{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "767e0881",
   "metadata": {},
   "source": [
    "# Coordinate-Based Meta-Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a7e2c26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:16.926626Z",
     "iopub.status.busy": "2026-09-30T09:32:16.926030Z",
     "iopub.status.idle": "2026-09-30T09:32:22.513373Z",
     "shell.execute_reply": "2026-09-30T09:32:22.512689Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# First, import the necessary modules and functions\nimport os\nfrom pprint import pprint\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom myst_nb import glue\nfrom nilearn import plotting\nfrom repo2data.repo2data import Repo2Data\n\nfrom nimare import dataset\n\n# Install the data if running locally, or points to cached data if running on neurolibre\nDATA_REQ_FILE = os.path.abspath(\"../binder/data_requirement.json\")\nrepo2data = Repo2Data(DATA_REQ_FILE)\ndata_path = repo2data.install()\ndata_path = os.path.join(data_path[0], \"data\")\n\n# Set an output directory for any files generated during the book building process\nout_dir = os.path.abspath(\"../outputs/\")\nos.makedirs(out_dir, exist_ok=True)\n\n# Now, load the Datasets we will use in this chapter\nsleuth_dset1 = dataset.Dataset.load(os.path.join(data_path, \"sleuth_dset1.pkl.gz\"))\nsleuth_dset2 = dataset.Dataset.load(os.path.join(data_path, \"sleuth_dset2.pkl.gz\"))\nneurosynth_dset = dataset.Dataset.load(os.path.join(data_path, \"neurosynth_dataset.pkl.gz\"))"
  },
  {
   "cell_type": "markdown",
   "id": "e90fc853",
   "metadata": {},
   "source": [
    "Coordinate-based meta-analysis (CBMA) is currently the most popular method for neuroimaging meta-analysis, given that the majority of fMRI papers currently report their findings as peaks of statistically significant clusters in standard space and do not release unthresholded statistical maps.\n",
    "These peaks indicate where significant results were found in the brain, and thus do not reflect an effect size estimate for each hypothesis test (i.e., each voxel) as one would expect for a typical meta-analysis.\n",
    "As such, standard methods for effect size-based meta-analysis cannot be applied.\n",
    "Over the past two decades, a number of algorithms have been developed to determine whether peaks converge across experiments in order to identify locations of consistent or specific activation associated with a given hypothesis {cite:p}`Samartsidis2017-ej,Muller2018-mt`.\n",
    "\n",
    "Kernel-based methods evaluate convergence of coordinates across studies by first convolving foci with a spatial kernel to produce study-specific modeled activation maps, then combining those modeled activation maps into a sample-wise map, which is compared to a null distribution to evaluate voxel-wise statistical significance.\n",
    "Additionally, for each of the following approaches, except for SCALE, voxel- or cluster-level multiple comparisons correction may be performed using Monte Carlo simulations or false discovery rate (FDR) {cite:p}`Laird2005-qh` correction. Basic multiple-comparisons correction methods (e.g., Bonferroni correction) are also supported."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbae7fd",
   "metadata": {},
   "source": "```{figure} images/figure_02.svg\n:name: meta_workflow_fig-notebook\n:align: center\n:width: 300px\n\nA flowchart of the typical workflow for coordinate-based meta-analyses in NiMARE.\n```\n\n## CBMA kernels\n\nCBMA kernels are available as {py:class}`~nimare.meta.kernel.KernelTransformer`s in the {py:mod}`nimare.meta.kernel` module.\nThere are three standard kernels that are currently available: {py:class}`~nimare.meta.kernel.MKDAKernel`, {py:class}`~nimare.meta.kernel.KDAKernel`, and {py:class}`~nimare.meta.kernel.ALEKernel`.\nEach class may be configured with certain parameters when a new object is initialized.\nFor example, `MKDAKernel` accepts an `r` parameter, which determines the radius of the spheres that will be created around each peak coordinate.\n`ALEKernel` automatically uses the sample size associated with each experiment in the `Dataset` to determine the appropriate full-width-at-half-maximum of its Gaussian distribution, as described in {cite:t}`EICKHOFF20122349`; however, users may provide a constant `sample_size` or `fwhm` parameter when sample size information is not available within the `Dataset` metadata.\n\nHere we show how these three kernels can be applied to the same `Dataset`."
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2987fb86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:22.524120Z",
     "iopub.status.busy": "2026-09-30T09:32:22.523569Z",
     "iopub.status.idle": "2026-09-30T09:32:25.025473Z",
     "shell.execute_reply": "2026-09-30T09:32:25.025089Z"
    }
   },
   "outputs": [],
   "source": "from nimare.meta import kernel\n\nmkda_kernel = kernel.MKDAKernel(r=10)\nmkda_ma_maps = mkda_kernel.transform(sleuth_dset1)\nkda_kernel = kernel.KDAKernel(r=10)\nkda_ma_maps = kda_kernel.transform(sleuth_dset1)\nale_kernel = kernel.ALEKernel(sample_size=20)\nale_ma_maps = ale_kernel.transform(sleuth_dset1)"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d47aacf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:25.028487Z",
     "iopub.status.busy": "2026-09-30T09:32:25.028217Z",
     "iopub.status.idle": "2026-09-30T09:32:25.030177Z",
     "shell.execute_reply": "2026-09-30T09:32:25.030379Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel mkda_kernel, kda_kernel, ale_kernel"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9147409",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:25.036196Z",
     "iopub.status.busy": "2026-09-30T09:32:25.035644Z",
     "iopub.status.idle": "2026-09-30T09:32:32.733248Z",
     "shell.execute_reply": "2026-09-30T09:32:32.733499Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/plotting/img_plotting.py:348: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n",
      "  anat_img = load_mni152_template()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": "<Figure size 600x600 with 15 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "figure_ma_maps"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": "#| label: figure_ma_mapscell\n# Generate figure\nstudy_idx = 10  # a study with overlapping kernels\nmax_value = np.max(kda_ma_maps[study_idx].get_fdata()) + 1\n\nma_maps = {\n    \"MKDA Kernel\": mkda_ma_maps[study_idx],\n    \"KDA Kernel\": kda_ma_maps[study_idx],\n    \"ALE Kernel\": ale_ma_maps[study_idx],\n}\n\nfig, axes = plt.subplots(nrows=3, figsize=(6, 6))\n\nfor i_meta, (name, img) in enumerate(ma_maps.items()):\n    if \"ALE\" in name:\n        vmax = None\n    else:\n        vmax = max_value\n\n    display = plotting.plot_stat_map(\n        img,\n        annotate=False,\n        axes=axes[i_meta],\n        cmap=\"Reds\",\n        cut_coords=[5, 0, 29],\n        draw_cross=False,\n        figure=fig,\n        vmax=vmax,\n    )\n    axes[i_meta].set_title(name)\n\n    colorbar = display._cbar\n    colorbar_ticks = colorbar.get_ticks()\n    if colorbar_ticks[0] < 0:\n        new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]]\n    else:\n        new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]]\n    colorbar.set_ticks(new_ticks, update_ticks=True)\n\nglue(\"figure_ma_maps\", fig, display=False)"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90e63cae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:32.736335Z",
     "iopub.status.busy": "2026-09-30T09:32:32.736021Z",
     "iopub.status.idle": "2026-09-30T09:32:32.738731Z",
     "shell.execute_reply": "2026-09-30T09:32:32.738464Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel mkda_ma_maps, kda_ma_maps, ale_ma_maps"
  },
  {
   "cell_type": "markdown",
   "id": "1ba4577a",
   "metadata": {},
   "source": "```{glue:figure} figure_ma_maps\n:name: \"figure_ma_maps-notebook\"\n:align: center\n\nModeled activation maps produced by NiMARE's `KernelTransformer` classes.\n```"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "affe2b96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:32:32.795429Z",
     "iopub.status.busy": "2026-09-30T09:32:32.794965Z",
     "iopub.status.idle": "2026-09-30T09:33:02.937307Z",
     "shell.execute_reply": "2026-09-30T09:33:02.937610Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nimare.utils:Shared path detected: '/tmp/paper/outputs/neurosynth_dataset_maps/'\n"
     ]
    }
   ],
   "source": "from nimare import dataset, meta\n\nneurosynth_dset_first500 = dataset.Dataset.load(\n    os.path.join(data_path, \"neurosynth_dataset_first500.pkl.gz\")\n)\n\n# Specify where images for this Dataset should be located\ntarget_folder = os.path.join(out_dir, \"neurosynth_dataset_maps\")\nos.makedirs(target_folder, exist_ok=True)\nneurosynth_dset_first500.update_path(target_folder)\n\n# Initialize a kernel transformer to use\nkern = meta.kernel.MKDAKernel(memory_limit=\"500mb\")\n\n# Run the kernel transformer with return_type set to \"dataset\" to return an updated Dataset\n# with the MA maps stored as files within its \"images\" attribute.\nneurosynth_dset_first500 = kern.transform(neurosynth_dset_first500, return_type=\"dataset\")\nneurosynth_dset_first500.save(\n    os.path.join(out_dir, \"neurosynth_dataset_first500_with_mkda_ma.pkl.gz\"),\n)"
  },
  {
   "cell_type": "markdown",
   "id": "75bc460f",
   "metadata": {},
   "source": [
    "(content:cbma:mkdad)=\n",
    "## Multilevel kernel density analysis\n",
    "\n",
    "**Multilevel kernel density analysis** (MKDA) {cite:p}`Wager2007-jc` is a kernel-based method that convolves each peak from each study with a binary sphere of a set radius.\n",
    "These peak-specific binary maps are then combined into study-specific maps by taking the maximum value for each voxel.\n",
    "Study-specific maps are then averaged across the meta-analytic sample.\n",
    "This averaging is generally weighted by studies’ sample sizes, although other covariates may be included, such as weights based on the type of inference (random or fixed effects) employed in the study’s analysis.\n",
    "An arbitrary threshold is generally employed to zero-out voxels with very low values, and then a Monte Carlo procedure is used to assess statistical significance, either at the voxel or cluster level.\n",
    "\n",
    "In NiMARE, the MKDA meta-analyses can be performed with the {py:class}`~nimare.meta.cbma.mkda.MKDADensity` class.\n",
    "This class, like most other CBMA classes in NiMARE, accepts a `null_method` parameter, which determines how voxel-wise (uncorrected) statistical significance is calculated.\n",
    "\n",
    "```{admonition} On CBMA \"null methods\"\n",
    ":class: tip\n",
    "The `null_method` parameter allows two options: \"approximate\" or \"montecarlo.\"\n",
    "The \"approximate\" option builds a histogram-based null distribution of summary-statistic values, which can then be used to determine the associated p-value for _observed_ summary-statistic values (i.e., the values in the meta-analytic map).\n",
    "The \"montecarlo\" option builds a null distribution of summary-statistic values by randomly shuffling the coordinates the `Dataset` many times, and computing the summary-statistic values for each permutation.\n",
    "In general, the \"montecarlo\" method is slightly more accurate when there are enough permutations, while the \"approximate\" method is much faster.\n",
    "```\n",
    "\n",
    "```{warning}\n",
    "Fitting the CBMA `Estimator` to a `Dataset` will produce p-value, z-statistic, and summary-statistic maps, but these are not corrected for multiple comparisons.\n",
    "\n",
    "When performing a meta-analysis with the goal of statistical inference, you will want to perform multiple comparisons correction with NiMARE's `Corrector`\n",
    "classes.\n",
    "Please see the multiple comparisons correction chapter for more information.\n",
    "```\n",
    "\n",
    "Here we perform an MKDADensity meta-analysis on one of the Sleuth-based Datasets.\n",
    "We will use the \"approximate\" null method for speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "afb0c4c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:03.000072Z",
     "iopub.status.busy": "2026-09-30T09:33:02.999622Z",
     "iopub.status.idle": "2026-09-30T09:33:03.601136Z",
     "shell.execute_reply": "2026-09-30T09:33:03.600793Z"
    }
   },
   "outputs": [],
   "source": "from nimare.meta.cbma import mkda\n\nmkdad_meta = mkda.MKDADensity(null_method=\"approximate\")\nmkdad_results = mkdad_meta.fit(sleuth_dset1)"
  },
  {
   "cell_type": "markdown",
   "id": "5262c6a3",
   "metadata": {},
   "source": [
    "(content:cbma:metaresult)=\n",
    "### The `MetaResult` class\n",
    "\n",
    "Fitting an `Estimator` to a `Dataset` produces a {py:class}`~nimare.results.MetaResult` object.\n",
    "The `MetaResult` class is a light container holding the different statistical maps produced by the `Estimator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dff766c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:03.605286Z",
     "iopub.status.busy": "2026-09-30T09:33:03.604920Z",
     "iopub.status.idle": "2026-09-30T09:33:03.607076Z",
     "shell.execute_reply": "2026-09-30T09:33:03.606803Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<nimare.results.MetaResult object at 0x7fffa8889e10>\n"
     ]
    }
   ],
   "source": "print(mkdad_results)"
  },
  {
   "cell_type": "markdown",
   "id": "7a45f1d0",
   "metadata": {},
   "source": [
    "This result is also retained as an attribute in the `Estimator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f85873c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:03.608978Z",
     "iopub.status.busy": "2026-09-30T09:33:03.608748Z",
     "iopub.status.idle": "2026-09-30T09:33:03.611283Z",
     "shell.execute_reply": "2026-09-30T09:33:03.611043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<nimare.results.MetaResult object at 0x7fffa8889e10>\n"
     ]
    }
   ],
   "source": "print(mkdad_meta.results)"
  },
  {
   "cell_type": "markdown",
   "id": "5b8f4a56",
   "metadata": {},
   "source": [
    "The `maps` attribute is a dictionary containing statistical map names and associated numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9721c80b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:03.615367Z",
     "iopub.status.busy": "2026-09-30T09:33:03.614442Z",
     "iopub.status.idle": "2026-09-30T09:33:03.617748Z",
     "shell.execute_reply": "2026-09-30T09:33:03.617281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'p': array([1., 1., 1., ..., 1., 1., 1.]),\n",
      " 'stat': array([0., 0., 0., ..., 0., 0., 0.]),\n",
      " 'z': array([0., 0., 0., ..., 0., 0., 0.])}\n"
     ]
    }
   ],
   "source": "pprint(mkdad_results.maps)"
  },
  {
   "cell_type": "markdown",
   "id": "aca84e17",
   "metadata": {},
   "source": [
    "These arrays can be transformed into image-like objects using the `masker` attribute.\n",
    "We can also use the `get_map` method to get that image object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10fb3ac0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:03.838725Z",
     "iopub.status.busy": "2026-09-30T09:33:03.838407Z",
     "iopub.status.idle": "2026-09-30T09:33:03.912220Z",
     "shell.execute_reply": "2026-09-30T09:33:03.912481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'nibabel.nifti1.Nifti1Image'>\n",
      "data shape (91, 109, 91)\n",
      "affine: \n",
      "[[  -2.    0.    0.   90.]\n",
      " [   0.    2.    0. -126.]\n",
      " [   0.    0.    2.  -72.]\n",
      " [   0.    0.    0.    1.]]\n",
      "metadata:\n",
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : b''\n",
      "db_name         : b''\n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : b''\n",
      "dim_info        : 0\n",
      "dim             : [  3  91 109  91   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : float64\n",
      "bitpix          : 64\n",
      "slice_start     : 0\n",
      "pixdim          : [-1.  2.  2.  2.  1.  1.  1.  1.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 0\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : b''\n",
      "aux_file        : b''\n",
      "qform_code      : unknown\n",
      "sform_code      : aligned\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 1.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : 90.0\n",
      "qoffset_y       : -126.0\n",
      "qoffset_z       : -72.0\n",
      "srow_x          : [-2.  0.  0. 90.]\n",
      "srow_y          : [   0.    2.    0. -126.]\n",
      "srow_z          : [  0.   0.   2. -72.]\n",
      "intent_name     : b''\n",
      "magic           : b'n+1'\n"
     ]
    }
   ],
   "source": "mkdad_img = mkdad_results.get_map(\"z\", return_type=\"image\")\nprint(mkdad_img)"
  },
  {
   "cell_type": "markdown",
   "id": "25e0a231",
   "metadata": {},
   "source": [
    "We can save the statistical maps to an output directory as gzipped nifti files, with a prefix.\n",
    "Here, we will save all of the statistical maps with the MKDADensity prefix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f0dca06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:04.009660Z",
     "iopub.status.busy": "2026-09-30T09:33:04.009299Z",
     "iopub.status.idle": "2026-09-30T09:33:04.412844Z",
     "shell.execute_reply": "2026-09-30T09:33:04.412552Z"
    }
   },
   "outputs": [],
   "source": "mkdad_results.save_maps(output_dir=out_dir, prefix=\"MKDADensity\")"
  },
  {
   "cell_type": "markdown",
   "id": "08630a49",
   "metadata": {},
   "source": [
    "We will also save the `Estimator` itself, which we will reuse when we get to multiple comparisons correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "566c819f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:04.415263Z",
     "iopub.status.busy": "2026-09-30T09:33:04.414955Z",
     "iopub.status.idle": "2026-09-30T09:33:05.300922Z",
     "shell.execute_reply": "2026-09-30T09:33:05.300643Z"
    }
   },
   "outputs": [],
   "source": "mkdad_meta.save(os.path.join(out_dir, \"MKDADensity.pkl.gz\"))"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4042093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:05.303264Z",
     "iopub.status.busy": "2026-09-30T09:33:05.302994Z",
     "iopub.status.idle": "2026-09-30T09:33:05.304913Z",
     "shell.execute_reply": "2026-09-30T09:33:05.305131Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel mkdad_meta, mkdad_results"
  },
  {
   "cell_type": "markdown",
   "id": "c3f94d90",
   "metadata": {},
   "source": [
    "Since this is a kernel-based algorithm, the kernel transformer is an optional input to the meta-analytic estimator, and can be controlled in a more fine-grained manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59a71713",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:05.307393Z",
     "iopub.status.busy": "2026-09-30T09:33:05.307154Z",
     "iopub.status.idle": "2026-09-30T09:33:05.310015Z",
     "shell.execute_reply": "2026-09-30T09:33:05.309842Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.meta.cbma.mkda:The KernelTransformer being used (<class 'nimare.meta.kernel.KDAKernel'>) is not optimized for the MKDADensity algorithm. Expect suboptimal performance and beware bugs.\n"
     ]
    }
   ],
   "source": "# These two approaches (initializing the kernel ahead of time or\n# providing the arguments with the kernel__ prefix) are equivalent.\nmkda_kernel = kernel.MKDAKernel(r=2)\nmkdad_meta = mkda.MKDADensity(kernel_transformer=mkda_kernel)\nmkdad_meta = mkda.MKDADensity(kernel_transformer=kernel.MKDAKernel, kernel__r=2)\n\n# A completely different kernel could even be provided, although this is not\n# recommended and should only be used for testing algorithms.\nmkdad_meta = mkda.MKDADensity(kernel_transformer=kernel.KDAKernel)"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c8cb6e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:05.311779Z",
     "iopub.status.busy": "2026-09-30T09:33:05.311493Z",
     "iopub.status.idle": "2026-09-30T09:33:05.314152Z",
     "shell.execute_reply": "2026-09-30T09:33:05.313957Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel mkda_kernel, mkdad_meta"
  },
  {
   "cell_type": "markdown",
   "id": "716b1f71",
   "metadata": {},
   "source": [
    "## Kernel density analysis\n",
    "\n",
    "**Kernel density analysis** (KDA) {cite:p}`Wager2003-no,Wager2004-ak` is a precursor algorithm that has been replaced in the field by MKDA.\n",
    "For the sake of completeness, NiMARE also includes a KDA estimator that implements the older KDA algorithm for comparison purposes.\n",
    "The interface is virtually identical, but since there are few if any legitimate uses of KDA (which models studies as fixed rather than random effects), we do not discuss the algorithm further here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5ba6a42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:05.316122Z",
     "iopub.status.busy": "2026-09-30T09:33:05.315899Z",
     "iopub.status.idle": "2026-09-30T09:33:06.000987Z",
     "shell.execute_reply": "2026-09-30T09:33:06.000669Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.meta.cbma.mkda:The KDA algorithm has been replaced in the literature with the MKDA algorithm. As such, this estimator should almost never be used, outside of systematic comparisons between algorithms.\n"
     ]
    }
   ],
   "source": "kda_meta = mkda.KDA(null_method=\"approximate\")\nkda_results = kda_meta.fit(sleuth_dset1)\n\n# Retain the z-statistic map for later use\nkda_img = kda_results.get_map(\"z\", return_type=\"image\")"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c8f2f44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:06.003545Z",
     "iopub.status.busy": "2026-09-30T09:33:06.003253Z",
     "iopub.status.idle": "2026-09-30T09:33:06.005174Z",
     "shell.execute_reply": "2026-09-30T09:33:06.004896Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel kda_meta, kda_results"
  },
  {
   "cell_type": "markdown",
   "id": "f4eb5174",
   "metadata": {},
   "source": [
    "## Activation likelihood estimation\n",
    "\n",
    "**Activation likelihood estimation** (ALE) {cite:p}`Eickhoff2012-hk,Turkeltaub2012-no,Turkeltaub2002-dn` assesses convergence of peaks across studies by first generating a modeled activation map for each study, in which each of the experiment’s peaks is convolved with a 3D Gaussian distribution determined by the experiment’s sample size, and then by combining these modeled activation maps across studies into an ALE map, which is compared to an empirical null distribution to assess voxel-wise statistical significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c2e94198",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:06.018664Z",
     "iopub.status.busy": "2026-09-30T09:33:06.017102Z",
     "iopub.status.idle": "2026-09-30T09:33:08.831035Z",
     "shell.execute_reply": "2026-09-30T09:33:08.830757Z"
    }
   },
   "outputs": [],
   "source": "from nimare.meta.cbma import ale\n\nale_meta = ale.ALE()\nale_results = ale_meta.fit(sleuth_dset1)\n\n# Retain the z-statistic map for later use\nale_img = ale_results.get_map(\"z\", return_type=\"image\")"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "199675f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:08.833396Z",
     "iopub.status.busy": "2026-09-30T09:33:08.833088Z",
     "iopub.status.idle": "2026-09-30T09:33:08.834786Z",
     "shell.execute_reply": "2026-09-30T09:33:08.834994Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel ale_meta, ale_results"
  },
  {
   "cell_type": "markdown",
   "id": "deb86cf0",
   "metadata": {},
   "source": [
    "## Specific coactivation likelihood estimation\n",
    "\n",
    "**Specific coactivation likelihood estimation** (SCALE) {cite:p}`Langner2014-ei` is an extension of the ALE algorithm developed for meta-analytic coactivation modeling (MACM) analyses.\n",
    "Rather than comparing convergence of foci within the sample to a null distribution derived under the assumption of spatial randomness within the brain, SCALE assesses whether the convergence at each voxel is greater than in the general literature.\n",
    "Each voxel in the brain is assigned a null distribution determined based on the base rate of activation for that voxel across an existing coordinate-based meta-analytic database.\n",
    "This approach allows for the generation of a statistical map for the sample, but no methods for multiple comparisons correction have yet been developed.\n",
    "While this method was developed to support analysis of joint activation or \"coactivation\" patterns, it is generic and can be applied to any CBMA; see [](08_about_derivative_analyses.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "374ed32c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:33:08.903551Z",
     "iopub.status.busy": "2026-09-30T09:33:08.894109Z",
     "iopub.status.idle": "2026-09-30T09:39:08.120020Z",
     "shell.execute_reply": "2026-09-30T09:39:08.121421Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb97397a2e884a3190780f79e01b3f4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d431d01c11544991b7e2fbe8034c60a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/228483 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "# Here we use the coordinates from Neurosynth as our measure of coordinate\n# base-rates, because we do not have access to the full BrainMap database.\n# However, one assumption of SCALE is that the Dataset being analyzed comes\n# from the same source as the database you use for calculating base-rates.\nxyz = neurosynth_dset.coordinates[[\"x\", \"y\", \"z\"]].values\n# Typically, you would have >=2500 iterations, but we're using 500 here.\nscale_meta = ale.SCALE(n_iters=500, xyz=xyz, memory_limit=\"100mb\", n_cores=1)\nscale_results = scale_meta.fit(sleuth_dset1)\n\n# Retain the z-statistic map for later use\nscale_img = scale_results.get_map(\"z\", return_type=\"image\")"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b8d2d8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:08.127292Z",
     "iopub.status.busy": "2026-09-30T09:39:08.126955Z",
     "iopub.status.idle": "2026-09-30T09:39:08.129338Z",
     "shell.execute_reply": "2026-09-30T09:39:08.129548Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel xyz, scale_meta, scale_results"
  },
  {
   "cell_type": "markdown",
   "id": "a6afa835",
   "metadata": {},
   "source": [
    "## MKDA Chi-Squared Analysis\n",
    "\n",
    "An alternative to the density-based approaches (i.e., MKDA, KDA, ALE, and SCALE) is the **MKDA Chi-squared** extension {cite:p}`Wager2007-jc`.\n",
    "Though still a kernel-based method in which foci are convolved with a binary sphere and combined within studies, this approach uses voxel-wise chi-squared tests to assess both consistency (i.e., higher convergence of foci within the meta-analytic sample than expected by chance) and specificity (i.e., higher convergence of foci within the meta-analytic sample than detected in an unrelated dataset) of activation.\n",
    "Such an analysis also requires access to a reference meta-analytic sample or database of studies.\n",
    "For example, to perform a chi-squared analysis of working memory studies, the researcher will also need a comprehensive set of studies which did not manipulate working memory—ideally one that is matched with the working memory study set on all relevant attributes _except_ the involvement of working memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ac99e9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:08.198981Z",
     "iopub.status.busy": "2026-09-30T09:39:08.143692Z",
     "iopub.status.idle": "2026-09-30T09:39:15.426071Z",
     "shell.execute_reply": "2026-09-30T09:39:15.425560Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nimare/meta/cbma/mkda.py:276: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pFgA = pAgF * pF / pA\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nimare/meta/cbma/mkda.py:280: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pFgA_prior = pAgF * self.prior / pAgF_prior\n"
     ]
    }
   ],
   "source": "mkdac_meta = mkda.MKDAChi2()\nmkdac_results = mkdac_meta.fit(sleuth_dset1, sleuth_dset2)\n\n# Retain the specificity analysis's z-statistic map for later use\nmkdac_img = mkdac_results.get_map(\"z_desc-specificity\", return_type=\"image\")"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "398b209a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:15.429406Z",
     "iopub.status.busy": "2026-09-30T09:39:15.429083Z",
     "iopub.status.idle": "2026-09-30T09:39:15.431882Z",
     "shell.execute_reply": "2026-09-30T09:39:15.432099Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel mkdac_meta, mkdac_results"
  },
  {
   "cell_type": "markdown",
   "id": "8aa1bb42",
   "metadata": {},
   "source": [
    "## Comparing algorithms\n",
    "\n",
    "Here we load the z-statistic map from each of the CBMA Estimators we've used throughout this chapter and plot them all side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a797d244",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:15.445824Z",
     "iopub.status.busy": "2026-09-30T09:39:15.441003Z",
     "iopub.status.idle": "2026-09-30T09:39:25.105136Z",
     "shell.execute_reply": "2026-09-30T09:39:25.105402Z"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": "<Figure size 1200x600 with 26 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "figure_cbma_uncorr"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": "#| label: figure_cbma_uncorrcell\nmeta_results = {\n    \"MKDA Density\": mkdad_img,\n    \"MKDA Chi-Squared\": mkdac_img,\n    \"KDA\": kda_img,\n    \"ALE\": ale_img,\n    \"SCALE\": scale_img,\n}\norder = [\n    [\"MKDA Density\", \"ALE\"],\n    [\"MKDA Chi-Squared\", \"SCALE\"],\n    [\"KDA\", None]\n]\n\nfig, axes = plt.subplots(figsize=(12, 6), nrows=3, ncols=2)\n\nfor i_row, row_names in enumerate(order):\n    for j_col, name in enumerate(row_names):\n        if not name:\n            axes[i_row, j_col].axis(\"off\")\n            continue\n\n        img = meta_results[name]\n        if name == \"MKDA Chi-Squared\":\n            cmap = \"RdBu_r\"\n        else:\n            cmap = \"Reds\"\n\n        display = plotting.plot_stat_map(\n            img,\n            annotate=False,\n            axes=axes[i_row, j_col],\n            cmap=cmap,\n            cut_coords=[5, -15, 10],\n            draw_cross=False,\n            figure=fig,\n        )\n        axes[i_row, j_col].set_title(name)\n\n        colorbar = display._cbar\n        colorbar_ticks = colorbar.get_ticks()\n        if colorbar_ticks[0] < 0:\n            new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]]\n        else:\n            new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]]\n        colorbar.set_ticks(new_ticks, update_ticks=True)\n\nglue(\"figure_cbma_uncorr\", fig, display=False)"
  },
  {
   "cell_type": "markdown",
   "id": "5f31ab2f",
   "metadata": {},
   "source": "```{glue:figure} figure_cbma_uncorr\n:name: \"figure_cbma_uncorr-notebook\"\n:align: center\n\nThresholded results from MKDA Density, KDA, ALE, and SCALE meta-analyses.\n```"
  },
  {
   "cell_type": "markdown",
   "id": "51ed8317",
   "metadata": {},
   "source": [
    "A number of other coordinate-based meta-analysis algorithms exist which are not yet implemented in NiMARE.\n",
    "We describe these algorithms briefly in [](13_future_directions.md)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "07f2f18e695b4d289c6bd698ca9177df": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "224853b81399449b9daed513dbdb0e07": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "27f896d96d814e95a4ff260f1bd523b8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "2c1a462d175e41dfb262d9b8eb363739": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3e4e75d36ec048c9a6e64edd6af2da00": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_ee7739f22e5e44bd97a89327f707828c",
       "max": 228483.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_b3c91979a8054fca8f5434191a1ff5c5",
       "value": 228483.0
      }
     },
     "3f1ba251feed4d05a21a1dc55ce2277e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_224853b81399449b9daed513dbdb0e07",
       "placeholder": "​",
       "style": "IPY_MODEL_64ef851dbd7d4b99b9a05aa63934150b",
       "value": " 500/500 [04:05&lt;00:00,  2.45it/s]"
      }
     },
     "50c8ab49101f45a886b7b94a6b5e5743": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_ff6ba0d41689488f8e4ab5ff03421398",
       "placeholder": "​",
       "style": "IPY_MODEL_c855a18b9a6e4290a720a0ed98e58602",
       "value": "100%"
      }
     },
     "5b9b688bbc334b3d883919397baaa219": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "63c8a22f5f73464e93311b737ed97367": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_2c1a462d175e41dfb262d9b8eb363739",
       "placeholder": "​",
       "style": "IPY_MODEL_07f2f18e695b4d289c6bd698ca9177df",
       "value": " 228483/228483 [01:52&lt;00:00, 2399.60it/s]"
      }
     },
     "64ef851dbd7d4b99b9a05aa63934150b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "7fdd575b0b604a40884df87c7139591e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_5b9b688bbc334b3d883919397baaa219",
       "max": 500.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_88a8f2edc30a460d80385bc60c12551d",
       "value": 500.0
      }
     },
     "8078797948e8453180e8186e551420c8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "88a8f2edc30a460d80385bc60c12551d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "a2df3224f50c4f0ca444134a6f3f1926": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "b3c91979a8054fca8f5434191a1ff5c5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "bda7444a81b64d00a86c221ef4ff9d8d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c855a18b9a6e4290a720a0ed98e58602": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "ca312335344740b9b42e187aadd7a0ef": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_8078797948e8453180e8186e551420c8",
       "placeholder": "​",
       "style": "IPY_MODEL_27f896d96d814e95a4ff260f1bd523b8",
       "value": "100%"
      }
     },
     "d431d01c11544991b7e2fbe8034c60a4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_50c8ab49101f45a886b7b94a6b5e5743",
        "IPY_MODEL_3e4e75d36ec048c9a6e64edd6af2da00",
        "IPY_MODEL_63c8a22f5f73464e93311b737ed97367"
       ],
       "layout": "IPY_MODEL_a2df3224f50c4f0ca444134a6f3f1926"
      }
     },
     "eb97397a2e884a3190780f79e01b3f4c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_ca312335344740b9b42e187aadd7a0ef",
        "IPY_MODEL_7fdd575b0b604a40884df87c7139591e",
        "IPY_MODEL_3f1ba251feed4d05a21a1dc55ce2277e"
       ],
       "layout": "IPY_MODEL_bda7444a81b64d00a86c221ef4ff9d8d"
      }
     },
     "ee7739f22e5e44bd97a89327f707828c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ff6ba0d41689488f8e4ab5ff03421398": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}