{ "cells": [ { "cell_type": "markdown", "id": "3edcfe0b-24d7-4321-b355-a6dc730c155d", "metadata": { "tags": [] }, "source": [ "# Image Deconvolution example for 511 keV with spacecraft attitude binning \n", "\n", "updated on 2025-03-19\n", "\n", "This notebook focuses on the image deconvolution with the spacecraft attitude (scatt) binning method. Using an older 511keV thin disk 3-month simulation data created for DC2, an example of the image analysis will be presented. If you have not run through 511keV-ScAtt-DataReduction.ipynb, please do that first as this analysis requires data that is binned slightly differently than the standard Galactic binning method shown in the DataIO tutorial." ] }, { "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": 2, "id": "e751bbd5", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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       "                  available                                                                                        \n",
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       "                  will not be available.                                                                           \n",
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       "                  require the C/C++ interface (currently HAWC)                                                     \n",
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       "                  software installed and configured?                                                               \n",
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       "                  software installed and configured?                                                               \n",
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       "                  performances in 3ML                                                                              \n",
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       "                  performances in 3ML                                                                              \n",
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       "                  performances in 3ML                                                                              \n",
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Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=897815;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML-2.4.3.dev1-py3.10.egg/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=576834;file:///Users/ckierans/Software/COSItools/COSItools/python-env/lib/python3.10/site-packages/threeML-2.4.3.dev1-py3.10.egg/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "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", "You should have used the 511keV-ScAtt-DataReduction.ipynb notebook to download and bin the data sets needed for this notebook. Specicially, you now should have:\n", "- 511keV_scatt_binning_DC2_bkg.hdf5\n", "- 511keV_scatt_binning_DC2_event.hdf5\n", "- ccm.hdf5\n", "\n", "From docs/tutorials/image_deconvolution/511keV/ScAttBinning, you will also need:\n", "- inputs_511keV.yaml\n", "- imagedeconvolution_parfile_scatt_511keV.yml\n" ] }, { "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": null, "id": "6c1dcdb3-0ed2-4ec6-8d47-7a629a7bd6ec", "metadata": {}, "outputs": [], "source": [ "path_data = \"/path/to/data/\"" ] }, { "cell_type": "code", "execution_count": 4, "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": 5, "id": "eab660b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "FILENAME: '/Users/ckierans/Software/COSItools/COSItools/cosipy/docs/tutorials/image_deconvolution/511keV/ScAttBinning/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": 5, "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": 6, "id": "04e15347-6b38-42de-a7c5-cd99b2ae66ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 117 ms, sys: 671 ms, total: 788 ms\n", "Wall time: 809 ms\n" ] } ], "source": [ "%%time\n", "\n", "# background \n", "data_bkg = BinnedData(\"inputs_511keV.yaml\")\n", "data_bkg.load_binned_data_from_hdf5(\"511keV_dc2_scatt_bkg.hdf5\")\n", "\n", "## signal + background\n", "data_511keV = BinnedData(\"inputs_511keV.yaml\")\n", "data_511keV.load_binned_data_from_hdf5(\"511keV_dc2_scatt_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": 7, "id": "daaf836a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.21 s, sys: 64 ms, total: 1.28 s\n", "Wall time: 1.28 s\n" ] } ], "source": [ "%%time\n", "\n", "ccm = CoordsysConversionMatrix.open(\"ccm_dc2.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": 8, "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", "... checking the axis ScAtt of the event and background files...\n", " --> pass (edges)\n", "... checking the axis Em of the event and background files...\n", " --> pass (edges)\n", "... checking the axis Phi of the event and background files...\n", " --> pass (edges)\n", "... checking the axis PsiChi of the event and background files...\n", " --> pass (edges)\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 6.67 s, sys: 1.49 s, total: 8.16 s\n", "Wall time: 8.18 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": 9, "id": "5fa73486", "metadata": {}, "outputs": [], "source": [ "parameter_filepath = \"imagedeconvolution_parfile_scatt_511keV.yml\"" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "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": 12, "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", "**On a MacBook Pro with M2 Max and 96 GB memory, it takes about 2.5 min for 30 iterations.**" ] }, { "cell_type": "code", "execution_count": 13, "id": "a57fbf71-2fcc-48c4-9ac7-4c545dca67c9", "metadata": { "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": "1f422577bfd94596957ee4fe6cbd7a83", "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: 2.4783944324338596\n", " background_normalization: {'albedo': 1.0003541905620614}\n", " loglikelihood: [-1197056.4545480786]\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: 1.575421707005236\n", " background_normalization: {'albedo': 0.9922607778263346}\n", " loglikelihood: [-1148892.2494289856]\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: 10\n", " background_normalization: {'albedo': 0.9989836398988254}\n", " loglikelihood: [-1366763.493999292]\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': 0.9807935141594855}\n", " loglikelihood: [-1130201.6042120028]\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': 0.9951524418084393}\n", " loglikelihood: [-1124841.8752257773]\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: 1.0\n", " background_normalization: {'albedo': 0.996710972292622}\n", " loglikelihood: [-1123882.6965031484]\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: 1.0\n", " background_normalization: {'albedo': 0.9968855622562821}\n", " loglikelihood: [-1123152.1905212747]\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': 0.9969083706965138}\n", " loglikelihood: [-1122553.294395423]\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': 0.9969134554559144}\n", " loglikelihood: [-1122056.1156774329]\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': 0.9969163516897256}\n", " loglikelihood: [-1121639.152483511]\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': 0.9969189620579678}\n", " loglikelihood: [-1121286.2048804823]\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': 0.9969215191961452}\n", " loglikelihood: [-1120984.8976566715]\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': 0.996924030389004}\n", " loglikelihood: [-1120725.6564115596]\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': 0.9969264790341887}\n", " loglikelihood: [-1120500.9815390154]\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': 0.9969288556146668}\n", " loglikelihood: [-1120304.963459809]\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': 0.9969311484060269}\n", " loglikelihood: [-1120132.8961275548]\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': 0.9969333516388736}\n", " loglikelihood: [-1119980.969809744]\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': 0.9969354690483336}\n", " loglikelihood: [-1119846.1195404662]\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': 0.9969374982210912}\n", " loglikelihood: [-1119725.820978066]\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': 0.9969394510025879}\n", " loglikelihood: [-1119618.0199778753]\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': 0.9969413216933766}\n", " loglikelihood: [-1119520.9964885826]\n", "<< Checking Stopping Criteria >>\n", "--> Continue\n", "## Iteration 22/30 ##\n", "<< Pre-processing >>\n", "<< E-step >>\n", "<< M-step >>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<< Post-processing >>\n", "<< Registering Result >>\n", " alpha: 1.0\n", " background_normalization: {'albedo': 0.9969431196567854}\n", " loglikelihood: [-1119433.3289185578]\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': 0.9969448441589266}\n", " loglikelihood: [-1119353.8044356154]\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': 0.9969465063444388}\n", " loglikelihood: [-1119281.4278713942]\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': 0.9969481006699753}\n", " loglikelihood: [-1119215.3384681274]\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': 0.9969496370088307}\n", " loglikelihood: [-1119154.8018173785]\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': 0.9969511182269674}\n", " loglikelihood: [-1119099.190130895]\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': 0.9969525475454646}\n", " loglikelihood: [-1119047.9797920918]\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': 0.9969539225343819}\n", " loglikelihood: [-1119000.7175890533]\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': 0.9969552449401047}\n", " loglikelihood: [-1118956.9770636673]\n", "<< Checking Stopping Criteria >>\n", "--> Stop\n", "<< Finalization >>\n", "#### Image Deconvolution Finished ####\n", "CPU times: user 18min 4s, sys: 3min 54s, total: 21min 58s\n", "Wall time: 2min 41s\n" ] } ], "source": [ "%%time\n", "\n", "image_deconvolution.run_deconvolution()" ] }, { "cell_type": "code", "execution_count": 14, "id": "cc64ea8d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'iteration': 1,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 1.0003541905620614},\n", " 'alpha': ,\n", " 'loglikelihood': [-1197056.4545480786]},\n", " {'iteration': 2,\n", " 'model': ,\n", " 'delta_model': ,\n", " 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[-1121639.152483511]},\n", " {'iteration': 11,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969189620579678},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1121286.2048804823]},\n", " {'iteration': 12,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969215191961452},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1120984.8976566715]},\n", " {'iteration': 13,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.996924030389004},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1120725.6564115596]},\n", " {'iteration': 14,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969264790341887},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1120500.9815390154]},\n", " {'iteration': 15,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969288556146668},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1120304.963459809]},\n", " {'iteration': 16,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969311484060269},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1120132.8961275548]},\n", " {'iteration': 17,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969333516388736},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119980.969809744]},\n", " {'iteration': 18,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969354690483336},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119846.1195404662]},\n", " {'iteration': 19,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969374982210912},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119725.820978066]},\n", " {'iteration': 20,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969394510025879},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119618.0199778753]},\n", " {'iteration': 21,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969413216933766},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119520.9964885826]},\n", " {'iteration': 22,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969431196567854},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119433.3289185578]},\n", " {'iteration': 23,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969448441589266},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119353.8044356154]},\n", " {'iteration': 24,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969465063444388},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119281.4278713942]},\n", " {'iteration': 25,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969481006699753},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119215.3384681274]},\n", " {'iteration': 26,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969496370088307},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119154.8018173785]},\n", " {'iteration': 27,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969511182269674},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119099.190130895]},\n", " {'iteration': 28,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969525475454646},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119047.9797920918]},\n", " {'iteration': 29,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969539225343819},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1119000.7175890533]},\n", " {'iteration': 30,\n", " 'model': ,\n", " 'delta_model': ,\n", " 'processed_delta_model': ,\n", " 'background_normalization': {'albedo': 0.9969552449401047},\n", " 'alpha': 1.0,\n", " 'loglikelihood': [-1118956.9770636673]}]" ] }, "execution_count": 14, "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": 15, "id": "445ee3d5", "metadata": {}, "outputs": [ { "data": { "image/png": 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arltoonXdiouL27Vd3IWstWvXavr06e3a9uWXX1ZBQUGUW3RihmFo4sSJWr16tUpLS9scED9+/HgNGzbM9768vFyzZ89W//79VVRUFLB9WVlZu/8RI8E0TZV+e2xqCqc9rcPOb+4s1xa1VPn69s/XoOJTQz5WR1+3RMF1Cx7XLDRct9Bw3UIT6+sWdyGrX79+mjVrVru2zc3NjXJr2sdbvaqqqmpzm7y8POXl5XVUk4JWvb9OdYePdRFax0lFm988Walx95UEACAkcXdHy83N1dixY2PdjKB8++23kqTs7OzYNiQMe7865Pe+Y0MWvy4EACSepHuszr59+0IedF5VVRUwfsrlcumVV15RSkpKu2eYj0d7Nx0Xsho6LmS5rJWs9LjL/QAAhCRh7mgvvviiJGn79u2SpA8++EDr1q2TJE2aNMm33SOPPKLS0lKtWLHCt6ympkZvvPGGJOkf//iHJOmvf/2rMjMzlZmZqQkTJkiSVq5cqZdeekkjR45Unz59VF1draVLl2rbtm26/fbb46b7MhT7jq9kNbhlekwZtuj/sIDH6gAAElHChKy5c+f6vV+yZInvtTVktaa6ujpg/4ULF0qSevfu7QtZhYWFKigo0NKlS3X48GE5HA4NGjRIDz30kK644opIfIyYqDvSqCN7agOWNze45MxIifr5/bsLE+YrCQBIcglzR7NWpk7kySefDFjWp0+fdu1fVFQUMIVDIji+iuXVXN9RIetYJctBJQsAkCCSbkwWAu3ddGwS0rQsp+91U0Pg/F3RQCULAJCICFk4NujdkPKHHJtmoqN+Yegd+G5zGLI7+EoCABIDd7Qk11TXrIPlLfN75eR3VWbesQded1TI8layqGIBABIJISvJ7d98WKbZ8rr3GTl+Uyg0dVjIajkPIQsAkEgIWUnOOglp76IcvykUOmqurGOVLAa9AwASByEryVkHvfc+o7uclkpWR3QXejymXI10FwIAEg8hK4m5m9068M0RSVLXHunq0j3Nr7uwuT76vy70BiyJ6RsAAImFkJXEDmytkrvZI6mliiWpw8dk+c/2TiULAJA4CFlJbN9Xx7oKe52RI0lyWoJOR4zJcvFwaABAgiJkJTHrQ6F7F7WErJQOHpNlPQeVLABAIiFkJSmPx9S+r1tCVlqWU936dJEUg5DVaAlZ6VSyAACJg5CVpA7trFZTXUvA6VWUI8MwJPl32XXImCzL4PqUVCpZAIDEQchKUvu+CuwqlCRHql1H85bfMwWjhYdDAwASFSErSfmNxzr6y0JJMgzD12XYMd2FlkpWOpUsAEDiIGQlIdM0tffoLwtT0uzKLejqt75DQ5Z14HsqlSwAQOIgZCWh6v31qjvUKEnqOShbNrv/18D7K78On8KBShYAIIEQspLQXsv8WL2Luges9z5ap7nBLdNjRrUtTX6TkVLJAgAkDkJWErKOx/JOQmplnUoh2tUsv0oWvy4EACQQQlYS8v6y0GY31HNgdsD6jpwry++xOnQXAgASCCErydQdadSRPbWSpLzCbnI4A7vorDOvN0V5GgfrNBFM4QAASCSErCTjNz9WK12F0rExWVIHV7J4rA4AIIEQspLM3k0nHvQuxbC7kCkcAAAJhJCVZHyD3g2p1+mtV7KsISvaj9bxDnx3pNpl2IyongsAgI5EyEoiTXXNOlheJUnKye+q1MyUVrezTqUQ7V8XesdkMX0DACDRELKSyP7Nh2UenfaqrfFYUmzGZDEeCwCQaAhZSWRvGw+FPp7/mKxo/7rQG7KoZAEAEgshK4n4DXo/o/VB71LHjclyuzzyuFpKaw4qWQCABEPIShLuZrcOfHNEktS1R7q6dE9rc1unJfBEc0wW0zcAABIZIStJVGyrkrvZI+nEVSyp46Zw8HukDt2FAIAEQ8hKEtauwtaeV2jVUSGrmYdDAwASGCErSbR30LvkH3iiOSar2a+SRXchACCxELKSgOkxfY/TSctyqlufLifc3pFql3F0XtDmKD670FolY+A7ACDRELKSwMFd1Wqqawk0vYtyZBgnnlndMAxfl2F0uwuPBThnOt2FAIDEQshKAvs2nfyh0MfrmJBlqWSlUskCACQWQlYS2GsNWW08FPp43jFS0R2TxcB3AEDiImQlONM0tferll8WpqTZ1b2ga7v28z5ax9Xolukxo9I2vykc0qlkAQASCyErwVXvr1fdoUZJUs9BObLZ2/dPnpIe/YdE+1WyUqlkAQASCyErwXmrWNLJp26w6oi5spqpZAEAEhghK8FZx2OdbBJSK+u8VU1RmsbBb+A7Y7IAAAmGkJXgvPNj2eyGeg7Mbvd+zo6uZPHrQgBAgiFkJbC6I406sqdWkpRX2E0OZ/urRR3TXWgZk8U8WQCABEPISmD7vgp+fiwva8iK1jQOPFYHAJDICFkJzPpQ6PbOj+VlnbcqWr8udB09rmEzZE/hqwgASCzc2RKYb9C7IfU6PbhKVkeOyUpJs5/0UT8AAHQ2hKwE1VTXrIPlVZKknPyuSs1MCWp//zFZ0f11IV2FAIBERMhKUPu3HJZ5dKL2YMdjSR07JovpGwAAiYiQlaD8n1cYfMhyWqpL0RiTZZqmb0wWlSwAQCIiZCWovX6/LAxu0LsU/Skc3E0eX6WNh0MDABIRISsBuZvdOrDlsCSpa490demeFvQxoh2ymqxzZFHJAgAkIEJWAqrYViV3s0dSaFUsyb+6FI0xWS6/kEUlCwCQeAhZCcg6P1Ywzyu0cqTa5Z1VoTkKzy5kIlIAQKIjZCUgv/FYIQx6lyTDMHxdhtHoLrQek18XAgASESErwZge0/c4nbQsp7r16RLysaIashqpZAEAEhshK8Ec3FWtprqWUNS7KCesmdS94ScaY7KswY0xWQCARJQQJYSKigotWrRIGzdu1KZNm1RfX6+SkhKde+657dr/448/1kcffaRNmzbp4MGD6tmzpy655BJNmjRJXbt2Ddj+k08+0QsvvKDy8nJlZ2frqquu0i233CKHI/aXc9+m0B8KfTzvo3VcjW6ZHlOGLXKPvnFZx2Slx/66AQAQaQlRydq5c6cWLFigAwcOqLCwMOj958yZo/Lyco0ePVrTp0/XRRddpDfffFPTpk1TY2Oj37arVq3Sv//7vyszM1PTp0/XiBEj9NJLL6mkpCRSHycs/pOQhvbLQq+U9Og9JNpvCodUKlkAgMSTECWEoqIivfvuu8rKytLy5cv1wAMPBLX/ww8/HFD1Kioq0qOPPqqlS5dq3LhxvuXPPPOMBgwYoN/97ne+ylVGRobmz5+va6+9VgUFBeF/oDBU7mh5XmFKml3dCwKrcME4fq4sZ0Zwzz88ERe/LgQAJLiEqGRlZGQoKysr5P1b61a87LLLJEnbt2/3Ldu+fbu2b9+u73//+35dgz/4wQ9kmqaWL18echsiZcLjw/XPj1yqy+4YIps9vH9ea/hpivA0DtbKGN2FAIBExN2tDZWVlZKk7Oxs37Kvv/5aUkuVyyovL089evTQ5s2bO6x9bbHZbcrr3015/buFfSxnFGd9t86T5aC7EACQgAhZbViwYIHsdrtGjhzpW+YNXrm5uQHb5+bm+ta3pqKiwm99eXl5BFsbHdF8tA6VLABAogvq7lZaWhryiYYOHdqu7Twej5qbm9u1rdPpDGuKgrYsXbpUixcv1g033KC+ffv6ljc1NfnO21pb6urq2jzmO++8o3nz5gUs37Ztm9zuwK64mpoalZWVhdD6yDlUdcT3euvm7Tpi3x+xY1fuPzZAf/uOrdpzJDJBKx6uW2fEdQse1yw0XLfQcN1CE63rVlxc3K7tgrqzTZ8+PeRQ097xSmvXrtX06dPbte3LL78c8YHma9eu1eOPP66LLrpIt912m986b7jyhi2rpqYmpaamtnnc8ePHa9iwYb735eXlmj17tvr37x/Q/ShJZWVl7f5HjJrd5SrXYUlSr7zeOr04P2KH3uH8TAfVEkqLB5+p1MzIDKqPi+vWCXHdgsc1Cw3XLTRct9DE+roFFbImTZoUELLKysq0evVq5efna/DgwcrJydGhQ4f0j3/8Qzt37tRFF10U1Afs16+fZs2a1a5tW+u2C8eWLVs0a9YsFRYW6uGHHw6Y98p7vsrKSvXq1ctvXWVlpc4888w2j52Xl6e8vLyItjfaOmpMFpORAgASUVAha8qUKX7v165dq1deeUUzZ87U1Vdf7RfATNPU3/72Nz355JO6+eab232O3NxcjR07NphmRcTu3bs1Y8YM5eTk6IknnlBGRkbANoMGDZIkffXVV37BsaKiQgcOHND48eM7rL0dwX9MVnR+XWhPscnmSIgfuQIA4Cesu9vcuXP1ne98R+PGjQuocBmGofHjx+viiy/W3Llzw2pkJO3bty9g0HllZaXuvfde2Ww2zZkzx+8XhVb9+/dXv3799Le//c1vHNVbb70lwzD8BsknAmvIivSjdbwhiyoWACBRhTXa+KuvvtK11157wm1OO+00LVq0KJzTtMuLL74o6di8Vh988IHWrVsnqaWb0+uRRx5RaWmpVqxY4Vs2c+ZMffvtt7rhhhu0fv16rV+/3rcuJydHF154oe/9z372M82aNUv33nuvRo0apa1bt+rNN9/UuHHjdNppp0XxE3Y8p2WerEjP+O7tLnQwESkAIEGFdYdLSUk56dxQX3/9tVJSIjdTeFuOr5YtWbLE99oaslqzZcsWSdKrr74asG7o0KF+IevSSy/V7NmzNW/ePJWUlKhbt2666aabNHny5DBaH586YgoHZnsHACSqsO5wF154oZYtW6b58+fr+uuv9wtTzc3NWrhwoT777DNdeeWVYTf0ZKyVqRN58sknQ97Xa8SIERoxYkRQ+3RG0QpZHo8pd5On5Rx0FwIAElRYIWvatGlat26d/vznP2vRokU644wzlJ2drcOHD2vTpk06fPiwcnNzdccdd0SqvehA1gAUyTFZLutEpIQsAECCCitk9ezZU3/605/03HPPadmyZfq///s/3zqn06nRo0dr6tSpEZ9qAR3DkWqXYUim6T/lQriaeTg0ACAJhH2Hy83N1b/927/pvvvu044dO1RbW6suXbqob9++HTIWC9FjGIZS0h1qqnNFtLvQeixCFgAgUUXsDudwOFRYWBipwyFORCVkNTIRKQAg8UUsZK1fv16bN29WXV2dMjIyNGjQIA0ePDhSh0eMeCtNkRyTZQ1sTOEAAEhUYd/h1q9fr8cee0y7d++W1DLTu3di0vz8fN1///06++yzwz0NYsT7aB1Xo1sejymbLfwHcjcz8B0AkATCClnbtm3TjBkz1NDQoAsuuEDnnnuucnNzdfDgQa1Zs0afffaZZsyYoT/+8Y8JN1FnskhJPxaCXA0uOTPCH2fnamTgOwAg8YV1h5s3b56am5v1xBNP6OKLL/Zb9+Mf/1iffvqpZs2apXnz5unBBx8M51SIkePnyopEyPIf+E4lCwCQmMJ6dmFpaakuv/zygIDldfHFF+vyyy/XmjVrwjkNYshaaWqK0DQOflM4pFPJAgAkprBCVm1trfr06XPCbfr06aPa2tpwToMYckZh1ne/MVmpVLIAAIkprJCVm5urDRs2nHCbsrIyJiPtxKLxaB1rJYtfFwIAElVYIWvYsGEqLS3Vn//8ZzU2Nvqta2xs1H/9139pzZo1Gj58eFiNROxYQ1akpnHwq2SlU8kCACSmsMoIkyZN0v/93/9p/vz5euedd3TmmWcqJydHhw4d8j278JRTTtGkSZMi1V50MOvAdGs4CoffmKxUKlkAgMQU1h2uW7duevbZZ/XHP/5RH374oVatWuVb53Q6NXbsWN1xxx3KysoKu6GIjWiMyfJ7QDQD3wEACSrsO1x2drbuv/9+zZgxQ+Xl5b4Z3wsKCuRwcAPt7PzHZEXh14VM4QAASFARfXbhgAEDInU4xImojskyJIeTkAUASEw8uxAn5LT8+i/SY7JSUu0yIvCYHgAA4hHPLsQJRWcKh5bjMH0DACCR8exCnFA0QpbLW8liPBYAIIHx7EKckDPCY7JM01TT0UoWD4cGACQynl2IE7I7bTra++v3q8BQeVwemW5TEpUsAEBi49mFOCHDMHxdhpHoLvSfvoFKFgAgcfHsQpxUZEPWsWM4qGQBABIYzy7ESXkrTpEYk0UlCwCQLHh2IU7KO/jd1eiWx2PKFsbcVtZqGGOyAACJjGcX4qRS0o+FIVeDS86MlJCP1dxIJQsAkBx4diFO6vi5ssIKWVSyAABJgmcX4qSsFaemBre6hHEsF2OyAABJIqyB70gOzgjO+t7caK1kEbIAAIkr7Lvc559/roULF2rTpk2qqamRaZoB2xiGoWXLloV7KsRIJB+tY92fKRwAAIksrJC1fPlyPfTQQ/J4POrVq5cKCgpkt3PjTDQpEXy0jt8UDulUsgAAiSusu9yLL74op9OpRx99VOeff36k2oQ4Yx2gbp1MNBTW/VNSCeQAgMQV1pisnTt3atSoUQSsBBfRMVkMfAcAJImwQlZWVpZSU1Mj1RbEKf8xWeE9JNrv14XpVLIAAIkrrJA1cuRIffHFF3K5wn/cCuJXZMdkWbsLqWQBABJXWCHr9ttvV2Zmph588EHt27cvUm1CnHFauvUiOiaLShYAIIEFVUq4/vrrA5a5XC6VlZXpk08+UWZmprp0CZyq0jAMvfbaa6G3EjEV0SkcjnYXGnZDNgfTtAEAEldQIau1ObDsdrt69ux5wm1aW4bOI7Ihq2V/Z5pDhhH6g6YBAIh3QYWsv/zlL9FqB+KYM4JjsrwD35mIFACQ6OivwUnZnTZ5i07WKRhC4a1k8XBoAECiI2ThpAzD8HUZhtNdaHpMNTe2hDTmyAIAJLqg7nTz5s2TYRj6wQ9+oKysLM2bN69d+xmGoUmTJoXSPsSJlHSHmupcYYUsV5NbOjo8j5AFAEh0Qd3pXnjhBRmGoSuvvFJZWVl64YUX2rUfIavz84aicMZk+c/2TnchACCxBRWySkpKJMn3a0LveyQ+7+B3V6NbHo8pmy34XwZaq2AOKlkAgAQX1J1u6NChJ3yPxGWdONTV4JIzIyXoYzQ3WiYipZIFAEhwDHxHu0RiriwXD4cGACQRQhbaxRqKQh2XZQ1nVLIAAIkuqHLCyJEjQ5ql2zAMLVu2LOj9ED+sE5KGOleWd/oGiUoWACDxBXWnO+ecc3gUSpKKRHchlSwAQDIJKmQ9+eST0WoH4lxKBB6tY62A8etCAECiY0wW2sVaefI+GidY/LoQAJBMIlZO2L59u8rLy9XQ0KDvfe97kTos4oQzIt2FjMkCACSPsO90Gzdu1G9/+1tt3brVt8wbskpLSzVz5kz96le/0vDhw8M9FWLIf0xWaAPfXZYKmPV4AAAkorC6C7dt26a7775be/bs0XXXXaeLL77Yb/0555yjbt26afny5eGcBnEg0mOyUlLpLgQAJLawygn/9V//JUl6/vnnlZ+frxdeeEGffvqpb71hGDrrrLO0adOm8Fp5EhUVFVq0aJE2btyoTZs2qb6+XiUlJTr33HPbtf/HH3+sjz76SJs2bdLBgwfVs2dPXXLJJZo0aZK6du3qt+3EiRO1d+/egGOMHz9eM2bMiMjniUfONOsUDqGGLMZkAQCSR1ghq7S0VCNHjlR+fn6b2/Tq1UurV68O5zQntXPnTi1YsED5+fkqLCzUhg0bgtp/zpw5ys3N1ejRo9WrVy998803evPNN7Vq1SrNnTtXqampftsPGjRI119/vd+yE12DRBCRKRyslSy6CwEACS6sO119fb1ycnJOuE1jY6M8Hk84pzmpoqIivfvuu8rKytLy5cv1wAMPBLX/ww8/HFD1Kioq0qOPPqqlS5dq3Lhxfuvy8vI0evTosNvdmUQmZFkeEE13IQAgwYU1JqtHjx5+A95b8/XXX+uUU04J5zQnlZGRoaysrJD3b61b8bLLLpPU8qvJ1jQ3N6u+vj7kc3Y2zgiOybI7bbLZmT0EAJDYwrrTXXrppfrss8/0+eeft7r+o48+UllZmUaMGBHOaWKisrJSkpSdnR2w7ssvv9To0aP1ve99TxMnTtTrr7/ewa3reHanTd7J/kN+rM7RShbTNwAAkkFYd7ubb75Zy5cv13333acxY8bo4MGDkqQ333xTGzZs0IcffqjevXtr4sSJEWlsR1qwYIHsdrtGjhzpt7ywsFBDhgxR3759VVVVpffee09PPfWUKioqNG3atDaPV1FR4QtuklReXh61tkeDYRhKSXeoqc4Vcnehyxey6CoEACS+sEJWdna2nnrqKc2ePVuLFy/2Lf/P//xPSVJxcbEeeOABZWZmtvuYHo9Hzc3N7drW6XRG5VmKS5cu1eLFi3XDDTeob9++fusee+wxv/dXXXWVZs6cqb/85S+aMGGCevbs2eox33nnHc2bNy9g+bZt2+R2B1aGampqVFZWFvqHiAaHKUmqq24IqW3ebka3XFH7bHF53ToBrlvwuGah4bqFhusWmmhdt+Li4nZtF3a/zSmnnKJnnnlGmzdvVllZmaqqqpSRkaHi4mKdeeaZQR9v7dq1mj59eru2ffnll1VQUBD0OU52/scff1wXXXSRbrvttpNubxiGJk6cqNWrV6u0tLTNAfHjx4/XsGHDfO/Ly8s1e/Zs9e/fX0VFRQHbl5WVtfsfsaOUda1UU1WNzOb2f8G8PG6P/u5qqd5lZneJ2meLx+vWGXDdgsc1Cw3XLTRct9DE+rqFFbJWrFjhGyA+aNAgDRo0qNXtnnrqKf3Lv/xLu47Zr18/zZo1q13b5ubmtq+h7bRlyxbNmjVLhYWFevjhh+VwtO/yeKtXVVVVbW6Tl5envLy8iLQzVryD312Nbnk8pmy29lcRreO4nIzJAgAkgbDudr/+9a81Z84cnXPOOW1u89RTT+mNN95od8jKzc3V2LFjw2lWSHbv3q0ZM2YoJydHTzzxhDIyMtq977fffiup9UHyiSQl/dhYKleDS86MlHbv6zd9A2OyAABJIKxfF55yyimaNWtWm9M4/OEPf9CiRYvi6rmF+/btCxh0XllZqXvvvVc2m01z5sxpMyxVVVUFjJ9yuVx65ZVXlJKS0u4Z5jurcObK8puIlEoWACAJhHW3++1vf6uf/exnmjlzpp555hn16tXLt+6ZZ57R66+/ruHDh+uhhx4Ku6En8+KLL0o6Nq/VBx98oHXr1kmSJk2a5NvukUceUWlpqVasWOFbNnPmTH377be64YYbtH79eq1fv963LicnRxdeeKEkaeXKlXrppZc0cuRI9enTR9XV1Vq6dKm2bdum22+/PeLdl/HGGo6a6l3qEsS+1lBGyAIAJIOw7nY9e/bUnDlzdNddd+nee+/V008/rW7duumZZ57RwoULdemll+qhhx6S3R797qG5c+f6vV+yZInvtTVktWbLli2SpFdffTVg3dChQ30hq7CwUAUFBVq6dKkOHz4sh8OhQYMG6aGHHtIVV1wR7keIe9YJSYOdK8vVaK1k0V0IAEh8YZcUTjvtND3++OO65557NHPmTA0ZMkSvv/66LrnkEv36179u9+DxcFkrUyfy5JNPhrxvUVFRwBQOySS87kIqWQCA5BKRZ5ucddZZevDBB7VlyxYtWrRI3/nOdzR79uwOC1joGClhPFrHGsoY+A4ASAZBpaD333//hOsvvPBClZWVadiwYfqf//kfv3VjxowJvnWIK9ZuPmtlqj2aG5nCAQCQXIK62/3mN79pdYZ10zRlGIZMs2VG8N///vd+ywzDIGQlAGc43YVUsgAASSaokHX//fdHqx3oBPzHZAU38J0pHAAAySaou10sJglF/AhnTBa/LgQAJJuIDHxHcrCOpQp6TBbzZAEAkgwhC+0WsSkc0qlkAQASX1AlhZEjR8pms+mll15S3759NXLkyFYHwh/PMAwtW7Ys5EYiPkTqsToOKlkAgCQQ1N3unHPOkWEYSk1N9XuP5OAMZ54sayUrlUoWACDxBRWyjp8tvbXZ05G47E6bDEMyzRAeq+Pd3pAchCwAQBJgTBbazTAMX5dhsN2FTUcrWSlpdqqfAICkQMhCUEINWd5KFr8sBAAki6DuePPmzQvpJIZhaNKkSSHti/jiDUmhjskiZAEAkkVQd7wXXnghpJMQshKHd/C7q9Etj8eUzXbyrj/TNH1juJiIFACQLIIKWSUlJdFqBzoJ6xxXrgaXnBkpJ93H3eyR6Wl5riXTNwAAkkVQd7yhQ4dGqRnoLI6fK6s9Ictv+gYqWQCAJMHAdwTFOqaqveOyXDwcGgCQhMK64+3bt++k2xiGoS5duqhLly7hnApxwjohaXvnymqikgUASEJhhayJEye2e86j7OxsXXbZZZo8ebK6d+8ezmkRQykhzPpOJQsAkIzC6i783ve+pyFDhsg0TWVmZmro0KG68sorNXToUHXt2lWmaeqcc87Rd77zHTmdTr399tu67bbbVFFREan2o4OF8vxC63ZUsgAAySKsssINN9ygO++8U5MmTdKNN96otLQ037rGxkYtWLBAixYt0tNPP61+/fpp/vz5mjt3rl566SXdc889YTceHc8akqwD2k+kuZGHQwMAkk9Ylaxnn31WxcXFmjJlil/AkqTU1FTdeuutKi4u1h//+EfZbDbdcsstOuOMM7Rq1aqwGo3YcYZSyWJMFgAgCYUVsv7xj3+oqKjohNucfvrpWrdune99cXGxDh48GM5pEUP+3YXtG/hu3Y4xWQCAZBFWyPJ4PNq9e/cJt9m1a5dM0/S9t9vtcjqd4ZwWMRTSwPdGayWLkAUASA5hhazBgwfr448/1ocfftjq+mXLlmnFihU6++yzfct27dql3NzccE6LGHKmWadwaF/IavKrZNFdCABIDmGVFe644w7deeed+vWvf60FCxZo8ODBysnJ0aFDh/SPf/xDW7ZsUVpamu644w5J0pEjR/T555/r6quvjkjj0fFC+XWhizFZAIAkFFbIGjBggP7whz/oP//zP7V+/Xpt2bLFb/3gwYM1ffp0DRgwQJKUmZmpt956K2CQPDqPkKZwsPy60Lo/AACJLOw73sCBA/WHP/xB+/bt05YtW1RbW6suXbpo4MCB6tWrl9+2drtdmZmZ4Z4SMeQMYUyWNYw5UglZAIDkELE7Xq9evQJCFRKP3WmTYUim2f7H6li3S0mnuxAAkBwiFrIOHDgQUMnq0aNHpA6POGEYhlLSHWqqc4U4TxaVLABAcgj7jrdr1y79/ve/15dffhmw7rzzztM999yj/Pz8cE+DOBJsyPIOfLfZDdkdYf2gFQCATiOskLVv3z7dddddOnTokPr166dzzjlHubm5OnjwoNauXasvvvhCd911l5577jm6EhOIM92hWgUxJutodyGD3gEAySSsu968efN06NAh3XPPPRo/frwMw/Bb//bbb+v3v/+9XnzxRd13331hNRTxw9vl52p0y+MxZbMZJ9ze213I9A0AgGQSVt/N6tWrdemll+qaa64JCFiSdM011+jSSy/Vp59+Gs5pEGesg9dd7ZiQ1FfJYjwWACCJhBWyDh8+rMLCwhNuU1hYqMOHD4dzGsSZYObKMj2mXEfnyXJQyQIAJJGwQlZ2dra2b99+wm22b9+u7OzscE6DOGOtSJ1sXJbfRKRUsgAASSSskHXhhRdq5cqVevfdd1tdv3jxYv3973/XRRddFM5pEGesE5KebK4spm8AACSrsO56t956q/7+979rzpw5ev311zV06FB1797d9+vC7du3q1u3bpo8eXKEmot4kBLErO+uBh4ODQBITmGFrF69eunpp5/WnDlzVFpaGtB1eO655+ree+9l+oYEE8yYLCpZAIBkFfZdr2/fviopKWnXswuRGKwVqeaT/LrQup6B7wCAZMKzCxE0Z1CVrGPdhU4qWQCAJBLUXe+xxx4L+UT3339/yPsivvh3F55k4Hs9lSwAQHIKKmS99957IZ3EMAxCVgIJZuA7UzgAAJJVUHe9hQsXRqsd6ESs3X4nG5Pl8hv4TiULAJA8ggpZvXv3jlY70IkE9evCeipZAIDkFNZkpEhOQYWsRkslK51KFgAgeRCyEDRnMGOyrJWsVCpZAIDkQchC0OxOmwyj5XUwj9Xh14UAgGRCyELQDMPwdRmevLvQUslKp5IFAEgehCyEpN0hy7I+JZVKFgAgeRCyEBLvuKxgHhDt4NeFAIAkQshCSLzTMbga3fJ4zDa3847JcqTaZbMZHdI2AADiASELIbFOx+A6wYSk3oHxTEQKAEg2CdF/U1FRoUWLFmnjxo3atGmT6uvrVVJSonPPPbdd+69YsUJvv/22tm7dqqqqKmVnZ6u4uFi33nqrCgsLA7b/5JNP9MILL6i8vFzZ2dm66qqrdMstt8jhSIjL2S7Hz5XlzEhpdTtvJYuJSAEAySYhKlk7d+7UggULdODAgVZD0cls3bpVXbt21bXXXquf//znuuaaa7R582ZNnTpVW7Zs8dt21apV+vd//3dlZmZq+vTpGjFihF566SWVlJRE6uN0CtbQdKJxWb7uQipZAIAkkxDlhaKiIr377rvKysrS8uXL9cADDwS1/+TJkwOWjRs3ThMmTNBbb72lGTNm+JY/88wzGjBggH73u9/5KlcZGRmaP3++rr32WhUUFIT1WToL64Skbc2V5XZ55HG1jNeikgUASDYJUcnKyMhQVlZWRI+Zk5OjtLQ01dTU+JZt375d27dv1/e//32/rsEf/OAHMk1Ty5cvj2gb4llKO2Z9b+bh0ACAJEZ5waK6ulput1uVlZV6/fXXVVtbq/PPP9+3/uuvv5bUUjmzysvLU48ePbR58+YObW8stef5hdbpG6hkAQCSDXc+i2nTpmnHjh2SpPT0dN1yyy26+uqrfesrKyslSbm5uQH75ubm+ta3pqKiwm99eXl5pJodE9bKVFshi0oWACCZxV3I8ng8am5ubte2TqdThhG5uZfuv/9+1dXV6dtvv9WSJUvU2Ngoj8cjm62lV7Wpqcl33tbaUldX1+ax33nnHc2bNy9g+bZt2+R2B45pqqmpUVlZWYifJPoOHDzWjbpz+y65elQFbFO9u9H3uqquqkM+T7xft3jFdQse1yw0XLfQcN1CE63rVlxc3K7t4i5krV27VtOnT2/Xti+//HJEB5qfffbZvtejRo3SzTffLEm68847JR0LV96wZdXU1KTU1NQ2jz1+/HgNGzbM9768vFyzZ89W//79A7ofJamsrKzd/4ixUF6/T5t1tLLXrYeKiwcEbLPbU6H12itJ6nVKTxUXnx71dsX7dYtXXLfgcc1Cw3ULDdctNLG+bnEXsvr166dZs2a1a9vWuu0ipWvXrjrvvPO0dOlSX8jynq+yslK9evXy276yslJnnnlmm8fLy8tTXl5e1Nrb0do18L3eOiaL7kIAQHKJu5CVm5ursWPHxroZkqTGxkbV1tb63g8aNEiS9NVXX/kl44qKCh04cEDjx4/v8DbGijPNOoVDGyGr0TomK+6+agAARFVCTOEQjH379gUMOj906FDAdnv27NEXX3zh15XXv39/9evXT3/729/8xlG99dZbMgxDI0eOjF7D40x7fl1oXU4lCwCQbBKmvPDiiy9KapnLSpI++OADrVu3TpI0adIk33aPPPKISktLtWLFCt+yyZMn6/zzz9fAgQPVtWtX7dq1S4sXL5bL5dLUqVP9zvOzn/1Ms2bN0r333qtRo0Zp69atevPNNzVu3Diddtpp0f2QcST4KRwIWQCA5JIwIWvu3Ll+75csWeJ7bQ1Zrbnmmmu0atUqffrpp6qrq1NOTo4uvPBC3XTTTRowwH9A96WXXqrZs2dr3rx5KikpUbdu3XTTTTe1Omt8InO2Z0xWI/NkAQCSV8Lc+ayVqRN58sknA5ZNmTJFU6ZMafe5RowYoREjRrR7+0Rkd9pkGJJptv1YHWuFy0HIAgAkmaQbk4XIMAzD12XY9mSklkpWOt2FAIDkQshCyE4esiwD31OpZAEAkgshCyHzjsviAdEAAAQiZCFk3sHsrka3PB4zYL3LOvA9nUoWACC5ELIQMmtwcrUyIam3G9EwJHsKXzUAQHLhzoeQWQeztzYuyzvwPSXdEdEHeQMA0BkQshAy69xXrY3L8o7JcjAeCwCQhAhZCJl1QtLW5sryVbKYIwsAkIQIWQhZyglmfTdN0zdOi5AFAEhGhCyE7ETPL3Q3eWQe/cEh0zcAAJIRIQshs4an40OW/xxZVLIAAMmHkIWQ+Y/JOlHIopIFAEg+hCyEzL+70H/gu99zC6lkAQCSECELITvRwHdr9yFTOAAAkhEhCyFzpp2gu7CRShYAILkRshCyE/260MWYLABAkiNkIWQnClnWMVpUsgAAyYiQhZA5TzQmq5FKFgAguRGyEDK70ybvc5+Pf6wOlSwAQLIjZCFkhmH4ugxPNBkpvy4EACQjQhbC0mbI4teFAIAkR8hCWLzjsk40TxZjsgAAyYiQhbB4q1SuRrc8HtO33GWd8T2dShYAIPkQshAWa4Cyzo3l9+zCVEIWACD5ELIQlpT0Y12B1i5C/2cX0l0IAEg+hCyExTqo3TouyztPlj3FJpuDrxkAIPlw90NYrBOSWqtX3qqWI5UqFgAgORGyEJaUNmZ99wYuBr0DAJIVIQthaev5hd6B74zHAgAkK0IWwmINUd6Q5fGYcjd5jq6nkgUASE6ELITFf0xWS8iyTuVAJQsAkKwIWQhLa2OyrAPgHVSyAABJipCFsPiPyWoJV9aJSJ2ELABAkiJkISzWEOUNV/6VLLoLAQDJiZCFsLT260K/R+pQyQIAJClCFsLSasiqZ+A7AACELITF2crAd1cjzy0EAICQhbDYnTYZRstr71gs/4dD010IAEhOhCyExTAMX5dha92FTOEAAEhWhCyELSBkNVqncKC7EACQnAhZCJt3XJZvMtJ6JiMFAICQhbB5x125Gt3yeMzjpnCgkgUASE6ELITNOo2Dq8F13K8LqWQBAJITIQthS0k/Vq1qrncxTxYAACJkIQKOnyvLbwqHdCpZAIDkRMhC2FL8nl/o9huT5XBSyQIAJCdCFsKW0kYly5Fql2EzYtUsAABiipCFsB3//ELX0Xmy6CoEACQzQhbCZh3c3jLwvaWSlZJKVyEAIHkRshA268D35gaXb0wWlSwAQDIjZCFs1jDVUN0sj9tsWc70DQCAJEbIQtisIavuUMOx5UxECgBIYoQshM2ZZg1Zjb7XDipZAIAkRshC2KhkAQAQKCHughUVFVq0aJE2btyoTZs2qb6+XiUlJTr33HPbtf+KFSv09ttva+vWraqqqlJ2draKi4t16623qrCw0G/biRMnau/evQHHGD9+vGbMmBGRz9PZ+IWsw8cqWYzJAgAks4QIWTt37tSCBQuUn5+vwsJCbdiwIaj9t27dqq5du+raa69Vt27ddPDgQS1ZskRTp07Vs88+q4EDB/ptP2jQIF1//fV+y/Lz88P+HJ2V9deF9VVNvtdUsgAAySwh7oJFRUV69913lZWVpeXLl+uBBx4Iav/JkycHLBs3bpwmTJigt956K6BClZeXp9GjR4fT5IRid9pkGJJpSjKPLaeSBQBIZgkRsjIyMiJ+zJycHKWlpammpqbV9c3NzXK5XEpPT4/4uTsbwzCUku5QU53LbzmVLABAMuMuaFFdXS23263Kykq9/vrrqq2t1fnnnx+w3ZdffqnRo0fL7Xard+/euu6663TdddfFoMXxo7WQxa8LAQDJjJBlMW3aNO3YsUOSlJ6erltuuUVXX3213zaFhYUaMmSI+vbtq6qqKr333nt66qmnVFFRoWnTprV57IqKClVWVvrel5eXR+dDxIgz3aHa45ZRyQIAJLO4uwt6PB41Nze3a1un0ynDMCJ27vvvv191dXX69ttvtWTJEjU2Nsrj8chmOzbTxWOPPea3z1VXXaWZM2fqL3/5iyZMmKCePXu2eux33nlH8+bNC1i+bds2ud3ugOU1NTUqKysL7wN1oGazKWDZnn27VV92sEPb0dmuW7zgugWPaxYarltouG6hidZ1Ky4ubtd2cRey1q5dq+nTp7dr25dfflkFBQURO/fZZ5/tez1q1CjdfPPNkqQ777yzzX0Mw9DEiRO1evVqlZaWtjkgfvz48Ro2bJjvfXl5uWbPnq3+/furqKgoYPuysrJ2/yPGg/LutarZXeG3bEBRoXqdntOh7ehs1y1ecN2CxzULDdctNFy30MT6usVdyOrXr59mzZrVrm1zc3Oj1o6uXbvqvPPO09KlS08YsiT5qldVVVVtbpOXl6e8vLyItjGepKQHjr+iuxAAkMzi7i6Ym5ursWPHxroZkqTGxkbV1h4/0ijQt99+K0nKzs6Ocovil3WuLC+mcAAAJLOke6zOvn37AgadHzp0KGC7PXv26IsvvvDryquqqgoYP+VyufTKK68oJSWl3TPMJ6LWqlZUsgAAySxh7oIvvviiJGn79u2SpA8++EDr1q2TJE2aNMm33SOPPKLS0lKtWLHCt2zy5Mk6//zzNXDgQHXt2lW7du3S4sWL5XK5NHXqVN92K1eu1EsvvaSRI0eqT58+qq6u1tKlS7Vt2zbdfvvtUe2+jHcprVSymMIBAJDMEiZkzZ071+/9kiVLfK+tIas111xzjVatWqVPP/1UdXV1ysnJ0YUXXqibbrpJAwYM8G1XWFiogoICLV26VIcPH5bD4dCgQYP00EMP6YorrojsB+pkjg9Zht2QPSXpCqUAAPgkTMiyVqZO5MknnwxYNmXKFE2ZMuWk+xYVFQVM4YAWx4+/Skm1R3R6DQAAOhtKDYiI4we+t9Z9CABAMiFkISKOD1X8shAAkOwIWYiIgJCVSiULAJDcCFmICGca3YUAAFgRshARx4cqpm8AACQ7QhYiInBMFpUsAEByI2QhIgJ+XUglCwCQ5AhZiAi70ybrtFhUsgAAyY6QhYgwDMOvy5BKFgAg2RGyEDHWkOWgkgUASHKELESMdVyWk0oWACDJEbIQMdZxWFSyAADJjpCFiGFMFgAAxxCyEDEp6ceCFb8uBAAkO0IWIsZJJQsAAB9CFiLmlLNyJUlpWU7l9O0a49YAABBb9OkgYgYOP1U5/boqs3u6HE4qWQCA5EbIQkTl9suKdRMAAIgLdBcCAABEASELAAAgCghZAAAAUUDIAgAAiAJCFgAAQBQQsgAAAKKAkAUAABAFhCwAAIAoIGQBAABEASELAAAgCghZAAAAUUDIAgAAiAJCFgAAQBQ4Yt2AZNXY2ChJKi8vb3X9jh07ZLfbO7JJCYHrFhquW/C4ZqHhuoWG6xaaaF63goICpaWlnXAbQlaM7N27V5I0e/bsGLcEAAAE6/nnn1dRUdEJtzFM0zQ7qD2wOHz4sFavXq0+ffrI6XT6rSsvL9fs2bP1y1/+UgUFBTFqYefDdQsN1y14XLPQcN1Cw3ULTbSvG5WsOJadna3Ro0efcJuCgoKTpmQE4rqFhusWPK5ZaLhuoeG6hSaW142B7wAAAFFAyAIAAIgCQlYcys3N1eTJk5WbmxvrpnQqXLfQcN2CxzULDdctNFy30MTDdWPgOwAAQBRQyQIAAIgCQhYAAEAUELIAAACigHmy4khTU5Pmzp2r//7v/1Z1dbUGDBign/70p7rwwgtj3bS4tWbNGk2fPr3Vdc8++6zOOuusDm5R/Kmrq9Nrr72msrIybdy4UdXV1Zo1a5bGjh0bsO327dv1hz/8QevXr5fD4dAll1yiu+66S9nZ2R3f8Bhr73V79NFH9f777wfs369fP82fP7+jmhsXNm7cqPfff19r1qzR3r17lZWVpbPOOks//elP1bdvX79t+a4d097rxnftmG3btumFF17QV199pYMHDyotLU0FBQW64YYbNGzYML9tY/ldI2TFkd/85jdavny5rrvuOuXn5+u9997Tfffdp5KSEg0ZMiTWzYtrEyZM0Jlnnum37NRTT41Ra+LLkSNHNG/ePPXq1UsDBw7UmjVrWt1u//79+pd/+RdlZmbqtttuU319vV577TVt3bpVzz33nFJSUjq45bHV3usmSU6nU/fdd5/fsi5dukS7iXFnwYIFWr9+va644goNGDBAlZWVevPNN/XTn/5Uzz77rAoLCyXxXTtee6+bxHfNa+/evaqrq9OYMWOUl5enhoYGffzxx5o1a5ZmzJih8ePHS4qD75qJuLBhwwZzxIgR5oIFC3zLGhoazB/96EfmHXfcEcOWxbcvv/zSHDFihLls2bJYNyVuNTY2mhUVFaZpmubGjRvNESNGmEuWLAnY7ne/+5353e9+19y7d69v2WeffWaOGDHCfPvttzusvfGivdftkUceMUePHt3RzYtL69atM5uamvyW7dixwxw1apT58MMP+5bxXfPX3uvGd+3EXC6Xeeutt5o//vGPfcti/V1jTFac+Pjjj2W3233pW5JSU1N19dVXa8OGDdq3b18MW9c51NXVyeVyxboZccfpdLZrnpiPP/5Yl156qXr16uVbdsEFF6hv375atmxZNJsYl9p73bzcbrdqa2uj2KL4N3jw4IDKQN++fXXaaaepvLzct4zvmr/2Xjcvvmuts9vt6tmzp2pqanzLYv1do7swTmzevFn5+fkBZV9vF9iWLVv8viTw95vf/Eb19fWy2+0aMmSIpk2bpjPOOCPWzeo0Dhw4oEOHDrX6fK8zzzxTq1atikGrOo+GhgaNHTtWDQ0N6tq1q0aNGqU77rhDGRkZsW5azJmmqUOHDum0006TxHetvY6/bl581/zV19ersbFRtbW1WrlypT799FNdccUVkuLju0bIihOVlZWt/n/N3mUVFRUd3aROweFwaOTIkfrOd76jbt26afv27Vq4cKHuuusuPfPMMzr99NNj3cROobKyUpLa/A5WVVWpqalJTqezo5sW93Jzc3XDDTfo9NNPl2ma+vTTT/XWW2/pm2++UUlJiRyO5P6f2aVLl+rAgQOaMmWKJL5r7XX8dZP4rrXm6aef1jvvvCNJstlsuuyyy/Tzn/9cUnx815LvXyRONTY2tjoAz/uP39jY2NFN6hQGDx6swYMH+94PHz5cl19+uW699Vb96U9/0pw5c2LYus7D+/062Xcw2W98rZk6darf+1GjRqlv3756/vnn9fHHH2vUqFExalnslZeX6z/+4z901llnacyYMZL4rrVHa9dN4rvWmuuuu06XX365KioqtGzZMrndbjU3N0uKj+8aY7LiRGpqqu+LYdXU1ORbj/bJz8/X8OHDtWbNGrnd7lg3p1Pwfr/4DkbGxIkTZbPZ9Pnnn8e6KTFTWVmpX/ziF+rSpYt+/etfy263S+K7djJtXbe2JPt3raCgQBdccIHGjBmjxx9/XPX19br//vtlmmZcfNcIWXEiNzfXV9q08i7Ly8vr6CZ1aj179lRzc7MaGhpi3ZROwVtOb+s7mJWVldSVhWClpqYqKytLVVVVsW5KTNTU1Oi+++5TTU2N5syZ4/e/X3zX2nai69aWZP+uHe/yyy/Xpk2btHPnzrj4rhGy4sTAgQO1a9eugF+MlJWV+daj/b799ls5nU6lp6fHuimdQo8ePZSdna2vvvoqYN3GjRv5/gWprq5OR44cScqJNRsbG3X//fdr586deuyxxwIGbvNda93Jrltbkvm71hpvF2FNTU1cfNcIWXHi8ssvl9vt9g3gk1rKmUuWLFFxcTG/LGzD4cOHA5Zt2bJFK1eu1IUXXiibja94e40cOVJ///vf/aYL+eKLL7Rz507fr3Xgr7GxUXV1dQHLX3zxRZmmqYsvvjgGrYodt9utBx98UBs2bNBDDz2ks88+u9Xt+K75a89147vm79ChQwHLXC6XPvjgA6WmpvpCaqy/awx8jxPFxcW64oor9Kc//UmHDx/Wqaeeqvfff1979+7VL37xi1g3L2796le/Umpqqs4++2zl5ORo+/bt+tvf/qa0tLSAQaLJ7I033lBNTY2vbL5y5Urt379fUsts+ZmZmbrpppu0fPly3X333br22mtVX1+vV199VYWFha0+gicZnOy6VVdX6yc/+Ym++93vql+/fpKk1atXa9WqVbr44os1fPjwmLU9Fp5++mmtXLlSl156qaqrq/Xf//3ffutHjx4tSXzXjtOe63bw4EG+axZz5sxRbW2tzjnnHPXo0UOVlZVaunSpduzYoTvvvNM3pUWsv2uGaZpm1M+CdmlsbPQ9u7CmpkaFhYX66U9/qosuuijWTYtbixYt0tKlS7V7927V1tYqOztb559/viZPnqz8/PxYNy9uTJw4UXv37m113cKFC9WnTx9JLc8DO/4ZX3feeae6d+/ekc2NGye7bpmZmSopKdGGDRtUWVkpj8ejU089Vf/0T/+kH/3oR0n3k/p//dd/VWlpaZvrV6xY4XvNd+2Y9ly36upqvmsWH374oRYvXqytW7fqyJEjysjIUFFRkX74wx8GBM5YftcIWQAAAFHAgBUAAIAoIGQBAABEASELAAAgCghZAAAAUUDIAgAAiAJCFgAAQBQQsgAAAKKAkAUAABAFhCwAAIAoIGQB6LT27Nmjyy67TI8++mismxIR7733ni677DK99957sW4KgAggZAFIKP/6r/+qyy67LNbNaFWihUIAJ5ZcT5QEkFB69Oihl19+WV26dIl1UyJixIgRKi4uVm5ubqybAiACCFkAOi2Hw6GCgoJYNyNiMjMzlZmZGetmAIgQwzRNM9aNAIBQ7NmzR9dff73GjBmjf/u3f2uzm9C73uubb77Ryy+/rNLSUlVVVSk3N1fDhg3Trbfeqm7durV6/BtvvFHPP/+81q5dq6qqKi1cuFB9+vTRihUrtGzZMm3atEkVFRVyOBwaMGCArr32Wl1++eW+Y7333nv6zW9+02r7SkpKdO655/q2mTVrlsaOHeu3zfr16/Xyyy9rw4YNamxsVO/evXXllVfqxhtvVFpamt+2l112mYYOHaoHH3xQzz77rFatWqX6+noNHDhQU6dO1bnnnhvspQYQAipZABLG5MmT9f7772vv3r2aPHmyb/mgQYN8rz/55BM9+OCDMgxDw4cPV8+ePbV9+3b99a9/1erVq/Xcc8+pa9eufsfdvXu3pk2bpsLCQo0ZM0ZVVVVKSUmRJP3pT3+Sw+HQ4MGDlZubq8OHD2vlypV64IEHNH36dE2YMEGSNHDgQF177bVatGiRBg4cqOHDh/uO37t37xN+rmXLlunhhx9WSkqKrrzySmVnZ+uzzz7TvHnztHr1apWUlCg1NdVvn5qaGt15553KzMzU6NGjdejQIS1btkwzZszQ888/r8LCwpCuMYD2I2QBSBhTpkxRaWmp9u7dqylTpgSsP3LkiB555BF169ZNTz/9tF+4+fDDD/XQQw9p7ty5uvvuu/32W79+vSZPntzqMZ944gmdcsopfsvq6ur0s5/9THPnztXVV1+ttLQ0DRo0SJmZmb6Q1dqxWlNbW6vf/va3stvtevbZZzVgwABJ0u23366HH35YH330kV577TVNmjTJb78tW7bon//5n3X33XfLZmv5jdN5552nJ554Qn/96181Y8aMdp0fQOj4dSGApPHBBx+otrZWt99+e0D1aNSoUTr99NP14YcfBuzXvXt33Xzzza0e8/iAJUkZGRkaO3asampqtGnTprDa/Mknn6impkZXXXWVL2BJks1m07Rp02S321ud8iE9PV133HGHL2BJLd2mdrs97DYBaB8qWQCSxoYNGyRJZWVl2r17d8D6pqYmHTlyRIcPH1Z2drZv+cCBA33dg8c7dOiQXnnlFa1atUr79u1TY2Oj3/qKioqw2rx582ZJ0tChQwPW9erVS6eccop27typuro6ZWRk+Nbl5+f7vZdafijQvXt31dTUhNUmAO1DyAKQNKqrqyVJb7755gm3a2ho8Hufk5PT6nZVVVW6/fbbtW/fPg0ePFgXXHCBMjMzZbPZtGXLFn3yySdqbm4Oq821tbWSWqpprcnNzdXOnTtVW1vrF6ramtbCbrfL4/GE1SYA7UPIApA0vCFk3rx5QQ38Ngyj1eWLFy/Wvn379JOf/CRgTNT8+fP1ySefhN7Yo7xh6eDBg62u9y5PlLnCgETCmCwACcU7BsntdgesKy4ulnSs2zBc3i5H6y8FvdatW9dm24KpJHl/GVlaWhqwbt++fdq9e7dOOeWUgK5BALFHyAKQULKysiRJ+/fvD1h31VVXKSMjQ88//7y2bdsWsL6hoSGoAOYdPL9+/Xq/5UuXLtWqVasCtu/atasMw2i1bW0ZPny4MjMztWTJEr82m6ap5557Tm63O2BOLQDxge5CAAnlvPPO0/Lly/X//t//08UXXyyn06mBAwdq2LBhys7O1q9+9Ss98MADmjJlii666CL169dPzc3N2rt3r0pLS3X22Wdrzpw57TrX6NGjtWDBApWUlGjNmjXq1auXtmzZoi+//FKXXXaZVqxY4bd9RkaGzjjjDK1du1azZ89Wfn6+DMPQ9773vTbnyurSpYtmzpyphx9+WHfccYeuuOIKZWdn64svvtBXX32lM888Uz/60Y/Cvm4AIo+QBSChjBs3Tnv27NFHH32kBQsWyO12a8yYMRo2bJgk6ZJLLtHcuXP16quv6osvvtDnn3+utLQ09ejRQ2PHjtXo0aPbfa6ePXvqySef1LPPPqvPP/9cbrdbp59+un73u99p//79ASFLkn75y1/qqaee0t///nfV1tbKNE0NGTLkhBOSXnHFFerevbvmz5+vFStW+GZ8nzRpkm688caAiUgBxAceqwMAABAFjMkCAACIAkIWAABAFBCyAAAAooCQBQAAEAWELAAAgCggZAEAAEQBIQsAACAKCFkAAABRQMgCAACIAkIWAABAFBCyAAAAooCQBQAAEAWELAAAgCj4/3jat4S+XSnWAAAAAElFTkSuQmCC", "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": 16, "id": "1695af05", "metadata": {}, "outputs": [ { "data": { "image/png": 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AnISwY2GBJHdP7nwP01gAACch7FhYsrsnR0SOjGAaCwDgJIQdCzPW7CQxsvNFEXPQH1IoEEpbuwAAyCWEHQszHgKaxMhOzPJz9toBADgFYcfC/CYLlCUOAwUAOAdhx8K6PY3FLsoAAAci7FiY37CpYPIFyhK7KAMAnIOwY2HdH9mJrdlhGgsA4AyEHQvzd3efnZgjJdhYEADgFIQdCzO7z07n1wIAYGeEHQvr/sgOq7EAAM5D2LGw7hwEKkleVmMBAByIsGNhkakol8clt/fov8o8VmMBAByIsGNhkWms/EKvXC7XUe837LPDNBYAwCEIOxYWGdnxJlGcLHFcBADAmQg7FhYpMk6mOLn9PgqUAQDOQ9ixqHAoHB3ZSaY4Weo8jcXIDgDAGQg7FmU8KiK5kR3DNBZhBwDgEIQdi4qdhkrmqAip0zQWx0UAAByCsGNR3d09WZLcXrdcHlfc6wEAsDPCjkUZNhRMskDZ5XJFd1EOsBoLAOAQhB2Lij21PNkCZakjGLHPDgDAKQg7FuVvjp3GSm5kR+rYk4dpLACAUxB2LMp4CGg3RnYi01gtAYXD4ZS3CwCAXEPYsShjgXJ3Rnba7w2HpaA/lPJ2AQCQawg7FmU88Tz5sMNhoAAAp0n+r2SO2rp1q5544glt2rRJbW1tGjx4sC688EJddNFF2W5aWhkKlAu6M40Vs4tya0CFyk9puwAAyDWWDjtvvvmm7rjjDh1//PGaO3euioqKtGvXLu3bty/bTUs7Q4FyN0Z2Yg8NpUgZAOAEKQk7wWBQjY2N8vv9CZ8vLy9PxccYNDU16ac//am++tWv6p577pHb7awZOWOBstlpLJafAwDsr0dhZ+vWrXrssce0ceNGBQKJ/3C6XC5VV1f35GMS+r//+z99/vnnuvrqq+V2u9Xc3KyCggLHhJ7YURlvd1ZjcRgoAMBhTIedbdu2af78+fJ4PDr99NO1bt06VVZWasCAAfrggw904MABjR8/Xsccc0wq2xv19ttvq3fv3qqvr9d//ud/qq6uTkVFRZo+fbrmz5+vgoKChK+rr69XQ0ND9HFtbW1a2pdufhNnY0kcBgoAcB7TYed3v/udJOnXv/61KioqNGXKFE2aNEnz5s1Ta2urFi1apFWrVun2229PWWNjffLJJwoGg/rBD36g888/X9dcc402bNigP//5z/L5fLrzzjsTvm758uVasmRJ3PUdO3YoGIz/4+/z+VRTU5Pq5vfYgfrG6M8f7twmT15yI1oNBw5Gf679qFaHezcc4W7zcrXfch39Zg791n30mTn0mznp6rexY8cmdZ/psLNp0yZVVVWpoqIiei2ySV1BQYFuueUWvffee1q8eLF+9KMfmf2YLjU3N6ulpUVf+9rXdPPNN0uSpkyZIr/fr+XLl+uKK67Q0KFD4143a9YsVVVVRR/X1tZqwYIFGjFihMaMGRN3f01NTdKdmUnbPAcktcrlkk4+5SS5XK6kXufd+4l2aL8kaVDZMTpx7LC0tC9X+y3X0W/m0G/dR5+ZQ7+Zk+1+Mx12mpqaNHjw4I438nrV3Nwcfex2uzV+/HitXLmyZy3sQmSaatq0aYbr5557rpYvX67NmzcnDDtlZWUqKytLS5syqe2LaSxvoTfpoNN+P9NYAABnMV3NW1JSokOHDkUfDxgwQJ988onhnra2NrW0tJhv3RGUlpZGPzdW//79JcnQNjuK1Ox05xBQyVigzGosAIATmA47FRUV+vjjj6OPx40bp7feekvvvfeeJGnnzp2qrq7W8OHDe97KBCJTTp331Kmvr5fUHsbsLLL7cewmgcnIY2QHAOAwpsPOhAkT9O6770bDxaWXXqpwOKz58+frwgsv1OWXXy6fz6fLLrssZY2NNXXqVEnS888/b7j+/PPPy+Px6NRTT03L5+aCcDgcPS6iOyuxpM6rsRjZAQDYn+mana997WuaOnWq+vTpI0mqrKzUgw8+qCeffFKffvqpxowZo9mzZ2vChAkpa2ys0aNH67zzztMLL7ygYDCo8ePHa8OGDaqurtZll11mi7qcrgTbQoocWN6dE8/b74+ZxmplZAcAYH+mw47X642rlxk3bpzuu+++HjcqWbfeeqvKy8u1YsUK/e1vf1N5ebnmz5+viy++OGNtyAazuydLnQqUCTsAAAew9NlYXq9Xl19+uS6//PJsNyWjjGGnmyM7BRwXAQBwlh6HnYaGBn3wwQc6dOiQQqFQwntmzJjR049BDLOHgErtS9Wj70OBMgDAAUyHndbWVt1///1auXJldDPBzsLhsFwuF2EnxXoyjeV2u+TJdyvYFiLsAAAcwXTYefTRR/XKK69o6NChmjZtmgYOHCiPp3tTKjAnNqR0dxqr/TVeBdvamMYCADiC6bBTXV2tiooKLV68WPn5+alsE46iJyM7UsfycwqUAQBOYHqfHZ/PpzPOOIOgkwU9KVCOfQ1LzwEATmA67AwbNkz79+9PZVuQpJ4UKEsduy4HWoMKhRLXWwEAYBemw86cOXP02muvxZ2HhfTr8TRWzGgQozsAALtL+i/lhg0bDI8HDhyoM844Q9dee62++c1vavTo0erVq1fC144fP74nbUQnsQXKXpMFyhGBloDyTYwOAQBgFUn/lbv55pvlcrnirofDYT3xxBMJn4tYtWqVqcYhsdiRnXwTIzscBgoAcJKk/1LOnTv3iIEGmRM5BFQyN7JjOAy0leXnAAB7SzrsXHHFFelsB7ohdsm4qQJlwzQWIzsAAHszXaCM7Ikd2enJPjsSe+0AAOyvx5Wp9fX1evXVV7Vt2zb5fD4VFxfr+OOP1znnnKOysrJUtBGdxI7GxB7smazYmh12UQYA2F2Pws5f/vIXPfLII/L7/YbzsV5++WUtXrxY3/nOd/T1r3+9x42EUaRA2Vvgkcvd/TqqPA4DBQA4iOmws3LlSi1cuFD9+vXTt7/9bZ1yyikaMGCAPv/8c23cuFHPPPNM9PlzzjknlW12vEjYMVOvIxmLmgk7AAC7Mx12li1bpn79+um3v/2tYbpq2LBhGj9+vGbOnKkrr7xSy5YtI+ykWCSgmJnCan9dTIEyq7EAADZnukC5trZWU6dO7bIuZ9CgQZo6dapqa2tNNw6JRQqUGdkBAODoTIed4uJiFRYWHvGeoqIiFRcXm/0IJBD0BxUKttdHmTkEVDKOCHFcBADA7kyHnaqqKq1bt06BQOJpkEAgoLVr12rixImmG4d4sSMxZpadd36dn9VYAACbMx12rr/+ehUVFel73/ueNm/ebHjuvffe0/e+9z316tVL1157bY8biQ49PQS0/XVMYwEAnMN0gfKVV16pQCCghoYG3XDDDfJ4POrXr58aGxsVDLb/AS0tLdWVV15peJ3L5dJTTz3Vs1Y7mL85dvdkc9NYxpodRnYAAPZmOuyEw2F5PB4NGjTIcL20tDTuviM9RvekZmQndjUWIzsAAHszHXb++Mc/prIdSJIx7Jgb2fHkuSWXpDDTWAAA++NsLItJRYGyy+WKBiWmsQAAdkfYsZjYcOI1ObIjdWwsyDQWAMDukh4aWLJkiakPcLlcmjt3rqnXIp6xQNn80WaRoBRgGgsAYHNJ/7V84oknTH0AYSe1Ykd28k1OY0kdU2BMYwEA7C7pv5YLFy5MZzuQpNianR5NY33x2lAwrGAgJI+XGU0AgD0lHXbGjx+fxmYgWalYei5J3gLjLsqe4vwetQsAgFxl/q9ljGAwqMbGRvn9/oTPl5eXp+JjoI5DQKWe1ewYzsdqCUocYQYAsKkehZ2tW7fqscce08aNG7s8I8vlcqm6uronH4MYAcPSc/PTWIZdlFmRBQCwMdNhZ9u2bZo/f748Ho9OP/10rVu3TpWVlRowYIA++OADHThwQOPHj9cxxxyTyvY6XqqmsQy7KFOkDACwMdN/LX/3u99Jkn7961+roqJCU6ZM0aRJkzRv3jy1trZq0aJFWrVqlW6//faUNRZSWwp2UO78WnZRBgDYmeklOJs2bVJVVZUqKiqi1yLnXhUUFOiWW25RWVmZFi9e3ONGokNkGsuT55bbY34FFYeBAgCcwvRfy6amJg0ePDj62Ov1qrm5ueON3W6NHz9e77zzTs9aCINIgXJPipOljh2UJXZRBgDYm+mwU1JSokOHDkUfDxgwQJ988onhnra2NrW0tJhvHeJERmF6MoUldR7ZIewAAOzLdNipqKjQxx9/HH08btw4vfXWW3rvvfckSTt37lR1dbWGDx/e81YiKhJMelKcLBmXnjONBQCwM9N/MSdMmKCHH35Y9fX1Kisr06WXXqo1a9Zo/vz56tOnj3w+n0KhkC677LJUttfRQsGQgv6QpBSEnUKmsQAAzmD6L+bXvvY1TZ06VX369JEkVVZW6sEHH9STTz6pTz/9VGPGjNHs2bM1YcKElDXW6YyHgDKNBQBAMkyHHa/XqwEDBhiujRs3Tvfdd1+PG4XE/K0xy84LUjiywzQWAMDGOP3RQoxHRfRsZMdQs8M0FgDAxgg7FuI3HBXR05EdCpQBAM5A2LGQ2FDi7WHYiT31PEDNDgDAxgg7FhJboJyfygJlprEAADZG2LGQ2AJlbw8LlD1et9xelyQKlAEA9kbYsZBUFihLHXU/LD0HANgZYcdCUlmg3P4e7YGJaSwAgJ0Rdiwkdrqpp2djSR1TYUxjAQDsjLBjISkf2SnoGNkJh8I9fj8AAHIRYcdC2gw1Oz0PO9EVWWEp0MZUFgDAngg7FhIwjOykrkBZ4jBQAIB9EXYsxG+o2UldgXL7exN2AAD2RNixEEPYScU0VsxePbF7+AAAYCeEHQuJjL64PS55vD3/1cWO7HBkBADArgg7FhLZVDAVozoSh4ECAJyBsGMhkUCSinodSfIWULMDALA/wo6FRAJJKlZitb8Pq7EAAPZH2LGIcCgcDSQpG9lhGgsA4ACEHYswrsRKw8gO01gAAJsi7FhE7GGd3hSN7OTF1uwwjQUAsCnCjkX4Y46KyE/ZNFbMPjtMYwEAbMpWYee///u/NXnyZM2dOzfbTUk5f4qPipCMIztMYwEA7Mo2YWfv3r1aunSpioqKst2UtIgdeUnVNBYFygAAJ0jNX80c8Ktf/Upjx45VKBRSY2NjtpuTcv7mNBcoU7MDALApW4zsbNiwQatXr9aNN96Y7aakjXEaKx07KBN2AAD2ZPmRnWAwqIULF+r888/XqFGjjnp/fX29Ghoaoo9ra2vT2byUCRhOPE/NyI43P3Y1FtNYAAB7snzYee6557Rnzx49+OCDSd2/fPlyLVmyJO76jh07FAzGj274fD7V1NT0tJk9tqv2YPTnPfW7Faw5lJL3dee5FPKH5Ws8nNJ/Z670m9XQb+bQb91Hn5lDv5mTrn4bO3ZsUvdZOuw0Njbqt7/9rf7t3/5NJSUlSb1m1qxZqqqqij6ura3VggULNGLECI0ZMybu/pqamqQ7M52aa7apVvslSSOOr9DQsQNT8r7re32m5sY2ecLelP47c6XfrIZ+M4d+6z76zBz6zZxs95ulw85vfvMb9enTR7Nnz076NWVlZSorK0tjq9IjtoA4dsl4T0UOA2U1FgDAriwbdurq6vTXv/5VN954o+rr66PX29raFAgE9Nlnn6l3797q27dvFluZOsbVWKn7tUWKnVmNBQCwK8uGnfr6eoVCIS1cuFALFy6Me/5b3/qWLrroIt10001ZaF3qGc7GStFqLKljr52gP6RQMCS3xxYL9AAAiLJs2BkxYoR+8pOfxF3/zW9+o8OHD+umm27S4MGDs9Cy9EjHDsqSlFdg3GsnvxdhBwBgL5YNOyUlJZo0aVLc9T/96U+SlPA5KzOeep7KaSzjXjv5vfJS9t4AAOQC/jPeIiI1Oy63S5681P3a8jgMFABgc5Yd2enKQw89lO0mpEVkGiuv0COXy5Wy9/VyGCgAwOYY2bGIyKhLKouTpU6HgbKLMgDAhgg7FhEd2UnRIaARsTU7jOwAAOyIsGMB4XA4ejZW7OqpVIh9Pz977QAAbIiwYwGB1qDC4fafU7kSS+q8GotpLACA/RB2LCB2xMWbwj122t8vdjUWIzsAAPsh7FiA4aiIFBcoU7MDALA7wo4FGI+KSPHITgHTWAAAeyPsWEDAcFREqkd2KFAGANgbYccCjCeep3HpOWEHAGBDhB0LaEvjyI63gOMiAAD2RtixgEALBcoAAJhF2LGAdBYos88OAMDuCDsW4G+OmcZK8aaCbq9bLnf7waLsswMAsCPCjgXEHtCZ6pEdl8sVfU8KlAEAdkTYsQDDyE6Ka3akjl2ZOfUcAGBHhB0L8KexQFnqOAyUAmUAgB0RdizAb1h6ntpprNj39LcEFY6cOAoAgE0QdizAuKlgOqax2t8zHAor6A+l/P0BAMgmwo4FRKexXJI3P30jOxJTWQAA+yHsWEBkGiuvwBNdJp5KhsNAKVIGANgMYccCIiM76ShO7vy+7LUDALAbwo4FRGp20lGvI7WPGEWw1w4AwG4IOzkuHA7L/0UAiZ1uSiVvIYeBAgDsi7CT44L+kMLB9uXgaRvZoUAZAGBjhJ0cl85DQBO9LyM7AAC7IezkuEBLeo+KkDqvxmJkBwBgL4SdHNeW5qMiOr8vq7EAAHZD2MlxhkNAi9I0jWVYjcU0FgDAXgg7OS6QgZEdLyM7AAAbI+zkuEwXKLMaCwBgN4SdHGc48TxtS8/ZZwcAYF+EnRxnGNlJ26aCrMYCANgXYSfHGQuUM3BcBCM7AACbIezkOH8mCpRj99mhZgcAYDOEnRxnqNlJU4Gy2+OWJ6/9q8BBoAAAuyHs5LhMjOy0v7cn7vMAALADwk6OM4SdNNXsSB177TCNBQCwG8JOjjMUKKdpGiv2vZnGAgDYDWEnx8WO7HjTOI0VKVIOtAYVCoXT9jkAAGQaYSfHRaaVPPluud2utH1OXkFHkGJ0BwBgJ4SdHBcZ2UlncXL7+8ceBkrYAQDYB2Enx/mb28NOfhqLk6XOh4GyIgsAYB+EnRwXGWXxprE4WeIwUACAfRF2clgoEFLQH5KUiWksRnYAAPZE2MlhbRnaUFDqdGQENTsAABsh7KRZKBAy/dpABo6KiL4/h4ECAGyKsJMmjZ816bXH39Mz//E3BU0GnkztniwZa4LYRRkAYCeEnTR5c9n7en9lnQ7uOawP131q6j0iK7GkDIzsFLLPDgDAngg7aXLKhSOjP2/63x0Km9iV2Hjieeb22aFAGQBgJ4SdNCkf3V/lo/tLkvZ/4lPdxn3dfg/jiefpHdkx7rPDyA4AwD4IO2l0yoUjoj+/+9ePuv16wyGgaa7ZMRQoM40FALARwk4aDTt1kPoN7i1J2v3+fu3dtr9br/e3xozsFGSyQJlpLACAfRB20sjldumUC2JGd/53R7debyhQLkr30nOmsQAA9kTYSbPKqsHqVVIgSdr59h4d+NSX9GuzVaDMNBYAwE4IO2nmyfPopJkV7Q/C0qbndyb92kwWKHNcBADArgg7GXDitKHRaahtf/tEhw+0JvU6w8hOmguUPfluydX+MweBAgDshLCTAfm98nTCtGGSpFAgrM0v7kzqdcZNBdMbdlwuV3T0KLYwGgAAqyPsZMjJMyrk9rQPnWz5v4/V1nz0QBE7suNN8zSW1FGkTIEyAMBOCDsZ0ntAoSonDpYktR0OaOurdUd9TWztTH6aR3akjpPPmcYCANgJYSeDxp3fsQz9vRU7j3pAaCTsePLccnvT/6uKTmNRoAwAsJH0DxekyZYtW/Tiiy9q/fr12r17t/r27auTTjpJV111lYYOHZrt5iXUf0gfDfvyIH38j71q+rxFH637TMdPPq7L+yMjLN6C9E9hSR1HRoSCYQUDIXkyELAAAEg3y/41W7ZsmVavXq2vfOUruummm3ThhRdq48aNuuqqq/TRR90/miFTjJsMfqRwuOsDQiN1PeleiRVh2GuHqSwAgE1YNuxcfPHF+tOf/qSbb75ZF1xwgebOnatf/vKXCgaD+v3vf5/t5nWpfEx/DTq+RFL7AaGfbOj6gNDI5n7pXokVYdhFmRVZAACbsGzYGTdunPLy8gzXhg4dqoqKCtXW1mapVUfncrn0pQtHRh9v7OIIiVAoHBN2MjWNFXs+FiM7AAB7sGzYSSQcDmv//v3q169ftptyRMO+PEj9jv3igNAtn2vv9gNx9wRaMrfHTvRzYk8+p0gZAGATtgo7r7zyivbt26dzzjmny3vq6+u1devW6P+yMQoUf0BofI2RcffkzBYod/58AACszLKrsTqrra3Vgw8+qJNOOkkzZszo8r7ly5dryZIlcdd37NihYDD+D7zP51NNTU0qmypJCpWGlVfskd8X1M439+idv21UUWnHtNzhen/056aW9LShs/0HD0R//mjbDh1w7TH9XunqN7uj38yh37qPPjOHfjMnXf02duzYpO6zRdhpaGjQ97//ffXu3Vv33HOPPJ6uR0JmzZqlqqqq6OPa2lotWLBAI0aM0JgxY+Lur6mpSbozuyt4QaHeeuoDSVLzVo++clXH5+z78IA26FNJ0sBjytLWhlih2h2qU6Mk6dhBgzVq7GDT75XOfrMz+s0c+q376DNz6Ddzst1vlg87Pp9Pt912m3w+nx5++GGVlZUd8f6ysrKj3pMpJ0wbpg3PfSh/c1Db/rZLX77oePUqKZDUaRorYwXKsauxmMYCANiDpWt2Wltbdfvtt6uurk4/+9nPVFFRke0mdUtB7zydcE77AaFBf0ibX+qoH/JToAwAQEpYNuwEg0Hddddd2rx5s3784x/r5JNPznaTTDl5xvCOA0JfqY1uJGg48TxDBcp5FCgDAGzIstNYixYt0tq1a3XWWWfp0KFDevnllw3PT58+PUst657epUUaVTVY29bsaj8gtLpO484bYZhGytTITuyxFIQdAIBdWDbsbN++XZK0bt06rVu3Lu55q4Qdqf0IiW1rdkmS3nthp06aPtw4spOhmh3jcRFMYwEA7MGyYeehhx7KdhNSpv+QPhp66kDVrd+nps9b9OHfP+tUoJyhkR0KlAEANmTZmh27iT1C4t3//cgwsuPNVIEyB4ECAGzIsiM7dlM+pr8GVZZo7/YD2l/nM4SNjBUocxAoAMCGGNnJES6XS6dc2HGExKF9zdGf8zM2jUWBMgDAfgg7OWTYV8qjB4TG8maoQNnjdUeXwVOgDACwC8JODnG7XRp3/oi465kqUI79LEZ2AAB2QdjJMZUTB6voiyMjJMnlccmTl7lfU2QUidVYAAC7IOzkGG++RyfPGB59nFfgkcvlytjnR1ZkBShQBgDYBGEnB50wbVg0dMSO8mRCZEWWvyWocDic0c8GACAdCDs5qKB3nqZcf4qOOXGAzrhkTEY/O1oMHZaCbaGMfjYAAOnAPjs5quL0Y1Rx+jEZ/1zjYaABw3lZAABYESM7MOAwUACA3RB2YBB7ZAS7KAMA7ICwA4PYaSzOxwIA2AFhBwaGaSz22gEA2ABhBwbGk8+ZxgIAWB9hBwbG1ViM7AAArI+wAwPjaixGdgAA1kfYgYFxGouRHQCA9RF2YOCNncaiQBkAYAOEHRjkxUxjBQg7AAAbIOzAoPNxEQAAWB1hBwbeQo6LAADYC2EHBsYdlBnZAQBYH2EHBhwECgCwG8IODIzHRTCyAwCwPsIODNxuVzTwsBoLAGAHhB3EiYQdprEAAHbgPfotcJq8Qo9aDkotB9u0cfmHpt5jz95G+bebe62T0W/m0G/dR5+ZQ7+Zs2dvo4YNalZxWVFWPp+wgziRFVmB1qDeeuoD0+/zsQ6kqEXOQr+ZQ791H31mDv1mzriqw1kLO0xjIc7gk0qz3QQAAFKGkR3EOfNfT9Dw08rV6vObfo+6T+o0dMjQFLbKGeg3c+i37qPPzKHfzKn7pE79j+uTtc8n7CCOy+3SsScO6NF7HO7doIqx5SlqkXPQb+bQb91Hn5lDv5lzuHeDCvvmZ+3zmcYCAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC25vhTz1tbWyVJtbW1CZ//+OOP5fF4MtkkW6DfzKHfzKHfuo8+M4d+Myed/TZ8+HAVFhYe8R7Hh53du3dLkhYsWJDllgAAgO5avHixxowZc8R7XOFwOJyh9uSkAwcO6M0339Sxxx6r/Px8w3O1tbVasGCBfvjDH2r48OFZaqH10G/m0G/m0G/dR5+ZQ7+Zk+5+Y2QnCSUlJZo+ffoR7xk+fPhRUyPi0W/m0G/m0G/dR5+ZQ7+Zk81+o0AZAADYGmEHAADYGmHnCEpLSzVv3jyVlpZmuymWQr+ZQ7+ZQ791H31mDv1mTi70m+MLlAEAgL0xsgMAAGyNsAMAAGyNsAMAAGyNsAMAAGzN8ZsKJtLW1qbHH39cL7/8sg4dOqRRo0bpqquu0umnn57tpuWs9evX6+abb0743COPPKKTTjopwy3KPYcPH9ZTTz2lmpoabdmyRYcOHdIdd9yhmTNnxt27c+dOPfzww9q0aZO8Xq8mTJig+fPnq6SkJPMNz7Jk++2nP/2pXnzxxbjXDxs2TEuXLs1Uc3PCli1b9OKLL2r9+vXavXu3+vbtq5NOOklXXXWVhg4dariX71qHZPuN75rRjh079MQTT2jr1q36/PPPVVhYqOHDh+uSSy5RVVWV4d5sfd8IOwnce++9WrVqlb75zW9qyJAhWrFihW677TYtXLhQp5xySrabl9Nmz56tE0880XDtuOOOy1JrcktjY6OWLFmi8vJyVVZWav369Qnv27t3r2688UYVFxfr6quvVnNzs5566il99NFHevTRR5WXl5fhlmdXsv0mSfn5+brtttsM13r37p3uJuacZcuWadOmTZo6dapGjRqlhoYGPfvss7rqqqv0yCOPaOTIkZL4rnWWbL9JfNdi7d69W4cPH9aMGTNUVlamlpYWrV69WnfccYduvfVWzZo1S1KWv29hGGzevDk8adKk8LJly6LXWlpawnPmzAlfd911WWxZbvvHP/4RnjRpUri6ujrbTclZra2t4fr6+nA4HA5v2bIlPGnSpPALL7wQd98vfvGL8LnnnhvevXt39Npbb70VnjRpUvi5557LWHtzRbL99pOf/CQ8ffr0TDcvJ7377rvhtrY2w7WPP/44PG3atPDdd98dvcZ3zSjZfuO7dnSBQCB8+eWXh//1X/81ei2b3zdqdjpZvXq1PB5PNIlKUkFBgc4//3xt3rxZe/bsyWLrrOHw4cMKBALZbkbOyc/PT2pTrdWrV+uss85SeXl59Nppp52moUOHqrq6Op1NzEnJ9ltEMBhUU1NTGluU+8aNGxf3X8lDhw5VRUWFamtro9f4rhkl228RfNe65vF4NGjQIPl8vui1bH7fmMbqZNu2bRoyZEjccGRkamb79u2GXxSM7r33XjU3N8vj8eiUU07R9ddfrxNOOCHbzbKMffv2af/+/QkPyzvxxBP1+uuvZ6FV1tHS0qKZM2eqpaVFffr00bRp03TdddepV69e2W5a1oXDYe3fv18VFRWS+K4lq3O/RfBdi9fc3KzW1lY1NTVp7dq1euONNzR16lRJ2f++EXY6aWhoSPhfkZFr9fX1mW6SJXi9Xk2ZMkVf/epX1a9fP+3cuVNPP/205s+fr1/96lcaPXp0tptoCQ0NDZLU5Xfw4MGDamtrU35+fqablvNKS0t1ySWXaPTo0QqHw3rjjTf0P//zP/rwww+1cOFCeb3O/r+7V155Rfv27dMVV1whie9asjr3m8R3rSuLFi3S8uXLJUlut1uTJ0/Wv//7v0vK/vfNmb+RI2htbU1YJBX5BbS2tma6SZYwbtw4jRs3Lvp44sSJOvvss3X55Zfrscce0wMPPJDF1llH5Pt1tO+g0/8AJXLttdcaHk+bNk1Dhw7V4sWLtXr1ak2bNi1LLcu+2tpaPfjggzrppJM0Y8YMSXzXkpGo3yS+a1355je/qbPPPlv19fWqrq5WMBiU3++XlP3vGzU7nRQUFER/ObHa2tqizyM5Q4YM0cSJE7V+/XoFg8FsN8cSIt8vvoOpcfHFF8vtduvtt9/OdlOypqGhQd///vfVu3dv3XPPPfJ4PJL4rh1NV/3WFb5r0vDhw3XaaadpxowZ+vnPf67m5mbdfvvtCofDWf++EXY6KS0tjQ63xYpcKysry3STLG3QoEHy+/1qaWnJdlMsITLE29V3sG/fvo7+L+3uKigoUN++fXXw4MFsNyUrfD6fbrvtNvl8Pj3wwAOG///iu9a1I/VbV5z+XUvk7LPP1vvvv6+6urqsf98IO51UVlbqk08+iauwr6mpiT6P5H366afKz89XUVFRtptiCQMHDlRJSYm2bt0a99yWLVv4/nXT4cOH1djY6MgN8lpbW3X77berrq5OP/vZz+IKbPmuJXa0fuuKk79rXYlMXfl8vqx/3wg7nZx99tkKBoPRIiupfYjthRde0NixY1mJ1YUDBw7EXdu+fbvWrl2r008/XW43X7VkTZkyRevWrTNsc/DOO++orq4uurIBRq2trTp8+HDc9d/97ncKh8M688wzs9Cq7AkGg7rrrru0efNm/fjHP9bJJ5+c8D6+a0bJ9BvftXj79++PuxYIBPTSSy+poKAgGhiz+X2jQLmTsWPHaurUqXrsscd04MABHXfccXrxxRe1e/duff/7389283LWnXfeqYKCAp188snq37+/du7cqb/+9a8qLCyMK+Zzsj//+c/y+XzRody1a9dq7969ktp3ny4uLtZll12mVatW6ZZbbtFFF12k5uZm/eEPf9DIkSMTHi3hBEfrt0OHDunKK6/Uueeeq2HDhkmS3nzzTb3++us688wzNXHixKy1PRsWLVqktWvX6qyzztKhQ4f08ssvG56fPn26JPFd6ySZfvv888/5rnXywAMPqKmpSV/60pc0cOBANTQ06JVXXtHHH3+sG264IbocP5vfN1c4HA6n9RMsqLW1NXo2ls/n08iRI3XVVVfpjDPOyHbTctYzzzyjV155Rbt27VJTU5NKSkr0la98RfPmzdOQIUOy3byccfHFF2v37t0Jn3v66ad17LHHSmo/a6bz+TE33HCDBgwYkMnm5oyj9VtxcbEWLlyozZs3q6GhQaFQSMcdd5z+6Z/+SXPmzHHcUuCbbrpJGzZs6PL5NWvWRH/mu9YhmX47dOgQ37VOVq5cqeeff14fffSRGhsb1atXL40ZM0bf+MY34sJftr5vhB0AAGBrFFIAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wA6LHPPvtMkydP1k9/+tNsNyUlVqxYocmTJ2vFihXZbgqAFCDsAEiLm266SZMnT852MxKyWzgDcGTOO7EMQMoNHDhQTz75pHr37p3tpqTEpEmTNHbsWJWWlma7KQBSgLADoMe8Xq+GDx+e7WakTHFxsYqLi7PdDAApwqnnAHrss88+07e+9S3NmDFDP/jBD7qcvoo8H/Hhhx/qySef1IYNG3Tw4EGVlpaqqqpKl19+ufr165fw/S+99FItXrxYGzdu1MGDB/X000/r2GOP1Zo1a1RdXa33339f9fX18nq9GjVqlC666CKdffbZ0fdasWKF7r333oTtW7hwoU499dToPXfccYdmzpxpuGfTpk168skntXnzZrW2tuqYY47ROeeco0svvVSFhYWGeydPnqzx48frrrvu0iOPPKLXX39dzc3Nqqys1LXXXqtTTz21u10NwARGdgCk3Lx58/Tiiy9q9+7dmjdvXvT68ccfH/35tdde01133SWXy6WJEydq0KBB2rlzp/7yl7/ozTff1KOPPqo+ffoY3nfXrl26/vrrNXLkSM2YMUMHDx5UXl6eJOmxxx6T1+vVuHHjVFpaqgMHDmjt2rX60Y9+pJtvvlmzZ8+WJFVWVuqiiy7SM888o8rKSk2cODH6/sccc8wR/13V1dW6++67lZeXp3POOUclJSV66623tGTJEr355ptauHChCgoKDK/x+Xy64YYbVFxcrOnTp2v//v2qrq7WrbfeqsWLF2vkyJGm+hhA8gg7AFLuiiuu0IYNG7R7925dccUVcc83NjbqJz/5ifr166dFixYZQsbKlSv14x//WI8//rhuueUWw+s2bdqkefPmJXzP++67T4MHDzZcO3z4sL7zne/o8ccf1/nnn6/CwkIdf/zxKi4ujoadRO+VSFNTk+6//355PB498sgjGjVqlCTpmmuu0d13361XX31VTz31lObOnWt43fbt2/Uv//IvuuWWW+R2t68J+fKXv6z77rtPf/nLX3Trrbcm9fkAzGM1FoCMe+mll9TU1KRrrrkmbjRl2rRpGj16tFauXBn3ugEDBujb3/52wvfsHHQkqVevXpo5c6Z8Pp/ef//9HrX5tddek8/n03nnnRcNOpLkdrt1/fXXy+PxJFyqXlRUpOuuuy4adKT26TyPx9PjNgFIDiM7ADJu8+bNkqSamhrt2rUr7vm2tjY1NjbqwIEDKikpiV6vrKyMTlt1tn//fv3+97/X66+/rj179qi1tdXwfH19fY/avG3bNknS+PHj454rLy/X4MGDVVdXp8OHD6tXr17R54YMGWJ4LLUXdA8YMEA+n69HbQKQHMIOgIw7dOiQJOnZZ5894n0tLS2Gx/37909438GDB3XNNddoz549GjdunE477TQVFxfL7XZr+/bteu211+T3+3vU5qamJknto0uJlJaWqq6uTk1NTYZw09VyfI/Ho1Ao1KM2AUgOYQdAxkXCwJIlS7pVoOtyuRJef/7557Vnzx5deeWVcTUzS5cu1WuvvWa+sV+IhJbPP/884fOR63bZawiwE2p2AKRFpEYlGAzGPTd27FhJHdNZPRWZCotdWRXx7rvvdtm27oysRFaSbdiwIe65PXv2aNeuXRo8eHDclBWA7CPsAEiLvn37SpL27t0b99x5552nXr16afHixdqxY0fc8y0tLd0KQpEi502bNhmuv/LKK3r99dfj7u/Tp49cLlfCtnVl4sSJKi4u1gsvvGBoczgc1qOPPqpgMBi3Jw+A3MA0FoC0+PKXv6xVq1bpv/7rv3TmmWcqPz9flZWVqqqqUklJie6880796Ec/0hVXXKEzzjhDw4YNk9/v1+7du7VhwwadfPLJeuCBB5L6rOnTp2vZsmVauHCh1q9fr/Lycm3fvl3/+Mc/NHnyZK1Zs8Zwf69evXTCCSdo48aNWrBggYYMGSKXy6V//ud/7nKvnd69e+s//uM/dPfdd+u6667T1KlTVVJSonfeeUdbt27ViSeeqDlz5vS43wCkHmEHQFpccMEF+uyzz/Tqq69q2bJlCgaDmjFjhqqqqiRJEyZM0OOPP64//OEPeuedd/T222+rsLBQAwcO1MyZMzV9+vSkP2vQoEF66KGH9Mgjj+jtt99WMBjU6NGj9Ytf/EJ79+6NCzuS9MMf/lC//OUvtW7dOjU1NSkcDuuUU0454saCU6dO1YABA7R06VKtWbMmuoPy3Llzdemll8ZtKAggN3BcBAAAsDVqdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK39P6rIIn+h7WV0AAAAAElFTkSuQmCC", "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": 17, "id": "71ad8d7a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "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": 18, "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": 19, "id": "2769b6e5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iteration = 29\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": 20, "id": "71f5f43f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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 = 2e-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-env", "language": "python", "name": "python-env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.15" } }, "nbformat": 4, "nbformat_minor": 5 }