The current implementation of lyap for complex scalar arguments is incorrect. We can see this by comparing the result obtained using 1x1 matrices vs scalars:

julia> A = (1.0+2.0im)*ones(1,1);

julia> C = (3.0+4.0im)*ones(1,1);

julia> lyap(A, C)  # expected
1×1 Matrix{ComplexF64}:
 -1.5 - 2.0im

julia> lyap(A[1], C[1])  # wrong! 
-1.1 + 0.2im

julia> -C[1] / 2real(A[1])  

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

I wrote this as an experiment, anyone should feel free to continue working on it! The first step is to try it out and make a list of TODOs.

https://user-images.githubusercontent.com/6933510/110047855-e9e5ba80-7d4e-11eb-9c25-ae3cf8f56b13.mov

This issue is to track the progress of migrating the modeling tutorials from JuMPTutorials.

• Tutorials should be added to some suitable subfolder of https://github.com/jump-dev/JuMP.jl/tree/master/docs/src/tutorials (make one if it doesn't fit. e.g., The examples have MILP, Nonlinear, Quadratic, and SDP). We c

Matplotlib is able to handle canvas events https://matplotlib.org/stable/users/event_handling.html
Perhaps, after mouse clicking or panning the ticks handling could be delegated back to matplotlib?
So this could perhaps elimate the need in ticks=:native for pyplot backend

The PR JuliaData/CategoricalArrays.jl#310 means that an array with elements of type Symbol can no longer be wrapped as a CategoricalArray.

This means all MLJ documentation and test code that uses symbols in categorical data must be refactored to use strings instead.

These repos, at least, need checking/refactoring, in order of priority:

• MLJ

• [x]

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.)

So that other people (like me) can help development of the interactive API. At the moment it is something alien that I Can just use through example. This is not the proper way forward...

(finally found Mouse in interaction/iodevices.jl...)

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.