQwXF"
+ },
+ "source": [
+ "The telescope pointing locations $(x_t, y_t)$ are in radians so we need to convert them to sky pixel indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-gAZYr0ZQoBV"
+ },
+ "outputs": [],
+ "source": [
+ "x_test = np.round(cmb_data_dict['test_x']/pixel_width).astype(int) \n",
+ "y_test = np.round(cmb_data_dict['test_y']/pixel_width).astype(int) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The two lines of code above round the (x,y) float coordinates to the pixels interger coordinates. "
+ ],
+ "metadata": {
+ "id": "vKYkcocNJQlP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lnMPWAm3Hw-1"
+ },
+ "source": [
+ "**QUESTION**: What are the two lines of code above exactly doing? What approximations on the telescope pointing are you making when you convert pointing locations to pixels in this way?\n",
+ "\n",
+ "Let's plot the test data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 574
+ },
+ "id": "bV5IXLyPP5sw",
+ "outputId": "ff9bd173-bd41-4f4e-cd75-8ca6d803f673"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "hf = figure(num=1, figsize=(10, 8))\n",
+ "matplotlib.rcParams.update({'font.size': 15})\n",
+ "subplot(311)\n",
+ "for k in ['test_white_noise', 'test_signal', 'test_red_noise']:\n",
+ " semilogy(abs(cmb_data_dict[k]), label=k)\n",
+ "#xlabel('Time (s)')\n",
+ "ylabel('$\\\\mu$K')\n",
+ "title('Time ordered test data')\n",
+ "legend()\n",
+ "\n",
+ "subplot(312)\n",
+ "plot(x_test, label='x')\n",
+ "plot(y_test, label='y')\n",
+ "ylabel('Sky pixel index')\n",
+ "title('Pointing. Test data')\n",
+ "legend()\n",
+ "\n",
+ "subplot(313)\n",
+ "plot(x_test[:5000], '.', label='x')\n",
+ "plot(y_test[:5000], '.', label='y')\n",
+ "ylabel('Sky pixel index')\n",
+ "xlabel('Time(s)')\n",
+ "title('Pointing. Test data. Zoom in')\n",
+ "tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uWYY5896-0Ms"
+ },
+ "source": [
+ "ON NOTATION: for objects in map space we use $i$ or $j$ to denote the index of\n",
+ "the pixel. However our pixels lie on a 2D grid, so it is convenient to give each\n",
+ "pixel a pair of indices for their $x$ and $y$ locations. This is how the\n",
+ "code snippet we have provided below implements map space. This creates a bit of a\n",
+ "disconnect between the algebra, and the implementation in the code.\n",
+ "You can think of the index $i$\n",
+ "as being a serialized/flattened version of 2D map array. Or, you can think of\n",
+ "$i$ as being a tuple with two components $(i_x, i_y)$. In either case, in the\n",
+ "code, the map is a 2D array $s_{(i_x, i_y)}$, and objects like the noise\n",
+ "covariance matrix are 4D arrays $C^N_{(i_x, i_y),(j_x, j_y)}$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s06nrM8fREhy"
+ },
+ "source": [
+ "### 1.1 Naive map maker\n",
+ "\n",
+ "A conceptually simple map-making algorithm is to find all times for which the\n",
+ "telescope is pointed at a given pixel, average the data for all these times\n",
+ "together, and repeat for all pixels. Implement this algorithm from scratch, without worrying\n",
+ "too much about efficiency (since you will re-implement it later) and use\n",
+ "it to make a map for \n",
+ "\n",
+ "1. the test data with no noise, and\n",
+ "2. the test data with white noise added.\n",
+ "\n",
+ "**QUESTION**: How well does this work when including red noise as well? Why? "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "hf = figure(num=1, figsize=(10, 8))\n",
+ "matplotlib.rcParams.update({'font.size': 15})\n",
+ "subplot(311)\n",
+ "for k in ['test_white_noise', 'test_signal', 'test_red_noise']:\n",
+ " semilogy(abs(cmb_data_dict[k]), label=k)\n",
+ "#xlabel('Time (s)')\n",
+ "ylabel('$\\\\mu$K')\n",
+ "title('Time ordered test data')\n",
+ "legend()\n",
+ "\n",
+ "subplot(312)\n",
+ "plot(x_test, label='x')\n",
+ "plot(y_test, label='y')\n",
+ "ylabel('Sky pixel index')\n",
+ "title('Pointing. Test data')\n",
+ "legend()\n",
+ "\n",
+ "subplot(313)\n",
+ "plot(x_test[:5000], '.', label='x')\n",
+ "plot(y_test[:5000], '.', label='y')\n",
+ "ylabel('Sky pixel index')\n",
+ "xlabel('Time(s)')\n",
+ "title('Pointing. Test data. Zoom in')\n",
+ "tight_layout()"
+ ],
+ "metadata": {
+ "id": "ib-7lKxfIUr1"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M31jwzgwCZbm"
+ },
+ "source": [
+ "### 1.2 Operators\n",
+ "\n",
+ "As already discussed in class, the optimal map estimate for the realistic noisy dataset can be found with\n",
+ "\n",
+ "\\begin{equation}\n",
+ "\\hat{s} = C_N P^T N^{-1}d, \\hspace{0.5in} C_N=(P^T N^{-1}P)^{-1}\n",
+ "\\end{equation}\n",
+ "\n",
+ "where $N$ is the noise covariance.\n",
+ "\n",
+ "The challenge in the map-making is not conceptually understanding the\n",
+ "algorithm, as it can be\n",
+ "derived in just over a page in Section 14.3 of D&S, and the equation above is just one line of code, where $\\hat{s}$ is just the serialized/flattened version of the 2D sky map. The issue is computational and the\n",
+ "sheer size of the matrices involved. CMB experiments typically record millions\n",
+ "of TOD points. The noise matrix $N_{t t'}$ thus contains trillions\n",
+ "of elements ($n_{\\rm side}\\sim 10^6$). Even if you could compute all of the elements and store the\n",
+ "matrix, inverting the matrix costs of order $n_{\\rm side}^3$ which is not doable\n",
+ "even on a computer cluster. Similarly, if the map has $10^4$ pixels, the\n",
+ "pointing matrix has $\\sim 10^{11}$ elements.\n",
+ "\n",
+ "The key is to notice that we do not actually need $N_{tt'}$ or $(N^{-1})_{tt'}$, but\n",
+ "rather $(N^{-1})_{tt'}$ *multiplied by another vector or matrix.*\n",
+ "That is, you should think of $(N^{-1})_{tt'}$ as an operator that inverse\n",
+ "noise-weights the data, rather than a matrix. Similarly $P_{ti}$ is an operator\n",
+ "that converts a map to time-ordered data (when summing over $i$), or\n",
+ "accumulates TOD into\n",
+ "map space (when summing over $t$). Both the TOD accumulation into map space and the computation of $C_N$ have a fairly\n",
+ "efficient implementation in `NoisePointingModel` class provided\n",
+ "below, which you should make sure you understand.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eLpDzK0IfMHH"
+ },
+ "outputs": [],
+ "source": [
+ "class NoisePointingModel:\n",
+ " \"\"\"Represents the pointing operator and noise matrix.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " x : 1D array of data length\n",
+ " x pixel index (will be rounded to an integer). Values must be between 0 and nx - 1.\n",
+ " y : 1D array of data length\n",
+ " y pixel index (will be rounded to an integer). Values must be between 0 and ny - 1.\n",
+ " nx : int\n",
+ " Map size in x direction\n",
+ " ny : int\n",
+ " Map size in y direction\n",
+ " noise_spec: 1D array\n",
+ " Noise power spectrum. Length should be the same as `fft.rfft(data)`. Entries\n",
+ " should be an estimate of < abs(rfft(data))**2 > / len(data).\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, x, y, nx, ny, noise_spec):\n",
+ " self._x = np.round(x).astype(int)\n",
+ " self._y = np.round(y).astype(int)\n",
+ " self._nx = nx\n",
+ " self._ny = ny\n",
+ " self._flat_inds = self._y + ny * self._x\n",
+ " self._noise_spec = noise_spec.copy()\n",
+ " # Replace the 0-frequency (mean mode) with twice the fundamental (frequency 1).\n",
+ " self._noise_spec[0] = noise_spec[1] * 2\n",
+ "\n",
+ " def apply_noise_weights(self, data):\n",
+ " \"\"\"Noise weight a time-order-data array.\n",
+ "\n",
+ " Performs the operation $N^{-1} d$.\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " # Note that I don't need unitary normalizations for the FFTs since the normalization\n",
+ " # factors cancel between the forward and inverse FFT. However, the noise power\n",
+ " # spectrum must be normalized to be that of the unitary case.\n",
+ " fdata = fft.rfft(data)\n",
+ " fdata /= self._noise_spec\n",
+ " #fdata[0] = 0\n",
+ " return fft.irfft(fdata)\n",
+ "\n",
+ " def grid_data(self, data, out):\n",
+ " \"\"\"Accumulate time-order data into a map.\n",
+ "\n",
+ " Performs the operation $P^{T} d$.\n",
+ "\n",
+ " For performance reasons, output must be preallocated.\n",
+ " It should be an array with shape (nx, ny).\n",
+ "\n",
+ " 80% of the the runtime of the function `noise_ing_to_map_domain`\n",
+ " is calling this function but I can't think of a simple way to\n",
+ " speed it up.\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " #out = np.zeros((self._nx, self._ny), dtype=float)\n",
+ " np.add.at(out, (self._x, self._y), data)\n",
+ " return out\n",
+ "\n",
+ " def map_noise_inv(self):\n",
+ " \"\"\"Calculate the map noise inverse matrix.\n",
+ "\n",
+ " Performs the operation $P^T N^{-1} P$.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " CN : 4D array with shape (nx, ny, nx, ny)\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " nx = self._nx\n",
+ " ny = self._ny\n",
+ " out = np.zeros((nx, ny, nx, ny), dtype=float)\n",
+ " colP = pyfftw.empty_aligned(len(self._x), dtype=float)\n",
+ " for ii in range(nx):\n",
+ " print(\"x-index\", ii)\n",
+ " for jj in range(ny):\n",
+ " #t0 = time.time()\n",
+ " colP[:] = np.logical_and(self._x == ii, self._y == jj)\n",
+ " #t1 = time.time() - t0\n",
+ " colP[:] = self.apply_noise_weights(colP)\n",
+ " #t2 = time.time() - t0\n",
+ " self.grid_data(colP, out[ii, jj])\n",
+ " #t3 = time.time() - t0\n",
+ " #print(t1, t2, t3)\n",
+ " return out"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hujxcSWJDpGX"
+ },
+ "source": [
+ "Re-implement the naive map-maker from above using the provided implementation of\n",
+ "the pointing operator. \n",
+ "\n",
+ "**QUESTION**: Is this implementation more or less efficient than your\n",
+ "first implementation?\n",
+ "\n",
+ "Applying $(N^{-1})_{tt'}$ to noise-weight the data is more\n",
+ "difficult and being able to do so efficiently depends on the noise model. We\n",
+ "will assume that the noise $\\eta_t$ is Gaussian and stationary, \n",
+ "which means\n",
+ "that $N_{tt'} \\equiv \\langle\\eta_t \\eta_t'\\rangle$ depends only on $t - t'$.\n",
+ "A key property of\n",
+ "stationary Gaussian random fields is that they are uncorrelated in Fourier\n",
+ "space.\n",
+ "This means that if we perform a *unitary* Fourier transform (this is the `numpy`\n",
+ "FFT divided by a factor $\\sqrt{N_{samples}}$ where $N_{samples}$ is the number of time samples) of $d_t$ into $d_\\omega$ then\n",
+ "\\begin{equation}\n",
+ " N_{\\omega \\omega'} \\equiv \\langle \\eta_\\omega \\eta_\\omega'\\rangle =\n",
+ " \\delta_{\\omega \\omega'} P_\\eta(\\omega),\n",
+ "\\end{equation}\n",
+ "where $P_\\eta(\\omega)$ is the noise power spectrum of the time-ordered data.\n",
+ "The matrix $N_{\\omega \\omega'}$ is diagonal, and thus trivially invertible to\n",
+ "\\begin{equation}\n",
+ " (N^{-1})_{\\omega \\omega'} =\n",
+ " \\delta_{\\omega \\omega'}\\frac{1}{P_\\eta(\\omega)}.\n",
+ "\\end{equation}\n",
+ "Thus, to calculate $(N^{-1})_{tt'} d_{t'}$ you should FFT $d_t$, divide by\n",
+ "$P_\\eta(\\omega)$, then inverse FFT the result.\n",
+ "\n",
+ "**QUESTION**: Is $\\eta_t$ in our data really stationary? Why?\n",
+ "\n",
+ "Similarly calculating $(C_N^{-1})_{ij}$ in D&S Equation 14.28, while being the\n",
+ "most computationally expensive part of the map-making, can be done far more\n",
+ "quickly than\n",
+ "you might expect. However, the implementation is challenging so we have\n",
+ "provided code that does this; see if you can figure out how it works."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2p0Rt82WRUvw"
+ },
+ "source": [
+ "### 1.3 Estimating the time stream noise power spectrum\n",
+ "\n",
+ "The Noise power spectrum $P_\\eta(\\omega)$ must be estimated directly from the\n",
+ "data.\n",
+ "There are typically two contributions to the noise power. The first is\n",
+ "white noise for which $P_\\eta(\\omega) \\sim \\textrm{constant}$, which is set by\n",
+ "the sensitivity of the detector. Note for white noise, both \n",
+ "$N_{t t'}$ and $N_{\\omega \\omega'}$ are diagonal (uncorrelated) and uniform.\n",
+ "The other type of noise is $1/f$ noise, also referred to as red noise or pink\n",
+ "noise, for which $P_\\eta(\\omega) \\sim \\omega^{-\\gamma}$ for some positive power\n",
+ "$\\gamma$. This noise typically comes from drifts in the detector response or,\n",
+ "in the case of ground-based telescopes, atmospheric turbulence.\n",
+ "Red noise is insidious since it adds correlations in the telescope noise on\n",
+ "long times scales. To overcome it, telescopes scan across the sky as fast as\n",
+ "they are able to bring the signal power to shorter timescales that are less\n",
+ "affected.\n",
+ "\n",
+ "In any case, we will estimate $P_\\eta(\\omega)$ directly from the data, which is\n",
+ "greatly simplified by the fact that, prior to gridding the data, the noise\n",
+ "dominates the signal so the power spectrum of $d_t$ is essentially the power\n",
+ "spectrum of $\\eta_t$. To estimate $P_\\eta(\\omega)$, you have to\n",
+ "\n",
+ "- FFT $d_t$ and divide by $\\sqrt{N_{samples}}$ to obtain $d_\\omega$\n",
+ "- Compute the quantity $d_\\omega d^*_\\omega$ which is a noisy estimate for $P_\\eta(\\omega)$.\n",
+ "- Accumulate the estimate over bins in $\\omega$ to reduce uncertainty\n",
+ "- Interpolate/extrapolate the result to any $\\omega$.\n",
+ "\n",
+ "A final subtlety is the treatment of the $P_\\eta(\\omega=0)$ where the red\n",
+ "noise diverges and thus requires careful treatment.\n",
+ "Without going into details, just set\n",
+ "$P_\\eta(\\omega=0) = 2 P_\\eta(\\omega=2\\pi/\\tau)$, where $\\tau$ is the survey\n",
+ "duration\n",
+ "(the provided code already does this)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OPwEMw66RqRd"
+ },
+ "source": [
+ "### 1.4 Noise covariance inverse\n",
+ "\n",
+ "The final step is to invert $(C_N^{-1})_{ij}$ to obtain $(C_N)_{ij}$ then\n",
+ "then multiply by the gridded noise-weighted data to obtain your final map\n",
+ "estimate. Note that in the provided code, `NoisePointingModel`, $(C_N^{-1})_{ij}$ is a 4D array\n",
+ "(since a pixel is specified by both an $x$ and $y$ index), so you will need to\n",
+ "reshape it into a 2D matrix prior to inversion (just take the output and apply `reshape(Nx*Ny, Nx*Ny)` where `(Nx,Ny)` are the number of pixels in the x and y directions).\n",
+ "\n",
+ "The maps for the large datasets will have roughly 16k pixels, and inverting a 16k$\\times$16k\n",
+ "matrix is doable on a laptop CPU, but takes some time. If you are using a Colab notebook then you can use a GPU to perform the matrix inversion much faster.\n",
+ "\n",
+ "To invert the matrix `A` with a GPU on Colab, just replace the traditional `numpy.linalg.inv` (or `scipy.linalg.inv`) with\n",
+ "\n",
+ "```\n",
+ "with tf.device('/device:GPU:0'): \n",
+ " A_inv = tf.linalg.inv(A)\n",
+ "```\n",
+ "\n",
+ "To observe the benefit of using a GPU for the computation of $C_N$, compare the GPU inverse (the code above) execution speed with that of the CPU for the `small` dataset (128×32 pixel maps, the difference will be bigger for large dataset inverse). You may want to check this [Colab Tensorflow speedup example](https://colab.research.google.com/notebooks/gpu.ipynb). \n",
+ "\n",
+ "**QUESTION**: What is the GPU speedup over CPU?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Bped1A7jR9Lu"
+ },
+ "source": [
+ "## 1.5 Testing\n",
+ "\n",
+ "There are many ways you can test your code using the provided test dataset.\n",
+ "First, you can make a noiseless map to compare to the output of your map maker.\n",
+ "Second, in either the noiseless case or the case where you add only white\n",
+ "noise, the simplified estimator in D&S 14.33 is optimal (You can\n",
+ "calculate the factor $m_i$ by gridding a vector of ones the same shape as\n",
+ "$d_t$.), and should give\n",
+ "almost exactly the same result as your final map maker.\n",
+ "\n",
+ "NOTE: Generating the maps for the `large` dataset takes time, even using the GPU. Make sure you save the final maps so you do not have to repeat this step."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4S7PzlmlTQQH"
+ },
+ "source": [
+ "## 2. Power spectrum estimation\n",
+ "\n",
+ "### 2.1 Map power spectrum\n",
+ "\n",
+ "The function `naive_PS_estimator` below can be used to estimate the power spectrum (with uncertainties) of a CMB map, or the cross-power spectrum of two different maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Bjl0biPlWau1"
+ },
+ "outputs": [],
+ "source": [
+ "def naive_PS_estimator(map1, map2, pix_size, l_bin_edges):\n",
+ " \"\"\"A simple angular power spectrum estimator.\n",
+ "\n",
+ " Implements the power spectrum estimator in 2D. This\n",
+ " is sub-optimal because it does not know about the correlations in the noise. It is\n",
+ " also wrong on large scales since it assumes a map with periodic boundary conditions.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " map1 : 2D array\n",
+ " Map 1\n",
+ " map2 : 2D array\n",
+ " Map 2\n",
+ " pix_size : float\n",
+ " Pixel size in radians.\n",
+ " l_bin_edges : 1d array len n_l + 1\n",
+ " Edges of the multipole (l) bins. The number of bins will be n_l = len(l_bin_edges)-1\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " Cl : 1d array len n_l\n",
+ " Angular power spectrum estimate\n",
+ " Cl_var : 1d array len n_l\n",
+ " variance of angular power spectrum estimate\n",
+ " n_modes : 1d array len n_l\n",
+ " Number of modes in each bin\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " al1 = fft.fft2(map1) * pix_size**2\n",
+ " al2 = fft.fft2(map2) * pix_size**2\n",
+ " Cl_2D_11 = abs(al1)**2\n",
+ " Cl_2D_22 = abs(al2)**2\n",
+ " Cl_2D_12 = np.real(al1.conj() * al2)\n",
+ " lx = fft.fftfreq(map1.shape[0], pix_size) * 2 * np.pi\n",
+ " ly = fft.fftfreq(map1.shape[1], pix_size) * 2 * np.pi\n",
+ " l = np.sqrt(lx[:, None]**2 + ly**2)\n",
+ " Cl_11 = np.zeros(len(l_bin_edges) - 1)\n",
+ " Cl_22 = np.zeros_like(Cl_11)\n",
+ " Cl_12 = np.zeros_like(Cl_11)\n",
+ " n_modes = np.zeros(len(l_bin_edges) - 1)\n",
+ " for ii in range(len(l_bin_edges) - 1):\n",
+ " ledge_l = l_bin_edges[ii]\n",
+ " ledge_h = l_bin_edges[ii + 1]\n",
+ " m = np.logical_and(l < ledge_h, l >= ledge_l)\n",
+ " Cl_11[ii] += np.sum(Cl_2D_11[m])\n",
+ " Cl_22[ii] += np.sum(Cl_2D_22[m])\n",
+ " Cl_12[ii] += np.sum(Cl_2D_12[m])\n",
+ " n_modes[ii] += np.sum(m)\n",
+ " Cl_11 /= (n_modes * map1.size * pix_size**2)\n",
+ " Cl_22 /= (n_modes * map1.size * pix_size**2)\n",
+ " Cl_12 /= (n_modes * map1.size * pix_size**2)\n",
+ " Cl_12_var = (Cl_11*Cl_22 + Cl_12**2)/n_modes\n",
+ " return Cl_12, n_modes, Cl_12_var"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-YSnFMvbadFy"
+ },
+ "source": [
+ "`naive_PS_estimator` uses the flat sky approximation to compute the angular power spectrum of the map: it first transforms the map to Fourier space ($k_x$ and $k_y$) by taking a 2D FFT, then it computes the magnitude squared to obtain the 2D power spectrum, and finally it averages Fourier modes in annular bins of $k = \\sqrt{k_x^2 + k_y^2}$ to obtain the 1D angular power spectrum. The spherical harmonic multipole moment is just $\\ell=2\\pi k$.\n",
+ "\n",
+ "**QUESTION**: Is it valid to use the flat-sky approximation for these maps? why?\n",
+ "\n",
+ "Before you compute the map power spectrum you need to choose the edges of the multipole bands. The $\\ell$ of each bin will be the bin center. First, we do not need bands at high $\\ell$ where there is high beam attenuation. Also, our estimator is sub-optimal because it doesn't know about the non-uniform correlated noise. It also assumes a periodic map which introduces distortions to the power spectrum, especially at low $\\ell$ (comparable to the inverse survey size). With these considerations, you can use choose ~30 bands in the $\\ell$ range ~100-1500 for our power spectrum.\n",
+ "\n",
+ "**QUESTION**: What does it mean that `naive_PS_estimator` assumes a periodic map?\n",
+ "\n",
+ "One subtlety is that the CMB is not the only source of structure in\n",
+ "the map, since even using an optimal map-maker there is still residual noise.\n",
+ "This leads to the issue of \"noise bias\" in the calculated power spectrum.\n",
+ "\n",
+ "There are two ways that CMB experiments deal with noise bias. The first is if\n",
+ "they understand the statistics of the noise very well then can estimate its\n",
+ "amplitude and subtract it off. A second, more robust, method is seasonal\n",
+ "cross-correlation. Take two maps of the same region of the sky made from\n",
+ "disjoint time-ordered datasets (typically collected on different observing\n",
+ "seasons). These will have\n",
+ "identical signal contributions, but different *realizations* of the noise.\n",
+ "Therefore if we *cross-correlate* the two maps, the signal contribution\n",
+ "will correlate but the noise will not.\n",
+ "\n",
+ "The provided power-spectrum code, `naive_PS_estimator`, can calculate either the auto-correlation\n",
+ "power spectrum or the cross-correlation power spectrum.\n",
+ "Calculate the auto-power spectrum of one of the `large` dataset maps. Then,\n",
+ "calculate the cross-power spectrum using the two independent `large` maps. \n",
+ "\n",
+ "**QUESTION**: Where do you see differences in the power\n",
+ "spectrum? Which has smaller uncertainties? By what factor are they smaller/bigger?\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XLyG6eBnaFV1"
+ },
+ "source": [
+ "### 2.2 Theoretically expected power spectrum\n",
+ "\n",
+ "Given a set of cosmological parameters $p_\\gamma = (\\Omega_b h^2,\n",
+ "\\Omega_b h^2, \\Omega_k, \\tau_{\\rm rei}, h, n_s, A_s)$ we can use the `pypico` module to compute the CMB angular power spectrum $C_{\\ell}$ quickly. Here is an example on how to use the module."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OBDVWROtTWBI",
+ "outputId": "088f0d25-bc73-43c0-e828-d27840e0d2d5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(['As', 'ns', 'tau', 'ombh2', 'omch2', 'H0', 'omk'], ['cl_TT'])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pico = pypico.load_pico(\"jcset_py3.dat\")\n",
+ "pico.inputs(), pico.outputs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Cg2iv4jXT_Eq",
+ "outputId": "c9ad8b44-acab-4b2c-9b04-a6153e11701d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2494,)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Test using fiducial values\n",
+ "results = pico.get(As=np.exp(3.0)/1e10, ns=0.965, H0=67.7, ombh2=0.022, omch2=0.122, omk=0, tau=0.057)\n",
+ "Dl = results['dl_TT']\n",
+ "ell = np.arange(len(Dl))\n",
+ "Dl.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 311
+ },
+ "id": "LOKZ25PKPC3K",
+ "outputId": "71b51290-d6d8-470c-a303-5d22a683594b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "hf = figure(num=1, figsize=(10, 4))\n",
+ "matplotlib.rcParams.update({'font.size': 16})\n",
+ "plot(ell, Dl, label='$C_\\\\ell$')\n",
+ "xlabel('$\\\\ell$')\n",
+ "ylabel('$D_{\\\\ell} (\\\\mu K^2)$')\n",
+ "title('CMB spectrum for fiducial paramerters')\n",
+ "grid()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KEJIEvqnU89y"
+ },
+ "source": [
+ "$D_{\\ell}$ is what you often see plotted in CMB-related papers. It is related to the power spectrum by $D_{\\ell}=\\ell(\\ell+1)C_\\ell/2\\pi$\n",
+ "\n",
+ "Another effect you need to take into account is the finite resolution of the\n",
+ "telescope making the measurement, which suppresses power on small scales. \n",
+ "The simulated data has a Gaussian beam with width\n",
+ "$\\theta_{\\rm beam} = 0.000667$ radians, and the\n",
+ "easiest way to account for this is by modifying the power spectrum.\n",
+ "\\begin{equation}\n",
+ " C_l^{\\rm obs} = \\exp(-l^2\\theta_{\\rm beam}^2) C_l.\n",
+ "\\end{equation}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gJQyZddhc92r"
+ },
+ "source": [
+ "Compare the expected CMB angular power spectrum with fiducial cosmological parameter values to that measured from the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lkHzqeQAXM8M"
+ },
+ "source": [
+ "## 3. Parameter inference\n",
+ "\n",
+ "The output of the previous part are the band-power estimates $\\hat c^\\alpha$,\n",
+ "and their variance $\\sigma_\\alpha^2$. We now want to use these\n",
+ "measurements to infer the $\\Lambda$CDM parameters $p_\\gamma = (\\Omega_b h^2,\n",
+ "\\Omega_b h^2, \\Omega_k, \\tau_{\\rm rei}, h, n_s, A_s)$. Note that the\n",
+ "simulated data were generated from a $\\Lambda$CDM model where the parameters\n",
+ "may be different from those of the Universe we live in.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eg6wul8kXU5S"
+ },
+ "source": [
+ "### 3.1 Least squares\n",
+ "\n",
+ "If we assume the uncertainty in the band powers is Gaussian, then this is a\n",
+ "non-linear least-squares problem with\n",
+ "\\begin{equation}\n",
+ " -2\\ln\\mathcal{L}(p_\\gamma) + \\textrm{constant} = \\chi^2(p_\\gamma) = \\sum_{\\alpha} \\frac{[\\hat c_\\alpha -\n",
+ " C^{\\rm obs}_{\\ell_\\alpha}(p_\\gamma)]^2}{\\sigma_\\alpha^2} ,\n",
+ "\\end{equation}\n",
+ "where $\\ell_\\alpha = (\\ell^{\\mathrm{low}}_{\\alpha} + \\ell^{\\mathrm{low}}_{\\alpha +\n",
+ "1}) / 2$, and $C^{\\rm obs}_{\\ell_\\alpha}$ is computed from `pypico` (properly\n",
+ "accounting for the beam).\n",
+ "\n",
+ "Here you will implement the $\\chi^2$ function and find the $\\Lambda$CDM cosmological parameters that minimize it.\n",
+ "\n",
+ "NOTE: the `pypico` module is designed to use machine learning to compute CMB power spectra really fast which you need since in this Section you will have to run `pypico` hundreds of times to find the best-fit parameters. However, the module is trained for a particular region of parameter space. If you try to run it outside this range you can get errors like\n",
+ "\n",
+ "`CantUsePICO: Parameter 'As'=1.488e-09 is outside the PICO region bounds 1.8229e-09 < As < 2.3757e-09`\n",
+ "\n",
+ "The pico bounds are:\n",
+ "```\n",
+ "As: (1.8229e-09, 2.3757e-09)\n",
+ "ommh3=(ombh2+omch2)*h: (0.044699, 0.154660)\n",
+ "ns: (0.925510, 1.026100)\n",
+ "omk: (-0.258240, 0.041995)\n",
+ "tau: (0.010038, 0.129130)\n",
+ "```\n",
+ "\n",
+ "By using the parameter `force=True` when calling `pypico` you can get results even outside the training region (check the [PICO](https://github.com/marius311/pypico) webpage).\n",
+ "\n",
+ "Even if you are slightly outside this region results may be accurate enough for calculations, so keep these bounds in mind when implementing the minimization algorithm."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kmL-G_obXf3-"
+ },
+ "source": [
+ "### 3.2 Markov Chain Monte Carlo\n",
+ "\n",
+ "Rather than simply maximizing the likelihood using least squares, we can sample\n",
+ "the likelihood using Markov Chain Monte Carlo (MCMC) techniques. This gives more\n",
+ "accurate estimates and uncertainties when the\n",
+ "likelihood is not an approximately-Gaussian function of the parameters\n",
+ "$p_\\gamma$. \n",
+ "\n",
+ "MCMC is summarized in D&S~14.6, and is surprisingly easy to deploy\n",
+ "using the very excellent `emcee` package (so please check the `emcee` documentation and tutorials). MCMC requires as an\n",
+ "input $\\ln \\mathcal{L}(p_\\gamma)$, which is trivial if you already have\n",
+ "$\\chi^2(p_\\gamma)$ coded up. Since `pypico` is fast calculating power spectra, it is possible to run relatively long MCMC chains quickly. You can start with something like $\\sim$64 walkers, 200 burn-in steps, and 2000 run steps. You can plot the\n",
+ "results using the excellent `corner`\n",
+ "package (so please check the `corner` documentation and tutorials)."
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Copy of MIT_8S50_CMB_analysis.ipynb",
+ "provenance": [],
+ "toc_visible": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "accelerator": "GPU"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file