{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "741c7211", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:41:36.075879Z", "iopub.status.busy": "2026-09-29T21:41:36.075490Z", "iopub.status.idle": "2026-09-29T21:41:38.941715Z", "shell.execute_reply": "2026-09-29T21:41:38.941340Z" }, "tags": [ "hide-input", "hide-output" ] }, "outputs": [], "source": "import warnings\n\nwarnings.filterwarnings(\"ignore\")\nfrom fmriprep_denoise.dataset.atlas import ATLAS_METADATA\nfrom fmriprep_denoise.visualization import figures, utils\n\nimport ipywidgets as widgets\nfrom ipywidgets import interactive, interact\n\n\npath_root = utils.repo2data_path() / \"denoise-metrics\"" }, { "cell_type": "markdown", "id": "06769637", "metadata": {}, "source": [ "# Results: atlas level\n", "\n", "```{Important}\n", "The dropdown menu only works in interactive mode!\n", "Please launch binder to see the alterative results.\n", "```\n", "\n", "It is possible to view the data at atlas level!\n", "\n", "In the report we used four atlases, three of them came with multiple parcellation schemes.\n", "\n", "- Gordon atlas {cite:p}`gordon_atlas_2014`: 333\n", "- Schaefer 7 network atlas {cite:p}`schaefer_local-global_2017`: 100, 200, 300, 400, 500, 600, 800\n", "- Multiresolution Intrinsic Segmentation Template (MIST) {cite:p}`urchs_mist_2019`: 7, 12, 20, 36, 64, 122, 197, 325, 444, “ROI” (210 parcels, 122 split by the midline)\n", "- DiFuMo {cite:p}`difumo_2020`: 64, 128, 256, 512, 1024\n", "\n", "## Before we start: Loss of temporal degrees of freedom\n", "\n", "As any denoising strategy aims at a particular trade-off between the amount of noise removed and the preservation of degrees of freedom for signals, first and foremost, we would like to presentthe loss of temporal degrees of freedom.\n", "\n", "This is an important consideration accompanying the remaining metrics." ] }, { "cell_type": "code", "execution_count": 3, "id": "3aff6c35", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:41:38.945500Z", "iopub.status.busy": "2026-09-29T21:41:38.945215Z", "iopub.status.idle": "2026-09-29T21:41:41.773463Z", "shell.execute_reply": "2026-09-29T21:41:41.773132Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "65c9d811be3f4a69848c10b151199ee2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Threshould: ', options=('stringent', 'minimal', None), value='stri…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": "from fmriprep_denoise.visualization import degrees_of_freedom_loss\n\n\ndef notebook_plot_loss_degrees_of_freedom(criteria_name, fmriprep_version):\n datasets = [\"ds000228\", \"ds000030\"]\n data = degrees_of_freedom_loss.load_data(\n path_root, datasets, criteria_name, fmriprep_version\n )\n degrees_of_freedom_loss.plot_stats(data)\n\n\ncriteria_name = widgets.Dropdown(\n options=[\"stringent\", \"minimal\", None],\n value=\"stringent\",\n description=\"Threshould: \",\n disabled=False,\n)\nfmriprep_version = widgets.Dropdown(\n options=[\"fmriprep-20.2.1lts\", \"fmriprep-20.2.5lts\"],\n value=\"fmriprep-20.2.1lts\",\n description=\"Preporcessing version : \",\n disabled=False,\n)\n\ninteractive(\n notebook_plot_loss_degrees_of_freedom,\n criteria_name=criteria_name,\n fmriprep_version=fmriprep_version,\n)" }, { "cell_type": "markdown", "id": "72fadf53", "metadata": {}, "source": [ "## Each parcellation scheme\n", "\n", "We can also plot them by each parcellation schemes.\n", "\n", "This is the original way Ciric and colleagues {cite:p}`ciric_benchmarking_2017` presented their results!\n", "\n", "### Gordon atlas" ] }, { "cell_type": "code", "execution_count": 4, "id": "eaabf88d", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:41:41.846844Z", "iopub.status.busy": "2026-09-29T21:41:41.846489Z", "iopub.status.idle": "2026-09-29T21:42:04.836703Z", "shell.execute_reply": "2026-09-29T21:42:04.837200Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d0515717d2bf47cd8eba88144a47ad06", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Dataset : ', options=('ds000228', 'ds000030'), value='ds000228'), …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": "atlas = \"gordon333\"\n\ndataset = widgets.Dropdown(\n options=[\"ds000228\", \"ds000030\"], description=\"Dataset : \", disabled=False\n)\nfmriprep_version = widgets.Dropdown(\n options=[\"fmriprep-20.2.1lts\", \"fmriprep-20.2.5lts\"],\n description=\"fmriprep : \",\n disabled=False,\n)\ndimension = widgets.Dropdown(\n description=\"dimensions : \",\n options=ATLAS_METADATA[atlas][\"dimensions\"],\n disabled=False,\n)\n\n@interact(ds=dataset, f=fmriprep_version, d=dimension)\ndef show_atlas(ds, f, d):\n print(ds, f, ATLAS_METADATA[atlas][\"atlas\"], \"dimensions: \", d)\n figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)" }, { "cell_type": "markdown", "id": "24775946", "metadata": {}, "source": [ "### MIST\n", "\n", "If selecting `None` for dimension, the figure will summmarise the metrics by the atlas collection.\n", "\n", "```{Important}\n", "The summary statistics are computed on the fly, it might take a bit of time, especially when displaying summary of the atlas collection.\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "id": "11dbaf47", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:42:04.857968Z", "iopub.status.busy": "2026-09-29T21:42:04.857524Z", "iopub.status.idle": "2026-09-29T21:42:08.430617Z", "shell.execute_reply": "2026-09-29T21:42:08.430227Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41f4292a3492492187214b583ba34af7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Dataset : ', options=('ds000228', 'ds000030'), value='ds000228'), …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": "atlas = \"mist\"\n\ndataset = widgets.Dropdown(\n options=[\"ds000228\", \"ds000030\"], description=\"Dataset : \", disabled=False\n)\nfmriprep_version = widgets.Dropdown(\n options=[\"fmriprep-20.2.1lts\", \"fmriprep-20.2.5lts\"],\n description=\"fmriprep : \",\n disabled=False,\n)\ndimension = widgets.Dropdown(\n description=\"dimensions : \",\n options=ATLAS_METADATA[atlas][\"dimensions\"] + [None],\n disabled=False,\n)\n\n@interact(ds=dataset, f=fmriprep_version, d=dimension)\ndef show_atlas(ds, f, d):\n print(ds, f, ATLAS_METADATA[atlas][\"atlas\"], \"dimensions: \", d)\n figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)" }, { "cell_type": "markdown", "id": "592a8cf4", "metadata": {}, "source": [ "### Schaefer 7 network\n", "\n", "If selecting `None` for dimension, the figure will summmarise the metrics by the atlas collection.\n", "\n", "```{Important}\n", "The summary statistics are computed on the fly, it might take a bit of time, especially when displaying summary of the atlas collection.\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "id": "2474ea99", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:42:08.450678Z", "iopub.status.busy": "2026-09-29T21:42:08.450326Z", "iopub.status.idle": "2026-09-29T21:42:15.631056Z", "shell.execute_reply": "2026-09-29T21:42:15.630479Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33202b5f194c4abc8dfce7bda632ce72", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Dataset : ', options=('ds000228', 'ds000030'), value='ds000228'), …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": "atlas = \"schaefer7networks\"\n\ndataset = widgets.Dropdown(\n options=[\"ds000228\", \"ds000030\"], description=\"Dataset : \", disabled=False\n)\nfmriprep_version = widgets.Dropdown(\n options=[\"fmriprep-20.2.1lts\", \"fmriprep-20.2.5lts\"],\n description=\"fmriprep : \",\n disabled=False,\n)\ndimension = widgets.Dropdown(\n description=\"dimensions : \",\n options=ATLAS_METADATA[atlas][\"dimensions\"] + [None],\n disabled=False,\n)\n\n@interact(ds=dataset, f=fmriprep_version, d=dimension)\ndef show_atlas(ds, f, d):\n print(ds, f, ATLAS_METADATA[atlas][\"atlas\"], \"dimensions: \", d)\n figures.plot_motion_resid(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_distance_dependence(ds, f, path_root, atlas_name=atlas, dimension=d)\n figures.plot_network_modularity(ds, f, path_root, atlas_name=atlas, dimension=d)" }, { "cell_type": "markdown", "id": "0bf2a2af", "metadata": {}, "source": [ "### DiFuMo\n", "\n", "If selecting `None` for dimension, the figure will summmarise the metrics by the atlas collection.\n", "\n", "```{Important}\n", "The summary statistics are computed on the fly, it might take a bit of time, especially when displaying summary of the atlas collection.\n", "```" ] }, { "cell_type": "code", "execution_count": 7, "id": "c9954154", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T21:42:15.650721Z", "iopub.status.busy": "2026-09-29T21:42:15.650286Z", "iopub.status.idle": "2026-09-29T21:42:22.240040Z", "shell.execute_reply": "2026-09-29T21:42:22.239609Z" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f6a7e5e33cf04d52a2df2871815602f5", "version_major": 2, "version_minor": 0 }, 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418\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspearmanr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpairwise_distance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/fmriprep_denoise/features/distance_dependency.py\u001b[0m in \u001b[0;36mget_atlas_pairwise_distance\u001b[0;34m(atlas_name, dimension)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mfile_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"atlas-gordon333_nroi-333_desc-distance.tsv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__file__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m\"data\"\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfile_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mcentroids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_centroid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matlas_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mpairwise_distance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcentroids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcentroids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpairwise_distance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/fmriprep_denoise/features/distance_dependency.py\u001b[0m in \u001b[0;36mget_centroid\u001b[0;34m(atlas_name, dimension)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0matlas_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"mist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m \u001b[0mcurrent_atlas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetch_atlas_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matlas_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcurrent_atlas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"z\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0matlas_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"difumo\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/fmriprep_denoise/dataset/atlas.py\u001b[0m in \u001b[0;36mfetch_atlas_path\u001b[0;34m(atlas_name, dimension, tf_dir)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"desc\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdimension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m img_path = templateflow.api.get(\n\u001b[0m\u001b[1;32m 93\u001b[0m \u001b[0mcur_atlas_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"template\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_empty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m )\n", "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/site-packages/templateflow/conf/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbids\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"A layout with PyBIDS <{__version__}> could not be initiated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 32\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/site-packages/templateflow/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(template, raise_empty, **kwargs)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mout_file\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mraise_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No results found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_file\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mException\u001b[0m: No results found" ] } ] } }, "be4b848538dc45c38dcb11f6ee3b0df4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DropdownModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DropdownModel", "_options_labels": [ "ds000228", "ds000030" ], "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "DropdownView", "description": "Dataset : ", "description_tooltip": null, "disabled": false, "index": 0, "layout": "IPY_MODEL_c751a184e49e4528acda818853299435", "style": "IPY_MODEL_5e0997a5abd042669e98846992ba4090" } }, "bfaaec7c85df46adb0eb9613ca6112be": { "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 } }, "bfb1905ba17d4a1b81f030aa22bd7f5c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_13944bc4c7be4c4cabfc0c225513015a", "msg_id": "", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Generating new graph\nds000228\ndescriptive stats for aroma aroma: 20.85333333333333(6.3303074801144374); range=7 - 38\ndescriptive stats for compcor compcor: 42.50666666666667(8.916085271943993); range=5 - 58\ndescriptive stats for scrubbing.5 excised_vol: 9.333333333333334(8.793937305515279); range=0 - 30\ndescriptive stats for scrubbing.2 excised_vol: 53.0(34.111898534406166); range=1 - 130\nds000030\ndescriptive stats for aroma aroma: 16.0(4.577999981688); range=6 - 29\ndescriptive stats for compcor compcor: 47.857142857142854(3.8680105429416756); range=35 - 54\ndescriptive stats for scrubbing.5 excised_vol: 2.505952380952381(4.4168932965296355); range=0 - 21\ndescriptive stats for scrubbing.2 excised_vol: 29.386904761904763(30.094447255791128); range=0 - 110\n" } ] } }, "c751a184e49e4528acda818853299435": { "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, 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