`scipy.integrate._quad_vec:quad_vec`

source: build-install/usr/lib/python3.14/site-packages/scipy/integrate/_quad_vec.py:97

Signature

def quad_vec(f, a, b, epsabs = 1e-200, epsrel = 1e-08, norm = 2, cache_size = 100000000.0, limit = 10000, workers = 1, points = None, quadrature = None, full_output = False, *, args = ())

Summary

Adaptive integration of a vector-valued function.

Parameters

f : callable: Vector-valued function f(x) to integrate.
a : float: Initial point.
b : float: Final point.
epsabs : float, optional: Absolute tolerance.
epsrel : float, optional: Relative tolerance.
norm : {'max', '2'}, optional: Vector norm to use for error estimation.
cache_size : int, optional: Number of bytes to use for memoization.
limit : float or int, optional: An upper bound on the number of subintervals used in the adaptive algorithm.
workers : int or map-like callable, optional: If workers is an integer, part of the computation is done in parallel subdivided to this many tasks (using python:multiprocessing.pool.Pool). Supply -1 to use all cores available to the Process. Alternatively, supply a map-like callable, such as python:multiprocessing.pool.Pool.map for evaluating the population in parallel. This evaluation is carried out as workers(func, iterable).
points : list, optional: List of additional breakpoints.
quadrature : {'gk21', 'gk15', 'trapezoid'}, optional: Quadrature rule to use on subintervals. Options: 'gk21' (Gauss-Kronrod 21-point rule), 'gk15' (Gauss-Kronrod 15-point rule), 'trapezoid' (composite trapezoid rule). Default: 'gk21' for finite intervals and 'gk15' for (semi-)infinite.
full_output : bool, optional: Return an additional info object.
args : tuple, optional: Extra arguments to pass to function, if any.
versionadded 1.8.0

Returns

res : {float, array-like}

Estimate for the result

err : float

Error estimate for the result in the given norm

info : object

Returned only when full_output=True. Result object with the attributes:

success: Whether integration reached target precision.
status: Indicator for convergence, success (0), failure (1), and failure due to rounding error (2).
neval: Number of function evaluations.
intervals: Start and end points of subdivision intervals.
integrals: Integral for each interval. Note that at most cache_size values are recorded, and the array may contains nan for missing items.
errors: Estimated integration error for each interval.

Notes

The algorithm mainly follows the implementation of QUADPACK's DQAG* algorithms, implementing global error control and adaptive subdivision.

The algorithm here has some differences to the QUADPACK approach:

Instead of subdividing one interval at a time, the algorithm subdivides N intervals with largest errors at once. This enables (partial) parallelization of the integration.

The logic of subdividing "next largest" intervals first is then not implemented, and we rely on the above extension to avoid concentrating on "small" intervals only.

The Wynn epsilon table extrapolation is not used (QUADPACK uses it for infinite intervals). This is because the algorithm here is supposed to work on vector-valued functions, in an user-specified norm, and the extension of the epsilon algorithm to this case does not appear to be widely agreed. For max-norm, using elementwise Wynn epsilon could be possible, but we do not do this here with the hope that the epsilon extrapolation is mainly useful in special cases.

Examples

We can compute integrations of a vector-valued function:

from scipy.integrate import quad_vec
import numpy as np
import matplotlib.pyplot as plt
alpha = np.linspace(0.0, 2.0, num=30)
f = lambda x: x**alpha
x0, x1 = 0, 2
y, err = quad_vec(f, x0, x1)

✓

plt.plot(alpha, y)
plt.xlabel(r"$\alpha$")
plt.ylabel(r"$\int_{0}^{2} x^\alpha dx$")

✗

Text(0, 0.5, '$\\int_{0}^{2} x^\\alpha dx$')

plt.show()

✓

When using the argument `workers`, one should ensure that the main module is import-safe, for instance by rewriting the example above as: .. code-block:: python from scipy.integrate import quad_vec import numpy as np import matplotlib.pyplot as plt alpha = np.linspace(0.0, 2.0, num=30) x0, x1 = 0, 2 def f(x): return x**alpha if __name__ == "__main__": y, err = quad_vec(f, x0, x1, workers=2)

Aliases

scipy.integrate.quad_vec