Hi,
I would suggest adding the line
if isinstance(x, np.ndarray) and len(x) == 1: x = x[0]
as line 679 in ctx_mp_python.py to convert numpy ndarrays of length 1.
Justification:
The following code fails without this:
from mpmath import *
from scipy.optimize import fsolve
fsolve (lambda x: exp(x) - 5, 1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 170, in fsolve
res = _root_hybr(_wrapped_func, x0, args, jac=fprime, **options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 238, in _root_hybr
shape, dtype = _check_func('fsolve', 'func', func, x0, args, n, (n,))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 23, in _check_func
res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 158, in _wrapped_func
return func(*fargs)
^^^^^^^^^^^^
File "<stdin>", line 1, in <lambda>
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/ctx_mp_python.py", line 991, in f
x = ctx.convert(x)
^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/ctx_mp_python.py", line 650, in convert
if type(x).__module__ == 'numpy': return ctx.npconvert(x)
^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/ctx_mp_python.py", line 683, in npconvert
raise TypeError("cannot create mpf from " + repr(x))
TypeError: cannot create mpf from array([1])
Of course, one could use findroot instead of fsolve above. But there are instances in which fsolve works and findroot does not:
n_1, n_obs_1, n_2, n_obs_2 = 62380000, 34539224.0, 62390000, 34542106.0
def f_1 (N0, N1):
return N0 * (1 - exp (-1 * n_1 / (N0 + 2 * N1))) + N1 * (1 - exp (-2 * n_1 / (N0 + 2 * N1))) - n_obs_1, \
N0 * (1 - exp (-1 * n_2 / (N0 + 2 * N1))) + N1 * (1 - exp (-2 * n_2 / (N0 + 2 * N1))) - n_obs_2
findroot (f_1, (27109937, 28500053))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/calculus/optimization.py", line 969, in findroot
for x, error in iterations:
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/calculus/optimization.py", line 660, in __iter__
s = self.ctx.lu_solve(Jx, fxn)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/matrices/linalg.py", line 224, in lu_solve
A, p = ctx.LU_decomp(A)
^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/mpmath/matrices/linalg.py", line 149, in LU_decomp
raise ZeroDivisionError('matrix is numerically singular')
ZeroDivisionError: matrix is numerically singular
def f_2 (param):
N0, N1 = param
return [N0 * (1 - exp (-1 * n_1 / (N0 + 2 * N1))) + N1 * (1 - exp (-2 * n_1 / (N0 + 2 * N1))) - n_obs_1, \
N0 * (1 - exp (-1 * n_2 / (N0 + 2 * N1))) + N1 * (1 - exp (-2 * n_2 / (N0 + 2 * N1))) - n_obs_2]
fsolve (f_2, [27109937, 28500053])
<stdin>:1: RuntimeWarning: The iteration is not making good progress, as measured by the
improvement from the last ten iterations.
array([36079713.35263181, 14271606.52089586])
Finally, there are instances in which fsolve runs into an overflow with the imports from math but not from mpmath:
from math import *
from scipy.optimize import fsolve
def f (param):
N0, N1, N2 = param
return [N0 * (1 - exp (-1 * n_1 / (N0 + 2 * N1 + 3 * N2))) + N1 * (1 - exp (-2 * n_1 / (N0 + 2 * N1 + 3 * N2))) + N2 * (1 - exp (-3 * n_1 / (N0 + 2 * N1 + 3 * N2))) - n_obs_1,
N0 * (1 - exp (-1 * n_2 / (N0 + 2 * N1 + 3 * N2))) + N1 * (1 - exp (-2 * n_2 / (N0 + 2 * N1 + 3 * N2))) + N2 * (1 - exp (-3 * n_1 / (N0 + 2 * N1 + 3 * N2))) - n_obs_2,
N0 * (1 - exp (-1 * n_3 / (N0 + 2 * N1 + 3 * N2))) + N1 * (1 - exp (-2 * n_3 / (N0 + 2 * N1 + 3 * N2))) + N2 * (1 - exp (-3 * n_1 / (N0 + 2 * N1 + 3 * N2))) - n_obs_3]
n_1, n_obs_1, n_2, n_obs_2, n_3, n_obs_3 = 62380000, 34539224.0, 62390000, 34542106.0, 62400000, 34544973.0
initial_estimate = [27376700.0, 18251133.333333332, 9125566.666666666]
fsolve (f, initial_estimate)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 170, in fsolve
res = _root_hybr(_wrapped_func, x0, args, jac=fprime, **options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 249, in _root_hybr
retval = _minpack._hybrd(func, x0, args, 1, xtol, maxfev,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/da/cbt/GX/schuisv1/python/envs/library-complexity-test/lib/python3.12/site-packages/scipy/optimize/_minpack_py.py", line 158, in _wrapped_func
return func(*fargs)
^^^^^^^^^^^^
File "<stdin>", line 3, in f
OverflowError: math range error
from mpmath import *
>>> fsolve (f, initial_estimate)
<stdin>:3: RuntimeWarning: invalid value encountered in multiply
<stdin>:4: RuntimeWarning: invalid value encountered in multiply
<stdin>:5: RuntimeWarning: invalid value encountered in multiply
<stdin>:1: RuntimeWarning: The iteration is not making good progress, as measured by the
improvement from the last ten iterations.
array([27376700. , 18251133.33333333, 9125566.66666667])
Best regards,
Sven
Hi,
I would suggest adding the line
as line 679 in ctx_mp_python.py to convert numpy ndarrays of length 1.
Justification:
The following code fails without this:
Of course, one could use findroot instead of fsolve above. But there are instances in which fsolve works and findroot does not:
Finally, there are instances in which fsolve runs into an overflow with the imports from math but not from mpmath:
Best regards,
Sven