Merge pull request #104 from VinceBaz/fix_plot_point_brain

Author: VinceBaz

netneurotools/plotting.py

@@ -11,7 +11,6 @@
 from mpl_toolkits.mplot3d import Axes3D  # noqa
 import nibabel as nib
 import numpy as np
-from scipy.stats import zscore
 
 from .freesurfer import FSIGNORE, _decode_list
 
@@ -726,8 +725,9 @@ def plot_fsvertex(data, *, order='lr', surf='pial', views='lat',
     return brain
 
 
-def plot_point_brain(data, coords, views=None, cbar=False, figsize=(4, 4.8),
-                     robust=True, size=50, **kwargs):
+def plot_point_brain(data, coords, views=None, views_orientation='vertical',
+                     views_size=(4, 2.4), cbar=False, robust=True, size=50,
+                     **kwargs):
     """
     Plots `data` as a cloud of points in 3D space based on specified `coords`
 
@@ -741,10 +741,13 @@ def plot_point_brain(data, coords, views=None, cbar=False, figsize=(4, 4.8),
         List specifying which views to use. Can be any of {'sagittal', 'sag',
         'coronal', 'cor', 'axial', 'ax'}. If not specified will use 'sagittal'
         and 'axial'. Default: None
+    views_orientation: str, optional
+        Orientation of the views. Can be either 'vertical' or 'horizontal'.
+        Default: 'vertical'.
+    views_size : tuple, optional
+        Figure size of each view. Default: (4, 2.4)
     cbar : bool, optional
         Whether to also show colorbar. Default: False
-    figsize : tuple, optional
-        Figure size. Default: (4, 4.8)
     robust : bool, optional
         Whether to use robust calculation of `vmin` and `vmax` for color scale.
     size : int, optional
@@ -761,18 +764,23 @@ def plot_point_brain(data, coords, views=None, cbar=False, figsize=(4, 4.8),
                   axial=(90, 180), ax=(90, 180),
                   coronal=(0, 90), cor=(0, 90))
 
-    # coordinate space needs to be centered around zero for aspect ratio
-    coords = zscore(coords)
     x, y, z = coords[:, 0], coords[:, 1], coords[:, 2]
+
     if views is None:
         views = [_views[f] for f in ['sagittal', 'axial']]
     else:
         if not isinstance(views, Iterable) or isinstance(views, str):
             views = [views]
         views = [_views[f] for f in views]
 
+    if views_orientation == 'vertical':
+        ncols, nrows = 1, len(views)
+    elif views_orientation == 'horizontal':
+        ncols, nrows = len(views), 1
+    figsize = (ncols * views_size[0], nrows * views_size[1])
+
     # create figure and axes (3d projections)
-    fig, axes = plt.subplots(ncols=1, nrows=len(views),
+    fig, axes = plt.subplots(ncols=ncols, nrows=nrows,
                              figsize=figsize,
                              subplot_kw=dict(projection='3d'))
 
@@ -790,10 +798,12 @@ def plot_point_brain(data, coords, views=None, cbar=False, figsize=(4, 4.8),
         col = ax.scatter(x, y, z, c=data, s=size, **opts)
         ax.view_init(*view)
         ax.axis('off')
-        ax.set(xlim=0.57 * np.array(ax.get_xlim()),
-               ylim=0.57 * np.array(ax.get_ylim()),
-               zlim=0.60 * np.array(ax.get_zlim()))
-    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
+        scaling = np.array([ax.get_xlim(),
+                            ax.get_ylim(),
+                            ax.get_zlim()])
+        ax.set_box_aspect(tuple(scaling[:, 1] - scaling[:, 0]))
+
+    fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0)
 
     # add colorbar to axes
     if cbar:

requirements.txt

@@ -5,4 +5,5 @@ nilearn
 numpy>=1.16
 scikit-learn
 scipy>=1.4.0
+matplotlib>=3.3.0
 tqdm

tools/install_dependencies.sh

@@ -9,6 +9,7 @@ if [ -n "${OPTIONAL_DEPENDS}" ]; then
     for DEP in ${OPTIONAL_DEPENDS}; do
         if [ ${DEP} == "mayavi" ]; then
             python -m pip install numpy vtk
+            sudo apt update
             sudo apt-get install -y xvfb \
                                     x11-utils \
                                     mencoder \