cumulative_trapezoid#
- scipy.integrate.cumulative_trapezoid(y, x=None, dx=1.0, axis=-1, initial=None)[source]#
Cumulatively integrate y(x) using the composite trapezoidal rule.
- Parameters:
- yarray_like
Values to integrate.
- xarray_like, optional
The coordinate to integrate along. If None (default), use spacing dx between consecutive elements in y.
- dxfloat, optional
Spacing between elements of y. Only used if x is None.
- axisint, optional
Specifies the axis to cumulate. Default is -1 (last axis).
- initialscalar, optional
If given, insert this value at the beginning of the returned result. 0 or None are the only values accepted. Default is None, which means res has one element less than y along the axis of integration.
Deprecated since version 1.12.0: The option for non-zero inputs for initial will be deprecated in SciPy 1.15.0. After this time, a ValueError will be raised if initial is not None or 0.
- Returns:
- resndarray
The result of cumulative integration of y along axis. If initial is None, the shape is such that the axis of integration has one less value than y. If initial is given, the shape is equal to that of y.
See also
numpy.cumsum
,numpy.cumprod
cumulative_simpson
cumulative integration using Simpson’s 1/3 rule
quad
adaptive quadrature using QUADPACK
fixed_quad
fixed-order Gaussian quadrature
dblquad
double integrals
tplquad
triple integrals
romb
integrators for sampled data
Examples
>>> from scipy import integrate >>> import numpy as np >>> import matplotlib.pyplot as plt
>>> x = np.linspace(-2, 2, num=20) >>> y = x >>> y_int = integrate.cumulative_trapezoid(y, x, initial=0) >>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-') >>> plt.show()