The Julia Language

I received this error message:

  MethodError: convert(::Type{Union{}}, ::Vector{Float32}) is ambiguous. Candidates:
    convert(::Type{T}, a::AbstractArray) where T<:Array in Base at array.jl:532
    convert(T::Type{var"#s79"} where var"#s79"<:BitArray, a::AbstractArray) in Base at bitarray.jl:577
    convert(T::Type{var"#s832"} where var"#s832"<:SparseArrays.SparseVector, m::AbstractVect

In recent versions (can't say from exactly when), there seems to be an off-by-one error in dcc.DatePickerRange. I set max_date_allowed = datetime.today().date(), but in the calendar, yesterday is the maximum date allowed. I see it in my apps, and it is also present in the first example on the DatePickerRange documentation page.

E

You can open a default notebook with the Pluto option
Pluto.run(notebook="test.jl")

Especially when using Pluto as a REST Server (with fonsp/Pluto.jl#1052 ) it would be good to have the possibility to start multiple notebooks from the command line.

Suggested syntax:
Pluto.run(notebook=["test.jl", "test2.jl"])

Currently, the opening of a notebook works by pass

See https://stackoverflow.com/questions/61922425/how-can-i-determine-whether-a-jump-model-solved-by-gurobi-is-a-mip

Issue is in functions like this:
https://github.com/JuliaOpt/MathOptInterface.jl/blob/3f872f9edb50b1a97cb65011430a0160fd56111e/src/Utilities/cachingoptimizer.jl#L539-L557
where we need a branch to see if it is possible to query directly from the optimizer.

Details

(This is related to JuliaPlots/Plots.jl#1706)

For example scatter(skipmissing([1.0, 2.0, missing, 1.0])) fails with Cannot convert Base.SkipMissing{Vector{Union{Missing, Float64}}} to series data for plotting.

Generally skipmissing is not required but it seems odd to see this fail.

Also I stumbled on this while trying to make a histogram, e.g.

I found that function mod2pi is not implemented yet, but mod works. Is there any list of implemented functions? Minimal working example is:

using Zygote

# This is working
gradient(x -> mod(x, 2pi), 1.)

# This is not
gradient(x -> mod2pi(x), 1.)

I've noticed that calling delete! on an Axis3 does not completely remove it:

julia> fig = Figure()

julia> ax = Axis3(fig[1, 1])
Axis3()

julia> surface!(ax, rand(100, 100))
Surface{Tuple{IntervalSets.ClosedInterval{Float32}, IntervalSets.ClosedInterval{Float32}, Matrix{Float32}}}

julia> delete!(ax)

![frame](https://user-images.githubusercontent.com/6333339/115960

In Exercises 10.1, the outer product in Take the outer product of a vector v with itself and assign it to variable cross_v should be cross product.