/usr/share/pyshared/surfer/viz.py is in python-surfer 0.3+git15-gae6cbb1-1.1.
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| import os
from os.path import join as pjoin
from warnings import warn
import numpy as np
from scipy import stats
from scipy import ndimage
from matplotlib.colors import colorConverter
from . import io
from . import utils
from .io import Surface
from .config import config
try:
from traits.api import (HasTraits, Range, Int, Float,
Bool, Enum, on_trait_change)
except ImportError:
from enthought.traits.api import (HasTraits, Range, Int, Float, \
Bool, Enum, on_trait_change)
try:
from traitsui.api import View, Item, VSplit, HSplit, Group
except ImportError:
try:
from traits.ui.api import View, Item, VSplit, HSplit, Group
except ImportError:
from enthought.traits.ui.api import View, Item, VSplit, HSplit, Group
lh_viewdict = {'lateral': {'v': (180., 90.), 'r': 90.},
'medial': {'v': (0., 90.), 'r': -90.},
'rostral': {'v': (90., 90.), 'r': -180.},
'caudal': {'v': (270., 90.), 'r': 0.},
'dorsal': {'v': (180., 0.), 'r': 90.},
'ventral': {'v': (180., 180.), 'r': 90.},
'frontal': {'v': (120., 80.), 'r': 106.739},
'parietal': {'v': (-120., 60.), 'r': 49.106}}
rh_viewdict = {'lateral': {'v': (180., -90.), 'r': -90.},
'medial': {'v': (0., -90.), 'r': 90.},
'rostral': {'v': (-90., -90.), 'r': 180.},
'caudal': {'v': (90., -90.), 'r': 0.},
'dorsal': {'v': (180., 0.), 'r': 90.},
'ventral': {'v': (180., 180.), 'r': 90.},
'frontal': {'v': (60., 80.), 'r': -106.739},
'parietal': {'v': (-60., 60.), 'r': -49.106}}
class Brain(object):
"""Brain object for visualizing with mlab."""
def __init__(self, subject_id, hemi, surf,
curv=True, title=None, config_opts={}):
"""Initialize a Brain object with Freesurfer-specific data.
Parameters
----------
subject_id : str
subject name in Freesurfer subjects dir
hemi : str
hemisphere id (ie 'lh' or 'rh')
surf : geometry name
freesurfer surface mesh name (ie 'white', 'inflated', etc.)
curv : boolean
if true, loads curv file and displays binary curvature
(default: True)
title : str
title for the mayavi figure
config_opts : dict
options to override visual options in config file
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
# Set the identifying info
self.subject_id = subject_id
self.hemi = hemi
if self.hemi == 'lh':
self.viewdict = lh_viewdict
elif self.hemi == 'rh':
self.viewdict = rh_viewdict
self.surf = surf
# Initialize the mlab figure
if title is None:
title = subject_id
self._set_scene_properties(config_opts)
self._f = mlab.figure(title,
**self.scene_properties)
mlab.clf()
self._f.scene.disable_render = True
# Set the lights so they are oriented by hemisphere
self._orient_lights()
# Initialize a Surface object as the geometry
self._geo = Surface(subject_id, hemi, surf)
# Load in the geometry and (maybe) curvature
self._geo.load_geometry()
if curv:
self._geo.load_curvature()
curv_data = self._geo.bin_curv
meshargs = dict(scalars=curv_data)
else:
curv_data = None
meshargs = dict()
# mlab pipeline mesh for geomtery
self._geo_mesh = mlab.pipeline.triangular_mesh_source(
self._geo.x, self._geo.y, self._geo.z,
self._geo.faces, **meshargs)
# mlab surface for the geometry
if curv:
colormap, vmin, vmax, reverse = self._get_geo_colors(config_opts)
self._geo_surf = mlab.pipeline.surface(self._geo_mesh,
colormap=colormap, vmin=vmin, vmax=vmax)
if reverse:
curv_bar = mlab.scalarbar(self._geo_surf)
curv_bar.reverse_lut = True
curv_bar.visible = False
else:
self._geo_surf = mlab.pipeline.surface(self._geo_mesh,
color=(.5, .5, .5))
# Initialize the overlay and label dictionaries
self.overlays = dict()
self.labels = dict()
self.foci = dict()
self.texts = dict()
# Bring up the lateral view
self.show_view(config.get("visual", "default_view"))
# Turn disable render off so that it displays
self._f.scene.disable_render = False
def show_view(self, view=None, roll=None):
"""Orient camera to display view
Parameters
----------
view : {'lateral' | 'medial' | 'rostral' | 'caudal' |
'dorsal' | 'ventral' | 'frontal' | 'parietal' |
dict}
brain surface to view or kwargs to pass to mlab.view()
Returns
-------
cv: tuple
tuple returned from mlab.view
cr: float
current camera roll
"""
if isinstance(view, basestring):
try:
vd = self.xfm_view(view, 'd')
view = dict(azimuth=vd['v'][0], elevation=vd['v'][1])
roll = vd['r']
except ValueError as v:
print(v)
raise
cv, cr = self.__view(view, roll)
return (cv, cr)
def __view(self, viewargs=None, roll=None):
"""Wrapper for mlab.view()
Parameters
----------
viewargs: dict
mapping with keys corresponding to mlab.view args
roll: num
int or float to set camera roll
Returns
-------
camera settings: tuple
view settings, roll setting
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
if viewargs:
viewargs['reset_roll'] = True
mlab.view(**viewargs)
if not roll is None:
mlab.roll(roll)
return mlab.view(), mlab.roll()
def _read_scalar_data(self, source, name=None, cast=True):
"""Load in scalar data from an image stored in a file or an array
Parameters
----------
source : str or numpy array
path to scalar data file or a numpy array
name : str or None, optional
name for the overlay in the internal dictionary
cast : bool, optional
either to cast float data into 64bit datatype as a
workaround. cast=True can fix a rendering problem with
certain versions of Mayavi
Returns
-------
scalar_data : numpy array
flat numpy array of scalar data
name : str
if no name was provided, deduces the name if filename was given
as a source
"""
# If source is a string, try to load a file
if isinstance(source, basestring):
if name is None:
basename = os.path.basename(source)
if basename.endswith(".gz"):
basename = basename[:-3]
if basename.startswith("%s." % self.hemi):
basename = basename[3:]
name = os.path.splitext(basename)[0]
scalar_data = io.read_scalar_data(source)
else:
# Can't think of a good way to check that this will work nicely
scalar_data = source
if cast:
if (scalar_data.dtype.char == 'f' and
scalar_data.dtype.itemsize < 8):
scalar_data = scalar_data.astype(np.float)
return scalar_data, name
def add_overlay(self, source, min=None, max=None, sign="abs", name=None):
"""Add an overlay to the overlay dict from a file or array.
Parameters
----------
source : str or numpy array
path to the overlay file or numpy array with data
min : float
threshold for overlay display
max : float
saturation point for overlay display
sign : {'abs' | 'pos' | 'neg'}
whether positive, negative, or both values should be displayed
name : str
name for the overlay in the internal dictionary
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
scalar_data, name = self._read_scalar_data(source, name)
min, max = self._get_display_range(scalar_data, min, max, sign)
if name in self.overlays:
"%s%d" % (name, len(self.overlays) + 1)
if not sign in ["abs", "pos", "neg"]:
raise ValueError("Overlay sign must be 'abs', 'pos', or 'neg'")
self._f.scene.disable_render = True
view = mlab.view()
self.overlays[name] = Overlay(scalar_data, self._geo, min, max, sign)
for bar in ["pos_bar", "neg_bar"]:
try:
self._format_cbar_text(getattr(self.overlays[name], bar))
except AttributeError:
pass
mlab.view(*view)
self._f.scene.disable_render = False
def add_data(self, array, min=None, max=None, thresh=None,
colormap="blue-red", alpha=1,
vertices=None, smoothing_steps=20, time=None,
time_label="time index=%d"):
"""Display data from a numpy array on the surface.
This provides a similar interface to add_overlay, but it displays
it with a single colormap. It offers more flexibility over the
colormap, and provides a way to display four dimensional data
(i.e. a timecourse).
Note that min sets the low end of the colormap, and is separate
from thresh (this is a different convention from add_overlay)
Parameters
----------
array : numpy array
data array (nvtx vector)
min : float
min value in colormap (uses real min if None)
max : float
max value in colormap (uses real max if None)
thresh : None or float
if not None, values below thresh will not be visible
colormap : str
name of Mayavi colormap to use
alpha : float in [0, 1]
alpha level to control opacity
vertices : numpy array
vertices for which the data is defined (needed if len(data) < nvtx)
smoothing_steps : int
number of smoothing steps (smooting is used if len(data) < nvtx)
Default : 20
time : numpy array
time points in the data array (if data is 2D)
time_label : str
format of the time label
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
self._f.scene.disable_render = True
view = mlab.view()
# Possibly remove old data
if hasattr(self, "data"):
self.data["surface"].remove()
self.data["colorbar"].remove()
if min is None:
min = array.min()
if max is None:
max = array.max()
# Create smoothing matrix if necessary
if len(array) < self._geo.x.shape[0]:
if vertices == None:
raise ValueError("len(data) < nvtx: need vertices")
adj_mat = utils.mesh_edges(self._geo.faces)
smooth_mat = utils.smoothing_matrix(vertices, adj_mat,
smoothing_steps)
else:
smooth_mat = None
# Calculate initial data to plot
if array.ndim == 1:
array_plot = array
elif array.ndim == 2:
array_plot = array[:, 0]
else:
raise ValueError("data has to be 1D or 2D")
if smooth_mat != None:
array_plot = smooth_mat * array_plot
# Copy and byteswap to deal with Mayavi bug
if array_plot.dtype.byteorder == '>':
mlab_plot = array_plot.copy()
mlab_plot.byteswap(True)
else:
mlab_plot = array_plot
# Set up the visualization pipeline
mesh = mlab.pipeline.triangular_mesh_source(self._geo.x,
self._geo.y,
self._geo.z,
self._geo.faces,
scalars=mlab_plot)
if thresh is not None:
if array_plot.min() >= thresh:
warn("Data min is greater than threshold.")
else:
mesh = mlab.pipeline.threshold(mesh, low=thresh)
surf = mlab.pipeline.surface(mesh, colormap=colormap,
vmin=min, vmax=max,
opacity=float(alpha))
# Get the colorbar
bar = mlab.scalarbar(surf)
self._format_cbar_text(bar)
bar.scalar_bar_representation.position2 = .8, 0.09
# Get the original colormap table
orig_ctable = \
surf.module_manager.scalar_lut_manager.lut.table.to_array().copy()
# Fill in the data dict
self.data = dict(surface=surf, colorbar=bar, orig_ctable=orig_ctable,
array=array, smoothing_steps=smoothing_steps,
fmin=min, fmid=(min + max) / 2, fmax=max,
transparent=False, time=0, time_idx=0)
if vertices != None:
self.data["vertices"] = vertices
self.data["smooth_mat"] = smooth_mat
mlab.view(*view)
# Create time array and add label if 2D
if array.ndim == 2:
if time == None:
time = np.arange(array.shape[1])
self.data["time_label"] = time_label
self.data["time"] = time
self.data["time_idx"] = 0
self.add_text(0.05, 0.1, time_label % time[0], name="time_label")
self._f.scene.disable_render = False
def add_annotation(self, annot, borders=True, alpha=1):
"""Add an annotation file.
Parameters
----------
annot : str
Either path to annotation file or annotation name
borders : bool
Show only borders of regions
alpha : float in [0, 1]
Alpha level to control opacity
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
self._f.scene.disable_render = True
view = mlab.view()
# Figure out where the data is coming from
if os.path.isfile(annot):
filepath = annot
annot = os.path.basename(filepath).split('.')[1]
else:
filepath = pjoin(os.environ['SUBJECTS_DIR'],
self.subject_id,
'label',
".".join([self.hemi, annot, 'annot']))
if not os.path.exists(filepath):
raise ValueError('Annotation file %s does not exist'
% filepath)
# Read in the data
labels, cmap, _ = io.read_annot(filepath, orig_ids=True)
# Maybe zero-out the non-border vertices
if borders:
n_vertices = labels.size
edges = utils.mesh_edges(self._geo.faces)
border_edges = labels[edges.row] != labels[edges.col]
show = np.zeros(n_vertices, dtype=np.int)
show[np.unique(edges.row[border_edges])] = 1
labels *= show
# Handle null labels properly
# (tksurfer doesn't use the alpha channel, so sometimes this
# is set weirdly. For our purposes, it should always be 0.
# Unless this sometimes causes problems?
cmap[np.where(cmap[:, 4] == 0), 3] = 0
if np.any(labels == 0) and not np.any(cmap[:, -1] == 0):
cmap = np.vstack((cmap, np.zeros(5, int)))
# Set label ids sensibly
ord = np.argsort(cmap[:, -1])
ids = ord[np.searchsorted(cmap[ord, -1], labels)]
cmap = cmap[:, :4]
# Set the alpha level
alpha_vec = cmap[:, 3]
alpha_vec[alpha_vec > 0] = alpha * 255
# Maybe get rid of old annot
if hasattr(self, "annot"):
self.annot['surface'].remove()
# Create an mlab surface to visualize the annot
mesh = mlab.pipeline.triangular_mesh_source(self._geo.x,
self._geo.y,
self._geo.z,
self._geo.faces,
scalars=ids)
surf = mlab.pipeline.surface(mesh, name=annot)
# Set the color table
surf.module_manager.scalar_lut_manager.lut.table = cmap
# Set the brain attributes
self.annot = dict(surface=surf, name=annot, colormap=cmap)
mlab.view(*view)
self._f.scene.disable_render = False
def add_label(self, label, color="crimson", alpha=1,
scalar_thresh=None, borders=False):
"""Add an ROI label to the image.
Parameters
----------
label : str
label filepath or name
color : matplotlib-style color
anything matplotlib accepts: string, RGB, hex, etc.
alpha : float in [0, 1]
alpha level to control opacity
scalar_thresh : None or number
threshold the label ids using this value in the label
file's scalar field (i.e. label only vertices with
scalar >= thresh)
borders : bool
show only label borders
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
self._f.scene.disable_render = True
view = mlab.view()
# Figure out where the data is coming from
if os.path.isfile(label):
filepath = label
label_name = os.path.basename(filepath).split('.')[1]
else:
label_name = label
filepath = pjoin(os.environ['SUBJECTS_DIR'],
self.subject_id,
'label',
".".join([self.hemi, label_name, 'label']))
if not os.path.exists(filepath):
raise ValueError('Label file %s does not exist'
% filepath)
# Load the label data and create binary overlay
if scalar_thresh is None:
ids = io.read_label(filepath)
else:
ids, scalars = io.read_label(filepath, read_scalars=True)
ids = ids[scalars >= scalar_thresh]
label = np.zeros(self._geo.coords.shape[0])
label[ids] = 1
if borders:
n_vertices = label.size
edges = utils.mesh_edges(self._geo.faces)
border_edges = label[edges.row] != label[edges.col]
show = np.zeros(n_vertices, dtype=np.int)
show[np.unique(edges.row[border_edges])] = 1
label *= show
mesh = mlab.pipeline.triangular_mesh_source(self._geo.x,
self._geo.y,
self._geo.z,
self._geo.faces,
scalars=label)
surf = mlab.pipeline.surface(mesh, name=label_name)
color = colorConverter.to_rgba(color, alpha)
cmap = np.array([(0, 0, 0, 0,), color]) * 255
surf.module_manager.scalar_lut_manager.lut.table = cmap
self.labels[label_name] = surf
mlab.view(*view)
self._f.scene.disable_render = False
def add_morphometry(self, measure, grayscale=False):
"""Add a morphometry overlay to the image.
Parameters
----------
measure : {'area' | 'curv' | 'jacobian_white' | 'sulc' | 'thickness'}
which measure to load
grayscale : bool
whether to load the overlay with a grayscale colormap
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
# Find the source data
surf_dir = pjoin(os.environ['SUBJECTS_DIR'], self.subject_id, 'surf')
morph_file = pjoin(surf_dir, '.'.join([self.hemi, measure]))
if not os.path.exists(morph_file):
raise ValueError(
'Could not find %s in subject directory' % morph_file)
# Preset colormaps
cmap_dict = dict(area="pink",
curv="RdBu",
jacobian_white="pink",
sulc="RdBu",
thickness="pink")
self._f.scene.disable_render = True
# Maybe get rid of an old overlay
if hasattr(self, "morphometry"):
self.morphometry['surface'].remove()
self.morphometry['colorbar'].visible = False
# Save the inital view
view = mlab.view()
# Read in the morphometric data
morph_data = io.read_morph_data(morph_file)
# Get a cortex mask for robust range
self._geo.load_label("cortex")
ctx_idx = self._geo.labels["cortex"]
# Get the display range
if measure == "thickness":
min, max = 1, 4
else:
min, max = stats.describe(morph_data[ctx_idx])[1]
# Set up the Mayavi pipeline
if morph_data.dtype.byteorder == '>':
morph_data.byteswap(True) # byte swap inplace; due to mayavi bug
mesh = mlab.pipeline.triangular_mesh_source(self._geo.x,
self._geo.y,
self._geo.z,
self._geo.faces,
scalars=morph_data)
if grayscale:
colormap = "gray"
else:
colormap = cmap_dict[measure]
surf = mlab.pipeline.surface(mesh, colormap=colormap,
vmin=min, vmax=max,
name=measure)
# Get the colorbar
bar = mlab.scalarbar(surf)
self._format_cbar_text(bar)
bar.scalar_bar_representation.position2 = .8, 0.09
# Fil in the morphometry dict
self.morphometry = dict(surface=surf,
colorbar=bar,
measure=measure)
mlab.view(*view)
self._f.scene.disable_render = False
def add_foci(self, coords, coords_as_verts=False, map_surface=None,
scale_factor=1, color="white", alpha=1, name=None):
"""Add spherical foci, possibly mapping to displayed surf.
The foci spheres can be displayed at the coordinates given, or
mapped through a surface geometry. In other words, coordinates
from a volume-based analysis in MNI space can be displayed on an
inflated average surface by finding the closest vertex on the
white surface and mapping to that vertex on the inflated mesh.
Parameters
----------
coords : numpy array
x, y, z coordinates in stereotaxic space or array of vertex ids
coords_as_verts : bool
whether the coords parameter should be interpreted as vertex ids
map_surface : Freesurfer surf or None
surface to map coordinates through, or None to use raw coords
scale_factor : int
controls the size of the foci spheres
color : matplotlib color code
HTML name, RBG tuple, or hex code
alpha : float in [0, 1]
opacity of focus gylphs
name : str
internal name to use
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
# Figure out how to interpret the first parameter
if coords_as_verts:
coords = self._geo.coords[coords]
map_surface = None
# Possibly map the foci coords through a surface
if map_surface is None:
foci_coords = np.atleast_2d(coords)
else:
foci_surf = io.Surface(self.subject_id, self.hemi, map_surface)
foci_surf.load_geometry()
foci_vtxs = utils.find_closest_vertices(foci_surf.coords, coords)
foci_coords = self._geo.coords[foci_vtxs]
# Get a unique name (maybe should take this approach elsewhere)
if name is None:
name = "foci_%d" % (len(self.foci) + 1)
# Convert the color code
color = colorConverter.to_rgb(color)
# Create the visualization
self._f.scene.disable_render = True
view = mlab.view()
points = mlab.points3d(foci_coords[:, 0],
foci_coords[:, 1],
foci_coords[:, 2],
np.ones(foci_coords.shape[0]),
scale_factor=(10. * scale_factor),
color=color, opacity=alpha, name=name)
self.foci[name] = points
mlab.view(*view)
self._f.scene.disable_render = False
def add_contour_overlay(self, source, min=None, max=None,
n_contours=7, line_width=1.5):
"""Add a topographic contour overlay of the positive data.
Note: This visualization will look best when using the "low_contrast"
cortical curvature colorscheme.
Parameters
----------
source : str or array
path to the overlay file or numpy array
min : float
threshold for overlay display
max : float
saturation point for overlay display
n_contours : int
number of contours to use in the display
line_width : float
width of contour lines
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
# Read the scalar data
scalar_data, _ = self._read_scalar_data(source)
min, max = self._get_display_range(scalar_data, min, max, "pos")
# Prep the viz
self._f.scene.disable_render = True
view = mlab.view()
# Maybe get rid of an old overlay
if hasattr(self, "contour"):
self.contour['surface'].remove()
self.contour['colorbar'].visible = False
# Deal with Mayavi bug
if scalar_data.dtype.byteorder == '>':
scalar_data.byteswap(True)
# Set up the pipeline
mesh = mlab.pipeline.triangular_mesh_source(self._geo.x, self._geo.y,
self._geo.z, self._geo.faces,
scalars=scalar_data)
thresh = mlab.pipeline.threshold(mesh, low=min)
surf = mlab.pipeline.contour_surface(thresh, contours=n_contours,
line_width=line_width)
# Set the colorbar and range correctly
bar = mlab.scalarbar(surf,
nb_colors=n_contours,
nb_labels=n_contours + 1)
bar.data_range = min, max
self._format_cbar_text(bar)
bar.scalar_bar_representation.position2 = .8, 0.09
# Set up a dict attribute with pointers at important things
self.contour = dict(surface=surf, colorbar=bar)
# Show the new overlay
mlab.view(*view)
self._f.scene.disable_render = False
def add_text(self, x, y, text, name, color=(1, 1, 1), opacity=1.0):
""" Add a text to the visualization
Parameters
----------
x : Float
x coordinate
y : Float
y coordinate
text : str
Text to add
name : str
Name of the text (text label can be updated using update_text())
color : Tuple
Color of the text. Default: (1, 1, 1)
opacity : Float
Opacity of the text. Default: 1.0
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
text = mlab.text(x, y, text, figure=None, name=name,
color=color, opacity=opacity)
self.texts[name] = text
def _set_scene_properties(self, config_opts):
"""Set the scene_prop dict from the config parser.
Parameters
----------
config_opts : dict
dictionary of config file "visual" options
"""
colors = dict(black=[0, 0, 0],
white=[256, 256, 256],
midnight=[12, 7, 32],
slate=[112, 128, 144],
charcoal=[59, 69, 79],
sand=[245, 222, 179])
try:
bg_color_name = config_opts['background']
except KeyError:
bg_color_name = config.get("visual", "background")
bg_color_code = colorConverter.to_rgb(bg_color_name)
try:
fg_color_name = config_opts['foreground']
except KeyError:
fg_color_name = config.get("visual", "foreground")
fg_color_code = colorConverter.to_rgb(fg_color_name)
try:
size = config_opts['size']
except KeyError:
size = config.getfloat("visual", "size")
size = (size, size)
self.scene_properties = dict(fgcolor=fg_color_code,
bgcolor=bg_color_code,
size=size)
def _orient_lights(self):
"""Set lights to come from same direction relative to brain."""
if self.hemi == "rh":
for light in self._f.scene.light_manager.lights:
light.azimuth *= -1
def _get_geo_colors(self, config_opts):
"""Return an mlab colormap name, vmin, and vmax for binary curvature.
Parameters
----------
config_opts : dict
dictionary of config file "visual" options
Returns
-------
colormap : string
mlab colormap name
vmin : float
curv colormap minimum
vmax : float
curv colormap maximum
reverse : boolean
boolean indicating whether the colormap should be reversed
"""
colormap_map = dict(classic=("Greys", -1, 2, False),
high_contrast=("Greys", -.1, 1.3, False),
low_contrast=("Greys", -5, 5, False),
bone=("bone", -.2, 2, True))
try:
cortex_color = config_opts['cortex']
except KeyError:
cortex_color = config.get("visual", "cortex")
try:
color_data = colormap_map[cortex_color]
except KeyError:
warn(""
"The 'cortex' setting in your config file must be one of "
"'classic', 'high_contrast', 'low_contrast', or 'bone', "
"but your value is '%s'. I'm setting the cortex colormap "
"to the 'classic' setting." % cortex_color)
color_data = colormap_map['classic']
return color_data
def get_data_properties(self):
""" Get properties of the data shown
Returns
-------
props : dict
Dictionary with data properties
props["fmin"] : minimum colormap
props["fmid"] : midpoint colormap
props["fmax"] : maximum colormap
props["transparent"] : lower part of colormap transparent?
props["time"] : time points
props["time_idx"] : current time index
props["smoothing_steps"] : number of smoothing steps
"""
props = dict()
try:
props["fmin"] = self.data["fmin"]
props["fmid"] = self.data["fmid"]
props["fmax"] = self.data["fmax"]
props["transparent"] = self.data["transparent"]
props["time"] = self.data["time"]
props["time_idx"] = self.data["time_idx"]
props["smoothing_steps"] = self.data["smoothing_steps"]
except KeyError:
# The user has not added any data
props["fmin"] = 0
props["fmid"] = 0
props["fmax"] = 0
props["transparent"] = 0
props["time"] = 0
props["time_idx"] = 0
props["smoothing_steps"] = 0
return props
def save_image(self, fname):
"""Save current view to disk
Only mayavi image types are supported:
(png jpg bmp tiff ps eps pdf rib oogl iv vrml obj
Parameters
----------
filename: string
path to new image file
"""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
ftype = fname[fname.rfind('.') + 1:]
good_ftypes = ['png', 'jpg', 'bmp', 'tiff', 'ps',
'eps', 'pdf', 'rib', 'oogl', 'iv', 'vrml', 'obj']
if not ftype in good_ftypes:
raise ValueError("Supported image types are %s"
% " ".join(good_ftypes))
mlab.savefig(fname)
def save_imageset(self, prefix, views, filetype='png'):
"""Convience wrapper for save_image
Files created are prefix+'_$view'+filetype
Parameters
----------
prefix: string
filename prefix for image to be created
views: list
desired views for images
filetype: string
image type
Returns
-------
images_written: list
all filenames written
"""
if isinstance(views, basestring):
raise ValueError("Views must be a non-string sequence"
"Use show_view & save_image for a single view")
images_written = []
for view in views:
try:
fname = "%s_%s.%s" % (prefix, view, filetype)
images_written.append(fname)
self.show_view(view)
try:
self.save_image(fname)
except ValueError:
print("Bad image type")
except ValueError:
print("Skipping %s: not in view dict" % view)
return images_written
def save_image_sequence(self, time_idx, fname_pattern, use_abs_idx=True):
"""Save a temporal image sequence
The files saved are named "fname_pattern % (pos)" where "pos" is a
relative or absolute index (controlled by "use_abs_idx")
Parameters
----------
time_idx : array-like
time indices to save
fname_pattern : str
filename pattern, e.g. 'movie-frame_%0.4d.png'
use_abs_idx : boolean
if True the indices given by "time_idx" are used in the filename
if False the index in the filename starts at zero and is
incremented by one for each image (Default: True)
Returns
-------
images_written: list
all filenames written
"""
current_time_idx = self.data["time_idx"]
images_written = list()
rel_pos = 0
for idx in time_idx:
self.set_data_time_index(idx)
fname = fname_pattern % (idx if use_abs_idx else rel_pos)
self.save_image(fname)
images_written.append(fname)
rel_pos += 1
# Restore original time index
self.set_data_time_index(current_time_idx)
return images_written
def scale_data_colormap(self, fmin, fmid, fmax, transparent):
"""Scale the data colormap.
Parameters
----------
fmin : float
minimum value of colormap
fmid : float
value corresponding to color midpoint
fmax : float
maximum value for colormap
transparent : boolean
if True: use a linear transparency between fmin and fmid
"""
if not (fmin < fmid) and (fmid < fmax):
raise ValueError("Invalid colormap, we need fmin<fmid<fmax")
# Cast inputs to float to prevent integer division
fmin = float(fmin)
fmid = float(fmid)
fmax = float(fmax)
print "colormap: fmin=%0.2e fmid=%0.2e fmax=%0.2e transparent=%d" \
% (fmin, fmid, fmax, transparent)
# Get the original colormap
table = self.data["orig_ctable"].copy()
# Add transparency if needed
if transparent:
n_colors = table.shape[0]
n_colors2 = int(n_colors / 2)
table[:n_colors2, -1] = np.linspace(0, 255, n_colors2)
table[n_colors2:, -1] = 255 * np.ones(n_colors - n_colors2)
# Scale the colormap
table_new = table.copy()
n_colors = table.shape[0]
n_colors2 = int(n_colors / 2)
# Index of fmid in new colorbar
fmid_idx = np.round(n_colors * ((fmid - fmin) / (fmax - fmin))) - 1
# Go through channels
for i in range(4):
part1 = np.interp(np.linspace(0, n_colors2 - 1, fmid_idx + 1),
np.arange(n_colors),
table[:, i])
table_new[:fmid_idx + 1, i] = part1
part2 = np.interp(np.linspace(n_colors2, n_colors - 1,
n_colors - fmid_idx - 1),
np.arange(n_colors),
table[:, i])
table_new[fmid_idx + 1:, i] = part2
# Get the new colormap
cmap = self.data["surface"].module_manager.scalar_lut_manager
cmap.lut.table = table_new
cmap.data_range = np.array([fmin, fmax])
# Update the data properties
self.data["fmin"] = fmin
self.data["fmid"] = fmid
self.data["fmax"] = fmax
self.data["transparent"] = transparent
def save_montage(self, filename, order=['lat', 'ven', 'med'],
orientation='h', border_size=15):
"""Create a montage from a given order of images
Parameters
----------
filename: string
path to final image
order: list
order of views to build montage
orientation: {'h' | 'v'}
montage image orientation (horizontal of vertical alignment)
border_size: int
Size of image border (more or less space between images)
"""
assert orientation in ['h', 'v']
import Image
fnames = self.save_imageset("tmp", order)
images = map(Image.open, fnames)
# get bounding box for cropping
boxes = []
for im in images:
red = np.array(im)[:, :, 0]
red[red == red[0, 0]] = 0 # hack for find_objects that wants 0
labels, n_labels = ndimage.label(red)
s = ndimage.find_objects(labels, n_labels)[0] # slice roi
# box = (left, top, width, height)
boxes.append([s[1].start - border_size, s[0].start - border_size,
s[1].stop + border_size, s[0].stop + border_size])
if orientation == 'v':
min_left = min(box[0] for box in boxes)
max_width = max(box[2] for box in boxes)
for box in boxes:
box[0] = min_left
box[2] = max_width
else:
min_top = min(box[1] for box in boxes)
max_height = max(box[3] for box in boxes)
for box in boxes:
box[1] = min_top
box[3] = max_height
# crop images
cropped_images = []
for im, box in zip(images, boxes):
cropped_images.append(im.crop(box))
images = cropped_images
# Get full image size
if orientation == 'h':
w = sum(i.size[0] for i in images)
h = max(i.size[1] for i in images)
else:
h = sum(i.size[1] for i in images)
w = max(i.size[0] for i in images)
new = Image.new("RGBA", (w, h))
x = 0
for i in images:
if orientation == 'h':
pos = (x, 0)
x += i.size[0]
else:
pos = (0, x)
x += i.size[1]
new.paste(i, pos)
try:
new.save(filename)
except Exception:
print("Error saving %s" % filename)
for f in fnames:
os.remove(f)
def set_data_time_index(self, time_idx):
""" Set the data time index to show
Parameters
----------
time_idx : int
time index
"""
if time_idx < 0 or time_idx >= self.data["array"].shape[1]:
raise ValueError("time index out of range")
plot_data = self.data["array"][:, time_idx]
if "smooth_mat" in self.data:
plot_data = self.data["smooth_mat"] * plot_data
self.data["surface"].mlab_source.scalars = plot_data
self.data["time_idx"] = time_idx
# Update time label
self.update_text(self.data["time_label"] % self.data["time"][time_idx],
"time_label")
def set_data_smoothing_steps(self, smoothing_steps):
""" Set the number of smoothing steps
Parameters
----------
smoothing_steps : int
Number of smoothing steps
"""
adj_mat = utils.mesh_edges(self._geo.faces)
smooth_mat = utils.smoothing_matrix(self.data["vertices"], adj_mat,
smoothing_steps)
self.data["smooth_mat"] = smooth_mat
# Redraw
if self.data["array"].ndim == 1:
plot_data = self.data["array"]
else:
plot_data = self.data["array"][:, self.data["time_idx"]]
plot_data = self.data["smooth_mat"] * plot_data
self.data["surface"].mlab_source.scalars = plot_data
# Update data properties
self.data["smoothing_steps"] = smoothing_steps
def update_text(self, text, name):
""" Update text label
Parameters
----------
text : str
New text for label
name : str
Name of text label
"""
self.texts[name].text = text
def min_diff(self, beg, end):
"""Determine minimum "camera distance" between two views.
Parameters
----------
beg: string
origin anatomical view
end: string
destination anatomical view
Returns
-------
diffs: tuple
(min view "distance", min roll "distance")
"""
beg = self.xfm_view(beg)
end = self.xfm_view(end)
if beg == end:
dv = [360., 0.]
dr = 0
else:
end_d = self.xfm_view(end, 'd')
beg_d = self.xfm_view(beg, 'd')
dv = []
for b, e in zip(beg_d['v'], end_d['v']):
diff = e - b
# to minimize the rotation we need -180 <= diff <= 180
if diff > 180:
dv.append(diff - 360)
elif diff < -180:
dv.append(diff + 360)
else:
dv.append(diff)
dr = np.array(end_d['r']) - np.array(beg_d['r'])
return (np.array(dv), dr)
def animate(self, views, n_steps=180., fname=None, use_cache=False):
"""Animate a rotation.
Currently only rotations through the axial plane are allowed.
Parameters
----------
views: sequence
views to animate through
n_steps: float
number of steps to take in between
fname: string
If not None, it saves the animation as a movie.
fname should end in '.avi' as only the AVI format is supported
use_cache: bool
Use previously generated images in ./.tmp/
"""
gviews = map(self.xfm_view, views)
allowed = ('lateral', 'caudal', 'medial', 'rostral')
if not len([v for v in gviews if v in allowed]) == len(gviews):
raise ValueError('Animate through %s views.' % ' '.join(allowed))
if fname is not None:
if not fname.endswith('.avi'):
raise ValueError('Can only output to AVI currently.')
tmp_dir = './.tmp'
tmp_fname = pjoin(tmp_dir, '%05d.png')
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir)
for i, beg in enumerate(gviews):
try:
end = gviews[i + 1]
dv, dr = self.min_diff(beg, end)
dv /= np.array((n_steps))
dr /= np.array((n_steps))
self.show_view(beg)
for i in range(int(n_steps)):
self._f.scene.camera.orthogonalize_view_up()
self._f.scene.camera.azimuth(dv[0])
self._f.scene.camera.elevation(dv[1])
self._f.scene.renderer.reset_camera_clipping_range()
self._f.scene.render()
if fname is not None:
if not (os.path.isfile(tmp_fname % i) and use_cache):
self.save_image(tmp_fname % i)
except IndexError:
pass
if fname is not None:
fps = 10
# we'll probably want some config options here
enc_cmd = " ".join(["mencoder",
"-ovc lavc",
"-mf fps=%d" % fps,
"mf://%s" % tmp_fname,
"-of avi",
"-lavcopts vcodec=mjpeg",
"-ofps %d" % fps,
"-noskip",
"-o %s" % fname])
ret = os.system(enc_cmd)
if ret:
print("\n\nError occured when exporting movie\n\n")
def xfm_view(self, view, out='s'):
"""Normalize a given string to available view
Parameters
----------
view: string
view which may match leading substring of available views
Returns
-------
good: string
matching view string
out: {'s' | 'd'}
's' to return string, 'd' to return dict
"""
if not view in self.viewdict:
good_view = [k for k in self.viewdict if view == k[:len(view)]]
if len(good_view) == 0:
raise ValueError('No views exist with this substring')
if len(good_view) > 1:
raise ValueError("Multiple views exist with this substring."
"Try a longer substring")
view = good_view[0]
if out == 'd':
return self.viewdict[view]
else:
return view
def close(self):
"""Close the figure and cleanup data structure."""
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
mlab.close(self._f)
#should we tear down other variables?
def _get_display_range(self, scalar_data, min, max, sign):
if scalar_data.min() >= 0:
sign = "pos"
elif scalar_data.max() <= 0:
sign = "neg"
self.sign = sign
# Get data with a range that will make sense for automatic thresholding
if sign == "neg":
range_data = np.abs(scalar_data[np.where(scalar_data < 0)])
elif sign == "pos":
range_data = scalar_data[np.where(scalar_data > 0)]
else:
range_data = np.abs(scalar_data)
# Get the min and max from among various places
if min is None:
try:
min = config.getfloat("overlay", "min_thresh")
except ValueError:
min_str = config.get("overlay", "min_thresh")
if min_str == "robust_min":
min = stats.scoreatpercentile(range_data, 2)
elif min_str == "actual_min":
min = range_data.min()
else:
min = 2.0
warn("The 'min_thresh' value in your config value must be "
"a float, 'robust_min', or 'actual_min', but it is %s. "
"I'm setting the overlay min to the config default of 2" % min)
if max is None:
try:
max = config.getfloat("overlay", "max_thresh")
except ValueError:
max_str = config.get("overlay", "max_thresh")
if max_str == "robust_max":
max = stats.scoreatpercentile(scalar_data, 98)
elif max_str == "actual_max":
max = range_data.max()
else:
max = stats.scoreatpercentile(range_data, 98)
warn("The 'max_thresh' value in your config value must be "
"a float, 'robust_min', or 'actual_min', but it is %s. "
"I'm setting the overlay min to the config default "
"of robust_max" % max)
return min, max
def _format_cbar_text(self, cbar):
bg_color = self.scene_properties["bgcolor"]
text_color = (1., 1., 1.) if sum(bg_color) < 2 else (0., 0., 0.)
cbar.label_text_property.color = text_color
class Overlay(object):
"""Encapsulation of statistical neuroimaging overlay visualization."""
def __init__(self, scalar_data, geo, min, max, sign):
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
if scalar_data.min() >= 0:
sign = "pos"
elif scalar_data.max() <= 0:
sign = "neg"
self.sign = sign
# Byte swap copy; due to mayavi bug
mlab_data = scalar_data.copy()
if scalar_data.dtype.byteorder == '>':
mlab_data.byteswap(True)
if sign in ["abs", "pos"]:
pos_mesh = mlab.pipeline.triangular_mesh_source(geo.x,
geo.y,
geo.z,
geo.faces,
scalars=mlab_data)
# Figure out the correct threshold to avoid TraitErrors
# This seems like not the cleanest way to do this
pos_data = scalar_data[np.where(scalar_data > 0)]
try:
pos_max = pos_data.max()
except ValueError:
pos_max = 0
if pos_max < min:
thresh_low = pos_max
else:
thresh_low = min
pos_thresh = mlab.pipeline.threshold(pos_mesh, low=thresh_low)
pos_surf = mlab.pipeline.surface(pos_thresh, colormap="YlOrRd",
vmin=min, vmax=max)
pos_bar = mlab.scalarbar(pos_surf, nb_labels=5)
pos_bar.reverse_lut = True
self.pos = pos_surf
self.pos_bar = pos_bar
if sign in ["abs", "neg"]:
neg_mesh = mlab.pipeline.triangular_mesh_source(geo.x,
geo.y,
geo.z,
geo.faces,
scalars=mlab_data)
# Figure out the correct threshold to avoid TraitErrors
# This seems even less clean due to negative convolutedness
neg_data = scalar_data[np.where(scalar_data < 0)]
try:
neg_min = neg_data.min()
except ValueError:
neg_min = 0
if neg_min > -min:
thresh_up = neg_min
else:
thresh_up = -min
neg_thresh = mlab.pipeline.threshold(neg_mesh, up=thresh_up)
neg_surf = mlab.pipeline.surface(neg_thresh, colormap="PuBu",
vmin=-max, vmax=-min)
neg_bar = mlab.scalarbar(neg_surf, nb_labels=5)
self.neg = neg_surf
self.neg_bar = neg_bar
self._format_colorbar()
def remove(self):
if self.sign in ["pos", "abs"]:
self.pos.remove()
self.pos_bar.visible = False
if self.sign in ["neg", "abs"]:
self.neg.remove()
self.neg_bar.visible = False
def _format_colorbar(self):
if self.sign in ["abs", "neg"]:
self.neg_bar.scalar_bar_representation.position = (0.05, 0.01)
self.neg_bar.scalar_bar_representation.position2 = (0.42, 0.09)
if self.sign in ["abs", "pos"]:
self.pos_bar.scalar_bar_representation.position = (0.53, 0.01)
self.pos_bar.scalar_bar_representation.position2 = (0.42, 0.09)
class TimeViewer(HasTraits):
""" TimeViewer object providing a GUI for visualizing time series, such
as M/EEG inverse solutions, on Brain object(s)
"""
min_time = Int(0)
max_time = Int(1E9)
current_time = Range(low="min_time", high="max_time", value=0)
# colormap: only update when user presses Enter
fmax = Float(enter_set=True, auto_set=False)
fmid = Float(enter_set=True, auto_set=False)
fmin = Float(enter_set=True, auto_set=False)
transparent = Bool(True)
smoothing_steps = Int(20, enter_set=True, auto_set=False)
orientation = Enum("lateral", "medial", "rostral", "caudal",
"dorsal", "ventral", "frontal", "parietal")
# GUI layout
view = View(VSplit(Item(name="current_time"),
Group(HSplit(Item(name="fmin"),
Item(name="fmid"),
Item(name="fmax"),
Item(name="transparent"),
),
label="Color scale",
show_border=True
),
Item(name="smoothing_steps"),
Item(name="orientation")
)
)
def __init__(self, brain):
"""Initialize TimeViewer
Parameters
----------
brain : Brain
brain to control
"""
super(TimeViewer, self).__init__()
self.brain = brain
# Initialize GUI with values from brain
props = brain.get_data_properties()
self._disable_updates = True
self.max_time = len(props["time"]) - 1
self.current_time = props["time_idx"]
self.fmin = props["fmin"]
self.fmid = props["fmid"]
self.fmax = props["fmax"]
self.transparent = props["transparent"]
self.smoothing_steps = props["smoothing_steps"]
self._disable_updates = False
# Show GUI
self.configure_traits()
@on_trait_change("smoothing_steps")
def set_smoothing_steps(self):
""" Change number of smooting steps
"""
if self._disable_updates:
return
self.brain.set_data_smoothing_steps(self.smoothing_steps)
@on_trait_change("orientation")
def set_orientation(self):
""" Set the orientation
"""
if self._disable_updates:
return
self.brain.show_view(view=self.orientation)
@on_trait_change("current_time")
def set_time_point(self):
""" Set the time point shown
"""
if self._disable_updates:
return
self.brain.set_data_time_index(self.current_time)
@on_trait_change("fmin, fmid, fmax, transparent")
def scale_colormap(self):
""" Scale the colormap
"""
if self._disable_updates:
return
self.brain.scale_data_colormap(self.fmin, self.fmid, self.fmax,
self.transparent)
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