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# Author: Travis Oliphant 2001 # Author: Nathan Woods 2013 (nquad &c)
'IntegrationWarning']
""" Warning on issues during integration. """
""" Print extra information about integrate.quad() parameters and returns.
Parameters ---------- output : instance with "write" method, optional Information about `quad` is passed to ``output.write()``. Default is ``sys.stdout``.
Returns ------- None
""" output.write(quad.__doc__)
limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50, limlst=50): """ 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.
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. Run scipy.integrate.quad_explain() for more 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. epsrel : float or int, optional Relative error tolerance. 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. 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 simps : 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. 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.
Examples -------- Calculate :math:`\\int^4_0 x^2 dx` and compare with an analytic result
>>> from scipy import integrate >>> 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)
>>> 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 weight is None: retval = _quad(func, a, b, args, full_output, epsabs, epsrel, limit, points) else: 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) 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 'errabs'<=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)
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 sinular points the_points = numpy.unique(points) the_points = the_points[a < the_points] the_points = the_points[the_points < b] the_points = numpy.concatenate((the_points, (0., 0.))) return _quadpack._qagpe(func,a,b,the_points,args,full_output,epsabs,epsrel,limit)
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)
""" 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. epsrel : float, optional Relative tolerance of the inner 1-D integrals. Default is 1.49e-8.
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 simps : integrator for sampled data romb : integrator for sampled data scipy.special : for coefficients and roots of orthogonal polynomials
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.
>>> from scipy import integrate >>> f = lambda y, x: x*y**2 >>> integrate.dblquad(f, 0, 2, lambda x: 0, lambda x: 1) (0.6666666666666667, 7.401486830834377e-15)
"""
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})
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 simps: Integrators for sampled data ode: ODE integrators odeint: ODE integrators scipy.special: For coefficients and roots of orthogonal polynomials
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.
>>> from scipy import integrate >>> f = lambda z, y, x: x*y*z >>> integrate.tplquad(f, 1, 2, lambda x: 2, lambda x: 3, ... lambda x, y: 0, lambda x, y: 1) (1.8750000000000002, 3.324644794257407e-14)
""" # 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 # Stupid 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})
""" 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. 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 extra parameters and args is an array of doubles of the additional parameters, the ``xx`` array contains the coordinates. The ``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` and `quad_explain`.
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-dimensional numerical integration dblquad, tplquad : double and triple integrals fixed_quad : fixed-order Gaussian quadrature quadrature : adaptive Gaussian quadrature
Examples -------- >>> 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) >>> points = [[lambda x1,x2,x3 : 0.2*x3 + 0.5 + 0.25*x1], [], [], []] >>> 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})
>>> scale = .1 >>> def func2(x0, x1, x2, x3, t0, t1): ... return x0*x1*x3**2 + np.sin(x2) + 1 + (1 if x0+t1*x1-t0>0 else 0) >>> def lim0(x1, x2, x3, t0, t1): ... return [scale * (x1**2 + x2 + np.cos(x3)*t0*t1 + 1) - 1, ... scale * (x1**2 + x2 + np.cos(x3)*t0*t1 + 1) + 1] >>> def lim1(x2, x3, t0, t1): ... return [scale * (t0*x2 + t1*x3) - 1, ... scale * (t0*x2 + t1*x3) + 1] >>> def lim2(x3, t0, t1): ... return [scale * (x3 + t0**2*t1**3) - 1, ... scale * (x3 + t0**2*t1**3) + 1] >>> def lim3(t0, t1): ... return [scale * (t0+t1) - 1, scale * (t0+t1) + 1] >>> def opts0(x1, x2, x3, t0, t1): ... return {'points' : [t0 - t1*x1]} >>> def opts1(x2, x3, t0, t1): ... return {} >>> def opts2(x3, t0, t1): ... return {} >>> def opts3(t0, t1): ... return {} >>> integrate.nquad(func2, [lim0, lim1, lim2, lim3], args=(0,0), ... opts=[opts0, opts1, opts2, opts3]) (25.066666666666666, 2.7829590483937256e-13)
""" 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)
self.range_ = range_
"""Return stored value.
*args needed because range_ can be float or func, and is called with variable number of parameters. """ return self.range_
self.opt = opt
"""Return stored dict.""" return self.opt
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}
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 |