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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_demo_glass_brain.html">Glass brain plotting in nilearn</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/01_plotting/plot_surf_stat_map.html">Seed-based connectivity on the surface</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_stimuli.html">Show stimuli of Haxby et al. dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_mixed_gambles_frem.html">FREM on Jimura et al “mixed gambles” dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_oasis_vbm_space_net.html">Voxel-Based Morphometry on Oasis dataset with Space-Net prior</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_multiclass.html">The haxby dataset: different multi-class strategies</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_full_analysis.html">ROI-based decoding analysis in Haxby et al. dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_grid_search.html">Setting a parameter by cross-validation</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_different_estimators.html">Different classifiers in decoding the Haxby dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_miyawaki_reconstruction.html">Reconstruction of visual stimuli from Miyawaki et al. 2008</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/03_connectivity/index.html">Functional connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Functional connectivity</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_inverse_covariance_connectome.html">Computing a connectome with sparse inverse covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.html">Extracting signals of a probabilistic atlas of functional regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_simulated_connectome.html">Connectivity structure estimation on simulated data</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_multi_subject_connectome.html">Group Sparse inverse covariance for multi-subject connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_seed_to_voxel_correlation.html">Producing single subject maps of seed-to-voxel correlation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html">Regions extraction using dictionary learning and functional connectomes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_atlas_comparison.html">Comparing connectomes on different reference atlases</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_group_level_connectivity.html">Classification of age groups using functional connectivity</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_signal_extraction.html">Extracting signals from a brain parcellation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html">Extract signals on spheres and plot a connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_data_driven_parcellations.html">Clustering methods to learn a brain parcellation from fMRI</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/04_glm_first_level/index.html">GLM: First level analysis</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of GLM: First level analysis</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_design_matrix.html">Examples of design matrices</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_write_events_file.html">Generate an events.tsv file for the NeuroSpin localizer task</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_adhd_dmn.html">Default Mode Network extraction of ADHD dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html">Single-subject data (two runs) in native space</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_hrf.html">Example of MRI response functions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_predictions_residuals.html">Predicted time series and residuals</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_bids_features.html">First level analysis of a complete BIDS dataset from openneuro</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html">Example of surface-based first-level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_two_runs_model.html">Simple example of two-runs fMRI model fitting</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_first_level_details.html">Understanding parameters of the first-level model</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/05_glm_second_level/index.html">GLM: Second level analysis</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of GLM: Second level analysis</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_proportion_activated_voxels.html">Second-level fMRI model: true positive proportion in clusters</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_thresholding.html">Statistical testing of a second-level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_oasis.html">Voxel-Based Morphometry on OASIS dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_two_sample_test.html">Second-level fMRI model: two-sample test, unpaired and paired</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_one_sample_test.html">Second-level fMRI model: one sample test</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/05_glm_second_level/plot_second_level_association_test.html">Example of generic design in second-level models</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/06_manipulating_images/index.html">Manipulating brain image volumes</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of Manipulating brain image volumes</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_negate_image.html">Negating an image with math_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_compare_mean_image.html">Comparing the means of 2 images</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_smooth_mean_image.html">Smoothing an image</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_regions_labels_image.html">Breaking an atlas of labels in separated regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_resample_to_template.html">Resample an image to a template</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.html">Regions Extraction of Default Mode Networks using Smith Atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_simple.html">Simple example of NiftiMasker use</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_labels_simple.html">Extracting signals from brain regions using the NiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_mask_computation.html">Understanding NiftiMasker and mask computation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_affine_transformation.html">Visualization of affine resamplings</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_roi_extraction.html">Computing a Region of Interest (ROI) mask manually</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/07_advanced/index.html">Advanced statistical analysis of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of Advanced statistical analysis of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_resting_state.html">Multivariate decompositions: Independent component analysis of fMRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_simple_analysis.html">Massively univariate analysis of a calculation task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_bids_analysis.html">BIDS dataset first and second level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_neurovault_meta_analysis.html">NeuroVault meta-analysis of stop-go paradigm studies</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_age_group_prediction_cross_val.html">Functional connectivity predicts age group</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_surface_bids_analysis.html">Surface-based dataset first and second level analysis of a dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_mass_univariate_methods.html">Massively univariate analysis of a motor task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_haxby_mass_univariate.html">Massively univariate analysis of face vs house recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_advanced_decoding_scikit.html">Advanced decoding using scikit learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_beta_series.html">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/08_experimental/index.html">Examples for experimental modules</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of Examples for experimental modules</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/08_experimental/plot_surface_image_and_maskers.html">A short demo of the surface images & maskers</a></li>
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<li class="toctree-l1 current has-children"><a class="reference internal" href="../user_guide.html">User guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of User guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../introduction.html">1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#what-is-nilearn">2. What is <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>?</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#using-nilearn-for-the-first-time">3. Using <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> for the first time</a></li>
<li class="toctree-l2"><a class="reference internal" href="../introduction.html#machine-learning-applications-to-neuroimaging">4. Machine learning applications to Neuroimaging</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../decoding/index.html">5. Decoding and MVPA: predicting from brain images</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of 5. Decoding and MVPA: predicting from brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../decoding/decoding_intro.html">5.1. An introduction to decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="../decoding/estimator_choice.html">5.2. Choosing the right predictive model for neuroimaging</a></li>
<li class="toctree-l3"><a class="reference internal" href="../decoding/frem.html">5.3. FREM: fast ensembling of regularized models for robust decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="../decoding/space_net.html">5.4. SpaceNet: decoding with spatial structure for better maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../decoding/searchlight.html">5.5. Searchlight : finding voxels containing information</a></li>
<li class="toctree-l3"><a class="reference internal" href="../decoding/going_further.html">5.6. Running scikit-learn functions for more control on the analysis</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../connectivity/index.html">6. Functional connectivity and resting state</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of 6. Functional connectivity and resting state</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/functional_connectomes.html">6.1. Extracting times series to build a functional connectome</a></li>
<li class="toctree-l3 has-children"><a class="reference internal" href="../connectivity/connectome_extraction.html">6.2. Connectome extraction: inverse covariance for direct connections</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of 6.2. Connectome extraction: inverse covariance for direct connections</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l4"><a class="reference internal" href="../developers/group_sparse_covariance.html">6.2.3.1. Group-sparse covariance estimation</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../connectivity/resting_state_networks.html">6.3. Extracting functional brain networks: ICA and related</a></li>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/region_extraction.html">6.4. Region Extraction for better brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../connectivity/parcellating.html">6.5. Clustering to parcellate the brain in regions</a></li>
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<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">7. Plotting brain images</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="../glm/index.html">8. Analyzing fMRI using GLMs</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of 8. Analyzing fMRI using GLMs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../glm/glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../glm/first_level_model.html">8.2. First level models</a></li>
<li class="toctree-l3"><a class="reference internal" href="../glm/second_level_model.html">8.3. Second level models</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../manipulating_images/index.html">9. Manipulation brain volumes with nilearn</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of 9. Manipulation brain volumes with nilearn</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/input_output.html">9.1. Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/manipulating_images.html">9.2. Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/masker_objects.html">9.3. From neuroimaging volumes to data matrices: the masker objects</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../building_blocks/index.html">10. Advanced usage: manual pipelines and scaling up</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of 10. Advanced usage: manual pipelines and scaling up</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/manual_pipeline.html">10.1. Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/neurovault.html">10.2. Downloading statistical maps from the Neurovault repository</a></li>
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<article role="main">
<section id="plotting-brain-images">
<span id="plotting"></span><h1><span class="section-number">7. </span>Plotting brain images<a class="headerlink" href="#plotting-brain-images" title="Link to this heading">#</a></h1>
<p>In this section, we detail the general tools to visualize
neuroimaging volumes and surfaces with nilearn.</p>
<p>Nilearn comes with plotting function to display brain maps coming from
Nifti-like images, in the <a class="reference internal" href="../modules/plotting.html#module-nilearn.plotting" title="nilearn.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting</span></code></a> module.</p>
<aside class="topic">
<p class="topic-title"><strong>Code examples</strong></p>
<p>Nilearn has a whole section of the example gallery on plotting.</p>
<p>A small tour of the plotting functions can be found in the example
<a class="reference internal" href="../auto_examples/01_plotting/plot_demo_plotting.html#sphx-glr-auto-examples-01-plotting-plot-demo-plotting-py"><span class="std std-ref">Plotting tools in nilearn</span></a>.</p>
<p>Finally, note that, as always in the nilearn documentation, clicking
on a figure will take you to the code that generates it.</p>
</aside>
<section id="different-plotting-functions">
<h2><span class="section-number">7.1. </span>Different plotting functions<a class="headerlink" href="#different-plotting-functions" title="Link to this heading">#</a></h2>
<p>Nilearn has a set of plotting functions to plot brain volumes that are
fined tuned to specific applications. Amongst other things, they use
different heuristics to find cutting coordinates.</p>
<div class="table-wrapper plotting-table docutils container">
<table class="plotting-table docutils align-default">
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_plotting.html"><img alt="plot_anat" src="../_images/sphx_glr_plot_demo_plotting_003.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_anat.html#nilearn.plotting.plot_anat" title="nilearn.plotting.plot_anat"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_anat</span></code></a>
<br>
Plotting an anatomical image</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_plotting.html"><img alt="plot_epi" src="../_images/sphx_glr_plot_demo_plotting_005.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_epi.html#nilearn.plotting.plot_epi" title="nilearn.plotting.plot_epi"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_epi</span></code></a>
<br>
Plotting an EPI, or T2* image</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_glass_brain_extensive.html"><img alt="plot_glass_brain" src="../_images/sphx_glr_plot_demo_glass_brain_extensive_001.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a>
<br>
Glass brain visualization. By default plots maximum
intensity projection of the absolute values. To plot
positive and negative values set plot_abs parameter to
False.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_plotting.html"><img alt="plot_stat_map" src="../_images/sphx_glr_plot_demo_plotting_001.png" style="width: 365.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_stat_map.html#nilearn.plotting.plot_stat_map" title="nilearn.plotting.plot_stat_map"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_stat_map</span></code></a>
<br>
Plotting a statistical map, like a T-map, a Z-map, or
an ICA, with an optional background</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_plotting.html"><img alt="plot_roi" src="../_images/sphx_glr_plot_demo_plotting_004.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_roi</span></code></a>
<br>
Plotting ROIs, or a mask, with an optional background</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html"><img alt="plot_connectome" src="../_images/sphx_glr_plot_sphere_based_connectome_003.png" style="width: 440.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_connectome.html#nilearn.plotting.plot_connectome" title="nilearn.plotting.plot_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_connectome</span></code></a>
<br>
Plotting a connectome</p>
<p>Functions for automatic extraction of coords based on
brain parcellations useful for <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_connectome.html#nilearn.plotting.plot_connectome" title="nilearn.plotting.plot_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_connectome</span></code></a>
are demonstrated in
<strong>Example:</strong> <a class="reference internal" href="../auto_examples/03_connectivity/plot_atlas_comparison.html#sphx-glr-auto-examples-03-connectivity-plot-atlas-comparison-py"><span class="std std-ref">Comparing connectomes on different reference atlases</span></a></p>
</td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html"><img alt="plot_nodes" src="../_images/sphx_glr_plot_sphere_based_connectome_007.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_markers</span></code></a>
<br>
Plotting network nodes (markers)</p>
<p>Function for automatic plotting of network nodes (markers)
and color coding them according to provided nodal measure
(i.e. connection strength) as demonstrated in
<strong>Example:</strong> <a class="reference internal" href="../auto_examples/03_connectivity/plot_atlas_comparison.html#sphx-glr-auto-examples-03-connectivity-plot-atlas-comparison-py"><span class="std std-ref">Comparing connectomes on different reference atlases</span></a></p>
</td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_prob_atlas.html"><img alt="plot_prob_atlas" src="../_images/sphx_glr_plot_prob_atlas_003.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_prob_atlas.html#nilearn.plotting.plot_prob_atlas" title="nilearn.plotting.plot_prob_atlas"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_prob_atlas</span></code></a>
<br>
Plotting 4D probabilistic atlas maps</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_carpet.html"><img alt="plot_carpet" src="../_images/sphx_glr_plot_carpet_001.png" style="width: 500.0px; height: 250.0px;" /></a></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_carpet</span></code></a>
<br>
Plotting voxel intensities across time.</p></td>
</tr>
<tr class="row-odd"><td><p><strong>plot_img</strong></p></td>
<td><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img.html#nilearn.plotting.plot_img" title="nilearn.plotting.plot_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_img</span></code></a>
<br>
General-purpose function, with no specific presets</p></td>
</tr>
</tbody>
</table>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><strong>Opening too many figures without closing</strong></p>
<p>Each call to a plotting function creates a new figure by default. When
used in non-interactive settings, such as a script or a program, these
are not displayed, but still accumulate and eventually lead to slowing
the execution and running out of memory.</p>
<p>To avoid this, you must close the plot as follow:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nilearn</span> <span class="kn">import</span> <span class="n">plotting</span>
<span class="n">display</span> <span class="o">=</span> <span class="n">plotting</span><span class="o">.</span><span class="n">plot_stat_map</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">display</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="../auto_examples/01_plotting/plot_dim_plotting.html#sphx-glr-auto-examples-01-plotting-plot-dim-plotting-py"><span class="std std-ref">Controlling the contrast of the background when plotting</span></a></p>
</div>
</section>
<section id="different-display-modes">
<h2><span class="section-number">7.2. </span>Different display modes<a class="headerlink" href="#different-display-modes" title="Link to this heading">#</a></h2>
<div class="table-wrapper plotting-table docutils container">
<table class="plotting-table docutils align-default">
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_ortho" src="../_images/sphx_glr_plot_demo_more_plotting_001.png" style="width: 365.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="ortho",</span> <span class="pre">cut_coords=[36,</span> <span class="pre">-27,</span> <span class="pre">60]</span></code>
<br>
Ortho slicer: 3 cuts along the x, y, z directions</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_z_many" src="../_images/sphx_glr_plot_demo_more_plotting_002.png" style="width: 351.0px; height: 96.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="z",</span> <span class="pre">cut_coords=5</span></code>
<br>
Cutting in the z direction, specifying the number of
cuts</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_x" src="../_images/sphx_glr_plot_demo_more_plotting_003.png" style="width: 295.0px; height: 114.5px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="x",</span> <span class="pre">cut_coords=[-36,</span> <span class="pre">36]</span></code>
<br>
Cutting in the x direction, specifying the exact
cuts</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_y_small" src="../_images/sphx_glr_plot_demo_more_plotting_004.png" style="width: 250.0px; height: 200.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="y",</span> <span class="pre">cut_coords=1</span></code>
<br>
Cutting in the y direction, with only 1 cut, that is
automatically positioned</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_z_small" src="../_images/sphx_glr_plot_demo_more_plotting_005.png" style="width: 250.0px; height: 350.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="z",</span> <span class="pre">cut_coords=1,</span> <span class="pre">colorbar=False</span></code>
<br>
Cutting in the z direction, with only 1 cut, that is
automatically positioned</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_xz" src="../_images/sphx_glr_plot_demo_more_plotting_006.png" style="width: 255.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="xz",</span> <span class="pre">cut_coords=[36,</span> <span class="pre">60]</span></code>
<br>
Cutting in the x and z direction, with cuts manually
positioned</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_yx" src="../_images/sphx_glr_plot_demo_more_plotting_007.png" style="width: 255.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="yx",</span> <span class="pre">cut_coords=[-27,</span> <span class="pre">36]</span></code>
<br>
Cutting in the y and x direction, with cuts manually
positioned</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_yz" src="../_images/sphx_glr_plot_demo_more_plotting_008.png" style="width: 255.0px; height: 150.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="yz",</span> <span class="pre">cut_coords=[-27,</span> <span class="pre">60]</span></code>
<br>
Cutting in the y and z direction, with cuts manually
positioned</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_tiled" src="../_images/sphx_glr_plot_demo_more_plotting_009.png" style="width: 335.0px; height: 380.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="tiled",</span> <span class="pre">cut_coords=[36,</span> <span class="pre">-27,</span> <span class="pre">60]</span></code>
<br>
Tiled slicer: 3 cuts along the x, y, z directions,
arranged in a 2x2 grid</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_mosaic" src="../_images/sphx_glr_plot_demo_more_plotting_010.png" style="width: 381.0px; height: 150.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display_mode="mosaic"</span></code>
<br>
Mosaic slicer: multiple cuts along the x, y, z directions,
with cuts automatically positioned by default</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_glass_brain_extensive.html"><img alt="plot_lzr" src="../_images/sphx_glr_plot_demo_glass_brain_extensive_006.png" style="width: 255.0px; height: 170.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">Glass</span> <span class="pre">brain</span> <span class="pre">display_mode="lzr"</span></code>
<br>
Glass brain and Connectome provide additional display modes
due to the possibility of doing hemispheric projections.
Check out: <code class="docutils literal notranslate"><span class="pre">'l'</span></code>, <code class="docutils literal notranslate"><span class="pre">'r'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lr'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lzr'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lyr'</span></code>, ‘<code class="docutils literal notranslate"><span class="pre">lzry'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lyrz'</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_glass_brain_extensive.html"><img alt="plot_lyrz" src="../_images/sphx_glr_plot_demo_glass_brain_extensive_007.png" style="width: 255.0px; height: 170.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">Glass</span> <span class="pre">brain</span> <span class="pre">display_mode="lyrz"</span></code>
<br>
Glass brain and Connectome provide additional display modes
due to the possibility of doing hemispheric projections.
Check out: <code class="docutils literal notranslate"><span class="pre">'l'</span></code>, <code class="docutils literal notranslate"><span class="pre">'r'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lr'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lzr'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lyr'</span></code>, ‘<code class="docutils literal notranslate"><span class="pre">lzry'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lyrz'</span></code>.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="available-colormaps">
<h2><span class="section-number">7.3. </span>Available Colormaps<a class="headerlink" href="#available-colormaps" title="Link to this heading">#</a></h2>
<p>Nilearn plotting library ships with a set of extra colormaps, as seen in the
image below</p>
<a class="reference external image-reference" href="../auto_examples/01_plotting/plot_colormaps.html"><img alt="../_images/sphx_glr_plot_colormaps_001.png" src="../_images/sphx_glr_plot_colormaps_001.png" style="width: 500.0px; height: 210.0px;" /></a>
<p>These colormaps can be used as any other matplotlib colormap.</p>
<a class="reference external image-reference" href="../auto_examples/01_plotting/plot_colormaps.html"><img alt="../_images/sphx_glr_plot_colormaps_002.png" src="../_images/sphx_glr_plot_colormaps_002.png" style="width: 500.0px; height: 250.0px;" /></a>
</section>
<section id="adding-overlays-edges-contours-contour-fillings-markers-scale-bar">
<span id="display-modules"></span><h2><span class="section-number">7.4. </span>Adding overlays, edges, contours, contour fillings, markers, scale bar<a class="headerlink" href="#adding-overlays-edges-contours-contour-fillings-markers-scale-bar" title="Link to this heading">#</a></h2>
<p>To add overlays, contours, or edges, use the return value of the plotting
functions. Indeed, these return a display object, such as the
<a class="reference internal" href="../modules/generated/nilearn.plotting.displays.OrthoSlicer.html#nilearn.plotting.displays.OrthoSlicer" title="nilearn.plotting.displays.OrthoSlicer"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.plotting.displays.OrthoSlicer</span></code></a>. This object represents the
plot, and has methods to add overlays, contours or edge maps:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">display</span> <span class="o">=</span> <span class="n">plotting</span><span class="o">.</span><span class="n">plot_epi</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<div class="table-wrapper plotting-table docutils container">
<table class="plotting-table docutils align-default">
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_edges" src="../_images/sphx_glr_plot_demo_more_plotting_013.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display.add_edges(img)</span></code>
<br>
Add a plot of the edges of <code class="docutils literal notranslate"><span class="pre">img</span></code>, where edges are
extracted using a Canny edge-detection routine. This
is typically useful to check registration. Note that
<code class="docutils literal notranslate"><span class="pre">img</span></code> should have some visible sharp edges. Typically
an EPI img does not, but a T1 does.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_contours" src="../_images/sphx_glr_plot_demo_more_plotting_014.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display.add_contours(img,</span> <span class="pre">levels=[0.5],</span> <span class="pre">colors="r")</span></code>
<br>
Add a plot of the contours of <code class="docutils literal notranslate"><span class="pre">img</span></code>, where contours
are computed for constant values, specified in
‘levels’. This is typically useful to outline a mask,
or ROI on top of another map.
<br>
<strong>Example:</strong> <a class="reference internal" href="../auto_examples/01_plotting/plot_haxby_masks.html#sphx-glr-auto-examples-01-plotting-plot-haxby-masks-py"><span class="std std-ref">Plot Haxby masks</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_demo_more_plotting.html"><img alt="plot_fill" src="../_images/sphx_glr_plot_demo_more_plotting_015.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display.add_contours(img,</span> <span class="pre">filled=True,</span> <span class="pre">alpha=0.7,</span> <span class="pre">levels=[0.5],</span> <span class="pre">colors="b")</span></code>
<br>
Add a plot of <code class="docutils literal notranslate"><span class="pre">img</span></code> with contours filled with colors</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="../auto_examples/01_plotting/plot_overlay.html"><img alt="plot_overlay" src="../_images/sphx_glr_plot_overlay_002.png" style="width: 330.0px; height: 175.0px;" /></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">display.add_overlay(img,</span> <span class="pre">cmap=plotting.cm.purple_green,</span> <span class="pre">threshold=3)</span></code>
<br>
Add a new overlay on the existing figure
<br>