Throw error on differentiation involving arrays #530
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@@ Coverage Diff @@
## master #530 +/- ##
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+ Coverage 9.02% 78.62% +69.59%
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Files 23 23
Lines 2272 2334 +62
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+ Hits 205 1835 +1630
+ Misses 2067 499 -1568
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AlCap23
reviewed
Mar 11, 2022
| is_scalar_indexed(ex) = istree(ex) && operation(x) == getindex && !(symtype(x) <: AbstractArray) | ||
| if is_scalar_indexed(x) && is_scalar_indexed(expr) && | ||
| isequal(first(arguments(x)), first(arguments(expr))) | ||
| return isequal(arguments(x), arguments(expr)) |
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I think this might be the problem for the DataDriven failure.
using ModelingToolkit
using Symbolics
@variables u[1:2] y[1:1] t
u = collect(u)
y = collect(y)
Symbolics.jacobian([u;u[1]^2; y], u)fails while
using ModelingToolkit
using Symbolics
@variables u[1:2] y[1:1] t
u = collect(u)
y = collect(y)
Symbolics.jacobian([u;u[1]^2], u)is valid. The problem is that I take the Jacobian w.r.t. to the state variables only to derive a variable transform. If the system has control variables, there are mixed terms. Could this be relaxed to
all([isin(xi, arguments(expr) for xi in arguments(x)]) ?
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Ah okay, that can be fixed.
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Try out the latest change.
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0 is very wrong.
As seen on https://stackoverflow.com/questions/71140858/with-julia-symbolics-how-can-i-do-a-mathematical-summation