def make_svg(fname, braindata, with_labels=False, with_curvature=True, layers=['rois'],
height=1024, overlay_file=None, **kwargs):
"""Save an svg file of the desired flatmap.
This function creates an SVG file with vector graphic ROIs overlaid on a single png image.
Ideally, this function would layer different images (curvature, data, dropout, etc), but
that has been left to implement at a future date if anyone really wants it.
Parameters
----------
fname : string
file name to save
braindata : Dataview
the data you would like to plot on a flatmap
with_labels : bool
Whether to display text labels on ROIs
with_curvature : bool
Whether to include background curvature
layers : list
List of layer names to show
height : int
Height of PNG in pixels
overlay_file : str
Custom ROI overlays file to use
"""
fp = io.BytesIO()
from matplotlib.pylab import imsave
## Render PNG file & retrieve image data
arr, extents = make_flatmap_image(braindata, height=height, **kwargs)
if hasattr(braindata, 'cmap'):
imsave(fp, arr, cmap=braindata.cmap, vmin=braindata.vmin, vmax=braindata.vmax)
else:
imsave(fp, arr)
fp.seek(0)
pngdata = binascii.b2a_base64(fp.read())
image_data = [pngdata]
if with_curvature:
# no options. learn to love it.
from cortex import db
fpc = io.BytesIO()
curv_vertices = db.get_surfinfo(braindata.subject)
curv_arr, _ = make_flatmap_image(curv_vertices, height=height)
mask = np.isnan(curv_arr)
curv_arr = np.where(curv_arr > 0, 0.5, 0.25)
curv_arr[mask] = np.nan
imsave(fpc, curv_arr, cmap='Greys_r', vmin=0, vmax=1)
fpc.seek(0)
image_data = [binascii.b2a_base64(fpc.read()), pngdata]
## Create and save SVG file
roipack = utils.db.get_overlay(braindata.subject, overlay_file)
roipack.get_svg(fname, layers=layers, labels=with_labels, with_ims=image_data)
def make_svg(fname, braindata, with_labels=False, with_curvature=True, layers=['rois'],
height=1024, **kwargs):
"""Save an svg file of the desired flatmap.
This function creates an SVG file with vector graphic ROIs overlaid on a single png image.
Ideally, this function would layer different images (curvature, data, dropout, etc), but
that has been left to implement at a future date if anyone really wants it.
Parameters
----------
fname : string
file name to save
braindata : Dataview
the data you would like to plot on a flatmap
with_labels : bool
Whether to display text labels on ROIs
with_curvature : bool
Whether to include background curvature
layers : list
List of layer names to show
height : int
Height of PNG in pixels
"""
fp = io.BytesIO()
from matplotlib.pylab import imsave
## Render PNG file & retrieve image data
arr, extents = make_flatmap_image(braindata, height=height, **kwargs)
if hasattr(braindata, 'cmap'):
imsave(fp, arr, cmap=braindata.cmap, vmin=braindata.vmin, vmax=braindata.vmax)
else:
imsave(fp, arr)
fp.seek(0)
pngdata = binascii.b2a_base64(fp.read())
image_data = [pngdata]
if with_curvature:
# no options. learn to love it.
from cortex import db
fpc = io.BytesIO()
curv_vertices = db.get_surfinfo(braindata.subject)
curv_arr, _ = make_flatmap_image(curv_vertices, height=height)
mask = np.isnan(curv_arr)
curv_arr = np.where(curv_arr > 0, 0.5, 0.25)
curv_arr[mask] = np.nan
imsave(fpc, curv_arr, cmap='Greys_r', vmin=0, vmax=1)
fpc.seek(0)
image_data = [binascii.b2a_base64(fpc.read()), pngdata]
## Create and save SVG file
roipack = utils.get_roipack(braindata.subject)
roipack.get_svg(fname, layers=layers, labels=with_labels, with_ims=image_data)