5 ms·
It looks like Narwhals; "Narwhals and scikit-Lego came together to achieve dataframe-agnosticism" https://news.ycombinator.com/item?id=40950813 https://news.yco
by westurner 1y ago
It looks like Narwhals; "Narwhals and scikit-Lego came together to achieve dataframe-agnosticism" https://news.ycombinator.com/item?id=40950813 https://news.ycombinator.com/item?id=40950813 :
> Narwhals: https://narwhals-dev.github.io/narwhals/ https://narwhals-dev.github.io/narwhals/ :
>> Extremely lightweight compatibility layer between [pandas, Polars, cuDF, Modin]
> Lancedb/lance works with [Pandas, DuckDB, Polars, Pyarrow,]; https://github.com/lancedb/lance https://github.com/lancedb/lance
SymPy has Solvers for ODEs and PDEs and other libraries do convex optimization. SymPy also has lambdify to compile from a relatively slow symbolic expression tree to faster 'vectorized' functions
From https://news.ycombinator.com/item?id=40683777 https://news.ycombinator.com/item?id=40683777 re: warp :
> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/master/sympy/utilities/lambdify.py#L182 https://github.com/sympy/sympy/blob/master/sympy/utilities/l... :
>>> """Convert a SymPy expression into a function that allows for fast numeric evaluation""" [with e.g. the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, PyTorch (*), SymPy, numexpr, but not yet cmath]
- physicsguy 1y agoI’m perfectly familiar with SymPy and it’s great but it doesn’t have methods comparable in performance in stiff PDEs to CVODE, and it’s not parallelised either. CVODES offers sensitivity analysis, ARKODE offers multi rate integrators for systems where the ODE can be decomposed into slow and fast rates, etc. etc. - it’s a much more sophisticated and specialist library.
- westurner 1y agoCVODE,: https://github.com/ufz/cvode https://github.com/ufz/cvode scikits.odes supports CVODE: scikits.odes.sundials.cvode: https://bmcage.github.io/odes/master/api/compat.html#module-scikits.odes.sundials.cvode https://bmcage.github.io/odes/master/api/compat.html#module-.... sckits.odes docs > Choosing a Solver: https://scikits-odes.readthedocs.io/en/latest/solvers.html https://scikits-odes.readthedocs.io/en/latest/solvers.html scipy.integrate.solve_ivp has Radau, BDF, and LSODA for stiff ODEs, in Python: https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html https://docs.scipy.org/doc/scipy/reference/generated/scipy.i... If you add Arrow RecordBatch or Table output to CVODE with arrow-cpp, e.g. Dask can zero-copy buffers to Python (pyarrow, pandas.DataFrame(dtype_backend=arrow), or narwhals) when it needs to gather / fan in at a computational barrier in a process-parallel workflow. Is sklearn-deap useful with scikits.odes and sundials (and dask or not)?