Initialize line split fixes following Catalyst.jl PR #1306 #982
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This commit creates the branch for fixing line splits following Catalyst.jl PR #1306 guidelines. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
Fixed 6 instances of unnecessary line splits across multiple files: - src/ffjord.jl: Fixed 2 mathematical expressions (logpz calculation and cat function call) - src/multiple_shooting.jl: Fixed 2 arithmetic expressions with continuity_loss - docs/src/examples/augmented_neural_ode.md: Fixed 2 identical arithmetic expressions in random_point_in_sphere function All changes follow Catalyst.jl PR #1306 guidelines to improve readability by keeping semantically related expressions on single lines while staying under 120 character limits. Related to: JuliaFormatter.jl PR #934 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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| function random_point_in_sphere(dim, min_radius, max_radius) | ||
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ | ||
| min_radius | ||
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ min_radius |
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| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ min_radius | |
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ | |
| min_radius |
| function random_point_in_sphere(dim, min_radius, max_radius) | ||
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ | ||
| min_radius | ||
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ min_radius |
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| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ min_radius | |
| distance = (max_radius - min_radius) .* (rand(Float32, 1) .^ (1.0f0 / dim)) .+ | |
| min_radius |
| if regularize | ||
| return cat(mz, -trace_jac, sum(abs2, mz; dims = 1:(N - 1)), | ||
| __norm_batched(eJ); dims = Val(N - 1)) | ||
| return cat(mz, -trace_jac, sum(abs2, mz; dims = 1:(N - 1)), __norm_batched(eJ); dims = Val(N - 1)) |
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| return cat(mz, -trace_jac, sum(abs2, mz; dims = 1:(N - 1)), __norm_batched(eJ); dims = Val(N - 1)) | |
| return cat(mz, -trace_jac, sum(abs2, mz; dims = 1:(N - 1)), | |
| __norm_batched(eJ); dims = Val(N - 1)) |
| if n.basedist === nothing | ||
| logpz = -sum(abs2, z; dims = 1:(N - 1)) / T(2) .- | ||
| T(prod(S[1:(N - 1)]) / 2 * log(2π)) | ||
| logpz = -sum(abs2, z; dims = 1:(N - 1)) / T(2) .- T(prod(S[1:(N - 1)]) / 2 * log(2π)) |
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| logpz = -sum(abs2, z; dims = 1:(N - 1)) / T(2) .- T(prod(S[1:(N - 1)]) / 2 * log(2π)) | |
| logpz = -sum(abs2, z; dims = 1:(N - 1)) / T(2) .- | |
| T(prod(S[1:(N - 1)]) / 2 * log(2π)) |
| # and current initial condition in ode_data | ||
| loss += continuity_term * | ||
| continuity_loss(group_predictions[i - 1][griddims..., end], u[griddims..., 1]) | ||
| loss += continuity_term * continuity_loss(group_predictions[i - 1][griddims..., end], u[griddims..., 1]) |
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| loss += continuity_term * continuity_loss(group_predictions[i - 1][griddims..., end], u[griddims..., 1]) | |
| loss += continuity_term * continuity_loss( | |
| group_predictions[i - 1][griddims..., end], u[griddims..., 1]) |
| # and current initial condition in ode_data | ||
| loss += continuity_term * | ||
| continuity_loss(group_predictions[i - 1][griddims..., end, :], u[griddims..., 1, :]) | ||
| loss += continuity_term * continuity_loss(group_predictions[i - 1][griddims..., end, :], u[griddims..., 1, :]) |
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| loss += continuity_term * continuity_loss(group_predictions[i - 1][griddims..., end, :], u[griddims..., 1, :]) | |
| loss += continuity_term * continuity_loss( | |
| group_predictions[i - 1][griddims..., end, :], u[griddims..., 1, :]) |
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Initialize fixing unnecessary line splits following Catalyst.jl PR #1306 guidelines. Part of systematic effort across 10+ SciML repositories. 🤖 Generated with Claude Code