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1.5.0-DEV-ed177d17d6.log
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1.5.0-DEV-ed177d17d6.log
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Julia Version 1.5.0-DEV.158
Commit ed177d17d6 (2020-01-27 16:45 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
Resolving package versions...
Installed SortingAlgorithms ─────────── v0.3.1
Installed ZygoteRules ───────────────── v0.2.0
Installed RecursiveFactorization ────── v0.1.0
Installed Compat ────────────────────── v2.2.0
Installed RecursiveArrayTools ───────── v2.0.4
Installed Parsers ───────────────────── v0.3.10
Installed ChainRulesCore ────────────── v0.6.1
Installed ModiaMath ─────────────────── v0.5.2
Installed ArrayInterface ────────────── v2.3.1
Installed BinaryProvider ────────────── v0.5.8
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed OrderedCollections ────────── v1.1.0
Installed Tables ────────────────────── v0.2.11
Installed Parameters ────────────────── v0.12.0
Installed DocStringExtensions ───────── v0.8.1
Installed Sundials ──────────────────── v3.8.1
Installed DataValueInterfaces ───────── v1.0.0
Installed ConstructionBase ──────────── v1.0.0
Installed DataStructures ────────────── v0.17.9
Installed Missings ──────────────────── v0.4.3
Installed MuladdMacro ───────────────── v0.2.2
Installed StaticArrays ──────────────── v0.12.1
Installed Unitful ───────────────────── v1.0.0
Installed JSON ──────────────────────── v0.21.0
Installed Roots ─────────────────────── v0.8.4
Installed FunctionWrappers ──────────── v1.0.0
Installed PooledArrays ──────────────── v0.5.3
Installed CategoricalArrays ─────────── v0.7.7
Installed DiffEqBase ────────────────── v6.13.1
Installed TableTraits ───────────────── v1.0.0
Installed IterativeSolvers ──────────── v0.8.1
Installed DataAPI ───────────────────── v1.1.0
Installed InvertedIndices ───────────── v1.0.0
Installed RecipesBase ───────────────── v0.7.0
Installed MacroTools ────────────────── v0.5.3
Installed Reexport ──────────────────── v0.2.0
Installed Requires ──────────────────── v1.0.0
Installed DataFrames ────────────────── v0.20.0
Installed TreeViews ─────────────────── v0.3.0
Updating `~/.julia/environments/v1.5/Project.toml`
[67ccffd1] + ModiaMath v0.5.2
Updating `~/.julia/environments/v1.5/Manifest.toml`
[4fba245c] + ArrayInterface v2.3.1
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.7
[d360d2e6] + ChainRulesCore v0.6.1
[34da2185] + Compat v2.2.0
[187b0558] + ConstructionBase v1.0.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.20.0
[864edb3b] + DataStructures v0.17.9
[e2d170a0] + DataValueInterfaces v1.0.0
[2b5f629d] + DiffEqBase v6.13.1
[ffbed154] + DocStringExtensions v0.8.1
[069b7b12] + FunctionWrappers v1.0.0
[41ab1584] + InvertedIndices v1.0.0
[42fd0dbc] + IterativeSolvers v0.8.1
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[1914dd2f] + MacroTools v0.5.3
[e1d29d7a] + Missings v0.4.3
[67ccffd1] + ModiaMath v0.5.2
[46d2c3a1] + MuladdMacro v0.2.2
[bac558e1] + OrderedCollections v1.1.0
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.3
[3cdcf5f2] + RecipesBase v0.7.0
[731186ca] + RecursiveArrayTools v2.0.4
[f2c3362d] + RecursiveFactorization v0.1.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v1.0.0
[f2b01f46] + Roots v0.8.4
[a2af1166] + SortingAlgorithms v0.3.1
[90137ffa] + StaticArrays v0.12.1
[c3572dad] + Sundials v3.8.1
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[a2a6695c] + TreeViews v0.3.0
[1986cc42] + Unitful v1.0.0
[700de1a5] + ZygoteRules v0.2.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Sundials → `~/.julia/packages/Sundials/MllUG/deps/build.log`
Updating `/tmp/jl_RLoox8/Project.toml`
[no changes]
Updating `/tmp/jl_RLoox8/Manifest.toml`
[no changes]
Testing ModiaMath
Updating `/tmp/jl_1k6TLX/Project.toml`
[no changes]
Updating `/tmp/jl_1k6TLX/Manifest.toml`
[no changes]
Running sandbox
Status `/tmp/jl_1k6TLX/Project.toml`
[a93c6f00] DataFrames v0.20.0
[864edb3b] DataStructures v0.17.9
[67ccffd1] ModiaMath v0.5.2
[ae029012] Requires v1.0.0
[90137ffa] StaticArrays v0.12.1
[c3572dad] Sundials v3.8.1
[1986cc42] Unitful v1.0.0
[37e2e46d] LinearAlgebra
[de0858da] Printf
[8dfed614] Test
Importing ModiaMath Version 0.5.2 (2019-07-10)
PyPlot not available (plot commands will be ignored).
Try to install PyPlot. See hints here:
https://github.com/ModiaSim/ModiaMath.jl/wiki/Installing-PyPlot-in-a-robust-way.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... Next plot should give a warning:
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... result variables = 2×4 DataFrames.DataFrame
│ Row │ name │ elType │ sizeOrValue │ unit │
│ │ String │ String │ String │ String │
├─────┼────────┼─────────┼─────────────┼────────┤
│ 1 │ phi │ Float64 │ (100,) │ │
│ 2 │ time │ Float64 │ (100,) │ │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... Next plot should give a warning:
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... Next plot should give a warning:
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... figure=4 is closed
... result variables =
│ Row │ name │ elType │ sizeOrValue │ unit │ info │
│ │ String │ String │ String │ String │ String │
├─────┼────────┼─────────┼─────────────┼────────┼────────┤
│ 1 │ phi │ Float64 │ (100,) │ rad │ │
│ 2 │ r │ Float64 │ (100, 3) │ m │ │
│ 3 │ time │ Float64 │ (100,) │ s │ │
│ 4 │ w │ Float64 │ (100,) │ rad/s │ │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... Next plot should give a warning:
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... Next plot should give a warning:
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... result variables = 7×4 DataFrames.DataFrame
│ Row │ name │ elType │ sizeOrValue │ unit │
│ │ String │ String │ String │ String │
├─────┼─────────────┼─────────┼─────────────┼──────────┤
│ 1 │ open │ Bool │ false │ │
│ 2 │ phi │ Float64 │ (100,) │ rad │
│ 3 │ phi2 │ Float64 │ (100,) │ rad │
│ 4 │ phi_max │ Float64 │ 1.1 │ rad │
│ 5 │ phi_max_int │ Int64 │ 1 │ │
│ 6 │ time │ Float64 │ (100,) │ │
│ 7 │ w │ Float64 │ (100,) │ rad s^-1 │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... result variables = 7×4 DataFrames.DataFrame
│ Row │ name │ elType │ sizeOrValue │ unit │
│ │ String │ String │ String │ String │
├─────┼────────┼─────────┼─────────────┼──────────┤
│ 1 │ phi │ Float64 │ (100,) │ rad │
│ 2 │ phi2 │ Float64 │ (100,) │ rad │
│ 3 │ r │ Float64 │ (100, 3) │ │
│ 4 │ r2 │ Float64 │ (100, 11) │ │
│ 5 │ time │ Float64 │ (100,) │ s │
│ 6 │ w │ Float64 │ (100,) │ rad s^-1 │
│ 7 │ w2 │ Float64 │ (100,) │ rad s^-1 │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... result variables =
│ Row │ name │ elType │ sizeOrValue │ unit │ info │
│ │ String │ String │ String │ String │ String │
├─────┼────────┼─────────┼─────────────┼────────┼────────┤
│ 1 │ phi │ Float64 │ (100,) │ rad │ │
│ 2 │ phi2 │ Float64 │ (100,) │ rad │ │
│ 3 │ r │ Float64 │ (100, 3) │ m │ │
│ 4 │ time │ Float64 │ (100,) │ s │ │
│ 5 │ w │ Float64 │ (100,) │ rad/s │ │
│ 6 │ w2 │ Float64 │ (100,) │ rad/s │ │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... result variables = 5×4 DataFrames.DataFrame
│ Row │ name │ elType │ sizeOrValue │ unit │
│ │ String │ String │ String │ String │
├─────┼────────┼─────────┼─────────────┼────────┤
│ 1 │ phi1 │ Float64 │ (100,) │ │
│ 2 │ phi2 │ Float64 │ (100,) │ │
│ 3 │ time │ Float64 │ (100,) │ │
│ 4 │ w1 │ Float64 │ (100,) │ │
│ 5 │ w2 │ Float64 │ (100,) │ │
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... robot = phi10 = 1.0
phi20 = 2.0
var10 = 3.0
r0[1] = 1.0
r0[2] = 2.0
r0[3] = 3.0
q0[1] = 0.5
q0[2] = 0.5
q0[3] = 0.0
q0[4] = 0.7071067811865476
rev1 = Revolute(
phi = 1.0 rad
w = 0.0 rad/s
a = 22.200000000000003 rad/s^2
tau = 0.0 N*m
)
rev2 = Revolute(
phi = 2.0 rad
w = 0.0 rad/s
a = 44.400000000000006 rad/s^2
tau = 0.0 N*m
)
var1 = 3.0
res1 = 0.0
frame = Revolute(
r = [1.0, 2.0, 3.0] m
q = [0.5, 0.5, 0.0, 0.7071067811865476]
v = [0.0, 0.0, 0.0] m/s
w = [0.0, 0.0, 0.0] rad/s
a = [0.0, 0.0, 0.0] m/s^2
z = [0.0, 0.0, 0.0] rad/s^2
f = [0.0, 0.0, 0.0] N
t = [0.0, 0.0, 0.0] N*m
)
)
... Print variables of robot
variables: . Omitted printing of 11 columns
│ Row │ name │ ValueType │ unit │ numericType │
│ │ Symbol │ Symbol │ String │ ModiaMat… │
├─────┼─────────────────┼──────────────────────────────┼─────────┼─────────────┤
│ 1 │ time │ Float64 │ s │ TIME │
│ 2 │ rev1.phi │ Float64 │ rad │ XD_EXP │
│ 3 │ rev1.w │ Float64 │ rad/s │ XD_EXP │
│ 4 │ rev1.a │ Float64 │ rad/s^2 │ DER_XD_EXP │
│ 5 │ rev1.tau │ Float64 │ N*m │ WR │
│ 6 │ rev2.phi │ Float64 │ rad │ XD_EXP │
│ 7 │ rev2.w │ Float64 │ rad/s │ XD_EXP │
⋮
│ 17 │ frame.a │ SArray{Tuple{3},Float64,1,3} │ m/s^2 │ DER_XD_IMP │
│ 18 │ frame.z │ SArray{Tuple{3},Float64,1,3} │ rad/s^2 │ DER_XD_IMP │
│ 19 │ frame.f │ SArray{Tuple{3},Float64,1,3} │ N │ WR │
│ 20 │ frame.t │ SArray{Tuple{3},Float64,1,3} │ N*m │ WR │
│ 21 │ frame.residue_w │ SArray{Tuple{3},Float64,1,3} │ │ FD_IMP │
│ 22 │ frame.residue_f │ SArray{Tuple{3},Float64,1,3} │ │ FD_IMP │
│ 23 │ frame.residue_t │ SArray{Tuple{3},Float64,1,3} │ │ FD_IMP │
│ 24 │ frame.residue_q │ Float64 │ │ FC │
x vector:
│ Row │ x │ name │ fixed │ start │
│ │ Symbol │ Symbol │ Bool │ Union… │
├─────┼──────────┼──────────┼───────┼───────────────────────────┤
│ 1 │ x[1] │ rev1.phi │ 1 │ 1.0 │
│ 2 │ x[2] │ rev1.w │ 1 │ 0.0 │
│ 3 │ x[3] │ rev2.phi │ 1 │ 2.0 │
│ 4 │ x[4] │ rev2.w │ 1 │ 0.0 │
│ 5 │ x[5:7] │ frame.r │ 1 │ [1.0, 2.0, 3.0] │
│ 6 │ x[8:11] │ frame.q │ 1 │ [0.5, 0.5, 0.0, 0.707107] │
│ 7 │ x[12:14] │ frame.v │ 1 │ [0.0, 0.0, 0.0] │
│ 8 │ x[15:17] │ frame.w │ 1 │ [0.0, 0.0, 0.0] │
│ 9 │ x[18] │ var1 │ 0 │ 3.0 │
copy to variables:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼─────────────┼────────────┤
│ 1 │ x[1] │ rev1.phi │
│ 2 │ x[2] │ rev1.w │
│ 3 │ x[3] │ rev2.phi │
│ 4 │ x[4] │ rev2.w │
│ 5 │ x[5:7] │ frame.r │
│ 6 │ x[8:11] │ frame.q │
│ 7 │ x[12:14] │ frame.v │
│ 8 │ x[15:17] │ frame.w │
│ 9 │ x[18] │ var1 │
│ 10 │ derx[8:11] │ frame.derq │
│ 11 │ derx[12:14] │ frame.a │
│ 12 │ derx[15:17] │ frame.z │
copy to residue vector:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼─────────────────────┼────────────────┤
│ 1 │ derx[1] - rev1.w │ residue[1] │
│ 2 │ derx[2] - rev1.a │ residue[2] │
│ 3 │ derx[3] - rev2.w │ residue[3] │
│ 4 │ derx[4] - rev2.a │ residue[4] │
│ 5 │ derx[5:7] - frame.v │ residue[5:7] │
│ 6 │ res1 │ residue[8] │
│ 7 │ frame.residue_w │ residue[9:11] │
│ 8 │ frame.residue_f │ residue[12:14] │
│ 9 │ frame.residue_t │ residue[15:17] │
│ 10 │ frame.residue_q │ residue[18] │
copy to results:
│ Row │ source │ target │ start │
│ │ Symbol │ Symbol │ Union… │
├─────┼────────────┼───────────────┼───────────────────────────┤
│ 1 │ time │ result[1] │ 0.0 │
│ 2 │ rev1.phi │ result[2] │ 1.0 │
│ 3 │ rev1.w │ result[3] │ 0.0 │
│ 4 │ rev1.a │ result[4] │ 0.0 │
│ 5 │ rev1.tau │ result[5] │ 0.0 │
│ 6 │ rev2.phi │ result[6] │ 2.0 │
│ 7 │ rev2.w │ result[7] │ 0.0 │
⋮
│ 12 │ frame.q │ result[14:17] │ [0.5, 0.5, 0.0, 0.707107] │
│ 13 │ frame.derq │ result[18:21] │ [0.0, 0.0, 0.0, 0.0] │
│ 14 │ frame.v │ result[22:24] │ [0.0, 0.0, 0.0] │
│ 15 │ frame.w │ result[25:27] │ [0.0, 0.0, 0.0] │
│ 16 │ frame.a │ result[28:30] │ [0.0, 0.0, 0.0] │
│ 17 │ frame.z │ result[31:33] │ [0.0, 0.0, 0.0] │
│ 18 │ frame.f │ result[34:36] │ [0.0, 0.0, 0.0] │
│ 19 │ frame.t │ result[37:39] │ [0.0, 0.0, 0.0] │
... Copy start values to x
... Copy x and der_x to variables
... Copy variables to residues
residue = [0.0, -3.552713678800501e-15, 0.0, -7.105427357601002e-15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
... robot2 = phi10 = 1.0
phi20 = 2.0
r0[1] = 0.0
r0[2] = 0.0
r0[3] = 0.0
q0[1] = 0.0
q0[2] = 0.0
q0[3] = 0.0
q0[4] = 1.0
rev1 = Revolute(
phi = 1.0 rad
w = 0.0 rad/s
a = 0.0 rad/s^2
tau = 0.0 N*m
)
rev2 = Revolute(
phi = 2.0 rad
w = 0.0 rad/s
a = 0.0 rad/s^2
tau = 0.0 N*m
)
frame = Revolute(
r = [0.0, 0.0, 0.0] m
q = [0.0, 0.0, 0.0, 1.0]
v = [0.0, 0.0, 0.0] m/s
w = [0.0, 0.0, 0.0] rad/s
a = [0.0, 0.0, 0.0] m/s^2
z = [0.0, 0.0, 0.0] rad/s^2
f = [0.0, 0.0, 0.0] N
t = [0.0, 0.0, 0.0] N*m
)
)
... Print variables of robot2
variables: . Omitted printing of 11 columns
│ Row │ name │ ValueType │ unit │ numericType │
│ │ Symbol │ Symbol │ String │ ModiaMat… │
├─────┼─────────────┼──────────────────────────────┼────────┼─────────────┤
│ 1 │ time │ Float64 │ s │ TIME │
│ 2 │ _dummy_x │ Float64 │ │ XD_EXP │
│ 3 │ _dummy_derx │ Float64 │ │ DER_XD_EXP │
│ 4 │ rev1.phi │ Float64 │ rad │ WR │
│ 5 │ rev2.phi │ Float64 │ rad │ WR │
│ 6 │ frame.r │ SArray{Tuple{3},Float64,1,3} │ m │ WR │
│ 7 │ frame.q │ SArray{Tuple{4},Float64,1,4} │ │ WR │
x vector:
│ Row │ x │ name │ fixed │ start │
│ │ Symbol │ Symbol │ Bool │ Union… │
├─────┼────────┼──────────┼───────┼────────┤
│ 1 │ x[1] │ _dummy_x │ 1 │ 0.0 │
copy to variables:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼────────┼──────────┤
│ 1 │ x[1] │ _dummy_x │
copy to residue vector:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼───────────────────────┼────────────┤
│ 1 │ derx[1] - _dummy_derx │ residue[1] │
copy to results:
│ Row │ source │ target │ start │
│ │ Symbol │ Symbol │ Union… │
├─────┼──────────┼──────────────┼──────────────────────┤
│ 1 │ time │ result[1] │ 0.0 │
│ 2 │ rev1.phi │ result[2] │ 1.0 │
│ 3 │ rev2.phi │ result[3] │ 2.0 │
│ 4 │ frame.r │ result[4:6] │ [0.0, 0.0, 0.0] │
│ 5 │ frame.q │ result[7:10] │ [0.0, 0.0, 0.0, 1.0] │
... Copy start values to x
... Copy x and der_x to variables
... Copy variables to residues
... robot3 = phi10 = 1.0
phi20 = 2.0
phi30 = -2.0
rev1 = Revolute(
phi = 1.0 rad
w = 0.0 rad/s
a = 0.0 rad/s^2
tau = 0.0 N*m
)
rev2 = Revolute(
phi = 2.0 rad
w = 0.0 rad/s
a = 0.0 rad/s^2
tau = 0.0 N*m
)
rev3 = Revolute(
phi = -2.0 rad
w = 0.0 rad/s
a = 0.0 rad/s^2
tau = 0.0 N*m
)
res1 = 0.0
)
... Print variables of robot3
variables: . Omitted printing of 9 columns
│ Row │ name │ ValueType │ unit │ numericType │ vec │ vecIndex │
│ │ Symbol │ Symbol │ String │ ModiaMat… │ Symbol │ Any │
├─────┼──────────┼───────────┼────────┼─────────────┼─────────┼──────────┤
│ 1 │ time │ Float64 │ s │ TIME │ │ 0 │
│ 2 │ rev1.phi │ Float64 │ rad │ WR │ │ 0 │
│ 3 │ rev2.phi │ Float64 │ rad │ WR │ │ 0 │
│ 4 │ rev3.phi │ Float64 │ rad │ XD_EXP │ x │ 1 │
│ 5 │ res1 │ Float64 │ │ FC │ residue │ 1 │
x vector:
│ Row │ x │ name │ fixed │ start │
│ │ Symbol │ Symbol │ Bool │ Union… │
├─────┼────────┼──────────┼───────┼────────┤
│ 1 │ x[1] │ rev3.phi │ 1 │ -2.0 │
copy to variables:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼────────┼──────────┤
│ 1 │ x[1] │ rev3.phi │
copy to residue vector:
│ Row │ source │ target │
│ │ Symbol │ Symbol │
├─────┼────────┼────────────┤
│ 1 │ res1 │ residue[1] │
copy to results:
│ Row │ source │ target │ start │
│ │ Symbol │ Symbol │ Union… │
├─────┼──────────┼───────────┼────────┤
│ 1 │ time │ result[1] │ 0.0 │
│ 2 │ rev1.phi │ result[2] │ 1.0 │
│ 3 │ rev2.phi │ result[3] │ 2.0 │
│ 4 │ rev3.phi │ result[4] │ -2.0 │
... Copy start values to x
... Copy x and der_x to variables
... Copy variables to residues
t_end = 2.8284271247461903
path.t = [0.0, 1.4142135623730951, 2.8284271247461903]
... time = 0.0, rt = [1.0, 0.0, 0.0]
... time = 0.1, rt = [0.9, 0.1, 0.0]
... time = 0.2, rt = [0.8, 0.2, 0.0]
... time = 0.30000000000000004, rt = [0.7, 0.30000000000000004, 0.0]
... time = 0.4, rt = [0.6, 0.4, 0.0]
... time = 0.5, rt = [0.5, 0.5, 0.0]
... time = 0.6, rt = [0.4, 0.6, 0.0]
... time = 0.7, rt = [0.30000000000000004, 0.7, 0.0]
... time = 0.7999999999999999, rt = [0.20000000000000007, 0.7999999999999999, 0.0]
... time = 0.8999999999999999, rt = [0.10000000000000009, 0.8999999999999999, 0.0]
... time = 0.9999999999999999, rt = [1.1102230246251565e-16, 0.9999999999999999, 0.0]
... time = 1.0999999999999999, rt = [0.0, 0.9000000000000002, 0.0999999999999998]
... time = 1.2, rt = [0.0, 0.8, 0.1999999999999999]
... time = 1.3, rt = [0.0, 0.7, 0.3]
... time = 1.4000000000000001, rt = [0.0, 0.5999999999999999, 0.40000000000000013]
... time = 1.5000000000000002, rt = [0.0, 0.4999999999999999, 0.5000000000000001]
... time = 1.6000000000000003, rt = [0.0, 0.3999999999999998, 0.6000000000000002]
... time = 1.7000000000000004, rt = [0.0, 0.2999999999999997, 0.7000000000000003]
... time = 1.8000000000000005, rt = [0.0, 0.19999999999999962, 0.8000000000000004]
... time = 1.9000000000000006, rt = [0.0, 0.09999999999999953, 0.9000000000000005]
... time = 2.0000000000000004, rt = [0.0, -2.220446049250313e-16, 1.0000000000000002]
... Results of Solve_SingleNonlinearEquations:
fun1:
analytical zero = 1.0000000000000000e+00
numerical zero = 1.0000000000000000e+00
absolute difference = 0.0000000000000000e+00
... Results of Solve_SingleNonlinearEquations:
fun2:
analytical zero = 6.4485440358400814e-01
numerical zero = 6.4485440358400814e-01
absolute difference = 0.0000000000000000e+00
... Results of Solve_SingleNonlinearEquations:
fun3:
analytical zero = 6.9368474072202186e+00
numerical zero = 6.9368474072202186e+00
absolute difference = 0.0000000000000000e+00
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Pendulum
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.5708 │ 1 │ 1.0 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
for given x, compute der(x)
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_ExplicitDerivatives
cpuTime = 1.9 s (init: 1 s, integration: 0.84 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 0.0001
nEquations = 2
nResults = 501
nSteps = 142
nResidues = 237 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 25
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 6
h0 = 5.8e-07 s
hMin = 5.8e-07 s
hMax = 0.069 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Pendulum
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.0472 │ 1 │ 1.0 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 10.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.27 s (init: 0.26 s, integration: 0.014 s)
startTime = 0.0 s
stopTime = 10.0 s
interval = 0.1 s
tolerance = 1.0e-8
nEquations = 2
nResults = 101
nSteps = 1408
nResidues = 1684 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 28
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 4.2e-11 s
hMin = 4.2e-11 s
hMax = 0.016 s
orderMax = 5
sparseSolver = false
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Pendulum
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.0472 │ 1 │ 1.0 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
for given x, compute der(x)
Simulation started
Simulation is terminated at time = 10.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_ExplicitDerivatives
cpuTime = 0.015 s (init: 0.00065 s, integration: 0.014 s)
startTime = 0.0 s
stopTime = 10.0 s
interval = 0.1 s
tolerance = 1.0e-8
nEquations = 2
nResults = 101
nSteps = 1408
nResidues = 1687 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 29
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 4.2e-11 s
hMin = 4.2e-11 s
hMax = 0.016 s
orderMax = 5
sparseSolver = false
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Pendulum
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.0472 │ 1 │ 1.0 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
for given x, determine consistent DAE variables der(x) (solving a linear equation system)
Simulation started
Simulation is terminated at time = 10.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.016 s (init: 0.00066 s, integration: 0.015 s)
startTime = 0.0 s
stopTime = 10.0 s
interval = 0.1 s
tolerance = 1.0e-8
nEquations = 2 (includes 0 constraints)
nResults = 101
nSteps = 1408
nResidues = 1684 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 28
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 4.2e-11 s
hMin = 4.2e-11 s
hMax = 0.016 s
orderMax = 5
sparseSolver = false
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: FreeBodyRotation
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ q[1] │ 0.1 │ 0 │ 0.1 │
│ 2 │ q[2] │ 0.5 │ 0 │ 0.5 │
│ 3 │ q[3] │ 0.0 │ 0 │ 1.0 │
│ 4 │ q[4] │ 1.0 │ 0 │ 1.0 │
│ 5 │ w[1] │ 0.0 │ 1 │ 1.0 │
│ 6 │ w[2] │ 0.0 │ 1 │ 1.0 │
│ 7 │ w[3] │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with analytical integral over time instant)
q[1] = 0.1 changed to 0.08908708957321489
q[2] = 0.5 changed to 0.4454354478660758
q[4] = 1.0 changed to 0.8908708957321516
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 1.3 s (init: 1.1 s, integration: 0.2 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 1.0e-6
nEquations = 7 (includes 1 constraints)
nResults = 501
nSteps = 601
nResidues = 1473 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 47
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 10
h0 = 7.5e-08 s
hMin = 7.5e-08 s
hMax = 0.039 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: FreeBodyRotation
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ q[1] │ 0.1 │ 0 │ 0.1 │
│ 2 │ q[2] │ 0.5 │ 0 │ 0.5 │
│ 3 │ q[3] │ 0.0 │ 0 │ 1.0 │
│ 4 │ q[4] │ 1.0 │ 0 │ 1.0 │
│ 5 │ w[1] │ 0.0 │ 1 │ 1.0 │
│ 6 │ w[2] │ 0.0 │ 1 │ 1.0 │
│ 7 │ w[3] │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with analytical integral over time instant)
q[1] = 0.1 changed to 0.0890870807438921
q[2] = 0.5 changed to 0.4454354037194605
q[4] = 1.0 changed to 0.890870807438921
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.11 s (init: 0.0027 s, integration: 0.11 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 1.0e-8
nEquations = 7 (includes 1 constraints)
nResults = 501
nSteps = 1292
nResidues = 2653 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 79
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 20
h0 = 7.5e-10 s
hMin = 7.5e-10 s
hMax = 0.018 s
orderMax = 5
sparseSolver = false
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: PendulumWithoutMacro
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.5708 │ 1 │ 1.5708 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
for given x, determine consistent DAE variables der(x) (solving a linear equation system)
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.024 s (init: 0.018 s, integration: 0.0058 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 0.0001
nEquations = 2 (includes 0 constraints)
nResults = 501
nSteps = 137
nResidues = 218 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 21
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 5.8e-07 s
hMin = 5.8e-07 s
hMax = 0.081 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: FreeBodyRotationWithoutMacro
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ q[1] │ 0.1 │ 0 │ 0.1 │
│ 2 │ q[2] │ 0.5 │ 0 │ 0.5 │
│ 3 │ q[3] │ 0.0 │ 0 │ 1.0 │
│ 4 │ q[4] │ 1.0 │ 0 │ 1.0 │
│ 5 │ w[1] │ 0.0 │ 1 │ 1.0 │
│ 6 │ w[2] │ 0.0 │ 1 │ 1.0 │
│ 7 │ w[3] │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with analytical integral over time instant)
q[1] = 0.1 changed to 0.0890870807438921
q[2] = 0.5 changed to 0.4454354037194605
q[4] = 1.0 changed to 0.890870807438921
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.22 s (init: 0.15 s, integration: 0.069 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 1.0e-8
nEquations = 7 (includes 1 constraints)
nResults = 501
nSteps = 1247
nResidues = 2687 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 73
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 21
h0 = 7.5e-10 s
hMin = 7.5e-10 s
hMax = 0.018 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: PendulumWithoutMacro
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ phi │ 1.0472 │ 1 │ 1.0472 │
│ 2 │ w │ 0.0 │ 1 │ 1.0 │
for given x, determine consistent DAE variables der(x) (solving a linear equation system)
Simulation started
Simulation is terminated at time = 10.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.014 s (init: 0.0013 s, integration: 0.013 s)
startTime = 0.0 s
stopTime = 10.0 s
interval = 0.1 s
tolerance = 1.0e-8
nEquations = 2 (includes 0 constraints)
nResults = 101
nSteps = 1383
nResidues = 1673 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 28
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 4.2e-11 s
hMin = 4.2e-11 s
hMax = 0.016 s
orderMax = 5
sparseSolver = false
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: FreeBodyRotationWithoutMacro
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ q[1] │ 0.1 │ 0 │ 0.1 │
│ 2 │ q[2] │ 0.5 │ 0 │ 0.5 │
│ 3 │ q[3] │ 0.0 │ 0 │ 1.0 │
│ 4 │ q[4] │ 1.0 │ 0 │ 1.0 │
│ 5 │ w[1] │ 0.0 │ 1 │ 1.0 │
│ 6 │ w[2] │ 0.0 │ 1 │ 1.0 │
│ 7 │ w[3] │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with analytical integral over time instant)
q[1] = 0.1 changed to 0.0890870807438921
q[2] = 0.5 changed to 0.4454354037194605
q[4] = 1.0 changed to 0.890870807438921
Simulation started
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_LinearDerivativesAndConstraints
cpuTime = 0.068 s (init: 0.0026 s, integration: 0.065 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.01 s
tolerance = 1.0e-8
nEquations = 7 (includes 1 constraints)
nResults = 501
nSteps = 1247
nResidues = 2687 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 73
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 21
h0 = 7.5e-10 s
hMin = 7.5e-10 s
hMax = 0.018 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: PT1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x[1] │ 1.5 │ 0 │ 1.5 │
for given x, compute der(x)
Simulation started
Simulation is terminated at time = 10.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_ExplicitDerivatives
cpuTime = 0.037 s (init: 0.036 s, integration: 0.00068 s)
startTime = 0.0 s
stopTime = 10.0 s