2d array vs 1d array inconsistency with optional argument to Plot3D

[ghost]

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The problem is based on the way that julia distinguishes between singleton arrays and nx1 two-dimensional arrays. The optional argument zs for plot3D( xs, ys, zs=zs ) is not robust to these two different representations but the first two arguments xs, ys are.

Say a matrix of data is generated and three sets of points for plotting are extracted from the columns of the matrix:

mat = [ 1 4 7;2 5 8;3 6 9 ]
x = mat[ :,1 ]
y = mat[ :,2 ]
z = mat[ :,3 ]

Each vector x, y, z is a 3-element Array{Int64,1}. These can be plotted using:

using PyPlot
close( gcf() )
plot3D( x, y, zs=z )
display( gcf() )

However, say the matrix is transposed and the rows are spliced and transposed:

x = mat'[ 1,: ]'
y = mat'[ 2,: ]'
z = mat'[ 3,: ]'

Each vector is now a 3x1 Array{Int64,2} and plotting as above gives:

using PyPlot
close( gcf() )
plot3D( x, y, zs=z )
display( gcf() )

PyError (PyObject_Call) <type 'exceptions.ValueError'>
ValueError('could not broadcast input array from shape (3,1) into shape (3)',)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/backend_bases.py", line 2177, in print_figure
    **kwargs)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/backends/backend_agg.py", line 509, in print_png
    FigureCanvasAgg.draw(self)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/backends/backend_agg.py", line 455, in draw
    self.figure.draw(self.renderer)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/artist.py", line 59, in draw_wrapper
    draw(artist, renderer, *args, **kwargs)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/figure.py", line 1050, in draw
    func(*args)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/mpl_toolkits/mplot3d/axes3d.py", line 275, in draw
    Axes.draw(self, renderer)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/artist.py", line 59, in draw_wrapper
    draw(artist, renderer, *args, **kwargs)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/axes/_base.py", line 2076, in draw
    a.draw(renderer)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/mpl_toolkits/mplot3d/art3d.py", line 124, in draw
    xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/mpl_toolkits/mplot3d/proj3d.py", line 198, in proj_transform
    vec = vec_pad_ones(xs, ys, zs)
  File "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/mpl_toolkits/mplot3d/proj3d.py", line 187, in vec_pad_ones
    vec = np.array([xs,ys,zs,np.ones(xs.shape)])

while loading In[16], in expression starting on line 4
 in pyerr_check at /Users/user/.julia/v0.3/PyCall/src/exception.jl:58
 in pycall at /Users/user/.julia/v0.3/PyCall/src/PyCall.jl:85
 in fn at /Users/user/.julia/v0.3/PyCall/src/conversions.jl:181
 in writemime at /Users/user/.julia/v0.3/pyplot/src/pyplot.jl:140
 in base64 at base64.jl:125
 in display_dict at /Users/user/.julia/v0.3/IJulia/src/execute_request.jl:30
 in display at /Users/user/.julia/v0.3/IJulia/src/inline.jl:34
 in display at multimedia.jl:150

Clearly Plot3D cannot handle this difference well. However, it turns out that the required arguments xs, ys are robust to this whereas zs is not. Using the 3x1 Array{Int64,2} vectors again:

using PyPlot
close( gcf() )
plot3D( x, y )
display( gcf() )

Works fine.

Sorry for the very contrived example but this caused me some confusion earlier! Thanks

[stevengj]

Ideally, these bugs would be fixed upstream.... currently, Matplotlib is very inconsistent about accepting row vs. column vectors (some functions allow it, some don't), and PyPlot has to work around this.

I didn't implement any workarounds for keyword arguments yet, though. It should be straightforward.

[ghost]

These singleton arrays seem to be quite the nuisance, I do hope the julia dev team are going to do away with the ambiguity

[stevengj]

@olaslett, I don't think that's going to happen. Unlike Matlab, Julia has 1d arrays (all arrays in Matlab are at least 2d). Unless Julia defines a separate Array type for row vectors, those will need to be 2d arrays.