{ "cells": [ { "cell_type": "markdown", "id": "3edcfe0b-24d7-4321-b355-a6dc730c155d", "metadata": { "tags": [] }, "source": [ "# DC2 Image Analysis, 511 keV, Image Deconvolution\n", "\n", "updated on 2024-07-29 (the commit 14fc4c42f8b33749bd2053603643ca693dbc3954)\n", "\n", "This notebook focuses on the image deconvolution with the spacecraft attitude (scatt) binning method for DC2.\n", "Using the 511 keV thin disk 3-month simulation data created for DC2, an example of the image analysis will be presented.\n", "If you have not run through 511keV-DC2-ScAtt-DataReduction.ipynb, please see it first." ] }, { "cell_type": "code", "execution_count": 1, "id": "6ac10677-610a-47f9-8915-f99850a2ae45", "metadata": {}, "outputs": [], "source": [ "import logging\n", "import sys\n", "logger = logging.getLogger('cosipy')\n", "logger.setLevel(logging.INFO)\n", "logger.addHandler(logging.StreamHandler(sys.stdout))" ] }, { "cell_type": "code", "execution_count": 3, "id": "e751bbd5", "metadata": { "scrolled": true }, "outputs": [], "source": [ "from histpy import Histogram, HealpixAxis, Axis, Axes\n", "from mhealpy import HealpixMap\n", "from astropy.coordinates import SkyCoord, cartesian_to_spherical, Galactic\n", "\n", "from cosipy.response import FullDetectorResponse\n", "from cosipy.spacecraftfile import SpacecraftFile\n", "from cosipy.ts_map.TSMap import TSMap\n", "from cosipy.data_io import UnBinnedData, BinnedData\n", "from cosipy.image_deconvolution import SpacecraftAttitudeExposureTable, CoordsysConversionMatrix, DataIF_COSI_DC2, ImageDeconvolution\n", "\n", "# cosipy uses astropy units\n", "import astropy.units as u\n", "from astropy.units import Quantity\n", "from astropy.coordinates import SkyCoord\n", "from astropy.time import Time\n", "from astropy.table import Table\n", "from astropy.io import fits\n", "from scoords import Attitude, SpacecraftFrame\n", "\n", "#3ML is needed for spectral modeling\n", "from threeML import *\n", "from astromodels import Band\n", "\n", "#Other standard libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec \n", "\n", "import healpy as hp\n", "from tqdm.autonotebook import tqdm\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "00f20cda-81f8-4685-b9c4-f9423e5ebcf7", "metadata": { "tags": [] }, "source": [ "## 0. Files needed for this notebook\n", "\n", "From wasabi\n", "- cosi-pipeline-public/COSI-SMEX/DC2/Responses/SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\n", "\n", "From docs/tutorials/image_deconvolution/511keV/ScAttBinning\n", "- inputs_511keV_DC2.yaml\n", "- imagedeconvolution_parfile_scatt_511keV.yml\n", "\n", "As outputs from the notebook 511keV-DC2-ScAtt-DataReduction.ipynb\n", "- 511keV_scatt_binning_DC2_bkg.hdf5\n", "- 511keV_scatt_binning_DC2_event.hdf5\n", "- ccm.hdf5" ] }, { "cell_type": "markdown", "id": "6c259412", "metadata": {}, "source": [ "## 1. Read the response matrix" ] }, { "cell_type": "markdown", "id": "573a7c60", "metadata": {}, "source": [ " please modify \"path_data\" corresponding to your environment." ] }, { "cell_type": "code", "execution_count": 4, "id": "fada24bc", "metadata": {}, "outputs": [], "source": [ "path_data = \"path/to/data/\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "98a778c2-73cf-467b-96b6-affc42f17102", "metadata": {}, "outputs": [], "source": [ "response_path = path_data + \"SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\"\n", "\n", "response = FullDetectorResponse.open(response_path)" ] }, { "cell_type": "code", "execution_count": 6, "id": "eab660b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "FILENAME: '/Users/yoneda/Work/Exp/COSI/cosipy-2/data_challenge/DC2/prework/data/Responses/SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5'\n", "AXES:\n", " NuLambda:\n", " DESCRIPTION: 'Location of the simulated source in the spacecraft coordinates'\n", " TYPE: 'healpix'\n", " NPIX: 3072\n", " NSIDE: 16\n", " SCHEME: 'RING'\n", " Ei:\n", " DESCRIPTION: 'Initial simulated energy'\n", " TYPE: 'log'\n", " UNIT: 'keV'\n", " NBINS: 1\n", " EDGES: [509.0 keV, 513.0 keV]\n", " Em:\n", " DESCRIPTION: 'Measured energy'\n", " TYPE: 'log'\n", " UNIT: 'keV'\n", " NBINS: 1\n", " EDGES: [509.0 keV, 513.0 keV]\n", " Phi:\n", " DESCRIPTION: 'Compton angle'\n", " TYPE: 'linear'\n", " UNIT: 'deg'\n", " NBINS: 60\n", " EDGES: [0.0 deg, 3.0 deg, 6.0 deg, 9.0 deg, 12.0 deg, 15.0 deg, 18.0 deg, 21.0 deg, 24.0 deg, 27.0 deg, 30.0 deg, 33.0 deg, 36.0 deg, 39.0 deg, 42.0 deg, 45.0 deg, 48.0 deg, 51.0 deg, 54.0 deg, 57.0 deg, 60.0 deg, 63.0 deg, 66.0 deg, 69.0 deg, 72.0 deg, 75.0 deg, 78.0 deg, 81.0 deg, 84.0 deg, 87.0 deg, 90.0 deg, 93.0 deg, 96.0 deg, 99.0 deg, 102.0 deg, 105.0 deg, 108.0 deg, 111.0 deg, 114.0 deg, 117.0 deg, 120.0 deg, 123.0 deg, 126.0 deg, 129.0 deg, 132.0 deg, 135.0 deg, 138.0 deg, 141.0 deg, 144.0 deg, 147.0 deg, 150.0 deg, 153.0 deg, 156.0 deg, 159.0 deg, 162.0 deg, 165.0 deg, 168.0 deg, 171.0 deg, 174.0 deg, 177.0 deg, 180.0 deg]\n", " PsiChi:\n", " DESCRIPTION: 'Location in the Compton Data Space'\n", " TYPE: 'healpix'\n", " NPIX: 3072\n", " NSIDE: 16\n", " SCHEME: 'RING'\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response" ] }, { "cell_type": "markdown", "id": "26d6eb3a", "metadata": {}, "source": [ "## 2. Read binned 511keV binned files (source and background)" ] }, { "cell_type": "code", "execution_count": 7, "id": "04e15347-6b38-42de-a7c5-cd99b2ae66ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 148 ms, sys: 850 ms, total: 998 ms\n", "Wall time: 1.01 s\n" ] } ], "source": [ "%%time\n", "\n", "# background \n", "data_bkg = BinnedData(\"inputs_511keV_DC2.yaml\")\n", "data_bkg.load_binned_data_from_hdf5(\"511keV_scatt_binning_DC2_bkg.hdf5\")\n", "\n", "## signal + background\n", "data_511keV = BinnedData(\"inputs_511keV_DC2.yaml\")\n", "data_511keV.load_binned_data_from_hdf5(\"511keV_scatt_binning_DC2_event.hdf5\")" ] }, { "cell_type": "markdown", "id": "a409aa7b-9bd8-443b-be46-ee5a053f8349", "metadata": { "tags": [] }, "source": [ "## 3. Load the coordsys conversion matrix" ] }, { "cell_type": "code", "execution_count": 8, "id": "daaf836a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.5 s, sys: 71.3 ms, total: 1.57 s\n", "Wall time: 1.58 s\n" ] } ], "source": [ "%%time\n", "\n", "ccm = CoordsysConversionMatrix.open(\"ccm.hdf5\")" ] }, { "cell_type": "markdown", "id": "31ec05ad-90b7-4fad-9ad0-98cfd6483d41", "metadata": {}, "source": [ "## 4. Imaging deconvolution" ] }, { "cell_type": "markdown", "id": "6e88ca7f", "metadata": {}, "source": [ "### Brief overview of the image deconvolution\n", "\n", "Basically, we have to maximize the following likelihood function\n", "\n", "$$\n", "\\log L = \\sum_i X_i \\log \\epsilon_i - \\sum_i \\epsilon_i\n", "$$\n", "\n", "$X_i$: detected counts at $i$-th bin ( $i$ : index of the Compton Data Space)\n", "\n", "$\\epsilon_i = \\sum_j R_{ij} \\lambda_j + b_i$ : expected counts ( $j$ : index of the model space)\n", "\n", "$\\lambda_j$ : the model map (basically gamma-ray flux at $j$-th pixel)\n", "\n", "$b_i$ : the background at $i$-th bin\n", "\n", "$R_{ij}$ : the response matrix\n", "\n", "Since we have to optimize the flux in each pixel, and the number of parameters is large, we adopt an iterative approach to find a solution of the above equation. The simplest one is the ML-EM (Maximum Likelihood Expectation Maximization) algorithm. It is also known as the Richardson-Lucy algorithm.\n", "\n", "$$\n", "\\lambda_{j}^{k+1} = \\lambda_{j}^{k} + \\delta \\lambda_{j}^{k}\n", "$$\n", "$$\n", "\\delta \\lambda_{j}^{k} = \\frac{\\lambda_{j}^{k}}{\\sum_{i} R_{ij}} \\sum_{i} \\left(\\frac{ X_{i} }{\\epsilon_{i}} - 1 \\right) R_{ij} \n", "$$\n", "\n", "We refer to $\\delta \\lambda_{j}^{k}$ as the delta map.\n", "\n", "As for now, the two improved algorithms are implemented in COSIpy.\n", "\n", "- Accelerated ML-EM algorithm (Knoedlseder+99)\n", "\n", "$$\n", "\\lambda_{j}^{k+1} = \\lambda_{j}^{k} + \\alpha^{k} \\delta \\lambda_{j}^{k}\n", "$$\n", "$$\n", "\\alpha^{k} < \\mathrm{max}(- \\lambda_{j}^{k} / \\delta \\lambda_{j}^{k})\n", "$$\n", "\n", "Practically, in order not to accelerate the algorithm excessively, we set the maximum value of $\\alpha$ ($\\alpha_{\\mathrm{max}}$). Then, $\\alpha$ is calculated as:\n", "\n", "$$\n", "\\alpha^{k} = \\mathrm{min}(\\mathrm{max}(- \\lambda_{j}^{k} / \\delta \\lambda_{j}^{k}), \\alpha_{\\mathrm{max}})\n", "$$\n", "\n", "- Noise damping using gaussian smoothing (Knoedlseder+05, Siegert+20)\n", "\n", "$$\n", "\\lambda_{j}^{k+1} = \\lambda_{j}^{k} + \\alpha^{k} \\left[ w_j \\delta \\lambda_{j}^{k} \\right]_{\\mathrm{gauss}}\n", "$$\n", "$$\n", "w_j = \\left(\\sum_{i} R_{ij}\\right)^\\beta\n", "$$\n", "\n", "$\\left[ ... \\right]_{\\mathrm{gauss}}$ means that the differential image is smoothed by a gaussian filter." ] }, { "cell_type": "markdown", "id": "e0a2582e", "metadata": {}, "source": [ "## 4-1. Prepare DataInterface containing all necessary datasets" ] }, { "cell_type": "code", "execution_count": 10, "id": "de8055f7-4aab-4a17-8751-42493f9e88d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading the response matrix onto your computer memory...\n", "Finished\n", "Note that _modify_axes() in DataLoader was implemented for a temporary use. It will be removed in the future.\n", "... checking the axis ScAtt of the event and background files...\n", " --> pass (edges)\n", " --> pass (unit)\n", "... checking the axis Em of the event and background files...\n", " --> pass (edges)\n", " --> pass (unit)\n", "... checking the axis Phi of the event and background files...\n", " --> pass (edges)\n", " --> pass (unit)\n", "... checking the axis PsiChi of the event and background files...\n", " --> pass (edges)\n", " --> pass (unit)\n", "...checking the axis Em of the event and response files...\n", " --> pass (edges)\n", "...checking the axis Phi of the event and response files...\n", " --> pass (edges)\n", "...checking the axis PsiChi of the event and response files...\n", " --> pass (edges)\n", "The axes in the event and background files are redefined. Now they are consistent with those of the response file.\n", "Calculating an exposure map...\n", "Finished...\n", "CPU times: user 14.4 s, sys: 2.03 s, total: 16.4 s\n", "Wall time: 16.4 s\n" ] } ], "source": [ "%%time\n", "\n", "data_interface = DataIF_COSI_DC2.load(name = \"511keV\",\n", " event_binned_data = data_511keV.binned_data,\n", " dict_bkg_binned_data = {\"albedo\": data_bkg.binned_data},\n", " rsp = response,\n", " coordsys_conv_matrix=ccm,\n", " is_miniDC2_format=False)" ] }, { "cell_type": "markdown", "id": "b1a0269e", "metadata": {}, "source": [ "### 4-2. Initialize the instance of the image deconvolution class\n", "\n", "First, we prepare an instance of the ImageDeconvolution class and then register the dataset and parameters for the deconvolution. After that, you can start the calculation." ] }, { "cell_type": "markdown", "id": "79eb910c", "metadata": {}, "source": [ " please modify this parameter_filepath corresponding to your environment." ] }, { "cell_type": "code", "execution_count": 34, "id": "5fa73486", "metadata": {}, "outputs": [], "source": [ "parameter_filepath = \"imagedeconvolution_parfile_scatt_511keV.yml\"" ] }, { "cell_type": "code", "execution_count": 35, "id": "a4b47308-3e13-400d-bebc-b5d1e093201d", "metadata": {}, "outputs": [], "source": [ "image_deconvolution = ImageDeconvolution()\n", "\n", "# set data_interface to image_deconvolution\n", "image_deconvolution.set_dataset([data_interface])\n", "\n", "# set a parameter file for the image deconvolution\n", "image_deconvolution.read_parameterfile(parameter_filepath)" ] }, { "cell_type": "markdown", "id": "a2345d9d", "metadata": {}, "source": [ "### Initialize image_deconvolution\n", "\n", "In this process, a model map is defined following the input parameters, and it is initialized. Also, it prepares ancillary data for the image deconvolution, e.g., the expected counts with the initial model map, gaussian smoothing filter etc.\n", "\n", "I describe parameters in the parameter file.\n", "\n", "#### model_property\n", "\n", "| Name | Unit | Description | Notes |\n", "| :---: | :---: | :---: | :---: |\n", "| coordinate | str | the coordinate system of the model map | As for now, it must be 'galactic' |\n", "| nside | int | NSIDE of the model map | it must be the same as NSIDE of 'lb' axis of the coordinate conversion matrix|\n", "| scheme | str | SCHEME of the model map | As for now, it must be 'ring' |\n", "| energy_edges | list of float [keV] | The definition of the energy bins of the model map | As for now, it must be the same as that of the response matrix |\n", "\n", "#### model_initialization\n", "\n", "| Name | Unit | Description | Notes |\n", "| :---: | :---: | :---: | :---: |\n", "| algorithm | str | the method name to initialize the model map | As for now, only 'flat' can be used |\n", "| parameter_flat:values | list of float [cm-2 s-1 sr-1] | the list of photon fluxes for each energy band | the length of the list should be the same as the length of \"energy_edges\" - 1 |\n", "\n", "#### deconvolution\n", "\n", "| Name | Unit | Description | Notes |\n", "| :---: | :---: | :---: | :---: |\n", "|algorithm | str | the name of the image deconvolution algorithm| As for now, only 'RL' is supported |\n", "|||||\n", "|parameter_RL:iteration | int | The maximum number of the iteration | |\n", "|parameter_RL:acceleration | bool | whether the accelerated ML-EM algorithm (Knoedlseder+99) is used | |\n", "|parameter_RL:alpha_max | float | the maximum value for the acceleration parameter | |\n", "|parameter_RL:save_results_each_iteration | bool | whether an updated model map, detal map, likelihood etc. are saved at the end of each iteration | |\n", "|parameter_RL:response_weighting | bool | whether a delta map is renormalized based on the exposure time on each pixel, namely $w_j = (\\sum_{i} R_{ij})^{\\beta}$ (see Knoedlseder+05, Siegert+20) | |\n", "|parameter_RL:response_weighting_index | float | $\\beta$ in the above equation | |\n", "|parameter_RL:smoothing | bool | whether a Gaussian filter is used (see Knoedlseder+05, Siegert+20) | |\n", "|parameter_RL:smoothing_FWHM | float, degree | the FWHM of the Gaussian in the filter | |\n", "|parameter_RL:background_normalization_fitting | bool | whether the background normalization factor is optimized at each iteration | As for now, the single background normalization factor is used in all of the bins |\n", "|parameter_RL:background_normalization_range | list of float | the range of the normalization factor | should be positive |" ] }, { "cell_type": "code", "execution_count": 36, "id": "879053e3-ac7b-4a0a-ad58-24e3fb137065", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#### Initialization Starts ####\n", "<< Instantiating the model class AllSkyImage >>\n", "---- parameters ----\n", "coordinate: galactic\n", "energy_edges:\n", " unit: keV\n", " value:\n", " - 509.0\n", " - 513.0\n", "nside: 16\n", "scheme: ring\n", "unit: cm-2 s-1 sr-1\n", "\n", "<< Setting initial values of the created model object >>\n", "---- parameters ----\n", "algorithm: flat\n", "parameter:\n", " unit: cm-2 s-1 sr-1\n", " value:\n", " - 1e-4\n", "\n", "<< Registering the deconvolution algorithm >>\n", "Gaussian filter with FWHM of 2.0 deg will be applied to delta images ...\n", "---- parameters ----\n", "algorithm: RL\n", "parameter:\n", " acceleration: true\n", " alpha_max: 10.0\n", " background_normalization_optimization: true\n", " background_normalization_range:\n", " albedo:\n", " - 0.01\n", " - 10.0\n", " iteration_max: 10\n", " response_weighting: true\n", " response_weighting_index: 0.5\n", " save_results: false\n", " save_results_directory: ./results\n", " smoothing: true\n", " smoothing_FWHM:\n", " unit: deg\n", " value: 2.0\n", "\n", "#### Initialization Finished ####\n" ] } ], "source": [ "image_deconvolution.initialize()" ] }, { "cell_type": "markdown", "id": "5fa48a9c", "metadata": {}, "source": [ "### (You can change the parameters as follows)\n", "\n", "Note that when you modify the parameters, do not forget to run \"initialize\" again!" ] }, { "cell_type": "code", "execution_count": 37, "id": "1a658d2a-4dee-4d05-83ae-d7ac06317c73", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#### Initialization Starts ####\n", "<< Instantiating the model class AllSkyImage >>\n", "---- parameters ----\n", "coordinate: galactic\n", "energy_edges:\n", " unit: keV\n", " value:\n", " - 509.0\n", " - 513.0\n", "nside: 16\n", "scheme: ring\n", "unit: cm-2 s-1 sr-1\n", "\n", "<< Setting initial values of the created model object >>\n", "---- parameters ----\n", "algorithm: flat\n", "parameter:\n", " unit: cm-2 s-1 sr-1\n", " value:\n", " - 1e-4\n", "\n", "<< Registering the deconvolution algorithm >>\n", "Gaussian filter with FWHM of 3.0 deg will be applied to delta images ...\n", "---- parameters ----\n", "algorithm: RL\n", "parameter:\n", " acceleration: true\n", " alpha_max: 10\n", " background_normalization_optimization: true\n", " background_normalization_range:\n", " albedo:\n", " - 0.01\n", " - 10.0\n", " iteration_max: 30\n", " response_weighting: true\n", " response_weighting_index: 0.5\n", " save_results: false\n", " save_results_directory: ./results\n", " smoothing: true\n", " smoothing_FWHM:\n", " unit: deg\n", " value: 3.0\n", "\n", "#### Initialization Finished ####\n" ] } ], "source": [ "image_deconvolution.override_parameter(\"deconvolution:parameter:iteration_max = 30\")\n", "image_deconvolution.override_parameter(\"deconvolution:parameter:background_normalization_optimization = True\")\n", "image_deconvolution.override_parameter(\"deconvolution:parameter:alpha_max = 10\")\n", "image_deconvolution.override_parameter(\"deconvolution:parameter:smoothing_FWHM:value = 3.0\")\n", "\n", "image_deconvolution.initialize()" ] }, { "cell_type": "markdown", "id": "f764066e", "metadata": {}, "source": [ "## 4-3. Start the image deconvolution\n", "\n", "**With MacBook Pro with M1 Max and 64 GB memory, it takes about 6 minutes for 30 iterations.**" ] }, { "cell_type": "code", "execution_count": 38, "id": "a57fbf71-2fcc-48c4-9ac7-4c545dca67c9", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#### Image Deconvolution Starts ####\n", "<< Initialization >>\n", "The response weighting filter was calculated.\n", "The expected count histograms were calculated with the initial model map.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc056b9cd5744eb5971029da7d2d8cfb", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/30 [00:00>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 2.1117259088539058\n", " background_normalization: {'albedo': 1.0050126418599168}\n", " loglikelihood: [-1551381.3046347937]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 2/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.4589369846150562\n", " background_normalization: {'albedo': 0.9959708325988547}\n", " loglikelihood: [-1512374.46901272]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 3/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 4.130808937204131\n", " background_normalization: {'albedo': 1.0012844690296692}\n", " loglikelihood: [-1509704.6551410041]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 4/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9962307721886375}\n", " loglikelihood: [-1493862.0484827922]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 5/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9994329904833922}\n", " loglikelihood: [-1492433.9595451895]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 6/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.6771693124541858\n", " background_normalization: {'albedo': 0.9998028740399162}\n", " loglikelihood: [-1490817.9922211317]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 7/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.449069075865903\n", " background_normalization: {'albedo': 0.9998675031710099}\n", " loglikelihood: [-1489842.2481823745]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 8/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998408770314386}\n", " loglikelihood: [-1489325.101495992]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 9/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998457175491627}\n", " loglikelihood: [-1488892.4039850137]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 10/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.99984625993385}\n", " loglikelihood: [-1488527.137553983]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 11/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998463424295027}\n", " loglikelihood: [-1488216.2200577823]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 12/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998464257441877}\n", " loglikelihood: [-1487949.4637732645]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 13/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998465535186982}\n", " loglikelihood: [-1487718.9020237564]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 14/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998467168565899}\n", " loglikelihood: [-1487518.2600785135]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 15/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998469023069589}\n", " loglikelihood: [-1487342.5363498037]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 16/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998471080648059}\n", " loglikelihood: [-1487187.7331807986]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 17/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998473246581261}\n", " loglikelihood: [-1487050.599753453]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 18/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998475565078137}\n", " loglikelihood: [-1486928.4722171663]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 19/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998478073767146}\n", " loglikelihood: [-1486819.1924637624]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 20/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998480657855443}\n", " loglikelihood: [-1486720.9625066128]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 21/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.999848334088258}\n", " loglikelihood: [-1486632.318098337]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 22/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998485984132034}\n", " loglikelihood: [-1486552.0271653645]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 23/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998488576362452}\n", " loglikelihood: [-1486479.030822393]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 24/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998491210871083}\n", " loglikelihood: [-1486412.4477046377]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 25/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998493834402365}\n", " loglikelihood: [-1486351.5230972534]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 26/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998496440561155}\n", " loglikelihood: [-1486295.6214640602]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 27/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998498967548999}\n", " loglikelihood: [-1486244.1778385974]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 28/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998501504408625}\n", " loglikelihood: [-1486196.7316539427]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 29/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998503876212085}\n", " loglikelihood: [-1486152.859412373]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 30/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n", "<< Post-processing >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9998506246389144}\n", " loglikelihood: [-1486112.1990728814]\n", "<< Checking Stopping Criteria >>\n", "--> Stop\n", "<< Finalization >>\n", "#### Image Deconvolution Finished ####\n", "CPU times: user 32min, sys: 2min 31s, total: 34min 31s\n", "Wall time: 5min 2s\n" ] } ], "source": [ "%%time\n", "\n", "image_deconvolution.run_deconvolution()" ] }, { "cell_type": "code", "execution_count": 39, "id": "cc64ea8d", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "data": { "text/plain": [ "[{'iteration': 1,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 1.0050126418599168},\n", " 'alpha': ,\n", " 'loglikelihood': [-1551381.3046347937]},\n", " {'iteration': 2,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9959708325988547},\n", " 'alpha': ,\n", " 'loglikelihood': [-1512374.46901272]},\n", " {'iteration': 3,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 1.0012844690296692},\n", " 'alpha': ,\n", " 'loglikelihood': [-1509704.6551410041]},\n", " {'iteration': 4,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9962307721886375},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1493862.0484827922]},\n", " {'iteration': 5,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9994329904833922},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1492433.9595451895]},\n", " {'iteration': 6,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998028740399162},\n", " 'alpha': ,\n", " 'loglikelihood': [-1490817.9922211317]},\n", " {'iteration': 7,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998675031710099},\n", " 'alpha': ,\n", " 'loglikelihood': [-1489842.2481823745]},\n", " {'iteration': 8,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998408770314386},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1489325.101495992]},\n", " {'iteration': 9,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998457175491627},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1488892.4039850137]},\n", " {'iteration': 10,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.99984625993385},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1488527.137553983]},\n", " {'iteration': 11,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998463424295027},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1488216.2200577823]},\n", " {'iteration': 12,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998464257441877},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487949.4637732645]},\n", " {'iteration': 13,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998465535186982},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487718.9020237564]},\n", " {'iteration': 14,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998467168565899},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487518.2600785135]},\n", " {'iteration': 15,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998469023069589},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487342.5363498037]},\n", " {'iteration': 16,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998471080648059},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487187.7331807986]},\n", " {'iteration': 17,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998473246581261},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1487050.599753453]},\n", " {'iteration': 18,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998475565078137},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486928.4722171663]},\n", " {'iteration': 19,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998478073767146},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486819.1924637624]},\n", " {'iteration': 20,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998480657855443},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486720.9625066128]},\n", " {'iteration': 21,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.999848334088258},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486632.318098337]},\n", " {'iteration': 22,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998485984132034},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486552.0271653645]},\n", " {'iteration': 23,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998488576362452},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486479.030822393]},\n", " {'iteration': 24,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998491210871083},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486412.4477046377]},\n", " {'iteration': 25,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998493834402365},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486351.5230972534]},\n", " {'iteration': 26,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998496440561155},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486295.6214640602]},\n", " {'iteration': 27,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998498967548999},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486244.1778385974]},\n", " {'iteration': 28,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998501504408625},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486196.7316539427]},\n", " {'iteration': 29,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998503876212085},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486152.859412373]},\n", " {'iteration': 30,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9998506246389144},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1486112.1990728814]}]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_deconvolution.results" ] }, { "cell_type": "markdown", "id": "9d32d0a8", "metadata": {}, "source": [ "## 5. Analyze the results\n", "Examples to see/analyze the results are shown below." ] }, { "cell_type": "markdown", "id": "f577c7ac", "metadata": {}, "source": [ "### Log-likelihood\n", "\n", "Plotting the log-likelihood vs the number of iterations" ] }, { "cell_type": "code", "execution_count": 40, "id": "445ee3d5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, y = [], []\n", "\n", "for result in image_deconvolution.results:\n", " x.append(result['iteration'])\n", " y.append(result['loglikelihood'])\n", " \n", "plt.plot(x, y)\n", "plt.grid()\n", "plt.xlabel(\"iteration\")\n", "plt.ylabel(\"loglikelihood\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3f085706", "metadata": {}, "source": [ "### Alpha (the factor used for the acceleration)\n", "\n", "Plotting $\\alpha$ vs the number of iterations. $\\alpha$ is a parameter to accelerate the EM algorithm (see the beginning of Section 4). If it is too large, reconstructed images may have artifacts." ] }, { "cell_type": "code", "execution_count": 41, "id": "1695af05", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, y = [], []\n", "\n", "for result in image_deconvolution.results:\n", " x.append(result['iteration'])\n", " y.append(result['alpha'])\n", " \n", "plt.plot(x, y)\n", "plt.grid()\n", "plt.xlabel(\"iteration\")\n", "plt.ylabel(\"alpha\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b3298aa5", "metadata": {}, "source": [ "### Background normalization\n", "\n", "Plotting the background nomalization factor vs the number of iterations. If the background model is accurate and the image is reconstructed perfectly, this factor should be close to 1." ] }, { "cell_type": "code", "execution_count": 42, "id": "71ad8d7a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, y = [], []\n", "\n", "for result in image_deconvolution.results:\n", " x.append(result['iteration'])\n", " y.append(result['background_normalization']['albedo'])\n", " \n", "plt.plot(x, y)\n", "plt.grid()\n", "plt.xlabel(\"iteration\")\n", "plt.ylabel(\"background_normalization\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "58e0d3a6", "metadata": {}, "source": [ "### The reconstructed images" ] }, { "cell_type": "code", "execution_count": 43, "id": "94ab604d-12d9-4f81-b8d1-7dcbe793b6e8", "metadata": { "scrolled": true }, "outputs": [], "source": [ "def plot_reconstructed_image(result, source_position = None): # source_position should be (l,b) in degrees\n", " iteration = result['iteration']\n", " image = result['model']\n", "\n", " for energy_index in range(image.axes['Ei'].nbins):\n", " map_healpxmap = HealpixMap(data = image[:,energy_index], unit = image.unit)\n", "\n", " _, ax = map_healpxmap.plot('mollview') \n", " \n", " _.colorbar.set_label(str(image.unit))\n", " \n", " if source_position is not None:\n", " ax.scatter(source_position[0]*u.deg, source_position[1]*u.deg, transform=ax.get_transform('world'), color = 'red')\n", "\n", " plt.title(label = f\"iteration = {iteration}, energy_index = {energy_index} ({image.axes['Ei'].bounds[energy_index][0]}-{image.axes['Ei'].bounds[energy_index][1]})\")" ] }, { "cell_type": "markdown", "id": "4b8cdf58", "metadata": {}, "source": [ "Plotting the reconstructed images in all of the energy bands at the 20th iteration" ] }, { "cell_type": "code", "execution_count": 44, "id": "2769b6e5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iteration = 19\n", "\n", "plot_reconstructed_image(image_deconvolution.results[iteration])" ] }, { "cell_type": "markdown", "id": "7ac96b22", "metadata": {}, "source": [ "An example to plot the image in the log scale" ] }, { "cell_type": "code", "execution_count": 46, "id": "71f5f43f", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iteration_idx = 29\n", "\n", "result = image_deconvolution.results[iteration_idx]\n", "\n", "iteration = result['iteration']\n", "image = result['model']\n", "\n", "data = image[:,0]\n", "data[data <= 0 * data.unit] = 1e-12 * data.unit\n", "\n", "hp.mollview(data, min = 1e-5, norm ='log', unit = str(data.unit), title = f'511 keV image at {iteration}th iteration', cmap = 'magma')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "8fd8b4e1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }