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Fix example for stand-alone usage and remove old notebooks #49

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Apr 24, 2024
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Fix example for stand-alone usage and remove old notebooks #49

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af20472
Fix example for usage without Pytensor
astoeriko Apr 24, 2024
b2d5606
Remove old notebooks
astoeriko Apr 24, 2024
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7 changes: 5 additions & 2 deletions doc/source/without_pymc.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,8 @@ ODE might look like this::
After defining states, parameters and right-hand-side function we can create a
`SympyProblem` instance::

import sunode

problem = sunode.SympyProblem(
params=params,
states=states,
Expand All @@ -99,12 +101,13 @@ create a solver for no derivatives or with forward derivatives
(``sunode.Solver``), or a solver that can compute gradients using
the adjoint ODE (``sunode.AdjointSolver``).::

solver = sunode.solver.Solver(problem, compute_sens=False, solver='BDF')
solver = sunode.solver.Solver(problem, solver='BDF')

We can use numpy structured arrays as input, so that we don't need to
think about how the different variables are stored in the array.
This does not introduce runtime overhead.::

import numpy as np
y0 = np.zeros((), dtype=problem.state_dtype)
y0['hares'] = 1
y0['lynxes'] = 0.1
Expand All @@ -128,4 +131,4 @@ We can convert the solution to an xarray Dataset or access the
individual states as numpy record array::

solver.as_xarray(tvals, output).solution_hares.plot()
plt.plot(output.view(tvals, problem.state_dtype)['hares'])
plt.plot(tvals, output.view(problem.state_dtype)['hares'])
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