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SciPyDiffEq.jl
Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
TensorFlow.jl and Torch.jl are very thin wrappers around their namesake's C or C++ libraries, but you do not really need them. Calling C from Julia efifciently is a single line without any boilerplate code. You can see those one liners in the generated code here: https://github.com/FluxML/Torch.jl/blob/master/src/wrap/libdoeye_caml_generated.jl
Overall, your analysis is very Python centric. It's not very clear to me why Julia should focus on convincing Python users or developers. There are many areas of numerical and scientific computing that are not well served by Python, and it's exactly those areas that Julia is pushing into. The whole SciML https://sciml.ai/ ecosystem is a great toolbox for writing models and optimizations that would have otherwise required FORTRAN, C, and MATLAB. Staying within Julia provides access to a consistent set of autodiff technologies to further accelerate those efforts.