From 4a1eaa7ae3e7275b299f97c59261f40247f2aec7 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Thu, 25 Aug 2022 12:29:23 -0700
Subject: [PATCH 01/33] surface searchlight pr start

---
 pr_notes.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 pr_notes.md

diff --git a/pr_notes.md b/pr_notes.md
new file mode 100644
index 00000000..48cdce85
--- /dev/null
+++ b/pr_notes.md
@@ -0,0 +1 @@
+placeholder

From 0664e5b948f3f8972a5b2f1e21b19b0847d46bf9 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Thu, 25 Aug 2022 16:00:22 -0400
Subject: [PATCH 02/33] first commit on surface searchlight

---
 rsatoolbox/searchlight/searchlight_rsa.py | 143 ++++++++++++++++++++++
 1 file changed, 143 insertions(+)
 create mode 100644 rsatoolbox/searchlight/searchlight_rsa.py

diff --git a/rsatoolbox/searchlight/searchlight_rsa.py b/rsatoolbox/searchlight/searchlight_rsa.py
new file mode 100644
index 00000000..cf5ae341
--- /dev/null
+++ b/rsatoolbox/searchlight/searchlight_rsa.py
@@ -0,0 +1,143 @@
+
+import warnings
+import numpy as np
+
+from joblib import Parallel, delayed, cpu_count
+from scipy.stats import pearsonr
+from scipy.spatial.distance import cdist, pdist, squareform
+from scipy.optimize import nnls
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.model_selection import KFold
+from sklearn import neighbors
+from nilearn import datasets, surface
+
+#def searchlight_rsa(targetspace, radius, betas,  metrix='correlation', n_jobs=-1, verbose=0):
+
+    # prepare surf_indices
+
+
+    # compute_brain_rdms
+
+#    return brain_rdms
+
+def prepare_surf_indices(targetspace, radius):
+    """prepare searchlight indices to be used to
+       sample searchlights from fMRI betas prepared
+       in surface space.
+
+    Args:
+        targetspace (string): what surface space are your betas
+                              prepared in? e.g. 'fsaverage'
+        radius (int): what radius do you want the searchlight 'spheres'
+                      to cover?
+
+    Returns:
+        sl_indices: list of searchlight indices for every vertex left
+                    and right. This list can then be used to run
+                    searchlight RSA, indexing the surface prepared betas
+    """
+
+    fsaverage = datasets.fetch_surf_fsaverage(mesh=targetspace)
+
+    hemis = ['left', 'right']
+
+    sl_indices = []
+
+    for hemi in hemis:
+
+        # we piggy back on nilearn to get inflated coordinates
+        infl_mesh = fsaverage['infl_' + hemi]
+        coords, _ = surface.load_surf_mesh(infl_mesh)
+
+        # prepare the nearest neighbours algo
+        nn = neighbors.NearestNeighbors(radius=radius)
+
+        # get the list of vertex indices using nearest neighbour
+        adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil()
+
+        # extend lists of indices for both hemispheres
+        sl_indices.append(adjacency)
+
+    return sl_indices
+
+
+def compute_searchlight_rdms(A, X, n_jobs=-1, verbose=0):
+    """compute searchlight RDMs takes a list of indices
+       and maps the betas to compute an RDM for each
+       searchlight of surface vertices. 
+
+    Args:
+        A (_type_): list of searchliht indices 
+                    (see prep_surf_indices)
+        X (_type_): betas in shape n_vertices by n_conditions
+        n_jobs (int, optional): number of cpus available. 
+                    Defaults to -1 (find number of cpus automatically).
+        verbose (int, optional): level of shouting. Defaults to 0.
+
+    Returns:
+        array: searchlight rdms in the shape 
+               n_vertices x n_pairwise_comparisons
+    """
+
+    group_iter = GroupIterator(A.shape[0], n_jobs)
+    with warnings.catch_warnings():  # might not converge
+        warnings.simplefilter('ignore', ConvergenceWarning)
+        rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
+            delayed(get_distance)(
+                A.rows[list_i],
+                X)
+            for list_i in group_iter)
+    return np.concatenate(rdms)
+
+
+class GroupIterator(object):
+    """Group iterator
+    Provides group of features for search_light loop
+    that may be used with Parallel.
+    Parameters
+    ----------
+    n_features : int
+        Total number of features
+    %(n_jobs)s
+    """
+    def __init__(self, n_features, n_jobs=1):
+        self.n_features = n_features
+        if n_jobs == -1:
+            n_jobs = cpu_count()
+        self.n_jobs = n_jobs
+
+    def __iter__(self):
+        split = np.array_split(np.arange(self.n_features), self.n_jobs)
+        for list_i in split:
+            yield list_i
+
+
+def get_distance(list_rows, X):
+
+    """get_distance returns the correlation distance
+       across condition patterns in X
+       get_distance uses numpy's einsum
+
+    Args:
+        list_rows : array of arrays of int
+        adjacency rows. For a voxel with index i in X, list_rows[i] is the list
+        of neighboring voxels indices (in X).
+
+        X : array-like of shape at least 2D
+        data to fit.
+
+    Returns:
+        par_rdms: pairwise distances between condition patterns in X
+             (in upper triangular vector form) for the list in list_rows
+    """
+    n_items = X.shape[1]
+    n_comparisons = (n_items*(n_items-1))/2
+
+    par_rdms = np.zeros((len(list_rows), int(n_comparisons)))
+
+    for i, row in enumerate(list_rows):
+        ind = np.array(row)
+        Xi = np.array(X[ind, :]).T
+        par_rdms[i, :] = pdist(Xi, metric='correlation')
+
+    return par_rdms
\ No newline at end of file

From d7dd86deb8f8aa3e1e6cc41879591126de46d621 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Mon, 29 Aug 2022 10:41:23 -0400
Subject: [PATCH 03/33] the surface searchlight now distribute dataset objects
 in calc_rdm_batch

---
 rsatoolbox/searchlight/__init__.py    |   2 +
 rsatoolbox/searchlight/searchlight.py | 177 ++++++++++++++++++++++++++
 2 files changed, 179 insertions(+)
 create mode 100644 rsatoolbox/searchlight/__init__.py
 create mode 100644 rsatoolbox/searchlight/searchlight.py

diff --git a/rsatoolbox/searchlight/__init__.py b/rsatoolbox/searchlight/__init__.py
new file mode 100644
index 00000000..490db86c
--- /dev/null
+++ b/rsatoolbox/searchlight/__init__.py
@@ -0,0 +1,2 @@
+from .searchlight import compute_searchlight_rdms
+from .searchlight import prepare_surf_indices
diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
new file mode 100644
index 00000000..24b29ba3
--- /dev/null
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -0,0 +1,177 @@
+
+import warnings
+import numpy as np
+
+from joblib import Parallel, delayed, cpu_count
+from scipy.stats import pearsonr
+from scipy.spatial.distance import cdist, pdist, squareform
+from scipy.optimize import nnls
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.model_selection import KFold
+from sklearn import neighbors
+from nilearn import datasets, surface
+import rsatoolbox.data as rsd
+import rsatoolbox.rdm as rsr
+
+
+class GroupIterator(object):
+    """Group iterator. cf. nilearn.
+    Provides group of features for search_light loop
+    that may be used with Parallel.
+    Parameters
+    ----------
+    n_features : int
+        Total number of features
+    %(n_jobs)s
+    """
+    def __init__(self, n_features, n_jobs=1):
+        self.n_features = n_features
+        if n_jobs == -1:
+            n_jobs = cpu_count()
+        self.n_jobs = n_jobs
+
+    def __iter__(self):
+        split = np.array_split(np.arange(self.n_features), self.n_jobs)
+        for list_i in split:
+            yield list_i
+
+
+def prepare_surf_indices(targetspace, radius):
+    """prepare searchlight indices to be used to
+       sample searchlights from fMRI betas prepared
+       in surface space.
+
+    Args:
+        targetspace (string): what surface space are your betas
+                              prepared in? e.g. 'fsaverage'
+        radius (int): what radius do you want the searchlight 'spheres'
+                      to cover?
+
+    Returns:
+        sl_indices: list of searchlight indices for every vertex left
+                    and right. This list can then be used to run
+                    searchlight RSA, indexing the surface prepared betas
+    """
+
+    fsaverage = datasets.fetch_surf_fsaverage(mesh=targetspace)
+
+    hemis = ['left', 'right']
+
+    sl_indices = []
+
+    for hemi in hemis:
+
+        # we piggy back on nilearn to get inflated coordinates
+        infl_mesh = fsaverage['infl_' + hemi]
+        coords, _ = surface.load_surf_mesh(infl_mesh)
+
+        # prepare the nearest neighbours algo
+        nn = neighbors.NearestNeighbors(radius=radius)
+
+        # get the list of vertex indices using nearest neighbour
+        adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil()
+
+        # append lists of indices for both hemispheres
+        sl_indices.append(adjacency)
+
+    return sl_indices
+
+
+def compute_searchlight_rdms(
+    indices,
+    betas,
+    des,
+    obs_des,
+    method='correlation',  cv_descriptor=None, prior_lambda=1,
+    prior_weight=0.1, noise=None, n_jobs=-1, verbose=0):
+    """compute searchlight RDMs takes a list of indices
+       and maps the betas to compute an RDM for each
+       searchlight of surface vertices. 
+
+    Args:
+        indices (_type_): list of searchliht indices 
+                    (see prep_surf_indices)
+        betas (_type_): betas in shape n_vertices by n_conditions
+        des : participant and session details
+        obs_des: conditions dictionary e.g. {'conds': 'cond_0',...}
+        method: metric for constructing rdm
+        for a full description of the arguments, refer to calc_rdm.
+        n_jobs (int, optional): number of cpus available. 
+                    Defaults to -1 (find number of cpus automatically).
+        verbose (int, optional): level of shouting. Defaults to 0.
+
+    Returns:
+        array: searchlight rdms in the shape 
+               n_vertices x n_pairwise_comparisons
+    """
+    # first deal with making datasets for the searchlights
+    data = []
+    for ind in indices.rows:
+        chan_des = {'verts': np.array(['vert_' + str(x) for x in ind])}
+
+        data.append(
+            rsd.Dataset(
+                measurements=betas[ind, :].T,
+                descriptors=des,
+                obs_descriptors=obs_des,
+                channel_descriptors=chan_des
+                )
+            )
+
+    # next we call calc_rdm. we use joblib parallel
+    # to distribute it if multiple cpus. this might be memory
+    # intense dependent on the number of conditions. 
+    group_iter = GroupIterator(len(data), n_jobs)
+    with warnings.catch_warnings():  # might not converge
+        warnings.simplefilter('ignore', ConvergenceWarning)
+        if noise is None:
+            rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
+                delayed(calc_rdm_batch)(
+                    np.array(data)[list_i],
+                    method=method, 
+                    descriptor='conds',
+                    cv_descriptor=cv_descriptor,
+                    prior_lambda=prior_lambda,
+                    prior_weight=prior_weight)
+                for list_i in group_iter)
+    
+    # TODO: repack to list of RDMs object with descriptors 
+    # and chan descriptors. for now rdms is a n_vertices
+    # by n_pairwise_comparisons dissimilarities array
+
+    return np.concatenate(rdms)
+
+
+def calc_rdm_batch(
+    data_batch,     
+    method='correlation', descriptor=None, cv_descriptor=None, prior_lambda=1,
+    prior_weight=0.1, noise=None, n_jobs=-1, verbose=0):
+    """ calc rdm batch
+
+    Args:
+        data_batch (_type_): _description_
+        method (str, optional): _description_. Defaults to 'correlation'.
+        descriptor (_type_, optional): _description_. Defaults to None.
+        cv_descriptor (_type_, optional): _description_. Defaults to None.
+        prior_lambda (int, optional): _description_. Defaults to 1.
+        prior_weight (float, optional): _description_. Defaults to 0.1.
+        noise (_type_, optional): _description_. Defaults to None.
+        n_jobs (int, optional): _description_. Defaults to -1.
+        verbose (int, optional): _description_. Defaults to 0.
+
+    Returns:
+        _type_: _description_
+    """
+
+    rdms = []
+    for data in data_batch:
+        rdm = rsr.calc_rdm(
+                    data,
+                    method=method, 
+                    descriptor=descriptor,
+                    cv_descriptor=cv_descriptor,
+                    prior_lambda=prior_lambda,
+                    prior_weight=prior_weight)
+        rdms.append(rdm.dissimilarities)
+    
+    return np.concatenate(rdms)

From d84e829a2cc38af3d8cc15de312366bb3b4e707e Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Mon, 29 Aug 2022 10:47:08 -0400
Subject: [PATCH 04/33] removed unused imports/

---
 rsatoolbox/searchlight/searchlight.py     |   4 -
 rsatoolbox/searchlight/searchlight_rsa.py | 143 ----------------------
 2 files changed, 147 deletions(-)
 delete mode 100644 rsatoolbox/searchlight/searchlight_rsa.py

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 24b29ba3..d5251a9b 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -3,11 +3,7 @@
 import numpy as np
 
 from joblib import Parallel, delayed, cpu_count
-from scipy.stats import pearsonr
-from scipy.spatial.distance import cdist, pdist, squareform
-from scipy.optimize import nnls
 from sklearn.exceptions import ConvergenceWarning
-from sklearn.model_selection import KFold
 from sklearn import neighbors
 from nilearn import datasets, surface
 import rsatoolbox.data as rsd
diff --git a/rsatoolbox/searchlight/searchlight_rsa.py b/rsatoolbox/searchlight/searchlight_rsa.py
deleted file mode 100644
index cf5ae341..00000000
--- a/rsatoolbox/searchlight/searchlight_rsa.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-import warnings
-import numpy as np
-
-from joblib import Parallel, delayed, cpu_count
-from scipy.stats import pearsonr
-from scipy.spatial.distance import cdist, pdist, squareform
-from scipy.optimize import nnls
-from sklearn.exceptions import ConvergenceWarning
-from sklearn.model_selection import KFold
-from sklearn import neighbors
-from nilearn import datasets, surface
-
-#def searchlight_rsa(targetspace, radius, betas,  metrix='correlation', n_jobs=-1, verbose=0):
-
-    # prepare surf_indices
-
-
-    # compute_brain_rdms
-
-#    return brain_rdms
-
-def prepare_surf_indices(targetspace, radius):
-    """prepare searchlight indices to be used to
-       sample searchlights from fMRI betas prepared
-       in surface space.
-
-    Args:
-        targetspace (string): what surface space are your betas
-                              prepared in? e.g. 'fsaverage'
-        radius (int): what radius do you want the searchlight 'spheres'
-                      to cover?
-
-    Returns:
-        sl_indices: list of searchlight indices for every vertex left
-                    and right. This list can then be used to run
-                    searchlight RSA, indexing the surface prepared betas
-    """
-
-    fsaverage = datasets.fetch_surf_fsaverage(mesh=targetspace)
-
-    hemis = ['left', 'right']
-
-    sl_indices = []
-
-    for hemi in hemis:
-
-        # we piggy back on nilearn to get inflated coordinates
-        infl_mesh = fsaverage['infl_' + hemi]
-        coords, _ = surface.load_surf_mesh(infl_mesh)
-
-        # prepare the nearest neighbours algo
-        nn = neighbors.NearestNeighbors(radius=radius)
-
-        # get the list of vertex indices using nearest neighbour
-        adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil()
-
-        # extend lists of indices for both hemispheres
-        sl_indices.append(adjacency)
-
-    return sl_indices
-
-
-def compute_searchlight_rdms(A, X, n_jobs=-1, verbose=0):
-    """compute searchlight RDMs takes a list of indices
-       and maps the betas to compute an RDM for each
-       searchlight of surface vertices. 
-
-    Args:
-        A (_type_): list of searchliht indices 
-                    (see prep_surf_indices)
-        X (_type_): betas in shape n_vertices by n_conditions
-        n_jobs (int, optional): number of cpus available. 
-                    Defaults to -1 (find number of cpus automatically).
-        verbose (int, optional): level of shouting. Defaults to 0.
-
-    Returns:
-        array: searchlight rdms in the shape 
-               n_vertices x n_pairwise_comparisons
-    """
-
-    group_iter = GroupIterator(A.shape[0], n_jobs)
-    with warnings.catch_warnings():  # might not converge
-        warnings.simplefilter('ignore', ConvergenceWarning)
-        rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
-            delayed(get_distance)(
-                A.rows[list_i],
-                X)
-            for list_i in group_iter)
-    return np.concatenate(rdms)
-
-
-class GroupIterator(object):
-    """Group iterator
-    Provides group of features for search_light loop
-    that may be used with Parallel.
-    Parameters
-    ----------
-    n_features : int
-        Total number of features
-    %(n_jobs)s
-    """
-    def __init__(self, n_features, n_jobs=1):
-        self.n_features = n_features
-        if n_jobs == -1:
-            n_jobs = cpu_count()
-        self.n_jobs = n_jobs
-
-    def __iter__(self):
-        split = np.array_split(np.arange(self.n_features), self.n_jobs)
-        for list_i in split:
-            yield list_i
-
-
-def get_distance(list_rows, X):
-
-    """get_distance returns the correlation distance
-       across condition patterns in X
-       get_distance uses numpy's einsum
-
-    Args:
-        list_rows : array of arrays of int
-        adjacency rows. For a voxel with index i in X, list_rows[i] is the list
-        of neighboring voxels indices (in X).
-
-        X : array-like of shape at least 2D
-        data to fit.
-
-    Returns:
-        par_rdms: pairwise distances between condition patterns in X
-             (in upper triangular vector form) for the list in list_rows
-    """
-    n_items = X.shape[1]
-    n_comparisons = (n_items*(n_items-1))/2
-
-    par_rdms = np.zeros((len(list_rows), int(n_comparisons)))
-
-    for i, row in enumerate(list_rows):
-        ind = np.array(row)
-        Xi = np.array(X[ind, :]).T
-        par_rdms[i, :] = pdist(Xi, metric='correlation')
-
-    return par_rdms
\ No newline at end of file

From 7e15982a599cbd353546e173566527840bd454a1 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 09:42:11 -0400
Subject: [PATCH 05/33] now returns rdm objects

---
 rsatoolbox/searchlight/searchlight.py | 102 +++++++++++++++++++-------
 1 file changed, 75 insertions(+), 27 deletions(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index d5251a9b..9a6525ef 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -1,6 +1,8 @@
 
 import warnings
 import numpy as np
+from copy import deepcopy
+from collections.abc import Iterable
 
 from joblib import Parallel, delayed, cpu_count
 from sklearn.exceptions import ConvergenceWarning
@@ -82,22 +84,22 @@ def compute_searchlight_rdms(
     prior_weight=0.1, noise=None, n_jobs=-1, verbose=0):
     """compute searchlight RDMs takes a list of indices
        and maps the betas to compute an RDM for each
-       searchlight of surface vertices. 
+       searchlight of surface vertices.
 
     Args:
-        indices (_type_): list of searchliht indices 
+        indices (_type_): list of searchliht indices
                     (see prep_surf_indices)
         betas (_type_): betas in shape n_vertices by n_conditions
         des : participant and session details
         obs_des: conditions dictionary e.g. {'conds': 'cond_0',...}
         method: metric for constructing rdm
         for a full description of the arguments, refer to calc_rdm.
-        n_jobs (int, optional): number of cpus available. 
+        n_jobs (int, optional): number of cpus available.
                     Defaults to -1 (find number of cpus automatically).
         verbose (int, optional): level of shouting. Defaults to 0.
 
     Returns:
-        array: searchlight rdms in the shape 
+        array: searchlight rdms in the shape
                n_vertices x n_pairwise_comparisons
     """
     # first deal with making datasets for the searchlights
@@ -110,13 +112,14 @@ def compute_searchlight_rdms(
                 measurements=betas[ind, :].T,
                 descriptors=des,
                 obs_descriptors=obs_des,
-                channel_descriptors=chan_des
+                channel_descriptors=chan_des,
                 )
             )
 
     # next we call calc_rdm. we use joblib parallel
-    # to distribute it if multiple cpus. this might be memory
-    # intense dependent on the number of conditions. 
+    # to distribute it if multiple cpus are present.
+    # this might be memory intense dependent on the
+    # number of conditions.
     group_iter = GroupIterator(len(data), n_jobs)
     with warnings.catch_warnings():  # might not converge
         warnings.simplefilter('ignore', ConvergenceWarning)
@@ -124,47 +127,92 @@ def compute_searchlight_rdms(
             rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
                 delayed(calc_rdm_batch)(
                     np.array(data)[list_i],
-                    method=method, 
+                    method=method,
                     descriptor='conds',
                     cv_descriptor=cv_descriptor,
                     prior_lambda=prior_lambda,
                     prior_weight=prior_weight)
                 for list_i in group_iter)
-    
-    # TODO: repack to list of RDMs object with descriptors 
-    # and chan descriptors. for now rdms is a n_vertices
-    # by n_pairwise_comparisons dissimilarities array
 
-    return np.concatenate(rdms)
+        elif isinstance(noise, np.ndarray) and noise.ndim == 2:
+            rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
+                delayed(calc_rdm_batch)(
+                    np.array(data)[list_i],
+                    method=method,
+                    descriptor='conds',
+                    noise=noise,
+                    cv_descriptor=cv_descriptor,
+                    prior_lambda=prior_lambda,
+                    prior_weight=prior_weight)
+                for list_i in group_iter)
+
+        elif isinstance(noise, Iterable):
+            rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
+                delayed(calc_rdm_batch)(
+                    np.array(data)[list_i],
+                    method=method,
+                    descriptor='conds',
+                    noise=np.array(noise)[list_i],
+                    cv_descriptor=cv_descriptor,
+                    prior_lambda=prior_lambda,
+                    prior_weight=prior_weight)
+                for list_i in group_iter)
+
+        # collect rdms in one array
+        rdms = np.concatenate(rdms)
+
+        # repack to list of RDMs object with descriptors
+        # and chan descriptors.
+        # the descriptor here becomes the index of the 
+        # centre of sphere related to the spherical indices
+        RDMs = [
+            rsr.RDMs(
+                dissimilarities=x,
+                dissimilarity_measure=method,
+                descriptors=des,
+                rdm_descriptors=deepcopy(data[y].descriptors),
+                pattern_descriptors=obs_des
+                ) for y, x in enumerate(rdms)
+            ]
+
+    return RDMs
 
 
 def calc_rdm_batch(
-    data_batch,     
-    method='correlation', descriptor=None, cv_descriptor=None, prior_lambda=1,
-    prior_weight=0.1, noise=None, n_jobs=-1, verbose=0):
+    data_batch,
+    method='correlation', descriptor='conds', cv_descriptor=None, prior_lambda=1,
+    prior_weight=0.1, noise=None):
     """ calc rdm batch
 
     Args:
-        data_batch (_type_): _description_
-        method (str, optional): _description_. Defaults to 'correlation'.
-        descriptor (_type_, optional): _description_. Defaults to None.
-        cv_descriptor (_type_, optional): _description_. Defaults to None.
-        prior_lambda (int, optional): _description_. Defaults to 1.
-        prior_weight (float, optional): _description_. Defaults to 0.1.
-        noise (_type_, optional): _description_. Defaults to None.
-        n_jobs (int, optional): _description_. Defaults to -1.
-        verbose (int, optional): _description_. Defaults to 0.
+        data_batch (list): list of rsa datasets
+        method (str, optional): metric to use. Defaults to 'correlation'.
+        descriptor (dict, optional): key in the dataset descriptors object. 
+                                     Defaults to conds.
+        noise (numpy.ndarray or list): 
+            dataset.n_channel x dataset.n_channel
+            precision matrix used to calculate the RDM
+            used only for Mahalanobis and Crossnobis estimators
+            defaults to an identity matrix, i.e. euclidean distance
+        cv_descriptor (string, optional):
+            obs_descriptor which determines the cross-validation folds.
+            Defaults to None.
+        prior_lambda (int, optional):
+            prior lambda used in symmetrized KL-divergence. Defaults to 1.
+        prior_weight (float, optional):
+            prior weight used in symmetrised KL-divergence. Defaults to 0.1.
 
     Returns:
-        _type_: _description_
+        rdms (numpy.ndarray): dissimilarities for the batch of datasets.
     """
 
     rdms = []
     for data in data_batch:
         rdm = rsr.calc_rdm(
                     data,
-                    method=method, 
+                    method=method,
                     descriptor=descriptor,
+                    noise=noise,
                     cv_descriptor=cv_descriptor,
                     prior_lambda=prior_lambda,
                     prior_weight=prior_weight)

From 6659cb095f81939ba5a85deef6ec5dfe44110961 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 11:39:34 -0400
Subject: [PATCH 06/33] added nilearn to reqs. updated general init

---
 requirements.txt       | 1 +
 rsatoolbox/__init__.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index 5ba4961a..e60c5792 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,3 +9,4 @@ matplotlib
 joblib
 petname==2.2
 pandas
+nilearn
diff --git a/rsatoolbox/__init__.py b/rsatoolbox/__init__.py
index 74d044c5..0802d27f 100644
--- a/rsatoolbox/__init__.py
+++ b/rsatoolbox/__init__.py
@@ -10,3 +10,4 @@
 from . import simulation
 from . import util
 from . import vis
+from . import searchlight
\ No newline at end of file

From 05c1e4f2818599f79cdb2348d2c4d0097b383901 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 12:11:29 -0400
Subject: [PATCH 07/33] some codefactor compliances

---
 rsatoolbox/__init__.py                |  2 +-
 rsatoolbox/searchlight/searchlight.py | 11 ++++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/rsatoolbox/__init__.py b/rsatoolbox/__init__.py
index 0802d27f..9c203313 100644
--- a/rsatoolbox/__init__.py
+++ b/rsatoolbox/__init__.py
@@ -10,4 +10,4 @@
 from . import simulation
 from . import util
 from . import vis
-from . import searchlight
\ No newline at end of file
+from . import searchlight
diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 9a6525ef..7fe77390 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -1,8 +1,9 @@
 
 import warnings
+from collections.abc import Iterable
+
 import numpy as np
 from copy import deepcopy
-from collections.abc import Iterable
 
 from joblib import Parallel, delayed, cpu_count
 from sklearn.exceptions import ConvergenceWarning
@@ -12,7 +13,7 @@
 import rsatoolbox.rdm as rsr
 
 
-class GroupIterator(object):
+class GroupIterator():
     """Group iterator. cf. nilearn.
     Provides group of features for search_light loop
     that may be used with Parallel.
@@ -163,7 +164,7 @@ def compute_searchlight_rdms(
 
         # repack to list of RDMs object with descriptors
         # and chan descriptors.
-        # the descriptor here becomes the index of the 
+        # the descriptor here becomes the index of the
         # centre of sphere related to the spherical indices
         RDMs = [
             rsr.RDMs(
@@ -187,9 +188,9 @@ def calc_rdm_batch(
     Args:
         data_batch (list): list of rsa datasets
         method (str, optional): metric to use. Defaults to 'correlation'.
-        descriptor (dict, optional): key in the dataset descriptors object. 
+        descriptor (dict, optional): key in the dataset descriptors object.
                                      Defaults to conds.
-        noise (numpy.ndarray or list): 
+        noise (numpy.ndarray or list):
             dataset.n_channel x dataset.n_channel
             precision matrix used to calculate the RDM
             used only for Mahalanobis and Crossnobis estimators

From f5d1cf6fb49a98239e2e16fe1fe1791bb2daa0ee Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 12:18:12 -0400
Subject: [PATCH 08/33] some more pep8 style issues

---
 rsatoolbox/searchlight/searchlight.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 7fe77390..e2353c89 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -1,9 +1,9 @@
 
 import warnings
 from collections.abc import Iterable
+from copy import deepcopy
 
 import numpy as np
-from copy import deepcopy
 
 from joblib import Parallel, delayed, cpu_count
 from sklearn.exceptions import ConvergenceWarning

From 77fbadb848c4660f53d85fe0cc962a0ff04a005d Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 12:40:39 -0400
Subject: [PATCH 09/33] some more cdacity fixes

---
 rsatoolbox/searchlight/searchlight.py | 45 +++++++++++++++++----------
 1 file changed, 28 insertions(+), 17 deletions(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index e2353c89..f6ace3a2 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -81,8 +81,14 @@ def compute_searchlight_rdms(
     betas,
     des,
     obs_des,
-    method='correlation',  cv_descriptor=None, prior_lambda=1,
-    prior_weight=0.1, noise=None, n_jobs=-1, verbose=0):
+    method='correlation',
+    cv_descriptor=None,
+    prior_lambda=1,
+    prior_weight=0.1,
+    noise=None,
+    n_jobs=-1,
+    verbose=0
+    ):
     """compute searchlight RDMs takes a list of indices
        and maps the betas to compute an RDM for each
        searchlight of surface vertices.
@@ -114,8 +120,8 @@ def compute_searchlight_rdms(
                 descriptors=des,
                 obs_descriptors=obs_des,
                 channel_descriptors=chan_des,
-                )
             )
+        )
 
     # next we call calc_rdm. we use joblib parallel
     # to distribute it if multiple cpus are present.
@@ -166,23 +172,28 @@ def compute_searchlight_rdms(
         # and chan descriptors.
         # the descriptor here becomes the index of the
         # centre of sphere related to the spherical indices
-        RDMs = [
+        rdm_objects = [
             rsr.RDMs(
                 dissimilarities=x,
                 dissimilarity_measure=method,
                 descriptors=des,
                 rdm_descriptors=deepcopy(data[y].descriptors),
-                pattern_descriptors=obs_des
-                ) for y, x in enumerate(rdms)
-            ]
+                pattern_descriptors=obs_des)
+                for y, x in enumerate(rdms)
+        ]
 
-    return RDMs
+    return rdm_objects
 
 
 def calc_rdm_batch(
     data_batch,
-    method='correlation', descriptor='conds', cv_descriptor=None, prior_lambda=1,
-    prior_weight=0.1, noise=None):
+    method='correlation',
+    descriptor='conds',
+    cv_descriptor=None,
+    prior_lambda=1,
+    prior_weight=0.1,
+    noise=None
+    ):
     """ calc rdm batch
 
     Args:
@@ -210,13 +221,13 @@ def calc_rdm_batch(
     rdms = []
     for data in data_batch:
         rdm = rsr.calc_rdm(
-                    data,
-                    method=method,
-                    descriptor=descriptor,
-                    noise=noise,
-                    cv_descriptor=cv_descriptor,
-                    prior_lambda=prior_lambda,
-                    prior_weight=prior_weight)
+            data,
+            method=method,
+            descriptor=descriptor,
+            noise=noise,
+            cv_descriptor=cv_descriptor,
+            prior_lambda=prior_lambda,
+            prior_weight=prior_weight)
         rdms.append(rdm.dissimilarities)
     
     return np.concatenate(rdms)

From 98c1bd9ace7f6de748d161c2dd0a4988f0443b35 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Tue, 30 Aug 2022 12:42:04 -0400
Subject: [PATCH 10/33] some more cdacity fixes

---
 rsatoolbox/searchlight/searchlight.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index f6ace3a2..748af6fd 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -140,7 +140,6 @@ def compute_searchlight_rdms(
                     prior_lambda=prior_lambda,
                     prior_weight=prior_weight)
                 for list_i in group_iter)
-
         elif isinstance(noise, np.ndarray) and noise.ndim == 2:
             rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
                 delayed(calc_rdm_batch)(
@@ -152,7 +151,6 @@ def compute_searchlight_rdms(
                     prior_lambda=prior_lambda,
                     prior_weight=prior_weight)
                 for list_i in group_iter)
-
         elif isinstance(noise, Iterable):
             rdms = Parallel(n_jobs=n_jobs, verbose=verbose)(
                 delayed(calc_rdm_batch)(

From a97c50cf3efea2b424f348692a60bd0bf1fe011e Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Thu, 1 Sep 2022 15:51:08 -0400
Subject: [PATCH 11/33] now handles subject's native spaces as well.

---
 rsatoolbox/searchlight/searchlight.py | 43 +++++++++++++++++++++++----
 1 file changed, 38 insertions(+), 5 deletions(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 748af6fd..d42565d7 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -1,8 +1,9 @@
 
 import warnings
+import os
 from collections.abc import Iterable
 from copy import deepcopy
-
+from glob import glob
 import numpy as np
 
 from joblib import Parallel, delayed, cpu_count
@@ -34,15 +35,47 @@ def __iter__(self):
         for list_i in split:
             yield list_i
 
+def fetch_surf(fspath, targetspace):
+    """ find a subject's native surface files
+
+    Args:
+        fspath (os.path): the absolute path pointing
+                        to the fs subjects directory
+        targetspace (string): targetspace of the data preparation
+                        e.g. 'fsaverage' if the data is prepared on 
+                        the fsaverage common space, 'subject01' if 
+                        the data is prepared on subject01's native
+                        surface space. 
+
+    Returns:
+        surface (list): list of absolute paths for 
+                        fs subjects inflated left and 
+                        right surfaces.
+    """
+
+    # if usual recon, surfaces are .inflated format
+    infl_list = glob(os.path.join(fspath, targetspace, 'surf', '*.inflated'))
+    infl_list.sort()
+
+    surface = {
+        'infl_left': infl_list[0],
+        'infl_right': infl_list[1]
+    }
+
+    return surface
+
+
 
-def prepare_surf_indices(targetspace, radius):
+def prepare_surf_indices(fspath, targetspace, radius):
     """prepare searchlight indices to be used to
        sample searchlights from fMRI betas prepared
        in surface space.
 
     Args:
+        fspath (os.path): the path where your freesurfer subject's
+                          live. e.g. /opt/freesurfer/subjects
         targetspace (string): what surface space are your betas
-                              prepared in? e.g. 'fsaverage'
+                              prepared in? e.g. 'fsaverage', 'subject01'
         radius (int): what radius do you want the searchlight 'spheres'
                       to cover?
 
@@ -52,7 +85,7 @@ def prepare_surf_indices(targetspace, radius):
                     searchlight RSA, indexing the surface prepared betas
     """
 
-    fsaverage = datasets.fetch_surf_fsaverage(mesh=targetspace)
+    surf = fetch_surf(fspath, targetspace)
 
     hemis = ['left', 'right']
 
@@ -61,7 +94,7 @@ def prepare_surf_indices(targetspace, radius):
     for hemi in hemis:
 
         # we piggy back on nilearn to get inflated coordinates
-        infl_mesh = fsaverage['infl_' + hemi]
+        infl_mesh = surf['infl_' + hemi]
         coords, _ = surface.load_surf_mesh(infl_mesh)
 
         # prepare the nearest neighbours algo

From 6f6da6cab73dbded1216638aa0bd537488efe06d Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Thu, 1 Sep 2022 15:51:57 -0400
Subject: [PATCH 12/33] removed now unused datasets class of nilearn

---
 rsatoolbox/searchlight/searchlight.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index d42565d7..32dbcecb 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -9,7 +9,7 @@
 from joblib import Parallel, delayed, cpu_count
 from sklearn.exceptions import ConvergenceWarning
 from sklearn import neighbors
-from nilearn import datasets, surface
+from nilearn import surface
 import rsatoolbox.data as rsd
 import rsatoolbox.rdm as rsr
 

From 677663f5e5d73c0a2e91bcadc54c078c74116fc8 Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Thu, 1 Sep 2022 15:55:59 -0400
Subject: [PATCH 13/33] fixes, trailing whitespaces..

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

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 32dbcecb..7a950af9 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -42,14 +42,14 @@ def fetch_surf(fspath, targetspace):
         fspath (os.path): the absolute path pointing
                         to the fs subjects directory
         targetspace (string): targetspace of the data preparation
-                        e.g. 'fsaverage' if the data is prepared on 
-                        the fsaverage common space, 'subject01' if 
+                        e.g. 'fsaverage' if the data is prepared on
+                        the fsaverage common space, 'subject01' if
                         the data is prepared on subject01's native
-                        surface space. 
+                        surface space.
 
     Returns:
-        surface (list): list of absolute paths for 
-                        fs subjects inflated left and 
+        surface (list): list of absolute paths for
+                        fs subjects inflated left and
                         right surfaces.
     """
 
@@ -57,12 +57,12 @@ def fetch_surf(fspath, targetspace):
     infl_list = glob(os.path.join(fspath, targetspace, 'surf', '*.inflated'))
     infl_list.sort()
 
-    surface = {
+    surfaces= {
         'infl_left': infl_list[0],
         'infl_right': infl_list[1]
     }
 
-    return surface
+    return surfaces
 
 
 

From f77f678b1f433b77b0cba10d8547be75287a16ba Mon Sep 17 00:00:00 2001
From: Ian Charest <charest.ian@gmail.com>
Date: Wed, 5 Oct 2022 17:20:07 -0400
Subject: [PATCH 14/33] added volumetric searchlight

---
 rsatoolbox/searchlight/searchlight.py | 86 ++++++++++++++++++++++++++-
 1 file changed, 84 insertions(+), 2 deletions(-)

diff --git a/rsatoolbox/searchlight/searchlight.py b/rsatoolbox/searchlight/searchlight.py
index 7a950af9..da2ec0ed 100644
--- a/rsatoolbox/searchlight/searchlight.py
+++ b/rsatoolbox/searchlight/searchlight.py
@@ -13,6 +13,9 @@
 import rsatoolbox.data as rsd
 import rsatoolbox.rdm as rsr
 
+from scipy.spatial.distance import cdist
+from itertools import compress
+
 
 class GroupIterator():
     """Group iterator. cf. nilearn.
@@ -109,6 +112,85 @@ def prepare_surf_indices(fspath, targetspace, radius):
     return sl_indices
 
 
+def _get_searchlight_neighbors(mask, centers, radius=3):
+    """Return indices for searchlight where distance
+        between a voxel and their center < radius (in voxels)
+    Args:
+        center (index):  point around which to make searchlight sphere
+    Returns:
+        list: the list of volume indices that respect the
+                searchlight radius for the input center.
+    """
+    all_indices = []
+    for center in centers:
+        mask_shape = mask.shape
+        cx, cy, cz = center
+        x = np.arange(mask_shape[0])
+        y = np.arange(mask_shape[1])
+        z = np.arange(mask_shape[2])
+
+        # First mask the obvious points
+        # - may actually slow down your calculation depending.
+        x = x[abs(x - cx) < radius]
+        y = y[abs(y - cy) < radius]
+        z = z[abs(z - cz) < radius]
+
+        # Generate grid of points
+        X, Y, Z = np.meshgrid(x, y, z)
+        data = np.vstack((X.ravel(), Y.ravel(), Z.ravel())).T
+        distance = cdist(data, center.reshape(1, -1), 'euclidean').ravel()
+
+        indices = data[distance < radius, :]
+
+        all_indices.append(indices)
+
+    return all_indices
+
+
+def prepare_vol_indices(mask, radius, threshold, n_jobs=-1, verbose=0):
+
+    # find the mask centers
+    centers = np.asarray(list(zip(*np.nonzero(mask))))
+
+    group_iter = GroupIterator(len(centers), n_jobs)
+
+    # get the list of spherical neighbor volume indices
+    with warnings.catch_warnings():  # might not converge
+        warnings.simplefilter('ignore', ConvergenceWarning)
+        neighbors = Parallel(n_jobs=n_jobs, verbose=verbose)(
+            delayed(_get_searchlight_neighbors)(
+                mask,
+                centers[list_i, :],
+                radius)
+            for list_i in group_iter)
+    # unpack
+    neighbors_list = [item for sublist in neighbors for item in sublist]
+
+    # janitor
+    neighbors = neighbors_list
+    
+    # turn the 3-dim coordinates to array coordinates
+    centers = np.ravel_multi_index(centers.T, mask.shape)
+    neighbors = [np.ravel_multi_index(n.T, mask.shape) for n in neighbors]
+
+    mask = mask.flatten()
+
+    # now we only keep those spheres that have at least threshold% brain.
+    good = [mask[n].mean() >=threshold for n in neighbors]
+
+    good_centers = list(compress(centers, good))
+
+    good_centers = np.array(good_centers)
+
+    good_neighbors = list(compress(neighbors, good))
+
+    assert good_centers.shape[0] == len(good_neighbors),\
+        "number of centers and sets of neighbors do not match"
+    print(f'Found {len(good_neighbors)} searchlights')
+
+    return good_centers, good_neighbors
+
+
 def compute_searchlight_rdms(
     indices,
     betas,
@@ -144,8 +226,8 @@ def compute_searchlight_rdms(
     """
     # first deal with making datasets for the searchlights
     data = []
-    for ind in indices.rows:
-        chan_des = {'verts': np.array(['vert_' + str(x) for x in ind])}
+    for ind in indices:
+        chan_des = {'chans': np.array(['chan_' + str(x) for x in ind])}
 
         data.append(
             rsd.Dataset(

From 0d76aa6a3a85044fdbb197e83d3276f363d7225a Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Sun, 19 Feb 2023 18:54:15 +0000
Subject: [PATCH 15/33] reconstructing sl nan error

---
 test_sitek.py | 92 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 test_sitek.py

diff --git a/test_sitek.py b/test_sitek.py
new file mode 100644
index 00000000..dae71ce7
--- /dev/null
+++ b/test_sitek.py
@@ -0,0 +1,92 @@
+"""
+
+Based on Kevin Sitek's code samples in #248 as well as Daniel's tutorial:
+https://rsatoolbox.readthedocs.io/en/latest/demo_searchlight.html
+"""
+
+from os.path import expanduser, join
+from glob import glob
+import numpy as np
+import nibabel as nib
+from rsatoolbox.rdm.rdms import RDMs
+from rsatoolbox.model.model import ModelFixed
+from rsatoolbox.inference import eval_fixed
+from rsatoolbox.util.searchlight import (
+    get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight)
+
+
+data_dir = expanduser('~/data/rsatoolbox/248b_sitek')
+mask_fpath = glob(join(data_dir, '*mask-gm.nii.gz'))[0]
+image_paths = glob(join(data_dir, '*beta.nii.gz'))[:2]
+
+
+mask_img = nib.load(mask_fpath)
+mask_data = mask_img.get_fdata()
+mask = ~np.isnan(mask_data) # daniel
+x, y, z = mask_data.shape
+
+# loop over all images
+data = np.zeros((len(image_paths), x, y, z))
+for x, im in enumerate(image_paths):
+    print(f'loading image {x+1}/{len(image_paths)}')
+    data[x] = nib.load(im).get_fdata()
+
+# only one pattern per image
+image_value = np.arange(len(image_paths))
+
+## 4mins
+centers, neighbors = get_volume_searchlight(mask, radius=5, threshold=0.5)
+
+# reshape data so we have n_observastions x n_voxels
+data_2d = data.reshape([data.shape[0], -1])
+data_2d = np.nan_to_num(data_2d)
+# Get RDMs (3m30)
+SL_RDM = get_searchlight_RDMs(data_2d, centers, neighbors, image_value, method='correlation')
+
+# /Users/jasper/projects/rsatoolbox/env/lib/python3.10/site-packages/rsatoolbox/rdm/calc.py:209: RuntimeWarning: invalid value encountered in divide
+#   ma /= np.sqrt(np.einsum('ij,ij->i', ma, ma))[:, None]
+
+raise ValueError('')
+## MODELS
+tone_rdm = np.array([[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,],
+                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,], ])
+
+talker_rdm = np.array([[0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, ],
+                       [1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ],
+                       [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ],
+                       [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, ],
+                       [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, ],
+                       [1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ],
+                       [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ],
+                       [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, ],
+                       [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, ],
+                       [1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ],
+                       [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ],
+                       [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, ],
+                       [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, ],
+                       [1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ],
+                       [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ],
+                       [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, ], ])
+
+model_rdms = RDMs(
+    np.asarray([tone_rdm, talker_rdm]),
+    rdm_descriptors={'categorical_model':['tone', 'talker'],},
+    dissimilarity_measure='Euclidean'
+)
+tone_model = ModelFixed( 'Tone RDM', model_rdms.subset('categorical_model', 'tone'))
+
+evaluate_models_searchlight(SL_RDM, tone_model, eval_fixed, method='spearman', n_jobs=4)
\ No newline at end of file

From ec52316a475a93f702fdc1b6bfbdae097766f5ad Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Sun, 19 Feb 2023 20:33:04 +0000
Subject: [PATCH 16/33] 248b replicates

---
 test_sitek.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/test_sitek.py b/test_sitek.py
index dae71ce7..a0917c61 100644
--- a/test_sitek.py
+++ b/test_sitek.py
@@ -14,39 +14,40 @@
 from rsatoolbox.util.searchlight import (
     get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight)
 
-
 data_dir = expanduser('~/data/rsatoolbox/248b_sitek')
 mask_fpath = glob(join(data_dir, '*mask-gm.nii.gz'))[0]
-image_paths = glob(join(data_dir, '*beta.nii.gz'))[:2]
+image_paths = glob(join(data_dir, '*beta.nii.gz'))
 
 
 mask_img = nib.load(mask_fpath)
 mask_data = mask_img.get_fdata()
 mask = ~np.isnan(mask_data) # daniel
-x, y, z = mask_data.shape
+
+## 5mins
+centers, neighbors = get_volume_searchlight(mask, radius=5, threshold=0.5)
 
 # loop over all images
+x, y, z = mask_data.shape
+print('reserving memory for betas..')
 data = np.zeros((len(image_paths), x, y, z))
 for x, im in enumerate(image_paths):
     print(f'loading image {x+1}/{len(image_paths)}')
     data[x] = nib.load(im).get_fdata()
 
-# only one pattern per image
-image_value = np.arange(len(image_paths))
-
-## 4mins
-centers, neighbors = get_volume_searchlight(mask, radius=5, threshold=0.5)
-
 # reshape data so we have n_observastions x n_voxels
 data_2d = data.reshape([data.shape[0], -1])
 data_2d = np.nan_to_num(data_2d)
-# Get RDMs (3m30)
-SL_RDM = get_searchlight_RDMs(data_2d, centers, neighbors, image_value, method='correlation')
+# Get RDMs (13m30)
+# only one pattern per image
+events = np.arange(len(image_paths))
+SL_RDM = get_searchlight_RDMs(data_2d, centers, neighbors, events, method='correlation')
 
-# /Users/jasper/projects/rsatoolbox/env/lib/python3.10/site-packages/rsatoolbox/rdm/calc.py:209: RuntimeWarning: invalid value encountered in divide
+#rsatoolbox/env/lib/python3.10/site-packages/rsatoolbox/rdm/calc.py:209: RuntimeWarning: invalid value encountered in divide
 #   ma /= np.sqrt(np.einsum('ij,ij->i', ma, ma))[:, None]
 
-raise ValueError('')
+# rsatoolbox/env/lib/python3.10/site-packages/rsatoolbox/data/computations.py:36: RuntimeWarning: invalid value encountered in multiply
+#   average = np.nan * np.empty(
+
 ## MODELS
 tone_rdm = np.array([[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],
                      [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,],

From 782eef3b1c53d095eb18e0ba473ad3fc42a2ceb4 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Sun, 19 Feb 2023 20:33:24 +0000
Subject: [PATCH 17/33] keeping notes for PR

---
 pr_notes.md | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/pr_notes.md b/pr_notes.md
index 48cdce85..f28b198d 100644
--- a/pr_notes.md
+++ b/pr_notes.md
@@ -1 +1,14 @@
-placeholder
+## Ian's surface todo list
+
+- [x] module to get surface searchlight indices
+- [x] module to get volume searchlight indices
+- [x] module to compute brain rdms from searchlight 
+indices and betas  
+- [x] re-pack dissimilarities in rdm object list to accomodate eval
+- [ ] make inference (and possibly integrating parallel joblib there too.)
+- [x] handle native, or fsaverage data preparation spaces.
+- [ ] prepare demos
+- [ ] unit tests
+- [x] refactor get_distance so that it flexibly uses the rsatoolboc compute_rdms functionalities
+- [x] add nilearn to requirements
+

From 77678dcce2067db2d680163b210a73bef870d88e Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 16:01:29 +0000
Subject: [PATCH 18/33] moved surface searchlight code into src

---
 rsatoolbox/searchlight/__init__.py                              | 2 --
 .../searchlight.py => src/rsatoolbox/searchlight/surface.py     | 0
 2 files changed, 2 deletions(-)
 delete mode 100644 rsatoolbox/searchlight/__init__.py
 rename rsatoolbox/searchlight/searchlight.py => src/rsatoolbox/searchlight/surface.py (100%)

diff --git a/rsatoolbox/searchlight/__init__.py b/rsatoolbox/searchlight/__init__.py
deleted file mode 100644
index 490db86c..00000000
--- a/rsatoolbox/searchlight/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .searchlight import compute_searchlight_rdms
-from .searchlight import prepare_surf_indices
diff --git a/rsatoolbox/searchlight/searchlight.py b/src/rsatoolbox/searchlight/surface.py
similarity index 100%
rename from rsatoolbox/searchlight/searchlight.py
rename to src/rsatoolbox/searchlight/surface.py

From 479c056b3fbd9c496ca308b1287f294d1dfef6f3 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 16:05:04 +0000
Subject: [PATCH 19/33] moved volume searchlight module to searchlight
 subpackage

---
 demos/demo_searchlight_volume.ipynb                |  6 +++---
 .../{util/searchlight.py => searchlight/volume.py} |  2 +-
 tests/test_searchlight.py                          | 14 ++++----------
 3 files changed, 8 insertions(+), 14 deletions(-)
 rename src/rsatoolbox/{util/searchlight.py => searchlight/volume.py} (99%)

diff --git a/demos/demo_searchlight_volume.ipynb b/demos/demo_searchlight_volume.ipynb
index 923ff6f5..5a64b524 100644
--- a/demos/demo_searchlight_volume.ipynb
+++ b/demos/demo_searchlight_volume.ipynb
@@ -53,7 +53,7 @@
     "from rsatoolbox.model import ModelFixed\n",
     "from rsatoolbox.rdm import RDMs\n",
     "from glob import glob\n",
-    "from rsatoolbox.util.searchlight import get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight"
+    "from rsatoolbox.searchlight.volume import get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight"
    ]
   },
   {
@@ -276,7 +276,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 864x216 with 14 Axes>"
       ]
diff --git a/src/rsatoolbox/util/searchlight.py b/src/rsatoolbox/searchlight/volume.py
similarity index 99%
rename from src/rsatoolbox/util/searchlight.py
rename to src/rsatoolbox/searchlight/volume.py
index d414b9bd..5070ad69 100644
--- a/src/rsatoolbox/util/searchlight.py
+++ b/src/rsatoolbox/searchlight/volume.py
@@ -157,7 +157,7 @@ def get_searchlight_RDMs(data_2d, centers, neighbors, events,
             RDM_corr = calc_rdm(center_data, method=method,
                                 descriptor='events')
             RDM[chunks, :] = RDM_corr.dissimilarities
-    else:
+    else: ## TODO: remove as this seems a very unlikely case
         center_data = []
         for c in range(n_centers):
             # grab this center and neighbors
diff --git a/tests/test_searchlight.py b/tests/test_searchlight.py
index e04d9e6b..35d9c9b4 100644
--- a/tests/test_searchlight.py
+++ b/tests/test_searchlight.py
@@ -8,8 +8,9 @@
 
 
 class TestSearchlight(unittest.TestCase):
+
     def test__get_searchlight_neighbors(self):
-        from rsatoolbox.util.searchlight import _get_searchlight_neighbors
+        from rsatoolbox.searchlight.volume import _get_searchlight_neighbors
 
         mask = np.zeros((5, 5, 5))
         center = [2, 2, 2]
@@ -17,13 +18,11 @@ def test__get_searchlight_neighbors(self):
         radius = 2
         # a radius of 2 will give us
         neighbors = _get_searchlight_neighbors(mask, center, radius=radius)
-
         assert np.array(neighbors).shape  == (3, 27)
         assert np.mean(mask[tuple(neighbors)]) == 10/27
 
     def test_get_volume_searchlight(self):
-        from rsatoolbox.util.searchlight import get_volume_searchlight
-
+        from rsatoolbox.searchlight.volume import get_volume_searchlight
         mask = np.array([[[False, False, False],
                         [False,  True, False],
                         [False, False, False]],
@@ -35,22 +34,17 @@ def test_get_volume_searchlight(self):
                         [[False, False, False],
                         [False,  True, False],
                         [False, False, False]]], dtype=int)
-
-
         centers, neighbors = get_volume_searchlight(mask, radius=1, threshold=1.0)
         assert len(centers) == 7
         assert len(neighbors) == 7
 
     def test_get_searchlight_RDMs(self):
-        from rsatoolbox.util.searchlight import get_searchlight_RDMs
-
+        from rsatoolbox.searchlight.volume import get_searchlight_RDMs
         n_observations = 5
         n_voxels = 5
         data_2d = np.random.random((n_observations, n_voxels))
         centers = np.array([1, 3])
         neighbors = [[0,1,2], [2,3,4]]
         events = np.arange(n_observations)
-
         sl_RDMs = get_searchlight_RDMs(data_2d, centers, neighbors, events)
-
         assert sl_RDMs.dissimilarities.shape == (2, 10)

From 4a88f4f5ea72f05417969559afdc771dcc97c6ae Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 16:07:16 +0000
Subject: [PATCH 20/33] removed top-level import of searchlight module

---
 src/rsatoolbox/__init__.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/rsatoolbox/__init__.py b/src/rsatoolbox/__init__.py
index 985ee74d..1f393210 100644
--- a/src/rsatoolbox/__init__.py
+++ b/src/rsatoolbox/__init__.py
@@ -11,4 +11,3 @@
 from . import simulation
 from . import util
 from . import vis
-from . import searchlight

From 83227d66231e5c03209d9cc366c64daef6d16d18 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 16:37:06 +0000
Subject: [PATCH 21/33] reran volume searchlight demo with new code

---
 demos/demo_searchlight_volume.ipynb | 79 ++++++++++++-----------------
 1 file changed, 32 insertions(+), 47 deletions(-)

diff --git a/demos/demo_searchlight_volume.ipynb b/demos/demo_searchlight_volume.ipynb
index 5a64b524..70b80778 100644
--- a/demos/demo_searchlight_volume.ipynb
+++ b/demos/demo_searchlight_volume.ipynb
@@ -29,18 +29,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/heiko/opt/anaconda3/envs/rsa/lib/python3.8/site-packages/nilearn/datasets/__init__.py:86: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
-      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -58,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,12 +102,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "# set this path to wherever you saved the folder containing the img-files\n",
-    "data_folder = '../fmri_data/subj02'\n",
+    "from os.path import expanduser\n",
+    "data_folder = expanduser('~/data/rsatoolbox/cichy2016/subj02')\n",
     "\n",
     "image_paths = list(glob(f\"{data_folder}/con_*.img\"))\n",
     "image_paths.sort()\n",
@@ -147,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -156,7 +148,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finding searchlights...: 100%|██████████| 61044/61044 [00:20<00:00, 3028.22it/s]\n"
+      "Finding searchlights...: 100%|██████████| 61044/61044 [00:08<00:00, 7597.19it/s]\n"
      ]
     },
     {
@@ -180,14 +172,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating RDMs...: 100%|██████████| 100/100 [10:59<00:00,  6.60s/it]\n"
+      "Calculating RDMs...: 100%|██████████| 100/100 [04:23<00:00,  2.64s/it]\n"
      ]
     }
    ],
@@ -210,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,14 +214,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Evaluating models for each searchlight: 100%|██████████| 56954/56954 [02:40<00:00, 355.41it/s]\n"
+      "Evaluating models for each searchlight: 100%|██████████| 56954/56954 [01:25<00:00, 668.46it/s]\n"
      ]
     }
    ],
@@ -238,12 +230,12 @@
     "\n",
     "# get the evaulation score for each voxel\n",
     "# We only have one model, but evaluations returns a list. By using float we just grab the value within that list\n",
-    "eval_score = [np.float(e.evaluations) for e in eval_results]"
+    "eval_score = [float(e.evaluations) for e in eval_results]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,32 +255,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/heiko/opt/anaconda3/envs/rsa/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.distplot(eval_score)\n",
+    "sns.displot(eval_score)\n",
     "plt.title('Distributions of correlations', size=18)\n",
     "plt.ylabel('Occurance', size=18)\n",
     "plt.xlabel('Spearmann correlation', size=18)\n",
@@ -305,19 +287,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 864x216 with 14 Axes>"
+       "<Figure size 1200x300 with 14 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -342,7 +322,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -356,7 +336,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "af6f0c1be22da210ce14b764d3d407b4e31df46360687c396ac7d1fbf0a9a76f"
+   }
   }
  },
  "nbformat": 4,

From b9cdbd6da4a51b6bf1addbf678d59abf8188260f Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 17:04:50 +0000
Subject: [PATCH 22/33] different masking strategy for sitek data

---
 test_sitek.py | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/test_sitek.py b/test_sitek.py
index a0917c61..25e7910a 100644
--- a/test_sitek.py
+++ b/test_sitek.py
@@ -11,7 +11,7 @@
 from rsatoolbox.rdm.rdms import RDMs
 from rsatoolbox.model.model import ModelFixed
 from rsatoolbox.inference import eval_fixed
-from rsatoolbox.util.searchlight import (
+from rsatoolbox.searchlight.volume import (
     get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight)
 
 data_dir = expanduser('~/data/rsatoolbox/248b_sitek')
@@ -21,23 +21,29 @@
 
 mask_img = nib.load(mask_fpath)
 mask_data = mask_img.get_fdata()
-mask = ~np.isnan(mask_data) # daniel
 
-## 5mins
-centers, neighbors = get_volume_searchlight(mask, radius=5, threshold=0.5)
 
 # loop over all images
 x, y, z = mask_data.shape
 print('reserving memory for betas..')
 data = np.zeros((len(image_paths), x, y, z))
-for x, im in enumerate(image_paths):
-    print(f'loading image {x+1}/{len(image_paths)}')
-    data[x] = nib.load(im).get_fdata()
+for i, im in enumerate(image_paths):
+    print(f'loading image {i+1}/{len(image_paths)}')
+    data[i] = nib.load(im).get_fdata()
 
 # reshape data so we have n_observastions x n_voxels
 data_2d = data.reshape([data.shape[0], -1])
 data_2d = np.nan_to_num(data_2d)
-# Get RDMs (13m30)
+
+
+## mask based on all-zero patterns
+mask_2d = ~np.all(data_2d==0, axis=0)
+mask_3d = mask_2d.reshape(x, y, z)
+
+## 5mins
+centers, neighbors = get_volume_searchlight(mask_3d, radius=5, threshold=0.5)
+
+# Get RDMs
 # only one pattern per image
 events = np.arange(len(image_paths))
 SL_RDM = get_searchlight_RDMs(data_2d, centers, neighbors, events, method='correlation')
@@ -90,4 +96,5 @@
 )
 tone_model = ModelFixed( 'Tone RDM', model_rdms.subset('categorical_model', 'tone'))
 
-evaluate_models_searchlight(SL_RDM, tone_model, eval_fixed, method='spearman', n_jobs=4)
\ No newline at end of file
+
+evaluate_models_searchlight(SL_RDM, tone_model, eval_fixed, method='spearman', n_jobs=4)

From a484a9cd316efdb7d0717a876e27fdce37daad7c Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Mon, 20 Feb 2023 17:33:28 +0000
Subject: [PATCH 23/33] value-based masking for the jones test data as well

---
 pr_notes.md   | 48 +++++++++++++++++++++++++++++++----------
 test_jones.py | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 97 insertions(+), 11 deletions(-)
 create mode 100644 test_jones.py

diff --git a/pr_notes.md b/pr_notes.md
index f28b198d..fbf6ce12 100644
--- a/pr_notes.md
+++ b/pr_notes.md
@@ -1,14 +1,40 @@
-## Ian's surface todo list
+## Ian's mutations
 
-- [x] module to get surface searchlight indices
-- [x] module to get volume searchlight indices
-- [x] module to compute brain rdms from searchlight 
+- module to get surface searchlight indices
+- module to get volume searchlight indices
+- module to compute brain rdms from searchlight 
 indices and betas  
-- [x] re-pack dissimilarities in rdm object list to accomodate eval
-- [ ] make inference (and possibly integrating parallel joblib there too.)
-- [x] handle native, or fsaverage data preparation spaces.
-- [ ] prepare demos
-- [ ] unit tests
-- [x] refactor get_distance so that it flexibly uses the rsatoolboc compute_rdms functionalities
-- [x] add nilearn to requirements
+- re-pack dissimilarities in rdm object list to accomodate eval
+- handle native, or fsaverage data preparation spaces.
+- refactor get_distance so that it flexibly uses the rsatoolboc compute_rdms functionalities
+- add nilearn to requirements
+
+## jasper's todo's
+
+- [ ] remove chunking "else" case of fewer than 1000 centres
+- [ ] evaluate fail silently if nan's
+- [ ] run daniel's tutorial with current code to see if that fails too??
+- [ ] subpress divide by zero warning in calc_corr
+- [ ] remove these notes
+- [ ] remove ad hoc test scripts
+- [ ] remove deprecated fns demo
+- [ ] test masking on sitek script
+- [ ] test masking on jones script
+
+## discussion
+
+- searchlight subpackage or .pipeline?
+
+```
+# ## this is the part that throws the error about nans.
+# ## the evaluate fn calls compare() which checks that both rdms only have nans
+# ## in the same locations. 
+# rdm1 = tone_model.predict_rdm(theta=None)
+# rdm2 = SL_RDM[1_000_000]
+# vector1 = rdm1.get_vectors()
+# vector2 = rdm2.get_vectors()
+# from rsatoolbox.rdm.compare import _parse_input_rdms
+# _parse_input_rdms(rdm1, rdm2)
+#eval_fixed(tone_model, SL_RDM[1_000_000], method='spearman', theta=None)
+```
 
diff --git a/test_jones.py b/test_jones.py
new file mode 100644
index 00000000..b00380bc
--- /dev/null
+++ b/test_jones.py
@@ -0,0 +1,60 @@
+from os.path import expanduser, join
+import numpy
+import rsatoolbox
+from scipy import io
+from rsatoolbox.inference import eval_fixed
+from rsatoolbox.searchlight.volume import get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight
+data_dir = expanduser('~/data/rsatoolbox/248a_jones-michael-s')
+
+
+# ----- load data ---------------------------------------------------
+
+# 2D array (nconditions x nvoxels) saved from matlab
+
+dataFile = join(data_dir, 'condition_data.mat')
+
+temp = io.matlab.loadmat(dataFile)
+varkey = list(temp)[-1]
+data = temp[varkey]
+# can convert nan here or convert nan in matlab before the
+# file is saved (or both) - you get the same result
+data = numpy.nan_to_num(data)
+
+# ---- load model ---------------------------------------------
+
+# 2D array (nconditions x nfeatures) saved from matlab
+
+modelFile = join(data_dir, 'model.mat')
+
+temp = io.matlab.loadmat(modelFile)
+varkey = list(temp)[-1]
+model_def = temp[varkey]
+
+model_features = [rsatoolbox.data.Dataset(model_def)]
+model_RDM = rsatoolbox.rdm.calc_rdm(model_features)
+model = rsatoolbox.model.ModelFixed('fixed_model',model_RDM)
+
+# ---- load mask -------------------------------------------
+
+# 0/1 mask saved as 3D array from matlab
+# [nx ny nz] where nx*ny*nz = nvoxels
+# converted to boolean (although 0/1 seems to work)
+
+maskFile = join(data_dir, 'searchlight_mask.mat')
+
+temp = io.matlab.loadmat(maskFile)
+varkey = list(temp)[-1]
+imask = temp[varkey]
+#mask = imask > 0
+
+## mask based on all-zero patterns
+mask_2d = ~numpy.all(data==0, axis=0)
+x,y,z = imask.shape
+mask = mask_2d.reshape(x, y, z)
+
+# ---- searchlight -----------------------------------------
+
+image_value = numpy.arange(data.shape[0])
+centers, neighbors = get_volume_searchlight(mask)
+SL_RDM = get_searchlight_RDMs(data, centers, neighbors, image_value, method='correlation')
+eval_results = evaluate_models_searchlight(SL_RDM, model, eval_fixed, method='spearman', n_jobs=3)
\ No newline at end of file

From 564a04349dddd6b60651761b8e06213c07e5a628 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Tue, 21 Feb 2023 19:26:35 +0000
Subject: [PATCH 24/33] started on surface sl demo

---
 demos/demo_searchlight_surface.py |  9 +++++++++
 pr_notes.md                       | 12 ++++++++----
 2 files changed, 17 insertions(+), 4 deletions(-)
 create mode 100644 demos/demo_searchlight_surface.py

diff --git a/demos/demo_searchlight_surface.py b/demos/demo_searchlight_surface.py
new file mode 100644
index 00000000..09aa52c1
--- /dev/null
+++ b/demos/demo_searchlight_surface.py
@@ -0,0 +1,9 @@
+'ds001499'
+
+https://nipy.org/nibabel/reference/nibabel.freesurfer.html#nibabel.freesurfer.io.read_geometry
+
+
+## how fast is it?
+## how much faster than volume?
+## how flexible is input?
+## what is missing?
\ No newline at end of file
diff --git a/pr_notes.md b/pr_notes.md
index fbf6ce12..37ba853c 100644
--- a/pr_notes.md
+++ b/pr_notes.md
@@ -11,19 +11,23 @@ indices and betas
 
 ## jasper's todo's
 
+- [x] test masking on sitek script
+- [x] test masking on jones script
+- [x] run daniel's tutorial with current code to see if that fails too??
+- [ ] bold5000 for surface?
 - [ ] remove chunking "else" case of fewer than 1000 centres
 - [ ] evaluate fail silently if nan's
-- [ ] run daniel's tutorial with current code to see if that fails too??
 - [ ] subpress divide by zero warning in calc_corr
 - [ ] remove these notes
 - [ ] remove ad hoc test scripts
 - [ ] remove deprecated fns demo
-- [ ] test masking on sitek script
-- [ ] test masking on jones script
+
 
 ## discussion
 
-- searchlight subpackage or .pipeline?
+- searchlight subpackage, .pipeline, some in .io
+- catch nans early: yes (in searchlight)
+- reason not to split into stages: potential to parallelize across nodes, need to not load verything in memory
 
 ```
 # ## this is the part that throws the error about nans.

From f49542458d48251c78969077ed1e6672ae2a271c Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Wed, 22 Feb 2023 19:26:25 +0000
Subject: [PATCH 25/33] some type annotations on rdm_utils fns

---
 src/rsatoolbox/util/rdm_utils.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/rsatoolbox/util/rdm_utils.py b/src/rsatoolbox/util/rdm_utils.py
index c65f6afc..e5b6813b 100644
--- a/src/rsatoolbox/util/rdm_utils.py
+++ b/src/rsatoolbox/util/rdm_utils.py
@@ -13,7 +13,7 @@
     from rsatoolbox.rdm.rdms import RDMs
 
 
-def batch_to_vectors(x):
+def batch_to_vectors(x: NDArray) -> Tuple[NDArray, int, int]:
     """converts a *stack* of RDMs in vector or matrix form into vector form
 
     Args:
@@ -26,7 +26,11 @@ def batch_to_vectors(x):
 
         **n_cond** (int): number of conditions
     """
-    if x.ndim == 2:
+    if x.ndim == 1:
+        v = np.array([x])
+        n_rdm = 1
+        n_cond = _get_n_from_reduced_vectors(v)
+    elif x.ndim == 2:
         v = x
         n_rdm = x.shape[0]
         n_cond = _get_n_from_reduced_vectors(x)
@@ -37,10 +41,8 @@ def batch_to_vectors(x):
         v = np.ndarray((n_rdm, int(n_cond * (n_cond - 1) / 2)))
         for idx in np.arange(n_rdm):
             v[idx, :] = squareform(m[idx, :, :], checks=False)
-    elif x.ndim == 1:
-        v = np.array([x])
-        n_rdm = 1
-        n_cond = _get_n_from_reduced_vectors(v)
+    else:
+        raise ValueError('[batch_to_vectors] input array has too many dims')
     return v, n_rdm, n_cond
 
 
@@ -71,7 +73,7 @@ def batch_to_matrices(x):
     return m, n_rdm, n_cond
 
 
-def _get_n_from_reduced_vectors(x):
+def _get_n_from_reduced_vectors(x: NDArray) -> int:
     """
     calculates the size of the RDM from the vector representation
 
@@ -85,7 +87,7 @@ def _get_n_from_reduced_vectors(x):
     return max(int(np.ceil(np.sqrt(x.shape[1] * 2))), 1)
 
 
-def _get_n_from_length(n):
+def _get_n_from_length(n: int) -> int:
     """
     calculates the size of the RDM from the vector length
 

From a02ed86d66ac7bfc7fa0b4fb3d33e277f9596515 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Wed, 22 Feb 2023 19:26:48 +0000
Subject: [PATCH 26/33] model module: absolute imports

---
 src/rsatoolbox/model/model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/rsatoolbox/model/model.py b/src/rsatoolbox/model/model.py
index 2537c2a7..d4f6362c 100644
--- a/src/rsatoolbox/model/model.py
+++ b/src/rsatoolbox/model/model.py
@@ -5,8 +5,8 @@
 """
 
 import numpy as np
-from rsatoolbox.rdm import RDMs
-from rsatoolbox.rdm import rdms_from_dict
+from rsatoolbox.rdm.rdms import RDMs
+from rsatoolbox.rdm.rdms import rdms_from_dict
 from rsatoolbox.util.rdm_utils import batch_to_vectors
 from .fitter import fit_mock, fit_optimize, fit_select, fit_interpolate
 

From 10e9134541a047e940a72172eecb96480f27303a Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Wed, 22 Feb 2023 19:40:43 +0000
Subject: [PATCH 27/33] volume sl demo removed unnecessary fns

---
 demos/demo_searchlight_volume.ipynb | 103 ++++++++--------------------
 1 file changed, 28 insertions(+), 75 deletions(-)

diff --git a/demos/demo_searchlight_volume.ipynb b/demos/demo_searchlight_volume.ipynb
index 70b80778..e9acf732 100644
--- a/demos/demo_searchlight_volume.ipynb
+++ b/demos/demo_searchlight_volume.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -8,7 +9,7 @@
     "By Daniel Lindh (dnllndh@gmail.com)\n",
     "\n",
     "Data used in this tutorial was used in:\n",
-    "<i>Cichy, R. M., Pantazis, D., & Oliva, A. (2016). Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition. Cerebral Cortex, 26(8), 3563-3579.</i>\n",
+    "*Cichy, R. M., Pantazis, D., & Oliva, A. (2016). Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition. Cerebral Cortex, 26(8), 3563-3579.*\n",
     "\n",
     "In this tutorial we will load a publically available fMRI data set for one participant. In this data set subjects passively viewed 118 images (see [project page](https://userpage.fu-berlin.de/rmcichy/fusion_project_page/main.html), where 27 images were animate and 91 were non-animate. Using RSA we will test an \"animate\" model to see which voxels most correspond to animate category. \n",
     "\n",
@@ -18,77 +19,34 @@
     "4. Plot results\n",
     "\n",
     "\n",
-    "On top of using rsatoolbox, this tutorial also make use of the following:\n",
-    "* numpy 1.18.1\n",
-    "* matplotlib 3.1.3\n",
-    "* nilearn 0.6.2\n",
-    "* pandas 1.0.1\n",
-    "* seaborn 0.10.0\n",
-    "* nibabel 3.1.0"
+    "On top of using rsatoolbox, this tutorial also makes use of the following:\n",
+    "\n",
+    "- numpy 1.18.1\n",
+    "- matplotlib 3.1.3\n",
+    "- nilearn 0.6.2\n",
+    "- seaborn 0.10.0\n",
+    "- nibabel 3.1.0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from glob import glob\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from nilearn.image import new_img_like\n",
-    "import pandas as pd\n",
+    "from nilearn import plotting\n",
     "import nibabel as nib\n",
     "import seaborn as sns\n",
-    "from nilearn import plotting\n",
     "from rsatoolbox.inference import eval_fixed\n",
     "from rsatoolbox.model import ModelFixed\n",
-    "from rsatoolbox.rdm import RDMs\n",
-    "from glob import glob\n",
+    "from rsatoolbox.util.rdm_utils import batch_to_vectors\n",
     "from rsatoolbox.searchlight.volume import get_volume_searchlight, get_searchlight_RDMs, evaluate_models_searchlight"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def upper_tri(RDM):\n",
-    "    \"\"\"upper_tri returns the upper triangular index of an RDM\n",
-    "    \n",
-    "    Args:\n",
-    "        RDM 2Darray: squareform RDM\n",
-    "    \n",
-    "    Returns:\n",
-    "        1D array: upper triangular vector of the RDM\n",
-    "    \"\"\"\n",
-    "    # returns the upper triangle\n",
-    "    m = RDM.shape[0]\n",
-    "    r, c = np.triu_indices(m, 1)\n",
-    "    return RDM[r, c]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.colors\n",
-    "def RDMcolormapObject(direction=1):\n",
-    "    \"\"\"\n",
-    "    Returns a matplotlib color map object for RSA and brain plotting\n",
-    "    \"\"\"\n",
-    "    if direction == 0:\n",
-    "        cs = ['yellow', 'red', 'gray', 'turquoise', 'blue']\n",
-    "    elif direction == 1:\n",
-    "        cs = ['blue', 'turquoise', 'gray', 'red', 'yellow']\n",
-    "    else:\n",
-    "        raise ValueError('Direction needs to be 0 or 1')\n",
-    "    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", cs)\n",
-    "    return cmap"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -102,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -148,7 +106,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Finding searchlights...: 100%|██████████| 61044/61044 [00:08<00:00, 7597.19it/s]\n"
+      "Finding searchlights...: 100%|██████████| 61044/61044 [00:08<00:00, 7528.27it/s]\n"
      ]
     },
     {
@@ -172,14 +130,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating RDMs...: 100%|██████████| 100/100 [04:23<00:00,  2.64s/it]\n"
+      "Calculating RDMs...: 100%|██████████| 100/100 [04:21<00:00,  2.62s/it]\n"
      ]
     }
    ],
@@ -202,26 +160,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load animal model\n",
-    "an_labels = np.load('118_images_files/118_animate_labels.npy')\n",
-    "an_RDM = np.load('118_images_files/118_animate_RDM.npy')\n",
-    "an_model = ModelFixed('Animate RDM', upper_tri(an_RDM))"
+    "animal_rdm_array = np.load('118_images_files/118_animate_RDM.npy')\n",
+    "an_model = ModelFixed('Animate RDM', batch_to_vectors(animal_rdm_array)[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Evaluating models for each searchlight: 100%|██████████| 56954/56954 [01:25<00:00, 668.46it/s]\n"
+      "Evaluating models for each searchlight: 100%|██████████| 56954/56954 [01:22<00:00, 691.96it/s]\n"
      ]
     }
    ],
@@ -235,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -287,12 +244,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x300 with 14 Axes>"
       ]
@@ -305,16 +262,12 @@
     "# lets plot the voxels above the 99th percentile\n",
     "threshold = np.percentile(eval_score, 99)\n",
     "plot_img = new_img_like(tmp_img, RDM_brain)\n",
-    "\n",
-    "cmap = RDMcolormapObject()\n",
-    "\n",
     "coords = range(-20, 40, 5)\n",
     "fig = plt.figure(figsize=(12, 3))\n",
-    "\n",
     "display = plotting.plot_stat_map(\n",
     "        plot_img, colorbar=True, cut_coords=coords,threshold=threshold,\n",
     "        display_mode='z', draw_cross=False, figure=fig, \n",
-    "        title=f'Animal model evaluation', cmap=cmap, \n",
+    "        title=f'Animal model evaluation', cmap='plasma', \n",
     "        black_bg=False, annotate=False)\n",
     "plt.show()"
    ]

From 4274942256a33f5514398fc4015467b78d8905c0 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <someone@somewhere.com>
Date: Sat, 25 Feb 2023 12:24:58 +0000
Subject: [PATCH 28/33] pr notes updated

---
 pr_notes.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pr_notes.md b/pr_notes.md
index 37ba853c..96b8a292 100644
--- a/pr_notes.md
+++ b/pr_notes.md
@@ -14,13 +14,15 @@ indices and betas
 - [x] test masking on sitek script
 - [x] test masking on jones script
 - [x] run daniel's tutorial with current code to see if that fails too??
+- [x] remove deprecated fns demo
 - [ ] bold5000 for surface?
 - [ ] remove chunking "else" case of fewer than 1000 centres
 - [ ] evaluate fail silently if nan's
 - [ ] subpress divide by zero warning in calc_corr
 - [ ] remove these notes
 - [ ] remove ad hoc test scripts
-- [ ] remove deprecated fns demo
+- [ ] mp vs jl
+
 
 
 ## discussion

From 2842b106180f0ec32148e80701b23d91af488103 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <japsai@gmail.com>
Date: Tue, 25 Jul 2023 10:09:06 +0100
Subject: [PATCH 29/33] reading bold 5k surface data

---
 demos/demo_searchlight_surface_bold5k.py | 113 +++++++++++++++++++++++
 src/rsatoolbox/searchlight/surface.py    |   5 +
 2 files changed, 118 insertions(+)
 create mode 100644 demos/demo_searchlight_surface_bold5k.py

diff --git a/demos/demo_searchlight_surface_bold5k.py b/demos/demo_searchlight_surface_bold5k.py
new file mode 100644
index 00000000..f90a7d34
--- /dev/null
+++ b/demos/demo_searchlight_surface_bold5k.py
@@ -0,0 +1,113 @@
+""""
+
+The 214 repeated images are spread out over 100 runs in 10 sessions (2 images per run).
+
+GLMsingle valid?
+
+"""
+## suppress warnings on nibabel
+# pyright: reportPrivateImportUsage=false 
+from os.path import expanduser, join
+import datalad.api as datalad
+import json, glob
+import nibabel, pandas
+
+#description: https://bold5000-dataset.github.io/website/overview.html
+# 112 images repeated four times, and one image repeated three times
+
+## this will setup a local copy of the dataset, but only downloads text files
+openneuro_id = 1499
+your_data_dir = expanduser('~/data')
+dataset_dir = join(your_data_dir, f'ds00{openneuro_id}')
+fmriprep_dir = join(dataset_dir, 'derivatives', 'fmriprep')
+dl = datalad.clone(
+    source=f'///openneuro/ds00{openneuro_id}', 
+    path=dataset_dir,
+    description='BOLD5000 v1'
+)
+
+
+## download fmriprep output for subject 1; ~49GB, 12h to download
+dl.get('derivatives/fmriprep/sub-CSI1/')
+
+# meta_fpath = join(root_dir, 'sub-04/ses-1/func/sub-04_ses-1_task-motor_run-01_bold.json')
+# with open(meta_fpath) as fhandle:
+#     metadata = json.load(fhandle)
+# TR = metadata['RepetitionTime']
+
+fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
+img_anat = nibabel.load(fpath_anat)
+coords = img_anat.agg_data('pointset')
+
+
+"""388s 194 volumes
+
+
+
+"""
+all_runs = []
+for s in range(1, 15+1):
+    ses_dir = join(dataset_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
+    ses_fmriprep_dir = join(fmriprep_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
+    run_evt_fpaths = glob.glob(join(ses_dir, '*_task-5000scenes_*_events.tsv'))
+    for fpath in run_evt_fpaths:
+        r = int(fpath.split('_')[-2][-2:])
+        run_df = pandas.read_csv(fpath, sep='\t')
+        bold_fname = f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_bold_space-fsnative.L.func.gii'
+        bold_fpath = join(ses_fmriprep_dir, bold_fname)
+        data = nibabel.load(bold_fpath).agg_data() # (135186, 194) # 100MB
+        all_runs.append(run_df)
+        raise ValueError
+df = pandas.concat(all_runs)
+
+## zip ses/run assume 10 runs, exit if run not there
+
+""" reps by cat
+In [44]: df[df.ImgType=='rep_coco'].ImgName.value_counts().size
+Out[44]: 45
+
+In [45]: df[df.ImgType=='rep_scenes'].ImgName.value_counts().size
+Out[45]: 23
+
+In [46]: df[df.ImgType=='rep_imagenet'].ImgName.value_counts().size
+Out[46]: 45
+"""
+
+
+
+
+
+
+"""from paper methods:
+
+In order to examine the effect of image repetition, we randomly 
+selected 112 of the 4,916 distinct images to be shown four times 
+and one image to be shown three times to each participant. 
+These 113 images were selected such that the image dataset breakdown 
+was proportionally to that of the 4,916 distinct images. 
+Specifically, 1/5 of the images were Scene images, 2/5 of the images were COCO images, 
+2/5 of the images were ImageNet images. When these image repetitions are considered, 
+we have a total of 5,254 image presentations shown to each participant 
+(4,803 distinct images +4 × 112 repeated images +3 × 1 repeated image). 
+For CSI3 and CSI4, a small number of repetitions varied from 2–5 times.
+
+----
+
+The following image presentation details apply for each run, each session, 
+and each participant. A slow event-related design was implemented for stimulus 
+presentation in order to isolate the blood oxygen level dependent (BOLD) signal 
+for each individual image trial. At the beginning and end of each run, 
+centered on a blank, black screen, a fixation cross was shown for 6 sec and 12 sec, 
+respectively. Following the initial fixation cross, all 37 stimuli were shown sequentially. 
+Each image was presented for 1 sec followed by a 9 sec fixation cross. Given that each run 
+contains 37 stimuli, there was a total of 370 sec of stimulus presentation plus fixation. 
+Including the pre- and post-stimulus fixations, there were a total of 388 sec 
+(6 min 28 sec) of data acquired in each run.
+
+For each stimulus image shown, each participant performed a valence judgment task, 
+responding with how much they liked the image using the metric: “like”, “neutral”, 
+“dislike”. Responses were collected during the 9 sec interval comprising the interstimulus 
+fixation, that is, subsequent to the stimulus presentation, and were made by 
+pressing buttons attached to an MRI-compatible response glove on their dominant hand 
+(right for all participants).
+"""
\ No newline at end of file
diff --git a/src/rsatoolbox/searchlight/surface.py b/src/rsatoolbox/searchlight/surface.py
index da2ec0ed..08d6b9d4 100644
--- a/src/rsatoolbox/searchlight/surface.py
+++ b/src/rsatoolbox/searchlight/surface.py
@@ -1,4 +1,9 @@
+"""
 
+Notes Jasper:
+- prep_vol .. -> for volume data only, supposed to be faster than DL implementation in volume.py
+- prep_surf .. -> does same but for surface
+"""
 import warnings
 import os
 from collections.abc import Iterable

From a4a3f3aef3831197a10ce82126153870f5e03db8 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <japsai@gmail.com>
Date: Tue, 25 Jul 2023 11:20:38 +0100
Subject: [PATCH 30/33] hrf

---
 demos/demo_searchlight_surface_bold5k.py | 103 +++++++++++++++++------
 demos/hrf.npy                            | Bin 0 -> 4048 bytes
 2 files changed, 77 insertions(+), 26 deletions(-)
 create mode 100644 demos/hrf.npy

diff --git a/demos/demo_searchlight_surface_bold5k.py b/demos/demo_searchlight_surface_bold5k.py
index f90a7d34..2d622364 100644
--- a/demos/demo_searchlight_surface_bold5k.py
+++ b/demos/demo_searchlight_surface_bold5k.py
@@ -2,15 +2,20 @@
 
 The 214 repeated images are spread out over 100 runs in 10 sessions (2 images per run).
 
-GLMsingle valid?
+## TODO
+
+- [ ] both hemis
+- [ ] coco, scenes, imagenet or all
+- [ ] if we cut out relevant events, must clip HRF
+- [ ] otherwise, get betas per run
 
 """
 ## suppress warnings on nibabel
 # pyright: reportPrivateImportUsage=false 
 from os.path import expanduser, join
 import datalad.api as datalad
-import json, glob
-import nibabel, pandas
+import nibabel, pandas, numpy
+from scipy.interpolate import pchip
 
 #description: https://bold5000-dataset.github.io/website/overview.html
 # 112 images repeated four times, and one image repeated three times
@@ -34,35 +39,84 @@
 # with open(meta_fpath) as fhandle:
 #     metadata = json.load(fhandle)
 # TR = metadata['RepetitionTime']
+tr = 2.0
+block_dur = 1.0
+
+STANDARD_HRF = numpy.load('demos/hrf.npy')
+STANDARD_TR = 0.1
+hrf = numpy.convolve(STANDARD_HRF, numpy.ones(int(block_dur/STANDARD_TR)))
+
+## timepoints in block
+timepts_block = numpy.arange(0, int((hrf.size-1)*STANDARD_TR), tr)
+
+# resample to desired TR
+hrf = pchip(numpy.arange(hrf.size)*STANDARD_TR, hrf)(timepts_block)
+hrf = hrf / hrf.max()
+
 
 fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
 img_anat = nibabel.load(fpath_anat)
 coords = img_anat.agg_data('pointset')
 
-
 """388s 194 volumes
 
+"""
+#sessions_runs = zip(range(1, 15+1), range(1, 10+1))
+sessions_runs = [(1, 1)]
+for (s, r) in sessions_runs:
+    ses_raw_dir = join(dataset_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
+    evt_fpath = join(ses_raw_dir, 
+        f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_events.tsv')
+    events_df = pandas.read_csv(evt_fpath, sep='\t')
 
+    ## 'rep' in ImgType
 
-"""
-all_runs = []
-for s in range(1, 15+1):
-    ses_dir = join(dataset_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
     ses_fmriprep_dir = join(fmriprep_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
-    run_evt_fpaths = glob.glob(join(ses_dir, '*_task-5000scenes_*_events.tsv'))
-    for fpath in run_evt_fpaths:
-        r = int(fpath.split('_')[-2][-2:])
-        run_df = pandas.read_csv(fpath, sep='\t')
-        bold_fname = f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_bold_space-fsnative.L.func.gii'
-        bold_fpath = join(ses_fmriprep_dir, bold_fname)
-        data = nibabel.load(bold_fpath).agg_data() # (135186, 194) # 100MB
-        all_runs.append(run_df)
-        raise ValueError
-df = pandas.concat(all_runs)
-
-## zip ses/run assume 10 runs, exit if run not there
-
-""" reps by cat
+    bold_fpath = join(ses_fmriprep_dir,
+        f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_bold_space-fsnative.L.func.gii')
+    data = nibabel.load(bold_fpath).agg_data() # (135186, 194) # 100MB
+
+
+    ## make design matrix
+    conditions = events_df.ImgName.unique()
+    n_vols = data.shape[-1]
+    dm = numpy.zeros((n_vols, conditions.size))
+    all_times = numpy.linspace(0, tr*(n_vols-1), n_vols)
+    hrf_times = numpy.linspace(0, tr*(len(hrf)-1), len(hrf))
+    for c, condition in enumerate(conditions):
+        onsets = events_df[events_df.trial_type == condition].onset.values
+        yvals = numpy.zeros((n_vols))
+        # loop over blocks
+        for o in onsets:
+            # interpolate to find values at the data sampling time points
+            f = pchip(o + hrf_times, hrf, extrapolate=False)(all_times)
+            yvals = yvals + numpy.nan_to_num(f)
+        dm[:, c] = yvals
+
+    ## add polynomials
+    pdata = wdata / wdata.mean(axis=0)
+
+    ## least square fitting
+    # The matrix addition is equivalent to concatenating the list of data and the list of
+    # design and fit it all at once. However, this is more memory efficient.
+    design = [dm]
+    data = [data.T]
+    X = numpy.vstack(design)
+    X = numpy.linalg.inv(X.T @ X) @ X.T
+
+    betas = 0
+    start_col = 0
+    for run in range(len(data)):
+        n_vols = data[run].shape[0]
+        these_cols = numpy.arange(n_vols) + start_col
+        betas += X[:, these_cols] @ data[run]
+        start_col += data[run].shape[0]
+
+
+
+
+"""from exploration
+
 In [44]: df[df.ImgType=='rep_coco'].ImgName.value_counts().size
 Out[44]: 45
 
@@ -71,11 +125,8 @@
 
 In [46]: df[df.ImgType=='rep_imagenet'].ImgName.value_counts().size
 Out[46]: 45
-"""
-
-
-
 
+"""
 
 
 """from paper methods:
diff --git a/demos/hrf.npy b/demos/hrf.npy
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From 9e13c6d02cd16380318580bf098cdb57fe0a3d5f Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <japsai@gmail.com>
Date: Tue, 25 Jul 2023 11:34:00 +0100
Subject: [PATCH 31/33] getting betas

---
 demos/demo_searchlight_surface_bold5k.py | 26 +++++++++++++-----------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/demos/demo_searchlight_surface_bold5k.py b/demos/demo_searchlight_surface_bold5k.py
index 2d622364..37c1eb6b 100644
--- a/demos/demo_searchlight_surface_bold5k.py
+++ b/demos/demo_searchlight_surface_bold5k.py
@@ -8,6 +8,7 @@
 - [ ] coco, scenes, imagenet or all
 - [ ] if we cut out relevant events, must clip HRF
 - [ ] otherwise, get betas per run
+- [ ] whitening
 
 """
 ## suppress warnings on nibabel
@@ -25,15 +26,15 @@
 your_data_dir = expanduser('~/data')
 dataset_dir = join(your_data_dir, f'ds00{openneuro_id}')
 fmriprep_dir = join(dataset_dir, 'derivatives', 'fmriprep')
-dl = datalad.clone(
-    source=f'///openneuro/ds00{openneuro_id}', 
-    path=dataset_dir,
-    description='BOLD5000 v1'
-)
 
 
+# dl = datalad.clone(
+#     source=f'///openneuro/ds00{openneuro_id}', 
+#     path=dataset_dir,
+#     description='BOLD5000 v1'
+# )
 ## download fmriprep output for subject 1; ~49GB, 12h to download
-dl.get('derivatives/fmriprep/sub-CSI1/')
+# dl.get('derivatives/fmriprep/sub-CSI1/')
 
 # meta_fpath = join(root_dir, 'sub-04/ses-1/func/sub-04_ses-1_task-motor_run-01_bold.json')
 # with open(meta_fpath) as fhandle:
@@ -53,10 +54,10 @@
 hrf = pchip(numpy.arange(hrf.size)*STANDARD_TR, hrf)(timepts_block)
 hrf = hrf / hrf.max()
 
-
-fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
-img_anat = nibabel.load(fpath_anat)
-coords = img_anat.agg_data('pointset')
+# ## get coords of vertices
+# fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
+# img_anat = nibabel.load(fpath_anat)
+# coords = img_anat.agg_data('pointset')
 
 """388s 194 volumes
 
@@ -68,6 +69,7 @@
     evt_fpath = join(ses_raw_dir, 
         f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_events.tsv')
     events_df = pandas.read_csv(evt_fpath, sep='\t')
+    events_df['trial_type'] = events_df['ImgName']
 
     ## 'rep' in ImgType
 
@@ -78,7 +80,7 @@
 
 
     ## make design matrix
-    conditions = events_df.ImgName.unique()
+    conditions = events_df.trial_type.unique()
     n_vols = data.shape[-1]
     dm = numpy.zeros((n_vols, conditions.size))
     all_times = numpy.linspace(0, tr*(n_vols-1), n_vols)
@@ -94,7 +96,7 @@
         dm[:, c] = yvals
 
     ## add polynomials
-    pdata = wdata / wdata.mean(axis=0)
+    data = data / data.mean(axis=0)
 
     ## least square fitting
     # The matrix addition is equivalent to concatenating the list of data and the list of

From 1b4a71cebad7a359ac45ca7d574cda76b653aa9b Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <japsai@gmail.com>
Date: Tue, 25 Jul 2023 14:38:58 +0100
Subject: [PATCH 32/33] process all runs

---
 demos/demo_searchlight_surface_bold5k.py | 44 ++++++++++++++++++------
 1 file changed, 33 insertions(+), 11 deletions(-)

diff --git a/demos/demo_searchlight_surface_bold5k.py b/demos/demo_searchlight_surface_bold5k.py
index 37c1eb6b..fa1c70dd 100644
--- a/demos/demo_searchlight_surface_bold5k.py
+++ b/demos/demo_searchlight_surface_bold5k.py
@@ -6,16 +6,17 @@
 
 - [ ] both hemis
 - [ ] coco, scenes, imagenet or all
-- [ ] if we cut out relevant events, must clip HRF
-- [ ] otherwise, get betas per run
+- [x] otherwise, get betas per run
 - [ ] whitening
+- [ ] model each individual rep within run
 
 """
 ## suppress warnings on nibabel
 # pyright: reportPrivateImportUsage=false 
-from os.path import expanduser, join
+from os.path import expanduser, join, isfile
+import itertools
 import datalad.api as datalad
-import nibabel, pandas, numpy
+import nibabel, pandas, numpy, tqdm
 from scipy.interpolate import pchip
 
 #description: https://bold5000-dataset.github.io/website/overview.html
@@ -31,7 +32,7 @@
 # dl = datalad.clone(
 #     source=f'///openneuro/ds00{openneuro_id}', 
 #     path=dataset_dir,
-#     description='BOLD5000 v1'
+#     description='BOLD5000 v1' 
 # )
 ## download fmriprep output for subject 1; ~49GB, 12h to download
 # dl.get('derivatives/fmriprep/sub-CSI1/')
@@ -62,16 +63,25 @@
 """388s 194 volumes
 
 """
-#sessions_runs = zip(range(1, 15+1), range(1, 10+1))
-sessions_runs = [(1, 1)]
-for (s, r) in sessions_runs:
+
+n_obs = (112 * 4) + (1 * 3) 
+
+sessions_runs = list(itertools.product(range(1, 15+1), range(1, 10+1)))
+#sessions_runs = [(1, 1)]
+all_events = []
+all_betas = []
+for (s, r) in tqdm.tqdm(sessions_runs): ## 2m35s
     ses_raw_dir = join(dataset_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
     evt_fpath = join(ses_raw_dir, 
         f'sub-CSI1_ses-{s:02}_task-5000scenes_run-{r:02}_events.tsv')
+    
+    if not isfile(evt_fpath):
+        print(f'Missing run: session {s} run {r}')
+        continue
+
     events_df = pandas.read_csv(evt_fpath, sep='\t')
     events_df['trial_type'] = events_df['ImgName']
 
-    ## 'rep' in ImgType
 
     ses_fmriprep_dir = join(fmriprep_dir, 'sub-CSI1', f'ses-{s:02}', 'func')
     bold_fpath = join(ses_fmriprep_dir,
@@ -95,7 +105,7 @@
             yvals = yvals + numpy.nan_to_num(f)
         dm[:, c] = yvals
 
-    ## add polynomials
+    # pct signal change
     data = data / data.mean(axis=0)
 
     ## least square fitting
@@ -114,7 +124,19 @@
         betas += X[:, these_cols] @ data[run]
         start_col += data[run].shape[0]
 
-
+    # gather betas if in rep conditions
+    conds_df = events_df.drop_duplicates(subset=['ImgName'])
+    relevant_conds = conds_df.ImgType.str.contains('rep_').values
+    all_events += [conds_df[relevant_conds]]
+    all_betas += [betas[relevant_conds, :]]
+all_df = pandas.concat(all_events).reset_index()
+## keep only columns we may need
+all_df = all_df[['Sess', 'Run', 'trial_type', 'ImgType', 'Response']]
+all_betas_a = numpy.vstack(all_betas)
+
+## save point
+all_df.to_csv('events.csv')
+numpy.save('betas.npy', all_betas_a)
 
 
 """from exploration

From 9278b3e655ec8bee5e36995f3cf859ca2d6876c9 Mon Sep 17 00:00:00 2001
From: Jasper van den Bosch <japsai@gmail.com>
Date: Wed, 26 Jul 2023 11:03:04 +0100
Subject: [PATCH 33/33] neighbors

---
 demos/demo_searchlight_surface_bold5k.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/demos/demo_searchlight_surface_bold5k.py b/demos/demo_searchlight_surface_bold5k.py
index fa1c70dd..ad81eb49 100644
--- a/demos/demo_searchlight_surface_bold5k.py
+++ b/demos/demo_searchlight_surface_bold5k.py
@@ -17,6 +17,7 @@
 import itertools
 import datalad.api as datalad
 import nibabel, pandas, numpy, tqdm
+from sklearn import neighbors as skl_neighbors
 from scipy.interpolate import pchip
 
 #description: https://bold5000-dataset.github.io/website/overview.html
@@ -55,10 +56,16 @@
 hrf = pchip(numpy.arange(hrf.size)*STANDARD_TR, hrf)(timepts_block)
 hrf = hrf / hrf.max()
 
-# ## get coords of vertices
-# fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
-# img_anat = nibabel.load(fpath_anat)
-# coords = img_anat.agg_data('pointset')
+print('starting neighbor search')
+
+## get coords of vertices
+fpath_anat = join(fmriprep_dir, 'sub-CSI1', 'anat', f'sub-CSI1_T1w_inflated.L.surf.gii')
+img_anat = nibabel.load(fpath_anat)
+coords = img_anat.agg_data('pointset')
+nn = skl_neighbors.NearestNeighbors(radius=5)
+adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil() # sparse
+
+#raise ValueError
 
 """388s 194 volumes