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Inzynierka/Lib/site-packages/scipy/integrate/_quadpack_py.py

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# Author: Travis Oliphant 2001
# Author: Nathan Woods 2013 (nquad &c)
import sys
import warnings
from functools import partial
from . import _quadpack
import numpy as np
from numpy import Inf
__all__ = ["quad", "dblquad", "tplquad", "nquad", "IntegrationWarning"]
error = _quadpack.error
class IntegrationWarning(UserWarning):
"""
Warning on issues during integration.
"""
pass
def quad(func, a, b, args=(), full_output=0, epsabs=1.49e-8, epsrel=1.49e-8,
limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50,
limlst=50, complex_func=False):
"""
Compute a definite integral.
Integrate func from `a` to `b` (possibly infinite interval) using a
technique from the Fortran library QUADPACK.
Parameters
----------
func : {function, scipy.LowLevelCallable}
A Python function or method to integrate. If `func` takes many
arguments, it is integrated along the axis corresponding to the
first argument.
If the user desires improved integration performance, then `f` may
be a `scipy.LowLevelCallable` with one of the signatures::
double func(double x)
double func(double x, void *user_data)
double func(int n, double *xx)
double func(int n, double *xx, void *user_data)
The ``user_data`` is the data contained in the `scipy.LowLevelCallable`.
In the call forms with ``xx``, ``n`` is the length of the ``xx``
array which contains ``xx[0] == x`` and the rest of the items are
numbers contained in the ``args`` argument of quad.
In addition, certain ctypes call signatures are supported for
backward compatibility, but those should not be used in new code.
a : float
Lower limit of integration (use -numpy.inf for -infinity).
b : float
Upper limit of integration (use numpy.inf for +infinity).
args : tuple, optional
Extra arguments to pass to `func`.
full_output : int, optional
Non-zero to return a dictionary of integration information.
If non-zero, warning messages are also suppressed and the
message is appended to the output tuple.
complex_func : bool, optional
Indicate if the function's (`func`) return type is real
(``complex_func=False``: default) or complex (``complex_func=True``).
In both cases, the function's argument is real.
If full_output is also non-zero, the `infodict`, `message`, and
`explain` for the real and complex components are returned in
a dictionary with keys "real output" and "imag output".
Returns
-------
y : float
The integral of func from `a` to `b`.
abserr : float
An estimate of the absolute error in the result.
infodict : dict
A dictionary containing additional information.
message
A convergence message.
explain
Appended only with 'cos' or 'sin' weighting and infinite
integration limits, it contains an explanation of the codes in
infodict['ierlst']
Other Parameters
----------------
epsabs : float or int, optional
Absolute error tolerance. Default is 1.49e-8. `quad` tries to obtain
an accuracy of ``abs(i-result) <= max(epsabs, epsrel*abs(i))``
where ``i`` = integral of `func` from `a` to `b`, and ``result`` is the
numerical approximation. See `epsrel` below.
epsrel : float or int, optional
Relative error tolerance. Default is 1.49e-8.
If ``epsabs <= 0``, `epsrel` must be greater than both 5e-29
and ``50 * (machine epsilon)``. See `epsabs` above.
limit : float or int, optional
An upper bound on the number of subintervals used in the adaptive
algorithm.
points : (sequence of floats,ints), optional
A sequence of break points in the bounded integration interval
where local difficulties of the integrand may occur (e.g.,
singularities, discontinuities). The sequence does not have
to be sorted. Note that this option cannot be used in conjunction
with ``weight``.
weight : float or int, optional
String indicating weighting function. Full explanation for this
and the remaining arguments can be found below.
wvar : optional
Variables for use with weighting functions.
wopts : optional
Optional input for reusing Chebyshev moments.
maxp1 : float or int, optional
An upper bound on the number of Chebyshev moments.
limlst : int, optional
Upper bound on the number of cycles (>=3) for use with a sinusoidal
weighting and an infinite end-point.
See Also
--------
dblquad : double integral
tplquad : triple integral
nquad : n-dimensional integrals (uses `quad` recursively)
fixed_quad : fixed-order Gaussian quadrature
quadrature : adaptive Gaussian quadrature
odeint : ODE integrator
ode : ODE integrator
simpson : integrator for sampled data
romb : integrator for sampled data
scipy.special : for coefficients and roots of orthogonal polynomials
Notes
-----
**Extra information for quad() inputs and outputs**
If full_output is non-zero, then the third output argument
(infodict) is a dictionary with entries as tabulated below. For
infinite limits, the range is transformed to (0,1) and the
optional outputs are given with respect to this transformed range.
Let M be the input argument limit and let K be infodict['last'].
The entries are:
'neval'
The number of function evaluations.
'last'
The number, K, of subintervals produced in the subdivision process.
'alist'
A rank-1 array of length M, the first K elements of which are the
left end points of the subintervals in the partition of the
integration range.
'blist'
A rank-1 array of length M, the first K elements of which are the
right end points of the subintervals.
'rlist'
A rank-1 array of length M, the first K elements of which are the
integral approximations on the subintervals.
'elist'
A rank-1 array of length M, the first K elements of which are the
moduli of the absolute error estimates on the subintervals.
'iord'
A rank-1 integer array of length M, the first L elements of
which are pointers to the error estimates over the subintervals
with ``L=K`` if ``K<=M/2+2`` or ``L=M+1-K`` otherwise. Let I be the
sequence ``infodict['iord']`` and let E be the sequence
``infodict['elist']``. Then ``E[I[1]], ..., E[I[L]]`` forms a
decreasing sequence.
If the input argument points is provided (i.e., it is not None),
the following additional outputs are placed in the output
dictionary. Assume the points sequence is of length P.
'pts'
A rank-1 array of length P+2 containing the integration limits
and the break points of the intervals in ascending order.
This is an array giving the subintervals over which integration
will occur.
'level'
A rank-1 integer array of length M (=limit), containing the
subdivision levels of the subintervals, i.e., if (aa,bb) is a
subinterval of ``(pts[1], pts[2])`` where ``pts[0]`` and ``pts[2]``
are adjacent elements of ``infodict['pts']``, then (aa,bb) has level l
if ``|bb-aa| = |pts[2]-pts[1]| * 2**(-l)``.
'ndin'
A rank-1 integer array of length P+2. After the first integration
over the intervals (pts[1], pts[2]), the error estimates over some
of the intervals may have been increased artificially in order to
put their subdivision forward. This array has ones in slots
corresponding to the subintervals for which this happens.
**Weighting the integrand**
The input variables, *weight* and *wvar*, are used to weight the
integrand by a select list of functions. Different integration
methods are used to compute the integral with these weighting
functions, and these do not support specifying break points. The
possible values of weight and the corresponding weighting functions are.
========== =================================== =====================
``weight`` Weight function used ``wvar``
========== =================================== =====================
'cos' cos(w*x) wvar = w
'sin' sin(w*x) wvar = w
'alg' g(x) = ((x-a)**alpha)*((b-x)**beta) wvar = (alpha, beta)
'alg-loga' g(x)*log(x-a) wvar = (alpha, beta)
'alg-logb' g(x)*log(b-x) wvar = (alpha, beta)
'alg-log' g(x)*log(x-a)*log(b-x) wvar = (alpha, beta)
'cauchy' 1/(x-c) wvar = c
========== =================================== =====================
wvar holds the parameter w, (alpha, beta), or c depending on the weight
selected. In these expressions, a and b are the integration limits.
For the 'cos' and 'sin' weighting, additional inputs and outputs are
available.
For finite integration limits, the integration is performed using a
Clenshaw-Curtis method which uses Chebyshev moments. For repeated
calculations, these moments are saved in the output dictionary:
'momcom'
The maximum level of Chebyshev moments that have been computed,
i.e., if ``M_c`` is ``infodict['momcom']`` then the moments have been
computed for intervals of length ``|b-a| * 2**(-l)``,
``l=0,1,...,M_c``.
'nnlog'
A rank-1 integer array of length M(=limit), containing the
subdivision levels of the subintervals, i.e., an element of this
array is equal to l if the corresponding subinterval is
``|b-a|* 2**(-l)``.
'chebmo'
A rank-2 array of shape (25, maxp1) containing the computed
Chebyshev moments. These can be passed on to an integration
over the same interval by passing this array as the second
element of the sequence wopts and passing infodict['momcom'] as
the first element.
If one of the integration limits is infinite, then a Fourier integral is
computed (assuming w neq 0). If full_output is 1 and a numerical error
is encountered, besides the error message attached to the output tuple,
a dictionary is also appended to the output tuple which translates the
error codes in the array ``info['ierlst']`` to English messages. The
output information dictionary contains the following entries instead of
'last', 'alist', 'blist', 'rlist', and 'elist':
'lst'
The number of subintervals needed for the integration (call it ``K_f``).
'rslst'
A rank-1 array of length M_f=limlst, whose first ``K_f`` elements
contain the integral contribution over the interval
``(a+(k-1)c, a+kc)`` where ``c = (2*floor(|w|) + 1) * pi / |w|``
and ``k=1,2,...,K_f``.
'erlst'
A rank-1 array of length ``M_f`` containing the error estimate
corresponding to the interval in the same position in
``infodict['rslist']``.
'ierlst'
A rank-1 integer array of length ``M_f`` containing an error flag
corresponding to the interval in the same position in
``infodict['rslist']``. See the explanation dictionary (last entry
in the output tuple) for the meaning of the codes.
**Details of QUADPACK level routines**
`quad` calls routines from the FORTRAN library QUADPACK. This section
provides details on the conditions for each routine to be called and a
short description of each routine. The routine called depends on
`weight`, `points` and the integration limits `a` and `b`.
================ ============== ========== =====================
QUADPACK routine `weight` `points` infinite bounds
================ ============== ========== =====================
qagse None No No
qagie None No Yes
qagpe None Yes No
qawoe 'sin', 'cos' No No
qawfe 'sin', 'cos' No either `a` or `b`
qawse 'alg*' No No
qawce 'cauchy' No No
================ ============== ========== =====================
The following provides a short desciption from [1]_ for each
routine.
qagse
is an integrator based on globally adaptive interval
subdivision in connection with extrapolation, which will
eliminate the effects of integrand singularities of
several types.
qagie
handles integration over infinite intervals. The infinite range is
mapped onto a finite interval and subsequently the same strategy as
in ``QAGS`` is applied.
qagpe
serves the same purposes as QAGS, but also allows the
user to provide explicit information about the location
and type of trouble-spots i.e. the abscissae of internal
singularities, discontinuities and other difficulties of
the integrand function.
qawoe
is an integrator for the evaluation of
:math:`\\int^b_a \\cos(\\omega x)f(x)dx` or
:math:`\\int^b_a \\sin(\\omega x)f(x)dx`
over a finite interval [a,b], where :math:`\\omega` and :math:`f`
are specified by the user. The rule evaluation component is based
on the modified Clenshaw-Curtis technique
An adaptive subdivision scheme is used in connection
with an extrapolation procedure, which is a modification
of that in ``QAGS`` and allows the algorithm to deal with
singularities in :math:`f(x)`.
qawfe
calculates the Fourier transform
:math:`\\int^\\infty_a \\cos(\\omega x)f(x)dx` or
:math:`\\int^\\infty_a \\sin(\\omega x)f(x)dx`
for user-provided :math:`\\omega` and :math:`f`. The procedure of
``QAWO`` is applied on successive finite intervals, and convergence
acceleration by means of the :math:`\\varepsilon`-algorithm is applied
to the series of integral approximations.
qawse
approximate :math:`\\int^b_a w(x)f(x)dx`, with :math:`a < b` where
:math:`w(x) = (x-a)^{\\alpha}(b-x)^{\\beta}v(x)` with
:math:`\\alpha,\\beta > -1`, where :math:`v(x)` may be one of the
following functions: :math:`1`, :math:`\\log(x-a)`, :math:`\\log(b-x)`,
:math:`\\log(x-a)\\log(b-x)`.
The user specifies :math:`\\alpha`, :math:`\\beta` and the type of the
function :math:`v`. A globally adaptive subdivision strategy is
applied, with modified Clenshaw-Curtis integration on those
subintervals which contain `a` or `b`.
qawce
compute :math:`\\int^b_a f(x) / (x-c)dx` where the integral must be
interpreted as a Cauchy principal value integral, for user specified
:math:`c` and :math:`f`. The strategy is globally adaptive. Modified
Clenshaw-Curtis integration is used on those intervals containing the
point :math:`x = c`.
**Integration of Complex Function of a Real Variable**
A complex valued function, :math:`f`, of a real variable can be written as
:math:`f = g + ih`. Similarly, the integral of :math:`f` can be
written as
.. math::
\\int_a^b f(x) dx = \\int_a^b g(x) dx + i\\int_a^b h(x) dx
assuming that the integrals of :math:`g` and :math:`h` exist
over the inteval :math:`[a,b]` [2]_. Therefore, ``quad`` integrates
complex-valued functions by integrating the real and imaginary components
separately.
References
----------
.. [1] Piessens, Robert; de Doncker-Kapenga, Elise;
Überhuber, Christoph W.; Kahaner, David (1983).
QUADPACK: A subroutine package for automatic integration.
Springer-Verlag.
ISBN 978-3-540-12553-2.
.. [2] McCullough, Thomas; Phillips, Keith (1973).
Foundations of Analysis in the Complex Plane.
Holt Rinehart Winston.
ISBN 0-03-086370-8
Examples
--------
Calculate :math:`\\int^4_0 x^2 dx` and compare with an analytic result
>>> from scipy import integrate
>>> import numpy as np
>>> x2 = lambda x: x**2
>>> integrate.quad(x2, 0, 4)
(21.333333333333332, 2.3684757858670003e-13)
>>> print(4**3 / 3.) # analytical result
21.3333333333
Calculate :math:`\\int^\\infty_0 e^{-x} dx`
>>> invexp = lambda x: np.exp(-x)
>>> integrate.quad(invexp, 0, np.inf)
(1.0, 5.842605999138044e-11)
Calculate :math:`\\int^1_0 a x \\,dx` for :math:`a = 1, 3`
>>> f = lambda x, a: a*x
>>> y, err = integrate.quad(f, 0, 1, args=(1,))
>>> y
0.5
>>> y, err = integrate.quad(f, 0, 1, args=(3,))
>>> y
1.5
Calculate :math:`\\int^1_0 x^2 + y^2 dx` with ctypes, holding
y parameter as 1::
testlib.c =>
double func(int n, double args[n]){
return args[0]*args[0] + args[1]*args[1];}
compile to library testlib.*
::
from scipy import integrate
import ctypes
lib = ctypes.CDLL('/home/.../testlib.*') #use absolute path
lib.func.restype = ctypes.c_double
lib.func.argtypes = (ctypes.c_int,ctypes.c_double)
integrate.quad(lib.func,0,1,(1))
#(1.3333333333333333, 1.4802973661668752e-14)
print((1.0**3/3.0 + 1.0) - (0.0**3/3.0 + 0.0)) #Analytic result
# 1.3333333333333333
Be aware that pulse shapes and other sharp features as compared to the
size of the integration interval may not be integrated correctly using
this method. A simplified example of this limitation is integrating a
y-axis reflected step function with many zero values within the integrals
bounds.
>>> y = lambda x: 1 if x<=0 else 0
>>> integrate.quad(y, -1, 1)
(1.0, 1.1102230246251565e-14)
>>> integrate.quad(y, -1, 100)
(1.0000000002199108, 1.0189464580163188e-08)
>>> integrate.quad(y, -1, 10000)
(0.0, 0.0)
"""
if not isinstance(args, tuple):
args = (args,)
# check the limits of integration: \int_a^b, expect a < b
flip, a, b = b < a, min(a, b), max(a, b)
if complex_func:
def imfunc(x, *args):
return np.imag(func(x, *args))
def refunc(x, *args):
return np.real(func(x, *args))
re_retval = quad(refunc, a, b, args, full_output, epsabs,
epsrel, limit, points, weight, wvar, wopts,
maxp1, limlst, complex_func=False)
im_retval = quad(imfunc, a, b, args, full_output, epsabs,
epsrel, limit, points, weight, wvar, wopts,
maxp1, limlst, complex_func=False)
integral = re_retval[0] + 1j*im_retval[0]
error_estimate = re_retval[1] + 1j*im_retval[1]
retval = integral, error_estimate
if full_output:
msgexp = {}
msgexp["real"] = re_retval[2:]
msgexp["imag"] = im_retval[2:]
retval = retval + (msgexp,)
return retval
if weight is None:
retval = _quad(func, a, b, args, full_output, epsabs, epsrel, limit,
points)
else:
if points is not None:
msg = ("Break points cannot be specified when using weighted integrand.\n"
"Continuing, ignoring specified points.")
warnings.warn(msg, IntegrationWarning, stacklevel=2)
retval = _quad_weight(func, a, b, args, full_output, epsabs, epsrel,
limlst, limit, maxp1, weight, wvar, wopts)
if flip:
retval = (-retval[0],) + retval[1:]
ier = retval[-1]
if ier == 0:
return retval[:-1]
msgs = {80: "A Python error occurred possibly while calling the function.",
1: "The maximum number of subdivisions (%d) has been achieved.\n If increasing the limit yields no improvement it is advised to analyze \n the integrand in order to determine the difficulties. If the position of a \n local difficulty can be determined (singularity, discontinuity) one will \n probably gain from splitting up the interval and calling the integrator \n on the subranges. Perhaps a special-purpose integrator should be used." % limit,
2: "The occurrence of roundoff error is detected, which prevents \n the requested tolerance from being achieved. The error may be \n underestimated.",
3: "Extremely bad integrand behavior occurs at some points of the\n integration interval.",
4: "The algorithm does not converge. Roundoff error is detected\n in the extrapolation table. It is assumed that the requested tolerance\n cannot be achieved, and that the returned result (if full_output = 1) is \n the best which can be obtained.",
5: "The integral is probably divergent, or slowly convergent.",
6: "The input is invalid.",
7: "Abnormal termination of the routine. The estimates for result\n and error are less reliable. It is assumed that the requested accuracy\n has not been achieved.",
'unknown': "Unknown error."}
if weight in ['cos','sin'] and (b == Inf or a == -Inf):
msgs[1] = "The maximum number of cycles allowed has been achieved., e.e.\n of subintervals (a+(k-1)c, a+kc) where c = (2*int(abs(omega)+1))\n *pi/abs(omega), for k = 1, 2, ..., lst. One can allow more cycles by increasing the value of limlst. Look at info['ierlst'] with full_output=1."
msgs[4] = "The extrapolation table constructed for convergence acceleration\n of the series formed by the integral contributions over the cycles, \n does not converge to within the requested accuracy. Look at \n info['ierlst'] with full_output=1."
msgs[7] = "Bad integrand behavior occurs within one or more of the cycles.\n Location and type of the difficulty involved can be determined from \n the vector info['ierlist'] obtained with full_output=1."
explain = {1: "The maximum number of subdivisions (= limit) has been \n achieved on this cycle.",
2: "The occurrence of roundoff error is detected and prevents\n the tolerance imposed on this cycle from being achieved.",
3: "Extremely bad integrand behavior occurs at some points of\n this cycle.",
4: "The integral over this cycle does not converge (to within the required accuracy) due to roundoff in the extrapolation procedure invoked on this cycle. It is assumed that the result on this interval is the best which can be obtained.",
5: "The integral over this cycle is probably divergent or slowly convergent."}
try:
msg = msgs[ier]
except KeyError:
msg = msgs['unknown']
if ier in [1,2,3,4,5,7]:
if full_output:
if weight in ['cos', 'sin'] and (b == Inf or a == -Inf):
return retval[:-1] + (msg, explain)
else:
return retval[:-1] + (msg,)
else:
warnings.warn(msg, IntegrationWarning, stacklevel=2)
return retval[:-1]
elif ier == 6: # Forensic decision tree when QUADPACK throws ier=6
if epsabs <= 0: # Small error tolerance - applies to all methods
if epsrel < max(50 * sys.float_info.epsilon, 5e-29):
msg = ("If 'epsabs'<=0, 'epsrel' must be greater than both"
" 5e-29 and 50*(machine epsilon).")
elif weight in ['sin', 'cos'] and (abs(a) + abs(b) == Inf):
msg = ("Sine or cosine weighted intergals with infinite domain"
" must have 'epsabs'>0.")
elif weight is None:
if points is None: # QAGSE/QAGIE
msg = ("Invalid 'limit' argument. There must be"
" at least one subinterval")
else: # QAGPE
if not (min(a, b) <= min(points) <= max(points) <= max(a, b)):
msg = ("All break points in 'points' must lie within the"
" integration limits.")
elif len(points) >= limit:
msg = ("Number of break points ({:d})"
" must be less than subinterval"
" limit ({:d})").format(len(points), limit)
else:
if maxp1 < 1:
msg = "Chebyshev moment limit maxp1 must be >=1."
elif weight in ('cos', 'sin') and abs(a+b) == Inf: # QAWFE
msg = "Cycle limit limlst must be >=3."
elif weight.startswith('alg'): # QAWSE
if min(wvar) < -1:
msg = "wvar parameters (alpha, beta) must both be >= -1."
if b < a:
msg = "Integration limits a, b must satistfy a<b."
elif weight == 'cauchy' and wvar in (a, b):
msg = ("Parameter 'wvar' must not equal"
" integration limits 'a' or 'b'.")
raise ValueError(msg)
def _quad(func,a,b,args,full_output,epsabs,epsrel,limit,points):
infbounds = 0
if (b != Inf and a != -Inf):
pass # standard integration
elif (b == Inf and a != -Inf):
infbounds = 1
bound = a
elif (b == Inf and a == -Inf):
infbounds = 2
bound = 0 # ignored
elif (b != Inf and a == -Inf):
infbounds = -1
bound = b
else:
raise RuntimeError("Infinity comparisons don't work for you.")
if points is None:
if infbounds == 0:
return _quadpack._qagse(func,a,b,args,full_output,epsabs,epsrel,limit)
else:
return _quadpack._qagie(func,bound,infbounds,args,full_output,epsabs,epsrel,limit)
else:
if infbounds != 0:
raise ValueError("Infinity inputs cannot be used with break points.")
else:
#Duplicates force function evaluation at singular points
the_points = np.unique(points)
the_points = the_points[a < the_points]
the_points = the_points[the_points < b]
the_points = np.concatenate((the_points, (0., 0.)))
return _quadpack._qagpe(func,a,b,the_points,args,full_output,epsabs,epsrel,limit)
def _quad_weight(func,a,b,args,full_output,epsabs,epsrel,limlst,limit,maxp1,weight,wvar,wopts):
if weight not in ['cos','sin','alg','alg-loga','alg-logb','alg-log','cauchy']:
raise ValueError("%s not a recognized weighting function." % weight)
strdict = {'cos':1,'sin':2,'alg':1,'alg-loga':2,'alg-logb':3,'alg-log':4}
if weight in ['cos','sin']:
integr = strdict[weight]
if (b != Inf and a != -Inf): # finite limits
if wopts is None: # no precomputed Chebyshev moments
return _quadpack._qawoe(func, a, b, wvar, integr, args, full_output,
epsabs, epsrel, limit, maxp1,1)
else: # precomputed Chebyshev moments
momcom = wopts[0]
chebcom = wopts[1]
return _quadpack._qawoe(func, a, b, wvar, integr, args, full_output,
epsabs, epsrel, limit, maxp1, 2, momcom, chebcom)
elif (b == Inf and a != -Inf):
return _quadpack._qawfe(func, a, wvar, integr, args, full_output,
epsabs,limlst,limit,maxp1)
elif (b != Inf and a == -Inf): # remap function and interval
if weight == 'cos':
def thefunc(x,*myargs):
y = -x
func = myargs[0]
myargs = (y,) + myargs[1:]
return func(*myargs)
else:
def thefunc(x,*myargs):
y = -x
func = myargs[0]
myargs = (y,) + myargs[1:]
return -func(*myargs)
args = (func,) + args
return _quadpack._qawfe(thefunc, -b, wvar, integr, args,
full_output, epsabs, limlst, limit, maxp1)
else:
raise ValueError("Cannot integrate with this weight from -Inf to +Inf.")
else:
if a in [-Inf,Inf] or b in [-Inf,Inf]:
raise ValueError("Cannot integrate with this weight over an infinite interval.")
if weight.startswith('alg'):
integr = strdict[weight]
return _quadpack._qawse(func, a, b, wvar, integr, args,
full_output, epsabs, epsrel, limit)
else: # weight == 'cauchy'
return _quadpack._qawce(func, a, b, wvar, args, full_output,
epsabs, epsrel, limit)
def dblquad(func, a, b, gfun, hfun, args=(), epsabs=1.49e-8, epsrel=1.49e-8):
"""
Compute a double integral.
Return the double (definite) integral of ``func(y, x)`` from ``x = a..b``
and ``y = gfun(x)..hfun(x)``.
Parameters
----------
func : callable
A Python function or method of at least two variables: y must be the
first argument and x the second argument.
a, b : float
The limits of integration in x: `a` < `b`
gfun : callable or float
The lower boundary curve in y which is a function taking a single
floating point argument (x) and returning a floating point result
or a float indicating a constant boundary curve.
hfun : callable or float
The upper boundary curve in y (same requirements as `gfun`).
args : sequence, optional
Extra arguments to pass to `func`.
epsabs : float, optional
Absolute tolerance passed directly to the inner 1-D quadrature
integration. Default is 1.49e-8. ``dblquad`` tries to obtain
an accuracy of ``abs(i-result) <= max(epsabs, epsrel*abs(i))``
where ``i`` = inner integral of ``func(y, x)`` from ``gfun(x)``
to ``hfun(x)``, and ``result`` is the numerical approximation.
See `epsrel` below.
epsrel : float, optional
Relative tolerance of the inner 1-D integrals. Default is 1.49e-8.
If ``epsabs <= 0``, `epsrel` must be greater than both 5e-29
and ``50 * (machine epsilon)``. See `epsabs` above.
Returns
-------
y : float
The resultant integral.
abserr : float
An estimate of the error.
See Also
--------
quad : single integral
tplquad : triple integral
nquad : N-dimensional integrals
fixed_quad : fixed-order Gaussian quadrature
quadrature : adaptive Gaussian quadrature
odeint : ODE integrator
ode : ODE integrator
simpson : integrator for sampled data
romb : integrator for sampled data
scipy.special : for coefficients and roots of orthogonal polynomials
Notes
-----
**Details of QUADPACK level routines**
`quad` calls routines from the FORTRAN library QUADPACK. This section
provides details on the conditions for each routine to be called and a
short description of each routine. For each level of integration, ``qagse``
is used for finite limits or ``qagie`` is used if either limit (or both!)
are infinite. The following provides a short description from [1]_ for each
routine.
qagse
is an integrator based on globally adaptive interval
subdivision in connection with extrapolation, which will
eliminate the effects of integrand singularities of
several types.
qagie
handles integration over infinite intervals. The infinite range is
mapped onto a finite interval and subsequently the same strategy as
in ``QAGS`` is applied.
References
----------
.. [1] Piessens, Robert; de Doncker-Kapenga, Elise;
Überhuber, Christoph W.; Kahaner, David (1983).
QUADPACK: A subroutine package for automatic integration.
Springer-Verlag.
ISBN 978-3-540-12553-2.
Examples
--------
Compute the double integral of ``x * y**2`` over the box
``x`` ranging from 0 to 2 and ``y`` ranging from 0 to 1.
That is, :math:`\\int^{x=2}_{x=0} \\int^{y=1}_{y=0} x y^2 \\,dy \\,dx`.
>>> import numpy as np
>>> from scipy import integrate
>>> f = lambda y, x: x*y**2
>>> integrate.dblquad(f, 0, 2, 0, 1)
(0.6666666666666667, 7.401486830834377e-15)
Calculate :math:`\\int^{x=\\pi/4}_{x=0} \\int^{y=\\cos(x)}_{y=\\sin(x)} 1
\\,dy \\,dx`.
>>> f = lambda y, x: 1
>>> integrate.dblquad(f, 0, np.pi/4, np.sin, np.cos)
(0.41421356237309503, 1.1083280054755938e-14)
Calculate :math:`\\int^{x=1}_{x=0} \\int^{y=2-x}_{y=x} a x y \\,dy \\,dx`
for :math:`a=1, 3`.
>>> f = lambda y, x, a: a*x*y
>>> integrate.dblquad(f, 0, 1, lambda x: x, lambda x: 2-x, args=(1,))
(0.33333333333333337, 5.551115123125783e-15)
>>> integrate.dblquad(f, 0, 1, lambda x: x, lambda x: 2-x, args=(3,))
(0.9999999999999999, 1.6653345369377348e-14)
Compute the two-dimensional Gaussian Integral, which is the integral of the
Gaussian function :math:`f(x,y) = e^{-(x^{2} + y^{2})}`, over
:math:`(-\\infty,+\\infty)`. That is, compute the integral
:math:`\\iint^{+\\infty}_{-\\infty} e^{-(x^{2} + y^{2})} \\,dy\\,dx`.
>>> f = lambda x, y: np.exp(-(x ** 2 + y ** 2))
>>> integrate.dblquad(f, -np.inf, np.inf, -np.inf, np.inf)
(3.141592653589777, 2.5173086737433208e-08)
"""
def temp_ranges(*args):
return [gfun(args[0]) if callable(gfun) else gfun,
hfun(args[0]) if callable(hfun) else hfun]
return nquad(func, [temp_ranges, [a, b]], args=args,
opts={"epsabs": epsabs, "epsrel": epsrel})
def tplquad(func, a, b, gfun, hfun, qfun, rfun, args=(), epsabs=1.49e-8,
epsrel=1.49e-8):
"""
Compute a triple (definite) integral.
Return the triple integral of ``func(z, y, x)`` from ``x = a..b``,
``y = gfun(x)..hfun(x)``, and ``z = qfun(x,y)..rfun(x,y)``.
Parameters
----------
func : function
A Python function or method of at least three variables in the
order (z, y, x).
a, b : float
The limits of integration in x: `a` < `b`
gfun : function or float
The lower boundary curve in y which is a function taking a single
floating point argument (x) and returning a floating point result
or a float indicating a constant boundary curve.
hfun : function or float
The upper boundary curve in y (same requirements as `gfun`).
qfun : function or float
The lower boundary surface in z. It must be a function that takes
two floats in the order (x, y) and returns a float or a float
indicating a constant boundary surface.
rfun : function or float
The upper boundary surface in z. (Same requirements as `qfun`.)
args : tuple, optional
Extra arguments to pass to `func`.
epsabs : float, optional
Absolute tolerance passed directly to the innermost 1-D quadrature
integration. Default is 1.49e-8.
epsrel : float, optional
Relative tolerance of the innermost 1-D integrals. Default is 1.49e-8.
Returns
-------
y : float
The resultant integral.
abserr : float
An estimate of the error.
See Also
--------
quad : Adaptive quadrature using QUADPACK
quadrature : Adaptive Gaussian quadrature
fixed_quad : Fixed-order Gaussian quadrature
dblquad : Double integrals
nquad : N-dimensional integrals
romb : Integrators for sampled data
simpson : Integrators for sampled data
ode : ODE integrators
odeint : ODE integrators
scipy.special : For coefficients and roots of orthogonal polynomials
Notes
-----
**Details of QUADPACK level routines**
`quad` calls routines from the FORTRAN library QUADPACK. This section
provides details on the conditions for each routine to be called and a
short description of each routine. For each level of integration, ``qagse``
is used for finite limits or ``qagie`` is used, if either limit (or both!)
are infinite. The following provides a short description from [1]_ for each
routine.
qagse
is an integrator based on globally adaptive interval
subdivision in connection with extrapolation, which will
eliminate the effects of integrand singularities of
several types.
qagie
handles integration over infinite intervals. The infinite range is
mapped onto a finite interval and subsequently the same strategy as
in ``QAGS`` is applied.
References
----------
.. [1] Piessens, Robert; de Doncker-Kapenga, Elise;
Überhuber, Christoph W.; Kahaner, David (1983).
QUADPACK: A subroutine package for automatic integration.
Springer-Verlag.
ISBN 978-3-540-12553-2.
Examples
--------
Compute the triple integral of ``x * y * z``, over ``x`` ranging
from 1 to 2, ``y`` ranging from 2 to 3, ``z`` ranging from 0 to 1.
That is, :math:`\\int^{x=2}_{x=1} \\int^{y=3}_{y=2} \\int^{z=1}_{z=0} x y z
\\,dz \\,dy \\,dx`.
>>> import numpy as np
>>> from scipy import integrate
>>> f = lambda z, y, x: x*y*z
>>> integrate.tplquad(f, 1, 2, 2, 3, 0, 1)
(1.8749999999999998, 3.3246447942574074e-14)
Calculate :math:`\\int^{x=1}_{x=0} \\int^{y=1-2x}_{y=0}
\\int^{z=1-x-2y}_{z=0} x y z \\,dz \\,dy \\,dx`.
Note: `qfun`/`rfun` takes arguments in the order (x, y), even though ``f``
takes arguments in the order (z, y, x).
>>> f = lambda z, y, x: x*y*z
>>> integrate.tplquad(f, 0, 1, 0, lambda x: 1-2*x, 0, lambda x, y: 1-x-2*y)
(0.05416666666666668, 2.1774196738157757e-14)
Calculate :math:`\\int^{x=1}_{x=0} \\int^{y=1}_{y=0} \\int^{z=1}_{z=0}
a x y z \\,dz \\,dy \\,dx` for :math:`a=1, 3`.
>>> f = lambda z, y, x, a: a*x*y*z
>>> integrate.tplquad(f, 0, 1, 0, 1, 0, 1, args=(1,))
(0.125, 5.527033708952211e-15)
>>> integrate.tplquad(f, 0, 1, 0, 1, 0, 1, args=(3,))
(0.375, 1.6581101126856635e-14)
Compute the three-dimensional Gaussian Integral, which is the integral of
the Gaussian function :math:`f(x,y,z) = e^{-(x^{2} + y^{2} + z^{2})}`, over
:math:`(-\\infty,+\\infty)`. That is, compute the integral
:math:`\\iiint^{+\\infty}_{-\\infty} e^{-(x^{2} + y^{2} + z^{2})} \\,dz
\\,dy\\,dx`.
>>> f = lambda x, y, z: np.exp(-(x ** 2 + y ** 2 + z ** 2))
>>> integrate.tplquad(f, -np.inf, np.inf, -np.inf, np.inf, -np.inf, np.inf)
(5.568327996830833, 4.4619078828029765e-08)
"""
# f(z, y, x)
# qfun/rfun(x, y)
# gfun/hfun(x)
# nquad will hand (y, x, t0, ...) to ranges0
# nquad will hand (x, t0, ...) to ranges1
# Only qfun / rfun is different API...
def ranges0(*args):
return [qfun(args[1], args[0]) if callable(qfun) else qfun,
rfun(args[1], args[0]) if callable(rfun) else rfun]
def ranges1(*args):
return [gfun(args[0]) if callable(gfun) else gfun,
hfun(args[0]) if callable(hfun) else hfun]
ranges = [ranges0, ranges1, [a, b]]
return nquad(func, ranges, args=args,
opts={"epsabs": epsabs, "epsrel": epsrel})
def nquad(func, ranges, args=None, opts=None, full_output=False):
r"""
Integration over multiple variables.
Wraps `quad` to enable integration over multiple variables.
Various options allow improved integration of discontinuous functions, as
well as the use of weighted integration, and generally finer control of the
integration process.
Parameters
----------
func : {callable, scipy.LowLevelCallable}
The function to be integrated. Has arguments of ``x0, ... xn``,
``t0, ... tm``, where integration is carried out over ``x0, ... xn``,
which must be floats. Where ``t0, ... tm`` are extra arguments
passed in args.
Function signature should be ``func(x0, x1, ..., xn, t0, t1, ..., tm)``.
Integration is carried out in order. That is, integration over ``x0``
is the innermost integral, and ``xn`` is the outermost.
If the user desires improved integration performance, then `f` may
be a `scipy.LowLevelCallable` with one of the signatures::
double func(int n, double *xx)
double func(int n, double *xx, void *user_data)
where ``n`` is the number of variables and args. The ``xx`` array
contains the coordinates and extra arguments. ``user_data`` is the data
contained in the `scipy.LowLevelCallable`.
ranges : iterable object
Each element of ranges may be either a sequence of 2 numbers, or else
a callable that returns such a sequence. ``ranges[0]`` corresponds to
integration over x0, and so on. If an element of ranges is a callable,
then it will be called with all of the integration arguments available,
as well as any parametric arguments. e.g., if
``func = f(x0, x1, x2, t0, t1)``, then ``ranges[0]`` may be defined as
either ``(a, b)`` or else as ``(a, b) = range0(x1, x2, t0, t1)``.
args : iterable object, optional
Additional arguments ``t0, ... tn``, required by ``func``, ``ranges``,
and ``opts``.
opts : iterable object or dict, optional
Options to be passed to `quad`. May be empty, a dict, or
a sequence of dicts or functions that return a dict. If empty, the
default options from scipy.integrate.quad are used. If a dict, the same
options are used for all levels of integraion. If a sequence, then each
element of the sequence corresponds to a particular integration. e.g.,
``opts[0]`` corresponds to integration over ``x0``, and so on. If a
callable, the signature must be the same as for ``ranges``. The
available options together with their default values are:
- epsabs = 1.49e-08
- epsrel = 1.49e-08
- limit = 50
- points = None
- weight = None
- wvar = None
- wopts = None
For more information on these options, see `quad`.
full_output : bool, optional
Partial implementation of ``full_output`` from scipy.integrate.quad.
The number of integrand function evaluations ``neval`` can be obtained
by setting ``full_output=True`` when calling nquad.
Returns
-------
result : float
The result of the integration.
abserr : float
The maximum of the estimates of the absolute error in the various
integration results.
out_dict : dict, optional
A dict containing additional information on the integration.
See Also
--------
quad : 1-D numerical integration
dblquad, tplquad : double and triple integrals
fixed_quad : fixed-order Gaussian quadrature
quadrature : adaptive Gaussian quadrature
Notes
-----
**Details of QUADPACK level routines**
`nquad` calls routines from the FORTRAN library QUADPACK. This section
provides details on the conditions for each routine to be called and a
short description of each routine. The routine called depends on
`weight`, `points` and the integration limits `a` and `b`.
================ ============== ========== =====================
QUADPACK routine `weight` `points` infinite bounds
================ ============== ========== =====================
qagse None No No
qagie None No Yes
qagpe None Yes No
qawoe 'sin', 'cos' No No
qawfe 'sin', 'cos' No either `a` or `b`
qawse 'alg*' No No
qawce 'cauchy' No No
================ ============== ========== =====================
The following provides a short desciption from [1]_ for each
routine.
qagse
is an integrator based on globally adaptive interval
subdivision in connection with extrapolation, which will
eliminate the effects of integrand singularities of
several types.
qagie
handles integration over infinite intervals. The infinite range is
mapped onto a finite interval and subsequently the same strategy as
in ``QAGS`` is applied.
qagpe
serves the same purposes as QAGS, but also allows the
user to provide explicit information about the location
and type of trouble-spots i.e. the abscissae of internal
singularities, discontinuities and other difficulties of
the integrand function.
qawoe
is an integrator for the evaluation of
:math:`\int^b_a \cos(\omega x)f(x)dx` or
:math:`\int^b_a \sin(\omega x)f(x)dx`
over a finite interval [a,b], where :math:`\omega` and :math:`f`
are specified by the user. The rule evaluation component is based
on the modified Clenshaw-Curtis technique
An adaptive subdivision scheme is used in connection
with an extrapolation procedure, which is a modification
of that in ``QAGS`` and allows the algorithm to deal with
singularities in :math:`f(x)`.
qawfe
calculates the Fourier transform
:math:`\int^\infty_a \cos(\omega x)f(x)dx` or
:math:`\int^\infty_a \sin(\omega x)f(x)dx`
for user-provided :math:`\omega` and :math:`f`. The procedure of
``QAWO`` is applied on successive finite intervals, and convergence
acceleration by means of the :math:`\varepsilon`-algorithm is applied
to the series of integral approximations.
qawse
approximate :math:`\int^b_a w(x)f(x)dx`, with :math:`a < b` where
:math:`w(x) = (x-a)^{\alpha}(b-x)^{\beta}v(x)` with
:math:`\alpha,\beta > -1`, where :math:`v(x)` may be one of the
following functions: :math:`1`, :math:`\log(x-a)`, :math:`\log(b-x)`,
:math:`\log(x-a)\log(b-x)`.
The user specifies :math:`\alpha`, :math:`\beta` and the type of the
function :math:`v`. A globally adaptive subdivision strategy is
applied, with modified Clenshaw-Curtis integration on those
subintervals which contain `a` or `b`.
qawce
compute :math:`\int^b_a f(x) / (x-c)dx` where the integral must be
interpreted as a Cauchy principal value integral, for user specified
:math:`c` and :math:`f`. The strategy is globally adaptive. Modified
Clenshaw-Curtis integration is used on those intervals containing the
point :math:`x = c`.
References
----------
.. [1] Piessens, Robert; de Doncker-Kapenga, Elise;
Überhuber, Christoph W.; Kahaner, David (1983).
QUADPACK: A subroutine package for automatic integration.
Springer-Verlag.
ISBN 978-3-540-12553-2.
Examples
--------
Calculate
.. math::
\int^{1}_{-0.15} \int^{0.8}_{0.13} \int^{1}_{-1} \int^{1}_{0}
f(x_0, x_1, x_2, x_3) \,dx_0 \,dx_1 \,dx_2 \,dx_3 ,
where
.. math::
f(x_0, x_1, x_2, x_3) = \begin{cases}
x_0^2+x_1 x_2-x_3^3+ \sin{x_0}+1 & (x_0-0.2 x_3-0.5-0.25 x_1 > 0) \\
x_0^2+x_1 x_2-x_3^3+ \sin{x_0}+0 & (x_0-0.2 x_3-0.5-0.25 x_1 \leq 0)
\end{cases} .
>>> import numpy as np
>>> from scipy import integrate
>>> func = lambda x0,x1,x2,x3 : x0**2 + x1*x2 - x3**3 + np.sin(x0) + (
... 1 if (x0-.2*x3-.5-.25*x1>0) else 0)
>>> def opts0(*args, **kwargs):
... return {'points':[0.2*args[2] + 0.5 + 0.25*args[0]]}
>>> integrate.nquad(func, [[0,1], [-1,1], [.13,.8], [-.15,1]],
... opts=[opts0,{},{},{}], full_output=True)
(1.5267454070738633, 2.9437360001402324e-14, {'neval': 388962})
Calculate
.. math::
\int^{t_0+t_1+1}_{t_0+t_1-1}
\int^{x_2+t_0^2 t_1^3+1}_{x_2+t_0^2 t_1^3-1}
\int^{t_0 x_1+t_1 x_2+1}_{t_0 x_1+t_1 x_2-1}
f(x_0,x_1, x_2,t_0,t_1)
\,dx_0 \,dx_1 \,dx_2,
where
.. math::
f(x_0, x_1, x_2, t_0, t_1) = \begin{cases}
x_0 x_2^2 + \sin{x_1}+2 & (x_0+t_1 x_1-t_0 > 0) \\
x_0 x_2^2 +\sin{x_1}+1 & (x_0+t_1 x_1-t_0 \leq 0)
\end{cases}
and :math:`(t_0, t_1) = (0, 1)` .
>>> def func2(x0, x1, x2, t0, t1):
... return x0*x2**2 + np.sin(x1) + 1 + (1 if x0+t1*x1-t0>0 else 0)
>>> def lim0(x1, x2, t0, t1):
... return [t0*x1 + t1*x2 - 1, t0*x1 + t1*x2 + 1]
>>> def lim1(x2, t0, t1):
... return [x2 + t0**2*t1**3 - 1, x2 + t0**2*t1**3 + 1]
>>> def lim2(t0, t1):
... return [t0 + t1 - 1, t0 + t1 + 1]
>>> def opts0(x1, x2, t0, t1):
... return {'points' : [t0 - t1*x1]}
>>> def opts1(x2, t0, t1):
... return {}
>>> def opts2(t0, t1):
... return {}
>>> integrate.nquad(func2, [lim0, lim1, lim2], args=(0,1),
... opts=[opts0, opts1, opts2])
(36.099919226771625, 1.8546948553373528e-07)
"""
depth = len(ranges)
ranges = [rng if callable(rng) else _RangeFunc(rng) for rng in ranges]
if args is None:
args = ()
if opts is None:
opts = [dict([])] * depth
if isinstance(opts, dict):
opts = [_OptFunc(opts)] * depth
else:
opts = [opt if callable(opt) else _OptFunc(opt) for opt in opts]
return _NQuad(func, ranges, opts, full_output).integrate(*args)
class _RangeFunc:
def __init__(self, range_):
self.range_ = range_
def __call__(self, *args):
"""Return stored value.
*args needed because range_ can be float or func, and is called with
variable number of parameters.
"""
return self.range_
class _OptFunc:
def __init__(self, opt):
self.opt = opt
def __call__(self, *args):
"""Return stored dict."""
return self.opt
class _NQuad:
def __init__(self, func, ranges, opts, full_output):
self.abserr = 0
self.func = func
self.ranges = ranges
self.opts = opts
self.maxdepth = len(ranges)
self.full_output = full_output
if self.full_output:
self.out_dict = {'neval': 0}
def integrate(self, *args, **kwargs):
depth = kwargs.pop('depth', 0)
if kwargs:
raise ValueError('unexpected kwargs')
# Get the integration range and options for this depth.
ind = -(depth + 1)
fn_range = self.ranges[ind]
low, high = fn_range(*args)
fn_opt = self.opts[ind]
opt = dict(fn_opt(*args))
if 'points' in opt:
opt['points'] = [x for x in opt['points'] if low <= x <= high]
if depth + 1 == self.maxdepth:
f = self.func
else:
f = partial(self.integrate, depth=depth+1)
quad_r = quad(f, low, high, args=args, full_output=self.full_output,
**opt)
value = quad_r[0]
abserr = quad_r[1]
if self.full_output:
infodict = quad_r[2]
# The 'neval' parameter in full_output returns the total
# number of times the integrand function was evaluated.
# Therefore, only the innermost integration loop counts.
if depth + 1 == self.maxdepth:
self.out_dict['neval'] += infodict['neval']
self.abserr = max(self.abserr, abserr)
if depth > 0:
return value
else:
# Final result of N-D integration with error
if self.full_output:
return value, self.abserr, self.out_dict
else:
return value, self.abserr
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