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vectorizing some trisurf functions for performance improvement #472

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vectorizing some trisurf functions for performance improvement #472

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90 changes: 43 additions & 47 deletions plotly/tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1464,11 +1464,10 @@ def _find_intermediate_color(lowcolor, highcolor, intermed):
diff_1 = float(highcolor[1] - lowcolor[1])
diff_2 = float(highcolor[2] - lowcolor[2])

new_tuple = (lowcolor[0] + intermed*diff_0,
lowcolor[1] + intermed*diff_1,
lowcolor[2] + intermed*diff_2)

return new_tuple
inter_colors = np.array([lowcolor[0] + intermed * diff_0,
lowcolor[1] + intermed * diff_1,
lowcolor[2] + intermed * diff_2])
return inter_colors

@staticmethod
def _unconvert_from_RGB_255(colors):
Expand All @@ -1491,7 +1490,7 @@ def _unconvert_from_RGB_255(colors):
return un_rgb_colors

@staticmethod
def _map_z2color(zval, colormap, vmin, vmax):
def _map_z2color(zvals, colormap, vmin, vmax):
"""
Returns the color corresponding zval's place between vmin and vmax

Expand All @@ -1508,21 +1507,14 @@ def _map_z2color(zval, colormap, vmin, vmax):
"of vmax.")
# find distance t of zval from vmin to vmax where the distance
# is normalized to be between 0 and 1
t = (zval - vmin)/float((vmax - vmin))
t_color = FigureFactory._find_intermediate_color(colormap[0],
colormap[1],
t)
t_color = (t_color[0]*255.0, t_color[1]*255.0, t_color[2]*255.0)
labelled_color = 'rgb{}'.format(t_color)

return labelled_color

@staticmethod
def _tri_indices(simplices):
"""
Returns a triplet of lists containing simplex coordinates
"""
return ([triplet[c] for triplet in simplices] for c in range(3))
t = (zvals - vmin) / float((vmax - vmin))
t_colors = FigureFactory._find_intermediate_color(colormap[0],
colormap[1],
t)
t_colors = t_colors * 255.
labelled_colors = ['rgb(%s, %s, %s)' % (i, j, k)
for i, j, k in t_colors.T]
return labelled_colors

@staticmethod
def _trisurf(x, y, z, simplices, colormap=None, dist_func=None,
Expand All @@ -1539,11 +1531,11 @@ def _trisurf(x, y, z, simplices, colormap=None, dist_func=None,
points3D = np.vstack((x, y, z)).T

# vertices of the surface triangles
tri_vertices = list(map(lambda index: points3D[index], simplices))
tri_vertices = points3D[simplices]

if not dist_func:
# mean values of z-coordinates of triangle vertices
mean_dists = [np.mean(tri[:, 2]) for tri in tri_vertices]
mean_dists = tri_vertices[:, :, 2].mean(-1)
else:
# apply user inputted function to calculate
# custom coloring for triangle vertices
Expand All @@ -1559,38 +1551,43 @@ def _trisurf(x, y, z, simplices, colormap=None, dist_func=None,

min_mean_dists = np.min(mean_dists)
max_mean_dists = np.max(mean_dists)
facecolor = ([FigureFactory._map_z2color(zz, colormap, min_mean_dists,
max_mean_dists) for zz in mean_dists])
ii, jj, kk = FigureFactory._tri_indices(simplices)
facecolor = FigureFactory._map_z2color(mean_dists, colormap,
min_mean_dists, max_mean_dists)
ii, jj, kk = zip(*simplices)

triangles = graph_objs.Mesh3d(x=x, y=y, z=z, facecolor=facecolor,
i=ii, j=jj, k=kk, name='')

if plot_edges is None: # the triangle sides are not plotted
if plot_edges is not True: # the triangle sides are not plotted
return graph_objs.Data([triangles])

# define the lists x_edge, y_edge and z_edge, of x, y, resp z
# coordinates of edge end points for each triangle
# None separates data corresponding to two consecutive triangles
lists_coord = ([[[T[k % 3][c] for k in range(4)]+[None]
for T in tri_vertices] for c in range(3)])
if x_edge is None:
x_edge = []
for array in lists_coord[0]:
for item in array:
x_edge.append(item)

if y_edge is None:
y_edge = []
for array in lists_coord[1]:
for item in array:
y_edge.append(item)

if z_edge is None:
z_edge = []
for array in lists_coord[2]:
for item in array:
z_edge.append(item)
is_none = [ii is None for ii in [x_edge, y_edge, z_edge]]
if any(is_none):
if not all(is_none):
raise ValueError('If any (x_edge, y_edge, z_edge) is None,'
' all must be None')
else:
x_edge = []
y_edge = []
z_edge = []
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cool logic.


# Pull indices we care about, then add a None column to separate tris
ixs_triangles = [0, 1, 2, 0]
pull_edges = tri_vertices[:, ixs_triangles, :]
x_edge_pull = np.hstack([pull_edges[:, :, 0],
np.tile(None, [pull_edges.shape[0], 1])])
y_edge_pull = np.hstack([pull_edges[:, :, 1],
np.tile(None, [pull_edges.shape[0], 1])])
z_edge_pull = np.hstack([pull_edges[:, :, 2],
np.tile(None, [pull_edges.shape[0], 1])])

# Now unravel the edges into a 1-d vector for plotting
x_edge = np.hstack([x_edge, x_edge_pull.reshape([1, -1])[0]])
y_edge = np.hstack([y_edge, y_edge_pull.reshape([1, -1])[0]])
z_edge = np.hstack([z_edge, z_edge_pull.reshape([1, -1])[0]])

# define the lines for plotting
lines = graph_objs.Scatter3d(
Expand Down Expand Up @@ -5621,4 +5618,3 @@ def make_table_annotations(self):
font=dict(color=font_color),
showarrow=False))
return annotations