From fdd52db0cef9f847d79c921728012666342b7426 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Mon, 5 Dec 2016 17:28:47 +0100
Subject: [PATCH 001/228] changing init

---
 nilearn/connectome/__init__.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/nilearn/connectome/__init__.py b/nilearn/connectome/__init__.py
index e2e85a5440..a8ca8dd5f6 100644
--- a/nilearn/connectome/__init__.py
+++ b/nilearn/connectome/__init__.py
@@ -10,6 +10,9 @@
 from .group_sparse_cov import (GroupSparseCovariance,
     GroupSparseCovarianceCV, group_sparse_covariance)
 
+from rena_clustering import ReNA
+
 __all__ = ['sym_to_vec', 'ConnectivityMeasure',
            'GroupSparseCovariance', 'GroupSparseCovarianceCV',
-           'group_sparse_covariance', 'cov_to_corr', 'prec_to_partial']
+           'group_sparse_covariance', 'cov_to_corr', 'prec_to_partial',
+           'ReNA']

From 77b94bc8810784eabf98742a6c18b7da36d41b95 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Mon, 5 Dec 2016 17:29:15 +0100
Subject: [PATCH 002/228] adding files

---
 .../03_connectivity/plot_fast_clustering.py   | 193 +++++++
 nilearn/connectome/rena_clustering.py         | 484 ++++++++++++++++++
 nilearn/connectome/tests/test_rena.py         |  29 ++
 3 files changed, 706 insertions(+)
 create mode 100644 examples/03_connectivity/plot_fast_clustering.py
 create mode 100644 nilearn/connectome/rena_clustering.py
 create mode 100644 nilearn/connectome/tests/test_rena.py

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
new file mode 100644
index 0000000000..74d7a6b64a
--- /dev/null
+++ b/examples/03_connectivity/plot_fast_clustering.py
@@ -0,0 +1,193 @@
+"""
+Ward clustering to learn a brain parcellation from rest fMRI
+====================================================================
+
+We use spatially-constrained Ward-clustering to create a set of
+parcels. These parcels are particularly interesting for creating a
+'compressed' representation of the data, replacing the data in the fMRI
+images by mean on the parcellation.
+
+This parcellation may be useful in a supervised learning, see for
+instance: `A supervised clustering approach for fMRI-based inference of
+brain states <https://hal.inria.fr/inria-00589201>`_, Michel et al,
+Pattern Recognition 2011.
+
+The big picture discussion corresponding to this example can be found
+in the documentation section :ref:`parcellating_brain`.
+"""
+
+##################################################################
+# Download a rest dataset and turn it to a data matrix
+# -----------------------------------------------------
+#
+# We we download one subject of the ADHD dataset from Internet
+
+from nilearn import datasets
+dataset = datasets.fetch_adhd(n_subjects=1)
+
+# print basic information on the dataset
+print('First subject functional nifti image (4D) is at: %s' %
+      dataset.func[0])  # 4D data
+
+
+##################################################################
+# Transform nifti files to a data matrix with the NiftiMasker
+from nilearn import input_data
+
+# The NiftiMasker will extract the data on a mask. We do not have a
+# mask, hence we need to compute one.
+#
+# This is resting-state data: the background has not been removed yet,
+# thus we need to use mask_strategy='epi' to compute the mask from the
+# EPI images
+nifti_masker = input_data.NiftiMasker(memory='nilearn_cache',
+                                      mask_strategy='epi', memory_level=1,
+                                      standardize=False)
+
+func_filename = dataset.func[0]
+# The fit_transform call computes the mask and extracts the time-series
+# from the files:
+fmri_masked = nifti_masker.fit_transform(func_filename)
+
+# We can retrieve the numpy array of the mask
+mask = nifti_masker.mask_img_.get_data().astype(bool)
+
+
+##################################################################
+# Perform Ward clustering
+# -----------------------
+#
+# We use spatially-constrained Ward clustering. For this, we need to
+# compute from the mask a matrix giving the voxel-to-voxel connectivity
+
+# Compute connectivity matrix: which voxel is connected to which
+from sklearn.feature_extraction import image
+shape = mask.shape
+connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
+                                   n_z=shape[2], mask=mask)
+
+
+##################################################################
+# Then we use FeatureAgglomeration from scikit-learn. Indeed, the voxels
+# are the features of the data matrix.
+#
+# In addition, we use caching. As a result, the clustering doesn't have
+# to be recomputed later.
+
+# Computing the ward for the first time, this is long...
+from sklearn.cluster import FeatureAgglomeration
+# If you have scikit-learn older than 0.14, you need to import
+# WardAgglomeration instead of FeatureAgglomeration
+import time
+start = time.time()
+ward = FeatureAgglomeration(n_clusters=2000, connectivity=connectivity,
+                            linkage='ward', memory='nilearn_cache')
+ward.fit(fmri_masked)
+print("Ward agglomeration 2000 clusters: %.2fs" % (time.time() - start))
+
+
+##################################################################
+# Perform ReNA clustering
+# -----------------------
+#
+from nilearn.connectome import ReNA
+start = time.time()
+rena = ReNA(scaling=True, n_clusters=2000, mask=nifti_masker,
+            memory='nilearn_cache')
+
+rena.fit_transform(func_filename)
+print("ReNA 2000 clusters: %.2fs" % (time.time() - start))
+
+##################################################################
+# Visualize results
+# ------------------
+#
+# First we display the labels of the clustering in the brain.
+#
+# To visualize results, we need to transform the clustering's labels back
+# to a neuroimaging volume. For this, we use the NiftiMasker's
+# inverse_transform method.
+from nilearn.plotting import plot_roi, plot_epi, show
+import numpy as np
+
+# Unmask the labels
+
+# Avoid 0 label
+ward_labels = ward.labels_ + 1
+ward_labels_img = nifti_masker.inverse_transform(ward_labels)
+
+rena_labels = rena.labels_ + 1
+permutation = np.random.permutation(rena_labels.shape[0])
+rena_labels = permutation[rena_labels]
+rena_labels_img = nifti_masker.inverse_transform(rena_labels)
+
+
+from nilearn.image import mean_img
+mean_func_img = mean_img(func_filename)
+
+# first_plot = plot_roi(rena_labels_img, mean_func_img,
+#                       title="ReNA parcellation",
+#                       display_mode='xz')
+
+# first_plot = plot_roi(ward_labels_img, mean_func_img,
+#                       title="Ward parcellation",
+#                       display_mode='xz')
+
+# import pdb; pdb.set_trace()  # XXX BREAKPOINT
+##################################################################
+# Second, we illustrate the effect that the clustering has on the
+# signal. We show the original data, and the approximation provided by
+# the clustering by averaging the signal on each parcel.
+#
+# As you can see below, this approximation is very good, although there
+# are only 2000 parcels, instead of the original 60000 voxels
+
+# Display the original data
+first_plot = plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
+                      title='Original (%i voxels)' % fmri_masked.shape[1],
+                      vmax=fmri_masked.max(), vmin=fmri_masked.min(),
+                      display_mode='xz')
+
+# common cut coordinates for all plots
+cut_coords = first_plot.cut_coords
+
+# A reduced data can be create by taking the parcel-level average:
+# Note that, as many objects in the scikit-learn, the ward object exposes
+# a transform method that modifies input features. Here it reduces their
+# dimension
+fmri_reduced_ward = ward.transform(fmri_masked)
+
+# Apply this sample mask to X (fMRI data).
+# Because the data is in one single large 4D image, we need to use
+# index_img to do the split easily
+from nilearn.image import index_img
+fmri_reduced_rena = rena.transform(index_img(func_filename, 0))
+
+
+# # Display the corresponding data compressed using the parcellation
+fmri_compressed_ward = ward.inverse_transform(fmri_reduced_ward)
+compressed_img_ward = nifti_masker.inverse_transform(fmri_compressed_ward[0])
+
+fmri_compressed_rena = rena.inverse_transform(fmri_reduced_rena)
+compressed_img_rena = nifti_masker.inverse_transform(fmri_compressed_rena[0])
+
+
+plot_epi(compressed_img_ward, cut_coords=cut_coords,
+         title='Ward: Compressed representation (2000 parcels)',
+         vmax=fmri_masked.max(), vmin=fmri_masked.min(),
+         display_mode='xz')
+
+plot_epi(compressed_img_rena, cut_coords=cut_coords,
+         title='ReNA: Compressed representation (2000 parcels)',
+         vmax=fmri_masked.max(), vmin=fmri_masked.min(),
+         display_mode='xz')
+
+
+##################################################################
+# labels_img is a Nifti1Image object, it can be saved to file with the
+# following code:
+# labels_img.to_filename('parcellation.nii')
+
+
+show()
+
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
new file mode 100644
index 0000000000..4987995611
--- /dev/null
+++ b/nilearn/connectome/rena_clustering.py
@@ -0,0 +1,484 @@
+"""Recursive nearest agglomeration (ReNA):
+    fastclustering for approximation of structured signals
+"""
+import numpy as np
+import warnings
+from sklearn.externals.joblib import Memory
+from sklearn.externals import six
+from scipy.sparse import csgraph, coo_matrix, dia_matrix
+from sklearn.base import BaseEstimator, clone
+from .._utils.fixes import check_array, check_is_fitted
+from ..input_data import NiftiMasker, MultiNiftiMasker
+
+
+def _compute_weights(masker, data_matrix):
+    """Compute the weights in the direction of each axis using the Euclidean
+    distance.
+
+    Note: Here we are assuming a square lattice (no diagonal connections)
+    """
+    dims = len(masker.mask_img_.shape)
+    data_graph = masker.inverse_transform(data_matrix).get_data()
+    weights = []
+
+    for axis in range(dims):
+        weights.append(
+            np.sum(np.diff(data_graph, axis=axis) ** 2, axis=-1).ravel())
+
+    return np.hstack(weights)
+
+
+def _compute_edges(data_graph, is_mask=False):
+    """
+    """
+    dims = len(data_graph.shape)
+    edges = []
+    for axis in range(dims):
+        vertices_axis = np.swapaxes(data_graph, 0, axis)
+
+        if is_mask:
+            edges.append(np.logical_and(
+                vertices_axis[:-1].swapaxes(axis, 0).ravel(),
+                vertices_axis[1:].swapaxes(axis, 0).ravel()))
+        else:
+            edges.append(
+                np.vstack([vertices_axis[:-1].swapaxes(axis, 0).ravel(),
+                           vertices_axis[1:].swapaxes(axis, 0).ravel()]))
+    edges = np.hstack(edges)
+
+    return edges
+
+
+def _create_ordered_edges(masker, data_matrix):
+    """
+    """
+    mask = masker.mask_img_.get_data()
+    shape = mask.shape
+    n_features = np.prod(shape)
+
+    vertices = np.arange(n_features).reshape(shape)
+    weights = _compute_weights(masker, data_matrix)
+    edges = _compute_edges(vertices)
+    edges_mask = _compute_edges(mask, is_mask=True)
+
+    # Apply the mask
+    weights = weights[edges_mask]
+    edges = edges[:, edges_mask]
+
+    # Reorder the indices of the graph
+    max_index = edges.max()
+    order = np.searchsorted(np.unique(edges.ravel()), np.arange(max_index + 1))
+    edges = order[edges]
+
+    return edges, weights, edges_mask
+
+
+def weighted_connectivity_graph(masker, data_matrix):
+    """ Creating weighted graph
+
+    data and topology, encoded by a connectivity matrix
+
+    """
+    n_features = masker.mask_img_.get_data().sum()
+
+    edges, weight, edges_mask = _create_ordered_edges(masker, data_matrix)
+    connectivity = coo_matrix(
+        (weight, edges), (n_features, n_features)).tocsr()
+
+    # Making it symmetrical
+    connectivity = (connectivity + connectivity.T) / 2
+
+    return connectivity
+
+
+def _nn_connectivity(connectivity, threshold):
+    """ Fast implementation of nearest neighbor connectivity
+
+    connectivity: weighted connectivity matrix
+    """
+    n_features = connectivity.shape[0]
+
+    connectivity_ = coo_matrix(
+        (1. / connectivity.data, connectivity.nonzero()),
+        (n_features, n_features)).tocsr()
+
+    inv_max = dia_matrix((1. / connectivity_.max(axis=0).toarray()[0], 0),
+                         shape=(n_features, n_features))
+
+    connectivity_ = inv_max * connectivity_
+
+    # Dealing with eccentricities
+    edge_mask = connectivity_.data > 1 - threshold
+
+    j_idx = connectivity_.nonzero()[1][edge_mask]
+    i_idx = connectivity_.nonzero()[0][edge_mask]
+
+    weight = np.ones_like(j_idx)
+    edges = np.array((i_idx, j_idx))
+
+    nn_connectivity = coo_matrix((weight, edges), (n_features, n_features))
+
+    return nn_connectivity
+
+
+def reduce_data_and_connectivity(labels, n_labels, connectivity, data_matrix,
+                                 threshold):
+    """
+    """
+    n_features = len(labels)
+
+    incidence = coo_matrix(
+        (np.ones(n_features), (labels, np.arange(n_features))),
+        shape=(n_labels, n_features), dtype=np.float32).tocsc()
+
+    inv_sum_col = dia_matrix(
+        (np.array(1. / incidence.sum(axis=1)).squeeze(), 0),
+        shape=(n_labels, n_labels))
+
+    incidence = inv_sum_col * incidence
+
+    # reduced data
+    reduced_data_matrix = (incidence * data_matrix.T).T
+    reduced_connectivity = (incidence * connectivity) * incidence.T
+
+    reduced_connectivity = reduced_connectivity - dia_matrix(
+        (reduced_connectivity.diagonal(), 0),
+        shape=(reduced_connectivity.shape))
+
+    i_idx, j_idx = reduced_connectivity.nonzero()
+
+    data_matrix_ = np.maximum(threshold, np.sum(
+        (reduced_data_matrix[:, i_idx] - reduced_data_matrix[:, j_idx]) ** 2, 0))
+    reduced_connectivity.data = data_matrix_
+
+    return reduced_connectivity, reduced_data_matrix
+
+
+def nearest_neighbor_grouping(connectivity, data_matrix, n_clusters,
+                              threshold):
+    """ Cluster according to nn and reduce the data and connectivity
+    """
+    # Nearest neighbor conenctivity
+    nn_connectivity = _nn_connectivity(connectivity, threshold)
+    n_features = connectivity.shape[0]
+
+    n_labels = n_features - (nn_connectivity + nn_connectivity.T).nnz / 2
+
+    if n_labels < n_clusters:
+        # cut some links to achieve the desired number of clusters
+        alpha = n_features - n_clusters
+
+        nn_connectivity = nn_connectivity + nn_connectivity.T
+
+        edges_ = np.array(nn_connectivity.nonzero())
+
+        plop = edges_[0] - edges_[1]
+
+        select = np.argsort(plop)[:alpha]
+
+        nn_connectivity = coo_matrix(
+            (np.ones(2 * alpha),
+             np.hstack((edges_[:, select], edges_[::-1, select]))),
+            (n_features, n_features))
+
+    # Clustering step: getting the connected components of the nn matrix
+    n_labels, labels = csgraph.connected_components(nn_connectivity)
+
+    # Reduction step: reduction by averaging
+    reduced_connectivity, reduced_data_matrix = reduce_data_and_connectivity(
+        labels, n_labels, connectivity, data_matrix, threshold)
+
+    return reduced_connectivity, reduced_data_matrix, labels
+
+
+def recursive_nearest_agglomeration(masker, data_matrix, n_clusters, n_iter,
+                                    threshold):
+    """
+    """
+    # Weighted connectivity matrix
+    connectivity = weighted_connectivity_graph(masker, data_matrix)
+
+    # Initialization
+    labels = np.arange(connectivity.shape[0])
+    n_labels = connectivity.shape[0]
+
+    for i in range(n_iter):
+        connectivity, data_matrix, reduced_labels = nearest_neighbor_grouping(
+            connectivity, data_matrix, n_clusters, threshold)
+
+        labels = reduced_labels[labels]
+        n_labels = connectivity.shape[0]
+
+        if n_labels <= n_clusters:
+            break
+
+    return n_labels, labels
+
+
+class ReNA(BaseEstimator):
+    """
+    Recursive nearest agglomeration.
+    Recursively merges the pair of clusters according to 1-nearest neighbors
+    criterion.
+
+    Parameters
+    ----------
+    mask : filename, niimg, NiftiMasker instance, optional default None)
+        Mask to be used on data. If an instance of masker is passed,
+        then its mask will be used. If no mask is it will be computed
+        automatically by a NiftiMasker.
+
+    n_cluster: int, optional (default 2)
+        The number of clusters to find.
+
+    scaling: bool, optional (default False)
+
+    memory: instance of joblib.Memory or string
+        Used to cache the masking process.
+        By default, no caching is done. If a string is given, it is the
+        path to the caching directory.
+
+    memory_level: integer, optional (default 1)
+        Rough estimator of the amount of memory used by caching. Higher value
+        means more memory for caching.
+
+    verbose : int, optional (default 1)
+        Verbosity level.
+
+    smoothing_fwhm: float, optional
+        If smoothing_fwhm is not None, it gives the size in millimeters of the
+        spatial smoothing to apply to the signal.
+
+    standardize : boolean, optional
+        If standardize is True, the time-series are centered and normed:
+        their variance is put to 1 in the time dimension.
+
+    target_affine : 3x3 or 4x4 matrix, optional (default None)
+        This parameter is passed to image.resample_img. An important use-case
+        of this parameter is for downsampling the input data to a coarser
+        resolution (to speed of the model fit). Please see the related
+        documentation for details.
+
+    target_shape : 3-tuple of integers, optional (default None)
+        This parameter is passed to image.resample_img. Please see the
+        related documentation for details.
+
+    low_pass: None or float, optional
+        This parameter is passed to signal.clean. Please see the related
+        documentation for details
+
+    high_pass: None or float, optional
+        This parameter is passed to signal.clean. Please see the related
+        documentation for details
+
+    t_r : float, optional (default None)
+        This parameter is passed to signal.clean. Please see the related
+        documentation for details.
+
+    n_iter: int, optional (default 10)
+        Number of iterations of the recursive nearest agglomeration
+
+    threshold: float in the opened interval (0., 1.), optional (default 1e-7)
+        Threshold used to handle eccentricities.
+
+    Attributes
+    ----------
+    `masker_` : instance of NiftiMasker
+        The nifti masker used to mask the data.
+
+    `mask_img_` : Nifti like image
+        The mask of the data. If no mask was supplied by the user,
+        this attribute is the mask image computed automatically from the
+        data `X`.
+
+    `labels_ ` : array-like, (n_features,)
+        cluster labels for each feature.
+
+    `n_clusters_` : int
+        Number of clusters
+
+    `sizes_`: array-like (n_features,)
+        It contains the size of each cluster
+
+    """
+    def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
+                 standardize=True, target_affine=None, target_shape=None,
+                 mask_strategy='background', memory=None, memory_level=0,
+                 verbose=False, scaling=False, n_iter=10, threshold=1e-7,):
+        self.n_clusters = n_clusters
+        self.scaling = scaling
+        self.n_iter = n_iter
+        self.mask = mask
+        self.threshold = threshold
+        self.smoothing_fwhm = smoothing_fwhm
+        self.standardize = standardize
+        self.target_affine = target_affine
+        self.target_shape = target_shape
+        self.mask_strategy = mask_strategy
+        self.memory = memory
+        self.memory_level = memory_level
+        self.verbose = verbose
+
+    def fit(self, X, y=None):
+        """Compute clustering of the data
+
+        Parameters
+        ----------
+        X : list of Niimg-like objects
+            See http://nilearn.github.io/manipulating_images/input_output.html
+            Data on which model is to be fitted. If this is a list,
+            the affine is considered the same for all.
+
+        Returns
+        -------
+        self : `ReNA` object
+        """
+
+        if self.memory is None or isinstance(self.memory, six.string_types):
+            self.memory_ = Memory(cachedir=self.memory,
+                                  verbose=max(0, self.verbose - 1))
+        else:
+            self.memory_ = self.memory
+
+        if self.n_clusters <= 0:
+            raise ValueError("n_clusters should be an integer greater than 0."
+                             " %s was provided." % str(self.n_clusters))
+
+        self.masker_ = _check_masking(self.mask, self.smoothing_fwhm,
+                                      self.target_affine, self.target_shape,
+                                      self.standardize, self.mask_strategy,
+                                      self.memory_, self.memory_level)
+
+        X = self.masker_.fit_transform(X)
+
+        X = check_array(X, ensure_min_features=2)
+
+        n_labels, labels = self.memory_.cache(recursive_nearest_agglomeration)(
+            self.masker_, X, self.n_clusters, n_iter=self.n_iter,
+            threshold=self.threshold)
+
+        sizes = np.bincount(labels)
+        sizes = sizes[sizes > 0]
+
+        self.labels_ = labels
+        self.n_clusters_ = np.unique(self.labels_).shape[0]
+        self.sizes_ = sizes
+
+        return self
+
+    def transform(self, X):
+        """Apply clustering, reduce the dimensionality of the data
+
+        Parameters
+        ----------
+        X : list of Niimg-like objects
+            See http://nilearn.github.io/manipulating_images/input_output.html
+            Data on which model is to be fitted. If this is a list,
+            the affine is considered the same for all.
+
+        Returns
+        -------
+        X : array
+
+        """
+
+        check_is_fitted(self, "masker_")
+        check_is_fitted(self, "labels_")
+
+        X = self.masker_.transform(X)
+
+        unique_labels = np.unique(self.labels_)
+
+        nX = []
+        for l in unique_labels:
+            nX.append(np.mean(X[:, self.labels_ == l], axis=1))
+        Xred = np.array(nX).T
+
+        if self.scaling:
+            Xred = Xred * np.sqrt(self.sizes_)
+
+        return Xred
+
+    def fit_transform(self, X, y=None):
+        """Fit to data, then perform the clustering (transformation)
+
+        Parameters
+        ----------
+        X : list of Niimg-like objects
+            See http://nilearn.github.io/manipulating_images/input_output.html
+            Data on which model is to be fitted. If this is a list,
+            the affine is considered the same for all.
+
+        Returns
+        -------
+        X : array
+
+        """
+
+        self.fit(X)
+        return self.transform(X)
+
+    def inverse_transform(self, Xred):
+        """
+        Parameters
+        ----------
+        Xred : array like
+        """
+
+        if not hasattr(self, "labels_"):
+            raise RuntimeError("This %s instance is not fitted yet!" % (
+                self.__class__.__name__))
+
+        _, inverse = np.unique(self.labels_, return_inverse=True)
+
+        if self.scaling:
+            Xred = Xred / np.sqrt(self.sizes_)
+        return Xred[..., inverse]
+
+
+def _check_masking(mask, smoothing_fwhm, target_affine, target_shape,
+                   standardize, mask_strategy, memory, memory_level):
+    """Setup a nifti masker."""
+    # mask is an image, not a masker
+    if mask is None or isinstance(mask, six.string_types):
+        masker = NiftiMasker(mask_img=mask,
+                             smoothing_fwhm=smoothing_fwhm,
+                             target_affine=target_affine,
+                             target_shape=target_shape,
+                             standardize=standardize,
+                             mask_strategy=mask_strategy,
+                             memory=memory,
+                             memory_level=memory_level)
+    # mask is a masker object
+    elif isinstance(mask, (NiftiMasker, MultiNiftiMasker)):
+        try:
+            masker = clone(mask)
+            if hasattr(mask, 'mask_img_'):
+                mask_img = mask.mask_img_
+                masker.set_params(mask_img=mask_img)
+                masker.fit()
+        except TypeError as e:
+            # Workaround for a joblib bug: in joblib 0.6, a Memory object
+            # with cachedir = None cannot be cloned.
+            masker_memory = mask.memory
+            if masker_memory.cachedir is None:
+                mask.memory = None
+                masker = clone(mask)
+                mask.memory = masker_memory
+                masker.memory = Memory(cachedir=None)
+            else:
+                # The error was raised for another reason
+                raise e
+
+        for param_name in ['target_affine', 'target_shape',
+                           'smoothing_fwhm', 'mask_strategy',
+                           'memory', 'memory_level']:
+            if getattr(mask, param_name) is not None:
+                warnings.warn('Parameter %s of the masker overriden'
+                              % param_name)
+                masker.set_params(**{param_name: getattr(mask, param_name)})
+        if hasattr(mask, 'mask_img_'):
+            warnings.warn('The mask_img_ of the masker will be copied')
+    return masker
+
+
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
new file mode 100644
index 0000000000..93a835f71a
--- /dev/null
+++ b/nilearn/connectome/tests/test_rena.py
@@ -0,0 +1,29 @@
+from nose.tools import assert_equal, assert_true, assert_raises
+
+import numpy as np
+from nilearn._utils.testing import generate_fake_fmri
+from nilearn.connectome import ReNA
+from nilearn.input_data import NiftiMasker
+
+
+def test_rena_clusterings():
+    data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=17)
+
+    nifti_masker = NiftiMasker(mask_img=mask_img).fit()
+    rena = ReNA(n_clusters=10, mask=nifti_masker)
+
+    #TODO assert not fitting
+
+    data_red = rena.fit_transform(data)
+    data_compress = rena.inverse_transform(data_red)
+
+    assert_equal(10, rena.n_clusters_)
+
+    rena2 = ReNA(n_clusters=-2, mask=nifti_masker)
+    # assert_raises(ValueError, rena2, fit, data)
+
+    # rena2.transform(data)
+
+    # import pdb; pdb.set_trace()  # XXX BREAKPOINT
+
+

From dd58e2cf9aec3c620c9807568beff3738830bc5d Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Mon, 5 Dec 2016 18:14:58 +0100
Subject: [PATCH 003/228] adding test

---
 nilearn/_utils/fixes/__init__.py      |  2 ++
 nilearn/connectome/tests/test_rena.py | 18 +++++++-----------
 2 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/nilearn/_utils/fixes/__init__.py b/nilearn/_utils/fixes/__init__.py
index a048b31f4b..b533c37438 100644
--- a/nilearn/_utils/fixes/__init__.py
+++ b/nilearn/_utils/fixes/__init__.py
@@ -35,10 +35,12 @@
 try:
     from sklearn.utils import check_X_y
     from sklearn.utils import check_is_fitted
+    from sklearn.utils import check_array
 except ImportError:
     # scikit-learn < 0.16
     from .sklearn_validation import check_X_y
     from .sklearn_validation import check_is_fitted
+    from sklearn.utils import check_array
 
 __all__ = ['f_regression', 'atleast2d_or_csr', 'roc_auc_score',
            'check_X_y', 'check_is_fitted', 'check_cv']
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
index 93a835f71a..b6bb428b61 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/connectome/tests/test_rena.py
@@ -1,29 +1,25 @@
 from nose.tools import assert_equal, assert_true, assert_raises
 
 import numpy as np
+from sklearn.externals.joblib import Memory
 from nilearn._utils.testing import generate_fake_fmri
 from nilearn.connectome import ReNA
 from nilearn.input_data import NiftiMasker
 
 
 def test_rena_clusterings():
-    data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=17)
+    data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=5)
 
     nifti_masker = NiftiMasker(mask_img=mask_img).fit()
-    rena = ReNA(n_clusters=10, mask=nifti_masker)
-
-    #TODO assert not fitting
+    rena = ReNA(n_clusters=10, mask=nifti_masker, scaling=False)
 
     data_red = rena.fit_transform(data)
     data_compress = rena.inverse_transform(data_red)
 
     assert_equal(10, rena.n_clusters_)
 
-    rena2 = ReNA(n_clusters=-2, mask=nifti_masker)
-    # assert_raises(ValueError, rena2, fit, data)
-
-    # rena2.transform(data)
-
-    # import pdb; pdb.set_trace()  # XXX BREAKPOINT
-
+    memory = Memory(cachedir=None)
+    rena2 = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
+    assert_raises(ValueError, rena2.fit, data)
 
+    rena3 = ReNA(n_clusters=10, mask=None, scaling=True).fit(data)

From cce8586b47da92170737a4866e33515b23751a02 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Mon, 5 Dec 2016 20:28:24 +0100
Subject: [PATCH 004/228] inproving test

---
 nilearn/connectome/rena_clustering.py | 5 ++---
 nilearn/connectome/tests/test_rena.py | 9 +++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 4987995611..090c1e7197 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -425,9 +425,7 @@ def inverse_transform(self, Xred):
         Xred : array like
         """
 
-        if not hasattr(self, "labels_"):
-            raise RuntimeError("This %s instance is not fitted yet!" % (
-                self.__class__.__name__))
+        check_is_fitted(self, "labels_")
 
         _, inverse = np.unique(self.labels_, return_inverse=True)
 
@@ -436,6 +434,7 @@ def inverse_transform(self, Xred):
         return Xred[..., inverse]
 
 
+# XXX this code is also replicated in the Metaestimator PR
 def _check_masking(mask, smoothing_fwhm, target_affine, target_shape,
                    standardize, mask_strategy, memory, memory_level):
     """Setup a nifti masker."""
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
index b6bb428b61..ead2afee2c 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/connectome/tests/test_rena.py
@@ -1,10 +1,9 @@
-from nose.tools import assert_equal, assert_true, assert_raises
-
-import numpy as np
+from nose.tools import assert_equal, assert_raises
 from sklearn.externals.joblib import Memory
 from nilearn._utils.testing import generate_fake_fmri
 from nilearn.connectome import ReNA
 from nilearn.input_data import NiftiMasker
+from nilearn.image import index_img
 
 
 def test_rena_clusterings():
@@ -22,4 +21,6 @@ def test_rena_clusterings():
     rena2 = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
     assert_raises(ValueError, rena2.fit, data)
 
-    rena3 = ReNA(n_clusters=10, mask=None, scaling=True).fit(data)
+    rena3 = ReNA(n_clusters=10, mask=None, scaling=True)
+    data_red2 = rena3.fit_transform(index_img(data, 0))
+    data_compress2 = rena3.inverse_transform(data_red2)

From 7ec1a83be2de8464927d67620c92050372198928 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 15:44:37 +0100
Subject: [PATCH 005/228] inproving doc

---
 nilearn/connectome/rena_clustering.py | 308 +++++++++++++++++++-------
 1 file changed, 232 insertions(+), 76 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 090c1e7197..51b6dc47cb 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -1,6 +1,9 @@
 """Recursive nearest agglomeration (ReNA):
     fastclustering for approximation of structured signals
 """
+# Author: Andres Hoyos idrobo, Gael Varoquaux, Jonas Kahn and  Bertrand Thirion
+# License: simplified BSD
+
 import numpy as np
 import warnings
 from sklearn.externals.joblib import Memory
@@ -11,57 +14,109 @@
 from ..input_data import NiftiMasker, MultiNiftiMasker
 
 
-def _compute_weights(masker, data_matrix):
+def _compute_weights(masker, masked_data):
     """Compute the weights in the direction of each axis using the Euclidean
-    distance.
+    distance --i.e. weights = (weight_deep, weights_right, weight_down).
+
+    Note: Here we assume a square lattice (no diagonal connections).
 
-    Note: Here we are assuming a square lattice (no diagonal connections)
+    Parameters
+    ----------
+    masker : NiftiMasker
+        The nifti masker used to mask the data.
+
+    masked_data : numpy array of shape [n_samples, n_features]
+        Image in brain space transformed into 2D data matrix.
+
+    Returns
+    -------
+    weights : numpy array
+        Weights corresponding to all edges in the mask.
+        shape: (n_edges,)
     """
-    dims = len(masker.mask_img_.shape)
-    data_graph = masker.inverse_transform(data_matrix).get_data()
-    weights = []
+    data = masker.inverse_transform(masked_data).get_data()
 
-    for axis in range(dims):
-        weights.append(
-            np.sum(np.diff(data_graph, axis=axis) ** 2, axis=-1).ravel())
+    weights_deep = np.sum(np.diff(data, axis=2) ** 2, axis=-1).ravel()
+    weights_right = np.sum(np.diff(data, axis=1) ** 2, axis=-1).ravel()
+    weights_down = np.sum(np.diff(data, axis=0) ** 2, axis=-1).ravel()
 
-    return np.hstack(weights)
+    weights = np.hstack([weights_deep, weights_right, weights_down])
 
+    return weights
 
-def _compute_edges(data_graph, is_mask=False):
-    """
+
+def _make_edges_3d(vertices, is_mask):
+    """Create the edges set: Returns a list of edges for a 3D image.
+
+    Parameters
+    ----------
+    vertices : numpy.ndarray
+        The indices of the voxels.
+
+    is_mask : boolean
+        If is_mask is true, it returns the mask of edges.
+        Retruns 1 if the edge is contained in the mask, 0 otherwise.
+
+    Returns
+    -------
+    edges : numpy array
+        Edges corresponding to the image or mask.
+        shape: (1, n_edges) if_mask,
+               (2, n_edges) otherwise.
     """
-    dims = len(data_graph.shape)
-    edges = []
-    for axis in range(dims):
-        vertices_axis = np.swapaxes(data_graph, 0, axis)
-
-        if is_mask:
-            edges.append(np.logical_and(
-                vertices_axis[:-1].swapaxes(axis, 0).ravel(),
-                vertices_axis[1:].swapaxes(axis, 0).ravel()))
-        else:
-            edges.append(
-                np.vstack([vertices_axis[:-1].swapaxes(axis, 0).ravel(),
-                           vertices_axis[1:].swapaxes(axis, 0).ravel()]))
-    edges = np.hstack(edges)
+
+    if is_mask:
+        edges_deep = np.logical_and(vertices[:, :, :-1].ravel(),
+                                    vertices[:, :, 1:].ravel())
+        edges_right = np.logical_and(vertices[:, :-1].ravel(),
+                                     vertices[:, 1:].ravel())
+        edges_down = np.logical_and(vertices[:-1].ravel(),
+                                    vertices[1:].ravel())
+    else:
+        edges_deep = np.vstack([vertices[:, :, :-1].ravel(),
+                                vertices[:, :, 1:].ravel()])
+        edges_right = np.vstack([vertices[:, :-1].ravel(),
+                                 vertices[:, 1:].ravel()])
+        edges_down = np.vstack([vertices[:-1].ravel(),
+                                vertices[1:].ravel()])
+
+    edges = np.hstack([edges_deep, edges_right, edges_down])
 
     return edges
 
 
-def _create_ordered_edges(masker, data_matrix):
-    """
+def _make_edges_and_weights(masker, masked_data):
+    """Compute the weights to all edges in the mask.
+
+    Parameters
+    ----------
+    masker : NiftiMasker
+        The nifti masker used to mask the data.
+
+    masked_data : numpy array of shape [n_samples, n_features]
+        Image in brain space transformed into 2D data matrix.
+
+    Returns
+    -------
+    edges : numpy array
+
+    weights : numpy array
+
+    edges_mask : numpy array
     """
     mask = masker.mask_img_.get_data()
     shape = mask.shape
     n_features = np.prod(shape)
 
+    # Indexing each voxel
     vertices = np.arange(n_features).reshape(shape)
-    weights = _compute_weights(masker, data_matrix)
-    edges = _compute_edges(vertices)
-    edges_mask = _compute_edges(mask, is_mask=True)
 
-    # Apply the mask
+    weights = _compute_weights(masker, masked_data)
+
+    edges = _make_edges_3d(vertices, is_mask=False)
+    edges_mask = _make_edges_3d(mask, is_mask=True)
+
+    # Apply mask to edges and weights
     weights = weights[edges_mask]
     edges = edges[:, edges_mask]
 
@@ -73,17 +128,25 @@ def _create_ordered_edges(masker, data_matrix):
     return edges, weights, edges_mask
 
 
-def weighted_connectivity_graph(masker, data_matrix):
-    """ Creating weighted graph
+def weighted_connectivity_graph(masker, masked_data):
+    """ Creating weighted graph: data and topology are encoded by a
+    connectivity matrix.
 
-    data and topology, encoded by a connectivity matrix
+    Parameters
+    ----------
+    masked_data : numpy array of shape [n_samples, n_features]
+        Image in brain space transformed into 2D data matrix
 
+    Returns
+    -------
+    connectivity : a sparse COO matrix
     """
     n_features = masker.mask_img_.get_data().sum()
 
-    edges, weight, edges_mask = _create_ordered_edges(masker, data_matrix)
-    connectivity = coo_matrix(
-        (weight, edges), (n_features, n_features)).tocsr()
+    edges, weight, edges_mask = _make_edges_and_weights(masker, masked_data)
+
+    connectivity = coo_matrix((weight, edges),
+                              (n_features, n_features)).tocsr()
 
     # Making it symmetrical
     connectivity = (connectivity + connectivity.T) / 2
@@ -94,7 +157,18 @@ def weighted_connectivity_graph(masker, data_matrix):
 def _nn_connectivity(connectivity, threshold):
     """ Fast implementation of nearest neighbor connectivity
 
-    connectivity: weighted connectivity matrix
+    Parameters
+    ----------
+    connectivity : a sparse matrix in COOrdinate format.
+        Weighted connectivity matrix
+
+    threshold : float in the close interval [0, 1]
+        The treshold is setted to handle eccentricities.
+        In practice it is 1e-7.
+
+    Returns
+    -------
+    nn_connectivity : a sparse matrix in COOrdinate format.
     """
     n_features = connectivity.shape[0]
 
@@ -107,12 +181,13 @@ def _nn_connectivity(connectivity, threshold):
 
     connectivity_ = inv_max * connectivity_
 
-    # Dealing with eccentricities
+    # Dealing with eccentricities, there are probably many neares neighbors
     edge_mask = connectivity_.data > 1 - threshold
 
     j_idx = connectivity_.nonzero()[1][edge_mask]
     i_idx = connectivity_.nonzero()[0][edge_mask]
 
+    # Set weights to 1
     weight = np.ones_like(j_idx)
     edges = np.array((i_idx, j_idx))
 
@@ -121,9 +196,31 @@ def _nn_connectivity(connectivity, threshold):
     return nn_connectivity
 
 
-def reduce_data_and_connectivity(labels, n_labels, connectivity, data_matrix,
-                                 threshold):
-    """
+def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
+                                  threshold):
+    """Perform feature grouping and reduce the connectivity matrix.
+
+    Parameters
+    ----------
+    labels : array like
+
+    n_labels : int
+
+    connectivity : a sparse matrix in COOrdinate format.
+
+    masked_data : array like
+        2D data matrix.
+
+    threshold : float in the close interval [0, 1]
+        The treshold is setted to handle eccentricities.
+        In practice it is 1e-7.
+
+    Returns
+    -------
+    reduced_connectivity : a sparse matrix in COOrdinate format.
+
+    reduced_masked_data: array like
+        2D data matrix.
     """
     n_features = len(labels)
 
@@ -137,8 +234,7 @@ def reduce_data_and_connectivity(labels, n_labels, connectivity, data_matrix,
 
     incidence = inv_sum_col * incidence
 
-    # reduced data
-    reduced_data_matrix = (incidence * data_matrix.T).T
+    reduced_masked_data = (incidence * masked_data.T).T
     reduced_connectivity = (incidence * connectivity) * incidence.T
 
     reduced_connectivity = reduced_connectivity - dia_matrix(
@@ -147,16 +243,44 @@ def reduce_data_and_connectivity(labels, n_labels, connectivity, data_matrix,
 
     i_idx, j_idx = reduced_connectivity.nonzero()
 
-    data_matrix_ = np.maximum(threshold, np.sum(
-        (reduced_data_matrix[:, i_idx] - reduced_data_matrix[:, j_idx]) ** 2, 0))
-    reduced_connectivity.data = data_matrix_
+    weights_ = np.sum(
+        (reduced_masked_data[:, i_idx] - reduced_masked_data[:, j_idx]) ** 2,
+        axis=0)
+    weights_ = np.maximum(threshold, weights_)
+    reduced_connectivity.data = weights_
 
-    return reduced_connectivity, reduced_data_matrix
+    return reduced_connectivity, reduced_masked_data
 
 
-def nearest_neighbor_grouping(connectivity, data_matrix, n_clusters,
+def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
                               threshold):
-    """ Cluster according to nn and reduce the data and connectivity
+    """Cluster using nearest agglomeration: merge clusters according to their
+    nearest neighbors, then the data and the connectivity are reduced.
+
+    Parameters
+    ----------
+    connectivity : a sparse matrix in COOrdinate format.
+        Weighted connectivity matrix
+
+    masked_data : numpy array of shape [n_samples, n_features]
+        Image in brain space transformed into 2D data matrix
+
+    n_clusters : int
+        The number of clusters to find.
+
+    threshold : float in the close interval [0, 1]
+        The treshold is setted to handle eccentricities.
+        In practice it is 1e-7.
+
+    Returns
+    -------
+    reduced_connectivity : a sparse matrix in COOrdinate format.
+
+    reduced_masked_data :  array like
+        2D data matrix.
+
+    labels : array like
+        It contains the clusters assignation.
     """
     # Nearest neighbor conenctivity
     nn_connectivity = _nn_connectivity(connectivity, threshold)
@@ -185,26 +309,52 @@ def nearest_neighbor_grouping(connectivity, data_matrix, n_clusters,
     n_labels, labels = csgraph.connected_components(nn_connectivity)
 
     # Reduction step: reduction by averaging
-    reduced_connectivity, reduced_data_matrix = reduce_data_and_connectivity(
-        labels, n_labels, connectivity, data_matrix, threshold)
+    reduced_connectivity, reduced_masked_data = _reduce_data_and_connectivity(
+        labels, n_labels, connectivity, masked_data, threshold)
 
-    return reduced_connectivity, reduced_data_matrix, labels
+    return reduced_connectivity, reduced_masked_data, labels
 
 
-def recursive_nearest_agglomeration(masker, data_matrix, n_clusters, n_iter,
+def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
                                     threshold):
+    """Recursive nearest agglomeration: it performs iteratively the nearest
+    neighbor grouping.
+
+    Parameters
+    ----------
+    connectivity : a sparse matrix in COOrdinate format.
+        Weighted connectivity matrix
+
+    masked_data : numpy array of shape [n_samples, n_features]
+        Image in brain space transformed into 2D data matrix
+
+    n_clusters : int
+        The number of clusters to find.
+
+    n_iter : int
+        Number of iterations.
+
+    threshold : float in the close interval [0, 1]
+        The treshold is setted to handle eccentricities.
+        In practice it is 1e-7.
+
+    Returns
+    -------
+    n_labels : int
+        Number of clusters.
+
+    labels : array
+        Cluster assignation.
     """
-    """
-    # Weighted connectivity matrix
-    connectivity = weighted_connectivity_graph(masker, data_matrix)
+    connectivity = weighted_connectivity_graph(masker, masked_data)
 
     # Initialization
     labels = np.arange(connectivity.shape[0])
     n_labels = connectivity.shape[0]
 
     for i in range(n_iter):
-        connectivity, data_matrix, reduced_labels = nearest_neighbor_grouping(
-            connectivity, data_matrix, n_clusters, threshold)
+        connectivity, masked_data, reduced_labels = nearest_neighbor_grouping(
+            connectivity, masked_data, n_clusters, threshold)
 
         labels = reduced_labels[labels]
         n_labels = connectivity.shape[0]
@@ -228,24 +378,24 @@ class ReNA(BaseEstimator):
         then its mask will be used. If no mask is it will be computed
         automatically by a NiftiMasker.
 
-    n_cluster: int, optional (default 2)
+    n_clusters : int, optional (default 2)
         The number of clusters to find.
 
-    scaling: bool, optional (default False)
+    scaling : bool, optional (default False)
 
-    memory: instance of joblib.Memory or string
+    memory : instance of joblib.Memory or string
         Used to cache the masking process.
         By default, no caching is done. If a string is given, it is the
         path to the caching directory.
 
-    memory_level: integer, optional (default 1)
+    memory_level : integer, optional (default 1)
         Rough estimator of the amount of memory used by caching. Higher value
         means more memory for caching.
 
     verbose : int, optional (default 1)
         Verbosity level.
 
-    smoothing_fwhm: float, optional
+    smoothing_fwhm : float, optional
         If smoothing_fwhm is not None, it gives the size in millimeters of the
         spatial smoothing to apply to the signal.
 
@@ -263,11 +413,11 @@ class ReNA(BaseEstimator):
         This parameter is passed to image.resample_img. Please see the
         related documentation for details.
 
-    low_pass: None or float, optional
+    low_pass : None or float, optional
         This parameter is passed to signal.clean. Please see the related
         documentation for details
 
-    high_pass: None or float, optional
+    high_pass : None or float, optional
         This parameter is passed to signal.clean. Please see the related
         documentation for details
 
@@ -275,10 +425,10 @@ class ReNA(BaseEstimator):
         This parameter is passed to signal.clean. Please see the related
         documentation for details.
 
-    n_iter: int, optional (default 10)
+    n_iter : int, optional (default 10)
         Number of iterations of the recursive nearest agglomeration
 
-    threshold: float in the opened interval (0., 1.), optional (default 1e-7)
+    threshold : float in the opened interval (0., 1.), optional (default 1e-7)
         Threshold used to handle eccentricities.
 
     Attributes
@@ -378,8 +528,8 @@ def transform(self, X):
 
         Returns
         -------
-        X : array
-
+        X : numpy array
+            2D data matrix of shape [n_sampels, n_clusters]
         """
 
         check_is_fitted(self, "masker_")
@@ -411,10 +561,9 @@ def fit_transform(self, X, y=None):
 
         Returns
         -------
-        X : array
-
+        X : numpy array
+            2D data matrix of shape [n_samples, n_clusters].
         """
-
         self.fit(X)
         return self.transform(X)
 
@@ -422,7 +571,12 @@ def inverse_transform(self, Xred):
         """
         Parameters
         ----------
-        Xred : array like
+        Xred : numpy array
+            2D data matrix of shape [n_samples, n_features]
+
+        Returns
+        -------
+        X_inv : Niimg
         """
 
         check_is_fitted(self, "labels_")
@@ -431,7 +585,9 @@ def inverse_transform(self, Xred):
 
         if self.scaling:
             Xred = Xred / np.sqrt(self.sizes_)
-        return Xred[..., inverse]
+        X_inv = Xred[..., inverse]
+
+        return self.masker_.inverse_transform(X_inv)
 
 
 # XXX this code is also replicated in the Metaestimator PR

From 4772d80b5c639e5622b786f48366a7c18008fb00 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 16:32:12 +0100
Subject: [PATCH 006/228] updating example

---
 .../03_connectivity/plot_fast_clustering.py   | 133 ++++++------------
 nilearn/connectome/rena_clustering.py         |   9 +-
 2 files changed, 49 insertions(+), 93 deletions(-)

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
index 74d7a6b64a..1b44a480ec 100644
--- a/examples/03_connectivity/plot_fast_clustering.py
+++ b/examples/03_connectivity/plot_fast_clustering.py
@@ -1,17 +1,21 @@
 """
-Ward clustering to learn a brain parcellation from rest fMRI
+ReNA clustering to learn a brain parcellation from rest fMRI
 ====================================================================
 
-We use spatially-constrained Ward-clustering to create a set of
-parcels. These parcels are particularly interesting for creating a
-'compressed' representation of the data, replacing the data in the fMRI
-images by mean on the parcellation.
+We use ReNA clustering to create a set of parcels. These parcels are
+particularly interesting for creating a 'compressed' representation of the
+data, replacing the data in the fMRI images by mean on the parcellation.
 
 This parcellation may be useful in a supervised learning, see for
 instance: `A supervised clustering approach for fMRI-based inference of
 brain states <https://hal.inria.fr/inria-00589201>`_, Michel et al,
 Pattern Recognition 2011.
 
+The computation time of this algorithm is linear in the number of features.
+This makes it well suited to use in the consensus on several random
+parcellations, see for instance: `Randomized parcellation based inference
+<https://hal.inria.fr/hal-00915243>`_, Da Mota et al, Neuroimage 2014.
+
 The big picture discussion corresponding to this example can be found
 in the documentation section :ref:`parcellating_brain`.
 """
@@ -45,52 +49,25 @@
                                       standardize=False)
 
 func_filename = dataset.func[0]
-# The fit_transform call computes the mask and extracts the time-series
-# from the files:
-fmri_masked = nifti_masker.fit_transform(func_filename)
-
-# We can retrieve the numpy array of the mask
-mask = nifti_masker.mask_img_.get_data().astype(bool)
-
+# The fit call computes the mask
+nifti_masker.fit(func_filename)
 
 ##################################################################
-# Perform Ward clustering
+# Perform ReNA clustering
 # -----------------------
+# The spatial constraints are implemented inside the ReNA object.
 #
-# We use spatially-constrained Ward clustering. For this, we need to
-# compute from the mask a matrix giving the voxel-to-voxel connectivity
-
-# Compute connectivity matrix: which voxel is connected to which
-from sklearn.feature_extraction import image
-shape = mask.shape
-connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
-                                   n_z=shape[2], mask=mask)
-
-
-##################################################################
-# Then we use FeatureAgglomeration from scikit-learn. Indeed, the voxels
-# are the features of the data matrix.
-#
-# In addition, we use caching. As a result, the clustering doesn't have
+# We use caching. As a result, the clustering doesn't have
 # to be recomputed later.
-
-# Computing the ward for the first time, this is long...
-from sklearn.cluster import FeatureAgglomeration
-# If you have scikit-learn older than 0.14, you need to import
-# WardAgglomeration instead of FeatureAgglomeration
 import time
+from nilearn.connectome import ReNA
 start = time.time()
-ward = FeatureAgglomeration(n_clusters=2000, connectivity=connectivity,
-                            linkage='ward', memory='nilearn_cache')
-ward.fit(fmri_masked)
-print("Ward agglomeration 2000 clusters: %.2fs" % (time.time() - start))
+rena = ReNA(scaling=True, n_clusters=1000, mask=nifti_masker,
+            memory='nilearn_cache')
 
+rena.fit_transform(func_filename)
+print("ReNA 1000 clusters: %.2fs" % (time.time() - start))
 
-##################################################################
-# Perform ReNA clustering
-# -----------------------
-#
-from nilearn.connectome import ReNA
 start = time.time()
 rena = ReNA(scaling=True, n_clusters=2000, mask=nifti_masker,
             memory='nilearn_cache')
@@ -113,27 +90,27 @@
 # Unmask the labels
 
 # Avoid 0 label
-ward_labels = ward.labels_ + 1
-ward_labels_img = nifti_masker.inverse_transform(ward_labels)
+labels = rena.labels_ + 1
+# Shuffling the labels for visualization
+permutation = np.random.permutation(labels.shape[0])
+labels = permutation[labels]
+labels_img = nifti_masker.inverse_transform(labels)
 
-rena_labels = rena.labels_ + 1
-permutation = np.random.permutation(rena_labels.shape[0])
-rena_labels = permutation[rena_labels]
-rena_labels_img = nifti_masker.inverse_transform(rena_labels)
+##################################################################
+# labels_img is a Nifti1Image object, it can be saved to file with the
+# following code:
+labels_img.to_filename('parcellation.nii')
 
 
 from nilearn.image import mean_img
 mean_func_img = mean_img(func_filename)
 
-# first_plot = plot_roi(rena_labels_img, mean_func_img,
-#                       title="ReNA parcellation",
-#                       display_mode='xz')
+first_plot = plot_roi(labels_img, mean_func_img, title="ReNA parcellation",
+                      display_mode='xz')
 
-# first_plot = plot_roi(ward_labels_img, mean_func_img,
-#                       title="Ward parcellation",
-#                       display_mode='xz')
+# common cut coordinates for all plots
+cut_coords = first_plot.cut_coords
 
-# import pdb; pdb.set_trace()  # XXX BREAKPOINT
 ##################################################################
 # Second, we illustrate the effect that the clustering has on the
 # signal. We show the original data, and the approximation provided by
@@ -142,52 +119,32 @@
 # As you can see below, this approximation is very good, although there
 # are only 2000 parcels, instead of the original 60000 voxels
 
+# The transform call extracts the time-series from the files:
+fmri_masked = nifti_masker.transform(func_filename)
+
 # Display the original data
-first_plot = plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
-                      title='Original (%i voxels)' % fmri_masked.shape[1],
-                      vmax=fmri_masked.max(), vmin=fmri_masked.min(),
-                      display_mode='xz')
+plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
+         title='Original (%i voxels)' % fmri_masked.shape[1],
+         cut_coords=cut_coords, vmax=fmri_masked.max(), vmin=fmri_masked.min(),
+         display_mode='xz')
 
-# common cut coordinates for all plots
-cut_coords = first_plot.cut_coords
 
 # A reduced data can be create by taking the parcel-level average:
-# Note that, as many objects in the scikit-learn, the ward object exposes
+# Note that, as many objects in the scikit-learn, the ReNA object exposes
 # a transform method that modifies input features. Here it reduces their
-# dimension
-fmri_reduced_ward = ward.transform(fmri_masked)
-
-# Apply this sample mask to X (fMRI data).
-# Because the data is in one single large 4D image, we need to use
-# index_img to do the split easily
+# dimension.
+# However, the data are in one single large 4D image, we need to use
+# index_img to do the split easily:
 from nilearn.image import index_img
 fmri_reduced_rena = rena.transform(index_img(func_filename, 0))
 
-
-# # Display the corresponding data compressed using the parcellation
-fmri_compressed_ward = ward.inverse_transform(fmri_reduced_ward)
-compressed_img_ward = nifti_masker.inverse_transform(fmri_compressed_ward[0])
-
-fmri_compressed_rena = rena.inverse_transform(fmri_reduced_rena)
-compressed_img_rena = nifti_masker.inverse_transform(fmri_compressed_rena[0])
-
-
-plot_epi(compressed_img_ward, cut_coords=cut_coords,
-         title='Ward: Compressed representation (2000 parcels)',
-         vmax=fmri_masked.max(), vmin=fmri_masked.min(),
-         display_mode='xz')
+# Display the corresponding data compression using the parcellation
+compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
 
 plot_epi(compressed_img_rena, cut_coords=cut_coords,
          title='ReNA: Compressed representation (2000 parcels)',
          vmax=fmri_masked.max(), vmin=fmri_masked.min(),
          display_mode='xz')
 
-
-##################################################################
-# labels_img is a Nifti1Image object, it can be saved to file with the
-# following code:
-# labels_img.to_filename('parcellation.nii')
-
-
 show()
 
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 51b6dc47cb..ad079e6a17 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -445,15 +445,14 @@ class ReNA(BaseEstimator):
         cluster labels for each feature.
 
     `n_clusters_` : int
-        Number of clusters
-
-    `sizes_`: array-like (n_features,)
-        It contains the size of each cluster
+        Number of clusters.
 
+    `sizes_` : array-like (n_features,)
+        It contains the size of each cluster.
     """
     def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
                  standardize=True, target_affine=None, target_shape=None,
-                 mask_strategy='background', memory=None, memory_level=0,
+                 mask_strategy='background', memory=None, memory_level=1,
                  verbose=False, scaling=False, n_iter=10, threshold=1e-7,):
         self.n_clusters = n_clusters
         self.scaling = scaling

From c9df8552e344f2d818f718de1a237264a4af3944 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 19:19:17 +0100
Subject: [PATCH 007/228] minor

---
 nilearn/_utils/fixes/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/_utils/fixes/__init__.py b/nilearn/_utils/fixes/__init__.py
index b533c37438..15b048117c 100644
--- a/nilearn/_utils/fixes/__init__.py
+++ b/nilearn/_utils/fixes/__init__.py
@@ -40,7 +40,7 @@
     # scikit-learn < 0.16
     from .sklearn_validation import check_X_y
     from .sklearn_validation import check_is_fitted
-    from sklearn.utils import check_array
+    from .sklearn_validation import check_array
 
 __all__ = ['f_regression', 'atleast2d_or_csr', 'roc_auc_score',
            'check_X_y', 'check_is_fitted', 'check_cv']

From cfb7bccff4c3659bea2fd04a86f9ee2b0e3c40db Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 19:20:35 +0100
Subject: [PATCH 008/228] minor

---
 nilearn/connectome/__init__.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/nilearn/connectome/__init__.py b/nilearn/connectome/__init__.py
index a8ca8dd5f6..b8e376b4bd 100644
--- a/nilearn/connectome/__init__.py
+++ b/nilearn/connectome/__init__.py
@@ -8,9 +8,10 @@
                                     cov_to_corr, prec_to_partial)
 
 from .group_sparse_cov import (GroupSparseCovariance,
-    GroupSparseCovarianceCV, group_sparse_covariance)
+                               GroupSparseCovarianceCV,
+                               group_sparse_covariance)
 
-from rena_clustering import ReNA
+from .rena_clustering import ReNA
 
 __all__ = ['sym_to_vec', 'ConnectivityMeasure',
            'GroupSparseCovariance', 'GroupSparseCovarianceCV',

From 98cfff0d73f574120bf1c45ececc8d42858b63b7 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 19:39:50 +0100
Subject: [PATCH 009/228] changing dot max to np.max

---
 nilearn/connectome/rena_clustering.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index ad079e6a17..f99e956dec 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -176,7 +176,7 @@ def _nn_connectivity(connectivity, threshold):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    inv_max = dia_matrix((1. / connectivity_.max(axis=0).toarray()[0], 0),
+    inv_max = dia_matrix((1. / np.max(connectivity_, axis=0).toarray()[0], 0),
                          shape=(n_features, n_features))
 
     connectivity_ = inv_max * connectivity_
@@ -229,7 +229,7 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
         shape=(n_labels, n_features), dtype=np.float32).tocsc()
 
     inv_sum_col = dia_matrix(
-        (np.array(1. / incidence.sum(axis=1)).squeeze(), 0),
+        (np.array(1. / np.sum(incidence, axis=1)).squeeze(), 0),
         shape=(n_labels, n_labels))
 
     incidence = inv_sum_col * incidence

From 52e5da855f4ed5a62cfcce9206d37acecc51875f Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Tue, 6 Dec 2016 20:25:44 +0100
Subject: [PATCH 010/228] not sure how to deal with max(axis) in former scipy
 versions

---
 nilearn/connectome/rena_clustering.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index f99e956dec..ad079e6a17 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -176,7 +176,7 @@ def _nn_connectivity(connectivity, threshold):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    inv_max = dia_matrix((1. / np.max(connectivity_, axis=0).toarray()[0], 0),
+    inv_max = dia_matrix((1. / connectivity_.max(axis=0).toarray()[0], 0),
                          shape=(n_features, n_features))
 
     connectivity_ = inv_max * connectivity_
@@ -229,7 +229,7 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
         shape=(n_labels, n_features), dtype=np.float32).tocsc()
 
     inv_sum_col = dia_matrix(
-        (np.array(1. / np.sum(incidence, axis=1)).squeeze(), 0),
+        (np.array(1. / incidence.sum(axis=1)).squeeze(), 0),
         shape=(n_labels, n_labels))
 
     incidence = inv_sum_col * incidence

From daef446d167fe86468ed43007378d84d006bed3c Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Wed, 7 Dec 2016 12:57:47 +0100
Subject: [PATCH 011/228] addressing Alex and Mehdi comments

---
 .../03_connectivity/plot_fast_clustering.py   |  5 ++-
 nilearn/connectome/rena_clustering.py         | 32 ++++++-------------
 2 files changed, 12 insertions(+), 25 deletions(-)

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
index 1b44a480ec..e13e2d5461 100644
--- a/examples/03_connectivity/plot_fast_clustering.py
+++ b/examples/03_connectivity/plot_fast_clustering.py
@@ -24,7 +24,7 @@
 # Download a rest dataset and turn it to a data matrix
 # -----------------------------------------------------
 #
-# We we download one subject of the ADHD dataset from Internet
+# We download one subject of the ADHD dataset from Internet
 
 from nilearn import datasets
 dataset = datasets.fetch_adhd(n_subjects=1)
@@ -87,13 +87,12 @@
 from nilearn.plotting import plot_roi, plot_epi, show
 import numpy as np
 
-# Unmask the labels
-
 # Avoid 0 label
 labels = rena.labels_ + 1
 # Shuffling the labels for visualization
 permutation = np.random.permutation(labels.shape[0])
 labels = permutation[labels]
+# Unmask the labels
 labels_img = nifti_masker.inverse_transform(labels)
 
 ##################################################################
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index ad079e6a17..1a911600dd 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -8,6 +8,7 @@
 import warnings
 from sklearn.externals.joblib import Memory
 from sklearn.externals import six
+from sklearn.base import TransformerMixin, ClusterMixin
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
 from sklearn.base import BaseEstimator, clone
 from .._utils.fixes import check_array, check_is_fitted
@@ -134,6 +135,8 @@ def weighted_connectivity_graph(masker, masked_data):
 
     Parameters
     ----------
+    masker : NiftiMasker instance
+
     masked_data : numpy array of shape [n_samples, n_features]
         Image in brain space transformed into 2D data matrix
 
@@ -198,13 +201,17 @@ def _nn_connectivity(connectivity, threshold):
 
 def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
                                   threshold):
-    """Perform feature grouping and reduce the connectivity matrix.
+    """Perform feature grouping and reduce the connectivity matrix: during the
+    reduction step one changes the value of each cluster by their mean.
+    In addition, connected nodes are merged.
 
     Parameters
     ----------
     labels : array like
+        Containts the label assignation for each voxel.
 
     n_labels : int
+        The number of clusters in the current iteration.
 
     connectivity : a sparse matrix in COOrdinate format.
 
@@ -322,8 +329,7 @@ def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
 
     Parameters
     ----------
-    connectivity : a sparse matrix in COOrdinate format.
-        Weighted connectivity matrix
+    masker : NiftiMasker instance
 
     masked_data : numpy array of shape [n_samples, n_features]
         Image in brain space transformed into 2D data matrix
@@ -365,7 +371,7 @@ def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
     return n_labels, labels
 
 
-class ReNA(BaseEstimator):
+class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     """
     Recursive nearest agglomeration.
     Recursively merges the pair of clusters according to 1-nearest neighbors
@@ -548,24 +554,6 @@ def transform(self, X):
 
         return Xred
 
-    def fit_transform(self, X, y=None):
-        """Fit to data, then perform the clustering (transformation)
-
-        Parameters
-        ----------
-        X : list of Niimg-like objects
-            See http://nilearn.github.io/manipulating_images/input_output.html
-            Data on which model is to be fitted. If this is a list,
-            the affine is considered the same for all.
-
-        Returns
-        -------
-        X : numpy array
-            2D data matrix of shape [n_samples, n_clusters].
-        """
-        self.fit(X)
-        return self.transform(X)
-
     def inverse_transform(self, Xred):
         """
         Parameters

From 68223cdb2a00729203d8b85dde268fd1c3f6b31d Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Thu, 8 Dec 2016 15:42:44 +0100
Subject: [PATCH 012/228] addressing Bertrand's comments

---
 .../03_connectivity/plot_fast_clustering.py   |   4 +-
 nilearn/connectome/rena_clustering.py         | 102 +++++++++++-------
 nilearn/connectome/tests/test_rena.py         |   4 +
 3 files changed, 67 insertions(+), 43 deletions(-)

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
index e13e2d5461..5791c2ee1b 100644
--- a/examples/03_connectivity/plot_fast_clustering.py
+++ b/examples/03_connectivity/plot_fast_clustering.py
@@ -128,8 +128,8 @@
          display_mode='xz')
 
 
-# A reduced data can be create by taking the parcel-level average:
-# Note that, as many objects in the scikit-learn, the ReNA object exposes
+# A reduced data can be created by taking the parcel-level average:
+# Note that, as many scikit-learn objects, the ReNA object exposes
 # a transform method that modifies input features. Here it reduces their
 # dimension.
 # However, the data are in one single large 4D image, we need to use
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 1a911600dd..017577f59b 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -46,7 +46,7 @@ def _compute_weights(masker, masked_data):
     return weights
 
 
-def _make_edges_3d(vertices, is_mask):
+def _make_3d_edges(vertices, is_mask):
     """Create the edges set: Returns a list of edges for a 3D image.
 
     Parameters
@@ -100,10 +100,11 @@ def _make_edges_and_weights(masker, masked_data):
     Returns
     -------
     edges : numpy array
+        Array containing [edges_deep, edges_right, edges_down]
 
     weights : numpy array
-
-    edges_mask : numpy array
+        Weights corresponding to all edges in the mask.
+        shape: (n_edges,)
     """
     mask = masker.mask_img_.get_data()
     shape = mask.shape
@@ -114,8 +115,8 @@ def _make_edges_and_weights(masker, masked_data):
 
     weights = _compute_weights(masker, masked_data)
 
-    edges = _make_edges_3d(vertices, is_mask=False)
-    edges_mask = _make_edges_3d(mask, is_mask=True)
+    edges = _make_3d_edges(vertices, is_mask=False)
+    edges_mask = _make_3d_edges(mask, is_mask=True)
 
     # Apply mask to edges and weights
     weights = weights[edges_mask]
@@ -126,7 +127,7 @@ def _make_edges_and_weights(masker, masked_data):
     order = np.searchsorted(np.unique(edges.ravel()), np.arange(max_index + 1))
     edges = order[edges]
 
-    return edges, weights, edges_mask
+    return edges, weights
 
 
 def weighted_connectivity_graph(masker, masked_data):
@@ -146,7 +147,7 @@ def weighted_connectivity_graph(masker, masked_data):
     """
     n_features = masker.mask_img_.get_data().sum()
 
-    edges, weight, edges_mask = _make_edges_and_weights(masker, masked_data)
+    edges, weight = _make_edges_and_weights(masker, masked_data)
 
     connectivity = coo_matrix((weight, edges),
                               (n_features, n_features)).tocsr()
@@ -192,7 +193,7 @@ def _nn_connectivity(connectivity, threshold):
 
     # Set weights to 1
     weight = np.ones_like(j_idx)
-    edges = np.array((i_idx, j_idx))
+    edges = np.array([i_idx, j_idx])
 
     nn_connectivity = coo_matrix((weight, edges), (n_features, n_features))
 
@@ -296,21 +297,24 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     n_labels = n_features - (nn_connectivity + nn_connectivity.T).nnz / 2
 
     if n_labels < n_clusters:
-        # cut some links to achieve the desired number of clusters
-        alpha = n_features - n_clusters
+        # remove edges so that the final number of clusters is not less than
+        # n_clusters (to achieve the desired number of clusters)
+        n_edges = n_features - n_clusters
 
-        nn_connectivity = nn_connectivity + nn_connectivity.T
+        nn_connectivity = (nn_connectivity + nn_connectivity.T)
 
-        edges_ = np.array(nn_connectivity.nonzero())
+        i_idx, j_idx = nn_connectivity.nonzero()
+        edges = np.array([i_idx, j_idx])
 
-        plop = edges_[0] - edges_[1]
+        # select n_edges to merge.
+        edge_mask = np.argsort(i_idx - j_idx)[:n_edges]
 
-        select = np.argsort(plop)[:alpha]
+        # Set weights to 1, and the connectivity matrix symetrical.
+        weight = np.ones(2 * n_edges)
+        edges = np.hstack([edges[:, edge_mask], edges[::-1, edge_mask]])
 
-        nn_connectivity = coo_matrix(
-            (np.ones(2 * alpha),
-             np.hstack((edges_[:, select], edges_[::-1, select]))),
-            (n_features, n_features))
+        nn_connectivity = coo_matrix((weight, edges),
+                                     (n_features, n_features))
 
     # Clustering step: getting the connected components of the nn matrix
     n_labels, labels = csgraph.connected_components(nn_connectivity)
@@ -322,9 +326,9 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     return reduced_connectivity, reduced_masked_data, labels
 
 
-def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
-                                    threshold):
-    """Recursive nearest agglomeration: it performs iteratively the nearest
+def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
+                                     n_iter=10, threshold=1e-7):
+    """Recursive neighbor agglomeration: it performs iteratively the nearest
     neighbor grouping.
 
     Parameters
@@ -337,10 +341,10 @@ def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
     n_clusters : int
         The number of clusters to find.
 
-    n_iter : int
+    n_iter : int, optional (default 10)
         Number of iterations.
 
-    threshold : float in the close interval [0, 1]
+    threshold : float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
 
@@ -372,8 +376,7 @@ def recursive_nearest_agglomeration(masker, masked_data, n_clusters, n_iter,
 
 
 class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
-    """
-    Recursive nearest agglomeration.
+    """Recursive Neighbor Agglomeration:
     Recursively merges the pair of clusters according to 1-nearest neighbors
     criterion.
 
@@ -432,9 +435,9 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
         documentation for details.
 
     n_iter : int, optional (default 10)
-        Number of iterations of the recursive nearest agglomeration
+        Number of iterations of the recursive neighbor agglomeration
 
-    threshold : float in the opened interval (0., 1.), optional (default 1e-7)
+    threshold : float in the open interval (0., 1.), optional (default 1e-7)
         Threshold used to handle eccentricities.
 
     Attributes
@@ -475,7 +478,7 @@ def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
         self.verbose = verbose
 
     def fit(self, X, y=None):
-        """Compute clustering of the data
+        """Compute clustering of the data.
 
         Parameters
         ----------
@@ -499,6 +502,10 @@ def fit(self, X, y=None):
             raise ValueError("n_clusters should be an integer greater than 0."
                              " %s was provided." % str(self.n_clusters))
 
+        if self.n_iter <= 0:
+            raise ValueError("n_iter should be an integer greater than 0."
+                             " %s was provided." % str(self.n_iter))
+
         self.masker_ = _check_masking(self.mask, self.smoothing_fwhm,
                                       self.target_affine, self.target_shape,
                                       self.standardize, self.mask_strategy,
@@ -506,11 +513,20 @@ def fit(self, X, y=None):
 
         X = self.masker_.fit_transform(X)
 
+        n_features = X.shape[1]
+
+        if self.n_clusters > n_features:
+            self.n_clusters = n_features
+            warnings.warn("n_clusters should be at most the number of "
+                          "features. Taking n_clusters = %s instead."
+                          % str(n_features))
+
         X = check_array(X, ensure_min_features=2)
 
-        n_labels, labels = self.memory_.cache(recursive_nearest_agglomeration)(
-            self.masker_, X, self.n_clusters, n_iter=self.n_iter,
-            threshold=self.threshold)
+        n_labels, labels = self.memory_.cache(
+            recursive_neighbor_agglomeration)(self.masker_, X, self.n_clusters,
+                                              n_iter=self.n_iter,
+                                              threshold=self.threshold)
 
         sizes = np.bincount(labels)
         sizes = sizes[sizes > 0]
@@ -521,8 +537,8 @@ def fit(self, X, y=None):
 
         return self
 
-    def transform(self, X):
-        """Apply clustering, reduce the dimensionality of the data
+    def transform(self, X, y=None):
+        """Apply clustering, reduce the dimensionality of the data.
 
         Parameters
         ----------
@@ -533,7 +549,7 @@ def transform(self, X):
 
         Returns
         -------
-        X : numpy array
+        Xred : numpy array
             2D data matrix of shape [n_sampels, n_clusters]
         """
 
@@ -544,10 +560,11 @@ def transform(self, X):
 
         unique_labels = np.unique(self.labels_)
 
-        nX = []
-        for l in unique_labels:
-            nX.append(np.mean(X[:, self.labels_ == l], axis=1))
-        Xred = np.array(nX).T
+        mean_cluster = []
+        for label in unique_labels:
+            mean_cluster.append(np.mean(X[:, self.labels_ == label], axis=1))
+
+        Xred = np.array(mean_cluster).T
 
         if self.scaling:
             Xred = Xred * np.sqrt(self.sizes_)
@@ -555,15 +572,18 @@ def transform(self, X):
         return Xred
 
     def inverse_transform(self, Xred):
-        """
+        """Transform the reduced 2D data matrix back to an image in brain
+        space.
+
         Parameters
         ----------
         Xred : numpy array
-            2D data matrix of shape [n_samples, n_features]
+            2D data matrix of shape [n_samples, n_clusters]
 
         Returns
         -------
-        X_inv : Niimg
+        X_inv : nibabel.Nifti1Image
+            shape: (n_x, n_y, n_z, n_samples)
         """
 
         check_is_fitted(self, "labels_")
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
index ead2afee2c..76bf4119d5 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/connectome/tests/test_rena.py
@@ -16,6 +16,7 @@ def test_rena_clusterings():
     data_compress = rena.inverse_transform(data_red)
 
     assert_equal(10, rena.n_clusters_)
+    assert_equal(data.shape, data_compress.shape)
 
     memory = Memory(cachedir=None)
     rena2 = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
@@ -24,3 +25,6 @@ def test_rena_clusterings():
     rena3 = ReNA(n_clusters=10, mask=None, scaling=True)
     data_red2 = rena3.fit_transform(index_img(data, 0))
     data_compress2 = rena3.inverse_transform(data_red2)
+
+    rena4 = ReNA(n_iter=-2, mask=nifti_masker, memory=memory)
+    assert_raises(ValueError, rena4.fit, data)

From 4c969bdf4b922da5c28b6d861de936aeb792255f Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Mon, 19 Dec 2016 15:34:34 +0100
Subject: [PATCH 013/228] addressing some comments

---
 nilearn/connectome/rena_clustering.py | 65 ++++++++++++++-------------
 nilearn/connectome/tests/test_rena.py | 14 ++++--
 2 files changed, 44 insertions(+), 35 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 017577f59b..3e1b495e9d 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -131,7 +131,7 @@ def _make_edges_and_weights(masker, masked_data):
 
 
 def weighted_connectivity_graph(masker, masked_data):
-    """ Creating weighted graph: data and topology are encoded by a
+    """ Creating symmetric weighted graph: data and topology are encoded by a
     connectivity matrix.
 
     Parameters
@@ -144,6 +144,7 @@ def weighted_connectivity_graph(masker, masked_data):
     Returns
     -------
     connectivity : a sparse COO matrix
+        sparse matrix representation of the weighted adjacency graph
     """
     n_features = masker.mask_img_.get_data().sum()
 
@@ -158,15 +159,15 @@ def weighted_connectivity_graph(masker, masked_data):
     return connectivity
 
 
-def _nn_connectivity(connectivity, threshold):
+def _nn_connectivity(connectivity, threshold=1e-7):
     """ Fast implementation of nearest neighbor connectivity
 
     Parameters
     ----------
     connectivity : a sparse matrix in COOrdinate format.
-        Weighted connectivity matrix
+        sparse matrix representation of the weighted adjacency graph
 
-    threshold : float in the close interval [0, 1]
+    threshold : float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
 
@@ -201,7 +202,7 @@ def _nn_connectivity(connectivity, threshold):
 
 
 def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
-                                  threshold):
+                                  threshold=1e-7):
     """Perform feature grouping and reduce the connectivity matrix: during the
     reduction step one changes the value of each cluster by their mean.
     In addition, connected nodes are merged.
@@ -215,11 +216,12 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
         The number of clusters in the current iteration.
 
     connectivity : a sparse matrix in COOrdinate format.
+        sparse matrix representation of the weighted adjacency graph
 
     masked_data : array like
         2D data matrix.
 
-    threshold : float in the close interval [0, 1]
+    threshold : float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
 
@@ -261,14 +263,14 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
 
 
 def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
-                              threshold):
+                              threshold=1e-7):
     """Cluster using nearest agglomeration: merge clusters according to their
     nearest neighbors, then the data and the connectivity are reduced.
 
     Parameters
     ----------
     connectivity : a sparse matrix in COOrdinate format.
-        Weighted connectivity matrix
+        sparse matrix representation of the weighted adjacency graph
 
     masked_data : numpy array of shape [n_samples, n_features]
         Image in brain space transformed into 2D data matrix
@@ -276,7 +278,7 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     n_clusters : int
         The number of clusters to find.
 
-    threshold : float in the close interval [0, 1]
+    threshold : float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
 
@@ -293,14 +295,12 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     # Nearest neighbor conenctivity
     nn_connectivity = _nn_connectivity(connectivity, threshold)
     n_features = connectivity.shape[0]
-
     n_labels = n_features - (nn_connectivity + nn_connectivity.T).nnz / 2
 
     if n_labels < n_clusters:
         # remove edges so that the final number of clusters is not less than
         # n_clusters (to achieve the desired number of clusters)
         n_edges = n_features - n_clusters
-
         nn_connectivity = (nn_connectivity + nn_connectivity.T)
 
         i_idx, j_idx = nn_connectivity.nonzero()
@@ -308,7 +308,6 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
 
         # select n_edges to merge.
         edge_mask = np.argsort(i_idx - j_idx)[:n_edges]
-
         # Set weights to 1, and the connectivity matrix symetrical.
         weight = np.ones(2 * n_edges)
         edges = np.hstack([edges[:, edge_mask], edges[::-1, edge_mask]])
@@ -376,21 +375,24 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
 
 class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
-    """Recursive Neighbor Agglomeration:
+    """Recursive nearest agglomeration (ReNA):
     Recursively merges the pair of clusters according to 1-nearest neighbors
     criterion.
 
     Parameters
     ----------
-    mask : filename, niimg, NiftiMasker instance, optional default None)
-        Mask to be used on data. If an instance of masker is passed,
-        then its mask will be used. If no mask is it will be computed
-        automatically by a NiftiMasker.
-
     n_clusters : int, optional (default 2)
         The number of clusters to find.
 
     scaling : bool, optional (default False)
+        If scaling is True, each cluster is scaled by the square root of its
+        size, preserving the l2-norm of the image.
+
+    n_iter : int, optional (default 10)
+        Number of iterations of the recursive neighbor agglomeration
+
+    threshold : float in the open interval (0., 1.), optional (default 1e-7)
+        Threshold used to handle eccentricities.
 
     memory : instance of joblib.Memory or string
         Used to cache the masking process.
@@ -404,6 +406,11 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     verbose : int, optional (default 1)
         Verbosity level.
 
+    mask : filename, niimg, NiftiMasker instance, optional default None)
+        Mask to be used on data. If an instance of masker is passed,
+        then its mask will be used. If no mask is passed, it will be computed
+        automatically by a NiftiMasker.
+
     smoothing_fwhm : float, optional
         If smoothing_fwhm is not None, it gives the size in millimeters of the
         spatial smoothing to apply to the signal.
@@ -434,22 +441,8 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
         This parameter is passed to signal.clean. Please see the related
         documentation for details.
 
-    n_iter : int, optional (default 10)
-        Number of iterations of the recursive neighbor agglomeration
-
-    threshold : float in the open interval (0., 1.), optional (default 1e-7)
-        Threshold used to handle eccentricities.
-
     Attributes
     ----------
-    `masker_` : instance of NiftiMasker
-        The nifti masker used to mask the data.
-
-    `mask_img_` : Nifti like image
-        The mask of the data. If no mask was supplied by the user,
-        this attribute is the mask image computed automatically from the
-        data `X`.
-
     `labels_ ` : array-like, (n_features,)
         cluster labels for each feature.
 
@@ -458,6 +451,14 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
 
     `sizes_` : array-like (n_features,)
         It contains the size of each cluster.
+
+    `masker_` : instance of NiftiMasker
+        The nifti masker used to mask the data.
+
+    `mask_img_` : Nifti like image
+        The mask of the data. If no mask was supplied by the user,
+        this attribute is the mask image computed automatically from the
+        data `X`.
     """
     def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
                  standardize=True, target_affine=None, target_shape=None,
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
index 76bf4119d5..ce0f788ac4 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/connectome/tests/test_rena.py
@@ -1,4 +1,4 @@
-from nose.tools import assert_equal, assert_raises
+from nose.tools import assert_equal, assert_not_equal, assert_raises
 from sklearn.externals.joblib import Memory
 from nilearn._utils.testing import generate_fake_fmri
 from nilearn.connectome import ReNA
@@ -10,6 +10,8 @@ def test_rena_clusterings():
     data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=5)
 
     nifti_masker = NiftiMasker(mask_img=mask_img).fit()
+    n_voxels = nifti_masker.transform(data).shape[1]
+
     rena = ReNA(n_clusters=10, mask=nifti_masker, scaling=False)
 
     data_red = rena.fit_transform(data)
@@ -26,5 +28,11 @@ def test_rena_clusterings():
     data_red2 = rena3.fit_transform(index_img(data, 0))
     data_compress2 = rena3.inverse_transform(data_red2)
 
-    rena4 = ReNA(n_iter=-2, mask=nifti_masker, memory=memory)
-    assert_raises(ValueError, rena4.fit, data)
+    for n_iter in [-2, 0]:
+        rena4 = ReNA(n_iter=n_iter, mask=nifti_masker, memory=memory)
+        assert_raises(ValueError, rena4.fit, data)
+
+    for n_clusters in [1, 2, 4, 8]:
+        rena5 = ReNA(n_clusters=n_clusters, n_iter=1, mask=nifti_masker,
+                    memory=memory).fit(data)
+        assert_not_equal(n_clusters, rena5.n_clusters_)

From 78930e8b418ff1b2f5152c9cf118901ea06d5e08 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Wed, 22 Feb 2017 15:53:09 +0100
Subject: [PATCH 014/228] removing check_masker, using check_embedded_marker
 instead

---
 nilearn/_utils/fixes/__init__.py      |  2 -
 nilearn/connectome/rena_clustering.py | 68 ++++-----------------------
 2 files changed, 9 insertions(+), 61 deletions(-)

diff --git a/nilearn/_utils/fixes/__init__.py b/nilearn/_utils/fixes/__init__.py
index a35cd020d6..3ae31e9d54 100644
--- a/nilearn/_utils/fixes/__init__.py
+++ b/nilearn/_utils/fixes/__init__.py
@@ -16,11 +16,9 @@
 try:
     from sklearn.utils import check_X_y
     from sklearn.utils import check_is_fitted
-    from sklearn.utils import check_array
 except ImportError:
     # scikit-learn < 0.16
     from .sklearn_validation import check_X_y
     from .sklearn_validation import check_is_fitted
-    from .sklearn_validation import check_array
 
 __all__ = ['check_X_y', 'check_is_fitted', 'check_cv']
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 3e1b495e9d..eb15ca5246 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -10,9 +10,13 @@
 from sklearn.externals import six
 from sklearn.base import TransformerMixin, ClusterMixin
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
-from sklearn.base import BaseEstimator, clone
-from .._utils.fixes import check_array, check_is_fitted
-from ..input_data import NiftiMasker, MultiNiftiMasker
+from sklearn.base import BaseEstimator
+try:
+    from sklearn.utils import atleast2d_or_csr
+except ImportError: # sklearn 0.15
+    from sklearn.utils import check_array as atleast2d_or_csr
+from ..input_data.masker_validation import check_embedded_nifti_masker
+from .._utils.fixes import check_is_fitted
 
 
 def _compute_weights(masker, masked_data):
@@ -507,13 +511,10 @@ def fit(self, X, y=None):
             raise ValueError("n_iter should be an integer greater than 0."
                              " %s was provided." % str(self.n_iter))
 
-        self.masker_ = _check_masking(self.mask, self.smoothing_fwhm,
-                                      self.target_affine, self.target_shape,
-                                      self.standardize, self.mask_strategy,
-                                      self.memory_, self.memory_level)
-
+        self.masker_ = check_embedded_nifti_masker(self, multi_subject=False)
         X = self.masker_.fit_transform(X)
 
+        X = atleast2d_or_csr(X)
         n_features = X.shape[1]
 
         if self.n_clusters > n_features:
@@ -522,8 +523,6 @@ def fit(self, X, y=None):
                           "features. Taking n_clusters = %s instead."
                           % str(n_features))
 
-        X = check_array(X, ensure_min_features=2)
-
         n_labels, labels = self.memory_.cache(
             recursive_neighbor_agglomeration)(self.masker_, X, self.n_clusters,
                                               n_iter=self.n_iter,
@@ -596,52 +595,3 @@ def inverse_transform(self, Xred):
         X_inv = Xred[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)
-
-
-# XXX this code is also replicated in the Metaestimator PR
-def _check_masking(mask, smoothing_fwhm, target_affine, target_shape,
-                   standardize, mask_strategy, memory, memory_level):
-    """Setup a nifti masker."""
-    # mask is an image, not a masker
-    if mask is None or isinstance(mask, six.string_types):
-        masker = NiftiMasker(mask_img=mask,
-                             smoothing_fwhm=smoothing_fwhm,
-                             target_affine=target_affine,
-                             target_shape=target_shape,
-                             standardize=standardize,
-                             mask_strategy=mask_strategy,
-                             memory=memory,
-                             memory_level=memory_level)
-    # mask is a masker object
-    elif isinstance(mask, (NiftiMasker, MultiNiftiMasker)):
-        try:
-            masker = clone(mask)
-            if hasattr(mask, 'mask_img_'):
-                mask_img = mask.mask_img_
-                masker.set_params(mask_img=mask_img)
-                masker.fit()
-        except TypeError as e:
-            # Workaround for a joblib bug: in joblib 0.6, a Memory object
-            # with cachedir = None cannot be cloned.
-            masker_memory = mask.memory
-            if masker_memory.cachedir is None:
-                mask.memory = None
-                masker = clone(mask)
-                mask.memory = masker_memory
-                masker.memory = Memory(cachedir=None)
-            else:
-                # The error was raised for another reason
-                raise e
-
-        for param_name in ['target_affine', 'target_shape',
-                           'smoothing_fwhm', 'mask_strategy',
-                           'memory', 'memory_level']:
-            if getattr(mask, param_name) is not None:
-                warnings.warn('Parameter %s of the masker overriden'
-                              % param_name)
-                masker.set_params(**{param_name: getattr(mask, param_name)})
-        if hasattr(mask, 'mask_img_'):
-            warnings.warn('The mask_img_ of the masker will be copied')
-    return masker
-
-

From 9b58fd60641d07203bf8f9d63fc09e4b96e6a37a Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Wed, 22 Feb 2017 15:56:35 +0100
Subject: [PATCH 015/228] testing

---
 nilearn/connectome/rena_clustering.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index eb15ca5246..e4fd0c4489 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -185,6 +185,8 @@ def _nn_connectivity(connectivity, threshold=1e-7):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
+    #
+    import pdb; pdb.set_trace()  # XXX BREAKPOINT
     inv_max = dia_matrix((1. / connectivity_.max(axis=0).toarray()[0], 0),
                          shape=(n_features, n_features))
 

From a2e3b43e5be6b23dc3ad826b7843e0f804a4b7a5 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Wed, 22 Feb 2017 17:13:42 +0100
Subject: [PATCH 016/228] adding backport to support scipy 0.13

---
 nilearn/connectome/rena_clustering.py | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index e4fd0c4489..8a1d634394 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -4,6 +4,9 @@
 # Author: Andres Hoyos idrobo, Gael Varoquaux, Jonas Kahn and  Bertrand Thirion
 # License: simplified BSD
 
+from distutils.version import LooseVersion
+import scipy
+
 import numpy as np
 import warnings
 from sklearn.externals.joblib import Memory
@@ -185,9 +188,8 @@ def _nn_connectivity(connectivity, threshold=1e-7):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    #
-    import pdb; pdb.set_trace()  # XXX BREAKPOINT
-    inv_max = dia_matrix((1. / connectivity_.max(axis=0).toarray()[0], 0),
+    max_connectivity = _get_max_connectivity(connectivity_)
+    inv_max = dia_matrix((1. / max_connectivity, 0),
                          shape=(n_features, n_features))
 
     connectivity_ = inv_max * connectivity_
@@ -597,3 +599,20 @@ def inverse_transform(self, Xred):
         X_inv = Xred[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)
+
+
+def _get_max_connectivity(connectivity):
+    """This function calculate of the maximum on the axis=0.
+    It is applied to the connectivity matrix.
+    This matrix is sparse and symetric.
+    """
+    if LooseVersion(scipy.__version__) >= LooseVersion('0.14'):
+        max_connectivity = connectivity.max(axis=0).toarray()[0]
+    else:
+        N = connectivity.shape[0]
+        max_connectivity = np.zeros((N))
+        for i in range(N):
+            max_connectivity[i] = connectivity[i].max()
+
+    return max_connectivity
+

From 67c9b000e941e33535bebf674afbcf0426d65736 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Wed, 22 Feb 2017 17:45:13 +0100
Subject: [PATCH 017/228] using getrow instead of standard indexing

---
 nilearn/connectome/rena_clustering.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 8a1d634394..21995b9725 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -608,11 +608,12 @@ def _get_max_connectivity(connectivity):
     """
     if LooseVersion(scipy.__version__) >= LooseVersion('0.14'):
         max_connectivity = connectivity.max(axis=0).toarray()[0]
+
     else:
         N = connectivity.shape[0]
         max_connectivity = np.zeros((N))
         for i in range(N):
-            max_connectivity[i] = connectivity[i].max()
+            max_connectivity[i] = connectivity.getrow(i).max()
 
     return max_connectivity
 

From 44e999dd25315bd51e7c79b8c6e44b13c68d87f6 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Thu, 23 Feb 2017 11:21:30 +0100
Subject: [PATCH 018/228] trying to increase coverage

---
 nilearn/connectome/rena_clustering.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 21995b9725..88fdc3d38d 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -608,7 +608,11 @@ def _get_max_connectivity(connectivity):
     """
     if LooseVersion(scipy.__version__) >= LooseVersion('0.14'):
         max_connectivity = connectivity.max(axis=0).toarray()[0]
-
+    elif LooseVersion(scipy.__version__) <= LooseVersion('0.13'):
+        N = connectivity.shape[0]
+        max_connectivity = np.zeros((N))
+        for i in range(N):
+            max_connectivity[i] = np.max(connectivity.getrow(i))
     else:
         N = connectivity.shape[0]
         max_connectivity = np.zeros((N))

From 200af8be96d1deae46a4730809365bfbaff65aa2 Mon Sep 17 00:00:00 2001
From: ahoyosid <ahoyosidrobo@gmail.com>
Date: Thu, 23 Feb 2017 11:45:45 +0100
Subject: [PATCH 019/228] adding workaround for scipy 0.9

---
 nilearn/connectome/rena_clustering.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 88fdc3d38d..9f10025cc9 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -608,11 +608,14 @@ def _get_max_connectivity(connectivity):
     """
     if LooseVersion(scipy.__version__) >= LooseVersion('0.14'):
         max_connectivity = connectivity.max(axis=0).toarray()[0]
-    elif LooseVersion(scipy.__version__) <= LooseVersion('0.13'):
+
+    if LooseVersion(scipy.__version__) < LooseVersion('0.10'):
         N = connectivity.shape[0]
         max_connectivity = np.zeros((N))
         for i in range(N):
-            max_connectivity[i] = np.max(connectivity.getrow(i))
+            row = connectivity.getrow(i)
+            ind = row.nonzero()
+            max_connectivity[i] = np.max(row[ind])
     else:
         N = connectivity.shape[0]
         max_connectivity = np.zeros((N))

From c4f935697e3c21045693eff97ad922dbf2b9cabb Mon Sep 17 00:00:00 2001
From: Ana Luisa <ana.pinho@inria.fr>
Date: Mon, 11 Mar 2019 14:21:06 +0100
Subject: [PATCH 020/228] ENH: add links to signal.clean() in docstring

---
 nilearn/input_data/nifti_masker.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 9a752b7c89..ab08c4b543 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -92,19 +92,19 @@ class NiftiMasker(BaseMasker, CacheMixin):
 
     detrend : boolean, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details
+        documentation for details. [1]
 
     low_pass: None or float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details
+        documentation for details. [1]
 
     high_pass: None or float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details
+        documentation for details. [1]
 
     t_r : float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details
+        documentation for details. [1]
 
     target_affine : 3x3 or 4x4 matrix, optional
         This parameter is passed to image.resample_img. Please see the
@@ -169,6 +169,10 @@ class NiftiMasker(BaseMasker, CacheMixin):
     nilearn.image.resample_img
     nilearn.masking.apply_mask
     nilearn.signal.clean
+
+    References
+    ----------
+    .. [1] https://nilearn.github.io/modules/generated/nilearn.signal.clean.html
     """
 
     def __init__(self, mask_img=None, sessions=None, smoothing_fwhm=None,

From fb1445646debd1ff137151bca69bc0c66d06a49e Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 12 Mar 2019 11:31:06 +0100
Subject: [PATCH 021/228] add symmetric_cmap option in view_img_on_surf

---
 nilearn/plotting/html_surface.py            | 8 ++++++--
 nilearn/plotting/tests/test_html_surface.py | 3 +++
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index f7dae14e80..67d8f5eda3 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -126,7 +126,7 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, vmax=None):
+                     black_bg=False, symmetric_cmap=True, vmax=None):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -158,6 +158,10 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         If True, image is plotted on a black background. Otherwise on a
         white background.
 
+    symmetric_cmap : bool, optional (default=True)
+        Make colormap symmetric (ranging from -vmax to vmax).
+        You can set it to False if you are plotting only positive values.
+
     vmax : float or None, optional (default=None)
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
@@ -180,7 +184,7 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     stat_map_img = check_niimg_3d(stat_map_img)
     info = full_brain_info(
         volume_img=stat_map_img, mesh=surf_mesh, threshold=threshold,
-        cmap=cmap, black_bg=black_bg, vmax=vmax)
+        cmap=cmap, black_bg=black_bg, vmax=vmax, symmetric_cmap=symmetric_cmap)
     return _fill_html_template(info, embed_js=True)
 
 
diff --git a/nilearn/plotting/tests/test_html_surface.py b/nilearn/plotting/tests/test_html_surface.py
index ab08489257..52974e4425 100644
--- a/nilearn/plotting/tests/test_html_surface.py
+++ b/nilearn/plotting/tests/test_html_surface.py
@@ -140,3 +140,6 @@ def test_view_img_on_surf():
     assert len(img_4d.shape) == 4
     html = html_surface.view_img_on_surf(img, threshold='92.3%')
     check_html(html)
+    np.clip(img.get_data(), 0, None, out=img.get_data())
+    html = html_surface.view_img_on_surf(img, symmetric_cmap=False)
+    check_html(html)

From 00ef627a7980de26bc1f9c17471b636209ba3961 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 22 Mar 2019 15:03:00 +0100
Subject: [PATCH 022/228] bigger tick labels in surface plot

---
 nilearn/plotting/data/js/surface-plot-utils.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nilearn/plotting/data/js/surface-plot-utils.js b/nilearn/plotting/data/js/surface-plot-utils.js
index d334d623dd..4f636193da 100644
--- a/nilearn/plotting/data/js/surface-plot-utils.js
+++ b/nilearn/plotting/data/js/surface-plot-utils.js
@@ -137,6 +137,7 @@ function addColorbar(colorscale, cmin, cmax, divId, layout, config) {
     // hack to get a colorbar
     let dummy = {
         "opacity": 0,
+        "colorbar": {"tickfont": {"size": 25}},
         "type": "mesh3d",
         "colorscale": colorscale,
         "x": [1, 0, 0],

From d13d1eeea9ad641530614128c0244938b79db689 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 22 Mar 2019 15:25:10 +0100
Subject: [PATCH 023/228] new argument at end of signature

---
 nilearn/plotting/html_surface.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 67d8f5eda3..076f720bd8 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -126,7 +126,7 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, symmetric_cmap=True, vmax=None):
+                     black_bg=False, vmax=None, symmetric_cmap=True):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -158,14 +158,14 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         If True, image is plotted on a black background. Otherwise on a
         white background.
 
-    symmetric_cmap : bool, optional (default=True)
-        Make colormap symmetric (ranging from -vmax to vmax).
-        You can set it to False if you are plotting only positive values.
-
     vmax : float or None, optional (default=None)
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
 
+    symmetric_cmap : bool, optional (default=True)
+        Make colormap symmetric (ranging from -vmax to vmax).
+        You can set it to False if you are plotting only positive values.
+
     Returns
     -------
     SurfaceView : plot of the stat map.

From 48282d57a0f11094d71c7310898ab347e6b847b3 Mon Sep 17 00:00:00 2001
From: Gilles de Hollander <gilles.de.hollander@gmail.com>
Date: Mon, 25 Mar 2019 10:29:35 +0100
Subject: [PATCH 024/228] Merge psc branch in single commit

---
 doc/whats_new.rst                             |   3 +
 nilearn/connectome/connectivity_matrices.py   |   2 +-
 nilearn/input_data/multi_nifti_masker.py      |  30 +++--
 nilearn/input_data/nifti_labels_masker.py     |  20 +--
 nilearn/input_data/nifti_maps_masker.py       |  43 ++++---
 nilearn/input_data/nifti_masker.py            |  12 +-
 nilearn/input_data/nifti_spheres_masker.py    |  20 +--
 nilearn/input_data/tests/test_base_masker.py  |   2 +-
 .../tests/test_masker_validation.py           |   4 +-
 .../tests/test_multi_nifti_masker.py          |  34 ++++++
 .../tests/test_nifti_labels_masker.py         |  33 +++++
 .../tests/test_nifti_maps_masker.py           |  35 ++++++
 nilearn/input_data/tests/test_nifti_masker.py |  28 +++++
 .../tests/test_nifti_spheres_masker.py        |  24 ++++
 nilearn/signal.py                             | 114 +++++++++++-------
 nilearn/tests/test_signal.py                  |  62 +++++++---
 16 files changed, 356 insertions(+), 110 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index feec3e49cd..429d3eff59 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -10,6 +10,9 @@ NEW
 - NiftiLabelsMasker now consumes less memory when extracting the signal from a 3D/4D
   image. This is especially noteworthy when extracting signals from large 4D images.
 - New function :func:`nilearn.datasets.fetch_atlas_schaefer_2018`
+- Rework of the standardize-options of :func:`nilearn.signal.clean` and the various Maskers
+  in `nilearn.input_data`. You can now set `standardize` to `zscore` or `psc`. `psc` stands
+  for `Percent Signal Change`, which can be a meaningful metric for BOLD.
 
 Changes
 -------
diff --git a/nilearn/connectome/connectivity_matrices.py b/nilearn/connectome/connectivity_matrices.py
index fce8fd064a..e2988d0632 100644
--- a/nilearn/connectome/connectivity_matrices.py
+++ b/nilearn/connectome/connectivity_matrices.py
@@ -486,7 +486,7 @@ def _fit_transform(self, X, do_transform=False, do_fit=False):
         # Compute all the matrices, stored in "connectivities"
         if self.kind == 'correlation':
             covariances_std = [self.cov_estimator_.fit(
-                signal._standardize(x, detrend=False, normalize=True)
+                signal._standardize(x, detrend=False, standardize=True)
                 ).covariance_ for x in X]
             connectivities = [cov_to_corr(cov) for cov in covariances_std]
         else:
diff --git a/nilearn/input_data/multi_nifti_masker.py b/nilearn/input_data/multi_nifti_masker.py
index f0f5737684..f552ef168b 100644
--- a/nilearn/input_data/multi_nifti_masker.py
+++ b/nilearn/input_data/multi_nifti_masker.py
@@ -36,12 +36,18 @@ class MultiNiftiMasker(NiftiMasker, CacheMixin):
         fine tune the mask extraction.
 
     smoothing_fwhm: float, optional
-        If smoothing_fwhm is not None, it gives the size in millimeters of the
-        spatial smoothing to apply to the signal.
-
-    standardize: boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their mean is put to 0 and their variance to 1 in the time dimension.
+        If smoothing_fwhm is not None, it gives the size in millimeters of
+        the spatial smoothing to apply to the signal.
+
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     detrend: boolean, optional
         This parameter is passed to signal.clean. Please see the related
@@ -121,13 +127,11 @@ class MultiNiftiMasker(NiftiMasker, CacheMixin):
     """
 
     def __init__(self, mask_img=None, smoothing_fwhm=None,
-                 standardize=False, detrend=False,
-                 low_pass=None, high_pass=None, t_r=None,
-                 target_affine=None, target_shape=None,
-                 mask_strategy='background', mask_args=None, dtype=None,
-                 memory=Memory(cachedir=None), memory_level=0,
-                 n_jobs=1, verbose=0
-                 ):
+                 standardize=False, detrend=False, low_pass=None,
+                 high_pass=None, t_r=None, target_affine=None,
+                 target_shape=None, mask_strategy='background',
+                 mask_args=None, dtype=None, memory=Memory(cachedir=None),
+                 memory_level=0, n_jobs=1, verbose=0):
         # Mask is provided or computed
         self.mask_img = mask_img
 
diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index f4e6dca9ea..4f1509abcc 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -56,9 +56,15 @@ class NiftiLabelsMasker(BaseMasker, CacheMixin):
         If smoothing_fwhm is not None, it gives the full-width half maximum in
         millimeters of the spatial smoothing to apply to the signal.
 
-    standardize: boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their mean is put to 0 and their variance to 1 in the time dimension.
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     detrend: boolean, optional
         This parameter is passed to signal.clean. Please see the related
@@ -184,8 +190,8 @@ def fit(self, X=None, y=None):
                     interpolation="nearest",
                     copy=True)
             else:
-                raise ValueError("Invalid value for resampling_target: " +
-                                 str(self.resampling_target))
+                raise ValueError("Invalid value for resampling_target: "
+                                 + str(self.resampling_target))
 
             mask_data, mask_affine = masking._load_mask_img(self.mask_img_)
 
@@ -254,8 +260,8 @@ def transform_single_imgs(self, imgs, confounds=None):
         params['target_affine'] = target_affine
 
         region_signals, labels_ = self._cache(
-                filter_and_extract,
-                ignore=['verbose', 'memory', 'memory_level'])(
+            filter_and_extract,
+            ignore=['verbose', 'memory', 'memory_level'])(
             # Images
             imgs, _ExtractionFunctor(self._resampled_labels_img_,
                                      self.background_label),
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index 0cb57567f9..fd3fb274d1 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -22,11 +22,11 @@ def __init__(self, _resampled_maps_img_, _resampled_mask_img_):
         self._resampled_mask_img_ = _resampled_mask_img_
 
     def __call__(self, imgs):
-            from ..regions import signal_extraction
+        from ..regions import signal_extraction
 
-            return signal_extraction.img_to_signals_maps(
-                imgs, self._resampled_maps_img_,
-                mask_img=self._resampled_mask_img_)
+        return signal_extraction.img_to_signals_maps(
+            imgs, self._resampled_maps_img_,
+            mask_img=self._resampled_mask_img_)
 
 
 class NiftiMapsMasker(BaseMasker, CacheMixin):
@@ -58,9 +58,15 @@ class NiftiMapsMasker(BaseMasker, CacheMixin):
         If smoothing_fwhm is not None, it gives the full-width half maximum in
         millimeters of the spatial smoothing to apply to the signal.
 
-    standardize: boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their mean is put to 0 and their variance to 1 in the time dimension.
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     detrend: boolean, optional
         This parameter is passed to signal.clean. Please see the related
@@ -116,10 +122,9 @@ class NiftiMapsMasker(BaseMasker, CacheMixin):
     # memory and memory_level are used by CacheMixin.
 
     def __init__(self, maps_img, mask_img=None,
-                 allow_overlap=True,
-                 smoothing_fwhm=None, standardize=False, detrend=False,
-                 low_pass=None, high_pass=None, t_r=None, dtype=None,
-                 resampling_target="data",
+                 allow_overlap=True, smoothing_fwhm=None, standardize=False,
+                 detrend=False, low_pass=None, high_pass=None, t_r=None,
+                 dtype=None, resampling_target="data",
                  memory=Memory(cachedir=None, verbose=0), memory_level=0,
                  verbose=0):
         self.maps_img = maps_img
@@ -269,18 +274,18 @@ def transform_single_imgs(self, imgs, confounds=None):
                 if self.verbose > 0:
                     print("Resampling maps")
                 self._resampled_maps_img_ = self._cache(image.resample_img)(
-                        self.maps_img_, interpolation="continuous",
-                        target_shape=ref_img.shape[:3],
-                        target_affine=ref_img.affine)
+                    self.maps_img_, interpolation="continuous",
+                    target_shape=ref_img.shape[:3],
+                    target_affine=ref_img.affine)
 
-            if (self.mask_img_ is not None and
-                    not _check_same_fov(ref_img, self.mask_img_)):
+            if (self.mask_img_ is not None
+                    and not _check_same_fov(ref_img, self.mask_img_)):
                 if self.verbose > 0:
                     print("Resampling mask")
                 self._resampled_mask_img_ = self._cache(image.resample_img)(
-                        self.mask_img_, interpolation="nearest",
-                        target_shape=ref_img.shape[:3],
-                        target_affine=ref_img.affine)
+                    self.mask_img_, interpolation="nearest",
+                    target_shape=ref_img.shape[:3],
+                    target_affine=ref_img.affine)
 
         if not self.allow_overlap:
             # Check if there is an overlap.
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 9a752b7c89..6af4b5f95d 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -86,9 +86,15 @@ class NiftiMasker(BaseMasker, CacheMixin):
         If smoothing_fwhm is not None, it gives the full-width half maximum in
         millimeters of the spatial smoothing to apply to the signal.
 
-    standardize : boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their mean is put to 0 and their variance to 1 in the time dimension.
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     detrend : boolean, optional
         This parameter is passed to signal.clean. Please see the related
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index fc7e829d58..e9ab855548 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -167,9 +167,15 @@ class NiftiSpheresMasker(BaseMasker, CacheMixin):
         If smoothing_fwhm is not None, it gives the full-width half maximum in
         millimeters of the spatial smoothing to apply to the signal.
 
-    standardize: boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their mean is set to 0 and their variance to 1 in the time dimension.
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     detrend: boolean, optional
         This parameter is passed to signal.clean. Please see the related
@@ -249,8 +255,8 @@ def fit(self, X=None, y=None):
                  "native space.\n")
 
         if not hasattr(self.seeds, '__iter__'):
-            raise ValueError(error + "Given seed list is of type: " +
-                             type(self.seeds))
+            raise ValueError(error + "Given seed list is of type: "
+                             + type(self.seeds))
 
         self.seeds_ = []
         # Check seeds and convert them to lists if needed
@@ -312,8 +318,8 @@ def transform_single_imgs(self, imgs, confounds=None):
         params = get_params(NiftiSpheresMasker, self)
 
         signals, _ = self._cache(
-                filter_and_extract,
-                ignore=['verbose', 'memory', 'memory_level'])(
+            filter_and_extract,
+            ignore=['verbose', 'memory', 'memory_level'])(
             # Images
             imgs, _ExtractionFunctor(self.seeds_, self.radius, self.mask_img,
                                      self.allow_overlap, self.dtype),
diff --git a/nilearn/input_data/tests/test_base_masker.py b/nilearn/input_data/tests/test_base_masker.py
index 3903e9935b..967b6d03c2 100644
--- a/nilearn/input_data/tests/test_base_masker.py
+++ b/nilearn/input_data/tests/test_base_masker.py
@@ -37,7 +37,7 @@ def test_cropping_code_paths():
                   "low_pass": None,
                   "t_r": None,
                   "detrend": None,
-                  "standardize": None
+                  "standardize": 'zscore',
                   }
 
     # Now do the two maskings
diff --git a/nilearn/input_data/tests/test_masker_validation.py b/nilearn/input_data/tests/test_masker_validation.py
index 7972769919..1914da40e2 100644
--- a/nilearn/input_data/tests/test_masker_validation.py
+++ b/nilearn/input_data/tests/test_masker_validation.py
@@ -54,10 +54,10 @@ def test_check_embedded_nifti_masker():
             if param_key not in ['memory', 'memory_level', 'n_jobs',
                                  'verbose']:
                 assert_equal(getattr(masker, param_key),
-                            getattr(mask, param_key))
+                             getattr(mask, param_key))
             else:
                 assert_equal(getattr(masker, param_key),
-                            getattr(owner, param_key))
+                             getattr(owner, param_key))
 
     # Check use of mask as mask_img
     shape = (6, 8, 10, 5)
diff --git a/nilearn/input_data/tests/test_multi_nifti_masker.py b/nilearn/input_data/tests/test_multi_nifti_masker.py
index 620864d10c..426481e827 100644
--- a/nilearn/input_data/tests/test_multi_nifti_masker.py
+++ b/nilearn/input_data/tests/test_multi_nifti_masker.py
@@ -196,3 +196,37 @@ def test_dtype():
 
     masked_img = masker.transform([[img]])
     assert(masked_img[0].dtype == np.float32)
+
+
+def test_standardization():
+    data_shape = (9, 9, 5)
+    n_samples = 500
+
+    signals = np.random.randn(2, np.prod(data_shape), n_samples)
+    means = np.random.randn(2, np.prod(data_shape), 1) * 50 + 1000
+    signals += means
+
+    img1 = Nifti1Image(signals[0].reshape(data_shape + (n_samples,)),
+                       np.eye(4))
+    img2 = Nifti1Image(signals[1].reshape(data_shape + (n_samples,)),
+                       np.eye(4))
+
+    mask = Nifti1Image(np.ones(data_shape), np.eye(4))
+
+    # z-score
+    masker = MultiNiftiMasker(mask, standardize='zscore')
+    trans_signals = masker.fit_transform([img1, img2])
+
+    for ts in trans_signals:
+        np.testing.assert_almost_equal(ts.mean(0), 0)
+        np.testing.assert_almost_equal(ts.std(0), 1)
+
+    # psc
+    masker = MultiNiftiMasker(mask, standardize='psc')
+    trans_signals = masker.fit_transform([img1, img2])
+
+    for ts, s in zip(trans_signals, signals):
+        np.testing.assert_almost_equal(ts.mean(0), 0)
+        np.testing.assert_almost_equal(ts,
+                                       (s / s.mean(1)[:, np.newaxis] *
+                                        100 - 100).T)
diff --git a/nilearn/input_data/tests/test_nifti_labels_masker.py b/nilearn/input_data/tests/test_nifti_labels_masker.py
index 18e7148e3b..ae1d71ee9f 100644
--- a/nilearn/input_data/tests/test_nifti_labels_masker.py
+++ b/nilearn/input_data/tests/test_nifti_labels_masker.py
@@ -266,3 +266,36 @@ def test_nifti_labels_masker_resampling():
         compressed_img2 = masker.inverse_transform(transformed2)
         np.testing.assert_array_equal(compressed_img.get_data(),
                                       compressed_img2.get_data())
+
+
+def test_standardization():
+    data_shape = (9, 9, 5)
+    n_samples = 500
+
+    signals = np.random.randn(np.prod(data_shape), n_samples)
+    means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+    signals += means
+    img = nibabel.Nifti1Image(signals.reshape(data_shape + (n_samples,)), np.eye(4))
+
+    labels = data_gen.generate_labeled_regions((9, 9, 5), 10)
+
+    # Unstandarized
+    masker = NiftiLabelsMasker(labels, standardize=False)
+    unstandarized_label_signals = masker.fit_transform(img)
+
+    # z-score
+    masker = NiftiLabelsMasker(labels, standardize='zscore')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+    # psc
+    masker = NiftiLabelsMasker(labels, standardize='psc')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals,
+                                   (unstandarized_label_signals /
+                                    unstandarized_label_signals.mean(0) *
+                                    100 - 100))
diff --git a/nilearn/input_data/tests/test_nifti_maps_masker.py b/nilearn/input_data/tests/test_nifti_maps_masker.py
index 82c09b9b76..dde2eae70e 100644
--- a/nilearn/input_data/tests/test_nifti_maps_masker.py
+++ b/nilearn/input_data/tests/test_nifti_maps_masker.py
@@ -298,3 +298,38 @@ def test_nifti_maps_masker_overlap():
                                              allow_overlap=False)
     assert_raises_regex(ValueError, 'Overlap detected',
                         non_overlapping_masker.fit_transform, fmri_img)
+
+
+def test_standardization():
+    data_shape = (9, 9, 5)
+    n_samples = 500
+
+    signals = np.random.randn(np.prod(data_shape), n_samples)
+    means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+    signals += means
+    img = nibabel.Nifti1Image(signals.reshape(data_shape + (n_samples,)),
+                              np.eye(4))
+
+    maps, _ = data_gen.generate_maps((9, 9, 5), 10)
+
+    # Unstandarized
+    masker = NiftiMapsMasker(maps, standardize=False)
+    unstandarized_label_signals = masker.fit_transform(img)
+
+    # z-score
+    masker = NiftiMapsMasker(maps,
+                             standardize='zscore')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+    # psc
+    masker = NiftiMapsMasker(maps, standardize='psc')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals,
+                                   unstandarized_label_signals
+                                   / unstandarized_label_signals.mean(0)
+                                   * 100 - 100)
diff --git a/nilearn/input_data/tests/test_nifti_masker.py b/nilearn/input_data/tests/test_nifti_masker.py
index 4c857ff820..1d761bcb4c 100644
--- a/nilearn/input_data/tests/test_nifti_masker.py
+++ b/nilearn/input_data/tests/test_nifti_masker.py
@@ -390,3 +390,31 @@ def test_dtype():
     masker_2 = NiftiMasker(dtype='float64')
     assert(masker_2.fit_transform(img_32).dtype == np.float64)
     assert(masker_2.fit_transform(img_64).dtype == np.float64)
+
+
+def test_standardization():
+    data_shape = (9, 9, 5)
+    n_samples = 500
+
+    signals = np.random.randn(np.prod(data_shape), n_samples)
+    means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+    signals += means
+    img = Nifti1Image(signals.reshape(data_shape + (n_samples,)), np.eye(4))
+
+    mask = Nifti1Image(np.ones(data_shape), np.eye(4))
+
+    # z-score
+    masker = NiftiMasker(mask, standardize='zscore')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+    # psc
+    masker = NiftiMasker(mask, standardize='psc')
+    trans_signals = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+    np.testing.assert_almost_equal(trans_signals,
+                                   (signals / signals.mean(1)[:, np.newaxis] *
+                                    100 - 100).T)
diff --git a/nilearn/input_data/tests/test_nifti_spheres_masker.py b/nilearn/input_data/tests/test_nifti_spheres_masker.py
index 21d1c2cef7..74e334a0eb 100644
--- a/nilearn/input_data/tests/test_nifti_spheres_masker.py
+++ b/nilearn/input_data/tests/test_nifti_spheres_masker.py
@@ -5,6 +5,7 @@
 from nilearn._utils.testing import assert_raises_regex
 from nose.tools import assert_false
 
+
 def test_seed_extraction():
     data = np.random.random((3, 3, 3, 5))
     img = nibabel.Nifti1Image(data, np.eye(4))
@@ -62,6 +63,7 @@ def test_anisotropic_sphere_extraction():
     affine_2[0, 0] = 4
     mask_img = nibabel.Nifti1Image(mask_img, affine=affine_2)
     masker = NiftiSpheresMasker([(2, 1, 2)], radius=1, mask_img=mask_img)
+
     masker.fit()
     s = masker.transform(img)
     assert_array_equal(s[:, 0], data[1, 0, 1])
@@ -151,3 +153,25 @@ def test_is_nifti_spheres_masker_give_nans():
     # When mask_img is provided, the seed interacts within the brain, so no nan
     masker = NiftiSpheresMasker(seeds=seed, radius=2., mask_img=mask_img)
     assert_false(np.isnan(np.sum(masker.fit_transform(img))))
+
+
+def test_standardization():
+    data = np.random.random((3, 3, 3, 5))
+    img = nibabel.Nifti1Image(data, np.eye(4))
+
+    # test zscore
+    masker = NiftiSpheresMasker([(1, 1, 1)], standardize='zscore')
+    # Test the fit
+    s = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(s.mean(), 0)
+    np.testing.assert_almost_equal(s.std(), 1)
+
+    # test psc
+    masker = NiftiSpheresMasker([(1, 1, 1)], standardize='psc')
+    # Test the fit
+    s = masker.fit_transform(img)
+
+    np.testing.assert_almost_equal(s.mean(), 0)
+    np.testing.assert_almost_equal(s.ravel(), data[1, 1, 1]
+                                   / data[1, 1, 1].mean() * 100 - 100)
diff --git a/nilearn/signal.py b/nilearn/signal.py
index d757df9191..562f955816 100644
--- a/nilearn/signal.py
+++ b/nilearn/signal.py
@@ -20,8 +20,8 @@
 NP_VERSION = distutils.version.LooseVersion(np.version.short_version).version
 
 
-def _standardize(signals, detrend=False, normalize=True):
-    """ Center and norm a given signal (time is along first axis)
+def _standardize(signals, detrend=False, standardize='zscore'):
+    """ Center and standardize a given signal (time is along first axis)
 
     Parameters
     ----------
@@ -31,34 +31,58 @@ def _standardize(signals, detrend=False, normalize=True):
     detrend: bool
         if detrending of timeseries is requested
 
-    normalize: bool
-        if True, shift timeseries to zero mean value and scale
-        to unit energy (sum of squares).
+    standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        True : the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        False : Do not standardize the data.
 
     Returns
     -------
     std_signals: numpy.ndarray
-        copy of signals, normalized.
+        copy of signals, standardized.
     """
 
+    if standardize not in [True, False, 'psc', 'zscore']:
+        raise ValueError('{} is no valid standardize strategy.'
+                         .format(standardize))
+
     if detrend:
         signals = _detrend(signals, inplace=False)
     else:
         signals = signals.copy()
 
-    if normalize:
+    if standardize:
         if signals.shape[0] == 1:
-            warnings.warn('Standardization of 3D signal has been requested but '
-                'would lead to zero values. Skipping.')
+            warnings.warn('Standardization of 3D signal has been requested but'
+                          ' would lead to zero values. Skipping.')
             return signals
 
-        if not detrend:
-            # remove mean if not already detrended
-            signals = signals - signals.mean(axis=0)
+        elif (standardize == 'zscore') or (standardize is True):
+            if not detrend:
+                # remove mean if not already detrended
+                signals = signals - signals.mean(axis=0)
+
+            std = signals.std(axis=0)
+            std[std < np.finfo(np.float).eps] = 1.  # avoid numerical problems
+            signals /= std
+
+        elif standardize == 'psc':
+            mean_signal = signals.mean(axis=0)
+            invalid_ix = mean_signal < np.finfo(np.float).eps
+            signals = (signals / mean_signal) * 100
+            signals -= 100
+
+            if np.any(invalid_ix):
+                warnings.warn('psc standardization strategy is meaningless '
+                              'for features that have a mean of 0 or '
+                              'less. These time series are set to 0.')
+                signals[:, invalid_ix] = 0
 
-        std = np.sqrt((signals ** 2).sum(axis=0))
-        std[std < np.finfo(np.float).eps] = 1.  # avoid numerical problems
-        signals /= std
     return signals
 
 
@@ -179,7 +203,7 @@ def _check_wn(btype, freq, nyq):
             'nyquist frequency). It has been lowered to %.2f (nyquist '
             'frequency).' % (btype, wn))
 
-    if wn < 0.0: # equal to 0.0 is okay
+    if wn < 0.0:  # equal to 0.0 is okay
         wn = np.finfo(1.).eps
         warnings.warn(
             'The frequency specified for the %s pass filter is '
@@ -354,9 +378,9 @@ def _ensure_float(data):
     return data
 
 
-def clean(signals, sessions=None, detrend=True, standardize=True,
-          confounds=None, low_pass=None, high_pass=None, t_r=None,
-          ensure_finite=False):
+def clean(signals, sessions=None, detrend=True, standardize='zscore',
+          confounds=None, low_pass=None,
+          high_pass=None, t_r=2.5, ensure_finite=False):
     """Improve SNR on masked fMRI signals.
 
     This function can do several things on the input signals, in
@@ -374,10 +398,6 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
 
     Filtering is only meaningful on evenly-sampled signals.
 
-    According to Lindquist et al. (2018), removal of confounds will be done
-    orthogonally to temporal filters (low- and/or high-pass filters), if both
-    are specified.
-
     Parameters
     ----------
     signals: numpy.ndarray
@@ -399,8 +419,7 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
         signal, as if all were in the same array.
 
     t_r: float
-        Repetition time, in second (sampling period). Set to None if not
-        specified. Mandatory if used together with low_pass or high_pass.
+        Repetition time, in second (sampling period).
 
     low_pass, high_pass: float
         Respectively low and high cutoff frequencies, in Hertz.
@@ -409,8 +428,13 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
         If detrending should be applied on timeseries (before
         confound removal)
 
-    standardize: bool
-        If True, returned signals are set to unit variance.
+    standardize: {'zscore', 'psc', False}, default is 'zscore'
+        Strategy to standardize the signal.
+        'zscore': the signal is z-scored. Timeseries are shifted
+        to zero mean and scaled to unit variance.
+        'psc':  Timeseries are shifted to zero mean value and scaled
+        to percent signal change (as compared to original mean signal).
+        False : Do not standardize the data.
 
     ensure_finite: bool
         If True, the non-finite values (NANs and infs) found in the data
@@ -430,11 +454,6 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
     Linear Approach". Human Brain Mapping 2, no 4 (1994): 189-210.
     <http://dx.doi.org/10.1002/hbm.460020402>`_
 
-    Orthogonalization between temporal filters and confound removal is based on
-    suggestions in `Lindquist, M., Geuter, S., Wager, T., & Caffo, B. (2018).
-    Modular preprocessing pipelines can reintroduce artifacts into fMRI data.
-    bioRxiv, 407676. <http://dx.doi.org/10.1101/407676>`_
-
     See Also
     --------
         nilearn.image.clean_img
@@ -454,7 +473,8 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
 
     if not isinstance(ensure_finite, bool):
         raise ValueError("'ensure_finite' must be boolean type True or False "
-                         "but you provided ensure_finite={0}".format(ensure_finite))
+                         "but you provided ensure_finite={0}"
+                         .format(ensure_finite))
 
     if not isinstance(signals, np.ndarray):
         signals = as_ndarray(signals)
@@ -503,7 +523,7 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
         if not len(sessions) == len(signals):
             raise ValueError(('The length of the session vector (%i) '
                               'does not match the length of the signals (%i)')
-                              % (len(sessions), len(signals)))
+                             % (len(sessions), len(signals)))
         for s in np.unique(sessions):
             session_confounds = None
             if confounds is not None:
@@ -514,23 +534,27 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
                       confounds=session_confounds, low_pass=low_pass,
                       high_pass=high_pass, t_r=t_r)
 
-    # detrend
     signals = _ensure_float(signals)
-    signals = _standardize(signals, normalize=False, detrend=detrend)
 
     # Apply low- and high-pass filters
     if low_pass is not None or high_pass is not None:
         if t_r is None:
             raise ValueError("Repetition time (t_r) must be specified for "
                              "filtering. You specified None.")
+    if detrend:
+        mean_signals = signals.mean(axis=0)
+        signals = _standardize(signals, standardize=False, detrend=detrend)
+
+    if low_pass is not None or high_pass is not None:
+        if t_r is None:
+            raise ValueError("Repetition time (t_r) must be specified for "
+                             "filtering")
 
         signals = butterworth(signals, sampling_rate=1. / t_r,
                               low_pass=low_pass, high_pass=high_pass)
-
     # Remove confounds
     if confounds is not None:
         confounds = _ensure_float(confounds)
-
         # Apply low- and high-pass filters to keep filters orthogonal
         # (according to Lindquist et al. (2018))
         if low_pass is not None or high_pass is not None:
@@ -538,7 +562,7 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
             confounds = butterworth(confounds, sampling_rate=1. / t_r,
                                     low_pass=low_pass, high_pass=high_pass)
 
-        confounds = _standardize(confounds, normalize=standardize,
+        confounds = _standardize(confounds, standardize=standardize,
                                  detrend=detrend)
 
         if not standardize:
@@ -554,8 +578,14 @@ def clean(signals, sessions=None, detrend=True, standardize=True,
         Q = Q[:, np.abs(np.diag(R)) > np.finfo(np.float).eps * 100.]
         signals -= Q.dot(Q.T).dot(signals)
 
-    if standardize:
-        signals = _standardize(signals, normalize=True, detrend=False)
-        signals *= np.sqrt(signals.shape[0])  # for unit variance
+    # Standardize
+    if detrend and (standardize == 'psc'):
+        # If the signal is detrended, we have to know the original mean
+        # signal to calculate the psc.
+        signals = _standardize(signals + mean_signals, standardize=standardize,
+                               detrend=False)
+    else:
+        signals = _standardize(signals, standardize=standardize,
+                               detrend=False)
 
     return signals
diff --git a/nilearn/tests/test_signal.py b/nilearn/tests/test_signal.py
index 6616dffbef..91122f5d60 100644
--- a/nilearn/tests/test_signal.py
+++ b/nilearn/tests/test_signal.py
@@ -133,8 +133,8 @@ def test_butterworth():
      causing it to fail tests.
      This hack prevents that and will be removed in future.
     '''
-    buggy_scipy = (LooseVersion(scipy.__version__) < LooseVersion('1.2')
-                   and LooseVersion(scipy.__version__) > LooseVersion('1.0')
+    buggy_scipy = (LooseVersion(scipy.__version__) < LooseVersion('1.2') and
+                   LooseVersion(scipy.__version__) > LooseVersion('1.0')
                    )
     if buggy_scipy:
         warnings.simplefilter('ignore')
@@ -189,20 +189,20 @@ def test_standardize():
 
     # transpose array to fit _standardize input.
     # Without trend removal
-    b = nisignal._standardize(a, normalize=True)
-    energies = (b ** 2).sum(axis=0)
-    np.testing.assert_almost_equal(energies, np.ones(n_features))
+    b = nisignal._standardize(a, standardize='zscore')
+    stds = np.std(b)
+    np.testing.assert_almost_equal(stds, np.ones(n_features))
     np.testing.assert_almost_equal(b.sum(axis=0), np.zeros(n_features))
 
     # With trend removal
     a = np.atleast_2d(np.linspace(0, 2., n_features)).T
-    b = nisignal._standardize(a, detrend=True, normalize=False)
+    b = nisignal._standardize(a, detrend=True, standardize=False)
     np.testing.assert_almost_equal(b, np.zeros(b.shape))
 
     length_1_signal = np.atleast_2d(np.linspace(0, 2., n_features))
     np.testing.assert_array_equal(length_1_signal,
                                   nisignal._standardize(length_1_signal,
-                                                        normalize=True))
+                                                        standardize='zscore'))
 
 
 def test_detrend():
@@ -218,8 +218,8 @@ def test_detrend():
 
     # Mean removal only (out-of-place)
     detrended = nisignal._detrend(x, inplace=False, type="constant")
-    assert_true(abs(detrended.mean(axis=0)).max()
-                < 15. * np.finfo(np.float).eps)
+    assert_true(abs(detrended.mean(axis=0)).max() <
+                15. * np.finfo(np.float).eps)
 
     # out-of-place detrending. Use scipy as a reference implementation
     detrended = nisignal._detrend(x, inplace=False)
@@ -227,8 +227,8 @@ def test_detrend():
 
     # "x" must be left untouched
     np.testing.assert_almost_equal(original, x, decimal=14)
-    assert_true(abs(detrended.mean(axis=0)).max() <
-                15. * np.finfo(np.float).eps)
+    assert_true(abs(detrended.mean(axis=0)).max()
+                < 15. * np.finfo(np.float).eps)
     np.testing.assert_almost_equal(detrended_scipy, detrended, decimal=14)
     # for this to work, there must be no trends at all in "signals"
     np.testing.assert_almost_equal(detrended, signals, decimal=14)
@@ -328,7 +328,8 @@ def test_clean_t_r():
                        'n_samples={}, n_features={}'.format(
                            tr1, tr2, low_cutoff, high_cutoff,
                            n_samples, n_features))
-                np.testing.assert_(np.any(np.not_equal(det_one_tr, det_diff_tr)),
+                np.testing.assert_(np.any(np.not_equal(det_one_tr,
+                                                       det_diff_tr)),
                                    msg)
                 del det_one_tr, det_diff_tr
 
@@ -442,8 +443,6 @@ def test_clean_confounds():
                                    np.zeros((20, 2)))
 
 
-
-
 def test_clean_frequencies():
 
     # Create signal
@@ -463,7 +462,7 @@ def test_clean_frequencies():
     res_low = clean(sx, detrend=False, standardize=False, low_pass=low_pass,
                     high_pass=None, t_r=t_r)
     res_high = clean(sx, detrend=False, standardize=False, low_pass=None,
-                    high_pass=high_pass, t_r=t_r)
+                     high_pass=high_pass, t_r=t_r)
 
     # Compute power spectrum density for both test
     f, Pxx_den_low = scipy.signal.welch(np.mean(res_low.T, axis=0), fs=t_r)
@@ -533,3 +532,36 @@ def test_high_variance_confounds():
     np.testing.assert_almost_equal(
         np.min(np.abs(np.dstack([outG - outGt, outG + outGt])), axis=2),
         np.zeros(outG.shape))
+
+
+def test_clean_psc():
+    rng = np.random.RandomState(0)
+    n_samples = 500
+    n_features = 5
+
+    signals, _, _ = generate_signals(n_features=n_features,
+                                     length=n_samples)
+    means = rng.randn(1, n_features)
+    signals += means
+
+    cleaned_signals = clean(signals, standardize='psc')
+    np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
+
+    std = cleaned_signals.std(axis=0)
+    np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
+    np.testing.assert_almost_equal(cleaned_signals,
+                                   signals / signals.mean(0) * 100 - 100)
+
+
+def test_clean_zscore():
+    rng = np.random.RandomState(0)
+    n_samples = 500
+    n_features = 5
+
+    signals, _, _ = generate_signals(n_features=n_features,
+                                     length=n_samples)
+
+    signals += rng.randn(1, n_features)
+    cleaned_signals = clean(signals, standardize='zscore')
+    np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
+    np.testing.assert_almost_equal(cleaned_signals.std(0), 1)

From e8c8e6459fb1275535af7535b6ac6344eeb1820c Mon Sep 17 00:00:00 2001
From: Gilles de Hollander <gilles.de.hollander@gmail.com>
Date: Mon, 25 Mar 2019 10:40:44 +0100
Subject: [PATCH 025/228] Some cosmetic fixes

---
 nilearn/input_data/nifti_labels_masker.py  |  8 ++++----
 nilearn/input_data/nifti_maps_masker.py    | 16 ++++++++--------
 nilearn/input_data/nifti_spheres_masker.py |  8 ++++----
 nilearn/signal.py                          |  8 ++++----
 nilearn/tests/test_signal.py               |  8 ++++----
 5 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index 4f1509abcc..c1a78f2408 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -190,8 +190,8 @@ def fit(self, X=None, y=None):
                     interpolation="nearest",
                     copy=True)
             else:
-                raise ValueError("Invalid value for resampling_target: "
-                                 + str(self.resampling_target))
+                raise ValueError("Invalid value for resampling_target: " +
+                                 str(self.resampling_target))
 
             mask_data, mask_affine = masking._load_mask_img(self.mask_img_)
 
@@ -260,8 +260,8 @@ def transform_single_imgs(self, imgs, confounds=None):
         params['target_affine'] = target_affine
 
         region_signals, labels_ = self._cache(
-            filter_and_extract,
-            ignore=['verbose', 'memory', 'memory_level'])(
+                filter_and_extract,
+                ignore=['verbose', 'memory', 'memory_level'])(
             # Images
             imgs, _ExtractionFunctor(self._resampled_labels_img_,
                                      self.background_label),
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index fd3fb274d1..4fab2a9791 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -274,18 +274,18 @@ def transform_single_imgs(self, imgs, confounds=None):
                 if self.verbose > 0:
                     print("Resampling maps")
                 self._resampled_maps_img_ = self._cache(image.resample_img)(
-                    self.maps_img_, interpolation="continuous",
-                    target_shape=ref_img.shape[:3],
-                    target_affine=ref_img.affine)
+                        self.maps_img_, interpolation="continuous",
+                        target_shape=ref_img.shape[:3],
+                        target_affine=ref_img.affine)
 
-            if (self.mask_img_ is not None
-                    and not _check_same_fov(ref_img, self.mask_img_)):
+            if (self.mask_img_ is not None and
+                    not _check_same_fov(ref_img, self.mask_img_)):
                 if self.verbose > 0:
                     print("Resampling mask")
                 self._resampled_mask_img_ = self._cache(image.resample_img)(
-                    self.mask_img_, interpolation="nearest",
-                    target_shape=ref_img.shape[:3],
-                    target_affine=ref_img.affine)
+                        self.mask_img_, interpolation="nearest",
+                        target_shape=ref_img.shape[:3],
+                        target_affine=ref_img.affine)
 
         if not self.allow_overlap:
             # Check if there is an overlap.
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index e9ab855548..d80f433804 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -255,8 +255,8 @@ def fit(self, X=None, y=None):
                  "native space.\n")
 
         if not hasattr(self.seeds, '__iter__'):
-            raise ValueError(error + "Given seed list is of type: "
-                             + type(self.seeds))
+            raise ValueError(error + "Given seed list is of type: " +
+                             type(self.seeds))
 
         self.seeds_ = []
         # Check seeds and convert them to lists if needed
@@ -318,8 +318,8 @@ def transform_single_imgs(self, imgs, confounds=None):
         params = get_params(NiftiSpheresMasker, self)
 
         signals, _ = self._cache(
-            filter_and_extract,
-            ignore=['verbose', 'memory', 'memory_level'])(
+                filter_and_extract,
+                ignore=['verbose', 'memory', 'memory_level'])(
             # Images
             imgs, _ExtractionFunctor(self.seeds_, self.radius, self.mask_img,
                                      self.allow_overlap, self.dtype),
diff --git a/nilearn/signal.py b/nilearn/signal.py
index 562f955816..436f96dbfe 100644
--- a/nilearn/signal.py
+++ b/nilearn/signal.py
@@ -58,8 +58,8 @@ def _standardize(signals, detrend=False, standardize='zscore'):
 
     if standardize:
         if signals.shape[0] == 1:
-            warnings.warn('Standardization of 3D signal has been requested but'
-                          ' would lead to zero values. Skipping.')
+            warnings.warn('Standardization of 3D signal has been requested but '
+                          'would lead to zero values. Skipping.')
             return signals
 
         elif (standardize == 'zscore') or (standardize is True):
@@ -203,7 +203,7 @@ def _check_wn(btype, freq, nyq):
             'nyquist frequency). It has been lowered to %.2f (nyquist '
             'frequency).' % (btype, wn))
 
-    if wn < 0.0:  # equal to 0.0 is okay
+    if wn < 0.0: # equal to 0.0 is okay
         wn = np.finfo(1.).eps
         warnings.warn(
             'The frequency specified for the %s pass filter is '
@@ -523,7 +523,7 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
         if not len(sessions) == len(signals):
             raise ValueError(('The length of the session vector (%i) '
                               'does not match the length of the signals (%i)')
-                             % (len(sessions), len(signals)))
+                              % (len(sessions), len(signals)))
         for s in np.unique(sessions):
             session_confounds = None
             if confounds is not None:
diff --git a/nilearn/tests/test_signal.py b/nilearn/tests/test_signal.py
index 91122f5d60..ab22376fe3 100644
--- a/nilearn/tests/test_signal.py
+++ b/nilearn/tests/test_signal.py
@@ -133,8 +133,8 @@ def test_butterworth():
      causing it to fail tests.
      This hack prevents that and will be removed in future.
     '''
-    buggy_scipy = (LooseVersion(scipy.__version__) < LooseVersion('1.2') and
-                   LooseVersion(scipy.__version__) > LooseVersion('1.0')
+    buggy_scipy = (LooseVersion(scipy.__version__) < LooseVersion('1.2')
+                   and LooseVersion(scipy.__version__) > LooseVersion('1.0')
                    )
     if buggy_scipy:
         warnings.simplefilter('ignore')
@@ -218,8 +218,8 @@ def test_detrend():
 
     # Mean removal only (out-of-place)
     detrended = nisignal._detrend(x, inplace=False, type="constant")
-    assert_true(abs(detrended.mean(axis=0)).max() <
-                15. * np.finfo(np.float).eps)
+    assert_true(abs(detrended.mean(axis=0)).max()
+                < 15. * np.finfo(np.float).eps)
 
     # out-of-place detrending. Use scipy as a reference implementation
     detrended = nisignal._detrend(x, inplace=False)

From abd49b0f37df97c759902e109402c48a861fb2aa Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Mon, 25 Mar 2019 21:38:15 +0100
Subject: [PATCH 026/228] update what's new

---
 doc/whats_new.rst | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 9ea51aab61..d63c2b7961 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -33,6 +33,9 @@ Changes
  - coords is now marker_coords
  - colors is now marker_color
 
+- :func:`nilearn.plotting.view_img_on_surf` now accepts a `symmetric_cmap`
+  argument to control whether the colormap is centered around 0.
+
 Fixes
 -----
 

From 6346db9b04fdddbef6e504d8f30b349d58d51f3b Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Mon, 25 Mar 2019 21:50:57 +0100
Subject: [PATCH 027/228] expose vmin in view_img_on_surf and view_surf

---
 doc/whats_new.rst                |  3 ++-
 nilearn/plotting/html_surface.py | 33 ++++++++++++++++++++++++--------
 2 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index d63c2b7961..51c4f0a3fd 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -34,7 +34,8 @@ Changes
  - colors is now marker_color
 
 - :func:`nilearn.plotting.view_img_on_surf` now accepts a `symmetric_cmap`
-  argument to control whether the colormap is centered around 0.
+  argument to control whether the colormap is centered around 0 and a `vmin`
+  argument.
 
 Fixes
 -----
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 076f720bd8..9cab3b064d 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -37,7 +37,7 @@ def _get_vertexcolor(surf_map, cmap, norm,
 
 def one_mesh_info(surf_map, surf_mesh, threshold=None, cmap=cm.cold_hot,
                   black_bg=False, bg_map=None, symmetric_cmap=True,
-                  vmax=None):
+                  vmax=None, vmin=None):
     """
     Prepare info for plotting one surface map on a single mesh.
 
@@ -49,7 +49,8 @@ def one_mesh_info(surf_map, surf_mesh, threshold=None, cmap=cm.cold_hot,
     """
     info = {}
     colors = colorscale(
-        cmap, surf_map, threshold, symmetric_cmap=symmetric_cmap, vmax=vmax)
+        cmap, surf_map, threshold, symmetric_cmap=symmetric_cmap,
+        vmax=vmax, vmin=vmin)
     info['inflated_left'] = mesh_to_plotly(surf_mesh)
     info['vertexcolor_left'] = _get_vertexcolor(
         surf_map, colors['cmap'], colors['norm'],
@@ -78,7 +79,7 @@ def _check_mesh(mesh):
 
 def full_brain_info(volume_img, mesh='fsaverage5', threshold=None,
                     cmap=cm.cold_hot, black_bg=False, symmetric_cmap=True,
-                    vmax=None, vol_to_surf_kwargs={}):
+                    vmax=None, vmin=None, vol_to_surf_kwargs={}):
     """
     Project 3D map on cortex; prepare info to plot both hemispheres.
 
@@ -97,7 +98,7 @@ def full_brain_info(volume_img, mesh='fsaverage5', threshold=None,
     }
     colors = colorscale(
         cmap, np.asarray(list(surface_maps.values())).ravel(), threshold,
-        symmetric_cmap=symmetric_cmap, vmax=vmax)
+        symmetric_cmap=symmetric_cmap, vmax=vmax, vmin=vmin)
 
     for hemi, surf_map in surface_maps.items():
         bg_map = surface.load_surf_data(mesh['sulc_{}'.format(hemi)])
@@ -126,7 +127,8 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, vmax=None, symmetric_cmap=True):
+                     black_bg=False, vmax=None, vmin=None,
+                     symmetric_cmap=True):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -162,6 +164,13 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
 
+    vmin : float, or None (default=None)
+        min value for mapping colors.
+        If `symmetric_cmap` is `True`, `vmin` is always equal to `-vmax` and
+        cannot be chosen.
+        If `symmetric_cmap` is `False`, `vmin` defaults to the min of the
+        image, or 0 when a threshold is used.
+
     symmetric_cmap : bool, optional (default=True)
         Make colormap symmetric (ranging from -vmax to vmax).
         You can set it to False if you are plotting only positive values.
@@ -184,12 +193,13 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     stat_map_img = check_niimg_3d(stat_map_img)
     info = full_brain_info(
         volume_img=stat_map_img, mesh=surf_mesh, threshold=threshold,
-        cmap=cmap, black_bg=black_bg, vmax=vmax, symmetric_cmap=symmetric_cmap)
+        cmap=cmap, black_bg=black_bg, vmax=vmax, vmin=vmin,
+        symmetric_cmap=symmetric_cmap)
     return _fill_html_template(info, embed_js=True)
 
 
 def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
-              cmap=cm.cold_hot, black_bg=False, vmax=None,
+              cmap=cm.cold_hot, black_bg=False, vmax=None, vmin=None,
               symmetric_cmap=True):
     """
     Insert a surface plot of a surface map into an HTML page.
@@ -239,6 +249,13 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
 
+    vmin : float, or None (default=None)
+        min value for mapping colors.
+        If `symmetric_cmap` is `True`, `vmin` is always equal to `-vmax` and
+        cannot be chosen.
+        If `symmetric_cmap` is `False`, `vmin` defaults to the min of the
+        image, or 0 when a threshold is used.
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -264,5 +281,5 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     info = one_mesh_info(
         surf_map=surf_map, surf_mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
-        symmetric_cmap=symmetric_cmap, vmax=vmax)
+        symmetric_cmap=symmetric_cmap, vmax=vmax, vmin=vmin)
     return _fill_html_template(info, embed_js=True)

From 415ae254c40484d45b95aae9647277b3af09b19b Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Wed, 3 Apr 2019 16:02:24 +0200
Subject: [PATCH 028/228] add title option to surface plots

---
 nilearn/plotting/data/html/connectome_plot_template.html | 6 ++++++
 nilearn/plotting/data/html/surface_plot_template.html    | 4 ++++
 nilearn/plotting/data/js/plotly-gl3d-latest.min.js       | 6 +++---
 nilearn/plotting/html_connectome.py                      | 9 +++++++--
 nilearn/plotting/html_surface.py                         | 6 +++++-
 5 files changed, 25 insertions(+), 6 deletions(-)

diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 92a72a5ddc..92bc4ef1e7 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -24,6 +24,12 @@
             data.push(info);
 
             let layout = getLayout("connectome-plot", "select-view", false);
+
+            layout['title'] = {
+                text: connectomeInfo['connectome']['title'],
+                yref: 'paper',
+                y: .9};
+
             let config = getConfig();
 
             Plotly.plot(divId, data, layout, config);
diff --git a/nilearn/plotting/data/html/surface_plot_template.html b/nilearn/plotting/data/html/surface_plot_template.html
index ef3d1487f4..77f480656b 100644
--- a/nilearn/plotting/data/html/surface_plot_template.html
+++ b/nilearn/plotting/data/html/surface_plot_template.html
@@ -21,6 +21,10 @@
             info['lighting'] = getLighting();
             let layout = getLayout("surface-plot", "select-view",
                                    surfaceMapInfo["black_bg"]);
+            layout['title'] = {
+                text: surfaceMapInfo['title'],
+                yref: 'paper',
+                y: .9};
             let config = getConfig();
 
             Plotly.react(divId, data, layout, config);
diff --git a/nilearn/plotting/data/js/plotly-gl3d-latest.min.js b/nilearn/plotting/data/js/plotly-gl3d-latest.min.js
index 2c2af6e989..2c8865bef8 100644
--- a/nilearn/plotting/data/js/plotly-gl3d-latest.min.js
+++ b/nilearn/plotting/data/js/plotly-gl3d-latest.min.js
@@ -1,7 +1,7 @@
 /**
-* plotly.js (gl3d - minified) v1.38.3
-* Copyright 2012-2018, Plotly, Inc.
+* plotly.js (gl3d - minified) v1.46.1
+* Copyright 2012-2019, Plotly, Inc.
 * All rights reserved.
 * Licensed under the MIT license
 */
-!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var u=new Error("Cannot find module '"+o+"'");throw u.code="MODULE_NOT_FOUND",u}var c=r[o]={exports:{}};e[o][0].call(c.exports,function(t){var r=e[o][1][t];return i(r||t)},c,c.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":"font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;","X input,X button":"font-family:'Open Sans', verdana, arial, sans-serif;","X input:focus,X 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a=t.length-r,o=a%n,s=Math.min(a,a-o)+r,u=0,c=r;c<s;c+=n)u=l(t,c,c+n,e),this.imuln(i),this.words[0]+u<67108864?this.words[0]+=u:this._iaddn(u);if(0!==o){var f=1;for(u=l(t,c,t.length,e),c=0;c<o;c++)f*=e;this.imuln(f),this.words[0]+u<67108864?this.words[0]+=u:this._iaddn(u)}},a.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},a.prototype.clone=function(){var t=new a(null);return this.copy(t),t},a.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},a.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},a.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},a.prototype.inspect=function(){return(this.red?"<BN-R: ":"<BN: ")+this.toString(16)+">"};var 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c=l>>>26,f=67108863&l,h=Math.min(u,e.length-1),d=Math.max(0,u-t.length+1);d<=h;d++){var p=u-d|0;c+=(o=(i=0|t.words[p])*(a=0|e.words[d])+f)/67108864|0,f=67108863&o}r.words[u]=0|f,l=0|c}return 0!==l?r.words[u]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||"hex"===t){r="";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?u[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}if(t===(0|t)&&t>=2&&t<=36){var h=c[t],d=f[t];r="";var p=this.clone();for(p.negative=0;!p.isZero();){var g=p.modn(d).toString(t);r=(p=p.idivn(d)).isZero()?g+r:u[h-g.length]+g+r}for(this.isZero()&&(r="0"+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}n(!1,"Base should be between 2 and 36")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(void 0!==o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,u=new t(a),c=this.clone();if(l){for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[s]=o;for(;s<a;s++)u[s]=0}else{for(s=0;s<a-i;s++)u[s]=0;for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[a-s-1]=o}return u},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 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mt=(u+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,U),i=(i=Math.imul(m,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(p,q)|0,i=(i=i+Math.imul(p,G)|0)+Math.imul(g,q)|0,a=a+Math.imul(g,G)|0;var yt=(u+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,Y)|0)+Math.imul(h,W)|0))<<13)|0;u=((a=a+Math.imul(h,Y)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(x,U),i=(i=Math.imul(x,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(m,q)|0,i=(i=i+Math.imul(m,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(p,W)|0,i=(i=i+Math.imul(p,Y)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,Y)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,J)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,J)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(A,U)|0,a=Math.imul(A,V),n=n+Math.imul(x,q)|0,i=(i=i+Math.imul(x,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,i=(i=i+Math.imul(m,Y)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,Y)|0,n=n+Math.imul(p,Q)|0,i=(i=i+Math.imul(p,J)|0)+Math.imul(g,Q)|0,a=a+Math.imul(g,J)|0;var xt=(u+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,U),i=(i=Math.imul(k,V))+Math.imul(E,U)|0,a=Math.imul(E,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(A,q)|0,a=a+Math.imul(A,G)|0,n=n+Math.imul(x,W)|0,i=(i=i+Math.imul(x,Y)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,Y)|0,n=n+Math.imul(m,Q)|0,i=(i=i+Math.imul(m,J)|0)+Math.imul(y,Q)|0,a=a+Math.imul(y,J)|0,n=n+Math.imul(p,$)|0,i=(i=i+Math.imul(p,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var 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wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(k,W)|0,i=(i=i+Math.imul(k,Y)|0)+Math.imul(E,W)|0,a=a+Math.imul(E,Y)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(A,Q)|0,a=a+Math.imul(A,J)|0,n=n+Math.imul(x,$)|0,i=(i=i+Math.imul(x,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,i=(i=i+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(p,at)|0,i=(i=i+Math.imul(p,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Mt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(N,U),i=(i=Math.imul(N,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(O,q)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,Y)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,Y)|0,n=n+Math.imul(k,Q)|0,i=(i=i+Math.imul(k,J)|0)+Math.imul(E,Q)|0,a=a+Math.imul(E,J)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(A,$)|0,a=a+Math.imul(A,tt)|0,n=n+Math.imul(x,rt)|0,i=(i=i+Math.imul(x,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(m,at)|0,i=(i=i+Math.imul(m,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(p,lt)|0,i=(i=i+Math.imul(p,ut)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ut)|0;var At=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(F,U),i=(i=Math.imul(F,V))+Math.imul(j,U)|0,a=Math.imul(j,V),n=n+Math.imul(N,q)|0,i=(i=i+Math.imul(N,G)|0)+Math.imul(z,q)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(O,W)|0,i=(i=i+Math.imul(O,Y)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Y)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(k,$)|0,i=(i=i+Math.imul(k,tt)|0)+Math.imul(E,$)|0,a=a+Math.imul(E,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(A,rt)|0,a=a+Math.imul(A,nt)|0,n=n+Math.imul(x,at)|0,i=(i=i+Math.imul(x,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,i=(i=i+Math.imul(m,ut)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ut)|0,n=n+Math.imul(p,ft)|0,i=(i=i+Math.imul(p,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var Tt=(u+(n=n+Math.imul(f,pt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,pt)|0))<<13)|0;u=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(F,q),i=(i=Math.imul(F,G))+Math.imul(j,q)|0,a=Math.imul(j,G),n=n+Math.imul(N,W)|0,i=(i=i+Math.imul(N,Y)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,Y)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,J)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(k,rt)|0,i=(i=i+Math.imul(k,nt)|0)+Math.imul(E,rt)|0,a=a+Math.imul(E,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(A,at)|0,a=a+Math.imul(A,ot)|0,n=n+Math.imul(x,lt)|0,i=(i=i+Math.imul(x,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(m,ft)|0,i=(i=i+Math.imul(m,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var kt=(u+(n=n+Math.imul(p,pt)|0)|0)+((8191&(i=(i=i+Math.imul(p,gt)|0)+Math.imul(g,pt)|0))<<13)|0;u=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(F,W),i=(i=Math.imul(F,Y))+Math.imul(j,W)|0,a=Math.imul(j,Y),n=n+Math.imul(N,Q)|0,i=(i=i+Math.imul(N,J)|0)+Math.imul(z,Q)|0,a=a+Math.imul(z,J)|0,n=n+Math.imul(O,$)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(k,at)|0,i=(i=i+Math.imul(k,ot)|0)+Math.imul(E,at)|0,a=a+Math.imul(E,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(A,lt)|0,a=a+Math.imul(A,ut)|0,n=n+Math.imul(x,ft)|0,i=(i=i+Math.imul(x,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var Et=(u+(n=n+Math.imul(m,pt)|0)|0)+((8191&(i=(i=i+Math.imul(m,gt)|0)+Math.imul(y,pt)|0))<<13)|0;u=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,J))+Math.imul(j,Q)|0,a=Math.imul(j,J),n=n+Math.imul(N,$)|0,i=(i=i+Math.imul(N,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(k,lt)|0,i=(i=i+Math.imul(k,ut)|0)+Math.imul(E,lt)|0,a=a+Math.imul(E,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(A,ft)|0,a=a+Math.imul(A,ht)|0;var St=(u+(n=n+Math.imul(x,pt)|0)|0)+((8191&(i=(i=i+Math.imul(x,gt)|0)+Math.imul(_,pt)|0))<<13)|0;u=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(F,$),i=(i=Math.imul(F,tt))+Math.imul(j,$)|0,a=Math.imul(j,tt),n=n+Math.imul(N,rt)|0,i=(i=i+Math.imul(N,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(k,ft)|0,i=(i=i+Math.imul(k,ht)|0)+Math.imul(E,ft)|0,a=a+Math.imul(E,ht)|0;var Lt=(u+(n=n+Math.imul(M,pt)|0)|0)+((8191&(i=(i=i+Math.imul(M,gt)|0)+Math.imul(A,pt)|0))<<13)|0;u=((a=a+Math.imul(A,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(j,rt)|0,a=Math.imul(j,nt),n=n+Math.imul(N,at)|0,i=(i=i+Math.imul(N,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(k,pt)|0)|0)+((8191&(i=(i=i+Math.imul(k,gt)|0)+Math.imul(E,pt)|0))<<13)|0;u=((a=a+Math.imul(E,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(j,at)|0,a=Math.imul(j,ot),n=n+Math.imul(N,lt)|0,i=(i=i+Math.imul(N,ut)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Pt=(u+(n=n+Math.imul(L,pt)|0)|0)+((8191&(i=(i=i+Math.imul(L,gt)|0)+Math.imul(C,pt)|0))<<13)|0;u=((a=a+Math.imul(C,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ut))+Math.imul(j,lt)|0,a=Math.imul(j,ut),n=n+Math.imul(N,ft)|0,i=(i=i+Math.imul(N,ht)|0)+Math.imul(z,ft)|0,a=a+Math.imul(z,ht)|0;var Ot=(u+(n=n+Math.imul(O,pt)|0)|0)+((8191&(i=(i=i+Math.imul(O,gt)|0)+Math.imul(R,pt)|0))<<13)|0;u=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(F,ft),i=(i=Math.imul(F,ht))+Math.imul(j,ft)|0,a=Math.imul(j,ht);var Rt=(u+(n=n+Math.imul(N,pt)|0)|0)+((8191&(i=(i=i+Math.imul(N,gt)|0)+Math.imul(z,pt)|0))<<13)|0;u=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Rt>>>26)|0,Rt&=67108863;var It=(u+(n=Math.imul(F,pt))|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(j,pt)|0))<<13)|0;return u=((a=Math.imul(j,gt))+(i>>>13)|0)+(It>>>26)|0,It&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=Mt,l[8]=At,l[9]=Tt,l[10]=kt,l[11]=Et,l[12]=St,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=Rt,l[18]=It,0!==u&&(l[19]=u,r.length++),r};function p(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):p(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,d=0;d<o;d++){var p=r[c+d],g=n[c+d],v=r[c+d+o],m=n[c+d+o],y=f*v-h*m;m=f*m+h*v,v=y,r[c+d]=p+v,n[c+d]=g+m,r[c+d+o]=p-v,n[c+d+o]=g-m,d!==s&&(y=l*f-u*h,h=l*h+u*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var d=0;d<n;d++){var p=s[d]*c[d]-l[d]*f[d];l[d]=s[d]*f[d]+l[d]*c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),p(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,d=1;0==(e.words[0]&d)&&h<26;++h,d<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var p=0,g=1;0==(r.words[0]&g)&&p<26;++p,g<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){m.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function x(){m.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function _(){m.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function w(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function M(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(x,m),i(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new y;else if("p224"===t)e=new b;else if("p192"===t)e=new x;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),d=this.pow(t,i),p=o;0!==d.cmp(s);){for(var g=d,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<p);var m=this.pow(f,new a(1).iushln(p-v-1));h=h.redMul(m),f=m.redSqr(),d=d.redMul(f),p=v}return h},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var 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e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new f(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new f(this,[])},c.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},c.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},c.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},c.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},c.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new 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a(r._color,r.key,r.value,r.left,e[c+1],r._count)}if((r=e[e.length-1]).left&&r.right){var f=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var h=e[f-1];e.push(new a(r._color,h.key,h.value,r.left,r.right,r._count)),e[f-1].key=r.key,e[f-1].value=r.value;for(c=e.length-2;c>=f;--c)r=e[c],e[c]=new a(r._color,r.key,r.value,r.left,e[c+1],r._count);e[f-1].left=e[f]}if((r=e[e.length-1])._color===n){var p=e[e.length-2];p.left===r?p.left=null:p.right===r&&(p.right=null),e.pop();for(c=0;c<e.length;++c)e[c]._count--;return new u(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=i;for(c=0;c<e.length-1;++c)e[c]._count--;return new u(this.tree._compare,e[0])}if(1===e.length)return new u(this.tree._compare,null);for(c=0;c<e.length;++c)e[c]._count--;var g=e[e.length-2];return function(t){for(var e,r,a,u,c=t.length-1;c>=0;--c){if(e=t[c],0===c)return void(e._color=i);if((r=t[c-1]).left===e){if((a=r.right).right&&a.right._color===n)return u=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=u,a._color=r._color,e._color=i,r._color=i,u._color=i,l(r),l(a),c>1&&((f=t[c-2]).left===r?f.left=a:f.right=a),void(t[c-1]=a);if(a.left&&a.left._color===n)return u=(a=r.right=o(a)).left=o(a.left),r.right=u.left,a.left=u.right,u.left=r,u.right=a,u._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(u),c>1&&((f=t[c-2]).left===r?f.left=u:f.right=u),void(t[c-1]=u);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),c>1&&((f=t[c-2]).left===r?f.left=a:f.right=a),t[c-1]=a,t[c]=r,c+1<t.length?t[c+1]=e:t.push(e),c+=2}else{if((a=r.left).left&&a.left._color===n)return u=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=u,a._color=r._color,e._color=i,r._color=i,u._color=i,l(r),l(a),c>1&&((f=t[c-2]).right===r?f.right=a:f.left=a),void(t[c-1]=a);if(a.right&&a.right._color===n)return u=(a=r.left=o(a)).right=o(a.right),r.left=u.right,a.right=u.left,u.right=r,u.left=a,u._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(u),c>1&&((f=t[c-2]).right===r?f.right=u:f.left=u),void(t[c-1]=u);if(a._color===i){if(r._color===n)return r._color=i,void(r.left=s(n,a));r.left=s(n,a);continue}var f;a=o(a),r.left=a.right,a.right=r,a._color=r._color,r._color=n,l(r),l(a),c>1&&((f=t[c-2]).right===r?f.right=a:f.left=a),t[c-1]=a,t[c]=r,c+1<t.length?t[c+1]=e:t.push(e),c+=2}}}(e),g.left===r?g.left=null:g.right=null,new u(this.tree._compare,e[0])},Object.defineProperty(h,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(h,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(h,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),h.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),h.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),n=e[e.length-1];r[r.length-1]=new a(n._color,n.key,t,n.left,n.right,n._count);for(var i=e.length-2;i>=0;--i)(n=e[i]).left===e[i+1]?r[i]=new a(n._color,n.key,n.value,r[i+1],n.right,n._count):r[i]=new a(n._color,n.key,n.value,n.left,r[i+1],n._count);return new u(this.tree._compare,r[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],93:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number("0/0");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(a,e+.5)*Math.exp(-a)*r},e.exports.log=o},{}],94:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],95:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),c=!1,f=!1;if("bounds"in t)for(var h=t.bounds,d=0;d<2;++d)for(var p=0;p<3;++p)h[d][p]!==this.bounds[d][p]&&(f=!0),this.bounds[d][p]=h[d][p];if("ticks"in t){r=t.ticks,c=!0,this.autoTicks=!1;for(d=0;d<3;++d)this.tickSpacing[d]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(d=0;d<3;++d)r[d].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?c=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(c=!0),a("tickSize"),a("tickAngle"),a("tickPad"),u("tickColor");var g=l("labels");l("labelFont")&&(g=!0),o("labelEnable"),a("labelSize"),a("labelPad"),u("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),u("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),u("lineTickColor"),o("gridEnable"),a("gridWidth"),u("gridColor"),o("zeroEnable"),u("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),u("backgroundColor"),this._text?this._text&&(g||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var d=[new h,new h,new h];function p(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,d=o,p=l;u&1<<c&&(f=s,h=a,d=l,p=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(d[c]=-1,p[c]=0):(d[c]=0,p[c]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],b=[0,0,0];f.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=o(r,n,i,a),c=s.cubeEdges,f=s.axis,h=n[12],x=n[13],_=n[14],w=n[15],M=this.pixelRatio*(i[3]*h+i[7]*x+i[11]*_+i[15]*w)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=c[A],this.lastCubeProps.axis[A]=f[A];var T=d;for(A=0;A<3;++A)p(d[A],A,this.bounds,c,f);e=this.gl;var k=g;for(A=0;A<3;++A)this.backgroundEnable[A]?k[A]=f[A]:k[A]=0;this._background.draw(r,n,i,a,k,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var E=[0,0,0];f[A]>0?E[A]=a[1][A]:E[A]=a[0][A];for(var S=0;S<2;++S){var L=(A+1+S)%3,C=(A+1+(1^S))%3;this.gridEnable[L]&&this._lines.drawGrid(L,C,this.bounds,E,this.gridColor[L],this.gridWidth[L]*this.pixelRatio)}for(S=0;S<2;++S){L=(A+1+S)%3,C=(A+1+(1^S))%3;this.zeroEnable[C]&&a[0][C]<=0&&a[1][C]>=0&&this._lines.drawZero(L,C,this.bounds,E,this.zeroLineColor[C],this.zeroLineWidth[C]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,T[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,T[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var P=u(m,T[A].primalMinor),O=u(y,T[A].mirrorMinor),R=this.lineTickLength;for(S=0;S<3;++S){var I=M/r[5*S];P[S]*=R[S]*I,O[S]*=R[S]*I}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,T[A].primalOffset,P,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,T[A].mirrorOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);for(A=0;A<3;++A){var N=T[A].primalMinor,z=u(b,T[A].primalOffset);for(S=0;S<3;++S)this.lineTickEnable[A]&&(z[S]+=M*N[S]*Math.max(this.lineTickLength[S],0)/r[5*S]);if(this.tickEnable[A]){for(S=0;S<3;++S)z[S]+=M*N[S]*this.tickPad[S]/r[5*S];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],z,this.tickColor[A])}if(this.labelEnable[A]){for(S=0;S<3;++S)z[S]+=M*N[S]*this.labelPad[S]/r[5*S];z[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],z,this.labelColor[A])}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":96,"./lib/cube.js":97,"./lib/lines.js":98,"./lib/text.js":100,"./lib/ticks.js":101}],96:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],d=-1;d<=1;d+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=d,h[l]=d;for(var p=-1;p<=1;p+=2){f[u]=p;for(var g=-1;g<=1;g+=2)f[c]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var v=u;u=c,c=v}var m=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),b=i(t,[{buffer:m,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:m,type:t.FLOAT,size:3,offset:12,stride:24}],y),x=a(t);return x.attributes.position.location=0,x.attributes.normal.location=1,new o(t,m,b,x)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":99,"gl-buffer":103,"gl-vao":161}],97:[function(t,e,r){"use strict";e.exports=function(t,e,r,a){i(s,e,t),i(s,r,s);for(var d=0,y=0;y<2;++y){c[2]=a[y][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var x=0;x<2;++x)c[0]=a[x][0],h(l[d],c,s),d+=1}}for(var _=-1,y=0;y<8;++y){for(var w=l[y][3],M=0;M<3;++M)u[y][M]=l[y][M]/w;w<0&&(_<0?_=y:u[y][2]<u[_][2]&&(_=y))}if(_<0){_=0;for(var A=0;A<3;++A){for(var T=(A+2)%3,k=(A+1)%3,E=-1,S=-1,L=0;L<2;++L){var C=L<<A,P=C+(L<<T)+(1-L<<k),O=C+(1-L<<T)+(L<<k);o(u[C],u[P],u[O],f)<0||(L?E=1:S=1)}if(E<0||S<0)S>E&&(_|=1<<A);else{for(var L=0;L<2;++L){var 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d=[0,0,0];c.drawLabel=function(t,e,r,n,i){this.labelCount[t]&&(this.shader.uniforms.axis=d,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},c.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t("_process"))},{"./shaders":99,_process:287,"gl-buffer":103,"gl-vao":161,"vectorize-text":331}],101:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=""+l;if(o<0&&(c="-"+c),i){for(var f=""+u;f.length<i;)f="0"+f;return c+"."+f}return c}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(var 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T=E[S],C=p(v,c,T,r,l),L=0;L<3;++L)x[L].lo=Math.min(x[L].lo,T[L]),x[L].hi=Math.max(x[L].hi,T[L]),L!==M&&(x[L].pixelsPerDataUnit=Math.min(x[L].pixelsPerDataUnit,Math.abs(C[L])))}}}return x};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],d=[0,0,0,1];function p(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=d,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],v=[0,0,0]},{"./lib/cube.js":97,"extract-frustum-planes":89,"gl-mat4/multiply":126,"gl-mat4/transpose":134,"gl-vec4/transformMat4":219,"split-polygon":318}],103:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,"uint16"):c(t,"float32"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:265,"ndarray-ops":259,"typedarray-pool":328}],104:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=(t("gl-vec4"),function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r}),a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var u=t[0],c=t[1],f=t[2],h=r[0].length,d=r[1].length,p=r[2].length,g=i(r[0],u),v=i(r[1],c),m=i(r[2],f),y=g+1,b=v+1,x=m+1;if(l&&(g=s(g,0,h-1),y=s(y,0,h-1),v=s(v,0,d-1),b=s(b,0,d-1),m=s(m,0,p-1),x=s(x,0,p-1)),g<0||v<0||m<0||y>=h||b>=d||x>=p)return n.create();var _=(u-r[0][g])/(r[0][y]-r[0][g]),w=(c-r[1][v])/(r[1][b]-r[1][v]),M=(f-r[2][m])/(r[2][x]-r[2][m]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(M<0||M>1||isNaN(M))&&(M=0);var A=m*h*d,T=x*h*d,k=v*h,E=b*h,S=g,L=y,C=e[k+A+S],P=e[k+A+L],O=e[E+A+S],R=e[E+A+L],I=e[k+T+S],N=e[k+T+L],z=e[E+T+S],D=e[E+T+L],F=n.create();return n.lerp(F,C,P,_),n.lerp(a,O,R,_),n.lerp(F,F,a,w),n.lerp(a,I,N,_),n.lerp(o,z,D,_),n.lerp(a,a,o,w),n.lerp(F,F,a,M),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),o;for(var s=0,u=1/0,c=-1/0,f=1/0,h=-1/0,d=1/0,p=-1/0,g=null,v=null,m=[],y=1/0,b=0;b<r.length;b++){var x,_=r[b];u=Math.min(_[0],u),c=Math.max(_[0],c),f=Math.min(_[1],f),h=Math.max(_[1],h),d=Math.min(_[2],d),p=Math.max(_[2],p),x=i?l(_,a,i,!0):a[b],n.length(x)>s&&(s=n.length(x)),b&&(y=Math.min(y,2*n.distance(g,_)/(n.length(v)+n.length(x)))),g=_,v=x,m.push(x)}var w=[u,f,d],M=[c,h,p];e&&(e[0]=w,e[1]=M),0===s&&(s=1);var A=1/s;isFinite(y)&&!isNaN(y)||(y=1),o.vectorScale=y;var T=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),k=t.coneSize||.5;t.absoluteConeSize&&(k=t.absoluteConeSize*A),o.coneScale=k;b=0;for(var E=0;b<r.length;b++)for(var S=(_=r[b])[0],L=_[1],C=_[2],P=m[b],O=n.length(P)*A,R=0;R<8;R++){o.positions.push([S,L,C,E++]),o.positions.push([S,L,C,E++]),o.positions.push([S,L,C,E++]),o.positions.push([S,L,C,E++]),o.positions.push([S,L,C,E++]),o.positions.push([S,L,C,E++]),o.vectors.push(P),o.vectors.push(P),o.vectors.push(P),o.vectors.push(P),o.vectors.push(P),o.vectors.push(P),o.vertexIntensity.push(O,O,O),o.vertexIntensity.push(O,O,O),o.vertexNormals.push(T,T,T),o.vertexNormals.push(T,T,T);var I=o.positions.length;o.cells.push([I-6,I-5,I-4],[I-3,I-2,I-1])}return o},e.exports.createConeMesh=t("./lib/conemesh")},{"./lib/conemesh":106,"gl-vec3":173,"gl-vec4":203}],105:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),c=0;c<t.length;++c)u[c]=o(t[c],r,a,s,l);for(var f=0,h=1/0,c=0;c<u.length;++c){for(var d=0,p=0;p<2;++p)d+=Math.pow(u[c][p]-e[p],2);d<h&&(h=d,f=c)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0,c=0;c<3;++c){if(g[c]<-.001||g[c]>1.0001)return null;v+=g[c]}if(Math.abs(v-1)>.001)return null;return[f,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:17,"polytope-closest-point/lib/closest_point_2d.js":286}],106:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),u=t("gl-mat4/invert"),c=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),d=t("typedarray-pool"),p=t("./shaders"),g=(t("./closest-point"),p.meshShader),v=p.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,u,c,f,h,d,p,g,v,y,b,x,_,w,M,A,T,k,E,S){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=u,this.triangleColors=f,this.triangleNormals=d,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=b,this.edgeIds=v,this.edgeVAO=x,this.edgeCount=0,this.pointPositions=_,this.pointColors=M,this.pointUVs=A,this.pointSizes=T,this.pointIds=w,this.pointVAO=k,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=E,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var b=y.prototype;function x(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}b.isOpaque=function(){return this.opacity>=1},b.isTransparent=function(){return this.opacity<1},b.pickSlots=1,b.setPickBase=function(t){this.pickId=t},b.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=d.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var p=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[p],b=this.positions[g],x=0;x<3;++x)o[s++]=v*y[x]+m*b[x]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),d.free(o)}else this.contourCount=0},b.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],u=[],h=[],d=[],p=[],g=[],v=[],m=[],y=[],b=[],x=[],_=[],w=[],M=[];this.cells=r,this.positions=n;var A=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,E=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,E)),T||A||(A=s.vertexNormals(r,n,k));var S=t.vertexColors,L=t.cellColors,C=t.meshColor||[1,1,1,1],P=t.vertexUVs,O=t.vertexIntensity,R=t.cellUVs,I=t.cellIntensity,N=1/0,z=-1/0;if(!P&&!R)if(O)if(t.vertexIntensityBounds)N=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var D=0;D<O.length;++D){var F=O[D];N=Math.min(N,F),z=Math.max(z,F)}else if(I)for(D=0;D<I.length;++D){F=I[D];N=Math.min(N,F),z=Math.max(z,F)}else for(D=0;D<n.length;++D){F=n[D][2];N=Math.min(N,F),z=Math.max(z,F)}this.intensity=O||(I?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,I):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var j=t.pointSizes,B=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var U=n[D],V=0;V<3;++V)!isNaN(U[V])&&isFinite(U[V])&&(this.bounds[0][V]=Math.min(this.bounds[0][V],U[V]),this.bounds[1][V]=Math.max(this.bounds[1][V],U[V]));var H=0,q=0,G=0;t:for(D=0;D<r.length;++D){var X=r[D];switch(X.length){case 1:for(U=n[Y=X[0]],V=0;V<3;++V)if(isNaN(U[V])||!isFinite(U[V]))continue t;b.push(U[0],U[1],U[2],U[3]),3===(Z=S?S[Y]:L?L[D]:C).length?x.push(Z[0],Z[1],Z[2],1):x.push(Z[0],Z[1],Z[2],Z[3]),Q=P?P[Y]:O?[(O[Y]-N)/(z-N),0]:R?R[D]:I?[(I[D]-N)/(z-N),0]:[(U[2]-N)/(z-N),0],_.push(Q[0],Q[1]),j?w.push(j[Y]):w.push(B),M.push(D),G+=1;break;case 2:for(V=0;V<2;++V){U=n[Y=X[V]];for(var W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t}for(V=0;V<2;++V){U=n[Y=X[V]];g.push(U[0],U[1],U[2]),3===(Z=S?S[Y]:L?L[D]:C).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),Q=P?P[Y]:O?[(O[Y]-N)/(z-N),0]:R?R[D]:I?[(I[D]-N)/(z-N),0]:[(U[2]-N)/(z-N),0],m.push(Q[0],Q[1]),y.push(D)}q+=1;break;case 3:for(V=0;V<3;++V)for(U=n[Y=X[V]],W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t;for(V=0;V<3;++V){var Y;U=n[Y=X[V]];a.push(U[0],U[1],U[2],U[3]);var Z,Q,J,K=i[Y];l.push(K[0],K[1],K[2]),3===(Z=S?S[Y]:L?L[D]:C).length?u.push(Z[0],Z[1],Z[2],1):u.push(Z[0],Z[1],Z[2],Z[3]),Q=P?P[Y]:O?[(O[Y]-N)/(z-N),0]:R?R[D]:I?[(I[D]-N)/(z-N),0]:[(U[2]-N)/(z-N),0],d.push(Q[0],Q[1]),J=A?A[Y]:T[D],h.push(J[0],J[1],J[2]),p.push(D)}H+=1}}this.pointCount=G,this.edgeCount=q,this.triangleCount=H,this.pointPositions.update(b),this.pointColors.update(x),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(M)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(u),this.triangleUVs.update(d),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(p))}},b.drawTransparent=b.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};this.texture.bind(0);var c=new Array(16);l(c,s.view,s.model),l(c,s.projection,c),u(c,c);for(o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var d=c[12+o],p=0;p<3;++p)d+=c[4*p+o]*this.lightPosition[p];s.lightPosition[o]=d/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},b.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},b.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},b.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=x(t),l=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var u=i(t),f=i(t),h=i(t),d=i(t),p=i(t),v=i(t),m=a(t,[{buffer:u,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:d,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),M=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:M,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),T=i(t),k=i(t),E=i(t),S=i(t),L=i(t),C=a(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2},{buffer:S,type:t.FLOAT,size:1}]),P=i(t),O=new y(t,l,r,null,null,s,null,null,u,f,v,h,d,p,m,b,M,_,w,A,T,L,k,E,S,C,P,a(t,[{buffer:P,type:t.FLOAT,size:3}]));return O.update(e),O}},{"./closest-point":105,"./shaders":107,colormap:67,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-shader":146,"gl-texture2d":157,"gl-vao":161,ndarray:265,normals:267,"simplicial-complex-contour":309,"typedarray-pool":328}],107:[function(t,e,r){var n=t("glslify"),i=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex <= 2.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float vectorScale;\nuniform float coneScale;\n\nuniform float coneOffset;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  normal = normalize(normal * inverse(mat3(model)));\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n  f_color          = color; //vec4(position.w, color.r, 0, 0);\n  f_normal         = normal;\n  f_data           = conePosition.xyz;\n  f_eyeDirection   = eyePosition   - conePosition.xyz;\n  f_lightDirection = lightPosition - conePosition.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  //if(any(lessThan(f_data, clipBounds[0])) || \n  //   any(greaterThan(f_data, clipBounds[1]))) {\n  //  discard;\n  //}\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n  \n  if(!gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor =  texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex <= 2.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nuniform float vectorScale;\nuniform float coneScale;\nuniform float coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:230}],108:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34000:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],109:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":108}],110:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return this.opacity>=1},l.isTransparent=function(){return this.opacity<1},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),"opacity"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var d=n[l],p=e[s];if(Array.isArray(p[0])&&(p=e[l]),3===p.length&&(p=[p[0],p[1],p[2],1]),!isNaN(d[0][s])&&!isNaN(d[1][s])){var g;if(d[0][s]<0)(g=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),u(this.bounds,g),o+=2+f(i,g,p,s);if(d[1][s]>0)(g=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),u(this.bounds,g),o+=2+f(i,g,p,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":111,"gl-buffer":103,"gl-vao":161}],111:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":146,glslify:230}],112:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension("WEBGL_draw_buffers");!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!u)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,d=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!d)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&d&&(h=t.FLOAT);var g=!0;"depth"in n&&(g=!!n.depth);var v=!1;"stencil"in n&&(v=!!n.stencil);return new p(t,e,r,h,f,g,v,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function d(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function p(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var p=0;p<i;++p)this.color[p]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,v=[0|e,0|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,p=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=h(r,i,a,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=d(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?p?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&p?t._depth_rb=d(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=d(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):p&&(t._depth_rb=d(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(b)}c(r,e)}(this)}var g=p.prototype;function v(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":157}],113:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var u=n("Error compiling %s shader %s:\n",l,s),c=n("%s%s",u,t),f=t.split("\n"),h={},d=0;d<f.length;d++){var p=f[d];if(""!==p&&"\0"!==p){var g=parseInt(p.split(":")[2]);if(isNaN(g))throw new Error(n("Could not parse error: %s",p));h[g]=p}}for(var v=o(e).split("\n"),d=0;d<v.length;d++)if(h[d+3]||h[d+2]||h[d+1]){var m=v[d];if(u+=m+"\n",h[d+1]){var y=h[d+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),u+=n("^^^ %s\n\n",y)}}return{long:u.trim(),short:c.trim()}}},{"add-line-numbers":12,"gl-constants/lookup":109,"glsl-shader-name":222,"sprintf-js":319}],114:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvoid main() {\n  vec4 projected = projection * view * model * vec4(position, 1.0);\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\n\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\nlowp vec4 encode_float_1540259130(highp float v) {\n  highp float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  highp vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  highp float e = floor(log2(av));\n  highp float m = av * pow(2.0, -e) - 1.0;\n  \n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n  \n  //Unpack exponent\n  highp float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0; \n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId/255.0, encode_float_1540259130(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":146,glslify:230}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=c(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),u=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),h=l(new Array(1024),[256,1,4]),d=0;d<1024;++d)h.data[d]=255;var p=a(e,h);p.wrap=e.REPEAT;var g=new v(e,r,o,s,u,p);return g.update(t),g};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=t("glsl-read-float"),s=t("binary-search-bounds"),l=t("ndarray"),u=t("./lib/shaders"),c=u.createShader,f=u.createPickShader,h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function p(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}var m=v.prototype;m.isTransparent=function(){return this.opacity<1},m.isOpaque=function(){return this.opacity>=1},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||h,view:t.view||h,projection:t.projection||h,clipBounds:p(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()},m.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||h,view:t.view||h,projection:t.projection||h,pickId:this.pickId,clipBounds:p(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),"opacity"in t&&(this.opacity=+t.opacity);var i=t.position||t.positions;if(i){var a=t.color||t.colors||[0,0,0,1],o=t.lineWidth||1,u=[],c=[],f=[],h=0,p=0,g=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],v=!1;t:for(e=1;e<i.length;++e){var m,y,b,x=i[e-1],_=i[e];for(c.push(h),f.push(x.slice()),r=0;r<3;++r){if(isNaN(x[r])||isNaN(_[r])||!isFinite(x[r])||!isFinite(_[r])){if(!n&&u.length>0){for(var w=0;w<24;++w)u.push(u[u.length-12]);p+=2,v=!0}continue t}g[0][r]=Math.min(g[0][r],x[r],_[r]),g[1][r]=Math.max(g[1][r],x[r],_[r])}Array.isArray(a[0])?(m=a[e-1],y=a[e]):m=y=a,3===m.length&&(m=[m[0],m[1],m[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),b=Array.isArray(o)?o[e-1]:o;var M=h;if(h+=d(x,_),v){for(r=0;r<2;++r)u.push(x[0],x[1],x[2],_[0],_[1],_[2],M,b,m[0],m[1],m[2],m[3]);p+=2,v=!1}u.push(x[0],x[1],x[2],_[0],_[1],_[2],M,b,m[0],m[1],m[2],m[3],x[0],x[1],x[2],_[0],_[1],_[2],M,-b,m[0],m[1],m[2],m[3],_[0],_[1],_[2],x[0],x[1],x[2],h,-b,y[0],y[1],y[2],y[3],_[0],_[1],_[2],x[0],x[1],x[2],h,b,y[0],y[1],y[2],y[3]),p+=4}if(this.buffer.update(u),c.push(h),f.push(i[i.length-1].slice()),this.bounds=g,this.vertexCount=p,this.points=f,this.arcLength=c,"dashes"in t){var A=t.dashes.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var T=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)T.set(e,0,r,0);1&s.le(A,A[A.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,u=[0,0,0],c=0;c<3;++c)u[c]=l*n[c]+a*i[c];var f=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,f,this.points[f])}},{"./lib/shaders":114,"binary-search-bounds":35,"gl-buffer":103,"gl-texture2d":157,"gl-vao":161,"glsl-read-float":221,ndarray:265}],116:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}},{}],117:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=c*o-s*u,h=-c*a+s*l,d=u*a-o*l,p=r*f+n*h+i*d;return p?(p=1/p,t[0]=f*p,t[1]=(-c*n+i*u)*p,t[2]=(s*n-i*o)*p,t[3]=h*p,t[4]=(c*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-u*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}},{}],118:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],119:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],120:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],d=t[12],p=t[13],g=t[14],v=t[15];return(e*o-r*a)*(f*v-h*g)-(e*s-n*a)*(c*v-h*p)+(e*l-i*a)*(c*g-f*p)+(r*s-n*o)*(u*v-h*d)-(r*l-i*o)*(u*g-f*d)+(n*l-i*s)*(u*p-c*d)}},{}],121:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,d=i*s,p=i*l,g=a*o,v=a*s,m=a*l;return t[0]=1-f-p,t[1]=c+m,t[2]=h-v,t[3]=0,t[4]=c-m,t[5]=1-u-p,t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],122:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,d=i*l,p=i*u,g=a*u,v=o*s,m=o*l,y=o*u;return t[0]=1-(d+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(c+g),t[6]=p+v,t[7]=0,t[8]=h+m,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],123:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],124:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],d=e[11],p=e[12],g=e[13],v=e[14],m=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,A=c*g-f*p,T=c*v-h*p,k=c*m-d*p,E=f*v-h*g,S=f*m-d*g,L=h*m-d*v,C=y*L-b*S+x*E+_*k-w*T+M*A;if(!C)return null;return C=1/C,t[0]=(s*L-l*S+u*E)*C,t[1]=(i*S-n*L-a*E)*C,t[2]=(g*M-v*w+m*_)*C,t[3]=(h*w-f*M-d*_)*C,t[4]=(l*k-o*L-u*T)*C,t[5]=(r*L-i*k+a*T)*C,t[6]=(v*x-p*M-m*b)*C,t[7]=(c*M-h*x+d*b)*C,t[8]=(o*S-s*k+u*A)*C,t[9]=(n*k-r*S-a*A)*C,t[10]=(p*w-g*x+m*y)*C,t[11]=(f*x-c*w-d*y)*C,t[12]=(s*T-o*E-l*A)*C,t[13]=(r*E-n*T+i*A)*C,t[14]=(g*b-p*_-v*y)*C,t[15]=(c*_-f*b+h*y)*C,t}},{}],125:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,d,p,g=e[0],v=e[1],m=e[2],y=i[0],b=i[1],x=i[2],_=r[0],w=r[1],M=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-M)<1e-6)return n(t);f=g-_,h=v-w,d=m-M,p=1/Math.sqrt(f*f+h*h+d*d),a=b*(d*=p)-x*(h*=p),o=x*(f*=p)-y*d,s=y*h-b*f,(p=Math.sqrt(a*a+o*o+s*s))?(a*=p=1/p,o*=p,s*=p):(a=0,o=0,s=0);l=h*s-d*o,u=d*a-f*s,c=f*o-h*a,(p=Math.sqrt(l*l+u*u+c*c))?(l*=p=1/p,u*=p,c*=p):(l=0,u=0,c=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=d,t[11]=0,t[12]=-(a*g+o*v+s*m),t[13]=-(l*g+u*v+c*m),t[14]=-(f*g+h*v+d*m),t[15]=1,t}},{"./identity":123}],126:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],d=e[10],p=e[11],g=e[12],v=e[13],m=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*f+w*g,t[1]=b*i+x*l+_*h+w*v,t[2]=b*a+x*u+_*d+w*m,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*f+w*g,t[5]=b*i+x*l+_*h+w*v,t[6]=b*a+x*u+_*d+w*m,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*f+w*g,t[9]=b*i+x*l+_*h+w*v,t[10]=b*a+x*u+_*d+w*m,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*f+w*g,t[13]=b*i+x*l+_*h+w*v,t[14]=b*a+x*u+_*d+w*m,t[15]=b*o+x*c+_*p+w*y,t}},{}],127:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],128:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,u,c,f,h,d,p,g,v,m,y,b,x,_,w,M,A,T,k,E,S=n[0],L=n[1],C=n[2],P=Math.sqrt(S*S+L*L+C*C);if(Math.abs(P)<1e-6)return null;S*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],u=e[2],c=e[3],f=e[4],h=e[5],d=e[6],p=e[7],g=e[8],v=e[9],m=e[10],y=e[11],b=S*S*o+a,x=L*S*o+C*i,_=C*S*o-L*i,w=S*L*o-C*i,M=L*L*o+a,A=C*L*o+S*i,T=S*C*o+L*i,k=L*C*o-S*i,E=C*C*o+a,t[0]=s*b+f*x+g*_,t[1]=l*b+h*x+v*_,t[2]=u*b+d*x+m*_,t[3]=c*b+p*x+y*_,t[4]=s*w+f*M+g*A,t[5]=l*w+h*M+v*A,t[6]=u*w+d*M+m*A,t[7]=c*w+p*M+y*A,t[8]=s*T+f*k+g*E,t[9]=l*T+h*k+v*E,t[10]=u*T+d*k+m*E,t[11]=c*T+p*k+y*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],129:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],130:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],131:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],132:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],133:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,d,p,g=r[0],v=r[1],m=r[2];e===t?(t[12]=e[0]*g+e[4]*v+e[8]*m+e[12],t[13]=e[1]*g+e[5]*v+e[9]*m+e[13],t[14]=e[2]*g+e[6]*v+e[10]*m+e[14],t[15]=e[3]*g+e[7]*v+e[11]*m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=d,t[11]=p,t[12]=n*g+s*v+f*m+e[12],t[13]=i*g+l*v+h*m+e[13],t[14]=a*g+u*v+d*m+e[14],t[15]=o*g+c*v+p*m+e[15]);return t}},{}],134:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],135:[function(t,e,r){"use strict";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:i(t,e);break;case 16:a(t,e);break;default:throw new Error("currently supports matrices up to 4x4")}return t};var n=t("gl-mat2/invert"),i=t("gl-mat3/invert"),a=t("gl-mat4/invert")},{"gl-mat2/invert":116,"gl-mat3/invert":117,"gl-mat4/invert":124}],136:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),c=0;c<t.length;++c)u[c]=o(t[c],r,a,s,l);for(var f=0,h=1/0,c=0;c<u.length;++c){for(var d=0,p=0;p<2;++p)d+=Math.pow(u[c][p]-e[p],2);d<h&&(h=d,f=c)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0,c=0;c<3;++c){if(g[c]<-.001||g[c]>1.0001)return null;v+=g[c]}if(Math.abs(v-1)>.001)return null;return[f,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:17,"polytope-closest-point/lib/closest_point_2d.js":286}],137:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec4 m_position  = model * vec4(position, 1.0);\n  vec4 t_position  = view * m_position;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = position;\n  f_eyeDirection   = eyePosition   - position;\n  f_lightDirection = lightPosition - position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nvec3 normals(vec3 pos) {\n  vec3 fdx = dFdx(pos);\n  vec3 fdy = dFdy(pos);\n  return normalize(cross(fdx, fdy));\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) ||\n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  vec3 normal = normals(f_data);\n\n  if (\n    dot(N, normal) < 0.0\n    ) {\n      N = -N;\n  }\n\n  float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) || \n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),u=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),c=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),d=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),p=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor,1);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:d,fragment:p,attributes:[{name:"position",type:"vec3"}]}},{glslify:230}],138:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),u=t("gl-mat4/invert"),c=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),d=t("typedarray-pool"),p=t("./lib/shaders"),g=t("./lib/closest-point"),v=p.meshShader,m=p.wireShader,y=p.pointShader,b=p.pickShader,x=p.pointPickShader,_=p.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function M(t,e,r,n,i,a,o,s,l,u,c,f,h,d,p,g,v,m,y,b,x,_,M,A,T,k,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=M,this.pointSizes=A,this.pointIds=x,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=k,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var A=M.prototype;function T(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function k(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}A.isOpaque=function(){return this.opacity>=1},A.isTransparent=function(){return this.opacity<1},A.pickSlots=1,A.setPickBase=function(t){this.pickId=t},A.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=d.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var p=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[p],b=this.positions[g],x=0;x<3;++x)o[s++]=v*y[x]+m*b[x]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),d.free(o)}else this.contourCount=0},A.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],d=[],p=[],g=[],v=[],m=[],y=[],b=[],x=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,M=t.cellNormals,A=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!M&&(M=s.faceNormals(r,n,T)),M||w||(w=s.vertexNormals(r,n,A));var k=t.vertexColors,E=t.cellColors,S=t.meshColor||[1,1,1,1],L=t.vertexUVs,C=t.vertexIntensity,P=t.cellUVs,O=t.cellIntensity,R=1/0,I=-1/0;if(!L&&!P)if(C)if(t.vertexIntensityBounds)R=+t.vertexIntensityBounds[0],I=+t.vertexIntensityBounds[1];else for(var N=0;N<C.length;++N){var z=C[N];R=Math.min(R,z),I=Math.max(I,z)}else if(O)for(N=0;N<O.length;++N){z=O[N];R=Math.min(R,z),I=Math.max(I,z)}else for(N=0;N<n.length;++N){z=n[N][2];R=Math.min(R,z),I=Math.max(I,z)}this.intensity=C||(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var D=t.pointSizes,F=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(N=0;N<n.length;++N)for(var j=n[N],B=0;B<3;++B)!isNaN(j[B])&&isFinite(j[B])&&(this.bounds[0][B]=Math.min(this.bounds[0][B],j[B]),this.bounds[1][B]=Math.max(this.bounds[1][B],j[B]));var U=0,V=0,H=0;t:for(N=0;N<r.length;++N){var q=r[N];switch(q.length){case 1:for(j=n[X=q[0]],B=0;B<3;++B)if(isNaN(j[B])||!isFinite(j[B]))continue t;m.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[N]:S).length?y.push(W[0],W[1],W[2],1):y.push(W[0],W[1],W[2],W[3]),Y=L?L[X]:C?[(C[X]-R)/(I-R),0]:P?P[N]:O?[(O[N]-R)/(I-R),0]:[(j[2]-R)/(I-R),0],b.push(Y[0],Y[1]),D?x.push(D[X]):x.push(F),_.push(N),H+=1;break;case 2:for(B=0;B<2;++B){j=n[X=q[B]];for(var G=0;G<3;++G)if(isNaN(j[G])||!isFinite(j[G]))continue t}for(B=0;B<2;++B){j=n[X=q[B]];d.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[N]:S).length?p.push(W[0],W[1],W[2],1):p.push(W[0],W[1],W[2],W[3]),Y=L?L[X]:C?[(C[X]-R)/(I-R),0]:P?P[N]:O?[(O[N]-R)/(I-R),0]:[(j[2]-R)/(I-R),0],g.push(Y[0],Y[1]),v.push(N)}V+=1;break;case 3:for(B=0;B<3;++B)for(j=n[X=q[B]],G=0;G<3;++G)if(isNaN(j[G])||!isFinite(j[G]))continue t;for(B=0;B<3;++B){var X,W,Y,Z;j=n[X=q[B]];i.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[N]:S).length?a.push(W[0],W[1],W[2],1):a.push(W[0],W[1],W[2],W[3]),Y=L?L[X]:C?[(C[X]-R)/(I-R),0]:P?P[N]:O?[(O[N]-R)/(I-R),0]:[(j[2]-R)/(I-R),0],u.push(Y[0],Y[1]),Z=w?w[X]:M[N],l.push(Z[0],Z[1],Z[2]),h.push(N)}U+=1}}this.pointCount=H,this.edgeCount=V,this.triangleCount=U,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(b),this.pointSizes.update(x),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(d),this.edgeColors.update(p),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},A.drawTransparent=A.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var c=new Array(16);l(c,s.view,s.model),l(c,s.projection,c),u(c,c);for(o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var d=c[12+o],p=0;p<3;++p)d+=c[4*p+o]*this.lightPosition[p];s.lightPosition[o]=d/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},A.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},A.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},A.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=T(t),u=k(t),f=E(t),h=S(t),d=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));d.generateMipmap(),d.minFilter=t.LINEAR_MIPMAP_LINEAR,d.magFilter=t.LINEAR;var p=i(t),g=i(t),y=i(t),b=i(t),x=i(t),_=a(t,[{buffer:p,type:t.FLOAT,size:3},{buffer:x,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:b,type:t.FLOAT,size:3}]),w=i(t),A=i(t),L=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:A,type:t.FLOAT,size:4},{buffer:L,type:t.FLOAT,size:2}]),O=i(t),R=i(t),I=i(t),N=i(t),z=i(t),D=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:z,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:R,type:t.FLOAT,size:4},{buffer:I,type:t.FLOAT,size:2},{buffer:N,type:t.FLOAT,size:1}]),F=i(t),j=new M(t,d,r,s,l,u,f,h,p,x,g,y,b,_,w,C,A,L,P,O,z,R,I,N,D,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return j.update(e),j}},{"./lib/closest-point":136,"./lib/shaders":137,colormap:67,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-shader":146,"gl-texture2d":157,"gl-vao":161,ndarray:265,normals:267,"simplicial-complex-contour":309,"typedarray-pool":328}],139:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":146,glslify:230}],140:[function(t,e,r){"use strict";e.exports=function(t){var e=!1,r=((t=t||{}).pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!r)if(r=document.createElement("canvas"),t.container){var m=t.container;m.appendChild(r)}else document.body.appendChild(r);var y=t.gl;y||(y=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(r,t.glOptions||{premultipliedAlpha:!0,antialias:!0}));if(!y)throw new Error("webgl not supported");var b=t.bounds||[[-10,-10,-10],[10,10,10]],x=new p,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!d}),w=h(y),M=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:"turntable"},A=t.axes||{},T=i(y,A);T.enable=!A.disable;var k=t.spikes||{},E=o(y,k),S=[],L=[],C=[],P=[],O=!0,R=!0,I=new Array(16),N=new Array(16),z={view:null,projection:I,model:N},R=!0,D=[y.drawingBufferWidth,y.drawingBufferHeight],F={gl:y,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:r,selection:x,camera:n(r,M),axes:T,axesPixels:null,spikes:E,bounds:b,objects:S,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:z,oncontextloss:null,mouseListener:null},j=[y.drawingBufferWidth/F.pixelRatio|0,y.drawingBufferHeight/F.pixelRatio|0];function B(){if(!e&&F.autoResize){var t=r.parentNode,n=1,i=1;t&&t!==document.body?(n=t.clientWidth,i=t.clientHeight):(n=window.innerWidth,i=window.innerHeight);var a=0|Math.ceil(n*F.pixelRatio),o=0|Math.ceil(i*F.pixelRatio);if(a!==r.width||o!==r.height){r.width=a,r.height=o;var s=r.style;s.position=s.position||"absolute",s.left="0px",s.top="0px",s.width=n+"px",s.height=i+"px",O=!0}}}F.autoResize&&B();function U(){for(var t=S.length,e=P.length,r=0;r<e;++r)C[r]=0;t:for(var r=0;r<t;++r){var n=S[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(C[a]+i<255){L[r]=a,n.setPickBase(C[a]+1),C[a]+=i;continue t}var o=s(y,D);L[r]=e,P.push(o),C.push(i),n.setPickBase(1),e+=1}else L[r]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}window.addEventListener("resize",B),F.update=function(t){e||(t=t||{},O=!0,R=!0)},F.add=function(t){e||(t.axes=T,S.push(t),L.push(-1),O=!0,R=!0,U())},F.remove=function(t){if(!e){var r=S.indexOf(t);r<0||(S.splice(r,1),L.pop(),O=!0,R=!0,U())}},F.dispose=function(){if(!e&&(e=!0,window.removeEventListener("resize",B),r.removeEventListener("webglcontextlost",q),F.mouseListener.enabled=!1,!F.contextLost)){T.dispose(),E.dispose();for(var t=0;t<S.length;++t)S[t].dispose();_.dispose();for(var t=0;t<P.length;++t)P[t].dispose();w.dispose(),y=null,T=null,E=null,S=[]}};var V=!1,H=0;function q(){if(F.contextLost)return!0;y.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.mouseListener=c(r,function(t,r,n){if(!e){var i=P.length,a=S.length,o=x.object;x.distance=1/0,x.mouse[0]=r,x.mouse[1]=n,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var s=!1;if(t&&H)V=!0;else{V&&(R=!0),V=!1;for(var l=0;l<i;++l){var u=P[l].query(r,j[1]-n-1,F.pickRadius);if(u){if(u.distance>x.distance)continue;for(var c=0;c<a;++c){var f=S[c];if(L[c]===l){var h=f.pick(u);h&&(x.buttons=t,x.screen=u.coord,x.distance=u.distance,x.object=f,x.index=h.distance,x.dataPosition=h.position,x.dataCoordinate=h.dataCoordinate,x.data=h,s=!0)}}}}}o&&o!==x.object&&(o.highlight&&o.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(s=s||x.object!==o)&&F.onselect&&F.onselect(x),1&t&&!(1&H)&&F.onclick&&F.onclick(x),H=t}}),r.addEventListener("webglcontextlost",q);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],X=[G[0].slice(),G[1].slice()];function W(){if(!q()){B();var t=F.camera.tick();z.view=F.camera.matrix,O=O||t,R=R||t,T.pixelRatio=F.pixelRatio,E.pixelRatio=F.pixelRatio;var e=S.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var i=0;i<e;++i){var o=S[i];o.pixelRatio=F.pixelRatio,o.axes=F.axes,O=O||!!o.dirty,R=R||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],c=s[1],h=0;h<3;++h)r[h]=Math.min(r[h],l[h]),n[h]=Math.max(n[h],c[h])}var d=F.bounds;if(F.autoBounds)for(var h=0;h<3;++h){if(n[h]<r[h])r[h]=-1,n[h]=1;else{r[h]===n[h]&&(r[h]-=1,n[h]+=1);var p=.05*(n[h]-r[h]);r[h]=r[h]-p,n[h]=n[h]+p}d[0][h]=r[h],d[1][h]=n[h]}for(var v=!1,h=0;h<3;++h)v=v||X[0][h]!==d[0][h]||X[1][h]!==d[1][h],X[0][h]=d[0][h],X[1][h]=d[1][h];if(R=R||v,O=O||v){if(v){for(var m=[0,0,0],i=0;i<3;++i)m[i]=g((d[1][i]-d[0][i])/10);T.autoTicks?T.update({bounds:d,tickSpacing:m}):T.update({bounds:d})}var b=y.drawingBufferWidth,M=y.drawingBufferHeight;D[0]=b,D[1]=M,j[0]=0|Math.max(b/F.pixelRatio,1),j[1]=0|Math.max(M/F.pixelRatio,1),f(I,F.fovy,b/M,F.zNear,F.zFar);for(var i=0;i<16;++i)N[i]=0;N[15]=1;for(var A=0,i=0;i<3;++i)A=Math.max(A,d[1][i]-d[0][i]);for(var i=0;i<3;++i)F.autoScale?N[5*i]=F.aspect[i]/(d[1][i]-d[0][i]):N[5*i]=1/A,F.autoCenter&&(N[12+i]=.5*-N[5*i]*(d[0][i]+d[1][i]));for(var i=0;i<e;++i){var o=S[i];o.axesBounds=d,F.clipToBounds&&(o.clipBounds=d)}x.object&&(F.snapToData?E.position=x.dataCoordinate:E.position=x.dataPosition,E.bounds=d),R&&(R=!1,function(){if(q())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST);for(var t=S.length,e=P.length,r=0;r<e;++r){var n=P[r];n.shape=j,n.begin();for(var i=0;i<t;++i)if(L[i]===r){var a=S[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(z))}n.end()}}()),F.axesPixels=a(F.axes,z,b,M),F.onrender&&F.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,b,M);var k=F.clearColor;y.clearColor(k[0],k[1],k[2],k[3]),y.clear(y.COLOR_BUFFER_BIT|y.DEPTH_BUFFER_BIT),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var C=!1;T.enable&&(C=C||T.isTransparent(),T.draw(z)),E.axes=T,x.object&&E.draw(z),y.disable(y.CULL_FACE);for(var i=0;i<e;++i){var o=S[i];o.axes=T,o.pixelRatio=F.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(z),o.isTransparent&&o.isTransparent()&&(C=!0)}if(C){_.shape=D,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),T.enable&&T.isTransparent()&&T.drawTransparent(z);for(var i=0;i<e;++i){var o=S[i];o.isOpaque&&o.isOpaque()&&o.draw(z)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),T.isTransparent()&&T.drawTransparent(z);for(var i=0;i<e;++i){var o=S[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(z)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,u(y),y.disable(y.BLEND)}O=!1;for(var i=0;i<e;++i)S[i].dirty=!1}}}return function t(){e||F.contextLost||(requestAnimationFrame(t),W())}(),F.redraw=function(){e||(O=!0,W())},F};var n=t("3d-view-controls"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),u=t("a-big-triangle"),c=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("./lib/shader"),d=t("is-mobile")();function p(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}},{"./lib/shader":139,"3d-view-controls":9,"a-big-triangle":11,"gl-axes3d":95,"gl-axes3d/properties":102,"gl-fbo":112,"gl-mat4/perspective":127,"gl-select-static":145,"gl-spikes3d":154,"is-mobile":240,"mouse-change":250}],141:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],d=r[0],p=r[1],g=r[2],v=r[3];(a=u*d+c*p+f*g+h*v)<0&&(a=-a,d=-d,p=-p,g=-g,v=-v);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*u+l*d,t[1]=s*c+l*p,t[2]=s*f+l*g,t[3]=s*h+l*v,t}},{}],142:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e){var r=i[e];r||(r=i[e]={});if(t in r)return r[t];for(var a=n(t,{textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),o=n(t,{triangles:!0,textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<a.positions.length;++l)for(var u=a.positions[l],c=0;c<2;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[o,a,s]};var i={}},{"vectorize-text":331}],143:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   || \n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n    \n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n    \n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   ||\n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = interpColor * opacity;\n  }\n}\n"]),u=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\n  }\n}"]),c=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},g={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};function m(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return m(t,f)},r.createOrtho=function(t){return m(t,h)},r.createProject=function(t){return m(t,d)},r.createPickPerspective=function(t){return m(t,p)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{"gl-shader":146,glslify:230}],144:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("typedarray-pool"),o=t("gl-mat4/multiply"),s=t("./lib/shaders"),l=t("./lib/glyphs"),u=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function c(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function f(t,e,r,n){return c(n,n),c(n,n),c(n,n)}function h(t,e){this.index=t,this.dataCoordinate=this.position=e}function d(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new h(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=s.createPerspective(e),a=s.createOrtho(e),o=s.createProject(e),l=s.createPickPerspective(e),u=s.createPickOrtho(e),c=s.createPickProject(e),f=n(e),h=n(e),p=n(e),g=n(e),v=i(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:g,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new d(e,r,a,o,f,h,p,g,v,l,u,c);return m.update(t),m};var p=d.prototype;p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},p.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var g=[0,0],v=[0,0,0],m=[0,0,0],y=[0,0,0,1],b=[0,0,0,1],x=u.slice(),_=[0,0,0],w=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function A(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function T(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function k(t,e,r,n,i){var a,s=e.axesProject,l=e.gl,c=t.uniforms,h=r.model||u,d=r.view||u,p=r.projection||u,k=e.axesBounds,E=function(t){for(var e=w,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);a=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],g[0]=2/l.drawingBufferWidth,g[1]=2/l.drawingBufferHeight,t.bind(),c.view=d,c.projection=p,c.screenSize=g,c.highlightId=e.highlightId,c.highlightScale=e.highlightScale,c.clipBounds=E,c.pickGroup=e.pickId/255,c.pixelRatio=e.pixelRatio;for(var 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s=t.uniforms;s.model=n.model||u,s.view=n.view||u,s.projection=n.projection||u,g[0]=2/o.drawingBufferWidth,g[1]=2/o.drawingBufferHeight,s.screenSize=g,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=E,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}k(e,r,n,i),r.vao.unbind()}p.draw=function(t){S(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},p.drawTransparent=function(t){S(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},p.drawPick=function(t){S(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var 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S(t,e,r,n,i,a,o,l,u,c,h,d,p,g){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=d,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new k([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=p,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=S.prototype;L.isTransparent=function(){return this.opacity<1},L.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[M.slice(),M.slice(),M.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],d(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var R={model:M,view:M,projection:M,inverseModel:M.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=M.slice(),N=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=R;n.model=t.model||M,n.view=t.view||M,n.projection=t.projection||M,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=p(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=N,n.vertexColor=this.vertexColor;var s=I;for(d(s,n.view,n.model),d(s,n.projection,s),p(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=T[i],r.lineWidth(this.contourWidth[i]),o=0;o<this.contourLevels[i].length;++o)this._contourCounts[i][o]&&(o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=T[o],r.lineWidth(this.contourWidth[o]);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g])}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=T[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return z.call(this,t,!1)},L.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:M,view:M,projection:M,inverseModel:M,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function j(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function B(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function U(t){if(Array.isArray(t)){if(Array.isArray(t))return[B(t[0]),B(t[1]),B(t[2])];var e=B(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||M,r.view=t.view||M,r.projection=t.projection||M,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=N;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),s.uniforms.permutation=T[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=T[a],e.lineWidth(this.contourWidth[a]);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var d=i+c,p=s+h,v=f*(h?l:1-l),m=0;m<3;++m)u[m]+=this._field[m].get(d,p)*v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=g.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;m<3;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},L.update=function(t){t=t||{},this.dirty=!0,"contourWidth"in t&&(this.contourWidth=j(t.contourWidth,Number)),"showContour"in t&&(this.showContour=j(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=j(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=U(t.contourColor)),"contourProject"in t&&(this.contourProject=j(t.contourProject,function(t){return j(t,Boolean)})),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=U(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=j(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=j(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),F(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var d=t.coords;if(!Array.isArray(d)||3!==d.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var p=d[o];for(x=0;x<2;++x)if(p.shape[x]!==a[x])throw new Error("gl-surface: coords have incorrect shape");F(this._field[o],p)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)||2!==g.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var m=g[o];if((Array.isArray(m)||m.length)&&(m=f(m)),m.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var y=f(m.data,a);y.stride[o]=m.stride[0],y.stride[1^o]=0,F(this._field[o],y)}}else{for(o=0;o<2;++o){var b=[0,0];b[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],b,0)}this._field[0].set(0,0,0);for(var x=0;x<a[0];++x)this._field[0].set(x+1,0,x);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),x=0;x<a[1];++x)this._field[1].set(0,x+1,x);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=f(s.mallocFloat(3*_[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)v(w.pick(o),_[o],"mirror");var M=f(s.mallocFloat(3*_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(x=0;x<a[1]+2;++x){var T=w.get(0,o,x,0),k=w.get(0,o,x,1),S=w.get(1,o,x,0),L=w.get(1,o,x,1),C=w.get(2,o,x,0),P=w.get(2,o,x,1),O=S*P-L*C,R=C*k-P*T,I=T*L-k*S,N=Math.sqrt(O*O+R*R+I*I);N<1e-8?(N=Math.max(Math.abs(O),Math.abs(R),Math.abs(I)))<1e-8?(I=1,R=O=0,N=1):N=1/N:N=1/Math.sqrt(N),M.set(o,x,0,O*N),M.set(o,x,1,R*N),M.set(o,x,2,I*N)}s.free(w.data);var z=[1/0,1/0,1/0],D=[-1/0,-1/0,-1/0],B=1/0,V=-1/0,H=(a[0]-1)*(a[1]-1)*6,q=s.mallocFloat(n.nextPow2(10*H)),G=0,X=0;for(o=0;o<a[0]-1;++o)t:for(x=0;x<a[1]-1;++x){for(var W=0;W<2;++W)for(var Y=0;Y<2;++Y)for(var Z=0;Z<3;++Z){var Q=this._field[Z].get(1+o+W,1+x+Y);if(isNaN(Q)||!isFinite(Q))continue t}for(Z=0;Z<6;++Z){var J=o+A[Z][0],K=x+A[Z][1],$=this._field[0].get(J+1,K+1),tt=this._field[1].get(J+1,K+1),et=Q=this._field[2].get(J+1,K+1);O=M.get(J+1,K+1,0),R=M.get(J+1,K+1,1),I=M.get(J+1,K+1,2),t.intensity&&(et=t.intensity.get(J,K)),q[G++]=J,q[G++]=K,q[G++]=$,q[G++]=tt,q[G++]=Q,q[G++]=0,q[G++]=et,q[G++]=O,q[G++]=R,q[G++]=I,z[0]=Math.min(z[0],$),z[1]=Math.min(z[1],tt),z[2]=Math.min(z[2],Q),B=Math.min(B,et),D[0]=Math.max(D[0],$),D[1]=Math.max(D[1],tt),D[2]=Math.max(D[2],Q),V=Math.max(V,et),X+=1}}for(t.intensityBounds&&(B=+t.intensityBounds[0],V=+t.intensityBounds[1]),o=6;o<G;o+=10)q[o]=(q[o]-B)/(V-B);this._vertexCount=X,this._coordinateBuffer.update(q.subarray(0,G)),s.freeFloat(q),s.free(M.data),this.bounds=[z,D],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===B&&this.intensityBounds[1]===V||(r=!0),this.intensityBounds=[B,V]}if("levels"in t){var rt=t.levels;for(rt=Array.isArray(rt[0])?rt.slice():[[],[],rt],o=0;o<3;++o)rt[o]=rt[o].slice(),rt.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if(rt[o].length!==this.contourLevels[o].length){r=!0;break}for(x=0;x<rt[o].length;++x)if(rt[o][x]!==this.contourLevels[o][x]){r=!0;break t}}this.contourLevels=rt}if(r){_=this._field,a=this.shape;for(var nt=[],it=0;it<3;++it){rt=this.contourLevels[it];var at=[],ot=[],st=[0,0,0];for(o=0;o<rt.length;++o){var lt=h(this._field[it],rt[o]);at.push(nt.length/5|0),X=0;t:for(x=0;x<lt.cells.length;++x){var ut=lt.cells[x];for(Z=0;Z<2;++Z){var ct=lt.positions[ut[Z]],ft=ct[0],ht=0|Math.floor(ft),dt=ft-ht,pt=ct[1],gt=0|Math.floor(pt),vt=pt-gt,mt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var bt=(it+yt+1)%3;for(W=0;W<2;++W){var xt=W?dt:1-dt;for(J=0|Math.min(Math.max(ht+W,0),a[0]),Y=0;Y<2;++Y){var _t=Y?vt:1-vt;if(K=0|Math.min(Math.max(gt+Y,0),a[1]),Q=yt<2?this._field[bt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Q)||isNaN(Q)){mt=!0;break e}var wt=xt*_t;st[yt]+=wt*Q}}}if(mt){if(Z>0){for(var Mt=0;Mt<5;++Mt)nt.pop();X-=1}continue t}nt.push(st[0],st[1],ct[0],ct[1],st[2]),X+=1}}ot.push(X)}this._contourOffsets[it]=at,this._contourCounts[it]=ot}var At=s.mallocFloat(nt.length);for(o=0;o<nt.length;++o)At[o]=nt[o];this._contourBuffer.update(At),s.freeFloat(At)}t.colormap&&this._colorMap.setPixels(function(t){var e=c([l({colormap:t,nshades:E,format:"rgba"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return u.divseq(e,255),e}(t.colormap))},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],d=this._field[u],p=(this.intensity,h(c,r[o])),g=p.cells,v=p.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var m=g[e],y=0;y<2;++y){var b=v[m[y]],x=+b[0],_=0|x,w=0|Math.min(_+1,i[0]),M=x-_,A=1-M,T=+b[1],k=0|T,E=0|Math.min(k+1,i[1]),S=T-k,L=1-S,C=A*L,P=A*S,O=M*L,R=M*S,I=C*f.get(_,k)+P*f.get(_,E)+O*f.get(w,k)+R*f.get(w,E),N=C*d.get(_,k)+P*d.get(_,E)+O*d.get(w,k)+R*d.get(w,E);if(isNaN(I)||isNaN(N)){y&&(n-=1);break}a[2*n+0]=I,a[2*n+1]=N,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":155,"binary-search-bounds":35,"bit-twiddle":36,colormap:67,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-texture2d":157,"gl-vao":161,ndarray:265,"ndarray-gradient":256,"ndarray-ops":259,"ndarray-pack":260,"surface-nets":321,"typedarray-pool":328}],157:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=p(o,e.stride.slice()),u=0;"float32"===r?u=t.FLOAT:"float64"===r?(u=t.FLOAT,l=!1,r="float32"):"uint8"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r="uint8");var f,d,v=0;if(2===o.length)v=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])v=t.ALPHA;else if(2===o[2])v=t.LUMINANCE_ALPHA;else if(3===o[2])v=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");v=t.RGBA}}u!==t.FLOAT||t.getExtension("OES_texture_float")||(u=t.UNSIGNED_BYTE,l=!1);var m=e.size;if(l)f=0===e.offset&&e.data.length===m?e.data:e.data.subarray(e.offset,e.offset+m);else{var y=[o[2],o[2]*o[0],1];d=a.malloc(m,r);var b=n(d,o,y,0);"float32"!==r&&"float64"!==r||u!==t.UNSIGNED_BYTE?i.assign(b,e):c(b,e),f=d.subarray(0,m)}var x=g(t);t.texImage2D(t.TEXTURE_2D,0,v,o[0],o[1],0,v,u,f),l||a.free(d);return new h(t,x,o[0],o[1],v,u)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=h.prototype;function p(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension("EXT_texture_filter_anisotropic");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error("gl-texture2d: Must specify wrap mode for rows and columns");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error("gl-texture2d: Invalid texture shape")}else t=[0|t,0|t];return 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l=u(t)?t:t.raw;if(l){this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l)}else{if(!(t.shape&&t.stride&&t.data))throw new Error("gl-texture2d: Unsupported data type");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error("gl-texture2d: Texture dimensions are out of bounds");!function(t,e,r,o,s,l,u,f){var h=f.dtype,d=f.shape.slice();if(d.length<2||d.length>3)throw new Error("gl-texture2d: Invalid ndarray, must be 2d or 3d");var g=0,v=0,m=p(d,f.stride.slice());"float32"===h?g=t.FLOAT:"float64"===h?(g=t.FLOAT,m=!1,h="float32"):"uint8"===h?g=t.UNSIGNED_BYTE:(g=t.UNSIGNED_BYTE,m=!1,h="uint8");if(2===d.length)v=t.LUMINANCE,d=[d[0],d[1],1],f=n(f.data,d,[f.stride[0],f.stride[1],1],f.offset);else{if(3!==d.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===d[2])v=t.ALPHA;else if(2===d[2])v=t.LUMINANCE_ALPHA;else if(3===d[2])v=t.RGB;else{if(4!==d[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");v=t.RGBA}d[2]}v!==t.LUMINANCE&&v!==t.ALPHA||s!==t.LUMINANCE&&s!==t.ALPHA||(v=s);if(v!==s)throw new Error("gl-texture2d: Incompatible texture format for setPixels");var y=f.size,b=u.indexOf(o)<0;b&&u.push(o);if(g===l&&m)0===f.offset&&f.data.length===y?b?t.texImage2D(t.TEXTURE_2D,o,s,d[0],d[1],0,s,l,f.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,d[0],d[1],s,l,f.data):b?t.texImage2D(t.TEXTURE_2D,o,s,d[0],d[1],0,s,l,f.data.subarray(f.offset,f.offset+y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,d[0],d[1],s,l,f.data.subarray(f.offset,f.offset+y));else{var x;x=l===t.FLOAT?a.mallocFloat32(y):a.mallocUint8(y);var _=n(x,d,[d[2],d[2]*d[0],1]);g===t.FLOAT&&l===t.UNSIGNED_BYTE?c(_,f):i.assign(_,f),b?t.texImage2D(t.TEXTURE_2D,o,s,d[0],d[1],0,s,l,x.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,d[0],d[1],s,l,x.subarray(0,y)),l===t.FLOAT?a.freeFloat32(x):a.freeUint8(x)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},{ndarray:265,"ndarray-ops":259,"typedarray-pool":328}],158:[function(t,e,r){"use strict";e.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error("gl-vao: Too many vertex attributes");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,u=!!a.normalized,c=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,u,c,f)}else{if("number"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else if(2===a.length)t.vertexAttrib2f(i,a[0],a[1]);else if(3===a.length)t.vertexAttrib3f(i,a[0],a[1],a[2]);else{if(4!==a.length)throw new Error("gl-vao: Invalid vertex attribute");t.vertexAttrib4f(i,a[0],a[1],a[2],a[3])}t.disableVertexAttribArray(i)}}for(;i<n;++i)t.disableVertexAttribArray(i)}else for(t.bindBuffer(t.ARRAY_BUFFER,null),i=0;i<n;++i)t.disableVertexAttribArray(i)}},{}],159:[function(t,e,r){"use strict";var n=t("./do-bind.js");function i(t){this.gl=t,this._elements=null,this._attributes=null,this._elementsType=t.UNSIGNED_SHORT}i.prototype.bind=function(){n(this.gl,this._elements,this._attributes)},i.prototype.update=function(t,e,r){this._elements=e,this._attributes=t,this._elementsType=r||this.gl.UNSIGNED_SHORT},i.prototype.dispose=function(){},i.prototype.unbind=function(){},i.prototype.draw=function(t,e,r){r=r||0;var n=this.gl;this._elements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t){return new i(t)}},{"./do-bind.js":158}],160:[function(t,e,r){"use strict";var n=t("./do-bind.js");function i(t,e,r,n,i,a){this.location=t,this.dimension=e,this.a=r,this.b=n,this.c=i,this.d=a}function a(t,e,r){this.gl=t,this._ext=e,this.handle=r,this._attribs=[],this._useElements=!1,this._elementsType=t.UNSIGNED_SHORT}i.prototype.bind=function(t){switch(this.dimension){case 1:t.vertexAttrib1f(this.location,this.a);break;case 2:t.vertexAttrib2f(this.location,this.a,this.b);break;case 3:t.vertexAttrib3f(this.location,this.a,this.b,this.c);break;case 4:t.vertexAttrib4f(this.location,this.a,this.b,this.c,this.d)}},a.prototype.bind=function(){this._ext.bindVertexArrayOES(this.handle);for(var t=0;t<this._attribs.length;++t)this._attribs[t].bind(this.gl)},a.prototype.unbind=function(){this._ext.bindVertexArrayOES(null)},a.prototype.dispose=function(){this._ext.deleteVertexArrayOES(this.handle)},a.prototype.update=function(t,e,r){if(this.bind(),n(this.gl,e,t),this.unbind(),this._attribs.length=0,t)for(var a=0;a<t.length;++a){var o=t[a];"number"==typeof o?this._attribs.push(new i(a,1,o)):Array.isArray(o)&&this._attribs.push(new i(a,o.length,o[0],o[1],o[2],o[3]))}this._useElements=!!e,this._elementsType=r||this.gl.UNSIGNED_SHORT},a.prototype.draw=function(t,e,r){r=r||0;var n=this.gl;this._useElements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t,e){return new a(t,e,e.createVertexArrayOES())}},{"./do-bind.js":158}],161:[function(t,e,r){"use strict";var n=t("./lib/vao-native.js"),i=t("./lib/vao-emulated.js");function a(t){this.bindVertexArrayOES=t.bindVertexArray.bind(t),this.createVertexArrayOES=t.createVertexArray.bind(t),this.deleteVertexArrayOES=t.deleteVertexArray.bind(t)}e.exports=function(t,e,r,o){var s,l=t.createVertexArray?new a(t):t.getExtension("OES_vertex_array_object");return(s=l?n(t,l):i(t)).update(e,r,o),s}},{"./lib/vao-emulated.js":159,"./lib/vao-native.js":160}],162:[function(t,e,r){e.exports=function(t,e,r){return 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r,a=i(t,e),o=a.on,s=a.off.concat(a.explicitOff),l={};if(!o.length&&!s.length)return;for(r=0;r<o.length;r++)l["annotations["+o[r]+"].visible"]=!0;for(r=0;r<s.length;r++)l["annotations["+s[r]+"].visible"]=!1;return n.call("update",t,{},l)}}},{"../../registry":577}],345:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../color");e.exports=function(t,e,r,a){a("opacity");var o=a("bgcolor"),s=a("bordercolor"),l=i.opacity(s);a("borderpad");var u=a("borderwidth"),c=a("showarrow");if(a("text",c?" 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l,u,c=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<c.length;h++)l=c[h],u="annotations["+h+"].",l[f+"ref"]===e._id&&d(f),l["a"+f+"ref"]===e._id&&d("a"+f);function d(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(u+t,s)}}},{"../../lib/to_log_range":506,"fast-isnumeric":90}],347:[function(t,e,r){"use strict";var n=t("../../plots/array_container_defaults"),i=t("./annotation_defaults");e.exports=function(t,e){n(t,e,{name:"annotations",handleItemDefaults:i})}},{"../../plots/array_container_defaults":521,"./annotation_defaults":340}],348:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=t("../../plots/cartesian/axes"),l=t("../color"),u=t("../drawing"),c=t("../fx"),f=t("../../lib/svg_text_utils"),h=t("../../lib/setcursor"),d=t("../dragelement"),p=t("./draw_arrow_head");function g(t,e){var r=t._fullLayout.annotations[e]||{};v(t,r,e,!1,s.getFromId(t,r.xref),s.getFromId(t,r.yref))}function v(t,e,r,a,s,g){var v,m,y=t._fullLayout,b=t._fullLayout._size,x=t._context.edits;a?(v="annotation-"+a,m=a+".annotations["+r+"]"):(v="annotation",m="annotations["+r+"]"),y._infolayer.selectAll("."+v+'[data-index="'+r+'"]').remove();var _="clip"+y._uid+"_ann"+r;if(e._input&&!1!==e.visible){var w={x:{},y:{}},M=+e.textangle||0,A=y._infolayer.append("g").classed(v,!0).attr("data-index",String(r)).style("opacity",e.opacity),T=A.append("g").classed("annotation-text-g",!0),k=x[e.showarrow?"annotationTail":"annotationPosition"],E=e.captureevents||x.annotationText||k,S=T.append("g").style("pointer-events",E?"all":null).call(h,"pointer").on("click",function(){t._dragging=!1;var i={index:r,annotation:e._input,fullAnnotation:e,event:n.event};a&&(i.subplotId=a),t.emit("plotly_clickannotation",i)});e.hovertext&&S.on("mouseover",function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();c.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:y._hoverlayer.node(),outerContainer:y._paper.node(),gd:t})}).on("mouseout",function(){c.loneUnhover(y._hoverlayer.node())});var L=e.borderwidth,C=e.borderpad,P=L+C,O=S.append("rect").attr("class","bg").style("stroke-width",L+"px").call(l.stroke,e.bordercolor).call(l.fill,e.bgcolor),R=e.width||e.height,I=y._topclips.selectAll("#"+_).data(R?[0]:[]);I.enter().append("clipPath").classed("annclip",!0).attr("id",_).append("rect"),I.exit().remove();var N=e.font,z=S.append("text").classed("annotation-text",!0).text(e.text);x.annotationText?z.call(f.makeEditable,{delegate:S,gd:t}).call(D).on("edit",function(r){e.text=r,this.call(D);var n={};n[m+".text"]=e.text,s&&s.autorange&&(n[s._name+".autorange"]=!0),g&&g.autorange&&(n[g._name+".autorange"]=!0),i.call("relayout",t,n)}):z.call(D)}else n.selectAll("#"+_).remove();function D(r){return r.call(u.font,N).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),f.convertToTspans(r,t,F),r}function F(){var r=z.selectAll("a");1===r.size()&&r.text()===z.text()&&S.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(O.node());var n=S.select(".annotation-text-math-group"),c=!n.empty(),v=u.bBox((c?n:z).node()),E=v.width,C=v.height,N=e.width||E,D=e.height||C,F=Math.round(N+2*P),j=Math.round(D+2*P);function B(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}e._w=N,e._h=D;for(var U=!1,V=["x","y"],H=0;H<V.length;H++){var q,G,X,W,Y,Z=V[H],Q=e[Z+"ref"]||Z,J=e["a"+Z+"ref"],K={x:s,y:g}[Z],$=(M+("x"===Z?0:-90))*Math.PI/180,tt=F*Math.cos($),et=j*Math.sin($),rt=Math.abs(tt)+Math.abs(et),nt=e[Z+"anchor"],it=e[Z+"shift"]*("x"===Z?1:-1),at=w[Z];if(K){var ot=K.r2fraction(e[Z]);if((t._dragging||!K.autorange)&&(ot<0||ot>1)&&(J===Q?((ot=K.r2fraction(e["a"+Z]))<0||ot>1)&&(U=!0):U=!0,U))continue;q=K._offset+K.r2p(e[Z]),W=.5}else"x"===Z?(X=e[Z],q=b.l+b.w*X):(X=1-e[Z],q=b.t+b.h*X),W=e.showarrow?.5:X;if(e.showarrow){at.head=q;var st=e["a"+Z];Y=tt*B(.5,e.xanchor)-et*B(.5,e.yanchor),J===Q?(at.tail=K._offset+K.r2p(st),G=Y):(at.tail=q+st,G=Y+st),at.text=at.tail+Y;var lt=y["x"===Z?"width":"height"];if("paper"===Q&&(at.head=o.constrain(at.head,1,lt-1)),"pixel"===J){var ut=-Math.max(at.tail-3,at.text),ct=Math.min(at.tail+3,at.text)-lt;ut>0?(at.tail+=ut,at.text+=ut):ct>0&&(at.tail-=ct,at.text-=ct)}at.tail+=it,at.head+=it}else G=Y=rt*B(W,nt),at.text=q+Y;at.text+=it,Y+=it,G+=it,e["_"+Z+"padplus"]=rt/2+G,e["_"+Z+"padminus"]=rt/2-G,e["_"+Z+"size"]=rt,e["_"+Z+"shift"]=Y}if(U)S.remove();else{var ft=0,ht=0;if("left"!==e.align&&(ft=(N-E)*("center"===e.align?.5:1)),"top"!==e.valign&&(ht=(D-C)*("middle"===e.valign?.5:1)),c)n.select("svg").attr({x:P+ft-1,y:P+ht}).call(u.setClipUrl,R?_:null);else{var dt=P+ht-v.top,pt=P+ft-v.left;z.call(f.positionText,pt,dt).call(u.setClipUrl,R?_:null)}I.select("rect").call(u.setRect,P,P,N,D),O.call(u.setRect,L/2,L/2,F-L,j-L),S.call(u.setTranslate,Math.round(w.x.text-F/2),Math.round(w.y.text-j/2)),T.attr({transform:"rotate("+M+","+w.x.text+","+w.y.text+")"});var gt,vt,mt=function(r,n){A.selectAll(".annotation-arrow-g").remove();var c=w.x.head,f=w.y.head,h=w.x.tail+r,v=w.y.tail+n,y=w.x.text+r,_=w.y.text+n,k=o.rotationXYMatrix(M,y,_),E=o.apply2DTransform(k),L=o.apply2DTransform2(k),C=+O.attr("width"),P=+O.attr("height"),R=y-.5*C,I=R+C,N=_-.5*P,z=N+P,D=[[R,N,R,z],[R,z,I,z],[I,z,I,N],[I,N,R,N]].map(L);if(!D.reduce(function(t,e){return t^!!o.segmentsIntersect(c,f,c+1e6,f+1e6,e[0],e[1],e[2],e[3])},!1)){D.forEach(function(t){var e=o.segmentsIntersect(h,v,c,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,v=e.y)});var F=e.arrowwidth,j=e.arrowcolor,B=e.arrowside,U=A.append("g").style({opacity:l.opacity(j)}).classed("annotation-arrow-g",!0),V=U.append("path").attr("d","M"+h+","+v+"L"+c+","+f).style("stroke-width",F+"px").call(l.stroke,l.rgb(j));if(p(V,B,e),x.annotationPosition&&V.node().parentNode&&!a){var H=c,q=f;if(e.standoff){var G=Math.sqrt(Math.pow(c-h,2)+Math.pow(f-v,2));H+=e.standoff*(h-c)/G,q+=e.standoff*(v-f)/G}var X,W,Y,Z=U.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-H)+","+(v-q),transform:"translate("+H+","+q+")"}).style("stroke-width",F+6+"px").call(l.stroke,"rgba(0,0,0,0)").call(l.fill,"rgba(0,0,0,0)");d.init({element:Z.node(),gd:t,prepFn:function(){var t=u.getTranslate(S);W=t.x,Y=t.y,X={},s&&s.autorange&&(X[s._name+".autorange"]=!0),g&&g.autorange&&(X[g._name+".autorange"]=!0)},moveFn:function(t,r){var n=E(W,Y),i=n[0]+t,a=n[1]+r;S.call(u.setTranslate,i,a),X[m+".x"]=s?s.p2r(s.r2p(e.x)+t):e.x+t/b.w,X[m+".y"]=g?g.p2r(g.r2p(e.y)+r):e.y-r/b.h,e.axref===e.xref&&(X[m+".ax"]=s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&(X[m+".ay"]=g.p2r(g.r2p(e.ay)+r)),U.attr("transform","translate("+t+","+r+")"),T.attr({transform:"rotate("+M+","+i+","+a+")"})},doneFn:function(){i.call("relayout",t,X);var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&mt(0,0),k)d.init({element:S.node(),gd:t,prepFn:function(){vt=T.attr("transform"),gt={}},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?gt[m+".ax"]=s.p2r(s.r2p(e.ax)+t):gt[m+".ax"]=e.ax+t,e.ayref===e.yref?gt[m+".ay"]=g.p2r(g.r2p(e.ay)+r):gt[m+".ay"]=e.ay+r,mt(t,r);else{if(a)return;if(s)gt[m+".x"]=s.p2r(s.r2p(e.x)+t);else{var i=e._xsize/b.w,o=e.x+(e._xshift-e.xshift)/b.w-i/2;gt[m+".x"]=d.align(o+t/b.w,i,0,1,e.xanchor)}if(g)gt[m+".y"]=g.p2r(g.r2p(e.y)+r);else{var l=e._ysize/b.h,u=e.y-(e._yshift+e.yshift)/b.h-l/2;gt[m+".y"]=d.align(u-r/b.h,l,0,1,e.yanchor)}s&&g||(n=d.getCursor(s?.5:gt[m+".x"],g?.5:gt[m+".y"],e.xanchor,e.yanchor))}T.attr({transform:"translate("+t+","+r+")"+vt}),h(S,n)},doneFn:function(){h(S),i.call("relayout",t,gt);var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&g(t,r);return a.previousPromises(t)},drawOne:g,drawRaw:v}},{"../../lib":481,"../../lib/setcursor":500,"../../lib/svg_text_utils":504,"../../plots/cartesian/axes":525,"../../plots/plots":568,"../../registry":577,"../color":357,"../dragelement":379,"../drawing":382,"../fx":399,"./draw_arrow_head":349,d3:80}],349:[function(t,e,r){"use strict";var n=t("d3"),i=t("../color"),a=t("./arrow_paths");e.exports=function(t,e,r){var o,s,l,u,c=t.node(),f=a[r.arrowhead||0],h=a[r.startarrowhead||0],d=(r.arrowwidth||1)*(r.arrowsize||1),p=(r.arrowwidth||1)*(r.startarrowsize||1),g=e.indexOf("start")>=0,v=e.indexOf("end")>=0,m=f.backoff*d+r.standoff,y=h.backoff*p+r.startstandoff;if("line"===c.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},s={x:+t.attr("x2"),y:+t.attr("y2")};var b=o.x-s.x,x=o.y-s.y;if(u=(l=Math.atan2(x,b))+Math.PI,m&&y&&m+y>Math.sqrt(b*b+x*x))return void P();if(m){if(m*m>b*b+x*x)return void P();var _=m*Math.cos(l),w=m*Math.sin(l);s.x+=_,s.y+=w,t.attr({x2:s.x,y2:s.y})}if(y){if(y*y>b*b+x*x)return void P();var M=y*Math.cos(l),A=y*Math.sin(l);o.x-=M,o.y-=A,t.attr({x1:o.x,y1:o.y})}}else if("path"===c.nodeName){var T=c.getTotalLength(),k="";if(T<m+y)return void P();var E=c.getPointAtLength(0),S=c.getPointAtLength(.1);l=Math.atan2(E.y-S.y,E.x-S.x),o=c.getPointAtLength(Math.min(y,T)),k="0px,"+y+"px,";var L=c.getPointAtLength(T),C=c.getPointAtLength(T-.1);u=Math.atan2(L.y-C.y,L.x-C.x),s=c.getPointAtLength(Math.max(0,T-m)),k+=T-(k?y+m:m)+"px,"+T+"px",t.style("stroke-dasharray",k)}function P(){t.style("stroke-dasharray","0px,100px")}function O(e,a,o,s){e.path&&(e.noRotate&&(o=0),n.select(c.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:"translate("+a.x+","+a.y+")"+(o?"rotate("+180*o/Math.PI+")":"")+"scale("+s+")"}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}g&&O(h,o,l,p),v&&O(f,s,u,d)}},{"../color":357,"./arrow_paths":341,d3:80}],350:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":535,"./attributes":342,"./calc_autorange":343,"./click":344,"./convert_coords":346,"./defaults":347,"./draw":348}],351:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll;e.exports=i({_isLinkedToArray:"annotation",visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents},"calc","from-root")},{"../../plot_api/edit_types":510,"../annotations/attributes":342}],352:[function(t,e,r){"use strict";var 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i=e(t[n]);i<=r.distance&&(r.index=n,r.distance=i)}return r},r.inbox=function(t,e,r){return t*e<0||0===t?r:1/0},r.quadrature=function(t,e){return function(r){var n=t(r),i=e(r);return Math.sqrt(n*n+i*i)}},r.makeEventData=function(t,e,n){var i="index"in t?t.index:t.pointNumber,a={data:e._input,fullData:e,curveNumber:e.index,pointNumber:i};if(e._indexToPoints){var o=e._indexToPoints[i];1===o.length?a.pointIndex=o[0]:a.pointIndices=o}else a.pointIndex=i;return e._module.eventData?a=e._module.eventData(a,t,e,n,i):("xVal"in t?a.x=t.xVal:"x"in t&&(a.x=t.x),"yVal"in t?a.y=t.yVal:"y"in t&&(a.y=t.y),t.xa&&(a.xaxis=t.xa),t.ya&&(a.yaxis=t.ya),void 0!==t.zLabelVal&&(a.z=t.zLabelVal)),r.appendArrayPointValue(a,e,i),a},r.appendArrayPointValue=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],u=a(l);if(void 0===t[u]){var c=o(n.nestedProperty(e,l).get(),r);void 0!==c&&(t[u]=c)}}},r.appendArrayMultiPointValues=function(t,e,r){var i=e._arrayAttrs;if(i)for(var 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E,S,L=a.readability(k.color,h)<1.5?f.contrast(h):k.color,C=r.spikemode,P=r.spikethickness,O=r.spikecolor||L,R=r._boundingBox,I=(R.top+R.bottom)/2<T?R.bottom:R.top;-1===C.indexOf("toaxis")&&-1===C.indexOf("across")||(-1!==C.indexOf("toaxis")&&(E=I,S=T),-1!==C.indexOf("across")&&(E=r._counterSpan[0],S=r._counterSpan[1]),i.insert("line",":first-child").attr({x1:A,x2:A,y1:E,y2:S,"stroke-width":P,stroke:O,"stroke-dasharray":c.dashStyle(r.spikedash,P)}).classed("spikeline",!0).classed("crisp",!0),i.insert("line",":first-child").attr({x1:A,x2:A,y1:E,y2:S,"stroke-width":P+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0)),-1!==C.indexOf("marker")&&i.insert("circle",":first-child").attr({cx:A,cy:I-("top"!==r.side?P:-P),r:P,fill:O}).classed("spikeline",!0)}}}function S(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}r.hover=function(t,e,r,a){t=o.getGraphDiv(t),o.throttle(t._fullLayout._uid+v.HOVERID,v.HOVERMINTIME,function(){!function(t,e,r,a){r||(r="xy");var l=Array.isArray(r)?r:[r],c=t._fullLayout,v=c._plots||[],m=v[r],y=c._has("cartesian");if(m){var x=m.overlays.map(function(t){return t.id});l=l.concat(x)}for(var _=l.length,w=new Array(_),M=new Array(_),L=!1,C=0;C<_;C++){var P=l[C],O=v[P];if(O)L=!0,w[C]=d.getFromId(t,O.xaxis._id),M[C]=d.getFromId(t,O.yaxis._id);else{var R=c[P]._subplot;w[C]=R.xaxis,M[C]=R.yaxis}}var I=e.hovermode||c.hovermode;I&&!L&&(I="closest");if(-1===["x","y","closest"].indexOf(I)||!t.calcdata||t.querySelector(".zoombox")||t._dragging)return h.unhoverRaw(t,e);var N,z,D,F,j,B,U,V,H,q,G,X,W,Y=-1===c.hoverdistance?1/0:c.hoverdistance,Z=-1===c.spikedistance?1/0:c.spikedistance,Q=[],J=[],K={hLinePoint:null,vLinePoint:null};if(Array.isArray(e))for(I="array",D=0;D<e.length;D++)"skip"!==(j=t.calcdata[e[D].curveNumber||0])[0].trace.hoverinfo&&J.push(j);else{for(F=0;F<t.calcdata.length;F++)j=t.calcdata[F],"skip"!==(B=j[0].trace).hoverinfo&&g.isTraceInSubplots(B,l)&&J.push(j);var $,tt,et=!e.target;if(et)$="xpx"in e?e.xpx:w[0]._length/2,tt="ypx"in e?e.ypx:M[0]._length/2;else{if(!1===s.triggerHandler(t,"plotly_beforehover",e))return;var rt=e.target.getBoundingClientRect();if($=e.clientX-rt.left,tt=e.clientY-rt.top,$<0||$>w[0]._length||tt<0||tt>M[0]._length)return h.unhoverRaw(t,e)}if(e.pointerX=$+w[0]._offset,e.pointerY=tt+M[0]._offset,N="xval"in e?g.flat(l,e.xval):g.p2c(w,$),z="yval"in e?g.flat(l,e.yval):g.p2c(M,tt),!i(N[0])||!i(z[0]))return o.warn("Fx.hover failed",e,t),h.unhoverRaw(t,e)}var 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n=t("../../lib");e.exports=function(t,e,r,i){r("hoverlabel.bgcolor",(i=i||{}).bgcolor),r("hoverlabel.bordercolor",i.bordercolor),r("hoverlabel.namelength",i.namelength),n.coerceFont(r,"hoverlabel.font",i.font)}},{"../../lib":481}],399:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",function(r){return 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b=s(e.yanchor);h=b-e.y0,d=b-e.y1}else h=s(e.y0),d=s(e.y1);if("line"===p)return"M"+l+","+h+"L"+u+","+d;if("rect"===p)return"M"+l+","+h+"H"+u+"V"+d+"H"+l+"Z";var x=(l+u)/2,_=(h+d)/2,w=Math.abs(x-l),M=Math.abs(_-h),A="A"+w+","+M,T=x+w+","+_;return"M"+T+A+" 0 1,1 "+(x+","+(_-M))+A+" 0 0,1 "+T+"Z"}function g(t,e,r){return t.replace(c.segmentRE,function(t){var n=0,i=t.charAt(0),a=c.paramIsX[i],o=c.paramIsY[i],s=c.numParams[i];return i+t.substr(1).replace(c.paramRE,function(t){return n>=s?t:(a[n]?t=e(t):o[n]&&(t=r(t)),n++,t)})})}e.exports={draw:function(t){var e=t._fullLayout;for(var r in e._shapeUpperLayer.selectAll("path").remove(),e._shapeLowerLayer.selectAll("path").remove(),e._plots){var n=e._plots[r].shapelayer;n&&n.selectAll("path").remove()}for(var i=0;i<e.shapes.length;i++)e.shapes[i].visible&&h(t,i)},drawOne:h}},{"../../lib":481,"../../lib/setcursor":500,"../../plots/cartesian/axes":525,"../../registry":577,"../color":357,"../dragelement":379,"../drawing":382,"./constants":439,"./helpers":442}],442:[function(t,e,r){"use strict";var n=t("./constants"),i=t("../../lib");r.rangeToShapePosition=function(t){return"log"===t.type?t.r2d:function(t){return t}},r.shapePositionToRange=function(t){return"log"===t.type?t.d2r:function(t){return t}},r.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace("_"," ")),t(e)}},r.encodeDate=function(t){return function(e){return t(e).replace(" ","_")}},r.extractPathCoords=function(t,e){var r=[];return t.match(n.segmentRE).forEach(function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}}),r},r.getDataToPixel=function(t,e,n){var i,a=t._fullLayout._size;if(e){var o=r.shapePositionToRange(e);i=function(t){return e._offset+e.r2p(o(t,!0))},"date"===e.type&&(i=r.decodeDate(i))}else i=n?function(t){return a.t+a.h*(1-t)}:function(t){return a.l+a.w*t};return i},r.getPixelToData=function(t,e,n){var i,a=t._fullLayout._size;if(e){var o=r.rangeToShapePosition(e);i=function(t){return o(e.p2r(t-e._offset))}}else i=n?function(t){return 1-(t-a.t)/a.h}:function(t){return(t-a.l)/a.w};return i},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n}},{"../../lib":481,"./constants":439}],443:[function(t,e,r){"use strict";var n=t("./draw");e.exports={moduleType:"component",name:"shapes",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("shapes"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne}},{"../../plots/cartesian/include_components":535,"./attributes":437,"./calc_autorange":438,"./defaults":440,"./draw":441}],444:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("./attributes"),o=t("./helpers");e.exports=function(t,e,r,s,l){function u(r,i){return n.coerce(t,e,a,r,i)}if(s=s||{},!u("visible",!(l=l||{}).itemIsNotPlainObject))return e;u("layer"),u("opacity"),u("fillcolor"),u("line.color"),u("line.width"),u("line.dash");for(var c=u("type",t.path?"path":"rect"),f=u("xsizemode"),h=u("ysizemode"),d=["x","y"],p=0;p<2;p++){var g,v,m,y=d[p],b=y+"anchor",x="x"===y?f:h,_={_fullLayout:r},w=i.coerceRef(t,e,_,y,"","paper");if("paper"!==w?(g=i.getFromId(_,w),m=o.rangeToShapePosition(g),v=o.shapePositionToRange(g)):v=m=n.identity,"path"!==c){var M=y+"0",A=y+"1",T=t[M],k=t[A];t[M]=v(t[M],!0),t[A]=v(t[A],!0),"pixel"===x?(u(M,0),u(A,10)):(i.coercePosition(e,_,u,w,M,.25),i.coercePosition(e,_,u,w,A,.75)),e[M]=m(e[M]),e[A]=m(e[A]),t[M]=T,t[A]=k}if("pixel"===x){var E=t[b];t[b]=v(t[b],!0),i.coercePosition(e,_,u,w,b,.25),e[b]=m(e[b]),t[b]=E}}return"path"===c?u("path"):n.noneOrAll(t,e,["x0","x1","y0","y1"]),e}},{"../../lib":481,"../../plots/cartesian/axes":525,"./attributes":437,"./helpers":442}],445:[function(t,e,r){"use strict";var n=t("../../plots/font_attributes"),i=t("../../plots/pad_attributes"),a=t("../../lib/extend").extendDeepAll,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/animation_attributes"),l=t("./constants");e.exports=o({_isLinkedToArray:"slider",visible:{valType:"boolean",dflt:!0},active:{valType:"number",min:0,dflt:0},steps:{_isLinkedToArray:"step",method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string"},value:{valType:"string"},execute:{valType:"boolean",dflt:!0}},lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",min:-2,max:3,dflt:0},pad:a({},i,{},{t:{dflt:20}}),xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left"},y:{valType:"number",min:-2,max:3,dflt:0},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},transition:{duration:{valType:"number",min:0,dflt:150},easing:{valType:"enumerated",values:s.transition.easing.values,dflt:"cubic-in-out"}},currentvalue:{visible:{valType:"boolean",dflt:!0},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},offset:{valType:"number",dflt:10},prefix:{valType:"string"},suffix:{valType:"string"},font:n({})},font:n({}),activebgcolor:{valType:"color",dflt:l.gripBgActiveColor},bgcolor:{valType:"color",dflt:l.railBgColor},bordercolor:{valType:"color",dflt:l.railBorderColor},borderwidth:{valType:"number",min:0,dflt:l.railBorderWidth},ticklen:{valType:"number",min:0,dflt:l.tickLength},tickcolor:{valType:"color",dflt:l.tickColor},tickwidth:{valType:"number",min:0,dflt:1},minorticklen:{valType:"number",min:0,dflt:l.minorTickLength}},"arraydraw","from-root")},{"../../lib/extend":473,"../../plot_api/edit_types":510,"../../plots/animation_attributes":520,"../../plots/font_attributes":551,"../../plots/pad_attributes":567,"./constants":446}],446:[function(t,e,r){"use strict";e.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],447:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/array_container_defaults"),a=t("./attributes"),o=t("./constants").name,s=a.steps;function l(t,e,r){function i(r,i){return n.coerce(t,e,a,r,i)}var o=function(t,e){var r,i,a=t.steps||[],o=e.steps=[];function l(t,e){return n.coerce(r,i,s,t,e)}for(var u=0;u<a.length;u++)r=a[u],i={},l("method"),n.isPlainObject(r)&&("skip"===i.method||Array.isArray(r.args))&&(l("args"),l("label","step-"+u),l("value",i.label),l("execute"),o.push(i));return o}(t,e);i("visible",o.length>0)&&(i("active"),i("x"),i("y"),n.noneOrAll(t,e,["x","y"]),i("xanchor"),i("yanchor"),i("len"),i("lenmode"),i("pad.t"),i("pad.r"),i("pad.b"),i("pad.l"),n.coerceFont(i,"font",r.font),i("currentvalue.visible")&&(i("currentvalue.xanchor"),i("currentvalue.prefix"),i("currentvalue.suffix"),i("currentvalue.offset"),n.coerceFont(i,"currentvalue.font",e.font)),i("transition.duration"),i("transition.easing"),i("bgcolor"),i("activebgcolor"),i("bordercolor"),i("borderwidth"),i("ticklen"),i("tickwidth"),i("tickcolor"),i("minorticklen"))}e.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},{"../../lib":481,"../../plots/array_container_defaults":521,"./attributes":445,"./constants":446}],448:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../plots/plots"),a=t("../color"),o=t("../drawing"),s=t("../../lib"),l=t("../../lib/svg_text_utils"),u=t("../legend/anchor_utils"),c=t("./constants"),f=t("../../constants/alignment"),h=f.LINE_SPACING,d=f.FROM_TL,p=f.FROM_BR;function g(t){return t._index}function v(t,e){var r=o.tester.selectAll("g."+c.labelGroupClass).data(e.steps);r.enter().append("g").classed(c.labelGroupClass,!0);var a=0,s=0;r.each(function(t){var r=b(n.select(this),{step:t},e).node();if(r){var i=o.bBox(r);s=Math.max(s,i.height),a=Math.max(a,i.width)}}),r.remove();var f=e._dims={};f.inputAreaWidth=Math.max(c.railWidth,c.gripHeight);var h=t._fullLayout._size;f.lx=h.l+h.w*e.x,f.ly=h.t+h.h*(1-e.y),"fraction"===e.lenmode?f.outerLength=Math.round(h.w*e.len):f.outerLength=e.len,f.inputAreaStart=0,f.inputAreaLength=Math.round(f.outerLength-e.pad.l-e.pad.r);var g=(f.inputAreaLength-2*c.stepInset)/(e.steps.length-1),v=a+c.labelPadding;if(f.labelStride=Math.max(1,Math.ceil(v/g)),f.labelHeight=s,f.currentValueMaxWidth=0,f.currentValueHeight=0,f.currentValueTotalHeight=0,f.currentValueMaxLines=1,e.currentvalue.visible){var y=o.tester.append("g");r.each(function(t){var r=m(y,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},i=l.lineCount(r);f.currentValueMaxWidth=Math.max(f.currentValueMaxWidth,Math.ceil(n.width)),f.currentValueHeight=Math.max(f.currentValueHeight,Math.ceil(n.height)),f.currentValueMaxLines=Math.max(f.currentValueMaxLines,i)}),f.currentValueTotalHeight=f.currentValueHeight+e.currentvalue.offset,y.remove()}f.height=f.currentValueTotalHeight+c.tickOffset+e.ticklen+c.labelOffset+f.labelHeight+e.pad.t+e.pad.b;var x="left";u.isRightAnchor(e)&&(f.lx-=f.outerLength,x="right"),u.isCenterAnchor(e)&&(f.lx-=f.outerLength/2,x="center");var _="top";u.isBottomAnchor(e)&&(f.ly-=f.height,_="bottom"),u.isMiddleAnchor(e)&&(f.ly-=f.height/2,_="middle"),f.outerLength=Math.ceil(f.outerLength),f.height=Math.ceil(f.height),f.lx=Math.round(f.lx),f.ly=Math.round(f.ly),i.autoMargin(t,c.autoMarginIdRoot+e._index,{x:e.x,y:e.y,l:f.outerLength*d[x],r:f.outerLength*p[x],b:f.height*p[_],t:f.height*d[_]})}function m(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-c.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=c.currentValueInset,i="left"}var u=s.ensureSingle(t,"text",c.labelClass,function(t){t.classed("user-select-none",!0).attr({"text-anchor":i,"data-notex":1})}),f=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)f+=r;else f+=e.steps[e.active].label;e.currentvalue.suffix&&(f+=e.currentvalue.suffix),u.call(o.font,e.currentvalue.font).text(f).call(l.convertToTspans,e._gd);var d=l.lineCount(u),p=(a.currentValueMaxLines+1-d)*e.currentvalue.font.size*h;return l.positionText(u,n,p),u}}function y(t,e,r){s.ensureSingle(t,"rect",c.gripRectClass,function(n){n.call(M,e,t,r).style("pointer-events","all")}).attr({width:c.gripWidth,height:c.gripHeight,rx:c.gripRadius,ry:c.gripRadius}).call(a.stroke,r.bordercolor).call(a.fill,r.bgcolor).style("stroke-width",r.borderwidth+"px")}function b(t,e,r){var n=s.ensureSingle(t,"text",c.labelClass,function(t){t.classed("user-select-none",!0).attr({"text-anchor":"middle","data-notex":1})});return n.call(o.font,r.font).text(e.step.label).call(l.convertToTspans,r._gd),n}function x(t,e){var r=s.ensureSingle(t,"g",c.labelsClass),i=e._dims,a=r.selectAll("g."+c.labelGroupClass).data(i.labelSteps);a.enter().append("g").classed(c.labelGroupClass,!0),a.exit().remove(),a.each(function(t){var r=n.select(this);r.call(b,t,e),o.setTranslate(r,k(e,t.fraction),c.tickOffset+e.ticklen+e.font.size*h+c.labelOffset+i.currentValueTotalHeight)})}function _(t,e,r,n,i){var a=Math.round(n*(r.steps.length-1));a!==r.active&&w(t,e,r,a,!0,i)}function w(t,e,r,n,a,o){var s=r.active;r._input.active=r.active=n;var l=r.steps[r.active];e.call(T,r,r.active/(r.steps.length-1),o),e.call(m,r),t.emit("plotly_sliderchange",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame(function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)})))}function M(t,e,r){var i=r.node(),o=n.select(e);function s(){return r.data()[0]}t.on("mousedown",function(){var t=s();e.emit("plotly_sliderstart",{slider:t});var l=r.select("."+c.gripRectClass);n.event.stopPropagation(),n.event.preventDefault(),l.call(a.fill,t.activebgcolor);var u=E(t,n.mouse(i)[0]);_(e,r,t,u,!0),t._dragging=!0,o.on("mousemove",function(){var t=s(),a=E(t,n.mouse(i)[0]);_(e,r,t,a,!1)}),o.on("mouseup",function(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on("mouseup",null),o.on("mousemove",null),e.emit("plotly_sliderend",{slider:t,step:t.steps[t.active]})})})}function A(t,e){var r=t.selectAll("rect."+c.tickRectClass).data(e.steps),i=e._dims;r.enter().append("rect").classed(c.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+"px","shape-rendering":"crispEdges"}),r.each(function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,k(e,r/(e.steps.length-1))-.5*e.tickwidth,(s?c.tickOffset:c.minorTickOffset)+i.currentValueTotalHeight)})}function T(t,e,r,n){var i=t.select("rect."+c.gripRectClass),a=k(e,r);if(!e._invokingCommand){var o=i;n&&e.transition.duration>0&&(o=o.transition().duration(e.transition.duration).ease(e.transition.easing)),o.attr("transform","translate("+(a-.5*c.gripWidth)+","+e._dims.currentValueTotalHeight+")")}}function k(t,e){var r=t._dims;return r.inputAreaStart+c.stepInset+(r.inputAreaLength-2*c.stepInset)*Math.min(1,Math.max(0,e))}function E(t,e){var r=t._dims;return Math.min(1,Math.max(0,(e-c.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*c.stepInset-2*r.inputAreaStart)))}function S(t,e,r){var n=r._dims,i=s.ensureSingle(t,"rect",c.railTouchRectClass,function(n){n.call(M,e,t,r).style("pointer-events","all")});i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,c.tickOffset+r.ticklen+n.labelHeight)}).call(a.fill,r.bgcolor).attr("opacity",0),o.setTranslate(i,0,n.currentValueTotalHeight)}function L(t,e){var r=e._dims,n=r.inputAreaLength-2*c.railInset,i=s.ensureSingle(t,"rect",c.railRectClass);i.attr({width:n,height:c.railWidth,rx:c.railRadius,ry:c.railRadius,"shape-rendering":"crispEdges"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style("stroke-width",e.borderwidth+"px"),o.setTranslate(i,c.railInset,.5*(r.inputAreaWidth-c.railWidth)+r.currentValueTotalHeight)}e.exports=function(t){var e=t._fullLayout,r=function(t,e){for(var r=t[c.name],n=[],i=0;i<r.length;i++){var a=r[i];a.visible&&a.steps.length&&(a._gd=e,n.push(a))}return n}(e,t),a=e._infolayer.selectAll("g."+c.containerClassName).data(r.length>0?[0]:[]);if(a.enter().append("g").classed(c.containerClassName,!0).style("cursor","ew-resize"),a.exit().remove(),a.exit().size()&&function(t){for(var e=t._fullLayout._pushmargin||{},r=Object.keys(e),n=0;n<r.length;n++){var a=r[n];-1!==a.indexOf(c.autoMarginIdRoot)&&i.autoMargin(t,a)}}(t),0!==r.length){var 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n=r._dims;o.setTranslate(e,n.lx+r.pad.l,n.ly+r.pad.t),e.call(T,r,r.active/(r.steps.length-1),!1),e.call(m,r)}(t,n.select(this),e)}})}}},{"../../constants/alignment":457,"../../lib":481,"../../lib/svg_text_utils":504,"../../plots/plots":568,"../color":357,"../drawing":382,"../legend/anchor_utils":409,"./constants":446,d3:80}],449:[function(t,e,r){"use strict";var n=t("./constants");e.exports={moduleType:"component",name:n.name,layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),draw:t("./draw")}},{"./attributes":445,"./constants":446,"./defaults":447,"./draw":448}],450:[function(t,e,r){"use strict";var n=t("d3"),i=t("fast-isnumeric"),a=t("../../plots/plots"),o=t("../../registry"),s=t("../../lib"),l=t("../drawing"),u=t("../color"),c=t("../../lib/svg_text_utils"),f=t("../../constants/interactions");e.exports={draw:function(t,e,r){var 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n=t("../../plots/font_attributes"),i=t("../color/attributes"),a=t("../../lib/extend").extendFlat,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/pad_attributes");e.exports=o({_isLinkedToArray:"updatemenu",_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:{_isLinkedToArray:"button",method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}},x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:a({},s,{}),font:n({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:i.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}},"arraydraw","from-root")},{"../../lib/extend":473,"../../plot_api/edit_types":510,"../../plots/font_attributes":551,"../../plots/pad_attributes":567,"../color/attributes":356}],452:[function(t,e,r){"use 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n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var l=n[i],u=o(r,"gl3d",l),c=e[l],f=c._scene;f||(f=new a({id:l,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),c._scene=f),f.cameraInitial||(f.cameraInitial=s.extendDeep({},c.camera)),f.plot(u,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var 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r&&(this.tickAngle[e]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180),"showticklabels"in r&&(this.tickEnable[e]=r.showticklabels),"tickfont"in r&&(r.tickfont.color&&(this.tickColor[e]=i(r.tickfont.color)),r.tickfont.family&&(this.tickFont[e]=r.tickfont.family),r.tickfont.size&&(this.tickSize[e]=r.tickfont.size)),"mirror"in r?-1!==["ticks","all","allticks"].indexOf(r.mirror)?(this.lineTickMirror[e]=!0,this.lineMirror[e]=!0):!0===r.mirror?(this.lineTickMirror[e]=!1,this.lineMirror[e]=!0):(this.lineTickMirror[e]=!1,this.lineMirror[e]=!1):this.lineMirror[e]=!1,"showbackground"in r&&!1!==r.showbackground?(this.backgroundEnable[e]=!0,this.backgroundColor[e]=i(r.backgroundcolor)):this.backgroundEnable[e]=!1):(this.tickEnable[e]=!1,this.labelEnable[e]=!1,this.lineEnable[e]=!1,this.lineTickEnable[e]=!1,this.gridEnable[e]=!1,this.zeroEnable[e]=!1,this.backgroundEnable[e]=!1)}},e.exports=function(t){var e=new o;return e.merge(t),e}},{"../../../lib/html2unicode":479,"../../../lib/str2rgbarray":503}],560:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes");function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],c=0;c<u.length;c++)r("camera."+u[c]+".x"),r("camera."+u[c]+".y"),r("camera."+u[c]+".z");var f=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),h=r("aspectmode",f?"manual":"auto");f||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===h&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n),r("dragmode",n.getDfltFromLayout("dragmode")),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":357,"../../../lib":481,"../../../registry":577,"../../subplot_defaults":576,"./axis_defaults":558,"./layout_attributes":561}],561:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],dflt:"turntable",editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":481,"../../../lib/extend":473,"../../domain":550,"./axis_attributes":557}],562:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":503}],563:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,l=t.fullSceneLayout,u=[[],[],[]],c=0;c<3;++c){var f=l[o[c]];if(f._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(f._length)===1/0)u[c]=[];else{f._input_range=f.range.slice(),f.range[0]=r[c].lo/t.dataScale[c],f.range[1]=r[c].hi/t.dataScale[c],f._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var h=f.tickmode;if("auto"===f.tickmode){f.tickmode="linear";var d=f.nticks||i.constrain(f._length/40,4,9);n.autoTicks(f,Math.abs(f.range[1]-f.range[0])/d)}for(var p=n.calcTicks(f),g=0;g<p.length;++g)p[g].x=p[g].x*t.dataScale[c],p[g].text=a(p[g].text);u[c]=p,f.tickmode=h}}e.ticks=u;for(var c=0;c<3;++c){s[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(var g=0;g<2;++g)e.bounds[g][c]=t.glplot.bounds[g][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(u)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=t("../../../lib/html2unicode"),o=["xaxis","yaxis","zaxis"],s=[0,0,0]},{"../../../lib":481,"../../../lib/html2unicode":479,"../../cartesian/axes":525}],564:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],565:[function(t,e,r){"use strict";var n,i,a=t("gl-plot3d"),o=t("webgl-context"),s=t("has-passive-events"),l=t("../../registry"),u=t("../../lib"),c=t("../../plots/cartesian/axes"),f=t("../../components/fx"),h=t("../../lib/str2rgbarray"),d=t("../../lib/show_no_webgl_msg"),p=t("./camera"),g=t("./project"),v=t("./layout/convert"),m=t("./layout/spikes"),y=t("./layout/tick_marks");function b(t,e,r,l){var h={canvas:r,gl:l,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=o({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");h.pixelRatio=t.pixelRatio,h.gl=i,h.canvas=n}try{t.glplot=a(h)}catch(e){d(t)}var v=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+".camera"]=A(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit("plotly_relayout",e)}};if(t.glplot.canvas.addEventListener("mouseup",v.bind(null,t)),t.glplot.canvas.addEventListener("wheel",v.bind(null,t),!!s&&{passive:!1}),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",function(t){u.warn("Lost WebGL context."),t.preventDefault()}),!t.camera){var m=t.fullSceneLayout.camera;t.camera=p(t.container,{center:[m.center.x,m.center.y,m.center.z],eye:[m.eye.x,m.eye.y,m.eye.z],up:[m.up.x,m.up.y,m.up.z],zoomMin:.1,zoomMax:100,mode:"orbit"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.svgContainer,n=t.container.getBoundingClientRect(),i=n.width,a=n.height;r.setAttributeNS(null,"viewBox","0 0 "+i+" "+a),r.setAttributeNS(null,"width",i),r.setAttributeNS(null,"height",a),y(t),t.glplot.axes.update(t.axesOptions);for(var o,s=Object.keys(t.traces),l=null,u=t.glplot.selection,h=0;h<s.length;++h)"skip"!==(e=t.traces[s[h]]).data.hoverinfo&&e.handlePick(u)&&(l=e),e.setContourLevels&&e.setContourLevels();function d(e,r){var n=t.fullSceneLayout[e];return c.tickText(n,n.d2l(r),"hover").text}if(null!==l){var p=g(t.glplot.cameraParams,u.dataCoordinate);e=l.data;var v,m=u.index,b=f.castHoverinfo(e,t.fullLayout,m),x=b.split("+"),_="all"===b,w=d("xaxis",u.traceCoordinate[0]),M=d("yaxis",u.traceCoordinate[1]),A=d("zaxis",u.traceCoordinate[2]);if(_||(-1===x.indexOf("x")&&(w=void 0),-1===x.indexOf("y")&&(M=void 0),-1===x.indexOf("z")&&(A=void 0),-1===x.indexOf("text")&&(u.textLabel=void 0),-1===x.indexOf("name")&&(l.name=void 0)),"cone"===e.type){var T=[];(_||-1!==x.indexOf("u"))&&T.push("u: "+d("xaxis",u.traceCoordinate[3])),(_||-1!==x.indexOf("v"))&&T.push("v: "+d("yaxis",u.traceCoordinate[4])),(_||-1!==x.indexOf("w"))&&T.push("w: "+d("zaxis",u.traceCoordinate[5])),(_||-1!==x.indexOf("norm"))&&T.push("norm: 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i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=v(e[this.id]),this.spikeOptions=m(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=l.getComponentMethod("annotations3d","convert"),this.drawAnnotations=l.getComponentMethod("annotations3d","draw"),b(this)}var _=x.prototype;_.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(function n(){e.isContextLost()?requestAnimationFrame(n):b(t,t.fullLayout,r,e)?t.plot.apply(t,t.plotArgs):u.error("Catastrophic and unrecoverable WebGL error. 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n,i,a,o,s,l,u=e[this.id],c=r[this.id];u.bgcolor?this.glplot.clearColor=h(u.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=u,this.glplotLayout=u,this.axesOptions.merge(u),this.spikeOptions.merge(u),this.setCamera(u.camera),this.updateFx(u.dragmode,u.hovermode),this.glplot.update({}),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&M(this,n,f);var d=[1,1,1];for(o=0;o<3;++o)f[0][o]>f[1][o]?d[o]=1:f[1][o]===f[0][o]?d[o]=1:d[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=d,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&((i=this.traces[n.uid])?i.update(n):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible)continue t;(i=this.traces[p[a]]).dispose(),delete 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o=0;o<3;o++)for(var s=0;s<3;s++)if(!a(e,n,o,s)){i=!0;break}return i&&r.set(e),i},_.updateFx=function(t,e){var r=this.camera;r&&("orbit"===t?(r.mode="orbit",r.keyBindingMode="rotate"):"turntable"===t?(r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate"):r.keyBindingMode=t),this.fullSceneLayout.hovermode=e},_.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement("canvas");f.width=r,f.height=i;var 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m=v.transformsRegistry,y=t("./command");v.executeAPICommand=y.executeAPICommand,v.computeAPICommandBindings=y.computeAPICommandBindings,v.manageCommandObserver=y.manageCommandObserver,v.hasSimpleAPICommandBindings=y.hasSimpleAPICommandBindings,v.redrawText=function(t){if(!((t=s.getGraphDiv(t)).data&&t.data[0]&&t.data[0].r))return new Promise(function(e){setTimeout(function(){a.getComponentMethod("annotations","draw")(t),a.getComponentMethod("legend","draw")(t),(t.calcdata||[]).forEach(function(t){t[0]&&t[0].t&&t[0].t.cb&&t[0].t.cb()}),e(v.previousPromises(t))},300)})},v.resize=function(t){return t=s.getGraphDiv(t),new Promise(function(e,r){t&&!function(t){var e=window.getComputedStyle(t).display;return!e||"none"===e}(t)||r(new Error("Resize must be passed a displayed plot div element.")),t._redrawTimer&&clearTimeout(t._redrawTimer),t._redrawTimer=setTimeout(function(){if(t.layout.width&&t.layout.height)e(t);else{delete t.layout.width,delete t.layout.height;var 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o=r.select(".js-link-to-tool"),u=r.select(".js-link-spacer"),c=r.select(".js-sourcelinks");t._context.showSources&&t._context.showSources(t),t._context.showLink&&function(t,e){e.text("");var r=e.append("a").attr({"xlink:xlink:href":"#",class:"link--impt link--embedview","font-weight":"bold"}).text(t._context.linkText+" "+String.fromCharCode(187));if(t._context.sendData)r.on("click",function(){v.sendDataToCloud(t)});else{var n=window.location.pathname.split("/"),i=window.location.search;r.attr({"xlink:xlink:show":"new","xlink:xlink:href":"/"+n[2].split(".")[0]+"/"+n[1]+i})}}(t,o),u.text(o.text()&&c.text()?" - ":"")}},v.sendDataToCloud=function(t){t.emit("plotly_beforeexport");var e=window.PLOTLYENV&&window.PLOTLYENV.BASE_URL||"https://plot.ly",r=n.select(t).append("div").attr("id","hiddenform").style("display","none"),i=r.append("form").attr({action:e+"/external",method:"post",target:"_blank"});return 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y=h.select(".legend-marks").selectAll("path.legend-mark").data(o);y.enter().append("path").classed("legend-mark",!0),y.attr({transform:function(t,e){return"translate("+[u/2,v(e)+u/2]+")"},d:function(t,e){var r,i,a,o=t.symbol;return a=3*(i=u),"line"===(r=o)?"M"+[[-i/2,-i/12],[i/2,-i/12],[i/2,i/12],[-i/2,i/12]]+"Z":-1!=n.svg.symbolTypes.indexOf(r)?n.svg.symbol().type(r).size(a)():n.svg.symbol().type("square").size(a)()},fill:function(t,e){return g(e)}}),y.exit().remove()}var b=n.svg.axis().scale(v).orient("right"),x=h.select("g.legend-axis").attr({transform:"translate("+[c?e.colorBandWidth:u,u/2]+")"}).call(b);return x.selectAll(".domain").style({fill:"none",stroke:"none"}),x.selectAll("line").style({fill:"none",stroke:c?e.textColor:"none"}),x.selectAll("text").style({fill:e.textColor,"font-size":e.fontSize}).text(function(t,e){return o[e].name}),r}return r.config=function(e){return 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h=a.padding,d=e.node().getBBox(),p={fill:a.color,stroke:s,"stroke-width":"2px"},g=d.width+2*h+l,v=d.height+2*h;return r.attr({d:"M"+[[l,-v/2],[l,-v/4],[a.hasTick?0:l,0],[l,v/4],[l,v/2],[g,v/2],[g,-v/2]].join("L")+"Z"}).style(p),t.attr({transform:"translate("+[l,-v/2+2*h]+")"}),t.style({display:"block"}),u},u.move=function(e){if(t)return t.attr({transform:"translate("+[e[0],e[1]]+")"}).style({display:"block"}),u},u.hide=function(){if(t)return t.style({display:"none"}),u},u.show=function(){if(t)return t.style({display:"block"}),u},u.config=function(t){return i(a,t),u},u},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var n=i({},t);return[[n,["marker","color"],["color"]],[n,["marker","opacity"],["opacity"]],[n,["marker","line","color"],["strokeColor"]],[n,["marker","line","dash"],["strokeDash"]],[n,["marker","line","width"],["strokeSize"]],[n,["marker","symbol"],["dotType"]],[n,["marker","size"],["dotSize"]],[n,["marker","barWidth"],["barWidth"]],[n,["line","interpolation"],["lineInterpolation"]],[n,["showlegend"],["visibleInLegend"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e||delete n.marker,e&&delete n.groupId,e?("LinePlot"===n.geometry?(n.type="scatter",!0===n.dotVisible?(delete n.dotVisible,n.mode="lines+markers"):n.mode="lines"):"DotPlot"===n.geometry?(n.type="scatter",n.mode="markers"):"AreaChart"===n.geometry?n.type="area":"BarChart"===n.geometry&&(n.type="bar"),delete n.geometry):("scatter"===n.type?"lines"===n.mode?n.geometry="LinePlot":"markers"===n.mode?n.geometry="DotPlot":"lines+markers"===n.mode&&(n.geometry="LinePlot",n.dotVisible=!0):"area"===n.type?n.geometry="AreaChart":"bar"===n.type&&(n.geometry="BarChart"),delete n.mode,delete n.type),n}),!e&&t.layout&&"stack"===t.layout.barmode)){var a=o.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=a.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var s=i({},t.layout);if([[s,["plot_bgcolor"],["backgroundColor"]],[s,["showlegend"],["showLegend"]],[s,["radialaxis"],["radialAxis"]],[s,["angularaxis"],["angularAxis"]],[s.angularaxis,["showline"],["gridLinesVisible"]],[s.angularaxis,["showticklabels"],["labelsVisible"]],[s.angularaxis,["nticks"],["ticksCount"]],[s.angularaxis,["tickorientation"],["tickOrientation"]],[s.angularaxis,["ticksuffix"],["ticksSuffix"]],[s.angularaxis,["range"],["domain"]],[s.angularaxis,["endpadding"],["endPadding"]],[s.radialaxis,["showline"],["gridLinesVisible"]],[s.radialaxis,["tickorientation"],["tickOrientation"]],[s.radialaxis,["ticksuffix"],["ticksSuffix"]],[s.radialaxis,["range"],["domain"]],[s.angularAxis,["showline"],["gridLinesVisible"]],[s.angularAxis,["showticklabels"],["labelsVisible"]],[s.angularAxis,["nticks"],["ticksCount"]],[s.angularAxis,["tickorientation"],["tickOrientation"]],[s.angularAxis,["ticksuffix"],["ticksSuffix"]],[s.angularAxis,["range"],["domain"]],[s.angularAxis,["endpadding"],["endPadding"]],[s.radialAxis,["showline"],["gridLinesVisible"]],[s.radialAxis,["tickorientation"],["tickOrientation"]],[s.radialAxis,["ticksuffix"],["ticksSuffix"]],[s.radialAxis,["range"],["domain"]],[s.font,["outlinecolor"],["outlineColor"]],[s.legend,["traceorder"],["reverseOrder"]],[s,["labeloffset"],["labelOffset"]],[s,["defaultcolorrange"],["defaultColorRange"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e?(void 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t,n,i=this,a=o.util.cloneJson(e);t=a,n=r,f.add({undo:function(){n&&i(n)},redo:function(){i(t)}}),r=o.util.cloneJson(a)},h.undo=function(){f.undo()},h.redo=function(){f.redo()},h},u.fillLayout=function(t){var e=n.select(t).selectAll(".plot-container"),r=e.selectAll(".svg-container"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{"../../../components/color":357,"../../../lib":481,"./micropolar":572,"./undo_manager":574,d3:80}],574:[function(t,e,r){"use strict";e.exports=function(){var t,e=[],r=-1,n=!1;function i(t,e){return t?(n=!0,t[e](),n=!1,this):this}return{add:function(t){return n?this:(e.splice(r+1,e.length-r),e.push(t),r=e.length-1,this)},setCallback:function(e){t=e},undo:function(){var n=e[r];return n?(i(n,"undo"),r-=1,t&&t(n.undo),this):this},redo:function(){var n=e[r+1];return 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n=t("../../components/colorscale/color_attributes"),i=t("../../components/colorscale/attributes"),a=t("../../components/colorbar/attributes"),o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,u={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},sizemode:{valType:"enumerated",values:["scaled","absolute"],editType:"calc",dflt:"scaled"},sizeref:{valType:"number",editType:"calc",min:0},anchor:{valType:"enumerated",editType:"calc",values:["tip","tail","cm","center"],dflt:"cm"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"}};l(u,n("","calc",!0),{showscale:i.showscale,colorbar:a}),delete 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n=t("../../components/colorscale/has_colorscale"),i=t("../../components/colorscale/calc"),a=t("./subtypes");e.exports=function(t){a.hasLines(t)&&n(t,"line")&&i(t,t.line.color,"line","c"),a.hasMarkers(t)&&(n(t,"marker")&&i(t,t.marker.color,"marker","c"),n(t,"marker.line")&&i(t,t.marker.line.color,"marker.line","c"))}},{"../../components/colorscale/calc":364,"../../components/colorscale/has_colorscale":371,"./subtypes":623}],607:[function(t,e,r){"use strict";e.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20}},{}],608:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./attributes"),o=t("./constants"),s=t("./subtypes"),l=t("./xy_defaults"),u=t("./marker_defaults"),c=t("./line_defaults"),f=t("./line_shape_defaults"),h=t("./text_defaults"),d=t("./fillcolor_defaults");e.exports=function(t,e,r,p){function g(r,i){return n.coerce(t,e,a,r,i)}var 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n=t("gl-surface3d"),i=t("ndarray"),a=t("ndarray-homography"),o=t("ndarray-fill"),s=t("ndarray-ops"),l=t("../../lib").isArrayOrTypedArray,u=t("../../lib/gl_format_color").parseColorScale,c=t("../../lib/str2rgbarray"),f=128;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.dataScale=1}var d=h.prototype;function p(t){var e=t.shape,r=[e[0]+2,e[1]+2],n=i(new Float32Array(r[0]*r[1]),r);return s.assign(n.lo(1,1).hi(e[0],e[1]),t),s.assign(n.lo(1).hi(e[0],1),t.hi(e[0],1)),s.assign(n.lo(1,r[1]-1).hi(e[0],1),t.lo(0,e[1]-1).hi(e[0],1)),s.assign(n.lo(0,1).hi(1,e[1]),t.hi(1)),s.assign(n.lo(r[0]-1,1).hi(1,e[1]),t.lo(e[0]-1)),n.set(0,0,t.get(0,0)),n.set(0,r[1]-1,t.get(0,e[1]-1)),n.set(r[0]-1,0,t.get(e[0]-1,0)),n.set(r[0]-1,r[1]-1,t.get(e[0]-1,e[1]-1)),n}d.handlePick=function(t){if(t.object===this.surface){var 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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,d=0|o[1],p=8191&d,g=d>>>13,v=0|o[2],m=8191&v,y=v>>>13,b=0|o[3],x=8191&b,_=b>>>13,w=0|o[4],A=8191&w,M=w>>>13,T=0|o[5],k=8191&T,E=T>>>13,L=0|o[6],S=8191&L,C=L>>>13,O=0|o[7],R=8191&O,P=O>>>13,z=0|o[8],I=8191&z,N=z>>>13,D=0|o[9],F=8191&D,j=D>>>13,B=0|s[0],U=8191&B,V=B>>>13,H=0|s[1],q=8191&H,G=H>>>13,X=0|s[2],W=8191&X,Y=X>>>13,Z=0|s[3],Q=8191&Z,$=Z>>>13,J=0|s[4],K=8191&J,tt=J>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,dt=0|s[9],pt=8191&dt,gt=dt>>>13;r.negative=t.negative^e.negative,r.length=19;var vt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(p,U),i=(i=Math.imul(p,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var mt=(u+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,U),i=(i=Math.imul(m,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(p,q)|0,i=(i=i+Math.imul(p,G)|0)+Math.imul(g,q)|0,a=a+Math.imul(g,G)|0;var yt=(u+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,Y)|0)+Math.imul(h,W)|0))<<13)|0;u=((a=a+Math.imul(h,Y)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(x,U),i=(i=Math.imul(x,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(m,q)|0,i=(i=i+Math.imul(m,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(p,W)|0,i=(i=i+Math.imul(p,Y)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,Y)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,$)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,$)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(M,U)|0,a=Math.imul(M,V),n=n+Math.imul(x,q)|0,i=(i=i+Math.imul(x,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,i=(i=i+Math.imul(m,Y)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,Y)|0,n=n+Math.imul(p,Q)|0,i=(i=i+Math.imul(p,$)|0)+Math.imul(g,Q)|0,a=a+Math.imul(g,$)|0;var xt=(u+(n=n+Math.imul(f,K)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,K)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,U),i=(i=Math.imul(k,V))+Math.imul(E,U)|0,a=Math.imul(E,V),n=n+Math.imul(A,q)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(M,q)|0,a=a+Math.imul(M,G)|0,n=n+Math.imul(x,W)|0,i=(i=i+Math.imul(x,Y)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,Y)|0,n=n+Math.imul(m,Q)|0,i=(i=i+Math.imul(m,$)|0)+Math.imul(y,Q)|0,a=a+Math.imul(y,$)|0,n=n+Math.imul(p,K)|0,i=(i=i+Math.imul(p,tt)|0)+Math.imul(g,K)|0,a=a+Math.imul(g,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(S,U),i=(i=Math.imul(S,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(k,q)|0,i=(i=i+Math.imul(k,G)|0)+Math.imul(E,q)|0,a=a+Math.imul(E,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,Y)|0)+Math.imul(M,W)|0,a=a+Math.imul(M,Y)|0,n=n+Math.imul(x,Q)|0,i=(i=i+Math.imul(x,$)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,$)|0,n=n+Math.imul(m,K)|0,i=(i=i+Math.imul(m,tt)|0)+Math.imul(y,K)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(p,rt)|0,i=(i=i+Math.imul(p,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(R,U),i=(i=Math.imul(R,V))+Math.imul(P,U)|0,a=Math.imul(P,V),n=n+Math.imul(S,q)|0,i=(i=i+Math.imul(S,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(k,W)|0,i=(i=i+Math.imul(k,Y)|0)+Math.imul(E,W)|0,a=a+Math.imul(E,Y)|0,n=n+Math.imul(A,Q)|0,i=(i=i+Math.imul(A,$)|0)+Math.imul(M,Q)|0,a=a+Math.imul(M,$)|0,n=n+Math.imul(x,K)|0,i=(i=i+Math.imul(x,tt)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,i=(i=i+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(p,at)|0,i=(i=i+Math.imul(p,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var At=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(R,q)|0,i=(i=i+Math.imul(R,G)|0)+Math.imul(P,q)|0,a=a+Math.imul(P,G)|0,n=n+Math.imul(S,W)|0,i=(i=i+Math.imul(S,Y)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,Y)|0,n=n+Math.imul(k,Q)|0,i=(i=i+Math.imul(k,$)|0)+Math.imul(E,Q)|0,a=a+Math.imul(E,$)|0,n=n+Math.imul(A,K)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(M,K)|0,a=a+Math.imul(M,tt)|0,n=n+Math.imul(x,rt)|0,i=(i=i+Math.imul(x,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(m,at)|0,i=(i=i+Math.imul(m,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(p,lt)|0,i=(i=i+Math.imul(p,ut)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ut)|0;var Mt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(F,U),i=(i=Math.imul(F,V))+Math.imul(j,U)|0,a=Math.imul(j,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(N,q)|0,a=a+Math.imul(N,G)|0,n=n+Math.imul(R,W)|0,i=(i=i+Math.imul(R,Y)|0)+Math.imul(P,W)|0,a=a+Math.imul(P,Y)|0,n=n+Math.imul(S,Q)|0,i=(i=i+Math.imul(S,$)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,$)|0,n=n+Math.imul(k,K)|0,i=(i=i+Math.imul(k,tt)|0)+Math.imul(E,K)|0,a=a+Math.imul(E,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(M,rt)|0,a=a+Math.imul(M,nt)|0,n=n+Math.imul(x,at)|0,i=(i=i+Math.imul(x,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,i=(i=i+Math.imul(m,ut)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ut)|0,n=n+Math.imul(p,ft)|0,i=(i=i+Math.imul(p,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var Tt=(u+(n=n+Math.imul(f,pt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,pt)|0))<<13)|0;u=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(F,q),i=(i=Math.imul(F,G))+Math.imul(j,q)|0,a=Math.imul(j,G),n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,Y)|0)+Math.imul(N,W)|0,a=a+Math.imul(N,Y)|0,n=n+Math.imul(R,Q)|0,i=(i=i+Math.imul(R,$)|0)+Math.imul(P,Q)|0,a=a+Math.imul(P,$)|0,n=n+Math.imul(S,K)|0,i=(i=i+Math.imul(S,tt)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(k,rt)|0,i=(i=i+Math.imul(k,nt)|0)+Math.imul(E,rt)|0,a=a+Math.imul(E,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(M,at)|0,a=a+Math.imul(M,ot)|0,n=n+Math.imul(x,lt)|0,i=(i=i+Math.imul(x,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(m,ft)|0,i=(i=i+Math.imul(m,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var kt=(u+(n=n+Math.imul(p,pt)|0)|0)+((8191&(i=(i=i+Math.imul(p,gt)|0)+Math.imul(g,pt)|0))<<13)|0;u=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(F,W),i=(i=Math.imul(F,Y))+Math.imul(j,W)|0,a=Math.imul(j,Y),n=n+Math.imul(I,Q)|0,i=(i=i+Math.imul(I,$)|0)+Math.imul(N,Q)|0,a=a+Math.imul(N,$)|0,n=n+Math.imul(R,K)|0,i=(i=i+Math.imul(R,tt)|0)+Math.imul(P,K)|0,a=a+Math.imul(P,tt)|0,n=n+Math.imul(S,rt)|0,i=(i=i+Math.imul(S,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(k,at)|0,i=(i=i+Math.imul(k,ot)|0)+Math.imul(E,at)|0,a=a+Math.imul(E,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ut)|0)+Math.imul(M,lt)|0,a=a+Math.imul(M,ut)|0,n=n+Math.imul(x,ft)|0,i=(i=i+Math.imul(x,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var Et=(u+(n=n+Math.imul(m,pt)|0)|0)+((8191&(i=(i=i+Math.imul(m,gt)|0)+Math.imul(y,pt)|0))<<13)|0;u=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,$))+Math.imul(j,Q)|0,a=Math.imul(j,$),n=n+Math.imul(I,K)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(N,K)|0,a=a+Math.imul(N,tt)|0,n=n+Math.imul(R,rt)|0,i=(i=i+Math.imul(R,nt)|0)+Math.imul(P,rt)|0,a=a+Math.imul(P,nt)|0,n=n+Math.imul(S,at)|0,i=(i=i+Math.imul(S,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(k,lt)|0,i=(i=i+Math.imul(k,ut)|0)+Math.imul(E,lt)|0,a=a+Math.imul(E,ut)|0,n=n+Math.imul(A,ft)|0,i=(i=i+Math.imul(A,ht)|0)+Math.imul(M,ft)|0,a=a+Math.imul(M,ht)|0;var Lt=(u+(n=n+Math.imul(x,pt)|0)|0)+((8191&(i=(i=i+Math.imul(x,gt)|0)+Math.imul(_,pt)|0))<<13)|0;u=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(F,K),i=(i=Math.imul(F,tt))+Math.imul(j,K)|0,a=Math.imul(j,tt),n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(N,rt)|0,a=a+Math.imul(N,nt)|0,n=n+Math.imul(R,at)|0,i=(i=i+Math.imul(R,ot)|0)+Math.imul(P,at)|0,a=a+Math.imul(P,ot)|0,n=n+Math.imul(S,lt)|0,i=(i=i+Math.imul(S,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(k,ft)|0,i=(i=i+Math.imul(k,ht)|0)+Math.imul(E,ft)|0,a=a+Math.imul(E,ht)|0;var St=(u+(n=n+Math.imul(A,pt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(M,pt)|0))<<13)|0;u=((a=a+Math.imul(M,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(j,rt)|0,a=Math.imul(j,nt),n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(N,at)|0,a=a+Math.imul(N,ot)|0,n=n+Math.imul(R,lt)|0,i=(i=i+Math.imul(R,ut)|0)+Math.imul(P,lt)|0,a=a+Math.imul(P,ut)|0,n=n+Math.imul(S,ft)|0,i=(i=i+Math.imul(S,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(k,pt)|0)|0)+((8191&(i=(i=i+Math.imul(k,gt)|0)+Math.imul(E,pt)|0))<<13)|0;u=((a=a+Math.imul(E,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(j,at)|0,a=Math.imul(j,ot),n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ut)|0)+Math.imul(N,lt)|0,a=a+Math.imul(N,ut)|0,n=n+Math.imul(R,ft)|0,i=(i=i+Math.imul(R,ht)|0)+Math.imul(P,ft)|0,a=a+Math.imul(P,ht)|0;var Ot=(u+(n=n+Math.imul(S,pt)|0)|0)+((8191&(i=(i=i+Math.imul(S,gt)|0)+Math.imul(C,pt)|0))<<13)|0;u=((a=a+Math.imul(C,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ut))+Math.imul(j,lt)|0,a=Math.imul(j,ut),n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(N,ft)|0,a=a+Math.imul(N,ht)|0;var Rt=(u+(n=n+Math.imul(R,pt)|0)|0)+((8191&(i=(i=i+Math.imul(R,gt)|0)+Math.imul(P,pt)|0))<<13)|0;u=((a=a+Math.imul(P,gt)|0)+(i>>>13)|0)+(Rt>>>26)|0,Rt&=67108863,n=Math.imul(F,ft),i=(i=Math.imul(F,ht))+Math.imul(j,ft)|0,a=Math.imul(j,ht);var Pt=(u+(n=n+Math.imul(I,pt)|0)|0)+((8191&(i=(i=i+Math.imul(I,gt)|0)+Math.imul(N,pt)|0))<<13)|0;u=((a=a+Math.imul(N,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863;var zt=(u+(n=Math.imul(F,pt))|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(j,pt)|0))<<13)|0;return u=((a=Math.imul(j,gt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=At,l[8]=Mt,l[9]=Tt,l[10]=kt,l[11]=Et,l[12]=Lt,l[13]=St,l[14]=Ct,l[15]=Ot,l[16]=Rt,l[17]=Pt,l[18]=zt,0!==u&&(l[19]=u,r.length++),r};function p(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):p(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,d=0;d<o;d++){var p=r[c+d],g=n[c+d],v=r[c+d+o],m=n[c+d+o],y=f*v-h*m;m=f*m+h*v,v=y,r[c+d]=p+v,n[c+d]=g+m,r[c+d+o]=p-v,n[c+d+o]=g-m,d!==s&&(y=l*f-u*h,h=l*h+u*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var d=0;d<n;d++){var p=s[d]*c[d]-l[d]*f[d];l[d]=s[d]*f[d]+l[d]*c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),p(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,d=1;0==(e.words[0]&d)&&h<26;++h,d<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var p=0,g=1;0==(r.words[0]&g)&&p<26;++p,g<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(x,m),i(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new y;else if("p224"===t)e=new b;else if("p192"===t)e=new x;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),d=this.pow(t,i),p=o;0!==d.cmp(s);){for(var g=d,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<p);var m=this.pow(f,new a(1).iushln(p-v-1));h=h.redMul(m),f=m.redSqr(),d=d.redMul(f),p=v}return h},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var 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e=this._time,r=e.length,o=i.le(e,t),s=this._scratch[0],l=this._state,u=this._velocity,c=this.dimension,f=this.bounds;if(o<0)for(var h=c-1,d=0;d<c;++d,--h)s[d]=l[h];else if(o>=r-1){h=l.length-1;var p=t-e[r-1];for(d=0;d<c;++d,--h)s[d]=l[h]+p*u[h]}else{h=c*(o+1)-1;var g=e[o],v=e[o+1]-g||1,m=this._scratch[1],y=this._scratch[2],b=this._scratch[3],x=this._scratch[4],_=!0;for(d=0;d<c;++d,--h)m[d]=l[h],b[d]=u[h]*v,y[d]=l[h+c],x[d]=u[h+c]*v,_=_&&m[d]===y[d]&&b[d]===x[d]&&0===b[d];if(_)for(d=0;d<c;++d)s[d]=m[d];else n(m,b,y,x,(t-g)/v,s)}var w=f[0],A=f[1];for(d=0;d<c;++d)s[d]=a(w[d],A[d],s[d]);return s},s.dcurve=function(t){var e=this._time,r=e.length,a=i.le(e,t),o=this._scratch[0],s=this._state,l=this._velocity,u=this.dimension;if(a>=r-1)for(var c=s.length-1,f=(e[r-1],0);f<u;++f,--c)o[f]=l[c];else{c=u*(a+1)-1;var 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n.length=i,new f(this,n)},c.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},c.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},c.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new f(this,n);r=i<=0?r.left:r.right}return new f(this,[])},c.remove=function(t){var e=this.find(t);return e?e.remove():this},c.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var h=f.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(h,"valid",{get:function(){return 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p=e[e.length-2];p.left===r?p.left=null:p.right===r&&(p.right=null),e.pop();for(c=0;c<e.length;++c)e[c]._count--;return new u(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=i;for(c=0;c<e.length-1;++c)e[c]._count--;return new u(this.tree._compare,e[0])}if(1===e.length)return new u(this.tree._compare,null);for(c=0;c<e.length;++c)e[c]._count--;var g=e[e.length-2];return function(t){for(var e,r,a,u,c=t.length-1;c>=0;--c){if(e=t[c],0===c)return void(e._color=i);if((r=t[c-1]).left===e){if((a=r.right).right&&a.right._color===n)return u=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=u,a._color=r._color,e._color=i,r._color=i,u._color=i,l(r),l(a),c>1&&((f=t[c-2]).left===r?f.left=a:f.right=a),void(t[c-1]=a);if(a.left&&a.left._color===n)return u=(a=r.right=o(a)).left=o(a.left),r.right=u.left,a.left=u.right,u.left=r,u.right=a,u._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(u),c>1&&((f=t[c-2]).left===r?f.left=u:f.right=u),void(t[c-1]=u);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),c>1&&((f=t[c-2]).left===r?f.left=a:f.right=a),t[c-1]=a,t[c]=r,c+1<t.length?t[c+1]=e:t.push(e),c+=2}else{if((a=r.left).left&&a.left._color===n)return u=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=u,a._color=r._color,e._color=i,r._color=i,u._color=i,l(r),l(a),c>1&&((f=t[c-2]).right===r?f.right=a:f.left=a),void(t[c-1]=a);if(a.right&&a.right._color===n)return u=(a=r.left=o(a)).right=o(a.right),r.left=u.right,a.right=u.left,u.right=r,u.left=a,u._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(u),c>1&&((f=t[c-2]).right===r?f.right=u:f.left=u),void(t[c-1]=u);if(a._color===i){if(r._color===n)return r._color=i,void(r.left=s(n,a));r.left=s(n,a);continue}var f;a=o(a),r.left=a.right,a.right=r,a._color=r._color,r._color=n,l(r),l(a),c>1&&((f=t[c-2]).right===r?f.right=a:f.left=a),t[c-1]=a,t[c]=r,c+1<t.length?t[c+1]=e:t.push(e),c+=2}}}(e),g.left===r?g.left=null:g.right=null,new u(this.tree._compare,e[0])},Object.defineProperty(h,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(h,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(h,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),h.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),h.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),n=e[e.length-1];r[r.length-1]=new a(n._color,n.key,t,n.left,n.right,n._count);for(var i=e.length-2;i>=0;--i)(n=e[i]).left===e[i+1]?r[i]=new a(n._color,n.key,n.value,r[i+1],n.right,n._count):r[i]=new a(n._color,n.key,n.value,n.left,r[i+1],n._count);return new u(this.tree._compare,r[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],93:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number("0/0");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(a,e+.5)*Math.exp(-a)*r},e.exports.log=o},{}],94:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],95:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),c=!1,f=!1;if("bounds"in t)for(var h=t.bounds,d=0;d<2;++d)for(var p=0;p<3;++p)h[d][p]!==this.bounds[d][p]&&(f=!0),this.bounds[d][p]=h[d][p];if("ticks"in t){r=t.ticks,c=!0,this.autoTicks=!1;for(d=0;d<3;++d)this.tickSpacing[d]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(d=0;d<3;++d)r[d].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?c=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(c=!0),a("tickSize"),a("tickAngle"),a("tickPad"),u("tickColor");var g=l("labels");l("labelFont")&&(g=!0),o("labelEnable"),a("labelSize"),a("labelPad"),u("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),u("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),u("lineTickColor"),o("gridEnable"),a("gridWidth"),u("gridColor"),o("zeroEnable"),u("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),u("backgroundColor"),this._text?this._text&&(g||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var d=[new h,new h,new h];function p(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,d=o,p=l;u&1<<c&&(f=s,h=a,d=l,p=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(d[c]=-1,p[c]=0):(d[c]=0,p[c]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],b=[0,0,0];f.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,x=n[12],_=n[13],w=n[14],A=n[15],M=(s?2:1)*this.pixelRatio*(i[3]*x+i[7]*_+i[11]*w+i[15]*A)/e.drawingBufferHeight,T=0;T<3;++T)this.lastCubeProps.cubeEdges[T]=f[T],this.lastCubeProps.axis[T]=h[T];var k=d;for(T=0;T<3;++T)p(d[T],T,this.bounds,f,h);e=this.gl;var E,L=g;for(T=0;T<3;++T)this.backgroundEnable[T]?L[T]=h[T]:L[T]=0;this._background.draw(r,n,i,a,L,this.backgroundColor),this._lines.bind(r,n,i,this);for(T=0;T<3;++T){var S=[0,0,0];h[T]>0?S[T]=a[1][T]:S[T]=a[0][T];for(var C=0;C<2;++C){var O=(T+1+C)%3,R=(T+1+(1^C))%3;this.gridEnable[O]&&this._lines.drawGrid(O,R,this.bounds,S,this.gridColor[O],this.gridWidth[O]*this.pixelRatio)}for(C=0;C<2;++C){O=(T+1+C)%3,R=(T+1+(1^C))%3;this.zeroEnable[R]&&Math.min(a[0][R],a[1][R])<=0&&Math.max(a[0][R],a[1][R])>=0&&this._lines.drawZero(O,R,this.bounds,S,this.zeroLineColor[R],this.zeroLineWidth[R]*this.pixelRatio)}}for(T=0;T<3;++T){this.lineEnable[T]&&this._lines.drawAxisLine(T,this.bounds,k[T].primalOffset,this.lineColor[T],this.lineWidth[T]*this.pixelRatio),this.lineMirror[T]&&this._lines.drawAxisLine(T,this.bounds,k[T].mirrorOffset,this.lineColor[T],this.lineWidth[T]*this.pixelRatio);var P=u(m,k[T].primalMinor),z=u(y,k[T].mirrorMinor),I=this.lineTickLength;for(C=0;C<3;++C){var N=M/r[5*C];P[C]*=I[C]*N,z[C]*=I[C]*N}this.lineTickEnable[T]&&this._lines.drawAxisTicks(T,k[T].primalOffset,P,this.lineTickColor[T],this.lineTickWidth[T]*this.pixelRatio),this.lineTickMirror[T]&&this._lines.drawAxisTicks(T,k[T].mirrorOffset,z,this.lineTickColor[T],this.lineTickWidth[T]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var D,F;function j(t){(F=[0,0,0])[t]=1}function B(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0?j(n):a>0&&l<0?j(n):a<0&&l>0?j(n):a<0&&l<0?j(n):o>0&&s>0?j(i):o>0&&s<0?j(i):o<0&&s>0?j(i):o<0&&s<0&&j(i)}for(T=0;T<3;++T){var U=k[T].primalMinor,V=k[T].mirrorMinor,H=u(b,k[T].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[T]&&(H[C]+=M*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[T]=1,this.tickEnable[T]){-3600===this.tickAngle[T]?(this.tickAngle[T]=0,this.tickAlign[T]="auto"):this.tickAlign[T]=-1,D=1,"auto"===(E=[this.tickAlign[T],.5,D])[0]?E[0]=0:E[0]=parseInt(""+E[0]),F=[0,0,0],B(T,U,V);for(C=0;C<3;++C)H[C]+=M*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(T,this.tickSize[T],this.tickAngle[T],H,this.tickColor[T],q,F,E)}if(this.labelEnable[T]){D=0,F=[0,0,0],this.labels[T].length>4&&(j(T),D=1),"auto"===(E=[this.labelAlign[T],.5,D])[0]?E[0]=0:E[0]=parseInt(""+E[0]);for(C=0;C<3;++C)H[C]+=M*U[C]*this.labelPad[C]/r[5*C];H[T]+=.5*(a[0][T]+a[1][T]),this._text.drawLabel(T,this.labelSize[T],this.labelAngle[T],H,this.labelColor[T],[0,0,0],F,E)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":96,"./lib/cube.js":97,"./lib/lines.js":98,"./lib/text.js":100,"./lib/ticks.js":101}],96:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],d=-1;d<=1;d+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=d,h[l]=d;for(var p=-1;p<=1;p+=2){f[u]=p;for(var g=-1;g<=1;g+=2)f[c]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var v=u;u=c,c=v}var m=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),b=i(t,[{buffer:m,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:m,type:t.FLOAT,size:3,offset:12,stride:24}],y),x=a(t);return x.attributes.position.location=0,x.attributes.normal.location=1,new o(t,m,b,x)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":99,"gl-buffer":103,"gl-vao":168}],97:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,d){i(s,e,t),i(s,r,s);for(var y=0,b=0;b<2;++b){c[2]=a[b][2];for(var x=0;x<2;++x){c[1]=a[x][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[y],c,s),y+=1}}for(var w=-1,b=0;b<8;++b){for(var A=l[b][3],M=0;M<3;++M)u[b][M]=l[b][M]/A;d&&(u[b][2]*=-1),A<0&&(w<0?w=b:u[b][2]<u[w][2]&&(w=b))}if(w<0){w=0;for(var T=0;T<3;++T){for(var k=(T+2)%3,E=(T+1)%3,L=-1,S=-1,C=0;C<2;++C){var O=C<<T,R=O+(C<<k)+(1-C<<E),P=O+(1-C<<k)+(C<<E);o(u[O],u[R],u[P],f)<0||(C?L=1:S=1)}if(L<0||S<0)S>L&&(w|=1<<T);else{for(var C=0;C<2;++C){var O=C<<T,R=O+(C<<k)+(1-C<<E),P=O+(1-C<<k)+(C<<E),z=p([l[O],l[R],l[P],l[O+(1<<k)+(1<<E)]]);C?L=z:S=z}S>L&&(w|=1<<T)}}}for(var I=7^w,N=-1,b=0;b<8;++b)b!==w&&b!==I&&(N<0?N=b:u[N][1]>u[b][1]&&(N=b));for(var D=-1,b=0;b<3;++b){var F=N^1<<b;if(F!==w&&F!==I){D<0&&(D=F);var E=u[F];E[0]<u[D][0]&&(D=F)}}for(var j=-1,b=0;b<3;++b){var F=N^1<<b;if(F!==w&&F!==I&&F!==D){j<0&&(j=F);var E=u[F];E[0]>u[j][0]&&(j=F)}}var B=g;B[0]=B[1]=B[2]=0,B[n.log2(D^N)]=N&D,B[n.log2(N^j)]=N&j;var U=7^j;U===w||U===I?(U=7^D,B[n.log2(j^U)]=U&j):B[n.log2(D^U)]=U&D;for(var V=v,H=w,T=0;T<3;++T)V[T]=H&1<<T?-1:1;return m};var n=t("bit-twiddle"),i=t("gl-mat4/multiply"),a=t("split-polygon"),o=t("robust-orientation"),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var d=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function p(t){for(var e=0;e<d.length;++e)if((t=a.positive(t,d[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{"bit-twiddle":36,"gl-mat4/multiply":126,"robust-orientation":322,"split-polygon":339}],98:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,p=0;p<r[f].length;++p){var g=+r[f][p].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;s[f]=h,l[f]=v-h;for(var h=o.length/3|0,m=0;m<r[f].length;++m){var g=+r[f][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3|0;u[f]=h,c[f]=v-h}var y=n(t,new Float32Array(o)),b=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),x=a(t);return x.attributes.position.location=0,new 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o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},p.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},p.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var d=h(u,n);d[e]+=r[0][e],this.shader.uniforms.offset=d;var p=f(o);p[t]=1,this.shader.uniforms.majorAxis=p;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},p.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var d=f(l);d[e]=1,this.shader.uniforms.screenAxis=d,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},p.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":99,"gl-buffer":103,"gl-vao":168}],99:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,u,c,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":149,glslify:250}],100:[function(t,e,r){(function(r){"use strict";e.exports=function(t,e,r,a,s,l){var c=n(t),f=i(t,[{buffer:c,size:3}]),h=o(t);h.attributes.position.location=0;var d=new u(t,h,c,f);return d.update(e,r,a,s,l),d};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function u(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var c=u.prototype,f=[0,0];c.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},c.unbind=function(){this.vao.unbind()},c.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var u=l[r];u||(u=l[r]={});var c=u[e];c||(c=u[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=c.positions,d=c.cells,p=0,g=d.length;p<g;++p)for(var v=d[p],m=2;m>=0;--m){var y=h[v[m]];o.push(f*y[0],-f*y[1],t)}}for(var u=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],d={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},p=0;p<3;++p){f[p]=o.length/3|0,s(.5*(t[0][p]+t[1][p]),e[p],r[p],12,1.25,d),h[p]=(o.length/3|0)-f[p],u[p]=o.length/3|0;for(var g=0;g<n[p].length;++g)n[p][g].text&&s(n[p][g].x,n[p][g].text,n[p][g].font||i,n[p][g].fontSize||12,1.25,d);c[p]=(o.length/3|0)-u[p]}this.buffer.update(o),this.tickOffset=u,this.tickCount=c,this.labelOffset=f,this.labelCount=h},c.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},c.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},c.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t("_process"))},{"./shaders":99,_process:308,"gl-buffer":103,"gl-vao":168,"vectorize-text":351}],101:[function(t,e,r){"use 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h=e.model||u,d=e.view||u,m=e.projection||u,y=e._ortho||!1,b=t.bounds,x=(f=f||a(h,d,m,b,y)).axis;o(c,d,h),o(c,m,c);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var A=n(s(c,c));s(c,c);for(var M=0;M<3;++M){var T=(M+1)%3,k=(M+2)%3,E=v;t:for(var w=0;w<2;++w){var L=[];if(x[M]<0!=!!w){E[M]=b[w][M];for(var S=0;S<2;++S){E[T]=b[S^w][T];for(var C=0;C<2;++C)E[k]=b[C^S^w][k],L.push(E.slice())}for(var O=y?5:4,S=O;S===O;++S){if(0===L.length)continue t;L=i.positive(L,A[S])}for(var S=0;S<L.length;++S)for(var k=L[S],R=p(v,c,k,r,l),C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,k[C]),_[C].hi=Math.max(_[C].hi,k[C]),C!==M&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(R[C])))}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],d=[0,0,0,1];function p(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=d,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],v=[0,0,0]},{"./lib/cube.js":97,"extract-frustum-planes":89,"gl-mat4/multiply":126,"gl-mat4/transpose":135,"gl-vec4/transformMat4":239,"split-polygon":339}],103:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&"undefined"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,"uint16"):c(t,"float32"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:286,"ndarray-ops":280,"typedarray-pool":348}],104:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r},a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var u=t[0],c=t[1],f=t[2],h=r[0].length,d=r[1].length,p=r[2].length,g=i(r[0],u),v=i(r[1],c),m=i(r[2],f),y=g+1,b=v+1,x=m+1;if(l&&(g=s(g,0,h-1),y=s(y,0,h-1),v=s(v,0,d-1),b=s(b,0,d-1),m=s(m,0,p-1),x=s(x,0,p-1)),g<0||v<0||m<0||y>=h||b>=d||x>=p)return n.create();var _=(u-r[0][g])/(r[0][y]-r[0][g]),w=(c-r[1][v])/(r[1][b]-r[1][v]),A=(f-r[2][m])/(r[2][x]-r[2][m]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(A<0||A>1||isNaN(A))&&(A=0);var M=m*h*d,T=x*h*d,k=v*h,E=b*h,L=g,S=y,C=e[k+M+L],O=e[k+M+S],R=e[E+M+L],P=e[E+M+S],z=e[k+T+L],I=e[k+T+S],N=e[E+T+L],D=e[E+T+S],F=n.create();return n.lerp(F,C,O,_),n.lerp(a,R,P,_),n.lerp(F,F,a,w),n.lerp(a,z,I,_),n.lerp(o,N,D,_),n.lerp(a,a,o,w),n.lerp(F,F,a,A),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),o;for(var s=0,u=1/0,c=-1/0,f=1/0,h=-1/0,d=1/0,p=-1/0,g=null,v=null,m=[],y=1/0,b=0;b<r.length;b++){var x,_=r[b];u=Math.min(_[0],u),c=Math.max(_[0],c),f=Math.min(_[1],f),h=Math.max(_[1],h),d=Math.min(_[2],d),p=Math.max(_[2],p),x=i?l(_,a,i,!0):a[b],n.length(x)>s&&(s=n.length(x)),b&&(y=Math.min(y,2*n.distance(g,_)/(n.length(v)+n.length(x)))),g=_,v=x,m.push(x)}var w=[u,f,d],A=[c,h,p];e&&(e[0]=w,e[1]=A),0===s&&(s=1);var M=1/s;isFinite(y)&&!isNaN(y)||(y=1),o.vectorScale=y;var T=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),k=t.coneSize||.5;t.absoluteConeSize&&(k=t.absoluteConeSize*M),o.coneScale=k;b=0;for(var E=0;b<r.length;b++)for(var L=(_=r[b])[0],S=_[1],C=_[2],O=m[b],R=n.length(O)*M,P=0;P<8;P++){o.positions.push([L,S,C,E++]),o.positions.push([L,S,C,E++]),o.positions.push([L,S,C,E++]),o.positions.push([L,S,C,E++]),o.positions.push([L,S,C,E++]),o.positions.push([L,S,C,E++]),o.vectors.push(O),o.vectors.push(O),o.vectors.push(O),o.vectors.push(O),o.vectors.push(O),o.vectors.push(O),o.vertexIntensity.push(R,R,R),o.vertexIntensity.push(R,R,R),o.vertexNormals.push(T,T,T),o.vertexNormals.push(T,T,T);var z=o.positions.length;o.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return o},e.exports.createConeMesh=t("./lib/conemesh")},{"./lib/conemesh":105,"gl-vec3":187}],105:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),u=t("gl-mat4/invert"),c=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),d=t("typedarray-pool"),p=t("./shaders"),g=p.meshShader,v=p.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,u,c,f,h,d,p,g,v,y,b,x,_,w,A,M){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleNormals=u,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.lineWidth=1,this.edgePositions=f,this.edgeColors=d,this.edgeUVs=p,this.edgeIds=h,this.edgeVAO=g,this.edgeCount=0,this.pointPositions=v,this.pointColors=b,this.pointUVs=x,this.pointSizes=_,this.pointIds=y,this.pointVAO=w,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=M,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var b=y.prototype;function x(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}b.isOpaque=function(){return this.opacity>=1},b.isTransparent=function(){return this.opacity<1},b.pickSlots=1,b.setPickBase=function(t){this.pickId=t},b.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=d.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var p=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[p],b=this.positions[g],x=0;x<3;++x)o[s++]=v*y[x]+m*b[x]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),d.free(o)}else this.contourCount=0},b.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],u=[],h=[],d=[],p=[],g=[],v=[],m=[],y=[],b=[],x=[],_=[],w=[],A=[];this.cells=r,this.positions=n;var M=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,E=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,E)),T||M||(M=s.vertexNormals(r,n,k));var L=t.vertexColors,S=t.cellColors,C=t.meshColor||[1,1,1,1],O=t.vertexUVs,R=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,I=1/0,N=-1/0;if(!O&&!P)if(R)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],N=+t.vertexIntensityBounds[1];else for(var D=0;D<R.length;++D){var F=R[D];I=Math.min(I,F),N=Math.max(N,F)}else if(z)for(D=0;D<z.length;++D){F=z[D];I=Math.min(I,F),N=Math.max(N,F)}else for(D=0;D<n.length;++D){F=n[D][2];I=Math.min(I,F),N=Math.max(N,F)}this.intensity=R||(z?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,z):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var j=t.pointSizes,B=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var U=n[D],V=0;V<3;++V)!isNaN(U[V])&&isFinite(U[V])&&(this.bounds[0][V]=Math.min(this.bounds[0][V],U[V]),this.bounds[1][V]=Math.max(this.bounds[1][V],U[V]));var H=0,q=0,G=0;t:for(D=0;D<r.length;++D){var X=r[D];switch(X.length){case 1:for(U=n[Y=X[0]],V=0;V<3;++V)if(isNaN(U[V])||!isFinite(U[V]))continue t;b.push(U[0],U[1],U[2],U[3]),3===(Z=L?L[Y]:S?S[D]:C).length?x.push(Z[0],Z[1],Z[2],1):x.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],_.push(Q[0],Q[1]),j?w.push(j[Y]):w.push(B),A.push(D),G+=1;break;case 2:for(V=0;V<2;++V){U=n[Y=X[V]];for(var W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t}for(V=0;V<2;++V){U=n[Y=X[V]];g.push(U[0],U[1],U[2]),3===(Z=L?L[Y]:S?S[D]:C).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],m.push(Q[0],Q[1]),y.push(D)}q+=1;break;case 3:for(V=0;V<3;++V)for(U=n[Y=X[V]],W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t;for(V=0;V<3;++V){var Y;U=n[Y=X[2-V]];a.push(U[0],U[1],U[2],U[3]);var Z,Q,$,J=i[Y];l.push(J[0],J[1],J[2]),3===(Z=L?L[Y]:S?S[D]:C).length?u.push(Z[0],Z[1],Z[2],1):u.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],d.push(Q[0],Q[1]),$=M?M[Y]:T[D],h.push($[0],$[1],$[2]),p.push(D)}H+=1}}this.pointCount=G,this.edgeCount=q,this.triangleCount=H,this.pointPositions.update(b),this.pointColors.update(x),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(A)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(u),this.triangleUVs.update(d),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(p))}},b.drawTransparent=b.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:m.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);l(c,s.view,s.model),l(c,s.projection,c),u(c,c);for(o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f=c[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var h=c[12+o],d=0;d<3;++d)h+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=h/f}if(this.triangleCount>0){var p=this.triShader;p.bind(),p.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},b.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind())},b.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},b.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=x(t),l=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var u=i(t),f=i(t),h=i(t),d=i(t),p=i(t),v=i(t),m=a(t,[{buffer:u,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:d,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),A=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:A,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),T=i(t),k=i(t),E=i(t),L=i(t),S=i(t),C=a(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:S,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2},{buffer:L,type:t.FLOAT,size:1}]),O=i(t),R=new y(t,l,r,s,u,f,v,h,d,p,m,b,A,_,w,M,T,S,k,E,L,C,O,a(t,[{buffer:O,type:t.FLOAT,size:3}]));return R.update(e),R}},{"./shaders":106,colormap:68,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-shader":149,"gl-texture2d":164,"gl-vao":168,ndarray:286,normals:288,"simplicial-complex-contour":330,"typedarray-pool":348}],106:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float vectorScale;\nuniform float coneScale;\n\nuniform float coneOffset;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data\n           , f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal,0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data\n           , f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nuniform float vectorScale;\nuniform float coneScale;\nuniform float coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:250}],107:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34000:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],108:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":107}],109:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var d=n[l],p=e[s];if(Array.isArray(p[0])&&(p=e[l]),3===p.length?p=[p[0],p[1],p[2],1]:4===p.length&&(p=[p[0],p[1],p[2],p[3]],!this.hasAlpha&&p[3]<1&&(this.hasAlpha=!0)),!isNaN(d[0][s])&&!isNaN(d[1][s])){var g;if(d[0][s]<0)(g=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),u(this.bounds,g),o+=2+f(i,g,p,s);if(d[1][s]>0)(g=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),u(this.bounds,g),o+=2+f(i,g,p,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":110,"gl-buffer":103,"gl-vao":168}],110:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":149,glslify:250}],111:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension("WEBGL_draw_buffers");!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!u)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,d=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!d)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&d&&(h=t.FLOAT);var g=!0;"depth"in n&&(g=!!n.depth);var v=!1;"stencil"in n&&(v=!!n.stencil);return new p(t,e,r,h,f,g,v,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function d(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function p(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var p=0;p<i;++p)this.color[p]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,v=[0|e,0|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,p=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=h(r,i,a,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=d(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?p?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&p?t._depth_rb=d(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=d(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):p&&(t._depth_rb=d(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(b)}c(r,e)}(this)}var g=p.prototype;function v(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":164}],112:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var u=n("Error compiling %s shader %s:\n",l,s),c=n("%s%s",u,t),f=t.split("\n"),h={},d=0;d<f.length;d++){var p=f[d];if(""!==p&&"\0"!==p){var g=parseInt(p.split(":")[2]);if(isNaN(g))throw new Error(n("Could not parse error: %s",p));h[g]=p}}for(var v=o(e).split("\n"),d=0;d<v.length;d++)if(h[d+3]||h[d+2]||h[d+1]){var m=v[d];if(u+=m+"\n",h[d+1]){var y=h[d+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),u+=n("^^^ %s\n\n",y)}}return{long:u.trim(),short:c.trim()}}},{"add-line-numbers":12,"gl-constants/lookup":108,"glsl-shader-name":242,"sprintf-js":340}],113:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\nlowp vec4 encode_float_1604150559(highp float v) {\n  highp float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  highp vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  highp float e = floor(log2(av));\n  highp float m = av * pow(2.0, -e) - 1.0;\n  \n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n  \n  //Unpack exponent\n  highp float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0; \n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, encode_float_1604150559(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":149,glslify:250}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=c(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),u=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),h=l(new Array(1024),[256,1,4]),d=0;d<1024;++d)h.data[d]=255;var p=a(e,h);p.wrap=e.REPEAT;var g=new v(e,r,o,s,u,p);return g.update(t),g};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=t("glsl-read-float"),s=t("binary-search-bounds"),l=t("ndarray"),u=t("./lib/shaders"),c=u.createShader,f=u.createPickShader,h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function p(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=v.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||h,view:t.view||h,projection:t.projection||h,clipBounds:p(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||h,view:t.view||h,projection:t.projection||h,pickId:this.pickId,clipBounds:p(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],u=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,v=!1;t:for(e=1;e<h.length;++e){var m,y,b,x=h[e-1],_=h[e];for(a.push(u),o.push(x.slice()),r=0;r<3;++r){if(isNaN(x[r])||isNaN(_[r])||!isFinite(x[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,v=!0}continue t}f[0][r]=Math.min(f[0][r],x[r],_[r]),f[1][r]=Math.max(f[1][r],x[r],_[r])}Array.isArray(p[0])?(m=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):m=y=p,3===m.length&&(m=[m[0],m[1],m[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&m[3]<1&&(this.hasAlpha=!0),b=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var A=u;if(u+=d(x,_),v){for(r=0;r<2;++r)i.push(x[0],x[1],x[2],_[0],_[1],_[2],A,b,m[0],m[1],m[2],m[3]);c+=2,v=!1}i.push(x[0],x[1],x[2],_[0],_[1],_[2],A,b,m[0],m[1],m[2],m[3],x[0],x[1],x[2],_[0],_[1],_[2],A,-b,m[0],m[1],m[2],m[3],_[0],_[1],_[2],x[0],x[1],x[2],u,-b,y[0],y[1],y[2],y[3],_[0],_[1],_[2],x[0],x[1],x[2],u,b,y[0],y[1],y[2],y[3]),c+=4}}if(this.buffer.update(i),a.push(u),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,"dashes"in t){var M=t.dashes.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var T=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)T.set(e,0,r,0);1&s.le(M,M[M.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,u=[0,0,0],c=0;c<3;++c)u[c]=l*n[c]+a*i[c];var f=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,f,this.points[f])}},{"./lib/shaders":113,"binary-search-bounds":115,"gl-buffer":103,"gl-texture2d":164,"gl-vao":168,"glsl-read-float":241,ndarray:286}],115:[function(t,e,r){arguments[4][54][0].apply(r,arguments)},{dup:54}],116:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}},{}],117:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=c*o-s*u,h=-c*a+s*l,d=u*a-o*l,p=r*f+n*h+i*d;return p?(p=1/p,t[0]=f*p,t[1]=(-c*n+i*u)*p,t[2]=(s*n-i*o)*p,t[3]=h*p,t[4]=(c*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-u*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}},{}],118:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],119:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],120:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],d=t[12],p=t[13],g=t[14],v=t[15];return(e*o-r*a)*(f*v-h*g)-(e*s-n*a)*(c*v-h*p)+(e*l-i*a)*(c*g-f*p)+(r*s-n*o)*(u*v-h*d)-(r*l-i*o)*(u*g-f*d)+(n*l-i*s)*(u*p-c*d)}},{}],121:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,d=i*s,p=i*l,g=a*o,v=a*s,m=a*l;return t[0]=1-f-p,t[1]=c+m,t[2]=h-v,t[3]=0,t[4]=c-m,t[5]=1-u-p,t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],122:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,d=i*l,p=i*u,g=a*u,v=o*s,m=o*l,y=o*u;return t[0]=1-(d+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(c+g),t[6]=p+v,t[7]=0,t[8]=h+m,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],123:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],124:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],d=e[11],p=e[12],g=e[13],v=e[14],m=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,A=i*u-a*l,M=c*g-f*p,T=c*v-h*p,k=c*m-d*p,E=f*v-h*g,L=f*m-d*g,S=h*m-d*v,C=y*S-b*L+x*E+_*k-w*T+A*M;if(!C)return null;return C=1/C,t[0]=(s*S-l*L+u*E)*C,t[1]=(i*L-n*S-a*E)*C,t[2]=(g*A-v*w+m*_)*C,t[3]=(h*w-f*A-d*_)*C,t[4]=(l*k-o*S-u*T)*C,t[5]=(r*S-i*k+a*T)*C,t[6]=(v*x-p*A-m*b)*C,t[7]=(c*A-h*x+d*b)*C,t[8]=(o*L-s*k+u*M)*C,t[9]=(n*k-r*L-a*M)*C,t[10]=(p*w-g*x+m*y)*C,t[11]=(f*x-c*w-d*y)*C,t[12]=(s*T-o*E-l*M)*C,t[13]=(r*E-n*T+i*M)*C,t[14]=(g*b-p*_-v*y)*C,t[15]=(c*_-f*b+h*y)*C,t}},{}],125:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,d,p,g=e[0],v=e[1],m=e[2],y=i[0],b=i[1],x=i[2],_=r[0],w=r[1],A=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-A)<1e-6)return n(t);f=g-_,h=v-w,d=m-A,p=1/Math.sqrt(f*f+h*h+d*d),a=b*(d*=p)-x*(h*=p),o=x*(f*=p)-y*d,s=y*h-b*f,(p=Math.sqrt(a*a+o*o+s*s))?(a*=p=1/p,o*=p,s*=p):(a=0,o=0,s=0);l=h*s-d*o,u=d*a-f*s,c=f*o-h*a,(p=Math.sqrt(l*l+u*u+c*c))?(l*=p=1/p,u*=p,c*=p):(l=0,u=0,c=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=d,t[11]=0,t[12]=-(a*g+o*v+s*m),t[13]=-(l*g+u*v+c*m),t[14]=-(f*g+h*v+d*m),t[15]=1,t}},{"./identity":123}],126:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],d=e[10],p=e[11],g=e[12],v=e[13],m=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*f+w*g,t[1]=b*i+x*l+_*h+w*v,t[2]=b*a+x*u+_*d+w*m,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*f+w*g,t[5]=b*i+x*l+_*h+w*v,t[6]=b*a+x*u+_*d+w*m,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*f+w*g,t[9]=b*i+x*l+_*h+w*v,t[10]=b*a+x*u+_*d+w*m,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*f+w*g,t[13]=b*i+x*l+_*h+w*v,t[14]=b*a+x*u+_*d+w*m,t[15]=b*o+x*c+_*p+w*y,t}},{}],127:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t}},{}],128:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],129:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,u,c,f,h,d,p,g,v,m,y,b,x,_,w,A,M,T,k,E,L=n[0],S=n[1],C=n[2],O=Math.sqrt(L*L+S*S+C*C);if(Math.abs(O)<1e-6)return null;L*=O=1/O,S*=O,C*=O,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],u=e[2],c=e[3],f=e[4],h=e[5],d=e[6],p=e[7],g=e[8],v=e[9],m=e[10],y=e[11],b=L*L*o+a,x=S*L*o+C*i,_=C*L*o-S*i,w=L*S*o-C*i,A=S*S*o+a,M=C*S*o+L*i,T=L*C*o+S*i,k=S*C*o-L*i,E=C*C*o+a,t[0]=s*b+f*x+g*_,t[1]=l*b+h*x+v*_,t[2]=u*b+d*x+m*_,t[3]=c*b+p*x+y*_,t[4]=s*w+f*A+g*M,t[5]=l*w+h*A+v*M,t[6]=u*w+d*A+m*M,t[7]=c*w+p*A+y*M,t[8]=s*T+f*k+g*E,t[9]=l*T+h*k+v*E,t[10]=u*T+d*k+m*E,t[11]=c*T+p*k+y*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],130:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],131:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],132:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],133:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],134:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,d,p,g=r[0],v=r[1],m=r[2];e===t?(t[12]=e[0]*g+e[4]*v+e[8]*m+e[12],t[13]=e[1]*g+e[5]*v+e[9]*m+e[13],t[14]=e[2]*g+e[6]*v+e[10]*m+e[14],t[15]=e[3]*g+e[7]*v+e[11]*m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=d,t[11]=p,t[12]=n*g+s*v+f*m+e[12],t[13]=i*g+l*v+h*m+e[13],t[14]=a*g+u*v+d*m+e[14],t[15]=o*g+c*v+p*m+e[15]);return t}},{}],135:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],136:[function(t,e,r){"use strict";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:i(t,e);break;case 16:a(t,e);break;default:throw new Error("currently supports matrices up to 4x4")}return t};var n=t("gl-mat2/invert"),i=t("gl-mat3/invert"),a=t("gl-mat4/invert")},{"gl-mat2/invert":116,"gl-mat3/invert":117,"gl-mat4/invert":124}],137:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),c=0;c<t.length;++c)u[c]=o(t[c],r,a,s,l);for(var f=0,h=1/0,c=0;c<u.length;++c){for(var d=0,p=0;p<2;++p)d+=Math.pow(u[c][p]-e[p],2);d<h&&(h=d,f=c)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0,c=0;c<3;++c){if(g[c]<-.001||g[c]>1.0001)return null;v+=g[c]}if(Math.abs(v-1)>.001)return null;return[f,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:17,"polytope-closest-point/lib/closest_point_2d.js":307}],138:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal,0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor,1);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:d,fragment:p,attributes:[{name:"position",type:"vec3"}]}},{glslify:250}],139:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),u=t("gl-mat4/invert"),c=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),d=t("typedarray-pool"),p=t("./lib/shaders"),g=t("./lib/closest-point"),v=p.meshShader,m=p.wireShader,y=p.pointShader,b=p.pickShader,x=p.pointPickShader,_=p.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function A(t,e,r,n,i,a,o,s,l,u,c,f,h,d,p,g,v,m,y,b,x,_,A,M,T,k,E){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=A,this.pointSizes=M,this.pointIds=x,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=k,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var M=A.prototype;function T(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function k(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}M.isOpaque=function(){return this.opacity>=1},M.isTransparent=function(){return this.opacity<1},M.pickSlots=1,M.setPickBase=function(t){this.pickId=t},M.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=d.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var p=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[p],b=this.positions[g],x=0;x<3;++x)o[s++]=v*y[x]+m*b[x]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),d.free(o)}else this.contourCount=0},M.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],d=[],p=[],g=[],v=[],m=[],y=[],b=[],x=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,A=t.cellNormals,M=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,T)),A||w||(w=s.vertexNormals(r,n,M));var k=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],S=t.vertexUVs,C=t.vertexIntensity,O=t.cellUVs,R=t.cellIntensity,P=1/0,z=-1/0;if(!S&&!O)if(C)if(t.vertexIntensityBounds)P=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var I=0;I<C.length;++I){var N=C[I];P=Math.min(P,N),z=Math.max(z,N)}else if(R)for(I=0;I<R.length;++I){N=R[I];P=Math.min(P,N),z=Math.max(z,N)}else for(I=0;I<n.length;++I){N=n[I][2];P=Math.min(P,N),z=Math.max(z,N)}this.intensity=C||(R?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,R):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var D=t.pointSizes,F=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(I=0;I<n.length;++I)for(var j=n[I],B=0;B<3;++B)!isNaN(j[B])&&isFinite(j[B])&&(this.bounds[0][B]=Math.min(this.bounds[0][B],j[B]),this.bounds[1][B]=Math.max(this.bounds[1][B],j[B]));var U=0,V=0,H=0;t:for(I=0;I<r.length;++I){var q=r[I];switch(q.length){case 1:for(j=n[X=q[0]],B=0;B<3;++B)if(isNaN(j[B])||!isFinite(j[B]))continue t;m.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[I]:L).length?y.push(W[0],W[1],W[2],1):y.push(W[0],W[1],W[2],W[3]),Y=S?S[X]:C?[(C[X]-P)/(z-P),0]:O?O[I]:R?[(R[I]-P)/(z-P),0]:[(j[2]-P)/(z-P),0],b.push(Y[0],Y[1]),D?x.push(D[X]):x.push(F),_.push(I),H+=1;break;case 2:for(B=0;B<2;++B){j=n[X=q[B]];for(var G=0;G<3;++G)if(isNaN(j[G])||!isFinite(j[G]))continue t}for(B=0;B<2;++B){j=n[X=q[B]];d.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[I]:L).length?p.push(W[0],W[1],W[2],1):p.push(W[0],W[1],W[2],W[3]),Y=S?S[X]:C?[(C[X]-P)/(z-P),0]:O?O[I]:R?[(R[I]-P)/(z-P),0]:[(j[2]-P)/(z-P),0],g.push(Y[0],Y[1]),v.push(I)}V+=1;break;case 3:for(B=0;B<3;++B)for(j=n[X=q[B]],G=0;G<3;++G)if(isNaN(j[G])||!isFinite(j[G]))continue t;for(B=0;B<3;++B){var X,W,Y,Z;j=n[X=q[2-B]];i.push(j[0],j[1],j[2]),3===(W=k?k[X]:E?E[I]:L).length?a.push(W[0],W[1],W[2],1):a.push(W[0],W[1],W[2],W[3]),Y=S?S[X]:C?[(C[X]-P)/(z-P),0]:O?O[I]:R?[(R[I]-P)/(z-P),0]:[(j[2]-P)/(z-P),0],u.push(Y[0],Y[1]),Z=w?w[X]:A[I],l.push(Z[0],Z[1],Z[2]),h.push(I)}U+=1}}this.pointCount=H,this.edgeCount=V,this.triangleCount=U,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(b),this.pointSizes.update(x),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(d),this.edgeColors.update(p),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},M.drawTransparent=M.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);l(c,s.view,s.model),l(c,s.projection,c),u(c,c);for(o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var d=c[12+o],p=0;p<3;++p)d+=c[4*p+o]*this.lightPosition[p];s.lightPosition[o]=d/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},M.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},M.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},M.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=T(t),u=k(t),f=E(t),h=L(t),d=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));d.generateMipmap(),d.minFilter=t.LINEAR_MIPMAP_LINEAR,d.magFilter=t.LINEAR;var p=i(t),g=i(t),y=i(t),b=i(t),x=i(t),_=a(t,[{buffer:p,type:t.FLOAT,size:3},{buffer:x,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:b,type:t.FLOAT,size:3}]),w=i(t),M=i(t),S=i(t),C=i(t),O=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:M,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2}]),R=i(t),P=i(t),z=i(t),I=i(t),N=i(t),D=a(t,[{buffer:R,type:t.FLOAT,size:3},{buffer:N,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:P,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:I,type:t.FLOAT,size:1}]),F=i(t),j=new A(t,d,r,s,l,u,f,h,p,x,g,y,b,_,w,C,M,S,O,R,N,P,z,I,D,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return j.update(e),j}},{"./lib/closest-point":137,"./lib/shaders":138,colormap:68,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-shader":149,"gl-texture2d":164,"gl-vao":168,ndarray:286,normals:288,"simplicial-complex-contour":330,"typedarray-pool":348}],140:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,d=t.clientHeight,p={keyBindingMode:"rotate",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===d;return h=t.clientWidth,d=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(p,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),"turntable"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",function(t){return t.preventDefault(),!1}),p._lastX=-1,p._lastY=-1,p._lastMods={shift:!1,control:!1,alt:!1,meta:!1},p.enableMouseListeners=function(){function e(e,r,i,a){var o=p.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,c="zoom"===o,h=!!a.control,d=!!a.alt,g=!!a.shift,v=!!(1&e),m=!!(2&e),y=!!(4&e),b=1/t.clientHeight,x=b*(r-p._lastX),_=b*(i-p._lastY),w=p.flipX?1:-1,A=p.flipY?1:-1,M=Math.PI*p.rotateSpeed,T=n();if(-1!==p._lastX&&-1!==p._lastY&&((s&&v&&!h&&!d&&!g||v&&!h&&!d&&g)&&u.rotate(T,w*M*x,-A*M*_,0),(l&&v&&!h&&!d&&!g||m||v&&h&&!d&&!g)&&u.pan(T,-p.translateSpeed*x*f,p.translateSpeed*_*f,0),c&&v&&!h&&!d&&!g||y||v&&!h&&d&&!g)){var k=-p.zoomSpeed*_/window.innerHeight*(T-u.lastT())*100;u.pan(T,0,0,f*(Math.exp(k)-1))}return p._lastX=r,p._lastY=i,p._lastMods=a,!0}}p.mouseListener=a(t,e),t.addEventListener("touchstart",function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],p._lastMods),e(1,n[0],n[1],p._lastMods),r.preventDefault()},!!l&&{passive:!1}),t.addEventListener("touchmove",function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],p._lastMods),r.preventDefault()},!!l&&{passive:!1}),t.addEventListener("touchend",function(t){e(0,p._lastX,p._lastY,p._lastMods),t.preventDefault()},!!l&&{passive:!1}),p.wheelListener=o(t,function(t,e){if(!1!==p.keyBindingMode&&p.enableWheel){var r=p.flipX?1:-1,i=p.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*p.rotateSpeed/window.innerWidth);else if(!p._ortho){var o=-p.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}},!0)},p.enableMouseListeners(),p};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":10,"has-passive-events":252,"mouse-change":271,"mouse-event-offset":272,"mouse-wheel":274,"right-now":316}],141:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":149,glslify:250}],142:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),u=t("a-big-triangle"),c=t("mouse-change"),f=t("mouse-wheel"),h=t("gl-mat4/perspective"),d=t("gl-mat4/ortho"),p=t("./lib/shader"),g=t("is-mobile")({tablet:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function y(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e)if(e=document.createElement("canvas"),t.container){var r=t.container;r.appendChild(e)}else document.body.appendChild(e);var b=t.gl;b||(b=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:g}));if(!b)throw new Error("webgl not supported");var x=t.bounds||[[-10,-10,-10],[10,10,10]],_=new v,w=l(b,[b.drawingBufferWidth,b.drawingBufferHeight],{preferFloat:!g}),A=p(b),M=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:M},k=t.axes||{},E=i(b,k);E.enable=!k.disable;var L=t.spikes||{},S=o(b,L),C=[],O=[],R=[],P=[],z=!0,I=!0,N=new Array(16),D=new Array(16),F={view:null,projection:N,model:D,_ortho:!1},I=!0,j=[b.drawingBufferWidth,b.drawingBufferHeight],B=t.cameraObject||n(e,T),U={gl:b,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:_,camera:B,axes:E,axesPixels:null,spikes:S,bounds:x,objects:C,shape:j,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:F,oncontextloss:null,mouseListener:null,_stopped:!1},V=[b.drawingBufferWidth/U.pixelRatio|0,b.drawingBufferHeight/U.pixelRatio|0];function H(){if(!U._stopped&&U.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*U.pixelRatio),a=0|Math.ceil(n*U.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",z=!0}}}U.autoResize&&H();function q(){for(var t=C.length,e=P.length,r=0;r<e;++r)R[r]=0;t:for(var r=0;r<t;++r){var n=C[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(R[a]+i<255){O[r]=a,n.setPickBase(R[a]+1),R[a]+=i;continue t}var o=s(b,j);O[r]=e,P.push(o),R.push(i),n.setPickBase(1),e+=1}else O[r]=-1}for(;e>0&&0===R[e-1];)R.pop(),P.pop().dispose()}function G(){if(U.contextLost)return!0;b.isContextLost()&&(U.contextLost=!0,U.mouseListener.enabled=!1,U.selection.object=null,U.oncontextloss&&U.oncontextloss())}window.addEventListener("resize",H),U.update=function(t){U._stopped||(t=t||{},z=!0,I=!0)},U.add=function(t){U._stopped||(t.axes=E,C.push(t),O.push(-1),z=!0,I=!0,q())},U.remove=function(t){if(!U._stopped){var e=C.indexOf(t);e<0||(C.splice(e,1),O.pop(),z=!0,I=!0,q())}},U.dispose=function(){if(!U._stopped&&(U._stopped=!0,window.removeEventListener("resize",H),e.removeEventListener("webglcontextlost",G),U.mouseListener.enabled=!1,!U.contextLost)){E.dispose(),S.dispose();for(var t=0;t<C.length;++t)C[t].dispose();w.dispose();for(var t=0;t<P.length;++t)P[t].dispose();A.dispose(),b=null,E=null,S=null,C=[]}},U.wheelListener=f(e,function(t,e){if(!1!==B.keyBindingMode&&B.enableWheel&&B._ortho){var r=t>e?1.1:1/1.1;U.aspect[0]*=r,U.aspect[1]*=r,U.aspect[2]*=r,U.redraw()}},!0),U._mouseRotating=!1,U._prevButtons=0,U.enableMouseListeners=function(){U.mouseListener=c(e,function(t,e,r){if(!U._stopped){var n=P.length,i=C.length,a=_.object;_.distance=1/0,_.mouse[0]=e,_.mouse[1]=r,_.object=null,_.screen=null,_.dataCoordinate=_.dataPosition=null;var o=!1;if(t&&U._prevButtons)U._mouseRotating=!0;else{U._mouseRotating&&(I=!0),U._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,V[1]-r-1,U.pickRadius);if(l){if(l.distance>_.distance)continue;for(var u=0;u<i;++u){var c=C[u];if(O[u]===s){var f=c.pick(l);f&&(_.buttons=t,_.screen=l.coord,_.distance=l.distance,_.object=c,_.index=f.distance,_.dataPosition=f.position,_.dataCoordinate=f.dataCoordinate,_.data=f,o=!0)}}}}}a&&a!==_.object&&(a.highlight&&a.highlight(null),z=!0),_.object&&(_.object.highlight&&_.object.highlight(_.data),z=!0),(o=o||_.object!==a)&&U.onselect&&U.onselect(_),1&t&&!(1&U._prevButtons)&&U.onclick&&U.onclick(_),U._prevButtons=t}})},e.addEventListener("webglcontextlost",G);var X=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],W=[X[0].slice(),X[1].slice()];function Y(){if(!G()){H();var t=U.camera.tick();F.view=U.camera.matrix,z=z||t,I=I||t,E.pixelRatio=U.pixelRatio,S.pixelRatio=U.pixelRatio;var e=C.length,r=X[0],n=X[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var i=0;i<e;++i){var o=C[i];o.pixelRatio=U.pixelRatio,o.axes=U.axes,z=z||!!o.dirty,I=I||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],c=s[1],f=0;f<3;++f)r[f]=Math.min(r[f],l[f]),n[f]=Math.max(n[f],c[f])}var p=U.bounds;if(U.autoBounds)for(var f=0;f<3;++f){if(n[f]<r[f])r[f]=-1,n[f]=1;else{r[f]===n[f]&&(r[f]-=1,n[f]+=1);var g=.05*(n[f]-r[f]);r[f]=r[f]-g,n[f]=n[f]+g}p[0][f]=r[f],p[1][f]=n[f]}for(var v=!1,f=0;f<3;++f)v=v||W[0][f]!==p[0][f]||W[1][f]!==p[1][f],W[0][f]=p[0][f],W[1][f]=p[1][f];if(I=I||v,z=z||v){if(v){for(var y=[0,0,0],i=0;i<3;++i)y[i]=m((p[1][i]-p[0][i])/10);E.autoTicks?E.update({bounds:p,tickSpacing:y}):E.update({bounds:p})}var x=b.drawingBufferWidth,T=b.drawingBufferHeight;j[0]=x,j[1]=T,V[0]=0|Math.max(x/U.pixelRatio,1),V[1]=0|Math.max(T/U.pixelRatio,1),M?(d(N,-x/T,x/T,-1,1,U.zNear,U.zFar),F._ortho=!0):(h(N,U.fovy,x/T,U.zNear,U.zFar),F._ortho=!1);for(var i=0;i<16;++i)D[i]=0;D[15]=1;for(var k=0,i=0;i<3;++i)k=Math.max(k,p[1][i]-p[0][i]);for(var i=0;i<3;++i)U.autoScale?D[5*i]=U.aspect[i]/(p[1][i]-p[0][i]):D[5*i]=1/k,U.autoCenter&&(D[12+i]=.5*-D[5*i]*(p[0][i]+p[1][i]));for(var i=0;i<e;++i){var o=C[i];o.axesBounds=p,U.clipToBounds&&(o.clipBounds=p)}_.object&&(U.snapToData?S.position=_.dataCoordinate:S.position=_.dataPosition,S.bounds=p),I&&(I=!1,function(){if(G())return;b.colorMask(!0,!0,!0,!0),b.depthMask(!0),b.disable(b.BLEND),b.enable(b.DEPTH_TEST);for(var t=C.length,e=P.length,r=0;r<e;++r){var n=P[r];n.shape=V,n.begin();for(var i=0;i<t;++i)if(O[i]===r){var a=C[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(F))}n.end()}}()),U.axesPixels=a(U.axes,F,x,T),U.onrender&&U.onrender(),b.bindFramebuffer(b.FRAMEBUFFER,null),b.viewport(0,0,x,T);var L=U.clearColor;b.clearColor(L[0],L[1],L[2],L[3]),b.clear(b.COLOR_BUFFER_BIT|b.DEPTH_BUFFER_BIT),b.depthMask(!0),b.colorMask(!0,!0,!0,!0),b.enable(b.DEPTH_TEST),b.depthFunc(b.LEQUAL),b.disable(b.BLEND),b.disable(b.CULL_FACE);var R=!1;E.enable&&(R=R||E.isTransparent(),E.draw(F)),S.axes=E,_.object&&S.draw(F),b.disable(b.CULL_FACE);for(var i=0;i<e;++i){var o=C[i];o.axes=E,o.pixelRatio=U.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(F),o.isTransparent&&o.isTransparent()&&(R=!0)}if(R){w.shape=j,w.bind(),b.clear(b.DEPTH_BUFFER_BIT),b.colorMask(!1,!1,!1,!1),b.depthMask(!0),b.depthFunc(b.LESS),E.enable&&E.isTransparent()&&E.drawTransparent(F);for(var i=0;i<e;++i){var o=C[i];o.isOpaque&&o.isOpaque()&&o.draw(F)}b.enable(b.BLEND),b.blendEquation(b.FUNC_ADD),b.blendFunc(b.ONE,b.ONE_MINUS_SRC_ALPHA),b.colorMask(!0,!0,!0,!0),b.depthMask(!1),b.clearColor(0,0,0,0),b.clear(b.COLOR_BUFFER_BIT),E.isTransparent()&&E.drawTransparent(F);for(var i=0;i<e;++i){var o=C[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(F)}b.bindFramebuffer(b.FRAMEBUFFER,null),b.blendFunc(b.ONE,b.ONE_MINUS_SRC_ALPHA),b.disable(b.DEPTH_TEST),A.bind(),w.color[0].bind(0),A.uniforms.accumBuffer=0,u(b),b.disable(b.BLEND)}z=!1;for(var i=0;i<e;++i)C[i].dirty=!1}}}return U.enableMouseListeners(),function t(){U._stopped||U.contextLost||(Y(),requestAnimationFrame(t))}(),U.redraw=function(){U._stopped||(z=!0,Y())},U},createCamera:n}},{"./camera.js":140,"./lib/shader":141,"a-big-triangle":11,"gl-axes3d":95,"gl-axes3d/properties":102,"gl-fbo":111,"gl-mat4/ortho":127,"gl-mat4/perspective":128,"gl-select-static":148,"gl-spikes3d":157,"is-mobile":260,"mouse-change":271,"mouse-wheel":274}],143:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],d=r[0],p=r[1],g=r[2],v=r[3];(a=u*d+c*p+f*g+h*v)<0&&(a=-a,d=-d,p=-p,g=-g,v=-v);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*u+l*d,t[1]=s*c+l*p,t[2]=s*f+l*g,t[3]=s*h+l*v,t}},{}],144:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],145:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(u=0;u<s.positions[l].length;++u)s.positions[l][u]/=r;for(l=0;l<c.positions.length;++l)for(u=0;u<c.positions[l].length;++u)c.positions[l][u]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=c.positions.length;for(l=0;l<h;++l){var d=c.positions[l];for(u=0;u<2;++u)f[0][u]=Math.min(f[0][u],d[u]),f[1][u]=Math.max(f[1][u],d[u])}return a[t]=[s,c,f]};var i={}},{"vectorize-text":351}],146:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),u=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),c=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},g={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};function m(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return m(t,f)},r.createOrtho=function(t){return m(t,h)},r.createProject=function(t){return m(t,d)},r.createPickPerspective=function(t){return m(t,p)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{"gl-shader":149,glslify:250}],147:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),u=t("./lib/glyphs"),c=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function d(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function p(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t?1:t>1?1:t}function v(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new p(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),d=i(e),p=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:d,size:2,type:e.FLOAT},{buffer:p,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new v(e,r,n,o,f,h,d,p,g,s,u,c);return m.update(t),m};var m=v.prototype;m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},m.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],b=[0,0,0],x=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],A=f.slice(),M=[0,0,0],T=[[0,0,0],[0,0,0]];function k(t){return t[0]=t[1]=t[2]=0,t}function E(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function L(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function S(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,p=e.axesBounds,g=function(t){for(var e=T,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var v=0;v<3;++v)if(a[v]){l.scale=e.projectScale[v],l.opacity=e.projectOpacity[v];for(var m=A,S=0;S<16;++S)m[S]=0;for(S=0;S<4;++S)m[5*S]=1;m[5*v]=0,i[v]<0?m[12+v]=p[0][v]:m[12+v]=p[1][v],s(m,u,m),l.model=m;var C=(v+1)%3,O=(v+2)%3,R=k(b),P=k(x);R[C]=1,P[O]=1;var z=d(0,0,0,E(_,R)),I=d(0,0,0,E(w,P));if(Math.abs(z[1])>Math.abs(I[1])){var N=z;z=I,I=N,N=R,R=P,P=N;var D=C;C=O,O=D}z[0]<0&&(R[C]=-1),I[1]>0&&(P[O]=-1);var F=0,j=0;for(S=0;S<4;++S)F+=Math.pow(u[4*C+S],2),j+=Math.pow(u[4*O+S],2);R[C]/=Math.sqrt(F),P[O]/=Math.sqrt(j),l.axes[0]=R,l.axes[1]=P,l.fragClipBounds[0]=L(M,g[0],v,-1e8),l.fragClipBounds[1]=L(M,g[1],v,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function O(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&S(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function R(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var 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float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float vectorScale;\nuniform float tubeScale;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data\n           , f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal,0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data\n           , f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:250}],159:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),u=t("gl-mat4/invert"),c=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),d=t("typedarray-pool"),p=t("./shaders"),g=p.meshShader,v=p.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,u,c,f,h,d,p,g,v,y,b,x,_,w,A,M){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleNormals=u,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.lineWidth=1,this.edgePositions=f,this.edgeColors=d,this.edgeUVs=p,this.edgeIds=h,this.edgeVAO=g,this.edgeCount=0,this.pointPositions=v,this.pointColors=b,this.pointUVs=x,this.pointSizes=_,this.pointIds=y,this.pointVAO=w,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=M,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!1,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.tubeScale=1,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1],this.pixelRatio=1}var b=y.prototype;function x(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}b.isOpaque=function(){return this.opacity>=1},b.isTransparent=function(){return this.opacity<1},b.pickSlots=1,b.setPickBase=function(t){this.pickId=t},b.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=d.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var p=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[p],b=this.positions[g],x=0;x<3;++x)o[s++]=v*y[x]+m*b[x]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),d.free(o)}else this.contourCount=0},b.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale);var a=[],l=[],u=[],h=[],d=[],p=[],g=[],v=[],m=[],y=[],b=[],x=[],_=[],w=[],A=[];this.cells=r,this.positions=n,this.vectors=i;var M=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,E=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,E)),T||M||(M=s.vertexNormals(r,n,k));var L=t.vertexColors,S=t.cellColors,C=t.meshColor||[1,1,1,1],O=t.vertexUVs,R=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,I=1/0,N=-1/0;if(!O&&!P)if(R)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],N=+t.vertexIntensityBounds[1];else for(var D=0;D<R.length;++D){var F=R[D];I=Math.min(I,F),N=Math.max(N,F)}else if(z)for(D=0;D<z.length;++D){F=z[D];I=Math.min(I,F),N=Math.max(N,F)}else for(D=0;D<n.length;++D){F=n[D][2];I=Math.min(I,F),N=Math.max(N,F)}this.intensity=R||(z?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,z):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var j=t.pointSizes,B=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var U=n[D],V=0;V<3;++V)!isNaN(U[V])&&isFinite(U[V])&&(this.bounds[0][V]=Math.min(this.bounds[0][V],U[V]),this.bounds[1][V]=Math.max(this.bounds[1][V],U[V]));var H=0,q=0,G=0;t:for(D=0;D<r.length;++D){var X=r[D];switch(X.length){case 1:for(U=n[Y=X[0]],V=0;V<3;++V)if(isNaN(U[V])||!isFinite(U[V]))continue t;b.push(U[0],U[1],U[2],U[3]),3===(Z=L?L[Y]:S?S[D]:C).length?x.push(Z[0],Z[1],Z[2],1):x.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],_.push(Q[0],Q[1]),j?w.push(j[Y]):w.push(B),A.push(D),G+=1;break;case 2:for(V=0;V<2;++V){U=n[Y=X[V]];for(var W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t}for(V=0;V<2;++V){U=n[Y=X[V]];g.push(U[0],U[1],U[2]),3===(Z=L?L[Y]:S?S[D]:C).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],m.push(Q[0],Q[1]),y.push(D)}q+=1;break;case 3:for(V=0;V<3;++V)for(U=n[Y=X[V]],W=0;W<3;++W)if(isNaN(U[W])||!isFinite(U[W]))continue t;for(V=0;V<3;++V){var Y;U=n[Y=X[2-V]];a.push(U[0],U[1],U[2],U[3]);var Z,Q,$,J=i[Y];l.push(J[0],J[1],J[2],J[3]),3===(Z=L?L[Y]:S?S[D]:C).length?u.push(Z[0],Z[1],Z[2],1):u.push(Z[0],Z[1],Z[2],Z[3]),Q=O?O[Y]:R?[(R[Y]-I)/(N-I),0]:P?P[D]:z?[(z[D]-I)/(N-I),0]:[(U[2]-I)/(N-I),0],d.push(Q[0],Q[1]),$=M?M[Y]:T[D],h.push($[0],$[1],$[2]),p.push(D)}H+=1}}this.pointCount=G,this.edgeCount=q,this.triangleCount=H,this.pointPositions.update(b),this.pointColors.update(x),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(A)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(u),this.triangleUVs.update(d),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(p))}},b.drawTransparent=b.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:m.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);l(c,s.view,s.model),l(c,s.projection,c),u(c,c);for(o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f=c[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var h=c[12+o],d=0;d<3;++d)h+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=h/f}if(this.triangleCount>0){var p=this.triShader;p.bind(),p.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},b.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||m,n=t.view||m,i=t.projection||m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind())},b.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:e,position:n,intensity:this.intensity[r[1]],velocity:this.vectors[r[1]].slice(0,3),divergence:this.vectors[r[1]][3],dataCoordinate:n}},b.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=x(t),l=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var u=i(t),f=i(t),h=i(t),d=i(t),p=i(t),v=i(t),m=a(t,[{buffer:u,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:d,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:4}]),b=i(t),_=i(t),w=i(t),A=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:A,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),T=i(t),k=i(t),E=i(t),L=i(t),S=i(t),C=a(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:S,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2},{buffer:L,type:t.FLOAT,size:1}]),O=i(t),R=new y(t,l,r,s,u,f,v,h,d,p,m,b,A,_,w,M,T,S,k,E,L,C,O,a(t,[{buffer:O,type:t.FLOAT,size:3}]));return R.update(e),R}},{"./shaders":158,colormap:68,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-shader":149,"gl-texture2d":164,"gl-vao":168,ndarray:286,normals:288,"simplicial-complex-contour":330,"typedarray-pool":348}],160:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++){var c=n.length(l[u]);c>o&&(o=c)}var f=t.map(function(t){return function(t,e,r,a){var o,s,l,u=t.points,c=t.velocities,f=t.divergences;n.set(n.create(),0,1,0),n.create(),n.create();n.create();for(var h=[],d=[],p=[],g=[],v=[],m=[],y=0,b=0,x=i.create(),_=i.create(),w=0;w<u.length;w++){o=u[w],s=c[w],l=f[w],0===e&&(l=.05*r),b=n.length(s)/a,x=i.create(),n.copy(x,s),x[3]=l;for(var A=0;A<8;A++)v[A]=[o[0],o[1],o[2],A];if(g.length>0)for(A=0;A<8;A++){var M=(A+1)%8;h.push(g[A],v[A],v[M],v[M],g[M],g[A]),p.push(_,x,x,x,_,_),m.push(y,b,b,b,y,y),d.push([h.length-6,h.length-5,h.length-4],[h.length-3,h.length-2,h.length-1])}var T=g;g=v,v=T,T=_,_=x,x=T,T=y,y=b,b=T}return{positions:h,cells:d,vectors:p,vertexIntensity:m}}(t,r,a,o)}),h=[],d=[],p=[],g=[];for(s=0;s<f.length;s++){var v=f[s],m=h.length;h=h.concat(v.positions),p=p.concat(v.vectors),g=g.concat(v.vertexIntensity);for(u=0;u<v.cells.length;u++){var y=v.cells[u],b=[];d.push(b);for(var x=0;x<y.length;x++)b.push(y[x]+m)}}return{positions:h,cells:d,vectors:p,vertexIntensity:g,colormap:e}},o=function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=this.getVelocity(r);n.subtract(a,a,e),n.scale(a,a,1e4),n.add(r,t,[0,i,0]);var o=this.getVelocity(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,i]);var s=this.getVelocity(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,a,o),n.add(r,r,s),r},s=function(t){return h(t,this.vectors,this.meshgrid,this.clampBorders)},l=function(t,e){for(var r=0;r<t.length;r++){var n=t[r];if(n===e)return r;if(n>e)return r-1}return r},u=n.create(),c=n.create(),f=function(t,e,r){return t<e?e:t>r?r:t},h=function(t,e,r,i){var a=t[0],o=t[1],s=t[2],h=r[0].length,d=r[1].length,p=r[2].length,g=l(r[0],a),v=l(r[1],o),m=l(r[2],s),y=g+1,b=v+1,x=m+1;if(r[0][g]===a&&(y=g),r[1][v]===o&&(b=v),r[2][m]===s&&(x=m),i&&(g=f(g,0,h-1),y=f(y,0,h-1),v=f(v,0,d-1),b=f(b,0,d-1),m=f(m,0,p-1),x=f(x,0,p-1)),g<0||v<0||m<0||y>=h||b>=d||x>=p)return n.create();var _=(a-r[0][g])/(r[0][y]-r[0][g]),w=(o-r[1][v])/(r[1][b]-r[1][v]),A=(s-r[2][m])/(r[2][x]-r[2][m]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(A<0||A>1||isNaN(A))&&(A=0);var M=m*h*d,T=x*h*d,k=v*h,E=b*h,L=g,S=y,C=e[k+M+L],O=e[k+M+S],R=e[E+M+L],P=e[E+M+S],z=e[k+T+L],I=e[k+T+S],N=e[E+T+L],D=e[E+T+S],F=n.create();return n.lerp(F,C,O,_),n.lerp(u,R,P,_),n.lerp(F,F,u,w),n.lerp(u,z,I,_),n.lerp(c,N,D,_),n.lerp(u,u,c,w),n.lerp(F,F,u,A),F},d=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=1;r<t.length;r++){var n=Math.abs(t[r]-t[r-1]);n<e&&(e=n)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,l=t.tubeSize||1,u=t.absoluteTubeSize;t.getDivergence||(t.getDivergence=o),t.getVelocity||(t.getVelocity=s),void 0===t.clampBorders&&(t.clampBorders=!0);var c=[],f=e[0][0],h=e[0][1],p=e[0][2],g=e[1][0],v=e[1][1],m=e[1][2],y=function(t,e){var r=e[0],n=e[1],i=e[2];return r>=f&&r<=g&&n>=h&&n<=v&&i>=p&&i<=m},b=10*n.distance(e[0],e[1])/i,x=b*b,_=1,w=0;n.create();r.length>=2&&(_=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=0;s<t.length;s++){var l=t[s],u=l[0],c=l[1],f=l[2];i[u]||(e.push(u),i[u]=!0),a[c]||(r.push(c),a[c]=!0),o[f]||(n.push(f),o[f]=!0)}var h=d(e),p=d(r),g=d(n),v=Math.min(h,p,g);return isFinite(v)?v:1}(r));for(var A=0;A<r.length;A++){var M=n.create();n.copy(M,r[A]);var T=[M],k=[],E=t.getVelocity(M),L=M;k.push(E);var S=[],C=t.getDivergence(M,E);(z=n.length(C))>w&&!isNaN(z)&&isFinite(z)&&(w=z),S.push(z),c.push({points:T,velocities:k,divergences:S});for(var O=0;O<100*i&&T.length<i&&y(0,M);){O++;var R=n.clone(E),P=n.squaredLength(R);if(0===P)break;if(P>x&&n.scale(R,R,b/Math.sqrt(P)),n.add(R,R,M),E=t.getVelocity(R),n.squaredDistance(L,R)-x>-1e-4*x){T.push(R),L=R,k.push(E);C=t.getDivergence(R,E);(z=n.length(C))>w&&!isNaN(z)&&isFinite(z)&&(w=z),S.push(z)}M=R}}for(A=0;A<S.length;A++){var z=S[A];!isNaN(z)&&isFinite(z)||(S[A]=w)}var I=a(c,t.colormap,w,_);return u?I.tubeScale=u:(0===w&&(w=1),I.tubeScale=.5*l*_/w),I},e.exports.createTubeMesh=t("./lib/tubemesh")},{"./lib/tubemesh":159,"gl-vec3":187,"gl-vec4":223}],161:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":149,glslify:250}],162:[function(t,e,r){arguments[4][54][0].apply(r,arguments)},{dup:54}],163:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=x(e),s=b(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),d=i(e),p=a(e,[{buffer:d,size:2,type:e.FLOAT}]),g=o(e,1,E,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var v=new L(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,g,s,l,f,h,d,p,[0,0,0]),m={levels:[[],[],[]]};for(var A in t)m[A]=t[A];return m.colormap=m.colormap||"jet",v.update(m),v};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),u=t("ndarray-ops"),c=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),d=t("gl-mat4/multiply"),p=t("gl-mat4/invert"),g=t("binary-search-bounds"),v=t("ndarray-gradient"),m=t("./lib/shaders"),y=m.createShader,b=m.createContourShader,x=m.createPickShader,_=m.createPickContourShader,w=40,A=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],M=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],T=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function k(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=T[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var E=256;function L(t,e,r,n,i,a,o,l,u,c,h,d,p,g,v){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=v,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=d,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new k([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=p,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=L.prototype;S.isTransparent=function(){return this.opacity<1},S.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var C=[0,0,0],O={showSurface:!1,showContour:!1,projections:[A.slice(),A.slice(),A.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function R(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=O.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],d(l,t.model,l);var u=O.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return O.showSurface=o,O.showContour=s,O}var P={model:A,view:A,projection:A,inverseModel:A.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},z=A.slice(),I=[1,0,0,0,1,0,0,0,1];function N(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||A,n.view=t.view||A,n.projection=t.projection||A,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=p(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=I,n.vertexColor=this.vertexColor;var s=z;for(d(s,n.view,n.model),d(s,n.projection,s),p(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=R(n,this);if(c.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=T[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=T[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=T[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return N.call(this,t,!1)},S.drawTransparent=function(t){return N.call(this,t,!0)};var D={model:A,view:A,projection:A,inverseModel:A,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function j(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[j(t[0]),j(t[1]),j(t[2])];var e=j(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||A,r.view=t.view||A,r.projection=t.projection||A,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=I;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=R(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=T[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=T[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var d=i+c,p=s+h,v=f*(h?l:1-l),m=0;m<3;++m)u[m]+=this._field[m].get(d,p)*v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=g.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;m<3;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=F(t.contourWidth,Number)),"showContour"in t&&(this.showContour=F(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=F(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=F(t.contourProject,function(t){return F(t,Boolean)})),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=F(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=F(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var d=t.coords;if(!Array.isArray(d)||3!==d.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var p=d[o];for(x=0;x<2;++x)if(p.shape[x]!==a[x])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],p)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)||2!==g.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var m=g[o];if((Array.isArray(m)||m.length)&&(m=f(m)),m.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var y=f(m.data,a);y.stride[o]=m.stride[0],y.stride[1^o]=0,this.padField(this._field[o],y)}}else{for(o=0;o<2;++o){var b=[0,0];b[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],b,0)}this._field[0].set(0,0,0);for(var x=0;x<a[0];++x)this._field[0].set(x+1,0,x);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),x=0;x<a[1];++x)this._field[1].set(0,x+1,x);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=f(s.mallocFloat(3*_[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)v(w.pick(o),_[o],"mirror");var A=f(s.mallocFloat(3*_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(x=0;x<a[1]+2;++x){var T=w.get(0,o,x,0),k=w.get(0,o,x,1),L=w.get(1,o,x,0),S=w.get(1,o,x,1),C=w.get(2,o,x,0),O=w.get(2,o,x,1),R=L*O-S*C,P=C*k-O*T,z=T*S-k*L,I=Math.sqrt(R*R+P*P+z*z);I<1e-8?(I=Math.max(Math.abs(R),Math.abs(P),Math.abs(z)))<1e-8?(z=1,P=R=0,I=1):I=1/I:I=1/Math.sqrt(I),A.set(o,x,0,R*I),A.set(o,x,1,P*I),A.set(o,x,2,z*I)}s.free(w.data);var N=[1/0,1/0,1/0],D=[-1/0,-1/0,-1/0],j=1/0,U=-1/0,V=(a[0]-1)*(a[1]-1)*6,H=s.mallocFloat(n.nextPow2(10*V)),q=0,G=0;for(o=0;o<a[0]-1;++o)t:for(x=0;x<a[1]-1;++x){for(var X=0;X<2;++X)for(var W=0;W<2;++W)for(var Y=0;Y<3;++Y){var Z=this._field[Y].get(1+o+X,1+x+W);if(isNaN(Z)||!isFinite(Z))continue t}for(Y=0;Y<6;++Y){var Q=o+M[Y][0],$=x+M[Y][1],J=this._field[0].get(Q+1,$+1),K=this._field[1].get(Q+1,$+1);Z=this._field[2].get(Q+1,$+1),R=A.get(Q+1,$+1,0),P=A.get(Q+1,$+1,1),z=A.get(Q+1,$+1,2),t.intensity&&(tt=t.intensity.get(Q,$));var tt=t.intensity?t.intensity.get(Q,$):Z+this.objectOffset[2];H[q++]=Q,H[q++]=$,H[q++]=J,H[q++]=K,H[q++]=Z,H[q++]=0,H[q++]=tt,H[q++]=R,H[q++]=P,H[q++]=z,N[0]=Math.min(N[0],J+this.objectOffset[0]),N[1]=Math.min(N[1],K+this.objectOffset[1]),N[2]=Math.min(N[2],Z+this.objectOffset[2]),j=Math.min(j,tt),D[0]=Math.max(D[0],J+this.objectOffset[0]),D[1]=Math.max(D[1],K+this.objectOffset[1]),D[2]=Math.max(D[2],Z+this.objectOffset[2]),U=Math.max(U,tt),G+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],U=+t.intensityBounds[1]),o=6;o<q;o+=10)H[o]=(H[o]-j)/(U-j);this._vertexCount=G,this._coordinateBuffer.update(H.subarray(0,q)),s.freeFloat(H),s.free(A.data),this.bounds=[N,D],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===U||(r=!0),this.intensityBounds=[j,U]}if("levels"in t){var et=t.levels;for(et=Array.isArray(et[0])?et.slice():[[],[],et],o=0;o<3;++o)et[o]=et[o].slice(),et[o].sort(function(t,e){return t-e});for(o=0;o<3;++o)for(x=0;x<et[o].length;++x)et[o][x]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(et[o].length!==this.contourLevels[o].length){r=!0;break}for(x=0;x<et[o].length;++x)if(et[o][x]!==this.contourLevels[o][x]){r=!0;break t}}this.contourLevels=et}if(r){_=this._field,a=this.shape;for(var rt=[],nt=0;nt<3;++nt){var it=this.contourLevels[nt],at=[],ot=[],st=[0,0,0];for(o=0;o<it.length;++o){var lt=h(this._field[nt],it[o]);at.push(rt.length/5|0),G=0;t:for(x=0;x<lt.cells.length;++x){var ut=lt.cells[x];for(Y=0;Y<2;++Y){var ct=lt.positions[ut[Y]],ft=ct[0],ht=0|Math.floor(ft),dt=ft-ht,pt=ct[1],gt=0|Math.floor(pt),vt=pt-gt,mt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var bt=(nt+yt+1)%3;for(X=0;X<2;++X){var xt=X?dt:1-dt;for(Q=0|Math.min(Math.max(ht+X,0),a[0]),W=0;W<2;++W){var _t=W?vt:1-vt;if($=0|Math.min(Math.max(gt+W,0),a[1]),Z=yt<2?this._field[bt].get(Q,$):(this.intensity.get(Q,$)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Z)||isNaN(Z)){mt=!0;break e}var wt=xt*_t;st[yt]+=wt*Z}}}if(mt){if(Y>0){for(var At=0;At<5;++At)rt.pop();G-=1}continue t}rt.push(st[0],st[1],ct[0],ct[1],st[2]),G+=1}}ot.push(G)}this._contourOffsets[nt]=at,this._contourCounts[nt]=ot}var Mt=s.mallocFloat(rt.length);for(o=0;o<rt.length;++o)Mt[o]=rt[o];this._contourBuffer.update(Mt),s.freeFloat(Mt)}t.colormap&&this._colorMap.setPixels(function(t){var e=c([l({colormap:t,nshades:E,format:"rgba"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return u.divseq(e,255),e}(t.colormap))},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],d=this._field[u],p=h(c,r[o]),g=p.cells,v=p.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var m=g[e],y=0;y<2;++y){var b=v[m[y]],x=+b[0],_=0|x,w=0|Math.min(_+1,i[0]),A=x-_,M=1-A,T=+b[1],k=0|T,E=0|Math.min(k+1,i[1]),L=T-k,S=1-L,C=M*S,O=M*L,R=A*S,P=A*L,z=C*f.get(_,k)+O*f.get(_,E)+R*f.get(w,k)+P*f.get(w,E),I=C*d.get(_,k)+O*d.get(_,E)+R*d.get(w,k)+P*d.get(w,E);if(isNaN(z)||isNaN(I)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=I,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":161,"binary-search-bounds":162,"bit-twiddle":36,colormap:68,"gl-buffer":103,"gl-mat4/invert":124,"gl-mat4/multiply":126,"gl-texture2d":164,"gl-vao":168,ndarray:286,"ndarray-gradient":277,"ndarray-ops":280,"ndarray-pack":281,"surface-nets":341,"typedarray-pool":348}],164:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=p(o,e.stride.slice()),u=0;"float32"===r?u=t.FLOAT:"float64"===r?(u=t.FLOAT,l=!1,r="float32"):"uint8"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r="uint8");var f,d,v=0;if(2===o.length)v=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])v=t.ALPHA;else if(2===o[2])v=t.LUMINANCE_ALPHA;else if(3===o[2])v=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");v=t.RGBA}}u!==t.FLOAT||t.getExtension("OES_texture_float")||(u=t.UNSIGNED_BYTE,l=!1);var m=e.size;if(l)f=0===e.offset&&e.data.length===m?e.data:e.data.subarray(e.offset,e.offset+m);else{var y=[o[2],o[2]*o[0],1];d=a.malloc(m,r);var b=n(d,o,y,0);"float32"!==r&&"float64"!==r||u!==t.UNSIGNED_BYTE?i.assign(b,e):c(b,e),f=d.subarray(0,m)}var x=g(t);t.texImage2D(t.TEXTURE_2D,0,v,o[0],o[1],0,v,u,f),l||a.free(d);return new h(t,x,o[0],o[1],v,u)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=h.prototype;function p(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return 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c=E.x.head,f=E.y.head,h=E.x.tail+r,p=E.y.tail+n,m=E.x.text+r,y=E.y.text+n,b=o.rotationXYMatrix(L,m,y),w=o.apply2DTransform(b),k=o.apply2DTransform2(b),O=+D.attr("width"),R=+D.attr("height"),z=m-.5*O,I=z+O,N=y-.5*R,F=N+R,j=[[z,N,z,F],[z,F,I,F],[I,F,I,N],[I,N,z,N]].map(k);if(!j.reduce(function(t,e){return t^!!o.segmentsIntersect(c,f,c+1e6,f+1e6,e[0],e[1],e[2],e[3])},!1)){j.forEach(function(t){var e=o.segmentsIntersect(h,p,c,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)});var B=e.arrowwidth,U=e.arrowcolor,V=e.arrowside,H=S.append("g").style({opacity:l.opacity(U)}).classed("annotation-arrow-g",!0),q=H.append("path").attr("d","M"+h+","+p+"L"+c+","+f).style("stroke-width",B+"px").call(l.stroke,l.rgb(U));if(g(q,V,e),_.annotationPosition&&q.node().parentNode&&!a){var G=c,X=f;if(e.standoff){var W=Math.sqrt(Math.pow(c-h,2)+Math.pow(f-p,2));G+=e.standoff*(h-c)/W,X+=e.standoff*(p-f)/W}var Y,Z,Q=H.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-G)+","+(p-X),transform:"translate("+G+","+X+")"}).style("stroke-width",B+6+"px").call(l.stroke,"rgba(0,0,0,0)").call(l.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(P);Y=t.x,Z=t.y,s&&s.autorange&&A(s._name+".autorange",!0),v&&v.autorange&&A(v._name+".autorange",!0)},moveFn:function(t,r){var n=w(Y,Z),i=n[0]+t,a=n[1]+r;P.call(u.setTranslate,i,a),M("x",s?s.p2r(s.r2p(e.x)+t):e.x+t/x.w),M("y",v?v.p2r(v.r2p(e.y)+r):e.y-r/x.h),e.axref===e.xref&&M("ax",s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&M("ay",v.p2r(v.r2p(e.ay)+r)),H.attr("transform","translate("+t+","+r+")"),C.attr({transform:"rotate("+L+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,T());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&yt(0,0),O)d.init({element:P.node(),gd:t,prepFn:function(){mt=C.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?M("ax",s.p2r(s.r2p(e.ax)+t)):M("ax",e.ax+t),e.ayref===e.yref?M("ay",v.p2r(v.r2p(e.ay)+r)):M("ay",e.ay+r),yt(t,r);else{if(a)return;var i,o;if(s)i=s.p2r(s.r2p(e.x)+t);else{var l=e._xsize/x.w,u=e.x+(e._xshift-e.xshift)/x.w-l/2;i=d.align(u+t/x.w,l,0,1,e.xanchor)}if(v)o=v.p2r(v.r2p(e.y)+r);else{var c=e._ysize/x.h,f=e.y-(e._yshift+e.yshift)/x.h-c/2;o=d.align(f-r/x.h,c,0,1,e.yanchor)}M("x",i),M("y",o),s&&v||(n=d.getCursor(s?.5:i,v?.5:o,e.xanchor,e.yanchor))}C.attr({transform:"translate("+t+","+r+")"+mt}),h(P,n)},doneFn:function(){h(P),i.call("_guiRelayout",t,T());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}else P.remove()}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:m}},{"../../lib":495,"../../lib/setcursor":514,"../../lib/svg_text_utils":518,"../../plot_api/plot_template":531,"../../plots/cartesian/axes":541,"../../plots/plots":584,"../../registry":592,"../color":376,"../dragelement":394,"../drawing":397,"../fx":415,"./draw_arrow_head":368,d3:81}],368:[function(t,e,r){"use strict";var n=t("d3"),i=t("../color"),a=t("./arrow_paths");e.exports=function(t,e,r){var o,s,l,u,c=t.node(),f=a[r.arrowhead||0],h=a[r.startarrowhead||0],d=(r.arrowwidth||1)*(r.arrowsize||1),p=(r.arrowwidth||1)*(r.startarrowsize||1),g=e.indexOf("start")>=0,v=e.indexOf("end")>=0,m=f.backoff*d+r.standoff,y=h.backoff*p+r.startstandoff;if("line"===c.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},s={x:+t.attr("x2"),y:+t.attr("y2")};var b=o.x-s.x,x=o.y-s.y;if(u=(l=Math.atan2(x,b))+Math.PI,m&&y&&m+y>Math.sqrt(b*b+x*x))return void O();if(m){if(m*m>b*b+x*x)return void O();var _=m*Math.cos(l),w=m*Math.sin(l);s.x+=_,s.y+=w,t.attr({x2:s.x,y2:s.y})}if(y){if(y*y>b*b+x*x)return void O();var A=y*Math.cos(l),M=y*Math.sin(l);o.x-=A,o.y-=M,t.attr({x1:o.x,y1:o.y})}}else if("path"===c.nodeName){var T=c.getTotalLength(),k="";if(T<m+y)return void O();var E=c.getPointAtLength(0),L=c.getPointAtLength(.1);l=Math.atan2(E.y-L.y,E.x-L.x),o=c.getPointAtLength(Math.min(y,T)),k="0px,"+y+"px,";var S=c.getPointAtLength(T),C=c.getPointAtLength(T-.1);u=Math.atan2(S.y-C.y,S.x-C.x),s=c.getPointAtLength(Math.max(0,T-m)),k+=T-(k?y+m:m)+"px,"+T+"px",t.style("stroke-dasharray",k)}function O(){t.style("stroke-dasharray","0px,100px")}function R(e,a,o,s){e.path&&(e.noRotate&&(o=0),n.select(c.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:"translate("+a.x+","+a.y+")"+(o?"rotate("+180*o/Math.PI+")":"")+"scale("+s+")"}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}g&&R(h,o,l,p),v&&R(f,s,u,d)}},{"../color":376,"./arrow_paths":360,d3:81}],369:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":551,"./attributes":361,"./calc_autorange":362,"./click":363,"./convert_coords":365,"./defaults":366,"./draw":367}],370:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":524,"../../plot_api/plot_template":531,"../annotations/attributes":361}],371:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes");function a(t,e){var r=e.fullSceneLayout.domain,a=e.fullLayout._size,o={pdata:null,type:"linear",autorange:!1,range:[-1/0,1/0]};t._xa={},n.extendFlat(t._xa,o),i.setConvert(t._xa),t._xa._offset=a.l+r.x[0]*a.w,t._xa.l2p=function(){return.5*(1+t._pdata[0]/t._pdata[3])*a.w*(r.x[1]-r.x[0])},t._ya={},n.extendFlat(t._ya,o),i.setConvert(t._ya),t._ya._offset=a.t+(1-r.y[1])*a.h,t._ya.l2p=function(){return.5*(1-t._pdata[1]/t._pdata[3])*a.h*(r.y[1]-r.y[0])}}e.exports=function(t){for(var e=t.fullSceneLayout.annotations,r=0;r<e.length;r++)a(e[r],t);t.fullLayout._infolayer.selectAll(".annotation-"+t.id).remove()}},{"../../lib":495,"../../plots/cartesian/axes":541}],372:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("../annotations/common_defaults"),s=t("./attributes");function l(t,e,r,a){function l(r,i){return n.coerce(t,e,s,r,i)}function u(t){var n=t+"axis",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l("visible")&&(o(t,e,a.fullLayout,l),u("x"),u("y"),u("z"),n.noneOrAll(t,e,["x","y","z"]),e.xref="x",e.yref="y",e.zref="z",l("xanchor"),l("yanchor"),l("xshift"),l("yshift"),e.showarrow&&(e.axref="pixel",e.ayref="pixel",l("ax",-10),l("ay",-30),n.noneOrAll(t,e,["ax","ay"])))}e.exports=function(t,e,r){a(t,e,{name:"annotations",handleItemDefaults:l,fullLayout:r.fullLayout})}},{"../../lib":495,"../../plots/array_container_defaults":537,"../../plots/cartesian/axes":541,"../annotations/common_defaults":364,"./attributes":370}],373:[function(t,e,r){"use strict";var n=t("../annotations/draw").drawRaw,i=t("../../plots/gl3d/project"),a=["x","y","z"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],u=!1,c=0;c<3;c++){var f=a[c],h=l[f],d=e[f+"axis"].r2fraction(h);if(d<0||d>1){u=!0;break}}u?t.fullLayout._infolayer.select(".annotation-"+t.id+'[data-index="'+s+'"]').remove():(l._pdata=i(t.glplot.cameraParams,[e.xaxis.r2l(l.x)*r[0],e.yaxis.r2l(l.y)*r[1],e.zaxis.r2l(l.z)*r[2]]),n(t.graphDiv,l,s,t.id,l._xa,l._ya))}}},{"../../plots/gl3d/project":579,"../annotations/draw":367}],374:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib");e.exports={moduleType:"component",name:"annotations3d",schema:{subplots:{scene:{annotations:t("./attributes")}}},layoutAttributes:t("./attributes"),handleDefaults:t("./defaults"),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(!r)return;for(var a=r.attrRegex,o=Object.keys(t),s=0;s<o.length;s++){var l=o[s];a.test(l)&&(t[l].annotations||[]).length&&(i.pushUnique(e._basePlotModules,r),i.pushUnique(e._subplots.gl3d,l))}},convert:t("./convert"),draw:t("./draw")}},{"../../lib":495,"../../registry":592,"./attributes":370,"./convert":371,"./defaults":372,"./draw":373}],375:[function(t,e,r){"use strict";r.defaults=["#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf"],r.defaultLine="#444",r.lightLine="#eee",r.background="#fff",r.borderLine="#BEC8D9",r.lightFraction=1e3/11},{}],376:[function(t,e,r){"use strict";var n=t("tinycolor2"),i=t("fast-isnumeric"),a=e.exports={},o=t("./attributes");a.defaults=o.defaults;var s=a.defaultLine=o.defaultLine;a.lightLine=o.lightLine;var l=a.background=o.background;function u(t){if(i(t)||"string"!=typeof t)return t;var e=t.trim();if("rgb"!==e.substr(0,3))return t;var r=e.match(/^rgba?\s*\(([^()]*)\)$/);if(!r)return t;var n=r[1].trim().split(/\s*[\s,]\s*/),a="a"===e.charAt(3)&&4===n.length;if(!a&&3!==n.length)return t;for(var o=0;o<n.length;o++){if(!n[o].length)return t;if(n[o]=Number(n[o]),!(n[o]>=0))return t;if(3===o)n[o]>1&&(n[o]=1);else if(n[o]>=1)return t}var s=Math.round(255*n[0])+", "+Math.round(255*n[1])+", "+Math.round(255*n[2]);return a?"rgba("+s+", "+n[3]+")":"rgb("+s+")"}a.tinyRGB=function(t){var e=t.toRgb();return"rgb("+Math.round(e.r)+", "+Math.round(e.g)+", "+Math.round(e.b)+")"},a.rgb=function(t){return a.tinyRGB(n(t))},a.opacity=function(t){return t?n(t).getAlpha():0},a.addOpacity=function(t,e){var r=n(t).toRgb();return"rgba("+Math.round(r.r)+", "+Math.round(r.g)+", "+Math.round(r.b)+", "+e+")"},a.combine=function(t,e){var r=n(t).toRgb();if(1===r.a)return n(t).toRgbString();var i=n(e||l).toRgb(),a=1===i.a?i:{r:255*(1-i.a)+i.r*i.a,g:255*(1-i.a)+i.g*i.a,b:255*(1-i.a)+i.b*i.a},o={r:a.r*(1-r.a)+r.r*r.a,g:a.g*(1-r.a)+r.g*r.a,b:a.b*(1-r.a)+r.b*r.a};return n(o).toRgbString()},a.contrast=function(t,e,r){var i=n(t);return 1!==i.getAlpha()&&(i=n(a.combine(t,l))),(i.isDark()?e?i.lighten(e):l:r?i.darken(r):s).toString()},a.stroke=function(t,e){var r=n(e);t.style({stroke:a.tinyRGB(r),"stroke-opacity":r.getAlpha()})},a.fill=function(t,e){var r=n(e);t.style({fill:a.tinyRGB(r),"fill-opacity":r.getAlpha()})},a.clean=function(t){if(t&&"object"==typeof t){var e,r,n,i,o=Object.keys(t);for(e=0;e<o.length;e++)if(i=t[n=o[e]],"color"===n.substr(n.length-5))if(Array.isArray(i))for(r=0;r<i.length;r++)i[r]=u(i[r]);else t[n]=u(i);else if("colorscale"===n.substr(n.length-10)&&Array.isArray(i))for(r=0;r<i.length;r++)Array.isArray(i[r])&&(i[r][1]=u(i[r][1]));else if(Array.isArray(i)){var s=i[0];if(!Array.isArray(s)&&s&&"object"==typeof s)for(r=0;r<i.length;r++)a.clean(i[r])}else i&&"object"==typeof i&&a.clean(i)}}},{"./attributes":375,"fast-isnumeric":90,tinycolor2:342}],377:[function(t,e,r){"use strict";var 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z=e.hovermode||c.hovermode;z&&!M&&(z="closest");if(-1===["x","y","closest"].indexOf(z)||!t.calcdata||t.querySelector(".zoombox")||t._dragging)return h.unhoverRaw(t,e);var I,N,D,F,j,B,U,V,H,q,G,X,W,Y=-1===c.hoverdistance?1/0:c.hoverdistance,Z=-1===c.spikedistance?1/0:c.spikedistance,Q=[],$=[],J={hLinePoint:null,vLinePoint:null},K=!1;if(Array.isArray(e))for(z="array",D=0;D<e.length;D++)(j=t.calcdata[e[D].curveNumber||0])&&(B=j[0].trace,"skip"!==j[0].trace.hoverinfo&&($.push(j),"h"===B.orientation&&(K=!0)));else{for(F=0;F<t.calcdata.length;F++)j=t.calcdata[F],"skip"!==(B=j[0].trace).hoverinfo&&g.isTraceInSubplots(B,l)&&($.push(j),"h"===B.orientation&&(K=!0));var tt,et,rt=!e.target;if(rt)tt="xpx"in e?e.xpx:w[0]._length/2,et="ypx"in e?e.ypx:A[0]._length/2;else{if(!1===s.triggerHandler(t,"plotly_beforehover",e))return;var nt=e.target.getBoundingClientRect();if(tt=e.clientX-nt.left,et=e.clientY-nt.top,tt<0||tt>w[0]._length||et<0||et>A[0]._length)return h.unhoverRaw(t,e)}if(e.pointerX=tt+w[0]._offset,e.pointerY=et+A[0]._offset,I="xval"in e?g.flat(l,e.xval):g.p2c(w,tt),N="yval"in e?g.flat(l,e.yval):g.p2c(A,et),!i(I[0])||!i(N[0]))return o.warn("Fx.hover failed",e,t),h.unhoverRaw(t,e)}var it=1/0;for(F=0;F<$.length;F++)if((j=$[F])&&j[0]&&j[0].trace&&!0===j[0].trace.visible&&(B=j[0].trace,-1===["carpet","contourcarpet"].indexOf(B._module.name))){if("splom"===B.type?U=l[V=0]:(U=g.getSubplot(B),V=l.indexOf(U)),H=z,X={cd:j,trace:B,xa:w[V],ya:A[V],maxHoverDistance:Y,maxSpikeDistance:Z,index:!1,distance:Math.min(it,Y),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:f.defaultLine,name:B.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},c[U]&&(X.subplot=c[U]._subplot),c._splomScenes&&c._splomScenes[B.uid]&&(X.scene=c._splomScenes[B.uid]),W=Q.length,"array"===H){var at=e[F];"pointNumber"in at?(X.index=at.pointNumber,H="closest"):(H="","xval"in at&&(q=at.xval,H="x"),"yval"in 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r,n=null,i=1/0,a=0;a<t.length;a++)(r=t[a].spikeDistance)<i&&r<=e&&(n=t[a],i=r);return n}function vt(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}var mt={fullLayout:c,container:c._hoverlayer,outerContainer:c._paperdiv,event:e},yt=t._spikepoints,bt={vLinePoint:J.vLinePoint,hLinePoint:J.hLinePoint};if(t._spikepoints=bt,y&&0!==Z&&0!==Q.length){var xt=Q.filter(function(t){return t.ya.showspikes}),_t=gt(xt,Z);J.hLinePoint=vt(_t);var wt=Q.filter(function(t){return t.xa.showspikes}),At=gt(wt,Z);J.vLinePoint=vt(At)}if(0===Q.length){var Mt=h.unhoverRaw(t,e);return!y||null===J.hLinePoint&&null===J.vLinePoint||S(yt)&&L(J,mt),Mt}y&&S(yt)&&L(J,mt);Q.sort(function(t,e){return t.distance-e.distance});var Tt=t._hoverdata,kt=[];for(D=0;D<Q.length;D++){var 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u=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&u++;for(o=t.length-1;o>=0&&!(u<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,u--);for(o=0;o<t.length&&!(u<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,u--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(u<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,u--)}}}for(t.each(function(t,n){var i=t[e],a="x"===i._id.charAt(0),o=i.range;!n&&o&&o[0]>o[1]!==a&&(f=-1),d[n]=[{datum:t,i:n,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?b:1)/2,pmin:0,pmax:a?r.width:r.height}]}),d.sort(function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)});!n&&c<=h;){for(c++,n=!0,o=0;o<d.length-1;){var g=d[o],v=d[o+1],m=g[g.length-1],y=v[0];if((i=m.pos+m.dp+m.size-y.pos-y.dp+y.size)>.01&&m.pmin===y.pmin&&m.pmax===y.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),d.splice(o+1,1),u=0,s=g.length-1;s>=0;s--)u+=g[s].dp;for(a=u/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}d.forEach(p)}for(o=d.length-1;o>=0;o--){var x=d[o];for(s=x.length-1;s>=0;s--){var _=x[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Pt,Ct?"xa":"ya",c),k(Pt,Ct),e.target&&e.target.tagName){var zt=p.getComponentMethod("annotations","hasClickToShow")(t,kt);u(n.select(e.target),zt?"pointer":"")}if(!e.target||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Tt))return;Tt&&t.emit("plotly_unhover",{event:e,points:Tt});t.emit("plotly_hover",{event:e,points:t._hoverdata,xaxes:w,yaxes:A,xvals:I,yvals:N})}(t,e,r,a)})},r.loneHover=function(t,e){var r={color:t.color||f.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:t.trace||{index:0,hoverinfo:""},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,eventData:t.eventData||!1,hovertemplateLabels:t.hovertemplateLabels||!1},i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:"closest",rotateLabels:!1,bgColor:e.bgColor||f.background,container:i,outerContainer:a},s=T([r],o,e.gd);return k(s,o.rotateLabels),s.node()},r.multiHovers=function(t,e){Array.isArray(t)||(t=[t]);var r=t.map(function(t){return{color:t.color||f.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:t.trace||{index:0,hoverinfo:""},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,eventData:t.eventData||!1,hovertemplateLabels:t.hovertemplateLabels||!1}}),i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:"closest",rotateLabels:!1,bgColor:e.bgColor||f.background,container:i,outerContainer:a},s=T(r,o,e.gd),l=0;return s.sort(function(t,e){return t.y0-e.y0}).each(function(t){var e=t.y0-t.by/2;t.offset=e-5<l?l-e+5:0,l=e+t.by+t.offset}),k(s,o.rotateLabels),s.node()};var M=/<extra>([\s\S]*)<\/extra>/;function T(t,e,r){var i=r._fullLayout,a=e.hovermode,s=e.rotateLabels,u=e.bgColor,h=e.container,d=e.outerContainer,p=e.commonLabelOpts||{},g=e.fontFamily||v.HOVERFONT,y=e.fontSize||v.HOVERFONTSIZE,b=t[0],x=b.xa,_=b.ya,T="y"===a?"yLabel":"xLabel",k=b[T],E=(String(k)||"").split(" ")[0],L=d.node().getBoundingClientRect(),S=L.top,C=L.width,O=L.height,R=void 0!==k&&b.distance<=e.hoverdistance&&("x"===a||"y"===a);if(R){var P,z,I=!0;for(P=0;P<t.length;P++)if(I&&void 0===t[P].zLabel&&(I=!1),z=t[P].hoverinfo||t[P].trace.hoverinfo){var N=Array.isArray(z)?z:z.split("+");if(-1===N.indexOf("all")&&-1===N.indexOf(a)){R=!1;break}}I&&(R=!1)}var D=h.selectAll("g.axistext").data(R?[0]:[]);D.enter().append("g").classed("axistext",!0),D.exit().remove(),D.each(function(){var e=n.select(this),i=o.ensureSingle(e,"path","",function(t){t.style({"stroke-width":"1px"})}),s=o.ensureSingle(e,"text","",function(t){t.attr("data-notex",1)}),u=p.bgcolor||f.defaultLine,h=p.bordercolor||f.contrast(u),d=f.contrast(u);i.style({fill:u,stroke:h}),s.text(k).call(c.font,p.font.family||g,p.font.size||y,p.font.color||d).call(l.positionText,0,0).call(l.convertToTspans,r),e.attr("transform","");var v=s.node().getBoundingClientRect();if("x"===a){s.attr("text-anchor","middle").call(l.positionText,0,"top"===x.side?S-v.bottom-w-A:S-v.top+w+A);var m="top"===x.side?"-":"";i.attr("d","M0,0L"+w+","+m+w+"H"+(A+v.width/2)+"v"+m+(2*A+v.height)+"H-"+(A+v.width/2)+"V"+m+w+"H-"+w+"Z"),e.attr("transform","translate("+(x._offset+(b.x0+b.x1)/2)+","+(_._offset+("top"===x.side?0:_._length))+")")}else{s.attr("text-anchor","right"===_.side?"start":"end").call(l.positionText,("right"===_.side?1:-1)*(A+w),S-v.top-v.height/2);var M="right"===_.side?"":"-";i.attr("d","M0,0L"+M+w+","+w+"V"+(A+v.height/2)+"h"+M+(2*A+v.width)+"V-"+(A+v.height/2)+"H"+M+w+"V-"+w+"Z"),e.attr("transform","translate("+(x._offset+("right"===_.side?x._length:0))+","+(_._offset+(b.y0+b.y1)/2)+")")}t=t.filter(function(t){return void 0!==t.zLabelVal||(t[T]||"").split(" ")[0]===E})});var F=h.selectAll("g.hovertext").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya||""].join(",")});return F.enter().append("g").classed("hovertext",!0).each(function(){var t=n.select(this);t.append("rect").call(f.fill,f.addOpacity(u,.8)),t.append("text").classed("name",!0),t.append("path").style("stroke-width","1px"),t.append("text").classed("nums",!0).call(c.font,g,y)}),F.exit().remove(),F.each(function(t){var e=n.select(this).attr("transform",""),h="",d="",p=t.bgcolor||t.color,v=f.combine(f.opacity(p)?p:f.defaultLine,u),b=f.combine(f.opacity(t.color)?t.color:f.defaultLine,u),x=t.borderColor||f.contrast(v);void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(i.meta&&(t.name=o.templateString(t.name,{meta:i.meta})),h=l.plainText(t.name||"",{len:t.nameLength,allowedTags:["br","sub","sup","b","i","em"]})),void 0!==t.zLabel?(void 0!==t.xLabel&&(d+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(d+="y: "+t.yLabel+"<br>"),d+=(d?"z: ":"")+t.zLabel):R&&t[a+"Label"]===k?d=t[("x"===a?"y":"x")+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&"scattercarpet"!==t.trace.type&&(d=t.yLabel):d=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",!t.text&&0!==t.text||Array.isArray(t.text)||(d+=(d?"<br>":"")+t.text),void 0!==t.extraText&&(d+=(d?"<br>":"")+t.extraText),""!==d||t.hovertemplate||(""===h&&e.remove(),d=h);var _=r._fullLayout._d3locale,T=t.hovertemplate||!1,E=t.hovertemplateLabels||t,L=t.eventData[0]||{};T&&(d=(d=o.hovertemplateString(T,E,_,L,{meta:i.meta})).replace(M,function(t,e){return h=e,""}));var P=e.select("text.nums").call(c.font,t.fontFamily||g,t.fontSize||y,t.fontColor||x).text(d).attr("data-notex",1).call(l.positionText,0,0).call(l.convertToTspans,r),z=e.select("text.name"),I=0,N=0;if(h&&h!==d){z.call(c.font,t.fontFamily||g,t.fontSize||y,b).text(h).attr("data-notex",1).call(l.positionText,0,0).call(l.convertToTspans,r);var D=z.node().getBoundingClientRect();I=D.width+2*A,N=D.height+2*A}else z.remove(),e.select("rect").remove();e.select("path").style({fill:v,stroke:x});var F,j,B=P.node().getBoundingClientRect(),U=t.xa._offset+(t.x0+t.x1)/2,V=t.ya._offset+(t.y0+t.y1)/2,H=Math.abs(t.x1-t.x0),q=Math.abs(t.y1-t.y0),G=B.width+w+A+I;if(t.ty0=S-B.top,t.bx=B.width+2*A,t.by=Math.max(B.height+2*A,N),t.anchor="start",t.txwidth=B.width,t.tx2width=I,t.offset=0,s)t.pos=U,F=V+q/2+G<=O,j=V-q/2-G>=0,"top"!==t.idealAlign&&F||!j?F?(V+=q/2,t.anchor="start"):t.anchor="middle":(V-=q/2,t.anchor="end");else 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n=t("./constants"),i=t("../../plots/font_attributes")({editType:"none"});i.family.dflt=n.HOVERFONT,i.size.dflt=n.HOVERFONTSIZE,e.exports={clickmode:{valType:"flaglist",flags:["event","select"],dflt:"event",editType:"plot",extras:["none"]},dragmode:{valType:"enumerated",values:["zoom","pan","select","lasso","orbit","turntable",!1],dflt:"zoom",editType:"modebar"},hovermode:{valType:"enumerated",values:["x","y","closest",!1],editType:"modebar"},hoverdistance:{valType:"integer",min:-1,dflt:20,editType:"none"},spikedistance:{valType:"integer",min:-1,dflt:20,editType:"none"},hoverlabel:{bgcolor:{valType:"color",editType:"none"},bordercolor:{valType:"color",editType:"none"},font:i,namelength:{valType:"integer",min:-1,dflt:15,editType:"none"},editType:"none"},selectdirection:{valType:"enumerated",values:["h","v","d","any"],dflt:"any",editType:"none"}}},{"../../plots/font_attributes":567,"./constants":409}],417:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}var o,s=a("clickmode");"select"===a("dragmode")&&a("selectdirection"),e._has("cartesian")?s.indexOf("select")>-1?o="closest":(e._isHoriz=function(t,e){for(var r=e._scatterStackOpts||{},n=0;n<t.length;n++){var i=t[n],a=i.xaxis+i.yaxis,o=r[a]||{},s=o[i.stackgroup]||{};if("h"!==i.orientation&&"h"!==s.orientation)return!1}return!0}(r,e),o=e._isHoriz?"y":"x"):o="closest",a("hovermode",o)&&(a("hoverdistance"),a("spikedistance"));var l=e._has("mapbox"),u=e._has("geo"),c=e._basePlotModules.length;"zoom"===e.dragmode&&((l||u)&&1===c||l&&u&&2===c)&&(e.dragmode="pan")}},{"../../lib":495,"./layout_attributes":416}],418:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./hoverlabel_defaults"),a=t("./layout_attributes");e.exports=function(t,e){i(t,e,function(r,i){return 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../plot_api/plot_template":531,"../../plots/cartesian/constants":547}],421:[function(t,e,r){"use 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n=t("../../lib"),i=t("./attributes"),a=t("./oppaxis_attributes"),o=t("./helpers");e.exports={moduleType:"component",name:"rangeslider",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:t("./attributes"),handleDefaults:t("./defaults"),calcAutorange:t("./calc_autorange"),draw:t("./draw"),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},{"../../lib":495,"./attributes":444,"./calc_autorange":445,"./defaults":447,"./draw":448,"./helpers":449,"./oppaxis_attributes":451}],451:[function(t,e,r){"use strict";e.exports={_isSubplotObj:!0,rangemode:{valType:"enumerated",values:["auto","fixed","match"],dflt:"match",editType:"calc"},range:{valType:"info_array",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},editType:"calc"}},{}],452:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../traces/scatter/attributes").line,a=t("../drawing/attributes").dash,o=t("../../lib/extend").extendFlat,s=t("../../plot_api/plot_template").templatedArray;e.exports=s("shape",{visible:{valType:"boolean",dflt:!0,editType:"calc+arraydraw"},type:{valType:"enumerated",values:["circle","rect","path","line"],editType:"calc+arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},xref:o({},n.xref,{}),xsizemode:{valType:"enumerated",values:["scaled","pixel"],dflt:"scaled",editType:"calc+arraydraw"},xanchor:{valType:"any",editType:"calc+arraydraw"},x0:{valType:"any",editType:"calc+arraydraw"},x1:{valType:"any",editType:"calc+arraydraw"},yref:o({},n.yref,{}),ysizemode:{valType:"enumerated",values:["scaled","pixel"],dflt:"scaled",editType:"calc+arraydraw"},yanchor:{valType:"any",editType:"calc+arraydraw"},y0:{valType:"any",editType:"calc+arraydraw"},y1:{valType:"any",editType:"calc+arraydraw"},path:{valType:"string",editType:"calc+arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},line:{color:o({},i.color,{editType:"arraydraw"}),width:o({},i.width,{editType:"calc+arraydraw"}),dash:o({},a,{editType:"arraydraw"}),editType:"calc+arraydraw"},fillcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"arraydraw"},editType:"arraydraw"})},{"../../lib/extend":488,"../../plot_api/plot_template":531,"../../traces/scatter/attributes":616,"../annotations/attributes":361,"../drawing/attributes":396}],453:[function(t,e,r){"use 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n=t("../../registry"),i=t("../../lib"),a=t("../../plots/cartesian/axes"),o=t("../color"),s=t("../drawing"),l=t("../../plot_api/plot_template").arrayEditor,u=t("../dragelement"),c=t("../../lib/setcursor"),f=t("./constants"),h=t("./helpers");function d(t,e){t._fullLayout._paperdiv.selectAll('.shapelayer [data-index="'+e+'"]').remove();var r=t._fullLayout.shapes[e]||{};if(r._input&&!1!==r.visible)if("below"!==r.layer)m(t._fullLayout._shapeUpperLayer);else if("paper"===r.xref||"paper"===r.yref)m(t._fullLayout._shapeLowerLayer);else{var d=t._fullLayout._plots[r.xref+r.yref];if(d)m((d.mainplotinfo||d).shapelayer);else m(t._fullLayout._shapeLowerLayer)}function m(d){var m={"data-index":e,"fill-rule":"evenodd",d:g(t,r)},y=r.line.width?r.line.color:"rgba(0,0,0,0)",b=d.append("path").attr(m).style("opacity",r.opacity).call(o.stroke,y).call(o.fill,r.fillcolor).call(s.dashLine,r.line.dash,r.line.width);p(b,t,r),t._context.edits.shapePosition&&function(t,e,r,o,d){var 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n=t("../../plots/font_attributes"),i=t("../../plots/pad_attributes"),a=t("../../lib/extend").extendDeepAll,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/animation_attributes"),l=t("../../plot_api/plot_template").templatedArray,u=t("./constants"),c=l("step",{visible:{valType:"boolean",dflt:!0},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string"},value:{valType:"string"},execute:{valType:"boolean",dflt:!0}});e.exports=o(l("slider",{visible:{valType:"boolean",dflt:!0},active:{valType:"number",min:0,dflt:0},steps:c,lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",min:-2,max:3,dflt:0},pad:a(i({editType:"arraydraw"}),{},{t:{dflt:20}}),xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left"},y:{valType:"number",min:-2,max:3,dflt:0},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},transition:{duration:{valType:"number",min:0,dflt:150},easing:{valType:"enumerated",values:s.transition.easing.values,dflt:"cubic-in-out"}},currentvalue:{visible:{valType:"boolean",dflt:!0},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},offset:{valType:"number",dflt:10},prefix:{valType:"string"},suffix:{valType:"string"},font:n({})},font:n({}),activebgcolor:{valType:"color",dflt:u.gripBgActiveColor},bgcolor:{valType:"color",dflt:u.railBgColor},bordercolor:{valType:"color",dflt:u.railBorderColor},borderwidth:{valType:"number",min:0,dflt:u.railBorderWidth},ticklen:{valType:"number",min:0,dflt:u.tickLength},tickcolor:{valType:"color",dflt:u.tickColor},tickwidth:{valType:"number",min:0,dflt:1},minorticklen:{valType:"number",min:0,dflt:u.minorTickLength}}),"arraydraw","from-root")},{"../../lib/extend":488,"../../plot_api/edit_types":524,"../../plot_api/plot_template":531,"../../plots/animation_attributes":536,"../../plots/font_attributes":567,"../../plots/pad_attributes":583,"./constants":460}],460:[function(t,e,r){"use 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p=u[d];c=t[p]||{},(f=a.newContainer(e,p))._id=p[0]+r.scene,f._name=p,s(c,f,h,r),l(c,f,h,{font:r.font,letter:p[0],data:r.data,showGrid:!0,noTickson:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",p[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":495,"../../../plot_api/plot_template":531,"../../cartesian/axis_defaults":543,"../../cartesian/type_defaults":564,"./axis_attributes":572,tinycolor2:342}],574:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t.meta?i.templateString(o.title.text,{meta:t.meta}):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":495,"../../../lib/str2rgbarray":517}],575:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),u=t("../../get_data").getSubplotData,c="gl3d";function f(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),f=["up","center","eye"],h=0;h<f.length;h++)r("camera."+f[h]+".x"),r("camera."+f[h]+".y"),r("camera."+f[h]+".z");r("camera.projection.type");var d=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",d?"manual":"auto");d||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var g=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:g,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var v=n.getDfltFromLayout("dragmode");if(!1!==v&&!v)if(v="orbit",t.camera&&t.camera.up){var m=t.camera.up.x,y=t.camera.up.y,b=t.camera.up.z;0!==b&&(m&&y&&b?b/Math.sqrt(m*m+y*y+b*b)>.999&&(v="turntable"):v="turntable")}else v="turntable";r("dragmode",v),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":376,"../../../lib":495,"../../../registry":592,"../../get_data":569,"../../subplot_defaults":591,"./axis_defaults":573,"./layout_attributes":576}],576:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":495,"../../../lib/extend":488,"../../domain":566,"./axis_attributes":572}],577:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":517}],578:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if("auto"===c.tickmode){c.tickmode="linear";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var d=n.calcTicks(c),p=0;p<d.length;++p)d[p].x=d[p].x*t.dataScale[u],"date"===c.type&&(d[p].text=d[p].text.replace(/\<br\>/g," "));l[u]=d,c.tickmode=f}}e.ticks=l;for(var u=0;u<3;++u){o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var p=0;p<2;++p)e.bounds[p][u]=t.glplot.bounds[p][u]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":495,"../../cartesian/axes":541}],579:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],580:[function(t,e,r){"use strict";var n,i,a=t("gl-plot3d").createCamera,o=t("gl-plot3d").createScene,s=t("webgl-context"),l=t("has-passive-events"),u=t("../../registry"),c=t("../../lib"),f=t("../../plots/cartesian/axes"),h=t("../../components/fx"),d=t("../../lib/str2rgbarray"),p=t("../../lib/show_no_webgl_msg"),g=t("./project"),v=t("./layout/convert"),m=t("./layout/spikes"),y=t("./layout/tick_marks");function b(t,e,r,a){if(t.initializeGLCamera(),!function(t,e,r,a,l){var u={canvas:a,gl:l,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e,pixelRatio:r};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=s({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");u.pixelRatio=t.pixelRatio,u.gl=i,u.canvas=n}try{t.glplot=o(u)}catch(t){return!1}return!0}(t,t.camera,e,r,a))return p(t);var u=t.graphDiv,d=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+".camera"]=M(t.camera),t.saveCamera(u.layout),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas.addEventListener("mouseup",function(){d(t)}),t.glplot.canvas.addEventListener("wheel",function(){u._context._scrollZoom.gl3d&&d(t)},!!l&&{passive:!1}),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",function(e){u&&u.emit&&u.emit("plotly_webglcontextlost",{event:e,layer:t.id})},!1),t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.svgContainer,n=t.container.getBoundingClientRect(),i=n.width,a=n.height;r.setAttributeNS(null,"viewBox","0 0 "+i+" "+a),r.setAttributeNS(null,"width",i),r.setAttributeNS(null,"height",a),y(t),t.glplot.axes.update(t.axesOptions);for(var o,s=Object.keys(t.traces),l=null,u=t.glplot.selection,d=0;d<s.length;++d)"skip"!==(e=t.traces[s[d]]).data.hoverinfo&&e.handlePick(u)&&(l=e),e.setContourLevels&&e.setContourLevels();function p(e,r){var n=t.fullSceneLayout[e];return f.tickText(n,n.d2l(r),"hover").text}if(null!==l){var v=g(t.glplot.cameraParams,u.dataCoordinate);e=l.data;var m,b=u.index,x={xLabel:p("xaxis",u.traceCoordinate[0]),yLabel:p("yaxis",u.traceCoordinate[1]),zLabel:p("zaxis",u.traceCoordinate[2])},_=h.castHoverinfo(e,t.fullLayout,b),w=(_||"").split("+"),A=_&&"all"===_;e.hovertemplate||A||(-1===w.indexOf("x")&&(x.xLabel=void 0),-1===w.indexOf("y")&&(x.yLabel=void 0),-1===w.indexOf("z")&&(x.zLabel=void 0),-1===w.indexOf("text")&&(u.textLabel=void 0),-1===w.indexOf("name")&&(l.name=void 0));var M=[];"cone"===e.type||"streamtube"===e.type?(x.uLabel=p("xaxis",u.traceCoordinate[3]),(A||-1!==w.indexOf("u"))&&M.push("u: "+x.uLabel),x.vLabel=p("yaxis",u.traceCoordinate[4]),(A||-1!==w.indexOf("v"))&&M.push("v: "+x.vLabel),x.wLabel=p("zaxis",u.traceCoordinate[5]),(A||-1!==w.indexOf("w"))&&M.push("w: "+x.wLabel),x.normLabel=u.traceCoordinate[6].toPrecision(3),(A||-1!==w.indexOf("norm"))&&M.push("norm: "+x.normLabel),"streamtube"===e.type&&(x.divergenceLabel=u.traceCoordinate[7].toPrecision(3),(A||-1!==w.indexOf("divergence"))&&M.push("divergence: "+x.divergenceLabel)),u.textLabel&&M.push(u.textLabel),m=M.join("<br>")):"isosurface"===e.type?(x.valueLabel=f.tickText(t.mockAxis,t.mockAxis.d2l(u.traceCoordinate[3]),"hover").text,M.push("value: "+x.valueLabel),u.textLabel&&M.push(u.textLabel),m=M.join("<br>")):m=u.textLabel;var T={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:e._input,fullData:e,curveNumber:e.index,pointNumber:b};h.appendArrayPointValue(T,e,b),e._module.eventData&&(T=e._module.eventData(T,u,e,{},b));var k={points:[T]};t.fullSceneLayout.hovermode&&h.loneHover({trace:e,x:(.5+.5*v[0]/v[3])*i,y:(.5-.5*v[1]/v[3])*a,xLabel:x.xLabel,yLabel:x.yLabel,zLabel:x.zLabel,text:m,name:l.name,color:h.castHoverOption(e,b,"bgcolor")||l.color,borderColor:h.castHoverOption(e,b,"bordercolor"),fontFamily:h.castHoverOption(e,b,"font.family"),fontSize:h.castHoverOption(e,b,"font.size"),fontColor:h.castHoverOption(e,b,"font.color"),hovertemplate:c.castOption(e,b,"hovertemplate"),hovertemplateLabels:c.extendFlat({},T,x),eventData:[T]},{container:r,gd:t.graphDiv}),u.buttons&&u.distance<5?t.graphDiv.emit("plotly_click",k):t.graphDiv.emit("plotly_hover",k),o=k}else h.loneUnhover(r),t.graphDiv.emit("plotly_unhover",o);t.drawAnnotations(t)}.bind(null,t),t.traces={},t.make4thDimension(),!0}function x(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=v(e,e[this.id]),this.spikeOptions=m(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),b(this,this.pixelRatio)}var _=x.prototype;_.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=a(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},_.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas,n=this.glplot.camera,i=this.glplot.pixelRatio;this.glplot.dispose(),requestAnimationFrame(function a(){e.isContextLost()?requestAnimationFrame(a):b(t,n,i,r)?t.plot.apply(t,t.plotArgs):c.error("Catastrophic and unrecoverable WebGL error. Context lost.")})};var w=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=w[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+"calendar"],f=e["_"+o+"length"];if(c.isArrayOrTypedArray(l))for(var h,d=0;d<(f||l.length);d++)if(c.isArrayOrTypedArray(l[d]))for(var p=0;p<l[d].length;++p)h=s.d2l(l[d][p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],f-1)}}function M(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}}_.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,u=e[this.id],c=r[this.id];u.bgcolor?this.glplot.clearColor=d(u.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=u,this.glplotLayout=u,this.axesOptions.merge(e,u),this.spikeOptions.merge(u),this.setCamera(u.camera),this.updateFx(u.dragmode,u.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.update({}),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=w[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,f);var 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n=t("./lib/loggers"),i=t("./lib/noop"),a=t("./lib/push_unique"),o=t("./lib/is_plain_object"),s=t("./lib/extend"),l=t("./plots/attributes"),u=t("./plots/layout_attributes"),c=s.extendFlat,f=s.extendDeepAll;function h(t){var e=t.name,i=t.categories,a=t.meta;if(r.modules[e])n.log("Type "+e+" already registered");else{r.subplotsRegistry[t.basePlotModule.name]||function(t){var e=t.name;if(r.subplotsRegistry[e])return void n.log("Plot type "+e+" already registered.");for(var i in v(t),r.subplotsRegistry[e]=t,r.componentsRegistry)b(i,t.name)}(t.basePlotModule);for(var o={},s=0;s<i.length;s++)o[i[s]]=!0,r.allCategories[i[s]]=!0;for(var l in r.modules[e]={_module:t,categories:o},a&&Object.keys(a).length&&(r.modules[e].meta=a),r.allTypes.push(e),r.componentsRegistry)m(l,e);t.layoutAttributes&&c(r.traceLayoutAttributes,t.layoutAttributes)}}function d(t){if("string"!=typeof t.name)throw new Error("Component module *name* must be a string.");var e=t.name;for(var n in 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n=t("../../components/color"),i=t("../../lib").isArrayOrTypedArray;e.exports=function(t,e,r,a){var o=!1;if(e.marker){var s=e.marker.color,l=(e.marker.line||{}).color;s&&!i(s)?o=s:l&&!i(l)&&(o=l)}a("fillcolor",n.addOpacity((e.line||{}).color||o||r,.5))}},{"../../components/color":376,"../../lib":495}],626:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./subtypes");e.exports=function(t,e){var r,a;if("lines"===t.mode)return(r=t.line.color)&&n.opacity(r)?r:t.fillcolor;if("none"===t.mode)return t.fill?t.fillcolor:"";var o=e.mcc||(t.marker||{}).color,s=e.mlcc||((t.marker||{}).line||{}).color;return(a=o&&n.opacity(o)?o:s&&n.opacity(s)&&(e.mlw||((t.marker||{}).line||{}).width)?s:"")?n.opacity(a)<.3?n.addOpacity(a,.3):a:(r=(t.line||{}).color)&&n.opacity(r)&&i.hasLines(t)&&t.line.width?r:t.fillcolor}},{"../../components/color":376,"./subtypes":640}],627:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/fx"),a=t("../../registry"),o=t("./get_trace_color"),s=t("../../components/color"),l=t("./fill_hover_text");e.exports=function(t,e,r,u){var c=t.cd,f=c[0].trace,h=t.xa,d=t.ya,p=h.c2p(e),g=d.c2p(r),v=[p,g],m=f.hoveron||"",y=-1!==f.mode.indexOf("markers")?3:.5;if(-1!==m.indexOf("points")){var b=function(t){var e=Math.max(y,t.mrc||0),r=h.c2p(t.x)-p,n=d.c2p(t.y)-g;return Math.max(Math.sqrt(r*r+n*n)-e,1-y/e)},x=i.getDistanceFunction(u,function(t){var e=Math.max(3,t.mrc||0),r=1-1/e,n=Math.abs(h.c2p(t.x)-p);return n<e?r*n/e:n-e+r},function(t){var e=Math.max(3,t.mrc||0),r=1-1/e,n=Math.abs(d.c2p(t.y)-g);return n<e?r*n/e:n-e+r},b);if(i.getClosest(c,x,t),!1!==t.index){var _=c[t.index],w=h.c2p(_.x,!0),A=d.c2p(_.y,!0),M=_.mrc||1;t.index=_.i;var T=c[0].t.orientation,k=T&&(_.sNorm||_.s),E="h"===T?k:_.x,L="v"===T?k:_.y;return 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g=e.exports=c({x:n.x,y:n.y,z:{valType:"data_array"},text:u({},n.text,{}),hovertext:u({},n.hovertext,{}),hovertemplate:a(),mode:u({},n.mode,{dflt:"lines+markers"}),surfaceaxis:{valType:"enumerated",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:"color"},projection:{x:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},y:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},z:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}}},connectgaps:n.connectgaps,line:p,marker:u({symbol:{valType:"enumerated",values:Object.keys(l),dflt:"circle",arrayOk:!0},size:u({},h.size,{dflt:8}),sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,opacity:u({},h.opacity,{arrayOk:!1}),colorbar:h.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},i("marker.line"))},i("marker")),textposition:u({},n.textposition,{dflt:"top 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l=a(+t[s],s);if("log"===i.type){var u=i.c2l(t[s]),c=t[s]-l[0],f=t[s]+l[1];if(o[s]=[(i.c2l(c,!0)-u)*r,(i.c2l(f,!0)-u)*r],c>0){var h=i.c2l(c);i._lowerLogErrorBound||(i._lowerLogErrorBound=h),i._lowerErrorBound=Math.min(i._lowerLogErrorBound,h)}}else o[s]=[-l[0]*r,l[1]*r]}return o}e.exports=function(t,e,r){var n=[i(t.x,t.error_x,e[0],r.xaxis),i(t.y,t.error_y,e[1],r.yaxis),i(t.z,t.error_z,e[2],r.zaxis)],a=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===a)return null;for(var o=new Array(a),s=0;s<a;s++){for(var l=[[0,0,0],[0,0,0]],u=0;u<3;u++)if(n[u])for(var c=0;c<2;c++)l[c][u]=n[u][s][c];o[s]=l}return o}},{"../../registry":592}],646:[function(t,e,r){"use strict";var n=t("gl-line3d"),i=t("gl-scatter3d"),a=t("gl-error3d"),o=t("gl-mesh3d"),s=t("delaunay-triangulate"),l=t("../../lib"),u=t("../../lib/str2rgbarray"),c=t("../../lib/gl_format_color").formatColor,f=t("../scatter/make_bubble_size_func"),h=t("../../constants/gl3d_dashes"),d=t("../../constants/gl3d_markers"),p=t("./calc_errors");function g(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var v=g.prototype;function m(t){return null==t?0:t.indexOf("left")>-1?-1:t.indexOf("right")>-1?1:0}function y(t){return null==t?0:t.indexOf("top")>-1?-1:t.indexOf("bottom")>-1?1:0}function b(t,e){return e(4*t)}function x(t){return d[t]}function _(t,e,r,n,i){var a=null;if(l.isArrayOrTypedArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,l.identity);return 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p=t.opacity;if(t.marker&&t.marker.opacity&&(p*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:d.position,color:d.scatterColor,size:d.scatterSize,glyph:d.scatterMarker,opacity:p,orthographic:!0,lineWidth:d.scatterLineWidth,lineColor:d.scatterLineColor,project:d.project,projectScale:d.projectScale,projectOpacity:d.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),u={gl:this.scene.glplot.gl,position:d.position,glyph:d.text,color:d.textColor,size:d.textSize,angle:d.textAngle,alignment:d.textOffset,font:d.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(u):(this.textMarkers=i(u),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:d.position,color:d.errorColor,error:d.errorBounds,lineWidth:d.errorLineWidth,capSize:d.errorCapSize,opacity:t.opacity},this.errorBars?d.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):d.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),d.delaunayAxis>=0){var g=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var u=t[n];!isNaN(u[i])&&isFinite(u[i])&&!isNaN(u[a])&&isFinite(u[a])&&(o.push([u[i],u[a]]),l.push(n))}var c=s(o);for(n=0;n<c.length;++n)for(var f=c[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:c,meshColor:e}}(d.position,d.delaunayColor,d.delaunayAxis);g.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(g):(g.gl=c,this.delaunayMesh=o(g),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},v.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new g(t,e.uid);return r.update(e),r}},{"../../constants/gl3d_dashes":472,"../../constants/gl3d_markers":473,"../../lib":495,"../../lib/gl_format_color":493,"../../lib/str2rgbarray":517,"../scatter/make_bubble_size_func":633,"./calc_errors":645,"delaunay-triangulate":83,"gl-error3d":109,"gl-line3d":114,"gl-mesh3d":139,"gl-scatter3d":147}],647:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),u=t("./attributes");e.exports=function(t,e,r,c){function f(r,n){return i.coerce(t,e,u,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,c)){f("text"),f("hovertext"),f("hovertemplate"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,c,f)),a.hasMarkers(e)&&o(t,e,r,c,f,{noSelect:!0}),a.hasText(e)&&l(t,e,c,f,{noSelect:!0});var h=(e.line||{}).color,d=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||d);for(var p=["x","y","z"],g=0;g<3;++g){var v="projection."+p[g];f(v+".show")&&(f(v+".opacity"),f(v+".scale"))}var m=n.getComponentMethod("errorbars","supplyDefaults");m(t,e,h||d||r,{axis:"z"}),m(t,e,h||d||r,{axis:"y",inherit:"z"}),m(t,e,h||d||r,{axis:"x",inherit:"z"})}else e.visible=!1}},{"../../lib":495,"../../registry":592,"../scatter/line_defaults":629,"../scatter/marker_defaults":635,"../scatter/subtypes":640,"../scatter/text_defaults":641,"./attributes":643}],648:[function(t,e,r){"use strict";var n={};n.plot=t("./convert"),n.attributes=t("./attributes"),n.markerSymbols=t("../../constants/gl3d_markers"),n.supplyDefaults=t("./defaults"),n.colorbar=[{container:"marker",min:"cmin",max:"cmax"},{container:"line",min:"cmin",max:"cmax"}],n.calc=t("./calc"),n.moduleType="trace",n.name="scatter3d",n.basePlotModule=t("../../plots/gl3d"),n.categories=["gl3d","symbols","showLegend"],n.meta={},e.exports=n},{"../../constants/gl3d_markers":473,"../../plots/gl3d":570,"./attributes":643,"./calc":644,"./convert":646,"./defaults":647}],649:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../components/colorbar/attributes"),a=t("../../components/fx/hovertemplate_attributes"),o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,u={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]})};l(u,n("",{colorAttr:"u/v/w 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m=s[r],y=l[r],b=u[r],x=Math.sqrt(m*m+y*y+b*b);g=Math.max(g,x),v=Math.min(v,x)}n(t,e,{vals:[v,g],containerStr:"",cLetter:"c"});var _=-1/0,w=1/0,A=-1/0,M=1/0,T=-1/0,k=1/0;for(r=0;r<d;r++){var E=c[r];_=Math.max(_,E),w=Math.min(w,E);var L=f[r];A=Math.max(A,L),M=Math.min(M,L);var S=h[r];T=Math.max(T,S),k=Math.min(k,S)}for(r=0;r<p;r++){var C=i[r];_=Math.max(_,C),w=Math.min(w,C);var O=a[r];A=Math.max(A,O),M=Math.min(M,O);var R=o[r];T=Math.max(T,R),k=Math.min(k,R)}e._len=d,e._slen=p,e._normMax=g,e._xbnds=[w,_],e._ybnds=[M,A],e._zbnds=[k,T]}},{"../../components/colorscale/calc":384}],651:[function(t,e,r){"use strict";var n=t("gl-streamtube3d"),i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../plots/gl3d/zip3"),l={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var c=u.prototype;function f(t){return a.distinctVals(t).vals}function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function d(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function p(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,c={};function p(t,e){var n=r[e],o=i[l[e]];return a.simpleMap(t,function(t){return n.d2l(t)*o})}c.vectors=s(p(e.u,"xaxis"),p(e.v,"yaxis"),p(e.w,"zaxis"),u);var g=f(e.x.slice(0,u)),v=f(e.y.slice(0,u)),m=f(e.z.slice(0,u));if(g.length*v.length*m.length>u)return{positions:[],cells:[]};var y=p(g,"xaxis"),b=p(v,"yaxis"),x=p(m,"zaxis");if(c.meshgrid=[y,b,x],e.starts){var _=e._slen;c.startingPositions=s(p(e.starts.x.slice(0,_),"xaxis"),p(e.starts.y.slice(0,_),"yaxis"),p(e.starts.z.slice(0,_),"zaxis"))}else{for(var w=b[0],A=h(y),M=h(x),T=new Array(A.length*M.length),k=0,E=0;E<A.length;E++)for(var L=0;L<M.length;L++)T[k++]=[A[E],w,M[L]];c.startingPositions=T}c.colormap=o(e),c.tubeSize=e.sizeref,c.maxLength=e.maxdisplayed;var S=p(e._xbnds,"xaxis"),C=p(e._ybnds,"yaxis"),O=p(e._zbnds,"zaxis"),R=d(y),P=d(b),z=d(x),I=[[S[0]-R[0],C[0]-P[0],O[0]-z[0]],[S[1]+R[1],C[1]+P[1],O[1]+z[1]]],N=n(c,I);N.vertexIntensityBounds=[e.cmin/e._normMax,e.cmax/e._normMax];var D=e.lightposition;return N.lightPosition=[D.x,D.y,D.z],N.ambient=e.lighting.ambient,N.diffuse=e.lighting.diffuse,N.specular=e.lighting.specular,N.roughness=e.lighting.roughness,N.fresnel=e.lighting.fresnel,N.opacity=e.opacity,e._pad=N.tubeScale*e.sizeref*2,N}c.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[l[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},c.update=function(t){this.data=t;var e=p(this.scene,t);this.mesh.update(e)},c.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=p(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../lib":495,"../../lib/gl_format_color":493,"../../plots/gl3d/zip3":581,"gl-streamtube3d":160}],652:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),u=s("v"),c=s("w"),f=s("x"),h=s("y"),d=s("z");l&&l.length&&u&&u.length&&c&&c.length&&f&&f.length&&h&&h.length&&d&&d.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":386,"../../lib":495,"./attributes":649}],653:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":570,"./attributes":649,"./calc":650,"./convert":651,"./defaults":652}],654:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../components/colorbar/attributes"),o=t("../../components/fx/hovertemplate_attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll;function c(t){return{show:{valType:"boolean",dflt:!1},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=u(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{colorbar:a,contours:{x:c(),y:c(),z:c()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo)}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":376,"../../components/colorbar/attributes":377,"../../components/colorscale/attributes":383,"../../components/fx/hovertemplate_attributes":414,"../../lib/extend":488,"../../plot_api/edit_types":524,"../../plots/attributes":538}],655:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":384}],656:[function(t,e,r){"use strict";var n=t("gl-surface3d"),i=t("ndarray"),a=t("ndarray-homography"),o=t("ndarray-fill"),s=t("../../lib").isArrayOrTypedArray,l=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),c=t("../heatmap/interp2d"),f=t("../heatmap/find_empties");function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this._interpolatedZ=!1}var d=h.prototype;d.getXat=function(t,e,r,n){var i=s(this.data.x)?s(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},d.getYat=function(t,e,r,n){var i=s(this.data.y)?s(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},d.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},d.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var p=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function v(t){for(var e=[],r=0;r<p.length;r++){var n=p[r];e.push(g(t,n))}return e}function m(t){for(var e=v(t),r=t,n=0;n<p.length;n++)if(e[n]>0){r=p[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=v(t),n=v(e),i=1,a=0;a<p.length;a++)i*=Math.pow(p[a],Math.max(r[a],n[a]));return i}}d.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},d.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var b=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],x=b[9],_=b[13];d.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<x;)r*=2;for(;r>_;)r--,r/=m(r),++r<x&&(r=_);var n=Math.round(r/t);return n>1?n:1},d.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],o=t[0].shape[1],s=0|Math.floor(t[0].shape[0]*e+1),l=0|Math.floor(t[0].shape[1]*r+1),u=1+n+1,c=1+o+1,f=i(new Float32Array(u*c),[u,c]),h=0;h<t.length;++h){this.surface.padField(f,t[h]);var d=i(new Float32Array(s*l),[s,l]);a(d,f,[e,0,0,0,r,0,0,0,1]),t[h]=d}},d.setContourLevels=function(){for(var t=[[],[],[]],e=!1,r=0;r<3;++r)this.showContour[r]&&(e=!0,t[r]=this.scene.contourLevels[r]);e&&this.surface.update({levels:t})},d.update=function(t){var e,r,n,a,s=this.scene,h=s.fullSceneLayout,d=this.surface,p=t.opacity,g=l(t,p),v=s.dataScale,m=t.z[0].length,y=t._ylength,b=s.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<y;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=f(x[2]),c(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<y;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<y;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=v[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)t._objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=t._objectOffset[e]);var w=[i(new Float32Array(m*y),[m,y]),i(new Float32Array(m*y),[m,y]),i(new Float32Array(m*y),[m,y])];o(w[0],function(t,e){return x[0][t][e]}),o(w[1],function(t,e){return x[1][t][e]}),o(w[2],function(t,e){return x[2][t][e]}),x=[];var A={colormap:g,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacity:t.opacity};if(A.intensityBounds=[t.cmin,t.cmax],t.surfacecolor){var M=i(new Float32Array(m*y),[m,y]);o(M,function(e,r){return t.surfacecolor[r][e]}),w.push(M)}else A.intensityBounds[0]*=v[2],A.intensityBounds[1]*=v[2];(_<w[0].shape[0]||_<w[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(w[0].shape[0],0),this.dataScaleY=this.estimateScale(w[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(w)),t.surfacecolor&&(A.intensity=w.pop());var T=[!0,!0,!0],k=["x","y","z"];for(e=0;e<3;++e){var E=t.contours[k[e]];T[e]=E.highlight,A.showContour[e]=E.show||E.highlight,A.showContour[e]&&(A.contourProject[e]=[E.project.x,E.project.y,E.project.z],E.show?(this.showContour[e]=!0,A.levels[e]=b[e],d.highlightColor[e]=A.contourColor[e]=u(E.color),E.usecolormap?d.highlightTint[e]=A.contourTint[e]=0:d.highlightTint[e]=A.contourTint[e]=1,A.contourWidth[e]=E.width):this.showContour[e]=!1,E.highlight&&(A.dynamicColor[e]=u(E.highlightcolor),A.dynamicWidth[e]=E.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(g)&&(A.vertexColor=!0),A.objectOffset=[t._objectOffset[0],t._objectOffset[1],t._objectOffset[2]],A.coords=w,d.update(A),d.visible=t.visible,d.enableDynamic=T,d.enableHighlight=T,d.snapToData=!0,"lighting"in t&&(d.ambientLight=t.lighting.ambient,d.diffuseLight=t.lighting.diffuse,d.specularLight=t.lighting.specular,d.roughness=t.lighting.roughness,d.fresnel=t.lighting.fresnel),"lightposition"in t&&(d.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z]),p&&p<1&&(d.supportsTransparency=!0)},d.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../lib":495,"../../lib/gl_format_color":493,"../../lib/str2rgbarray":517,"../heatmap/find_empties":606,"../heatmap/interp2d":607,"gl-surface3d":163,ndarray:286,"ndarray-fill":276,"ndarray-homography":278}],657:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");function s(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports=function(t,e,r,l){var u,c;function f(r,n){return i.coerce(t,e,o,r,n)}var h=f("x"),d=f("y"),p=f("z");if(!p||!p.length||h&&h.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(h)&&i.isArrayOrTypedArray(h[0])?p.length:p[0].length,e._ylength=p.length,e._objectOffset=[0,0,0],n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],l),f("text"),f("hovertext"),f("hovertemplate"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach(function(t){f(t)});var g=f("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var m="contours."+v[u],y=f(m+".show"),b=f(m+".highlight");if(y||b)for(c=0;c<3;++c)f(m+".project."+v[c]);y&&(f(m+".color"),f(m+".width"),f(m+".usecolormap")),b&&(f(m+".highlightcolor"),f(m+".highlightwidth"))}g||(s(t,"zmin","cmin"),s(t,"zmax","cmax"),s(t,"zauto","cauto")),a(t,e,l,f,{prefix:"",cLetter:"c"}),e._length=null}}},{"../../components/colorscale/defaults":386,"../../lib":495,"../../registry":592,"./attributes":654}],658:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.colorbar={min:"cmin",max:"cmax"},n.calc=t("./calc"),n.plot=t("./convert"),n.moduleType="trace",n.name="surface",n.basePlotModule=t("../../plots/gl3d"),n.categories=["gl3d","2dMap","noOpacity"],n.meta={},e.exports=n},{"../../plots/gl3d":570,"./attributes":654,"./calc":655,"./convert":656,"./defaults":657}]},{},[5])(5)});
\ No newline at end of file
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 5a290c2299..a504cfa99b 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -105,7 +105,7 @@ def _replacement_params_view_connectome():
                     )
 def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
                     edge_cmap=cm.bwr, symmetric_cmap=True,
-                    linewidth=6., node_size=3.,
+                    linewidth=6., node_size=3., title=None
                     ):
     """
     Insert a 3d plot of a connectome into an HTML page.
@@ -164,6 +164,8 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
         adjacency_matrix, node_coords, threshold=edge_threshold, cmap=edge_cmap,
         symmetric_cmap=symmetric_cmap, marker_size=node_size)
     connectome_info['line_width'] = linewidth
+    if title is not None:
+        connectome_info['title'] = title
     return _make_connectome_html(connectome_info)
 
 
@@ -181,7 +183,8 @@ def _replacement_params_view_markers():
                     end_version='0.6.0',
                     lib_name='Nilearn',
                     )
-def view_markers(marker_coords, marker_color=None, marker_size=5.):
+def view_markers(marker_coords, marker_color=None, marker_size=5.,
+                 title=None):
     """
     Insert a 3d plot of markers in a brain into an HTML page.
 
@@ -227,4 +230,6 @@ def view_markers(marker_coords, marker_color=None, marker_size=5.):
     if hasattr(marker_size, 'tolist'):
         marker_size = marker_size.tolist()
     connectome_info["marker_size"] = marker_size
+    if title is not None:
+        connectome_info['title'] = title
     return _make_connectome_html(connectome_info)
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index f7dae14e80..f7b46e2c22 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -126,7 +126,7 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, vmax=None):
+                     black_bg=False, vmax=None, title=None):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -181,6 +181,8 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     info = full_brain_info(
         volume_img=stat_map_img, mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, vmax=vmax)
+    if title is not None:
+        info['title'] = title
     return _fill_html_template(info, embed_js=True)
 
 
@@ -261,4 +263,6 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         surf_map=surf_map, surf_mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
         symmetric_cmap=symmetric_cmap, vmax=vmax)
+    if title is not None:
+        info['title'] = title
     return _fill_html_template(info, embed_js=True)

From 1e326270ea173ddedff26d3358199df841c2a34c Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 5 Apr 2019 18:38:13 +0200
Subject: [PATCH 029/228] shorter colorbar

---
 nilearn/plotting/data/js/surface-plot-utils.js | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nilearn/plotting/data/js/surface-plot-utils.js b/nilearn/plotting/data/js/surface-plot-utils.js
index 4f636193da..d4b1970f3b 100644
--- a/nilearn/plotting/data/js/surface-plot-utils.js
+++ b/nilearn/plotting/data/js/surface-plot-utils.js
@@ -137,7 +137,8 @@ function addColorbar(colorscale, cmin, cmax, divId, layout, config) {
     // hack to get a colorbar
     let dummy = {
         "opacity": 0,
-        "colorbar": {"tickfont": {"size": 25}},
+        "colorbar": {"tickfont": {"size": 25},
+                     "len": .5},
         "type": "mesh3d",
         "colorscale": colorscale,
         "x": [1, 0, 0],

From 6cd971be6bebf8aaa8a2a5b7b4c2e67a00a5f86b Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sat, 6 Apr 2019 16:50:14 +0200
Subject: [PATCH 030/228] make colorbar optional

---
 .../data/html/connectome_plot_template.html      | 16 +++++++++-------
 .../data/html/surface_plot_template.html         | 10 ++++++----
 nilearn/plotting/html_connectome.py              |  7 +++++--
 nilearn/plotting/html_surface.py                 | 13 +++++++++++--
 4 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 92a72a5ddc..4a13f11669 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -37,13 +37,15 @@
             if(connectomeInfo["connectome"]["markers_only"]){
                 return;
             }
-            addColorbar(
-                connectomeInfo["connectome"]["colorscale"],
-                connectomeInfo["connectome"]["cmin"],
-                connectomeInfo["connectome"]["cmax"],
-                "connectome-plot", getLayout("connectome-plot",
-                                             "select-view", false),
-                getConfig());
+            if(connectomeInfo["connectome"]["colorbar"]){
+                addColorbar(
+                    connectomeInfo["connectome"]["colorscale"],
+                    connectomeInfo["connectome"]["cmin"],
+                    connectomeInfo["connectome"]["cmax"],
+                    "connectome-plot", getLayout("connectome-plot",
+                                                 "select-view", false),
+                    getConfig());
+            }
         }
 
         function updateOpacity() {
diff --git a/nilearn/plotting/data/html/surface_plot_template.html b/nilearn/plotting/data/html/surface_plot_template.html
index ef3d1487f4..5583e886d8 100644
--- a/nilearn/plotting/data/html/surface_plot_template.html
+++ b/nilearn/plotting/data/html/surface_plot_template.html
@@ -25,10 +25,12 @@
 
             Plotly.react(divId, data, layout, config);
 
-            addColorbar(surfaceMapInfo["colorscale"],
-                        surfaceMapInfo["cmin"],
-                        surfaceMapInfo["cmax"],
-                        divId, layout, config);
+            if(surfaceMapInfo["colorbar"]){
+                addColorbar(surfaceMapInfo["colorscale"],
+                            surfaceMapInfo["cmin"],
+                            surfaceMapInfo["cmax"],
+                            divId, layout, config);
+            }
         }
 
         function addPlot() {
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 5a290c2299..531f35be2d 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -105,8 +105,7 @@ def _replacement_params_view_connectome():
                     )
 def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
                     edge_cmap=cm.bwr, symmetric_cmap=True,
-                    linewidth=6., node_size=3.,
-                    ):
+                    linewidth=6., node_size=3., colorbar=True):
     """
     Insert a 3d plot of a connectome into an HTML page.
 
@@ -137,6 +136,9 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
     node_size : float, optional (default=3.)
         Size of the markers showing the seeds in pixels.
 
+    colorbar : bool, optional (default=True)
+        add a colorbar
+
     Returns
     -------
     ConnectomeView : plot of the connectome.
@@ -164,6 +166,7 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
         adjacency_matrix, node_coords, threshold=edge_threshold, cmap=edge_cmap,
         symmetric_cmap=symmetric_cmap, marker_size=node_size)
     connectome_info['line_width'] = linewidth
+    connectome_info['colorbar'] = colorbar
     return _make_connectome_html(connectome_info)
 
 
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index f7dae14e80..4a3639a0ef 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -126,7 +126,7 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, vmax=None):
+                     black_bg=False, vmax=None, colorbar=True):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -162,6 +162,9 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
 
+    colorbar : bool, optional (default=True)
+        add a colorbar
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -181,12 +184,13 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     info = full_brain_info(
         volume_img=stat_map_img, mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, vmax=vmax)
+    info['colorbar'] = colorbar
     return _fill_html_template(info, embed_js=True)
 
 
 def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
               cmap=cm.cold_hot, black_bg=False, vmax=None,
-              symmetric_cmap=True):
+              symmetric_cmap=True, colorbar=True):
     """
     Insert a surface plot of a surface map into an HTML page.
 
@@ -235,6 +239,9 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         upper bound for the colorbar. if None, use the absolute max of the
         brain map.
 
+    colorbar : bool, optional (default=True)
+        add a colorbar
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -261,4 +268,6 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         surf_map=surf_map, surf_mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
         symmetric_cmap=symmetric_cmap, vmax=vmax)
+    if colorbar:
+        info['colorbar'] = True
     return _fill_html_template(info, embed_js=True)

From 0b073db26ac5ccf8e85b7ee152a65f7ca28cb87d Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sat, 6 Apr 2019 17:12:05 +0200
Subject: [PATCH 031/228] expose colorbar height and fontsize

---
 .../data/html/connectome_plot_template.html   |  4 ++-
 .../data/html/surface_plot_template.html      |  5 +++-
 .../plotting/data/js/surface-plot-utils.js    |  7 +++---
 nilearn/plotting/html_connectome.py           | 11 +++++++-
 nilearn/plotting/html_surface.py              | 25 ++++++++++++++++---
 5 files changed, 42 insertions(+), 10 deletions(-)

diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 4a13f11669..b257638630 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -44,7 +44,9 @@
                     connectomeInfo["connectome"]["cmax"],
                     "connectome-plot", getLayout("connectome-plot",
                                                  "select-view", false),
-                    getConfig());
+                    getConfig(),
+                    connectomeInfo["connectome"]["cbar_fontsize"],
+                    connectomeInfo["connectome"]["cbar_height"]);
             }
         }
 
diff --git a/nilearn/plotting/data/html/surface_plot_template.html b/nilearn/plotting/data/html/surface_plot_template.html
index 5583e886d8..9854b5a6d8 100644
--- a/nilearn/plotting/data/html/surface_plot_template.html
+++ b/nilearn/plotting/data/html/surface_plot_template.html
@@ -9,6 +9,7 @@
     <script>
         var surfaceMapInfo = $INSERT_STAT_MAP_JSON_HERE;
 
+
         function makePlot(surface, hemisphere, divId) {
 
             decodeHemisphere(surfaceMapInfo, surface, hemisphere);
@@ -29,7 +30,9 @@
                 addColorbar(surfaceMapInfo["colorscale"],
                             surfaceMapInfo["cmin"],
                             surfaceMapInfo["cmax"],
-                            divId, layout, config);
+                            divId, layout, config,
+                            surfaceMapInfo["cbar_fontsize"],
+                            surfaceMapInfo["cbar_height"]);
             }
         }
 
diff --git a/nilearn/plotting/data/js/surface-plot-utils.js b/nilearn/plotting/data/js/surface-plot-utils.js
index d4b1970f3b..4784a34bc0 100644
--- a/nilearn/plotting/data/js/surface-plot-utils.js
+++ b/nilearn/plotting/data/js/surface-plot-utils.js
@@ -133,12 +133,13 @@ function updateLayout(plotDivId, viewSelectId, blackBg) {
     Plotly.relayout(plotDivId, layout);
 }
 
-function addColorbar(colorscale, cmin, cmax, divId, layout, config) {
+function addColorbar(colorscale, cmin, cmax, divId, layout, config,
+                     fontsize=25, height=.5) {
     // hack to get a colorbar
     let dummy = {
         "opacity": 0,
-        "colorbar": {"tickfont": {"size": 25},
-                     "len": .5},
+        "colorbar": {"tickfont": {"size": fontsize},
+                     "len": height},
         "type": "mesh3d",
         "colorscale": colorscale,
         "x": [1, 0, 0],
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 531f35be2d..6d069f8da0 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -105,7 +105,8 @@ def _replacement_params_view_connectome():
                     )
 def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
                     edge_cmap=cm.bwr, symmetric_cmap=True,
-                    linewidth=6., node_size=3., colorbar=True):
+                    linewidth=6., node_size=3., colorbar=True,
+                    cbar_height=.5, cbar_fontsize=25):
     """
     Insert a 3d plot of a connectome into an HTML page.
 
@@ -139,6 +140,12 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
     colorbar : bool, optional (default=True)
         add a colorbar
 
+    cbar_height : float, optional (default=.5)
+        height of the colorbar, relative to the figure height
+
+    cbar_fontsize : int, optional (default=25)
+        fontsize of the colorbar tick labels
+
     Returns
     -------
     ConnectomeView : plot of the connectome.
@@ -167,6 +174,8 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
         symmetric_cmap=symmetric_cmap, marker_size=node_size)
     connectome_info['line_width'] = linewidth
     connectome_info['colorbar'] = colorbar
+    connectome_info['cbar_height'] = cbar_height
+    connectome_info['cbar_fontsize'] = cbar_fontsize
     return _make_connectome_html(connectome_info)
 
 
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 4a3639a0ef..99061f7a06 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -126,7 +126,8 @@ def _fill_html_template(info, embed_js=True):
 
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
-                     black_bg=False, vmax=None, colorbar=True):
+                     black_bg=False, vmax=None, colorbar=True,
+                     cbar_height=.5, cbar_fontsize=25):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -165,6 +166,12 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     colorbar : bool, optional (default=True)
         add a colorbar
 
+    cbar_height : float, optional (default=.5)
+        height of the colorbar, relative to the figure height
+
+    cbar_fontsize : int, optional (default=25)
+        fontsize of the colorbar tick labels
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -185,12 +192,15 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         volume_img=stat_map_img, mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, vmax=vmax)
     info['colorbar'] = colorbar
+    info['cbar_height'] = cbar_height
+    info['cbar_fontsize'] = cbar_fontsize
     return _fill_html_template(info, embed_js=True)
 
 
 def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
               cmap=cm.cold_hot, black_bg=False, vmax=None,
-              symmetric_cmap=True, colorbar=True):
+              symmetric_cmap=True, colorbar=True, cbar_height=.5,
+              cbar_fontsize=25):
     """
     Insert a surface plot of a surface map into an HTML page.
 
@@ -242,6 +252,12 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     colorbar : bool, optional (default=True)
         add a colorbar
 
+    cbar_height : float, optional (default=.5)
+        height of the colorbar, relative to the figure height
+
+    cbar_fontsize : int, optional (default=25)
+        fontsize of the colorbar tick labels
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -268,6 +284,7 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         surf_map=surf_map, surf_mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
         symmetric_cmap=symmetric_cmap, vmax=vmax)
-    if colorbar:
-        info['colorbar'] = True
+    info['colorbar'] = colorbar
+    info['cbar_height'] = cbar_height
+    info['cbar_fontsize'] = cbar_fontsize
     return _fill_html_template(info, embed_js=True)

From 972caa5b78753cfb20970dbf746169a5928570f5 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sat, 6 Apr 2019 17:40:03 +0200
Subject: [PATCH 032/228] doc

---
 doc/whats_new.rst                     | 5 +++++
 nilearn/plotting/js_plotting_utils.py | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index feec3e49cd..888355d38f 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -33,6 +33,11 @@ Changes
  - coords is now marker_coords
  - colors is now marker_color
 
+- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
+  and :func:`nilearn.plotting.view_connectome` now allow disabling the colorbar,
+  and setting its height and the fontsize of its ticklabels.
+
+
 Fixes
 -----
 
diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index cad247b36d..f30aa0285a 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -165,7 +165,7 @@ def open_in_browser(self, file_name=None, temp_file_lifetime=30):
 
         """
         if file_name is None:
-            fd, file_name = tempfile.mkstemp('.html', 'nilearn_surface_plot_')
+            fd, file_name = tempfile.mkstemp('.html', 'nilearn_plot_')
             os.close(fd)
         self.save_as_html(file_name)
         self._temp_file = file_name

From 9998387098b3f1c128be3e11f889fda06890df54 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:17:16 +0200
Subject: [PATCH 033/228] added seitzman files and merge script

---
 .../ROIs_300inVol_MNI_allInfo.txt             | 301 ++++++++++++++++++
 .../seitzman_2018/ROIs_anatomicalLabels.txt   | 301 ++++++++++++++++++
 .../seitzman_2018/merge_seitzman_files.py     |  30 ++
 .../data/seitzman_2018/seitzman_2018_rois.csv | 301 ++++++++++++++++++
 4 files changed, 933 insertions(+)
 create mode 100644 nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt
 create mode 100644 nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt
 create mode 100644 nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
 create mode 100644 nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv

diff --git a/nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt b/nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt
new file mode 100644
index 0000000000..6e73eb8025
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt
@@ -0,0 +1,301 @@
+x y z radius(mm) netWorkbenchLabel netName
+-56.16 -44.76 -24.23 5 0 unassigned
+-24.66 -97.84 -12.33 5 0 unassigned
+8.13 41.12 -24.31 5 0 unassigned
+26.68 -97.3 -13.49 5 0 unassigned
+48.52 -2.85 -38.49 5 0 unassigned
+51.79 -34.17 -27.23 5 0 unassigned
+55.18 -30.8 -16.93 5 0 unassigned
+-57.97 -25.69 -14.73 5 0 unassigned
+64.6 -24.41 -18.57 5 0 unassigned
+-50.06 -7.09 -39.24 5 0 unassigned
+33.55 38.46 -12.03 5 0 unassigned
+-46.68 -50.91 -20.91 5 0 unassigned
+-7.12 -52.22 60.71 5 10 SomatomotorDorsal
+0.05 -14.53 46.74 5 10 SomatomotorDorsal
+-53.52 -22.54 43.1 5 10 SomatomotorDorsal
+-45.1 -31.85 46.63 5 10 SomatomotorDorsal
+-39.63 -19.04 54.21 5 10 SomatomotorDorsal
+-38.28 -27.17 69.45 5 10 SomatomotorDorsal
+-38.24 -14.57 68.72 5 10 SomatomotorDorsal
+-29.1 -43 60.66 5 10 SomatomotorDorsal
+-22.5 -30.1 72.44 5 10 SomatomotorDorsal
+-20.66 -31.33 60.85 5 10 SomatomotorDorsal
+-16.25 -45.8 73.22 5 10 SomatomotorDorsal
+-13.74 -17.95 39.84 5 10 SomatomotorDorsal
+-12.96 -17.34 74.66 5 10 SomatomotorDorsal
+-6.9 -20.59 65.21 5 10 SomatomotorDorsal
+-6.79 -33.09 72.27 5 10 SomatomotorDorsal
+2.4 -27.94 60.15 5 10 SomatomotorDorsal
+3.45 -17.44 58.45 5 10 SomatomotorDorsal
+9.5 -1.84 44.73 5 10 SomatomotorDorsal
+9.94 -45.52 72.63 5 10 SomatomotorDorsal
+10.09 -17.1 74.14 5 10 SomatomotorDorsal
+13.19 -32.82 74.98 5 10 SomatomotorDorsal
+20.21 -28.8 59.8 5 10 SomatomotorDorsal
+22.45 -42.29 68.99 5 10 SomatomotorDorsal
+28.88 -16.95 70.55 5 10 SomatomotorDorsal
+28.54 -39.24 59.17 5 10 SomatomotorDorsal
+37.74 -17.3 45.01 5 10 SomatomotorDorsal
+42.14 -20.24 54.59 5 10 SomatomotorDorsal
+44.34 -7.55 56.98 5 10 SomatomotorDorsal
+47.21 -29.75 48.7 5 10 SomatomotorDorsal
+50.24 -20.37 41.74 5 10 SomatomotorDorsal
+19.33 -7.71 63.88 5 10 SomatomotorDorsal
+54.22 -27.83 33.64 5 10 SomatomotorDorsal
+-52.84 -10.23 24.41 5 11 SomatomotorLateral
+-49.47 -11.06 34.95 5 11 SomatomotorLateral
+36.04 -9.44 13.95 5 11 SomatomotorLateral
+51.14 -5.8 32.42 5 11 SomatomotorLateral
+65.64 -7.88 24.83 5 11 SomatomotorLateral
+-51.26 8.26 -2.06 5 9 CinguloOpercular
+-44.76 0.1 8.83 5 9 CinguloOpercular
+-34.37 3.29 4.19 5 9 CinguloOpercular
+-16.14 -4.82 70.83 5 9 CinguloOpercular
+-10.48 -2.1 42.02 5 9 CinguloOpercular
+-5.33 17.8 34.41 5 9 CinguloOpercular
+-2.88 2.38 53.21 5 9 CinguloOpercular
+6.52 7.69 50.58 5 9 CinguloOpercular
+13.21 -1.36 69.98 5 9 CinguloOpercular
+35.83 10.32 1.18 5 9 CinguloOpercular
+36.73 0.78 -3.57 5 9 CinguloOpercular
+49.4 8.32 -1.12 5 9 CinguloOpercular
+-30.12 -27.02 12.2 5 9 CinguloOpercular
+42.05 -0.39 47.1 5 9 CinguloOpercular
+35.91 21.91 2.62 5 9 CinguloOpercular
+-39.12 50.79 17.38 5 9 CinguloOpercular
+31.24 32.79 26.39 5 9 CinguloOpercular
+31.07 55.71 14.49 5 9 CinguloOpercular
+36.89 32.35 -2.24 5 9 CinguloOpercular
+-52.92 -21.83 22.97 5 12 Auditory
+43.45 -22.93 19.85 5 12 Auditory
+59.4 -17.34 28.69 5 12 Auditory
+-60.48 -25.22 13.82 5 12 Auditory
+-55.22 -9.42 11.73 5 12 Auditory
+-49.77 -34.36 25.74 5 12 Auditory
+-49.14 -26.3 5.18 5 12 Auditory
+-38.43 -33.34 16.98 5 12 Auditory
+31.75 -26.33 12.91 5 12 Auditory
+55.96 -5.03 13.25 5 12 Auditory
+57.88 -15.62 7.49 5 12 Auditory
+65.43 -33.2 19.97 5 12 Auditory
+-68.47 -22.66 -15.74 5 1 DefaultMode
+-68.3 -41.41 -5.14 5 1 DefaultMode
+-57.75 -29.7 -3.94 5 1 DefaultMode
+-55.72 -12.96 -10.24 5 1 DefaultMode
+-52.89 2.55 -27.06 5 1 DefaultMode
+-49.3 -42.15 0.83 5 1 DefaultMode
+-45.79 -60.69 20.85 5 1 DefaultMode
+-46.17 31.26 -13.03 5 1 DefaultMode
+-44.45 -64.64 34.78 5 1 DefaultMode
+-43.58 11.99 -34.15 5 1 DefaultMode
+-40.5 -75.27 25.8 5 1 DefaultMode
+-39.05 -74.95 43.72 5 1 DefaultMode
+-35.36 19.86 50.8 5 1 DefaultMode
+-19.78 45.07 39.48 5 1 DefaultMode
+-20.16 63.65 19.39 5 1 DefaultMode
+-17.65 63.19 -9.17 5 1 DefaultMode
+-16.4 28.52 53.05 5 1 DefaultMode
+-12.6 -39.64 0.93 5 1 DefaultMode
+-11.29 -56.2 15.6 5 1 DefaultMode
+-10.09 39.09 52.29 5 1 DefaultMode
+-10.33 54.63 38.71 5 1 DefaultMode
+-11.06 44.62 7.61 5 1 DefaultMode
+-7.55 48.08 23.18 5 1 DefaultMode
+-6.84 -54.9 27.05 5 1 DefaultMode
+-7.04 50.82 -1.29 5 1 DefaultMode
+-2.94 -48.79 12.87 5 1 DefaultMode
+-2.2 -36.68 43.85 5 1 DefaultMode
+-3.06 44.41 -9.46 5 1 DefaultMode
+-2.06 37.85 36.34 5 1 DefaultMode
+-2.5 41.7 16.05 5 1 DefaultMode
+5.55 66.69 -3.55 5 1 DefaultMode
+5.91 -58.82 35.45 5 1 DefaultMode
+5.94 54.42 16.18 5 1 DefaultMode
+6.11 63.98 21.96 5 1 DefaultMode
+7.94 -48.37 30.57 5 1 DefaultMode
+7.51 42.49 -5.35 5 1 DefaultMode
+8.36 47.59 -15.18 5 1 DefaultMode
+8.8 54.23 3.45 5 1 DefaultMode
+10.77 -53.83 17.09 5 1 DefaultMode
+12.73 54.87 38.19 5 1 DefaultMode
+13.08 29.99 58.65 5 1 DefaultMode
+12.25 35.63 20.3 5 1 DefaultMode
+15.12 -63.09 25.98 5 1 DefaultMode
+22.11 39.21 38.9 5 1 DefaultMode
+23.33 33.07 47.68 5 1 DefaultMode
+43.43 -72.21 28 5 1 DefaultMode
+45.64 16.2 -30.02 5 1 DefaultMode
+46.68 -50.08 28.76 5 1 DefaultMode
+49.26 35.47 -12.2 5 1 DefaultMode
+52.04 -59.37 35.52 5 1 DefaultMode
+52.16 -2.43 -16.4 5 1 DefaultMode
+51.9 6.81 -29.61 5 1 DefaultMode
+64.64 -11.8 -19.3 5 1 DefaultMode
+64.8 -30.55 -8.7 5 1 DefaultMode
+-33.93 -38.06 -15.6 5 1 DefaultMode
+4.2 -48.06 50.71 5 15 ParietoMedial
+-6.58 -71.47 41.74 5 15 ParietoMedial
+-2.47 -34.8 31.07 5 15 ParietoMedial
+1.75 -24.25 30.36 5 15 ParietoMedial
+11.27 -66.01 42.09 5 15 ParietoMedial
+-12.08 -94.56 -12.8 5 2 Visual
+7.98 -91.08 -7.1 5 2 Visual
+17.27 -91.09 -13.64 5 2 Visual
+-46.54 -75.95 -9.95 5 2 Visual
+-42.1 -73.62 0.38 5 2 Visual
+-40.21 -88.44 -6.19 5 2 Visual
+-33 -79.02 -13.24 5 2 Visual
+-28.07 -79.45 19.43 5 2 Visual
+-26.39 -90.23 3.12 5 2 Visual
+-23.94 -90.98 18.96 5 2 Visual
+-17.87 -68.03 4.81 5 2 Visual
+-16.21 -76.97 33.82 5 2 Visual
+-15.85 -52.34 -1.43 5 2 Visual
+-15.02 -72.42 -7.68 5 2 Visual
+-14.22 -90.66 31.4 5 2 Visual
+-8.43 -80.5 7.44 5 2 Visual
+-2.88 -81.25 21.1 5 2 Visual
+5.59 -71.65 23.52 5 2 Visual
+6.21 -81.41 6.11 5 2 Visual
+8.45 -71.84 10.79 5 2 Visual
+15.27 -87.09 36.89 5 2 Visual
+15.18 -76.68 31 5 2 Visual
+17.53 -46.86 -9.88 5 2 Visual
+19.81 -65.56 1.72 5 2 Visual
+19.64 -85.62 -2.39 5 2 Visual
+24.41 -87.21 24.01 5 2 Visual
+25.66 -79.47 -15.56 5 2 Visual
+26.93 -59.37 -9.36 5 2 Visual
+28.68 -76.62 25.42 5 2 Visual
+36.76 -84.11 12.99 5 2 Visual
+36.51 -81.16 1.2 5 2 Visual
+39.98 -72.49 14.36 5 2 Visual
+41.6 -65.5 -8.27 5 2 Visual
+42.52 -78.17 -11.78 5 2 Visual
+-52.6 -48.83 42.5 5 3 FrontoParietal
+-46.5 10.85 23.04 5 3 FrontoParietal
+-43.93 1.8 45.7 5 3 FrontoParietal
+-42.23 38.21 21.35 5 3 FrontoParietal
+-42.09 -54.98 44.74 5 3 FrontoParietal
+-42.1 24.68 29.53 5 3 FrontoParietal
+-41.06 5.81 32.72 5 3 FrontoParietal
+-41.68 45.16 -2.31 5 3 FrontoParietal
+-34.16 54.83 4.36 5 3 FrontoParietal
+-28.4 -57.93 47.78 5 3 FrontoParietal
+-22.53 10.76 63.73 5 3 FrontoParietal
+-2.98 26.41 44.42 5 3 FrontoParietal
+24.07 44.61 -15.35 5 3 FrontoParietal
+31.83 14.37 55.98 5 3 FrontoParietal
+33.38 -53.12 44.02 5 3 FrontoParietal
+33.6 54.22 -12.95 5 3 FrontoParietal
+37.45 -64.7 40.38 5 3 FrontoParietal
+38.37 43.18 15.06 5 3 FrontoParietal
+39.87 18.39 39.72 5 3 FrontoParietal
+43.93 -52.95 46.95 5 3 FrontoParietal
+43.25 49.25 -2.31 5 3 FrontoParietal
+47.01 9.93 32.66 5 3 FrontoParietal
+47.98 24.56 26.5 5 3 FrontoParietal
+49.18 -42.41 45.16 5 3 FrontoParietal
+58.31 -52.79 -13.61 5 3 FrontoParietal
+47.6 22.16 9.74 5 3 FrontoParietal
+55.27 -44.59 36.7 5 3 FrontoParietal
+-35.44 20.03 0.07 5 8 Salience
+-27.5 52.04 21.28 5 8 Salience
+-10.76 25.99 24.54 5 8 Salience
+-0.94 14.86 43.99 5 8 Salience
+-0.2 30.35 27.22 5 8 Salience
+5.23 23.22 37.03 5 8 Salience
+10.26 22.06 27.48 5 8 Salience
+26.07 49.56 26.58 5 8 Salience
+33.56 16.45 -7.58 5 8 Salience
+51.28 -28.52 -4.3 5 7 VentralAttention
+-56.47 -50.48 9.92 5 7 VentralAttention
+-55.3 -39.89 13.51 5 7 VentralAttention
+51.52 -32.52 7.55 5 7 VentralAttention
+55.75 -46.07 11.42 5 7 VentralAttention
+-49.07 25.13 -0.98 5 7 VentralAttention
+-9.88 10.95 66.61 5 7 VentralAttention
+52.68 32.58 0.57 5 7 VentralAttention
+53.9 -42.76 21.83 5 7 VentralAttention
+-26.6 -70.72 36.86 5 5 DorsalAttention
+-37.26 -28.8 -25.58 5 5 DorsalAttention
+45.68 -46.67 -16.85 5 5 DorsalAttention
+10.51 -38.54 50.02 5 5 DorsalAttention
+-52.44 -63.14 5.29 5 5 DorsalAttention
+-42.26 -60.12 -8.85 5 5 DorsalAttention
+-32.56 -46.42 47.2 5 5 DorsalAttention
+-32.23 -1.08 54.06 5 5 DorsalAttention
+-16.5 -58.57 64.46 5 5 DorsalAttention
+9.61 -61.5 60.88 5 5 DorsalAttention
+21.9 -64.74 48.12 5 5 DorsalAttention
+25.34 -58.18 60.34 5 5 DorsalAttention
+28.56 -4.62 53.99 5 5 DorsalAttention
+46.09 -58.93 3.93 5 5 DorsalAttention
+-31.13 -9.99 -36.32 5 17 MedialTemporalLobe
+32.85 -12.41 -34.41 5 17 MedialTemporalLobe
+-25.57 -11.78 -21.54 4 17 MedialTemporalLobe
+24.73 -11.25 -22.68 4 17 MedialTemporalLobe
+-25.24 -38.78 -2.01 4 1 DefaultMode
+25.08 -37.18 -2.16 4 1 DefaultMode
+-30.63 18.71 -18.98 5 4 Reward
+-21.14 40.87 -20.48 5 4 Reward
+23.96 31.94 -17.78 5 4 Reward
+27.06 16.22 -16.93 5 4 Reward
+-20.3 -2.27 -22.21 4 4 Reward
+19.51 -1.85 -23.11 4 4 Reward
+12.66 17.32 -5.06 4 4 Reward
+-12.49 17.05 -4.49 4 4 Reward
+11.87 17.51 6.57 4 1 DefaultMode
+-13.28 17.24 7.14 4 1 DefaultMode
+14.15 -1.19 18.18 4 3 FrontoParietal
+-15.19 -1.5 18.84 4 3 FrontoParietal
+28.56 -7.53 7.66 4 10 SomatomotorDorsal
+-28.02 -10.23 9.07 4 10 SomatomotorDorsal
+28.28 -6.7 -5.06 4 10 SomatomotorDorsal
+-28.31 -10.45 -3.6 4 10 SomatomotorDorsal
+25.26 1.75 -1.26 4 10 SomatomotorDorsal
+-28.26 -0.97 -3.09 4 10 SomatomotorDorsal
+25.46 5.05 7 4 10 SomatomotorDorsal
+-24.83 7.67 7.95 4 10 SomatomotorDorsal
+18.88 -4.68 -3.91 4 10 SomatomotorDorsal
+-18.84 -5.08 -3.06 4 10 SomatomotorDorsal
+3.42 -7.79 8.23 4 1 DefaultMode
+-2.88 -9.96 8.5 4 1 DefaultMode
+18.89 -28.79 0.66 4 2 Visual
+-18.84 -29.18 1.51 4 2 Visual
+12.9 -13.94 11.55 4 9 CinguloOpercular
+-15.39 -14.24 12.19 4 9 CinguloOpercular
+9.56 -7.84 1.76 4 9 CinguloOpercular
+-9.35 -10.17 0.2 4 9 CinguloOpercular
+13.67 -19.39 0.23 4 10 SomatomotorDorsal
+-13.57 -19.67 0.84 4 10 SomatomotorDorsal
+15.95 -22.37 8.8 4 10 SomatomotorDorsal
+-18.63 -22.73 9.59 4 10 SomatomotorDorsal
+32 -49 -51 4 0 unassigned
+-13 -52 -50 4 0 unassigned
+14 -48 -52 4 0 unassigned
+-32 -78 -38 4 1 DefaultMode
+32 -81 -38 4 1 DefaultMode
+-24 -76 -28 4 1 DefaultMode
+24 -76 -28.01 4 1 DefaultMode
+-5.72 -50.8 -40.84 4 1 DefaultMode
+8 -50 -40 4 1 DefaultMode
+0 -74 -25 4 2 Visual
+-10 -78 -28 4 3 FrontoParietal
+10 -78 -28 4 3 FrontoParietal
+-34 -72.01 -48 4 3 FrontoParietal
+34 -72 -48 4 3 FrontoParietal
+-30.5 -66 -30 4 3 FrontoParietal
+31.68 -62.83 -30.4 4 3 FrontoParietal
+40 -44 -38 4 3 FrontoParietal
+43.5 -60 -30 4 9 CinguloOpercular
+-43.5 -60 -30 4 9 CinguloOpercular
+-34 -42 -44 4 9 CinguloOpercular
+-33 -51 -50 4 10 SomatomotorDorsal
+-6 -74 -42 4 10 SomatomotorDorsal
+7.5 -72 -39 4 10 SomatomotorDorsal
+-10 -62 -18 4 10 SomatomotorDorsal
+10 -62 -18 4 10 SomatomotorDorsal
+-12 -44 -18 4 10 SomatomotorDorsal
+12 -44 -18 4 10 SomatomotorDorsal
diff --git a/nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt b/nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt
new file mode 100644
index 0000000000..9b41c0b51a
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt
@@ -0,0 +1,301 @@
+0=cortexMid,1=cortexL,2=cortexR,3=hippocampus,4=amygdala,5=basalGanglia,6=thalamus,7=cerebellum
+1
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diff --git a/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py b/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
new file mode 100644
index 0000000000..3226eba166
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
@@ -0,0 +1,30 @@
+"""This script takes original files from Seitzman atlas
+    - ROIs_300inVol_MNI_allInfo.txt
+    - ROIs_anatomicalLabels.txt
+and creates one nicely formatted csv file (seitzman_2018_rois.csv) with
+coordinates, network and region names
+"""
+import pandas as pd
+
+
+def merge_seitzman_files():
+    rois = (pd.read_csv("ROIs_300inVol_MNI_allInfo.txt", sep=" ").
+            rename(columns={"netName": "network", "radius(mm)": "radius"})
+            )
+
+    labels = pd.read_csv("ROIs_anatomicalLabels.txt", sep=" ")
+
+    region_mapping = {}
+    for r in labels.columns[0].split(","):
+        i, region = r.split("=")
+        region_mapping[int(i)] = region
+
+    labels.columns = ["region"]
+    labels.replace(to_replace=region_mapping, inplace=True)
+
+    rois = pd.merge(rois, labels, left_index=True, right_index=True)
+    rois.to_csv("seitzman_2018_rois.csv", index=False)
+
+
+if __name__ == "__main__":
+    merge_seitzman_files()
diff --git a/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv b/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv
new file mode 100644
index 0000000000..c7f1f69968
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv
@@ -0,0 +1,301 @@
+x,y,z,radius,netWorkbenchLabel,network,region
+-56.16,-44.76,-24.23,5,0,unassigned,cortexL
+-24.66,-97.84,-12.33,5,0,unassigned,cortexL
+8.13,41.12,-24.31,5,0,unassigned,cortexR
+26.68,-97.3,-13.49,5,0,unassigned,cortexR
+48.52,-2.85,-38.49,5,0,unassigned,cortexR
+51.79,-34.17,-27.23,5,0,unassigned,cortexR
+55.18,-30.8,-16.93,5,0,unassigned,cortexR
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+-30.63,18.71,-18.98,5,4,Reward,cortexL
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+23.96,31.94,-17.78,5,4,Reward,cortexR
+27.06,16.22,-16.93,5,4,Reward,cortexR
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+12.66,17.32,-5.06,4,4,Reward,basalGanglia
+-12.49,17.05,-4.49,4,4,Reward,basalGanglia
+11.87,17.51,6.57,4,1,DefaultMode,basalGanglia
+-13.28,17.24,7.14,4,1,DefaultMode,basalGanglia
+14.15,-1.19,18.18,4,3,FrontoParietal,basalGanglia
+-15.19,-1.5,18.84,4,3,FrontoParietal,basalGanglia
+28.56,-7.53,7.66,4,10,SomatomotorDorsal,basalGanglia
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+28.28,-6.7,-5.06,4,10,SomatomotorDorsal,basalGanglia
+-28.31,-10.45,-3.6,4,10,SomatomotorDorsal,basalGanglia
+25.26,1.75,-1.26,4,10,SomatomotorDorsal,basalGanglia
+-28.26,-0.97,-3.09,4,10,SomatomotorDorsal,basalGanglia
+25.46,5.05,7.0,4,10,SomatomotorDorsal,basalGanglia
+-24.83,7.67,7.95,4,10,SomatomotorDorsal,basalGanglia
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+-18.84,-5.08,-3.06,4,10,SomatomotorDorsal,basalGanglia
+3.42,-7.79,8.23,4,1,DefaultMode,thalamus
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+18.89,-28.79,0.66,4,2,Visual,thalamus
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+12.9,-13.94,11.55,4,9,CinguloOpercular,thalamus
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+9.56,-7.84,1.76,4,9,CinguloOpercular,thalamus
+-9.35,-10.17,0.2,4,9,CinguloOpercular,thalamus
+13.67,-19.39,0.23,4,10,SomatomotorDorsal,thalamus
+-13.57,-19.67,0.84,4,10,SomatomotorDorsal,thalamus
+15.95,-22.37,8.8,4,10,SomatomotorDorsal,thalamus
+-18.63,-22.73,9.59,4,10,SomatomotorDorsal,thalamus
+32.0,-49.0,-51.0,4,0,unassigned,cerebellum
+-13.0,-52.0,-50.0,4,0,unassigned,cerebellum
+14.0,-48.0,-52.0,4,0,unassigned,cerebellum
+-32.0,-78.0,-38.0,4,1,DefaultMode,cerebellum
+32.0,-81.0,-38.0,4,1,DefaultMode,cerebellum
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+-5.72,-50.8,-40.84,4,1,DefaultMode,cerebellum
+8.0,-50.0,-40.0,4,1,DefaultMode,cerebellum
+0.0,-74.0,-25.0,4,2,Visual,cerebellum
+-10.0,-78.0,-28.0,4,3,FrontoParietal,cerebellum
+10.0,-78.0,-28.0,4,3,FrontoParietal,cerebellum
+-34.0,-72.01,-48.0,4,3,FrontoParietal,cerebellum
+34.0,-72.0,-48.0,4,3,FrontoParietal,cerebellum
+-30.5,-66.0,-30.0,4,3,FrontoParietal,cerebellum
+31.68,-62.83,-30.4,4,3,FrontoParietal,cerebellum
+40.0,-44.0,-38.0,4,3,FrontoParietal,cerebellum
+43.5,-60.0,-30.0,4,9,CinguloOpercular,cerebellum
+-43.5,-60.0,-30.0,4,9,CinguloOpercular,cerebellum
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+-33.0,-51.0,-50.0,4,10,SomatomotorDorsal,cerebellum
+-6.0,-74.0,-42.0,4,10,SomatomotorDorsal,cerebellum
+7.5,-72.0,-39.0,4,10,SomatomotorDorsal,cerebellum
+-10.0,-62.0,-18.0,4,10,SomatomotorDorsal,cerebellum
+10.0,-62.0,-18.0,4,10,SomatomotorDorsal,cerebellum
+-12.0,-44.0,-18.0,4,10,SomatomotorDorsal,cerebellum
+12.0,-44.0,-18.0,4,10,SomatomotorDorsal,cerebellum

From ace460d5efe9beb839c172f6beef7c7770312476 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:18:22 +0200
Subject: [PATCH 034/228] seitzman atlas fetcher

---
 nilearn/datasets/__init__.py                  |  2 +
 nilearn/datasets/atlas.py                     | 50 +++++++++++++++++++
 .../datasets/description/seitzman_2018.rst    | 35 +++++++++++++
 nilearn/datasets/tests/test_atlas.py          | 16 ++++++
 setup.py                                      |  1 +
 5 files changed, 104 insertions(+)
 create mode 100644 nilearn/datasets/description/seitzman_2018.rst

diff --git a/nilearn/datasets/__init__.py b/nilearn/datasets/__init__.py
index a599286bd4..b607fac2ca 100644
--- a/nilearn/datasets/__init__.py
+++ b/nilearn/datasets/__init__.py
@@ -17,6 +17,7 @@
 from .atlas import (fetch_atlas_craddock_2012, fetch_atlas_destrieux_2009,
                     fetch_atlas_harvard_oxford, fetch_atlas_msdl,
                     fetch_coords_power_2011,
+                    fetch_coords_seitzman_2018,
                     fetch_atlas_smith_2009,
                     fetch_atlas_yeo_2011, fetch_atlas_aal,
                     fetch_atlas_basc_multiscale_2015,
@@ -44,6 +45,7 @@
            'fetch_atlas_harvard_oxford', 'fetch_atlas_msdl',
            'fetch_atlas_schaefer_2018',
            'fetch_coords_power_2011',
+           'fetch_coords_seitzman_2018',
            'fetch_atlas_smith_2009',
            'fetch_atlas_allen_2011',
            'fetch_atlas_yeo_2011', 'fetch_mixed_gambles', 'fetch_atlas_aal',
diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 36689b8105..2fbc45b689 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -10,6 +10,7 @@
 
 import nibabel as nb
 import numpy as np
+from numpy.lib import recfunctions
 from sklearn.datasets.base import Bunch
 
 from .utils import _get_dataset_dir, _fetch_files, _get_dataset_descr
@@ -808,6 +809,55 @@ def fetch_coords_dosenbach_2010(ordered_regions=True):
     return Bunch(**params)
 
 
+def fetch_coords_seitzman_2018(ordered_regions=True):
+    """Load the Seitzman et al. 300 ROIs. These ROIs cover cortical,
+    subcortical and cerebellar regions and are assigned to one of 13
+    networks (Auditory, CinguloOpercular, DefaultMode, DorsalAttention,
+    FrontoParietal, MedialTemporalLobe, ParietoMedial, Reward, Salience,
+    SomatomotorDorsal, SomatomotorLateral, VentralAttention, Visual) and
+    have a regional label (cortexL, cortexR, cerebellum, thalamus, hippocampus,
+    basalGanglia, amygdala, cortexMid).
+
+    Parameters
+    ----------
+    ordered_regions : bool, optional
+        ROIs from same networks are grouped together and ordered with respect
+        to their locations (anterior to posterior).
+
+    Returns
+    -------
+    data: sklearn.datasets.base.Bunch
+        dictionary-like object, contains:
+        - "rois": Coordinates of 300 ROIs in MNI space
+        - "radius": Radius of each ROI in mm
+        - "networks": Network names
+        - "regions": Region names
+
+    References
+    ----------
+    Seitzman, B. A., Gratton, C., Marek, S., Raut, R. V., Dosenbach, N. U.,
+    Schlaggar, B. L., et al. (2018). A set of functionally-defined brain
+    regions with improved representation of the subcortex and cerebellum.
+    bioRxiv, 450452. http://doi.org/10.1101/450452
+    """
+    dataset_name = 'seitzman_2018'
+    fdescr = _get_dataset_descr(dataset_name)
+    package_directory = os.path.dirname(os.path.abspath(__file__))
+    csv_file = os.path.join(package_directory, "data", "seitzman_2018",
+                            "seitzman_2018_rois.csv")
+    out_csv = np.recfromcsv(csv_file)
+
+    if ordered_regions:
+        out_csv = np.sort(out_csv, order=['network', 'y'])
+
+    params = dict(rois=out_csv[['x', 'y', 'z']],
+                  radius=out_csv['radius'],
+                  networks=out_csv['network'],
+                  regions=out_csv['region'], description=fdescr)
+
+    return Bunch(**params)
+
+
 def fetch_atlas_allen_2011(data_dir=None, url=None, resume=True, verbose=1):
     """Download and return file names for the Allen and MIALAB ICA atlas
     (dated 2011).
diff --git a/nilearn/datasets/description/seitzman_2018.rst b/nilearn/datasets/description/seitzman_2018.rst
new file mode 100644
index 0000000000..26c9e62686
--- /dev/null
+++ b/nilearn/datasets/description/seitzman_2018.rst
@@ -0,0 +1,35 @@
+Seitzman 2018 atlas
+
+
+Notes
+-----
+300 ROI coordinates in cortical, subcortical and cerebellar regions.
+
+"Here, we apply a winner-take-all partitioning method to resting-state fMRI data and careful consideration of
+anatomy to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and
+cerebellum. We validate these ROIs in three datasets via several anatomical and functional criteria, including known
+anatomical divisions and functions, as well as agreement with existing literature.
+Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends
+previously described functional network organization." (Seitzman et. al, 2018)
+
+
+
+Content
+-------
+    :"rois": Coordinates of 300 ROIs in MNI space
+    :"radius": Radius of each ROI in mm
+    :"networks": Network names
+    :"regions": Region names
+
+References
+----------
+For more information see:
+https://greenelab.wustl.edu/data_software
+
+ROI coordinates downloaded from:
+https://wustl.box.com/s/twpyb1pflj6vrlxgh3rohyqanxbdpelw
+
+
+Seitzman, B. A., Gratton, C., Marek, S., Raut, R. V., Dosenbach, N. U., Schlaggar, B. L., et al. (2018).
+A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.
+bioRxiv, 450452. http://doi.org/10.1101/450452
\ No newline at end of file
diff --git a/nilearn/datasets/tests/test_atlas.py b/nilearn/datasets/tests/test_atlas.py
index f6cffc48d1..88fe7bc708 100644
--- a/nilearn/datasets/tests/test_atlas.py
+++ b/nilearn/datasets/tests/test_atlas.py
@@ -308,6 +308,22 @@ def test_fetch_coords_power_2011():
     assert_not_equal(bunch.description, '')
 
 
+def test_fetch_coords_seitzman_2018():
+    bunch = atlas.fetch_coords_seitzman_2018()
+    assert_equal(len(bunch.rois), 300)
+    assert_equal(len(bunch.radius), 300)
+    assert_equal(len(bunch.networks), 300)
+    assert_equal(len(bunch.regions), 300)
+    assert_equal(len(np.unique(bunch.networks)), 14)
+    assert_equal(len(np.unique(bunch.regions)), 8)
+    np.testing.assert_array_equal(bunch.networks, np.sort(bunch.networks))
+    assert_not_equal(bunch.description, '')
+
+    bunch = atlas.fetch_coords_seitzman_2018(ordered_regions=False)
+    assert_true(np.any(bunch.networks != np.sort(bunch.networks)))
+
+
+
 @with_setup(setup_mock, teardown_mock)
 @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
 def test_fetch_atlas_destrieux_2009():
diff --git a/setup.py b/setup.py
index 878e089ddf..13bfe1b361 100755
--- a/setup.py
+++ b/setup.py
@@ -88,6 +88,7 @@ def is_installing():
           packages=find_packages(),
           package_data={'nilearn.datasets.data': ['*.nii.gz', '*.csv'],
                         'nilearn.datasets.data.fsaverage5': ['*.gz'],
+                        'nilearn.datasets.data.seitzman_2018': ['*.csv'],
                         'nilearn.surface.data': ['*.csv'],
                         'nilearn.plotting.data.js': ['*.js'],
                         'nilearn.plotting.data.html': ['*.html'],

From 00d2bfbdf794b25b32b147f830d757acda12e931 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:19:44 +0200
Subject: [PATCH 035/228] added seitzman atlas to sphere connectome example

---
 .../plot_sphere_based_connectome.py           | 55 ++++++++++++++++++-
 1 file changed, 54 insertions(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index d52a006513..1d903168ad 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -3,7 +3,8 @@
 ==============================================================
 
 This example shows how to extract signals from spherical regions
-centered on coordinates from Power-264 atlas [1] and Dosenbach-160 [2].
+centered on coordinates from the Power-264 atlas [1], the Dosenbach-160
+atlas [2], and the Seitzman-300 atlas [3].
 We estimate connectome using **sparse inverse covariance**, to recover
 the functional brain **networks structure**.
 
@@ -15,6 +16,9 @@
 [2] Dosenbach N.U., Nardos B., et al. "Prediction of individual brain maturity
 using fMRI.", 2010, Science 329, 1358-1361.
 
+[3] Seitzman, B. A., et al. "A set of functionally-defined brain regions with
+improved representation of the subcortex and cerebellum.", 2018, bioRxiv,
+450452. http://doi.org/10.1101/450452
 """
 
 ###############################################################################
@@ -146,3 +150,52 @@
 print('Dosenbach networks names are {0}'.format(np.unique(dosenbach.networks)))
 
 plotting.show()
+
+
+###############################################################################
+# Connectome extracted from Seitzman's atlas
+# -----------------------------------------------------
+# We repeat the same steps for Seitzman's atlas.
+seitzman = datasets.fetch_coords_seitzman_2018()
+
+coords = np.vstack((
+    seitzman.rois['x'],
+    seitzman.rois['y'],
+    seitzman.rois['z'],
+)).T
+
+###############################################################################
+# Before calculating the connectivity matrix, let's look at the distribution
+# of the regions of the default mode network.
+dmn_rois = seitzman.networks == b"DefaultMode"
+dmn_coords = coords[dmn_rois]
+zero_matrix = np.zeros((len(dmn_coords), len(dmn_coords)))
+plotting.plot_connectome(zero_matrix, dmn_coords,
+                         title='Seitzman default mode network',
+                         node_color='darkred', node_size=20)
+
+###############################################################################
+# Now let's calculate connectivity for the Seitzman atlas.
+spheres_masker = input_data.NiftiSpheresMasker(
+    seeds=coords, smoothing_fwhm=4, radius=4,
+    detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2.5,
+    allow_overlap=True)
+
+timeseries = spheres_masker.fit_transform(fmri_filename,
+                                          confounds=confounds_filename)
+
+covariance_estimator = GraphLassoCV()
+covariance_estimator.fit(timeseries)
+matrix = covariance_estimator.covariance_
+
+plotting.plot_matrix(matrix, vmin=-1., vmax=1., colorbar=True,
+                     title='Seitzman correlation matrix')
+
+plotting.plot_connectome(matrix, coords, title='Seitzman correlation graph',
+                         edge_threshold="99.7%", node_size=20, colorbar=True)
+
+
+###############################################################################
+# We can easily identify the networks from the matrix blocks.
+print('Seitzman networks names are {0}'.format(np.unique(seitzman.networks)))
+plotting.show()

From 0e1db7c03311f8c8c4ddf095de320256f5763744 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:22:24 +0200
Subject: [PATCH 036/228] added seitzman atlas

---
 doc/whats_new.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index feec3e49cd..2ab0000865 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -10,6 +10,7 @@ NEW
 - NiftiLabelsMasker now consumes less memory when extracting the signal from a 3D/4D
   image. This is especially noteworthy when extracting signals from large 4D images.
 - New function :func:`nilearn.datasets.fetch_atlas_schaefer_2018`
+- New function :func:`nilearn.datasets.fetch_coords_seitzman_2018`
 
 Changes
 -------

From 27434be6ef52aa67fc199b5c1d40d9cf379c3f17 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:24:52 +0200
Subject: [PATCH 037/228] clean import

---
 nilearn/datasets/atlas.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 2fbc45b689..77a5073832 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -10,7 +10,6 @@
 
 import nibabel as nb
 import numpy as np
-from numpy.lib import recfunctions
 from sklearn.datasets.base import Bunch
 
 from .utils import _get_dataset_dir, _fetch_files, _get_dataset_descr

From b81cb988343b5331d2a065f3fe1de80bce2f6b4d Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 06:31:55 +0200
Subject: [PATCH 038/228] added versionadded

---
 nilearn/datasets/atlas.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 77a5073832..bbf9dde81d 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -817,6 +817,8 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
     have a regional label (cortexL, cortexR, cerebellum, thalamus, hippocampus,
     basalGanglia, amygdala, cortexMid).
 
+    .. versionadded:: 0.5.1
+
     Parameters
     ----------
     ordered_regions : bool, optional

From 9d59c90214064fc22c60a750fdf089a8605c6099 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Tue, 9 Apr 2019 08:26:33 +0200
Subject: [PATCH 039/228] added fetch_coords_seitzman_2018

---
 doc/modules/reference.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/modules/reference.rst b/doc/modules/reference.rst
index 442ec197db..0ef060edfe 100644
--- a/doc/modules/reference.rst
+++ b/doc/modules/reference.rst
@@ -71,6 +71,7 @@ uses.
    fetch_atlas_harvard_oxford
    fetch_atlas_msdl
    fetch_coords_power_2011
+   fetch_coords_seitzman_2018
    fetch_atlas_smith_2009
    fetch_atlas_yeo_2011
    fetch_atlas_aal

From 90c04b68e655dd22bca03382ecb3fb37b3cddf43 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 11 Apr 2019 06:43:35 +0200
Subject: [PATCH 040/228] removed seitzman roi script

---
 .../seitzman_2018/merge_seitzman_files.py     |  30 --
 .../data/seitzman_2018/seitzman_2018_rois.csv | 301 ------------------
 2 files changed, 331 deletions(-)
 delete mode 100644 nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
 delete mode 100644 nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv

diff --git a/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py b/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
deleted file mode 100644
index 3226eba166..0000000000
--- a/nilearn/datasets/data/seitzman_2018/merge_seitzman_files.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""This script takes original files from Seitzman atlas
-    - ROIs_300inVol_MNI_allInfo.txt
-    - ROIs_anatomicalLabels.txt
-and creates one nicely formatted csv file (seitzman_2018_rois.csv) with
-coordinates, network and region names
-"""
-import pandas as pd
-
-
-def merge_seitzman_files():
-    rois = (pd.read_csv("ROIs_300inVol_MNI_allInfo.txt", sep=" ").
-            rename(columns={"netName": "network", "radius(mm)": "radius"})
-            )
-
-    labels = pd.read_csv("ROIs_anatomicalLabels.txt", sep=" ")
-
-    region_mapping = {}
-    for r in labels.columns[0].split(","):
-        i, region = r.split("=")
-        region_mapping[int(i)] = region
-
-    labels.columns = ["region"]
-    labels.replace(to_replace=region_mapping, inplace=True)
-
-    rois = pd.merge(rois, labels, left_index=True, right_index=True)
-    rois.to_csv("seitzman_2018_rois.csv", index=False)
-
-
-if __name__ == "__main__":
-    merge_seitzman_files()
diff --git a/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv b/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv
deleted file mode 100644
index c7f1f69968..0000000000
--- a/nilearn/datasets/data/seitzman_2018/seitzman_2018_rois.csv
+++ /dev/null
@@ -1,301 +0,0 @@
-x,y,z,radius,netWorkbenchLabel,network,region
--56.16,-44.76,-24.23,5,0,unassigned,cortexL
--24.66,-97.84,-12.33,5,0,unassigned,cortexL
-8.13,41.12,-24.31,5,0,unassigned,cortexR
-26.68,-97.3,-13.49,5,0,unassigned,cortexR
-48.52,-2.85,-38.49,5,0,unassigned,cortexR
-51.79,-34.17,-27.23,5,0,unassigned,cortexR
-55.18,-30.8,-16.93,5,0,unassigned,cortexR
--57.97,-25.69,-14.73,5,0,unassigned,cortexL
-64.6,-24.41,-18.57,5,0,unassigned,cortexR
--50.06,-7.09,-39.24,5,0,unassigned,cortexL
-33.55,38.46,-12.03,5,0,unassigned,cortexR
--46.68,-50.91,-20.91,5,0,unassigned,cortexL
--7.12,-52.22,60.71,5,10,SomatomotorDorsal,cortexL
-0.05,-14.53,46.74,5,10,SomatomotorDorsal,cortexMid
--53.52,-22.54,43.1,5,10,SomatomotorDorsal,cortexL
--45.1,-31.85,46.63,5,10,SomatomotorDorsal,cortexL
--39.63,-19.04,54.21,5,10,SomatomotorDorsal,cortexL
--38.28,-27.17,69.45,5,10,SomatomotorDorsal,cortexL
--38.24,-14.57,68.72,5,10,SomatomotorDorsal,cortexL
--29.1,-43.0,60.66,5,10,SomatomotorDorsal,cortexL
--22.5,-30.1,72.44,5,10,SomatomotorDorsal,cortexL
--20.66,-31.33,60.85,5,10,SomatomotorDorsal,cortexL
--16.25,-45.8,73.22,5,10,SomatomotorDorsal,cortexL
--13.74,-17.95,39.84,5,10,SomatomotorDorsal,cortexL
--12.96,-17.34,74.66,5,10,SomatomotorDorsal,cortexL
--6.9,-20.59,65.21,5,10,SomatomotorDorsal,cortexL
--6.79,-33.09,72.27,5,10,SomatomotorDorsal,cortexL
-2.4,-27.94,60.15,5,10,SomatomotorDorsal,cortexR
-3.45,-17.44,58.45,5,10,SomatomotorDorsal,cortexR
-9.5,-1.84,44.73,5,10,SomatomotorDorsal,cortexR
-9.94,-45.52,72.63,5,10,SomatomotorDorsal,cortexR
-10.09,-17.1,74.14,5,10,SomatomotorDorsal,cortexR
-13.19,-32.82,74.98,5,10,SomatomotorDorsal,cortexR
-20.21,-28.8,59.8,5,10,SomatomotorDorsal,cortexR
-22.45,-42.29,68.99,5,10,SomatomotorDorsal,cortexR
-28.88,-16.95,70.55,5,10,SomatomotorDorsal,cortexR
-28.54,-39.24,59.17,5,10,SomatomotorDorsal,cortexR
-37.74,-17.3,45.01,5,10,SomatomotorDorsal,cortexR
-42.14,-20.24,54.59,5,10,SomatomotorDorsal,cortexR
-44.34,-7.55,56.98,5,10,SomatomotorDorsal,cortexR
-47.21,-29.75,48.7,5,10,SomatomotorDorsal,cortexR
-50.24,-20.37,41.74,5,10,SomatomotorDorsal,cortexR
-19.33,-7.71,63.88,5,10,SomatomotorDorsal,cortexR
-54.22,-27.83,33.64,5,10,SomatomotorDorsal,cortexR
--52.84,-10.23,24.41,5,11,SomatomotorLateral,cortexL
--49.47,-11.06,34.95,5,11,SomatomotorLateral,cortexL
-36.04,-9.44,13.95,5,11,SomatomotorLateral,cortexR
-51.14,-5.8,32.42,5,11,SomatomotorLateral,cortexR
-65.64,-7.88,24.83,5,11,SomatomotorLateral,cortexR
--51.26,8.26,-2.06,5,9,CinguloOpercular,cortexL
--44.76,0.1,8.83,5,9,CinguloOpercular,cortexL
--34.37,3.29,4.19,5,9,CinguloOpercular,cortexL
--16.14,-4.82,70.83,5,9,CinguloOpercular,cortexL
--10.48,-2.1,42.02,5,9,CinguloOpercular,cortexL
--5.33,17.8,34.41,5,9,CinguloOpercular,cortexL
--2.88,2.38,53.21,5,9,CinguloOpercular,cortexL
-6.52,7.69,50.58,5,9,CinguloOpercular,cortexR
-13.21,-1.36,69.98,5,9,CinguloOpercular,cortexR
-35.83,10.32,1.18,5,9,CinguloOpercular,cortexR
-36.73,0.78,-3.57,5,9,CinguloOpercular,cortexR
-49.4,8.32,-1.12,5,9,CinguloOpercular,cortexR
--30.12,-27.02,12.2,5,9,CinguloOpercular,cortexL
-42.05,-0.39,47.1,5,9,CinguloOpercular,cortexR
-35.91,21.91,2.62,5,9,CinguloOpercular,cortexR
--39.12,50.79,17.38,5,9,CinguloOpercular,cortexL
-31.24,32.79,26.39,5,9,CinguloOpercular,cortexR
-31.07,55.71,14.49,5,9,CinguloOpercular,cortexR
-36.89,32.35,-2.24,5,9,CinguloOpercular,cortexR
--52.92,-21.83,22.97,5,12,Auditory,cortexL
-43.45,-22.93,19.85,5,12,Auditory,cortexR
-59.4,-17.34,28.69,5,12,Auditory,cortexR
--60.48,-25.22,13.82,5,12,Auditory,cortexL
--55.22,-9.42,11.73,5,12,Auditory,cortexL
--49.77,-34.36,25.74,5,12,Auditory,cortexL
--49.14,-26.3,5.18,5,12,Auditory,cortexL
--38.43,-33.34,16.98,5,12,Auditory,cortexL
-31.75,-26.33,12.91,5,12,Auditory,cortexR
-55.96,-5.03,13.25,5,12,Auditory,cortexR
-57.88,-15.62,7.49,5,12,Auditory,cortexR
-65.43,-33.2,19.97,5,12,Auditory,cortexR
--68.47,-22.66,-15.74,5,1,DefaultMode,cortexL
--68.3,-41.41,-5.14,5,1,DefaultMode,cortexL
--57.75,-29.7,-3.94,5,1,DefaultMode,cortexL
--55.72,-12.96,-10.24,5,1,DefaultMode,cortexL
--52.89,2.55,-27.06,5,1,DefaultMode,cortexL
--49.3,-42.15,0.83,5,1,DefaultMode,cortexL
--45.79,-60.69,20.85,5,1,DefaultMode,cortexL
--46.17,31.26,-13.03,5,1,DefaultMode,cortexL
--44.45,-64.64,34.78,5,1,DefaultMode,cortexL
--43.58,11.99,-34.15,5,1,DefaultMode,cortexL
--40.5,-75.27,25.8,5,1,DefaultMode,cortexL
--39.05,-74.95,43.72,5,1,DefaultMode,cortexL
--35.36,19.86,50.8,5,1,DefaultMode,cortexL
--19.78,45.07,39.48,5,1,DefaultMode,cortexL
--20.16,63.65,19.39,5,1,DefaultMode,cortexL
--17.65,63.19,-9.17,5,1,DefaultMode,cortexL
--16.4,28.52,53.05,5,1,DefaultMode,cortexL
--12.6,-39.64,0.93,5,1,DefaultMode,cortexL
--11.29,-56.2,15.6,5,1,DefaultMode,cortexL
--10.09,39.09,52.29,5,1,DefaultMode,cortexL
--10.33,54.63,38.71,5,1,DefaultMode,cortexL
--11.06,44.62,7.61,5,1,DefaultMode,cortexL
--7.55,48.08,23.18,5,1,DefaultMode,cortexL
--6.84,-54.9,27.05,5,1,DefaultMode,cortexL
--7.04,50.82,-1.29,5,1,DefaultMode,cortexL
--2.94,-48.79,12.87,5,1,DefaultMode,cortexL
--2.2,-36.68,43.85,5,1,DefaultMode,cortexL
--3.06,44.41,-9.46,5,1,DefaultMode,cortexL
--2.06,37.85,36.34,5,1,DefaultMode,cortexL
--2.5,41.7,16.05,5,1,DefaultMode,cortexL
-5.55,66.69,-3.55,5,1,DefaultMode,cortexR
-5.91,-58.82,35.45,5,1,DefaultMode,cortexR
-5.94,54.42,16.18,5,1,DefaultMode,cortexR
-6.11,63.98,21.96,5,1,DefaultMode,cortexR
-7.94,-48.37,30.57,5,1,DefaultMode,cortexR
-7.51,42.49,-5.35,5,1,DefaultMode,cortexR
-8.36,47.59,-15.18,5,1,DefaultMode,cortexR
-8.8,54.23,3.45,5,1,DefaultMode,cortexR
-10.77,-53.83,17.09,5,1,DefaultMode,cortexR
-12.73,54.87,38.19,5,1,DefaultMode,cortexR
-13.08,29.99,58.65,5,1,DefaultMode,cortexR
-12.25,35.63,20.3,5,1,DefaultMode,cortexR
-15.12,-63.09,25.98,5,1,DefaultMode,cortexR
-22.11,39.21,38.9,5,1,DefaultMode,cortexR
-23.33,33.07,47.68,5,1,DefaultMode,cortexR
-43.43,-72.21,28.0,5,1,DefaultMode,cortexR
-45.64,16.2,-30.02,5,1,DefaultMode,cortexR
-46.68,-50.08,28.76,5,1,DefaultMode,cortexR
-49.26,35.47,-12.2,5,1,DefaultMode,cortexR
-52.04,-59.37,35.52,5,1,DefaultMode,cortexR
-52.16,-2.43,-16.4,5,1,DefaultMode,cortexR
-51.9,6.81,-29.61,5,1,DefaultMode,cortexR
-64.64,-11.8,-19.3,5,1,DefaultMode,cortexR
-64.8,-30.55,-8.7,5,1,DefaultMode,cortexR
--33.93,-38.06,-15.6,5,1,DefaultMode,cortexL
-4.2,-48.06,50.71,5,15,ParietoMedial,cortexR
--6.58,-71.47,41.74,5,15,ParietoMedial,cortexL
--2.47,-34.8,31.07,5,15,ParietoMedial,cortexL
-1.75,-24.25,30.36,5,15,ParietoMedial,cortexR
-11.27,-66.01,42.09,5,15,ParietoMedial,cortexR
--12.08,-94.56,-12.8,5,2,Visual,cortexL
-7.98,-91.08,-7.1,5,2,Visual,cortexR
-17.27,-91.09,-13.64,5,2,Visual,cortexR
--46.54,-75.95,-9.95,5,2,Visual,cortexL
--42.1,-73.62,0.38,5,2,Visual,cortexL
--40.21,-88.44,-6.19,5,2,Visual,cortexL
--33.0,-79.02,-13.24,5,2,Visual,cortexL
--28.07,-79.45,19.43,5,2,Visual,cortexL
--26.39,-90.23,3.12,5,2,Visual,cortexL
--23.94,-90.98,18.96,5,2,Visual,cortexL
--17.87,-68.03,4.81,5,2,Visual,cortexL
--16.21,-76.97,33.82,5,2,Visual,cortexL
--15.85,-52.34,-1.43,5,2,Visual,cortexL
--15.02,-72.42,-7.68,5,2,Visual,cortexL
--14.22,-90.66,31.4,5,2,Visual,cortexL
--8.43,-80.5,7.44,5,2,Visual,cortexL
--2.88,-81.25,21.1,5,2,Visual,cortexL
-5.59,-71.65,23.52,5,2,Visual,cortexR
-6.21,-81.41,6.11,5,2,Visual,cortexR
-8.45,-71.84,10.79,5,2,Visual,cortexR
-15.27,-87.09,36.89,5,2,Visual,cortexR
-15.18,-76.68,31.0,5,2,Visual,cortexR
-17.53,-46.86,-9.88,5,2,Visual,cortexR
-19.81,-65.56,1.72,5,2,Visual,cortexR
-19.64,-85.62,-2.39,5,2,Visual,cortexR
-24.41,-87.21,24.01,5,2,Visual,cortexR
-25.66,-79.47,-15.56,5,2,Visual,cortexR
-26.93,-59.37,-9.36,5,2,Visual,cortexR
-28.68,-76.62,25.42,5,2,Visual,cortexR
-36.76,-84.11,12.99,5,2,Visual,cortexR
-36.51,-81.16,1.2,5,2,Visual,cortexR
-39.98,-72.49,14.36,5,2,Visual,cortexR
-41.6,-65.5,-8.27,5,2,Visual,cortexR
-42.52,-78.17,-11.78,5,2,Visual,cortexR
--52.6,-48.83,42.5,5,3,FrontoParietal,cortexL
--46.5,10.85,23.04,5,3,FrontoParietal,cortexL
--43.93,1.8,45.7,5,3,FrontoParietal,cortexL
--42.23,38.21,21.35,5,3,FrontoParietal,cortexL
--42.09,-54.98,44.74,5,3,FrontoParietal,cortexL
--42.1,24.68,29.53,5,3,FrontoParietal,cortexL
--41.06,5.81,32.72,5,3,FrontoParietal,cortexL
--41.68,45.16,-2.31,5,3,FrontoParietal,cortexL
--34.16,54.83,4.36,5,3,FrontoParietal,cortexL
--28.4,-57.93,47.78,5,3,FrontoParietal,cortexL
--22.53,10.76,63.73,5,3,FrontoParietal,cortexL
--2.98,26.41,44.42,5,3,FrontoParietal,cortexL
-24.07,44.61,-15.35,5,3,FrontoParietal,cortexR
-31.83,14.37,55.98,5,3,FrontoParietal,cortexR
-33.38,-53.12,44.02,5,3,FrontoParietal,cortexR
-33.6,54.22,-12.95,5,3,FrontoParietal,cortexR
-37.45,-64.7,40.38,5,3,FrontoParietal,cortexR
-38.37,43.18,15.06,5,3,FrontoParietal,cortexR
-39.87,18.39,39.72,5,3,FrontoParietal,cortexR
-43.93,-52.95,46.95,5,3,FrontoParietal,cortexR
-43.25,49.25,-2.31,5,3,FrontoParietal,cortexR
-47.01,9.93,32.66,5,3,FrontoParietal,cortexR
-47.98,24.56,26.5,5,3,FrontoParietal,cortexR
-49.18,-42.41,45.16,5,3,FrontoParietal,cortexR
-58.31,-52.79,-13.61,5,3,FrontoParietal,cortexR
-47.6,22.16,9.74,5,3,FrontoParietal,cortexR
-55.27,-44.59,36.7,5,3,FrontoParietal,cortexR
--35.44,20.03,0.07,5,8,Salience,cortexL
--27.5,52.04,21.28,5,8,Salience,cortexL
--10.76,25.99,24.54,5,8,Salience,cortexL
--0.94,14.86,43.99,5,8,Salience,cortexL
--0.2,30.35,27.22,5,8,Salience,cortexL
-5.23,23.22,37.03,5,8,Salience,cortexR
-10.26,22.06,27.48,5,8,Salience,cortexR
-26.07,49.56,26.58,5,8,Salience,cortexR
-33.56,16.45,-7.58,5,8,Salience,cortexR
-51.28,-28.52,-4.3,5,7,VentralAttention,cortexR
--56.47,-50.48,9.92,5,7,VentralAttention,cortexL
--55.3,-39.89,13.51,5,7,VentralAttention,cortexL
-51.52,-32.52,7.55,5,7,VentralAttention,cortexR
-55.75,-46.07,11.42,5,7,VentralAttention,cortexR
--49.07,25.13,-0.98,5,7,VentralAttention,cortexL
--9.88,10.95,66.61,5,7,VentralAttention,cortexL
-52.68,32.58,0.57,5,7,VentralAttention,cortexR
-53.9,-42.76,21.83,5,7,VentralAttention,cortexR
--26.6,-70.72,36.86,5,5,DorsalAttention,cortexL
--37.26,-28.8,-25.58,5,5,DorsalAttention,cortexL
-45.68,-46.67,-16.85,5,5,DorsalAttention,cortexR
-10.51,-38.54,50.02,5,5,DorsalAttention,cortexR
--52.44,-63.14,5.29,5,5,DorsalAttention,cortexL
--42.26,-60.12,-8.85,5,5,DorsalAttention,cortexL
--32.56,-46.42,47.2,5,5,DorsalAttention,cortexL
--32.23,-1.08,54.06,5,5,DorsalAttention,cortexL
--16.5,-58.57,64.46,5,5,DorsalAttention,cortexL
-9.61,-61.5,60.88,5,5,DorsalAttention,cortexR
-21.9,-64.74,48.12,5,5,DorsalAttention,cortexR
-25.34,-58.18,60.34,5,5,DorsalAttention,cortexR
-28.56,-4.62,53.99,5,5,DorsalAttention,cortexR
-46.09,-58.93,3.93,5,5,DorsalAttention,cortexR
--31.13,-9.99,-36.32,5,17,MedialTemporalLobe,cortexL
-32.85,-12.41,-34.41,5,17,MedialTemporalLobe,cortexR
--25.57,-11.78,-21.54,4,17,MedialTemporalLobe,hippocampus
-24.73,-11.25,-22.68,4,17,MedialTemporalLobe,hippocampus
--25.24,-38.78,-2.01,4,1,DefaultMode,hippocampus
-25.08,-37.18,-2.16,4,1,DefaultMode,hippocampus
--30.63,18.71,-18.98,5,4,Reward,cortexL
--21.14,40.87,-20.48,5,4,Reward,cortexL
-23.96,31.94,-17.78,5,4,Reward,cortexR
-27.06,16.22,-16.93,5,4,Reward,cortexR
--20.3,-2.27,-22.21,4,4,Reward,amygdala
-19.51,-1.85,-23.11,4,4,Reward,amygdala
-12.66,17.32,-5.06,4,4,Reward,basalGanglia
--12.49,17.05,-4.49,4,4,Reward,basalGanglia
-11.87,17.51,6.57,4,1,DefaultMode,basalGanglia
--13.28,17.24,7.14,4,1,DefaultMode,basalGanglia
-14.15,-1.19,18.18,4,3,FrontoParietal,basalGanglia
--15.19,-1.5,18.84,4,3,FrontoParietal,basalGanglia
-28.56,-7.53,7.66,4,10,SomatomotorDorsal,basalGanglia
--28.02,-10.23,9.07,4,10,SomatomotorDorsal,basalGanglia
-28.28,-6.7,-5.06,4,10,SomatomotorDorsal,basalGanglia
--28.31,-10.45,-3.6,4,10,SomatomotorDorsal,basalGanglia
-25.26,1.75,-1.26,4,10,SomatomotorDorsal,basalGanglia
--28.26,-0.97,-3.09,4,10,SomatomotorDorsal,basalGanglia
-25.46,5.05,7.0,4,10,SomatomotorDorsal,basalGanglia
--24.83,7.67,7.95,4,10,SomatomotorDorsal,basalGanglia
-18.88,-4.68,-3.91,4,10,SomatomotorDorsal,basalGanglia
--18.84,-5.08,-3.06,4,10,SomatomotorDorsal,basalGanglia
-3.42,-7.79,8.23,4,1,DefaultMode,thalamus
--2.88,-9.96,8.5,4,1,DefaultMode,thalamus
-18.89,-28.79,0.66,4,2,Visual,thalamus
--18.84,-29.18,1.51,4,2,Visual,thalamus
-12.9,-13.94,11.55,4,9,CinguloOpercular,thalamus
--15.39,-14.24,12.19,4,9,CinguloOpercular,thalamus
-9.56,-7.84,1.76,4,9,CinguloOpercular,thalamus
--9.35,-10.17,0.2,4,9,CinguloOpercular,thalamus
-13.67,-19.39,0.23,4,10,SomatomotorDorsal,thalamus
--13.57,-19.67,0.84,4,10,SomatomotorDorsal,thalamus
-15.95,-22.37,8.8,4,10,SomatomotorDorsal,thalamus
--18.63,-22.73,9.59,4,10,SomatomotorDorsal,thalamus
-32.0,-49.0,-51.0,4,0,unassigned,cerebellum
--13.0,-52.0,-50.0,4,0,unassigned,cerebellum
-14.0,-48.0,-52.0,4,0,unassigned,cerebellum
--32.0,-78.0,-38.0,4,1,DefaultMode,cerebellum
-32.0,-81.0,-38.0,4,1,DefaultMode,cerebellum
--24.0,-76.0,-28.0,4,1,DefaultMode,cerebellum
-24.0,-76.0,-28.01,4,1,DefaultMode,cerebellum
--5.72,-50.8,-40.84,4,1,DefaultMode,cerebellum
-8.0,-50.0,-40.0,4,1,DefaultMode,cerebellum
-0.0,-74.0,-25.0,4,2,Visual,cerebellum
--10.0,-78.0,-28.0,4,3,FrontoParietal,cerebellum
-10.0,-78.0,-28.0,4,3,FrontoParietal,cerebellum
--34.0,-72.01,-48.0,4,3,FrontoParietal,cerebellum
-34.0,-72.0,-48.0,4,3,FrontoParietal,cerebellum
--30.5,-66.0,-30.0,4,3,FrontoParietal,cerebellum
-31.68,-62.83,-30.4,4,3,FrontoParietal,cerebellum
-40.0,-44.0,-38.0,4,3,FrontoParietal,cerebellum
-43.5,-60.0,-30.0,4,9,CinguloOpercular,cerebellum
--43.5,-60.0,-30.0,4,9,CinguloOpercular,cerebellum
--34.0,-42.0,-44.0,4,9,CinguloOpercular,cerebellum
--33.0,-51.0,-50.0,4,10,SomatomotorDorsal,cerebellum
--6.0,-74.0,-42.0,4,10,SomatomotorDorsal,cerebellum
-7.5,-72.0,-39.0,4,10,SomatomotorDorsal,cerebellum
--10.0,-62.0,-18.0,4,10,SomatomotorDorsal,cerebellum
-10.0,-62.0,-18.0,4,10,SomatomotorDorsal,cerebellum
--12.0,-44.0,-18.0,4,10,SomatomotorDorsal,cerebellum
-12.0,-44.0,-18.0,4,10,SomatomotorDorsal,cerebellum

From e3e47b7f46b094f705033c7b91638f93b71024cc Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 11 Apr 2019 07:15:17 +0200
Subject: [PATCH 041/228] ref to use np rec

---
 nilearn/datasets/atlas.py                     | 38 +++++++++++++++----
 ...itzman_2018_ROIs_300inVol_MNI_allInfo.txt} |  0
 ...> seitzman_2018_ROIs_anatomicalLabels.txt} |  0
 setup.py                                      |  4 +-
 4 files changed, 32 insertions(+), 10 deletions(-)
 rename nilearn/datasets/data/{seitzman_2018/ROIs_300inVol_MNI_allInfo.txt => seitzman_2018_ROIs_300inVol_MNI_allInfo.txt} (100%)
 rename nilearn/datasets/data/{seitzman_2018/ROIs_anatomicalLabels.txt => seitzman_2018_ROIs_anatomicalLabels.txt} (100%)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index bbf9dde81d..114d77fc56 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -10,6 +10,7 @@
 
 import nibabel as nb
 import numpy as np
+from numpy.lib import recfunctions
 from sklearn.datasets.base import Bunch
 
 from .utils import _get_dataset_dir, _fetch_files, _get_dataset_descr
@@ -844,17 +845,38 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
     dataset_name = 'seitzman_2018'
     fdescr = _get_dataset_descr(dataset_name)
     package_directory = os.path.dirname(os.path.abspath(__file__))
-    csv_file = os.path.join(package_directory, "data", "seitzman_2018",
-                            "seitzman_2018_rois.csv")
-    out_csv = np.recfromcsv(csv_file)
+    roi_file = os.path.join(package_directory, "data",
+                            "seitzman_2018_ROIs_300inVol_MNI_allInfo.txt")
+    anatomical_file = os.path.join(package_directory, "data",
+                                   "seitzman_2018_ROIs_anatomicalLabels.txt")
+
+    rois = np.recfromcsv(roi_file, delimiter=" ")
+    rois = recfunctions.rename_fields(rois, {"netname": "network",
+                                             "radiusmm": "radius"})
+
+    # get integer regional labels and convert to text labels with mapping
+    # from header line
+    with open(anatomical_file, 'r') as fi:
+        header = fi.readline()
+    region_mapping = {}
+    for r in header.strip().split(","):
+        i, region = r.split("=")
+        region_mapping[int(i)] = region
+
+    anatomical = np.genfromtxt(anatomical_file, skip_header=1)
+    anatomical_names = np.array([region_mapping[a] for a in anatomical],
+                                dtype=[('region', '<S18')])
+
+    rois = recfunctions.merge_arrays((rois, anatomical_names),
+                                     asrecarray=True, flatten=True)
 
     if ordered_regions:
-        out_csv = np.sort(out_csv, order=['network', 'y'])
+        rois = np.sort(rois, order=['network', 'y'])
 
-    params = dict(rois=out_csv[['x', 'y', 'z']],
-                  radius=out_csv['radius'],
-                  networks=out_csv['network'],
-                  regions=out_csv['region'], description=fdescr)
+    params = dict(rois=rois[['x', 'y', 'z']],
+                  radius=rois['radius'],
+                  networks=rois['network'],
+                  regions=rois['region'], description=fdescr)
 
     return Bunch(**params)
 
diff --git a/nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt b/nilearn/datasets/data/seitzman_2018_ROIs_300inVol_MNI_allInfo.txt
similarity index 100%
rename from nilearn/datasets/data/seitzman_2018/ROIs_300inVol_MNI_allInfo.txt
rename to nilearn/datasets/data/seitzman_2018_ROIs_300inVol_MNI_allInfo.txt
diff --git a/nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt b/nilearn/datasets/data/seitzman_2018_ROIs_anatomicalLabels.txt
similarity index 100%
rename from nilearn/datasets/data/seitzman_2018/ROIs_anatomicalLabels.txt
rename to nilearn/datasets/data/seitzman_2018_ROIs_anatomicalLabels.txt
diff --git a/setup.py b/setup.py
index 13bfe1b361..355e6de4ab 100755
--- a/setup.py
+++ b/setup.py
@@ -86,9 +86,9 @@ def is_installing():
               'Programming Language :: Python :: 3.7',
           ],
           packages=find_packages(),
-          package_data={'nilearn.datasets.data': ['*.nii.gz', '*.csv'],
+          package_data={'nilearn.datasets.data': ['*.nii.gz', '*.csv', '*.txt'
+                                                  ],
                         'nilearn.datasets.data.fsaverage5': ['*.gz'],
-                        'nilearn.datasets.data.seitzman_2018': ['*.csv'],
                         'nilearn.surface.data': ['*.csv'],
                         'nilearn.plotting.data.js': ['*.js'],
                         'nilearn.plotting.data.html': ['*.html'],

From 325e6d23d2a8c0768cf888b21d3c85ae3da12e4a Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 15 Apr 2019 10:17:25 +0200
Subject: [PATCH 042/228] testing that regions is not empty

---
 nilearn/datasets/tests/test_atlas.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/nilearn/datasets/tests/test_atlas.py b/nilearn/datasets/tests/test_atlas.py
index 88fe7bc708..c059524c8e 100644
--- a/nilearn/datasets/tests/test_atlas.py
+++ b/nilearn/datasets/tests/test_atlas.py
@@ -319,6 +319,10 @@ def test_fetch_coords_seitzman_2018():
     np.testing.assert_array_equal(bunch.networks, np.sort(bunch.networks))
     assert_not_equal(bunch.description, '')
 
+    r1 = bunch.regions[0]
+    r1 = r1.decode("utf-8") if isinstance(r1, bytes) else r1
+    assert r1 == "cortexL"
+
     bunch = atlas.fetch_coords_seitzman_2018(ordered_regions=False)
     assert_true(np.any(bunch.networks != np.sort(bunch.networks)))
 

From 32760904c754ae445a0cbf9543289954c4ec33b3 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 15 Apr 2019 10:45:04 +0200
Subject: [PATCH 043/228] fixed enc

---
 nilearn/datasets/tests/test_atlas.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/nilearn/datasets/tests/test_atlas.py b/nilearn/datasets/tests/test_atlas.py
index c059524c8e..e6984c4d6c 100644
--- a/nilearn/datasets/tests/test_atlas.py
+++ b/nilearn/datasets/tests/test_atlas.py
@@ -319,9 +319,7 @@ def test_fetch_coords_seitzman_2018():
     np.testing.assert_array_equal(bunch.networks, np.sort(bunch.networks))
     assert_not_equal(bunch.description, '')
 
-    r1 = bunch.regions[0]
-    r1 = r1.decode("utf-8") if isinstance(r1, bytes) else r1
-    assert r1 == "cortexL"
+    assert bunch.regions[0] == "cortexL"
 
     bunch = atlas.fetch_coords_seitzman_2018(ordered_regions=False)
     assert_true(np.any(bunch.networks != np.sort(bunch.networks)))

From 70ac9e50d6eb964fe2e78c15b5205709f8e5b665 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 15 Apr 2019 10:46:42 +0200
Subject: [PATCH 044/228] fixed import encoding

---
 nilearn/datasets/atlas.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 114d77fc56..fe4fe1b4eb 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -850,7 +850,7 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
     anatomical_file = os.path.join(package_directory, "data",
                                    "seitzman_2018_ROIs_anatomicalLabels.txt")
 
-    rois = np.recfromcsv(roi_file, delimiter=" ")
+    rois = np.recfromcsv(roi_file, delimiter=" ", encoding="ascii")
     rois = recfunctions.rename_fields(rois, {"netname": "network",
                                              "radiusmm": "radius"})
 
@@ -863,9 +863,10 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
         i, region = r.split("=")
         region_mapping[int(i)] = region
 
-    anatomical = np.genfromtxt(anatomical_file, skip_header=1)
+    anatomical = np.genfromtxt(anatomical_file, skip_header=1,
+                               encoding="ascii")
     anatomical_names = np.array([region_mapping[a] for a in anatomical],
-                                dtype=[('region', '<S18')])
+                                dtype=[('region', np.dtype((str, 20)))])
 
     rois = recfunctions.merge_arrays((rois, anatomical_names),
                                      asrecarray=True, flatten=True)

From 7fea9b260848fbaeee8069cccd30e7e91cca2df5 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 15 Apr 2019 14:42:45 +0200
Subject: [PATCH 045/228] Moved changelog into v0.5.2 in whats_new.rst

---
 doc/whats_new.rst | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 413cebe38d..5a433a0c1c 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -15,6 +15,12 @@ Changes
 - All the connectivity examples are changed from ADHD to brain development
   fmri dataset.
 
+- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
+  and :func:`nilearn.plotting.view_connectome` now allow disabling the colorbar,
+  and setting its height and the fontsize of its ticklabels.
+
+
+
 Fixes
 -----
 
@@ -64,10 +70,6 @@ Changes
   - coords is now marker_coords
   - colors is now marker_color
 
-- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
-  and :func:`nilearn.plotting.view_connectome` now allow disabling the colorbar,
-  and setting its height and the fontsize of its ticklabels.
-
 
 Fixes
 -----

From 2803d574c36097fe319c4d143f5c9812f5e16384 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 15 Apr 2019 15:04:50 +0200
Subject: [PATCH 046/228] fix encoding in example

---
 examples/03_connectivity/plot_sphere_based_connectome.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 06ceb77bde..48e94ac40f 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -167,7 +167,7 @@
 ###############################################################################
 # Before calculating the connectivity matrix, let's look at the distribution
 # of the regions of the default mode network.
-dmn_rois = seitzman.networks == b"DefaultMode"
+dmn_rois = seitzman.networks == "DefaultMode"
 dmn_coords = coords[dmn_rois]
 zero_matrix = np.zeros((len(dmn_coords), len(dmn_coords)))
 plotting.plot_connectome(zero_matrix, dmn_coords,

From 5c4bc7acb4db92550e733566f3b26e4c67b8c5b5 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 15 Apr 2019 16:28:13 +0200
Subject: [PATCH 047/228] minor modif of presentation

---
 examples/03_connectivity/plot_fast_clustering.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
index 5791c2ee1b..31c88470ef 100644
--- a/examples/03_connectivity/plot_fast_clustering.py
+++ b/examples/03_connectivity/plot_fast_clustering.py
@@ -2,9 +2,10 @@
 ReNA clustering to learn a brain parcellation from rest fMRI
 ====================================================================
 
-We use ReNA clustering to create a set of parcels. These parcels are
-particularly interesting for creating a 'compressed' representation of the
-data, replacing the data in the fMRI images by mean on the parcellation.
+We use Recursive Neighbor Agglomeration (ReNA) clustering to create a set of
+parcels. These parcels are particularly interesting for creating a 'compressed'
+representation of the data, replacing the data in the fMRI images by mean on
+the parcellation.
 
 This parcellation may be useful in a supervised learning, see for
 instance: `A supervised clustering approach for fMRI-based inference of
@@ -146,4 +147,3 @@
          display_mode='xz')
 
 show()
-

From bca01a22d365f08f83d0dd1dcab48c44089776c7 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 15 Apr 2019 16:30:29 +0200
Subject: [PATCH 048/228] adressing comments : cleaning + renaming variables +
 updating imports + fixing minor type error

---
 nilearn/connectome/rena_clustering.py | 56 +++++++++++++--------------
 1 file changed, 26 insertions(+), 30 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 9f10025cc9..e6e9e3f9b2 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -1,4 +1,4 @@
-"""Recursive nearest agglomeration (ReNA):
+"""Recursive Neighbor Agglomeration (ReNA):
     fastclustering for approximation of structured signals
 """
 # Author: Andres Hoyos idrobo, Gael Varoquaux, Jonas Kahn and  Bertrand Thirion
@@ -9,17 +9,14 @@
 
 import numpy as np
 import warnings
+from scipy.sparse import csgraph, coo_matrix, dia_matrix
 from sklearn.externals.joblib import Memory
 from sklearn.externals import six
 from sklearn.base import TransformerMixin, ClusterMixin
-from scipy.sparse import csgraph, coo_matrix, dia_matrix
 from sklearn.base import BaseEstimator
-try:
-    from sklearn.utils import atleast2d_or_csr
-except ImportError: # sklearn 0.15
-    from sklearn.utils import check_array as atleast2d_or_csr
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils import check_array
 from ..input_data.masker_validation import check_embedded_nifti_masker
-from .._utils.fixes import check_is_fitted
 
 
 def _compute_weights(masker, masked_data):
@@ -120,14 +117,14 @@ def _make_edges_and_weights(masker, masked_data):
     # Indexing each voxel
     vertices = np.arange(n_features).reshape(shape)
 
-    weights = _compute_weights(masker, masked_data)
+    weights_unmasked = _compute_weights(masker, masked_data)
 
-    edges = _make_3d_edges(vertices, is_mask=False)
+    edges_unmasked = _make_3d_edges(vertices, is_mask=False)
     edges_mask = _make_3d_edges(mask, is_mask=True)
 
     # Apply mask to edges and weights
-    weights = weights[edges_mask]
-    edges = edges[:, edges_mask]
+    weights = np.copy(weights_unmasked[edges_mask])
+    edges = np.copy(edges_unmasked[:, edges_mask])
 
     # Reorder the indices of the graph
     max_index = edges.max()
@@ -153,7 +150,7 @@ def weighted_connectivity_graph(masker, masked_data):
     connectivity : a sparse COO matrix
         sparse matrix representation of the weighted adjacency graph
     """
-    n_features = masker.mask_img_.get_data().sum()
+    n_features = int(masker.mask_img_.get_data().sum())
 
     edges, weight = _make_edges_and_weights(masker, masked_data)
 
@@ -209,8 +206,8 @@ def _nn_connectivity(connectivity, threshold=1e-7):
     return nn_connectivity
 
 
-def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
-                                  threshold=1e-7):
+def _reduce_data_and_connectivity(labels, n_components, connectivity,
+                                  masked_data, threshold=1e-7):
     """Perform feature grouping and reduce the connectivity matrix: during the
     reduction step one changes the value of each cluster by their mean.
     In addition, connected nodes are merged.
@@ -220,7 +217,7 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
     labels : array like
         Containts the label assignation for each voxel.
 
-    n_labels : int
+    n_components : int
         The number of clusters in the current iteration.
 
     connectivity : a sparse matrix in COOrdinate format.
@@ -244,11 +241,11 @@ def _reduce_data_and_connectivity(labels, n_labels, connectivity, masked_data,
 
     incidence = coo_matrix(
         (np.ones(n_features), (labels, np.arange(n_features))),
-        shape=(n_labels, n_features), dtype=np.float32).tocsc()
+        shape=(n_components, n_features), dtype=np.float32).tocsc()
 
     inv_sum_col = dia_matrix(
         (np.array(1. / incidence.sum(axis=1)).squeeze(), 0),
-        shape=(n_labels, n_labels))
+        shape=(n_components, n_components))
 
     incidence = inv_sum_col * incidence
 
@@ -303,9 +300,9 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     # Nearest neighbor conenctivity
     nn_connectivity = _nn_connectivity(connectivity, threshold)
     n_features = connectivity.shape[0]
-    n_labels = n_features - (nn_connectivity + nn_connectivity.T).nnz / 2
+    n_components = n_features - (nn_connectivity + nn_connectivity.T).nnz / 2
 
-    if n_labels < n_clusters:
+    if n_components < n_clusters:
         # remove edges so that the final number of clusters is not less than
         # n_clusters (to achieve the desired number of clusters)
         n_edges = n_features - n_clusters
@@ -324,11 +321,11 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
                                      (n_features, n_features))
 
     # Clustering step: getting the connected components of the nn matrix
-    n_labels, labels = csgraph.connected_components(nn_connectivity)
+    n_components, labels = csgraph.connected_components(nn_connectivity)
 
     # Reduction step: reduction by averaging
     reduced_connectivity, reduced_masked_data = _reduce_data_and_connectivity(
-        labels, n_labels, connectivity, masked_data, threshold)
+        labels, n_components, connectivity, masked_data, threshold)
 
     return reduced_connectivity, reduced_masked_data, labels
 
@@ -357,7 +354,7 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
     Returns
     -------
-    n_labels : int
+    n_components : int
         Number of clusters.
 
     labels : array
@@ -367,23 +364,23 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
     # Initialization
     labels = np.arange(connectivity.shape[0])
-    n_labels = connectivity.shape[0]
+    n_components = connectivity.shape[0]
 
     for i in range(n_iter):
         connectivity, masked_data, reduced_labels = nearest_neighbor_grouping(
             connectivity, masked_data, n_clusters, threshold)
 
         labels = reduced_labels[labels]
-        n_labels = connectivity.shape[0]
+        n_components = connectivity.shape[0]
 
-        if n_labels <= n_clusters:
+        if n_components <= n_clusters:
             break
 
-    return n_labels, labels
+    return n_components, labels
 
 
 class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
-    """Recursive nearest agglomeration (ReNA):
+    """Recursive Neighbor Agglomeration (ReNA):
     Recursively merges the pair of clusters according to 1-nearest neighbors
     criterion.
 
@@ -518,7 +515,7 @@ def fit(self, X, y=None):
         self.masker_ = check_embedded_nifti_masker(self, multi_subject=False)
         X = self.masker_.fit_transform(X)
 
-        X = atleast2d_or_csr(X)
+        X = check_array(X)
         n_features = X.shape[1]
 
         if self.n_clusters > n_features:
@@ -527,7 +524,7 @@ def fit(self, X, y=None):
                           "features. Taking n_clusters = %s instead."
                           % str(n_features))
 
-        n_labels, labels = self.memory_.cache(
+        n_components, labels = self.memory_.cache(
             recursive_neighbor_agglomeration)(self.masker_, X, self.n_clusters,
                                               n_iter=self.n_iter,
                                               threshold=self.threshold)
@@ -623,4 +620,3 @@ def _get_max_connectivity(connectivity):
             max_connectivity[i] = connectivity.getrow(i).max()
 
     return max_connectivity
-

From ac4e5c3b4cd56cbbc366a7ed3546bbc11edb07c8 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 15 Apr 2019 16:32:11 +0200
Subject: [PATCH 049/228] improving variable names + updating imports

---
 nilearn/connectome/tests/test_rena.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/connectome/tests/test_rena.py
index ce0f788ac4..38df942232 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/connectome/tests/test_rena.py
@@ -1,7 +1,7 @@
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 from sklearn.externals.joblib import Memory
-from nilearn._utils.testing import generate_fake_fmri
-from nilearn.connectome import ReNA
+from nilearn._utils.data_gen import generate_fake_fmri
+from nilearn.connectome.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker
 from nilearn.image import index_img
 
@@ -21,18 +21,20 @@ def test_rena_clusterings():
     assert_equal(data.shape, data_compress.shape)
 
     memory = Memory(cachedir=None)
-    rena2 = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
-    assert_raises(ValueError, rena2.fit, data)
+    rena = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
+    assert_raises(ValueError, rena.fit, data)
 
-    rena3 = ReNA(n_clusters=10, mask=None, scaling=True)
-    data_red2 = rena3.fit_transform(index_img(data, 0))
-    data_compress2 = rena3.inverse_transform(data_red2)
+    rena = ReNA(n_clusters=10, mask=None, scaling=True)
+    data_red = rena.fit_transform(index_img(data, 0))
+    data_compress = rena.inverse_transform(data_red)
 
     for n_iter in [-2, 0]:
-        rena4 = ReNA(n_iter=n_iter, mask=nifti_masker, memory=memory)
-        assert_raises(ValueError, rena4.fit, data)
+        rena = ReNA(n_iter=n_iter, mask=nifti_masker, memory=memory)
+        assert_raises(ValueError, rena.fit, data)
 
     for n_clusters in [1, 2, 4, 8]:
-        rena5 = ReNA(n_clusters=n_clusters, n_iter=1, mask=nifti_masker,
+        rena = ReNA(n_clusters=n_clusters, n_iter=1, mask=nifti_masker,
                     memory=memory).fit(data)
-        assert_not_equal(n_clusters, rena5.n_clusters_)
+        assert_not_equal(n_clusters, rena.n_clusters_)
+
+    del n_voxels, data_compress

From f10f5e7878e121bcf684070d40ebbed5898bbb05 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Tue, 16 Apr 2019 14:32:25 +0200
Subject: [PATCH 050/228] removing _get_max_connectivity function since scipy
 version is at least 0.17 (>0.14)

---
 nilearn/connectome/rena_clustering.py | 29 +--------------------------
 1 file changed, 1 insertion(+), 28 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index e6e9e3f9b2..bc28650efc 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -4,9 +4,6 @@
 # Author: Andres Hoyos idrobo, Gael Varoquaux, Jonas Kahn and  Bertrand Thirion
 # License: simplified BSD
 
-from distutils.version import LooseVersion
-import scipy
-
 import numpy as np
 import warnings
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
@@ -185,7 +182,7 @@ def _nn_connectivity(connectivity, threshold=1e-7):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    max_connectivity = _get_max_connectivity(connectivity_)
+    max_connectivity = connectivity.max(axis=0).toarray()[0]
     inv_max = dia_matrix((1. / max_connectivity, 0),
                          shape=(n_features, n_features))
 
@@ -596,27 +593,3 @@ def inverse_transform(self, Xred):
         X_inv = Xred[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)
-
-
-def _get_max_connectivity(connectivity):
-    """This function calculate of the maximum on the axis=0.
-    It is applied to the connectivity matrix.
-    This matrix is sparse and symetric.
-    """
-    if LooseVersion(scipy.__version__) >= LooseVersion('0.14'):
-        max_connectivity = connectivity.max(axis=0).toarray()[0]
-
-    if LooseVersion(scipy.__version__) < LooseVersion('0.10'):
-        N = connectivity.shape[0]
-        max_connectivity = np.zeros((N))
-        for i in range(N):
-            row = connectivity.getrow(i)
-            ind = row.nonzero()
-            max_connectivity[i] = np.max(row[ind])
-    else:
-        N = connectivity.shape[0]
-        max_connectivity = np.zeros((N))
-        for i in range(N):
-            max_connectivity[i] = connectivity.getrow(i).max()
-
-    return max_connectivity

From 58cdc74b8c431543fa4a70416b5972cf13469f17 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Tue, 16 Apr 2019 15:14:17 +0200
Subject: [PATCH 051/228] adding references

---
 nilearn/connectome/rena_clustering.py | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index bc28650efc..e2ccb6551e 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -266,8 +266,8 @@ def _reduce_data_and_connectivity(labels, n_components, connectivity,
 
 def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
                               threshold=1e-7):
-    """Cluster using nearest agglomeration: merge clusters according to their
-    nearest neighbors, then the data and the connectivity are reduced.
+    """Cluster using nearest neighbor agglomeration: merge clusters according
+    to their nearest neighbors, then the data and the connectivity are reduced.
 
     Parameters
     ----------
@@ -329,8 +329,8 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
 
 def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
                                      n_iter=10, threshold=1e-7):
-    """Recursive neighbor agglomeration: it performs iteratively the nearest
-    neighbor grouping.
+    """Recursive neighbor agglomeration (ReNA): it performs iteratively
+    the nearest neighbor grouping [1]_.
 
     Parameters
     ----------
@@ -356,6 +356,14 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
     labels : array
         Cluster assignation.
+
+    References
+    ----------
+    .. [1] A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
+           Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
+           Structured Signals," in IEEE Transactions on Pattern Analysis and
+           Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
+           https://hal.archives-ouvertes.fr/hal-01366651/
     """
     connectivity = weighted_connectivity_graph(masker, masked_data)
 
@@ -379,7 +387,7 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     """Recursive Neighbor Agglomeration (ReNA):
     Recursively merges the pair of clusters according to 1-nearest neighbors
-    criterion.
+    criterion [1]_.
 
     Parameters
     ----------
@@ -461,6 +469,14 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
         The mask of the data. If no mask was supplied by the user,
         this attribute is the mask image computed automatically from the
         data `X`.
+
+    References
+    ----------
+    .. [1] A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
+           Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
+           Structured Signals," in IEEE Transactions on Pattern Analysis and
+           Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
+           https://hal.archives-ouvertes.fr/hal-01366651/
     """
     def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
                  standardize=True, target_affine=None, target_shape=None,

From 10b0bb64d3b659c51759ffa8aa7108ee8c61a3d5 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 11:03:21 +0200
Subject: [PATCH 052/228] adding back _get_max_connectivity but refactored +
 adding verbose

---
 nilearn/connectome/rena_clustering.py | 34 +++++++++++++++++++++++----
 1 file changed, 30 insertions(+), 4 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index e2ccb6551e..1ea01798ed 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -182,7 +182,7 @@ def _nn_connectivity(connectivity, threshold=1e-7):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    max_connectivity = connectivity.max(axis=0).toarray()[0]
+    max_connectivity = _get_max_connectivity(connectivity_)
     inv_max = dia_matrix((1. / max_connectivity, 0),
                          shape=(n_features, n_features))
 
@@ -328,7 +328,8 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
 
 
 def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
-                                     n_iter=10, threshold=1e-7):
+                                     n_iter=10, threshold=1e-7,
+                                     verbose=0):
     """Recursive neighbor agglomeration (ReNA): it performs iteratively
     the nearest neighbor grouping [1]_.
 
@@ -349,6 +350,9 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
 
+    verbose : int, optional (default 1)
+        Verbosity level.
+
     Returns
     -------
     n_components : int
@@ -372,12 +376,18 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
     n_components = connectivity.shape[0]
 
     for i in range(n_iter):
+
         connectivity, masked_data, reduced_labels = nearest_neighbor_grouping(
             connectivity, masked_data, n_clusters, threshold)
 
         labels = reduced_labels[labels]
         n_components = connectivity.shape[0]
 
+        if verbose > 0:
+            print('After iteration number %s, features are '
+                  ' grouped into %s clusters'
+                  % (str(i + 1), str(n_components)))
+
         if n_components <= n_clusters:
             break
 
@@ -481,7 +491,7 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
                  standardize=True, target_affine=None, target_shape=None,
                  mask_strategy='background', memory=None, memory_level=1,
-                 verbose=False, scaling=False, n_iter=10, threshold=1e-7,):
+                 verbose=0, scaling=False, n_iter=10, threshold=1e-7,):
         self.n_clusters = n_clusters
         self.scaling = scaling
         self.n_iter = n_iter
@@ -540,7 +550,8 @@ def fit(self, X, y=None):
         n_components, labels = self.memory_.cache(
             recursive_neighbor_agglomeration)(self.masker_, X, self.n_clusters,
                                               n_iter=self.n_iter,
-                                              threshold=self.threshold)
+                                              threshold=self.threshold,
+                                              verbose=self.verbose)
 
         sizes = np.bincount(labels)
         sizes = sizes[sizes > 0]
@@ -609,3 +620,18 @@ def inverse_transform(self, Xred):
         X_inv = Xred[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)
+
+
+def _get_max_connectivity(connectivity):
+    """This function calculate of the maximum on the axis=0.
+    It is applied to the connectivity matrix.
+    This matrix is sparse and symetric.
+    """
+    max_connectivity = connectivity.max(axis=0).toarray()[0]
+
+    N = connectivity.shape[0]
+    max_connectivity = np.zeros((N))
+    for i in range(N):
+        max_connectivity[i] = connectivity.getrow(i).max()
+
+    return max_connectivity

From 75da2ab6168d63864c23e51e40a6b5bef02b3616 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 11:14:50 +0200
Subject: [PATCH 053/228] Removing definitely _get_max_connectivity

---
 nilearn/connectome/rena_clustering.py | 18 ++----------------
 1 file changed, 2 insertions(+), 16 deletions(-)

diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/connectome/rena_clustering.py
index 1ea01798ed..7c4146d901 100644
--- a/nilearn/connectome/rena_clustering.py
+++ b/nilearn/connectome/rena_clustering.py
@@ -182,7 +182,8 @@ def _nn_connectivity(connectivity, threshold=1e-7):
         (1. / connectivity.data, connectivity.nonzero()),
         (n_features, n_features)).tocsr()
 
-    max_connectivity = _get_max_connectivity(connectivity_)
+    # maximum on the axis = 0
+    max_connectivity = connectivity_.max(axis=0).toarray()[0]
     inv_max = dia_matrix((1. / max_connectivity, 0),
                          shape=(n_features, n_features))
 
@@ -620,18 +621,3 @@ def inverse_transform(self, Xred):
         X_inv = Xred[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)
-
-
-def _get_max_connectivity(connectivity):
-    """This function calculate of the maximum on the axis=0.
-    It is applied to the connectivity matrix.
-    This matrix is sparse and symetric.
-    """
-    max_connectivity = connectivity.max(axis=0).toarray()[0]
-
-    N = connectivity.shape[0]
-    max_connectivity = np.zeros((N))
-    for i in range(N):
-        max_connectivity[i] = connectivity.getrow(i).max()
-
-    return max_connectivity

From c0fec0e7948ece9e5636452fbc8d728562434f0e Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 12:16:24 +0200
Subject: [PATCH 054/228] Meging ReNA example in plot_data_driven_parcellations
 + references

---
 .../plot_data_driven_parcellations.py         | 117 +++++++++++++++++-
 1 file changed, 111 insertions(+), 6 deletions(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 336095b9ce..108f6a4321 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -2,8 +2,8 @@
 Clustering methods to learn a brain parcellation from fMRI
 ==========================================================
 
-We use spatially-constrained Ward-clustering and KMeans to create a set
-of parcels.
+We use spatially-constrained Ward-clustering, KMeans and Recursive Neighbor
+Agglomeration (ReNA) to create a set of parcels.
 
 In a high dimensional regime, these methods can be interesting
 to create a 'compressed' representation of the data, replacing the data
@@ -33,6 +33,14 @@
       <http://dx.doi.org/10.1016/j.patcog.2011.04.006>`_.
       Pattern Recognition, Elsevier, 2011.
 
+More about ReNA clustering algorithm in the original paper
+
+    * A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
+      Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
+      Structured Signals," in IEEE Transactions on Pattern Analysis and
+      Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
+      https://hal.archives-ouvertes.fr/hal-01366651/
+
 The big picture discussion corresponding to this example can be found
 in the documentation section :ref:`parcellating_brain`.
 """
@@ -110,6 +118,7 @@
 
 # Grab cut coordinates from this plot to use as a common for all plots
 cut_coords = first_plot.cut_coords
+
 ###########################################################################
 # Compressed representation of Ward clustering
 # --------------------------------------------
@@ -165,13 +174,15 @@
 
 # This object uses method='kmeans' for KMeans clustering with 10mm smoothing
 # and standardization ON
-kmeans = Parcellations(method='kmeans', n_parcels=50,
+start = time.time()
+kmeans = Parcellations(method='kmeans', n_parcels=2000,
                        standardize=True, smoothing_fwhm=10.,
                        memory='nilearn_cache', memory_level=1,
                        verbose=1)
 # Call fit on functional dataset: single subject (less samples)
 kmeans.fit(dataset.func)
-print("KMeans 50 clusters: %.2fs" % (time.time() - start))
+print("KMeans 2000 clusters: %.2fs" % (time.time() - start))
+
 ###########################################################################
 # Visualize: Brain parcellations (KMeans)
 # ---------------------------------------
@@ -188,6 +199,100 @@
 kmeans_labels_img.to_filename('kmeans_parcellation.nii.gz')
 
 ##################################################################
-# Finally show them
+# Brain parcellations with ReNA Clustering
+# ----------------------------------------
+# The spatial constraints are implemented inside the ReNA object.
+#
+# We use caching. As a result, the clustering doesn't have
+# to be recomputed later.
+from nilearn.connectome import ReNA
+start = time.time()
+rena = ReNA(scaling=True, n_clusters=1000, memory='nilearn_cache')
+
+rena.fit_transform(dataset.func[0])
+print("ReNA 1000 clusters: %.2fs" % (time.time() - start))
+
+start = time.time()
+rena = ReNA(scaling=True, n_clusters=2000, memory='nilearn_cache')
+
+rena.fit_transform(dataset.func[0])
+print("ReNA 2000 clusters: %.2fs" % (time.time() - start))
+
+# We notice that, at the first computation (without using precomputed data),
+# for 1000 clusters, the computation time is almost divided by 2 comparing to
+# Ward clustering. This is due to the interesting algorithmic properties of
+# ReNA. In some cases (higher number of features), this reduction in time
+# computation can be much more significant.
+
+##################################################################
+# Visualize: Brain parcellations (ReNA)
+# -------------------------------------
+#
+# First we display the labels of the clustering in the brain.
+#
+# To visualize results, we need to transform the clustering's labels back
+# to a neuroimaging volume. For this, we use the NiftiMasker's
+# inverse_transform method.
+
+# Avoid 0 label
+labels = rena.labels_ + 1
+# Shuffling the labels for visualization
+permutation = np.random.permutation(labels.shape[0])
+labels = permutation[labels]
+# Obtain the masker
+nifti_masker = rena.masker_
+# Unmask the labels
+labels_img = nifti_masker.inverse_transform(labels)
+
+##################################################################
+# labels_img is a Nifti1Image object, it can be saved to file with the
+# following code:
+labels_img.to_filename('rena_parcellation.nii.gz')
+
+
+from nilearn.image import mean_img
+
+plotting.plot_roi(labels_img, mean_func_img, title="ReNA parcellation",
+                  display_mode='xz')
+
+# common cut coordinates for all plots
+cut_coords = first_plot.cut_coords
+
+##################################################################
+# Compressed representation of ReNA clustering
+# --------------------------------------------
+#
+# We illustrate the effect that the clustering has on the signal.
+# We show the original data, and the approximation provided by
+# the clustering by averaging the signal on each parcel.
+#
+# We can then compare the results with the compressed representation
+# obtained with Ward.
+
+# Display the original data
+plotting.plot_epi(mean_func_img, cut_coords=cut_coords,
+                  title='Original (%i voxels)' % original_voxels,
+                  vmax=vmax, vmin=vmin, display_mode='xz')
+
+# A reduced data can be created by taking the parcel-level average:
+# Note that, as many scikit-learn objects, the ReNA object exposes
+# a transform method that modifies input features. Here it reduces their
+# dimension.
+# However, the data are in one single large 4D image, we need to use
+# index_img to do the split easily:
+from nilearn.image import index_img
+fmri_reduced_rena = rena.transform(index_img(dataset.func[0], 0))
+
+# Display the corresponding data compression using the parcellation
+compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
+
+plotting.plot_epi(compressed_img_rena, cut_coords=cut_coords,
+                  title='Ward compressed representation (2000 parcels)',
+                  vmin=vmin, vmax=vmax, display_mode='xz')
 
-plotting.show()
+# Even if the compressed signal is relatively close
+# to the original signal, we can notice that Ward Clustering
+# gives a slightly more accurate compressed representation.
+# However, as said in the previous section, the computation time is
+# reduced which could still make ReNA more relevant than Ward in
+# some cases.

From c241ec956d0fda3f80108fa1423b6c32f47c087d Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 15:56:34 +0200
Subject: [PATCH 055/228] deleting plot_fast_clustering

---
 .../03_connectivity/plot_fast_clustering.py   | 149 ------------------
 1 file changed, 149 deletions(-)
 delete mode 100644 examples/03_connectivity/plot_fast_clustering.py

diff --git a/examples/03_connectivity/plot_fast_clustering.py b/examples/03_connectivity/plot_fast_clustering.py
deleted file mode 100644
index 31c88470ef..0000000000
--- a/examples/03_connectivity/plot_fast_clustering.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""
-ReNA clustering to learn a brain parcellation from rest fMRI
-====================================================================
-
-We use Recursive Neighbor Agglomeration (ReNA) clustering to create a set of
-parcels. These parcels are particularly interesting for creating a 'compressed'
-representation of the data, replacing the data in the fMRI images by mean on
-the parcellation.
-
-This parcellation may be useful in a supervised learning, see for
-instance: `A supervised clustering approach for fMRI-based inference of
-brain states <https://hal.inria.fr/inria-00589201>`_, Michel et al,
-Pattern Recognition 2011.
-
-The computation time of this algorithm is linear in the number of features.
-This makes it well suited to use in the consensus on several random
-parcellations, see for instance: `Randomized parcellation based inference
-<https://hal.inria.fr/hal-00915243>`_, Da Mota et al, Neuroimage 2014.
-
-The big picture discussion corresponding to this example can be found
-in the documentation section :ref:`parcellating_brain`.
-"""
-
-##################################################################
-# Download a rest dataset and turn it to a data matrix
-# -----------------------------------------------------
-#
-# We download one subject of the ADHD dataset from Internet
-
-from nilearn import datasets
-dataset = datasets.fetch_adhd(n_subjects=1)
-
-# print basic information on the dataset
-print('First subject functional nifti image (4D) is at: %s' %
-      dataset.func[0])  # 4D data
-
-
-##################################################################
-# Transform nifti files to a data matrix with the NiftiMasker
-from nilearn import input_data
-
-# The NiftiMasker will extract the data on a mask. We do not have a
-# mask, hence we need to compute one.
-#
-# This is resting-state data: the background has not been removed yet,
-# thus we need to use mask_strategy='epi' to compute the mask from the
-# EPI images
-nifti_masker = input_data.NiftiMasker(memory='nilearn_cache',
-                                      mask_strategy='epi', memory_level=1,
-                                      standardize=False)
-
-func_filename = dataset.func[0]
-# The fit call computes the mask
-nifti_masker.fit(func_filename)
-
-##################################################################
-# Perform ReNA clustering
-# -----------------------
-# The spatial constraints are implemented inside the ReNA object.
-#
-# We use caching. As a result, the clustering doesn't have
-# to be recomputed later.
-import time
-from nilearn.connectome import ReNA
-start = time.time()
-rena = ReNA(scaling=True, n_clusters=1000, mask=nifti_masker,
-            memory='nilearn_cache')
-
-rena.fit_transform(func_filename)
-print("ReNA 1000 clusters: %.2fs" % (time.time() - start))
-
-start = time.time()
-rena = ReNA(scaling=True, n_clusters=2000, mask=nifti_masker,
-            memory='nilearn_cache')
-
-rena.fit_transform(func_filename)
-print("ReNA 2000 clusters: %.2fs" % (time.time() - start))
-
-##################################################################
-# Visualize results
-# ------------------
-#
-# First we display the labels of the clustering in the brain.
-#
-# To visualize results, we need to transform the clustering's labels back
-# to a neuroimaging volume. For this, we use the NiftiMasker's
-# inverse_transform method.
-from nilearn.plotting import plot_roi, plot_epi, show
-import numpy as np
-
-# Avoid 0 label
-labels = rena.labels_ + 1
-# Shuffling the labels for visualization
-permutation = np.random.permutation(labels.shape[0])
-labels = permutation[labels]
-# Unmask the labels
-labels_img = nifti_masker.inverse_transform(labels)
-
-##################################################################
-# labels_img is a Nifti1Image object, it can be saved to file with the
-# following code:
-labels_img.to_filename('parcellation.nii')
-
-
-from nilearn.image import mean_img
-mean_func_img = mean_img(func_filename)
-
-first_plot = plot_roi(labels_img, mean_func_img, title="ReNA parcellation",
-                      display_mode='xz')
-
-# common cut coordinates for all plots
-cut_coords = first_plot.cut_coords
-
-##################################################################
-# Second, we illustrate the effect that the clustering has on the
-# signal. We show the original data, and the approximation provided by
-# the clustering by averaging the signal on each parcel.
-#
-# As you can see below, this approximation is very good, although there
-# are only 2000 parcels, instead of the original 60000 voxels
-
-# The transform call extracts the time-series from the files:
-fmri_masked = nifti_masker.transform(func_filename)
-
-# Display the original data
-plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
-         title='Original (%i voxels)' % fmri_masked.shape[1],
-         cut_coords=cut_coords, vmax=fmri_masked.max(), vmin=fmri_masked.min(),
-         display_mode='xz')
-
-
-# A reduced data can be created by taking the parcel-level average:
-# Note that, as many scikit-learn objects, the ReNA object exposes
-# a transform method that modifies input features. Here it reduces their
-# dimension.
-# However, the data are in one single large 4D image, we need to use
-# index_img to do the split easily:
-from nilearn.image import index_img
-fmri_reduced_rena = rena.transform(index_img(func_filename, 0))
-
-# Display the corresponding data compression using the parcellation
-compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
-
-plot_epi(compressed_img_rena, cut_coords=cut_coords,
-         title='ReNA: Compressed representation (2000 parcels)',
-         vmax=fmri_masked.max(), vmin=fmri_masked.min(),
-         display_mode='xz')
-
-show()

From 84e1582cc6607334685c53cecfc8b2e1cf974f19 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 15:59:02 +0200
Subject: [PATCH 056/228] minor changes

---
 .../03_connectivity/plot_data_driven_parcellations.py     | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 108f6a4321..72565014ab 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -221,7 +221,7 @@
 # We notice that, at the first computation (without using precomputed data),
 # for 1000 clusters, the computation time is almost divided by 2 comparing to
 # Ward clustering. This is due to the interesting algorithmic properties of
-# ReNA. In some cases (higher number of features), this reduction in time
+# ReNA. In some cases (higher number of voxels), this reduction in time
 # computation can be much more significant.
 
 ##################################################################
@@ -235,10 +235,10 @@
 # inverse_transform method.
 
 # Avoid 0 label
-labels = rena.labels_ + 1
+labels = rena.labels_
 # Shuffling the labels for visualization
-permutation = np.random.permutation(labels.shape[0])
-labels = permutation[labels]
+permutation = np.random.permutation(np.unique(labels).shape[0])
+labels = 1 + permutation[labels]
 # Obtain the masker
 nifti_masker = rena.masker_
 # Unmask the labels

From c2f3623193ec00b92d8a4af448e465da14ee6171 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 16:16:24 +0200
Subject: [PATCH 057/228] Deleting timing for KMeans

---
 examples/03_connectivity/plot_data_driven_parcellations.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 72565014ab..e00b28b885 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -181,7 +181,6 @@
                        verbose=1)
 # Call fit on functional dataset: single subject (less samples)
 kmeans.fit(dataset.func)
-print("KMeans 2000 clusters: %.2fs" % (time.time() - start))
 
 ###########################################################################
 # Visualize: Brain parcellations (KMeans)

From 55592e6bea2ed20360c067a3d3ca754a4f3dce00 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 16:48:52 +0200
Subject: [PATCH 058/228] relocating ReNA from nilearn.connectome to
 nilearn.regions

---
 examples/03_connectivity/plot_data_driven_parcellations.py    | 2 +-
 nilearn/connectome/__init__.py                                | 4 +---
 nilearn/regions/__init__.py                                   | 4 +++-
 nilearn/{connectome => regions}/rena_clustering.py            | 0
 .../test_rena.py => regions/tests/test_rena_clustering.py}    | 4 ++--
 5 files changed, 7 insertions(+), 7 deletions(-)
 rename nilearn/{connectome => regions}/rena_clustering.py (100%)
 rename nilearn/{connectome/tests/test_rena.py => regions/tests/test_rena_clustering.py} (94%)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index e00b28b885..9b81d5d249 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -204,7 +204,7 @@
 #
 # We use caching. As a result, the clustering doesn't have
 # to be recomputed later.
-from nilearn.connectome import ReNA
+from nilearn.regions import ReNA
 start = time.time()
 rena = ReNA(scaling=True, n_clusters=1000, memory='nilearn_cache')
 
diff --git a/nilearn/connectome/__init__.py b/nilearn/connectome/__init__.py
index 205c497c9f..701675768b 100644
--- a/nilearn/connectome/__init__.py
+++ b/nilearn/connectome/__init__.py
@@ -12,9 +12,7 @@
                                GroupSparseCovarianceCV,
                                group_sparse_covariance)
 
-from .rena_clustering import ReNA
-
 __all__ = ['sym_matrix_to_vec', 'vec_to_sym_matrix', 'sym_to_vec',
            'ConnectivityMeasure', 'cov_to_corr', 'prec_to_partial',
            'GroupSparseCovariance', 'GroupSparseCovarianceCV',
-           'group_sparse_covariance', 'ReNA']
+           'group_sparse_covariance']
diff --git a/nilearn/regions/__init__.py b/nilearn/regions/__init__.py
index 520d825dc1..1202bc58f2 100644
--- a/nilearn/regions/__init__.py
+++ b/nilearn/regions/__init__.py
@@ -10,9 +10,11 @@
 )
 from .parcellations import Parcellations
 
+from .rena_clustering import ReNA
+
 __all__ = [
     'connected_regions', 'RegionExtractor',
     'connected_label_regions',
     'img_to_signals_labels', 'signals_to_img_labels',
     'img_to_signals_maps', 'signals_to_img_maps',
-    'Parcellations']
+    'Parcellations', 'ReNA']
diff --git a/nilearn/connectome/rena_clustering.py b/nilearn/regions/rena_clustering.py
similarity index 100%
rename from nilearn/connectome/rena_clustering.py
rename to nilearn/regions/rena_clustering.py
diff --git a/nilearn/connectome/tests/test_rena.py b/nilearn/regions/tests/test_rena_clustering.py
similarity index 94%
rename from nilearn/connectome/tests/test_rena.py
rename to nilearn/regions/tests/test_rena_clustering.py
index 38df942232..d2814f2b1a 100644
--- a/nilearn/connectome/tests/test_rena.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,12 +1,12 @@
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 from sklearn.externals.joblib import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
-from nilearn.connectome.rena_clustering import ReNA
+from nilearn.regions.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker
 from nilearn.image import index_img
 
 
-def test_rena_clusterings():
+def test_rena_clustering():
     data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=5)
 
     nifti_masker = NiftiMasker(mask_img=mask_img).fit()

From 63bbda8930a7570a892be8188bdf4bac38f9f129 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 17 Apr 2019 18:17:44 +0200
Subject: [PATCH 059/228] no change

---
 nilearn/regions/tests/test_rena_clustering.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index d2814f2b1a..40005072a4 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -33,8 +33,8 @@ def test_rena_clustering():
         assert_raises(ValueError, rena.fit, data)
 
     for n_clusters in [1, 2, 4, 8]:
-        rena = ReNA(n_clusters=n_clusters, n_iter=1, mask=nifti_masker,
-                    memory=memory).fit(data)
+        rena = ReNA(n_clusters=n_clusters, n_iter=1,
+                    mask=nifti_masker, memory=memory).fit(data)
         assert_not_equal(n_clusters, rena.n_clusters_)
 
     del n_voxels, data_compress

From 335c9cae02f726f682e33a03d9e9cdd8811e360d Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Wed, 17 Apr 2019 20:49:23 +0200
Subject: [PATCH 060/228] fix tr

---
 examples/03_connectivity/plot_sphere_based_connectome.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index b36f7c21f1..84f1449c35 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -178,7 +178,7 @@
 # Now let's calculate connectivity for the Seitzman atlas.
 spheres_masker = input_data.NiftiSpheresMasker(
     seeds=coords, smoothing_fwhm=4, radius=4,
-    detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2.5,
+    detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2,
     allow_overlap=True)
 
 timeseries = spheres_masker.fit_transform(fmri_filename,

From 96fb9623e0e371747e68c8d7703df09642c87620 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 18 Apr 2019 06:17:28 +0200
Subject: [PATCH 061/228] removed encoding flag as this is not implemented in
 old np versions

---
 nilearn/datasets/atlas.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index fe4fe1b4eb..77d2cd82ec 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -850,9 +850,11 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
     anatomical_file = os.path.join(package_directory, "data",
                                    "seitzman_2018_ROIs_anatomicalLabels.txt")
 
-    rois = np.recfromcsv(roi_file, delimiter=" ", encoding="ascii")
+    #rois = np.recfromcsv(roi_file, delimiter=" ", encoding="ascii")
+    rois = np.recfromcsv(roi_file, delimiter=" ")
     rois = recfunctions.rename_fields(rois, {"netname": "network",
                                              "radiusmm": "radius"})
+    rois.network = rois.network.astype(str)
 
     # get integer regional labels and convert to text labels with mapping
     # from header line
@@ -863,20 +865,21 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
         i, region = r.split("=")
         region_mapping[int(i)] = region
 
-    anatomical = np.genfromtxt(anatomical_file, skip_header=1,
-                               encoding="ascii")
-    anatomical_names = np.array([region_mapping[a] for a in anatomical],
-                                dtype=[('region', np.dtype((str, 20)))])
+    #anatomical = np.genfromtxt(anatomical_file, skip_header=1,
+    #                           encoding="ascii")
+    anatomical = np.genfromtxt(anatomical_file, skip_header=1)
+    anatomical_names = np.array([region_mapping[a] for a in anatomical])
 
     rois = recfunctions.merge_arrays((rois, anatomical_names),
                                      asrecarray=True, flatten=True)
+    rois.dtype.names = rois.dtype.names[:-1] + ("region",)
 
     if ordered_regions:
         rois = np.sort(rois, order=['network', 'y'])
 
     params = dict(rois=rois[['x', 'y', 'z']],
                   radius=rois['radius'],
-                  networks=rois['network'],
+                  networks=rois['network'].astype(str),
                   regions=rois['region'], description=fdescr)
 
     return Bunch(**params)

From 965f5019f60c87fcb7e25559a5a6d897cfa848f4 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 18 Apr 2019 07:00:35 +0200
Subject: [PATCH 062/228] cleanup

---
 nilearn/datasets/atlas.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 77d2cd82ec..2be84139d8 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -850,7 +850,6 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
     anatomical_file = os.path.join(package_directory, "data",
                                    "seitzman_2018_ROIs_anatomicalLabels.txt")
 
-    #rois = np.recfromcsv(roi_file, delimiter=" ", encoding="ascii")
     rois = np.recfromcsv(roi_file, delimiter=" ")
     rois = recfunctions.rename_fields(rois, {"netname": "network",
                                              "radiusmm": "radius"})
@@ -865,8 +864,6 @@ def fetch_coords_seitzman_2018(ordered_regions=True):
         i, region = r.split("=")
         region_mapping[int(i)] = region
 
-    #anatomical = np.genfromtxt(anatomical_file, skip_header=1,
-    #                           encoding="ascii")
     anatomical = np.genfromtxt(anatomical_file, skip_header=1)
     anatomical_names = np.array([region_mapping[a] for a in anatomical])
 

From 3d54950a4ca60f2303979e6670188ef20825d76c Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 18 Apr 2019 07:41:03 +0200
Subject: [PATCH 063/228] change smoothing for seitzman to 6 to avoid
 FloatingPointError: The system is too ill-conditioned for this solver

---
 .../03_connectivity/plot_sphere_based_connectome.py    | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 84f1449c35..e0ca3781f2 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -16,15 +16,17 @@
 [2] Dosenbach N.U., Nardos B., et al. "Prediction of individual brain maturity
 using fMRI.", 2010, Science 329, 1358-1361.
 
-[3] Seitzman, B. A., et al. "A set of functionally-defined brain regions with
+[3] `Seitzman, B. A., et al. "A set of functionally-defined brain regions with
 improved representation of the subcortex and cerebellum.", 2018, bioRxiv,
-450452. http://doi.org/10.1101/450452
+450452
+<http://doi.org/10.1101/450452>`_
+
+
 """
 
 ###############################################################################
 # Load fMRI data and Power atlas
 # ------------------------------
-#
 # We are going to use a single subject from the movie watching fmri dataset.
 from nilearn import datasets
 
@@ -177,7 +179,7 @@
 ###############################################################################
 # Now let's calculate connectivity for the Seitzman atlas.
 spheres_masker = input_data.NiftiSpheresMasker(
-    seeds=coords, smoothing_fwhm=4, radius=4,
+    seeds=coords, smoothing_fwhm=6, radius=4,
     detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2,
     allow_overlap=True)
 

From c622ed2c6dc2368fe355014a549c9eda998c1753 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Thu, 18 Apr 2019 10:16:11 +0200
Subject: [PATCH 064/228] formatting

---
 examples/03_connectivity/plot_sphere_based_connectome.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index e0ca3781f2..9fd13ea0bb 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -21,12 +21,12 @@
 450452
 <http://doi.org/10.1101/450452>`_
 
-
 """
 
 ###############################################################################
 # Load fMRI data and Power atlas
 # ------------------------------
+#
 # We are going to use a single subject from the movie watching fmri dataset.
 from nilearn import datasets
 

From f5d882c106aa2202d03ead930f7af2ee5d612b4c Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 18 Apr 2019 19:29:14 +0200
Subject: [PATCH 065/228] Change to make ReNA integrable in Parcellation
 (closer to sklearn objects)

---
 nilearn/regions/rena_clustering.py | 211 ++++++++++++-----------------
 1 file changed, 84 insertions(+), 127 deletions(-)

diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index 7c4146d901..7260d598a2 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -13,10 +13,11 @@
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils import check_array
-from ..input_data.masker_validation import check_embedded_nifti_masker
+from nilearn.masking import _unmask_from_to_3d_array
+from nibabel import Nifti1Image
 
 
-def _compute_weights(masker, masked_data):
+def _compute_weights(X, mask_img):
     """Compute the weights in the direction of each axis using the Euclidean
     distance --i.e. weights = (weight_deep, weights_right, weight_down).
 
@@ -24,11 +25,10 @@ def _compute_weights(masker, masked_data):
 
     Parameters
     ----------
-    masker : NiftiMasker
-        The nifti masker used to mask the data.
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
 
-    masked_data : numpy array of shape [n_samples, n_features]
-        Image in brain space transformed into 2D data matrix.
+    mask_img : Niimg-like object used for masking the data.
 
     Returns
     -------
@@ -36,7 +36,16 @@ def _compute_weights(masker, masked_data):
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
-    data = masker.inverse_transform(masked_data).get_data()
+
+    n_samples, n_features = X.shape
+
+    mask = mask_img.get_data().astype('bool')
+    shape = mask.shape
+
+    data = np.empty((shape[0], shape[1], shape[2], n_samples))
+    for sample in range(n_samples):
+        data[:, :, :, sample] = \
+            _unmask_from_to_3d_array(X[sample].copy(), mask)
 
     weights_deep = np.sum(np.diff(data, axis=2) ** 2, axis=-1).ravel()
     weights_right = np.sum(np.diff(data, axis=1) ** 2, axis=-1).ravel()
@@ -87,16 +96,15 @@ def _make_3d_edges(vertices, is_mask):
     return edges
 
 
-def _make_edges_and_weights(masker, masked_data):
+def _make_edges_and_weights(X, mask_img):
     """Compute the weights to all edges in the mask.
 
     Parameters
     ----------
-    masker : NiftiMasker
-        The nifti masker used to mask the data.
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
 
-    masked_data : numpy array of shape [n_samples, n_features]
-        Image in brain space transformed into 2D data matrix.
+    mask_img : Niimg-like object used for masking the data.
 
     Returns
     -------
@@ -107,14 +115,14 @@ def _make_edges_and_weights(masker, masked_data):
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
-    mask = masker.mask_img_.get_data()
+    mask = mask_img.get_data()
     shape = mask.shape
-    n_features = np.prod(shape)
+    n_vertices = np.prod(shape)
 
     # Indexing each voxel
-    vertices = np.arange(n_features).reshape(shape)
+    vertices = np.arange(n_vertices).reshape(shape)
 
-    weights_unmasked = _compute_weights(masker, masked_data)
+    weights_unmasked = _compute_weights(X, mask_img)
 
     edges_unmasked = _make_3d_edges(vertices, is_mask=False)
     edges_mask = _make_3d_edges(mask, is_mask=True)
@@ -131,25 +139,25 @@ def _make_edges_and_weights(masker, masked_data):
     return edges, weights
 
 
-def weighted_connectivity_graph(masker, masked_data):
+def weighted_connectivity_graph(X, mask_img):
     """ Creating symmetric weighted graph: data and topology are encoded by a
     connectivity matrix.
 
     Parameters
     ----------
-    masker : NiftiMasker instance
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
 
-    masked_data : numpy array of shape [n_samples, n_features]
-        Image in brain space transformed into 2D data matrix
+    mask_img : Niimg-like object used for masking the data.
 
     Returns
     -------
     connectivity : a sparse COO matrix
         sparse matrix representation of the weighted adjacency graph
     """
-    n_features = int(masker.mask_img_.get_data().sum())
+    n_features = X.shape[1]
 
-    edges, weight = _make_edges_and_weights(masker, masked_data)
+    edges, weight = _make_edges_and_weights(X, mask_img)
 
     connectivity = coo_matrix((weight, edges),
                               (n_features, n_features)).tocsr()
@@ -204,14 +212,17 @@ def _nn_connectivity(connectivity, threshold=1e-7):
     return nn_connectivity
 
 
-def _reduce_data_and_connectivity(labels, n_components, connectivity,
-                                  masked_data, threshold=1e-7):
+def _reduce_data_and_connectivity(X, labels, n_components, connectivity,
+                                  threshold=1e-7):
     """Perform feature grouping and reduce the connectivity matrix: during the
     reduction step one changes the value of each cluster by their mean.
     In addition, connected nodes are merged.
 
     Parameters
     ----------
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
+
     labels : array like
         Containts the label assignation for each voxel.
 
@@ -221,9 +232,6 @@ def _reduce_data_and_connectivity(labels, n_components, connectivity,
     connectivity : a sparse matrix in COOrdinate format.
         sparse matrix representation of the weighted adjacency graph
 
-    masked_data : array like
-        2D data matrix.
-
     threshold : float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
         In practice it is 1e-7.
@@ -232,7 +240,7 @@ def _reduce_data_and_connectivity(labels, n_components, connectivity,
     -------
     reduced_connectivity : a sparse matrix in COOrdinate format.
 
-    reduced_masked_data: array like
+    reduced_X: array like
         2D data matrix.
     """
     n_features = len(labels)
@@ -247,7 +255,7 @@ def _reduce_data_and_connectivity(labels, n_components, connectivity,
 
     incidence = inv_sum_col * incidence
 
-    reduced_masked_data = (incidence * masked_data.T).T
+    reduced_X = (incidence * X.T).T
     reduced_connectivity = (incidence * connectivity) * incidence.T
 
     reduced_connectivity = reduced_connectivity - dia_matrix(
@@ -257,27 +265,26 @@ def _reduce_data_and_connectivity(labels, n_components, connectivity,
     i_idx, j_idx = reduced_connectivity.nonzero()
 
     weights_ = np.sum(
-        (reduced_masked_data[:, i_idx] - reduced_masked_data[:, j_idx]) ** 2,
+        (reduced_X[:, i_idx] - reduced_X[:, j_idx]) ** 2,
         axis=0)
     weights_ = np.maximum(threshold, weights_)
     reduced_connectivity.data = weights_
 
-    return reduced_connectivity, reduced_masked_data
+    return reduced_connectivity, reduced_X
 
 
-def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
-                              threshold=1e-7):
+def nearest_neighbor_grouping(X, connectivity, n_clusters, threshold=1e-7):
     """Cluster using nearest neighbor agglomeration: merge clusters according
     to their nearest neighbors, then the data and the connectivity are reduced.
 
     Parameters
     ----------
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
+
     connectivity : a sparse matrix in COOrdinate format.
         sparse matrix representation of the weighted adjacency graph
 
-    masked_data : numpy array of shape [n_samples, n_features]
-        Image in brain space transformed into 2D data matrix
-
     n_clusters : int
         The number of clusters to find.
 
@@ -289,7 +296,7 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     -------
     reduced_connectivity : a sparse matrix in COOrdinate format.
 
-    reduced_masked_data :  array like
+    reduced_X :  array like
         2D data matrix.
 
     labels : array like
@@ -322,13 +329,13 @@ def nearest_neighbor_grouping(connectivity, masked_data, n_clusters,
     n_components, labels = csgraph.connected_components(nn_connectivity)
 
     # Reduction step: reduction by averaging
-    reduced_connectivity, reduced_masked_data = _reduce_data_and_connectivity(
-        labels, n_components, connectivity, masked_data, threshold)
+    reduced_connectivity, reduced_X = _reduce_data_and_connectivity(
+        X, labels, n_components, connectivity, threshold)
 
-    return reduced_connectivity, reduced_masked_data, labels
+    return reduced_connectivity, reduced_X, labels
 
 
-def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
+def recursive_neighbor_agglomeration(X, mask_img, n_clusters,
                                      n_iter=10, threshold=1e-7,
                                      verbose=0):
     """Recursive neighbor agglomeration (ReNA): it performs iteratively
@@ -336,10 +343,10 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
     Parameters
     ----------
-    masker : NiftiMasker instance
+    X : array-like, shape = [n_samples, n_features]
+        Training data.
 
-    masked_data : numpy array of shape [n_samples, n_features]
-        Image in brain space transformed into 2D data matrix
+    mask_img : Niimg-like object used for masking the data.
 
     n_clusters : int
         The number of clusters to find.
@@ -370,7 +377,7 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
            Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
            https://hal.archives-ouvertes.fr/hal-01366651/
     """
-    connectivity = weighted_connectivity_graph(masker, masked_data)
+    connectivity = weighted_connectivity_graph(X, mask_img)
 
     # Initialization
     labels = np.arange(connectivity.shape[0])
@@ -378,8 +385,8 @@ def recursive_neighbor_agglomeration(masker, masked_data, n_clusters,
 
     for i in range(n_iter):
 
-        connectivity, masked_data, reduced_labels = nearest_neighbor_grouping(
-            connectivity, masked_data, n_clusters, threshold)
+        connectivity, X, reduced_labels = nearest_neighbor_grouping(
+            X, connectivity, n_clusters, threshold)
 
         labels = reduced_labels[labels]
         n_components = connectivity.shape[0]
@@ -402,6 +409,8 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
 
     Parameters
     ----------
+    mask_img : Niimg-like object used for masking the data.
+
     n_clusters : int, optional (default 2)
         The number of clusters to find.
 
@@ -427,41 +436,6 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     verbose : int, optional (default 1)
         Verbosity level.
 
-    mask : filename, niimg, NiftiMasker instance, optional default None)
-        Mask to be used on data. If an instance of masker is passed,
-        then its mask will be used. If no mask is passed, it will be computed
-        automatically by a NiftiMasker.
-
-    smoothing_fwhm : float, optional
-        If smoothing_fwhm is not None, it gives the size in millimeters of the
-        spatial smoothing to apply to the signal.
-
-    standardize : boolean, optional
-        If standardize is True, the time-series are centered and normed:
-        their variance is put to 1 in the time dimension.
-
-    target_affine : 3x3 or 4x4 matrix, optional (default None)
-        This parameter is passed to image.resample_img. An important use-case
-        of this parameter is for downsampling the input data to a coarser
-        resolution (to speed of the model fit). Please see the related
-        documentation for details.
-
-    target_shape : 3-tuple of integers, optional (default None)
-        This parameter is passed to image.resample_img. Please see the
-        related documentation for details.
-
-    low_pass : None or float, optional
-        This parameter is passed to signal.clean. Please see the related
-        documentation for details
-
-    high_pass : None or float, optional
-        This parameter is passed to signal.clean. Please see the related
-        documentation for details
-
-    t_r : float, optional (default None)
-        This parameter is passed to signal.clean. Please see the related
-        documentation for details.
-
     Attributes
     ----------
     `labels_ ` : array-like, (n_features,)
@@ -473,14 +447,6 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
     `sizes_` : array-like (n_features,)
         It contains the size of each cluster.
 
-    `masker_` : instance of NiftiMasker
-        The nifti masker used to mask the data.
-
-    `mask_img_` : Nifti like image
-        The mask of the data. If no mask was supplied by the user,
-        this attribute is the mask image computed automatically from the
-        data `X`.
-
     References
     ----------
     .. [1] A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
@@ -489,20 +455,13 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
            Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
            https://hal.archives-ouvertes.fr/hal-01366651/
     """
-    def __init__(self, n_clusters=2, mask=None, smoothing_fwhm=None,
-                 standardize=True, target_affine=None, target_shape=None,
-                 mask_strategy='background', memory=None, memory_level=1,
-                 verbose=0, scaling=False, n_iter=10, threshold=1e-7,):
+    def __init__(self, mask_img, n_clusters=2, scaling=False, n_iter=10,
+                 threshold=1e-7, memory=None, memory_level=1, verbose=0):
+        self.mask_img = mask_img
         self.n_clusters = n_clusters
         self.scaling = scaling
         self.n_iter = n_iter
-        self.mask = mask
         self.threshold = threshold
-        self.smoothing_fwhm = smoothing_fwhm
-        self.standardize = standardize
-        self.target_affine = target_affine
-        self.target_shape = target_shape
-        self.mask_strategy = mask_strategy
         self.memory = memory
         self.memory_level = memory_level
         self.verbose = verbose
@@ -512,16 +471,24 @@ def fit(self, X, y=None):
 
         Parameters
         ----------
-        X : list of Niimg-like objects
-            See http://nilearn.github.io/manipulating_images/input_output.html
-            Data on which model is to be fitted. If this is a list,
-            the affine is considered the same for all.
+        X : array-like, shape = [n_samples, n_features]
+            Training data.
+        y : Ignored
 
         Returns
         -------
         self : `ReNA` object
         """
 
+        X = check_array(X, ensure_min_features=2, ensure_min_samples=2,
+                        estimator=self)
+        n_features = X.shape[1]
+
+        if not isinstance(self.mask_img, (str, Nifti1Image)):
+            raise ValueError("The mask image should be a Niimg-like"
+                             "object. Instead a %s object was provided."
+                             % type(self.mask_img))
+
         if self.memory is None or isinstance(self.memory, six.string_types):
             self.memory_ = Memory(cachedir=self.memory,
                                   verbose=max(0, self.verbose - 1))
@@ -536,12 +503,6 @@ def fit(self, X, y=None):
             raise ValueError("n_iter should be an integer greater than 0."
                              " %s was provided." % str(self.n_iter))
 
-        self.masker_ = check_embedded_nifti_masker(self, multi_subject=False)
-        X = self.masker_.fit_transform(X)
-
-        X = check_array(X)
-        n_features = X.shape[1]
-
         if self.n_clusters > n_features:
             self.n_clusters = n_features
             warnings.warn("n_clusters should be at most the number of "
@@ -549,7 +510,8 @@ def fit(self, X, y=None):
                           % str(n_features))
 
         n_components, labels = self.memory_.cache(
-            recursive_neighbor_agglomeration)(self.masker_, X, self.n_clusters,
+            recursive_neighbor_agglomeration)(X, self.mask_img,
+                                              self.n_clusters,
                                               n_iter=self.n_iter,
                                               threshold=self.threshold,
                                               verbose=self.verbose)
@@ -568,42 +530,37 @@ def transform(self, X, y=None):
 
         Parameters
         ----------
-        X : list of Niimg-like objects
-            See http://nilearn.github.io/manipulating_images/input_output.html
-            Data on which model is to be fitted. If this is a list,
-            the affine is considered the same for all.
+        X : array-like, shape = [n_samples, n_features]
+            Data to transform with the fitted clustering.
 
         Returns
         -------
-        Xred : numpy array
-            2D data matrix of shape [n_sampels, n_clusters]
+        X_red : numpy array
+            2D data matrix of shape [n_samples, n_clusters]
         """
 
-        check_is_fitted(self, "masker_")
         check_is_fitted(self, "labels_")
 
-        X = self.masker_.transform(X)
-
         unique_labels = np.unique(self.labels_)
 
         mean_cluster = []
         for label in unique_labels:
             mean_cluster.append(np.mean(X[:, self.labels_ == label], axis=1))
 
-        Xred = np.array(mean_cluster).T
+        X_red = np.array(mean_cluster).T
 
         if self.scaling:
-            Xred = Xred * np.sqrt(self.sizes_)
+            X_red = X_red * np.sqrt(self.sizes_)
 
-        return Xred
+        return X_red
 
-    def inverse_transform(self, Xred):
+    def inverse_transform(self, X_red):
         """Transform the reduced 2D data matrix back to an image in brain
         space.
 
         Parameters
         ----------
-        Xred : numpy array
+        X_red : numpy array
             2D data matrix of shape [n_samples, n_clusters]
 
         Returns
@@ -617,7 +574,7 @@ def inverse_transform(self, Xred):
         _, inverse = np.unique(self.labels_, return_inverse=True)
 
         if self.scaling:
-            Xred = Xred / np.sqrt(self.sizes_)
-        X_inv = Xred[..., inverse]
+            X_red = X_red / np.sqrt(self.sizes_)
+        X_inv = X_red[..., inverse]
 
         return self.masker_.inverse_transform(X_inv)

From b32c8a990a744e135c8787456ef5b62ab40d6da7 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 18 Apr 2019 19:29:59 +0200
Subject: [PATCH 066/228] Adapting the tests accordingly

---
 nilearn/regions/tests/test_rena_clustering.py | 41 +++++++++++--------
 1 file changed, 24 insertions(+), 17 deletions(-)

diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index 40005072a4..e25f4d82e6 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,40 +1,47 @@
+import numpy as np
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 from sklearn.externals.joblib import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
 from nilearn.regions.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker
-from nilearn.image import index_img
 
 
 def test_rena_clustering():
-    data, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=5)
+    data_img, mask_img = generate_fake_fmri(shape=(10, 11, 12), length=5)
+
+    data = data_img.get_data()
+    mask = mask_img.get_data()
+
+    X = np.empty((data.shape[3], int(mask.sum())))
+    for i in range(data.shape[3]):
+        X[i, :] = np.copy(data[:, :, :, i])[mask_img.get_data() != 0]
 
     nifti_masker = NiftiMasker(mask_img=mask_img).fit()
-    n_voxels = nifti_masker.transform(data).shape[1]
+    n_voxels = nifti_masker.transform(data_img).shape[1]
 
-    rena = ReNA(n_clusters=10, mask=nifti_masker, scaling=False)
+    rena = ReNA(mask_img, n_clusters=10)
 
-    data_red = rena.fit_transform(data)
-    data_compress = rena.inverse_transform(data_red)
+    X_red = rena.fit_transform(X)
+    # X_compress = rena.inverse_transform(X_red)
 
     assert_equal(10, rena.n_clusters_)
-    assert_equal(data.shape, data_compress.shape)
+    # assert_equal(X.shape, X_compress.shape)
 
     memory = Memory(cachedir=None)
-    rena = ReNA(n_clusters=-2, mask=nifti_masker, memory=memory)
-    assert_raises(ValueError, rena.fit, data)
+    rena = ReNA(mask_img, n_clusters=-2, memory=memory)
+    assert_raises(ValueError, rena.fit, X)
 
-    rena = ReNA(n_clusters=10, mask=None, scaling=True)
-    data_red = rena.fit_transform(index_img(data, 0))
-    data_compress = rena.inverse_transform(data_red)
+    rena = ReNA(mask_img, n_clusters=10, scaling=True)
+    X_red = rena.fit_transform(X)
+    # X_compress = rena.inverse_transform(X_red)
 
     for n_iter in [-2, 0]:
-        rena = ReNA(n_iter=n_iter, mask=nifti_masker, memory=memory)
-        assert_raises(ValueError, rena.fit, data)
+        rena = ReNA(mask_img, n_iter=n_iter, memory=memory)
+        assert_raises(ValueError, rena.fit, X)
 
     for n_clusters in [1, 2, 4, 8]:
-        rena = ReNA(n_clusters=n_clusters, n_iter=1,
-                    mask=nifti_masker, memory=memory).fit(data)
+        rena = ReNA(mask_img, n_clusters=n_clusters, n_iter=1,
+                    memory=memory).fit(X)
         assert_not_equal(n_clusters, rena.n_clusters_)
 
-    del n_voxels, data_compress
+    del n_voxels, X_red

From 8dd377c1b39047773708778a2ca04add48b3c8bd Mon Sep 17 00:00:00 2001
From: Sylvain Takerkart <sylvain.takerkart@univ-amu.fr>
Date: Fri, 19 Apr 2019 12:10:10 +0200
Subject: [PATCH 067/228] Added a fetcher for the fsaverage spherical meshes,
 as an option to the fsaverage fetcher

---
 .../description/fsaverage5_sphere.rst         | 16 +++++++++++++++
 nilearn/datasets/struct.py                    | 20 +++++++++++++++++++
 nilearn/datasets/tests/test_struct.py         | 11 ++++++++++
 3 files changed, 47 insertions(+)
 create mode 100644 nilearn/datasets/description/fsaverage5_sphere.rst

diff --git a/nilearn/datasets/description/fsaverage5_sphere.rst b/nilearn/datasets/description/fsaverage5_sphere.rst
new file mode 100644
index 0000000000..8b2aba6ea8
--- /dev/null
+++ b/nilearn/datasets/description/fsaverage5_sphere.rst
@@ -0,0 +1,16 @@
+fsaverage5
+
+
+Notes
+-----
+Fsaverage5 standard spheres as distributed with Freesurfer (Fischl et al, 1999)
+
+Content
+-------
+    :'sphere_left': Gifti file, left hemisphere spherical surface mesh
+    :'sphere_right': Gifti file, right hemisphere spherical surface mesh
+
+References
+----------
+Fischl et al, (1999). High-resolution intersubject averaging and a
+coordinate system for the cortical surface. Hum Brain Mapp 8, 272-284.
diff --git a/nilearn/datasets/struct.py b/nilearn/datasets/struct.py
index 8393e44bd4..ca892784a1 100644
--- a/nilearn/datasets/struct.py
+++ b/nilearn/datasets/struct.py
@@ -440,6 +440,7 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
     mesh: str, optional (default='fsaverage5')
         Which mesh to fetch.
         'fsaverage5': the low-resolution fsaverage5 mesh (10242 nodes)
+        'fsaverage5_sphere': the low-resolution fsaverage5 spheres (10242 nodes)
         'fsaverage': the high-resolution fsaverage mesh (163842 nodes)
         (high-resolution fsaverage will result in
         more computation time and memory usage)
@@ -467,6 +468,7 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
 
     """
     meshes = {'fsaverage5': _fetch_surf_fsaverage5,
+              'fsaverage5_sphere': _fetch_surf_fsaverage5_sphere,
               'fsaverage': _fetch_surf_fsaverage}
     if mesh not in meshes:
         raise ValueError(
@@ -571,3 +573,21 @@ def _fetch_surf_fsaverage5(data_dir=None, url=None, resume=True, verbose=1):
                  sulc_left=sulcs[0],
                  sulc_right=sulcs[1],
                  description=fdescr)
+
+def _fetch_surf_fsaverage5_sphere(data_dir=None):
+    fsaverage_dir = _get_dataset_dir('fsaverage', data_dir=data_dir)
+    dataset_dir = _get_dataset_dir('fsaverage5_sphere', data_dir=fsaverage_dir)
+    print("bouh", dataset_dir)
+    url = 'https://osf.io/b79fy/download'
+    opts = {'uncompress': True}
+    names = ['sphere_right', 'sphere_left']
+    filenames = [('{}.gii'.format(name), url, opts)
+                 for name in names]
+    #if not os.path.isdir(dataset_dir):
+    _fetch_files(dataset_dir, filenames)
+    result = {
+        name: os.path.join(dataset_dir, '{}.gii'.format(name))
+        for name in names}
+
+    result['description'] = str(_get_dataset_descr('fsaverage'))
+    return Bunch(**result)
diff --git a/nilearn/datasets/tests/test_struct.py b/nilearn/datasets/tests/test_struct.py
index f94a99833e..fbcce318f4 100644
--- a/nilearn/datasets/tests/test_struct.py
+++ b/nilearn/datasets/tests/test_struct.py
@@ -175,3 +175,14 @@ def test_fetch_surf_fsaverage():
 
         assert keys.issubset(set(dataset.keys()))
         assert_not_equal(dataset.description, '')
+
+def test_fetch_surf_fsaverage5_sphere():
+    for mesh in ['fsaverage5_sphere']:
+
+        dataset = struct.fetch_surf_fsaverage(
+            mesh, data_dir=tst.tmpdir)
+
+        keys = {'sphere_left', 'sphere_right'}
+
+        assert keys.issubset(set(dataset.keys()))
+        assert_not_equal(dataset.description, '')

From 2db4c4d5284df5769257785ef86d67ef11fd8853 Mon Sep 17 00:00:00 2001
From: Sylvain Takerkart <Sylvain.Takerkart@univ-amu.fr>
Date: Fri, 19 Apr 2019 14:07:20 +0200
Subject: [PATCH 068/228] Added docstring to helper functions and removed some
 useless lines of code...

---
 nilearn/datasets/struct.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/nilearn/datasets/struct.py b/nilearn/datasets/struct.py
index ca892784a1..82461401c7 100644
--- a/nilearn/datasets/struct.py
+++ b/nilearn/datasets/struct.py
@@ -478,6 +478,11 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
 
 
 def _fetch_surf_fsaverage(data_dir=None):
+    """Helper function to ship fsaverage (highest resolution) surfaces
+    and sulcal information with Nilearn.
+
+    The source of the data is downloaded from nitrc.
+    """
     dataset_dir = _get_dataset_dir('fsaverage', data_dir=data_dir)
     url = 'https://www.nitrc.org/frs/download.php/10846/fsaverage.tar.gz'
     if not os.path.isdir(os.path.join(dataset_dir, 'fsaverage')):
@@ -575,19 +580,22 @@ def _fetch_surf_fsaverage5(data_dir=None, url=None, resume=True, verbose=1):
                  description=fdescr)
 
 def _fetch_surf_fsaverage5_sphere(data_dir=None):
+    """Helper function to ship fsaverage5 spherical meshes.
+
+    The source of the data is downloaded from OSF.
+    """
+
     fsaverage_dir = _get_dataset_dir('fsaverage', data_dir=data_dir)
     dataset_dir = _get_dataset_dir('fsaverage5_sphere', data_dir=fsaverage_dir)
-    print("bouh", dataset_dir)
     url = 'https://osf.io/b79fy/download'
     opts = {'uncompress': True}
     names = ['sphere_right', 'sphere_left']
     filenames = [('{}.gii'.format(name), url, opts)
                  for name in names]
-    #if not os.path.isdir(dataset_dir):
     _fetch_files(dataset_dir, filenames)
     result = {
         name: os.path.join(dataset_dir, '{}.gii'.format(name))
         for name in names}
 
-    result['description'] = str(_get_dataset_descr('fsaverage'))
+    result['description'] = str(_get_dataset_descr('fsaverage5_sphere'))
     return Bunch(**result)

From 611989285e10d1631fbca59711b9e83a8c7364e2 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 22 Apr 2019 08:38:32 +0200
Subject: [PATCH 069/228] fix tr for DMN example

---
 examples/03_connectivity/plot_sphere_based_connectome.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 4d72b63edd..2b41b6d7c5 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -76,7 +76,7 @@
 masker = input_data.NiftiSpheresMasker(
     dmn_coords, radius=8,
     detrend=True, standardize=True,
-    low_pass=0.1, high_pass=0.01, t_r=2.5,
+    low_pass=0.1, high_pass=0.01, t_r=2,
     memory='nilearn_cache', memory_level=1, verbose=2)
 
 # Additionally, we pass confound information so ensure our extracted 

From 132342bc0ab1cd841c6b97a7366ef6121df69cb0 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 22 Apr 2019 08:39:06 +0200
Subject: [PATCH 070/228] fix typo

---
 examples/03_connectivity/plot_sphere_based_connectome.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 2b41b6d7c5..07226bc4ea 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -80,7 +80,7 @@
     memory='nilearn_cache', memory_level=1, verbose=2)
 
 # Additionally, we pass confound information so ensure our extracted 
-# signal is cleaned from counfounds.
+# signal is cleaned from confounds.
 
 func_filename = dataset.func[0]
 confounds_filename = dataset.confounds[0]

From aec1973a82f0a77b013c3ec85ef8bdf26029fa51 Mon Sep 17 00:00:00 2001
From: fliem <franz.liem@gmail.com>
Date: Mon, 22 Apr 2019 08:54:00 +0200
Subject: [PATCH 071/228] fwhm 6 for all atlases

---
 .../03_connectivity/plot_sphere_based_connectome.py  | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 07226bc4ea..4ea44844e9 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -2,12 +2,6 @@
 Extract signals on spheres and plot a connectome
 ==============================================================
 
-This example shows how to extract signals from spherical regions
-centered on coordinates from the Power-264 atlas [1], the Dosenbach-160
-atlas [2], and the Seitzman-300 atlas [3].
-We estimate connectome using **sparse inverse covariance**, to recover
-the functional brain **networks structure**.
-
 This example shows how to extract signals from spherical regions.
 We show how to build spheres around user-defined coordinates, as well as
 centered on coordinates from the Power-264 atlas [1], the Dosenbach-160
@@ -198,7 +192,7 @@
 # and define spheres masker, with small enough radius to avoid regions overlap.
 
 spheres_masker = input_data.NiftiSpheresMasker(
-    seeds=coords, smoothing_fwhm=4, radius=5.,
+    seeds=coords, smoothing_fwhm=6, radius=5.,
     detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2)
 
 timeseries = spheres_masker.fit_transform(func_filename,
@@ -271,7 +265,7 @@
 )).T
 
 spheres_masker = input_data.NiftiSpheresMasker(
-    seeds=coords, smoothing_fwhm=4, radius=4.5,
+    seeds=coords, smoothing_fwhm=6, radius=4.5,
     detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2)
 
 timeseries = spheres_masker.fit_transform(func_filename,
@@ -324,7 +318,7 @@
     detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2,
     allow_overlap=True)
 
-timeseries = spheres_masker.fit_transform(fmri_filename,
+timeseries = spheres_masker.fit_transform(func_filename,
                                           confounds=confounds_filename)
 
 covariance_estimator = GraphLassoCV()

From f7e778147e6754608374f47f5838ff0fd9d6d875 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Tue, 23 Apr 2019 22:40:55 +0200
Subject: [PATCH 072/228] adressing requested changes in
 plot_data_driven_parcellations example

---
 .../plot_data_driven_parcellations.py         | 49 +++++++------------
 1 file changed, 19 insertions(+), 30 deletions(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 9b81d5d249..a6ab9425a5 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -33,14 +33,6 @@
       <http://dx.doi.org/10.1016/j.patcog.2011.04.006>`_.
       Pattern Recognition, Elsevier, 2011.
 
-More about ReNA clustering algorithm in the original paper
-
-    * A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
-      Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
-      Structured Signals," in IEEE Transactions on Pattern Analysis and
-      Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
-      https://hal.archives-ouvertes.fr/hal-01366651/
-
 The big picture discussion corresponding to this example can be found
 in the documentation section :ref:`parcellating_brain`.
 """
@@ -175,7 +167,7 @@
 # This object uses method='kmeans' for KMeans clustering with 10mm smoothing
 # and standardization ON
 start = time.time()
-kmeans = Parcellations(method='kmeans', n_parcels=2000,
+kmeans = Parcellations(method='kmeans', n_parcels=50,
                        standardize=True, smoothing_fwhm=10.,
                        memory='nilearn_cache', memory_level=1,
                        verbose=1)
@@ -200,28 +192,28 @@
 ##################################################################
 # Brain parcellations with ReNA Clustering
 # ----------------------------------------
+#
 # The spatial constraints are implemented inside the ReNA object.
+# One interesting algorithmic property of ReNA is that it is very
+# fast for a large number of parcels (notably faster than Ward).
 #
-# We use caching. As a result, the clustering doesn't have
-# to be recomputed later.
-from nilearn.regions import ReNA
-start = time.time()
-rena = ReNA(scaling=True, n_clusters=1000, memory='nilearn_cache')
-
-rena.fit_transform(dataset.func[0])
-print("ReNA 1000 clusters: %.2fs" % (time.time() - start))
+# References
+# ----------
+#
+# More about ReNA clustering algorithm in the original paper
+#
+#     * A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
+#       Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
+#       Structured Signals," in IEEE Transactions on Pattern Analysis and
+#       Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
+#       https://hal.archives-ouvertes.fr/hal-01366651/
 
+from nilearn.regions import ReNA
 start = time.time()
-rena = ReNA(scaling=True, n_clusters=2000, memory='nilearn_cache')
+rena = ReNA(n_clusters=5000, scaling=True)
 
 rena.fit_transform(dataset.func[0])
-print("ReNA 2000 clusters: %.2fs" % (time.time() - start))
-
-# We notice that, at the first computation (without using precomputed data),
-# for 1000 clusters, the computation time is almost divided by 2 comparing to
-# Ward clustering. This is due to the interesting algorithmic properties of
-# ReNA. In some cases (higher number of voxels), this reduction in time
-# computation can be much more significant.
+print("ReNA 5000 clusters: %.2fs" % (time.time() - start))
 
 ##################################################################
 # Visualize: Brain parcellations (ReNA)
@@ -247,10 +239,6 @@
 # labels_img is a Nifti1Image object, it can be saved to file with the
 # following code:
 labels_img.to_filename('rena_parcellation.nii.gz')
-
-
-from nilearn.image import mean_img
-
 plotting.plot_roi(labels_img, mean_func_img, title="ReNA parcellation",
                   display_mode='xz')
 
@@ -286,9 +274,10 @@
 compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
 
 plotting.plot_epi(compressed_img_rena, cut_coords=cut_coords,
-                  title='Ward compressed representation (2000 parcels)',
+                  title='ReNA compressed representation (5000 parcels)',
                   vmin=vmin, vmax=vmax, display_mode='xz')
 
+####################################################################
 # Even if the compressed signal is relatively close
 # to the original signal, we can notice that Ward Clustering
 # gives a slightly more accurate compressed representation.

From 57fbacd4d4e19723597db678c43a2d634acba1ab Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Tue, 23 Apr 2019 23:57:20 +0200
Subject: [PATCH 073/228] doc string changes + fixing inverse_transform method

---
 nilearn/regions/rena_clustering.py | 62 +++++++++++++++---------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index 7260d598a2..fe6972331c 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -25,14 +25,14 @@ def _compute_weights(X, mask_img):
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
     mask_img : Niimg-like object used for masking the data.
 
     Returns
     -------
-    weights : numpy array
+    weights : ndarray
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
@@ -61,7 +61,7 @@ def _make_3d_edges(vertices, is_mask):
 
     Parameters
     ----------
-    vertices : numpy.ndarray
+    vertices : ndarray
         The indices of the voxels.
 
     is_mask : boolean
@@ -70,7 +70,7 @@ def _make_3d_edges(vertices, is_mask):
 
     Returns
     -------
-    edges : numpy array
+    edges : ndarray
         Edges corresponding to the image or mask.
         shape: (1, n_edges) if_mask,
                (2, n_edges) otherwise.
@@ -101,17 +101,17 @@ def _make_edges_and_weights(X, mask_img):
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
     mask_img : Niimg-like object used for masking the data.
 
     Returns
     -------
-    edges : numpy array
+    edges : ndarray
         Array containing [edges_deep, edges_right, edges_down]
 
-    weights : numpy array
+    weights : ndarray
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
@@ -145,7 +145,7 @@ def weighted_connectivity_graph(X, mask_img):
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
     mask_img : Niimg-like object used for masking the data.
@@ -220,10 +220,10 @@ def _reduce_data_and_connectivity(X, labels, n_components, connectivity,
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
-    labels : array like
+    labels : ndarray
         Containts the label assignation for each voxel.
 
     n_components : int
@@ -240,8 +240,8 @@ def _reduce_data_and_connectivity(X, labels, n_components, connectivity,
     -------
     reduced_connectivity : a sparse matrix in COOrdinate format.
 
-    reduced_X: array like
-        2D data matrix.
+    reduced_X : ndarray
+        Data reduced with agglomerated signal for each cluster
     """
     n_features = len(labels)
 
@@ -279,7 +279,7 @@ def nearest_neighbor_grouping(X, connectivity, n_clusters, threshold=1e-7):
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
     connectivity : a sparse matrix in COOrdinate format.
@@ -296,10 +296,10 @@ def nearest_neighbor_grouping(X, connectivity, n_clusters, threshold=1e-7):
     -------
     reduced_connectivity : a sparse matrix in COOrdinate format.
 
-    reduced_X :  array like
-        2D data matrix.
+    reduced_X : ndarray
+        Data reduced with agglomerated signal for each cluster
 
-    labels : array like
+    labels : ndarray, shape = [n_features]
         It contains the clusters assignation.
     """
     # Nearest neighbor conenctivity
@@ -343,7 +343,7 @@ def recursive_neighbor_agglomeration(X, mask_img, n_clusters,
 
     Parameters
     ----------
-    X : array-like, shape = [n_samples, n_features]
+    X : ndarray, shape = [n_samples, n_features]
         Training data.
 
     mask_img : Niimg-like object used for masking the data.
@@ -366,7 +366,7 @@ def recursive_neighbor_agglomeration(X, mask_img, n_clusters,
     n_components : int
         Number of clusters.
 
-    labels : array
+    labels : ndarray, shape = [n_features]
         Cluster assignation.
 
     References
@@ -438,13 +438,13 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
 
     Attributes
     ----------
-    `labels_ ` : array-like, (n_features,)
+    `labels_ ` : ndarray, shape = [n_features]
         cluster labels for each feature.
 
     `n_clusters_` : int
         Number of clusters.
 
-    `sizes_` : array-like (n_features,)
+    `sizes_` : ndarray, shape = [n_features]
         It contains the size of each cluster.
 
     References
@@ -471,7 +471,7 @@ def fit(self, X, y=None):
 
         Parameters
         ----------
-        X : array-like, shape = [n_samples, n_features]
+        X : ndarray, shape = [n_samples, n_features]
             Training data.
         y : Ignored
 
@@ -530,13 +530,13 @@ def transform(self, X, y=None):
 
         Parameters
         ----------
-        X : array-like, shape = [n_samples, n_features]
+        X : ndarray, shape = [n_samples, n_features]
             Data to transform with the fitted clustering.
 
         Returns
         -------
-        X_red : numpy array
-            2D data matrix of shape [n_samples, n_clusters]
+        X_red : ndarray, shape = [n_samples, n_clusters]
+            Data reduced with agglomerated signal for each cluster
         """
 
         check_is_fitted(self, "labels_")
@@ -555,18 +555,18 @@ def transform(self, X, y=None):
         return X_red
 
     def inverse_transform(self, X_red):
-        """Transform the reduced 2D data matrix back to an image in brain
-        space.
+        """Send the reduced 2D data matrix back to the original feature
+        space (voxels).
 
         Parameters
         ----------
-        X_red : numpy array
-            2D data matrix of shape [n_samples, n_clusters]
+        X_red : ndarray , shape = [n_samples, n_clusters]
+            Data reduced with agglomerated signal for each cluster
 
         Returns
         -------
-        X_inv : nibabel.Nifti1Image
-            shape: (n_x, n_y, n_z, n_samples)
+        X_inv : ndarray, shape = [n_samples, n_features]
+            Data reduced expanded to the original feature space
         """
 
         check_is_fitted(self, "labels_")
@@ -577,4 +577,4 @@ def inverse_transform(self, X_red):
             X_red = X_red / np.sqrt(self.sizes_)
         X_inv = X_red[..., inverse]
 
-        return self.masker_.inverse_transform(X_inv)
+        return X_inv

From fb6c20472e715446cdd32be2dba6afcd80b5db89 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Tue, 23 Apr 2019 23:57:41 +0200
Subject: [PATCH 074/228] improving tests accordingly

---
 nilearn/regions/tests/test_rena_clustering.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index e25f4d82e6..8f236b29ec 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -22,10 +22,10 @@ def test_rena_clustering():
     rena = ReNA(mask_img, n_clusters=10)
 
     X_red = rena.fit_transform(X)
-    # X_compress = rena.inverse_transform(X_red)
+    X_compress = rena.inverse_transform(X_red)
 
     assert_equal(10, rena.n_clusters_)
-    # assert_equal(X.shape, X_compress.shape)
+    assert_equal(X.shape, X_compress.shape)
 
     memory = Memory(cachedir=None)
     rena = ReNA(mask_img, n_clusters=-2, memory=memory)
@@ -33,7 +33,7 @@ def test_rena_clustering():
 
     rena = ReNA(mask_img, n_clusters=10, scaling=True)
     X_red = rena.fit_transform(X)
-    # X_compress = rena.inverse_transform(X_red)
+    X_compress = rena.inverse_transform(X_red)
 
     for n_iter in [-2, 0]:
         rena = ReNA(mask_img, n_iter=n_iter, memory=memory)
@@ -44,4 +44,4 @@ def test_rena_clustering():
                     memory=memory).fit(X)
         assert_not_equal(n_clusters, rena.n_clusters_)
 
-    del n_voxels, X_red
+    del n_voxels, X_red, X_compress

From 31cc746f37e85bcbaae6e2dd7ad15c2d87a43184 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 09:51:52 +0200
Subject: [PATCH 075/228] minor change in masking.py

---
 nilearn/masking.py | 36 ++++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 16 deletions(-)

diff --git a/nilearn/masking.py b/nilearn/masking.py
index 711d005f36..88d0453cff 100644
--- a/nilearn/masking.py
+++ b/nilearn/masking.py
@@ -52,7 +52,7 @@ def _load_mask_img(mask_img, allow_empty=False):
                 'The mask is invalid as it is empty: it masks all data.')
     elif len(values) == 2:
         # If there are 2 different values, one of them must be 0 (background)
-        if not 0 in values:
+        if 0 not in values:
             raise ValueError('Background of the mask must be represented with'
                              '0. Given mask contains: %s.' % values)
     elif len(values) != 2:
@@ -91,8 +91,8 @@ def _extrapolate_out_mask(data, mask, iterations=1):
         extrapolation.append(masked_data[this_x, this_y, this_z])
 
     extrapolation = np.array(extrapolation)
-    extrapolation = (np.nansum(extrapolation, axis=0)
-                     / np.sum(np.isfinite(extrapolation), axis=0))
+    extrapolation = (np.nansum(extrapolation, axis=0) /
+                     np.sum(np.isfinite(extrapolation), axis=0))
     extrapolation[np.logical_not(np.isfinite(extrapolation))] = 0
     new_data = np.zeros_like(masked_data)
     new_data[outer_shell] = extrapolation
@@ -162,7 +162,8 @@ def intersect_masks(mask_imgs, threshold=0.5, connected=True):
     if np.any(grp_mask > 0) and connected:
         grp_mask = largest_connected_component(grp_mask)
     grp_mask = _utils.as_ndarray(grp_mask, dtype=np.int8)
-    return new_img_like(_utils.check_niimg_3d(mask_imgs[0]), grp_mask, ref_affine)
+    return new_img_like(_utils.check_niimg_3d(mask_imgs[0]), grp_mask,
+                        ref_affine)
 
 
 def _post_process_mask(mask, affine, opening=2, connected=True,
@@ -258,10 +259,10 @@ def compute_epi_mask(epi_img, lower_cutoff=0.2, upper_cutoff=0.85,
 
     # Delayed import to avoid circular imports
     from .image.image import _compute_mean
-    mean_epi, affine = cache(_compute_mean, memory)(epi_img,
-                                                    target_affine=target_affine,
-                                                    target_shape=target_shape,
-                                                    smooth=(1 if opening else False))
+    mean_epi, affine = \
+        cache(_compute_mean, memory)(epi_img, target_affine=target_affine,
+                                     target_shape=target_shape,
+                                     smooth=(1 if opening else False))
 
     if ensure_finite:
         # Get rid of memmapping
@@ -278,13 +279,14 @@ def compute_epi_mask(epi_img, lower_cutoff=0.2, upper_cutoff=0.85,
     delta = sorted_input[lower_cutoff + 1:upper_cutoff + 1] \
         - sorted_input[lower_cutoff:upper_cutoff]
     ia = delta.argmax()
-    threshold = 0.5 * (sorted_input[ia + lower_cutoff]
-                       + sorted_input[ia + lower_cutoff + 1])
+    threshold = 0.5 * (sorted_input[ia + lower_cutoff] +
+                       sorted_input[ia + lower_cutoff + 1])
 
     mask = mean_epi >= threshold
 
     mask, affine = _post_process_mask(mask, affine, opening=opening,
-                                      connected=connected, warning_msg="Are you sure that input "
+                                      connected=connected,
+                                      warning_msg="Are you sure that input "
                                       "data are EPI images not detrended. ")
     return new_img_like(epi_img, mask, affine)
 
@@ -426,7 +428,8 @@ def compute_background_mask(data_imgs, border_size=2,
     # Delayed import to avoid circular imports
     from .image.image import _compute_mean
     data, affine = cache(_compute_mean, memory)(data_imgs,
-                                                target_affine=target_affine, target_shape=target_shape,
+                                                target_affine=target_affine,
+                                                target_shape=target_shape,
                                                 smooth=False)
 
     background = np.median(get_border_data(data, border_size))
@@ -438,7 +441,8 @@ def compute_background_mask(data_imgs, border_size=2,
         mask = data != background
 
     mask, affine = _post_process_mask(mask, affine, opening=opening,
-                                      connected=connected, warning_msg="Are you sure that input "
+                                      connected=connected,
+                                      warning_msg="Are you sure that input "
                                       "images have a homogeneous background.")
     return new_img_like(data_imgs, mask, affine)
 
@@ -866,9 +870,9 @@ def unmask(X, mask_img, order="F"):
 
 
 def _unmask_from_to_3d_array(w, mask):
-    """Unmask an image into whole brain, with off-mask voxels set to 0. Used as
-    a stand-alone function in low-level decoding (SpaceNet) and clustering (ReNA)
-    functions.
+    """Unmask an image into whole brain, with off-mask voxels set to 0.
+    Used as a stand-alone function in low-level decoding (SpaceNet) and
+    clustering (ReNA) functions.
 
     Parameters
     ----------

From 8c1a79b725583bb6497b682b6532e0e90f301eef Mon Sep 17 00:00:00 2001
From: Sylvain Takerkart <Sylvain.Takerkart@univ-amu.fr>
Date: Wed, 24 Apr 2019 13:30:25 +0200
Subject: [PATCH 076/228] Added something in the docstring of
 _fetch_surf_fsaverage5_sphere to explain what these spherical meshes are used
 for...

---
 nilearn/datasets/struct.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/nilearn/datasets/struct.py b/nilearn/datasets/struct.py
index 82461401c7..11efe39e1c 100644
--- a/nilearn/datasets/struct.py
+++ b/nilearn/datasets/struct.py
@@ -582,6 +582,9 @@ def _fetch_surf_fsaverage5(data_dir=None, url=None, resume=True, verbose=1):
 def _fetch_surf_fsaverage5_sphere(data_dir=None):
     """Helper function to ship fsaverage5 spherical meshes.
 
+    These meshes can be used for visualization purposes, but also to run
+    cortical surface-based searchlight decoding.
+
     The source of the data is downloaded from OSF.
     """
 

From b525271320ecc3d8984bdf471978a9892d8ee2a8 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 14:22:22 +0200
Subject: [PATCH 077/228] P8 changes

---
 nilearn/decomposition/base.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/nilearn/decomposition/base.py b/nilearn/decomposition/base.py
index aef70a7219..bb6fad4044 100644
--- a/nilearn/decomposition/base.py
+++ b/nilearn/decomposition/base.py
@@ -84,7 +84,6 @@ def fast_svd(X, n_components, random_state=None):
     return U, S, V
 
 
-
 def mask_and_reduce(masker, imgs,
                     confounds=None,
                     reduction_ratio='auto',
@@ -218,10 +217,10 @@ def _mask_and_reduce_single(masker,
         n_samples = int(ceil(data_n_samples * reduction_ratio))
 
     U, S, V = cache(fast_svd, memory,
-                        memory_level=memory_level,
-                        func_memory_level=3)(this_data.T,
-                                             n_samples,
-                                             random_state=random_state)
+                    memory_level=memory_level,
+                    func_memory_level=3)(this_data.T,
+                                         n_samples,
+                                         random_state=random_state)
     U = U.T.copy()
     U = U * S[:, np.newaxis]
     return U

From 5e0ccaeb03a498e3fb6a155091990bbad50b8d7b Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 14:22:52 +0200
Subject: [PATCH 078/228] P8 changes

---
 nilearn/decomposition/multi_pca.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nilearn/decomposition/multi_pca.py b/nilearn/decomposition/multi_pca.py
index 1cc60f6229..5b888c615d 100644
--- a/nilearn/decomposition/multi_pca.py
+++ b/nilearn/decomposition/multi_pca.py
@@ -32,7 +32,8 @@ class MultiPCA(BaseDecomposition):
         If smoothing_fwhm is not None, it gives the size in millimeters of the
         spatial smoothing to apply to the signal.
 
-    mask: Niimg-like object, instance of NiftiMasker or MultiNiftiMasker, optional
+    mask: Niimg-like object, instance of NiftiMasker or MultiNiftiMasker,
+        optional
         Mask to be used on data. If an instance of masker is passed,
         then its mask will be used. If no mask is given,
         it will be computed automatically by a MultiNiftiMasker with default

From bc258b6557fc1dd7c949aa15b41701271656fab9 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 14:23:49 +0200
Subject: [PATCH 079/228] integrating ReNA into Parcellations

---
 nilearn/regions/parcellations.py | 84 ++++++++++++++++++++++++++++----
 1 file changed, 74 insertions(+), 10 deletions(-)

diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 47e1476aaf..702b724720 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -7,6 +7,7 @@
 from sklearn.feature_extraction import image
 from sklearn.externals.joblib import Memory, delayed, Parallel
 
+from .rena_clustering import ReNA
 from ..decomposition.multi_pca import MultiPCA
 from ..input_data import NiftiLabelsMasker
 from .._utils.compat import _basestring
@@ -14,7 +15,7 @@
 from .._utils.niimg_conversions import _iter_check_niimg
 
 
-def _estimator_fit(data, estimator):
+def _estimator_fit(data, estimator, method=None):
     """ Estimator to fit on the data matrix
 
     Parameters
@@ -25,15 +26,32 @@ def _estimator_fit(data, estimator):
     estimator : instance of estimator from sklearn
         MiniBatchKMeans or AgglomerativeClustering
 
+    method : str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
+        A method to choose between for brain parcellations.
+
     Returns
     -------
     labels_ : numpy.ndarray
         labels_ estimated from estimator
     """
-    estimator = clone(estimator)
-    estimator.fit(data.T)
+    if method == 'rena':
+        rena = ReNA(mask_img=estimator.mask_img,
+                    n_clusters=estimator.n_clusters,
+                    scaling=estimator.scaling,
+                    n_iter=estimator.n_iter,
+                    threshold=estimator.threshold,
+                    memory=estimator.memory,
+                    memory_level=estimator.memory_level,
+                    verbose=estimator.verbose)
+        rena.fit(data)
+        labels_ = rena.labels_
+
+    else:
+        estimator = clone(estimator)
+        estimator.fit(data.T)
+        labels_ = estimator.labels_
 
-    return estimator.labels_
+    return labels_
 
 
 def _check_parameters_transform(imgs, confounds):
@@ -101,7 +119,7 @@ class Parcellations(MultiPCA):
 
     Parameters
     ----------
-    method : str, {'kmeans', 'ward', 'complete', 'average'}
+    method : str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
         A method to choose between for brain parcellations.
 
     n_parcels : int, default=50
@@ -153,6 +171,35 @@ class Parcellations(MultiPCA):
         This parameter is passed to image.resample_img. Please see the
         related documentation for details.
 
+    mask_strategy: {'background', 'epi' or 'template'}, optional
+        The strategy used to compute the mask: use 'background' if your
+        images present a clear homogeneous background, 'epi' if they
+        are raw EPI images, or you could use 'template' which will
+        extract the gray matter part of your data by resampling the MNI152
+        brain mask for your data's field of view.
+        Depending on this value, the mask will be computed from
+        masking.compute_background_mask, masking.compute_epi_mask or
+        masking.compute_gray_matter_mask. Default is 'epi'.
+
+    mask_args: dict, optional
+        If mask is None, these are additional parameters passed to
+        masking.compute_background_mask or masking.compute_epi_mask
+        to fine-tune mask computation. Please see the related documentation
+        for details.
+
+    scaling : bool, optional (default False)
+        Used only when the method selected is 'rena'. If scaling is True, each
+        cluster is scaled by the square root of its size, preserving the
+        l2-norm of the image.
+
+    n_iter : int, optional (default 10)
+        Used only when the method selected is 'rena'. Number of iterations of
+        the recursive neighbor agglomeration.
+
+    threshold : float in the open interval (0., 1.), optional (default 1e-7)
+        Used only when the method selected is 'rena'.
+        Threshold used to handle eccentricities.
+
     memory : instance of joblib.Memory or str
         Used to cache the masking process.
         By default, no caching is done. If a string is given, it is the
@@ -189,12 +236,13 @@ class Parcellations(MultiPCA):
           parcellations using KMeans or various Agglomerative methods.
 
         * This object uses spatially-constrained AgglomerativeClustering for
-          method='ward' or 'complete' or 'average'. Spatial connectivity matrix
+          method='ward' or 'complete' or 'average' and spatially-constrained
+          ReNA clustering for method='rena'. Spatial connectivity matrix
           (voxel-to-voxel) is built-in object which means no need of explicitly
           giving the matrix.
 
     """
-    VALID_METHODS = ['kmeans', 'ward', 'complete', 'average']
+    VALID_METHODS = ['kmeans', 'ward', 'complete', 'average', 'rena']
 
     def __init__(self, method, n_parcels=50,
                  random_state=0, mask=None, smoothing_fwhm=4.,
@@ -202,10 +250,15 @@ def __init__(self, method, n_parcels=50,
                  low_pass=None, high_pass=None, t_r=None,
                  target_affine=None, target_shape=None,
                  mask_strategy='epi', mask_args=None,
+                 scaling=False, n_iter=10, threshold=1e-7,
                  memory=Memory(cachedir=None),
                  memory_level=0, n_jobs=1, verbose=1):
+
         self.method = method
         self.n_parcels = n_parcels
+        self.scaling = scaling
+        self.n_iter = n_iter
+        self.threshold = threshold
 
         MultiPCA.__init__(self, n_components=200,
                           random_state=random_state,
@@ -262,7 +315,7 @@ def _raw_fit(self, data):
         # happening in Travis
         try:
             from nilearn import plotting
-        except:
+        except Exception:
             pass
 
         components = MultiPCA._raw_fit(self, data)
@@ -280,6 +333,18 @@ def _raw_fit(self, data):
                                      verbose=max(0, self.verbose - 1))
             labels = self._cache(_estimator_fit,
                                  func_memory_level=1)(components.T, kmeans)
+
+        elif self.method == 'rena':
+            rena = ReNA(mask_img_, n_clusters=self.n_parcels,
+                        scaling=self.scaling, n_iter=self.n_iter,
+                        threshold=self.threshold, memory=self.memory,
+                        memory_level=self.memory_level,
+                        verbose=max(0, self.verbose - 1))
+            method = 'rena'
+            labels = \
+                self._cache(_estimator_fit, func_memory_level=1)(components.T,
+                                                                 rena, method)
+
         else:
             mask_ = _safe_get_data(mask_img_).astype(np.bool)
             shape = mask_.shape
@@ -387,8 +452,7 @@ def fit_transform(self, imgs, confounds=None):
             Example, for single image shape will be
             (number of scans, number of labels)
         """
-        return self.fit(imgs, confounds=confounds).transform(imgs,
-                                                             confounds=confounds)
+        return self.fit(imgs, confounds=confounds).transform(imgs, confounds)
 
     def inverse_transform(self, signals):
         """Transform signals extracted from parcellations back to brain

From a34ed07c49e605273945f6ea6539a3915b686073 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 14:24:09 +0200
Subject: [PATCH 080/228] Modifying example accordingly

---
 .../plot_data_driven_parcellations.py         | 50 +++++++------------
 1 file changed, 17 insertions(+), 33 deletions(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index a6ab9425a5..4f92d96f0d 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -193,9 +193,10 @@
 # Brain parcellations with ReNA Clustering
 # ----------------------------------------
 #
-# The spatial constraints are implemented inside the ReNA object.
-# One interesting algorithmic property of ReNA is that it is very
-# fast for a large number of parcels (notably faster than Ward).
+# One interesting algorithmic property of ReNA (see References) is that
+# it is very fast for a large number of parcels (notably faster than Ward).
+# As before, the parcellation is done with a Parcellations object.
+# The spatial constraints are implemented inside the Parcellations object.
 #
 # References
 # ----------
@@ -207,43 +208,27 @@
 #       Structured Signals," in IEEE Transactions on Pattern Analysis and
 #       Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
 #       https://hal.archives-ouvertes.fr/hal-01366651/
-
-from nilearn.regions import ReNA
 start = time.time()
-rena = ReNA(n_clusters=5000, scaling=True)
+rena = Parcellations(method='rena', n_parcels=5000, standardize=False,
+                     smoothing_fwhm=2., scaling=True)
 
-rena.fit_transform(dataset.func[0])
+rena.fit_transform(dataset.func)
 print("ReNA 5000 clusters: %.2fs" % (time.time() - start))
 
 ##################################################################
 # Visualize: Brain parcellations (ReNA)
 # -------------------------------------
 #
-# First we display the labels of the clustering in the brain.
-#
-# To visualize results, we need to transform the clustering's labels back
-# to a neuroimaging volume. For this, we use the NiftiMasker's
-# inverse_transform method.
-
-# Avoid 0 label
-labels = rena.labels_
-# Shuffling the labels for visualization
-permutation = np.random.permutation(np.unique(labels).shape[0])
-labels = 1 + permutation[labels]
-# Obtain the masker
-nifti_masker = rena.masker_
-# Unmask the labels
-labels_img = nifti_masker.inverse_transform(labels)
+# First, we display the parcellations of the brain image stored in attribute
+# `labels_img_`
+rena_labels_img = rena.labels_img_
 
-##################################################################
-# labels_img is a Nifti1Image object, it can be saved to file with the
-# following code:
-labels_img.to_filename('rena_parcellation.nii.gz')
-plotting.plot_roi(labels_img, mean_func_img, title="ReNA parcellation",
-                  display_mode='xz')
+# Now, rena_labels_img are Nifti1Image object, it can be saved to file
+# with the following code:
+rena_labels_img.to_filename('rena_parcellation.nii.gz')
 
-# common cut coordinates for all plots
-cut_coords = first_plot.cut_coords
+plotting.plot_roi(ward_labels_img, title="ReNA parcellation",
+                  display_mode='xz', cut_coords=cut_coords)
 
 ##################################################################
 # Compressed representation of ReNA clustering
@@ -267,13 +252,12 @@
 # dimension.
 # However, the data are in one single large 4D image, we need to use
 # index_img to do the split easily:
-from nilearn.image import index_img
-fmri_reduced_rena = rena.transform(index_img(dataset.func[0], 0))
+fmri_reduced_rena = rena.transform(dataset.func)
 
 # Display the corresponding data compression using the parcellation
 compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
 
-plotting.plot_epi(compressed_img_rena, cut_coords=cut_coords,
+plotting.plot_epi(index_img(compressed_img_rena, 0), cut_coords=cut_coords,
                   title='ReNA compressed representation (5000 parcels)',
                   vmin=vmin, vmax=vmax, display_mode='xz')
 

From ca6de6596d37d47779abbfb17ee83116601db92e Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 17:16:56 +0200
Subject: [PATCH 081/228] updating doc for ReNA

---
 doc/modules/reference.rst | 1 +
 doc/whats_new.rst         | 5 +++++
 2 files changed, 6 insertions(+)

diff --git a/doc/modules/reference.rst b/doc/modules/reference.rst
index 894ed0df2b..87d4816632 100644
--- a/doc/modules/reference.rst
+++ b/doc/modules/reference.rst
@@ -269,6 +269,7 @@ uses.
 
    RegionExtractor
    Parcellations
+   ReNA
 
 
 :mod:`nilearn.mass_univariate`: Mass-univariate analysis
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 4f3783d2bf..ce3ec9a10d 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -3,6 +3,11 @@
 
 NEW
 ---
+- Parcellation method ReNA: Fast agglomerative clustering based on recursive
+  nearest neighbor grouping.
+  Yields very fast & accurate models, without creation of giant
+  clusters.
+  :class:`nilearn.regions.rena_clustering.ReNA`
 
 - Optimization to image resampling
   :func:`nilearn.image.resample_img` has been optimized to pad rather than

From efaab10aa897721cb87c14e9fb1c013c10cd0c6b Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 17:17:43 +0200
Subject: [PATCH 082/228] improving doc string in Parcellations to take into
 account rena

---
 nilearn/regions/parcellations.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 702b724720..8ff540720a 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -110,10 +110,13 @@ def _labels_masker_extraction(img, masker, confound):
 class Parcellations(MultiPCA):
     """Learn parcellations on fMRI images.
 
-    Four different types of clustering methods can be used such as kmeans,
-    ward, complete, average. Kmeans will call MiniBatchKMeans whereas
-    ward, complete, average are used within in Agglomerative Clustering.
-    All methods are leveraged from scikit-learn.
+    Five different types of clustering methods can be used:
+    kmeans, ward, complete, average and rena.
+    kmeans will call MiniBatchKMeans whereas
+    ward, complete, average are used within in Agglomerative Clustering and
+    rena will call ReNA.
+    kmeans, ward, complete, average are leveraged from scikit-learn.
+    rena is buit into nilearn.
 
     .. versionadded:: 0.4.1
 

From e8abba4dbc0746867307a8fd105f6808cae215a7 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Wed, 24 Apr 2019 17:33:19 +0200
Subject: [PATCH 083/228] Adding rena to the tests

---
 nilearn/regions/tests/test_parcellations.py | 24 ++++++++++++++-------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/nilearn/regions/tests/test_parcellations.py b/nilearn/regions/tests/test_parcellations.py
index df77cd68eb..584feb8f72 100644
--- a/nilearn/regions/tests/test_parcellations.py
+++ b/nilearn/regions/tests/test_parcellations.py
@@ -37,7 +37,7 @@ def test_parcellations_fit_on_single_nifti_image():
     data[4, 9, 3] = 2
     fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
 
-    methods = ['kmeans', 'ward', 'complete', 'average']
+    methods = ['kmeans', 'ward', 'complete', 'average', 'rena']
     n_parcels = [5, 10, 15]
     for n_parcel, method in zip(n_parcels, methods):
         parcellator = Parcellations(method=method, n_parcels=n_parcel)
@@ -47,7 +47,7 @@ def test_parcellations_fit_on_single_nifti_image():
         # Test object returns attribute masker_
         assert_true(parcellator.masker_ is not None)
         assert_true(parcellator.mask_img_ is not None)
-        if method != 'kmeans':
+        if method not in ['kmeans', 'rena']:
             # Test that object returns attribute connectivity_
             # only for AgglomerativeClustering methods
             assert_true(parcellator.connectivity_ is not None)
@@ -76,6 +76,10 @@ def test_parcellations_fit_on_multi_nifti_images():
     parcellator.fit(fmri_imgs)
     assert_true(parcellator.labels_img_ is not None)
 
+    parcellator = Parcellations(method='rena', n_parcels=5)
+    parcellator.fit(fmri_imgs)
+    assert_true(parcellator.labels_img_ is not None)
+
     # Smoke test with explicit mask image
     mask_img = np.ones((10, 11, 12))
     mask_img = nibabel.Nifti1Image(mask_img, np.eye(4))
@@ -88,6 +92,10 @@ def test_parcellations_fit_on_multi_nifti_images():
                                 mask=mask_img)
     parcellator.fit(fmri_imgs)
 
+    parcellator = Parcellations(method='rena', n_parcels=5,
+                                mask=mask_img)
+    parcellator.fit(fmri_imgs)
+
 
 def test_parcellations_transform_single_nifti_image():
     # Test with NiftiLabelsMasker extraction of timeseries data
@@ -102,7 +110,7 @@ def test_parcellations_transform_single_nifti_image():
 
     fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
 
-    for method in ['kmeans', 'ward', 'complete', 'average']:
+    for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
         parcellator = Parcellations(method=method, n_parcels=parcels)
         parcellator.fit(fmri_img)
         # transform to signals
@@ -125,7 +133,7 @@ def test_parcellations_transform_multi_nifti_images():
     fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
     fmri_imgs = [fmri_img, fmri_img, fmri_img]
 
-    for method in ['kmeans', 'ward', 'complete', 'average']:
+    for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
         parcellator = Parcellations(method=method, n_parcels=parcels)
         parcellator.fit(fmri_imgs)
         # transform multi images to signals. In return, we have length
@@ -184,7 +192,7 @@ def test_parcellations_transform_with_multi_confounds_multi_images():
     confounds = rng.randn(*(10, 3))
     confounds_list = (confounds, confounds, confounds)
 
-    for method in ['kmeans', 'ward', 'complete', 'average']:
+    for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
         parcellator = Parcellations(method=method, n_parcels=5)
         parcellator.fit(fmri_imgs)
 
@@ -207,11 +215,11 @@ def test_fit_transform():
     confounds = rng.randn(*(10, 3))
     confounds_list = [confounds, confounds, confounds]
 
-    for method in ['kmeans', 'ward', 'complete', 'average']:
+    for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
         parcellator = Parcellations(method=method, n_parcels=5)
         signals = parcellator.fit_transform(fmri_imgs)
         assert_true(parcellator.labels_img_ is not None)
-        if method != 'kmeans':
+        if method not in ['kmeans', 'rena']:
             assert_true(parcellator.connectivity_ is not None)
         assert_true(parcellator.masker_ is not None)
         # fit_transform with confounds
@@ -227,7 +235,7 @@ def test_inverse_transform_single_nifti_image():
     data[9, 10, 11] = 3
 
     fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
-    methods = ['kmeans', 'ward', 'complete', 'average']
+    methods = ['kmeans', 'ward', 'complete', 'average', 'rena']
 
     for method in methods:
         parcellate = Parcellations(method=method, n_parcels=5)

From c8a0b8559a31cdcfcce462ca5be617424d631d21 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 29 Apr 2019 16:29:38 +0200
Subject: [PATCH 084/228] updating whats_new.rst

---
 doc/whats_new.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index da6b015141..46ebc2c495 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -7,7 +7,7 @@ NEW
   nearest neighbor grouping.
   Yields very fast & accurate models, without creation of giant
   clusters.
-  :class:`nilearn.regions.rena_clustering.ReNA`
+  :class:`nilearn.regions.ReNA`
 
 - Optimization to image resampling
   :func:`nilearn.image.resample_img` has been optimized to pad rather than
@@ -15,7 +15,7 @@ NEW
   between two spaces. This is a common case in :class:`nilearn.input_data.NiftiMasker`
   when using the `mask_strategy="template"` option for brains in MNI space.
 - New brain development fMRI dataset fetcher
-  :func:`nilearn.datasets.fetch_development_fmri` can be used to download
+  :func:`nilearn.datashets.fetch_development_fmri` can be used to download
   movie-watching data in children and adults. A light-weight dataset implemented
   for teaching and usage in the examples.
 

From 45f00ee9a511996c93ebbd9092f7e42e5c958525 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 29 Apr 2019 18:08:13 +0200
Subject: [PATCH 085/228] fixing whats_new.rst

---
 doc/whats_new.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 46ebc2c495..bd1d8f0222 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -15,9 +15,9 @@ NEW
   between two spaces. This is a common case in :class:`nilearn.input_data.NiftiMasker`
   when using the `mask_strategy="template"` option for brains in MNI space.
 - New brain development fMRI dataset fetcher
-  :func:`nilearn.datashets.fetch_development_fmri` can be used to download
-  movie-watching data in children and adults. A light-weight dataset implemented
-  for teaching and usage in the examples.
+  :func:`nilearn.datasets.fetch_development_fmri` can be used to download
+  movie-watching data in children and adults. A light-weight dataset
+  implemented for teaching and usage in the examples.
 
 Changes
 -------

From 2416b10c04d5c7ed1eb0969a6d372c958e924533 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 2 May 2019 14:31:17 +0200
Subject: [PATCH 086/228] make html templates valid

---
 .../data/html/connectome_plot_template.html      |  5 +++--
 .../plotting/data/html/stat_map_template.html    | 16 +++++++++-------
 nilearn/plotting/html_stat_map.py                |  4 ++--
 3 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 92a72a5ddc..48deaa7d74 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -3,7 +3,8 @@
 
 <head>
     <title>connectome plot</title>
-    <meta charset="UTF-8" /> $INSERT_JS_LIBRARIES_HERE
+    <meta charset="UTF-8" />
+    $INSERT_JS_LIBRARIES_HERE
     <script>
         var connectomeInfo = $INSERT_CONNECTOME_JSON_HERE;
         var data = [];
@@ -155,7 +156,7 @@
         <option value="bottom">view: Bottom</option>
         <option value="custom">view: -</option>
     </select>
-    <input id="opacity-range" type="range" min="0" max="100" value="30"></input>
+    <input id="opacity-range" type="range" min="0" max="100" value="30"/>
 
 </body>
 
diff --git a/nilearn/plotting/data/html/stat_map_template.html b/nilearn/plotting/data/html/stat_map_template.html
index 7816d676fa..7b7393b5fc 100644
--- a/nilearn/plotting/data/html/stat_map_template.html
+++ b/nilearn/plotting/data/html/stat_map_template.html
@@ -1,24 +1,26 @@
 <!DOCTYPE html>
-<html>
-
+<html lang="en">
 <head>
+    <title>slice viewer</title>
+    <meta charset="UTF-8" />
 </head>
 <body>
   <div id="div_viewer">
     <!-- this is the canvas that will feature the brain slices -->
     <canvas id="3Dviewer">
     <!-- load a hidden version of the sprite image that includes all (sagital) brain slices -->
-    <img id="spriteImg" class="hidden" src="data:image/png;base64, $bg_base64" alt="background" />
+    <img id="spriteImg" class="hidden" src="data:image/png;base64,$bg_base64" alt="background" />
     <!-- the colormap -->
-    <img id="colorMap" class="hidden" src="data:image/png;base64, $cm_base64" alt="colormap">
+    <img id="colorMap" class="hidden" src="data:image/png;base64,$cm_base64" alt="colormap">
     <!-- another sprite image, with an overlay-->
-    <img id="overlayImg" class="hidden" src="data:image/png;base64, $stat_map_base64" alt="overlay">
+    <img id="overlayImg" class="hidden" src="data:image/png;base64,$stat_map_base64" alt="overlay">
+    </canvas>
   </div>
 
-  <script type="text/javascript" >
+  <script>
     $js_jquery
   </script>
-  <script type="text/javascript">
+  <script>
     $js_brainsprite
   </script>
 
diff --git a/nilearn/plotting/html_stat_map.py b/nilearn/plotting/html_stat_map.py
index 7c4e90abdf..fb8e762b10 100644
--- a/nilearn/plotting/html_stat_map.py
+++ b/nilearn/plotting/html_stat_map.py
@@ -5,6 +5,7 @@
 import json
 import warnings
 from io import BytesIO
+from base64 import b64encode
 
 import numpy as np
 from matplotlib.image import imsave
@@ -20,7 +21,6 @@
 from .._utils.param_validation import check_threshold
 from .._utils.extmath import fast_abs_percentile
 from .._utils.niimg import _safe_get_data
-from .._utils.compat import _encodebytes
 from ..datasets import load_mni152_template
 
 
@@ -110,7 +110,7 @@ def _bytesIO_to_base64(handle_io):
         Returns: data
     """
     handle_io.seek(0)
-    data = _encodebytes(handle_io.read()).decode('utf-8')
+    data = b64encode(handle_io.read()).decode('utf-8')
     handle_io.close()
     return data
 

From d115d8658f6bf15dc52dcf11dc6a60ad47941ded Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 2 May 2019 19:37:18 +0200
Subject: [PATCH 087/228] minor documentation changes

---
 .../plot_data_driven_parcellations.py              | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 4f92d96f0d..552230d765 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -2,7 +2,7 @@
 Clustering methods to learn a brain parcellation from fMRI
 ==========================================================
 
-We use spatially-constrained Ward-clustering, KMeans and Recursive Neighbor
+We use spatially-constrained Ward-clustering, KMeans, and Recursive Neighbor
 Agglomeration (ReNA) to create a set of parcels.
 
 In a high dimensional regime, these methods can be interesting
@@ -92,7 +92,7 @@
 
 ###########################################################################
 # Visualize: Brain parcellations (Ward)
-# -------------------------------------
+# .....................................
 #
 # First, we display the parcellations of the brain image stored in attribute
 # `labels_img_`
@@ -113,7 +113,7 @@
 
 ###########################################################################
 # Compressed representation of Ward clustering
-# --------------------------------------------
+# ............................................
 #
 # Second, we illustrate the effect that the clustering has on the signal.
 # We show the original data, and the approximation provided by the
@@ -176,7 +176,7 @@
 
 ###########################################################################
 # Visualize: Brain parcellations (KMeans)
-# ---------------------------------------
+# .......................................
 #
 # Grab parcellations of brain image stored in attribute `labels_img_`
 kmeans_labels_img = kmeans.labels_img_
@@ -199,7 +199,7 @@
 # The spatial constraints are implemented inside the Parcellations object.
 #
 # References
-# ----------
+# ..........
 #
 # More about ReNA clustering algorithm in the original paper
 #
@@ -217,7 +217,7 @@
 
 ##################################################################
 # Visualize: Brain parcellations (ReNA)
-# -------------------------------------
+# .....................................
 #
 # First, we display the parcellations of the brain image stored in attribute
 # `labels_img_`
@@ -232,7 +232,7 @@
 
 ##################################################################
 # Compressed representation of ReNA clustering
-# --------------------------------------------
+# ............................................
 #
 # We illustrate the effect that the clustering has on the signal.
 # We show the original data, and the approximation provided by

From 59b58b59ff5a8ec61f1190ce0a5dea1324e0d937 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 2 May 2019 19:37:40 +0200
Subject: [PATCH 088/228] improving doc string

---
 nilearn/regions/parcellations.py | 85 ++++++++++++++++----------------
 1 file changed, 42 insertions(+), 43 deletions(-)

diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 8ff540720a..7213addb80 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -20,18 +20,18 @@ def _estimator_fit(data, estimator, method=None):
 
     Parameters
     ----------
-    data : numpy array
+    data: numpy array
         Data matrix
 
-    estimator : instance of estimator from sklearn
+    estimator: instance of estimator from sklearn
         MiniBatchKMeans or AgglomerativeClustering
 
-    method : str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
+    method: str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
         A method to choose between for brain parcellations.
 
     Returns
     -------
-    labels_ : numpy.ndarray
+    labels_: numpy.ndarray
         labels_ estimated from estimator
     """
     if method == 'rena':
@@ -87,19 +87,19 @@ def _labels_masker_extraction(img, masker, confound):
 
     Parameters
     ----------
-    img : 4D Nifti image like object
+    img: 4D Nifti image like object
         Image to process.
 
-    masker : instance of NiftiLabelsMasker
+    masker: instance of NiftiLabelsMasker
         Used for extracting signals with fit_transform
 
-    confound : csv file or numpy array
+    confound: csv file or numpy array
         Confound used for signal cleaning while extraction.
         Passed to signal.clean
 
     Returns
     -------
-    signals : numpy array
+    signals: numpy array
         Signals extracted on given img
     """
     masker = clone(masker)
@@ -122,16 +122,21 @@ class Parcellations(MultiPCA):
 
     Parameters
     ----------
-    method : str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
+    method: str, {'kmeans', 'ward', 'complete', 'average', 'rena'}
         A method to choose between for brain parcellations.
+        For a small number of parcels, kmeans is usually advisable.
+        For a large number of parcellations (several hundreds, or thousands),
+        ward and rena are the best options. Ward will give higher quality
+        parcels, but with increased computation time. ReNA is most useful as a
+        fast data-reduction step, typically dividing the signal size by ten.
 
-    n_parcels : int, default=50
+    n_parcels: int, default=50
         Number of parcellations to divide the brain data into.
 
-    random_state : int or RandomState
+    random_state: int or RandomState
         Pseudo number generator state used for random sampling.
 
-    mask : Niimg-like object or NiftiMasker, MultiNiftiMasker instance
+    mask: Niimg-like object or NiftiMasker, MultiNiftiMasker instance
         Mask/Masker used for masking the data.
         If mask image if provided, it will be used in the MultiNiftiMasker.
         If an instance of MultiNiftiMasker is provided, then this instance
@@ -140,15 +145,15 @@ class Parcellations(MultiPCA):
         If None, mask will be automatically computed by a MultiNiftiMasker
         with default parameters.
 
-    smoothing_fwhm : float, optional default=4.
+    smoothing_fwhm: float, optional default=4.
         If smoothing_fwhm is not None, it gives the full-width half maximum in
         millimeters of the spatial smoothing to apply to the signal.
 
-    standardize : boolean, optional
+    standardize: boolean, optional
         If standardize is True, the time-series are centered and normed:
         their mean is put to 0 and their variance to 1 in the time dimension.
 
-    detrend : boolean, optional
+    detrend: boolean, optional
         Whether to detrend signals or not.
         This parameter is passed to signal.clean. Please see the related
         documentation for details
@@ -161,16 +166,16 @@ class Parcellations(MultiPCA):
         This parameter is passed to signal.clean. Please see the related
         documentation for details
 
-    t_r : float, optional
+    t_r: float, optional
         This parameter is passed to signal.clean. Please see the related
         documentation for details
 
-    target_affine : 3x3 or 4x4 matrix, optional
+    target_affine: 3x3 or 4x4 matrix, optional
         This parameter is passed to image.resample_img. Please see the
         related documentation for details. The given affine will be
         considered as same for all given list of images.
 
-    target_shape : 3-tuple of integers, optional
+    target_shape: 3-tuple of integers, optional
         This parameter is passed to image.resample_img. Please see the
         related documentation for details.
 
@@ -190,44 +195,40 @@ class Parcellations(MultiPCA):
         to fine-tune mask computation. Please see the related documentation
         for details.
 
-    scaling : bool, optional (default False)
+    scaling: bool, optional (default False)
         Used only when the method selected is 'rena'. If scaling is True, each
         cluster is scaled by the square root of its size, preserving the
         l2-norm of the image.
 
-    n_iter : int, optional (default 10)
+    n_iter: int, optional (default 10)
         Used only when the method selected is 'rena'. Number of iterations of
         the recursive neighbor agglomeration.
 
-    threshold : float in the open interval (0., 1.), optional (default 1e-7)
-        Used only when the method selected is 'rena'.
-        Threshold used to handle eccentricities.
-
-    memory : instance of joblib.Memory or str
+    memory: instance of joblib.Memory or str
         Used to cache the masking process.
         By default, no caching is done. If a string is given, it is the
         path to the caching directory.
 
-    memory_level : integer, optional
+    memory_level: integer, optional
         Rough estimator of the amount of memory used by caching. Higher value
         means more memory for caching.
 
-    n_jobs : integer, optional
+    n_jobs: integer, optional
         The number of CPUs to use to do the computation. -1 means
         'all CPUs', -2 'all CPUs but one', and so on.
 
-    verbose : integer, optional
+    verbose: integer, optional
         Indicate the level of verbosity. By default, nothing is printed.
 
     Returns
     -------
-    labels_img_ : Nifti1Image
+    labels_img_: Nifti1Image
         Labels image to each parcellation learned on fmri images.
 
-    masker_ : instance of NiftiMasker or MultiNiftiMasker
+    masker_: instance of NiftiMasker or MultiNiftiMasker
         The masker used to mask the data
 
-    connectivity_ : numpy.ndarray
+    connectivity_: numpy.ndarray
         voxel-to-voxel connectivity matrix computed from a mask.
         Note that this attribute is only seen if selected methods are
         Agglomerative Clustering type, 'ward', 'complete', 'average'.
@@ -253,7 +254,7 @@ def __init__(self, method, n_parcels=50,
                  low_pass=None, high_pass=None, t_r=None,
                  target_affine=None, target_shape=None,
                  mask_strategy='epi', mask_args=None,
-                 scaling=False, n_iter=10, threshold=1e-7,
+                 scaling=False, n_iter=10,
                  memory=Memory(cachedir=None),
                  memory_level=0, n_jobs=1, verbose=1):
 
@@ -261,7 +262,6 @@ def __init__(self, method, n_parcels=50,
         self.n_parcels = n_parcels
         self.scaling = scaling
         self.n_iter = n_iter
-        self.threshold = threshold
 
         MultiPCA.__init__(self, n_components=200,
                           random_state=random_state,
@@ -286,15 +286,15 @@ def _raw_fit(self, data):
 
         Parameters
         ----------
-        data : ndarray
+        data: ndarray
             Shape (n_samples, n_features)
 
         Returns
         -------
-        labels_ : numpy.ndarray
+        labels_: numpy.ndarray
             Labels to each cluster in the brain.
 
-        connectivity_ : numpy.ndarray
+        connectivity_: numpy.ndarray
             voxel-to-voxel connectivity matrix computed from a mask.
             Note that, this attribute is returned only for selected methods
             such as 'ward', 'complete', 'average'.
@@ -340,8 +340,7 @@ def _raw_fit(self, data):
         elif self.method == 'rena':
             rena = ReNA(mask_img_, n_clusters=self.n_parcels,
                         scaling=self.scaling, n_iter=self.n_iter,
-                        threshold=self.threshold, memory=self.memory,
-                        memory_level=self.memory_level,
+                        memory=self.memory, memory_level=self.memory_level,
                         verbose=max(0, self.verbose - 1))
             method = 'rena'
             labels = \
@@ -383,7 +382,7 @@ def transform(self, imgs, confounds=None):
 
         Parameters
         ----------
-        imgs : List of Nifti-like images
+        imgs: List of Nifti-like images
             See http://nilearn.github.io/manipulating_images/input_output.html.
             Images to process.
 
@@ -435,11 +434,11 @@ def fit_transform(self, imgs, confounds=None):
 
         Parameters
         ----------
-        imgs : List of Nifti-like images
+        imgs: List of Nifti-like images
             See http://nilearn.github.io/manipulating_images/input_output.html.
             Images for process for fit as well for transform to signals.
 
-        confounds : List of CSV files or arrays-like, optional
+        confounds: List of CSV files or arrays-like, optional
             Each file or numpy array in a list should have shape
             (number of scans, number of confounds).
             This parameter is passed to signal.clean. Given confounds
@@ -465,12 +464,12 @@ def inverse_transform(self, signals):
 
         Parameters
         ----------
-        signals : List of 2D numpy.ndarray
+        signals: List of 2D numpy.ndarray
             Each 2D array with shape (number of scans, number of regions)
 
         Returns
         -------
-        imgs : List of or Nifti-like image
+        imgs: List of or Nifti-like image
             Brain image(s)
         """
         from .signal_extraction import signals_to_img_labels

From 98d12adb96ef8ce7d9a892bf507783384a7b40c4 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 2 May 2019 19:38:00 +0200
Subject: [PATCH 089/228] improving doc string

---
 nilearn/regions/rena_clustering.py | 126 ++++++++++++++---------------
 1 file changed, 62 insertions(+), 64 deletions(-)

diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index fe6972331c..f3974f1974 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -1,5 +1,5 @@
 """Recursive Neighbor Agglomeration (ReNA):
-    fastclustering for approximation of structured signals
+fastclustering for approximation of structured signals
 """
 # Author: Andres Hoyos idrobo, Gael Varoquaux, Jonas Kahn and  Bertrand Thirion
 # License: simplified BSD
@@ -7,8 +7,10 @@
 import numpy as np
 import warnings
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
-from sklearn.externals.joblib import Memory
-from sklearn.externals import six
+try:
+    from sklearn.externals.joblib import Memory
+except ImportError:
+    from joblib import Memory
 from sklearn.base import TransformerMixin, ClusterMixin
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted
@@ -25,14 +27,14 @@ def _compute_weights(X, mask_img):
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    mask_img : Niimg-like object used for masking the data.
+    mask_img: Niimg-like object used for masking the data.
 
     Returns
     -------
-    weights : ndarray
+    weights: ndarray
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
@@ -61,16 +63,16 @@ def _make_3d_edges(vertices, is_mask):
 
     Parameters
     ----------
-    vertices : ndarray
+    vertices: ndarray
         The indices of the voxels.
 
-    is_mask : boolean
+    is_mask: boolean
         If is_mask is true, it returns the mask of edges.
         Retruns 1 if the edge is contained in the mask, 0 otherwise.
 
     Returns
     -------
-    edges : ndarray
+    edges: ndarray
         Edges corresponding to the image or mask.
         shape: (1, n_edges) if_mask,
                (2, n_edges) otherwise.
@@ -101,17 +103,17 @@ def _make_edges_and_weights(X, mask_img):
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    mask_img : Niimg-like object used for masking the data.
+    mask_img: Niimg-like object used for masking the data.
 
     Returns
     -------
-    edges : ndarray
+    edges: ndarray
         Array containing [edges_deep, edges_right, edges_down]
 
-    weights : ndarray
+    weights: ndarray
         Weights corresponding to all edges in the mask.
         shape: (n_edges,)
     """
@@ -145,14 +147,14 @@ def weighted_connectivity_graph(X, mask_img):
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    mask_img : Niimg-like object used for masking the data.
+    mask_img: Niimg-like object used for masking the data.
 
     Returns
     -------
-    connectivity : a sparse COO matrix
+    connectivity: a CSR matrix
         sparse matrix representation of the weighted adjacency graph
     """
     n_features = X.shape[1]
@@ -173,16 +175,15 @@ def _nn_connectivity(connectivity, threshold=1e-7):
 
     Parameters
     ----------
-    connectivity : a sparse matrix in COOrdinate format.
+    connectivity: a sparse matrix in COOrdinate format.
         sparse matrix representation of the weighted adjacency graph
 
-    threshold : float in the close interval [0, 1], optional (default 1e-7)
+    threshold: float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
-        In practice it is 1e-7.
 
     Returns
     -------
-    nn_connectivity : a sparse matrix in COOrdinate format.
+    nn_connectivity: a sparse matrix in COOrdinate format.
     """
     n_features = connectivity.shape[0]
 
@@ -220,27 +221,26 @@ def _reduce_data_and_connectivity(X, labels, n_components, connectivity,
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    labels : ndarray
+    labels: ndarray
         Containts the label assignation for each voxel.
 
-    n_components : int
+    n_components: int
         The number of clusters in the current iteration.
 
-    connectivity : a sparse matrix in COOrdinate format.
+    connectivity: a sparse matrix in COOrdinate format.
         sparse matrix representation of the weighted adjacency graph
 
-    threshold : float in the close interval [0, 1], optional (default 1e-7)
+    threshold: float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
-        In practice it is 1e-7.
 
     Returns
     -------
-    reduced_connectivity : a sparse matrix in COOrdinate format.
+    reduced_connectivity: a sparse matrix in COOrdinate format.
 
-    reduced_X : ndarray
+    reduced_X: ndarray
         Data reduced with agglomerated signal for each cluster
     """
     n_features = len(labels)
@@ -279,27 +279,26 @@ def nearest_neighbor_grouping(X, connectivity, n_clusters, threshold=1e-7):
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    connectivity : a sparse matrix in COOrdinate format.
+    connectivity: a sparse matrix in COOrdinate format.
         sparse matrix representation of the weighted adjacency graph
 
-    n_clusters : int
+    n_clusters: int
         The number of clusters to find.
 
-    threshold : float in the close interval [0, 1], optional (default 1e-7)
+    threshold: float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
-        In practice it is 1e-7.
 
     Returns
     -------
-    reduced_connectivity : a sparse matrix in COOrdinate format.
+    reduced_connectivity: a sparse matrix in COOrdinate format.
 
-    reduced_X : ndarray
+    reduced_X: ndarray
         Data reduced with agglomerated signal for each cluster
 
-    labels : ndarray, shape = [n_features]
+    labels: ndarray, shape = [n_features]
         It contains the clusters assignation.
     """
     # Nearest neighbor conenctivity
@@ -343,30 +342,29 @@ def recursive_neighbor_agglomeration(X, mask_img, n_clusters,
 
     Parameters
     ----------
-    X : ndarray, shape = [n_samples, n_features]
+    X: ndarray, shape = [n_samples, n_features]
         Training data.
 
-    mask_img : Niimg-like object used for masking the data.
+    mask_img: Niimg-like object used for masking the data.
 
-    n_clusters : int
+    n_clusters: int
         The number of clusters to find.
 
-    n_iter : int, optional (default 10)
+    n_iter: int, optional (default 10)
         Number of iterations.
 
-    threshold : float in the close interval [0, 1], optional (default 1e-7)
+    threshold: float in the close interval [0, 1], optional (default 1e-7)
         The treshold is setted to handle eccentricities.
-        In practice it is 1e-7.
 
-    verbose : int, optional (default 1)
+    verbose: int, optional (default 1)
         Verbosity level.
 
     Returns
     -------
-    n_components : int
+    n_components: int
         Number of clusters.
 
-    labels : ndarray, shape = [n_features]
+    labels: ndarray, shape = [n_features]
         Cluster assignation.
 
     References
@@ -409,42 +407,42 @@ class ReNA(BaseEstimator, ClusterMixin, TransformerMixin):
 
     Parameters
     ----------
-    mask_img : Niimg-like object used for masking the data.
+    mask_img: Niimg-like object used for masking the data.
 
-    n_clusters : int, optional (default 2)
+    n_clusters: int, optional (default 2)
         The number of clusters to find.
 
-    scaling : bool, optional (default False)
+    scaling: bool, optional (default False)
         If scaling is True, each cluster is scaled by the square root of its
         size, preserving the l2-norm of the image.
 
-    n_iter : int, optional (default 10)
+    n_iter: int, optional (default 10)
         Number of iterations of the recursive neighbor agglomeration
 
-    threshold : float in the open interval (0., 1.), optional (default 1e-7)
+    threshold: float in the open interval (0., 1.), optional (default 1e-7)
         Threshold used to handle eccentricities.
 
-    memory : instance of joblib.Memory or string
+    memory: instance of joblib.Memory or string
         Used to cache the masking process.
         By default, no caching is done. If a string is given, it is the
         path to the caching directory.
 
-    memory_level : integer, optional (default 1)
+    memory_level: integer, optional (default 1)
         Rough estimator of the amount of memory used by caching. Higher value
         means more memory for caching.
 
-    verbose : int, optional (default 1)
+    verbose: int, optional (default 1)
         Verbosity level.
 
     Attributes
     ----------
-    `labels_ ` : ndarray, shape = [n_features]
+    `labels_ `: ndarray, shape = [n_features]
         cluster labels for each feature.
 
-    `n_clusters_` : int
+    `n_clusters_`: int
         Number of clusters.
 
-    `sizes_` : ndarray, shape = [n_features]
+    `sizes_`: ndarray, shape = [n_features]
         It contains the size of each cluster.
 
     References
@@ -471,13 +469,13 @@ def fit(self, X, y=None):
 
         Parameters
         ----------
-        X : ndarray, shape = [n_samples, n_features]
+        X: ndarray, shape = [n_samples, n_features]
             Training data.
-        y : Ignored
+        y: Ignored
 
         Returns
         -------
-        self : `ReNA` object
+        self: `ReNA` object
         """
 
         X = check_array(X, ensure_min_features=2, ensure_min_samples=2,
@@ -489,7 +487,7 @@ def fit(self, X, y=None):
                              "object. Instead a %s object was provided."
                              % type(self.mask_img))
 
-        if self.memory is None or isinstance(self.memory, six.string_types):
+        if self.memory is None or isinstance(self.memory, str):
             self.memory_ = Memory(cachedir=self.memory,
                                   verbose=max(0, self.verbose - 1))
         else:
@@ -530,12 +528,12 @@ def transform(self, X, y=None):
 
         Parameters
         ----------
-        X : ndarray, shape = [n_samples, n_features]
+        X: ndarray, shape = [n_samples, n_features]
             Data to transform with the fitted clustering.
 
         Returns
         -------
-        X_red : ndarray, shape = [n_samples, n_clusters]
+        X_red: ndarray, shape = [n_samples, n_clusters]
             Data reduced with agglomerated signal for each cluster
         """
 
@@ -560,12 +558,12 @@ def inverse_transform(self, X_red):
 
         Parameters
         ----------
-        X_red : ndarray , shape = [n_samples, n_clusters]
+        X_red: ndarray , shape = [n_samples, n_clusters]
             Data reduced with agglomerated signal for each cluster
 
         Returns
         -------
-        X_inv : ndarray, shape = [n_samples, n_features]
+        X_inv: ndarray, shape = [n_samples, n_features]
             Data reduced expanded to the original feature space
         """
 

From d774c629a4f688052d826c3ba5414e82e3074e78 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Thu, 2 May 2019 19:38:36 +0200
Subject: [PATCH 090/228] managing Memory import

---
 nilearn/regions/tests/test_rena_clustering.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index 8f236b29ec..d491c29795 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,6 +1,9 @@
 import numpy as np
 from nose.tools import assert_equal, assert_not_equal, assert_raises
-from sklearn.externals.joblib import Memory
+try:
+    from sklearn.externals.joblib import Memory
+except ImportError:
+    from joblib import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
 from nilearn.regions.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker

From 3b711e9be5acd896350188689882b495d75035f9 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 3 May 2019 00:03:41 +0200
Subject: [PATCH 091/228] fix test

---
 nilearn/plotting/tests/test_html_stat_map.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/tests/test_html_stat_map.py b/nilearn/plotting/tests/test_html_stat_map.py
index 38a6baa6f1..fbf55cb5d1 100644
--- a/nilearn/plotting/tests/test_html_stat_map.py
+++ b/nilearn/plotting/tests/test_html_stat_map.py
@@ -115,7 +115,7 @@ def test_save_sprite():
     sprite_base64 = html_stat_map._bytesIO_to_base64(sprite_io)
 
     # Check the sprite is correct
-    assert sprite_base64 == '////AP////8=\n'
+    assert sprite_base64 == '////AP////8='
 
 
 def test_save_cmap():
@@ -130,7 +130,7 @@ def test_save_cmap():
     cmap_base64 = html_stat_map._bytesIO_to_base64(cmap_io)
 
     # Check the colormap is correct
-    assert cmap_base64 == '//////////8=\n'
+    assert cmap_base64 == '//////////8='
 
 
 def test_mask_stat_map():

From 16d496245cedce22fbaf8c2725fab8280c506e9d Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Fri, 3 May 2019 13:54:50 +0200
Subject: [PATCH 092/228] typo

---
 nilearn/regions/rena_clustering.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index f3974f1974..e3dfaa5aa9 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -198,7 +198,7 @@ def _nn_connectivity(connectivity, threshold=1e-7):
 
     connectivity_ = inv_max * connectivity_
 
-    # Dealing with eccentricities, there are probably many neares neighbors
+    # Dealing with eccentricities, there are probably many nearest neighbors
     edge_mask = connectivity_.data > 1 - threshold
 
     j_idx = connectivity_.nonzero()[1][edge_mask]

From 06203b36a8612329bd468deefecae1e2eb724012 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 6 May 2019 15:49:17 +0200
Subject: [PATCH 093/228] Improving doc string

---
 nilearn/regions/parcellations.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 7213addb80..33a998b7fc 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -220,8 +220,8 @@ class Parcellations(MultiPCA):
     verbose: integer, optional
         Indicate the level of verbosity. By default, nothing is printed.
 
-    Returns
-    -------
+    Attributes
+    ----------
     labels_img_: Nifti1Image
         Labels image to each parcellation learned on fmri images.
 

From 149bb3dde391ca0a71fbc7912c8621df96f3eac8 Mon Sep 17 00:00:00 2001
From: jerome-alexis_chevalier <jerome-alexis.chevalier@inria.fr>
Date: Mon, 6 May 2019 16:06:26 +0200
Subject: [PATCH 094/228] improving doc string

---
 nilearn/regions/parcellations.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 33a998b7fc..1c4932ce9e 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -222,13 +222,13 @@ class Parcellations(MultiPCA):
 
     Attributes
     ----------
-    labels_img_: Nifti1Image
+    `labels_img_`: Nifti1Image
         Labels image to each parcellation learned on fmri images.
 
-    masker_: instance of NiftiMasker or MultiNiftiMasker
+    `masker_`: instance of NiftiMasker or MultiNiftiMasker
         The masker used to mask the data
 
-    connectivity_: numpy.ndarray
+    `connectivity_`: numpy.ndarray
         voxel-to-voxel connectivity matrix computed from a mask.
         Note that this attribute is only seen if selected methods are
         Agglomerative Clustering type, 'ward', 'complete', 'average'.
@@ -291,10 +291,10 @@ def _raw_fit(self, data):
 
         Returns
         -------
-        labels_: numpy.ndarray
+        labels: numpy.ndarray
             Labels to each cluster in the brain.
 
-        connectivity_: numpy.ndarray
+        connectivity: numpy.ndarray
             voxel-to-voxel connectivity matrix computed from a mask.
             Note that, this attribute is returned only for selected methods
             such as 'ward', 'complete', 'average'.

From eb31a98a172c7fc665caf5b32b78cbe77c2f7ebd Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 10 May 2019 19:24:24 +0200
Subject: [PATCH 095/228] whatsnew

---
 doc/whats_new.rst | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index bf4dd00c7a..7e30342fcd 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -113,6 +113,9 @@ Changes
   argument to control whether the colormap is centered around 0 and a `vmin`
   argument.
 
+- Users can now control the size and fontsize of colorbars in interactive
+  surface and connectome plots, or disable the colorbar alltogether.
+
 Fixes
 -----
 

From 3e9a5387ca779d93bf45c1ad55040d87a37e314e Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 14 May 2019 12:35:18 +0200
Subject: [PATCH 096/228] turn print into warning and don't warn when filename
 is specified in open_in_browser

---
 nilearn/plotting/js_plotting_utils.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index cad247b36d..081aef6465 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -167,14 +167,19 @@ def open_in_browser(self, file_name=None, temp_file_lifetime=30):
         if file_name is None:
             fd, file_name = tempfile.mkstemp('.html', 'nilearn_surface_plot_')
             os.close(fd)
+            named_file = False
+        else:
+            named_file = True
         self.save_as_html(file_name)
         self._temp_file = file_name
         file_size = os.path.getsize(file_name) / 1e6
         if temp_file_lifetime is None:
-            print(("Saved HTML in temporary file: {}\n"
-                   "file size is {:.1f}M, delete it when you're done, "
-                   "for example by calling this.remove_temp_file").format(
-                       file_name, file_size))
+            if not named_file:
+                warnings.warn(
+                    ("Saved HTML in temporary file: {}\n"
+                     "file size is {:.1f}M, delete it when you're done, "
+                     "for example by calling this.remove_temp_file").format(
+                         file_name, file_size))
         else:
             _remove_after_n_seconds(self._temp_file, temp_file_lifetime)
         webbrowser.open('file://{}'.format(file_name))

From 8656e8a66ad0c79db512df9677da56bd64644d94 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 14 May 2019 14:37:26 +0200
Subject: [PATCH 097/228] update whatsnew

---
 doc/whats_new.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 7e30342fcd..e3ed2da13b 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -114,7 +114,7 @@ Changes
   argument.
 
 - Users can now control the size and fontsize of colorbars in interactive
-  surface and connectome plots, or disable the colorbar alltogether.
+  surface and connectome plots, or disable the colorbar.
 
 Fixes
 -----

From 1232b08b8fc28eefc3a7d604a156cacef692d050 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 14 May 2019 16:43:33 +0200
Subject: [PATCH 098/228] cbar -> colorbar

---
 nilearn/plotting/html_connectome.py | 20 ++++++++++----------
 nilearn/plotting/html_surface.py    | 22 +++++++++++-----------
 2 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 6d069f8da0..bc97179619 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -1,6 +1,4 @@
-import functools
 import json
-import warnings
 
 import numpy as np
 from scipy import sparse
@@ -97,16 +95,17 @@ def _replacement_params_view_connectome():
         'threshold': 'edge_threshold',
         'cmap': 'edge_cmap',
         'marker_size': 'node_size',
-        }
+    }
+
 
 @replace_parameters(replacement_params=_replacement_params_view_connectome(),
                     end_version='0.6.0',
-                    lib_name='Nilearn',
+                    lib_name='Nilearn'
                     )
 def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
                     edge_cmap=cm.bwr, symmetric_cmap=True,
                     linewidth=6., node_size=3., colorbar=True,
-                    cbar_height=.5, cbar_fontsize=25):
+                    colorbar_height=.5, colorbar_fontsize=25):
     """
     Insert a 3d plot of a connectome into an HTML page.
 
@@ -140,10 +139,10 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
     colorbar : bool, optional (default=True)
         add a colorbar
 
-    cbar_height : float, optional (default=.5)
+    colorbar_height : float, optional (default=.5)
         height of the colorbar, relative to the figure height
 
-    cbar_fontsize : int, optional (default=25)
+    colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
     Returns
@@ -170,12 +169,13 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
 
     """
     connectome_info = _get_connectome(
-        adjacency_matrix, node_coords, threshold=edge_threshold, cmap=edge_cmap,
+        adjacency_matrix, node_coords,
+        threshold=edge_threshold, cmap=edge_cmap,
         symmetric_cmap=symmetric_cmap, marker_size=node_size)
     connectome_info['line_width'] = linewidth
     connectome_info['colorbar'] = colorbar
-    connectome_info['cbar_height'] = cbar_height
-    connectome_info['cbar_fontsize'] = cbar_fontsize
+    connectome_info['cbar_height'] = colorbar_height
+    connectome_info['cbar_fontsize'] = colorbar_fontsize
     return _make_connectome_html(connectome_info)
 
 
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index a0b932ee77..b6a5537213 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -128,7 +128,7 @@ def _fill_html_template(info, embed_js=True):
 def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
                      black_bg=False, vmax=None, vmin=None, symmetric_cmap=True,
-                     colorbar=True, cbar_height=.5, cbar_fontsize=25):
+                     colorbar=True, colorbar_height=.5, colorbar_fontsize=25):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -178,10 +178,10 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     colorbar : bool, optional (default=True)
         add a colorbar
 
-    cbar_height : float, optional (default=.5)
+    colorbar_height : float, optional (default=.5)
         height of the colorbar, relative to the figure height
 
-    cbar_fontsize : int, optional (default=25)
+    colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
     Returns
@@ -205,15 +205,15 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
         cmap=cmap, black_bg=black_bg, vmax=vmax, vmin=vmin,
         symmetric_cmap=symmetric_cmap)
     info['colorbar'] = colorbar
-    info['cbar_height'] = cbar_height
-    info['cbar_fontsize'] = cbar_fontsize
+    info['cbar_height'] = colorbar_height
+    info['cbar_fontsize'] = colorbar_fontsize
     return _fill_html_template(info, embed_js=True)
 
 
 def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
               cmap=cm.cold_hot, black_bg=False, vmax=None, vmin=None,
-              symmetric_cmap=True, colorbar=True, cbar_height=.5,
-              cbar_fontsize=25):
+              symmetric_cmap=True, colorbar=True, colorbar_height=.5,
+              colorbar_fontsize=25):
     """
     Insert a surface plot of a surface map into an HTML page.
 
@@ -272,10 +272,10 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     colorbar : bool, optional (default=True)
         add a colorbar
 
-    cbar_height : float, optional (default=.5)
+    colorbar_height : float, optional (default=.5)
         height of the colorbar, relative to the figure height
 
-    cbar_fontsize : int, optional (default=25)
+    colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
     Returns
@@ -305,6 +305,6 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
         symmetric_cmap=symmetric_cmap, vmax=vmax, vmin=vmin)
     info['colorbar'] = colorbar
-    info['cbar_height'] = cbar_height
-    info['cbar_fontsize'] = cbar_fontsize
+    info['cbar_height'] = colorbar_height
+    info['cbar_fontsize'] = colorbar_fontsize
     return _fill_html_template(info, embed_js=True)

From b1932debf3b9c4ed2a01e205c1ccc86625c7e8d4 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Wed, 15 May 2019 12:38:09 +0200
Subject: [PATCH 099/228] load surf data in view_surf

---
 nilearn/plotting/html_surface.py            | 5 +++--
 nilearn/plotting/tests/test_html_surface.py | 4 ++++
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 9cab3b064d..20e90949eb 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -275,9 +275,10 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     if surf_map is None:
         surf_map = np.ones(len(surf_mesh[0]))
     if surf_map is not None:
-        surface.check_mesh_and_data(surf_mesh, surf_map)
+        surf_mesh, surf_map = surface.check_mesh_and_data(
+            surf_mesh, surf_map)
     if bg_map is not None:
-        surface.check_mesh_and_data(surf_mesh, bg_map)
+        _, bg_map = surface.check_mesh_and_data(surf_mesh, bg_map)
     info = one_mesh_info(
         surf_map=surf_map, surf_mesh=surf_mesh, threshold=threshold,
         cmap=cmap, black_bg=black_bg, bg_map=bg_map,
diff --git a/nilearn/plotting/tests/test_html_surface.py b/nilearn/plotting/tests/test_html_surface.py
index 52974e4425..3c31669d76 100644
--- a/nilearn/plotting/tests/test_html_surface.py
+++ b/nilearn/plotting/tests/test_html_surface.py
@@ -116,6 +116,10 @@ def test_view_surf():
     html = html_surface.view_surf(
         fsaverage['pial_left'], destrieux, symmetric_cmap=False)
     check_html(html)
+    html = html_surface.view_surf(fsaverage['pial_right'],
+                                  fsaverage['sulc_right'],
+                                  threshold=None, cmap='Greys')
+    check_html(html)
     assert_raises(ValueError, html_surface.view_surf, mesh, mesh[0][::2, 0])
     assert_raises(ValueError, html_surface.view_surf, mesh, mesh[0][:, 0],
                   bg_map=mesh[0][::2, 0])

From 2bf12b7febb0cbc48d79a3338c8b70650a45a695 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 16 May 2019 13:10:10 +0200
Subject: [PATCH 100/228] update whatsnew

---
 doc/whats_new.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index b5adc92749..003e243160 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -44,6 +44,8 @@ Fixes
 - When :func:`nilearn.plotting.plot_surf_stat_map` is used with a thresholded map
   but without a background map, the surface mesh is displayed in
   half-transparent grey to maintain a 3D perception.
+- :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
+  path.
 
 0.5.2
 =====

From 5d168c899666941c213e996e308922df8a5bd1b0 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 16 May 2019 13:28:58 +0200
Subject: [PATCH 101/228] complete test

---
 nilearn/plotting/tests/test_js_plotting_utils.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index 6320afbc0f..7d33175adb 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -295,11 +295,16 @@ def test_temp_file_removing():
     html = js_plotting_utils.HTMLDocument('hello')
     wb_open = webbrowser.open
     webbrowser.open = _open_mock
+    fd, tmpfile = tempfile.mkstemp()
     try:
+        os.close(fd)
+        assert_no_warnings(html.open_in_browser,
+                           file_name=tmpfile, temp_file_lifetime=None)
         html.open_in_browser(temp_file_lifetime=.5)
         assert os.path.isfile(html._temp_file)
         time.sleep(1.5)
         assert not os.path.isfile(html._temp_file)
+        assert_warns(UserWarning, html.open_in_browser, temp_file_lifetime=None)
         html.open_in_browser(temp_file_lifetime=None)
         assert os.path.isfile(html._temp_file)
         time.sleep(1.5)
@@ -310,6 +315,10 @@ def test_temp_file_removing():
             os.remove(html._temp_file)
         except Exception:
             pass
+        try:
+            os.remove(tmpfile)
+        except Exception:
+            pass
 
 
 def _open_views():

From 1f6fa47ec51ef8b9fb3678096b2c1f1eaafe1c3f Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 16 May 2019 15:47:03 +0200
Subject: [PATCH 102/228] iter

---
 nilearn/plotting/html_surface.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 5d138784b7..097ad7a3b7 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -296,7 +296,7 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     surf_mesh = surface.load_surf_mesh(surf_mesh)
     if surf_map is None:
         surf_map = np.ones(len(surf_mesh[0]))
-    if surf_map is not None:
+    else:
         surf_mesh, surf_map = surface.check_mesh_and_data(
             surf_mesh, surf_map)
     if bg_map is not None:

From 6bbc8b72034349ba54ed7c0f399a9dc3b0ffd69c Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 16 May 2019 18:10:45 +0200
Subject: [PATCH 103/228] 3 fold cv -> 100 fold shuffle split

---
 examples/03_connectivity/plot_group_level_connectivity.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/03_connectivity/plot_group_level_connectivity.py b/examples/03_connectivity/plot_group_level_connectivity.py
index cf15f87899..23df6db9fa 100644
--- a/examples/03_connectivity/plot_group_level_connectivity.py
+++ b/examples/03_connectivity/plot_group_level_connectivity.py
@@ -185,10 +185,10 @@ def plot_matrices(matrices, matrix_kind):
 # We stratify the dataset into homogeneous classes according to phenotypic
 # and scan site. We then split the subjects into 3 folds with the same
 # proportion of each class as in the whole cohort
-from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import StratifiedShuffleSplit
 
 _, classes = np.unique(groups, return_inverse=True)
-cv = StratifiedKFold(n_splits=3)
+cv = StratifiedShuffleSplit(n_splits=100)
 ###############################################################################
 # and use the connectivity coefficients to classify children vs adults.
 

From b9ee8ad519cec4b7f7da1e7b7cd04ac7b4c38705 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 16 May 2019 18:47:19 +0200
Subject: [PATCH 104/228] iter

---
 examples/03_connectivity/plot_group_level_connectivity.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/03_connectivity/plot_group_level_connectivity.py b/examples/03_connectivity/plot_group_level_connectivity.py
index 23df6db9fa..12542f4165 100644
--- a/examples/03_connectivity/plot_group_level_connectivity.py
+++ b/examples/03_connectivity/plot_group_level_connectivity.py
@@ -183,12 +183,12 @@ def plot_matrices(matrices, matrix_kind):
 
 ###############################################################################
 # We stratify the dataset into homogeneous classes according to phenotypic
-# and scan site. We then split the subjects into 3 folds with the same
-# proportion of each class as in the whole cohort
+# and scan site. We then split the subjects into 30 folds with the same
+# proportion of each class as in the whole cohort for cross-validation
 from sklearn.model_selection import StratifiedShuffleSplit
 
 _, classes = np.unique(groups, return_inverse=True)
-cv = StratifiedShuffleSplit(n_splits=100)
+cv = StratifiedShuffleSplit(n_splits=30)
 ###############################################################################
 # and use the connectivity coefficients to classify children vs adults.
 

From d44d8eba857f3cb0f3ceb850e8c69dfe68bdf525 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sun, 19 May 2019 20:24:55 +0200
Subject: [PATCH 105/228] import norm from scipy.linalg not
 sklearn.utils.extmath

---
 nilearn/decoding/tests/test_graph_net.py | 18 +++++++++---------
 nilearn/decoding/tests/test_space_net.py |  4 ++--
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/nilearn/decoding/tests/test_graph_net.py b/nilearn/decoding/tests/test_graph_net.py
index 9a7ddb1a65..45cf046ffc 100644
--- a/nilearn/decoding/tests/test_graph_net.py
+++ b/nilearn/decoding/tests/test_graph_net.py
@@ -2,7 +2,7 @@
 import numpy as np
 import scipy as sp
 from numpy.testing import assert_almost_equal
-from sklearn.utils import extmath
+from scipy import linalg
 from sklearn.utils import check_random_state
 from nilearn.decoding.objective_functions import _gradient, _div
 from nilearn.decoding.space_net_solvers import (
@@ -167,12 +167,12 @@ def test__squared_loss_derivative_lipschitz_constant():
     for _ in range(20):
         x_1 = rng.rand(*w.shape) * rng.randint(1000)
         x_2 = rng.rand(*w.shape) * rng.randint(1000)
-        gradient_difference = extmath.norm(
+        gradient_difference = linalg.norm(
             _squared_loss_and_spatial_grad_derivative(X, y, x_1, mask,
                                                       grad_weight)
             - _squared_loss_and_spatial_grad_derivative(X, y, x_2, mask,
                                                         grad_weight))
-        point_difference = extmath.norm(x_1 - x_2)
+        point_difference = linalg.norm(x_1 - x_2)
         assert_true(
             gradient_difference <= lipschitz_constant * point_difference)
 
@@ -186,12 +186,12 @@ def test_logistic_derivative_lipschitz_constant():
     for _ in range(20):
         x_1 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
         x_2 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
-        gradient_difference = extmath.norm(
+        gradient_difference = linalg.norm(
             _logistic_data_loss_and_spatial_grad_derivative(
                 X, y, x_1, mask, grad_weight)
             - _logistic_data_loss_and_spatial_grad_derivative(
                 X, y, x_2, mask, grad_weight))
-        point_difference = extmath.norm(x_1 - x_2)
+        point_difference = linalg.norm(x_1 - x_2)
         assert_true(
             gradient_difference <= lipschitz_constant * point_difference)
 
@@ -224,12 +224,12 @@ def test_tikhonov_regularization_vs_graph_net():
         screening_percentile=100., standardize=False)
     graph_net.fit(X_, y.copy())
     coef_ = graph_net.coef_[0]
-    graph_net_perf = 0.5 / y.size * extmath.norm(
+    graph_net_perf = 0.5 / y.size * linalg.norm(
         np.dot(X, coef_) - y) ** 2\
-        + 0.5 * extmath.norm(np.dot(G, coef_)) ** 2
-    optimal_model_perf = 0.5 / y.size * extmath.norm(
+        + 0.5 * linalg.norm(np.dot(G, coef_)) ** 2
+    optimal_model_perf = 0.5 / y.size * linalg.norm(
         np.dot(X, optimal_model) - y) ** 2\
-        + 0.5 * extmath.norm(np.dot(G, optimal_model)) ** 2
+        + 0.5 * linalg.norm(np.dot(G, optimal_model)) ** 2
     assert_almost_equal(graph_net_perf, optimal_model_perf, decimal=1)
 
 
diff --git a/nilearn/decoding/tests/test_space_net.py b/nilearn/decoding/tests/test_space_net.py
index f75b23ebd0..bd18818bb9 100644
--- a/nilearn/decoding/tests/test_space_net.py
+++ b/nilearn/decoding/tests/test_space_net.py
@@ -4,8 +4,8 @@
 from nose.tools import (assert_equal, assert_true, assert_false,
                         assert_raises)
 import numpy as np
+from scipy import linalg
 from sklearn.datasets import load_iris
-from sklearn.utils import extmath
 from sklearn.linear_model import Lasso
 from sklearn.utils import check_random_state
 from sklearn.linear_model import LogisticRegression
@@ -241,7 +241,7 @@ def test_lasso_vs_graph_net():
                              penalty="graph-net", max_iter=100)
     lasso.fit(X_, y)
     graph_net.fit(X, y)
-    lasso_perf = 0.5 / y.size * extmath.norm(np.dot(
+    lasso_perf = 0.5 / y.size * linalg.norm(np.dot(
         X_, lasso.coef_) - y) ** 2 + np.sum(np.abs(lasso.coef_))
     graph_net_perf = 0.5 * ((graph_net.predict(X) - y) ** 2).mean()
     np.testing.assert_almost_equal(graph_net_perf, lasso_perf, decimal=3)

From a4eaf1156ad3492d389bfb4c8e70fb3f63a0454c Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sun, 19 May 2019 20:57:16 +0200
Subject: [PATCH 106/228] doctest normalize_whitespace

---
 doc/manipulating_images/masker_objects.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/manipulating_images/masker_objects.rst b/doc/manipulating_images/masker_objects.rst
index 5e2a2c2330..fa77719bda 100644
--- a/doc/manipulating_images/masker_objects.rst
+++ b/doc/manipulating_images/masker_objects.rst
@@ -177,7 +177,7 @@ preparation::
 
    >>> from nilearn import input_data
    >>> masker = input_data.NiftiMasker()
-   >>> masker # doctest: +ELLIPSIS
+   >>> masker # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    NiftiMasker(detrend=False, dtype=None, high_pass=None, low_pass=None,
          mask_args=None, mask_img=None, mask_strategy='background',
          memory=Memory(...), memory_level=1, sample_mask=None,

From 6cf7ba7129f42ac80fdec36e8203c61d389f79dd Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 4 Jun 2019 13:19:10 +0200
Subject: [PATCH 107/228] don't set aspect of 3d axes: raises starting from
 matplotlib 3.1

---
 examples/01_plotting/plot_surface_projection_strategies.py | 1 -
 nilearn/plotting/surf_plotting.py                          | 1 -
 2 files changed, 2 deletions(-)

diff --git a/examples/01_plotting/plot_surface_projection_strategies.py b/examples/01_plotting/plot_surface_projection_strategies.py
index 06d87f7596..15b614a5cf 100644
--- a/examples/01_plotting/plot_surface_projection_strategies.py
+++ b/examples/01_plotting/plot_surface_projection_strategies.py
@@ -61,7 +61,6 @@
 for sample_points in [line_sample_points, ball_sample_points]:
     fig = plt.figure()
     ax = plt.subplot(projection='3d')
-    ax.set_aspect(1)
 
     ax.plot_trisurf(x, y, z, triangles=triangulation.triangles)
 
diff --git a/nilearn/plotting/surf_plotting.py b/nilearn/plotting/surf_plotting.py
index 01e1b3bfd1..0369256d82 100644
--- a/nilearn/plotting/surf_plotting.py
+++ b/nilearn/plotting/surf_plotting.py
@@ -188,7 +188,6 @@ def plot_surf(surf_mesh, surf_map=None, bg_map=None,
             figure = axes.get_figure()
         axes.set_xlim(*limits)
         axes.set_ylim(*limits)
-    axes.set_aspect(.74)
     axes.view_init(elev=elev, azim=azim)
     axes.set_axis_off()
 

From 66dde8264cd899e1aa4b91bac063808f47f70047 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 4 Jun 2019 13:41:29 +0200
Subject: [PATCH 108/228] don't save sprites to RAW format (removed in
 matplotlib 3.1)

---
 nilearn/plotting/tests/test_html_stat_map.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/nilearn/plotting/tests/test_html_stat_map.py b/nilearn/plotting/tests/test_html_stat_map.py
index fbf55cb5d1..08998aa4d3 100644
--- a/nilearn/plotting/tests/test_html_stat_map.py
+++ b/nilearn/plotting/tests/test_html_stat_map.py
@@ -109,13 +109,14 @@ def test_save_sprite():
     # Save the sprite using BytesIO
     sprite_io = BytesIO()
     html_stat_map._save_sprite(data, sprite_io, vmin=0, vmax=1,
-                               mask=mask, format='raw')
+                               mask=mask, format='png')
 
     # Load the sprite back in base64
     sprite_base64 = html_stat_map._bytesIO_to_base64(sprite_io)
 
     # Check the sprite is correct
-    assert sprite_base64 == '////AP////8='
+    assert sprite_base64.startswith('iVBORw0KG')
+    assert sprite_base64.endswith('ABJRU5ErkJggg==')
 
 
 def test_save_cmap():
@@ -124,13 +125,14 @@ def test_save_cmap():
 
     # Save the cmap using BytesIO
     cmap_io = BytesIO()
-    html_stat_map._save_cm(cmap_io, 'cold_hot', format='raw', n_colors=2)
+    html_stat_map._save_cm(cmap_io, 'cold_hot', format='png', n_colors=2)
 
     # Load the colormap back in base64
     cmap_base64 = html_stat_map._bytesIO_to_base64(cmap_io)
 
     # Check the colormap is correct
-    assert cmap_base64 == '//////////8='
+    assert cmap_base64.startswith('iVBORw0KG')
+    assert cmap_base64.endswith('ElFTkSuQmCC')
 
 
 def test_mask_stat_map():

From f76aef5225368b9c1e077ba0ba38069d30662266 Mon Sep 17 00:00:00 2001
From: Colin Reininger <reiningc@oregonstate.edu>
Date: Sun, 9 Jun 2019 14:35:16 -0600
Subject: [PATCH 109/228] Correct typos and re-word Smoothing step in ROI mask
 recipe

---
 doc/manipulating_images/manipulating_images.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/manipulating_images/manipulating_images.rst b/doc/manipulating_images/manipulating_images.rst
index 57993de8ad..c5a4a514cc 100644
--- a/doc/manipulating_images/manipulating_images.rst
+++ b/doc/manipulating_images/manipulating_images.rst
@@ -8,7 +8,7 @@ This chapter discusses how nilearn can be used to do simple operations on
 brain images.
 
 
-.. contents:: **Chapters contents**
+.. contents:: **Chapter contents**
     :local:
     :depth: 1
 
@@ -198,7 +198,7 @@ brain. It is thus convenient to apply a brain mask in order to convert the
     :width: 100%
 
 Note that in an analysis pipeline, this operation is best done using the
-:ref:`masker objects <masker_objects>`. For completness, we give code to
+:ref:`masker objects <masker_objects>`. For completeness, we give code to
 do it manually below:
 
 .. literalinclude:: ../../examples/01_plotting/plot_visualization.py
@@ -219,8 +219,8 @@ statistical test. This requires a chain of image
 operations on the input data. Here is a possible recipe for computing an
 ROI mask:
 
- * **Smoothing**: Before a statistical test, it is often use to smooth a bit
-   the image using :func:`nilearn.image.smooth_img`, typically fwhm=6 for
+ * **Smoothing**: Before a statistical test, it is often useful to smooth the image a bit
+   using :func:`nilearn.image.smooth_img`, typically fwhm=6 for
    fMRI.
 
  * **Selecting voxels**: Given the smoothed data, we can select voxels

From 00e1b0c15164f76cb2699f89e38667f6e504955e Mon Sep 17 00:00:00 2001
From: Colin Reininger <reiningc@oregonstate.edu>
Date: Tue, 11 Jun 2019 14:29:18 -0600
Subject: [PATCH 110/228] Fix note in Masking data

---
 doc/manipulating_images/manipulating_images.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/manipulating_images/manipulating_images.rst b/doc/manipulating_images/manipulating_images.rst
index c5a4a514cc..8c388ff426 100644
--- a/doc/manipulating_images/manipulating_images.rst
+++ b/doc/manipulating_images/manipulating_images.rst
@@ -198,7 +198,7 @@ brain. It is thus convenient to apply a brain mask in order to convert the
     :width: 100%
 
 Note that in an analysis pipeline, this operation is best done using the
-:ref:`masker objects <masker_objects>`. For completeness, we give code to
+:ref:`masker objects <masker_objects>`. For completeness, we give the code to
 do it manually below:
 
 .. literalinclude:: ../../examples/01_plotting/plot_visualization.py

From 845220550f220465573a8cab1604612541117ab9 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 13 Jun 2019 08:42:40 +0200
Subject: [PATCH 111/228] better checking of warnings in test

---
 nilearn/plotting/tests/test_js_plotting_utils.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index 7d33175adb..6008d08330 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -13,6 +13,7 @@
     LXML_INSTALLED = True
 except ImportError:
     LXML_INSTALLED = False
+import pytest
 
 from nilearn.plotting import js_plotting_utils
 from nilearn import surface
@@ -298,13 +299,16 @@ def test_temp_file_removing():
     fd, tmpfile = tempfile.mkstemp()
     try:
         os.close(fd)
-        assert_no_warnings(html.open_in_browser,
-                           file_name=tmpfile, temp_file_lifetime=None)
+        with pytest.warns(None) as record:
+            html.open_in_browser(file_name=tmpfile, temp_file_lifetime=None)
+        for warning in record:
+            assert "Saved HTML in temporary file" not in str(warning.message)
         html.open_in_browser(temp_file_lifetime=.5)
         assert os.path.isfile(html._temp_file)
         time.sleep(1.5)
         assert not os.path.isfile(html._temp_file)
-        assert_warns(UserWarning, html.open_in_browser, temp_file_lifetime=None)
+        with pytest.warns(UserWarning, match="Saved HTML in temporary file"):
+            html.open_in_browser(temp_file_lifetime=None)
         html.open_in_browser(temp_file_lifetime=None)
         assert os.path.isfile(html._temp_file)
         time.sleep(1.5)

From 69816c9143cb747cbbdbc0f401660516a033318d Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 18 Jun 2019 10:38:34 +0200
Subject: [PATCH 112/228] allow setting or disabling warning about too many
 open views

---
 nilearn/plotting/__init__.py                   |  2 ++
 nilearn/plotting/js_plotting_utils.py          | 18 +++++++++++++++++-
 .../plotting/tests/test_js_plotting_utils.py   |  8 ++++++++
 3 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/nilearn/plotting/__init__.py b/nilearn/plotting/__init__.py
index bfa0b879e0..629ca5b4e3 100644
--- a/nilearn/plotting/__init__.py
+++ b/nilearn/plotting/__init__.py
@@ -45,6 +45,7 @@ def _set_mpl_backend():
 from .html_stat_map import view_img
 from .html_connectome import view_connectome, view_markers
 from .surf_plotting import plot_surf, plot_surf_stat_map, plot_surf_roi
+from .js_plotting_utils import set_max_img_views_before_warning
 
 __all__ = ['cm', 'plot_img', 'plot_anat', 'plot_epi',
            'plot_roi', 'plot_stat_map', 'plot_glass_brain',
@@ -54,4 +55,5 @@ def _set_mpl_backend():
            'view_img', 'view_connectome', 'view_markers',
            'find_parcellation_cut_coords', 'find_probabilistic_atlas_cut_coords',
            'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi',
+           'set_max_img_views_before_warning'
            ]
diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index f30aa0285a..4da47c0bdf 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -24,6 +24,17 @@
 from .._utils.param_validation import check_threshold
 from .. import surface
 
+MAX_IMG_VIEWS_BEFORE_WARNING = 10
+
+
+def set_max_img_views_before_warning(new_value):
+    """Set the number of open views which triggers a warning.
+
+    If `None` or a negative number, disable the memory warning.
+    """
+    global MAX_IMG_VIEWS_BEFORE_WARNING
+    MAX_IMG_VIEWS_BEFORE_WARNING = new_value
+
 
 def add_js_lib(html, embed_js=True):
     """
@@ -99,7 +110,12 @@ def __init__(self, html, width=600, height=400):
 
     def _check_n_open(self):
         HTMLDocument._all_open_html_repr.add(self)
-        if len(HTMLDocument._all_open_html_repr) > 9:
+        if MAX_IMG_VIEWS_BEFORE_WARNING is None:
+            return
+        if MAX_IMG_VIEWS_BEFORE_WARNING < 0:
+            return
+        if len(HTMLDocument._all_open_html_repr
+               ) > MAX_IMG_VIEWS_BEFORE_WARNING - 1:
             warnings.warn('It seems you have created more than 10 '
                           'nilearn views. As each view uses dozens '
                           'of megabytes of RAM, you might want to '
diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index 6320afbc0f..45e007392b 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -327,6 +327,14 @@ def test_open_view_warning():
     # should raise a warning about memory usage
     assert_warns(UserWarning, _open_views)
     assert_no_warnings(_open_one_view)
+    js_plotting_utils.set_max_img_views_before_warning(15)
+    assert_no_warnings(_open_views)
+    js_plotting_utils.set_max_img_views_before_warning(-1)
+    assert_no_warnings(_open_views)
+    js_plotting_utils.set_max_img_views_before_warning(None)
+    assert_no_warnings(_open_views)
+    js_plotting_utils.set_max_img_views_before_warning(6)
+    assert_warns(UserWarning, _open_views)
 
 
 def test_to_color_strings():

From 0363178ae4405bc7657e774a8c9013523e89eb1e Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 18 Jun 2019 23:43:36 +0200
Subject: [PATCH 113/228] get right number of views in warning

---
 nilearn/plotting/js_plotting_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index 4da47c0bdf..be3e575c20 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -116,10 +116,11 @@ def _check_n_open(self):
             return
         if len(HTMLDocument._all_open_html_repr
                ) > MAX_IMG_VIEWS_BEFORE_WARNING - 1:
-            warnings.warn('It seems you have created more than 10 '
+            warnings.warn('It seems you have created more than {} '
                           'nilearn views. As each view uses dozens '
                           'of megabytes of RAM, you might want to '
-                          'delete some of them.')
+                          'delete some of them.'.format(
+                              MAX_IMG_VIEWS_BEFORE_WARNING))
 
     def resize(self, width, height):
         """Resize the plot displayed in a Jupyter notebook."""

From 91c03fee0901e7950920348d7c2816677cfd21eb Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 20 Jun 2019 16:37:03 +0200
Subject: [PATCH 114/228] Install Pytest in CI

---
 appveyor.yml        | 2 +-
 azure-pipelines.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 605c79581b..6d1e06b2c9 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -24,7 +24,7 @@ install:
   # See similar fix which made for travis and circleci
   # https://github.com/nilearn/nilearn/pull/1525
   # Should be removed after a new matplotlib release 2.1.1
-  - "conda install pip numpy scipy scikit-learn nose wheel matplotlib -y -q"
+  - "conda install pip numpy scipy scikit-learn nose pytest wheel matplotlib -y -q"
 
   # Install other nilearn dependencies
   - "pip install nibabel coverage nose-timer"
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 4601af26a9..5ae4de4c1a 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -30,7 +30,7 @@ jobs:
 
   - script: |
       pip install .
-      nosetests ./nilearn -v
+      pytest ./nilearn -v
     displayName: 'test'
 
   - task: PublishTestResults@2

From 389cc45c278f9cad11e1683b5e61d7cb209aca28 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 26 Jun 2019 20:50:50 +0200
Subject: [PATCH 115/228] Replaced yield with function call since PyTest4.0
 does not support yield tests

---
 nilearn/tests/test_extmath.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/tests/test_extmath.py b/nilearn/tests/test_extmath.py
index 589948a5c2..ad0093cb17 100644
--- a/nilearn/tests/test_extmath.py
+++ b/nilearn/tests/test_extmath.py
@@ -15,7 +15,7 @@ def test_fast_abs_percentile():
     data = np.arange(100)
     rng.shuffle(data)
     for p in data:
-        yield nose.tools.assert_equal, fast_abs_percentile(data, p), p
+        nose.tools.assert_equal(fast_abs_percentile(data, p), p)
 
 
 def test_is_spd_with_non_symmetrical_matrix():

From 04d6cd4f67bd78843519fadd0e183b804cd9750a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 15:11:35 +0200
Subject: [PATCH 116/228] Replace nosetests with pytest in makefile (WIP)

---
 Makefile | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/Makefile b/Makefile
index 15d6f8d4dd..3a5cad797c 100644
--- a/Makefile
+++ b/Makefile
@@ -4,9 +4,11 @@
 
 PYTHON ?= python
 CYTHON ?= cython
-NOSETESTS ?= nosetests
-NOSETESTS_OPTIONS := $(shell pip list | grep nose-timer > /dev/null && \
-                       echo '--with-timer --timer-top-n 50')
+TEST_RUNNER ?= pytest
+TEST_RUNNER_OPTIONS := --duration=0 -vv
+# TEST_RUNNER ?= nosetests
+# TEST_RUNNER_OPTIONS := $(shell pip list | grep nose-timer > /dev/null && \
+#                       echo '--with-timer --timer-top-n 50')
 CTAGS ?= ctags
 
 all: clean test doc-noplot
@@ -32,14 +34,14 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	$(NOSETESTS) -s nilearn $(NOSETESTS_OPTIONS)
+	$(TEST_RUNNER) nilearn TEST_RUNNER_OPTIONS
 test-doc:
-	$(NOSETESTS) -s --with-doctest --doctest-tests --doctest-extension=rst \
+	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
 	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
 
 test-coverage:
 	rm -rf coverage .coverage
-	$(NOSETESTS) -s --with-coverage --cover-html --cover-html-dir=coverage \
+	$(TEST_RUNNER) -s --with-coverage --cover-html --cover-html-dir=coverage \
 	--cover-package=nilearn nilearn
 
 test: test-code test-doc

From dae421403dbca27e72ce64b82338d8e9338b2cef Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 15:15:54 +0200
Subject: [PATCH 117/228] Add pytest for installation

---
 continuous_integration/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 512cbdf2f3..eac59ae670 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -22,7 +22,7 @@ create_new_venv() {
     deactivate
     virtualenv --system-site-packages testvenv
     source testvenv/bin/activate
-    pip install nose
+    pip install nose pytest
 }
 
 print_conda_requirements() {

From 359cacaa2be6598e1e6d6d482cb28047adb581d3 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 15:24:20 +0200
Subject: [PATCH 118/228] Add pytest for installation in a few more places for
 Posix systems

---
 Makefile                          | 2 +-
 continuous_integration/install.sh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 3a5cad797c..70712f7f8f 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	$(TEST_RUNNER) nilearn TEST_RUNNER_OPTIONS
+	$(TEST_RUNNER) nilearn $(TEST_RUNNER_OPTIONS)
 test-doc:
 	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
 	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index eac59ae670..5076107055 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -33,7 +33,7 @@ print_conda_requirements() {
     # if yes which version to install. For example:
     #   - for numpy, NUMPY_VERSION is used
     #   - for scikit-learn, SCIKIT_LEARN_VERSION is used
-    TO_INSTALL_ALWAYS="pip nose"
+    TO_INSTALL_ALWAYS="pip nose pytest"
     REQUIREMENTS="$TO_INSTALL_ALWAYS"
     TO_INSTALL_MAYBE="python numpy scipy matplotlib scikit-learn pandas \
 flake8 lxml"

From 79aa9411b8853947af55c3f9604a2bae6c55e94c Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 15:42:07 +0200
Subject: [PATCH 119/228] Attempting to make pytest work attempt 5/n

---
 Makefile | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 70712f7f8f..55f01ff19d 100644
--- a/Makefile
+++ b/Makefile
@@ -34,10 +34,14 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	$(TEST_RUNNER) nilearn $(TEST_RUNNER_OPTIONS)
+    pwd
+    pytest -s -vv --duration=0
+	# $(TEST_RUNNER) -s $(TEST_RUNNER_OPTIONS)
 test-doc:
-	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
-	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
+    pwd
+    pytest -s -vv --duration=0
+	# $(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
+	# --doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
 
 test-coverage:
 	rm -rf coverage .coverage

From 64fee99814928f51525c73527586dfcaa6903b40 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 15:52:25 +0200
Subject: [PATCH 120/228] Attempting to make pytest work attempt 6/n

---
 requirements-dev.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements-dev.txt b/requirements-dev.txt
index 1ae2fca753..4dceacf41c 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,4 +1,5 @@
 nose
+pytest
 coverage
 boto3
 patsy

From 7b90e58022b58c0fde0758e0f1278dbc618a4ea9 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:00:57 +0200
Subject: [PATCH 121/228] Replaced `make clean` with `make  clean`-Travis
 executing `makeclean` (weird)

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 9554e60eee..63b8c9b85e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -44,7 +44,7 @@ matrix:
 
 install: source continuous_integration/install.sh
 
-before_script: make clean
+before_script: make  clean
 
 script: source continuous_integration/test_script.sh
 

From f93b53e5b27165c7730f8624534de08f2bb102c7 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:04:40 +0200
Subject: [PATCH 122/228] Iter 8/n

---
 Makefile | 2 --
 1 file changed, 2 deletions(-)

diff --git a/Makefile b/Makefile
index 55f01ff19d..7c81cf1078 100644
--- a/Makefile
+++ b/Makefile
@@ -34,11 +34,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-    pwd
     pytest -s -vv --duration=0
 	# $(TEST_RUNNER) -s $(TEST_RUNNER_OPTIONS)
 test-doc:
-    pwd
     pytest -s -vv --duration=0
 	# $(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
 	# --doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`

From 8d2cf79382b9862863d7234acdb1f49b7783b1d7 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:10:31 +0200
Subject: [PATCH 123/228] Iter 9/n

---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 7c81cf1078..ff514ccaff 100644
--- a/Makefile
+++ b/Makefile
@@ -35,11 +35,11 @@ inplace:
 
 test-code:
     pytest -s -vv --duration=0
-	# $(TEST_RUNNER) -s $(TEST_RUNNER_OPTIONS)
+#	$(TEST_RUNNER) -s $(TEST_RUNNER_OPTIONS)
 test-doc:
     pytest -s -vv --duration=0
-	# $(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
-	# --doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
+#	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
+#	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
 
 test-coverage:
 	rm -rf coverage .coverage

From f2d134dc42a8e85f069564c5b8645af1a3be84aa Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:31:10 +0200
Subject: [PATCH 124/228] Iter 10/n

---
 Makefile | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/Makefile b/Makefile
index ff514ccaff..1783da59a1 100644
--- a/Makefile
+++ b/Makefile
@@ -6,9 +6,6 @@ PYTHON ?= python
 CYTHON ?= cython
 TEST_RUNNER ?= pytest
 TEST_RUNNER_OPTIONS := --duration=0 -vv
-# TEST_RUNNER ?= nosetests
-# TEST_RUNNER_OPTIONS := $(shell pip list | grep nose-timer > /dev/null && \
-#                       echo '--with-timer --timer-top-n 50')
 CTAGS ?= ctags
 
 all: clean test doc-noplot
@@ -35,11 +32,8 @@ inplace:
 
 test-code:
     pytest -s -vv --duration=0
-#	$(TEST_RUNNER) -s $(TEST_RUNNER_OPTIONS)
 test-doc:
     pytest -s -vv --duration=0
-#	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
-#	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
 
 test-coverage:
 	rm -rf coverage .coverage

From 3444f14375a2c068f012022f172394ebe5f6e40a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:33:45 +0200
Subject: [PATCH 125/228] Iter 11/n: Replaced spaces with Tabs

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 1783da59a1..ad3d252695 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-    pytest -s -vv --duration=0
+	pytest -s -vv --duration=0
 test-doc:
-    pytest -s -vv --duration=0
+	pytest -s -vv --duration=0
 
 test-coverage:
 	rm -rf coverage .coverage

From eead6d9da9db2729b5edca747a48aa531ca1ebcf Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:53:57 +0200
Subject: [PATCH 126/228] Iter 12/n

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index ad3d252695..1849b6a299 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	pytest -s -vv --duration=0
+	pytest nilearn -s -vv --duration=0
 test-doc:
-	pytest -s -vv --duration=0
+	pytest nilearn -s -vv --duration=0
 
 test-coverage:
 	rm -rf coverage .coverage

From 537a240492f4da44667758082219613971299d59 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 16:59:09 +0200
Subject: [PATCH 127/228] Iter 13/n : Added dir marker for pytest test
 ceolection

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 1849b6a299..685dbc8b62 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	pytest nilearn -s -vv --duration=0
+	pytest nilearn/ -s -vv --duration=0
 test-doc:
-	pytest nilearn -s -vv --duration=0
+	pytest nilearn/ -s -vv --duration=0
 
 test-coverage:
 	rm -rf coverage .coverage

From 519d79f8b118fcd32aa90e9576041e8727d345de Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 17:02:48 +0200
Subject: [PATCH 128/228] Iter 14/n : Invoked pytest as a python module

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 685dbc8b62..e8ed37a485 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	pytest nilearn/ -s -vv --duration=0
+	python -m pytest -s -vv --duration=0
 test-doc:
-	pytest nilearn/ -s -vv --duration=0
+	python -m pytest -s -vv --duration=0
 
 test-coverage:
 	rm -rf coverage .coverage

From 48984be28aa95e9d628c70e058b9d02848d84aa0 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 1 Jul 2019 17:06:59 +0200
Subject: [PATCH 129/228] Iter 15/n : Added full path for pytest test discovery

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index e8ed37a485..b61d2efcf4 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest -s -vv --duration=0
+	python -m pytest /tmp/nilearn/ -s -vv --duration=0
 test-doc:
-	python -m pytest -s -vv --duration=0
+	python -m pytest /tmp/nilearn/ -s -vv --duration=0
 
 test-coverage:
 	rm -rf coverage .coverage

From c85bcde23167696cef30b62776ee2931298a828e Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 2 Jul 2019 15:06:32 +0200
Subject: [PATCH 130/228] Bumped up the minimum supported versions of
 scikit-learn, scipy to 0.19

 - Conda default channel dropped support for these versions.
 - Updated Whats_new.rst
---
 .travis.yml       | 4 ++--
 README.rst        | 4 ++--
 doc/whats_new.rst | 9 +++++++++
 3 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 9554e60eee..0a6b0a866c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,8 +21,8 @@ matrix:
   include:
     # without matplotlib
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
-           NUMPY_VERSION="1.11" SCIPY_VERSION="0.17" PANDAS_VERSION="*"
-           SCIKIT_LEARN_VERSION="0.18" COVERAGE="true"
+           NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
+           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
diff --git a/README.rst b/README.rst
index d9916729c4..f2d1b50d36 100644
--- a/README.rst
+++ b/README.rst
@@ -41,8 +41,8 @@ The required dependencies to use the software are:
 * Python >= 3.5,
 * setuptools
 * Numpy >= 1.11
-* SciPy >= 0.17
-* Scikit-learn >= 0.18
+* SciPy >= 0.19
+* Scikit-learn >= 0.19
 * Nibabel >= 2.0.2
 
 If you are using nilearn plotting functionalities or running the
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 003e243160..3bb1354f4f 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -3,6 +3,15 @@
 
 NEW
 ---
+
+.. warning::
+
+ | **Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.**
+ |
+ | **Minimum supported versions of packages have been bumped up.**
+ | - scikit-learn -- v0.19
+ | - scipy -- v0.19
+
 - Parcellation method ReNA: Fast agglomerative clustering based on recursive
   nearest neighbor grouping.
   Yields very fast & accurate models, without creation of giant

From e73bf7127446728eef6f87a00077ba9372663dd7 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 2 Jul 2019 17:38:54 +0200
Subject: [PATCH 131/228] Pinned conda=v4.6 to avoid file corruption issue. See
 nilearn issue #2079

---
 continuous_integration/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 512cbdf2f3..6b107af031 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -67,7 +67,7 @@ create_new_conda_env() {
     chmod +x ~/miniconda.sh && ~/miniconda.sh -b
     export PATH=$HOME/miniconda3/bin:$PATH
     echo $PATH
-    conda update --quiet --yes conda
+    conda update --quiet --yes conda=4.6
 
     # Configure the conda environment and put it in the path using the
     # provided versions

From dd503d41091e79e111815e0a32ea1590fec9308d Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 3 Jul 2019 10:24:04 +0200
Subject: [PATCH 132/228] Corrected conda upgrade command

---
 continuous_integration/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 6b107af031..e4b33c4ece 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -67,7 +67,7 @@ create_new_conda_env() {
     chmod +x ~/miniconda.sh && ~/miniconda.sh -b
     export PATH=$HOME/miniconda3/bin:$PATH
     echo $PATH
-    conda update --quiet --yes conda=4.6
+    conda install conda=4.6 --yes
 
     # Configure the conda environment and put it in the path using the
     # provided versions

From dad251cb590894f188411ad0a6e7b234c4e2fa66 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 3 Jul 2019 13:05:28 +0200
Subject: [PATCH 133/228] Updated min version of libs

 - Numpy=1.2
 - Scipy=0.19
 - Scikit-Learn=0.19
 - Matplotlib=2.0
---
 .travis.yml        | 2 +-
 README.rst         | 2 +-
 doc/whats_new.rst  | 6 ++++--
 nilearn/version.py | 8 ++++----
 4 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 0a6b0a866c..fcfbd9d3e6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,7 +21,7 @@ matrix:
   include:
     # without matplotlib
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
-           NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
+           NUMPY_VERSION="1.12" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="0.19" COVERAGE="true"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
diff --git a/README.rst b/README.rst
index f2d1b50d36..6361dede4d 100644
--- a/README.rst
+++ b/README.rst
@@ -40,7 +40,7 @@ The required dependencies to use the software are:
 
 * Python >= 3.5,
 * setuptools
-* Numpy >= 1.11
+* Numpy >= 1.12
 * SciPy >= 0.19
 * Scikit-learn >= 0.19
 * Nibabel >= 2.0.2
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 3bb1354f4f..1c4418781e 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -9,8 +9,10 @@ NEW
  | **Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.**
  |
  | **Minimum supported versions of packages have been bumped up.**
- | - scikit-learn -- v0.19
- | - scipy -- v0.19
+ | - Numpy -- v1.12
+ | - Matplotlib -- v2.0
+ | - Scikit-learn -- v0.19
+ | - Scipy -- v0.19
 
 - Parcellation method ReNA: Fast agglomerative clustering based on recursive
   nearest neighbor grouping.
diff --git a/nilearn/version.py b/nilearn/version.py
index 326ad9d28d..f26d515e86 100644
--- a/nilearn/version.py
+++ b/nilearn/version.py
@@ -30,22 +30,22 @@
 #   in some meaningful order (more => less 'core').
 REQUIRED_MODULE_METADATA = (
     ('numpy', {
-        'min_version': '1.11',
+        'min_version': '1.12',
         'required_at_installation': True,
         'install_info': _NILEARN_INSTALL_MSG}),
     ('scipy', {
-        'min_version': '0.17',
+        'min_version': '0.19',
         'required_at_installation': True,
         'install_info': _NILEARN_INSTALL_MSG}),
     ('sklearn', {
-        'min_version': '0.18',
+        'min_version': '0.19',
         'required_at_installation': True,
         'install_info': _NILEARN_INSTALL_MSG}),
     ('nibabel', {
         'min_version': '2.0.2',
         'required_at_installation': False}))
 
-OPTIONAL_MATPLOTLIB_MIN_VERSION = '1.5.1'
+OPTIONAL_MATPLOTLIB_MIN_VERSION = '2.0'
 
 
 def _import_module_with_version_check(

From bda81612eb382265f926f213c96c8e10da25f41b Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 3 Jul 2019 16:09:14 +0200
Subject: [PATCH 134/228] Removed conda==4.6 version pinning, restored min
 numpy=1.11

---
 .travis.yml                       | 2 +-
 README.rst                        | 2 +-
 continuous_integration/install.sh | 2 +-
 doc/whats_new.rst                 | 1 -
 nilearn/version.py                | 2 +-
 5 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index fcfbd9d3e6..0a6b0a866c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,7 +21,7 @@ matrix:
   include:
     # without matplotlib
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
-           NUMPY_VERSION="1.12" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
+           NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="0.19" COVERAGE="true"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
diff --git a/README.rst b/README.rst
index 6361dede4d..f2d1b50d36 100644
--- a/README.rst
+++ b/README.rst
@@ -40,7 +40,7 @@ The required dependencies to use the software are:
 
 * Python >= 3.5,
 * setuptools
-* Numpy >= 1.12
+* Numpy >= 1.11
 * SciPy >= 0.19
 * Scikit-learn >= 0.19
 * Nibabel >= 2.0.2
diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index e4b33c4ece..894340c9cb 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -67,7 +67,7 @@ create_new_conda_env() {
     chmod +x ~/miniconda.sh && ~/miniconda.sh -b
     export PATH=$HOME/miniconda3/bin:$PATH
     echo $PATH
-    conda install conda=4.6 --yes
+#    conda install conda=4.6 --yes
 
     # Configure the conda environment and put it in the path using the
     # provided versions
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 1c4418781e..75d38b5504 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -9,7 +9,6 @@ NEW
  | **Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.**
  |
  | **Minimum supported versions of packages have been bumped up.**
- | - Numpy -- v1.12
  | - Matplotlib -- v2.0
  | - Scikit-learn -- v0.19
  | - Scipy -- v0.19
diff --git a/nilearn/version.py b/nilearn/version.py
index f26d515e86..8c4bf2dcfa 100644
--- a/nilearn/version.py
+++ b/nilearn/version.py
@@ -30,7 +30,7 @@
 #   in some meaningful order (more => less 'core').
 REQUIRED_MODULE_METADATA = (
     ('numpy', {
-        'min_version': '1.12',
+        'min_version': '1.11',
         'required_at_installation': True,
         'install_info': _NILEARN_INSTALL_MSG}),
     ('scipy', {

From 44573597af41c6342e07d8cd5878d0fa3a12077b Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 3 Jul 2019 19:56:22 +0200
Subject: [PATCH 135/228] Removed commented out line for conda==4.6
 installation

---
 continuous_integration/install.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 894340c9cb..43ef8eaf4b 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -67,7 +67,6 @@ create_new_conda_env() {
     chmod +x ~/miniconda.sh && ~/miniconda.sh -b
     export PATH=$HOME/miniconda3/bin:$PATH
     echo $PATH
-#    conda install conda=4.6 --yes
 
     # Configure the conda environment and put it in the path using the
     # provided versions

From a8d1d46e40b3a6d536273d4db82d40a0f606eecd Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Mon, 8 Jul 2019 17:56:08 +0200
Subject: [PATCH 136/228] fix incorrect docstring in neurovault localizer
 fetchers

---
 nilearn/datasets/neurovault.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/nilearn/datasets/neurovault.py b/nilearn/datasets/neurovault.py
index 0f51b319ac..7843ddcd3e 100644
--- a/nilearn/datasets/neurovault.py
+++ b/nilearn/datasets/neurovault.py
@@ -2629,8 +2629,6 @@ def fetch_neurovault_motor_task(data_dir=None, verbose=1):
     Notes
     ------
 
-    This function is only a caller for the fetch_localizer_contrasts in order
-    to simplify examples reading and understanding.
     The 'left vs right button press' contrast is used:
     https://neurovault.org/images/10426/
 
@@ -2673,8 +2671,6 @@ def fetch_neurovault_auditory_computation_task(data_dir=None, verbose=1):
     Notes
     ------
 
-    This function is only a caller for the fetch_localizer_contrasts in order
-    to simplify examples reading and understanding.
     The 'auditory_calculation_vs_baseline' contrast is used:
     https://neurovault.org/images/32980/
 

From b05c5ec35a97a3ea2bf3f401263d77afa86ffa9e Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Fri, 12 Jul 2019 14:29:26 +0200
Subject: [PATCH 137/228] expose fontsize + white text if black_bg

---
 .../data/html/connectome_plot_template.html   |  5 +++-
 .../data/html/surface_plot_template.html      |  7 ++++--
 .../plotting/data/js/surface-plot-utils.js    | 11 +++++++--
 nilearn/plotting/html_connectome.py           | 24 ++++++++++++++-----
 nilearn/plotting/html_surface.py              | 24 ++++++++++++++-----
 5 files changed, 54 insertions(+), 17 deletions(-)

diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 5329e47de3..11e592a173 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -28,6 +28,8 @@
 
             layout['title'] = {
                 text: connectomeInfo['connectome']['title'],
+                font: {size: connectomeInfo['connectome']["title_fontsize"],
+                       color: textColor(connectomeInfo["black_bg"])},
                 yref: 'paper',
                 y: .9};
 
@@ -53,7 +55,8 @@
                                                  "select-view", false),
                     getConfig(),
                     connectomeInfo["connectome"]["cbar_fontsize"],
-                    connectomeInfo["connectome"]["cbar_height"]);
+                    connectomeInfo["connectome"]["cbar_height"],
+                    textColor(connectomeInfo["black_bg"]));
             }
         }
 
diff --git a/nilearn/plotting/data/html/surface_plot_template.html b/nilearn/plotting/data/html/surface_plot_template.html
index 583bb94e13..ba56e986fe 100644
--- a/nilearn/plotting/data/html/surface_plot_template.html
+++ b/nilearn/plotting/data/html/surface_plot_template.html
@@ -24,8 +24,10 @@
                                    surfaceMapInfo["black_bg"]);
             layout['title'] = {
                 text: surfaceMapInfo['title'],
+                font: {size: surfaceMapInfo["title_fontsize"],
+                       color: textColor(surfaceMapInfo["black_bg"])},
                 yref: 'paper',
-                y: .9};
+                y: .95};
             let config = getConfig();
 
             Plotly.react(divId, data, layout, config);
@@ -36,7 +38,8 @@
                             surfaceMapInfo["cmax"],
                             divId, layout, config,
                             surfaceMapInfo["cbar_fontsize"],
-                            surfaceMapInfo["cbar_height"]);
+                            surfaceMapInfo["cbar_height"],
+                            color=textColor(surfaceMapInfo["black_bg"]));
             }
         }
 
diff --git a/nilearn/plotting/data/js/surface-plot-utils.js b/nilearn/plotting/data/js/surface-plot-utils.js
index 4784a34bc0..29659516b9 100644
--- a/nilearn/plotting/data/js/surface-plot-utils.js
+++ b/nilearn/plotting/data/js/surface-plot-utils.js
@@ -133,12 +133,19 @@ function updateLayout(plotDivId, viewSelectId, blackBg) {
     Plotly.relayout(plotDivId, layout);
 }
 
+function textColor(black_bg){
+    if (black_bg){
+        return "white";
+    }
+    return "black";
+}
+
 function addColorbar(colorscale, cmin, cmax, divId, layout, config,
-                     fontsize=25, height=.5) {
+                     fontsize=25, height=.5, color="black") {
     // hack to get a colorbar
     let dummy = {
         "opacity": 0,
-        "colorbar": {"tickfont": {"size": fontsize},
+        "colorbar": {"tickfont": {"size": fontsize, "color": color},
                      "len": height},
         "type": "mesh3d",
         "colorscale": colorscale,
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 56dfdda0fd..e1d08b1c8d 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -106,7 +106,7 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
                     edge_cmap=cm.bwr, symmetric_cmap=True,
                     linewidth=6., node_size=3., colorbar=True,
                     colorbar_height=.5, colorbar_fontsize=25,
-                    title=None):
+                    title=None, title_fontsize=25):
     """
     Insert a 3d plot of a connectome into an HTML page.
 
@@ -146,6 +146,12 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
     colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
+    title : str, optional (default=None)
+        title for the plot
+
+    title_fontsize : int, optional (default=25)
+        fontsize of the title
+
     Returns
     -------
     ConnectomeView : plot of the connectome.
@@ -177,8 +183,8 @@ def view_connectome(adjacency_matrix, node_coords, edge_threshold=None,
     connectome_info['colorbar'] = colorbar
     connectome_info['cbar_height'] = colorbar_height
     connectome_info['cbar_fontsize'] = colorbar_fontsize
-    if title is not None:
-        connectome_info['title'] = title
+    connectome_info['title'] = title
+    connectome_info['title_fontsize'] = title_fontsize
     return _make_connectome_html(connectome_info)
 
 
@@ -197,7 +203,7 @@ def _replacement_params_view_markers():
                     lib_name='Nilearn',
                     )
 def view_markers(marker_coords, marker_color=None, marker_size=5.,
-                 title=None):
+                 title=None, title_fontsize=25):
     """
     Insert a 3d plot of markers in a brain into an HTML page.
 
@@ -214,6 +220,12 @@ def view_markers(marker_coords, marker_color=None, marker_size=5.,
     marker_size : float or array-like, optional (default=3.)
         Size of the markers showing the seeds in pixels.
 
+    title : str, optional (default=None)
+        title for the plot
+
+    title_fontsize : int, optional (default=25)
+        fontsize of the title
+
     Returns
     -------
     ConnectomeView : plot of the markers.
@@ -243,6 +255,6 @@ def view_markers(marker_coords, marker_color=None, marker_size=5.,
     if hasattr(marker_size, 'tolist'):
         marker_size = marker_size.tolist()
     connectome_info["marker_size"] = marker_size
-    if title is not None:
-        connectome_info['title'] = title
+    connectome_info['title'] = title
+    connectome_info['title_fontsize'] = title_fontsize
     return _make_connectome_html(connectome_info)
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 004aed0fce..858959935d 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -129,7 +129,7 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
                      threshold=None, cmap=cm.cold_hot,
                      black_bg=False, vmax=None, vmin=None, symmetric_cmap=True,
                      colorbar=True, colorbar_height=.5, colorbar_fontsize=25,
-                     title=None):
+                     title=None, title_fontsize=25):
     """
     Insert a surface plot of a statistical map into an HTML page.
 
@@ -185,6 +185,12 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
+    title : str, optional (default=None)
+        title for the plot
+
+    title_fontsize : int, optional (default=25)
+        fontsize of the title
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -208,15 +214,15 @@ def view_img_on_surf(stat_map_img, surf_mesh='fsaverage5',
     info['colorbar'] = colorbar
     info['cbar_height'] = colorbar_height
     info['cbar_fontsize'] = colorbar_fontsize
-    if title is not None:
-        info['title'] = title
+    info['title'] = title
+    info['title_fontsize'] = title_fontsize
     return _fill_html_template(info, embed_js=True)
 
 
 def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
               cmap=cm.cold_hot, black_bg=False, vmax=None, vmin=None,
               symmetric_cmap=True, colorbar=True, colorbar_height=.5,
-              colorbar_fontsize=25, title=None):
+              colorbar_fontsize=25, title=None, title_fontsize=25):
     """
     Insert a surface plot of a surface map into an HTML page.
 
@@ -281,6 +287,12 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     colorbar_fontsize : int, optional (default=25)
         fontsize of the colorbar tick labels
 
+    title : str, optional (default=None)
+        title for the plot
+
+    title_fontsize : int, optional (default=25)
+        fontsize of the title
+
     Returns
     -------
     SurfaceView : plot of the stat map.
@@ -311,6 +323,6 @@ def view_surf(surf_mesh, surf_map=None, bg_map=None, threshold=None,
     info['colorbar'] = colorbar
     info['cbar_height'] = colorbar_height
     info['cbar_fontsize'] = colorbar_fontsize
-    if title is not None:
-        info['title'] = title
+    info['title'] = title
+    info['title_fontsize'] = title_fontsize
     return _fill_html_template(info, embed_js=True)

From 890c5b15353c118b899230f432009d554105f26c Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sat, 13 Jul 2019 14:10:41 +0200
Subject: [PATCH 138/228] update tests

---
 nilearn/plotting/tests/test_html_surface.py      | 2 +-
 nilearn/plotting/tests/test_js_plotting_utils.py | 6 +-----
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/nilearn/plotting/tests/test_html_surface.py b/nilearn/plotting/tests/test_html_surface.py
index 3c31669d76..99fdcc5cbb 100644
--- a/nilearn/plotting/tests/test_html_surface.py
+++ b/nilearn/plotting/tests/test_html_surface.py
@@ -97,7 +97,7 @@ def test_fill_html_template():
     info = html_surface.full_brain_info(img)
     html = html_surface._fill_html_template(info)
     check_html(html)
-    assert "* plotly.js (gl3d - minified) v1.38.3" in html.html
+    assert "* plotly.js (gl3d - minified) v1." in html.html
 
 
 def test_view_surf():
diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index 45e007392b..bbd573f355 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -41,11 +41,7 @@ def test_add_js_lib():
     assert _normalize_ws("""/*! jQuery v3.3.1 | (c) JS Foundation and other
                             contributors | jquery.org/license */""") in inline
     assert _normalize_ws("""**
-                            * plotly.js (gl3d - minified) v1.38.3
-                            * Copyright 2012-2018, Plotly, Inc.
-                            * All rights reserved.
-                            * Licensed under the MIT license
-                            */ """) in inline
+                            * plotly.js (gl3d - minified)""") in inline
     assert "decodeBase64" in inline
 
 

From 6a6d913df1f0c0a975254124d5822bfae28c373b Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Sat, 13 Jul 2019 14:12:39 +0200
Subject: [PATCH 139/228] update whatsnew

---
 doc/whats_new.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 75d38b5504..1a46320c98 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -39,7 +39,8 @@ Changes
   and :func:`nilearn.plotting.view_connectome` now allow disabling the colorbar,
   and setting its height and the fontsize of its ticklabels.
 
-
+- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
+  and :func:`nilearn.plotting.view_connectome` can now display a title.
 
 Fixes
 -----

From 256d8d811e1d9baf32c88bf5532d88dd2e99a6b9 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Tue, 16 Jul 2019 10:38:48 +0200
Subject: [PATCH 140/228] test surface plot title

---
 nilearn/plotting/tests/test_html_connectome.py | 4 +++-
 nilearn/plotting/tests/test_html_surface.py    | 7 +++++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/nilearn/plotting/tests/test_html_connectome.py b/nilearn/plotting/tests/test_html_connectome.py
index 94c454fe81..6a67a67ebb 100644
--- a/nilearn/plotting/tests/test_html_connectome.py
+++ b/nilearn/plotting/tests/test_html_connectome.py
@@ -53,8 +53,10 @@ def test_view_connectome():
     adj, coord = _make_connectome()
     html = html_connectome.view_connectome(adj, coord)
     check_html(html, False, 'connectome-plot')
-    html = html_connectome.view_connectome(adj, coord, '85.3%')
+    html = html_connectome.view_connectome(adj, coord, '85.3%',
+                                           title="SOME_TITLE")
     check_html(html, False, 'connectome-plot')
+    assert "SOME_TITLE" in html.html
     html = html_connectome.view_connectome(adj, coord, '85.3%',
                                            linewidth=8.5, node_size=4.2)
     check_html(html, False, 'connectome-plot')
diff --git a/nilearn/plotting/tests/test_html_surface.py b/nilearn/plotting/tests/test_html_surface.py
index 99fdcc5cbb..14a2b2950f 100644
--- a/nilearn/plotting/tests/test_html_surface.py
+++ b/nilearn/plotting/tests/test_html_surface.py
@@ -108,8 +108,10 @@ def test_view_surf():
                                   fsaverage['sulc_right'], '90%')
     check_html(html)
     html = html_surface.view_surf(fsaverage['pial_right'], surf_map,
-                                  fsaverage['sulc_right'], .3)
+                                  fsaverage['sulc_right'], .3,
+                                  title="SOME_TITLE")
     check_html(html)
+    assert "SOME_TITLE" in html.html
     html = html_surface.view_surf(fsaverage['pial_right'])
     check_html(html)
     destrieux = datasets.fetch_atlas_surf_destrieux()['map_left']
@@ -132,7 +134,8 @@ def test_view_img_on_surf():
     check_html(html)
     html = html_surface.view_img_on_surf(img, threshold=0, surf_mesh=fsaverage)
     check_html(html)
-    html = html_surface.view_img_on_surf(img, threshold=.4)
+    html = html_surface.view_img_on_surf(img, threshold=.4, title="SOME_TITLE")
+    assert "SOME_TITLE" in html.html
     check_html(html)
     html = html_surface.view_img_on_surf(
         img, threshold=.4, cmap='hot', black_bg=True)

From 77d765eb57ca70902f746b594bef5e3713c27520 Mon Sep 17 00:00:00 2001
From: celinede <cdelettre.tts@gmail.com>
Date: Wed, 17 Jul 2019 11:31:20 +0200
Subject: [PATCH 141/228] fix 2082: vol_to_surf samples out of image np.round
 to np.floor

---
 nilearn/surface/surface.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/surface/surface.py b/nilearn/surface/surface.py
index d17b0a27a8..fac0282204 100644
--- a/nilearn/surface/surface.py
+++ b/nilearn/surface/surface.py
@@ -236,7 +236,7 @@ def _masked_indices(sample_locations, img_shape, mask=None):
     for dim, size in enumerate(img_shape):
         kept = np.logical_and(kept, sample_locations[:, dim] < size)
     if mask is not None:
-        indices = np.asarray(np.round(sample_locations[kept]), dtype=int)
+        indices = np.asarray(np.floor(sample_locations[kept]), dtype=int)
         kept[kept] = mask[
             indices[:, 0], indices[:, 1], indices[:, 2]] != 0
     return ~kept

From 53693c23515c8e13537a4af3030227fcc6cdc7e0 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Wed, 17 Jul 2019 15:44:48 +0200
Subject: [PATCH 142/228] don't import joblib from sklearn.externals

---
 examples/03_connectivity/plot_multi_subject_connectome.py | 2 +-
 nilearn/_utils/cache_mixin.py                             | 2 +-
 nilearn/_utils/niimg_conversions.py                       | 2 +-
 nilearn/connectome/group_sparse_cov.py                    | 2 +-
 nilearn/decoding/searchlight.py                           | 2 +-
 nilearn/decoding/space_net.py                             | 2 +-
 nilearn/decoding/tests/test_searchlight.py                | 4 ++--
 nilearn/decomposition/base.py                             | 2 +-
 nilearn/decomposition/canica.py                           | 2 +-
 nilearn/decomposition/dict_learning.py                    | 2 +-
 nilearn/decomposition/multi_pca.py                        | 2 +-
 nilearn/image/image.py                                    | 2 +-
 nilearn/input_data/base_masker.py                         | 2 +-
 nilearn/input_data/multi_nifti_masker.py                  | 2 +-
 nilearn/input_data/nifti_labels_masker.py                 | 2 +-
 nilearn/input_data/nifti_maps_masker.py                   | 2 +-
 nilearn/input_data/nifti_masker.py                        | 2 +-
 nilearn/input_data/nifti_spheres_masker.py                | 2 +-
 nilearn/input_data/tests/test_masker_validation.py        | 2 +-
 nilearn/input_data/tests/test_multi_nifti_masker.py       | 4 ++--
 nilearn/input_data/tests/test_nifti_masker.py             | 2 +-
 nilearn/masking.py                                        | 2 +-
 nilearn/mass_univariate/permuted_least_squares.py         | 2 +-
 nilearn/regions/parcellations.py                          | 2 +-
 nilearn/regions/region_extractor.py                       | 2 +-
 nilearn/regions/rena_clustering.py                        | 2 +-
 nilearn/regions/tests/test_rena_clustering.py             | 2 +-
 nilearn/tests/test_cache_mixin.py                         | 2 +-
 nilearn/tests/test_niimg.py                               | 2 +-
 29 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index c7f45f0ce4..9e7ed5d2b6 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -52,7 +52,7 @@ def plot_matrices(cov, prec, title, labels):
 from nilearn import input_data
 
 # A "memory" to avoid recomputation
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 mem = Memory('nilearn_cache')
 
 masker = input_data.NiftiMapsMasker(
diff --git a/nilearn/_utils/cache_mixin.py b/nilearn/_utils/cache_mixin.py
index 30bde14440..d8be896758 100644
--- a/nilearn/_utils/cache_mixin.py
+++ b/nilearn/_utils/cache_mixin.py
@@ -13,7 +13,7 @@
 import nibabel
 import sklearn
 
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 MEMORY_CLASSES = (Memory, )
 
diff --git a/nilearn/_utils/niimg_conversions.py b/nilearn/_utils/niimg_conversions.py
index efdfad485c..a534db656b 100644
--- a/nilearn/_utils/niimg_conversions.py
+++ b/nilearn/_utils/niimg_conversions.py
@@ -10,7 +10,7 @@
 import nilearn as ni
 import numpy as np
 import itertools
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .cache_mixin import cache
 from .niimg import _safe_get_data, load_niimg
diff --git a/nilearn/connectome/group_sparse_cov.py b/nilearn/connectome/group_sparse_cov.py
index f43076b992..24f3086244 100644
--- a/nilearn/connectome/group_sparse_cov.py
+++ b/nilearn/connectome/group_sparse_cov.py
@@ -15,7 +15,7 @@
 
 from sklearn.base import BaseEstimator
 from sklearn.covariance import empirical_covariance
-from sklearn.externals.joblib import Memory, delayed, Parallel
+from joblib import Memory, delayed, Parallel
 from sklearn.model_selection import check_cv
 from sklearn.utils.extmath import fast_logdet
 
diff --git a/nilearn/decoding/searchlight.py b/nilearn/decoding/searchlight.py
index 5829b1ff5b..80c6c5740e 100644
--- a/nilearn/decoding/searchlight.py
+++ b/nilearn/decoding/searchlight.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from sklearn.externals.joblib import Parallel, delayed, cpu_count
+from joblib import Parallel, delayed, cpu_count
 from sklearn import svm
 from sklearn.base import BaseEstimator
 from sklearn.exceptions import ConvergenceWarning
diff --git a/nilearn/decoding/space_net.py b/nilearn/decoding/space_net.py
index 9867b3ca21..3b312a15e3 100644
--- a/nilearn/decoding/space_net.py
+++ b/nilearn/decoding/space_net.py
@@ -24,7 +24,7 @@
 from sklearn.linear_model.base import LinearModel
 from sklearn.feature_selection import (SelectPercentile, f_regression,
                                        f_classif)
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from joblib import Memory, Parallel, delayed
 from sklearn.preprocessing import LabelBinarizer
 from sklearn.metrics import accuracy_score
 from ..input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/decoding/tests/test_searchlight.py b/nilearn/decoding/tests/test_searchlight.py
index c731ad92e0..fc7b42f9df 100644
--- a/nilearn/decoding/tests/test_searchlight.py
+++ b/nilearn/decoding/tests/test_searchlight.py
@@ -100,10 +100,10 @@ def test_searchlight():
     rand = np.random.RandomState(0)
     data = rand.rand(5, 5, 5)
     data_img = nibabel.Nifti1Image(data, affine=np.eye(4))
-    imgs = [data_img, data_img, data_img, data_img, data_img, data_img]
+    imgs = [data_img] * 12
 
     # labels
-    y = [0, 1, 0, 1, 0, 1]
+    y = [0, 1] * 6
 
     # run searchlight on list of 3D images
     sl = searchlight.SearchLight(mask_img)
diff --git a/nilearn/decomposition/base.py b/nilearn/decomposition/base.py
index bb6fad4044..0a282072a6 100644
--- a/nilearn/decomposition/base.py
+++ b/nilearn/decomposition/base.py
@@ -14,7 +14,7 @@
 import sklearn
 import nilearn
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from joblib import Memory, Parallel, delayed
 from sklearn.linear_model import LinearRegression
 from sklearn.utils import check_random_state
 from sklearn.utils.extmath import randomized_svd, svd_flip
diff --git a/nilearn/decomposition/canica.py b/nilearn/decomposition/canica.py
index ef6d77d63a..368ce7c7b1 100644
--- a/nilearn/decomposition/canica.py
+++ b/nilearn/decomposition/canica.py
@@ -10,7 +10,7 @@
 import numpy as np
 from scipy.stats import scoreatpercentile
 from sklearn.decomposition import fastica
-from sklearn.externals.joblib import Memory, delayed, Parallel
+from joblib import Memory, delayed, Parallel
 from sklearn.utils import check_random_state
 
 from .multi_pca import MultiPCA
diff --git a/nilearn/decomposition/dict_learning.py b/nilearn/decomposition/dict_learning.py
index e31b2e734f..c8f02dd90e 100644
--- a/nilearn/decomposition/dict_learning.py
+++ b/nilearn/decomposition/dict_learning.py
@@ -15,7 +15,7 @@
 import numpy as np
 import sklearn
 from sklearn.decomposition import dict_learning_online
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 from sklearn.linear_model import Ridge
 
 from .base import BaseDecomposition
diff --git a/nilearn/decomposition/multi_pca.py b/nilearn/decomposition/multi_pca.py
index 5b888c615d..9d66a2bc45 100644
--- a/nilearn/decomposition/multi_pca.py
+++ b/nilearn/decomposition/multi_pca.py
@@ -3,7 +3,7 @@
 This is a good initialization method for ICA.
 """
 import numpy as np
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 from sklearn.utils.extmath import randomized_svd
 
 from .base import BaseDecomposition
diff --git a/nilearn/image/image.py b/nilearn/image/image.py
index 0387ee1ea5..0f6faffe5c 100644
--- a/nilearn/image/image.py
+++ b/nilearn/image/image.py
@@ -14,7 +14,7 @@
 from scipy.stats import scoreatpercentile
 import copy
 import nibabel
-from sklearn.externals.joblib import Parallel, delayed
+from joblib import Parallel, delayed
 
 from .. import signal
 from .._utils import (check_niimg_4d, check_niimg_3d, check_niimg, as_ndarray,
diff --git a/nilearn/input_data/base_masker.py b/nilearn/input_data/base_masker.py
index f90b54785f..8c8f68d007 100644
--- a/nilearn/input_data/base_masker.py
+++ b/nilearn/input_data/base_masker.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .. import masking
 from .. import image
diff --git a/nilearn/input_data/multi_nifti_masker.py b/nilearn/input_data/multi_nifti_masker.py
index f0f5737684..863c925784 100644
--- a/nilearn/input_data/multi_nifti_masker.py
+++ b/nilearn/input_data/multi_nifti_masker.py
@@ -8,7 +8,7 @@
 import itertools
 import warnings
 
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from joblib import Memory, Parallel, delayed
 
 from .. import _utils
 from .. import image
diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index f4e6dca9ea..a754e8b232 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin, _compose_err_msg
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index 0cb57567f9..89dfbe1f76 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -3,7 +3,7 @@
 """
 
 import numpy as np
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 9a752b7c89..8fe6438052 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -6,7 +6,7 @@
 
 from copy import copy as copy_object
 
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .base_masker import BaseMasker, filter_and_extract
 from .. import _utils
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index fc7e829d58..42e1f6ccc5 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -7,7 +7,7 @@
 import numpy as np
 import warnings
 from sklearn import neighbors
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from ..image.resampling import coord_transform
 from .._utils.niimg_conversions import _safe_get_data
diff --git a/nilearn/input_data/tests/test_masker_validation.py b/nilearn/input_data/tests/test_masker_validation.py
index 7972769919..df62565099 100644
--- a/nilearn/input_data/tests/test_masker_validation.py
+++ b/nilearn/input_data/tests/test_masker_validation.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from nilearn._utils.testing import assert_warns
 from nilearn.input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/input_data/tests/test_multi_nifti_masker.py b/nilearn/input_data/tests/test_multi_nifti_masker.py
index 620864d10c..2cd4e268b7 100644
--- a/nilearn/input_data/tests/test_multi_nifti_masker.py
+++ b/nilearn/input_data/tests/test_multi_nifti_masker.py
@@ -14,7 +14,7 @@
 from nose import SkipTest
 from nose.tools import assert_true, assert_false, assert_raises, assert_equal
 from numpy.testing import assert_array_equal
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from nilearn._utils.exceptions import DimensionError
 from nilearn._utils.testing import assert_raises_regex, write_tmp_imgs
@@ -116,7 +116,7 @@ def test_3d_images():
 
 
 def test_joblib_cache():
-    from sklearn.externals.joblib import hash
+    from joblib import hash
     # Dummy mask
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
diff --git a/nilearn/input_data/tests/test_nifti_masker.py b/nilearn/input_data/tests/test_nifti_masker.py
index 4c857ff820..c68e45233d 100644
--- a/nilearn/input_data/tests/test_nifti_masker.py
+++ b/nilearn/input_data/tests/test_nifti_masker.py
@@ -233,7 +233,7 @@ def test_sessions():
 
 
 def test_joblib_cache():
-    from sklearn.externals.joblib import hash, Memory
+    from joblib import hash, Memory
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
     mask_img = Nifti1Image(mask, np.eye(4))
diff --git a/nilearn/masking.py b/nilearn/masking.py
index 88d0453cff..b340e12e28 100644
--- a/nilearn/masking.py
+++ b/nilearn/masking.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 from scipy import ndimage
-from sklearn.externals.joblib import Parallel, delayed
+from joblib import Parallel, delayed
 
 from . import _utils
 from .image import new_img_like
diff --git a/nilearn/mass_univariate/permuted_least_squares.py b/nilearn/mass_univariate/permuted_least_squares.py
index bf4e616bf6..02ce7c3cf2 100644
--- a/nilearn/mass_univariate/permuted_least_squares.py
+++ b/nilearn/mass_univariate/permuted_least_squares.py
@@ -8,7 +8,7 @@
 import numpy as np
 from scipy import linalg
 from sklearn.utils import check_random_state
-import sklearn.externals.joblib as joblib
+import joblib as joblib
 
 
 def normalize_matrix_on_axis(m, axis=0):
diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 1c4932ce9e..141b838a8c 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -5,7 +5,7 @@
 
 from sklearn.base import clone
 from sklearn.feature_extraction import image
-from sklearn.externals.joblib import Memory, delayed, Parallel
+from joblib import Memory, delayed, Parallel
 
 from .rena_clustering import ReNA
 from ..decomposition.multi_pca import MultiPCA
diff --git a/nilearn/regions/region_extractor.py b/nilearn/regions/region_extractor.py
index a8fd9b4bfe..0d807cb238 100644
--- a/nilearn/regions/region_extractor.py
+++ b/nilearn/regions/region_extractor.py
@@ -9,7 +9,7 @@
 from scipy import ndimage
 from scipy.stats import scoreatpercentile
 
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 from .. import masking
 from ..input_data import NiftiMapsMasker
diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index e3dfaa5aa9..ca32b8346c 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -8,7 +8,7 @@
 import warnings
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
 try:
-    from sklearn.externals.joblib import Memory
+    from joblib import Memory
 except ImportError:
     from joblib import Memory
 from sklearn.base import TransformerMixin, ClusterMixin
diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index d491c29795..11b6585793 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,7 +1,7 @@
 import numpy as np
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 try:
-    from sklearn.externals.joblib import Memory
+    from joblib import Memory
 except ImportError:
     from joblib import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
diff --git a/nilearn/tests/test_cache_mixin.py b/nilearn/tests/test_cache_mixin.py
index 0578c60a68..ff42e17337 100644
--- a/nilearn/tests/test_cache_mixin.py
+++ b/nilearn/tests/test_cache_mixin.py
@@ -10,7 +10,7 @@
 
 import sklearn
 from nose.tools import assert_false, assert_true, assert_equal
-from sklearn.externals.joblib import Memory
+from joblib import Memory
 
 import nilearn
 from nilearn._utils import cache_mixin, CacheMixin
diff --git a/nilearn/tests/test_niimg.py b/nilearn/tests/test_niimg.py
index dfc4900e02..3b7fe02fcf 100644
--- a/nilearn/tests/test_niimg.py
+++ b/nilearn/tests/test_niimg.py
@@ -6,7 +6,7 @@
 import nibabel as nb
 from nibabel import Nifti1Image
 from nibabel.tmpdirs import InTemporaryDirectory
-from sklearn.externals import joblib
+import joblib
 
 from nilearn.image import new_img_like
 from nilearn._utils import niimg

From 57bd2115baea1fbdb6b97ec7960bbfc4545c5b32 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Wed, 17 Jul 2019 15:59:54 +0200
Subject: [PATCH 143/228] update requirements doc and config

---
 .circleci/config.yml                                    | 1 +
 .travis.yml                                             | 6 ++++--
 README.rst                                              | 1 +
 appveyor.yml                                            | 2 +-
 continuous_integration/install.sh                       | 4 ++--
 continuous_integration/show-python-packages-versions.py | 2 +-
 doc/install_doc_component.html                          | 9 ++++++---
 doc/whats_new.rst                                       | 2 ++
 nilearn/version.py                                      | 4 ++++
 requirements-build-docs.txt                             | 1 +
 requirements-dev.txt                                    | 1 +
 11 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 95000f104e..b4bde91685 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -15,6 +15,7 @@ jobs:
       NUMPY_VERSION: "*"
       SCIPY_VERSION: "*"
       SCIKIT_LEARN_VERSION: "*"
+      JOBLIB_VERSION: "*"_
       MATPLOTLIB_VERSION: "*"
 
     steps:
diff --git a/.travis.yml b/.travis.yml
index 0a6b0a866c..b9480dbf90 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -22,20 +22,22 @@ matrix:
     # without matplotlib
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
            NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
-           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true"
+           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true" JOBLIB_VERSION="*"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.5"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
+           JOBLIB_VERSION="0.11"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.6"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
+           JOBLIB_VERSION="*"
            LXML_VERSION="*"
     - env: DISTRIB="conda" PYTHON_VERSION="3.7"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
-           LXML_VERSION="*"
+           JOBLIB_VERSION="0.12" LXML_VERSION="*"
 
   # FLAKE8 linting on diff wrt common ancestor with upstream/master
     # Note: the python value is only there to trigger allow_failures
diff --git a/README.rst b/README.rst
index f2d1b50d36..e2f42830a9 100644
--- a/README.rst
+++ b/README.rst
@@ -43,6 +43,7 @@ The required dependencies to use the software are:
 * Numpy >= 1.11
 * SciPy >= 0.19
 * Scikit-learn >= 0.19
+* Joblib >= 0.11
 * Nibabel >= 2.0.2
 
 If you are using nilearn plotting functionalities or running the
diff --git a/appveyor.yml b/appveyor.yml
index 605c79581b..0cc65dbcf3 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -24,7 +24,7 @@ install:
   # See similar fix which made for travis and circleci
   # https://github.com/nilearn/nilearn/pull/1525
   # Should be removed after a new matplotlib release 2.1.1
-  - "conda install pip numpy scipy scikit-learn nose wheel matplotlib -y -q"
+  - "conda install pip numpy scipy scikit-learn joblib nose wheel matplotlib -y -q"
 
   # Install other nilearn dependencies
   - "pip install nibabel coverage nose-timer"
diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 43ef8eaf4b..a335b7fce0 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -36,7 +36,7 @@ print_conda_requirements() {
     TO_INSTALL_ALWAYS="pip nose"
     REQUIREMENTS="$TO_INSTALL_ALWAYS"
     TO_INSTALL_MAYBE="python numpy scipy matplotlib scikit-learn pandas \
-flake8 lxml"
+flake8 lxml joblib"
     for PACKAGE in $TO_INSTALL_MAYBE; do
         # Capitalize package name and add _VERSION
         PACKAGE_VERSION_VARNAME="${PACKAGE^^}_VERSION"
@@ -90,7 +90,7 @@ if [[ "$DISTRIB" == "neurodebian" ]]; then
     create_new_venv
     pip install nose-timer
     bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh)
-    sudo apt-get install -qq python-scipy python-nose python-nibabel python-sklearn
+    sudo apt-get install -qq python-scipy python-nose python-nibabel python-sklearn python-joblib
 
 elif [[ "$DISTRIB" == "conda" ]]; then
     create_new_conda_env
diff --git a/continuous_integration/show-python-packages-versions.py b/continuous_integration/show-python-packages-versions.py
index 1822dd172e..3c2f3319a3 100644
--- a/continuous_integration/show-python-packages-versions.py
+++ b/continuous_integration/show-python-packages-versions.py
@@ -1,6 +1,6 @@
 import sys
 
-DEPENDENCIES = ['numpy', 'scipy', 'sklearn', 'matplotlib', 'nibabel']
+DEPENDENCIES = ['numpy', 'scipy', 'sklearn', 'joblib', 'matplotlib', 'nibabel']
 
 
 def print_package_version(package_name, indent='  '):
diff --git a/doc/install_doc_component.html b/doc/install_doc_component.html
index c6584d3961..d1e97934b9 100644
--- a/doc/install_doc_component.html
+++ b/doc/install_doc_component.html
@@ -53,7 +53,8 @@
        as an alternative.</p>
 
       <p>Nilearn requires a Python installation and the following
-      dependencies: ipython, scipy, scikit-learn, matplotlib and nibabel.</p>
+          dependencies: ipython, scipy, scikit-learn, joblib, matplotlib
+          and nibabel.</p>
 
       <p class="emphasize">Second: open a Command Prompt</p>
       <p><strong>(Press "Win-R", type "cmd" and press "Enter". This will open
@@ -83,7 +84,8 @@
       it will save you time and trouble.</p>
 
       <p>Nilearn requires a Python installation and the following
-      dependencies: ipython, scipy, scikit-learn, matplotlib and nibabel.</p>
+          dependencies: ipython, scipy, scikit-learn, joblib,
+          matplotlib and nibabel.</p>
 
       <p class="emphasize">Second: open a Terminal</p>
       <p><strong>(Navigate to /Applications/Utilities and double-click on
@@ -114,7 +116,8 @@
       <p>Install or ask your system administrator to install the following
       packages using the distribution package manager: <strong>ipython</strong>
       , <strong>scipy</strong>, <strong>scikit-learn</strong> (sometimes called <cite>sklearn</cite>,
-      or <cite>python-sklearn</cite>), <strong>matplotlib</strong> (sometimes
+          or <cite>python-sklearn</cite>), <strong>joblib</strong>,
+          <strong>matplotlib</strong> (sometimes
       called <cite>python-matplotlib</cite>) and <strong>nibabel</strong>
       (sometimes called <cite>python-nibabel</cite>).</p>
 
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 75d38b5504..20f0152335 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -13,6 +13,8 @@ NEW
  | - Scikit-learn -- v0.19
  | - Scipy -- v0.19
 
+- joblib is now a dependency
+
 - Parcellation method ReNA: Fast agglomerative clustering based on recursive
   nearest neighbor grouping.
   Yields very fast & accurate models, without creation of giant
diff --git a/nilearn/version.py b/nilearn/version.py
index 8c4bf2dcfa..913654d4f5 100644
--- a/nilearn/version.py
+++ b/nilearn/version.py
@@ -41,6 +41,10 @@
         'min_version': '0.19',
         'required_at_installation': True,
         'install_info': _NILEARN_INSTALL_MSG}),
+    ('joblib', {
+        'min_version': '0.11',
+        'required_at_installation': True,
+        'install_info': _NILEARN_INSTALL_MSG}),
     ('nibabel', {
         'min_version': '2.0.2',
         'required_at_installation': False}))
diff --git a/requirements-build-docs.txt b/requirements-build-docs.txt
index 88aad0f937..27e6262424 100644
--- a/requirements-build-docs.txt
+++ b/requirements-build-docs.txt
@@ -17,4 +17,5 @@ pandas
 nose-timer
 nibabel
 scikit-learn
+joblib
 matplotlib
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 1ae2fca753..e2e583d550 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -4,5 +4,6 @@ boto3
 patsy
 scipy
 scikit-learn
+joblib
 pandas
 matplotlib

From 8dd08ccad865c0d301267c2a415f9c4b0b518ea1 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Wed, 17 Jul 2019 16:03:48 +0200
Subject: [PATCH 144/228] typo in config

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index b4bde91685..9ec84ad53f 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -15,7 +15,7 @@ jobs:
       NUMPY_VERSION: "*"
       SCIPY_VERSION: "*"
       SCIKIT_LEARN_VERSION: "*"
-      JOBLIB_VERSION: "*"_
+      JOBLIB_VERSION: "*"
       MATPLOTLIB_VERSION: "*"
 
     steps:

From 2b48243268e2a0797ea52934e4963eef3498e9e7 Mon Sep 17 00:00:00 2001
From: Daniel Gomez <d.gomez@posteo.org>
Date: Wed, 17 Jul 2019 16:47:59 -0400
Subject: [PATCH 145/228] Add copy=True parameter to threshold_img

The threshold_img was not following the conventions of the other
image functions and was performing in-place thresholding without
offering the possibility to turn it off. This is now fixed.
---
 nilearn/image/image.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/nilearn/image/image.py b/nilearn/image/image.py
index 0387ee1ea5..8f442b85cb 100644
--- a/nilearn/image/image.py
+++ b/nilearn/image/image.py
@@ -172,8 +172,8 @@ def _smooth_array(arr, affine, fwhm=None, ensure_finite=True, copy=True):
         filtering.
 
     copy: bool
-        if True, input array is not modified. False by default: the filtering
-        is performed in-place.
+        if True, input array is not modified. True by default: the filtering
+        is not performed in-place.
 
     Returns
     -------
@@ -705,7 +705,7 @@ def new_img_like(ref_niimg, data, affine=None, copy_header=False):
     return klass(data, affine, header=header)
 
 
-def threshold_img(img, threshold, mask_img=None):
+def threshold_img(img, threshold, mask_img=None, copy=True):
     """ Threshold the given input image, mostly statistical or atlas images.
 
     Thresholding can be done based on direct image intensities or selection
@@ -732,6 +732,10 @@ def threshold_img(img, threshold, mask_img=None):
         Mask image applied to mask the input data.
         If None, no masking will be applied.
 
+    copy: bool
+        if True, input array is not modified. True by default: the filtering
+        is not performed in-place.
+
     Returns
     -------
     threshold_img: Nifti1Image
@@ -742,6 +746,8 @@ def threshold_img(img, threshold, mask_img=None):
 
     img = check_niimg(img)
     img_data = _safe_get_data(img, ensure_finite=True)
+    if copy:
+        img_data = img_data.copy()
     affine = img.affine
 
     if mask_img is not None:

From e82d8ec73de61fa85bc65108611e7046484c3fd9 Mon Sep 17 00:00:00 2001
From: Daniel Gomez <d.gomez@posteo.org>
Date: Wed, 17 Jul 2019 17:20:09 -0400
Subject: [PATCH 146/228] Keep old behaviour in the threshold image examples

---
 examples/02_decoding/plot_miyawaki_encoding.py                | 2 +-
 .../plot_extract_rois_statistical_maps.py                     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/02_decoding/plot_miyawaki_encoding.py b/examples/02_decoding/plot_miyawaki_encoding.py
index 8520076823..e0e3b3697b 100644
--- a/examples/02_decoding/plot_miyawaki_encoding.py
+++ b/examples/02_decoding/plot_miyawaki_encoding.py
@@ -152,7 +152,7 @@
 # bring the scores into the shape of the background brain
 score_map_img = masker.inverse_transform(cut_score)
 
-thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6)
+thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6, copy=False)
 
 ##############################################################################
 # Plotting the statistical map on a background brain, we mark four voxels
diff --git a/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py b/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
index 72773ff068..d63f7518c6 100644
--- a/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
+++ b/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
@@ -28,12 +28,12 @@
 
 # Two types of strategies can be used from this threshold function
 # Type 1: strategy used will be based on scoreatpercentile
-threshold_percentile_img = threshold_img(tmap_filename, threshold='97%')
+threshold_percentile_img = threshold_img(tmap_filename, threshold='97%', copy=False)
 
 
 # Type 2: threshold strategy used will be based on image intensity
 # Here, threshold value should be within the limits i.e. less than max value.
-threshold_value_img = threshold_img(tmap_filename, threshold=3.0)
+threshold_value_img = threshold_img(tmap_filename, threshold=3.0, copy=False)
 
 ################################################################################
 # Visualization

From cbf114393a79ee9e31e7f0a399aac178e4e192ac Mon Sep 17 00:00:00 2001
From: Daniel Gomez <d.gomez@posteo.org>
Date: Wed, 17 Jul 2019 17:20:40 -0400
Subject: [PATCH 147/228] threshold_img behaviour changes in RegionExtractor.
 Now copy

Although the default already changed to copy, we make this explicit here.
---
 nilearn/regions/region_extractor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/regions/region_extractor.py b/nilearn/regions/region_extractor.py
index a8fd9b4bfe..fab04b4b17 100644
--- a/nilearn/regions/region_extractor.py
+++ b/nilearn/regions/region_extractor.py
@@ -403,7 +403,7 @@ def fit(self, X=None, y=None):
             else:
                 if self.thresholding_strategy == 'percentile':
                     self.threshold = "{0}%".format(self.threshold)
-                threshold_maps = threshold_img(maps_img, mask_img=self.mask_img,
+                threshold_maps = threshold_img(maps_img, mask_img=self.mask_img, copy=True,
                                                threshold=self.threshold)
 
         # connected component extraction

From 05246c6f8d1fc38c266514f0d4cea905787fc67b Mon Sep 17 00:00:00 2001
From: Daniel Gomez <d.gomez@posteo.org>
Date: Wed, 17 Jul 2019 17:21:28 -0400
Subject: [PATCH 148/228] Add a test to check that threshold_img copy is
 working.

---
 nilearn/image/tests/test_image.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/nilearn/image/tests/test_image.py b/nilearn/image/tests/test_image.py
index 92232c75e2..fe78003965 100644
--- a/nilearn/image/tests/test_image.py
+++ b/nilearn/image/tests/test_image.py
@@ -499,6 +499,27 @@ def test_threshold_img():
         # when threshold is a percentile
         thr_maps_percent2 = threshold_img(img, threshold='2%')
 
+def test_threshold_img_copy():
+
+    img_zeros = Nifti1Image(np.zeros((10, 10, 10, 10)), np.eye(4))
+    img_ones = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
+
+    # Check that copy does not mutate. It returns modified copy.
+    thresholded = threshold_img(img_ones, 2)  # threshold 2 > 1
+
+    # Original img_ones should have all ones.
+    assert_array_equal(img_ones.get_data(), np.ones((10, 10, 10, 10)))
+    # Thresholded should have all zeros.
+    assert_array_equal(thresholded.get_data(), np.zeros((10, 10, 10, 10)))
+
+    # Check that not copying does mutate.
+    img_to_mutate = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
+    thresholded = threshold_img(img_to_mutate, 2, copy=False)
+    # Check that original mutates
+    assert_array_equal(img_to_mutate.get_data(), np.zeros((10, 10, 10, 10)))
+    # And that returned value is also thresholded.
+    assert_array_equal(img_to_mutate.get_data(), thresholded.get_data())
+
 
 def test_isnan_threshold_img_data():
     shape = (10, 10, 10)

From 3519c39d91b2b764d9f1c6e6ce7aafc54f4a3667 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 18 Jul 2019 15:02:17 +0200
Subject: [PATCH 149/228] joblib in compat

---
 nilearn/_utils/compat.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/nilearn/_utils/compat.py b/nilearn/_utils/compat.py
index f76de77cae..f7eaa9ca28 100644
--- a/nilearn/_utils/compat.py
+++ b/nilearn/_utils/compat.py
@@ -7,6 +7,7 @@
 from distutils.version import LooseVersion
 
 import nibabel
+import sklearn
 
 
 if sys.version_info[0] == 3:
@@ -66,3 +67,9 @@ def md5_hash(string):
         m = hashlib.md5()
         m.update(string)
         return m.hexdigest()
+
+
+if sklearn.__version__ < '0.21':
+    from sklearn.utils.compat import joblib
+else:
+    import joblib

From a518ba03ac41e7efbaf8f1990ac4c7ecc0de0237 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 18 Jul 2019 15:24:35 +0200
Subject: [PATCH 150/228] import joblib from compat

---
 examples/03_connectivity/plot_multi_subject_connectome.py | 2 +-
 nilearn/_utils/cache_mixin.py                             | 4 ++--
 nilearn/_utils/niimg_conversions.py                       | 2 +-
 nilearn/connectome/group_sparse_cov.py                    | 2 +-
 nilearn/decoding/searchlight.py                           | 2 +-
 nilearn/decoding/space_net.py                             | 2 +-
 nilearn/decomposition/base.py                             | 2 +-
 nilearn/decomposition/canica.py                           | 2 +-
 nilearn/decomposition/dict_learning.py                    | 2 +-
 nilearn/decomposition/multi_pca.py                        | 2 +-
 nilearn/image/image.py                                    | 2 +-
 nilearn/input_data/base_masker.py                         | 2 +-
 nilearn/input_data/multi_nifti_masker.py                  | 2 +-
 nilearn/input_data/nifti_labels_masker.py                 | 2 +-
 nilearn/input_data/nifti_maps_masker.py                   | 2 +-
 nilearn/input_data/nifti_masker.py                        | 2 +-
 nilearn/input_data/nifti_spheres_masker.py                | 2 +-
 nilearn/input_data/tests/test_masker_validation.py        | 2 +-
 nilearn/input_data/tests/test_multi_nifti_masker.py       | 4 ++--
 nilearn/input_data/tests/test_nifti_masker.py             | 2 +-
 nilearn/masking.py                                        | 2 +-
 nilearn/mass_univariate/permuted_least_squares.py         | 2 +-
 nilearn/regions/parcellations.py                          | 2 +-
 nilearn/regions/region_extractor.py                       | 2 +-
 nilearn/regions/rena_clustering.py                        | 4 ++--
 nilearn/regions/tests/test_rena_clustering.py             | 4 ++--
 nilearn/tests/test_cache_mixin.py                         | 2 +-
 nilearn/tests/test_niimg.py                               | 2 +-
 28 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index 9e7ed5d2b6..3fee94f0ea 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -52,7 +52,7 @@ def plot_matrices(cov, prec, title, labels):
 from nilearn import input_data
 
 # A "memory" to avoid recomputation
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 mem = Memory('nilearn_cache')
 
 masker = input_data.NiftiMapsMasker(
diff --git a/nilearn/_utils/cache_mixin.py b/nilearn/_utils/cache_mixin.py
index d8be896758..0266759796 100644
--- a/nilearn/_utils/cache_mixin.py
+++ b/nilearn/_utils/cache_mixin.py
@@ -13,12 +13,12 @@
 import nibabel
 import sklearn
 
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 MEMORY_CLASSES = (Memory, )
 
 try:
-    from joblib import Memory as JoblibMemory
+    from nilearn._utils.compat.joblib import Memory as JoblibMemory
     MEMORY_CLASSES = (Memory, JoblibMemory)
 except ImportError:
     pass
diff --git a/nilearn/_utils/niimg_conversions.py b/nilearn/_utils/niimg_conversions.py
index a534db656b..41da21f81e 100644
--- a/nilearn/_utils/niimg_conversions.py
+++ b/nilearn/_utils/niimg_conversions.py
@@ -10,7 +10,7 @@
 import nilearn as ni
 import numpy as np
 import itertools
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .cache_mixin import cache
 from .niimg import _safe_get_data, load_niimg
diff --git a/nilearn/connectome/group_sparse_cov.py b/nilearn/connectome/group_sparse_cov.py
index 24f3086244..f8429bce8c 100644
--- a/nilearn/connectome/group_sparse_cov.py
+++ b/nilearn/connectome/group_sparse_cov.py
@@ -15,7 +15,7 @@
 
 from sklearn.base import BaseEstimator
 from sklearn.covariance import empirical_covariance
-from joblib import Memory, delayed, Parallel
+from nilearn._utils.compat.joblib import Memory, delayed, Parallel
 from sklearn.model_selection import check_cv
 from sklearn.utils.extmath import fast_logdet
 
diff --git a/nilearn/decoding/searchlight.py b/nilearn/decoding/searchlight.py
index 80c6c5740e..2ab9a1473d 100644
--- a/nilearn/decoding/searchlight.py
+++ b/nilearn/decoding/searchlight.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from joblib import Parallel, delayed, cpu_count
+from nilearn._utils.compat.joblib import Parallel, delayed, cpu_count
 from sklearn import svm
 from sklearn.base import BaseEstimator
 from sklearn.exceptions import ConvergenceWarning
diff --git a/nilearn/decoding/space_net.py b/nilearn/decoding/space_net.py
index 3b312a15e3..aa9cb4769b 100644
--- a/nilearn/decoding/space_net.py
+++ b/nilearn/decoding/space_net.py
@@ -24,7 +24,7 @@
 from sklearn.linear_model.base import LinearModel
 from sklearn.feature_selection import (SelectPercentile, f_regression,
                                        f_classif)
-from joblib import Memory, Parallel, delayed
+from nilearn._utils.compat.joblib import Memory, Parallel, delayed
 from sklearn.preprocessing import LabelBinarizer
 from sklearn.metrics import accuracy_score
 from ..input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/decomposition/base.py b/nilearn/decomposition/base.py
index 0a282072a6..dee190017f 100644
--- a/nilearn/decomposition/base.py
+++ b/nilearn/decomposition/base.py
@@ -14,7 +14,7 @@
 import sklearn
 import nilearn
 from sklearn.base import BaseEstimator, TransformerMixin
-from joblib import Memory, Parallel, delayed
+from nilearn._utils.compat.joblib import Memory, Parallel, delayed
 from sklearn.linear_model import LinearRegression
 from sklearn.utils import check_random_state
 from sklearn.utils.extmath import randomized_svd, svd_flip
diff --git a/nilearn/decomposition/canica.py b/nilearn/decomposition/canica.py
index 368ce7c7b1..a1204dc6ec 100644
--- a/nilearn/decomposition/canica.py
+++ b/nilearn/decomposition/canica.py
@@ -10,7 +10,7 @@
 import numpy as np
 from scipy.stats import scoreatpercentile
 from sklearn.decomposition import fastica
-from joblib import Memory, delayed, Parallel
+from nilearn._utils.compat.joblib import Memory, delayed, Parallel
 from sklearn.utils import check_random_state
 
 from .multi_pca import MultiPCA
diff --git a/nilearn/decomposition/dict_learning.py b/nilearn/decomposition/dict_learning.py
index c8f02dd90e..1243b91309 100644
--- a/nilearn/decomposition/dict_learning.py
+++ b/nilearn/decomposition/dict_learning.py
@@ -15,7 +15,7 @@
 import numpy as np
 import sklearn
 from sklearn.decomposition import dict_learning_online
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 from sklearn.linear_model import Ridge
 
 from .base import BaseDecomposition
diff --git a/nilearn/decomposition/multi_pca.py b/nilearn/decomposition/multi_pca.py
index 9d66a2bc45..b5d6030b09 100644
--- a/nilearn/decomposition/multi_pca.py
+++ b/nilearn/decomposition/multi_pca.py
@@ -3,7 +3,7 @@
 This is a good initialization method for ICA.
 """
 import numpy as np
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 from sklearn.utils.extmath import randomized_svd
 
 from .base import BaseDecomposition
diff --git a/nilearn/image/image.py b/nilearn/image/image.py
index 0f6faffe5c..c83125a597 100644
--- a/nilearn/image/image.py
+++ b/nilearn/image/image.py
@@ -14,7 +14,7 @@
 from scipy.stats import scoreatpercentile
 import copy
 import nibabel
-from joblib import Parallel, delayed
+from nilearn._utils.compat.joblib import Parallel, delayed
 
 from .. import signal
 from .._utils import (check_niimg_4d, check_niimg_3d, check_niimg, as_ndarray,
diff --git a/nilearn/input_data/base_masker.py b/nilearn/input_data/base_masker.py
index 8c8f68d007..9dc4d358fd 100644
--- a/nilearn/input_data/base_masker.py
+++ b/nilearn/input_data/base_masker.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator, TransformerMixin
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .. import masking
 from .. import image
diff --git a/nilearn/input_data/multi_nifti_masker.py b/nilearn/input_data/multi_nifti_masker.py
index 863c925784..7d1babb554 100644
--- a/nilearn/input_data/multi_nifti_masker.py
+++ b/nilearn/input_data/multi_nifti_masker.py
@@ -8,7 +8,7 @@
 import itertools
 import warnings
 
-from joblib import Memory, Parallel, delayed
+from nilearn._utils.compat.joblib import Memory, Parallel, delayed
 
 from .. import _utils
 from .. import image
diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index a754e8b232..777ee2667d 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin, _compose_err_msg
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index 89dfbe1f76..4621b02e8a 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -3,7 +3,7 @@
 """
 
 import numpy as np
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 8fe6438052..9029c7b8a4 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -6,7 +6,7 @@
 
 from copy import copy as copy_object
 
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .base_masker import BaseMasker, filter_and_extract
 from .. import _utils
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index 42e1f6ccc5..287e6dd0b0 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -7,7 +7,7 @@
 import numpy as np
 import warnings
 from sklearn import neighbors
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from ..image.resampling import coord_transform
 from .._utils.niimg_conversions import _safe_get_data
diff --git a/nilearn/input_data/tests/test_masker_validation.py b/nilearn/input_data/tests/test_masker_validation.py
index df62565099..d44617603b 100644
--- a/nilearn/input_data/tests/test_masker_validation.py
+++ b/nilearn/input_data/tests/test_masker_validation.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from nilearn._utils.testing import assert_warns
 from nilearn.input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/input_data/tests/test_multi_nifti_masker.py b/nilearn/input_data/tests/test_multi_nifti_masker.py
index 2cd4e268b7..436f196305 100644
--- a/nilearn/input_data/tests/test_multi_nifti_masker.py
+++ b/nilearn/input_data/tests/test_multi_nifti_masker.py
@@ -14,7 +14,7 @@
 from nose import SkipTest
 from nose.tools import assert_true, assert_false, assert_raises, assert_equal
 from numpy.testing import assert_array_equal
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from nilearn._utils.exceptions import DimensionError
 from nilearn._utils.testing import assert_raises_regex, write_tmp_imgs
@@ -116,7 +116,7 @@ def test_3d_images():
 
 
 def test_joblib_cache():
-    from joblib import hash
+    from nilearn._utils.compat.joblib import hash
     # Dummy mask
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
diff --git a/nilearn/input_data/tests/test_nifti_masker.py b/nilearn/input_data/tests/test_nifti_masker.py
index c68e45233d..458f5a0ec6 100644
--- a/nilearn/input_data/tests/test_nifti_masker.py
+++ b/nilearn/input_data/tests/test_nifti_masker.py
@@ -233,7 +233,7 @@ def test_sessions():
 
 
 def test_joblib_cache():
-    from joblib import hash, Memory
+    from nilearn._utils.compat.joblib import hash, Memory
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
     mask_img = Nifti1Image(mask, np.eye(4))
diff --git a/nilearn/masking.py b/nilearn/masking.py
index b340e12e28..3cc529266f 100644
--- a/nilearn/masking.py
+++ b/nilearn/masking.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 from scipy import ndimage
-from joblib import Parallel, delayed
+from nilearn._utils.compat.joblib import Parallel, delayed
 
 from . import _utils
 from .image import new_img_like
diff --git a/nilearn/mass_univariate/permuted_least_squares.py b/nilearn/mass_univariate/permuted_least_squares.py
index 02ce7c3cf2..fa26be4c23 100644
--- a/nilearn/mass_univariate/permuted_least_squares.py
+++ b/nilearn/mass_univariate/permuted_least_squares.py
@@ -8,7 +8,7 @@
 import numpy as np
 from scipy import linalg
 from sklearn.utils import check_random_state
-import joblib as joblib
+from nilearn._utils.compat import joblib
 
 
 def normalize_matrix_on_axis(m, axis=0):
diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 141b838a8c..658573dd65 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -5,7 +5,7 @@
 
 from sklearn.base import clone
 from sklearn.feature_extraction import image
-from joblib import Memory, delayed, Parallel
+from nilearn._utils.compat.joblib import Memory, delayed, Parallel
 
 from .rena_clustering import ReNA
 from ..decomposition.multi_pca import MultiPCA
diff --git a/nilearn/regions/region_extractor.py b/nilearn/regions/region_extractor.py
index 0d807cb238..6735b15500 100644
--- a/nilearn/regions/region_extractor.py
+++ b/nilearn/regions/region_extractor.py
@@ -9,7 +9,7 @@
 from scipy import ndimage
 from scipy.stats import scoreatpercentile
 
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 from .. import masking
 from ..input_data import NiftiMapsMasker
diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index ca32b8346c..5715229781 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -8,9 +8,9 @@
 import warnings
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
 try:
-    from joblib import Memory
+    from nilearn._utils.compat.joblib import Memory
 except ImportError:
-    from joblib import Memory
+    from nilearn._utils.compat.joblib import Memory
 from sklearn.base import TransformerMixin, ClusterMixin
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted
diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index 11b6585793..55a16c27b0 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,9 +1,9 @@
 import numpy as np
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 try:
-    from joblib import Memory
+    from nilearn._utils.compat.joblib import Memory
 except ImportError:
-    from joblib import Memory
+    from nilearn._utils.compat.joblib import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
 from nilearn.regions.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker
diff --git a/nilearn/tests/test_cache_mixin.py b/nilearn/tests/test_cache_mixin.py
index ff42e17337..722562c8ce 100644
--- a/nilearn/tests/test_cache_mixin.py
+++ b/nilearn/tests/test_cache_mixin.py
@@ -10,7 +10,7 @@
 
 import sklearn
 from nose.tools import assert_false, assert_true, assert_equal
-from joblib import Memory
+from nilearn._utils.compat.joblib import Memory
 
 import nilearn
 from nilearn._utils import cache_mixin, CacheMixin
diff --git a/nilearn/tests/test_niimg.py b/nilearn/tests/test_niimg.py
index 3b7fe02fcf..3127c982d5 100644
--- a/nilearn/tests/test_niimg.py
+++ b/nilearn/tests/test_niimg.py
@@ -6,7 +6,7 @@
 import nibabel as nb
 from nibabel import Nifti1Image
 from nibabel.tmpdirs import InTemporaryDirectory
-import joblib
+from nilearn._utils.compat import joblib
 
 from nilearn.image import new_img_like
 from nilearn._utils import niimg

From 63ec0997a64df674d116bdfbdb1247a2352dc8fc Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 18 Jul 2019 16:03:22 +0200
Subject: [PATCH 151/228] memory etc. in compat

---
 examples/03_connectivity/plot_multi_subject_connectome.py | 2 +-
 nilearn/_utils/cache_mixin.py                             | 8 +-------
 nilearn/_utils/compat.py                                  | 6 ++++++
 nilearn/_utils/niimg_conversions.py                       | 2 +-
 nilearn/connectome/group_sparse_cov.py                    | 2 +-
 nilearn/decoding/searchlight.py                           | 2 +-
 nilearn/decoding/space_net.py                             | 2 +-
 nilearn/decomposition/base.py                             | 2 +-
 nilearn/decomposition/canica.py                           | 2 +-
 nilearn/decomposition/dict_learning.py                    | 2 +-
 nilearn/decomposition/multi_pca.py                        | 2 +-
 nilearn/image/image.py                                    | 2 +-
 nilearn/input_data/base_masker.py                         | 2 +-
 nilearn/input_data/multi_nifti_masker.py                  | 2 +-
 nilearn/input_data/nifti_labels_masker.py                 | 2 +-
 nilearn/input_data/nifti_maps_masker.py                   | 2 +-
 nilearn/input_data/nifti_masker.py                        | 2 +-
 nilearn/input_data/nifti_spheres_masker.py                | 2 +-
 nilearn/input_data/tests/test_masker_validation.py        | 2 +-
 nilearn/input_data/tests/test_multi_nifti_masker.py       | 4 ++--
 nilearn/input_data/tests/test_nifti_masker.py             | 2 +-
 nilearn/masking.py                                        | 2 +-
 nilearn/regions/parcellations.py                          | 2 +-
 nilearn/regions/region_extractor.py                       | 2 +-
 nilearn/regions/rena_clustering.py                        | 4 ++--
 nilearn/regions/tests/test_rena_clustering.py             | 4 ++--
 nilearn/tests/test_cache_mixin.py                         | 2 +-
 27 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index 3fee94f0ea..ac85c7a84b 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -52,7 +52,7 @@ def plot_matrices(cov, prec, title, labels):
 from nilearn import input_data
 
 # A "memory" to avoid recomputation
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 mem = Memory('nilearn_cache')
 
 masker = input_data.NiftiMapsMasker(
diff --git a/nilearn/_utils/cache_mixin.py b/nilearn/_utils/cache_mixin.py
index 0266759796..3f82def65f 100644
--- a/nilearn/_utils/cache_mixin.py
+++ b/nilearn/_utils/cache_mixin.py
@@ -13,16 +13,10 @@
 import nibabel
 import sklearn
 
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 MEMORY_CLASSES = (Memory, )
 
-try:
-    from nilearn._utils.compat.joblib import Memory as JoblibMemory
-    MEMORY_CLASSES = (Memory, JoblibMemory)
-except ImportError:
-    pass
-
 import nilearn
 
 from .compat import _basestring
diff --git a/nilearn/_utils/compat.py b/nilearn/_utils/compat.py
index f7eaa9ca28..9629410a90 100644
--- a/nilearn/_utils/compat.py
+++ b/nilearn/_utils/compat.py
@@ -73,3 +73,9 @@ def md5_hash(string):
     from sklearn.utils.compat import joblib
 else:
     import joblib
+
+Memory = joblib.Memory
+Parallel = joblib.Parallel
+hash = joblib.hash
+delayed = joblib.delayed
+cpu_count = joblib.cpu_count
diff --git a/nilearn/_utils/niimg_conversions.py b/nilearn/_utils/niimg_conversions.py
index 41da21f81e..3ecf93a159 100644
--- a/nilearn/_utils/niimg_conversions.py
+++ b/nilearn/_utils/niimg_conversions.py
@@ -10,7 +10,7 @@
 import nilearn as ni
 import numpy as np
 import itertools
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .cache_mixin import cache
 from .niimg import _safe_get_data, load_niimg
diff --git a/nilearn/connectome/group_sparse_cov.py b/nilearn/connectome/group_sparse_cov.py
index f8429bce8c..9822f537ef 100644
--- a/nilearn/connectome/group_sparse_cov.py
+++ b/nilearn/connectome/group_sparse_cov.py
@@ -15,7 +15,7 @@
 
 from sklearn.base import BaseEstimator
 from sklearn.covariance import empirical_covariance
-from nilearn._utils.compat.joblib import Memory, delayed, Parallel
+from nilearn._utils.compat import Memory, delayed, Parallel
 from sklearn.model_selection import check_cv
 from sklearn.utils.extmath import fast_logdet
 
diff --git a/nilearn/decoding/searchlight.py b/nilearn/decoding/searchlight.py
index 2ab9a1473d..c853f9e0de 100644
--- a/nilearn/decoding/searchlight.py
+++ b/nilearn/decoding/searchlight.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from nilearn._utils.compat.joblib import Parallel, delayed, cpu_count
+from nilearn._utils.compat import Parallel, delayed, cpu_count
 from sklearn import svm
 from sklearn.base import BaseEstimator
 from sklearn.exceptions import ConvergenceWarning
diff --git a/nilearn/decoding/space_net.py b/nilearn/decoding/space_net.py
index aa9cb4769b..368513a8ef 100644
--- a/nilearn/decoding/space_net.py
+++ b/nilearn/decoding/space_net.py
@@ -24,7 +24,7 @@
 from sklearn.linear_model.base import LinearModel
 from sklearn.feature_selection import (SelectPercentile, f_regression,
                                        f_classif)
-from nilearn._utils.compat.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
 from sklearn.preprocessing import LabelBinarizer
 from sklearn.metrics import accuracy_score
 from ..input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/decomposition/base.py b/nilearn/decomposition/base.py
index dee190017f..d479dd2078 100644
--- a/nilearn/decomposition/base.py
+++ b/nilearn/decomposition/base.py
@@ -14,7 +14,7 @@
 import sklearn
 import nilearn
 from sklearn.base import BaseEstimator, TransformerMixin
-from nilearn._utils.compat.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
 from sklearn.linear_model import LinearRegression
 from sklearn.utils import check_random_state
 from sklearn.utils.extmath import randomized_svd, svd_flip
diff --git a/nilearn/decomposition/canica.py b/nilearn/decomposition/canica.py
index a1204dc6ec..2a4ad45aac 100644
--- a/nilearn/decomposition/canica.py
+++ b/nilearn/decomposition/canica.py
@@ -10,7 +10,7 @@
 import numpy as np
 from scipy.stats import scoreatpercentile
 from sklearn.decomposition import fastica
-from nilearn._utils.compat.joblib import Memory, delayed, Parallel
+from nilearn._utils.compat import Memory, delayed, Parallel
 from sklearn.utils import check_random_state
 
 from .multi_pca import MultiPCA
diff --git a/nilearn/decomposition/dict_learning.py b/nilearn/decomposition/dict_learning.py
index 1243b91309..25c79a435f 100644
--- a/nilearn/decomposition/dict_learning.py
+++ b/nilearn/decomposition/dict_learning.py
@@ -15,7 +15,7 @@
 import numpy as np
 import sklearn
 from sklearn.decomposition import dict_learning_online
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 from sklearn.linear_model import Ridge
 
 from .base import BaseDecomposition
diff --git a/nilearn/decomposition/multi_pca.py b/nilearn/decomposition/multi_pca.py
index b5d6030b09..84b6daeb45 100644
--- a/nilearn/decomposition/multi_pca.py
+++ b/nilearn/decomposition/multi_pca.py
@@ -3,7 +3,7 @@
 This is a good initialization method for ICA.
 """
 import numpy as np
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 from sklearn.utils.extmath import randomized_svd
 
 from .base import BaseDecomposition
diff --git a/nilearn/image/image.py b/nilearn/image/image.py
index c83125a597..63d5b956c9 100644
--- a/nilearn/image/image.py
+++ b/nilearn/image/image.py
@@ -14,7 +14,7 @@
 from scipy.stats import scoreatpercentile
 import copy
 import nibabel
-from nilearn._utils.compat.joblib import Parallel, delayed
+from nilearn._utils.compat import Parallel, delayed
 
 from .. import signal
 from .._utils import (check_niimg_4d, check_niimg_3d, check_niimg, as_ndarray,
diff --git a/nilearn/input_data/base_masker.py b/nilearn/input_data/base_masker.py
index 9dc4d358fd..af604b581e 100644
--- a/nilearn/input_data/base_masker.py
+++ b/nilearn/input_data/base_masker.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator, TransformerMixin
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .. import masking
 from .. import image
diff --git a/nilearn/input_data/multi_nifti_masker.py b/nilearn/input_data/multi_nifti_masker.py
index 7d1babb554..c0efab0957 100644
--- a/nilearn/input_data/multi_nifti_masker.py
+++ b/nilearn/input_data/multi_nifti_masker.py
@@ -8,7 +8,7 @@
 import itertools
 import warnings
 
-from nilearn._utils.compat.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
 
 from .. import _utils
 from .. import image
diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index 777ee2667d..563632040c 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin, _compose_err_msg
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index 4621b02e8a..4f45c22d2c 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -3,7 +3,7 @@
 """
 
 import numpy as np
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .. import _utils
 from .._utils import logger, CacheMixin
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 9029c7b8a4..d8a869fa94 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -6,7 +6,7 @@
 
 from copy import copy as copy_object
 
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .base_masker import BaseMasker, filter_and_extract
 from .. import _utils
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index 287e6dd0b0..fd82055486 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -7,7 +7,7 @@
 import numpy as np
 import warnings
 from sklearn import neighbors
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from ..image.resampling import coord_transform
 from .._utils.niimg_conversions import _safe_get_data
diff --git a/nilearn/input_data/tests/test_masker_validation.py b/nilearn/input_data/tests/test_masker_validation.py
index d44617603b..77bbc56fbf 100644
--- a/nilearn/input_data/tests/test_masker_validation.py
+++ b/nilearn/input_data/tests/test_masker_validation.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from sklearn.base import BaseEstimator
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from nilearn._utils.testing import assert_warns
 from nilearn.input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/input_data/tests/test_multi_nifti_masker.py b/nilearn/input_data/tests/test_multi_nifti_masker.py
index 436f196305..bc0729f931 100644
--- a/nilearn/input_data/tests/test_multi_nifti_masker.py
+++ b/nilearn/input_data/tests/test_multi_nifti_masker.py
@@ -14,7 +14,7 @@
 from nose import SkipTest
 from nose.tools import assert_true, assert_false, assert_raises, assert_equal
 from numpy.testing import assert_array_equal
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from nilearn._utils.exceptions import DimensionError
 from nilearn._utils.testing import assert_raises_regex, write_tmp_imgs
@@ -116,7 +116,7 @@ def test_3d_images():
 
 
 def test_joblib_cache():
-    from nilearn._utils.compat.joblib import hash
+    from nilearn._utils.compat import hash
     # Dummy mask
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
diff --git a/nilearn/input_data/tests/test_nifti_masker.py b/nilearn/input_data/tests/test_nifti_masker.py
index 458f5a0ec6..caa84812ed 100644
--- a/nilearn/input_data/tests/test_nifti_masker.py
+++ b/nilearn/input_data/tests/test_nifti_masker.py
@@ -233,7 +233,7 @@ def test_sessions():
 
 
 def test_joblib_cache():
-    from nilearn._utils.compat.joblib import hash, Memory
+    from nilearn._utils.compat import hash, Memory
     mask = np.zeros((40, 40, 40))
     mask[20, 20, 20] = 1
     mask_img = Nifti1Image(mask, np.eye(4))
diff --git a/nilearn/masking.py b/nilearn/masking.py
index 3cc529266f..6708151537 100644
--- a/nilearn/masking.py
+++ b/nilearn/masking.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 from scipy import ndimage
-from nilearn._utils.compat.joblib import Parallel, delayed
+from nilearn._utils.compat import Parallel, delayed
 
 from . import _utils
 from .image import new_img_like
diff --git a/nilearn/regions/parcellations.py b/nilearn/regions/parcellations.py
index 658573dd65..d37c3f8d7f 100644
--- a/nilearn/regions/parcellations.py
+++ b/nilearn/regions/parcellations.py
@@ -5,7 +5,7 @@
 
 from sklearn.base import clone
 from sklearn.feature_extraction import image
-from nilearn._utils.compat.joblib import Memory, delayed, Parallel
+from nilearn._utils.compat import Memory, delayed, Parallel
 
 from .rena_clustering import ReNA
 from ..decomposition.multi_pca import MultiPCA
diff --git a/nilearn/regions/region_extractor.py b/nilearn/regions/region_extractor.py
index 6735b15500..712d744f80 100644
--- a/nilearn/regions/region_extractor.py
+++ b/nilearn/regions/region_extractor.py
@@ -9,7 +9,7 @@
 from scipy import ndimage
 from scipy.stats import scoreatpercentile
 
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 from .. import masking
 from ..input_data import NiftiMapsMasker
diff --git a/nilearn/regions/rena_clustering.py b/nilearn/regions/rena_clustering.py
index 5715229781..fac552aa5a 100644
--- a/nilearn/regions/rena_clustering.py
+++ b/nilearn/regions/rena_clustering.py
@@ -8,9 +8,9 @@
 import warnings
 from scipy.sparse import csgraph, coo_matrix, dia_matrix
 try:
-    from nilearn._utils.compat.joblib import Memory
+    from nilearn._utils.compat import Memory
 except ImportError:
-    from nilearn._utils.compat.joblib import Memory
+    from nilearn._utils.compat import Memory
 from sklearn.base import TransformerMixin, ClusterMixin
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted
diff --git a/nilearn/regions/tests/test_rena_clustering.py b/nilearn/regions/tests/test_rena_clustering.py
index 55a16c27b0..6ae6e0515e 100644
--- a/nilearn/regions/tests/test_rena_clustering.py
+++ b/nilearn/regions/tests/test_rena_clustering.py
@@ -1,9 +1,9 @@
 import numpy as np
 from nose.tools import assert_equal, assert_not_equal, assert_raises
 try:
-    from nilearn._utils.compat.joblib import Memory
+    from nilearn._utils.compat import Memory
 except ImportError:
-    from nilearn._utils.compat.joblib import Memory
+    from nilearn._utils.compat import Memory
 from nilearn._utils.data_gen import generate_fake_fmri
 from nilearn.regions.rena_clustering import ReNA
 from nilearn.input_data import NiftiMasker
diff --git a/nilearn/tests/test_cache_mixin.py b/nilearn/tests/test_cache_mixin.py
index 722562c8ce..7f9b711a68 100644
--- a/nilearn/tests/test_cache_mixin.py
+++ b/nilearn/tests/test_cache_mixin.py
@@ -10,7 +10,7 @@
 
 import sklearn
 from nose.tools import assert_false, assert_true, assert_equal
-from nilearn._utils.compat.joblib import Memory
+from nilearn._utils.compat import Memory
 
 import nilearn
 from nilearn._utils import cache_mixin, CacheMixin

From b423f908fa950a582de3a49f5ffeb5079d8b8112 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Thu, 18 Jul 2019 16:12:12 +0200
Subject: [PATCH 152/228] externals not utils

---
 nilearn/_utils/compat.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/_utils/compat.py b/nilearn/_utils/compat.py
index 9629410a90..27b648fdae 100644
--- a/nilearn/_utils/compat.py
+++ b/nilearn/_utils/compat.py
@@ -70,7 +70,7 @@ def md5_hash(string):
 
 
 if sklearn.__version__ < '0.21':
-    from sklearn.utils.compat import joblib
+    from sklearn.externals import joblib
 else:
     import joblib
 

From da87bb5ebb28943df7c931f146e455c46b212002 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 18 Jul 2019 16:46:09 +0200
Subject: [PATCH 153/228] Added pytest for installation in travis

---
 continuous_integration/install.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 5076107055..f4ca444f57 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -75,6 +75,7 @@ create_new_conda_env() {
     echo "conda requirements string: $REQUIREMENTS"
     conda create -n testenv --quiet --yes $REQUIREMENTS
     source activate testenv
+    conda install pytest --yes
 
     if [[ "$INSTALL_MKL" == "true" ]]; then
         # Make sure that MKL is used

From 9b5969dfc40ca365de63735d7dfbc4fec4e62782 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 18 Jul 2019 17:49:24 +0200
Subject: [PATCH 154/228] Changed pytest switch --duration to --durations

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index b61d2efcf4..442c99d29f 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest /tmp/nilearn/ -s -vv --duration=0
+	python -m pytest /tmp/nilearn/ -s -vv --durations=0
 test-doc:
-	python -m pytest /tmp/nilearn/ -s -vv --duration=0
+	python -m pytest /tmp/nilearn/ -s -vv --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage

From b3e2ffa33ae489588d33f7063f4d47fde4843d5b Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 18 Jul 2019 17:59:12 +0200
Subject: [PATCH 155/228] Changed pytest start path

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 442c99d29f..7afbb8bba9 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest /tmp/nilearn/ -s -vv --durations=0
+	python -m pytest -s -vv --durations=0
 test-doc:
-	python -m pytest /tmp/nilearn/ -s -vv --durations=0
+	python -m pytest -s -vv --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage

From ee81eb890f2bc3d6c4f44f4f8f8afaf15523bd71 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 18 Jul 2019 20:22:33 +0200
Subject: [PATCH 156/228] Added find command to find nilearn dir

---
 continuous_integration/install.sh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index f4ca444f57..41c093a422 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -121,3 +121,5 @@ fi
 if [[ "$SKIP_TESTS" != "true" ]]; then
     python setup.py install
 fi
+
+find . -type f -name "nilearn"

From 5ab0d2a9603cdf288d0f2cf0d8077e6917304b8f Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Thu, 18 Jul 2019 20:33:46 +0200
Subject: [PATCH 157/228] Changed pytest path, Removed find command (cant see
 anything)

---
 Makefile                          | 4 ++--
 continuous_integration/install.sh | 2 --
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index 7afbb8bba9..392fff47f6 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest -s -vv --durations=0
+	python -m pytest nilearn -s -vv --durations=0
 test-doc:
-	python -m pytest -s -vv --durations=0
+	python -m pytest nilearn -s -vv --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage
diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 41c093a422..f4ca444f57 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -121,5 +121,3 @@ fi
 if [[ "$SKIP_TESTS" != "true" ]]; then
     python setup.py install
 fi
-
-find . -type f -name "nilearn"

From bd9f8a57bcd95d236a7afc465fbbc3ad03f6b196 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 00:15:50 +0200
Subject: [PATCH 158/228] Use --pyargs to pass package names instead of dir
 name to pytest

 - pip installs nilearn clone in site packages & we are not in that dir.
 - Suggested by jeromedockes .
---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 392fff47f6..33a34e7813 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest nilearn -s -vv --durations=0
+	python -m pytest --pyargs nilearn -s -vv --durations=0
 test-doc:
-	python -m pytest nilearn -s -vv --durations=0
+	python -m pytest --pyargs nilearn -s -vv --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage

From 4a34cc18665cafc3a86b355f924211b6df879a62 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 00:43:06 +0200
Subject: [PATCH 159/228] Reduce pytest log verbosity

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 33a34e7813..0cb6ab0d9c 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,9 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest --pyargs nilearn -s -vv --durations=0
+	python -m pytest --pyargs nilearn -s -v --durations=0
 test-doc:
-	python -m pytest --pyargs nilearn -s -vv --durations=0
+	python -m pytest --pyargs nilearn -s -v --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage

From 67c37383230498c666da2d4d1706248267d0ca5b Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 10:12:03 +0200
Subject: [PATCH 160/228] Changed coverage parameter to work with pytest in
 site-packages

---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 0cb6ab0d9c..9117fd8f70 100644
--- a/Makefile
+++ b/Makefile
@@ -31,13 +31,13 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m pytest --pyargs nilearn -s -v --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn -s -v --durations=0
 test-doc:
-	python -m pytest --pyargs nilearn -s -v --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn -s -v --durations=0
 
 test-coverage:
 	rm -rf coverage .coverage
-	$(TEST_RUNNER) -s --with-coverage --cover-html --cover-html-dir=coverage \
+	$(TEST_RUNNER) -s --cov=nilearn --cover-html --cover-html-dir=coverage \
 	--cover-package=nilearn nilearn
 
 test: test-code test-doc

From 6ea37e5faba4388bbf9cd00f56e09a5bce824523 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 12:07:21 +0200
Subject: [PATCH 161/228] Changed coverage parameter to work with pytest in
 site-packages attempt 2

---
 Makefile | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 9117fd8f70..b24c0e5413 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,11 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m $(TEST_RUNNER) --pyargs nilearn -s -v --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -v --durations=0
 test-doc:
-	python -m $(TEST_RUNNER) --pyargs nilearn -s -v --durations=0
+	$(NOSETESTS) -s --with-doctest --doctest-tests --doctest-extension=rst \
+	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
+
 
 test-coverage:
 	rm -rf coverage .coverage

From d3d597d338484a0094ecacc791300131f5fad37f Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 12:22:13 +0200
Subject: [PATCH 162/228] Added pytest-cov to installation for pytest coverage
 customization

---
 continuous_integration/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index f4ca444f57..55257efd49 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -75,7 +75,7 @@ create_new_conda_env() {
     echo "conda requirements string: $REQUIREMENTS"
     conda create -n testenv --quiet --yes $REQUIREMENTS
     source activate testenv
-    conda install pytest --yes
+    conda install pytest pytest-cov --yes
 
     if [[ "$INSTALL_MKL" == "true" ]]; then
         # Make sure that MKL is used

From e25d31f35117ace0e1b66bef68c10f21907cb83a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 13:01:05 +0200
Subject: [PATCH 163/228] Added conftest.py to configure pytest

 - Skips plotting test when no matplotlib.
---
 conftest.py | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 conftest.py

diff --git a/conftest.py b/conftest.py
new file mode 100644
index 0000000000..8826b2370e
--- /dev/null
+++ b/conftest.py
@@ -0,0 +1,4 @@
+try:
+    import matplotlib
+except ImportError:
+    collect_ignore = ["nilearn/plotting"]

From 21959ac798614b4660b9b9ed6d97fac8bc47a67c Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 13:26:15 +0200
Subject: [PATCH 164/228] Restored extra verbosity for Pytest log

---
 Makefile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index b24c0e5413..952ebdd962 100644
--- a/Makefile
+++ b/Makefile
@@ -31,9 +31,10 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -v --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -vv --durations=0
+
 test-doc:
-	$(NOSETESTS) -s --with-doctest --doctest-tests --doctest-extension=rst \
+	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
 	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
 
 

From bb800db32b74987479be7556194fcb5a1a7a5301 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 14:05:36 +0200
Subject: [PATCH 165/228] Moved conftest.py into Nilearn package & fixed flake8
 error in it

---
 conftest.py         | 4 ----
 nilearn/conftest.py | 6 ++++++
 2 files changed, 6 insertions(+), 4 deletions(-)
 delete mode 100644 conftest.py
 create mode 100644 nilearn/conftest.py

diff --git a/conftest.py b/conftest.py
deleted file mode 100644
index 8826b2370e..0000000000
--- a/conftest.py
+++ /dev/null
@@ -1,4 +0,0 @@
-try:
-    import matplotlib
-except ImportError:
-    collect_ignore = ["nilearn/plotting"]
diff --git a/nilearn/conftest.py b/nilearn/conftest.py
new file mode 100644
index 0000000000..fad9be2d67
--- /dev/null
+++ b/nilearn/conftest.py
@@ -0,0 +1,6 @@
+try:
+    import matplotlib
+except ImportError:
+    collect_ignore = ['plotting']
+else:
+    matplotlib

From 8594a42d280c309407863cd57a8b47cdc84e50ad Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 14:26:08 +0200
Subject: [PATCH 166/228] Changed doc test command in makefile to work with
 pytest

---
 Makefile | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 952ebdd962..30abfb6b4c 100644
--- a/Makefile
+++ b/Makefile
@@ -34,8 +34,7 @@ test-code:
 	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -vv --durations=0
 
 test-doc:
-	$(TEST_RUNNER) -s --with-doctest --doctest-tests --doctest-extension=rst \
-	--doctest-extension=inc --doctest-fixtures=_fixture `find doc/ -name '*.rst'`
+	$(TEST_RUNNER) -s --doctest-glob='*.rst'
 
 
 test-coverage:

From 99475eef84eab49439c2fe05c96f6fe579fee478 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 14:43:52 +0200
Subject: [PATCH 167/228] Added doctest option to test module docstrings

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 30abfb6b4c..02f41f90b9 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ test-code:
 	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -vv --durations=0
 
 test-doc:
-	$(TEST_RUNNER) -s --doctest-glob='*.rst'
+	$(TEST_RUNNER) -s --doctest-glob='*.rst' --doctest-modules
 
 
 test-coverage:

From f8dcdde55339ba3d8f4c9b2f94f57577a04aef57 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 15:47:02 +0200
Subject: [PATCH 168/228] Restored doctest only checks; WIP tweak to coverage
 only test

---
 Makefile             | 9 ++++-----
 requirements-dev.txt | 3 +--
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/Makefile b/Makefile
index 02f41f90b9..fe7c3e8c8c 100644
--- a/Makefile
+++ b/Makefile
@@ -5,7 +5,7 @@
 PYTHON ?= python
 CYTHON ?= cython
 TEST_RUNNER ?= pytest
-TEST_RUNNER_OPTIONS := --duration=0 -vv
+TEST_RUNNER_OPTIONS := --durations=0 -vv
 CTAGS ?= ctags
 
 all: clean test doc-noplot
@@ -31,16 +31,15 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn -s -vv --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn --doctest-modules --cov=nilearn -s -vv --durations=0
 
 test-doc:
-	$(TEST_RUNNER) -s --doctest-glob='*.rst' --doctest-modules
+	$(TEST_RUNNER) -s --doctest-glob='*.rst' `find doc/ -name '*.rst'`
 
 
 test-coverage:
 	rm -rf coverage .coverage
-	$(TEST_RUNNER) -s --cov=nilearn --cover-html --cover-html-dir=coverage \
-	--cover-package=nilearn nilearn
+	$(PYTEST) --pyargs nilearn --showlocals -vv --cov=nilearn --cov-report=html:coverage
 
 test: test-code test-doc
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 4dceacf41c..38cea01bbb 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,8 +1,7 @@
 nose
 pytest
+pytest-cov
 coverage
-boto3
-patsy
 scipy
 scikit-learn
 pandas

From b80cb80de0722484ab104e5f969dad0b0fc6d04a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 16:34:20 +0200
Subject: [PATCH 169/228] Restored doctest only checks; WIP tweak to coverage
 only testFixed failing docstring test

---
 nilearn/mass_univariate/permuted_least_squares.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nilearn/mass_univariate/permuted_least_squares.py b/nilearn/mass_univariate/permuted_least_squares.py
index bf4e616bf6..0f4335a621 100644
--- a/nilearn/mass_univariate/permuted_least_squares.py
+++ b/nilearn/mass_univariate/permuted_least_squares.py
@@ -34,11 +34,11 @@ def normalize_matrix_on_axis(m, axis=0):
     ...     normalize_matrix_on_axis)
     >>> X = np.array([[0, 4], [1, 0]])
     >>> normalize_matrix_on_axis(X)
-    array([[ 0.,  1.],
-           [ 1.,  0.]])
+    array([[0., 1.],
+           [1., 0.]])
     >>> normalize_matrix_on_axis(X, axis=1)
-    array([[ 0.,  1.],
-           [ 1.,  0.]])
+    array([[0., 1.],
+           [1., 0.]])
 
     """
     if m.ndim > 2:

From f6d3109da43c8af04d5974492359b3460fde0094 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Fri, 19 Jul 2019 16:59:17 +0200
Subject: [PATCH 170/228] Fixed doctests failing due to spacing

 - Added doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS to setup.cfg
---
 setup.cfg | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/setup.cfg b/setup.cfg
index 48ddd82a97..3cd4625b2b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -14,6 +14,9 @@ with-doctest=1
 doctest-extension=rst
 ignore-files=(plot_.*.py|conf\.py)
 
+[tool:pytest]
+doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
+
 [wheel]
 universal=1
 

From f008b06bef1f42c055f6c3c3af91b5cd5a477786 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 12:36:12 +0200
Subject: [PATCH 171/228] Pytest skips running doctest for numpy < 1.14

 - Added Pytest plugin collection hook
---
 nilearn/conftest.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/nilearn/conftest.py b/nilearn/conftest.py
index fad9be2d67..46b7bf3b25 100644
--- a/nilearn/conftest.py
+++ b/nilearn/conftest.py
@@ -1,6 +1,30 @@
+from distutils.version import LooseVersion
+
+import numpy as np
+import pytest
+
+from _pytest.doctest import DoctestItem
+
 try:
     import matplotlib
 except ImportError:
     collect_ignore = ['plotting']
 else:
     matplotlib
+
+
+def pytest_collection_modifyitems(items):
+    
+    # numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
+    # run doctests only for numpy >= 1.14.Adapted from scikit-learn
+    if LooseVersion(np.__version__) < LooseVersion('1.14'):
+        reason = 'doctests are only run for numpy >= 1.14'
+        skip_doctests = True
+    else:
+        skip_doctests = False
+        
+    if skip_doctests:
+        skip_marker = pytest.mark.skip(reason=reason)
+        for item in items:
+            if isinstance(item, DoctestItem):
+                item.add_marker(skip_marker)

From 0ee60217116b668abbcf816189ed37f673e5556b Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 12:56:23 +0200
Subject: [PATCH 172/228] Fixed extra whitespace flake8 errors in conftest.py

---
 nilearn/conftest.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/conftest.py b/nilearn/conftest.py
index 46b7bf3b25..540e3db41f 100644
--- a/nilearn/conftest.py
+++ b/nilearn/conftest.py
@@ -14,7 +14,7 @@
 
 
 def pytest_collection_modifyitems(items):
-    
+
     # numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
     # run doctests only for numpy >= 1.14.Adapted from scikit-learn
     if LooseVersion(np.__version__) < LooseVersion('1.14'):
@@ -22,7 +22,7 @@ def pytest_collection_modifyitems(items):
         skip_doctests = True
     else:
         skip_doctests = False
-        
+
     if skip_doctests:
         skip_marker = pytest.mark.skip(reason=reason)
         for item in items:

From e83fa28bb288556ab5bb25b71bf00a83003e01bc Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 12:59:51 +0200
Subject: [PATCH 173/228] Added pytest config options to pytest.ini instead  of
 setup.cfg (best practice)

---
 pytest.ini | 2 ++
 setup.cfg  | 3 ---
 2 files changed, 2 insertions(+), 3 deletions(-)
 create mode 100644 pytest.ini

diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000..83642323d1
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,2 @@
+[pytest]
+doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
diff --git a/setup.cfg b/setup.cfg
index 3cd4625b2b..48ddd82a97 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -14,9 +14,6 @@ with-doctest=1
 doctest-extension=rst
 ignore-files=(plot_.*.py|conf\.py)
 
-[tool:pytest]
-doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
-
 [wheel]
 universal=1
 

From c6747aecfd73bc5540b15f64435a98fcfdb53ed1 Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Mon, 22 Jul 2019 13:09:27 +0200
Subject: [PATCH 174/228] fix imread import in example

---
 examples/02_decoding/plot_haxby_stimuli.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/02_decoding/plot_haxby_stimuli.py b/examples/02_decoding/plot_haxby_stimuli.py
index ba63648a27..0740b1aab3 100644
--- a/examples/02_decoding/plot_haxby_stimuli.py
+++ b/examples/02_decoding/plot_haxby_stimuli.py
@@ -7,7 +7,6 @@
 Cortex" (Science 2001)
 """
 
-from scipy.misc import imread
 import matplotlib.pyplot as plt
 
 from nilearn import datasets
@@ -27,8 +26,8 @@
     for i in range(48):
         plt.subplot(6, 8, i + 1)
         try:
-            plt.imshow(imread(file_names[i]), cmap=plt.cm.gray)
-        except:
+            plt.imshow(plt.imread(file_names[i]), cmap=plt.cm.gray)
+        except Exception:
             # just go to the next one if the file is not present
             pass
         plt.axis("off")

From 252811ae50ea9a307077ed00c72cbd4c0aa0a84a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 13:33:12 +0200
Subject: [PATCH 175/228] Remove nose config options & add coverage options

---
 .coveragerc |  8 ++++++++
 setup.cfg   | 14 +++++++-------
 2 files changed, 15 insertions(+), 7 deletions(-)
 create mode 100644 .coveragerc

diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000000..64f4eb42b6
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,8 @@
+[run]
+branch = True
+source = nilearn
+parallel = True
+omit =
+	plot_.*.py
+	conf\.py
+
diff --git a/setup.cfg b/setup.cfg
index 48ddd82a97..afbf71ff74 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -4,15 +4,15 @@
 doc-files = doc
 
 [nosetests]
-verbosity = 2
-detailed-errors = 1
-with-coverage = 1
-cover-package = nilearn
+;verbosity = 2
+;detailed-errors = 1
+;with-coverage = 1
+;cover-package = nilearn
 #pdb = 1
 #pdb-failures = 1
-with-doctest=1
-doctest-extension=rst
-ignore-files=(plot_.*.py|conf\.py)
+;with-doctest=1
+;doctest-extension=rst
+;ignore-files=(plot_.*.py|conf\.py)
 
 [wheel]
 universal=1

From 8bed4ebd90582ac888d5d7e9a2796d80e9ca1b5e Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 14:03:05 +0200
Subject: [PATCH 176/228] Moved pytest options from command line in makefile to
 pytest.ini

 - A central location to default tweak settings.
---
 Makefile   | 2 +-
 pytest.ini | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index fe7c3e8c8c..07196cd10a 100644
--- a/Makefile
+++ b/Makefile
@@ -31,7 +31,7 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m $(TEST_RUNNER) --pyargs nilearn --doctest-modules --cov=nilearn -s -vv --durations=0
+	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn
 
 test-doc:
 	$(TEST_RUNNER) -s --doctest-glob='*.rst' `find doc/ -name '*.rst'`
diff --git a/pytest.ini b/pytest.ini
index 83642323d1..c9c6dc96d9 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,2 +1,4 @@
 [pytest]
 doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
+junit_family = xunit2
+addopts = --doctest-modules -s -vv --durations=0

From 844b266e0d51b8212699cb9055808637c64c60b5 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 14:31:10 +0200
Subject: [PATCH 177/228] Removed nosetest config section completely

---
 setup.cfg | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index afbf71ff74..b04a460c55 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,17 +3,6 @@
 [bdist_rpm]
 doc-files = doc
 
-[nosetests]
-;verbosity = 2
-;detailed-errors = 1
-;with-coverage = 1
-;cover-package = nilearn
-#pdb = 1
-#pdb-failures = 1
-;with-doctest=1
-;doctest-extension=rst
-;ignore-files=(plot_.*.py|conf\.py)
-
 [wheel]
 universal=1
 

From 9b9095f877f603d91495c72f91e4fddf61f1724e Mon Sep 17 00:00:00 2001
From: Jerome Dockes <jerome@dockes.org>
Date: Mon, 22 Jul 2019 14:48:40 +0200
Subject: [PATCH 178/228] simplify example

---
 examples/02_decoding/plot_haxby_stimuli.py | 31 ++++++++++------------
 1 file changed, 14 insertions(+), 17 deletions(-)

diff --git a/examples/02_decoding/plot_haxby_stimuli.py b/examples/02_decoding/plot_haxby_stimuli.py
index 0740b1aab3..ac72643b92 100644
--- a/examples/02_decoding/plot_haxby_stimuli.py
+++ b/examples/02_decoding/plot_haxby_stimuli.py
@@ -15,22 +15,19 @@
 haxby_dataset = datasets.fetch_haxby(subjects=[], fetch_stimuli=True)
 stimulus_information = haxby_dataset.stimuli
 
-for stim_type in sorted(stimulus_information.keys()):
-    if stim_type == b'controls':
-        # skip control images, there are too many
-        continue
-
-    file_names = stimulus_information[stim_type]
-
-    plt.figure()
-    for i in range(48):
-        plt.subplot(6, 8, i + 1)
-        try:
-            plt.imshow(plt.imread(file_names[i]), cmap=plt.cm.gray)
-        except Exception:
-            # just go to the next one if the file is not present
-            pass
-        plt.axis("off")
-    plt.suptitle(stim_type)
+for stim_type in stimulus_information:
+    # skip control images, there are too many
+    if stim_type != 'controls':
+
+        file_names = stimulus_information[stim_type]
+
+        fig, axes = plt.subplots(6, 8)
+        fig.suptitle(stim_type)
+
+        for img_path, ax in zip(file_names, axes.ravel()):
+            ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
+
+        for ax in axes.ravel():
+            ax.axis("off")
 
 show()

From 9e10e7bcc470416f38cd9e96ab0d1165801539d5 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 15:13:07 +0200
Subject: [PATCH 179/228] Removed redundant pytest config options & env vars in
 makefile

---
 Makefile | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/Makefile b/Makefile
index 07196cd10a..50ada3bd2a 100644
--- a/Makefile
+++ b/Makefile
@@ -4,8 +4,6 @@
 
 PYTHON ?= python
 CYTHON ?= cython
-TEST_RUNNER ?= pytest
-TEST_RUNNER_OPTIONS := --durations=0 -vv
 CTAGS ?= ctags
 
 all: clean test doc-noplot
@@ -31,15 +29,15 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	python -m $(TEST_RUNNER) --pyargs nilearn --cov=nilearn
+	python -m pytest --pyargs nilearn --cov=nilearn
 
 test-doc:
-	$(TEST_RUNNER) -s --doctest-glob='*.rst' `find doc/ -name '*.rst'`
+	pytest --doctest-glob='*.rst' `find doc/ -name '*.rst'`
 
 
 test-coverage:
 	rm -rf coverage .coverage
-	$(PYTEST) --pyargs nilearn --showlocals -vv --cov=nilearn --cov-report=html:coverage
+	pytest --pyargs nilearn --showlocals --cov=nilearn --cov-report=html:coverage
 
 test: test-code test-doc
 

From 283023aecacbe0ba783c133a9e680d588d2a1e1f Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 15:15:40 +0200
Subject: [PATCH 180/228] Added comment

---
 nilearn/conftest.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/conftest.py b/nilearn/conftest.py
index 540e3db41f..7d2d5abfe6 100644
--- a/nilearn/conftest.py
+++ b/nilearn/conftest.py
@@ -10,7 +10,7 @@
 except ImportError:
     collect_ignore = ['plotting']
 else:
-    matplotlib
+    matplotlib  # Prevents flake8 erring due to unused entities.
 
 
 def pytest_collection_modifyitems(items):

From f26d92e5e28c5944bba7718e267f5bbe529e47a8 Mon Sep 17 00:00:00 2001
From: Ana Luisa Pinho <ana.pinho@inria.fr>
Date: Mon, 22 Jul 2019 15:43:25 +0200
Subject: [PATCH 181/228] FIXUP: add trailing underscore

---
 nilearn/input_data/nifti_masker.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index ab08c4b543..594ab2cf5a 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -92,19 +92,19 @@ class NiftiMasker(BaseMasker, CacheMixin):
 
     detrend : boolean, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details. [1]
+        documentation for details. [1]_
 
     low_pass: None or float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details. [1]
+        documentation for details. [1]_
 
     high_pass: None or float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details. [1]
+        documentation for details. [1]_
 
     t_r : float, optional
         This parameter is passed to signal.clean. Please see the related
-        documentation for details. [1]
+        documentation for details. [1]_
 
     target_affine : 3x3 or 4x4 matrix, optional
         This parameter is passed to image.resample_img. Please see the

From 1e62b6c4ca8540c6311e995053d8da3cca2ac922 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 22 Jul 2019 17:34:51 +0200
Subject: [PATCH 182/228] Included pytest.ini & setup.cfg into the package

---
 MANIFEST.in | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/MANIFEST.in b/MANIFEST.in
index d6af1ad4eb..24dbe770b6 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,5 @@
 include AUTHORS.rst
 include LICENSE
 include README.rst
+include pytest.ini
+include setup.cfg

From dd87a033a206e9b83e335776be435a58ae807b96 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 23 Jul 2019 13:06:37 +0200
Subject: [PATCH 183/228] Included package data in setup.py

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index 355e6de4ab..ac04623e8e 100755
--- a/setup.py
+++ b/setup.py
@@ -98,4 +98,5 @@ def is_installing():
                         'nilearn.surface.tests.data': ['*.annot', '*.label'],
                         'nilearn.datasets.tests.data': ['*.*'],
                         'nilearn.datasets.description': ['*.rst']},
+          include_package_data=True,
           install_requires=install_requires,)

From 5574e153816191b423b86d7b74009bf5d6f2ab49 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 23 Jul 2019 16:45:37 +0200
Subject: [PATCH 184/228] Moved around test config, undid some options not
 working, some to minimize change

---
 MANIFEST.in                           | 2 --
 continuous_integration/test_script.sh | 1 +
 pytest.ini                            | 4 ----
 setup.cfg                             | 9 +++++++++
 setup.py                              | 1 -
 5 files changed, 10 insertions(+), 7 deletions(-)
 delete mode 100644 pytest.ini

diff --git a/MANIFEST.in b/MANIFEST.in
index 24dbe770b6..d6af1ad4eb 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,3 @@
 include AUTHORS.rst
 include LICENSE
 include README.rst
-include pytest.ini
-include setup.cfg
diff --git a/continuous_integration/test_script.sh b/continuous_integration/test_script.sh
index 1dfa2578d1..4fbb32ba2e 100755
--- a/continuous_integration/test_script.sh
+++ b/continuous_integration/test_script.sh
@@ -11,6 +11,7 @@ if [[ "$SKIP_TESTS" != "true" ]]; then
     # Copy setup.cfg to TEST_RUN_FOLDER where we are going to run the tests from
     # Mainly for nose config settings
     cp setup.cfg "$TEST_RUN_FOLDER"
+    cp .coveragerc "$TEST_RUN_FOLDER"
     # We want to back out of the current working directory to make
     # sure we are using nilearn installed in site-packages rather
     # than the one from the current working directory
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index c9c6dc96d9..0000000000
--- a/pytest.ini
+++ /dev/null
@@ -1,4 +0,0 @@
-[pytest]
-doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
-junit_family = xunit2
-addopts = --doctest-modules -s -vv --durations=0
diff --git a/setup.cfg b/setup.cfg
index b04a460c55..3e61ea08c1 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -11,3 +11,12 @@ universal=1
 # http://pep8.readthedocs.org/en/latest/intro.html#error-codes
 # E402: module level import not at top of file
 ignore=E402
+
+[tool:pytest]
+doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
+junit_family = xunit2
+addopts =
+	--doctest-modules
+	-s
+	-vv
+	--durations=0
diff --git a/setup.py b/setup.py
index ac04623e8e..355e6de4ab 100755
--- a/setup.py
+++ b/setup.py
@@ -98,5 +98,4 @@ def is_installing():
                         'nilearn.surface.tests.data': ['*.annot', '*.label'],
                         'nilearn.datasets.tests.data': ['*.*'],
                         'nilearn.datasets.description': ['*.rst']},
-          include_package_data=True,
           install_requires=install_requires,)

From 216c1337ed08415858f07345d95ccc0b613339e8 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 23 Jul 2019 17:10:28 +0200
Subject: [PATCH 185/228] Added [report] section to skip certain files in
 coverage report

---
 .coveragerc | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.coveragerc b/.coveragerc
index 64f4eb42b6..6b66028225 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -6,3 +6,8 @@ omit =
 	plot_.*.py
 	conf\.py
 
+[report]
+omit =
+	plot_.*.py
+	conf\.py
+

From 98b357b6f6882613a0756c8359bd7ba5ea7f981f Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 23 Jul 2019 19:51:56 +0200
Subject: [PATCH 186/228] plots_* are no longer omitted from test coverage
 check

---
 .coveragerc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.coveragerc b/.coveragerc
index 6b66028225..625dc70865 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,11 +3,9 @@ branch = True
 source = nilearn
 parallel = True
 omit =
-	plot_.*.py
 	conf\.py
 
 [report]
 omit =
-	plot_.*.py
 	conf\.py
 

From de05be5192a7a6213e599230a5b50e0e2356e4e4 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Tue, 23 Jul 2019 23:57:39 +0200
Subject: [PATCH 187/228] Replace GraphLasso* with GraphicalLasso*

---
 doc/connectivity/connectome_extraction.rst         | 14 +++++++-------
 doc/developers/group_sparse_covariance.rst         |  2 +-
 .../plot_inverse_covariance_connectome.py          |  4 ++--
 .../plot_multi_subject_connectome.py               |  6 +++---
 .../03_connectivity/plot_simulated_connectome.py   |  4 ++--
 .../plot_sphere_based_connectome.py                | 10 +++++-----
 nilearn/connectome/group_sparse_cov.py             |  2 +-
 7 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/doc/connectivity/connectome_extraction.rst b/doc/connectivity/connectome_extraction.rst
index 6707f081d4..fb62356326 100644
--- a/doc/connectivity/connectome_extraction.rst
+++ b/doc/connectivity/connectome_extraction.rst
@@ -49,12 +49,12 @@ conditioned on all the others.
 
 
 To recover well the interaction structure, a **sparse inverse covariance
-estimator** is necessary. The GraphLasso, implemented in scikit-learn's
-estimator :class:`sklearn.covariance.GraphLassoCV` is a good, simple
+estimator** is necessary. The GraphicalLasso, implemented in scikit-learn's
+estimator :class:`sklearn.covariance.GraphicalLassoCV` is a good, simple
 solution. To use it, you need to create an estimator object::
 
-    >>> from sklearn.covariance import GraphLassoCV
-    >>> estimator = GraphLassoCV()
+    >>> from sklearn.covariance import GraphicalLassoCV
+    >>> estimator = GraphicalLassoCV()
 
 And then you can fit it on the activation time series, for instance
 extracted in :ref:`the previous section <functional_connectomes>`::
@@ -95,7 +95,7 @@ of the estimator::
     The parameter controlling the sparsity is set by `cross-validation
     <http://scikit-learn.org/stable/modules/cross_validation.html>`_
     scheme. If you want to specify it manually, use the estimator
-    :class:`sklearn.covariance.GraphLasso`.
+    :class:`sklearn.covariance.GraphicalLasso`.
 
 .. topic:: **Full example**
 
@@ -126,7 +126,7 @@ differing connection values across subjects.
 
 For this, nilearn provides the
 :class:`nilearn.connectome.GroupSparseCovarianceCV`
-estimator. Its usage is similar to the GraphLassoCV object, but it takes
+estimator. Its usage is similar to the GraphicalLassoCV object, but it takes
 a list of time series::
 
     >>> estimator.fit([time_series_1, time_series_2, ...])  # doctest: +SKIP
@@ -186,7 +186,7 @@ with different precision matrices, but sharing a common sparsity pattern:
 10 brain regions, for 20 subjects.
 
 A single-subject estimation can be performed using the
-:class:`sklearn.covariance.GraphLassoCV` estimator from scikit-learn.
+:class:`sklearn.covariance.GraphicalLassoCV` estimator from scikit-learn.
 
 It is also possible to fit a graph lasso on data from every subject all
 together.
diff --git a/doc/developers/group_sparse_covariance.rst b/doc/developers/group_sparse_covariance.rst
index 56e15e1c45..b95eadf867 100644
--- a/doc/developers/group_sparse_covariance.rst
+++ b/doc/developers/group_sparse_covariance.rst
@@ -369,7 +369,7 @@ used), then a tighter grid near the found maximum is computed, and so
 on. This allows for a very precise determination of the maximum
 location while reducing a lot the required evaluation number. The code
 is very close to what is done in
-:class:`sklearn.covariance.GraphLassoCV`.
+:class:`sklearn.covariance.GraphicalLassoCV`.
 
 
 Warm restart
diff --git a/examples/03_connectivity/plot_inverse_covariance_connectome.py b/examples/03_connectivity/plot_inverse_covariance_connectome.py
index 5ee1cef823..68b6555417 100644
--- a/examples/03_connectivity/plot_inverse_covariance_connectome.py
+++ b/examples/03_connectivity/plot_inverse_covariance_connectome.py
@@ -51,8 +51,8 @@
 ##############################################################################
 # Compute the sparse inverse covariance
 # --------------------------------------
-from sklearn.covariance import GraphLassoCV
-estimator = GraphLassoCV()
+from sklearn.covariance import GraphicalLassoCV
+estimator = GraphicalLassoCV()
 
 estimator.fit(time_series)
 
diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index ac85c7a84b..fd419b1c4b 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -85,7 +85,7 @@ def plot_matrices(cov, prec, title, labels):
 gsc.fit(subject_time_series)
 
 from sklearn import covariance
-gl = covariance.GraphLassoCV(verbose=2)
+gl = covariance.GraphicalLassoCV(verbose=2)
 gl.fit(np.concatenate(subject_time_series))
 
 
@@ -102,10 +102,10 @@ def plot_matrices(cov, prec, title, labels):
                          display_mode="lzr")
 plotting.plot_connectome(-gl.precision_, atlas_region_coords,
                          edge_threshold='90%',
-                         title="Sparse inverse covariance (GraphLasso)",
+                         title="Sparse inverse covariance (GraphicalLasso)",
                          display_mode="lzr",
                          edge_vmax=.5, edge_vmin=-.5)
-plot_matrices(gl.covariance_, gl.precision_, "GraphLasso", labels)
+plot_matrices(gl.covariance_, gl.precision_, "GraphicalLasso", labels)
 
 title = "GroupSparseCovariance"
 plotting.plot_connectome(-gsc.precisions_[..., 0],
diff --git a/examples/03_connectivity/plot_simulated_connectome.py b/examples/03_connectivity/plot_simulated_connectome.py
index 385676c08d..36cc5ddd52 100644
--- a/examples/03_connectivity/plot_simulated_connectome.py
+++ b/examples/03_connectivity/plot_simulated_connectome.py
@@ -49,8 +49,8 @@
 
 
 # Fit one graph lasso per subject
-from sklearn.covariance import GraphLassoCV
-gl = GraphLassoCV(verbose=1)
+from sklearn.covariance import GraphicalLassoCV
+gl = GraphicalLassoCV(verbose=1)
 
 for n, subject in enumerate(subjects[:n_displayed]):
     gl.fit(subject)
diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 4ea44844e9..0e558060c3 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -211,13 +211,13 @@
 ###############################################################################
 # in which situation the graphical lasso **sparse inverse covariance**
 # estimator captures well the covariance **structure**.
-from sklearn.covariance import GraphLassoCV
+from sklearn.covariance import GraphicalLassoCV
 
-covariance_estimator = GraphLassoCV(cv=3, verbose=1)
+covariance_estimator = GraphicalLassoCV(cv=3, verbose=1)
 
 
 ###############################################################################
-# We just fit our regions signals into the `GraphLassoCV` object
+# We just fit our regions signals into the `GraphicalLassoCV` object
 covariance_estimator.fit(timeseries)
 
 
@@ -271,7 +271,7 @@
 timeseries = spheres_masker.fit_transform(func_filename,
                                           confounds=confounds_filename)
 
-covariance_estimator = GraphLassoCV()
+covariance_estimator = GraphicalLassoCV()
 covariance_estimator.fit(timeseries)
 matrix = covariance_estimator.covariance_
 
@@ -321,7 +321,7 @@
 timeseries = spheres_masker.fit_transform(func_filename,
                                           confounds=confounds_filename)
 
-covariance_estimator = GraphLassoCV()
+covariance_estimator = GraphicalLassoCV()
 covariance_estimator.fit(timeseries)
 matrix = covariance_estimator.covariance_
 
diff --git a/nilearn/connectome/group_sparse_cov.py b/nilearn/connectome/group_sparse_cov.py
index 9822f537ef..06317097aa 100644
--- a/nilearn/connectome/group_sparse_cov.py
+++ b/nilearn/connectome/group_sparse_cov.py
@@ -891,7 +891,7 @@ class GroupSparseCovarianceCV(BaseEstimator, CacheMixin):
     See also
     --------
     GroupSparseCovariance,
-    sklearn.covariance.GraphLassoCV
+    sklearn.covariance.GraphicalLassoCV
 
     Notes
     -----

From 8f3b7abc553cff5c0fca726f423570170ac9db68 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 00:34:41 +0200
Subject: [PATCH 188/228] Remove coverage of tester code

---
 .coveragerc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.coveragerc b/.coveragerc
index 625dc70865..756591a983 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,9 +3,11 @@ branch = True
 source = nilearn
 parallel = True
 omit =
+	*test*
 	conf\.py
 
 [report]
 omit =
+	*test*
 	conf\.py
 

From 67fe538d1d8f8e1c6ac1ab05e88cbd09c03bcc83 Mon Sep 17 00:00:00 2001
From: mrahim <mrahim@users.noreply.github.com>
Date: Wed, 24 Jul 2019 08:54:43 +0200
Subject: [PATCH 189/228] Set verbose=1 to fetchers

---
 nilearn/datasets/func.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
index 64281abfdd..f0171eda26 100644
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -1360,7 +1360,7 @@ def _load_mixed_gambles(zmap_imgs):
 
 
 def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True,
-                        return_raw_data=False, verbose=0):
+                        return_raw_data=False, verbose=1):
     """Fetch Jimura "mixed gambles" dataset.
 
     Parameters
@@ -1995,7 +1995,7 @@ def _fetch_development_fmri_functional(participants, data_dir, url, verbose):
 
 
 def fetch_development_fmri(n_subjects=None, reduce_confounds=True,
-                           data_dir=None, resume=True, verbose=0):
+                           data_dir=None, resume=True, verbose=1):
     """Fetch movie watching based brain development dataset (fMRI)
 
     The data is downsampled to 4mm resolution for convenience. The origin of

From 065246c1f4381e0b6b0c98294c26abe14b0b6626 Mon Sep 17 00:00:00 2001
From: mrahim <mrahim@users.noreply.github.com>
Date: Wed, 24 Jul 2019 09:56:42 +0200
Subject: [PATCH 190/228] update docstring

---
 nilearn/datasets/func.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
index f0171eda26..fb13e055b6 100644
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -1380,7 +1380,7 @@ def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True,
     resume: bool, optional (default True)
         If true, try resuming download if possible.
 
-    verbose: int, optional (default 0)
+    verbose: int, optional (default 1)
         Defines the level of verbosity of the output.
 
     return_raw_data: bool, optional (default True)
@@ -2024,7 +2024,7 @@ def fetch_development_fmri(n_subjects=None, reduce_confounds=True,
     resume: bool, optional (default True)
         Whether to resume download of a partly-downloaded file.
 
-    verbose: int, optional (default 0)
+    verbose: int, optional (default 1)
         Defines the level of verbosity of the output.
 
     Returns

From d791f48cc0427c2c5d46a53657e96d98c8c9aa79 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 10:34:06 +0200
Subject: [PATCH 191/228] Doc links no now point to the latest version of SKL

---
 doc/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/conf.py b/doc/conf.py
index 3f88540004..d2dd72133c 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -287,7 +287,7 @@
     'numpy': ('http://docs.scipy.org/doc/numpy', None),
     'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),
     'matplotlib': ('http://matplotlib.org/', None),
-    'sklearn': ('http://scikit-learn.org/0.18', None),
+    'sklearn': ('http://scikit-learn.org/', None),
     'nibabel': ('http://nipy.org/nibabel', None),
     'pandas': ('http://pandas.pydata.org', None),
 }

From 0155d6ab93fdf85114bd4bdca6bbd11b89040726 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 10:40:56 +0200
Subject: [PATCH 192/228] Doc links no now point to the latest version of SKL
 attempt 2

---
 doc/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/conf.py b/doc/conf.py
index d2dd72133c..cebd87c270 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -287,7 +287,7 @@
     'numpy': ('http://docs.scipy.org/doc/numpy', None),
     'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),
     'matplotlib': ('http://matplotlib.org/', None),
-    'sklearn': ('http://scikit-learn.org/', None),
+    'sklearn': ('http://scikit-learn.org/latest', None),
     'nibabel': ('http://nipy.org/nibabel', None),
     'pandas': ('http://pandas.pydata.org', None),
 }

From d8e73c87cd03c2d589138e6bf2d6096fae46b16a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 10:41:47 +0200
Subject: [PATCH 193/228] Doc links no now point to the latest version of SKL
 attempt 3

---
 doc/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/conf.py b/doc/conf.py
index cebd87c270..387905d121 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -287,7 +287,7 @@
     'numpy': ('http://docs.scipy.org/doc/numpy', None),
     'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),
     'matplotlib': ('http://matplotlib.org/', None),
-    'sklearn': ('http://scikit-learn.org/latest', None),
+    'sklearn': ('http://scikit-learn.org/stable/', None),
     'nibabel': ('http://nipy.org/nibabel', None),
     'pandas': ('http://pandas.pydata.org', None),
 }

From de106d3fc480dc75225556165e74455430f8e0ba Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 12:47:09 +0200
Subject: [PATCH 194/228] Handled GraphicalLasso import cases where SKL <
 0.20.0

---
 .../plot_inverse_covariance_connectome.py                | 8 ++++++--
 .../03_connectivity/plot_multi_subject_connectome.py     | 9 +++++++--
 examples/03_connectivity/plot_simulated_connectome.py    | 7 ++++++-
 examples/03_connectivity/plot_sphere_based_connectome.py | 6 +++++-
 4 files changed, 24 insertions(+), 6 deletions(-)

diff --git a/examples/03_connectivity/plot_inverse_covariance_connectome.py b/examples/03_connectivity/plot_inverse_covariance_connectome.py
index 68b6555417..779344101b 100644
--- a/examples/03_connectivity/plot_inverse_covariance_connectome.py
+++ b/examples/03_connectivity/plot_inverse_covariance_connectome.py
@@ -51,9 +51,13 @@
 ##############################################################################
 # Compute the sparse inverse covariance
 # --------------------------------------
-from sklearn.covariance import GraphicalLassoCV
-estimator = GraphicalLassoCV()
+try:
+    from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+    # for Scitkit-Learn < v0.20.0
+    from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
 
+estimator = GraphicalLassoCV()
 estimator.fit(time_series)
 
 ##############################################################################
diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index fd419b1c4b..78dc2ce2b6 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -84,8 +84,13 @@ def plot_matrices(cov, prec, title, labels):
 gsc = GroupSparseCovarianceCV(verbose=2)
 gsc.fit(subject_time_series)
 
-from sklearn import covariance
-gl = covariance.GraphicalLassoCV(verbose=2)
+try:
+    from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+    # for Scitkit-Learn < v0.20.0
+    from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
+
+gl = GraphicalLassoCV(verbose=2)
 gl.fit(np.concatenate(subject_time_series))
 
 
diff --git a/examples/03_connectivity/plot_simulated_connectome.py b/examples/03_connectivity/plot_simulated_connectome.py
index 36cc5ddd52..3df491300c 100644
--- a/examples/03_connectivity/plot_simulated_connectome.py
+++ b/examples/03_connectivity/plot_simulated_connectome.py
@@ -49,7 +49,12 @@
 
 
 # Fit one graph lasso per subject
-from sklearn.covariance import GraphicalLassoCV
+try:
+    from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+    # for Scitkit-Learn < v0.20.0
+    from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
+
 gl = GraphicalLassoCV(verbose=1)
 
 for n, subject in enumerate(subjects[:n_displayed]):
diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 0e558060c3..9247c53faa 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -211,7 +211,11 @@
 ###############################################################################
 # in which situation the graphical lasso **sparse inverse covariance**
 # estimator captures well the covariance **structure**.
-from sklearn.covariance import GraphicalLassoCV
+try:
+    from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+    # for Scitkit-Learn < v0.20.0
+    from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
 
 covariance_estimator = GraphicalLassoCV(cv=3, verbose=1)
 

From 6697b67fafa4ae2af5b790197bfc3f6367be9e2f Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 13:29:13 +0200
Subject: [PATCH 195/228] Removed source=nilearn to avoid confusion between
 path & package name

---
 .coveragerc | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.coveragerc b/.coveragerc
index 756591a983..80a297d359 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -1,6 +1,5 @@
 [run]
 branch = True
-source = nilearn
 parallel = True
 omit =
 	*test*

From d2f07fbf839796b0ee2ffa1afa797828bc5b82a4 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 15:27:15 +0200
Subject: [PATCH 196/228] Removed omit=*test* to fix error during coverage
 reporting

---
 .coveragerc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.coveragerc b/.coveragerc
index 80a297d359..0e4f6b34fd 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -2,11 +2,9 @@
 branch = True
 parallel = True
 omit =
-	*test*
 	conf\.py
 
 [report]
 omit =
-	*test*
 	conf\.py
 

From f41f1fbe274af6a92dae89d8840892c16e1dbf77 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 16:13:52 +0200
Subject: [PATCH 197/228] Corrected docstring for bg_img in plot_stat_map

---
 nilearn/plotting/img_plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/plotting/img_plotting.py b/nilearn/plotting/img_plotting.py
index d7965a0b11..d49077d5ab 100644
--- a/nilearn/plotting/img_plotting.py
+++ b/nilearn/plotting/img_plotting.py
@@ -941,7 +941,7 @@ def plot_stat_map(stat_map_img, bg_img=MNI152TEMPLATE, cut_coords=None,
             See http://nilearn.github.io/manipulating_images/input_output.html
             The background image that the ROI/mask will be plotted on top of.
             If nothing is specified, the MNI152 template will be used.
-            To turn off background image, just pass "bg_img=False".
+            To turn off background image, just pass "bg_img=None".
         cut_coords : None, a tuple of floats, or an integer
             The MNI coordinates of the point where the cut is performed
             If display_mode is 'ortho' or 'tiled',

From 0ee7df1e5ececc263c97b5d3ba3d13f26016041c Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 24 Jul 2019 16:30:18 +0200
Subject: [PATCH 198/228] Removed omit sections (nothing to omit, it would
 seem)

---
 .coveragerc | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.coveragerc b/.coveragerc
index 0e4f6b34fd..a7668d330c 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -1,10 +1,4 @@
 [run]
 branch = True
 parallel = True
-omit =
-	conf\.py
-
-[report]
-omit =
-	conf\.py
 

From 4e7704ecf2bf892d1a3d056d98ec452b32f8f343 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Sat, 3 Aug 2019 20:46:50 +0200
Subject: [PATCH 199/228] Replaced conda latest version with 4.6.* pending
 replacement

---
 continuous_integration/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 202055ab8f..6304eda955 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -62,7 +62,7 @@ create_new_conda_env() {
 
     # Use the miniconda installer for faster download / install of conda
     # itself
-    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    wget https://repo.continuum.io/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh \
         -O ~/miniconda.sh
     chmod +x ~/miniconda.sh && ~/miniconda.sh -b
     export PATH=$HOME/miniconda3/bin:$PATH

From 2e60c19ea8dc821107edd8e66b01d62c64ffb56a Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Mon, 5 Aug 2019 12:22:29 +0200
Subject: [PATCH 200/228] Undid some documentation changes as their reason is
 not understood

 -  Might be added back in future once the original PR author Gilles86
 clarifies them.
---
 nilearn/signal.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/nilearn/signal.py b/nilearn/signal.py
index 436f96dbfe..10eda20701 100644
--- a/nilearn/signal.py
+++ b/nilearn/signal.py
@@ -397,6 +397,10 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
     sensitivity.
 
     Filtering is only meaningful on evenly-sampled signals.
+    
+    According to Lindquist et al. (2018), removal of confounds will be done
+    orthogonally to temporal filters (low- and/or high-pass filters), if both
+    are specified.
 
     Parameters
     ----------
@@ -419,7 +423,7 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
         signal, as if all were in the same array.
 
     t_r: float
-        Repetition time, in second (sampling period).
+        Repetition time, in second (sampling period). Set to None if not.
 
     low_pass, high_pass: float
         Respectively low and high cutoff frequencies, in Hertz.
@@ -454,6 +458,11 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
     Linear Approach". Human Brain Mapping 2, no 4 (1994): 189-210.
     <http://dx.doi.org/10.1002/hbm.460020402>`_
 
+    Orthogonalization between temporal filters and confound removal is based on
+    suggestions in `Lindquist, M., Geuter, S., Wager, T., & Caffo, B. (2018).
+    Modular preprocessing pipelines can reintroduce artifacts into fMRI data.
+    bioRxiv, 407676. <http://dx.doi.org/10.1101/407676>`_
+
     See Also
     --------
         nilearn.image.clean_img

From f0ea7511c5751a43fe744313d95fbd7ddf9506a4 Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Thu, 11 Jul 2019 16:33:30 -0400
Subject: [PATCH 201/228] Change such that empty lists are recognized as False

---
 nilearn/plotting/matrix_plotting.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nilearn/plotting/matrix_plotting.py b/nilearn/plotting/matrix_plotting.py
index 120177255d..d533e09451 100644
--- a/nilearn/plotting/matrix_plotting.py
+++ b/nilearn/plotting/matrix_plotting.py
@@ -81,7 +81,7 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
             Axes image.
     """
     if reorder:
-        if labels is None or labels is False:
+        if not labels:
             raise ValueError("Labels are needed to show the reordering.")
         try:
             from scipy.cluster.hierarchy import (linkage, optimal_leaf_ordering,
@@ -134,10 +134,10 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
                         cmap=cmap, **kwargs)
     axes.set_autoscale_on(False)
     ymin, ymax = axes.get_ylim()
-    if labels is False:
+    if not labels:
         axes.xaxis.set_major_formatter(plt.NullFormatter())
         axes.yaxis.set_major_formatter(plt.NullFormatter())
-    elif labels is not None:
+    else:
         axes.set_xticks(np.arange(len(labels)))
         axes.set_xticklabels(labels, size='x-small')
         for label in axes.get_xticklabels():
@@ -178,7 +178,7 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
     axes.set_ylim(ymin, ymax)
 
     if auto_fit:
-        if labels is not None and labels is not False:
+        if labels:
             fit_axes(axes)
         elif own_fig:
             plt.tight_layout(pad=.1,

From 27ed996eb0c97af15be2db8912a972d356e4b197 Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Thu, 11 Jul 2019 17:22:47 -0400
Subject: [PATCH 202/228] ensure labels is a list, not an ndarray

---
 nilearn/plotting/matrix_plotting.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/matrix_plotting.py b/nilearn/plotting/matrix_plotting.py
index d533e09451..941569bdbb 100644
--- a/nilearn/plotting/matrix_plotting.py
+++ b/nilearn/plotting/matrix_plotting.py
@@ -37,7 +37,7 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
             Matrix to be plotted.
         title : string or None, optional
             A text to add in the upper left corner.
-        labels : list of strings, optional
+        labels : list or ndarray of strings, optional
             The label of each row and column
         figure : figure instance, figsize tuple, or None
             Sets the figure used. This argument can be either an existing
@@ -80,6 +80,10 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
         display : instance of matplotlib
             Axes image.
     """
+    # we need a list so an empty one will be cast to False
+    if isinstance(labels, np.ndarray):
+        labels = labels.tolist()
+
     if reorder:
         if not labels:
             raise ValueError("Labels are needed to show the reordering.")
@@ -103,7 +107,7 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
         labels = np.array(labels).copy()
         mat = mat.copy()
         # and reorder labels and matrix
-        labels = labels[index]
+        labels = labels[index].tolist()
         mat = mat[index, :][:, index]
 
     if tri == 'lower':

From fdb01f9ba479e5a48b9792c94a870a8c52e86007 Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Fri, 12 Jul 2019 10:40:20 -0400
Subject: [PATCH 203/228] Added tests for empty list case and labels as ndarray

---
 nilearn/plotting/tests/test_matrix_plotting.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/nilearn/plotting/tests/test_matrix_plotting.py b/nilearn/plotting/tests/test_matrix_plotting.py
index 848089c295..f055f4f2fe 100644
--- a/nilearn/plotting/tests/test_matrix_plotting.py
+++ b/nilearn/plotting/tests/test_matrix_plotting.py
@@ -10,7 +10,7 @@
 
 
 def test_matrix_plotting():
-    from numpy import zeros
+    from numpy import zeros, array
     from distutils.version import LooseVersion
     mat = zeros((10, 10))
     labels = [str(i) for i in range(10)]
@@ -21,6 +21,13 @@ def test_matrix_plotting():
     # test if it returns an AxesImage
     ax.axes.set_title('Title')
     plt.close()
+    # test if an empty list works as an argument for labels
+    ax = plot_matrix(mat, labels=[])
+    plt.close()
+    # test if an array gets correctly cast to a list
+    ax = plot_matrix(mat, labels=array(labels))
+    plt.close()
+
     import scipy
     if LooseVersion(scipy.__version__) >= LooseVersion('1.0.0'):
         # test if a ValueError is raised when reorder=True without labels

From 7ba0296894be6c0e02b236688b0e2b941bc0cef9 Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Mon, 15 Jul 2019 11:43:40 -0400
Subject: [PATCH 204/228] Test for right length of labels

---
 nilearn/plotting/matrix_plotting.py            | 2 ++
 nilearn/plotting/tests/test_matrix_plotting.py | 1 +
 2 files changed, 3 insertions(+)

diff --git a/nilearn/plotting/matrix_plotting.py b/nilearn/plotting/matrix_plotting.py
index 941569bdbb..f2c2959c47 100644
--- a/nilearn/plotting/matrix_plotting.py
+++ b/nilearn/plotting/matrix_plotting.py
@@ -83,6 +83,8 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
     # we need a list so an empty one will be cast to False
     if isinstance(labels, np.ndarray):
         labels = labels.tolist()
+    if labels and len(labels) != mat.shape[0]:
+        raise ValueError("Length of labels unequal to length of matrix.")
 
     if reorder:
         if not labels:
diff --git a/nilearn/plotting/tests/test_matrix_plotting.py b/nilearn/plotting/tests/test_matrix_plotting.py
index f055f4f2fe..36710d6792 100644
--- a/nilearn/plotting/tests/test_matrix_plotting.py
+++ b/nilearn/plotting/tests/test_matrix_plotting.py
@@ -27,6 +27,7 @@ def test_matrix_plotting():
     # test if an array gets correctly cast to a list
     ax = plot_matrix(mat, labels=array(labels))
     plt.close()
+    assert_raises(ValueError, plot_matrix, mat, labels=[0, 1, 2])
 
     import scipy
     if LooseVersion(scipy.__version__) >= LooseVersion('1.0.0'):

From 605be5733f4023adb34b2363f58187d52f360422 Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Thu, 25 Jul 2019 11:30:09 -0400
Subject: [PATCH 205/228] More explicit doc string

---
 nilearn/plotting/matrix_plotting.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/nilearn/plotting/matrix_plotting.py b/nilearn/plotting/matrix_plotting.py
index f2c2959c47..075205a7fa 100644
--- a/nilearn/plotting/matrix_plotting.py
+++ b/nilearn/plotting/matrix_plotting.py
@@ -37,8 +37,10 @@ def plot_matrix(mat, title=None, labels=None, figure=None, axes=None,
             Matrix to be plotted.
         title : string or None, optional
             A text to add in the upper left corner.
-        labels : list or ndarray of strings, optional
-            The label of each row and column
+        labels : list, ndarray of strings, empty list, False, or None, optional
+            The label of each row and column. Needs to be the same
+            length as rows/columns of mat. If False, None, or an
+            empty list, no labels are plotted.
         figure : figure instance, figsize tuple, or None
             Sets the figure used. This argument can be either an existing
             figure, or a pair (width, height) that gives the size of a

From c8f6cc7e4be50ec7a69e85ebafacc8370b164b5c Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Fri, 2 Aug 2019 22:01:51 -0400
Subject: [PATCH 206/228] Added test for labels=None

---
 nilearn/plotting/tests/test_matrix_plotting.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/nilearn/plotting/tests/test_matrix_plotting.py b/nilearn/plotting/tests/test_matrix_plotting.py
index 36710d6792..9a1aa04939 100644
--- a/nilearn/plotting/tests/test_matrix_plotting.py
+++ b/nilearn/plotting/tests/test_matrix_plotting.py
@@ -27,6 +27,9 @@ def test_matrix_plotting():
     # test if an array gets correctly cast to a list
     ax = plot_matrix(mat, labels=array(labels))
     plt.close()
+    # test if labels can be None
+    ax = plot_matrix(mat, labels=None)
+    plt.close()
     assert_raises(ValueError, plot_matrix, mat, labels=[0, 1, 2])
 
     import scipy

From cd928fd764e0729a054391cca7694a3eaddc156e Mon Sep 17 00:00:00 2001
From: mjboos <moritz.boos@uni-oldenburg.de>
Date: Tue, 6 Aug 2019 23:45:08 -0400
Subject: [PATCH 207/228] added whats_new

---
 doc/whats_new.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index b0b09f77ac..7c98dfe919 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -59,6 +59,7 @@ Fixes
   half-transparent grey to maintain a 3D perception.
 - :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
   path.
+- :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
 
 0.5.2
 =====

From a29a9a3eaa1db419a4c7a80e1a5a15979c473054 Mon Sep 17 00:00:00 2001
From: "Kshitij Chawla (kchawla-pi)" <kc.insight.pi@gmail.com>
Date: Wed, 7 Aug 2019 18:43:42 +0200
Subject: [PATCH 208/228] Made Flake8 happy

---
 nilearn/input_data/tests/test_nifti_labels_masker.py  | 4 +++-
 nilearn/input_data/tests/test_nifti_maps_masker.py    | 9 +++++----
 nilearn/input_data/tests/test_nifti_spheres_masker.py | 5 +++--
 nilearn/signal.py                                     | 3 +--
 nilearn/tests/test_signal.py                          | 7 ++++---
 5 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/nilearn/input_data/tests/test_nifti_labels_masker.py b/nilearn/input_data/tests/test_nifti_labels_masker.py
index ae1d71ee9f..fe936028a6 100644
--- a/nilearn/input_data/tests/test_nifti_labels_masker.py
+++ b/nilearn/input_data/tests/test_nifti_labels_masker.py
@@ -275,7 +275,9 @@ def test_standardization():
     signals = np.random.randn(np.prod(data_shape), n_samples)
     means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
     signals += means
-    img = nibabel.Nifti1Image(signals.reshape(data_shape + (n_samples,)), np.eye(4))
+    img = nibabel.Nifti1Image(
+            signals.reshape(data_shape + (n_samples,)), np.eye(4)
+            )
 
     labels = data_gen.generate_labeled_regions((9, 9, 5), 10)
 
diff --git a/nilearn/input_data/tests/test_nifti_maps_masker.py b/nilearn/input_data/tests/test_nifti_maps_masker.py
index dde2eae70e..e931220999 100644
--- a/nilearn/input_data/tests/test_nifti_maps_masker.py
+++ b/nilearn/input_data/tests/test_nifti_maps_masker.py
@@ -329,7 +329,8 @@ def test_standardization():
     trans_signals = masker.fit_transform(img)
 
     np.testing.assert_almost_equal(trans_signals.mean(0), 0)
-    np.testing.assert_almost_equal(trans_signals,
-                                   unstandarized_label_signals
-                                   / unstandarized_label_signals.mean(0)
-                                   * 100 - 100)
+    np.testing.assert_almost_equal(
+            trans_signals,
+            unstandarized_label_signals /
+            unstandarized_label_signals.mean(0) * 100 - 100,
+            )
diff --git a/nilearn/input_data/tests/test_nifti_spheres_masker.py b/nilearn/input_data/tests/test_nifti_spheres_masker.py
index 74e334a0eb..0df537ec1c 100644
--- a/nilearn/input_data/tests/test_nifti_spheres_masker.py
+++ b/nilearn/input_data/tests/test_nifti_spheres_masker.py
@@ -173,5 +173,6 @@ def test_standardization():
     s = masker.fit_transform(img)
 
     np.testing.assert_almost_equal(s.mean(), 0)
-    np.testing.assert_almost_equal(s.ravel(), data[1, 1, 1]
-                                   / data[1, 1, 1].mean() * 100 - 100)
+    np.testing.assert_almost_equal(s.ravel(), data[1, 1, 1] /
+                                   data[1, 1, 1].mean() * 100 - 100,
+                                   )
diff --git a/nilearn/signal.py b/nilearn/signal.py
index b0e0077d57..0f3c983d52 100644
--- a/nilearn/signal.py
+++ b/nilearn/signal.py
@@ -397,7 +397,7 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
     sensitivity.
 
     Filtering is only meaningful on evenly-sampled signals.
-    
+
     According to Lindquist et al. (2018), removal of confounds will be done
     orthogonally to temporal filters (low- and/or high-pass filters), if both
     are specified.
@@ -467,7 +467,6 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
     --------
         nilearn.image.clean_img
     """
-
     if isinstance(low_pass, bool):
         raise TypeError("low pass must be float or None but you provided "
                         "low_pass='{0}'".format(low_pass))
diff --git a/nilearn/tests/test_signal.py b/nilearn/tests/test_signal.py
index ab22376fe3..184033682a 100644
--- a/nilearn/tests/test_signal.py
+++ b/nilearn/tests/test_signal.py
@@ -227,8 +227,9 @@ def test_detrend():
 
     # "x" must be left untouched
     np.testing.assert_almost_equal(original, x, decimal=14)
-    assert_true(abs(detrended.mean(axis=0)).max()
-                < 15. * np.finfo(np.float).eps)
+    assert_true(
+            abs(detrended.mean(axis=0)).max() < 15. * np.finfo(np.float).eps
+            )
     np.testing.assert_almost_equal(detrended_scipy, detrended, decimal=14)
     # for this to work, there must be no trends at all in "signals"
     np.testing.assert_almost_equal(detrended, signals, decimal=14)
@@ -547,7 +548,7 @@ def test_clean_psc():
     cleaned_signals = clean(signals, standardize='psc')
     np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
 
-    std = cleaned_signals.std(axis=0)
+    cleaned_signals.std(axis=0)
     np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
     np.testing.assert_almost_equal(cleaned_signals,
                                    signals / signals.mean(0) * 100 - 100)

From 358e749643006ab5c8d8356ac6da94352056ab7a Mon Sep 17 00:00:00 2001
From: juhuntenburg <ju.huntenburg@gmail.com>
Date: Wed, 14 Aug 2019 17:50:07 +0100
Subject: [PATCH 209/228] fix wrong urls in nki dataset

---
 nilearn/datasets/func.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
index fb13e055b6..303247aec7 100644
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -1808,7 +1808,7 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
            'A00056097', 'A00056098', 'A00056164', 'A00056372', 'A00056452',
            'A00056489', 'A00056949']
 
-    nitrc_ids = range(8260, 8470)
+    nitrc_ids = range(8260, 8464)
     max_subjects = len(ids)
     if n_subjects is None:
         n_subjects = max_subjects
@@ -1816,7 +1816,6 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
         warnings.warn('Warning: there are only %d subjects' % max_subjects)
         n_subjects = max_subjects
     ids = ids[:n_subjects]
-    nitrc_ids = nitrc_ids[:n_subjects]
 
     # Dataset description
     fdescr = _get_dataset_descr(dataset_name)
@@ -1850,13 +1849,13 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
         func = os.path.join('%s', '%s_%s_preprocessed_fwhm6.gii')
         rh = _fetch_files(data_dir,
                           [(func % (ids[i], ids[i], 'right'),
-                           archive % (nitrc_ids[i], ids[i], 'rh'),
+                           archive % (nitrc_ids[2*i+1], ids[i], 'rh'),
                            {'move': func % (ids[i], ids[i], 'right')}
                             )],
                           resume=resume, verbose=verbose)
         lh = _fetch_files(data_dir,
                           [(func % (ids[i], ids[i], 'left'),
-                           archive % (nitrc_ids[i], ids[i], 'lh'),
+                           archive % (nitrc_ids[2*i], ids[i], 'lh'),
                            {'move': func % (ids[i], ids[i], 'left')}
                             )],
                           resume=resume, verbose=verbose)

From d27cda504af89b26d2ac63beb4327fd16acb4ebf Mon Sep 17 00:00:00 2001
From: juhuntenburg <ju.huntenburg@gmail.com>
Date: Wed, 28 Aug 2019 11:23:27 +0100
Subject: [PATCH 210/228] adding fix to whatsnew

---
 doc/whats_new.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index d060421a33..c1fa513d82 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -64,6 +64,8 @@ Fixes
 - :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
   path.
 - :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
+- :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
+  left and right functional surface data for each subject
 
 0.5.2
 =====
@@ -1403,4 +1405,3 @@ Contributors (from ``git shortlog -ns 0.1``)::
      1  Matthias Ekman
      1  Michael Waskom
      1  Vincent Michel
-

From a2c33401793a09cc1b9b0ae97e02a08b297c503d Mon Sep 17 00:00:00 2001
From: jeromedockes <jerome@dockes.org>
Date: Wed, 4 Sep 2019 18:33:51 +0200
Subject: [PATCH 211/228] change nose to pytest on appveyor (#2130)

This was missed in Pytest switch in PR #2076
---
 appveyor.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 0e8214374a..ed77a89874 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -27,7 +27,7 @@ install:
   - "conda install pip numpy scipy scikit-learn joblib nose pytest wheel matplotlib -y -q"
 
   # Install other nilearn dependencies
-  - "pip install nibabel coverage nose-timer"
+  - "pip install nibabel coverage nose-timer pytest-cov"
   - "python setup.py bdist_wheel"
   - ps: "ls dist"
 
@@ -41,7 +41,7 @@ test_script:
   # Change to a non-source folder to make sure we run the tests on the
   # installed library.
   - "cd C:\\"
-  - "python -c \"import nose; nose.main()\" -v -s nilearn --with-timer --timer-top-n 50"
+  - "pytest --pyargs nilearn -v"
 
 artifacts:
   # Archive the generated packages in the ci.appveyor.com build report.

From 0d53f6d90c82bdc16d782a49a28dc89b5392f278 Mon Sep 17 00:00:00 2001
From: jeromedockes <jerome@dockes.org>
Date: Mon, 23 Sep 2019 23:28:42 +0200
Subject: [PATCH 212/228] fix openmp crash (#2140)

---
 nilearn/__init__.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/nilearn/__init__.py b/nilearn/__init__.py
index 7bfee2f735..5d39246d7b 100644
--- a/nilearn/__init__.py
+++ b/nilearn/__init__.py
@@ -35,11 +35,17 @@
 import gzip
 import sys
 import warnings
+import os
 
 from distutils.version import LooseVersion
 
 from .version import _check_module_dependencies, __version__
 
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+#
+# see also https://github.com/scikit-learn/scikit-learn/pull/15020
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
 
 def _py2_deprecation_warning():
     py2_warning = ('Python2 support is deprecated and will be removed in '

From bf652530fa5ef44cd30314124123ae823d822828 Mon Sep 17 00:00:00 2001
From: Eric Larson <larson.eric.d@gmail.com>
Date: Wed, 25 Sep 2019 14:39:55 -0400
Subject: [PATCH 213/228] MAINT: Future-compatible cmap reversal (#2131)

* MAINT: Future-compatible cmap reversal

* STY: PEP8

* DOC: whats_new.rst
---
 doc/whats_new.rst      |  2 ++
 nilearn/plotting/cm.py | 75 +++++++++++++++++++++++-------------------
 2 files changed, 43 insertions(+), 34 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index c1fa513d82..eed06930d0 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -66,6 +66,8 @@ Fixes
 - :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
 - :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
   left and right functional surface data for each subject
+- Colormap creation functions have been updated to avoid matplotlib deprecation warnings
+  about colormap reversal
 
 0.5.2
 =====
diff --git a/nilearn/plotting/cm.py b/nilearn/plotting/cm.py
index 1c989f4caa..9ee553e664 100644
--- a/nilearn/plotting/cm.py
+++ b/nilearn/plotting/cm.py
@@ -38,16 +38,16 @@ def _pigtailed_cmap(cmap, swap_order=('green', 'red', 'blue')):
     orig_cdict = cmap._segmentdata.copy()
 
     cdict = dict()
-    cdict['green'] = [(0.5*(1-p), c1, c2)
-                        for (p, c1, c2) in reversed(orig_cdict[swap_order[0]])]
-    cdict['blue'] = [(0.5*(1-p), c1, c2)
-                        for (p, c1, c2) in reversed(orig_cdict[swap_order[1]])]
-    cdict['red'] = [(0.5*(1-p), c1, c2)
-                        for (p, c1, c2) in reversed(orig_cdict[swap_order[2]])]
+    cdict['green'] = [(0.5 * (1 - p), c1, c2)
+                      for (p, c1, c2) in reversed(orig_cdict[swap_order[0]])]
+    cdict['blue'] = [(0.5 * (1 - p), c1, c2)
+                     for (p, c1, c2) in reversed(orig_cdict[swap_order[1]])]
+    cdict['red'] = [(0.5 * (1 - p), c1, c2)
+                    for (p, c1, c2) in reversed(orig_cdict[swap_order[2]])]
 
     for color in ('red', 'green', 'blue'):
-        cdict[color].extend([(0.5*(1+p), c1, c2) 
-                                    for (p, c1, c2) in orig_cdict[color]])
+        cdict[color].extend([(0.5 * (1 + p), c1, c2)
+                             for (p, c1, c2) in orig_cdict[color]])
 
     return cdict
 
@@ -149,19 +149,28 @@ def alpha_cmap(color, name='', alpha_min=0.5, alpha_max=1.):
 _cmaps_data['bwr'] = _colors.LinearSegmentedColormap.from_list(
     'bwr', _bwr_data)._segmentdata.copy()
 
+
 ################################################################################
 # Build colormaps and their reverse.
+
+# backported and adapted from matplotlib since it's deprecated in 3.2
+def _revcmap(data):
+    data_r = {}
+    for key, val in data.items():
+        data_r[key] = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)]
+    return data_r
+
+
 _cmap_d = dict()
 
 for _cmapname in list(_cmaps_data.keys()):  # needed as dict changes within loop
     _cmapname_r = _cmapname + '_r'
     _cmapspec = _cmaps_data[_cmapname]
-    _cmaps_data[_cmapname_r] = _cm.revcmap(_cmapspec)
+    _cmaps_data[_cmapname_r] = _revcmap(_cmapspec)
     _cmap_d[_cmapname] = _colors.LinearSegmentedColormap(
-                            _cmapname, _cmapspec, _cm.LUTSIZE)
+        _cmapname, _cmapspec, _cm.LUTSIZE)
     _cmap_d[_cmapname_r] = _colors.LinearSegmentedColormap(
-                            _cmapname_r, _cmaps_data[_cmapname_r],
-                            _cm.LUTSIZE)
+        _cmapname_r, _cmaps_data[_cmapname_r], _cm.LUTSIZE)
 
 ################################################################################
 # A few transparent colormaps
@@ -212,13 +221,15 @@ def alpha_cmap(color, name='', alpha_min=0.5, alpha_max=1.):
 def dim_cmap(cmap, factor=.3, to_white=True):
     """ Dim a colormap to white, or to black.
     """
-    assert factor >= 0 and factor <=1, ValueError(
-            'Dimming factor must be larger than 0 and smaller than 1, %s was passed.' 
-                                                        % factor)
+    assert 0 <= factor <= 1, ValueError(
+        'Dimming factor must be larger than 0 and smaller than 1, %s was '
+        'passed.' % factor)
     if to_white:
-        dimmer = lambda c: 1 - factor*(1-c)
+        def dimmer(c):
+            return 1 - factor * (1 - c)
     else:
-        dimmer = lambda c: factor*c
+        def dimmer(c):
+            return factor * c
     cdict = cmap._segmentdata.copy()
     for c_index, color in enumerate(('red', 'green', 'blue')):
         color_lst = list()
@@ -227,19 +238,17 @@ def dim_cmap(cmap, factor=.3, to_white=True):
         cdict[color] = color_lst
 
     return _colors.LinearSegmentedColormap(
-                                '%s_dimmed' % cmap.name,
-                                cdict,
-                                _cm.LUTSIZE)
+        '%s_dimmed' % cmap.name, cdict, _cm.LUTSIZE)
 
 
 def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
     """ Replace a colormap by another inside a pair of values.
     """
     assert vmin < vmax, ValueError('vmin must be smaller than vmax')
-    assert vmin >= 0,    ValueError('vmin must be larger than 0, %s was passed.' 
-                                        % vmin)
-    assert vmax <= 1,    ValueError('vmax must be smaller than 1, %s was passed.' 
-                                        % vmax)
+    assert vmin >= 0, ValueError('vmin must be larger than 0, %s was passed.'
+                                 % vmin)
+    assert vmax <= 1, ValueError('vmax must be smaller than 1, %s was passed.'
+                                 % vmax)
     outer_cdict = outer_cmap._segmentdata.copy()
     inner_cdict = inner_cmap._segmentdata.copy()
 
@@ -257,7 +266,6 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
                 this_cdict['green'].append((p, g, g))
                 this_cdict['blue'].append((p, b, b))
 
-
     for c_index, color in enumerate(('red', 'green', 'blue')):
         color_lst = list()
 
@@ -266,8 +274,9 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
                 break
             color_lst.append((value, c1, c2))
 
-        color_lst.append((vmin, outer_cmap(vmin)[c_index], 
-                                inner_cmap(vmin)[c_index]))
+        color_lst.append((vmin,
+                          outer_cmap(vmin)[c_index],
+                          inner_cmap(vmin)[c_index]))
 
         for value, c1, c2 in inner_cdict[color]:
             if value <= vmin:
@@ -276,8 +285,9 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
                 break
             color_lst.append((value, c1, c2))
 
-        color_lst.append((vmax, inner_cmap(vmax)[c_index],
-                                outer_cmap(vmax)[c_index]))
+        color_lst.append((vmax,
+                          inner_cmap(vmax)[c_index],
+                          outer_cmap(vmax)[c_index]))
 
         for value, c1, c2 in outer_cdict[color]:
             if value <= vmax:
@@ -287,8 +297,5 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
         cdict[color] = color_lst
 
     return _colors.LinearSegmentedColormap(
-                                '%s_inside_%s' % (inner_cmap.name, outer_cmap.name),
-                                cdict,
-                                _cm.LUTSIZE)
-
-
+        '%s_inside_%s' % (inner_cmap.name, outer_cmap.name),
+        cdict, _cm.LUTSIZE)

From 920f7bd665a62382b672c99dfebd23d512748aa3 Mon Sep 17 00:00:00 2001
From: Dan Gale <danjgale@gmail.com>
Date: Wed, 25 Sep 2019 14:41:44 -0400
Subject: [PATCH 214/228] Update Schaefer parcelation to v0.14.3 (#2138)

* updated fetching of schaefer atlas to v0.14.3 by default for correct region labels

* include options for 700 and 900 region parcelations for schaefer atlas

* added 700 and 900 parcelations to schaefer test

* fixed url in schaefer reference
---
 doc/whats_new.rst                    |  3 +++
 nilearn/datasets/atlas.py            | 19 +++++++++++++------
 nilearn/datasets/tests/test_atlas.py |  2 +-
 3 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index eed06930d0..b341c8cdb2 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -66,6 +66,9 @@ Fixes
 - :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
 - :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
   left and right functional surface data for each subject
+- :func:`nilearn.datasets.fetch_atlas_schaefer_2018` now downloads from release
+  version 0.14.3 (instead of 0.8.1) by default, which includes corrected region label
+  names along with 700 and 900 region parcelations.
 - Colormap creation functions have been updated to avoid matplotlib deprecation warnings
   about colormap reversal
 
diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 2be84139d8..3897f95110 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -1275,7 +1275,7 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
     ----------
     n_rois: int
         number of regions of interest {100, 200, 300, 400 (default), 500, 600,
-        800, 1000}
+        700, 800, 900, 1000}
 
     yeo_networks: int
         ROI annotation according to yeo networks {7 (default), 17}
@@ -1307,7 +1307,7 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
     References
     ----------
     For more information on this dataset, see
-    https://github.com/ThomasYeoLab/CBIG/tree/v0.8.1-Schaefer2018_LocalGlobal/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal
+    https://github.com/ThomasYeoLab/CBIG/tree/v0.14.3-Update_Yeo2011_Schaefer2018_labelname/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations
 
     Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ,
     Eickhoff SB, Yeo BTT. Local-Global parcellation of the human
@@ -1320,8 +1320,15 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
     intrinsic functional connectivity. J Neurophysiol 106(3):1125-65, 2011.
 
     Licence: MIT.
+
+    Notes
+    -----
+    Release v0.14.3 of the Schaefer 2018 parcellation is used by
+    default. Versions prior to v0.14.3 are known to contain erroneous region
+    label names. For more details, see
+    https://github.com/ThomasYeoLab/CBIG/blob/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/Updates/Update_20190916_README.md
     """
-    valid_n_rois = [100, 200, 300, 400, 500, 600, 800, 1000]
+    valid_n_rois = list(range(100, 1100, 100))
     valid_yeo_networks = [7, 17]
     valid_resolution_mm = [1, 2]
     if n_rois not in valid_n_rois:
@@ -1338,9 +1345,9 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
 
     if base_url is None:
         base_url = ('https://raw.githubusercontent.com/ThomasYeoLab/CBIG/'
-                    'v0.8.1-Schaefer2018_LocalGlobal/stable_projects/'
-                    'brain_parcellation/Schaefer2018_LocalGlobal/'
-                    'Parcellations/MNI/'
+                    'v0.14.3-Update_Yeo2011_Schaefer2018_labelname/'
+                    'stable_projects/brain_parcellation/'
+                    'Schaefer2018_LocalGlobal/Parcellations/MNI/'
                     )
 
     files = []
diff --git a/nilearn/datasets/tests/test_atlas.py b/nilearn/datasets/tests/test_atlas.py
index e6984c4d6c..a2ce5b8cf1 100644
--- a/nilearn/datasets/tests/test_atlas.py
+++ b/nilearn/datasets/tests/test_atlas.py
@@ -565,7 +565,7 @@ def test_fetch_atlas_pauli_2017():
 
 @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
 def test_fetch_atlas_schaefer_2018():
-    valid_n_rois = [100, 200, 300, 400, 500, 600, 800, 1000]
+    valid_n_rois = list(range(100, 1100, 100))
     valid_yeo_networks = [7, 17]
     valid_resolution_mm = [1, 2]
 

From 64d97dd1fef1993e8fe19c741f59e7a731d94898 Mon Sep 17 00:00:00 2001
From: Alexandre Abraham <abraham.alexandre@gmail.com>
Date: Wed, 25 Sep 2019 20:42:55 +0200
Subject: [PATCH 215/228] Fixes #2029 Handle gzip files without extensions
 (#2126)

---
 nilearn/datasets/utils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/nilearn/datasets/utils.py b/nilearn/datasets/utils.py
index 446cae359e..d9aade43fa 100644
--- a/nilearn/datasets/utils.py
+++ b/nilearn/datasets/utils.py
@@ -336,9 +336,13 @@ def _uncompress_file(file_, delete_archive=True, verbose=1):
             processed = True
         elif ext == '.gz' or header.startswith(b'\x1f\x8b'):
             import gzip
-            gz = gzip.open(file_)
             if ext == '.tgz':
                 filename = filename + '.tar'
+            elif ext == '':
+                # For gzip file, we rely on the assumption that there is an extenstion
+                shutil.move(file_, file_ + '.gz')
+                file_ = file_ + '.gz'
+            gz = gzip.open(file_)
             out = open(filename, 'wb')
             shutil.copyfileobj(gz, out, 8192)
             gz.close()

From 66d01ef60efe524339c4a96e2069bf8e06b2ab1d Mon Sep 17 00:00:00 2001
From: Kshitij Chawla <kc.insight.pi@gmail.com>
Date: Tue, 1 Oct 2019 15:18:16 +0200
Subject: [PATCH 216/228] Renamed test to deduplicate name (#2144)

LGTM. Thanks.
---
 nilearn/tests/test_signal.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nilearn/tests/test_signal.py b/nilearn/tests/test_signal.py
index 184033682a..cbfeef1513 100644
--- a/nilearn/tests/test_signal.py
+++ b/nilearn/tests/test_signal.py
@@ -444,7 +444,7 @@ def test_clean_confounds():
                                    np.zeros((20, 2)))
 
 
-def test_clean_frequencies():
+def test_clean_frequencies_using_power_spectrum_density():
 
     # Create signal
     sx = np.array([np.sin(np.linspace(0, 100, 100) * 1.5),

From e0ae31ca1f53e399a4b66803654eac588e5922be Mon Sep 17 00:00:00 2001
From: Elizabeth DuPre <emd222@cornell.edu>
Date: Wed, 2 Oct 2019 06:49:45 -0400
Subject: [PATCH 217/228] [ENH] Initial visual reports (#2019)

* Add externals directory for tempita

* Add html report template

* wip: initial commit of NiftiMasker report

* Embed images in browser

* Minor CSS updates

* Add parameters table, non-responsive

* Add tooltip for docstring

* ENH: Avoid the use of iframes in the notebook

(nicer layout)

* MISC: cleaner inheritance

* Remove ReportMixin class

* Patch merge conflict

* Create reporting module

* Move computation to reporting method of masker

* Call local style sheets

* New nose-exclude dependency

* Correct handling of 3D images

* Revert nose-excludes, unpackage externals

* Re-introduce ReportMixin

* Re-package externals, add nose plugin to ignore

* temporarily remove non-functioning nose plugin

* Define __test__ attr to skip nosetest

* Fix doc error, ignore linting in tempita

* Remove externals from coverage

* Unpackage pure style sheets

* Remove externals from coverage reporting

* Add pure attribution, license

* Ask codecov directly (nicely)

* Initial tests for reporting

* Explicitly cast pathlib to str

* Add overlay on hover

* patch handling of single display images

* Try to be careful about closing things...

* fix: patch CI failure, add new text if hover

* Address @jeromedockes review

* Update images files to svg

* omit externals from coverage

* Move HTMLDocument to reporting

* Add warning re missing MPL import, fix check_html calls

* Move rm_file.py into reporting module

* Fix check_html again

* Move HTMLDocument to its own module

* Adding additional tests

* Retrigger Travis

* Fix flake8 errors... the joys of switching text editors

* Update examples to reference generate_report method

* Change language from HTML to report, add comment on use

* Update plot_nifti_simple example

* Add code comment to plot_nifti_simple

* Add in screenshots to showcase reporting

* Adjust scaling

* embed html reports

* Address review comments

* Fix iframe fenching

* Update path to static HTML

* Set height auto

* Testing raw html directive

* Break plot_mixed_gambles death spiral

* Initial commit of MNE report scraper

* Patch _ReportScraper

* Use iframe rather than html page

* define image_scrapers in sphinx conf

* Update nilearn/reporting/sphinx_report.py

Co-Authored-By: Eric Larson <larson.eric.d@gmail.com>

* Use get_iframe method

* Re-introduce whitespace in _SCRAPER_TEXT

* Re-trigger CI

* fix report in sphinx

* Fix sphinx_report

Indentation is also necessary

* STY: fix CSS

* Better example

* FIX: avoid displaying reports twice

* STY: more compact view

Without this, the font is too big compared to the font of the examples

* ENH: sphinx-gallery capture with repr_html

Also works for interactive plots such as view_img

* Update nilearn/reporting/sphinx_report.py

Co-Authored-By: Elizabeth DuPre <emd222@cornell.edu>

* fix: correct dataset fetching

* Add niftimasker report screenshots for rst

* tst: initial tests for sphinx HTMLDocument embedding

* fix: patch location of overlay

* fix tst: do not rely on pytest fixture

* tst: ensure tmpdir is closed after testing

* fix: plot_nifti_simple fetcher

* STY: fix whitespace in embedeed reports

The CSS specified by the unclosing page (the nilearn website) was
getting in the way.

* STY: more styling to isolate from encompasing CSS

* STY: enclose reports with a border

* STY: tweak to limit scrollbars

* CSS to get a better box

This design makes the report really look like a box with a title.

* STY: change colormap on masker report

* STY: smaller reports in docs

* STY: deal gracefully with details overflow

* FIX: remove duplicate CSS rule

* STY: fix scrolling in jupyter

* Address @jeromedockes review

* STY: make reports work in vscode

Two problems arose:

- Default theme is dark
- The width was set wrong

* Add example with resampling to plot_mask_computation

* sty: update private attr in reporting

* Differentiate warns for not fit, no reporting

* fix: patch text in plot_mask_computation

* sty: linting changes

* fix: remove scaling to avoid jumping in page

* sty: make contour thicker for contrast

* Revert "fix: remove scaling to avoid jumping in page"

This reverts commit 250e995097c3f75249e1d97d4d3f39eb9ee05bea.

* fix: typo in sphinx_report

* fix: remove overlay for saving svg images

* Update nilearn/reporting/sphinx_report.py

Co-Authored-By: Gael Varoquaux <gael.varoquaux@normalesup.org>

* fix: update and extend test_html_report

* fix: remove leftover nosetests attribute

* Address @jeromedockes review

* Re-introduce mask_img_ in examples

* Generalize warnings in reporting

* Add whats_new entry for generate_report

* fix: patch whats_new entry

* sty: Update display of plot_roi figs for NiftiMasker docs
---
 .coveragerc                                   |    3 +-
 Makefile                                      |    1 -
 codecov.yml                                   |    2 +
 doc/conf.py                                   |    8 +-
 doc/images/niftimasker_report.png             |  Bin 0 -> 97128 bytes
 doc/images/niftimasker_report_params.png      |  Bin 0 -> 93637 bytes
 doc/manipulating_images/masker_objects.rst    |   32 +-
 doc/themes/nilearn/static/nature.css_t        |   19 +
 doc/themes/nilearn/static/sphinxdoc.css       |  339 -----
 doc/whats_new.rst                             |    3 +
 .../plot_3d_map_to_surface_projection.py      |    9 +-
 examples/01_plotting/plot_demo_plotting.py    |   11 +-
 examples/01_plotting/plot_surf_atlas.py       |    8 +-
 .../plot_inverse_covariance_connectome.py     |   11 +-
 .../plot_probabilistic_atlas_extraction.py    |   11 +-
 .../plot_sphere_based_connectome.py           |   17 +-
 .../plot_mask_computation.py                  |   41 +-
 .../plot_nifti_simple.py                      |   15 +-
 nilearn/externals/README.md                   |    5 +
 nilearn/externals/__init__.py                 |    7 +
 nilearn/externals/conftest.py                 |    8 +
 nilearn/externals/install_tempita.sh          |   22 +
 nilearn/externals/tempita/__init__.py         | 1311 +++++++++++++++++
 nilearn/externals/tempita/_looper.py          |  163 ++
 nilearn/externals/tempita/compat3.py          |   56 +
 nilearn/input_data/nifti_masker.py            |   95 +-
 nilearn/plotting/__init__.py                  |    4 +-
 nilearn/plotting/displays.py                  |    2 +-
 nilearn/plotting/html_connectome.py           |    3 +-
 nilearn/plotting/html_stat_map.py             |    3 +-
 nilearn/plotting/html_surface.py              |    3 +-
 nilearn/plotting/js_plotting_utils.py         |  150 --
 .../plotting/tests/test_html_connectome.py    |    7 +-
 .../plotting/tests/test_js_plotting_utils.py  |   76 +-
 nilearn/reporting/__init__.py                 |   12 +
 nilearn/reporting/data/README.txt             |    4 +
 nilearn/reporting/data/__init__.py            |    0
 nilearn/reporting/data/html/__init__.py       |    0
 .../data/html/report_body_template.html       |  226 +++
 .../data/html/report_head_template.html       |   18 +
 nilearn/reporting/html_document.py            |  151 ++
 nilearn/reporting/html_report.py              |  199 +++
 nilearn/{plotting => reporting}/rm_file.py    |    0
 nilearn/reporting/sphinx_report.py            |   48 +
 nilearn/reporting/tests/__init__.py           |    3 +
 nilearn/reporting/tests/test_html_document.py |   74 +
 nilearn/reporting/tests/test_html_report.py   |  102 ++
 nilearn/reporting/tests/test_sphinx_report.py |   45 +
 setup.py                                      |    3 +-
 49 files changed, 2699 insertions(+), 631 deletions(-)
 create mode 100644 codecov.yml
 create mode 100644 doc/images/niftimasker_report.png
 create mode 100644 doc/images/niftimasker_report_params.png
 delete mode 100644 doc/themes/nilearn/static/sphinxdoc.css
 create mode 100644 nilearn/externals/README.md
 create mode 100644 nilearn/externals/__init__.py
 create mode 100644 nilearn/externals/conftest.py
 create mode 100644 nilearn/externals/install_tempita.sh
 create mode 100644 nilearn/externals/tempita/__init__.py
 create mode 100644 nilearn/externals/tempita/_looper.py
 create mode 100644 nilearn/externals/tempita/compat3.py
 create mode 100644 nilearn/reporting/__init__.py
 create mode 100644 nilearn/reporting/data/README.txt
 create mode 100644 nilearn/reporting/data/__init__.py
 create mode 100644 nilearn/reporting/data/html/__init__.py
 create mode 100644 nilearn/reporting/data/html/report_body_template.html
 create mode 100644 nilearn/reporting/data/html/report_head_template.html
 create mode 100644 nilearn/reporting/html_document.py
 create mode 100644 nilearn/reporting/html_report.py
 rename nilearn/{plotting => reporting}/rm_file.py (100%)
 create mode 100644 nilearn/reporting/sphinx_report.py
 create mode 100644 nilearn/reporting/tests/__init__.py
 create mode 100644 nilearn/reporting/tests/test_html_document.py
 create mode 100644 nilearn/reporting/tests/test_html_report.py
 create mode 100644 nilearn/reporting/tests/test_sphinx_report.py

diff --git a/.coveragerc b/.coveragerc
index a7668d330c..5e2d545938 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -1,4 +1,5 @@
 [run]
 branch = True
 parallel = True
-
+omit =
+    */nilearn/externals/*
diff --git a/Makefile b/Makefile
index 50ada3bd2a..ec14caa46b 100644
--- a/Makefile
+++ b/Makefile
@@ -63,4 +63,3 @@ doc:
 .PHONY : pdf
 pdf:
 	make -C doc pdf
-
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 0000000000..9fd6de2da3
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,2 @@
+ignore:
+  - "*externals/.*"  # ignore folders and all its contents
diff --git a/doc/conf.py b/doc/conf.py
index 387905d121..392ccb353d 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -281,6 +281,11 @@
 
 _python_doc_base = 'http://docs.python.org/3.6'
 
+# Scraper, copied from https://github.com/mne-tools/mne-python/
+from nilearn.reporting import _ReportScraper
+report_scraper = _ReportScraper()
+scrapers = ('matplotlib', report_scraper)
+
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     'python': (_python_doc_base, None),
@@ -302,6 +307,7 @@
     'backreferences_dir': os.path.join('modules', 'generated'),
     'reference_url': {'nilearn': None},
     'junit': '../test-results/sphinx-gallery/junit.xml',
+    'image_scrapers': scrapers,
     }
 
 # Get rid of spurious warnings due to some interaction between
@@ -310,7 +316,6 @@
 # details
 numpydoc_show_class_members = False
 
-
 def touch_example_backreferences(app, what, name, obj, options, lines):
     # generate empty examples files, so that we don't get
     # inclusion errors if there are no examples for a class / module
@@ -327,3 +332,4 @@ def touch_example_backreferences(app, what, name, obj, options, lines):
 def setup(app):
     app.add_javascript('copybutton.js')
     app.connect('autodoc-process-docstring', touch_example_backreferences)
+    report_scraper.app = app
diff --git a/doc/images/niftimasker_report.png b/doc/images/niftimasker_report.png
new file mode 100644
index 0000000000000000000000000000000000000000..664469b9a3b02e9a41011e5504975a1054cf690a
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diff --git a/doc/manipulating_images/masker_objects.rst b/doc/manipulating_images/masker_objects.rst
index fa77719bda..dc2f06dafd 100644
--- a/doc/manipulating_images/masker_objects.rst
+++ b/doc/manipulating_images/masker_objects.rst
@@ -136,12 +136,25 @@ mask computation parameters.
 The mask can be retrieved and visualized from the `mask_img_` attribute
 of the masker:
 
+.. literalinclude:: ../../examples/04_manipulating_images/plot_mask_computation.py
+    :start-after: # A NiftiMasker with the default strategy
+    :end-before: # Plot the generated mask using the .generate_report method
+
+.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_002.png
+    :target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
+    :align: center
+    :scale: 40
+
+Alternatively, the mask can be visualized using the `generate_report`
+method of the masker. The generated report can be viewed in a Jupyter notebook,
+opened in a new browser tab using `report.open_in_browser()`,
+or saved as a portable HTML file `report.save_as_html(output_filepath)`.
+
 .. literalinclude:: ../../examples/04_manipulating_images/plot_mask_computation.py
     :start-after: # We need to specify an 'epi' mask_strategy, as this is raw EPI data
     :end-before: # Generate mask with strong opening
 
-
-.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_004.png
+.. figure:: /images/niftimasker_report.png
     :target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
     :scale: 50%
 
@@ -163,7 +176,7 @@ Controling these arguments set the fine aspects of the mask. See the
 functions documentation, or :doc:`the NiftiMasker example
 <../auto_examples/04_manipulating_images/plot_mask_computation>`.
 
-.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_005.png
+.. figure:: /images/niftimasker_report_params.png
     :target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
     :scale: 50%
 
@@ -180,9 +193,10 @@ preparation::
    >>> masker # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    NiftiMasker(detrend=False, dtype=None, high_pass=None, low_pass=None,
          mask_args=None, mask_img=None, mask_strategy='background',
-         memory=Memory(...), memory_level=1, sample_mask=None,
-         sessions=None, smoothing_fwhm=None, standardize=False, t_r=None,
-         target_affine=None, target_shape=None, verbose=0)
+         memory=Memory(...), memory_level=1, reports=True,
+         sample_mask=None, sessions=None, smoothing_fwhm=None,
+         standardize=False, t_r=None, target_affine=None, target_shape=None,
+         verbose=0)
 
 .. note::
 
@@ -234,7 +248,7 @@ Temporal Filtering and confound removal
 properties, before conversion to voxel signals.
 
 - **Standardization**. Parameter ``standardize``: Signals can be
-  standardized (scaled to unit variance). 
+  standardized (scaled to unit variance).
 
 - **Frequency filtering**. Low-pass and high-pass filters can be used to
   remove artifacts. Parameters: ``high_pass`` and ``low_pass``, specified
@@ -242,7 +256,7 @@ properties, before conversion to voxel signals.
   the ``t_r`` parameter: ``loss_pass=.5, t_r=2.1``).
 
 - **Confound removal**. Two ways of removing confounds are provided: simple
-  detrending or using prespecified confounds, such as behavioral or movement 
+  detrending or using prespecified confounds, such as behavioral or movement
   information.
 
   * Linear trends can be removed by activating the `detrend` parameter.
@@ -251,7 +265,7 @@ properties, before conversion to voxel signals.
     signal of interest (e.g., the neural correlates of cognitive tasks).
     It is not activated by default in :class:`NiftiMasker` but is recommended
     in almost all scenarios.
-    
+
   * More complex confounds, measured during the acquision, can be removed
     by passing them to :meth:`NiftiMasker.transform`. If the dataset
     provides a confounds file, just pass its path to the masker.
diff --git a/doc/themes/nilearn/static/nature.css_t b/doc/themes/nilearn/static/nature.css_t
index 6d5c73b8ff..7397860e39 100644
--- a/doc/themes/nilearn/static/nature.css_t
+++ b/doc/themes/nilearn/static/nature.css_t
@@ -1689,3 +1689,22 @@ ul#tab li div.contents p{
 p.sphx-glr-horizontal {
     margin-top: 2em;
 }
+
+
+/* Sphinx-gallery Report embedding */
+div.sg-report {
+    padding: 0pt;
+    transform: scale(.95);
+}
+
+div.sg-report iframe {
+    display: block;
+    border-style: none;
+    transform: scale(.85);
+    height: 470px;
+    margin-left: -12%; /* Negative because of .8 scaling */
+    margin-top: -4%;
+    padding: 0pt;
+    margin-bottom: 0pt;
+    width: 126%; /* More than 100% because of .8 scaling */
+}
diff --git a/doc/themes/nilearn/static/sphinxdoc.css b/doc/themes/nilearn/static/sphinxdoc.css
deleted file mode 100644
index b680a95710..0000000000
--- a/doc/themes/nilearn/static/sphinxdoc.css
+++ /dev/null
@@ -1,339 +0,0 @@
-/*
- * sphinxdoc.css_t
- * ~~~~~~~~~~~~~~~
- *
- * Sphinx stylesheet -- sphinxdoc theme.  Originally created by
- * Armin Ronacher for Werkzeug.
- *
- * :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-@import url("basic.css");
-
-/* -- page layout ----------------------------------------------------------- */
-
-body {
-    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
-                 'Verdana', sans-serif;
-    font-size: 14px;
-    letter-spacing: -0.01em;
-    line-height: 150%;
-    text-align: center;
-    background-color: #BFD1D4;
-    color: black;
-    padding: 0;
-    border: 1px solid #aaa;
-
-    margin: 0px 80px 0px 80px;
-    min-width: 740px;
-}
-
-div.document {
-    background-color: white;
-    text-align: left;
-    background-image: url(contents.png);
-    background-repeat: repeat-x;
-}
-
-div.bodywrapper {
-    margin: 0 240px 0 0;
-    border-right: 1px solid #ccc;
-}
-
-div.body {
-    margin: 0;
-    padding: 0.5em 20px 20px 20px;
-}
-
-div.related {
-    font-size: 1em;
-}
-
-div.related ul {
-    background-image: url(navigation.png);
-    height: 2em;
-    border-top: 1px solid #ddd;
-    border-bottom: 1px solid #ddd;
-}
-
-div.related ul li {
-    margin: 0;
-    padding: 0;
-    height: 2em;
-    float: left;
-}
-
-div.related ul li.right {
-    float: right;
-    margin-right: 5px;
-}
-
-div.related ul li a {
-    margin: 0;
-    padding: 0 5px 0 5px;
-    line-height: 1.75em;
-    color: #EE9816;
-}
-
-div.related ul li a:hover {
-    color: #3CA8E7;
-}
-
-div.sphinxsidebarwrapper {
-    padding: 0;
-}
-
-div.sphinxsidebar {
-    margin: 0;
-    padding: 0.5em 15px 15px 0;
-    width: 210px;
-    float: right;
-    font-size: 1em;
-    text-align: left;
-}
-
-div.sphinxsidebar h3, div.sphinxsidebar h4 {
-    margin: 1em 0 0.5em 0;
-    font-size: 1em;
-    padding: 0.1em 0 0.1em 0.5em;
-    color: white;
-    border: 1px solid #86989B;
-    background-color: #AFC1C4;
-}
-
-div.sphinxsidebar h3 a {
-    color: white;
-}
-
-div.sphinxsidebar ul {
-    padding-left: 1.5em;
-    margin-top: 7px;
-    padding: 0;
-    line-height: 130%;
-}
-
-div.sphinxsidebar ul ul {
-    margin-left: 20px;
-}
-
-div.footer {
-    background-color: #E3EFF1;
-    color: #86989B;
-    padding: 3px 8px 3px 0;
-    clear: both;
-    font-size: 0.8em;
-    text-align: right;
-}
-
-div.footer a {
-    color: #86989B;
-    text-decoration: underline;
-}
-
-/* -- body styles ----------------------------------------------------------- */
-
-p {    
-    margin: 0.8em 0 0.5em 0;
-}
-
-a {
-    color: #CA7900;
-    text-decoration: none;
-}
-
-a:hover {
-    color: #2491CF;
-}
-
-div.body a {
-    text-decoration: underline;
-}
-
-h1 {
-    margin: 0;
-    padding: 0.7em 0 0.3em 0;
-    font-size: 1.5em;
-    color: #11557C;
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-div.body h1 a, div.body h2 a, div.body h3 a, div.body h4 a, div.body h5 a, div.body h6 a {
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-}
-
-h1 a.anchor, h2 a.anchor, h3 a.anchor, h4 a.anchor, h5 a.anchor, h6 a.anchor {
-    display: none;
-    margin: 0 0 0 0.3em;
-    padding: 0 0.2em 0 0.2em;
-    color: #aaa!important;
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-
-h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
-h5:hover a.anchor, h6:hover a.anchor {
-    display: inline;
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-
-h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
-h5 a.anchor:hover, h6 a.anchor:hover {
-    color: #777;
-    background-color: #eee;
-}
-
-a.headerlink {
-    color: #c60f0f!important;
-    font-size: 1em;
-    margin-left: 6px;
-    padding: 0 4px 0 4px;
-    text-decoration: none!important;
-}
-
-a.headerlink:hover {
-    background-color: #ccc;
-    color: white!important;
-}
-
-cite, code, tt {
-    font-family: 'Consolas', 'Deja Vu Sans Mono',
-                 'Bitstream Vera Sans Mono', monospace;
-    font-size: 0.95em;
-    letter-spacing: 0.01em;
-}
-
-tt {
-    background-color: #f2f2f2;
-    border-bottom: 1px solid #ddd;
-    color: #333;
-}
-
-tt.descname, tt.descclassname, tt.xref {
-    border: 0;
-}
-
-hr {
-    border: 1px solid #abc;
-    margin: 2em;
-}
-
-a tt {
-    border: 0;
-    color: #CA7900;
-}
-
-a tt:hover {
-    color: #2491CF;
-}
-
-pre {
-    font-family: 'Consolas', 'Deja Vu Sans Mono',
-                 'Bitstream Vera Sans Mono', monospace;
-    font-size: 0.95em;
-    letter-spacing: 0.015em;
-    line-height: 120%;
-    padding: 0.5em;
-    border: 1px solid #ccc;
-    background-color: #f8f8f8;
-}
-
-pre a {
-    color: inherit;
-    text-decoration: underline;
-}
-
-td.linenos pre {
-    padding: 0.5em 0;
-}
-
-div.quotebar {
-    background-color: #f8f8f8;
-    max-width: 250px;
-    float: right;
-    padding: 2px 7px;
-    border: 1px solid #ccc;
-}
-
-div.topic {
-    background-color: #f8f8f8;
-}
-
-table {
-    border-collapse: collapse;
-    margin: 0 -0.5em 0 -0.5em;
-}
-
-table td, table th {
-    padding: 0.2em 0.5em 0.2em 0.5em;
-}
-
-div.admonition, div.warning {
-    font-size: 0.9em;
-    margin: 1em 0 1em 0;
-    border: 1px solid #86989B;
-    background-color: #f7f7f7;
-    padding: 0;
-}
-
-div.admonition p, div.warning p {
-    margin: 0.5em 1em 0.5em 1em;
-    padding: 0;
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-
-div.admonition pre, div.warning pre {
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-}
-
-div.admonition p.admonition-title,
-div.warning p.admonition-title {
-    margin: 0;
-    padding: 0.1em 0 0.1em 0.5em;
-    color: white;
-    border-bottom: 1px solid #86989B;
-    font-weight: bold;
-    background-color: #AFC1C4;
-}
-
-div.warning {
-    border: 1px solid #940000;
-}
-
-div.warning p.admonition-title {
-    background-color: #CF0000;
-    border-bottom-color: #940000;
-}
-
-div.admonition ul, div.admonition ol,
-div.warning ul, div.warning ol {
-    margin: 0.1em 0.5em 0.5em 3em;
-    padding: 0;
-}
-
-div.versioninfo {
-    margin: 1em 0 0 0;
-    border: 1px solid #ccc;
-    background-color: #DDEAF0;
-    padding: 8px;
-    line-height: 1.3em;
-    font-size: 0.9em;
-}
-
-.viewcode-back {
-    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
-                 'Verdana', sans-serif;
-}
-
-div.viewcode-block:target {
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\ No newline at end of file
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index b341c8cdb2..df88e6ed22 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -13,6 +13,9 @@ NEW
  | - Scikit-learn -- v0.19
  | - Scipy -- v0.19
 
+- A new method for :class:`nilearn.input_data.NiftiMasker` instances
+  for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.
+
 - joblib is now a dependency
 
 - Parcellation method ReNA: Fast agglomerative clustering based on recursive
diff --git a/examples/01_plotting/plot_3d_map_to_surface_projection.py b/examples/01_plotting/plot_3d_map_to_surface_projection.py
index c8d08a0e6e..0b01f4197e 100644
--- a/examples/01_plotting/plot_3d_map_to_surface_projection.py
+++ b/examples/01_plotting/plot_3d_map_to_surface_projection.py
@@ -85,15 +85,16 @@
 
 view = plotting.view_surf(fsaverage.infl_right, texture, threshold='90%',
                           bg_map=fsaverage.sulc_right)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
 
-##############################################################################
 # In a Jupyter notebook, if ``view`` is the output of a cell, it will
 # be displayed below the cell
-
 view
 
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
 ##############################################################################
 # We don't need to do the projection ourselves, we can use view_img_on_surf:
 
diff --git a/examples/01_plotting/plot_demo_plotting.py b/examples/01_plotting/plot_demo_plotting.py
index 629864a913..9123648522 100644
--- a/examples/01_plotting/plot_demo_plotting.py
+++ b/examples/01_plotting/plot_demo_plotting.py
@@ -33,6 +33,7 @@
 motor_images = datasets.fetch_neurovault_motor_task()
 stat_img = motor_images.images[0]
 
+
 ###############################################################################
 # Plotting statistical maps with function `plot_stat_map`
 # --------------------------------------------------------
@@ -54,15 +55,15 @@
 # for more details.
 
 view = plotting.view_img(stat_img, threshold=3)
+# In a Jupyter notebook, if ``view`` is the output of a cell, it will
+# be displayed below the cell
+view
+
+##############################################################################
 
 # uncomment this to open the plot in a web browser:
 # view.open_in_browser()
 
-##############################################################################
-# In a Jupyter notebook, if ``view`` is the output of a cell, it will
-# be displayed below the cell
-
-view
 
 ###############################################################################
 # Plotting statistical maps in a glass brain with function `plot_glass_brain`
diff --git a/examples/01_plotting/plot_surf_atlas.py b/examples/01_plotting/plot_surf_atlas.py
index 90fb6996dd..85b35a3dab 100644
--- a/examples/01_plotting/plot_surf_atlas.py
+++ b/examples/01_plotting/plot_surf_atlas.py
@@ -124,14 +124,14 @@
 
 view = plotting.view_surf(fsaverage.infl_left, parcellation,
                           cmap='gist_ncar', symmetric_cmap=False)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
 # In a Jupyter notebook, if ``view`` is the output of a cell, it will
 # be displayed below the cell
 
 view
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
 
 ##############################################################################
 # you can also use :func:`nilearn.plotting.view_connectome` to open an
diff --git a/examples/03_connectivity/plot_inverse_covariance_connectome.py b/examples/03_connectivity/plot_inverse_covariance_connectome.py
index 779344101b..88c430659b 100644
--- a/examples/03_connectivity/plot_inverse_covariance_connectome.py
+++ b/examples/03_connectivity/plot_inverse_covariance_connectome.py
@@ -107,11 +107,12 @@
 
 view = plotting.view_connectome(-estimator.precision_, coords)
 
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
 # In a Jupyter notebook, if ``view`` is the output of a cell, it will
 # be displayed below the cell
-
 view
+
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
diff --git a/examples/03_connectivity/plot_probabilistic_atlas_extraction.py b/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
index 938cd97ca1..21fb2153b6 100644
--- a/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
+++ b/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
@@ -88,11 +88,12 @@
 
 view = plotting.view_connectome(correlation_matrix, coords, threshold='80%')
 
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
 # In a Jupyter notebook, if ``view`` is the output of a cell, it will
 # be displayed below the cell
-
 view
+
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index 9247c53faa..4c5bd58440 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -143,16 +143,15 @@
 
 view = plotting.view_connectome(partial_correlation_matrix, dmn_coords)
 
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-
-##############################################################################
 # In a Jupyter notebook, if ``view`` is the output of a cell, it will
 # be displayed below the cell
-
 view
 
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
 
 ##########################################################################
 # Extract signals on spheres from an atlas
@@ -344,8 +343,6 @@
 ###############################################################################
 # .. seealso::
 #
-#     :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
-#
-# .. seealso::
+#    * :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
 #
-#     :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py`
+#    * :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py`
diff --git a/examples/04_manipulating_images/plot_mask_computation.py b/examples/04_manipulating_images/plot_mask_computation.py
index d6b76db8e2..470a2506c4 100644
--- a/examples/04_manipulating_images/plot_mask_computation.py
+++ b/examples/04_manipulating_images/plot_mask_computation.py
@@ -17,7 +17,6 @@
 
 """
 
-
 from nilearn.input_data import NiftiMasker
 import nilearn.image as image
 from nilearn.plotting import plot_roi, plot_epi, show
@@ -48,10 +47,15 @@
 masker = NiftiMasker()
 masker.fit(miyawaki_filename)
 
-# Plot the generated mask
+# Plot the generated mask using the mask_img_ attribute
 plot_roi(masker.mask_img_, miyawaki_mean_img,
          title="Mask from already masked data")
 
+###############################################################################
+# Plot the generated mask using the .generate_report method
+report = masker.generate_report()
+report
+
 
 ###############################################################################
 # Computing a mask from raw EPI data
@@ -77,7 +81,8 @@
 # We need to specify an 'epi' mask_strategy, as this is raw EPI data
 masker = NiftiMasker(mask_strategy='epi')
 masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img, title='EPI automatic mask')
+report = masker.generate_report()
+report
 
 ###############################################################################
 # Generate mask with strong opening
@@ -90,7 +95,8 @@
 # skull parts in the image.
 masker = NiftiMasker(mask_strategy='epi', mask_args=dict(opening=10))
 masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img, title='EPI Mask with strong opening')
+report = masker.generate_report()
+report
 
 ###############################################################################
 # Generate mask with a high lower cutoff
@@ -107,8 +113,8 @@
                      mask_args=dict(upper_cutoff=.9, lower_cutoff=.8,
                                     opening=False))
 masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img,
-         title='EPI Mask: high lower_cutoff')
+report = masker.generate_report()
+report
 
 ###############################################################################
 # Computing the mask from the MNI template
@@ -119,9 +125,27 @@
 
 masker = NiftiMasker(mask_strategy='template')
 masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img,
-         title='Mask from template')
+report = masker.generate_report()
+report
+
+###############################################################################
+# Compute and resample a mask
+###############################################################################
+#
+# NiftiMasker also allows passing parameters directly to `image.resample_img`.
+# We can specify a `target_affine`, a `target_shape`, or both.
+# For more information on these arguments,
+# see :doc:`plot_affine_transformation`.
+#
+# The NiftiMasker report allows us to see the mask before and after resampling.
+# Simply hover over the report to see the mask from the original image.
+
+import numpy as np
 
+masker = NiftiMasker(mask_strategy='epi', target_affine=np.eye(3) * 8)
+masker.fit(epi_img)
+report = masker.generate_report()
+report
 
 ###############################################################################
 # After mask computation: extracting time series
@@ -136,7 +160,6 @@
 detrended_data = detrended.fit_transform(epi_img)
 
 # The timeseries are numpy arrays, so we can manipulate them with numpy
-import numpy as np
 
 print("Trended: mean %.2f, std %.2f" %
       (np.mean(trended_data), np.std(trended_data)))
diff --git a/examples/04_manipulating_images/plot_nifti_simple.py b/examples/04_manipulating_images/plot_nifti_simple.py
index 6f3045f4c9..b24122490c 100644
--- a/examples/04_manipulating_images/plot_nifti_simple.py
+++ b/examples/04_manipulating_images/plot_nifti_simple.py
@@ -10,7 +10,7 @@
 # Retrieve the brain development functional dataset
 
 from nilearn import datasets
-dataset = datasets.fetch_haxby()
+dataset = datasets.fetch_development_fmri(n_subjects=1)
 func_filename = dataset.func[0]
 
 # print basic information on the dataset
@@ -30,7 +30,7 @@
 mask_img = nifti_masker.mask_img_
 
 ###########################################################################
-# Visualize the mask
+# Visualize the mask using the plot_roi method
 from nilearn.plotting import plot_roi
 from nilearn.image.image import mean_img
 
@@ -39,6 +39,13 @@
 
 plot_roi(mask_img, mean_func_img, display_mode='y', cut_coords=4, title="Mask")
 
+###########################################################################
+# Visualize the mask using the 'generate_report' method
+# This report can be displayed in a Jupyter Notebook,
+# opened in-browser using the .open_in_browser() method,
+# or saved to a file using the .save_as_html(output_filepath) method.
+report = nifti_masker.generate_report()
+report
 
 ###########################################################################
 # Preprocess data with the NiftiMasker
@@ -60,6 +67,10 @@
 # Visualize results
 from nilearn.plotting import plot_stat_map, show
 from nilearn.image import index_img
+from nilearn.image.image import mean_img
+
+# calculate mean image for the background
+mean_func_img = mean_img(func_filename)
 
 plot_stat_map(index_img(components, 0), mean_func_img,
               display_mode='y', cut_coords=4, title="Component 0")
diff --git a/nilearn/externals/README.md b/nilearn/externals/README.md
new file mode 100644
index 0000000000..0e6519bbed
--- /dev/null
+++ b/nilearn/externals/README.md
@@ -0,0 +1,5 @@
+This directory contains bundled external dependencies.
+
+Note for distribution packagers: if you want to remove the duplicated
+code and depend on a packaged version, we suggest that you simply do a
+symbolic link in this directory.
diff --git a/nilearn/externals/__init__.py b/nilearn/externals/__init__.py
new file mode 100644
index 0000000000..c861213890
--- /dev/null
+++ b/nilearn/externals/__init__.py
@@ -0,0 +1,7 @@
+"""
+External, bundled dependencies for Nilearn.
+
+To ignore linting on these files, at the top define:
+
+# flake8: noqa
+"""
diff --git a/nilearn/externals/conftest.py b/nilearn/externals/conftest.py
new file mode 100644
index 0000000000..f3bb9d9e9a
--- /dev/null
+++ b/nilearn/externals/conftest.py
@@ -0,0 +1,8 @@
+# Do not collect any tests in externals. This is more robust than using
+# --ignore because --ignore needs a path and it is not convenient to pass in
+# the externals path (very long install-dependent path in site-packages) when
+# using --pyargs
+
+
+def pytest_ignore_collect(path, config):
+    return True
diff --git a/nilearn/externals/install_tempita.sh b/nilearn/externals/install_tempita.sh
new file mode 100644
index 0000000000..9e6c1d4425
--- /dev/null
+++ b/nilearn/externals/install_tempita.sh
@@ -0,0 +1,22 @@
+#!/bin/sh
+# Script to do a local install of tempita
+set +x
+export LC_ALL=C
+INSTALL_FOLDER=tmp/tempita_install
+rm -rf tempita $INSTALL_FOLDER
+if [ -z "$1" ]
+then
+        TEMPITA=tempita
+else
+        TEMPITA=$1
+fi
+
+pip install --no-cache $TEMPITA --target $INSTALL_FOLDER
+cp -r $INSTALL_FOLDER/tempita tempita
+rm -rf $INSTALL_FOLDER
+
+# Needed to rewrite the doctests
+# Note: BSD sed -i needs an argument unders OSX
+# so first renaming to .bak and then deleting backup files
+find tempita -name "*.py" | xargs sed -i.bak "s/from tempita/from nilearn.externals.tempita/"
+find tempita -name "*.bak" | xargs rm
diff --git a/nilearn/externals/tempita/__init__.py b/nilearn/externals/tempita/__init__.py
new file mode 100644
index 0000000000..91f4091672
--- /dev/null
+++ b/nilearn/externals/tempita/__init__.py
@@ -0,0 +1,1311 @@
+# flake8: noqa
+"""
+A small templating language
+
+This implements a small templating language.  This language implements
+if/elif/else, for/continue/break, expressions, and blocks of Python
+code.  The syntax is::
+
+  {{any expression (function calls etc)}}
+  {{any expression | filter}}
+  {{for x in y}}...{{endfor}}
+  {{if x}}x{{elif y}}y{{else}}z{{endif}}
+  {{py:x=1}}
+  {{py:
+  def foo(bar):
+      return 'baz'
+  }}
+  {{default var = default_value}}
+  {{# comment}}
+
+You use this with the ``Template`` class or the ``sub`` shortcut.
+The ``Template`` class takes the template string and the name of
+the template (for errors) and a default namespace.  Then (like
+``string.Template``) you can call the ``tmpl.substitute(**kw)``
+method to make a substitution (or ``tmpl.substitute(a_dict)``).
+
+``sub(content, **kw)`` substitutes the template immediately.  You
+can use ``__name='tmpl.html'`` to set the name of the template.
+
+If there are syntax errors ``TemplateError`` will be raised.
+"""
+from __future__ import absolute_import, division, print_function
+
+import re
+import sys
+try:
+    from urllib.parse import quote as url_quote
+    from io import StringIO
+    from html import escape as html_escape
+except ImportError:
+    from urllib import quote as url_quote
+    from cStringIO import StringIO
+    from cgi import escape as html_escape
+import os
+import tokenize
+from ._looper import looper
+from .compat3 import (
+    PY3, bytes, basestring_, next, is_unicode, coerce_text, iteritems)
+
+__all__ = ['TemplateError', 'Template', 'sub', 'HTMLTemplate',
+           'sub_html', 'html', 'bunch']
+
+in_re = re.compile(r'\s+in\s+')
+var_re = re.compile(r'^[a-z_][a-z0-9_]*$', re.I)
+
+
+class TemplateError(Exception):
+    """Exception raised while parsing a template
+    """
+
+    def __init__(self, message, position, name=None):
+        Exception.__init__(self, message)
+        self.position = position
+        self.name = name
+
+    def __str__(self):
+        msg = ' '.join(self.args)
+        if self.position:
+            msg = '%s at line %s column %s' % (
+                msg, self.position[0], self.position[1])
+        if self.name:
+            msg += ' in %s' % self.name
+        return msg
+
+
+class _TemplateContinue(Exception):
+    pass
+
+
+class _TemplateBreak(Exception):
+    pass
+
+
+def get_file_template(name, from_template):
+    path = os.path.join(os.path.dirname(from_template.name), name)
+    return from_template.__class__.from_filename(
+        path, namespace=from_template.namespace,
+        get_template=from_template.get_template)
+
+
+class Template(object):
+
+    default_namespace = {
+        'start_braces': '{{',
+        'end_braces': '}}',
+        'looper': looper,
+    }
+
+    default_encoding = 'utf8'
+    default_inherit = None
+
+    def __init__(self, content, name=None, namespace=None, stacklevel=None,
+                 get_template=None, default_inherit=None, line_offset=0,
+                 delimeters=None):
+        self.content = content
+
+        # set delimeters
+        if delimeters is None:
+            delimeters = (self.default_namespace['start_braces'],
+                          self.default_namespace['end_braces'])
+        else:
+            assert len(delimeters) == 2 and all(
+                [isinstance(delimeter, basestring_)
+                    for delimeter in delimeters])
+            self.default_namespace = self.__class__.default_namespace.copy()
+            self.default_namespace['start_braces'] = delimeters[0]
+            self.default_namespace['end_braces'] = delimeters[1]
+        self.delimeters = delimeters
+
+        self._unicode = is_unicode(content)
+        if name is None and stacklevel is not None:
+            try:
+                caller = sys._getframe(stacklevel)
+            except ValueError:
+                pass
+            else:
+                globals = caller.f_globals
+                lineno = caller.f_lineno
+                if '__file__' in globals:
+                    name = globals['__file__']
+                    if name.endswith('.pyc') or name.endswith('.pyo'):
+                        name = name[:-1]
+                elif '__name__' in globals:
+                    name = globals['__name__']
+                else:
+                    name = '<string>'
+                if lineno:
+                    name += ':%s' % lineno
+        self.name = name
+        self._parsed = parse(
+            content, name=name, line_offset=line_offset,
+            delimeters=self.delimeters)
+        if namespace is None:
+            namespace = {}
+        self.namespace = namespace
+        self.get_template = get_template
+        if default_inherit is not None:
+            self.default_inherit = default_inherit
+
+    def from_filename(cls, filename, namespace=None, encoding=None,
+                      default_inherit=None, get_template=get_file_template):
+        f = open(filename, 'rb')
+        c = f.read()
+        f.close()
+        if encoding:
+            c = c.decode(encoding)
+        elif PY3:
+            c = c.decode('latin-1')
+        return cls(content=c, name=filename, namespace=namespace,
+                   default_inherit=default_inherit, get_template=get_template)
+
+    from_filename = classmethod(from_filename)
+
+    def __repr__(self):
+        return '<%s %s name=%r>' % (
+            self.__class__.__name__,
+            hex(id(self))[2:], self.name)
+
+    def substitute(self, *args, **kw):
+        if args:
+            if kw:
+                raise TypeError(
+                    "You can only give positional *or* keyword arguments")
+            if len(args) > 1:
+                raise TypeError(
+                    "You can only give one positional argument")
+            if not hasattr(args[0], 'items'):
+                raise TypeError(
+                    ("If you pass in a single argument, you must pass in a ",
+                     "dict-like object (with a .items() method); you gave %r")
+                    % (args[0],))
+            kw = args[0]
+        ns = kw
+        ns['__template_name__'] = self.name
+        if self.namespace:
+            ns.update(self.namespace)
+        result, defs, inherit = self._interpret(ns)
+        if not inherit:
+            inherit = self.default_inherit
+        if inherit:
+            result = self._interpret_inherit(result, defs, inherit, ns)
+        return result
+
+    def _interpret(self, ns):
+        # __traceback_hide__ = True
+        parts = []
+        defs = {}
+        self._interpret_codes(self._parsed, ns, out=parts, defs=defs)
+        if '__inherit__' in defs:
+            inherit = defs.pop('__inherit__')
+        else:
+            inherit = None
+        return ''.join(parts), defs, inherit
+
+    def _interpret_inherit(self, body, defs, inherit_template, ns):
+        # __traceback_hide__ = True
+        if not self.get_template:
+            raise TemplateError(
+                'You cannot use inheritance without passing in get_template',
+                position=None, name=self.name)
+        templ = self.get_template(inherit_template, self)
+        self_ = TemplateObject(self.name)
+        for name, value in iteritems(defs):
+            setattr(self_, name, value)
+        self_.body = body
+        ns = ns.copy()
+        ns['self'] = self_
+        return templ.substitute(ns)
+
+    def _interpret_codes(self, codes, ns, out, defs):
+        # __traceback_hide__ = True
+        for item in codes:
+            if isinstance(item, basestring_):
+                out.append(item)
+            else:
+                self._interpret_code(item, ns, out, defs)
+
+    def _interpret_code(self, code, ns, out, defs):
+        # __traceback_hide__ = True
+        name, pos = code[0], code[1]
+        if name == 'py':
+            self._exec(code[2], ns, pos)
+        elif name == 'continue':
+            raise _TemplateContinue()
+        elif name == 'break':
+            raise _TemplateBreak()
+        elif name == 'for':
+            vars, expr, content = code[2], code[3], code[4]
+            expr = self._eval(expr, ns, pos)
+            self._interpret_for(vars, expr, content, ns, out, defs)
+        elif name == 'cond':
+            parts = code[2:]
+            self._interpret_if(parts, ns, out, defs)
+        elif name == 'expr':
+            parts = code[2].split('|')
+            base = self._eval(parts[0], ns, pos)
+            for part in parts[1:]:
+                func = self._eval(part, ns, pos)
+                base = func(base)
+            out.append(self._repr(base, pos))
+        elif name == 'default':
+            var, expr = code[2], code[3]
+            if var not in ns:
+                result = self._eval(expr, ns, pos)
+                ns[var] = result
+        elif name == 'inherit':
+            expr = code[2]
+            value = self._eval(expr, ns, pos)
+            defs['__inherit__'] = value
+        elif name == 'def':
+            name = code[2]
+            signature = code[3]
+            parts = code[4]
+            ns[name] = defs[name] = TemplateDef(
+                self, name, signature, body=parts, ns=ns, pos=pos)
+        elif name == 'comment':
+            return
+        else:
+            assert 0, "Unknown code: %r" % name
+
+    def _interpret_for(self, vars, expr, content, ns, out, defs):
+        # __traceback_hide__ = True
+        for item in expr:
+            if len(vars) == 1:
+                ns[vars[0]] = item
+            else:
+                if len(vars) != len(item):
+                    raise ValueError(
+                        'Need %i items to unpack (got %i items)'
+                        % (len(vars), len(item)))
+                for name, value in zip(vars, item):
+                    ns[name] = value
+            try:
+                self._interpret_codes(content, ns, out, defs)
+            except _TemplateContinue:
+                continue
+            except _TemplateBreak:
+                break
+
+    def _interpret_if(self, parts, ns, out, defs):
+        # __traceback_hide__ = True
+        # @@: if/else/else gets through
+        for part in parts:
+            assert not isinstance(part, basestring_)
+            name, pos = part[0], part[1]
+            if name == 'else':
+                result = True
+            else:
+                result = self._eval(part[2], ns, pos)
+            if result:
+                self._interpret_codes(part[3], ns, out, defs)
+                break
+
+    def _eval(self, code, ns, pos):
+        # __traceback_hide__ = True
+        try:
+            try:
+                value = eval(code, self.default_namespace, ns)
+            except SyntaxError as e:
+                raise SyntaxError(
+                    'invalid syntax in expression: %s' % code)
+            return value
+        except:
+            exc_info = sys.exc_info()
+            e = exc_info[1]
+            if getattr(e, 'args', None):
+                arg0 = e.args[0]
+            else:
+                arg0 = coerce_text(e)
+            e.args = (self._add_line_info(arg0, pos),)
+            if PY3:
+                raise(e)
+            else:
+                raise (exc_info[1], e, exc_info[2])
+
+    def _exec(self, code, ns, pos):
+        # __traceback_hide__ = True
+        try:
+            exec(code, self.default_namespace, ns)
+        except:
+            exc_info = sys.exc_info()
+            e = exc_info[1]
+            if e.args:
+                e.args = (self._add_line_info(e.args[0], pos),)
+            else:
+                e.args = (self._add_line_info(None, pos),)
+            if PY3:
+                raise(e)
+            else:
+                raise (exc_info[1], e, exc_info[2])
+
+    def _repr(self, value, pos):
+        # __traceback_hide__ = True
+        try:
+            if value is None:
+                return ''
+            if self._unicode:
+                value = str(value)
+                if not is_unicode(value):
+                    value = value.decode('utf-8')
+            else:
+                if not isinstance(value, basestring_):
+                    value = coerce_text(value)
+                if (is_unicode(value) and self.default_encoding):
+                    value = value.encode(self.default_encoding)
+        except:
+            exc_info = sys.exc_info()
+            e = exc_info[1]
+            e.args = (self._add_line_info(e.args[0], pos),)
+            if PY3:
+                raise(e)
+            else:
+                raise (exc_info[1], e, exc_info[2])
+        else:
+            if self._unicode and isinstance(value, bytes):
+                if not self.default_encoding:
+                    raise UnicodeDecodeError(
+                        'Cannot decode bytes value %r into unicode '
+                        '(no default_encoding provided)' % value)
+                try:
+                    value = value.decode(self.default_encoding)
+                except UnicodeDecodeError as e:
+                    raise UnicodeDecodeError(
+                        e.encoding,
+                        e.object,
+                        e.start,
+                        e.end,
+                        e.reason + ' in string %r' % value)
+            elif not self._unicode and is_unicode(value):
+                if not self.default_encoding:
+                    raise UnicodeEncodeError(
+                        'Cannot encode unicode value %r into bytes '
+                        '(no default_encoding provided)' % value)
+                value = value.encode(self.default_encoding)
+            return value
+
+    def _add_line_info(self, msg, pos):
+        msg = "%s at line %s column %s" % (
+            msg, pos[0], pos[1])
+        if self.name:
+            msg += " in file %s" % self.name
+        return msg
+
+
+def sub(content, delimeters=None, **kw):
+    name = kw.get('__name')
+    tmpl = Template(content, name=name, delimeters=delimeters)
+    return tmpl.substitute(kw)
+
+
+def paste_script_template_renderer(content, vars, filename=None):
+    tmpl = Template(content, name=filename)
+    return tmpl.substitute(vars)
+
+
+class bunch(dict):
+
+    def __init__(self, **kw):
+        for name, value in iteritems(kw):
+            setattr(self, name, value)
+
+    def __setattr__(self, name, value):
+        self[name] = value
+
+    def __getattr__(self, name):
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __getitem__(self, key):
+        if 'default' in self:
+            try:
+                return dict.__getitem__(self, key)
+            except KeyError:
+                return dict.__getitem__(self, 'default')
+        else:
+            return dict.__getitem__(self, key)
+
+    def __repr__(self):
+        items = [
+            (k, v) for k, v in iteritems(self)]
+        items.sort()
+        return '<%s %s>' % (
+            self.__class__.__name__,
+            ' '.join(['%s=%r' % (k, v) for k, v in items]))
+
+############################################################
+# HTML Templating
+############################################################
+
+
+class html(object):
+
+    def __init__(self, value):
+        self.value = value
+
+    def __str__(self):
+        return self.value
+
+    def __html__(self):
+        return self.value
+
+    def __repr__(self):
+        return '<%s %r>' % (
+            self.__class__.__name__, self.value)
+
+
+def html_quote(value, force=True):
+    if not force and hasattr(value, '__html__'):
+        return value.__html__()
+    if value is None:
+        return ''
+    if not isinstance(value, basestring_):
+        value = coerce_text(value)
+    if sys.version >= "3" and isinstance(value, bytes):
+        value = html_escape(value.decode('latin1'), 1)
+        value = value.encode('latin1')
+    else:
+        value = html_escape(value, 1)
+    if sys.version < "3":
+        if is_unicode(value):
+            value = value.encode('ascii', 'xmlcharrefreplace')
+    return value
+
+
+def url(v):
+    v = coerce_text(v)
+    if is_unicode(v):
+        v = v.encode('utf8')
+    return url_quote(v)
+
+
+def attr(**kw):
+    kw = list(iteritems(kw))
+    kw.sort()
+    parts = []
+    for name, value in kw:
+        if value is None:
+            continue
+        if name.endswith('_'):
+            name = name[:-1]
+        parts.append('%s="%s"' % (html_quote(name), html_quote(value)))
+    return html(' '.join(parts))
+
+
+class HTMLTemplate(Template):
+
+    default_namespace = Template.default_namespace.copy()
+    default_namespace.update(dict(
+        html=html,
+        attr=attr,
+        url=url,
+        html_quote=html_quote))
+
+    def _repr(self, value, pos):
+        if hasattr(value, '__html__'):
+            value = value.__html__()
+            quote = False
+        else:
+            quote = True
+        plain = Template._repr(self, value, pos)
+        if quote:
+            return html_quote(plain)
+        else:
+            return plain
+
+
+def sub_html(content, **kw):
+    name = kw.get('__name')
+    tmpl = HTMLTemplate(content, name=name)
+    return tmpl.substitute(kw)
+
+
+class TemplateDef(object):
+    def __init__(self, template, func_name, func_signature,
+                 body, ns, pos, bound_self=None):
+        self._template = template
+        self._func_name = func_name
+        self._func_signature = func_signature
+        self._body = body
+        self._ns = ns
+        self._pos = pos
+        self._bound_self = bound_self
+
+    def __repr__(self):
+        return '<tempita function %s(%s) at %s:%s>' % (
+            self._func_name, self._func_signature,
+            self._template.name, self._pos)
+
+    def __str__(self):
+        return self()
+
+    def __call__(self, *args, **kw):
+        values = self._parse_signature(args, kw)
+        ns = self._ns.copy()
+        ns.update(values)
+        if self._bound_self is not None:
+            ns['self'] = self._bound_self
+        out = []
+        subdefs = {}
+        self._template._interpret_codes(self._body, ns, out, subdefs)
+        return ''.join(out)
+
+    def __get__(self, obj, type=None):
+        if obj is None:
+            return self
+        return self.__class__(
+            self._template, self._func_name, self._func_signature,
+            self._body, self._ns, self._pos, bound_self=obj)
+
+    def _parse_signature(self, args, kw):
+        values = {}
+        sig_args, var_args, var_kw, defaults = self._func_signature
+        extra_kw = {}
+        for name, value in iteritems(kw):
+            if not var_kw and name not in sig_args:
+                raise TypeError(
+                    'Unexpected argument %s' % name)
+            if name in sig_args:
+                values[sig_args] = value
+            else:
+                extra_kw[name] = value
+        args = list(args)
+        sig_args = list(sig_args)
+        while args:
+            while sig_args and sig_args[0] in values:
+                sig_args.pop(0)
+            if sig_args:
+                name = sig_args.pop(0)
+                values[name] = args.pop(0)
+            elif var_args:
+                values[var_args] = tuple(args)
+                break
+            else:
+                raise TypeError(
+                    'Extra position arguments: %s'
+                    % ', '.join(repr(v) for v in args))
+        for name, value_expr in iteritems(defaults):
+            if name not in values:
+                values[name] = self._template._eval(
+                    value_expr, self._ns, self._pos)
+        for name in sig_args:
+            if name not in values:
+                raise TypeError(
+                    'Missing argument: %s' % name)
+        if var_kw:
+            values[var_kw] = extra_kw
+        return values
+
+
+class TemplateObject(object):
+
+    def __init__(self, name):
+        self.__name = name
+        self.get = TemplateObjectGetter(self)
+
+    def __repr__(self):
+        return '<%s %s>' % (self.__class__.__name__, self.__name)
+
+
+class TemplateObjectGetter(object):
+
+    def __init__(self, template_obj):
+        self.__template_obj = template_obj
+
+    def __getattr__(self, attr):
+        return getattr(self.__template_obj, attr, Empty)
+
+    def __repr__(self):
+        return '<%s around %r>' % (
+            self.__class__.__name__, self.__template_obj)
+
+
+class _Empty(object):
+    def __call__(self, *args, **kw):
+        return self
+
+    def __str__(self):
+        return ''
+
+    def __repr__(self):
+        return 'Empty'
+
+    def __unicode__(self):
+        return '' if PY3 else u''
+
+    def __iter__(self):
+        return iter(())
+
+    def __bool__(self):
+        return False
+
+    if sys.version < "3":
+        __nonzero__ = __bool__
+
+Empty = _Empty()
+del _Empty
+
+############################################################
+# Lexing and Parsing
+############################################################
+
+
+def lex(s, name=None, trim_whitespace=True, line_offset=0, delimeters=None):
+    if delimeters is None:
+        delimeters = (Template.default_namespace['start_braces'],
+                      Template.default_namespace['end_braces'])
+    in_expr = False
+    chunks = []
+    last = 0
+    last_pos = (line_offset + 1, 1)
+    token_re = re.compile(r'%s|%s' % (re.escape(delimeters[0]),
+                                      re.escape(delimeters[1])))
+    for match in token_re.finditer(s):
+        expr = match.group(0)
+        pos = find_position(s, match.end(), last, last_pos)
+        if expr == delimeters[0] and in_expr:
+            raise TemplateError('%s inside expression' % delimeters[0],
+                                position=pos,
+                                name=name)
+        elif expr == delimeters[1] and not in_expr:
+            raise TemplateError('%s outside expression' % delimeters[1],
+                                position=pos,
+                                name=name)
+        if expr == delimeters[0]:
+            part = s[last:match.start()]
+            if part:
+                chunks.append(part)
+            in_expr = True
+        else:
+            chunks.append((s[last:match.start()], last_pos))
+            in_expr = False
+        last = match.end()
+        last_pos = pos
+    if in_expr:
+        raise TemplateError('No %s to finish last expression' % delimeters[1],
+                            name=name, position=last_pos)
+    part = s[last:]
+    if part:
+        chunks.append(part)
+    if trim_whitespace:
+        chunks = trim_lex(chunks)
+    return chunks
+
+lex.__doc__ = """
+Lex a string into chunks:
+
+    >>> lex('hey')
+    ['hey']
+    >>> lex('hey {{you}}')
+    ['hey ', ('you', (1, 7))]
+    >>> lex('hey {{')
+    Traceback (most recent call last):
+        ...
+    tempita.TemplateError: No }} to finish last expression at line 1 column 7
+    >>> lex('hey }}')
+    Traceback (most recent call last):
+        ...
+    tempita.TemplateError: }} outside expression at line 1 column 7
+    >>> lex('hey {{ {{')
+    Traceback (most recent call last):
+        ...
+    tempita.TemplateError: {{ inside expression at line 1 column 10
+
+""" if PY3 else """
+Lex a string into chunks:
+
+    >>> lex('hey')
+    ['hey']
+    >>> lex('hey {{you}}')
+    ['hey ', ('you', (1, 7))]
+    >>> lex('hey {{')
+    Traceback (most recent call last):
+        ...
+    TemplateError: No }} to finish last expression at line 1 column 7
+    >>> lex('hey }}')
+    Traceback (most recent call last):
+        ...
+    TemplateError: }} outside expression at line 1 column 7
+    >>> lex('hey {{ {{')
+    Traceback (most recent call last):
+        ...
+    TemplateError: {{ inside expression at line 1 column 10
+
+"""
+
+statement_re = re.compile(r'^(?:if |elif |for |def |inherit |default |py:)')
+single_statements = ['else', 'endif', 'endfor', 'enddef', 'continue', 'break']
+trail_whitespace_re = re.compile(r'\n\r?[\t ]*$')
+lead_whitespace_re = re.compile(r'^[\t ]*\n')
+
+
+def trim_lex(tokens):
+    last_trim = None
+    for i in range(len(tokens)):
+        current = tokens[i]
+        if isinstance(tokens[i], basestring_):
+            # we don't trim this
+            continue
+        item = current[0]
+        if not statement_re.search(item) and item not in single_statements:
+            continue
+        if not i:
+            prev = ''
+        else:
+            prev = tokens[i - 1]
+        if i + 1 >= len(tokens):
+            next_chunk = ''
+        else:
+            next_chunk = tokens[i + 1]
+        if (not
+                isinstance(next_chunk, basestring_) or
+                not isinstance(prev, basestring_)):
+            continue
+        prev_ok = not prev or trail_whitespace_re.search(prev)
+        if i == 1 and not prev.strip():
+            prev_ok = True
+        if last_trim is not None and last_trim + 2 == i and not prev.strip():
+            prev_ok = 'last'
+        if (prev_ok and (not next_chunk or lead_whitespace_re.search(
+                         next_chunk) or (
+                         i == len(tokens) - 2 and not next_chunk.strip()))):
+            if prev:
+                if ((i == 1 and not prev.strip()) or prev_ok == 'last'):
+                    tokens[i - 1] = ''
+                else:
+                    m = trail_whitespace_re.search(prev)
+                    # +1 to leave the leading \n on:
+                    prev = prev[:m.start() + 1]
+                    tokens[i - 1] = prev
+            if next_chunk:
+                last_trim = i
+                if i == len(tokens) - 2 and not next_chunk.strip():
+                    tokens[i + 1] = ''
+                else:
+                    m = lead_whitespace_re.search(next_chunk)
+                    next_chunk = next_chunk[m.end():]
+                    tokens[i + 1] = next_chunk
+    return tokens
+
+trim_lex.__doc__ = r"""
+    Takes a lexed set of tokens, and removes whitespace when there is
+    a directive on a line by itself:
+
+       >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False)
+       >>> tokens
+       [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny']
+       >>> trim_lex(tokens)
+       [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y']
+    """ if PY3 else r"""
+    Takes a lexed set of tokens, and removes whitespace when there is
+    a directive on a line by itself:
+
+       >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False)
+       >>> tokens
+       [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny']
+       >>> trim_lex(tokens)
+       [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y']
+    """
+
+
+def find_position(string, index, last_index, last_pos):
+    """
+    Given a string and index, return (line, column)
+    """
+    lines = string.count('\n', last_index, index)
+    if lines > 0:
+        column = index - string.rfind('\n', last_index, index)
+    else:
+        column = last_pos[1] + (index - last_index)
+    return (last_pos[0] + lines, column)
+
+
+def parse(s, name=None, line_offset=0, delimeters=None):
+
+    if delimeters is None:
+        delimeters = (Template.default_namespace['start_braces'],
+                      Template.default_namespace['end_braces'])
+    tokens = lex(s, name=name, line_offset=line_offset, delimeters=delimeters)
+    result = []
+    while tokens:
+        next_chunk, tokens = parse_expr(tokens, name)
+        result.append(next_chunk)
+    return result
+
+parse.__doc__ = r"""
+    Parses a string into a kind of AST
+
+        >>> parse('{{x}}')
+        [('expr', (1, 3), 'x')]
+        >>> parse('foo')
+        ['foo']
+        >>> parse('{{if x}}test{{endif}}')
+        [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))]
+        >>> parse(
+        ...    'series->{{for x in y}}x={{x}}{{endfor}}'
+        ... )  #doctest: +NORMALIZE_WHITESPACE
+        ['series->',
+            ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])]
+        >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}')
+        [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])]
+        >>> parse('{{py:x=1}}')
+        [('py', (1, 3), 'x=1')]
+        >>> parse(
+        ...    '{{if x}}a{{elif y}}b{{else}}c{{endif}}'
+        ... )  #doctest: +NORMALIZE_WHITESPACE
+        [('cond', (1, 3), ('if', (1, 3), 'x', ['a']),
+            ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))]
+
+    Some exceptions::
+
+        >>> parse('{{continue}}')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: continue outside of for loop at line 1 column 3
+        >>> parse('{{if x}}foo')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: No {{endif}} at line 1 column 3
+        >>> parse('{{else}}')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: else outside of an if block at line 1 column 3
+        >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: Unexpected endif at line 1 column 25
+        >>> parse('{{if}}{{endif}}')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: if with no expression at line 1 column 3
+        >>> parse('{{for x y}}{{endfor}}')
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: Bad for (no "in") in 'x y' at line 1 column 3
+        >>> parse('{{py:x=1\ny=2}}')  #doctest: +NORMALIZE_WHITESPACE
+        Traceback (most recent call last):
+            ...
+        tempita.TemplateError: Multi-line py blocks must start
+            with a newline at line 1 column 3
+    """ if PY3 else r"""
+    Parses a string into a kind of AST
+
+        >>> parse('{{x}}')
+        [('expr', (1, 3), 'x')]
+        >>> parse('foo')
+        ['foo']
+        >>> parse('{{if x}}test{{endif}}')
+        [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))]
+        >>> parse(
+        ...    'series->{{for x in y}}x={{x}}{{endfor}}'
+        ... )  #doctest: +NORMALIZE_WHITESPACE
+        ['series->',
+            ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])]
+        >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}')
+        [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])]
+        >>> parse('{{py:x=1}}')
+        [('py', (1, 3), 'x=1')]
+        >>> parse(
+        ...    '{{if x}}a{{elif y}}b{{else}}c{{endif}}'
+        ... )  #doctest: +NORMALIZE_WHITESPACE
+        [('cond', (1, 3), ('if', (1, 3), 'x', ['a']),
+            ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))]
+
+    Some exceptions::
+
+        >>> parse('{{continue}}')
+        Traceback (most recent call last):
+            ...
+        TemplateError: continue outside of for loop at line 1 column 3
+        >>> parse('{{if x}}foo')
+        Traceback (most recent call last):
+            ...
+        TemplateError: No {{endif}} at line 1 column 3
+        >>> parse('{{else}}')
+        Traceback (most recent call last):
+            ...
+        TemplateError: else outside of an if block at line 1 column 3
+        >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}')
+        Traceback (most recent call last):
+            ...
+        TemplateError: Unexpected endif at line 1 column 25
+        >>> parse('{{if}}{{endif}}')
+        Traceback (most recent call last):
+            ...
+        TemplateError: if with no expression at line 1 column 3
+        >>> parse('{{for x y}}{{endfor}}')
+        Traceback (most recent call last):
+            ...
+        TemplateError: Bad for (no "in") in 'x y' at line 1 column 3
+        >>> parse('{{py:x=1\ny=2}}')  #doctest: +NORMALIZE_WHITESPACE
+        Traceback (most recent call last):
+            ...
+        TemplateError: Multi-line py blocks must start
+            with a newline at line 1 column 3
+    """
+
+
+def parse_expr(tokens, name, context=()):
+    if isinstance(tokens[0], basestring_):
+        return tokens[0], tokens[1:]
+    expr, pos = tokens[0]
+    expr = expr.strip()
+    if expr.startswith('py:'):
+        expr = expr[3:].lstrip(' \t')
+        if expr.startswith('\n') or expr.startswith('\r'):
+            expr = expr.lstrip('\r\n')
+            if '\r' in expr:
+                expr = expr.replace('\r\n', '\n')
+                expr = expr.replace('\r', '')
+            expr += '\n'
+        else:
+            if '\n' in expr:
+                raise TemplateError(
+                    'Multi-line py blocks must start with a newline',
+                    position=pos, name=name)
+        return ('py', pos, expr), tokens[1:]
+    elif expr in ('continue', 'break'):
+        if 'for' not in context:
+            raise TemplateError(
+                'continue outside of for loop',
+                position=pos, name=name)
+        return (expr, pos), tokens[1:]
+    elif expr.startswith('if '):
+        return parse_cond(tokens, name, context)
+    elif (expr.startswith('elif ') or expr == 'else'):
+        raise TemplateError(
+            '%s outside of an if block' % expr.split()[0],
+            position=pos, name=name)
+    elif expr in ('if', 'elif', 'for'):
+        raise TemplateError(
+            '%s with no expression' % expr,
+            position=pos, name=name)
+    elif expr in ('endif', 'endfor', 'enddef'):
+        raise TemplateError(
+            'Unexpected %s' % expr,
+            position=pos, name=name)
+    elif expr.startswith('for '):
+        return parse_for(tokens, name, context)
+    elif expr.startswith('default '):
+        return parse_default(tokens, name, context)
+    elif expr.startswith('inherit '):
+        return parse_inherit(tokens, name, context)
+    elif expr.startswith('def '):
+        return parse_def(tokens, name, context)
+    elif expr.startswith('#'):
+        return ('comment', pos, tokens[0][0]), tokens[1:]
+    return ('expr', pos, tokens[0][0]), tokens[1:]
+
+
+def parse_cond(tokens, name, context):
+    start = tokens[0][1]
+    pieces = []
+    context = context + ('if',)
+    while 1:
+        if not tokens:
+            raise TemplateError(
+                'Missing {{endif}}',
+                position=start, name=name)
+        if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endif'):
+            return ('cond', start) + tuple(pieces), tokens[1:]
+        next_chunk, tokens = parse_one_cond(tokens, name, context)
+        pieces.append(next_chunk)
+
+
+def parse_one_cond(tokens, name, context):
+    (first, pos), tokens = tokens[0], tokens[1:]
+    content = []
+    if first.endswith(':'):
+        first = first[:-1]
+    if first.startswith('if '):
+        part = ('if', pos, first[3:].lstrip(), content)
+    elif first.startswith('elif '):
+        part = ('elif', pos, first[5:].lstrip(), content)
+    elif first == 'else':
+        part = ('else', pos, None, content)
+    else:
+        assert 0, "Unexpected token %r at %s" % (first, pos)
+    while 1:
+        if not tokens:
+            raise TemplateError(
+                'No {{endif}}',
+                position=pos, name=name)
+        if (isinstance(tokens[0], tuple) and (
+                tokens[0][0] == 'endif' or tokens[0][0].startswith(
+                    'elif ') or tokens[0][0] == 'else')):
+            return part, tokens
+        next_chunk, tokens = parse_expr(tokens, name, context)
+        content.append(next_chunk)
+
+
+def parse_for(tokens, name, context):
+    first, pos = tokens[0]
+    tokens = tokens[1:]
+    context = ('for',) + context
+    content = []
+    assert first.startswith('for ')
+    if first.endswith(':'):
+        first = first[:-1]
+    first = first[3:].strip()
+    match = in_re.search(first)
+    if not match:
+        raise TemplateError(
+            'Bad for (no "in") in %r' % first,
+            position=pos, name=name)
+    vars = first[:match.start()]
+    if '(' in vars:
+        raise TemplateError(
+            'You cannot have () in the variable section of a for loop (%r)'
+            % vars, position=pos, name=name)
+    vars = tuple([
+        v.strip() for v in first[:match.start()].split(',')
+        if v.strip()])
+    expr = first[match.end():]
+    while 1:
+        if not tokens:
+            raise TemplateError(
+                'No {{endfor}}',
+                position=pos, name=name)
+        if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endfor'):
+            return ('for', pos, vars, expr, content), tokens[1:]
+        next_chunk, tokens = parse_expr(tokens, name, context)
+        content.append(next_chunk)
+
+
+def parse_default(tokens, name, context):
+    first, pos = tokens[0]
+    assert first.startswith('default ')
+    first = first.split(None, 1)[1]
+    parts = first.split('=', 1)
+    if len(parts) == 1:
+        raise TemplateError(
+            "Expression must be {{default var=value}}; no = found in %r" %
+            first, position=pos, name=name)
+    var = parts[0].strip()
+    if ',' in var:
+        raise TemplateError(
+            "{{default x, y = ...}} is not supported",
+            position=pos, name=name)
+    if not var_re.search(var):
+        raise TemplateError(
+            "Not a valid variable name for {{default}}: %r"
+            % var, position=pos, name=name)
+    expr = parts[1].strip()
+    return ('default', pos, var, expr), tokens[1:]
+
+
+def parse_inherit(tokens, name, context):
+    first, pos = tokens[0]
+    assert first.startswith('inherit ')
+    expr = first.split(None, 1)[1]
+    return ('inherit', pos, expr), tokens[1:]
+
+
+def parse_def(tokens, name, context):
+    first, start = tokens[0]
+    tokens = tokens[1:]
+    assert first.startswith('def ')
+    first = first.split(None, 1)[1]
+    if first.endswith(':'):
+        first = first[:-1]
+    if '(' not in first:
+        func_name = first
+        sig = ((), None, None, {})
+    elif not first.endswith(')'):
+        raise TemplateError("Function definition doesn't end with ): %s" %
+                            first, position=start, name=name)
+    else:
+        first = first[:-1]
+        func_name, sig_text = first.split('(', 1)
+        sig = parse_signature(sig_text, name, start)
+    context = context + ('def',)
+    content = []
+    while 1:
+        if not tokens:
+            raise TemplateError(
+                'Missing {{enddef}}',
+                position=start, name=name)
+        if (isinstance(tokens[0], tuple) and tokens[0][0] == 'enddef'):
+            return ('def', start, func_name, sig, content), tokens[1:]
+        next_chunk, tokens = parse_expr(tokens, name, context)
+        content.append(next_chunk)
+
+
+def parse_signature(sig_text, name, pos):
+    tokens = tokenize.generate_tokens(StringIO(sig_text).readline)
+    sig_args = []
+    var_arg = None
+    var_kw = None
+    defaults = {}
+
+    def get_token(pos=False):
+        try:
+            tok_type, tok_string, (srow, scol), (erow, ecol), line = next(
+                tokens)
+        except StopIteration:
+            return tokenize.ENDMARKER, ''
+        if pos:
+            return tok_type, tok_string, (srow, scol), (erow, ecol)
+        else:
+            return tok_type, tok_string
+    while 1:
+        var_arg_type = None
+        tok_type, tok_string = get_token()
+        if tok_type == tokenize.ENDMARKER:
+            break
+        if tok_type == tokenize.OP and (
+                tok_string == '*' or tok_string == '**'):
+            var_arg_type = tok_string
+            tok_type, tok_string = get_token()
+        if tok_type != tokenize.NAME:
+            raise TemplateError('Invalid signature: (%s)' % sig_text,
+                                position=pos, name=name)
+        var_name = tok_string
+        tok_type, tok_string = get_token()
+        if tok_type == tokenize.ENDMARKER or (
+                tok_type == tokenize.OP and tok_string == ','):
+            if var_arg_type == '*':
+                var_arg = var_name
+            elif var_arg_type == '**':
+                var_kw = var_name
+            else:
+                sig_args.append(var_name)
+            if tok_type == tokenize.ENDMARKER:
+                break
+            continue
+        if var_arg_type is not None:
+            raise TemplateError('Invalid signature: (%s)' % sig_text,
+                                position=pos, name=name)
+        if tok_type == tokenize.OP and tok_string == '=':
+            nest_type = None
+            unnest_type = None
+            nest_count = 0
+            start_pos = end_pos = None
+            parts = []
+            while 1:
+                tok_type, tok_string, s, e = get_token(True)
+                if start_pos is None:
+                    start_pos = s
+                end_pos = e
+                if tok_type == tokenize.ENDMARKER and nest_count:
+                    raise TemplateError('Invalid signature: (%s)' % sig_text,
+                                        position=pos, name=name)
+                if (not nest_count and
+                    (tok_type == tokenize.ENDMARKER or
+                        (tok_type == tokenize.OP and tok_string == ','))):
+                    default_expr = isolate_expression(
+                        sig_text, start_pos, end_pos)
+                    defaults[var_name] = default_expr
+                    sig_args.append(var_name)
+                    break
+                parts.append((tok_type, tok_string))
+                if nest_count \
+                        and tok_type == tokenize.OP \
+                        and tok_string == nest_type:
+                    nest_count += 1
+                elif nest_count \
+                        and tok_type == tokenize.OP \
+                        and tok_string == unnest_type:
+                    nest_count -= 1
+                    if not nest_count:
+                        nest_type = unnest_type = None
+                elif not nest_count \
+                        and tok_type == tokenize.OP \
+                        and tok_string in ('(', '[', '{'):
+                    nest_type = tok_string
+                    nest_count = 1
+                    unnest_type = {'(': ')', '[': ']', '{': '}'}[nest_type]
+    return sig_args, var_arg, var_kw, defaults
+
+
+def isolate_expression(string, start_pos, end_pos):
+    srow, scol = start_pos
+    srow -= 1
+    erow, ecol = end_pos
+    erow -= 1
+    lines = string.splitlines(True)
+    if srow == erow:
+        return lines[srow][scol:ecol]
+    parts = [lines[srow][scol:]]
+    parts.extend(lines[srow + 1:erow])
+    if erow < len(lines):
+        # It'll sometimes give (end_row_past_finish, 0)
+        parts.append(lines[erow][:ecol])
+    return ''.join(parts)
+
+_fill_command_usage = """\
+%prog [OPTIONS] TEMPLATE arg=value
+
+Use py:arg=value to set a Python value; otherwise all values are
+strings.
+"""
+
+
+def fill_command(args=None):
+    import sys
+    import optparse
+    import pkg_resources
+    import os
+    if args is None:
+        args = sys.argv[1:]
+    dist = pkg_resources.get_distribution('Paste')
+    parser = optparse.OptionParser(
+        version=coerce_text(dist),
+        usage=_fill_command_usage)
+    parser.add_option(
+        '-o', '--output',
+        dest='output',
+        metavar="FILENAME",
+        help="File to write output to (default stdout)")
+    parser.add_option(
+        '--html',
+        dest='use_html',
+        action='store_true',
+        help="Use HTML style filling (including automatic HTML quoting)")
+    parser.add_option(
+        '--env',
+        dest='use_env',
+        action='store_true',
+        help="Put the environment in as top-level variables")
+    options, args = parser.parse_args(args)
+    if len(args) < 1:
+        print('You must give a template filename')
+        sys.exit(2)
+    template_name = args[0]
+    args = args[1:]
+    vars = {}
+    if options.use_env:
+        vars.update(os.environ)
+    for value in args:
+        if '=' not in value:
+            print('Bad argument: %r' % value)
+            sys.exit(2)
+        name, value = value.split('=', 1)
+        if name.startswith('py:'):
+            name = name[:3]
+            value = eval(value)
+        vars[name] = value
+    if template_name == '-':
+        template_content = sys.stdin.read()
+        template_name = '<stdin>'
+    else:
+        f = open(template_name, 'rb', encoding="latin-1")
+        template_content = f.read()
+        f.close()
+    if options.use_html:
+        TemplateClass = HTMLTemplate
+    else:
+        TemplateClass = Template
+    template = TemplateClass(template_content, name=template_name)
+    result = template.substitute(vars)
+    if options.output:
+        f = open(options.output, 'wb')
+        f.write(result)
+        f.close()
+    else:
+        sys.stdout.write(result)
+
+if __name__ == '__main__':
+    fill_command()
diff --git a/nilearn/externals/tempita/_looper.py b/nilearn/externals/tempita/_looper.py
new file mode 100644
index 0000000000..a2a67800d8
--- /dev/null
+++ b/nilearn/externals/tempita/_looper.py
@@ -0,0 +1,163 @@
+"""
+Helper for looping over sequences, particular in templates.
+
+Often in a loop in a template it's handy to know what's next up,
+previously up, if this is the first or last item in the sequence, etc.
+These can be awkward to manage in a normal Python loop, but using the
+looper you can get a better sense of the context.  Use like::
+
+    >>> for loop, item in looper(['a', 'b', 'c']):
+    ...     print(loop.number, item)
+    ...     if not loop.last:
+    ...         print('---')
+    1 a
+    ---
+    2 b
+    ---
+    3 c
+
+"""
+from __future__ import absolute_import, division, print_function
+
+import sys
+from .compat3 import basestring_
+
+__all__ = ['looper']
+
+
+class looper(object):
+    """
+    Helper for looping (particularly in templates)
+
+    Use this like::
+
+        for loop, item in looper(seq):
+            if loop.first:
+                ...
+    """
+
+    def __init__(self, seq):
+        self.seq = seq
+
+    def __iter__(self):
+        return looper_iter(self.seq)
+
+    def __repr__(self):
+        return '<%s for %r>' % (
+            self.__class__.__name__, self.seq)
+
+
+class looper_iter(object):
+
+    def __init__(self, seq):
+        self.seq = list(seq)
+        self.pos = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.pos >= len(self.seq):
+            raise StopIteration
+        result = loop_pos(self.seq, self.pos), self.seq[self.pos]
+        self.pos += 1
+        return result
+
+    if sys.version < "3":
+        next = __next__
+
+
+class loop_pos(object):
+
+    def __init__(self, seq, pos):
+        self.seq = seq
+        self.pos = pos
+
+    def __repr__(self):
+        return '<loop pos=%r at %r>' % (
+            self.seq[self.pos], self.pos)
+
+    def index(self):
+        return self.pos
+    index = property(index)
+
+    def number(self):
+        return self.pos + 1
+    number = property(number)
+
+    def item(self):
+        return self.seq[self.pos]
+    item = property(item)
+
+    def __next__(self):
+        try:
+            return self.seq[self.pos + 1]
+        except IndexError:
+            return None
+    __next__ = property(__next__)
+
+    if sys.version < "3":
+        next = __next__
+
+    def previous(self):
+        if self.pos == 0:
+            return None
+        return self.seq[self.pos - 1]
+    previous = property(previous)
+
+    def odd(self):
+        return not self.pos % 2
+    odd = property(odd)
+
+    def even(self):
+        return self.pos % 2
+    even = property(even)
+
+    def first(self):
+        return self.pos == 0
+    first = property(first)
+
+    def last(self):
+        return self.pos == len(self.seq) - 1
+    last = property(last)
+
+    def length(self):
+        return len(self.seq)
+    length = property(length)
+
+    def first_group(self, getter=None):
+        """
+        Returns true if this item is the start of a new group,
+        where groups mean that some attribute has changed.  The getter
+        can be None (the item itself changes), an attribute name like
+        ``'.attr'``, a function, or a dict key or list index.
+        """
+        if self.first:
+            return True
+        return self._compare_group(self.item, self.previous, getter)
+
+    def last_group(self, getter=None):
+        """
+        Returns true if this item is the end of a new group,
+        where groups mean that some attribute has changed.  The getter
+        can be None (the item itself changes), an attribute name like
+        ``'.attr'``, a function, or a dict key or list index.
+        """
+        if self.last:
+            return True
+        return self._compare_group(self.item, self.__next__, getter)
+
+    def _compare_group(self, item, other, getter):
+        if getter is None:
+            return item != other
+        elif (isinstance(getter, basestring_) and getter.startswith('.')):
+            getter = getter[1:]
+            if getter.endswith('()'):
+                getter = getter[:-2]
+                return getattr(item, getter)() != getattr(other, getter)()
+            else:
+                return getattr(item, getter) != getattr(other, getter)
+        elif hasattr(getter, '__call__'):
+            return getter(item) != getter(other)
+        else:
+            return item[getter] != other[getter]
diff --git a/nilearn/externals/tempita/compat3.py b/nilearn/externals/tempita/compat3.py
new file mode 100644
index 0000000000..861f5aaf1a
--- /dev/null
+++ b/nilearn/externals/tempita/compat3.py
@@ -0,0 +1,56 @@
+# flake8: noqa
+from __future__ import absolute_import, division, print_function
+
+import sys
+
+__all__ = ['PY3', 'b', 'basestring_', 'bytes', 'next', 'is_unicode',
+           'iteritems']
+
+PY3 = True if sys.version_info[0] == 3 else False
+
+if sys.version_info[0] < 3:
+
+    def next(obj):
+        return obj.next()
+
+    def iteritems(d, **kw):
+        return d.iteritems(**kw)
+
+    b = bytes = str
+    basestring_ = basestring
+
+else:
+
+    def b(s):
+        if isinstance(s, str):
+            return s.encode('latin1')
+        return bytes(s)
+
+    def iteritems(d, **kw):
+        return iter(d.items(**kw))
+
+    next = next
+    basestring_ = (bytes, str)
+    bytes = bytes
+
+text = str
+
+
+def is_unicode(obj):
+    if sys.version_info[0] < 3:
+        return isinstance(obj, unicode)
+    else:
+        return isinstance(obj, str)
+
+
+def coerce_text(v):
+    if not isinstance(v, basestring_):
+        if sys.version_info[0] < 3:
+            attr = '__unicode__'
+        else:
+            attr = '__str__'
+        if hasattr(v, attr):
+            return unicode(v)
+        else:
+            return bytes(v)
+    return v
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index f6016d82ed..cd1a29fa36 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -4,6 +4,7 @@
 # Author: Gael Varoquaux, Alexandre Abraham
 # License: simplified BSD
 
+import warnings
 from copy import copy as copy_object
 
 from nilearn._utils.compat import Memory
@@ -12,6 +13,7 @@
 from .. import _utils
 from .. import image
 from .. import masking
+from nilearn.reporting import ReportMixin
 from .._utils import CacheMixin
 from .._utils.class_inspect import get_params
 from .._utils.niimg import img_data_dtype
@@ -63,7 +65,7 @@ def filter_and_mask(imgs, mask_img_, parameters,
     return data
 
 
-class NiftiMasker(BaseMasker, CacheMixin):
+class NiftiMasker(BaseMasker, CacheMixin, ReportMixin):
     """Applying a mask to extract time-series from Niimg-like objects.
 
     NiftiMasker is useful when preprocessing (detrending, standardization,
@@ -184,7 +186,7 @@ def __init__(self, mask_img=None, sessions=None, smoothing_fwhm=None,
                  mask_strategy='background',
                  mask_args=None, sample_mask=None, dtype=None,
                  memory_level=1, memory=Memory(cachedir=None),
-                 verbose=0
+                 verbose=0, reports=True,
                  ):
         # Mask is provided or computed
         self.mask_img = mask_img
@@ -206,9 +208,78 @@ def __init__(self, mask_img=None, sessions=None, smoothing_fwhm=None,
         self.memory = memory
         self.memory_level = memory_level
         self.verbose = verbose
+        self.reports = reports
+        self._report_description = ('This report shows the input Nifti '
+                                    'image overlaid with the outlines of the '
+                                    'mask (in green). We recommend to inspect '
+                                    'the report for the overlap between the '
+                                    'mask and its input image. ')
+        self._overlay_text = ('\n To see the input Nifti image before '
+                              'resampling, hover over the displayed image.')
 
         self._shelving = False
 
+    def _reporting(self):
+        """
+        Returns
+        -------
+        displays : list
+            A list of all displays to be rendered.
+        """
+        try:
+            from nilearn import plotting
+        except ImportError:
+            with warnings.catch_warnings():
+                mpl_unavail_msg = ('Matplotlib is not imported! '
+                                'No reports will be generated.')
+                warnings.filterwarnings('always', message=mpl_unavail_msg)
+                warnings.warn(category=ImportWarning,
+                            message=mpl_unavail_msg)
+                return [None]
+
+        img = self._reporting_data['images']
+        mask = self._reporting_data['mask']
+        if img is not None:
+            dim = image.load_img(img).shape
+            if len(dim) == 4:
+                # compute middle image from 4D series for plotting
+                img = image.index_img(img, dim[-1] // 2)
+        else:  # images were not provided to fit
+            img = mask
+
+        # create display of retained input mask, image
+        # for visual comparison
+        init_display = plotting.plot_img(img,
+                                         black_bg=False,
+                                         cmap='CMRmap_r')
+        init_display.add_contours(mask, levels=[.5], colors='g',
+                                  linewidths=2.5)
+
+        if 'transform' not in self._reporting_data:
+            return [init_display]
+
+        else:  # if resampling was performed
+            self._report_description = (self._report_description +
+                                        self._overlay_text)
+
+            # create display of resampled NiftiImage and mask
+            # assuming that resampl_img has same dim as img
+            resampl_img, resampl_mask = self._reporting_data['transform']
+            if resampl_img is not None:
+                if len(dim) == 4:
+                    # compute middle image from 4D series for plotting
+                    resampl_img = image.index_img(resampl_img, dim[-1] // 2)
+            else:  # images were not provided to fit
+                resampl_img = resampl_mask
+
+            final_display = plotting.plot_img(resampl_img,
+                                              black_bg=False,
+                                              cmap='CMRmap_r')
+            final_display.add_contours(resampl_mask, levels=[.5],
+                                       colors='g', linewidths=2.5)
+
+        return [init_display, final_display]
+
     def _check_fitted(self):
         if not hasattr(self, 'mask_img_'):
             raise ValueError('It seems that %s has not been fitted. '
@@ -254,6 +325,11 @@ def fit(self, imgs=None, y=None):
         else:
             self.mask_img_ = _utils.check_niimg_3d(self.mask_img)
 
+        if self.reports:  # save inputs for reporting
+            self._reporting_data = {'images': imgs, 'mask': self.mask_img_}
+        else:
+            self._reporting_data = None
+
         # If resampling is requested, resample also the mask
         # Resampling: allows the user to change the affine, the shape or both
         if self.verbose > 0:
@@ -263,14 +339,25 @@ def fit(self, imgs=None, y=None):
             target_affine=self.target_affine,
             target_shape=self.target_shape,
             copy=False, interpolation='nearest')
-        if self.target_affine is not None:
+        if self.target_affine is not None:  # resample image to target affine
             self.affine_ = self.target_affine
-        else:
+        else:  # resample image to mask affine
             self.affine_ = self.mask_img_.affine
         # Load data in memory
         self.mask_img_.get_data()
         if self.verbose > 10:
             print("[%s.fit] Finished fit" % self.__class__.__name__)
+
+        if (self.target_shape is not None) or (self.target_affine is not None):
+            if self.reports:
+                if imgs is not None:
+                    resampl_imgs = self._cache(image.resample_img)(
+                        imgs, target_affine=self.affine_,
+                        copy=False, interpolation='nearest')
+                else:  # imgs not provided to fit
+                    resampl_imgs = None
+                self._reporting_data['transform'] = [resampl_imgs, self.mask_img_]
+
         return self
 
     def transform_single_imgs(self, imgs, confounds=None, copy=True):
diff --git a/nilearn/plotting/__init__.py b/nilearn/plotting/__init__.py
index 629ca5b4e3..a47a92d3e2 100644
--- a/nilearn/plotting/__init__.py
+++ b/nilearn/plotting/__init__.py
@@ -45,7 +45,6 @@ def _set_mpl_backend():
 from .html_stat_map import view_img
 from .html_connectome import view_connectome, view_markers
 from .surf_plotting import plot_surf, plot_surf_stat_map, plot_surf_roi
-from .js_plotting_utils import set_max_img_views_before_warning
 
 __all__ = ['cm', 'plot_img', 'plot_anat', 'plot_epi',
            'plot_roi', 'plot_stat_map', 'plot_glass_brain',
@@ -54,6 +53,5 @@ def _set_mpl_backend():
            'show', 'plot_matrix', 'view_surf', 'view_img_on_surf',
            'view_img', 'view_connectome', 'view_markers',
            'find_parcellation_cut_coords', 'find_probabilistic_atlas_cut_coords',
-           'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi',
-           'set_max_img_views_before_warning'
+           'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi'
            ]
diff --git a/nilearn/plotting/displays.py b/nilearn/plotting/displays.py
index 9f2119e6b1..2ef2594652 100644
--- a/nilearn/plotting/displays.py
+++ b/nilearn/plotting/displays.py
@@ -1020,7 +1020,7 @@ def savefig(self, filename, dpi=None):
         Parameters
         ----------
         filename: string
-            The file name to save to. It's extension determines the
+            The file name to save to. Its extension determines the
             file type, typically '.png', '.svg' or '.pdf'.
 
         dpi: None or scalar
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index e1d08b1c8d..66ffe6ba2d 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -7,9 +7,10 @@
 from .. import datasets
 from . import cm
 
-from .js_plotting_utils import (add_js_lib, HTMLDocument, mesh_to_plotly,
+from .js_plotting_utils import (add_js_lib, mesh_to_plotly,
                                 encode, colorscale, get_html_template,
                                 to_color_strings)
+from nilearn.reporting import HTMLDocument
 
 
 class ConnectomeView(HTMLDocument):
diff --git a/nilearn/plotting/html_stat_map.py b/nilearn/plotting/html_stat_map.py
index fb8e762b10..edc0af760e 100644
--- a/nilearn/plotting/html_stat_map.py
+++ b/nilearn/plotting/html_stat_map.py
@@ -13,10 +13,11 @@
 from nibabel.affines import apply_affine
 
 from ..image import resample_to_img, new_img_like, reorder_img
-from .js_plotting_utils import get_html_template, HTMLDocument, colorscale
+from .js_plotting_utils import get_html_template, colorscale
 from ..plotting import cm
 from ..plotting.find_cuts import find_xyz_cut_coords
 from ..plotting.img_plotting import _load_anat
+from nilearn.reporting import HTMLDocument
 from .._utils.niimg_conversions import check_niimg_3d
 from .._utils.param_validation import check_threshold
 from .._utils.extmath import fast_abs_percentile
diff --git a/nilearn/plotting/html_surface.py b/nilearn/plotting/html_surface.py
index 858959935d..536a779551 100644
--- a/nilearn/plotting/html_surface.py
+++ b/nilearn/plotting/html_surface.py
@@ -7,9 +7,10 @@
 
 from .._utils.niimg_conversions import check_niimg_3d
 from .. import datasets, surface
+from nilearn.reporting import HTMLDocument
 from . import cm
 from .js_plotting_utils import (
-    HTMLDocument, colorscale, mesh_to_plotly, get_html_template, add_js_lib,
+    colorscale, mesh_to_plotly, get_html_template, add_js_lib,
     to_color_strings)
 
 
diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index b78a127f9c..2d398b23a3 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -5,16 +5,8 @@
 
 import os
 import base64
-import webbrowser
-import tempfile
 import warnings
-import subprocess
 from string import Template
-import weakref
-try:
-    from html import escape  # Unavailable in Py2
-except ImportError:  # Can be removed once we EOL Py2 support for NiLearn
-    from cgi import escape  # Deprecated in Py3, necessary for Py2
 
 import matplotlib as mpl
 import numpy as np
@@ -27,15 +19,6 @@
 MAX_IMG_VIEWS_BEFORE_WARNING = 10
 
 
-def set_max_img_views_before_warning(new_value):
-    """Set the number of open views which triggers a warning.
-
-    If `None` or a negative number, disable the memory warning.
-    """
-    global MAX_IMG_VIEWS_BEFORE_WARNING
-    MAX_IMG_VIEWS_BEFORE_WARNING = new_value
-
-
 def add_js_lib(html, embed_js=True):
     """
     Add javascript libraries to html template.
@@ -81,139 +64,6 @@ def get_html_template(template_name):
         return Template(f.read().decode('utf-8'))
 
 
-def _remove_after_n_seconds(file_name, n_seconds):
-    script = os.path.join(os.path.dirname(__file__), 'rm_file.py')
-    subprocess.Popen(['python', script, file_name, str(n_seconds)])
-
-
-class HTMLDocument(object):
-    """
-    Embeds a plot in a web page.
-
-    If you are running a Jupyter notebook, the plot will be displayed
-    inline if this object is the output of a cell.
-    Otherwise, use open_in_browser() to open it in a web browser (or
-    save_as_html("filename.html") to save it as an html file).
-
-    use str(document) or document.html to get the content of the web page,
-    and document.get_iframe() to have it wrapped in an iframe.
-
-    """
-    _all_open_html_repr = weakref.WeakSet()
-
-    def __init__(self, html, width=600, height=400):
-        self.html = html
-        self.width = width
-        self.height = height
-        self._temp_file = None
-        self._check_n_open()
-
-    def _check_n_open(self):
-        HTMLDocument._all_open_html_repr.add(self)
-        if MAX_IMG_VIEWS_BEFORE_WARNING is None:
-            return
-        if MAX_IMG_VIEWS_BEFORE_WARNING < 0:
-            return
-        if len(HTMLDocument._all_open_html_repr
-               ) > MAX_IMG_VIEWS_BEFORE_WARNING - 1:
-            warnings.warn('It seems you have created more than {} '
-                          'nilearn views. As each view uses dozens '
-                          'of megabytes of RAM, you might want to '
-                          'delete some of them.'.format(
-                              MAX_IMG_VIEWS_BEFORE_WARNING))
-
-    def resize(self, width, height):
-        """Resize the plot displayed in a Jupyter notebook."""
-        self.width, self.height = width, height
-        return self
-
-    def get_iframe(self, width=None, height=None):
-        """
-        Get the document wrapped in an inline frame.
-
-        For inserting in another HTML page of for display in a Jupyter
-        notebook.
-
-        """
-        if width is None:
-            width = self.width
-        if height is None:
-            height = self.height
-        escaped = escape(self.html, quote=True)
-        wrapped = ('<iframe srcdoc="{}" width="{}" height="{}" '
-                   'frameBorder="0"></iframe>').format(escaped, width, height)
-        return wrapped
-
-    def get_standalone(self):
-        """ Get the plot in an HTML page."""
-        return self.html
-
-    def _repr_html_(self):
-        """
-        Used by the Jupyter notebook.
-
-        Users normally won't call this method explicitely.
-        """
-        return self.get_iframe()
-
-    def __str__(self):
-        return self.html
-
-    def save_as_html(self, file_name):
-        """
-        Save the plot in an HTML file, that can later be opened in a browser.
-        """
-        with open(file_name, 'wb') as f:
-            f.write(self.html.encode('utf-8'))
-
-    def open_in_browser(self, file_name=None, temp_file_lifetime=30):
-        """
-        Save the plot to a temporary HTML file and open it in a browser.
-
-        Parameters
-        ----------
-
-        file_name : str, optional
-            .html file to use as temporary file
-
-        temp_file_lifetime : float, optional (default=30.)
-            Time, in seconds, after which the temporary file is removed.
-            If None, it is never removed.
-
-        """
-        if file_name is None:
-            fd, file_name = tempfile.mkstemp('.html', 'nilearn_plot_')
-            os.close(fd)
-            named_file = False
-        else:
-            named_file = True
-        self.save_as_html(file_name)
-        self._temp_file = file_name
-        file_size = os.path.getsize(file_name) / 1e6
-        if temp_file_lifetime is None:
-            if not named_file:
-                warnings.warn(
-                    ("Saved HTML in temporary file: {}\n"
-                     "file size is {:.1f}M, delete it when you're done, "
-                     "for example by calling this.remove_temp_file").format(
-                         file_name, file_size))
-        else:
-            _remove_after_n_seconds(self._temp_file, temp_file_lifetime)
-        webbrowser.open('file://{}'.format(file_name))
-
-    def remove_temp_file(self):
-        """
-        Remove the temporary file created by `open_in_browser`, if necessary.
-        """
-        if self._temp_file is None:
-            return
-        if not os.path.isfile(self._temp_file):
-            return
-        os.remove(self._temp_file)
-        print('removed {}'.format(self._temp_file))
-        self._temp_file = None
-
-
 def colorscale(cmap, values, threshold=None, symmetric_cmap=True,
                vmax=None, vmin=None):
     """Normalize a cmap, put it in plotly format, get threshold and range."""
diff --git a/nilearn/plotting/tests/test_html_connectome.py b/nilearn/plotting/tests/test_html_connectome.py
index 6a67a67ebb..50378f2173 100644
--- a/nilearn/plotting/tests/test_html_connectome.py
+++ b/nilearn/plotting/tests/test_html_connectome.py
@@ -65,7 +65,6 @@ def test_view_connectome():
     check_html(html, False, 'connectome-plot')
 
 
-
 def test_params_deprecation_view_connectome():
     deprecated_params = {'coords': 'node_coords',
                          'threshold': 'edge_threshold',
@@ -79,7 +78,7 @@ def test_params_deprecation_view_connectome():
     warning_msgs = {old_: deprecation_msg.format(old_, new_)
                     for old_, new_ in deprecated_params.items()
                     }
-    
+
     adj, coord = _make_connectome()
     with warnings.catch_warnings(record=True) as raised_warnings:
         html_connectome.view_connectome(adjacency_matrix=adj,
@@ -129,12 +128,12 @@ def test_params_deprecation_view_connectome():
                                         4.2,
                                         )
     old_params = ['coords', 'threshold', 'cmap', 'marker_size']
-    
+
     assert len(raised_warnings) == 4
     for old_param_, raised_warning_ in zip(old_params, raised_warnings):
         assert warning_msgs[old_param_] == str(raised_warning_.message)
         assert raised_warning_.category is DeprecationWarning
-        
+
 
 def test_get_markers():
     coords = np.arange(12).reshape((4, 3))
diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index edca130fba..854b9d07df 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -2,27 +2,21 @@
 import re
 import base64
 import webbrowser
-import time
 import tempfile
 
 import numpy as np
 import matplotlib
-from numpy.testing import assert_warns, assert_no_warnings
-try:
-    from lxml import etree
-    LXML_INSTALLED = True
-except ImportError:
-    LXML_INSTALLED = False
-import pytest
 
 from nilearn.plotting import js_plotting_utils
 from nilearn import surface
 from nilearn.datasets import fetch_surf_fsaverage
 
-
-# Note: html output by nilearn view_* functions
-# should validate as html5 using https://validator.w3.org/nu/ with no
-# warnings
+from numpy.testing import assert_warns
+try:
+    from lxml import etree
+    LXML_INSTALLED = True
+except ImportError:
+    LXML_INSTALLED = False
 
 
 def _normalize_ws(text):
@@ -288,64 +282,6 @@ def _check_open_in_browser(html):
             pass
 
 
-def test_temp_file_removing():
-    html = js_plotting_utils.HTMLDocument('hello')
-    wb_open = webbrowser.open
-    webbrowser.open = _open_mock
-    fd, tmpfile = tempfile.mkstemp()
-    try:
-        os.close(fd)
-        with pytest.warns(None) as record:
-            html.open_in_browser(file_name=tmpfile, temp_file_lifetime=None)
-        for warning in record:
-            assert "Saved HTML in temporary file" not in str(warning.message)
-        html.open_in_browser(temp_file_lifetime=.5)
-        assert os.path.isfile(html._temp_file)
-        time.sleep(1.5)
-        assert not os.path.isfile(html._temp_file)
-        with pytest.warns(UserWarning, match="Saved HTML in temporary file"):
-            html.open_in_browser(temp_file_lifetime=None)
-        html.open_in_browser(temp_file_lifetime=None)
-        assert os.path.isfile(html._temp_file)
-        time.sleep(1.5)
-        assert os.path.isfile(html._temp_file)
-    finally:
-        webbrowser.open = wb_open
-        try:
-            os.remove(html._temp_file)
-        except Exception:
-            pass
-        try:
-            os.remove(tmpfile)
-        except Exception:
-            pass
-
-
-def _open_views():
-    return [js_plotting_utils.HTMLDocument('') for i in range(12)]
-
-
-def _open_one_view():
-    for i in range(12):
-        v = js_plotting_utils.HTMLDocument('')
-    return v
-
-
-def test_open_view_warning():
-    # opening many views (without deleting the SurfaceView objects)
-    # should raise a warning about memory usage
-    assert_warns(UserWarning, _open_views)
-    assert_no_warnings(_open_one_view)
-    js_plotting_utils.set_max_img_views_before_warning(15)
-    assert_no_warnings(_open_views)
-    js_plotting_utils.set_max_img_views_before_warning(-1)
-    assert_no_warnings(_open_views)
-    js_plotting_utils.set_max_img_views_before_warning(None)
-    assert_no_warnings(_open_views)
-    js_plotting_utils.set_max_img_views_before_warning(6)
-    assert_warns(UserWarning, _open_views)
-
-
 def test_to_color_strings():
     colors = [[0, 0, 1], [1, 0, 0], [.5, .5, .5]]
     as_str = js_plotting_utils.to_color_strings(colors)
diff --git a/nilearn/reporting/__init__.py b/nilearn/reporting/__init__.py
new file mode 100644
index 0000000000..2a97d557b5
--- /dev/null
+++ b/nilearn/reporting/__init__.py
@@ -0,0 +1,12 @@
+"""
+Reporting code for nilearn
+"""
+
+from .html_report import (ReportMixin, HTMLReport)
+
+from .html_document import (HTMLDocument, set_max_img_views_before_warning)
+
+from .sphinx_report import (_ReportScraper)
+
+__all__ = ['ReportMixin', 'HTMLDocument', 'HTMLReport',
+           'set_max_img_views_before_warning', '_ReportScraper']
diff --git a/nilearn/reporting/data/README.txt b/nilearn/reporting/data/README.txt
new file mode 100644
index 0000000000..9a9a58765f
--- /dev/null
+++ b/nilearn/reporting/data/README.txt
@@ -0,0 +1,4 @@
+This directory contains files required for nilearn reporting.
+
+html/                        : templates for HTML files
+css/                         : styling sheets
diff --git a/nilearn/reporting/data/__init__.py b/nilearn/reporting/data/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/nilearn/reporting/data/html/__init__.py b/nilearn/reporting/data/html/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/nilearn/reporting/data/html/report_body_template.html b/nilearn/reporting/data/html/report_body_template.html
new file mode 100644
index 0000000000..f662559f53
--- /dev/null
+++ b/nilearn/reporting/data/html/report_body_template.html
@@ -0,0 +1,226 @@
+<!-- CSS for the report -->
+<link rel="stylesheet" href="https://unpkg.com/purecss@1.0.0/build/pure-min.css" integrity="sha384-nn4HPE8lTHyVtfCBi5yW9d20FjT8BJwUXyWZT9InLYax14RDjBj46LmSztkmNP9w" crossorigin="anonymous">
+<link rel="stylesheet" href="https://unpkg.com/purecss@1.0.0/build/grids-responsive-min.css">
+<style type="text/css">
+
+/* Add a gutter to Pure's Columns */
+.pure-g > div {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+
+.pure-g > div {
+  padding: 0 0.5em;
+}
+
+/*!
+Pure-button class copied from pure-min.css,
+in case reports are displayed offline.
+
+Pure v1.0.0
+Copyright 2013 Yahoo!
+Licensed under the BSD License.
+https://github.com/yahoo/pure/blob/master/LICENSE.md
+*/
+
+.pure-button {
+    /* Structure */
+    display: inline-block;
+    zoom: 1;
+    line-height: normal;
+    white-space: nowrap;
+    vertical-align: middle;
+    text-align: center;
+    cursor: pointer;
+    -webkit-user-drag: none;
+    -webkit-user-select: none;
+       -moz-user-select: none;
+        -ms-user-select: none;
+            user-select: none;
+    box-sizing: border-box;
+}
+
+.pure-button {
+    /* Button styling */
+    font-family: inherit;
+    font-size: 100%;
+    padding: 0.5em 1em;
+    color: #444; /* rgba not supported (IE 8) */
+    color: rgba(0, 0, 0, 0.80); /* rgba supported */
+    border: 1px solid #999;  /*IE 6/7/8*/
+    border: none rgba(0, 0, 0, 0);  /*IE9 + everything else*/
+    background-color: #E6E6E6;
+    text-decoration: none;
+    border-radius: 2px;
+}
+
+/*!
+End pure-button class definition from pure-min.css
+*/
+
+div.nilearn_report {
+    padding-top: 0px;
+    border: solid rgb(150, 150, 150);
+    border-width: 1pt;
+    border-radius: 6pt;
+    overflow: hidden;  /* Needed to avoid scrolling in jupyter :( */
+    color: black;
+    background-color: white;
+}
+
+.terminal div.nilearn_report {
+    max-width: min(50ex, 100vw); /* Needed in vscode */
+}
+
+/* Isolate us a bit from the styles specified by other stylesheets */
+
+div.nilearn_report h1 {
+    text-align: left;
+    margin-left: 0pt;
+    margin-right: 0pt;
+    padding-top: 5pt;
+    font-size: 1.8em;
+    padding-left: 5pt;
+}
+
+div.nilearn_report h1:first-child {
+    margin-top: 0pt;
+    background-color: #f5f5f5;
+    border-radius: 6pt 6pt 0pt 0pt;
+    padding-left: 5pt;
+}
+
+div.nilearn_report summary::-webkit-details-marker {
+    margin-right: 2px;
+}
+
+div.nilearn_report summary:focus {
+    outline-style: none;
+}
+
+div.nilearn_report table {
+    max-width: 100%;
+    border-collapse: collapse;
+    margin:50px auto;
+    float: right;
+}
+
+div.nilearn_report thead th {
+  text-align: center;
+}
+
+div.nilearn_report tbody tr:nth-child(even) {
+   background-color: #ddd;
+}
+
+div.nilearn_report details {
+    overflow-x: visible;
+}
+
+div.nilearn_report div.raise {
+    z-index: 1;
+
+}
+
+/* Tooltip container */
+div.nilearn_report .withtooltip {
+  position: relative;
+  border-bottom: 1px dotted black; /* If you want dots under the hoverable text */
+}
+
+/* Tooltip text */
+div.nilearn_report .withtooltip .tooltiptext {
+  visibility: hidden;
+  max-width: 35em;
+  width: 90%;
+  background-color: rgba(24,24,24,0.9);
+  color: #fff;
+  text-align: left;
+  font-size: 12pt;
+  font-weight: 200;
+  border-radius: 6px;
+  padding: 5px;css=resource_path.joinpath('css'),
+
+  margin-left: 3pt;
+
+  /* Position the tooltip text - see examples below! */
+  position: absolute;
+  z-index: 2;
+
+  width: 99%;
+  left: 2pt;
+  top: 133%;
+}
+
+/* Show the tooltip text when you mouse over the tooltip container */
+div.nilearn_report .withtooltip:hover .tooltiptext {
+  visibility: visible;
+}
+
+div.nilearn_report .image {
+  position: relative;
+  height: auto;
+  width: 100%;
+}
+
+div.nilearn_report .image .overlay {
+  position: absolute;
+  top: 0;
+  left: 0;
+  opacity: 0;
+  transition: .5s ease;
+  height: auto;
+  width: 100%;
+  -webkit-transition: .5s ease;
+}
+
+div.nilearn_report .image:hover .overlay {
+  opacity: 1;
+}
+
+</style>
+<div class="nilearn_report">
+  <h1 class="withtooltip">
+    {{title}}
+    <span class="tooltiptext">{{docstring}}</span>
+  </h1>
+  <div class="pure-g">
+  <div class="pure-u-1 pure-u-md-2-3">
+      <div class="image">
+      <img class="pure-img" width="100%" src="data:image/svg+xml;base64,{{content}}" alt="image"/>
+      {{if overlay}}
+      <div class="overlay">
+        <img class="pure-img" width="100%" src="data:image/svg+xml;base64,{{overlay}}" alt="overlay"/>
+      </div>
+      {{endif}}
+      </div>
+  </div>
+  <div class="pure-u-1 pure-u-md-1-3 raise">
+    <p class="elem-desc">{{description}}</p>
+    <p></p>
+    <details>
+      <summary class="pure-button">Parameters</summary>
+      <table class="pure-table">
+        <thead>
+          <tr>
+            <th>Parameter</th>
+            <th>Value</th>
+          </tr>
+        </thead>
+
+        <tbody>
+          {{py: params = parameters.items()}}
+          {{for p, v in params}}
+            <tr>
+              <td data-column="Parameter">{{p}}</td>
+              <td data-column="Value">{{v}}</td>
+            </tr>
+          {{endfor}}
+        </tbody>
+      </table>
+
+    </details>
+    </div>
+  </div>
+</div>
diff --git a/nilearn/reporting/data/html/report_head_template.html b/nilearn/reporting/data/html/report_head_template.html
new file mode 100644
index 0000000000..4656912025
--- /dev/null
+++ b/nilearn/reporting/data/html/report_head_template.html
@@ -0,0 +1,18 @@
+<!DOCTYPE html >
+<html lang="en">
+
+<head>
+<title>HTML report</title>
+<meta name = "viewport" content = "width = device-width, initial-scale = 1" charset="UTF-8">
+<style type="text/css">
+
+body {
+    padding: 10px 20px 10px 20px;
+}
+
+</style>
+</head>
+<body>
+    $body
+</body>
+</html>
diff --git a/nilearn/reporting/html_document.py b/nilearn/reporting/html_document.py
new file mode 100644
index 0000000000..9398497bb3
--- /dev/null
+++ b/nilearn/reporting/html_document.py
@@ -0,0 +1,151 @@
+import os
+import weakref
+import warnings
+import tempfile
+import webbrowser
+import subprocess
+from html import escape
+
+MAX_IMG_VIEWS_BEFORE_WARNING = 10
+
+
+def set_max_img_views_before_warning(new_value):
+    """Set the number of open views which triggers a warning.
+
+    If `None` or a negative number, disable the memory warning.
+    """
+    global MAX_IMG_VIEWS_BEFORE_WARNING
+    MAX_IMG_VIEWS_BEFORE_WARNING = new_value
+
+
+def _remove_after_n_seconds(file_name, n_seconds):
+    script = os.path.join(os.path.dirname(__file__), 'rm_file.py')
+    subprocess.Popen(['python', script, file_name, str(n_seconds)])
+
+
+class HTMLDocument(object):
+    """
+    Embeds a plot in a web page.
+
+    If you are running a Jupyter notebook, the plot will be displayed
+    inline if this object is the output of a cell.
+    Otherwise, use open_in_browser() to open it in a web browser (or
+    save_as_html("filename.html") to save it as an html file).
+
+    use str(document) or document.html to get the content of the web page,
+    and document.get_iframe() to have it wrapped in an iframe.
+
+    """
+    _all_open_html_repr = weakref.WeakSet()
+
+    def __init__(self, html, width=600, height=400):
+        self.html = html
+        self.width = width
+        self.height = height
+        self._temp_file = None
+        self._check_n_open()
+
+    def _check_n_open(self):
+        HTMLDocument._all_open_html_repr.add(self)
+        if MAX_IMG_VIEWS_BEFORE_WARNING is None:
+            return
+        if MAX_IMG_VIEWS_BEFORE_WARNING < 0:
+            return
+        if len(HTMLDocument._all_open_html_repr
+               ) > MAX_IMG_VIEWS_BEFORE_WARNING - 1:
+            warnings.warn('It seems you have created more than {} '
+                          'nilearn views. As each view uses dozens '
+                          'of megabytes of RAM, you might want to '
+                          'delete some of them.'.format(
+                              MAX_IMG_VIEWS_BEFORE_WARNING))
+
+    def resize(self, width, height):
+        """Resize the plot displayed in a Jupyter notebook."""
+        self.width, self.height = width, height
+        return self
+
+    def get_iframe(self, width=None, height=None):
+        """
+        Get the document wrapped in an inline frame.
+
+        For inserting in another HTML page of for display in a Jupyter
+        notebook.
+
+        """
+        if width is None:
+            width = self.width
+        if height is None:
+            height = self.height
+        escaped = escape(self.html, quote=True)
+        wrapped = ('<iframe srcdoc="{}" width="{}" height="{}" '
+                   'frameBorder="0"></iframe>').format(escaped, width, height)
+        return wrapped
+
+    def get_standalone(self):
+        """ Get the plot in an HTML page."""
+        return self.html
+
+    def _repr_html_(self):
+        """
+        Used by the Jupyter notebook.
+
+        Users normally won't call this method explicitely.
+        """
+        return self.get_iframe()
+
+    def __str__(self):
+        return self.html
+
+    def save_as_html(self, file_name):
+        """
+        Save the plot in an HTML file, that can later be opened in a browser.
+        """
+        with open(file_name, 'wb') as f:
+            f.write(self.get_standalone().encode('utf-8'))
+
+    def open_in_browser(self, file_name=None, temp_file_lifetime=30):
+        """
+        Save the plot to a temporary HTML file and open it in a browser.
+
+        Parameters
+        ----------
+
+        file_name : str, optional
+            .html file to use as temporary file
+
+        temp_file_lifetime : float, optional (default=30.)
+            Time, in seconds, after which the temporary file is removed.
+            If None, it is never removed.
+
+        """
+        if file_name is None:
+            fd, file_name = tempfile.mkstemp('.html', 'nilearn_plot_')
+            os.close(fd)
+            named_file = False
+        else:
+            named_file = True
+        self.save_as_html(file_name)
+        self._temp_file = file_name
+        file_size = os.path.getsize(file_name) / 1e6
+        if temp_file_lifetime is None:
+            if not named_file:
+                warnings.warn(
+                    ("Saved HTML in temporary file: {}\n"
+                     "file size is {:.1f}M, delete it when you're done, "
+                     "for example by calling this.remove_temp_file").format(
+                         file_name, file_size))
+        else:
+            _remove_after_n_seconds(self._temp_file, temp_file_lifetime)
+        webbrowser.open('file://{}'.format(file_name))
+
+    def remove_temp_file(self):
+        """
+        Remove the temporary file created by `open_in_browser`, if necessary.
+        """
+        if self._temp_file is None:
+            return
+        if not os.path.isfile(self._temp_file):
+            return
+        os.remove(self._temp_file)
+        print('removed {}'.format(self._temp_file))
+        self._temp_file = None
diff --git a/nilearn/reporting/html_report.py b/nilearn/reporting/html_report.py
new file mode 100644
index 0000000000..dc2b95b9f0
--- /dev/null
+++ b/nilearn/reporting/html_report.py
@@ -0,0 +1,199 @@
+import io
+import copy
+import base64
+import warnings
+from pathlib import Path
+from string import Template
+
+from .html_document import HTMLDocument
+from nilearn.externals import tempita
+
+
+def _embed_img(display):
+    """
+    Parameters
+    ----------
+    display: obj
+        A Nilearn plotting object to display
+
+    Returns
+    -------
+    embed : str
+        Binary image string
+    """
+    if display is None:  # no image to display
+        return None
+
+    else:  # we were passed a matplotlib display
+        io_buffer = io.BytesIO()
+        display.frame_axes.figure.savefig(io_buffer, format='svg',
+                                          facecolor='white',
+                                          edgecolor='white')
+        display.close()
+
+        io_buffer.seek(0)
+        data = base64.b64encode(io_buffer.read())
+
+        return '{}'.format(data.decode())
+
+
+def _str_params(params):
+    """
+    Convert NoneType values to the string 'None'
+    for display.
+
+    Parameters
+    ----------
+    params: dict
+        A dictionary of input values to a function
+    """
+    params_str = copy.deepcopy(params)
+    for k, v in params_str.items():
+        if v is None:
+            params_str[k] = 'None'
+    return params_str
+
+
+def _update_template(title, docstring, content, overlay,
+                     parameters, description=None):
+    """
+    Populate a report with content.
+
+    Parameters
+    ----------
+    title : str
+        The title for the report
+    docstring : str
+        The introductory docstring for the reported object
+    content : img
+        The content to display
+    overlay : img
+        Overlaid content, to appear on hover
+    parameters : dict
+        A dictionary of object parameters and their values
+    description : str
+        An optional description of the content
+
+    Returns
+    -------
+    HTMLReport : an instance of a populated HTML report
+    """
+    resource_path = Path(__file__).resolve().parent.joinpath('data', 'html')
+
+    body_template_name = 'report_body_template.html'
+    body_template_path = resource_path.joinpath(body_template_name)
+    tpl = tempita.HTMLTemplate.from_filename(str(body_template_path),
+                                             encoding='utf-8')
+    body = tpl.substitute(title=title, content=content,
+                          overlay=overlay,
+                          docstring=docstring,
+                          parameters=parameters,
+                          description=description)
+
+    head_template_name = 'report_head_template.html'
+    head_template_path = resource_path.joinpath(head_template_name)
+    with open(str(head_template_path), 'r') as head_file:
+        head_tpl = Template(head_file.read())
+
+    return HTMLReport(body=body, head_tpl=head_tpl)
+
+
+class ReportMixin:
+    """
+    A class to provide general reporting functionality
+    """
+
+    def _define_overlay(self):
+        """
+        Determine whether an overlay was provided and
+        update the report text as appropriate.
+
+        Parameters
+        ----------
+
+        Returns
+        -------
+        """
+        displays = self._reporting()
+
+        if len(displays) == 1:  # set overlay to None
+            overlay, image = None, displays[0]
+
+        elif len(displays) == 2:
+            overlay, image = displays[0], displays[1]
+
+        return overlay, image
+
+    def generate_report(self):
+        """
+        Generate a report for Nilearn objects.
+
+        Reports are useful to visualize steps in a processing pipeline.
+        Example use case: visualize the overlap of a mask and reference image
+        in NiftiMasker.
+
+        Returns
+        -------
+        report : HTMLReport
+        """
+        if not hasattr(self, '_reporting_data'):
+            warnings.warn('This object has not been fitted yet ! '
+                          'Make sure to run `fit` before inspecting reports.')
+            report = _update_template(title='Empty Report',
+                                      docstring=('This report was not '
+                                                 'generated. Please `fit` the '
+                                                 'object.'),
+                                      content=_embed_img(None),
+                                      overlay=None,
+                                      parameters=dict())
+
+        elif self._reporting_data is None:
+            warnings.warn('Report generation not enabled ! '
+                          'No visual outputs will be created.')
+            report = _update_template(title='Empty Report',
+                                      docstring=('This report was not '
+                                                 'generated. Please check '
+                                                 'that reporting is enabled.'),
+                                      content=_embed_img(None),
+                                      overlay=None,
+                                      parameters=dict())
+
+        else:  # We can create a report
+            overlay, image = self._define_overlay()
+            description = self._report_description
+            parameters = _str_params(self.get_params())
+            docstring = self.__doc__
+            snippet = docstring.partition('Parameters\n    ----------\n')[0]
+            report = _update_template(title=self.__class__.__name__,
+                                      docstring=snippet,
+                                      content=_embed_img(image),
+                                      overlay=_embed_img(overlay),
+                                      parameters=parameters,
+                                      description=description)
+        return report
+
+
+class HTMLReport(HTMLDocument):
+    """
+    A report written as HTML.
+    Methods such as save_as_html(), open_in_browser()
+    are inherited from HTMLDocument
+    """
+    def __init__(self, head_tpl, body):
+        """ The head_tpl is meant for display as a full page, eg writing on
+            disk. The body is used for embedding in an existing page.
+        """
+        html = head_tpl.substitute(body=body)
+        super(HTMLReport, self).__init__(html)
+        self.head_tpl = head_tpl
+        self.body = body
+
+    def _repr_html_(self):
+        """
+        Used by the Jupyter notebook.
+        Users normally won't call this method explicitly.
+        """
+        return self.body
+
+    def __str__(self):
+        return self.body
diff --git a/nilearn/plotting/rm_file.py b/nilearn/reporting/rm_file.py
similarity index 100%
rename from nilearn/plotting/rm_file.py
rename to nilearn/reporting/rm_file.py
diff --git a/nilearn/reporting/sphinx_report.py b/nilearn/reporting/sphinx_report.py
new file mode 100644
index 0000000000..814ec3336f
--- /dev/null
+++ b/nilearn/reporting/sphinx_report.py
@@ -0,0 +1,48 @@
+# Scraper for sphinx-gallery
+# Inspired from https://github.com/mne-tools/mne-python/
+import weakref
+
+from nilearn.reporting import HTMLDocument
+
+
+_SCRAPER_TEXT = '''
+.. only:: builder_html
+
+    .. container:: row sg-report
+
+        .. raw:: html
+
+            {0}
+
+'''  # noqa: E501
+
+
+def indent_and_escape(text, amount=12):
+    "Indent, skip empty lines, and escape string delimiters"
+    return (''.join(amount * ' ' + line.replace("'", '"')
+                    for line in text.splitlines(True)
+                    if line) + amount * ' ')
+
+
+class _ReportScraper:
+    """Scrape Reports for Sphinx display.
+    """
+
+    def __init__(self):
+        self.app = None
+        self.displayed_reports = weakref.WeakSet()
+
+    def __repr__(self):
+        return '<ReportScraper>'
+
+    def __call__(self, block, block_vars, gallery_conf):
+        for report in block_vars['example_globals'].values():
+            if (isinstance(report, HTMLDocument) and
+                    gallery_conf['builder_name'] == 'html'):
+                if report in self.displayed_reports:
+                    continue
+                report_str = report._repr_html_()
+                data = _SCRAPER_TEXT.format(indent_and_escape(report_str))
+                self.displayed_reports.add(report)
+                return data
+        return ''
diff --git a/nilearn/reporting/tests/__init__.py b/nilearn/reporting/tests/__init__.py
new file mode 100644
index 0000000000..20f6b77ed9
--- /dev/null
+++ b/nilearn/reporting/tests/__init__.py
@@ -0,0 +1,3 @@
+"""
+Testing reporting code in nilearn
+"""
diff --git a/nilearn/reporting/tests/test_html_document.py b/nilearn/reporting/tests/test_html_document.py
new file mode 100644
index 0000000000..87b5817f04
--- /dev/null
+++ b/nilearn/reporting/tests/test_html_document.py
@@ -0,0 +1,74 @@
+import os
+import time
+import pytest
+import tempfile
+import webbrowser
+from nilearn import reporting
+
+from numpy.testing import assert_warns, assert_no_warnings
+
+# Note: html output by nilearn view_* functions
+# should validate as html5 using https://validator.w3.org/nu/ with no
+# warnings
+
+
+def _open_mock(f):
+    print('opened {}'.format(f))
+
+
+def test_temp_file_removing():
+    html = reporting.HTMLDocument('hello')
+    wb_open = webbrowser.open
+    webbrowser.open = _open_mock
+    fd, tmpfile = tempfile.mkstemp()
+    try:
+        os.close(fd)
+        with pytest.warns(None) as record:
+            html.open_in_browser(file_name=tmpfile, temp_file_lifetime=None)
+        for warning in record:
+            assert "Saved HTML in temporary file" not in str(warning.message)
+        html.open_in_browser(temp_file_lifetime=0.5)
+        assert os.path.isfile(html._temp_file)
+        time.sleep(1.5)
+        assert not os.path.isfile(html._temp_file)
+        with pytest.warns(UserWarning, match="Saved HTML in temporary file"):
+            html.open_in_browser(temp_file_lifetime=None)
+        html.open_in_browser(temp_file_lifetime=None)
+        assert os.path.isfile(html._temp_file)
+        time.sleep(1.5)
+        assert os.path.isfile(html._temp_file)
+    finally:
+        webbrowser.open = wb_open
+        try:
+            os.remove(html._temp_file)
+        except Exception:
+            pass
+        try:
+            os.remove(tmpfile)
+        except Exception:
+            pass
+
+
+def _open_views():
+    return [reporting.HTMLDocument('') for i in range(12)]
+
+
+def _open_one_view():
+    for i in range(12):
+        v = reporting.HTMLDocument('')
+    return v
+
+
+def test_open_view_warning():
+    # opening many views (without deleting the SurfaceView objects)
+    # should raise a warning about memory usage
+    assert_warns(UserWarning, _open_views)
+    assert_no_warnings(_open_one_view)
+    reporting.set_max_img_views_before_warning(15)
+    assert_no_warnings(_open_views)
+    reporting.set_max_img_views_before_warning(-1)
+    assert_no_warnings(_open_views)
+    reporting.set_max_img_views_before_warning(None)
+    assert_no_warnings(_open_views)
+    reporting.set_max_img_views_before_warning(6)
+    assert_warns(UserWarning, _open_views)
diff --git a/nilearn/reporting/tests/test_html_report.py b/nilearn/reporting/tests/test_html_report.py
new file mode 100644
index 0000000000..1c1499fd76
--- /dev/null
+++ b/nilearn/reporting/tests/test_html_report.py
@@ -0,0 +1,102 @@
+import pytest
+import numpy as np
+from nibabel import Nifti1Image
+from nilearn import input_data
+from numpy.testing import assert_warns
+
+
+# Note: html output by nilearn view_* functions
+# should validate as html5 using https://validator.w3.org/nu/ with no
+# warnings
+
+
+def _check_html(html_view):
+    """ Check the presence of some expected code in the html viewer
+    """
+    assert "Parameters" in str(html_view)
+    assert "data:image/svg+xml;base64," in str(html_view)
+
+
+def test_3d_reports():
+    # Dummy 3D data
+    data = np.zeros((9, 9, 9))
+    data[3:-3, 3:-3, 3:-3] = 10
+    data_img_3d = Nifti1Image(data, np.eye(4))
+
+    # Dummy mask
+    mask = np.zeros((9, 9, 9))
+    mask[4:-4, 4:-4, 4:-4] = True
+    mask_img_3d = Nifti1Image(data, np.eye(4))
+
+    # test .fit method
+    mask = input_data.NiftiMasker()
+    mask.fit(data_img_3d)
+    html = mask.generate_report()
+    _check_html(html)
+
+    # check providing mask to init
+    masker = input_data.NiftiMasker(mask_img=mask_img_3d)
+    masker.fit(data_img_3d)
+    html = masker.generate_report()
+    _check_html(html)
+
+    # check providing mask to init and no images to .fit
+    masker = input_data.NiftiMasker(mask_img=mask_img_3d)
+    masker.fit()
+    html = masker.generate_report()
+    _check_html(html)
+
+
+def test_4d_reports():
+    # Dummy 4D data
+    data = np.zeros((10, 10, 10, 3), dtype=int)
+    data[..., 0] = 1
+    data[..., 1] = 2
+    data[..., 2] = 3
+    data_img_4d = Nifti1Image(data, np.eye(4))
+
+    # Dummy mask
+    mask = np.zeros((10, 10, 10), dtype=int)
+    mask[3:7, 3:7, 3:7] = 1
+    mask_img = Nifti1Image(mask, np.eye(4))
+
+    # test .fit method
+    mask = input_data.NiftiMasker(mask_strategy='epi')
+    mask.fit(data_img_4d)
+    html = mask.generate_report()
+    _check_html(html)
+
+    # test .fit_transform method
+    masker = input_data.NiftiMasker(mask_img=mask_img, standardize=True)
+    masker.fit_transform(data_img_4d)
+    html = masker.generate_report()
+    _check_html(html)
+
+
+def _generate_empty_report():
+    data = np.zeros((9, 9, 9))
+    data[3:-3, 3:-3, 3:-3] = 10
+    data_img_3d = Nifti1Image(data, np.eye(4))
+
+    # turn off reporting
+    mask = input_data.NiftiMasker(reports=False)
+    mask.fit(data_img_3d)
+    mask.generate_report()
+
+
+def test_empty_report():
+    assert_warns(UserWarning, _generate_empty_report)
+
+
+def test_overlaid_report():
+    pytest.importorskip('matplotlib')
+
+    # Dummy 3D data
+    data = np.zeros((9, 9, 9))
+    data[3:-3, 3:-3, 3:-3] = 10
+    data_img_3d = Nifti1Image(data, np.eye(4))
+
+    mask = input_data.NiftiMasker(target_affine=np.eye(3) * 8)
+    mask.fit(data_img_3d)
+    html = mask.generate_report()
+    assert '<div class="overlay">' in str(html)
diff --git a/nilearn/reporting/tests/test_sphinx_report.py b/nilearn/reporting/tests/test_sphinx_report.py
new file mode 100644
index 0000000000..7a44306777
--- /dev/null
+++ b/nilearn/reporting/tests/test_sphinx_report.py
@@ -0,0 +1,45 @@
+import numpy as np
+import os.path as op
+from nibabel import Nifti1Image
+from sklearn.utils import Bunch
+from nilearn.input_data import NiftiMasker
+from nilearn.reporting import _ReportScraper
+
+
+def _gen_report():
+    """ Generate an empty HTMLReport for testing """
+
+    data = np.zeros((9, 9, 9))
+    data[3:-3, 3:-3, 3:-3] = 10
+    data_img_3d = Nifti1Image(data, np.eye(4))
+
+    # turn off reporting
+    mask = NiftiMasker()
+    mask.fit(data_img_3d)
+    report = mask.generate_report()
+    return report
+
+
+def test_scraper(tmpdir):
+    """Test report scraping."""
+    # Mock a Sphinx + sphinx_gallery config
+    app = Bunch(builder=Bunch(srcdir=str(tmpdir),
+                              outdir=op.join(str(tmpdir), '_build', 'html')))
+    scraper = _ReportScraper()
+    scraper.app = app
+    gallery_conf = dict(src_dir=app.builder.srcdir, builder_name='html')
+    img_fname = op.join(app.builder.srcdir, 'auto_examples', 'images',
+                        'sg_img.png')
+    target_file = op.join(app.builder.srcdir, 'auto_examples', 'sg.py')
+    block_vars = dict(image_path_iterator=(img for img in [img_fname]),
+                      example_globals=dict(a=1), target_file=target_file)
+    # Confirm that HTML isn't accidentally inserted
+    block = None
+    rst = scraper(block, block_vars, gallery_conf)
+    assert rst == ''
+
+    # Confirm that HTML is correctly inserted for HTMLReport
+    report = _gen_report()
+    block_vars['example_globals']['report'] = report
+    rst = scraper(block, block_vars, gallery_conf)
+    assert "<detail" in rst
diff --git a/setup.py b/setup.py
index 355e6de4ab..a88c17ae81 100755
--- a/setup.py
+++ b/setup.py
@@ -97,5 +97,6 @@ def is_installing():
                         'nilearn.image.tests.data': ['*.mgz'],
                         'nilearn.surface.tests.data': ['*.annot', '*.label'],
                         'nilearn.datasets.tests.data': ['*.*'],
-                        'nilearn.datasets.description': ['*.rst']},
+                        'nilearn.datasets.description': ['*.rst'],
+                        'nilearn.reporting.data.html': ['*.html']},
           install_requires=install_requires,)

From 2ead4d01df71d51c205a437a8fc5419ecb2a0beb Mon Sep 17 00:00:00 2001
From: Ian Abenes <40700608+boredStats@users.noreply.github.com>
Date: Tue, 8 Oct 2019 09:25:40 -0500
Subject: [PATCH 218/228] Add check for vmin, vmax in plot_surf_roi (#2052)

* added check for vmin, vmax

Passed vmin, vmax values were being overwritten by default values. Added check to use default vmin, vmax if no parameters are passed

* remove commented out code

* unit test for plot_surf_roi

* add plot_surf_roi fixes to whats_new
---
 doc/whats_new.rst                            | 1 +
 nilearn/plotting/surf_plotting.py            | 5 ++++-
 nilearn/plotting/tests/test_surf_plotting.py | 8 ++++++++
 3 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index df88e6ed22..59616ef6c3 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -67,6 +67,7 @@ Fixes
 - :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
   path.
 - :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
+- :func:`nilearn.plotting.plot_surf_roi` now takes vmin, vmax parameters
 - :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
   left and right functional surface data for each subject
 - :func:`nilearn.datasets.fetch_atlas_schaefer_2018` now downloads from release
diff --git a/nilearn/plotting/surf_plotting.py b/nilearn/plotting/surf_plotting.py
index 0369256d82..fb25f762a9 100644
--- a/nilearn/plotting/surf_plotting.py
+++ b/nilearn/plotting/surf_plotting.py
@@ -514,7 +514,10 @@ def plot_surf_roi(surf_mesh, roi_map, bg_map=None,
     # messages in case of wrong inputs
 
     roi = load_surf_data(roi_map)
-    vmin, vmax = np.min(roi), 1 + np.max(roi)
+    if vmin is None:
+        vmin = np.min(roi)
+    if vmax is None:
+        vmax = 1 + np.max(roi)
 
     mesh = load_surf_mesh(surf_mesh)
 
diff --git a/nilearn/plotting/tests/test_surf_plotting.py b/nilearn/plotting/tests/test_surf_plotting.py
index e1e77c7b9a..73872e8f47 100644
--- a/nilearn/plotting/tests/test_surf_plotting.py
+++ b/nilearn/plotting/tests/test_surf_plotting.py
@@ -162,6 +162,14 @@ def test_plot_surf_roi():
     # plot roi
     plot_surf_roi(mesh, roi_map=roi_map)
     plot_surf_roi(mesh, roi_map=roi_map, colorbar=True)
+    # change vmin, vmax
+    img = plot_surf_roi(mesh, roi_map=roi_map,
+						vmin=1.2, vmax=8.9, colorbar=True)
+    cbar = img.axes[-1]
+    cbar_vmin = float(cbar.get_yticklabels()[0].get_text())
+    cbar_vmax = float(cbar.get_yticklabels()[-1].get_text())
+    assert cbar_vmin == 1.2
+    assert cbar_vmax == 8.9
 
     # plot parcellation
     plot_surf_roi(mesh, roi_map=parcellation)

From 607ba06efe89698a911ccc8c77c0bb3b42bd1cf9 Mon Sep 17 00:00:00 2001
From: Gael Varoquaux <gael.varoquaux@normalesup.org>
Date: Tue, 8 Oct 2019 10:52:12 -0400
Subject: [PATCH 219/228] Core devs doc and add @emdupre (#2151)

* DOC: make core devs explicit

* Add @emdupre to core-devs

* Address typo (review by @aabadie)

* Address @bthirion 's comment
---
 AUTHORS.rst          |  71 ++++++++++++++++++++++---------------------
 doc/development.rst  |   5 +--
 doc/logos/dataia.png | Bin 0 -> 4930 bytes
 3 files changed, 38 insertions(+), 38 deletions(-)
 create mode 100644 doc/logos/dataia.png

diff --git a/AUTHORS.rst b/AUTHORS.rst
index c67751492c..ed04bc5d1a 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -3,38 +3,36 @@
 People
 ------
 
-This work is made available by a community of people, amongst which
+This work is made available by a community of people, which
+originated from
 the `INRIA Parietal Project Team <https://team.inria.fr/parietal/>`_
-and the `scikit-learn <http://scikit-learn.org/>`_ folks, in
-particular:
-
-* Alexandre Abraham
-* `Alexandre Gramfort <http://alexandre.gramfort.net>`_
-* Vincent Michel
-* Bertrand Thirion
-* `Fabian Pedregosa <http://fa.bianp.net/>`_
-* `Gael Varoquaux <http://gael-varoquaux.info/>`_
-* Philippe Gervais
-* Michael Eickenberg
-* Danilo Bzdok
-* Loïc Estève
-* Kamalakar Reddy Daddy
-* Elvis Dohmatob
-* Alexandre Abadie
-* Andres Hoyos Idrobo
-* Salma Bougacha
-* Mehdi Rahim
-* Sylvain Lanuzel
-* `Kshitij Chawla <https://github.com/kchawla-pi>`_
-
-Many of also contributed outside of Parietal, notably:
-
-* `Chris Filo Gorgolewski <http://multiplecomparisons.blogspot.fr/>`_
-* `Ben Cipollini <http://cseweb.ucsd.edu/~bcipolli/>`_
-* Julia Huntenburg
-* Martin Perez-Guevara
-
-Thanks to M. Hanke and Y. Halchenko for data and packaging.
+and the `scikit-learn <http://scikit-learn.org/>`_ but grew much further.
+
+An up-to-date list of contributors can be seen in on `gitub
+<https://github.com/nilearn/nilearn/graphs/contributors>`_
+
+Additional credit goes to M. Hanke and Y. Halchenko for data and packaging.
+
+.. _core_devs:
+
+Core developers
+.................
+
+The nilearn core developers are:
+
+* Alexandre Gramfort https://github.com/agramfort
+* Ben Cipollini https://github.com/bcipolli
+* Bertrand Thirion https://github.com/bthirion
+* Chris Gorgolewski https://github.com/chrisgorgo
+* Danilo Bzdok https://github.com/banilo
+* Elizabeth DuPre https://github.com/emdupre
+* Gael Varoquaux https://github.com/GaelVaroquaux
+* Jerome Dockes https://github.com/jeromedockes
+* Julia Huntenburg https://github.com/juhuntenburg
+* KamalakerDadi https://github.com/KamalakerDadi
+* Kshitij Chawla https://github.com/kchawla-pi
+* Medhi Rahim https://github.com/mrahim
+* Salma Bougacha https://github.com/salma1601
 
 Funding
 ........
@@ -45,7 +43,8 @@ Mehdi Rahim, Philippe Gervais where payed by the `NiConnect
 project, funded by the French `Investissement d'Avenir
 <http://www.gouvernement.fr/investissements-d-avenir-cgi>`_.
 
-NiLearn is also supported by `DigiCosme <https://digicosme.lri.fr>`_ |digicomse logo|
+NiLearn is also supported by `DigiCosme <https://digicosme.lri.fr>`_
+|digicosme logo| and `DataIA <https://dataia.eu/en>`_ |dataia_logo|.
 
 .. _citing:
 
@@ -74,6 +73,10 @@ See the scikit-learn documentation on `how to cite
 <http://scikit-learn.org/stable/about.html#citing-scikit-learn>`_.
 
 
-.. |digicomse logo| image:: logos/digi-saclay-logo-small.png
+.. |digicosme logo| image:: logos/digi-saclay-logo-small.png
+    :height: 25
+    :alt: DigiComse Logo
+
+.. |dataia_logo| image:: logos/dataia.png
     :height: 25
-    :alt: DigiComse Logo
\ No newline at end of file
+    :alt: DataIA Logo
diff --git a/doc/development.rst b/doc/development.rst
index a0ea29cdd6..4edac4347f 100644
--- a/doc/development.rst
+++ b/doc/development.rst
@@ -122,12 +122,9 @@ Decisions are made public, through discussion on issues and pull requests
 in Github.
 
 The decisions are made by the core-contributors, ie people with write
-access to the repository, as listed `here
-<https://github.com/orgs/nilearn/people>`__
+access to the repository, as listed :ref:`here <core_devs>`
 
 If there are open questions, final decisions are made by the Temporary
 Benevolent Dictator, currently Gaël Varoquaux.
 
-
-
 .. include:: ../CONTRIBUTING.rst
diff --git a/doc/logos/dataia.png b/doc/logos/dataia.png
new file mode 100644
index 0000000000000000000000000000000000000000..fae26199d35c1219cc503321e2466c261c1e3214
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From e0d6b6e4a899ef9c1a374a913d7ce9c04f998037 Mon Sep 17 00:00:00 2001
From: Kshitij Chawla <kc.insight.pi@gmail.com>
Date: Tue, 8 Oct 2019 18:47:33 +0200
Subject: [PATCH 220/228] Replac conda with pip in TravisCI setup (#2141)

* Replaced conda with pip in CI setup

* Removed conda related commands & conditions

* Simpler adaptation of existing script - conda to create venv, pip to install

* Fix: Failure de to conflict between conda's python & pip's python

* Removed --quiet fag to aid debugging

* Corrected order of args for conda create

* Changed package version specifier syntax to be pip compatible

* Addressed pip does not handle "*" in package version spec

* Using TravisCI's multiple Python version to move completely away from Conda

* Bypassing conda download & installation

* Removing system site packages use since using virtual envs for each build

* Corrected allow_failures, joblib min & latest installed in appropriate builds
 - Removed unused code.

* Corrected Joblib version spec string in a build

* Removed references to conda in symbols

* Changed [] to [[]], $VAR to $(VAR)

* Remove unnecessary comments, add a useful one
---
 .travis.yml                       | 41 +++++++++++++----------
 continuous_integration/install.sh | 55 ++++++++++---------------------
 2 files changed, 41 insertions(+), 55 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 657fd24dfb..6c9abc7da9 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,47 +2,54 @@ sudo: required
 dist: xenial
 
 language: python
-python: "3.5"
-
-virtualenv:
-  system_site_packages: true
 
 env:
   global:
-    - TEST_RUN_FOLDER="/tmp" # folder where the tests are run from
+    - TEST_RUN_FOLDER="/tmp"
 
 matrix:
   # Do not wait for the allowed_failures entry to finish before
   # setting the status
   fast_finish: true
   allow_failures:
-    # allow_failures seems to be keyed on the python version.
-    - python: 3.5
+    # allow_failures keyed to skipping tests.
+    - env: SKIP_TESTS="true"
   include:
     # without matplotlib
-    - env: DISTRIB="conda" PYTHON_VERSION="3.5"
+    - name: "Python 3.5 minimum package versions"
+      python: "3.5"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5"
            NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
-           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true" JOBLIB_VERSION="*"
+           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true" JOBLIB_VERSION="0.11"
            LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.5"
+    - name: "Python 3.5 latest package versions"
+      python: "3.5"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
            JOBLIB_VERSION="0.11"
            LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.6"
+    - name: "Python 3.6 latest package versions"
+      python: "3.6"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.6"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
-           JOBLIB_VERSION="*"
-           LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.7"
+           JOBLIB_VERSION="0.12" LXML_VERSION="*"
+      # joblib.Memory switches from keyword cachedir to location in version 0.12
+      # Making sure we get the deprecation warning.
+
+    - name: "Python 3.7 latest package versions"
+      python: "3.7"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.7"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
-           JOBLIB_VERSION="0.12" LXML_VERSION="*"
+           JOBLIB_VERSION="*" LXML_VERSION="*"
 
   # FLAKE8 linting on diff wrt common ancestor with upstream/master
     # Note: the python value is only there to trigger allow_failures
-    - python: 3.5
-      env: DISTRIB="conda" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
+    - name: Python 3.5 Flake8 no tests
+      python: 3.5
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
 
 install: source continuous_integration/install.sh
 
diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh
index 6304eda955..a387010a74 100755
--- a/continuous_integration/install.sh
+++ b/continuous_integration/install.sh
@@ -25,8 +25,8 @@ create_new_venv() {
     pip install nose pytest
 }
 
-print_conda_requirements() {
-    # Echo a conda requirement string for example
+echo_requirements_string() {
+    # Echo a requirement string for example
     # "pip nose python='2.7.3 scikit-learn=*". It has a hardcoded
     # list of possible packages to install and looks at _VERSION
     # environment variables to know whether to install a given package and
@@ -35,8 +35,7 @@ print_conda_requirements() {
     #   - for scikit-learn, SCIKIT_LEARN_VERSION is used
     TO_INSTALL_ALWAYS="pip nose pytest"
     REQUIREMENTS="$TO_INSTALL_ALWAYS"
-    TO_INSTALL_MAYBE="python numpy scipy matplotlib scikit-learn pandas \
-flake8 lxml joblib"
+    TO_INSTALL_MAYBE="numpy scipy matplotlib scikit-learn pandas flake8 lxml joblib"
     for PACKAGE in $TO_INSTALL_MAYBE; do
         # Capitalize package name and add _VERSION
         PACKAGE_VERSION_VARNAME="${PACKAGE^^}_VERSION"
@@ -45,45 +44,25 @@ flake8 lxml joblib"
         # dereference $PACKAGE_VERSION_VARNAME to figure out the
         # version to install
         PACKAGE_VERSION="${!PACKAGE_VERSION_VARNAME}"
-        if [ -n "$PACKAGE_VERSION" ]; then
-            REQUIREMENTS="$REQUIREMENTS $PACKAGE=$PACKAGE_VERSION"
+        if [[ -n "$PACKAGE_VERSION" ]]; then
+            if [[ "$PACKAGE_VERSION" == "*" ]]; then
+                REQUIREMENTS="$REQUIREMENTS $PACKAGE"
+            else
+                REQUIREMENTS="$REQUIREMENTS $PACKAGE==$PACKAGE_VERSION"
+            fi
         fi
     done
     echo $REQUIREMENTS
 }
 
-create_new_conda_env() {
-    # Skip Travis related code on circle ci.
-    if [ -z $CIRCLECI ]; then
-        # Deactivate the travis-provided virtual environment and setup a
-        # conda-based environment instead
-        deactivate
-    fi
-
-    # Use the miniconda installer for faster download / install of conda
-    # itself
-    wget https://repo.continuum.io/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh \
-        -O ~/miniconda.sh
-    chmod +x ~/miniconda.sh && ~/miniconda.sh -b
-    export PATH=$HOME/miniconda3/bin:$PATH
-    echo $PATH
-
-    # Configure the conda environment and put it in the path using the
-    # provided versions
-    REQUIREMENTS=$(print_conda_requirements)
-    echo "conda requirements string: $REQUIREMENTS"
-    conda create -n testenv --quiet --yes $REQUIREMENTS
-    source activate testenv
-    conda install pytest pytest-cov --yes
+create_new_travisci_env() {
+    REQUIREMENTS=$(echo_requirements_string)
+    pip install ${REQUIREMENTS}
+    pip install pytest pytest-cov
 
     if [[ "$INSTALL_MKL" == "true" ]]; then
         # Make sure that MKL is used
-        conda install --quiet --yes mkl
-    elif [[ -z $CIRCLECI ]]; then
-        # Travis doesn't use MKL but circle ci does for speeding up examples
-        # generation in the html documentation.
-        # Make sure that MKL is not used
-        conda remove --yes --features mkl || echo "MKL not installed"
+        pip install mkl
     fi
 }
 
@@ -93,14 +72,14 @@ if [[ "$DISTRIB" == "neurodebian" ]]; then
     bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh)
     sudo apt-get install -qq python-scipy python-nose python-nibabel python-sklearn python-joblib
 
-elif [[ "$DISTRIB" == "conda" ]]; then
-    create_new_conda_env
+elif [[ "$DISTRIB" == "travisci" ]]; then
+    create_new_travisci_env
     pip install nose-timer
     # Note: nibabel is in setup.py install_requires so nibabel will
     # always be installed eventually. Defining NIBABEL_VERSION is only
     # useful if you happen to want a specific nibabel version rather
     # than the latest available one.
-    if [ -n "$NIBABEL_VERSION" ]; then
+    if [[ -n "$NIBABEL_VERSION" ]]; then
         pip install nibabel=="$NIBABEL_VERSION"
     fi
 

From f1d268c4ad0c42c5669af730ca56e65f8f24826b Mon Sep 17 00:00:00 2001
From: jeromedockes <jerome@dockes.org>
Date: Tue, 8 Oct 2019 20:11:59 +0200
Subject: [PATCH 221/228] Expose bg_img, vmin and vmax in plot_img signature
 (#2157)

* expose bg_img, vmin and vmax in plot_img signature

* whats new
---
 doc/whats_new.rst                |  5 +++++
 nilearn/plotting/img_plotting.py | 14 ++++++++++++--
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 59616ef6c3..c712eea951 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -51,6 +51,11 @@ Changes
   in `nilearn.input_data`. You can now set `standardize` to `zscore` or `psc`. `psc` stands
   for `Percent Signal Change`, which can be a meaningful metric for BOLD.
 
+- :func:`nilearn.plotting.plot_img` now has explicit keyword arguments `bg_img`,
+  `vmin` and `vmax` to control the background image and the bounds of the
+  colormap. These arguments were already accepted in `kwargs` but not documented
+  before.
+
 Fixes
 -----
 
diff --git a/nilearn/plotting/img_plotting.py b/nilearn/plotting/img_plotting.py
index d49077d5ab..a06d16195e 100644
--- a/nilearn/plotting/img_plotting.py
+++ b/nilearn/plotting/img_plotting.py
@@ -232,7 +232,8 @@ def _crop_colorbar(cbar, cbar_vmin, cbar_vmax):
 def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
              figure=None, axes=None, title=None, threshold=None,
              annotate=True, draw_cross=True, black_bg=False, colorbar=False,
-             resampling_interpolation='continuous', **kwargs):
+             resampling_interpolation='continuous',
+             bg_img=None, vmin=None, vmax=None, **kwargs):
     """ Plot cuts of a given image (by default Frontal, Axial, and Lateral)
 
         Parameters
@@ -290,6 +291,14 @@ def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
             space. Can be "continuous" (default) to use 3rd-order spline
             interpolation, or "nearest" to use nearest-neighbor mapping.
             "nearest" is faster but can be noisier in some cases.
+        bg_img : Niimg-like object, optional
+            See http://nilearn.github.io/manipulating_images/input_output.html
+            The background image that the ROI/mask will be plotted on top of.
+            If nothing is specified, no background image is plotted.
+        vmin : float, optional
+            lower bound of the colormap. If `None`, the min of the image is used.
+        vmax : float, optional
+            upper bound of the colormap. If `None`, the max of the image is used.
         kwargs: extra keyword arguments, optional
             Extra keyword arguments passed to matplotlib.pyplot.imshow
     """  # noqa: E501
@@ -300,7 +309,8 @@ def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
         threshold=threshold, annotate=annotate,
         draw_cross=draw_cross,
         resampling_interpolation=resampling_interpolation,
-        black_bg=black_bg, colorbar=colorbar, **kwargs)
+        black_bg=black_bg, colorbar=colorbar,
+        bg_img=bg_img, vmin=vmin, vmax=vmax, **kwargs)
 
     return display
 

From eedc39e495b2a9be29742f7c2d172949607b8767 Mon Sep 17 00:00:00 2001
From: jeromedockes <jerome@dockes.org>
Date: Tue, 8 Oct 2019 22:32:37 +0200
Subject: [PATCH 222/228] Iter age group prediction example (#2063)

* simplify cv + include transformer in cv

* note about dataset size

* don't use "passthrough"

* iter

* simplify matrix plotting

* don't use GridSearch or Pipeline (move to advanced example)

* rename example

* Apply suggestions from code review

Co-Authored-By: Elizabeth DuPre <emd222@cornell.edu>
Co-Authored-By: bthirion <bertrand.thirion@inria.fr>

* address review

* address review

* whatsnew
---
 doc/whats_new.rst                             |   4 +
 .../plot_group_level_connectivity.py          | 176 ++++++++----------
 .../plot_age_group_prediction_cross_val.py    | 105 +++++++++++
 3 files changed, 187 insertions(+), 98 deletions(-)
 create mode 100644 examples/05_advanced/plot_age_group_prediction_cross_val.py

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index c712eea951..782a24d26c 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -33,6 +33,10 @@ NEW
   :func:`nilearn.datasets.fetch_development_fmri` can be used to download
   movie-watching data in children and adults. A light-weight dataset
   implemented for teaching and usage in the examples.
+- New example in `examples/05_advanced/plot_age_group_prediction_cross_val.py`
+  to compare methods for classifying subjects into age groups based on
+  functional connectivity. Similar example in
+  `examples/03_connectivity/plot_group_level_connectivity.py` simplified.
 
 Changes
 -------
diff --git a/examples/03_connectivity/plot_group_level_connectivity.py b/examples/03_connectivity/plot_group_level_connectivity.py
index 12542f4165..daf21f8f70 100644
--- a/examples/03_connectivity/plot_group_level_connectivity.py
+++ b/examples/03_connectivity/plot_group_level_connectivity.py
@@ -1,35 +1,17 @@
 """
-Functional connectivity matrices for group analysis of connectomes
-==================================================================
+Classification of age groups using functional connectivity
+==========================================================
 
 This example compares different kinds of functional connectivity between
-regions of interest : correlation, partial correlation, as well as a kind
-called **tangent**.
+regions of interest : correlation, partial correlation, and tangent space
+embedding.
 
 The resulting connectivity coefficients can be used to
-discriminate children from adults. In general, the **tangent kind**
+discriminate children from adults. In general, the tangent space embedding
 **outperforms** the standard correlations: see `Dadi et al 2019
 <https://www.sciencedirect.com/science/article/pii/S1053811919301594>`_
 for a careful study.
 """
-# Matrix plotting from Nilearn: nilearn.plotting.plot_matrix
-import numpy as np
-import matplotlib.pylab as plt
-
-
-def plot_matrices(matrices, matrix_kind):
-    n_matrices = len(matrices)
-    fig = plt.figure(figsize=(n_matrices * 4, 4))
-    for n_subject, matrix in enumerate(matrices):
-        plt.subplot(1, n_matrices, n_subject + 1)
-        matrix = matrix.copy()  # avoid side effects
-        # Set diagonal to zero, for better visualization
-        np.fill_diagonal(matrix, 0)
-        vmax = np.max(np.abs(matrix))
-        title = '{0}, subject {1}'.format(matrix_kind, n_subject)
-        plotting.plot_matrix(matrix, vmin=-vmax, vmax=vmax, cmap='RdBu_r',
-                             title=title, figure=fig, colorbar=False)
-
 
 ###############################################################################
 # Load brain development fMRI dataset and MSDL atlas
@@ -37,7 +19,7 @@ def plot_matrices(matrices, matrix_kind):
 # We study only 30 subjects from the dataset, to save computation time.
 from nilearn import datasets
 
-rest_data = datasets.fetch_development_fmri(n_subjects=30)
+development_dataset = datasets.fetch_development_fmri(n_subjects=30)
 
 ###############################################################################
 # We use probabilistic regions of interest (ROIs) from the MSDL atlas.
@@ -57,7 +39,7 @@ def plot_matrices(matrices, matrix_kind):
 
 masker = input_data.NiftiMapsMasker(
     msdl_data.maps, resampling_target="data", t_r=2, detrend=True,
-    low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1)
+    low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1).fit()
 
 ###############################################################################
 # Then we compute region signals and extract useful phenotypic informations.
@@ -65,13 +47,13 @@ def plot_matrices(matrices, matrix_kind):
 pooled_subjects = []
 groups = []  # child or adult
 for func_file, confound_file, phenotypic in zip(
-        rest_data.func, rest_data.confounds, rest_data.phenotypic):
-    time_series = masker.fit_transform(func_file, confounds=confound_file)
+        development_dataset.func,
+        development_dataset.confounds,
+        development_dataset.phenotypic):
+    time_series = masker.transform(func_file, confounds=confound_file)
     pooled_subjects.append(time_series)
-    is_child = phenotypic['Child_Adult'] == 'child'
-    if is_child:
+    if phenotypic['Child_Adult'] == 'child':
         children.append(time_series)
-
     groups.append(phenotypic['Child_Adult'])
 
 print('Data has {0} children.'.format(len(children)))
@@ -101,10 +83,14 @@ def plot_matrices(matrices, matrix_kind):
 print('Mean correlation has shape {0}.'.format(mean_correlation_matrix.shape))
 
 ###############################################################################
-# We display the connectome matrices of the first 4 children
+# We display the connectome matrices of the first 3 children
 from nilearn import plotting
+from matplotlib import pyplot as plt
 
-plot_matrices(correlation_matrices[:4], 'correlation')
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(correlation_matrices, axes)):
+    plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+                         title='correlation, child {}'.format(i))
 ###############################################################################
 # The blocks structure that reflect functional networks are visible.
 
@@ -119,19 +105,17 @@ def plot_matrices(matrices, matrix_kind):
 # We can also study **direct connections**, revealed by partial correlation
 # coefficients. We just change the `ConnectivityMeasure` kind
 partial_correlation_measure = ConnectivityMeasure(kind='partial correlation')
-
-###############################################################################
-# and repeat the previous operation.
 partial_correlation_matrices = partial_correlation_measure.fit_transform(
     children)
 
 ###############################################################################
-# Most of direct connections are weaker than full connections,
-plot_matrices(partial_correlation_matrices[:4], 'partial')
+# Most of direct connections are weaker than full connections.
 
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(partial_correlation_matrices, axes)):
+    plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+                         title='partial correlation, child {}'.format(i))
 ###############################################################################
-# Compared to a connectome computed on correlations, the connectome graph
-# with partial correlations is more sparse:
 plotting.plot_connectome(
     partial_correlation_measure.mean_, msdl_coords,
     title='mean partial correlation over all children')
@@ -140,8 +124,8 @@ def plot_matrices(matrices, matrix_kind):
 # Extract subjects variabilities around a group connectivity
 # ----------------------------------------------------------
 # We can use **both** correlations and partial correlations to capture
-# reproducible connectivity patterns at the group-level and build a **robust**
-# **group connectivity matrix**. This is done by the **tangent** kind.
+# reproducible connectivity patterns at the group-level.
+# This is done by the tangent space embedding.
 tangent_measure = ConnectivityMeasure(kind='tangent')
 
 ###############################################################################
@@ -154,84 +138,80 @@ def plot_matrices(matrices, matrix_kind):
 # `tangent_matrices` model individual connectivities as
 # **perturbations** of the group connectivity matrix `tangent_measure.mean_`.
 # Keep in mind that these subjects-to-group variability matrices do not
-# straight reflect individual brain connections. For instance negative
+# directly reflect individual brain connections. For instance negative
 # coefficients can not be interpreted as anticorrelated regions.
-plot_matrices(tangent_matrices[:4], 'tangent variability')
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(tangent_matrices, axes)):
+    plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+                         title='tangent offset, child {}'.format(i))
+
 
 ###############################################################################
-# The average tangent matrix cannot be interpreted, as the average
-# variation is expected to be zero
+# The average tangent matrix cannot be interpreted, as individual matrices
+# represent deviations from the mean, which is set to 0.
 
 ###############################################################################
 # What kind of connectivity is most powerful for classification?
 # --------------------------------------------------------------
-# *ConnectivityMeasure* can output the estimated subjects coefficients
-# as a 1D arrays through the parameter *vectorize*.
-connectivity_biomarkers = {}
-kinds = ['correlation', 'partial correlation', 'tangent']
-for kind in kinds:
-    conn_measure = ConnectivityMeasure(kind=kind, vectorize=True)
-    connectivity_biomarkers[kind] = conn_measure.fit_transform(pooled_subjects)
-
-# For each kind, all individual coefficients are stacked in a unique 2D matrix.
-print('{0} correlation biomarkers for each subject.'.format(
-    connectivity_biomarkers['correlation'].shape[1]))
-
-###############################################################################
-# Note that we use the **pooled groups**. This is crucial for **tangent** kind,
-# to get the displacements from a **unique** `mean_` of all subjects.
-
-###############################################################################
-# We stratify the dataset into homogeneous classes according to phenotypic
-# and scan site. We then split the subjects into 30 folds with the same
-# proportion of each class as in the whole cohort for cross-validation
+# We will use connectivity matrices as features to distinguish children from
+# adults. We use cross-validation and measure classification accuracy to
+# compare the different kinds of connectivity matrices.
+# We use random splits of the subjects into training/testing sets.
+# StratifiedShuffleSplit allows preserving the proportion of children in the
+# test set.
+from sklearn.svm import LinearSVC
 from sklearn.model_selection import StratifiedShuffleSplit
+from sklearn.metrics import accuracy_score
+import numpy as np
 
+kinds = ['correlation', 'partial correlation', 'tangent']
 _, classes = np.unique(groups, return_inverse=True)
-cv = StratifiedShuffleSplit(n_splits=30)
-###############################################################################
-# and use the connectivity coefficients to classify children vs adults.
+cv = StratifiedShuffleSplit(n_splits=15, random_state=0, test_size=5)
+pooled_subjects = np.asarray(pooled_subjects)
 
-# Note that in cv.split(X, y),
-# providing y is sufficient to generate the splits and
-# hence np.zeros(n_samples) may be used as a placeholder for X
-# instead of actual training data.
-from sklearn.svm import LinearSVC
-from sklearn.model_selection import cross_val_score
-
-mean_scores = []
+scores = {}
 for kind in kinds:
-    svc = LinearSVC(random_state=0)
-    cv_scores = cross_val_score(svc,
-                                connectivity_biomarkers[kind],
-                                y=classes,
-                                cv=cv,
-                                groups=groups,
-                                scoring='accuracy',
-                                )
-    mean_scores.append(cv_scores.mean())
-
-###############################################################################
-# Finally, we can display the classification scores.
-
-###############################################################################
-# Finally, we can display the classification scores.
-from nilearn.plotting import show
+    scores[kind] = []
+    for train, test in cv.split(pooled_subjects, classes):
+        # *ConnectivityMeasure* can output the estimated subjects coefficients
+        # as a 1D arrays through the parameter *vectorize*.
+        connectivity = ConnectivityMeasure(kind=kind, vectorize=True)
+        # build vectorized connectomes for subjects in the train set
+        connectomes = connectivity.fit_transform(pooled_subjects[train])
+        # fit the classifier
+        classifier = LinearSVC().fit(connectomes, classes[train])
+        # make predictions for the left-out test subjects
+        predictions = classifier.predict(
+            connectivity.transform(pooled_subjects[test]))
+        # store the accuracy for this cross-validation fold
+        scores[kind].append(accuracy_score(classes[test], predictions))
+
+
+######################################################################
+# display the results
+
+mean_scores = [np.mean(scores[kind]) for kind in kinds]
+scores_std = [np.std(scores[kind]) for kind in kinds]
 
 plt.figure(figsize=(6, 4))
 positions = np.arange(len(kinds)) * .1 + .1
-plt.barh(positions, mean_scores, align='center', height=.05)
-yticks = [kind.replace(' ', '\n') for kind in kinds]
+plt.barh(positions, mean_scores, align='center', height=.05, xerr=scores_std)
+yticks = [k.replace(' ', '\n') for k in kinds]
 plt.yticks(positions, yticks)
-plt.xlabel('Classification accuracy')
-plt.grid(True)
+plt.gca().grid(True)
+plt.gca().set_axisbelow(True)
+plt.gca().axvline(.8, color='red', linestyle='--')
+plt.xlabel('Classification accuracy\n(red line = chance level)')
 plt.tight_layout()
 
+
 ###############################################################################
-# While the comparison is not fully conclusive on this small dataset,
+# This is a small example to showcase nilearn features. In practice such
+# comparisons need to be performed on much larger cohorts and several
+# datasets.
 # `Dadi et al 2019
 # <https://www.sciencedirect.com/science/article/pii/S1053811919301594>`_
 # Showed that across many cohorts and clinical questions, the tangent
 # kind should be preferred.
 
-show()
+plotting.show()
diff --git a/examples/05_advanced/plot_age_group_prediction_cross_val.py b/examples/05_advanced/plot_age_group_prediction_cross_val.py
new file mode 100644
index 0000000000..8a4f193efe
--- /dev/null
+++ b/examples/05_advanced/plot_age_group_prediction_cross_val.py
@@ -0,0 +1,105 @@
+"""
+Functional connectivity predicts age group
+==========================================
+
+This example compares different kinds of functional connectivity between
+regions of interest : correlation, partial correlation, and tangent space
+embedding.
+
+The resulting connectivity coefficients can be used to
+discriminate children from adults. In general, the tangent space embedding
+**outperforms** the standard correlations: see `Dadi et al 2019
+<https://www.sciencedirect.com/science/article/pii/S1053811919301594>`_
+for a careful study.
+"""
+
+###############################################################################
+# Load brain development fMRI dataset and MSDL atlas
+# -------------------------------------------------------------------
+# We study only 60 subjects from the dataset, to save computation time.
+from nilearn import datasets
+
+development_dataset = datasets.fetch_development_fmri(n_subjects=60)
+
+###############################################################################
+# We use probabilistic regions of interest (ROIs) from the MSDL atlas.
+from nilearn.input_data import NiftiMapsMasker
+
+msdl_data = datasets.fetch_atlas_msdl()
+msdl_coords = msdl_data.region_coords
+
+masker = NiftiMapsMasker(
+    msdl_data.maps, resampling_target="data", t_r=2, detrend=True,
+    low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1).fit()
+masked_data = [masker.transform(func, confounds) for
+               (func, confounds) in zip(
+                   development_dataset.func, development_dataset.confounds)]
+
+###############################################################################
+# What kind of connectivity is most powerful for classification?
+# --------------------------------------------------------------
+# we will use connectivity matrices as features to distinguish children from
+# adults. We use cross-validation and measure classification accuracy to
+# compare the different kinds of connectivity matrices.
+
+# prepare the classification pipeline
+from sklearn.pipeline import Pipeline
+from nilearn.connectome import ConnectivityMeasure
+from sklearn.svm import LinearSVC
+from sklearn.dummy import DummyClassifier
+from sklearn.model_selection import GridSearchCV
+
+kinds = ['correlation', 'partial correlation', 'tangent']
+
+pipe = Pipeline(
+    [('connectivity', ConnectivityMeasure(vectorize=True)),
+     ('classifier', GridSearchCV(LinearSVC(), {'C': [.1, 1., 10.]}, cv=5))])
+
+param_grid = [
+    {'classifier': [DummyClassifier('most_frequent')]},
+    {'connectivity__kind': kinds}
+]
+
+######################################################################
+# We use random splits of the subjects into training/testing sets.
+# StratifiedShuffleSplit allows preserving the proportion of children in the
+# test set.
+from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit
+from sklearn.preprocessing import LabelEncoder
+
+groups = [pheno['Child_Adult'] for pheno in development_dataset.phenotypic]
+classes = LabelEncoder().fit_transform(groups)
+
+cv = StratifiedShuffleSplit(n_splits=30, random_state=0, test_size=10)
+gs = GridSearchCV(pipe, param_grid, scoring='accuracy', cv=cv, verbose=1,
+                  refit=False, n_jobs=8)
+gs.fit(masked_data, classes)
+mean_scores = gs.cv_results_['mean_test_score']
+scores_std = gs.cv_results_['std_test_score']
+
+######################################################################
+# display the results
+from matplotlib import pyplot as plt
+
+plt.figure(figsize=(6, 4))
+positions = [.1, .2, .3, .4]
+plt.barh(positions, mean_scores, align='center', height=.05, xerr=scores_std)
+yticks = ['dummy'] + list(gs.cv_results_['param_connectivity__kind'].data[1:])
+yticks = [t.replace(' ', '\n') for t in yticks]
+plt.yticks(positions, yticks)
+plt.xlabel('Classification accuracy')
+plt.gca().grid(True)
+plt.gca().set_axisbelow(True)
+plt.tight_layout()
+
+
+###############################################################################
+# This is a small example to showcase nilearn features. In practice such
+# comparisons need to be performed on much larger cohorts and several
+# datasets.
+# `Dadi et al 2019
+# <https://www.sciencedirect.com/science/article/pii/S1053811919301594>`_
+# Showed that across many cohorts and clinical questions, the tangent
+# kind should be preferred.
+
+plt.show()

From e7b118b7b1a817ae69300bb0cfad6a815e713966 Mon Sep 17 00:00:00 2001
From: Alexandre Abraham <abraham.alexandre@gmail.com>
Date: Wed, 9 Oct 2019 08:07:57 +0200
Subject: [PATCH 223/228] Remove inplace modification in signal.clean (#2125)

* Remove inplace modification in signal.clean

PR #1271 introduces this regression where ensure_finite=True modifies the data inplace instead of copying it (nan_to_num does an implicit copy). I preferred to get back to nan_to_num instead of just doing a copy because I believe that relying on numpy functions is safer and more future proof.

* Do the replacement manually to be compliant with older numpy

* Added test to verify no inplace modification occurs when ensure_finite=True
---
 nilearn/signal.py            |  5 ++++-
 nilearn/tests/test_signal.py | 25 +++++++++++++++++++++++++
 2 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/nilearn/signal.py b/nilearn/signal.py
index 0f3c983d52..733c0de3e4 100644
--- a/nilearn/signal.py
+++ b/nilearn/signal.py
@@ -488,7 +488,10 @@ def clean(signals, sessions=None, detrend=True, standardize='zscore',
         signals = as_ndarray(signals)
 
     if ensure_finite:
-        signals[np.logical_not(np.isfinite(signals))] = 0
+        mask = np.logical_not(np.isfinite(signals))
+        if mask.any():
+            signals = signals.copy()
+            signals[mask] = 0
 
     # Read confounds
     if confounds is not None:
diff --git a/nilearn/tests/test_signal.py b/nilearn/tests/test_signal.py
index cbfeef1513..9fcdddaccd 100644
--- a/nilearn/tests/test_signal.py
+++ b/nilearn/tests/test_signal.py
@@ -474,6 +474,31 @@ def test_clean_frequencies_using_power_spectrum_density():
     assert_true(np.sum(Pxx_den_high[f <= high_pass / 2.]) <= 1e-4)
 
 
+def test_clean_finite_no_inplace_mod():
+    """
+    Test for verifying that the passed in signal array is not modified.
+    For PR #2125 . This test is failing on master, passing in this PR.
+    """
+    rng = np.random.RandomState(0)
+    n_samples = 2
+    # n_features  Must be higher than 500
+    n_features = 501
+    x_orig, _, _ = generate_signals(n_features=n_features,
+                              length=n_samples)
+    x_orig_inital_copy = x_orig.copy()
+
+    x_orig_with_nans = x_orig.copy()
+    x_orig_with_nans[0, 0] = np.nan
+    x_orig_with_nans_initial_copy = x_orig_with_nans.copy()
+
+    cleaned_x_orig = clean(x_orig)
+    assert np.array_equal(x_orig, x_orig_inital_copy)
+
+    cleaned_x_orig_with_nans = clean(x_orig_with_nans, ensure_finite=True)
+    assert np.isnan(x_orig_with_nans_initial_copy[0, 0])
+    assert np.isnan(x_orig_with_nans[0, 0])
+
+
 def test_high_variance_confounds():
 
     # C and F order might take different paths in the function. Check that the

From 796a5a7b4d8c2d484da0f518f65318b7a741ad5a Mon Sep 17 00:00:00 2001
From: AGrigis <antoine.grigis@cea.fr>
Date: Wed, 9 Oct 2019 11:54:45 +0200
Subject: [PATCH 224/228] Update Brainomics fetcher (#2097)

* nilearn/datasets/func: update localizer fetcher (CW -> OSF).

* nilearn/datasets/func: update brainimics fetcher + test.

* examples/01_plotting/plot_dim_plotting: update localizer fetcher.

* nilearn/datasets/func: remove pandas dependency in localizer fetcher.

* nilearn/datasets/func: remove use of isin numpy function.

* continuous_integration/install: no SSL verification in TravisCI.

* continuous_integration/install: just export the variable.

* continuous_integration/test_script: propagate option to tests.

* nilearn/datasets/func: update URL for Win.

* nilearn/datasets/func: fix index search.

* nilearn/datasets/func: add behavioural data.

* continuous_integration: remove SSL hack.

* doc/whats_new: describe new localizer organization.

* nilearn/datasets/func: restore default output for fetch_localizer_button_task (compatibility).

* nilearn/datasets/tests/test_func: update.

* nilearn/datasets/tests/test_func: localizer tests offline.

* nilearn/datasets/func: restaure button_task fetcher default behaviour.

* nilearn/datasets/tests/test_func: resize localizer index resource.

* nilearn/datasets/tests/test_func: skip 2 lines before functions.

* nilearn/datasets/tests/test_func: use simple quote.

* pep8.
---
 doc/whats_new.rst                             |   2 +
 examples/01_plotting/plot_dim_plotting.py     |   4 +-
 .../plot_localizer_mass_univariate_methods.py |   2 +-
 nilearn/datasets/func.py                      | 230 ++++++++--------
 .../tests/data/localizer_behavioural.tsv      |  95 +++++++
 .../datasets/tests/data/localizer_index.json  | 114 ++++++++
 .../tests/data/localizer_participants.tsv     |  95 +++++++
 nilearn/datasets/tests/test_func.py           | 260 +++++++-----------
 8 files changed, 529 insertions(+), 273 deletions(-)
 create mode 100644 nilearn/datasets/tests/data/localizer_behavioural.tsv
 create mode 100755 nilearn/datasets/tests/data/localizer_index.json
 create mode 100644 nilearn/datasets/tests/data/localizer_participants.tsv

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 782a24d26c..36e224f7c5 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -38,6 +38,8 @@ NEW
   functional connectivity. Similar example in
   `examples/03_connectivity/plot_group_level_connectivity.py` simplified.
 
+- the Localizer dataset now follows the BIDS organization.
+
 Changes
 -------
 
diff --git a/examples/01_plotting/plot_dim_plotting.py b/examples/01_plotting/plot_dim_plotting.py
index afecc379c3..11253a33ad 100644
--- a/examples/01_plotting/plot_dim_plotting.py
+++ b/examples/01_plotting/plot_dim_plotting.py
@@ -21,9 +21,9 @@
 
 localizer_dataset = datasets.fetch_localizer_button_task()
 # Contrast map of motor task
-localizer_tmap_filename = localizer_dataset.tmap
+localizer_tmap_filename = localizer_dataset.tmaps[0]
 # Subject specific anatomical image
-localizer_anat_filename = localizer_dataset.anat
+localizer_anat_filename = localizer_dataset.anats[0]
 ###########################################################################
 # Plotting with enhancement of background image with dim=-.5
 # --------------------------------------------------------------------------
diff --git a/examples/05_advanced/plot_localizer_mass_univariate_methods.py b/examples/05_advanced/plot_localizer_mass_univariate_methods.py
index 9c8f4d5e2a..12531abf6c 100644
--- a/examples/05_advanced/plot_localizer_mass_univariate_methods.py
+++ b/examples/05_advanced/plot_localizer_mass_univariate_methods.py
@@ -35,7 +35,7 @@
 
 tested_var = localizer_dataset.ext_vars['pseudo']
 # Quality check / Remove subjects with bad tested variate
-mask_quality_check = np.where(tested_var != b'None')[0]
+mask_quality_check = np.where(tested_var != b'n/a')[0]
 n_samples = mask_quality_check.size
 contrast_map_filenames = [localizer_dataset.cmaps[i]
                           for i in mask_quality_check]
diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
index 303247aec7..38844a93b1 100644
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -13,7 +13,7 @@
 from sklearn.utils import deprecated
 
 from .utils import (_get_dataset_dir, _fetch_files, _get_dataset_descr,
-                    _read_md5_sum_file, _tree, _filter_columns)
+                    _read_md5_sum_file, _tree, _filter_columns, _fetch_file)
 from .._utils import check_niimg
 from .._utils.compat import BytesIO, _basestring, _urllib
 from .._utils.numpy_conversions import csv_to_array
@@ -824,7 +824,7 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
     if n_subjects is None:
         n_subjects = 94  # 94 subjects available
     if (isinstance(n_subjects, numbers.Number) and
-                    ((n_subjects > 94) or (n_subjects < 1))):
+            ((n_subjects > 94) or (n_subjects < 1))):
         warnings.warn("Wrong value for \'n_subjects\' (%d). The maximum "
                       "value will be used instead (\'n_subjects=94\')")
         n_subjects = 94  # 94 subjects available
@@ -886,6 +886,7 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
         "button press vs calculation and sentence listening/reading":
             "auditory&visual motor vs cognitive processing"}
     allowed_contrasts = list(contrast_name_wrapper.values())
+
     # convert contrast names
     contrasts_wrapped = []
     # get a unique ID for each contrast. It is used to give a unique name to
@@ -893,21 +894,27 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
     contrasts_indices = []
     for contrast in contrasts:
         if contrast in allowed_contrasts:
-            contrasts_wrapped.append(contrast)
+            contrasts_wrapped.append(contrast.title().replace(" ", ""))
             contrasts_indices.append(allowed_contrasts.index(contrast))
         elif contrast in contrast_name_wrapper:
             name = contrast_name_wrapper[contrast]
-            contrasts_wrapped.append(name)
+            contrasts_wrapped.append(name.title().replace(" ", ""))
             contrasts_indices.append(allowed_contrasts.index(name))
         else:
             raise ValueError("Contrast \'%s\' is not available" % contrast)
 
-    # It is better to perform several small requests than a big one because:
-    # - Brainomics server has no cache (can lead to timeout while the archive
-    #   is generated on the remote server)
-    # - Local (cached) version of the files can be checked for each contrast
-    opts = {'uncompress': True}
+    # Get the dataset OSF index
+    dataset_name = "brainomics_localizer"
+    index_url = "https://osf.io/hwbm2/download"
+    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
+                                verbose=verbose)
+    index_file = _fetch_file(index_url, data_dir, verbose=verbose)
+    with open(index_file, "rt") as of:
+        index = json.load(of)
 
+    # Build data URLs that will be fetched
+    files = {}
+    root_url = "https://osf.io/download/{0}"
     if isinstance(n_subjects, numbers.Number):
         subject_mask = np.arange(1, n_subjects + 1)
         subject_id_max = "S%02d" % n_subjects
@@ -916,26 +923,18 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
         subject_id_max = "S%02d" % np.max(n_subjects)
         n_subjects = len(n_subjects)
     subject_ids = ["S%02d" % s for s in subject_mask]
-    data_types = ["c map"]
+    data_types = ["cmaps"]
     if get_tmaps:
-        data_types.append("t map")
-    rql_types = str.join(", ", ["\"%s\"" % x for x in data_types])
-    root_url = "http://brainomics.cea.fr/localizer/"
-
-    base_query = ("Any X,XT,XL,XI,XF,XD WHERE X is Scan, X type XT, "
-                  "X concerns S, "
-                  "X label XL, X identifier XI, "
-                  "X format XF, X description XD, "
-                  'S identifier <= "%s", ' % (subject_id_max, ) +
-                  'X type IN(%(types)s), X label "%(label)s"')
-
-    urls = ["%sbrainomics_data_%d.zip?rql=%s&vid=data-zip"
-            % (root_url, i,
-               _urllib.parse.quote(base_query % {"types": rql_types,
-                                          "label": c},
-                            safe=',()'))
-            for c, i in zip(contrasts_wrapped, contrasts_indices)]
+        data_types.append("tmaps")
     filenames = []
+
+    def _is_valid_path(path, index, verbose):
+        if path not in index:
+            if verbose > 0:
+                print("Skiping path '{0}'...".format(path))
+            return False
+        return True
+
     for subject_id in subject_ids:
         for data_type in data_types:
             for contrast_id, contrast in enumerate(contrasts_wrapped):
@@ -943,80 +942,87 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
                     str.join('_', [data_type, contrast]), ' ', '_')
                 file_path = os.path.join(
                     "brainomics_data", subject_id, "%s.nii.gz" % name_aux)
-                file_tarball_url = urls[contrast_id]
-                filenames.append((file_path, file_tarball_url, opts))
+                path = "/".join([
+                    "/localizer", "derivatives", "spm_1st_level",
+                    "sub-%s" % subject_id,
+                    "sub-%s_task-localizer_acq-%s_%s.nii.gz" % (
+                        subject_id, contrast, data_type)])
+                if _is_valid_path(path, index, verbose=verbose):
+                    file_url = root_url.format(index[path][1:])
+                    opts = {"move": file_path}
+                    filenames.append((file_path, file_url, opts))
+                    files.setdefault(data_type, []).append(file_path)
+
     # Fetch masks if asked by user
     if get_masks:
-        urls.append("%sbrainomics_data_masks.zip?rql=%s&vid=data-zip"
-                    % (root_url,
-                       _urllib.parse.quote(base_query % {"types": '"boolean mask"',
-                                                  "label": "mask"},
-                                    safe=',()')))
         for subject_id in subject_ids:
             file_path = os.path.join(
                 "brainomics_data", subject_id, "boolean_mask_mask.nii.gz")
-            file_tarball_url = urls[-1]
-            filenames.append((file_path, file_tarball_url, opts))
+            path = "/".join([
+                "/localizer", "derivatives", "spm_1st_level",
+                "sub-%s" % subject_id, "sub-%s_mask.nii.gz" % subject_id])
+            if _is_valid_path(path, index, verbose=verbose):
+                file_url = root_url.format(index[path][1:])
+                opts = {"move": file_path}
+                filenames.append((file_path, file_url, opts))
+                files.setdefault("masks", []).append(file_path)
+
     # Fetch anats if asked by user
     if get_anats:
-        urls.append("%sbrainomics_data_anats.zip?rql=%s&vid=data-zip"
-                    % (root_url,
-                       _urllib.parse.quote(base_query % {"types": '"normalized T1"',
-                                                  "label": "anatomy"},
-                                    safe=',()')))
         for subject_id in subject_ids:
             file_path = os.path.join(
                 "brainomics_data", subject_id,
                 "normalized_T1_anat_defaced.nii.gz")
-            file_tarball_url = urls[-1]
-            filenames.append((file_path, file_tarball_url, opts))
-    # Fetch subject characteristics (separated in two files)
-    if url is None:
-        url_csv = ("%sdataset/cubicwebexport.csv?rql=%s&vid=csvexport"
-                   % (root_url, _urllib.parse.quote("Any X WHERE X is Subject")))
-        url_csv2 = ("%sdataset/cubicwebexport2.csv?rql=%s&vid=csvexport"
-                    % (root_url,
-                       _urllib.parse.quote("Any X,XI,XD WHERE X is QuestionnaireRun, "
-                                    "X identifier XI, X datetime "
-                                    "XD", safe=',')
-                       ))
-    else:
-        url_csv = "%s/cubicwebexport.csv" % url
-        url_csv2 = "%s/cubicwebexport2.csv" % url
-    filenames += [("cubicwebexport.csv", url_csv, {}),
-                  ("cubicwebexport2.csv", url_csv2, {})]
+            path = "/".join([
+                "/localizer", "derivatives", "spm_preprocessing",
+                "sub-%s" % subject_id, "sub-%s_T1w.nii.gz" % subject_id])
+            if _is_valid_path(path, index, verbose=verbose):
+                file_url = root_url.format(index[path][1:])
+                opts = {"move": file_path}
+                filenames.append((file_path, file_url, opts))
+                files.setdefault("anats", []).append(file_path)
+
+    # Fetch subject characteristics
+    participants_file = os.path.join("brainomics_data", "participants.tsv")
+    path = "/localizer/participants.tsv"
+    if _is_valid_path(path, index, verbose=verbose):
+        file_url = root_url.format(index[path][1:])
+        opts = {"move": participants_file}
+        filenames.append((participants_file, file_url, opts))
+
+    # Fetch behavioural
+    behavioural_file = os.path.join(
+        "brainomics_data", "phenotype", "behavioural.tsv")
+    path = "/localizer/phenotype/behavioural.tsv"
+    if _is_valid_path(path, index, verbose=verbose):
+        file_url = root_url.format(index[path][1:])
+        opts = {"move": behavioural_file}
+        filenames.append((behavioural_file, file_url, opts))
 
     # Actual data fetching
-    dataset_name = 'brainomics_localizer'
-    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
-                                verbose=verbose)
     fdescr = _get_dataset_descr(dataset_name)
-    files = _fetch_files(data_dir, filenames, verbose=verbose)
-    anats = None
-    masks = None
-    tmaps = None
-    # combine data from both covariates files into one single recarray
+    _fetch_files(data_dir, filenames, verbose=verbose)
+    for key, value in files.items():
+        files[key] = [os.path.join(data_dir, val) for val in value]
+
+    # Load covariates file
     from numpy.lib.recfunctions import join_by
-    ext_vars_file2 = files[-1]
-    csv_data2 = np.recfromcsv(ext_vars_file2, delimiter=';')
-    files = files[:-1]
-    ext_vars_file = files[-1]
-    csv_data = np.recfromcsv(ext_vars_file, delimiter=';')
-    files = files[:-1]
-    # join_by sorts the output along the key
-    csv_data = join_by('subject_id', csv_data, csv_data2,
-                       usemask=False, asrecarray=True)[subject_mask - 1]
-    if get_anats:
-        anats = files[-n_subjects:]
-        files = files[:-n_subjects]
-    if get_masks:
-        masks = files[-n_subjects:]
-        files = files[:-n_subjects]
-    if get_tmaps:
-        tmaps = files[1::2]
-        files = files[::2]
-    return Bunch(cmaps=files, tmaps=tmaps, masks=masks, anats=anats,
-                 ext_vars=csv_data, description=fdescr)
+    participants_file = os.path.join(data_dir, participants_file)
+    csv_data = np.recfromcsv(participants_file, delimiter='\t')
+    behavioural_file = os.path.join(data_dir, behavioural_file)
+    csv_data2 = np.recfromcsv(behavioural_file, delimiter='\t')
+    csv_data = join_by(
+        "participant_id", csv_data, csv_data2, usemask=False, asrecarray=True)
+    subject_names = csv_data["participant_id"].tolist()
+    subjects_indices = []
+    for name in subject_ids:
+        name = name.encode("utf8")
+        if name not in subject_names:
+            continue
+        subjects_indices.append(subject_names.index(name))
+    csv_data = csv_data[subjects_indices]
+
+    return Bunch(ext_vars=csv_data, description=fdescr, **files)
 
 
 def fetch_localizer_calculation_task(n_subjects=1, data_dir=None, url=None,
@@ -1064,20 +1070,20 @@ def fetch_localizer_calculation_task(n_subjects=1, data_dir=None, url=None,
                                      get_tmaps=False, get_masks=False,
                                      get_anats=False, data_dir=data_dir,
                                      url=url, resume=True, verbose=verbose)
-    data.pop('tmaps')
-    data.pop('masks')
-    data.pop('anats')
     return data
 
 
-def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
+def fetch_localizer_button_task(n_subjects=None, data_dir=None, url=None,
+                                verbose=1):
     """Fetch left vs right button press contrast maps from the localizer.
 
-    This function ships only 2nd subject (S02) specific tmap and
-    its normalized T1 image.
-
     Parameters
     ----------
+    n_subjects: int, optional
+        The number of subjects to load. If None is given,
+        this function ships only 2nd subject (S02) specific tmap and
+        its normalized T1 image.
+
     data_dir: string, optional
         Path of the data directory. Used to force data storage in a specified
         location.
@@ -1093,6 +1099,7 @@ def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
     -------
     data: Bunch
         Dictionary-like object, the interest attributes are :
+        'cmaps': string list, giving paths to nifti contrast maps
         'tmap': string, giving paths to nifti contrast maps
         'anat': string, giving paths to normalized anatomical image
 
@@ -1109,30 +1116,19 @@ def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
     nilearn.datasets.fetch_localizer_contrasts
 
     """
-    # The URL can be retrieved from the nilearn account on OSF (Open
-    # Science Framework). Uploaded files specific to S02 from
-    # fetch_localizer_contrasts ['left vs right button press']
-    if url is None:
-        url = 'https://osf.io/dx9jn/download'
-
-    tmap = "t_map_left_auditory_&_visual_click_vs_right_auditory&visual_click.nii.gz"
-    anat = "normalized_T1_anat_defaced.nii.gz"
-
-    opts = {'uncompress': True}
-
-    options = ('tmap', 'anat')
-    filenames = [(os.path.join('localizer_button_task', name), url, opts)
-                 for name in (tmap, anat)]
-
-    dataset_name = 'brainomics'
-    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
-                                verbose=verbose)
-    files = _fetch_files(data_dir, filenames, verbose=verbose)
-
-    fdescr = _get_dataset_descr('brainomics_localizer')
-
-    params = dict([('description', fdescr)] + list(zip(options, files)))
-    return Bunch(**params)
+    if n_subjects is None:
+        n_subjects = [2]
+    data = fetch_localizer_contrasts(["left vs right button press"],
+                                     n_subjects=n_subjects,
+                                     get_tmaps=True, get_masks=False,
+                                     get_anats=True, data_dir=data_dir,
+                                     url=url, resume=True, verbose=verbose)
+    # TODO: remove -> only here for compatibility
+    if len(data["tmaps"]) == 1:
+        setattr(data, "tmap", data["tmaps"][0])
+    if len(data["anats"]) == 1:
+        setattr(data, "anat", data["anats"][0])
+    return data
 
 
 def fetch_abide_pcp(data_dir=None, n_subjects=None, pipeline='cpac',
diff --git a/nilearn/datasets/tests/data/localizer_behavioural.tsv b/nilearn/datasets/tests/data/localizer_behavioural.tsv
new file mode 100644
index 0000000000..d7c18f0a3c
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_behavioural.tsv
@@ -0,0 +1,95 @@
+participant_id	nuage2	pente_dots_lin	couleur	nom_des_gens	pb_phono	lin_vs_log	aspect	pb_express	facilite_addition	espece_plante	visu_vb_pour_calcul	tv	r2_dots_log	ratio_pw_w	pb_allocentrique	pb_rotation	details	score_cal_complexe	activites_pratiques_nb	bruxe	espece_animales	mot	pb_lecture	moyenne_pw_w	trajet	score_3d	nuage10	nuage11	nuage12	pb_ecriture	gaucherie	score_addition	probleme_ecole	facilite_soustraction	pente_dots_log	arc	perspective	score_multiplication	r2_dots_lin	pair1	pair3	pair2	pair5	pair4	itineraire_plan	score_soustraction	erreur_w	imagerie	type_pb_langage	decomposition	niveau_etude	francais1erl	geometrie	coef_bisec	pente_sujet_estim_lin	pb_prononc	seine	parle_tard	pb_a_l_ecole	autoestimation_calcul	lettre_miroir	rapport_pente_lin_groupe	comment	nuage3	nuage1	nuage6	nuage7	nuage4	nuage5	strategie	nuage8	nuage9	score_tableau_langage	pb_binaire	erreur_langage	droitier	erreur_ps_w	pb_consonne	facilite_multiplication	dyslexie	visage	coef_estim_qt	pb_nonlangage	bilingue	dessin_fini	bus	billet	pente_sujet_estim_log	autoevaluation_cx	pb_reconnaissance	difference_pw_w	score_pb_g_d	car	synaesthete	error	difficult_apprend_lire	pente_bisec	orthophoniste	lieux	orientation	normalized_pw	autoevaluation_dessin	bonne_reconnaissance	lycee	avion	id	difficulte_calc_localizer	dessin_note_sur_20	rapport_pente_log_groupe	score_imageur	visuel_verbal	algebre	pain	edimburgh	arbre	g_contrarie	lecture_ok	pb_en_3d	pb_2eme_langue	oiseau	pb_egocentrique	pseudo	pente_estim_qt	autoevaluation_rotation	pb_orthogr
+S01	35.0	0.416922	n/a	n/a	False	-0.0952051	4.0	False	4.0	n/a	1.0	17.0	0.916693	1.875	False	False	2.0	0.966667	n/a	500.0	n/a	8.0	False	11.5	9.0	0.916667	20.0	10.0	35.0	False	0.0	10.0	0.0	4.0	0.749906	40.0	2.0	10.0	0.821488	3.0	23.0	5.0	68.0	43.0	n/a	10.0	1.0	3.0	0	False	4.0	True	4.0	0.985224	0.642131	False	60.0	False	False	3.0	False	0.620551	n/a	10.0	25.0	20.0	40.0	50.0	65.0	u	15.0	30.0	4.0	False	0.0	True	3.0	False	4.0	False	1.0	0.969218	False	False	1.0	17.0	13.0	0.862849	4.0	0	7.0	0.0	53.0	0	n/a	False	0.953448	False	2.0	4.0	0.304348	2.0	lieux	4.0	70.0	165	n/a	18.0	0.85468	9.51389	4.0	4.0	60.0	10.0	30.0	False	True	False	False	2.4	False	15.0	0.988097	4.0	False
+S02	30.0	0.529093	n/a	n/a	False	0.0151144	3.5	False	3.0	n/a	3.0	20.0	0.954898	1.77778	False	False	1.0	0.933333	3.4	700.0	n/a	9.0	False	12.5	5.0	0.916667	30.0	10.0	40.0	False	0.0	10.0	0.0	4.0	0.76985	20.0	2.0	10.0	0.970012	3.0	29.0	4.0	65.0	43.0	n/a	8.0	0.0	3.0	0	True	3.0	True	3.0	0.999742	0.776734	False	50.0	False	False	3.0	False	0.787507	n/a	15.0	25.0	20.0	50.0	55.0	70.0	n/a	20.0	60.0	2.5	False	0.0	True	2.0	False	5.0	False	1.0	0.90535	False	False	1.0	8.0	10.0	0.873827	3.0	0	7.0	0.0	52.0	n/a	n/a	False	1.00269	False	1.0	3.0	0.28	1.0	0	3.0	20.0	174	n/a	15.0	0.87741	7.80556	8.0	4.0	90.0	10.0	80.0	False	True	False	False	1.5	False	16.0	1.15221	3.0	False
+S03	35.0	0.45857	n/a	n/a	False	-0.0038151	2.0	False	2.0	n/a	3.0	10.0	0.894501	1.55556	False	False	1.0	0.866667	3.6	40.0	0.0	9.0	False	11.5	10.0	0.5	30.0	10.0	40.0	False	0.25	10.0	0.0	3.0	0.715695	100.0	2.0	10.0	0.890686	3.0	33.0	5.0	63.0	42.0	n/a	9.0	0.0	2.0	0	False	4.0	True	3.0	0.995588	0.682245	False	30.0	False	False	2.0	False	0.68254	n/a	15.0	30.0	15.0	30.0	50.0	70.0	d	20.0	50.0	4.0	False	0.0	True	0.0	False	4.0	False	2.0	0.658032	False	False	1.0	12.0	10.0	0.816513	2.33333	animaux	5.0	0.0	60.0	0	n/a	False	0.966338	False	2.0	4.0	0.217391	2.0	visages, lieux	4.0	100.0	204	n/a	12.0	0.815689	7.375	6.0	4.0	80.0	11.0	80.0	False	False	False	False	2.5	False	14.0	0.740778	2.0	False
+S04	20.0	0.240791	1.0	1.0	False	0.0366737	3.5	False	5.0	1.0	1.0	45.0	0.937822	1.58333	True	True	2.0	0.766667	3.6	450.0	1.0	12.0	False	15.5	10.0	0.833333	15.0	10.0	25.0	False	0.333333	10.0	0.0	2.0	0.584904	70.0	2.0	9.0	0.974495	3.0	32.0	4.0	55.0	44.0	1.0	9.0	0.0	1.0	0	False	3.0	True	4.0	0.997322	0.356258	False	100.0	False	False	3.0	False	0.358396	n/a	10.0	15.0	15.0	25.0	30.0	35.0	d	10.0	30.0	4.0	False	0.0	True	0.0	False	5.0	False	2.0	0.350432	False	False	1.0	25.0	12.0	0.668016	4.0	LIEUX	7.0	2.0	50.0	0	n/a	False	0.984509	False	0.0	2.0	0.225806	1.0	VISAGE	5.0	250.0	322	n/a	17.0	0.666625	5.52778	6.0	5.0	1.0	14.0	100.0	True	True	True	False	3.5	False	19.0	0.791295	2.0	False
+S05	40.0	0.3345	1.0	1.0	False	-0.0530016	1.5	False	3.0	1.0	1.0	10.0	0.914142	1.77778	False	True	0.0	0.433333	1.8	400.0	1.0	9.0	False	12.5	8.0	0.583333	20.0	10.0	30.0	False	0.0	9.0	0.0	2.0	0.635478	20.0	0.0	6.0	0.86114	3.0	23.0	3.0	60.0	40.0	1.0	4.0	0.0	3.0	0	True	2.0	True	0.0	0.991584	0.503873	False	50.0	False	False	2.0	False	0.497873	n/a	15.0	30.0	20.0	30.0	45.0	50.0	n/a	15.0	40.0	2.0	False	0.0	True	4.0	False	3.0	False	1.0	0.917151	False	False	1.0	10.0	10.0	0.724752	0.0	n/a	7.0	0.0	30.0	0	n/a	False	1.01049	False	1.0	2.5	0.28	1.0	n/a	0.0	30.0	298	n/a	5.0	0.724264	6.40278	6.0	2.0	40.0	11.0	15.0	False	True	True	True	1.0	False	16.0	1.02732	3.0	False
+S06	30.0	0.52638	n/a	n/a	False	-0.00565731	3.5	False	4.0	n/a	1.0	25.0	0.933403	2.0	False	False	2.0	0.633333	3.6	400.0	n/a	9.0	False	13.5	8.0	0.75	20.0	10.0	50.0	False	0.0	10.0	2.0	3.0	0.834941	75.0	2.0	10.0	0.927745	3.0	34.0	4.0	64.0	43.0	n/a	8.0	0.0	3.0	0	False	2.0	True	5.0	0.998702	0.781213	False	20.0	True	False	3.0	False	0.783469	n/a	10.0	20.0	20.0	50.0	50.0	70.0	u	20.0	50.0	2.0	True	0.0	True	5.0	False	5.0	False	2.0	0.881779	False	False	1.0	15.0	10.0	0.951082	0.0	n/a	9.0	0.0	45.0	0	n/a	True	1.01283	False	2.0	3.0	0.333333	1.0	LIEUX VISAGES	3.0	200.0	311	n/a	17.0	0.951596	8.0	4.0	4.0	75.0	10.0	75.0	False	True	False	True	2.0	False	18.0	1.0508	n/a	False
+S07	60.0	0.887654	1.0	1.0	False	-0.013893	2.0	False	3.0	1.0	1.0	10.0	0.923668	2.0	False	True	1.0	0.7	3.8	300.0	1.0	11.0	True	16.5	7.0	0.666667	50.0	15.0	80.0	False	0.25	9.0	2.0	2.0	0.967964	10.0	0.0	10.0	0.909775	3.0	30.0	3.0	62.0	40.0	1.0	6.0	0.0	3.0	aprentissage lecture , difference d/t , p/b a 6ans duree 1ans	False	4.0	True	5.0	0.994361	1.30039	False	50.0	False	False	2.0	False	1.32119	n/a	10.0	40.0	30.0	100.0	80.0	100.0	d	30.0	100.0	2.0	True	0.0	True	6.0	False	3.0	False	1.0	0.884219	False	False	1.0	4.0	10.0	1.09569	2.0	n/a	11.0	0.0	40.0	0	n/a	True	1.03761	True	2.0	1.0	0.333333	1.0	LIEUX	4.0	30.0	333	n/a	8.0	1.1032	5.84722	6.0	5.0	40.0	10.0	30.0	False	True	True	False	2.0	False	22.0	0.958523	1.0	False
+S08	30.0	0.378301	n/a	n/a	False	0.0574613	3.0	False	4.0	n/a	2.0	5.0	0.914426	1.35714	False	False	2.0	0.833333	7.0	350.0	n/a	14.0	False	16.5	64.0	0.416667	20.0	15.0	35.0	False	0.0	10.0	0.0	3.0	0.657123	80.0	2.0	10.0	0.971887	3.0	32.0	4.0	63.0	42.0	n/a	8.0	2.0	1.0	0	False	4.0	False	5.0	0.999458	0.551338	False	100.0	False	False	3.0	True	0.563067	n/a	10.0	20.0	18.0	35.0	40.0	50.0	u	15.0	50.0	4.0	False	0.0	True	2.0	False	5.0	False	1.0	0.721875	False	True	1.0	20.0	10.0	0.74894	4.0	LIEUX	5.0	0.0	30.0	0	n/a	False	1.00225	False	0.0	1.5	0.151515	2.0	PHYSIONOMIE	4.0	100.0	290	n/a	16.0	0.748933	4.84722	8.0	4.0	90.0	10.0	80.0	False	True	False	False	1.9	False	19.0	0.959285	3.0	False
+S09	100.0	1.29591	n/a	n/a	False	-0.0193175	4.0	False	3.0	n/a	1.0	12.0	0.943651	1.47826	False	True	2.0	0.533333	2.9	800.0	n/a	11.5	False	14.25	10.0	0.5	80.0	15.0	100.0	False	0.2	10.0	0.0	2.0	1.03413	200.0	2.0	10.0	0.924333	3.0	35.0	3.0	55.0	42.0	n/a	6.0	0.0	3.0	0	False	3.0	True	2.0	0.988621	1.94941	False	50.0	False	False	1.0	False	1.92885	n/a	20.0	50.0	35.0	110.0	130.0	160.0	u	25.0	120.0	3.0	False	0.0	True	2.0	False	4.0	False	1.0	0.85892	False	False	1.0	15.0	8.0	1.18304	0.0	n/a	5.5	0.0	50.0	0	n/a	False	1.03538	False	2.0	3.0	0.192982	1.0	LIEUX	2.0	300.0	336	n/a	18.0	1.17861	7.5	4.0	2.0	45.0	14.0	30.0	False	True	True	False	1.5	False	17.0	1.22631	1.0	False
+S10	43.0	0.534045	n/a	n/a	False	0.0017111	4.5	False	3.0	n/a	1.0	40.0	0.973282	1.66667	False	True	2.0	0.6	4.2	500.0	0.0	9.0	True	12.0	10.0	0.583333	27.0	12.0	39.0	False	0.25	8.0	4.0	2.0	0.780877	50.0	2.0	10.0	0.974993	3.0	30.0	3.0	66.0	43.0	n/a	7.0	1.0	3.0	APPRENTISSAGE LECTURE/DYSLEXIE/BEGAIEMENT ZEZAIEMENT	False	4.0	True	5.0	0.994773	0.792601	False	70.0	True	False	2.0	False	0.794878	n/a	12.0	25.0	25.0	48.0	58.0	70.0	d	19.0	60.0	4.0	True	2.0	True	4.0	True	3.0	True	2.0	0.944997	False	False	1.0	12.0	11.0	0.890339	2.33333	animaux	6.0	0.0	50.0	0	n/a	True	1.05757	True	1.0	3.0	0.25	3.0	lieux	3.0	80.0	163	n/a	19.0	0.889978	8.06944	6.0	3.0	70.0	10.0	100.0	False	True	False	False	3.0	False	15.0	0.999617	2.0	False
+S11	50.0	0.501391	n/a	n/a	False	-0.155042	3.5	False	4.0	n/a	1.0	25.0	0.877283	1.25	True	True	2.0	0.466667	1.6	400.0	n/a	8.0	False	9.0	8.0	0.583333	30.0	10.0	40.0	False	0.0	10.0	0.0	2.0	0.786632	200.0	2.0	7.0	0.722241	3.0	30.0	5.0	70.0	41.0	n/a	6.0	0.0	3.0	MATHS	False	2.0	True	1.0	0.994483	0.767568	False	80.0	False	True	1.0	True	0.746275	n/a	16.0	38.0	20.0	75.0	55.0	70.0	d	16.0	40.0	2.0	False	3.0	True	1.0	False	4.0	False	2.0	0.918997	False	True	1.0	15.0	11.0	0.901555	2.66667	0	2.0	3.0	46.0	Lettres, chiffres, sons, temps, en espace et en couleur	n/a	False	0.973921	False	2.0	n/a	0.111111	3.0	visages, lieux	1.0	90.0	176	n/a	17.0	0.896537	4.81944	6.0	1.0	80.0	16.0	80.0	False	True	n/a	False	3.0	True	10.0	1.03446	2.0	False
+S12	40.0	0.45318	n/a	n/a	False	-0.0294667	0.5	False	4.0	n/a	2.0	5.0	0.923912	1.75	False	True	1.0	0.633333	3.2	500.0	n/a	12.0	False	16.5	8.0	0.5	35.0	12.0	37.0	False	0.25	8.0	2.0	3.0	0.722995	20.0	1.0	7.0	0.894445	3.0	32.0	4.0	60.0	45.0	n/a	8.0	1.0	3.0	FRANCAIS ORTHOGRAPHE LECTURE	False	3.0	True	3.0	0.999026	0.676939	False	100.0	False	False	3.0	False	0.674518	n/a	12.0	30.0	25.0	35.0	50.0	70.0	u	15.0	50.0	2.0	True	0.0	True	4.0	False	3.0	False	1.0	0.883083	False	False	1.0	8.0	15.0	0.822753	3.33333	0	9.0	0.0	56.0	n/a	n/a	True	1.00245	False	1.0	1.5	0.272727	2.0	0	4.0	40.0	182	n/a	7.0	0.824009	5.91667	6.0	4.0	50.0	11.0	80.0	True	True	True	True	2.0	False	21.0	1.08707	2.0	True
+S13	30.0	0.553522	1.0	1.0	False	-0.0442015	3.5	False	3.0	1.0	1.0	12.0	0.962172	1.54545	False	True	2.0	1.0	3.8	350.0	1.0	11.0	False	14.0	10.0	0.666667	25.0	10.0	50.0	False	0.2	10.0	0.0	4.0	0.840788	80.0	2.0	10.0	0.91797	3.0	33.0	4.0	70.0	44.0	1.0	10.0	0.0	3.0	0	False	3.0	True	4.0	0.999519	0.797884	False	75.0	False	False	3.0	False	0.823867	n/a	12.0	30.0	20.0	59.0	55.0	60.0	d	20.0	70.0	2.0	False	0.0	True	2.0	False	5.0	False	1.0	0.96621	False	False	1.0	10.0	15.0	0.94617	5.0	n/a	6.0	0.0	60.0	0	n/a	False	1.02888	False	2.0	1.5	0.214286	1.0	LIEUX	5.0	100.0	318	n/a	17.0	0.958259	6.84722	4.0	5.0	85.0	12.0	80.0	False	True	False	False	2.5	False	17.0	1.04223	2.0	False
+S14	40.0	0.536215	n/a	n/a	False	-0.0167022	1.5	False	4.0	n/a	1.0	7.0	0.982381	1.90909	False	False	1.0	0.8	3.4	350.0	n/a	11.0	False	16.0	9.0	0.75	25.0	10.0	50.0	False	0.11	10.0	0.0	3.0	0.842689	50.0	1.0	10.0	0.965678	3.0	36.0	4.0	62.0	45.0	n/a	9.0	0.0	3.0	0	False	3.0	True	3.0	0.997089	0.788024	False	60.0	False	False	3.0	False	0.798107	n/a	12.0	30.0	20.0	40.0	50.0	70.0	d	15.0	60.0	4.0	False	0.0	True	2.0	False	4.0	False	2.0	0.910639	False	False	1.0	20.0	10.0	0.955723	3.33333	0	10.0	0.0	50.0	n/a	n/a	False	1.01764	False	2.0	2.5	0.3125	1.0	LIEUX VISAGES	4.0	100.0	219	n/a	9.0	0.960426	7.14583	5.0	3.0	60.0	10.0	30.0	False	True	True	n/a	4.0	False	21.0	0.927176	2.0	False
+S15	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	366	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S16	55.0	0.432409	n/a	n/a	False	-0.114653	2.0	False	3.0	n/a	1.0	40.0	0.930288	2.0	False	False	1.0	0.633333	3.4	350.0	n/a	7.0	False	10.5	8.0	0.666667	30.0	10.0	55.0	False	0.0	9.0	0.0	2.0	0.735609	50.0	1.0	8.0	0.815635	3.0	30.0	4.0	67.0	43.0	n/a	6.0	0.0	3.0	0	False	4.0	True	3.0	0.99974	0.651471	False	100.0	False	False	2.0	False	0.643601	n/a	15.0	35.0	25.0	45.0	50.0	60.0	d	20.0	50.0	4.0	False	0.0	True	3.0	False	3.0	False	2.0	0.896679	False	False	1.0	15.0	15.0	0.835647	2.33333	0	7.0	2.0	50.0	n/a	n/a	False	1.01069	False	1.0	3.5	0.333333	2.0	physionomie	3.0	40.0	194	n/a	10.0	0.838385	7.74306	7.0	3.0	45.0	10.0	80.0	False	True	n/a	False	2.0	True	14.0	0.951493	2.0	False
+S17	50.0	1.90848	n/a	n/a	False	0.00262361	5.0	False	2.0	n/a	1.0	20.0	0.934938	2.0	True	True	2.0	0.266667	1.8	200.0	n/a	11.0	False	16.5	8.0	0.416667	20.0	10.0	100.0	False	0.0	8.0	0.0	n/a	1.48161	30.0	2.0	7.0	0.937562	3.0	30.0	5.0	60.0	40.0	n/a	0.0	1.0	3.0	0	False	3.0	True	4.0	0.995747	2.7377	False	200.0	False	False	2.0	False	2.8406	n/a	10.0	50.0	20.0	100.0	130.0	200.0	u	10.0	200.0	3.0	False	0.0	True	2.0	False	3.0	False	2.0	0.744411	False	False	1.0	50.0	8.0	1.67618	2.0	0	11.0	2.0	57.0	0	n/a	False	0.944038	False	1.0	3.0	0.333333	3.0	Visages	3.0	150.0	205	n/a	20.0	1.68861	8.18056	4.0	3.0	50.0	11.0	100.0	False	True	False	False	2.0	False	22.0	0.940993	3.0	False
+S18	40.0	2.15291	1.0	1.0	False	-0.169848	3.5	False	3.0	1.0	1.0	10.0	0.890863	1.41667	False	True	2.0	0.466667	3.0	300.0	1.0	12.0	False	14.5	9.0	0.583333	35.0	7.0	40.0	False	0.5	10.0	1.0	2.0	1.41522	20.0	2.0	10.0	0.721015	3.0	30.0	4.0	63.0	43.0	1.0	4.0	0.0	1.0	0	False	2.0	True	3.0	1.0	2.99655	False	10.0	False	False	2.0	False	3.20441	n/a	10.0	35.0	25.0	70.0	150.0	200.0	u	20.0	300.0	3.0	True	0.0	True	2.0	False	5.0	False	2.0	0.764588	False	False	1.0	4.0	15.0	1.59256	0.0	n/a	5.0	0.0	50.0	0	n/a	True	1.0	False	1.0	3.0	0.172414	2.0	VISAGES	1.0	10.0	332	n/a	17.0	1.61294	5.98611	6.0	2.0	80.0	12.0	50.0	False	True	True	True	1.5	False	17.0	0.944911	2.0	False
+S19	40.0	0.396693	n/a	n/a	False	-0.018355	1.0	True	3.0	n/a	2.0	10.0	0.958646	1.64286	True	False	1.0	0.5	3.4	600.0	n/a	14.0	False	18.5	8.5	0.666667	30.0	14.0	45.0	False	0.0	6.0	4.0	2.0	0.623431	50.0	0.0	8.0	0.940291	3.0	25.0	4.0	59.0	42.0	n/a	5.0	0.0	3.0	DYSLEXIE DYSORTHOGRAPHIE PB EXPRESSION	False	2.0	True	4.0	0.997793	0.589586	False	60.0	True	True	2.0	False	0.590441	n/a	15.0	35.0	20.0	45.0	50.0	58.0	d	25.0	50.0	1.0	True	1.0	True	3.0	False	4.0	True	1.0	0.969703	False	False	1.0	12.0	11.0	0.708503	2.0	0	9.0	2.0	40.0	0	n/a	False	0.970941	True	2.0	4.0	0.243243	1.0	lieux	2.0	110.0	183	n/a	6.0	0.710534	8.53472	5.0	2.0	50.0	10.0	60.0	False	True	False	True	1.5	False	23.0	1.14744	3.0	True
+S20	65.0	0.580366	n/a	n/a	False	-0.0676551	2.5	False	4.0	n/a	1.0	15.0	0.938926	1.875	False	True	1.0	0.833333	2.0	300.0	n/a	8.0	False	11.5	8.0	0.666	30.0	15.0	45.0	False	0.0	9.0	0.0	3.0	0.757288	100.0	1.0	9.0	0.871271	3.0	30.0	5.0	63.0	45.0	n/a	8.0	0.0	3.0	0	False	4.0	True	2.0	0.996139	0.879881	False	50.0	False	False	2.0	False	0.863822	n/a	15.0	40.0	25.0	50.0	70.0	80.0	d	20.0	60.0	3.0	False	0.0	True	2.0	False	5.0	False	2.0	0.675853	False	True	1.0	10.0	20.0	0.867901	3.5	0	7.0	0.0	50.0	n/a	n/a	False	0.96733	False	1.0	3.0	0.304348	2.0	Visages	3.0	25.0	166	n/a	11.0	0.863093	7.51333	4.0	3.0	70.0	13.0	500.0	False	True	False	False	7.0	False	15.0	0.861326	2.0	False
+S21	75.0	0.827894	n/a	n/a	False	-0.0550406	1.0	False	3.0	n/a	3.0	10.0	0.933896	1.5	False	True	1.0	0.6	2.8	500.0	n/a	10.0	False	12.5	11.0	0.25	25.0	10.0	60.0	False	0.2	6.0	0.0	3.0	0.97081	30.0	0.0	9.0	0.878856	3.0	30.0	5.0	60.0	40.0	n/a	4.0	0.0	1.0	n/a	False	4.0	True	2.0	0.995747	1.23959	False	20.0	False	False	3.0	False	1.23225	n/a	15.0	40.0	25.0	45.0	90.0	100.0	d	20.0	85.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.885648	False	False	0.0	10.0	13.0	1.10749	3.0	lieux	5.0	0.0	100.0	n/a	n/a	False	0.944038	False	0.0	0.0	0.2	1.0	visage	3.0	40.0	104	n/a	4.0	1.10645	1.875	12.0	3.0	60.0	11.0	80.0	False	True	True	False	3.0	False	15.0	0.975003	2.0	False
+S22	50.0	0.310351	n/a	n/a	False	-0.249045	4.0	False	5.0	n/a	1.0	20.0	0.783854	1.5	False	False	2.0	0.866667	4.4	300.0	n/a	12.0	False	15.0	7.0	0.666667	20.0	10.0	35.0	False	0.0	9.0	0.0	4.0	0.695147	50.0	2.0	10.0	0.534809	3.0	28.0	4.0	62.0	45.0	n/a	10.0	0.0	3.0	0	False	4.0	True	5.0	0.999154	0.471502	False	40.0	False	False	3.0	False	0.461929	n/a	10.0	50.0	20.0	30.0	40.0	50.0	d	15.0	35.0	4.0	False	0.0	True	2.0	False	5.0	False	2.0	0.977155	False	True	1.0	15.0	10.0	0.786574	4.33333	0	6.0	0.0	40.0	0	n/a	False	0.997375	False	0.0	4.0	0.2	3.0	Visages	4.0	80.0	188	n/a	18.0	0.79227	9.20139	5.0	5.0	70.0	10.0	50.0	False	True	False	False	2.5	False	18.0	0.955999	3.0	False
+S23	50.0	0.508968	1.0	1.0	False	-0.0350254	2.5	False	3.0	1.0	1.0	10.0	0.871767	1.54545	False	True	1.0	0.433333	2.8	800.0	1.0	11.0	False	14.0	5.0	0.5	20.0	10.0	30.0	False	0.0	10.0	0.0	2.0	0.800143	15.0	0.0	10.0	0.836741	3.0	23.0	4.0	64.0	44.0	1.0	3.0	0.0	3.0	0	False	4.0	True	2.0	0.992409	0.7681	False	30.0	False	True	2.0	False	0.757553	n/a	15.0	40.0	15.0	40.0	60.0	70.0	u	15.0	50.0	2.0	False	0.0	True	3.0	False	4.0	False	1.0	0.855351	False	False	1.0	10.0	10.0	0.916341	0.0	n/a	6.0	0.0	40.0	0	n/a	False	0.984198	False	1.0	2.0	0.214286	2.0	n/a	2.0	40.0	323	n/a	9.0	0.911936	6.14583	7.0	2.0	50.0	10.0	200.0	False	True	True	False	2.0	False	17.0	1.14246	2.0	False
+S24	60.0	0.671362	1.0	1.0	False	-0.0573619	3.0	False	3.0	1.0	2.0	25.0	0.944763	0.947368	True	True	2.0	0.5	3.2	300.0	1.0	19.0	False	18.5	8.0	0.333333	35.0	10.0	75.0	False	0.0	9.0	0.0	2.0	0.951386	600.0	1.0	9.0	0.887401	3.0	34.0	3.0	57.0	45.0	1.0	6.0	0.0	1.0	0	False	2.0	True	2.0	0.990636	1.00301	False	45.0	False	True	1.0	False	0.999262	n/a	10.0	35.0	25.0	60.0	70.0	80.0	u	25.0	70.0	2.0	False	0.0	True	2.0	False	3.0	False	0.0	0.673082	False	False	1.0	20.0	20.0	1.08045	0.0	VISAGES	-1.0	2.0	30.0	0	n/a	False	1.04792	False	2.0	1.5	-0.027027	1.0	LIEUX	3.0	500.0	335	n/a	14.0	1.08431	4.13194	7.0	4.0	90.0	10.0	300.0	False	True	True	True	3.0	False	18.0	1.06108	2.0	False
+S25	50.0	0.688949	1.0	1.0	False	-0.0489149	1.0	False	4.0	1.0	1.0	20.0	0.948051	2.0	False	True	0.0	0.566667	3.0	500.0	0.0	10.0	False	15.0	8.5	0.666667	40.0	10.0	60.0	False	0.0	10.0	0.0	3.0	0.875224	800.0	1.0	10.0	0.899136	3.0	40.0	5.0	76.0	35.0	1.0	7.0	0.0	3.0	n/a	False	2.0	True	2.0	0.979872	1.04007	False	70.0	False	False	2.0	False	1.02544	n/a	15.0	30.0	25.0	50.0	70.0	100.0	u	20.0	60.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.575015	False	False	1.0	4.0	15.0	1.00105	3.33333	animaux	10.0	2.0	50.0	0	n/a	False	0.991503	False	1.0	2.5	0.333333	2.0	n/a	3.0	10.0	9	n/a	6.0	0.997507	7.05556	4.0	3.0	50.0	16.0	6.0	False	True	False	False	1.2	True	20.0	1.09846	3.0	False
+S26	35.0	0.455105	n/a	n/a	False	-0.00552304	1.0	False	2.0	n/a	2.0	5.0	0.96991	2.07692	True	True	1.0	0.433333	1.8	240.0	n/a	13.0	True	20.0	12.0	0.25	20.0	10.0	35.0	False	0.2	9.0	3.0	1.0	0.801922	5.0	0.0	10.0	0.964387	3.0	32.0	5.0	64.0	43.0	n/a	4.0	1.0	3.0	dyslexie, pblm de: lecture, inversion de syllabes,dorganisation, emploi du tps	False	4.0	True	4.0	0.996225	0.68226	False	10.0	True	False	1.0	False	0.677383	n/a	10.0	20.0	17.0	40.0	50.0	60.0	d	15.0	45.0	2.0	True	0.0	True	3.0	False	4.0	True	2.0	0.610165	False	False	1.0	6.0	7.0	0.919298	0.0	n/a	14.0	3.0	50.0	0	n/a	False	0.969552	True	2.0	2.0	0.35	3.0	VISAGES LIEUX	3.0	15.0	347	n/a	6.0	0.913964	6.52083	5.0	3.0	60.0	10.0	4.0	False	True	True	True	1.5	True	27.0	0.830617	3.0	False
+S27	30.0	0.400822	1.0	1.0	False	-0.0253511	2.0	False	4.0	1.0	2.0	4.0	0.941145	1.38462	False	False	0.0	0.966667	4.2	300.0	1.0	13.0	False	15.5	8.0	0.75	20.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.676112	60.0	1.0	10.0	0.915794	3.0	27.0	4.0	65.0	42.0	1.0	10.0	0.0	3.0	0	False	4.0	True	4.0	0.998677	0.60153	False	80.0	False	False	3.0	False	0.596588	n/a	15.0	30.0	20.0	40.0	50.0	60.0	d	20.0	40.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.870445	False	True	1.0	15.0	16.0	0.768781	3.0	n/a	5.0	0.0	80.0	0	n/a	False	0.993971	False	1.0	1.5	0.16129	1.0	VISAGES	4.0	100.0	331	n/a	8.0	0.770575	6.22917	7.0	4.0	50.0	11.0	100.0	False	True	False	False	2.0	False	18.0	1.06635	2.0	False
+S28	53.0	1.08503	n/a	n/a	False	-0.0193036	4.0	False	3.0	n/a	3.0	15.0	0.918141	1.77778	True	False	2.0	0.666667	3.2	600.0	n/a	9.0	False	12.5	8.0	0.583333	20.0	10.0	55.0	False	0.0	10.0	0.0	3.0	1.09877	25.0	2.0	9.0	0.898837	3.0	25.0	4.0	65.0	42.0	n/a	7.0	0.0	3.0	0	False	2.0	True	5.0	0.99648	1.60522	False	30.0	False	False	2.0	False	1.61497	n/a	15.0	25.0	25.0	105.0	76.0	135.0	u	15.0	90.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.814187	False	False	1.0	9.0	16.0	1.25582	2.0	n/a	7.0	2.0	50.0	0	n/a	False	0.987764	False	1.0	1.5	0.28	3.0	VIGAGE	5.0	50.0	294	n/a	18.0	1.25228	6.56944	8.0	5.0	70.0	13.0	50.0	False	True	False	True	12.0	False	16.0	0.81182	2.0	False
+S29	34.0	0.403552	n/a	n/a	False	-0.032888	3.5	False	3.0	n/a	2.0	5.0	0.915869	1.8	False	True	2.0	0.433333	3.0	800.0	2.0	10.0	False	14.0	10.0	0.666667	15.0	10.0	30.0	False	0.0	8.0	0.0	3.0	0.737564	500.0	2.0	10.0	0.882981	3.0	33.0	4.0	57.0	42.0	1.0	5.0	0.0	3.0	0	False	2.0	True	4.0	0.997662	0.613726	False	40.0	False	False	2.0	False	0.600651	n/a	10.0	20.0	20.0	35.0	47.0	60.0	d	15.0	35.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.404527	False	False	1.0	10.0	15.0	0.84771	0.0	n/a	8.0	0.0	40.0	0	n/a	False	0.987342	False	1.0	1.5	0.285714	3.0	VISAGES ANIMAUX	4.0	500.0	330	n/a	17.0	0.840613	7.01389	4.0	4.0	1.0	11.0	300.0	False	True	False	False	3.0	False	18.0	1.08016	3.0	False
+S30	60.0	0.46732	n/a	n/a	False	-0.281506	1.0	False	4.0	n/a	1.0	50.0	0.818848	1.88889	False	False	0.0	0.566667	3.6	400.0	n/a	9.0	False	13.0	10.0	0.75	30.0	10.0	40.0	False	0.5	9.0	0.0	3.0	0.757308	70.0	1.0	8.0	0.537342	3.0	25.0	5.0	62.0	42.0	n/a	7.0	0.0	3.0	0	False	4.0	True	4.0	0.9921	0.72291	False	250.0	False	False	2.0	False	0.695564	n/a	15.0	40.0	30.0	90.0	60.0	60.0	u	20.0	40.0	3.0	False	0.0	True	3.0	False	3.0	False	2.0	0.78383	False	False	1.0	15.0	15.0	0.864595	3.33333	0	8.0	0.0	50.0	nombre / espace	n/a	False	0.941167	False	1.0	4.0	0.307692	1.0	Visages	3.0	60.0	177	n/a	6.0	0.863115	8.60417	5.0	2.0	50.0	10.0	20.0	False	True	False	False	1.0	False	17.0	1.05261	3.0	False
+S31	40.0	0.580558	n/a	n/a	False	0.0131732	2.5	False	4.0	n/a	2.0	5.0	0.967509	1.46154	False	True	1.0	0.533333	4.4	800.0	n/a	13.0	False	16.0	11.0	0.666667	20.0	11.0	40.0	False	0.0	10.0	0.0	1.0	0.868769	200.0	1.0	10.0	0.980682	3.0	30.0	4.0	63.0	45.0	n/a	4.0	0.0	2.0	0	False	4.0	True	3.0	0.999827	0.86083	False	600.0	False	False	2.0	False	0.864108	n/a	12.0	25.0	18.0	50.0	60.0	70.0	d	14.0	60.0	4.0	False	0.0	True	5.0	False	4.0	False	0.0	0.789305	False	False	1.0	7.0	12.0	0.993075	4.0	VISAGE	6.0	0.0	50.0	0	n/a	False	1.00572	False	2.0	3.0	0.1875	2.0	LIEUX	3.0	150.0	295	n/a	11.0	0.99015	6.74306	5.0	4.0	80.0	10.0	300.0	False	True	True	False	2.5	False	19.0	1.33373	2.0	False
+S32	60.0	1.50179	n/a	n/a	False	-0.0628016	0.0	False	4.0	n/a	1.0	10.0	0.937975	1.57143	False	True	0.0	0.833333	3.6	500.0	0.0	14.0	False	18.0	7.0	n/a	40.0	15.0	120.0	False	0.0	10.0	0.0	3.0	1.12305	20.0	0.0	8.0	0.875174	3.0	27.0	4.0	64.0	43.0	n/a	9.0	0.0	1.0	0	False	4.0	True	5.0	0.998799	2.15269	False	60.0	False	False	3.0	False	2.23528	n/a	15.0	40.0	30.0	150.0	100.0	150.0	d	30.0	170.0	4.0	False	1.0	True	0.0	False	4.0	False	0.0	0.866485	False	False	1.0	10.0	15.0	1.26973	3.33333	Visages animaux	8.0	0.0	40.0	0	n/a	False	0.994238	False	2.0	2.5	0.222222	1.0	lieux	5.0	150.0	185	n/a	2.0	1.27996	4.5625	5.0	5.0	80.0	14.0	15.0	True	True	False	False	2.0	False	22.0	1.06178	n/a	False
+S33	80.0	1.00954	n/a	n/a	False	-0.037087	2.0	False	3.0	n/a	3.0	6.0	0.970922	1.875	True	True	1.0	0.733333	2.8	400.0	n/a	8.0	True	11.5	11.0	0.583333	35.0	10.0	90.0	False	0.2	7.0	2.0	4.0	1.07425	30.0	2.0	9.0	0.933835	3.0	32.0	4.0	65.0	42.0	n/a	10.0	0.0	3.0	apprentissage lecture	False	3.0	True	4.0	0.999484	1.50266	False	80.0	False	False	2.0	False	1.5026	n/a	15.0	50.0	25.0	90.0	100.0	110.0	u	25.0	100.0	3.0	True	0.0	True	1.0	False	5.0	False	1.0	0.882616	False	False	1.0	20.0	15.0	1.22089	3.33333	0	7.0	2.0	100.0	0	n/a	True	1.00767	False	1.0	2.0	0.304348	2.0	0	3.0	150.0	216	n/a	12.0	1.22434	6.61111	6.0	4.0	80.0	10.0	70.0	False	True	False	False	2.5	False	15.0	1.04328	1.0	False
+S34	39.0	0.546959	n/a	n/a	False	-0.0169346	3.5	False	2.0	n/a	1.0	20.0	0.978523	1.77778	False	False	2.0	0.766667	3.0	350.0	n/a	9.0	False	12.5	8.0	1.0	29.0	13.0	55.0	False	0.33	10.0	0.0	2.0	0.790664	50.0	2.0	10.0	0.961589	3.0	33.0	4.0	72.0	44.0	n/a	9.0	0.0	3.0	timbre de voix	False	3.0	True	n/a	0.999336	0.808399	False	90.0	False	False	2.0	False	0.8141	n/a	13.0	36.0	21.0	50.0	60.0	70.0	d	18.0	60.0	4.0	False	0.0	True	0.0	False	3.0	False	1.0	0.95104	True	False	1.0	17.0	13.0	0.897307	1.66667	0	7.0	0.0	40.0	n/a	n/a	False	1.03377	True	2.0	3.0	0.28	2.0	lieux	2.0	50.0	191	n/a	17.0	0.901132	7.875	4.0	2.0	75.0	16.0	100.0	False	True	True	False	3.0	False	16.0	0.952927	3.0	False
+S35	45.0	0.739662	n/a	n/a	False	-0.0104976	3.0	False	4.0	n/a	1.0	4.0	0.956899	2.1	True	False	1.0	0.533333	2.4	350.0	n/a	10.0	False	15.5	9.0	0.833333	30.0	12.0	50.0	False	0.6	7.0	0.0	1.0	0.966636	30.0	0.0	8.0	0.946401	3.0	32.0	4.0	70.0	43.0	n/a	3.0	0.0	1.0	positionement main gauche	False	4.0	True	4.0	0.999391	1.07854	False	8.0	False	False	2.0	False	1.10092	n/a	10.0	30.0	30.0	75.0	70.0	80.0	d	15.0	85.0	3.0	False	1.0	True	2.0	False	4.0	False	1.0	0.814216	True	False	1.0	5.0	6.0	1.09427	2.66667	0	11.0	2.0	60.0	0	n/a	False	1.02351	True	1.0	4.0	0.354839	1.0	0	4.0	60.0	192	n/a	10.0	1.10169	6.90278	6.0	1.0	50.0	13.0	20.0	True	True	False	False	2.5	False	21.0	0.995345	2.0	False
+S36	24.0	0.373996	n/a	n/a	False	0.0331708	0.5	False	4.0	n/a	1.0	4.0	0.885147	2.22222	False	True	0.0	0.8	2.8	450.0	n/a	9.0	False	14.5	7.0	0.583333	20.0	11.0	39.0	False	0.5	9.0	0.0	3.0	0.668698	167.0	0.0	9.0	0.918318	3.0	28.0	4.0	55.0	43.0	n/a	7.0	0.0	3.0	0	False	4.0	True	2.0	0.998932	0.556324	False	30.0	False	False	3.0	False	0.556659	n/a	13.0	15.0	16.0	35.0	45.0	50.0	u	15.0	41.0	4.0	False	0.0	True	2.0	False	4.0	False	1.0	0.856854	False	True	1.0	7.0	11.0	0.768194	3.66667	0	11.0	0.0	45.0	0	n/a	False	0.970847	False	1.0	1.0	0.37931	2.0	tout	2.0	30.0	200	n/a	3.0	0.762126	5.50694	5.0	2.0	70.0	12.0	40.0	False	True	True	False	2.5	False	20.0	1.08659	3.0	False
+S37	60.0	1.20749	n/a	n/a	False	0.00848409	4.0	False	0.0	n/a	3.0	20.0	0.911086	1.33333	False	False	2.0	0.733333	3.8	300.0	n/a	9.0	False	10.5	9.0	0.833333	45.0	15.0	85.0	False	0.0	6.0	0.0	0.0	0.988676	60.0	2.0	10.0	0.91957	3.0	30.0	4.0	64.0	44.0	n/a	8.0	0.0	3.0	0	False	4.0	True	3.0	0.999972	1.76399	False	45.0	False	False	3.0	False	1.79724	n/a	16.0	26.0	30.0	60.0	100.0	150.0	d	25.0	130.0	4.0	False	0.0	True	5.0	False	0.0	False	1.0	0.957402	False	True	1.0	15.0	12.0	1.12971	0.0	n/a	3.0	0.0	50.0	0	n/a	False	1.00563	False	2.0	4.0	0.142857	1.0	LIEUX,VISAGE	5.0	35.0	291	n/a	18.0	1.12681	8.63194	7.0	2.0	90.0	13.0	80.0	False	True	True	False	2.5	False	12.0	0.938097	2.0	False
+S38	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	368	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S39	50.0	0.621052	n/a	n/a	False	-0.0259647	0.5	False	5.0	n/a	1.0	25.0	0.923161	1.58333	False	False	0.0	0.733333	2.8	800.0	n/a	12.0	False	15.5	10.0	0.666667	25.0	10.0	30.0	False	0.0	9.0	0.0	4.0	0.840421	110.0	0.0	10.0	0.897196	3.0	30.0	5.0	61.0	41.0	n/a	10.0	0.0	3.0	 LANGUES	False	4.0	True	5.0	0.995976	0.934155	False	50.0	False	False	2.0	False	0.924379	n/a	15.0	30.0	20.0	60.0	70.0	80.0	d	20.0	60.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.947885	True	False	1.0	13.0	10.0	0.962968	4.0	0	7.0	0.0	58.0	n/a	n/a	False	0.950016	False	2.0	4.0	0.225806	1.0	visages, lieux	4.0	100.0	139	n/a	3.0	0.957841	8.68056	4.0	5.0	80.0	10.0	30.0	False	True	False	True	2.0	False	19.0	1.13179	3.0	False
+S40	60.0	0.669262	n/a	n/a	False	-0.120663	3.0	False	4.0	n/a	1.0	6.0	0.869703	1.26667	False	True	2.0	0.85	2.8	400.0	n/a	15.0	False	17.0	6.0	0.75	30.0	12.0	35.0	False	0.29	10.0	0.0	3.0	0.798569	80.0	1.0	10.0	0.74904	3.0	22.0	3.0	61.0	42.0	0.0	7.0	0.0	2.0	0	False	4.0	True	2.0	0.988815	1.03409	False	100.0	False	False	1.0	True	0.996137	n/a	15.0	30.0	28.0	50.0	70.0	110.0	d	18.0	45.0	4.0	False	0.0	True	0.0	False	3.0	False	2.0	0.917762	False	False	1.0	7.0	18.0	0.923717	3.5	lieux	4.0	0.0	60.0	Chiffres lettres, noms, mots / couleurs, prenoms / gout, toucher, nombres / temps	n/a	False	1.01591	False	1.0	2.0	0.117647	2.0	Visages	3.0	50.0	197	n/a	14.0	0.910141	5.83333	6.0	2.0	70.0	10.0	90.0	False	True	False	False	2.0	False	19.0	1.1343	2.0	False
+S41	50.0	0.634911	n/a	n/a	False	-0.123459	1.5	False	4.0	n/a	3.0	6.0	0.911603	1.33333	False	True	0.0	0.8	5.0	400.0	n/a	12.0	False	14.0	10.0	0.583333	25.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.892371	60.0	1.0	10.0	0.788143	3.0	27.0	3.0	63.0	41.0	n/a	10.0	0.0	3.0	0	False	2.0	True	2.0	0.994467	0.985329	False	75.0	False	False	2.0	False	0.945008	n/a	12.0	30.0	20.0	50.0	80.0	90.0	d	15.0	40.0	4.0	False	0.0	True	1.0	False	5.0	False	1.0	0.910247	False	False	1.0	20.0	10.0	1.03135	2.0	n/a	4.0	0.0	50.0	0	n/a	False	1.035	False	1.0	1.0	0.142857	1.0	n/a	4.0	80.0	348	n/a	7.0	1.01705	5.29861	7.0	4.0	40.0	11.0	50.0	False	True	True	False	3.0	False	16.0	0.979685	2.0	False
+S42	70.0	0.971599	n/a	n/a	False	-0.0145183	3.0	False	3.0	n/a	3.0	20.0	0.93927	1.83333	False	False	1.0	0.9	3.4	500.0	n/a	12.0	False	17.0	10.0	0.666667	30.0	15.0	77.0	False	0.0	10.0	0.0	2.0	0.955744	50.0	1.0	10.0	0.924752	3.0	32.0	4.0	65.0	44.0	2.0	10.0	0.0	3.0	0	True	4.0	True	4.0	0.99983	1.46297	False	250.0	False	False	3.0	False	1.44614	n/a	15.0	40.0	20.0	70.0	90.0	130.0	d	25.0	80.0	4.0	False	0.0	True	0.0	False	4.0	False	2.0	0.893647	False	False	1.0	15.0	15.0	1.09782	2.0	n/a	10.0	0.0	70.0	0	n/a	False	1.01353	False	2.0	2.0	0.294118	3.0	VISAGE, LIEUX,PHYSIONOMIE	5.0	130.0	324	n/a	12.0	1.08928	6.53472	9.0	4.0	65.0	11.0	50.0	False	True	False	False	4.0	False	22.0	1.00161	2.0	False
+S43	50.0	0.444483	n/a	n/a	False	-0.0592845	2.5	False	3.0	n/a	2.0	35.0	0.889422	1.91667	False	False	2.0	0.933333	3.2	600.0	n/a	12.0	False	17.5	7.0	0.916667	25.0	15.0	40.0	False	0.0	10.0	0.0	2.0	0.608799	40.0	1.0	10.0	0.830138	3.0	22.0	4.0	68.0	43.0	n/a	9.0	1.0	3.0	0	False	2.0	True	3.0	0.988576	0.669578	False	100.0	False	False	2.0	False	0.661573	n/a	20.0	50.0	25.0	50.0	60.0	70.0	u	25.0	50.0	4.0	False	0.0	True	1.0	False	4.0	False	0.0	0.8933	False	False	1.0	12.0	18.0	0.692751	3.0	VISAGES	11.0	0.0	50.0	0	n/a	False	0.988251	False	2.0	3.0	0.314286	1.0	LIEUX	5.0	40.0	349	n/a	13.0	0.693858	7.93056	6.0	4.0	60.0	12.0	25.0	False	True	False	True	2.5	False	23.0	0.986973	3.0	False
+S44	60.0	1.05413	n/a	n/a	False	-0.0291271	5.0	False	4.0	n/a	2.0	10.0	0.913654	1.07692	False	True	2.0	0.666667	2.4	600.0	n/a	13.0	False	13.5	10.0	0.833333	50.0	15.0	65.0	False	0.0	10.0	0.0	3.0	0.863541	20.0	2.0	7.0	0.884527	3.0	25.0	5.0	55.0	42.0	n/a	10.0	0.0	3.0	0	False	4.0	True	2.0	0.99285	1.59484	False	100.0	False	True	2.0	True	1.56897	n/a	25.0	40.0	25.0	78.0	115.0	150.0	u	30.0	85.0	3.0	False	1.0	True	2.0	False	3.0	False	2.0	0.6925	False	False	1.0	6.0	15.0	0.99406	0.0	n/a	1.0	0.0	50.0	0	n/a	False	0.920358	False	2.0	1.5	0.037037	3.0	VISAGE ET LIEUX	2.0	15.0	293	n/a	20.0	0.984191	6.73611	6.0	1.0	80.0	13.0	6.0	False	True	False	False	3.0	False	14.0	0.943294	3.0	False
+S45	30.0	0.287899	n/a	n/a	False	-0.0142111	1.5	False	4.0	n/a	3.0	30.0	0.89745	2.4	False	True	1.0	0.733333	3.4	600.0	n/a	10.0	False	17.0	6.0	0.583333	20.0	6.0	20.0	False	0.25	9.0	0.0	3.0	0.706925	80.0	1.0	9.0	0.883239	3.0	27.0	5.0	70.0	45.0	n/a	7.0	0.0	3.0	0	False	4.0	True	3.0	0.992768	0.419834	False	30.0	False	False	2.0	False	0.428512	n/a	12.0	25.0	15.0	30.0	30.0	40.0	u	12.0	40.0	2.0	False	0.0	True	2.0	False	5.0	False	1.0	0.912762	False	False	1.0	8.0	12.0	0.796829	3.33333	0	14.0	0.0	40.0	0	n/a	False	0.977134	False	2.0	2.5	0.411765	1.0	lieux	3.0	60.0	170	n/a	9.0	0.805694	7.23611	6.0	3.0	50.0	10.0	100.0	False	True	False	False	3.0	False	24.0	1.04265	4.0	False
+S46	50.0	0.719223	n/a	n/a	False	-0.0376729	3.0	False	2.0	n/a	1.0	150.0	0.917437	1.69231	True	True	2.0	0.733333	2.6	400.0	n/a	13.0	False	17.5	6.0	0.5	25.0	15.0	80.0	False	0.0	10.0	0.0	2.0	0.927479	40.0	1.0	10.0	0.879764	3.0	30.0	5.0	52.0	40.0	0.0	8.0	0.0	3.0	0	False	4.0	True	4.0	0.994182	1.05988	False	200.0	False	False	2.0	False	1.0705	n/a	10.0	30.0	20.0	70.0	65.0	80.0	d	20.0	75.0	1.0	False	0.0	True	4.0	False	3.0	False	2.0	0.743006	False	False	1.0	20.0	13.0	1.05708	1.0	FLEURS	9.0	2.0	50.0	0	n/a	False	0.919182	False	2.0	2.5	0.257143	1.0	VISAGES LIEUX	4.0	150.0	337	n/a	14.0	1.05706	7.20833	6.0	4.0	80.0	15.0	100.0	False	True	True	True	3.0	False	22.0	0.976954	2.0	False
+S47	50.0	0.747239	n/a	n/a	False	-0.0657451	4.0	False	4.0	n/a	2.0	n/a	0.91669	1.81818	False	True	2.0	0.533333	3.6	n/a	n/a	11.0	False	15.5	n/a	0.916667	25.0	12.0	45.0	False	0.0	6.0	0.0	2.0	0.839307	n/a	2.0	10.0	0.850945	3.0	30.0	4.0	60.0	45.0	n/a	9.0	0.0	3.0	0	False	2.0	True	2.0	0.999447	1.11427	False	n/a	False	False	3.0	False	1.1122	n/a	18.0	40.0	30.0	45.0	100.0	90.0	d	20.0	80.0	1.0	False	2.0	True	1.0	False	4.0	False	1.0	n/a	False	False	1.0	n/a	n/a	0.95479	2.66667	0	9.0	0.0	n/a	n/a	n/a	False	0.997244	False	1.0	2.5	0.290323	2.0	0	4.0	n/a	218	n/a	18.0	0.956572	7.24306	9.0	4.0	n/a	10.0	n/a	False	True	False	False	n/a	False	20.0	n/a	3.0	False
+S48	30.0	0.507131	n/a	n/a	False	-0.00956098	0.5	False	2.0	n/a	1.0	20.0	0.939729	2.77778	False	True	0.0	0.733333	4.0	500.0	n/a	9.0	False	17.0	7.0	0.833333	20.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.824241	40.0	1.0	10.0	0.930168	3.0	30.0	5.0	65.0	43.0	n/a	9.0	0.0	2.0	0	False	4.0	True	4.0	0.996104	0.752623	False	20.0	False	False	2.0	True	0.754819	n/a	10.0	20.0	15.0	30.0	50.0	70.0	d	15.0	50.0	4.0	False	0.0	True	0.0	False	4.0	False	1.0	0.917877	False	False	1.0	15.0	7.0	0.943009	2.33333	lieux	16.0	0.0	52.0	n/a	n/a	False	0.96704	False	0.0	0.0	0.470588	1.0	Visages	4.0	70.0	168	n/a	5.0	0.9394	4.09028	7.0	5.0	50.0	13.0	30.0	False	True	True	False	3.0	False	25.0	0.966885	2.0	False
+S49	30.0	0.329163	n/a	n/a	False	-0.0534856	2.0	False	3.0	n/a	1.0	7.0	0.781903	2.09091	False	False	1.0	0.8	3.0	400.0	n/a	11.0	True	17.0	10.0	0.833333	20.0	10.0	25.0	False	0.0	9.0	3.0	3.0	0.616801	30.0	2.0	9.0	0.728418	3.0	27.0	4.0	62.0	42.0	n/a	7.0	0.0	3.0	APPRENTISSAGE LECTURE / DYSLEXIE	False	4.0	True	3.0	0.999214	0.50128	False	50.0	False	False	2.0	False	0.489929	n/a	10.0	20.0	30.0	25.0	50.0	55.0	u	20.0	35.0	2.0	True	0.0	True	1.0	True	3.0	True	2.0	0.942988	False	False	1.0	8.0	10.0	0.704073	3.0	0	12.0	0.0	40.0	0	n/a	True	0.985763	True	1.0	3.5	0.352941	2.0	Visages	3.0	100.0	171	n/a	12.0	0.702978	8.17361	5.0	4.0	45.0	10.0	60.0	False	True	False	False	1.2	False	23.0	1.1189	2.0	False
+S50	60.0	0.801137	n/a	n/a	False	-0.0291117	0.5	False	4.0	n/a	1.0	50.0	0.98375	1.66667	False	True	0.5	0.666667	2.6	n/a	n/a	9.0	False	12.0	10.0	0.666667	40.0	15.0	80.0	False	0.25	10.0	0.0	3.0	0.826063	60.0	0.0	10.0	0.954638	3.0	25.0	5.0	63.0	43.0	n/a	7.0	1.0	3.0	n/a	False	4.0	True	3.0	0.991647	1.16622	False	30.0	False	False	2.0	False	1.19242	n/a	20.0	50.0	30.0	70.0	80.0	95.0	u	27.0	100.0	4.0	False	0.0	True	1.0	False	4.0	False	0.0	n/a	False	False	1.0	8.0	12.5	0.933209	3.33333	visage	6.0	0.0	70.0	n/a	n/a	False	0.946907	False	1.0	1.5	0.25	1.0	n/a	3.0	150.0	117	n/a	4.0	0.941477	6.13889	4.0	3.0	75.0	10.0	250.0	False	False	True	True	1.5	False	15.0	n/a	2.0	False
+S51	120.0	0.902599	n/a	n/a	False	-0.231889	4.0	False	3.0	n/a	2.0	10.0	0.752758	1.71429	False	True	1.0	0.833333	2.6	700.0	n/a	7.0	False	9.5	7.0	0.833333	50.0	17.0	54.0	False	0.17	10.0	0.0	2.0	0.812932	30.0	2.0	10.0	0.520869	3.0	30.0	4.0	60.0	40.0	n/a	7.0	0.0	2.0	0	False	4.0	True	3.0	0.999712	1.25798	False	200.0	False	False	2.0	False	1.34344	n/a	20.0	80.0	42.0	120.0	50.0	90.0	d	30.0	160.0	4.0	False	0.0	True	2.0	False	4.0	False	1.0	0.89375	False	False	1.0	10.0	18.0	0.90344	2.66667	0	5.0	0.0	40.0	n/a	n/a	False	0.982427	False	1.0	1.5	0.263158	2.0	0	2.0	100.0	173	n/a	16.0	0.926512	5.69444	8.0	3.0	60.0	11.0	115.0	False	True	True	False	2.0	False	12.0	1.1715	3.0	False
+S52	55.0	0.87824	n/a	n/a	False	-0.00832569	2.0	False	5.0	n/a	1.0	20.0	0.971116	1.77778	True	True	1.0	0.766667	2.6	400.0	n/a	9.0	False	12.5	7.0	0.5	35.0	10.0	75.0	False	0.0	8.0	0.0	2.0	1.0228	75.0	1.0	7.0	0.96279	3.0	25.0	4.0	61.0	42.0	n/a	9.0	0.0	1.0	0	False	4.0	True	1.0	0.997485	1.29562	False	150.0	False	False	1.0	True	1.30718	n/a	12.0	30.0	25.0	80.0	80.0	100.0	u	25.0	90.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.855701	False	False	1.0	20.0	12.0	1.16281	3.66667	0	7.0	2.0	60.0	0	n/a	False	0.976732	False	1.0	2.0	0.28	2.0	Visages	3.0	100.0	206	n/a	10.0	1.1657	4.66667	4.0	5.0	60.0	10.0	15.0	False	True	False	False	2.0	False	16.0	1.02892	2.0	False
+S53	57.0	0.721953	1.0	1.0	False	-0.038159	2.0	False	3.0	1.0	2.0	21.0	0.949123	2.0	False	True	1.0	0.6	2.0	400.0	1.0	10.0	False	15.0	7.0	0.666667	18.0	8.0	47.0	False	0.5	10.0	0.0	2.0	1.04459	500.0	2.0	9.0	0.910964	3.0	25.0	4.0	53.0	34.0	1.0	8.0	0.0	3.0	"confusion entre ""p""et""b"",""a"" et""d"",""v""et""f"" a9ans pendant3-4mois "	True	2.0	False	3.0	0.999318	1.09054	False	40.0	False	False	2.0	False	1.07456	n/a	11.0	36.0	22.0	52.0	78.0	89.0	u	13.0	61.0	2.0	False	1.0	True	6.0	True	3.0	False	1.0	0.727899	False	False	1.0	9.0	17.0	1.19193	0.0	n/a	10.0	0.0	40.0	0	n/a	False	0.92573	True	1.0	1.5	0.333333	1.0	n/a	4.0	60.0	299	n/a	12.0	1.19054	6.53472	5.0	4.0	70.0	12.0	12.0	False	True	False	False	1.8	False	20.0	1.04877	3.0	False
+S54	38.0	0.383918	n/a	0.0	False	-0.103711	1.0	False	3.0	n/a	3.0	5.0	0.86639	1.8	True	True	1.0	0.666667	2.8	300.0	n/a	10.0	False	14.0	12.0	0.75	20.0	10.0	30.0	False	0.33	8.0	1.0	3.0	0.731965	10.0	1.0	9.0	0.76268	3.0	30.0	4.0	64.0	42.0	n/a	9.0	0.0	2.0	DYSORTHOGRAPHIE DYSLEXIE	False	3.0	True	2.0	0.999901	0.594111	False	20.0	False	False	2.0	False	0.571427	n/a	10.0	25.0	15.0	25.0	50.0	60.0	u	12.0	30.0	4.0	True	0.0	True	1.0	True	4.0	True	2.0	0.799681	False	False	0.0	4.0	8.0	0.846727	3.0	noms	8.0	2.0	45.0	n/a	n/a	False	0.999775	True	1.0	1.5	0.285714	1.0	Visages	2.0	12.0	179	n/a	6.0	0.834232	4.625	12.0	2.0	40.0	10.0	50.0	False	True	True	False	1.5	False	18.0	0.95225	3.0	False
+S55	38.0	0.351037	n/a	n/a	False	-0.0959172	2.5	False	2.0	n/a	2.0	15.0	0.874188	1.4	True	True	2.0	0.53	2.6	500.0	n/a	10.0	False	12.0	8.0	0.5	19.0	9.0	25.0	False	0.0	10.0	0.0	2.0	0.670693	75.0	1.0	7.0	0.778271	3.0	28.0	4.0	75.0	45.0	n/a	5.0	1.0	3.0	0	False	3.0	True	1.0	0.997022	0.542072	False	35.0	False	False	1.0	True	0.522487	n/a	13.0	27.0	20.0	39.0	45.0	57.0	d	15.0	29.0	4.0	False	0.0	True	2.0	False	3.0	False	1.0	0.969419	False	False	1.0	10.0	13.0	0.771524	2.0	n/a	4.0	3.0	45.0	0	n/a	False	1.03044	False	2.0	3.0	0.166667	3.0	LIEUX	2.0	50.0	297	n/a	13.0	0.764399	7.08333	6.0	1.0	55.0	10.0	75.0	False	True	True	False	2.5	True	14.0	1.02732	2.0	False
+S56	50.0	0.728235	1.0	1.0	False	-0.0200327	2.5	False	3.0	1.0	2.0	13.0	0.958526	1.55556	False	False	1.0	0.7	3.0	450.0	1.0	9.0	False	11.5	9.0	0.666667	30.0	10.0	40.0	False	0.0	8.0	0.0	2.0	0.891965	35.0	2.0	9.0	0.938493	3.0	32.0	5.0	62.0	44.0	1.0	6.0	0.0	3.0	zozottement	False	3.0	True	4.0	0.996171	1.08598	False	45.0	False	False	2.0	False	1.08391	n/a	15.0	30.0	20.0	50.0	70.0	100.0	d	20.0	70.0	0.0	False	1.0	True	1.0	False	4.0	False	2.0	0.976141	True	False	1.0	13.0	14.0	1.02026	2.66667	n/a	5.0	0.0	45.0	0	n/a	False	0.966929	True	1.0	1.0	0.217391	1.0	visage	3.0	75.0	39	n/a	13.0	1.01659	6.15972	5.0	3.0	80.0	13.0	35.0	False	True	True	True	3.0	False	14.0	0.967209	2.0	False
+S57	45.0	0.543197	n/a	n/a	False	-0.036005	0.5	False	4.0	n/a	1.0	20.0	0.969654	2.0	False	False	0.0	0.766667	2.4	700.0	n/a	9.0	False	13.5	6.5	0.583333	30.0	10.0	60.0	False	0.13	10.0	0.0	1.0	0.817328	30.0	1.0	10.0	0.933649	3.0	25.0	4.0	60.0	45.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.996329	0.804771	False	50.0	False	False	2.0	False	0.8085	n/a	15.0	30.0	25.0	50.0	55.0	70.0	d	17.0	60.0	1.0	False	0.0	True	0.0	False	4.0	False	2.0	0.930744	False	False	0.0	10.0	15.0	0.927787	2.33333	0	9.0	0.0	50.0	0	n/a	False	0.98254	False	2.0	3.0	0.333333	2.0	visages, lieux	4.0	25.0	181	n/a	3.0	0.931521	7.61111	4.0	2.0	80.0	12.0	80.0	False	True	True	False	1.2	False	18.0	1.14636	3.0	False
+S58	49.0	0.482002	n/a	n/a	False	-0.0484288	1.5	False	5.0	n/a	1.0	10.0	0.95826	2.5	False	False	1.0	0.933333	4.2	450.0	n/a	8.0	False	14.0	6.0	0.833333	30.0	10.0	50.0	False	0.2	9.0	0.0	4.0	0.770706	30.0	1.0	9.0	0.909831	3.0	35.0	4.0	62.0	45.0	n/a	9.0	0.0	3.0	0	False	4.0	True	4.0	0.997881	0.72737	False	50.0	False	False	3.0	False	0.717416	n/a	15.0	30.0	20.0	50.0	55.0	65.0	u	20.0	50.0	4.0	False	0.0	True	4.0	False	5.0	False	1.0	0.950643	False	True	1.0	14.0	12.0	0.880939	4.33333	0	12.0	0.0	50.0	0	n/a	False	1.01537	False	1.0	3.0	0.428571	2.0	0	4.0	30.0	201	n/a	9.0	0.878385	8.06944	4.0	4.0	100.0	10.0	40.0	False	True	True	False	2.5	False	20.0	1.01326	3.0	False
+S59	80.0	1.21402	n/a	n/a	False	-0.0345568	0.0	False	3.0	n/a	1.0	15.0	0.975677	1.46154	False	False	1.0	0.6	4.2	400.0	n/a	13.0	False	16.0	8.0	0.583333	40.0	10.0	100.0	False	0.0	8.0	0.0	4.0	1.14589	100.0	0.0	8.0	0.94112	3.0	35.0	5.0	60.0	40.0	n/a	4.0	0.0	3.0	0	False	4.0	True	3.0	0.992796	1.81669	False	50.0	False	False	3.0	False	1.80695	n/a	15.0	40.0	30.0	100.0	100.0	150.0	d	20.0	100.0	3.0	False	0.0	True	1.0	False	3.0	False	1.0	0.748922	False	False	0.0	20.0	100.0	1.30699	3.5	0	6.0	0.0	50.0	0	n/a	False	0.956471	False	1.0	3.0	0.1875	3.0	0	3.5	60.0	203	n/a	2.0	1.30599	7.65278	n/a	4.0	100.0	15.0	30.0	False	True	True	False	5.0	False	19.0	0.806267	2.0	False
+S60	45.0	0.705189	1.0	1.0	False	-0.0101898	0.0	False	3.0	1.0	1.0	7.0	0.974984	1.55556	False	False	0.0	0.933333	3.6	380.0	1.0	9.0	False	11.5	8.5	0.416667	30.0	12.0	55.0	False	0.333333	10.0	0.0	3.0	0.828774	10.0	0.0	9.0	0.964794	3.0	30.0	5.0	64.0	45.0	1.0	10.0	0.0	2.0	n/a	False	4.0	True	2.0	0.996178	1.04281	False	20.0	False	n/a	3.0	False	1.04961	n/a	15.0	35.0	30.0	50.0	75.0	95.0	d	25.0	75.0	3.0	False	0.0	True	1.0	False	5.0	False	2.0	0.680142	False	False	0.0	12.0	12.0	0.939798	3.0	lieux	5.0	0.0	50.0	0	n/a	False	0.970066	False	0.0	2.5	0.217391	1.0	visage	3.0	40.0	132	n/a	0.0	0.944566	5.28472	7.0	5.0	50.0	12.0	10.0	False	True	True	False	7.0	False	14.0	0.738193	2.0	False
+S61	30.0	0.420859	n/a	n/a	False	-0.0346947	1.0	False	3.0	n/a	3.0	17.5	0.946556	2.45455	False	False	2.0	0.5	3.4	400.0	n/a	11.0	False	19.0	8.0	0.833333	20.0	10.0	30.0	False	0.666667	9.0	0.0	2.0	0.753665	100.0	1.0	8.0	0.911862	3.0	29.0	3.0	60.0	45.0	n/a	1.0	1.0	3.0	0	False	2.0	True	3.0	0.994075	0.603338	False	40.0	False	False	1.0	False	0.626411	n/a	10.0	20.0	15.0	30.0	40.0	50.0	u	15.0	60.0	1.0	False	0.0	True	3.0	False	3.0	False	0.0	0.853415	False	False	1.0	10.0	15.0	0.854203	2.5	physionomie	16.0	0.0	52.0	n/a	n/a	False	1.044	False	2.0	4.0	0.421053	2.0	lieux	3.0	25.0	199	n/a	10.0	0.858964	8.25694	9.0	3.0	50.0	11.0	200.0	False	True	False	False	2.0	False	27.0	1.02602	3.0	False
+S62	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	367	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S63	40.0	0.710036	1.0	1.0	False	-0.00756445	1.0	False	4.0	1.0	3.0	5.0	0.952403	1.85714	False	True	0.0	0.833333	2.4	1000.0	1.0	7.0	False	10.0	8.0	0.583333	25.0	10.0	60.0	False	0.0	10.0	0.0	2.0	0.912774	800.0	1.0	10.0	0.944839	3.0	25.0	3.5	60.0	42.0	1.0	9.0	0.0	2.0	0	False	2.0	True	1.0	0.997006	1.03295	False	400.0	False	True	2.0	False	1.05682	n/a	15.0	25.0	20.0	70.0	60.0	80.0	u	20.0	80.0	4.0	False	0.0	True	3.0	False	4.0	False	2.0	0.235942	False	False	0.0	4.0	8.0	1.03665	3.0	LIEUX	6.0	0.0	50.0	0	n/a	False	0.996833	False	0.0	1.0	0.3	1.0	VISAGES	2.0	200.0	325	n/a	4.0	1.0403	3.75694	11.0	1.0	60.0	14.0	500.0	False	True	True	False	500.0	False	13.0	0.782561	2.0	False
+S64	40.0	0.526118	n/a	n/a	False	-0.000933116	4.0	False	4.0	n/a	1.0	50.0	0.971842	1.7	False	False	2.0	0.4	2.4	600.0	0.0	10.0	False	13.5	7.0	0.75	20.0	10.0	40.0	False	0.75	8.0	1.0	3.0	0.88201	30.0	2.0	7.0	0.970909	3.0	36.0	5.0	60.0	40.0	n/a	4.0	0.0	3.0	0	False	2.0	True	1.0	0.991759	0.772525	False	10.0	False	False	1.0	False	0.783079	n/a	10.0	25.0	15.0	50.0	50.0	60.0	u	15.0	60.0	1.0	True	0.0	True	3.0	False	4.0	False	2.0	0.726744	False	False	1.0	3.0	20.0	1.00469	0.0	ESPECES ANIMALES	7.0	0.0	45.0	0	n/a	True	0.958743	False	1.0	2.5	0.259259	2.0	VISAGE	1.0	40.0	296	n/a	18.0	1.00524	7.35417	5.0	1.0	50.0	12.0	20.0	False	True	False	False	2.0	False	17.0	0.975383	3.0	False
+S65	30.0	0.422172	1.0	1.0	False	-0.0217241	3.5	False	3.0	1.0	1.0	10.0	0.956833	1.58333	False	False	2.0	0.866667	3.8	500.0	1.0	12.0	False	15.5	7.0	0.916667	20.0	10.0	40.0	False	0.0	10.0	0.0	4.0	0.757414	40.0	2.0	10.0	0.935109	3.0	30.0	5.0	64.0	43.0	1.0	8.0	0.0	3.0	0	False	3.0	True	4.0	0.996173	0.615527	False	60.0	False	False	3.0	False	0.628364	n/a	10.0	30.0	20.0	30.0	40.0	60.0	d	15.0	50.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.939091	False	False	1.0	30.0	12.0	0.853278	2.0	n/a	7.0	0.0	55.0	0	n/a	False	0.964347	False	1.0	2.5	0.225806	2.0	n/a	4.0	70.0	327	n/a	17.0	0.863237	8.05556	4.0	4.0	80.0	10.0	40.0	False	True	False	False	3.0	False	19.0	0.994149	3.0	False
+S66	50.0	0.602292	n/a	n/a	False	-0.0338606	1.5	False	4.0	n/a	1.0	20.0	0.934056	2.11111	False	False	0.0	0.6	3.2	400.0	n/a	9.0	False	14.0	9.0	0.5	15.0	7.0	35.0	False	0.0	10.0	0.0	3.0	1.0295	25.0	1.0	9.0	0.900195	3.0	25.0	3.0	65.0	42.0	n/a	8.0	0.0	3.0	0	False	4.0	True	3.0	0.992627	0.904788	False	35.0	False	False	3.0	False	0.896458	n/a	10.0	30.0	15.0	50.0	50.0	80.0	u	10.0	50.0	4.0	False	0.0	True	1.0	False	5.0	False	2.0	0.894138	False	False	1.0	20.0	15.0	1.17632	0.0	n/a	10.0	0.0	50.0	0	n/a	False	1.03726	False	2.0	3.0	0.357143	1.0	VISAGES LIEUX	3.0	90.0	344	n/a	7.0	1.17333	7.27083	5.0	3.0	80.0	12.0	20.0	False	True	False	True	2.0	False	19.0	0.942724	2.0	False
+S67	50.0	0.760311	n/a	n/a	False	-0.090743	0.5	False	3.0	n/a	2.0	40.0	0.957141	1.75	False	False	0.0	0.7	2.8	300.0	n/a	8.0	False	11.0	7.0	0.166667	40.0	12.0	60.0	False	0.25	8.0	0.0	2.0	0.895573	50.0	0.0	10.0	0.866398	3.0	32.0	5.0	65.0	44.0	2.0	5.0	0.0	3.0	0	False	3.0	True	2.0	0.996432	1.11832	False	150.0	False	False	2.0	True	1.13165	n/a	15.0	45.0	25.0	100.0	70.0	90.0	u	25.0	80.0	3.0	False	0.0	True	2.0	False	5.0	False	1.0	0.719087	False	False	1.0	3.0	12.0	1.01291	2.33333	0	6.0	0.0	58.0	Chiffres, voyelles en couleur	n/a	False	0.975137	False	2.0	3.0	0.272727	1.0	Lieux, itineraires	2.0	40.0	215	n/a	3.0	1.0207	6.49306	5.0	2.0	80.0	10.0	8.0	False	True	True	False	2.0	False	14.0	1.00441	1.0	False
+S68	42.0	0.518261	n/a	n/a	False	-0.0204349	2.0	False	5.0	n/a	1.0	5.0	0.936235	1.88889	False	False	1.0	1.0	4.2	700.0	n/a	9.0	False	13.0	7.0	0.833333	27.0	12.0	47.0	False	0.666667	10.0	1.0	3.0	0.753259	15.0	1.0	10.0	0.9158	3.0	33.0	4.0	63.0	43.0	n/a	10.0	0.0	3.0	n/a	False	4.0	True	5.0	0.999161	0.76944	False	50.0	True	False	3.0	False	0.771384	n/a	12.0	27.0	23.0	32.0	58.0	72.0	u	21.0	59.0	1.0	True	0.0	True	0.0	False	5.0	False	1.0	0.850019	False	False	1.0	8.0	15.0	0.858164	4.0	n/a	8.0	0.0	40.0	0	n/a	False	1.00769	False	2.0	4.0	0.307692	1.0	lieux	5.0	50.0	94	n/a	10.0	0.858501	8.90278	6.0	5.0	60.0	16.0	130.0	False	True	False	False	3.0	False	17.0	1.08324	3.0	False
+S69	40.0	0.320483	n/a	n/a	False	-0.0823311	4.0	False	4.0	n/a	1.0	32.5	0.906829	2.14286	False	False	2.0	0.7	4.2	450.0	n/a	7.0	False	11.0	9.0	0.5	20.0	13.0	40.0	False	0.0	7.0	0.0	4.0	0.601481	150.0	2.0	9.0	0.824498	3.0	25.0	5.0	60.0	48.0	n/a	8.0	0.0	3.0	n/a	False	4.0	True	4.0	0.989427	0.488542	False	2500.0	False	False	2.0	True	0.47701	n/a	12.0	30.0	25.0	40.0	45.0	50.0	u	20.0	35.0	4.0	False	0.0	True	3.0	False	5.0	False	2.0	0.554342	False	False	1.0	8.0	15.0	0.68626	4.0	n/a	8.0	0.0	35.0	couleur, mot, espace	n/a	False	0.95227	False	2.0	4.0	0.363636	2.0	visage, lieux	4.0	200.0	105	n/a	18.0	0.685518	9.0	4.0	5.0	70.0	13.0	10.0	False	True	False	False	2.5	False	15.0	1.11756	3.0	False
+S70	350.0	4.37116	n/a	n/a	False	-0.082708	3.0	False	4.0	n/a	1.0	15.0	0.923019	1.5	False	False	2.0	0.766667	3.8	500.0	n/a	10.0	False	12.5	9.0	0.833333	50.0	15.0	250.0	False	0.0	8.0	0.0	3.0	1.71511	20.0	2.0	10.0	0.840311	3.0	25.0	4.0	68.0	43.0	n/a	8.0	0.0	3.0	0	False	4.0	True	4.0	0.995391	6.66643	False	200.0	False	False	3.0	False	6.50608	n/a	15.0	100.0	30.0	200.0	400.0	500.0	d	20.0	300.0	2.0	False	0.0	True	1.0	False	5.0	False	2.0	0.832625	False	False	1.0	5.0	6.0	1.97093	3.33333	0	5.0	0.0	40.0	Odeurs, images : souvenirs	n/a	False	0.998561	False	0.0	3.0	0.2	1.0	Visages	5.0	70.0	187	n/a	16.0	1.95474	8.42361	5.0	5.0	40.0	14.0	50.0	False	True	True	False	3.0	False	15.0	1.0604	4.0	False
+S71	20.0	0.212617	n/a	n/a	False	-0.0629865	1.5	False	n/a	n/a	2.0	15.0	0.830302	1.55556	False	False	0.0	0.5	3.2	400.0	n/a	9.0	False	11.5	8.0	0.5	15.0	10.0	20.0	False	0.0	6.0	0.0	n/a	0.516706	40.0	1.0	7.0	0.767316	3.0	25.0	4.0	60.0	42.0	n/a	3.0	0.0	3.0	0	False	3.0	True	2.0	0.997657	0.325419	False	50.0	False	False	2.0	False	0.316461	n/a	10.0	20.0	15.0	20.0	30.0	40.0	u	10.0	20.0	1.0	False	0.0	True	2.0	False	n/a	False	2.0	0.968262	False	False	1.0	15.0	10.0	0.594702	n/a	0	5.0	0.0	50.0	n/a	n/a	False	0.97386	False	1.0	3.0	0.217391	1.0	Visages	2.0	60.0	159	n/a	7.0	0.588898	7.16667	6.0	3.0	100.0	10.0	100.0	False	True	True	False	2.0	False	14.0	1.05611	3.0	False
+S72	60.0	0.715723	1.0	1.0	False	-0.0551373	2.0	False	2.0	2.0	1.0	40.0	0.958627	1.33333	True	True	1.0	0.433333	3.6	450.0	1.0	15.0	False	17.5	7.0	0.416667	30.0	10.0	70.0	False	0.0	8.0	2.0	0.0	1.0041	30.0	1.0	7.0	0.90349	3.0	21.0	3.0	67.0	45.0	1.0	5.0	0.0	3.0	pbl de deglutition a9ans(suce son pouce)	False	3.0	True	3.0	0.98314	1.07017	False	50.0	False	False	1.0	False	1.06529	n/a	10.0	30.0	20.0	70.0	70.0	80.0	d	20.0	70.0	2.0	True	0.0	True	2.0	False	3.0	False	0.0	0.830847	False	False	1.0	20.0	14.0	1.14585	0.0	PHYSIONOMIE	5.0	3.0	60.0	0	n/a	True	1.03714	True	1.0	2.5	0.142857	1.0	ESPECES VEGETALES	3.0	110.0	329	n/a	10.0	1.14439	6.59722	8.0	2.0	50.0	10.0	20.0	False	True	True	False	5.0	True	20.0	0.841678	2.0	True
+S73	40.0	0.927448	1.0	1.0	False	-0.00182578	n/a	False	n/a	1.0	3.0	15.0	0.979823	1.90476	False	False	n/a	0.766667	3.2	300.0	0.0	8.4	False	12.2	8.0	0.916667	25.0	10.0	50.0	False	0.0	9.0	0.0	n/a	1.07666	60.0	n/a	10.0	0.977997	3.0	30.0	3.0	63.0	41.0	1.0	10.0	0.0	3.0	n/a	n/a	2.0	True	4.0	0.994564	1.36044	False	70.0	False	False	n/a	False	1.38042	n/a	10.0	30.0	20.0	70.0	80.0	110.0	n/a	18.0	90.0	4.0	False	0.0	True	3.0	False	n/a	False	2.0	0.979557	False	False	n/a	15.0	13.0	1.22344	n/a	espece animal	7.6	0.0	60.0	n/a	n/a	False	1.0435	False	1.0	1.0	0.311475	1.0	visage	3.0	60.0	365	n/a	n/a	1.22709	n/a	7.0	3.0	50.0	10.0	55.0	False	True	n/a	False	3.0	False	16.0	0.929294	3.0	False
+S74	30.0	0.382676	n/a	n/a	False	-0.0665277	2.5	True	4.0	n/a	1.0	40.0	0.920906	1.66667	False	True	1.0	0.8	3.6	250.0	n/a	9.0	False	12.0	12.0	0.833333	30.0	12.0	50.0	False	0.2	9.0	1.0	3.0	0.637316	20.0	1.0	10.0	0.854378	3.0	32.0	4.0	62.0	42.0	n/a	7.0	0.0	3.0	EXPRESSION ORALE	False	4.0	True	5.0	0.999392	0.562843	False	30.0	False	False	3.0	False	0.569578	n/a	15.0	30.0	20.0	50.0	50.0	50.0	U?	25.0	50.0	1.0	True	2.0	True	3.0	True	4.0	False	2.0	0.859567	False	False	1.0	10.0	10.0	0.720039	3.5	lieux	6.0	2.0	60.0	0	n/a	False	0.999466	True	0.0	0.0	0.25	1.0	Visages	5.0	50.0	207	n/a	11.0	0.726359	5.81944	4.0	5.0	50.0	10.0	30.0	False	True	True	False	2.5	True	15.0	0.819761	3.0	False
+S75	90.0	1.17674	1.0	1.0	False	-0.0322937	1.5	False	4.0	1.0	1.0	30.0	0.962962	1.5	False	False	1.0	0.966667	5.0	300.0	1.0	10.0	False	12.5	7.0	0.666667	40.0	10.0	80.0	False	0.2	10.0	0.0	5.0	1.06134	15.0	1.0	10.0	0.930669	3.0	32.0	4.0	64.0	43.0	1.0	10.0	0.0	3.0	0	False	3.0	True	3.0	0.999677	1.77613	False	40.0	False	False	3.0	False	1.75148	n/a	20.0	50.0	30.0	80.0	120.0	150.0	u	25.0	100.0	3.0	False	0.0	True	1.0	False	5.0	False	2.0	0.843897	False	False	0.0	7.0	10.0	1.2123	4.0	n/a	5.0	0.0	60.0	0	n/a	False	1.00794	False	2.0	3.5	0.2	2.0	LIEUX VISAGES	3.0	60.0	319	n/a	7.0	1.20962	7.72222	6.0	5.0	70.0	13.0	120.0	False	True	True	True	3.0	False	15.0	0.945441	2.0	False
+S76	50.0	0.611865	n/a	n/a	False	-0.0818007	3.5	False	4.0	n/a	1.0	20.0	0.921049	1.66667	False	False	2.0	0.833333	2.6	500.0	n/a	9.0	False	12.0	7.0	0.25	30.0	10.0	35.0	False	0.0	10.0	0.0	3.0	0.873615	150.0	2.0	10.0	0.839249	3.0	33.0	4.0	66.0	45.0	n/a	6.0	0.0	3.0	0	False	4.0	True	3.0	0.999685	0.93416	False	200.0	False	False	2.0	True	0.910705	n/a	10.0	30.0	20.0	40.0	70.0	90.0	u	20.0	50.0	3.0	False	0.0	True	3.0	False	3.0	False	2.0	0.825401	False	True	1.0	50.0	15.0	1.0031	3.33333	0	6.0	0.0	30.0	Couleur /  mots	n/a	False	1.02149	False	2.0	3.5	0.25	2.0	visages, lieux	3.0	200.0	172	n/a	17.0	0.995672	7.95833	6.0	3.0	65.0	10.0	100.0	False	True	False	False	2.0	False	15.0	1.09488	3.0	False
+S77	45.0	0.689299	n/a	n/a	False	-0.0180914	1.0	False	5.0	n/a	2.0	10.0	0.968552	2.11111	True	True	0.0	0.966667	3.0	500.0	n/a	9.0	False	14.0	11.0	0.416667	20.0	10.0	40.0	False	0.0	9.0	0.0	4.0	0.981267	15.0	1.0	10.0	0.950461	3.0	30.0	4.0	64.0	43.0	n/a	10.0	n/a	3.0	n/a	False	4.0	True	3.0	0.999983	1.01458	False	500.0	False	False	3.0	False	1.02596	n/a	10.0	30.0	20.0	70.0	60.0	80.0	u	15.0	70.0	2.0	False	1.0	True	3.0	False	5.0	False	2.0	0.671399	False	False	1.0	7.0	10.0	1.11533	4.33333	lieux	10.0	3.0	60.0	0	n/a	False	1.00274	False	1.0	0.0	0.357143	2.0	visage	4.0	700.0	115	n/a	6.0	1.11837	4.65972	5.0	4.0	80.0	15.0	20.0	False	True	True	False	2.0	True	19.0	1.19359	2.0	False
+S78	40.0	0.548867	n/a	n/a	False	-0.0193456	3.0	False	4.0	n/a	2.0	25.0	0.950317	1.7	False	False	2.0	0.7	3.8	600.0	n/a	10.0	False	13.5	15.0	0.916667	40.0	10.0	50.0	False	0.0	10.0	0.0	3.0	0.813199	60.0	1.0	10.0	0.930971	3.0	30.0	4.0	62.0	41.0	1.0	7.0	2.0	3.0	0	False	2.0	True	3.0	0.999859	0.815318	False	45.0	False	False	2.0	False	0.816939	n/a	15.0	30.0	20.0	60.0	60.0	70.0	u	20.0	60.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.905331	False	False	1.0	10.0	15.0	0.925195	2.0	n/a	7.0	0.0	50.0	0	n/a	False	0.991229	False	1.0	2.5	0.259259	1.0	VISAGES	1.0	30.0	328	n/a	14.0	0.926816	6.74306	9.0	2.0	60.0	16.0	112.0	False	True	False	False	3.0	False	17.0	0.960007	n/a	False
+S79	42.0	0.449873	n/a	0.0	False	0.00970278	4.0	False	3.0	n/a	1.0	12.0	0.905277	1.4	False	False	2.0	0.566667	1.8	350.0	n/a	10.0	False	12.0	7.0	0.666667	28.0	16.0	30.0	False	0.25	9.0	0.0	1.0	0.603731	30.0	2.0	9.0	0.914979	3.0	27.0	4.0	68.0	45.0	n/a	6.0	0.0	3.0	0	False	4.0	True	1.0	0.997786	0.671734	False	100.0	False	True	1.0	False	0.669595	n/a	18.0	28.0	21.0	50.0	59.0	63.0	d	22.0	55.0	3.0	False	0.0	True	3.0	True	5.0	False	1.0	0.917245	False	False	1.0	20.0	12.0	0.693804	1.66667	Mettre nom sur un visage	4.0	0.0	70.0	Chiffres ds espace	n/a	False	1.01049	False	1.0	3.5	0.166667	3.0	tout	1.0	80.0	198	n/a	18.0	0.688082	8.22222	4.0	1.0	50.0	12.0	100.0	False	False	False	False	2.0	False	14.0	1.03246	3.0	False
+S80	48.0	0.634019	n/a	n/a	False	-0.00810036	2.5	False	4.0	n/a	2.0	30.0	0.948885	1.5	False	True	1.0	0.733333	2.6	530.0	n/a	10.0	False	12.5	13.0	0.5	31.0	8.0	41.0	False	0.0	7.0	0.0	2.0	0.91701	250.0	0.0	10.0	0.940784	3.0	25.0	4.0	62.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	3.0	0.996996	0.946805	False	20.0	False	False	2.0	False	0.94368	n/a	12.0	35.0	34.0	56.0	68.0	86.0	u	19.0	63.0	2.0	False	1.0	True	1.0	False	5.0	False	2.0	0.798489	False	False	1.0	6.0	18.0	1.03757	3.33333	0	5.0	0.0	62.0	0	n/a	False	0.982516	False	2.0	2.0	0.2	1.0	visages, lieux	3.0	45.0	180	n/a	9.0	1.04513	6.375	8.0	3.0	75.0	10.0	50.0	False	True	True	False	3.0	False	15.0	0.987773	3.0	False
+S81	n/a	n/a	n/a	n/a	False	n/a	n/a	False	n/a	n/a	n/a	15.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	500.0	n/a	n/a	False	n/a	8.0	n/a	n/a	n/a	n/a	False	n/a	n/a	n/a	n/a	n/a	40.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	False	40.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	1.0	0.979913	False	n/a	n/a	10.0	15.0	n/a	n/a	n/a	n/a	n/a	55.0	n/a	n/a	n/a	n/a	n/a	1.0	n/a	n/a	n/a	n/a	n/a	50.0	217	n/a	n/a	n/a	n/a	n/a	n/a	90.0	n/a	60.0	n/a	n/a	n/a	n/a	3.0	n/a	n/a	1.00486	n/a	False
+S82	55.0	0.611077	n/a	n/a	False	-0.0475203	3.5	False	3.0	n/a	1.0	15.0	0.963192	2.0	True	True	1.0	0.85	2.4	1000.0	n/a	10.0	False	15.0	8.0	0.583333	25.0	11.0	45.0	False	0.33	10.0	0.0	3.0	0.877608	15.0	2.0	10.0	0.915672	3.0	30.0	3.0	66.0	43.0	n/a	7.0	0.0	1.0	0	False	3.0	True	3.0	0.994773	0.92664	False	25.0	False	False	2.0	False	0.909533	n/a	12.0	30.0	20.0	50.0	68.0	80.0	u	17.0	55.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.745845	False	False	1.0	8.0	12.0	1.0073	3.0	lieux	10.0	2.0	50.0	0	n/a	False	1.05757	False	0.0	2.0	0.333333	2.0	Visages	3.0	20.0	195	n/a	15.0	1.00022	5.27778	4.0	4.0	50.0	13.0	6.0	False	True	False	False	2.0	False	20.0	1.00696	2.0	False
+S83	55.0	0.752174	n/a	n/a	False	-0.0102645	3.0	False	3.0	n/a	1.0	7.0	0.983114	1.25	False	True	1.0	1.0	3.4	600.0	n/a	12.0	False	13.5	8.0	0.75	30.0	10.0	60.0	False	0.0	10.0	0.0	3.0	0.946814	15.0	2.0	10.0	0.972849	3.0	35.0	3.0	65.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.992436	1.10866	False	100.0	False	False	3.0	False	1.11954	n/a	13.0	40.0	25.0	75.0	70.0	90.0	d	25.0	80.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.777745	False	False	1.0	8.0	12.0	1.07195	2.66667	lieux	3.0	0.0	50.0	0	n/a	False	1.06735	False	0.0	1.0	0.111111	3.0	Visages	4.0	30.0	184	n/a	14.0	1.0791	6.4375	5.0	4.0	60.0	11.0	50.0	False	True	False	False	8.0	False	15.0	0.904109	3.0	False
+S84	70.0	0.918418	n/a	n/a	False	0.053325	1.0	False	4.0	n/a	1.0	7.0	0.888236	1.54545	False	False	1.0	0.8	3.4	450.0	n/a	11.0	False	14.0	11.0	0.75	40.0	12.0	75.0	False	0.25	10.0	0.0	3.0	1.15816	80.0	1.0	10.0	0.941561	3.0	33.0	5.0	63.0	42.0	n/a	9.0	0.0	3.0	0	False	4.0	True	3.0	0.995588	1.37754	False	70.0	False	False	3.0	False	1.36698	n/a	5.0	40.0	35.0	70.0	95.0	110.0	d	20.0	85.0	4.0	False	0.0	True	0.0	False	4.0	False	2.0	0.818761	False	False	1.0	5.0	14.0	1.3146	3.33333	0	6.0	0.0	45.0	0	n/a	False	0.966338	False	2.0	2.5	0.214286	2.0	visages, lieux	3.0	25.0	193	n/a	8.0	1.31998	7.10417	5.0	3.0	60.0	10.0	9.0	False	True	True	False	1.5	False	17.0	1.05465	2.0	False
+S85	25.0	0.278677	n/a	n/a	False	-0.022051	3.0	False	4.0	n/a	2.0	20.0	0.94183	2.0	False	False	1.0	0.6	4.0	500.0	0.0	9.0	False	13.5	9.0	0.833333	25.0	10.0	30.0	False	0.33	8.0	0.0	2.0	0.562786	60.0	2.0	9.0	0.919779	3.0	30.0	5.0	60.0	40.0	n/a	9.0	0.0	3.0	0	False	3.0	True	3.0	0.995747	0.400515	False	80.0	False	False	2.0	False	0.414786	n/a	15.0	25.0	20.0	30.0	35.0	40.0	u	15.0	45.0	4.0	False	0.0	True	3.0	False	4.0	False	2.0	0.962147	False	False	1.0	10.0	12.0	0.631332	3.0	animaux	9.0	0.0	65.0	0	n/a	False	0.944038	False	1.0	n/a	0.333333	2.0	Visages	3.0	50.0	169	n/a	14.0	0.641416	5.56944	6.0	4.0	60.0	13.0	30.0	False	True	n/a	False	2.0	False	18.0	1.04717	4.0	False
+S86	40.0	0.780401	1.0	1.0	False	-0.0458562	1.5	False	4.0	1.0	1.0	50.0	0.878883	1.75	False	True	0.0	0.5	3.4	750.0	1.0	10.0	False	13.75	9.0	0.416667	40.0	12.0	90.0	True	0.0	9.0	3.0	3.0	0.993049	90.0	2.0	10.0	0.833027	3.0	32.0	4.0	63.0	45.0	1.0	6.0	0.0	3.0	dyslexie, confusion che/jeu, f/v	False	4.0	True	4.0	0.999649	1.14484	True	30.0	False	False	2.0	False	1.16156	n/a	10.0	20.0	28.0	70.0	65.0	90.0	u	15.0	80.0	4.0	True	0.0	True	2.0	True	4.0	True	1.0	0.89167	False	False	1.0	12.0	11.0	1.13006	0.0	n/a	7.5	0.0	60.0	0	n/a	False	1.01092	True	1.0	1.5	0.272727	1.0	n/a	2.0	80.0	346	n/a	9.0	1.13179	6.26389	6.0	4.0	70.0	13.0	30.0	False	True	n/a	True	2.5	False	17.5	1.04276	3.0	False
+S87	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	369	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S88	45.0	0.525943	n/a	0.0	False	-0.0442923	0.0	False	4.0	n/a	1.0	35.0	0.958444	1.875	False	False	0.0	0.6	3.6	500.0	n/a	8.0	True	11.5	10.0	0.75	25.0	10.0	40.0	False	0.5	8.0	2.0	2.0	0.825556	60.0	1.0	10.0	0.914152	3.0	32.0	5.0	60.0	42.0	n/a	8.0	0.0	3.0	APPRENTISSAGE LECTURE, math	False	3.0	True	3.0	0.995658	0.77895	False	100.0	True	True	1.0	False	0.782818	n/a	15.0	35.0	20.0	60.0	60.0	60.0	d	15.0	60.0	3.0	True	1.0	True	0.0	False	3.0	False	2.0	0.860257	False	True	0.0	20.0	15.0	0.93793	2.33333	noms	7.0	0.0	60.0	nombre / espace, gouts et sons	n/a	False	0.955381	True	2.0	3.0	0.304348	1.0	visages, lieux	1.0	150.0	178	n/a	2.0	0.940899	7.375	4.0	1.0	80.0	10.0	400.0	False	True	True	False	3.0	False	15.0	1.05283	2.0	False
+S89	70.0	1.09973	n/a	n/a	False	-0.0425949	1.0	False	5.0	n/a	3.0	40.0	0.927972	2.55556	False	True	0.0	0.666667	3.0	200.0	n/a	9.0	False	16.0	20.0	0.583333	40.0	11.0	60.0	False	0.0	10.0	0.0	3.0	1.01285	150.0	0.0	10.0	0.885377	3.0	24.0	3.0	62.0	38.0	n/a	7.0	0.0	2.0	0	True	4.0	True	3.0	0.992471	1.64304	False	40.0	False	False	3.0	False	1.63685	n/a	15.0	30.0	25.0	50.0	100.0	150.0	d	25.0	100.0	2.0	False	0.0	True	1.0	False	5.0	False	2.0	0.00909366	False	True	0.0	15.0	10.0	1.16136	4.0	LIEUX	14.0	0.0	5.0	0	n/a	False	1.01316	False	0.0	0.0	0.4375	1.0	VISAGE	5.0	1000.0	289	n/a	2.0	1.15436	3.38194	9.0	3.0	50.0	18.0	300.0	False	True	True	False	1000.0	False	23.0	0.126279	2.0	False
+S90	80.0	1.19538	n/a	n/a	False	-0.00869327	3.0	False	2.0	n/a	3.0	15.0	0.894658	1.71429	False	False	2.0	0.7	2.8	400.0	n/a	7.0	False	9.5	7.0	0.666667	60.0	10.0	40.0	False	0.33	10.0	1.0	1.0	1.11558	20.0	2.0	10.0	0.885965	3.0	22.0	5.0	62.0	45.0	n/a	7.0	0.0	3.0	en histoire	False	4.0	True	2.0	0.983444	1.75143	False	30.0	True	False	1.0	False	1.77922	n/a	15.0	60.0	30.0	80.0	100.0	150.0	d	15.0	130.0	2.0	True	1.0	True	0.0	False	4.0	False	1.0	0.905508	False	False	1.0	8.0	10.0	1.26396	1.66667	lieux	5.0	0.0	60.0	n/a	n/a	False	0.939536	False	0.0	1.5	0.263158	1.0	0	3.0	20.0	213	n/a	16.0	1.27144	5.84722	10.0	2.0	80.0	10.0	30.0	False	True	True	False	2.5	False	12.0	0.953995	2.0	False
+S91	50.0	0.611427	1.0	1.0	False	-0.00681096	3.5	False	3.0	1.0	2.0	20.0	0.966239	1.6	False	False	2.0	0.9	4.2	500.0	1.0	10.0	False	13.0	7.0	0.833333	20.0	10.0	50.0	False	0.0	10.0	0.0	3.0	0.920033	40.0	2.0	10.0	0.959428	3.0	23.0	5.0	61.0	43.0	1.0	10.0	0.0	3.0	deglutition a 15ans pendant 4mois	False	4.0	True	4.0	0.987777	0.899697	False	120.0	False	False	2.0	False	0.910054	n/a	10.0	30.0	20.0	50.0	60.0	70.0	u	20.0	70.0	2.0	False	0.0	True	1.0	False	4.0	False	1.0	n/a	False	False	1.0	15.0	10.0	1.04483	2.0	n/a	6.0	0.0	60.0	0	n/a	False	0.934578	True	2.0	3.0	0.230769	2.0	LIEUX	4.0	60.0	334	n/a	17.0	1.04858	8.25694	7.0	3.0	n/a	10.0	140.0	False	True	False	True	2.0	False	16.0	n/a	4.0	False
+S92	38.0	0.475912	n/a	n/a	False	-0.0850252	1.5	False	5.0	n/a	1.0	6.0	0.785702	1.44444	False	True	1.0	0.8	3.2	500.0	n/a	9.0	False	11.0	9.0	0.75	25.0	12.0	29.0	False	0.0	10.0	0.0	3.0	0.696258	120.0	1.0	9.0	0.700677	3.0	32.0	5.0	65.0	43.0	n/a	8.0	2.0	3.0	0	False	4.0	True	4.0	0.99624	0.744678	False	200.0	False	False	3.0	False	0.708352	n/a	15.0	25.0	24.0	44.0	66.0	77.0	u	12.0	29.0	4.0	False	0.0	True	n/a	False	5.0	False	1.0	0.815552	False	True	1.0	15.0	12.0	0.810939	3.66667	0	4.0	0.0	52.0	Suites ds espaCe et couleurs	n/a	False	0.972245	False	1.0	2.0	0.181818	2.0	0	5.0	20.0	196	n/a	9.0	0.793536	6.95833	6.0	5.0	80.0	10.0	50.0	False	True	False	False	3.0	False	13.0	1.04952	3.0	False
+S93	50.0	0.717351	n/a	0.0	False	-0.0384674	3.5	False	3.0	n/a	1.0	20.0	0.959921	2.08333	False	False	1.0	1.0	4.8	280.0	n/a	12.0	False	18.5	8.0	0.916667	40.0	12.0	70.0	False	0.0	10.0	0.0	3.0	0.856346	30.0	1.0	10.0	0.921454	3.0	35.0	4.0	60.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.997048	1.0501	False	30.0	False	False	3.0	False	1.06771	n/a	15.0	40.0	30.0	50.0	60.0	100.0	d	20.0	80.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.907422	False	False	1.0	10.0	12.0	0.967146	3.0	NOMS	13.0	0.0	50.0	NUM DE TELEPHONE 	n/a	False	1.00396	False	2.0	3.0	0.351351	1.0	LIEUX CHIFFRES	4.0	120.0	343	n/a	13.0	0.975992	8.03472	5.0	5.0	50.0	12.0	100.0	False	True	n/a	False	2.0	False	25.0	0.980029	2.0	False
+S94	53.0	0.913518	n/a	n/a	False	-0.0127455	4.0	False	3.0	n/a	1.0	17.0	0.992355	2.1	False	False	1.0	0.9	4.7	250.0	n/a	10.0	False	15.5	6.5	1.0	30.0	10.0	60.0	False	0.0	9.0	0.0	3.0	1.03356	50.0	2.0	10.0	0.97961	3.0	36.0	5.0	64.0	43.0	n/a	9.0	0.0	3.0	0	False	1.0	True	5.0	0.994238	1.35131	False	65.0	False	False	3.0	False	1.35969	n/a	13.0	38.0	25.0	60.0	90.0	110.0	d	18.0	90.0	4.0	False	0.0	True	5.0	False	4.0	False	1.0	0.937513	False	False	1.0	15.0	15.0	1.17454	3.33333	0	11.0	0.0	50.0	nombre / espace	n/a	False	0.979051	False	1.0	4.0	0.354839	3.0	0	4.0	55.0	189	n/a	16.0	1.17797	9.66667	4.0	4.0	60.0	10.0	120.0	False	True	n/a	False	2.75	False	21.0	0.933082	4.0	False
diff --git a/nilearn/datasets/tests/data/localizer_index.json b/nilearn/datasets/tests/data/localizer_index.json
new file mode 100755
index 0000000000..2d0bb9fb2f
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_index.json
@@ -0,0 +1,114 @@
+[
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_mask.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_spm.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculationVsSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculationVsSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculationVsSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculation_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculation_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculation_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualMotorVsCognitiveProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualMotorVsCognitiveProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualMotorVsCognitiveProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculationVsAuditorySentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculationVsAuditorySentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculationVsAuditorySentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculation_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculation_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryCalculation_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_design.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_design.npy",
+ "/localizer/derivatives/spm_preprocessing/sub-{0}/sub-{0}_T1w.nii.gz",
+ "/localizer/derivatives/spm_preprocessing/sub-{0}/sub-{0}_task-localizer_bold.nii.gz",
+ "/localizer/sub-{0}/anat/sub-{0}_T1w.nii.gz",
+ "/localizer/sub-{0}/func/sub-{0}_task-localizer_bold.json",
+ "/localizer/sub-{0}/func/sub-{0}_task-localizer_bold.nii.gz"
+]
+
diff --git a/nilearn/datasets/tests/data/localizer_participants.tsv b/nilearn/datasets/tests/data/localizer_participants.tsv
new file mode 100644
index 0000000000..a029d440d6
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_participants.tsv
@@ -0,0 +1,95 @@
+participant_id	age	sex	site	family	language
+S01	24	M	SHFJ	F01	French
+S02	20	M	SHFJ	F02	French
+S03	22	F	SHFJ	F03	French
+S04	20	M	Neurospin	F04	French
+S05	23	F	Neurospin	F05	French
+S06	19	M	Neurospin	F06	French
+S07	26	F	Neurospin	F07	French
+S08	27	M	Neurospin	F89	French
+S09	21	F	Neurospin	F09	French
+S10	24	F	SHFJ	F10	French
+S11	47	F	SHFJ	F11	French
+S12	23	F	SHFJ	F12	French
+S13	19	M	Neurospin	F13	French
+S14	21	F	SHFJ	F14	French
+S15	22	F	SHFJ	F15	French
+S16	24	F	SHFJ	F16	French
+S17	22	M	SHFJ	F17	French
+S18	20	F	Neurospin	F18	French
+S19	18	M	SHFJ	F52	French
+S20	25	F	SHFJ	F20	French
+S21	21	M	SHFJ	F21	French
+S22	40	M	SHFJ	F22	French
+S23	24	F	Neurospin	F23	French
+S24	18	F	Neurospin	F24	French
+S25	24	F	SHFJ	F25	French
+S26	24	F	Neurospin	F26	French
+S27	30	M	Neurospin	F27	French
+S28	18	F	Neurospin	F28	French
+S29	21	F	Neurospin	F29	French
+S30	34	F	SHFJ	F30	French
+S31	22	F	Neurospin	F31	French
+S32	26	F	SHFJ	F32	French
+S33	21	F	SHFJ	F33	French
+S34	21	M	SHFJ	F34	French
+S35	24	F	SHFJ	F35	French
+S36	25	F	SHFJ	F36	French
+S37	22	M	Neurospin	F37	French
+S38	n/a	M	SHFJ	F38	French
+S39	22	M	SHFJ	F39	French
+S40	24	F	SHFJ	F40	French
+S41	18	M	Neurospin	F41	French
+S42	26	M	Neurospin	F42	French
+S43	49	M	Neurospin	F43	French
+S44	34	F	SHFJ	F44	French
+S45	21	M	SHFJ	F45	French
+S46	24	F	Neurospin	F46	French
+S47	19	M	SHFJ	F47	French
+S48	21	F	SHFJ	F48	French
+S49	22	F	SHFJ	F49	French
+S50	32	F	SHFJ	F50	French
+S51	30	M	SHFJ	F51	French
+S52	22	F	SHFJ	F52	French
+S53	18	F	Neurospin	F53	French
+S54	20	F	SHFJ	F54	French
+S55	21	F	Neurospin	F55	French
+S56	23	M	Neurospin	F56	French
+S57	20	F	SHFJ	F57	French
+S58	34	M	SHFJ	F58	French
+S59	30	M	SHFJ	F59	French
+S60	21	M	Neurospin	F60	French
+S61	23	M	SHFJ	F61	French
+S62	n/a	F	SHFJ	F62	French
+S63	19	F	Neurospin	F63	French
+S64	21	F	Neurospin	F64	French
+S65	19	M	Neurospin	F65	French
+S66	26	M	Neurospin	F66	French
+S67	44	F	SHFJ	F67	French
+S68	20	M	SHFJ	F68	French
+S69	21	F	SHFJ	F69	French
+S70	25	M	SHFJ	F59	French
+S71	21	M	SHFJ	F71	French
+S72	37	F	Neurospin	F72	French
+S73	19	M	Neurospin	F73	French
+S74	21	M	SHFJ	F74	French
+S75	20	M	Neurospin	F75	French
+S76	28	F	SHFJ	F76	French
+S77	23	F	SHFJ	F77	French
+S78	29	M	Neurospin	F78	French
+S79	43	F	SHFJ	F79	French
+S80	19	F	SHFJ	F80	French
+S81	n/a	M	SHFJ	F81	French
+S82	20	F	SHFJ	F82	French
+S83	20	F	Neurospin	F83	French
+S84	23	F	SHFJ	F84	French
+S85	21	M	SHFJ	F85	French
+S86	31	M	Neurospin	F86	French
+S87	n/a	M	SHFJ	F87	French
+S88	19	M	SHFJ	F88	French
+S89	29	M	Neurospin	F89	French
+S90	21	M	SHFJ	F90	French
+S91	23	M	Neurospin	F91	French
+S92	26	F	SHFJ	F92	French
+S93	36	M	Neurospin	F93	French
+S94	41	M	SHFJ	F94	French
diff --git a/nilearn/datasets/tests/test_func.py b/nilearn/datasets/tests/test_func.py
index 27193e9ac0..9c52e3dbca 100644
--- a/nilearn/datasets/tests/test_func.py
+++ b/nilearn/datasets/tests/test_func.py
@@ -5,6 +5,7 @@
 # License: simplified BSD
 
 import os
+import uuid
 import numpy as np
 import json
 import nibabel
@@ -12,7 +13,8 @@
 from sklearn.utils import check_random_state
 
 from nose import with_setup
-from nose.tools import assert_true, assert_equal, assert_not_equal
+from nose.tools import (
+    assert_true, assert_false, assert_equal, assert_not_equal)
 from . import test_utils as tst
 
 from nilearn.datasets import utils, func
@@ -146,193 +148,145 @@ def test_miyawaki2008():
     assert_not_equal(dataset.description, '')
 
 
+with open(os.path.join(tst.datadir, 'localizer_index.json')) as of:
+    localizer_template = json.load(of)
+LOCALIZER_INDEX = {}
+for idx in range(1, 95):
+    idx = str(idx).zfill(2)
+    sid = 'S{0}'.format(idx)
+    LOCALIZER_INDEX.update(dict(
+        (key.format(sid), uuid.uuid4().hex)
+        for key in localizer_template))
+LOCALIZER_INDEX['/localizer/phenotype/behavioural.tsv'] = uuid.uuid4().hex
+LOCALIZER_PARTICIPANTS = np.recfromcsv(
+    os.path.join(tst.datadir, 'localizer_participants.tsv'), delimiter='\t')
+LOCALIZER_BEHAVIOURAL = np.recfromcsv(
+    os.path.join(tst.datadir, 'localizer_behavioural.tsv'), delimiter='\t')
+
+
+def mock_localizer_index(*args, **kwargs):
+    return LOCALIZER_INDEX
+
+
+def mock_np_recfromcsv(*args, **kwargs):
+    if args[0].endswith('participants.tsv'):
+        return LOCALIZER_PARTICIPANTS
+    elif args[0].endswith('behavioural.tsv'):
+        return LOCALIZER_BEHAVIOURAL
+    else:
+        raise ValueError('Unexpected args!')
+
+
+def setup_localizer():
+    global original_json_load
+    global mock_json_load
+    mock_json_load = mock_localizer_index
+    original_json_load = json.load
+    json.load = mock_json_load
+
+    global original_np_recfromcsv
+    global mock_np_recfromcsv
+    mock_np_recfromcsv = mock_np_recfromcsv
+    original_np_recfromcsv = np.recfromcsv
+    np.recfromcsv = mock_np_recfromcsv
+
+
+def teardown_localizer():
+    global original_json_load
+    json.load = original_json_load
+
+    global original_np_recfromcsv
+    np.recfromcsv = original_np_recfromcsv
+
+
 @with_setup(setup_mock, teardown_mock)
 @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
 def test_fetch_localizer_contrasts():
-    local_url = "file://" + tst.datadir
-    ids = np.asarray([('S%2d' % i).encode() for i in range(94)])
-    ids = ids.view(dtype=[('subject_id', 'S3')])
-    tst.mock_fetch_files.add_csv('cubicwebexport.csv', ids)
-    tst.mock_fetch_files.add_csv('cubicwebexport2.csv', ids)
-
-    # Disabled: cannot be tested without actually fetching covariates CSV file
-    # All subjects
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             verbose=0)
-    assert_true(dataset.anats is None)
-    assert_true(dataset.tmaps is None)
-    assert_true(dataset.masks is None)
-    assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 94)
-    assert_equal(len(dataset.cmaps), 94)
-
-    # 20 subjects
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             n_subjects=20,
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             verbose=0)
-    assert_true(dataset.anats is None)
-    assert_true(dataset.tmaps is None)
-    assert_true(dataset.masks is None)
+    # 2 subjects
+    dataset = func.fetch_localizer_contrasts(
+        ['checkerboard'],
+        n_subjects=2,
+        data_dir=tst.tmpdir,
+        verbose=1)
+    assert_false(hasattr(dataset, 'anats'))
+    assert_false(hasattr(dataset, 'tmaps'))
+    assert_false(hasattr(dataset, 'masks'))
     assert_true(isinstance(dataset.cmaps[0], _basestring))
     assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_equal(len(dataset.cmaps), 20)
-    assert_equal(dataset.ext_vars.size, 20)
+    assert_equal(len(dataset.cmaps), 2)
+    assert_equal(dataset.ext_vars.size, 2)
 
     # Multiple contrasts
     dataset = func.fetch_localizer_contrasts(
-        ["checkerboard", "horizontal checkerboard"],
-        n_subjects=20, data_dir=tst.tmpdir,
-        verbose=0)
-    assert_true(dataset.anats is None)
-    assert_true(dataset.tmaps is None)
-    assert_true(dataset.masks is None)
+        ['checkerboard', 'horizontal checkerboard'],
+        n_subjects=2,
+        data_dir=tst.tmpdir,
+        verbose=1)
     assert_true(isinstance(dataset.ext_vars, np.recarray))
     assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_equal(len(dataset.cmaps), 20 * 2)  # two contrasts are fetched
-    assert_equal(dataset.ext_vars.size, 20)
-
-    # get_anats=True
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             get_anats=True,
-                                             verbose=0)
-    assert_true(dataset.masks is None)
-    assert_true(dataset.tmaps is None)
-    assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_true(isinstance(dataset.anats[0], _basestring))
-    assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 94)
-    assert_equal(len(dataset.anats), 94)
-    assert_equal(len(dataset.cmaps), 94)
-
-    # get_masks=True
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             get_masks=True,
-                                             verbose=0)
-    assert_true(dataset.anats is None)
-    assert_true(dataset.tmaps is None)
-    assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_true(isinstance(dataset.masks[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 94)
-    assert_equal(len(dataset.cmaps), 94)
-    assert_equal(len(dataset.masks), 94)
-
-    # get_tmaps=True
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             get_tmaps=True,
-                                             verbose=0)
-    assert_true(dataset.anats is None)
-    assert_true(dataset.masks is None)
-    assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_true(isinstance(dataset.tmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 94)
-    assert_equal(len(dataset.cmaps), 94)
-    assert_equal(len(dataset.tmaps), 94)
+    assert_equal(len(dataset.cmaps), 2 * 2)  # two contrasts are fetched
+    assert_equal(dataset.ext_vars.size, 2)
 
     # all get_*=True
-    dataset = func.fetch_localizer_contrasts(["checkerboard"],
-                                             data_dir=tst.tmpdir,
-                                             url=local_url,
-                                             get_anats=True,
-                                             get_masks=True,
-                                             get_tmaps=True,
-                                             verbose=0)
-
+    dataset = func.fetch_localizer_contrasts(
+        ['checkerboard'],
+        n_subjects=1,
+        data_dir=tst.tmpdir,
+        get_anats=True,
+        get_masks=True,
+        get_tmaps=True,
+        verbose=1)
     assert_true(isinstance(dataset.ext_vars, np.recarray))
     assert_true(isinstance(dataset.anats[0], _basestring))
     assert_true(isinstance(dataset.cmaps[0], _basestring))
     assert_true(isinstance(dataset.masks[0], _basestring))
     assert_true(isinstance(dataset.tmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 94)
-    assert_equal(len(dataset.anats), 94)
-    assert_equal(len(dataset.cmaps), 94)
-    assert_equal(len(dataset.masks), 94)
-    assert_equal(len(dataset.tmaps), 94)
+    assert_equal(dataset.ext_vars.size, 1)
+    assert_equal(len(dataset.anats), 1)
+    assert_equal(len(dataset.cmaps), 1)
+    assert_equal(len(dataset.masks), 1)
+    assert_equal(len(dataset.tmaps), 1)
     assert_not_equal(dataset.description, '')
 
     # grab a given list of subjects
-    dataset2 = func.fetch_localizer_contrasts(["checkerboard"],
-                                              n_subjects=[2, 3, 5],
-                                              data_dir=tst.tmpdir,
-                                              url=local_url,
-                                              get_anats=True,
-                                              get_masks=True,
-                                              get_tmaps=True,
-                                              verbose=0)
-
-    # Check that we are getting only 3 subjects
+    dataset2 = func.fetch_localizer_contrasts(
+        ['checkerboard'],
+        n_subjects=[2, 3, 5],
+        data_dir=tst.tmpdir,
+        verbose=1)
     assert_equal(dataset2.ext_vars.size, 3)
-    assert_equal(len(dataset2.anats), 3)
     assert_equal(len(dataset2.cmaps), 3)
-    assert_equal(len(dataset2.masks), 3)
-    assert_equal(len(dataset2.tmaps), 3)
-    np.testing.assert_array_equal(dataset2.ext_vars,
-                                  dataset.ext_vars[[1, 2, 4]])
-    np.testing.assert_array_equal(dataset2.anats,
-                                  np.array(dataset.anats)[[1, 2, 4]])
-    np.testing.assert_array_equal(dataset2.cmaps,
-                                  np.array(dataset.cmaps)[[1, 2, 4]])
-    np.testing.assert_array_equal(dataset2.masks,
-                                  np.array(dataset.masks)[[1, 2, 4]])
-    np.testing.assert_array_equal(dataset2.tmaps,
-                                  np.array(dataset.tmaps)[[1, 2, 4]])
+    assert_equal([row[0] for row in dataset2.ext_vars],
+                 [b'S02', b'S03', b'S05'])
 
 
 @with_setup(setup_mock, teardown_mock)
 @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
 def test_fetch_localizer_calculation_task():
-    local_url = "file://" + tst.datadir
-    ids = np.asarray(['S%2d' % i for i in range(94)])
-    ids = ids.view(dtype=[('subject_id', 'S3')])
-    tst.mock_fetch_files.add_csv('cubicwebexport.csv', ids)
-    tst.mock_fetch_files.add_csv('cubicwebexport2.csv', ids)
-
-    # Disabled: cannot be tested without actually fetching covariates CSV file
-    # All subjects
-    dataset = func.fetch_localizer_calculation_task(data_dir=tst.tmpdir,
-                                                    url=local_url,
-                                                    verbose=0)
-    assert_true(isinstance(dataset.ext_vars, np.recarray))
-    assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 1)
-    assert_equal(len(dataset.cmaps), 1)
-
-    # 20 subjects
-    dataset = func.fetch_localizer_calculation_task(n_subjects=20,
-                                                    data_dir=tst.tmpdir,
-                                                    url=local_url,
-                                                    verbose=0)
+    # 2 subjects
+    dataset = func.fetch_localizer_calculation_task(
+        n_subjects=2,
+        data_dir=tst.tmpdir,
+        verbose=1)
     assert_true(isinstance(dataset.ext_vars, np.recarray))
     assert_true(isinstance(dataset.cmaps[0], _basestring))
-    assert_equal(dataset.ext_vars.size, 20)
-    assert_equal(len(dataset.cmaps), 20)
+    assert_equal(dataset.ext_vars.size, 2)
+    assert_equal(len(dataset.cmaps), 2)
     assert_not_equal(dataset.description, '')
 
 
 @with_setup(setup_mock, teardown_mock)
 @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
 def test_fetch_localizer_button_task():
-    local_url = "file://" + tst.datadir
-
-    # Disabled: cannot be tested without actually fetching covariates CSV file
-    # Only one subject
-    dataset = func.fetch_localizer_button_task(data_dir=tst.tmpdir,
-                                               url=local_url,
-                                               verbose=0)
-    assert_true(isinstance(dataset.tmap, _basestring))
-    assert_true(isinstance(dataset.anat, _basestring))
+    # 2 subjects
+    dataset = func.fetch_localizer_button_task(
+        data_dir=tst.tmpdir,
+        verbose=1)
+    assert_true(isinstance(dataset.tmaps[0], _basestring))
+    assert_true(isinstance(dataset.anats[0], _basestring))
     assert_not_equal(dataset.description, '')
 
 

From dbdf0e057a4617fb415f1e601f3765826eda4b03 Mon Sep 17 00:00:00 2001
From: Kshitij Chawla <kc.insight.pi@gmail.com>
Date: Wed, 9 Oct 2019 11:57:29 +0200
Subject: [PATCH 225/228] Fixing malfunctioning allowed-failure section in
 Travis (#2160)

* Fixing malfunctioning allowed-failure section in Travis

 - Deliberate Flake8 violation for testing.

* Changed: Maybe Python version spec needs to be a double quoted string

 - Changed comments to be more useful, less redundant.

* Replicated env info exactly and completely to fix allowed failures

* Replicated env info exactly and completely to fix allowed failures- 2

* Remove deliberate flake8 violations now that allowed_failures works as expected
---
 .travis.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 6c9abc7da9..e63407049c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,11 +12,11 @@ matrix:
   # setting the status
   fast_finish: true
   allow_failures:
-    # allow_failures keyed to skipping tests.
-    - env: SKIP_TESTS="true"
+    # allow_failures keyed to python 3.5 & skipping tests.
+    - python: "3.5"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
   include:
-    # without matplotlib
-    - name: "Python 3.5 minimum package versions"
+    - name: "Python 3.5 minimum package versions without Matplotlib"
       python: "3.5"
       env: DISTRIB="travisci" PYTHON_VERSION="3.5"
            NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
@@ -48,7 +48,7 @@ matrix:
   # FLAKE8 linting on diff wrt common ancestor with upstream/master
     # Note: the python value is only there to trigger allow_failures
     - name: Python 3.5 Flake8 no tests
-      python: 3.5
+      python: "3.5"
       env: DISTRIB="travisci" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
 
 install: source continuous_integration/install.sh

From 8d1a058db3b2fe815a4932662830295735e80d80 Mon Sep 17 00:00:00 2001
From: Kshitij Chawla <kc.insight.pi@gmail.com>
Date: Wed, 9 Oct 2019 14:25:10 +0200
Subject: [PATCH 226/228] Import HTMLDocument in its original module to
 preserve backwards compatibility (#2162)

* Imported HTMLDocument in its original module to preserve backwards compat

* Imported set_max_img_views_before_warning in orig module for backwards compat

* Added tests & comments
---
 nilearn/plotting/js_plotting_utils.py            | 3 +++
 nilearn/plotting/tests/test_js_plotting_utils.py | 5 +++++
 2 files changed, 8 insertions(+)

diff --git a/nilearn/plotting/js_plotting_utils.py b/nilearn/plotting/js_plotting_utils.py
index 2d398b23a3..5e41e2ae54 100644
--- a/nilearn/plotting/js_plotting_utils.py
+++ b/nilearn/plotting/js_plotting_utils.py
@@ -12,6 +12,9 @@
 import numpy as np
 from matplotlib import cm as mpl_cm
 
+# included here for backward compatibility
+from nilearn.reporting.html_document import (
+    HTMLDocument, set_max_img_views_before_warning,)  # noqa
 from .._utils.extmath import fast_abs_percentile
 from .._utils.param_validation import check_threshold
 from .. import surface
diff --git a/nilearn/plotting/tests/test_js_plotting_utils.py b/nilearn/plotting/tests/test_js_plotting_utils.py
index 854b9d07df..97154dcb07 100644
--- a/nilearn/plotting/tests/test_js_plotting_utils.py
+++ b/nilearn/plotting/tests/test_js_plotting_utils.py
@@ -309,3 +309,8 @@ def test_to_color_strings():
     colors = ['#0000ffff', '#ff0000ab', '#7f7f7f00']
     as_str = js_plotting_utils.to_color_strings(colors)
     assert as_str == ['#0000ff', '#ff0000', '#7f7f7f']
+
+
+def test_import_html_document_from_js_plotting():
+    from nilearn.plotting.js_plotting_utils import (
+        HTMLDocument, set_max_img_views_before_warning)  #noqa

From 6c240057d3c0e64f0dd0b0b52d73ae087735286f Mon Sep 17 00:00:00 2001
From: KamalakerDadi <dkamalakarreddy@gmail.com>
Date: Wed, 9 Oct 2019 17:21:52 +0200
Subject: [PATCH 227/228] [MRG] FIX: orientation problem with plot_glass_brain
 (#1888)

* FIX: orientation problem with plot_glass_brain

* TST: FIX issues with matplotlib 2.1.0

* FIX: glass brain orientation compatible with all matplotlib versions

* fix for display.add_overlay

* DOC: added entry into whats new

* FIX: display.add_graph with plot_connectome

* DOC: add about this fix in whats new
---
 doc/whats_new.rst                           |  4 +++-
 nilearn/plotting/displays.py                | 20 ++++++++++++++------
 nilearn/plotting/img_plotting.py            |  5 +++++
 nilearn/plotting/tests/test_displays.py     |  8 +++++++-
 nilearn/plotting/tests/test_img_plotting.py | 18 ++++++++++++++++++
 5 files changed, 47 insertions(+), 8 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 36e224f7c5..a96d2dd40f 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -77,6 +77,8 @@ Fixes
   half-transparent grey to maintain a 3D perception.
 - :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
   path.
+- :func:`nilearn.plotting.plot_glass_brain` now correctly displays the left 'l' orientation even when
+  the given images are completely masked (empty images).
 - :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
 - :func:`nilearn.plotting.plot_surf_roi` now takes vmin, vmax parameters
 - :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
@@ -203,7 +205,7 @@ The following people contributed to this release::
 0.5.0
 =====
 
-    **Released November 2018**
+**Released November 2018**
 
 NEW
 ---
diff --git a/nilearn/plotting/displays.py b/nilearn/plotting/displays.py
index 2ef2594652..b6f8b46ec1 100644
--- a/nilearn/plotting/displays.py
+++ b/nilearn/plotting/displays.py
@@ -369,6 +369,13 @@ def transform_to_2d(self, data, affine):
         else:
             maximum_intensity_data = np.abs(data_selection).max(axis=max_axis)
 
+        # This work around can be removed bumping matplotlib > 2.1.0. See #1815
+        # in nilearn for the invention of this work around
+
+        if self.direction == 'l' and data_selection.min() is np.ma.masked and \
+                not (self.ax.get_xlim()[0] > self.ax.get_xlim()[1]):
+            self.ax.invert_xaxis()
+
         return np.rot90(maximum_intensity_data)
 
     def draw_position(self, size, bg_color, **kwargs):
@@ -789,12 +796,13 @@ def _map_show(self, img, type='imshow',
         ims = []
         to_iterate_over = zip(self.axes.values(), data_2d_list)
         for display_ax, data_2d in to_iterate_over:
-            if data_2d is not None:
+            if data_2d is not None and data_2d.min() is not np.ma.masked:
                 # If data_2d is completely masked, then there is nothing to
-                # plot. Hence, continued to loop over. This problem came up
-                # with matplotlib 2.1.0. See issue #9280 in matplotlib.
-                if data_2d.min() is np.ma.masked:
-                    continue
+                # plot. Hence, no point to do imshow(). Moreover, we see
+                # problem came up with matplotlib 2.1.0 (issue #9280) when
+                # data is completely masked or with numpy < 1.14
+                # (issue #4595). This work aroung can be removed when bumping
+                # matplotlib version above 2.1.0
                 im = display_ax.draw_2d(data_2d, data_bounds, bounding_box,
                                         type=type, **kwargs)
                 ims.append(im)
@@ -1837,7 +1845,7 @@ def add_graph(self, adjacency_matrix, node_coords,
                               vmin=edge_vmin, vmax=edge_vmax,
                               **edge_kwargs)
             # To obtain the brain left view, we simply invert the x axis
-            if ax.direction == 'l':
+            if ax.direction == 'l' and not (ax.ax.get_xlim()[0] > ax.ax.get_xlim()[1]):
                 ax.ax.invert_xaxis()
 
         if colorbar:
diff --git a/nilearn/plotting/img_plotting.py b/nilearn/plotting/img_plotting.py
index a06d16195e..b3dae99f69 100644
--- a/nilearn/plotting/img_plotting.py
+++ b/nilearn/plotting/img_plotting.py
@@ -1079,6 +1079,8 @@ def plot_glass_brain(stat_map_img,
         The plotted image should be in MNI space for this function to work
         properly.
 
+        Only glass brain can be plotted by switching stat_map_img to None.
+
         Parameters
         ----------
         stat_map_img : Niimg-like object
@@ -1185,6 +1187,9 @@ def display_factory(display_mode):
         cbar_vmin=cbar_vmin, cbar_vmax=cbar_vmax, brain_color=brain_color,
         resampling_interpolation=resampling_interpolation, **kwargs)
 
+    if stat_map_img is None and 'l' in display.axes:
+        display.axes['l'].ax.invert_xaxis()
+
     return display
 
 
diff --git a/nilearn/plotting/tests/test_displays.py b/nilearn/plotting/tests/test_displays.py
index d71813c5cd..051f9f7b36 100644
--- a/nilearn/plotting/tests/test_displays.py
+++ b/nilearn/plotting/tests/test_displays.py
@@ -9,6 +9,7 @@
 from nilearn.plotting.displays import OrthoSlicer, XSlicer, OrthoProjector
 from nilearn.plotting.displays import TiledSlicer
 from nilearn.plotting.displays import LZRYProjector
+from nilearn.plotting.displays import LYRZProjector
 from nilearn.datasets import load_mni152_template
 
 ##############################################################################
@@ -45,7 +46,7 @@ def test_tiled_slicer():
     img = load_mni152_template()
     slicer = TiledSlicer.init_with_figure(img=img, cut_coords=(0, 0, 0),
                                           colorbar=True)
-    slicer.add_overlay(img, cmap=plt.cm.gray,colorbar=True)
+    slicer.add_overlay(img, cmap=plt.cm.gray, colorbar=True)
     # Forcing a layout here, to test the locator code
     with tempfile.TemporaryFile() as fp:
         slicer.savefig(fp)
@@ -85,6 +86,7 @@ def test_contour_fillings_levels_in_add_contours():
     # without passing levels, should work with default levels from
     # matplotlib
     oslicer.add_contours(img, filled=True)
+    oslicer.close()
 
 
 def test_user_given_cmap_with_colorbar():
@@ -112,6 +114,10 @@ def test_data_complete_mask():
     oslicer.add_overlay(img)
     oslicer.close()
 
+    lyrz_projector = LYRZProjector(cut_coords=(0, 0, 0, 0))
+    lyrz_projector.add_overlay(img)
+    lyrz_projector.close()
+
 
 def test_add_markers_cut_coords_is_none():
     # A special case test for add_markers when cut_coords are None. This
diff --git a/nilearn/plotting/tests/test_img_plotting.py b/nilearn/plotting/tests/test_img_plotting.py
index 5157993f69..34269dc93d 100644
--- a/nilearn/plotting/tests/test_img_plotting.py
+++ b/nilearn/plotting/tests/test_img_plotting.py
@@ -1009,3 +1009,21 @@ def test_plot_glass_brain_colorbar_having_nans():
     img = nibabel.Nifti1Image(data, np.eye(4))
     plot_glass_brain(img, colorbar=True)
     plt.close()
+
+
+def test_plot_glass_brain_display_modes_without_img():
+    # Smoke test for work around from PR #1888
+    fig = plot_glass_brain(None, display_mode='lr')
+    fig = plot_glass_brain(None, display_mode='lzry')
+    fig.close()
+
+
+def test_plot_glass_brain_with_completely_masked_img():
+    # Smoke test for PR #1888 with display modes having 'l'
+    data = np.zeros((10, 20, 30))
+    affine = np.eye(4)
+
+    img = nibabel.Nifti1Image(data, affine)
+    plot_glass_brain(img, display_mode='lzry')
+    plot_glass_brain(img, display_mode='lr')
+    plt.close()

From d566678483f319ff4686507ea37930c979cbc682 Mon Sep 17 00:00:00 2001
From: jeromedockes <jerome@dockes.org>
Date: Thu, 10 Oct 2019 11:47:02 +0200
Subject: [PATCH 228/228] [MRG] Nans in view connectome (#2166)

* nan_to_num in view_connectome

* update warning test

* whatsnew
---
 doc/whats_new.rst                              |  3 +++
 nilearn/plotting/html_connectome.py            |  2 +-
 nilearn/plotting/tests/test_html_connectome.py | 14 ++++++++++----
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index a96d2dd40f..0d568d8502 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -62,6 +62,9 @@ Changes
   colormap. These arguments were already accepted in `kwargs` but not documented
   before.
 
+- :func:`nilearn.plotting.view_connectome` now converts NaNs in the adjacency
+  matrix to 0.
+
 Fixes
 -----
 
diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
index 66ffe6ba2d..559ef019f2 100644
--- a/nilearn/plotting/html_connectome.py
+++ b/nilearn/plotting/html_connectome.py
@@ -31,7 +31,7 @@ def _get_connectome(adjacency_matrix, coords, threshold=None,
                     marker_size=None, cmap=cm.cold_hot, symmetric_cmap=True):
     connectome = {}
     coords = np.asarray(coords, dtype='<f4')
-    adjacency_matrix = adjacency_matrix.copy()
+    adjacency_matrix = np.nan_to_num(adjacency_matrix, copy=True)
     colors = colorscale(
         cmap, adjacency_matrix.ravel(), threshold=threshold,
         symmetric_cmap=symmetric_cmap)
diff --git a/nilearn/plotting/tests/test_html_connectome.py b/nilearn/plotting/tests/test_html_connectome.py
index 50378f2173..790b1d3cfe 100644
--- a/nilearn/plotting/tests/test_html_connectome.py
+++ b/nilearn/plotting/tests/test_html_connectome.py
@@ -47,6 +47,11 @@ def test_get_connectome():
     assert {'_con_x', '_con_y', '_con_z', '_con_w', 'colorscale'
             }.issubset(connectome.keys())
     assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5)
+    adj[adj == 0] = np.nan
+    connectome = html_connectome._get_connectome(adj, coord)
+    con_x = decode(connectome['_con_x'], '<f4')
+    assert (con_x == expected_x).all()
+    assert (connectome['cmin'], connectome['cmax']) == (-2.5, 2.5)
 
 
 def test_view_connectome():
@@ -129,10 +134,11 @@ def test_params_deprecation_view_connectome():
                                         )
     old_params = ['coords', 'threshold', 'cmap', 'marker_size']
 
-    assert len(raised_warnings) == 4
-    for old_param_, raised_warning_ in zip(old_params, raised_warnings):
-        assert warning_msgs[old_param_] == str(raised_warning_.message)
-        assert raised_warning_.category is DeprecationWarning
+    raised_warning_messages = ''.join(
+        str(warning.message) for warning in raised_warnings)
+    print(raised_warning_messages)
+    for old_param_ in old_params:
+        assert warning_msgs[old_param_] in raised_warning_messages
 
 
 def test_get_markers():