ENH,FIX add dropout option to make_svg, fix transparency (#426)

* ENH,FIX add dropout option to make_svg, fix transparency

* DOC improve description of dropout

cortex/quickflat/utils.py

@@ -320,9 +320,15 @@ def _make_hatch_image(hatch_data, height, sampler='nearest', hatch_space=4, reca
         space between hatch lines (in pixels)
     recache : boolean
 
-
+    Returns
+    -------
+    hatchim : RGBA array
+        flatmap image with hatches over hatch_data
     """
-    dmap, _ = make_flatmap_image(hatch_data, height=height, sampler=sampler, recache=recache)
+    dmap, _ = make_flatmap_image(
+        hatch_data, height=height, sampler=sampler, recache=recache, nanmean=True
+    )
+    mask_nans = np.isnan(dmap)
     hx, hy = np.meshgrid(range(dmap.shape[1]), range(dmap.shape[0]))
 
     hatchpat = (hx+hy)%(2*hatch_space) < 2
@@ -332,7 +338,9 @@ def _make_hatch_image(hatch_data, height, sampler='nearest', hatch_space=4, reca
 
     hatchpat = np.logical_or(hatchpat, hatchpat[:,::-1]).astype(float)
     hatchim = np.dstack([1-hatchpat]*3 + [hatchpat])
-    hatchim[:, : ,3] *= np.clip(dmap, 0, 1).astype(float)
+    hatchim[:, :, 3] *= np.clip(dmap, 0, 1).astype(float)
+    # Set nans to alpha = 0. for transparency
+    hatchim[mask_nans, 3] = 0.
 
     return hatchim
 

cortex/quickflat/view.py

@@ -319,7 +319,7 @@ def make_png(fname, braindata, recache=False, pixelwise=True, sampler='nearest',
     plt.close(fig)
 
 def make_svg(fname, braindata, with_labels=False, with_curvature=True, layers=['rois'],
-             height=1024, overlay_file=None, **kwargs):
+             height=1024, overlay_file=None, with_dropout=False, **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.
@@ -341,14 +341,23 @@ def make_svg(fname, braindata, with_labels=False, with_curvature=True, layers=['
     height : int
         Height of PNG in pixels
     overlay_file : str
-    	Custom ROI overlays file to use
-
+        Custom ROI overlays file to use
+    with_dropout : bool or Dataview
+        If True or a cortex.Dataview object, hatches will be overlaid on top of the
+        flatmap to indicate areas with dropout. If set to True, the dropout areas will
+        be estimated from the intensity of the reference image. If set to a
+        cortex.Dataview object, values in the dataset will be considered dropout areas.
+        The transparency of the hatches is proportional to the intensity of the values
+        in the dropout dataset.
     """
     fp = io.BytesIO()
-    from matplotlib.pylab import imsave
+    from matplotlib.pyplot import imsave
 
     ## Render PNG file & retrieve image data
     arr, extents = make_flatmap_image(braindata, height=height, **kwargs)
+    # Set nans to alpha = 0. to enable transparency when saving as PNG
+    mask_nans = np.isnan(arr[..., 3])
+    arr[mask_nans, 3] = 0.
 
     if hasattr(braindata, 'cmap'):
         imsave(fp, arr, cmap=braindata.cmap, vmin=braindata.vmin, vmax=braindata.vmax)
@@ -372,6 +381,30 @@ def make_svg(fname, braindata, with_labels=False, with_curvature=True, layers=['
         fpc.seek(0)
         image_data = [binascii.b2a_base64(fpc.read()), pngdata]
 
+    # Add dropout -- modified from quickflat.view.make_figure
+    if with_dropout:
+        dataview = dataset.normalize(braindata)
+        # Support old api:
+        if isinstance(with_dropout, dataset.Dataview):
+            hatch_data = with_dropout
+        else:
+            hatch_data = utils.get_dropout(dataview.subject, dataview.xfmname)
+        sampler = kwargs.get("sampler", "nearest")
+        recache = kwargs.get("recache", False)
+        hatch_space = 4
+        hatch_color = (0, 0, 0)
+        hatchim = composite._make_hatch_image(
+            hatch_data, height, sampler, recache=recache, hatch_space=hatch_space
+        )
+        hatchim[:, :, 0] = hatch_color[0]
+        hatchim[:, :, 1] = hatch_color[1]
+        hatchim[:, :, 2] = hatch_color[2]
+        fpc = io.BytesIO()
+        imsave(fpc, hatchim)
+        fpc.seek(0)
+        # Add dropout above data layer
+        image_data += [binascii.b2a_base64(fpc.read())]
+
     ## 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)