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    "# Analysis of simulated EDX data\n",
    "\n",
    "In this notebook we showcase the analysis of the built-in simulated dataset. We use the espm EDXS modelling to simulate the data.\n",
    "We first perform a KL-NMF decomposition of the data and then plot the results."
   ]
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   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "# Generic imports \n",
    "import hyperspy.api as hs\n",
    "import numpy as np\n",
    "\n",
    "# espm imports\n",
    "from espm.estimators import SmoothNMF\n",
    "import espm.datasets as ds"
   ]
  },
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   "metadata": {},
   "source": [
    "## Generating artificial datasets and loading them\n",
    "\n",
    "If the datasets were already generated, they are not generated again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Generate the data\n",
    "# Here `seeds_range` is the number of different samples to generate\n",
    "ds.generate_built_in_datasets(seeds_range=1)\n",
    "\n",
    "# Load the data\n",
    "spim = ds.load_particules(sample = 0)\n",
    "# We need to change the data to floats for the decomposition to run\n",
    "spim.change_dtype('float64')"
   ]
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   "source": [
    "## Building G\n",
    "\n",
    "The information in the metadata of the spim object are used to build the G matrix. This matrix contains a model of the characteristic X-rays and the bremsstrahlung."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spim.build_G()"
   ]
  },
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   "source": [
    "## Problem solving\n",
    "\n",
    "### Picking analysis parameters\n",
    "\n",
    "- 3 components for 3 phases\n",
    "- convergence criterions : tol and max_iter. tol is the minimum change of the loss function between two iterations. max_iter is the max number of iterations.\n",
    "- G : The EDX model is integrated in the decomposition. The algorithm learns the concentration of each chemical element and the bremsstrahlung parameters.\n",
    "- hspy_comp is a required parameter if you want to use the hyperspy api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = SmoothNMF( n_components = 3, tol = 1e-6, max_iter = 200, G = spim.model,hspy_comp = True)"
   ]
  },
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   "source": [
    "### Calculating the decomposition\n",
    "\n",
    "/!\\ It should take a minute to execute in the case of the built-in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = spim.decomposition(algorithm = est, return_info=True)"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the losses and the results of the decomposition\n",
    "\n",
    "- First cell : Printing the resulting concentrations.\n",
    "- Second cell : Ploting the resulting spectra\n",
    "- Thrid cell : Ploting the resulting abundances\n",
    "\n",
    "Hyperspy is mainly designed to be used with the qt graphical backend of matplotlib. Thus two plots will appear if you are in inline mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spim.print_concentration_report()"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
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   "outputs": [],
   "source": [
    "spim.plot_decomposition_loadings(3)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spim.plot_decomposition_factors(3)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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