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Passing a value of missing, a first class type in Julia Base, to matplotlib should simply not the plot missing data. It should handle missing just like it presently does NaN.
However, passing a missing to matplotlib results in long and unhelpful errors from PyPlot and matplotlib:
plot([0,missing,2])
ERROR: PyError ($(Expr(:escape, :(ccall(#= ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'TypeError'>
TypeError("float() argument must be a string or a real number, not 'PyCall.jlwrap'")
File "mypath/python3.12/site-packages/matplotlib/pyplot.py", line 3794, in plot
return gca().plot(
^^^^^^^^^^^
File "/mypath/python3.12/site-packages/matplotlib/axes/_axes.py", line 1781, in plot
self.add_line(line)
File "/mypath/python3.12/site-packages/matplotlib/axes/_base.py", line 2339, in add_line
self._update_line_limits(line)
File "/mypath/python3.12/site-packages/matplotlib/axes/_base.py", line 2362, in _update_line_limits
path = line.get_path()
^^^^^^^^^^^^^^^
File "/mypath/python3.12/site-packages/matplotlib/lines.py", line 1037, in get_path
self.recache()
File "/mypath/python3.12/site-packages/matplotlib/lines.py", line 679, in recache
y = _to_unmasked_float_array(yconv).ravel()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mypath/python3.12/site-packages/matplotlib/cbook.py", line 1398, in _to_unmasked_float_array
return np.asarray(x, float)
^^^^^^^^^^^^^^^^^^^^
Stacktrace:
[1] pyerr_check
@ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:75 [inlined]
[2] pyerr_check
@ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:79 [inlined]
[3] _handle_error(msg::String)
@ PyCall ~/.julia/packages/PyCall/1gn3u/src/exception.jl:96
[4] macro expansion
@ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:110 [inlined]
[5] #107
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:43 [inlined]
[6] disable_sigint
@ ./c.jl:473 [inlined]
[7] __pycall!
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:42 [inlined]
[8] _pycall!(ret::PyObject, o::PyObject, args::Tuple{Vector{Union{Missing, Int64}}}, nargs::Int64, kw::Ptr{Nothing})
@ PyCall ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:29
[9] _pycall!
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:11 [inlined]
[10] pycall
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:83 [inlined]
[11] plot(args::Vector{Union{Missing, Int64}}; kws::@Kwargs{})
@ PyPlot ~/.julia/packages/PyPlot/rWSdf/src/PyPlot.jl:194
[12] plot(args::Vector{Union{Missing, Int64}})
@ PyPlot ~/.julia/packages/PyPlot/rWSdf/src/PyPlot.jl:190
[13] top-level scope
@ REPL[56]:1
For years I have wrapped my inputs to matplotlib in the helper function
m2n(x) = ismissing(x) ? NaN : x
It is awkward.
I propose then, that
missingvalues are simply (not) plotted, just like NaNs.- a brief warning catch and warn the user of
missings in the data.
The proposed change will make PyPlot more compatible with Julia and Plots.jl, cf.
JuliaPlots/Plots.jl#1706
I expect this will not break most cases, because missing is just a newer use pattern than NaN for missing data.
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