def get_svg_html(mpl_figures):
svg_images = []
with make_tmp_folder() as tmp_dir:
for fig in mpl_figures:
tmp_svg = "%s/mplfig.svg" % (tmp_dir)
fig.savefig(tmp_svg)
fig_data = open(tmp_svg, "rb").readlines()
svg_images.append(fig_data)
return svg_images
def get_svg_html(mpl_figures):
svg_images = []
with make_tmp_folder() as tmp_dir:
for fig in mpl_figures:
tmp_svg = "%s/mplfig.svg" %(tmp_dir)
fig.savefig(tmp_svg)
fig_data = open(tmp_svg,"rb").readlines()
svg_images.append(fig_data)
return svg_images
def get_static_svg(self):
'''Generate static svg of atlas (cannot manipulate in d3)'''
svg_data = []
with make_tmp_folder() as temp_dir:
output_file='%s/atlas.svg' %(temp_dir)
plotting.plot_roi(self.mr,annotate=False,draw_cross=False,cmap="nipy_spectral",black_bg=False, output_file=output_file)
svg_file = open(output_file,'r')
svg_data = svg_file.readlines()
svg_file.close()
return svg_data[4:]
def get_static_svg(self):
'''Generate static svg of atlas (cannot manipulate in d3)'''
svg_data = []
with make_tmp_folder() as temp_dir:
output_file='%s/atlas.svg' %(temp_dir)
plotting.plot_roi(self.mr,annotate=False,draw_cross=False,cmap="nipy_spectral",black_bg=False, output_file=output_file)
svg_file = open(output_file,'r')
svg_data = svg_file.readlines()
svg_file.close()
return svg_data[4:]
def view(html_snippet):
with make_tmp_folder() as tmp_dir:
# Write to temporary file
tmp_file = "%s/pycompare.html" % (tmp_dir)
internal_view(html_snippet, tmp_file)
def make_svg(self,views):
'''Generate path-based svg of atlas (paths we can manipulate in d3)'''
import cairo
# We will save complete svg (for file), partial (for embedding), and paths
svg_data = dict(); svg_data_partial = dict(); svg_data_file = dict();
if isinstance(views,str):
views = [views]
views = [v.lower() for v in views]
self.views = views
mr = self.mr.get_data()
middles = [numpy.round(x/2) for x in self.mr.get_shape()]
# Create a color lookup table
colors_html = get_colors(len(self.labels),"hex")
self.color_lookup = self.make_color_lookup(colors_html)
with make_tmp_folder() as temp_dir:
# Get all unique regions (may not be present in every slice)
regions = [ x for x in numpy.unique(mr) if x != 0]
# Get colors - will later be changed
colors = get_colors(len(self.labels),"decimal")
# Generate an axial, sagittal, coronal view
slices = dict()
for v in views:
# Keep a list of region names that correspond to paths
region_names = []
# Generate each of the views
if v == "axial": slices[v] = numpy.rot90(mr[:,:,middles[0]],2)
elif v == "sagittal" : slices[v] = numpy.rot90(mr[middles[1],:,:],2)
elif v == "coronal" : slices[v] = numpy.rot90(mr[:,middles[2],:],2)
# For each region in the view, but not 0
regions = [ x for x in numpy.unique(slices[v]) if x != 0]
# Write svg to temporary file
output_file = '%s/%s_atlas.svg' %(temp_dir,v)
fo = file(output_file, 'wb')
# Set up the "context" - what cairo calls a canvas
width, height = numpy.shape(slices[v])
surface = cairo.SVGSurface (fo, width*3, height*3)
ctx = cairo.Context (surface)
ctx.scale(3.,3.)
# 90 degree rotation matrix
rotation_matrix = cairo.Matrix.init_rotate(numpy.pi/2)
for rr in range(0,len(regions)):
index_value = regions[rr]
#region_name = self.labels[str(index_value)].label
filtered = numpy.zeros(numpy.shape(slices[v]))
filtered[slices[v] == regions[rr]] = 1
region = img_as_float(find_boundaries(filtered)) # We aren't using Canny anymore...
ctx.set_source_rgb (float(colors[index_value-1][0]), float(colors[index_value-1][1]), float(colors[index_value-1][2])) # Solid color
# Segment!
segments_fz = felzenszwalb(region, scale=100, sigma=0.1, min_size=10)
# For each cluster in the region, skipping value of 0
for c in range(1,len(numpy.unique(segments_fz))):
cluster = numpy.zeros(numpy.shape(region))
cluster[segments_fz==c] = 1
# Create distance matrix for points
x,y = numpy.where(cluster==1)
points = [[x[i],y[i]] for i in range(0,len(x))]
disty = squareform(pdist(points, 'euclidean'))
# This keeps track of which we have already visited
visited = []; row = 0; current = points[row]
visited.append(row)
# We need to remember the first point, for the last one
fp = current
while len(visited) != len(points):
thisx = current[0]
thisy = current[1]
ctx.move_to(thisx, thisy)
# Find closest point, only include columns we have not visited
distances = disty[row,:]
distance_lookup = dict()
# We need to preserve indices but still eliminate visited
for j in range(0,len(distances)):
if j not in visited: distance_lookup[j] = distances[j]
# Get key minimum distance
row = min(distance_lookup, key=distance_lookup.get)
next = points[row]
nextx = next[0]
nexty = next[1]
# If the distance is more than N pixels, close the path
# This resolves some of the rough edges too
if min(distance_lookup) > 70:
ctx.line_to(fp[0],fp[1])
#cp = [(current[0]+fp[0])/2,(current[1]+fp[1])/2]
#ctx.curve_to(fp[0],fp[1],cp[0],cp[1],cp[0],cp[1])
ctx.set_line_width(1)
ctx.close_path()
fp = next
else:
#cp = [(current[0]+nextx)/2,(current[1]+nexty)/2]
#ctx.curve_to(nextx,nexty,cp[0],cp[1],cp[0],cp[1])
ctx.line_to(nextx, nexty)
# Set next point to be current
visited.append(row)
current = next
# Go back to the first point
ctx.move_to(current[0],current[1])
#cp = [(current[0]+fp[0])/2,(current[1]+fp[1])/2]
#ctx.curve_to(fp[0],fp[1],cp[0],cp[1],cp[0],cp[1])
ctx.line_to(fp[0],fp[1])
# Close the path
ctx.set_line_width (1)
ctx.stroke()
# Finish the surface
surface.finish()
fo.close()
# Now grab the file, set attributes
# Give group name based on atlas, region id based on matching color
dom = minidom.parse(output_file)
for group in dom.getElementsByTagName("g"):
group.setAttribute("id",os.path.split(self.file)[-1])
group.setAttribute("class",v)
expression = re.compile("stroke:rgb")
# Add class to svg - important so can manipulate in d3
dom.getElementsByTagName("svg")[0].setAttribute("class",v)
for path in dom.getElementsByTagName("path"):
style = path.getAttribute("style")
# This is lame - but we have to use the color to look up the region
color = [x for x in style.split(";") if expression.search(x)][0]
color = [percent_to_float(x) for x in color.replace("stroke:rgb(","").replace(")","").split(",")]
region_index = [x for x in range(0,len(colors)) if numpy.equal(colors[x],color).all()][0]+1
region_label = self.labels[str(region_index)].label
# We don't want to rely on cairo to style the paths
self.remove_attributes(path,"style")
self.set_attributes(path,["id","stroke"],[region_label,self.color_lookup[region_label]])
svg_data_file[v] = dom.toxml()
svg_data[v] = dom.toxml().replace("<?xml version=\"1.0\" ?>","") # get rid of just xml tag
svg_data_partial[v] = "/n".join(dom.toxml().split("\n")[1:-1])
return svg_data, svg_data_partial, svg_data_file
def view(html_snippet):
with make_tmp_folder() as tmp_dir:
# Write to temporary file
tmp_file = "%s/pycompare.html" %(tmp_dir)
internal_view(html_snippet,tmp_file)
def make_svg(self, views):
'''Generate path-based svg of atlas (paths we can manipulate in d3)'''
import cairo
# We will save complete svg for file, partial for embedding, and paths
svg_data = dict()
svg_data_partial = dict()
svg_data_file = dict()
if isinstance(views, str):
views = [views]
self.views = [v.lower() for v in views]
mr = self.mr.get_data()
middles = [numpy.round(old_div(x, 2)) for x in self.mr.get_shape()]
# Create a color lookup table
colors_html = get_colors(len(self.labels), "hex")
self.color_lookup = self.make_color_lookup(colors_html)
with make_tmp_folder() as temp_dir:
# Get all unique regions (may not be present in every slice)
regions = [x for x in numpy.unique(mr) if x != 0]
# Get colors - will later be changed
colors = get_colors(len(self.labels), "decimal")
# Generate an axial, sagittal, coronal view
slices = dict()
for v in views:
# Keep a list of region names that correspond to paths
region_names = []
# Generate each of the views
if v == "axial":
slices[v] = numpy.rot90(mr[:, :, middles[0]], 2)
elif v == "sagittal":
slices[v] = numpy.rot90(mr[middles[1], :, :], 2)
elif v == "coronal":
slices[v] = numpy.rot90(mr[:, middles[2], :], 2)
# For each region in the view, but not 0
regions = [x for x in numpy.unique(slices[v]) if x != 0]
# Write svg to temporary file
output_file = '%s/%s_atlas.svg' % (temp_dir, v)
fo = file(output_file, 'wb')
# Set up the "context" - what cairo calls a canvas
width, height = numpy.shape(slices[v])
surface = cairo.SVGSurface(fo, width * 3, height * 3)
ctx = cairo.Context(surface)
ctx.scale(3., 3.)
# 90 degree rotation matrix
rotation_matrix = cairo.Matrix.init_rotate(old_div(
numpy.pi, 2))
for rr in range(0, len(regions)):
index_value = regions[rr]
#region_name = self.labels[str(index_value)].label
filtered = numpy.zeros(numpy.shape(slices[v]))
filtered[slices[v] == regions[rr]] = 1
# We aren't using Canny anymore...
region = img_as_float(find_boundaries(filtered))
# Solid color
ctx.set_source_rgb(float(colors[index_value - 1][0]),
float(colors[index_value - 1][1]),
float(colors[index_value - 1][2]))
# Segment!
segments_fz = felzenszwalb(region,
scale=100,
sigma=0.1,
min_size=10)
# For each cluster in the region, skipping value of 0
for c in range(1, len(numpy.unique(segments_fz))):
cluster = numpy.zeros(numpy.shape(region))
cluster[segments_fz == c] = 1
# Create distance matrix for points
x, y = numpy.where(cluster == 1)
points = [[x[i], y[i]] for i in range(0, len(x))]
disty = squareform(pdist(points, 'euclidean'))
# This keeps track of which we have already visited
visited = []
row = 0
current = points[row]
visited.append(row)
# We need to remember the first point, for the last one
fp = current
while len(visited) != len(points):
thisx = current[0]
thisy = current[1]
ctx.move_to(thisx, thisy)
# Find closest point, only include cols not visited
distances = disty[row, :]
distance_lookup = dict()
# preserve indices but still eliminate visited
for j in range(0, len(distances)):
if j not in visited:
distance_lookup[j] = distances[j]
# Get key minimum distance
row = min(distance_lookup, key=distance_lookup.get)
next = points[row]
nextx = next[0]
nexty = next[1]
# If the distance is more than N pixels, close path
# This resolves some of the rough edges too
if min(distance_lookup) > 70:
ctx.line_to(fp[0], fp[1])
ctx.set_line_width(1)
ctx.close_path()
fp = next
else:
ctx.line_to(nextx, nexty)
# Set next point to be current
visited.append(row)
current = next
# Go back to the first point
ctx.move_to(current[0], current[1])
ctx.line_to(fp[0], fp[1])
# Close the path
ctx.set_line_width(1)
ctx.stroke()
# Finish the surface
surface.finish()
fo.close()
# Now grab the file, set attributes
# group name based on atlas, region id based on matching color
dom = minidom.parse(output_file)
for group in dom.getElementsByTagName("g"):
group.setAttribute("id", os.path.split(self.file)[-1])
group.setAttribute("class", v)
regexp = re.compile("stroke:rgb")
# Add class to svg - important so can manipulate in d3
dom.getElementsByTagName("svg")[0].setAttribute("class", v)
for path in dom.getElementsByTagName("path"):
style = path.getAttribute("style")
# we have to use the color to look up the region
color = [x for x in style.split(";")
if regexp.search(x)][0]
color = [
percent_to_float(x) for x in color.replace(
"stroke:rgb(", "").replace(")", "").split(",")
]
region_index = [
x for x in range(0, len(colors))
if numpy.equal(colors[x], color).all()
][0] + 1
region_label = self.labels[str(region_index)].label
# We don't want to rely on cairo to style the paths
self.remove_attributes(path, "style")
self.set_attributes(
path, ["id", "stroke"],
[region_label, self.color_lookup[region_label]])
svg_data_file[v] = dom.toxml()
# get rid of just xml tag
svg_data[v] = dom.toxml().replace("<?xml version=\"1.0\" ?>",
"")
svg_data_partial[v] = "/n".join(dom.toxml().split("\n")[1:-1])
return svg_data, svg_data_partial, svg_data_file