# scipy/scipy

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 """ Classes for interpolating values. """ from __future__ import division, print_function, absolute_import __all__ = ['interp1d', 'interp2d', 'spline', 'spleval', 'splmake', 'spltopp', 'lagrange', 'PPoly', 'BPoly', 'NdPPoly', 'RegularGridInterpolator', 'interpn'] import itertools import warnings import functools import operator import numpy as np from numpy import (array, transpose, searchsorted, atleast_1d, atleast_2d, dot, ravel, poly1d, asarray, intp) import scipy.linalg import scipy.special as spec from scipy.special import comb from scipy._lib.six import xrange, integer_types, string_types from . import fitpack from . import dfitpack from . import _fitpack from .polyint import _Interpolator1D from . import _ppoly from .fitpack2 import RectBivariateSpline from .interpnd import _ndim_coords_from_arrays from ._bsplines import make_interp_spline, BSpline def prod(x): """Product of a list of numbers; ~40x faster vs np.prod for Python tuples""" if len(x) == 0: return 1 return functools.reduce(operator.mul, x) def lagrange(x, w): r""" Return a Lagrange interpolating polynomial. Given two 1-D arrays x and w, returns the Lagrange interpolating polynomial through the points (x, w). Warning: This implementation is numerically unstable. Do not expect to be able to use more than about 20 points even if they are chosen optimally. Parameters ---------- x : array_like x represents the x-coordinates of a set of datapoints. w : array_like w represents the y-coordinates of a set of datapoints, i.e. f(x). Returns ------- lagrange : numpy.poly1d instance The Lagrange interpolating polynomial. Examples -------- Interpolate :math:f(x) = x^3 by 3 points. >>> from scipy.interpolate import lagrange >>> x = np.array([0, 1, 2]) >>> y = x**3 >>> poly = lagrange(x, y) Since there are only 3 points, Lagrange polynomial has degree 2. Explicitly, it is given by .. math:: \begin{aligned} L(x) &= 1\times \frac{x (x - 2)}{-1} + 8\times \frac{x (x-1)}{2} \\ &= x (-2 + 3x) \end{aligned} >>> from numpy.polynomial.polynomial import Polynomial >>> Polynomial(poly).coef array([ 3., -2., 0.]) """ M = len(x) p = poly1d(0.0) for j in xrange(M): pt = poly1d(w[j]) for k in xrange(M): if k == j: continue fac = x[j]-x[k] pt *= poly1d([1.0, -x[k]])/fac p += pt return p # !! Need to find argument for keeping initialize. If it isn't # !! found, get rid of it! class interp2d(object): """ interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=nan) Interpolate over a 2-D grid. x, y and z are arrays of values used to approximate some function f: z = f(x, y). This class returns a function whose call method uses spline interpolation to find the value of new points. If x and y represent a regular grid, consider using RectBivariateSpline. Note that calling interp2d with NaNs present in input values results in undefined behaviour. Methods ------- __call__ Parameters ---------- x, y : array_like Arrays defining the data point coordinates. If the points lie on a regular grid, x can specify the column coordinates and y the row coordinates, for example:: >>> x = [0,1,2]; y = [0,3]; z = [[1,2,3], [4,5,6]] Otherwise, x and y must specify the full coordinates for each point, for example:: >>> x = [0,1,2,0,1,2]; y = [0,0,0,3,3,3]; z = [1,2,3,4,5,6] If x and y are multi-dimensional, they are flattened before use. z : array_like The values of the function to interpolate at the data points. If z is a multi-dimensional array, it is flattened before use. The length of a flattened z array is either len(x)*len(y) if x and y specify the column and row coordinates or len(z) == len(x) == len(y) if x and y specify coordinates for each point. kind : {'linear', 'cubic', 'quintic'}, optional The kind of spline interpolation to use. Default is 'linear'. copy : bool, optional If True, the class makes internal copies of x, y and z. If False, references may be used. The default is to copy. bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data (x,y), a ValueError is raised. If False, then fill_value is used. fill_value : number, optional If provided, the value to use for points outside of the interpolation domain. If omitted (None), values outside the domain are extrapolated. See Also -------- RectBivariateSpline : Much faster 2D interpolation if your input data is on a grid bisplrep, bisplev : Spline interpolation based on FITPACK BivariateSpline : a more recent wrapper of the FITPACK routines interp1d : one dimension version of this function Notes ----- The minimum number of data points required along the interpolation axis is (k+1)**2, with k=1 for linear, k=3 for cubic and k=5 for quintic interpolation. The interpolator is constructed by bisplrep, with a smoothing factor of 0. If more control over smoothing is needed, bisplrep should be used directly. Examples -------- Construct a 2-D grid and interpolate on it: >>> from scipy import interpolate >>> x = np.arange(-5.01, 5.01, 0.25) >>> y = np.arange(-5.01, 5.01, 0.25) >>> xx, yy = np.meshgrid(x, y) >>> z = np.sin(xx**2+yy**2) >>> f = interpolate.interp2d(x, y, z, kind='cubic') Now use the obtained interpolation function and plot the result: >>> import matplotlib.pyplot as plt >>> xnew = np.arange(-5.01, 5.01, 1e-2) >>> ynew = np.arange(-5.01, 5.01, 1e-2) >>> znew = f(xnew, ynew) >>> plt.plot(x, z[0, :], 'ro-', xnew, znew[0, :], 'b-') >>> plt.show() """ def __init__(self, x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=None): x = ravel(x) y = ravel(y) z = asarray(z) rectangular_grid = (z.size == len(x) * len(y)) if rectangular_grid: if z.ndim == 2: if z.shape != (len(y), len(x)): raise ValueError("When on a regular grid with x.size = m " "and y.size = n, if z.ndim == 2, then z " "must have shape (n, m)") if not np.all(x[1:] >= x[:-1]): j = np.argsort(x) x = x[j] z = z[:, j] if not np.all(y[1:] >= y[:-1]): j = np.argsort(y) y = y[j] z = z[j, :] z = ravel(z.T) else: z = ravel(z) if len(x) != len(y): raise ValueError( "x and y must have equal lengths for non rectangular grid") if len(z) != len(x): raise ValueError( "Invalid length for input z for non rectangular grid") try: kx = ky = {'linear': 1, 'cubic': 3, 'quintic': 5}[kind] except KeyError: raise ValueError("Unsupported interpolation type.") if not rectangular_grid: # TODO: surfit is really not meant for interpolation! self.tck = fitpack.bisplrep(x, y, z, kx=kx, ky=ky, s=0.0) else: nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth( x, y, z, None, None, None, None, kx=kx, ky=ky, s=0.0) self.tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)], kx, ky) self.bounds_error = bounds_error self.fill_value = fill_value self.x, self.y, self.z = [array(a, copy=copy) for a in (x, y, z)] self.x_min, self.x_max = np.amin(x), np.amax(x) self.y_min, self.y_max = np.amin(y), np.amax(y) def __call__(self, x, y, dx=0, dy=0, assume_sorted=False): """Interpolate the function. Parameters ---------- x : 1D array x-coordinates of the mesh on which to interpolate. y : 1D array y-coordinates of the mesh on which to interpolate. dx : int >= 0, < kx Order of partial derivatives in x. dy : int >= 0, < ky Order of partial derivatives in y. assume_sorted : bool, optional If False, values of x and y can be in any order and they are sorted first. If True, x and y have to be arrays of monotonically increasing values. Returns ------- z : 2D array with shape (len(y), len(x)) The interpolated values. """ x = atleast_1d(x) y = atleast_1d(y) if x.ndim != 1 or y.ndim != 1: raise ValueError("x and y should both be 1-D arrays") if not assume_sorted: x = np.sort(x) y = np.sort(y) if self.bounds_error or self.fill_value is not None: out_of_bounds_x = (x < self.x_min) | (x > self.x_max) out_of_bounds_y = (y < self.y_min) | (y > self.y_max) any_out_of_bounds_x = np.any(out_of_bounds_x) any_out_of_bounds_y = np.any(out_of_bounds_y) if self.bounds_error and (any_out_of_bounds_x or any_out_of_bounds_y): raise ValueError("Values out of range; x must be in %r, y in %r" % ((self.x_min, self.x_max), (self.y_min, self.y_max))) z = fitpack.bisplev(x, y, self.tck, dx, dy) z = atleast_2d(z) z = transpose(z) if self.fill_value is not None: if any_out_of_bounds_x: z[:, out_of_bounds_x] = self.fill_value if any_out_of_bounds_y: z[out_of_bounds_y, :] = self.fill_value if len(z) == 1: z = z[0] return array(z) def _check_broadcast_up_to(arr_from, shape_to, name): """Helper to check that arr_from broadcasts up to shape_to""" shape_from = arr_from.shape if len(shape_to) >= len(shape_from): for t, f in zip(shape_to[::-1], shape_from[::-1]): if f != 1 and f != t: break else: # all checks pass, do the upcasting that we need later if arr_from.size != 1 and arr_from.shape != shape_to: arr_from = np.ones(shape_to, arr_from.dtype) * arr_from return arr_from.ravel() # at least one check failed raise ValueError('%s argument must be able to broadcast up ' 'to shape %s but had shape %s' % (name, shape_to, shape_from)) def _do_extrapolate(fill_value): """Helper to check if fill_value == "extrapolate" without warnings""" return (isinstance(fill_value, string_types) and fill_value == 'extrapolate') class interp1d(_Interpolator1D): """ Interpolate a 1-D function. x and y are arrays of values used to approximate some function f: y = f(x). This class returns a function whose call method uses interpolation to find the value of new points. Note that calling interp1d with NaNs present in input values results in undefined behaviour. Parameters ---------- x : (N,) array_like A 1-D array of real values. y : (...,N,...) array_like A N-D array of real values. The length of y along the interpolation axis must be equal to the length of x. kind : str or int, optional Specifies the kind of interpolation as a string ('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic' where 'zero', 'slinear', 'quadratic' and 'cubic' refer to a spline interpolation of zeroth, first, second or third order) or as an integer specifying the order of the spline interpolator to use. Default is 'linear'. axis : int, optional Specifies the axis of y along which to interpolate. Interpolation defaults to the last axis of y. copy : bool, optional If True, the class makes internal copies of x and y. If False, references to x and y are used. The default is to copy. bounds_error : bool, optional If True, a ValueError is raised any time interpolation is attempted on a value outside of the range of x (where extrapolation is necessary). If False, out of bounds values are assigned fill_value. By default, an error is raised unless fill_value="extrapolate". fill_value : array-like or (array-like, array_like) or "extrapolate", optional - if a ndarray (or float), this value will be used to fill in for requested points outside of the data range. If not provided, then the default is NaN. The array-like must broadcast properly to the dimensions of the non-interpolation axes. - If a two-element tuple, then the first element is used as a fill value for x_new < x[0] and the second element is used for x_new > x[-1]. Anything that is not a 2-element tuple (e.g., list or ndarray, regardless of shape) is taken to be a single array-like argument meant to be used for both bounds as below, above = fill_value, fill_value. .. versionadded:: 0.17.0 - If "extrapolate", then points outside the data range will be extrapolated. .. versionadded:: 0.17.0 assume_sorted : bool, optional If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values. Methods ------- __call__ See Also -------- splrep, splev Spline interpolation/smoothing based on FITPACK. UnivariateSpline : An object-oriented wrapper of the FITPACK routines. interp2d : 2-D interpolation Examples -------- >>> import matplotlib.pyplot as plt >>> from scipy import interpolate >>> x = np.arange(0, 10) >>> y = np.exp(-x/3.0) >>> f = interpolate.interp1d(x, y) >>> xnew = np.arange(0, 9, 0.1) >>> ynew = f(xnew) # use interpolation function returned by interp1d >>> plt.plot(x, y, 'o', xnew, ynew, '-') >>> plt.show() """ def __init__(self, x, y, kind='linear', axis=-1, copy=True, bounds_error=None, fill_value=np.nan, assume_sorted=False): """ Initialize a 1D linear interpolation class.""" _Interpolator1D.__init__(self, x, y, axis=axis) self.bounds_error = bounds_error # used by fill_value setter self.copy = copy if kind in ['zero', 'slinear', 'quadratic', 'cubic']: order = {'zero': 0, 'slinear': 1, 'quadratic': 2, 'cubic': 3}[kind] kind = 'spline' elif isinstance(kind, int): order = kind kind = 'spline' elif kind not in ('linear', 'nearest'): raise NotImplementedError("%s is unsupported: Use fitpack " "routines for other types." % kind) x = array(x, copy=self.copy) y = array(y, copy=self.copy) if not assume_sorted: ind = np.argsort(x) x = x[ind] y = np.take(y, ind, axis=axis) if x.ndim != 1: raise ValueError("the x array must have exactly one dimension.") if y.ndim == 0: raise ValueError("the y array must have at least one dimension.") # Force-cast y to a floating-point type, if it's not yet one if not issubclass(y.dtype.type, np.inexact): y = y.astype(np.float_) # Backward compatibility self.axis = axis % y.ndim # Interpolation goes internally along the first axis self.y = y self._y = self._reshape_yi(self.y) self.x = x del y, x # clean up namespace to prevent misuse; use attributes self._kind = kind self.fill_value = fill_value # calls the setter, can modify bounds_err # Adjust to interpolation kind; store reference to *unbound* # interpolation methods, in order to avoid circular references to self # stored in the bound instance methods, and therefore delayed garbage # collection. See: http://docs.python.org/2/reference/datamodel.html if kind in ('linear', 'nearest'): # Make a "view" of the y array that is rotated to the interpolation # axis. minval = 2 if kind == 'nearest': # Do division before addition to prevent possible integer # overflow self.x_bds = self.x / 2.0 self.x_bds = self.x_bds[1:] + self.x_bds[:-1] self._call = self.__class__._call_nearest else: # Check if we can delegate to numpy.interp (2x-10x faster). cond = self.x.dtype == np.float_ and self.y.dtype == np.float_ cond = cond and self.y.ndim == 1 cond = cond and not _do_extrapolate(fill_value) if cond: self._call = self.__class__._call_linear_np else: self._call = self.__class__._call_linear else: minval = order + 1 rewrite_nan = False xx, yy = self.x, self._y if order > 1: # Quadratic or cubic spline. If input contains even a single # nan, then the output is all nans. We cannot just feed data # with nans to make_interp_spline because it calls LAPACK. # So, we make up a bogus x and y with no nans and use it # to get the correct shape of the output, which we then fill # with nans. # For slinear or zero order spline, we just pass nans through. if np.isnan(self.x).any(): xx = np.linspace(min(self.x), max(self.x), len(self.x)) rewrite_nan = True if np.isnan(self._y).any(): yy = np.ones_like(self._y) rewrite_nan = True self._spline = make_interp_spline(xx, yy, k=order, check_finite=False) if rewrite_nan: self._call = self.__class__._call_nan_spline else: self._call = self.__class__._call_spline if len(self.x) < minval: raise ValueError("x and y arrays must have at " "least %d entries" % minval) @property def fill_value(self): # backwards compat: mimic a public attribute return self._fill_value_orig @fill_value.setter def fill_value(self, fill_value): # extrapolation only works for nearest neighbor and linear methods if _do_extrapolate(fill_value): if self.bounds_error: raise ValueError("Cannot extrapolate and raise " "at the same time.") self.bounds_error = False self._extrapolate = True else: broadcast_shape = (self.y.shape[:self.axis] + self.y.shape[self.axis + 1:]) if len(broadcast_shape) == 0: broadcast_shape = (1,) # it's either a pair (_below_range, _above_range) or a single value # for both above and below range if isinstance(fill_value, tuple) and len(fill_value) == 2: below_above = [np.asarray(fill_value[0]), np.asarray(fill_value[1])] names = ('fill_value (below)', 'fill_value (above)') for ii in range(2): below_above[ii] = _check_broadcast_up_to( below_above[ii], broadcast_shape, names[ii]) else: fill_value = np.asarray(fill_value) below_above = [_check_broadcast_up_to( fill_value, broadcast_shape, 'fill_value')] * 2 self._fill_value_below, self._fill_value_above = below_above self._extrapolate = False if self.bounds_error is None: self.bounds_error = True # backwards compat: fill_value was a public attr; make it writeable self._fill_value_orig = fill_value def _call_linear_np(self, x_new): # Note that out-of-bounds values are taken care of in self._evaluate return np.interp(x_new, self.x, self.y) def _call_linear(self, x_new): # 2. Find where in the original data, the values to interpolate # would be inserted. # Note: If x_new[n] == x[m], then m is returned by searchsorted. x_new_indices = searchsorted(self.x, x_new) # 3. Clip x_new_indices so that they are within the range of # self.x indices and at least 1. Removes mis-interpolation # of x_new[n] = x[0] x_new_indices = x_new_indices.clip(1, len(self.x)-1).astype(int) # 4. Calculate the slope of regions that each x_new value falls in. lo = x_new_indices - 1 hi = x_new_indices x_lo = self.x[lo] x_hi = self.x[hi] y_lo = self._y[lo] y_hi = self._y[hi] # Note that the following two expressions rely on the specifics of the # broadcasting semantics. slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None] # 5. Calculate the actual value for each entry in x_new. y_new = slope*(x_new - x_lo)[:, None] + y_lo return y_new def _call_nearest(self, x_new): """ Find nearest neighbour interpolated y_new = f(x_new).""" # 2. Find where in the averaged data the values to interpolate # would be inserted. # Note: use side='left' (right) to searchsorted() to define the # halfway point to be nearest to the left (right) neighbour x_new_indices = searchsorted(self.x_bds, x_new, side='left') # 3. Clip x_new_indices so that they are within the range of x indices. x_new_indices = x_new_indices.clip(0, len(self.x)-1).astype(intp) # 4. Calculate the actual value for each entry in x_new. y_new = self._y[x_new_indices] return y_new def _call_spline(self, x_new): return self._spline(x_new) def _call_nan_spline(self, x_new): out = self._spline(x_new) out[...] = np.nan return out def _evaluate(self, x_new): # 1. Handle values in x_new that are outside of x. Throw error, # or return a list of mask array indicating the outofbounds values. # The behavior is set by the bounds_error variable. x_new = asarray(x_new) y_new = self._call(self, x_new) if not self._extrapolate: below_bounds, above_bounds = self._check_bounds(x_new) if len(y_new) > 0: # Note fill_value must be broadcast up to the proper size # and flattened to work here y_new[below_bounds] = self._fill_value_below y_new[above_bounds] = self._fill_value_above return y_new def _check_bounds(self, x_new): """Check the inputs for being in the bounds of the interpolated data. Parameters ---------- x_new : array Returns ------- out_of_bounds : bool array The mask on x_new of values that are out of the bounds. """ # If self.bounds_error is True, we raise an error if any x_new values # fall outside the range of x. Otherwise, we return an array indicating # which values are outside the boundary region. below_bounds = x_new < self.x[0] above_bounds = x_new > self.x[-1] # !! Could provide more information about which values are out of bounds if self.bounds_error and below_bounds.any(): raise ValueError("A value in x_new is below the interpolation " "range.") if self.bounds_error and above_bounds.any(): raise ValueError("A value in x_new is above the interpolation " "range.") # !! Should we emit a warning if some values are out of bounds? # !! matlab does not. return below_bounds, above_bounds class _PPolyBase(object): """Base class for piecewise polynomials.""" __slots__ = ('c', 'x', 'extrapolate', 'axis') def __init__(self, c, x, extrapolate=None, axis=0): self.c = np.asarray(c) self.x = np.ascontiguousarray(x, dtype=np.float64) if extrapolate is None: extrapolate = True elif extrapolate != 'periodic': extrapolate = bool(extrapolate) self.extrapolate = extrapolate if self.c.ndim < 2: raise ValueError("Coefficients array must be at least " "2-dimensional.") if not (0 <= axis < self.c.ndim - 1): raise ValueError("axis=%s must be between 0 and %s" % (axis, self.c.ndim-1)) self.axis = axis if axis != 0: # roll the interpolation axis to be the first one in self.c # More specifically, the target shape for self.c is (k, m, ...), # and axis !=0 means that we have c.shape (..., k, m, ...) # ^ # axis # So we roll two of them. self.c = np.rollaxis(self.c, axis+1) self.c = np.rollaxis(self.c, axis+1) if self.x.ndim != 1: raise ValueError("x must be 1-dimensional") if self.x.size < 2: raise ValueError("at least 2 breakpoints are needed") if self.c.ndim < 2: raise ValueError("c must have at least 2 dimensions") if self.c.shape[0] == 0: raise ValueError("polynomial must be at least of order 0") if self.c.shape[1] != self.x.size-1: raise ValueError("number of coefficients != len(x)-1") dx = np.diff(self.x) if not (np.all(dx >= 0) or np.all(dx <= 0)): raise ValueError("x must be strictly increasing or decreasing.") dtype = self._get_dtype(self.c.dtype) self.c = np.ascontiguousarray(self.c, dtype=dtype) def _get_dtype(self, dtype): if np.issubdtype(dtype, np.complexfloating) \ or np.issubdtype(self.c.dtype, np.complexfloating): return np.complex_ else: return np.float_ @classmethod def construct_fast(cls, c, x, extrapolate=None, axis=0): """ Construct the piecewise polynomial without making checks. Takes the same parameters as the constructor. Input arguments c and x must be arrays of the correct shape and type. The c array can only be of dtypes float and complex, and x array must have dtype float. """ self = object.__new__(cls) self.c = c self.x = x self.axis = axis if extrapolate is None: extrapolate = True self.extrapolate = extrapolate return self def _ensure_c_contiguous(self): """ c and x may be modified by the user. The Cython code expects that they are C contiguous. """ if not self.x.flags.c_contiguous: self.x = self.x.copy() if not self.c.flags.c_contiguous: self.c = self.c.copy() def extend(self, c, x, right=None): """ Add additional breakpoints and coefficients to the polynomial. Parameters ---------- c : ndarray, size (k, m, ...) Additional coefficients for polynomials in intervals. Note that the first additional interval will be formed using one of the self.x end points. x : ndarray, size (m,) Additional breakpoints. Must be sorted in the same order as self.x and either to the right or to the left of the current breakpoints. right Deprecated argument. Has no effect. .. deprecated:: 0.19 """ if right is not None: warnings.warn("right is deprecated and will be removed.") c = np.asarray(c) x = np.asarray(x) if c.ndim < 2: raise ValueError("invalid dimensions for c") if x.ndim != 1: raise ValueError("invalid dimensions for x") if x.shape[0] != c.shape[1]: raise ValueError("x and c have incompatible sizes") if c.shape[2:] != self.c.shape[2:] or c.ndim != self.c.ndim: raise ValueError("c and self.c have incompatible shapes") if c.size == 0: return dx = np.diff(x) if not (np.all(dx >= 0) or np.all(dx <= 0)): raise ValueError("x is not sorted.") if self.x[-1] >= self.x[0]: if not x[-1] >= x[0]: raise ValueError("x is in the different order " "than self.x.") if x[0] >= self.x[-1]: action = 'append' elif x[-1] <= self.x[0]: action = 'prepend' else: raise ValueError("x is neither on the left or on the right " "from self.x.") else: if not x[-1] <= x[0]: raise ValueError("x is in the different order " "than self.x.") if x[0] <= self.x[-1]: action = 'append' elif x[-1] >= self.x[0]: action = 'prepend' else: raise ValueError("x is neither on the left or on the right " "from self.x.") dtype = self._get_dtype(c.dtype) k2 = max(c.shape[0], self.c.shape[0]) c2 = np.zeros((k2, self.c.shape[1] + c.shape[1]) + self.c.shape[2:], dtype=dtype) if action == 'append': c2[k2-self.c.shape[0]:, :self.c.shape[1]] = self.c c2[k2-c.shape[0]:, self.c.shape[1]:] = c self.x = np.r_[self.x, x] elif action == 'prepend': c2[k2-self.c.shape[0]:, :c.shape[1]] = c c2[k2-c.shape[0]:, c.shape[1]:] = self.c self.x = np.r_[x, self.x] self.c = c2 def __call__(self, x, nu=0, extrapolate=None): """ Evaluate the piecewise polynomial or its derivative. Parameters ---------- x : array_like Points to evaluate the interpolant at. nu : int, optional Order of derivative to evaluate. Must be non-negative. extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), use self.extrapolate. Returns ------- y : array_like Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of x. Notes ----- Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals are considered half-open, [a, b), except for the last interval which is closed [a, b]. """ if extrapolate is None: extrapolate = self.extrapolate x = np.asarray(x) x_shape, x_ndim = x.shape, x.ndim x = np.ascontiguousarray(x.ravel(), dtype=np.float_) # With periodic extrapolation we map x to the segment # [self.x[0], self.x[-1]]. if extrapolate == 'periodic': x = self.x[0] + (x - self.x[0]) % (self.x[-1] - self.x[0]) extrapolate = False out = np.empty((len(x), prod(self.c.shape[2:])), dtype=self.c.dtype) self._ensure_c_contiguous() self._evaluate(x, nu, extrapolate, out) out = out.reshape(x_shape + self.c.shape[2:]) if self.axis != 0: # transpose to move the calculated values to the interpolation axis l = list(range(out.ndim)) l = l[x_ndim:x_ndim+self.axis] + l[:x_ndim] + l[x_ndim+self.axis:] out = out.transpose(l) return out class PPoly(_PPolyBase): """ Piecewise polynomial in terms of coefficients and breakpoints The polynomial between x[i] and x[i + 1] is written in the local power basis:: S = sum(c[m, i] * (xp - x[i])**(k-m) for m in range(k+1)) where k is the degree of the polynomial. Parameters ---------- c : ndarray, shape (k, m, ...) Polynomial coefficients, order k and m intervals x : ndarray, shape (m+1,) Polynomial breakpoints. Must be sorted in either increasing or decreasing order. extrapolate : bool or 'periodic', optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. axis : int, optional Interpolation axis. Default is zero. Attributes ---------- x : ndarray Breakpoints. c : ndarray Coefficients of the polynomials. They are reshaped to a 3-dimensional array with the last dimension representing the trailing dimensions of the original coefficient array. axis : int Interpolation axis. Methods ------- __call__ derivative antiderivative integrate solve roots extend from_spline from_bernstein_basis construct_fast See also -------- BPoly : piecewise polynomials in the Bernstein basis Notes ----- High-order polynomials in the power basis can be numerically unstable. Precision problems can start to appear for orders larger than 20-30. """ def _evaluate(self, x, nu, extrapolate, out): _ppoly.evaluate(self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, x, nu, bool(extrapolate), out) def derivative(self, nu=1): """ Construct a new piecewise polynomial representing the derivative. Parameters ---------- nu : int, optional Order of derivative to evaluate. Default is 1, i.e. compute the first derivative. If negative, the antiderivative is returned. Returns ------- pp : PPoly Piecewise polynomial of order k2 = k - n representing the derivative of this polynomial. Notes ----- Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals are considered half-open, [a, b), except for the last interval which is closed [a, b]. """ if nu < 0: return self.antiderivative(-nu) # reduce order if nu == 0: c2 = self.c.copy() else: c2 = self.c[:-nu, :].copy() if c2.shape[0] == 0: # derivative of order 0 is zero c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype) # multiply by the correct rising factorials factor = spec.poch(np.arange(c2.shape[0], 0, -1), nu) c2 *= factor[(slice(None),) + (None,)*(c2.ndim-1)] # construct a compatible polynomial return self.construct_fast(c2, self.x, self.extrapolate, self.axis) def antiderivative(self, nu=1): """ Construct a new piecewise polynomial representing the antiderivative. Antiderivative is also the indefinite integral of the function, and derivative is its inverse operation. Parameters ---------- nu : int, optional Order of antiderivative to evaluate. Default is 1, i.e. compute the first integral. If negative, the derivative is returned. Returns ------- pp : PPoly Piecewise polynomial of order k2 = k + n representing the antiderivative of this polynomial. Notes ----- The antiderivative returned by this function is continuous and continuously differentiable to order n-1, up to floating point rounding error. If antiderivative is computed and self.extrapolate='periodic', it will be set to False for the returned instance. This is done because the antiderivative is no longer periodic and its correct evaluation outside of the initially given x interval is difficult. """ if nu <= 0: return self.derivative(-nu) c = np.zeros((self.c.shape[0] + nu, self.c.shape[1]) + self.c.shape[2:], dtype=self.c.dtype) c[:-nu] = self.c # divide by the correct rising factorials factor = spec.poch(np.arange(self.c.shape[0], 0, -1), nu) c[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)] # fix continuity of added degrees of freedom self._ensure_c_contiguous() _ppoly.fix_continuity(c.reshape(c.shape[0], c.shape[1], -1), self.x, nu - 1) if self.extrapolate == 'periodic': extrapolate = False else: extrapolate = self.extrapolate # construct a compatible polynomial return self.construct_fast(c, self.x, extrapolate, self.axis) def integrate(self, a, b, extrapolate=None): """ Compute a definite integral over a piecewise polynomial. Parameters ---------- a : float Lower integration bound b : float Upper integration bound extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), use self.extrapolate. Returns ------- ig : array_like Definite integral of the piecewise polynomial over [a, b] """ if extrapolate is None: extrapolate = self.extrapolate # Swap integration bounds if needed sign = 1 if b < a: a, b = b, a sign = -1 range_int = np.empty((prod(self.c.shape[2:]),), dtype=self.c.dtype) self._ensure_c_contiguous() # Compute the integral. if extrapolate == 'periodic': # Split the integral into the part over period (can be several # of them) and the remaining part. xs, xe = self.x[0], self.x[-1] period = xe - xs interval = b - a n_periods, left = divmod(interval, period) if n_periods > 0: _ppoly.integrate( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, xs, xe, False, out=range_int) range_int *= n_periods else: range_int.fill(0) # Map a to [xs, xe], b is always a + left. a = xs + (a - xs) % period b = a + left # If b <= xe then we need to integrate over [a, b], otherwise # over [a, xe] and from xs to what is remained. remainder_int = np.empty_like(range_int) if b <= xe: _ppoly.integrate( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, a, b, False, out=remainder_int) range_int += remainder_int else: _ppoly.integrate( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, a, xe, False, out=remainder_int) range_int += remainder_int _ppoly.integrate( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, xs, xs + left + a - xe, False, out=remainder_int) range_int += remainder_int else: _ppoly.integrate( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, a, b, bool(extrapolate), out=range_int) # Return range_int *= sign return range_int.reshape(self.c.shape[2:]) def solve(self, y=0., discontinuity=True, extrapolate=None): """ Find real solutions of the the equation pp(x) == y. Parameters ---------- y : float, optional Right-hand side. Default is zero. discontinuity : bool, optional Whether to report sign changes across discontinuities at breakpoints as roots. extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to return roots from the polynomial extrapolated based on first and last intervals, 'periodic' works the same as False. If None (default), use self.extrapolate. Returns ------- roots : ndarray Roots of the polynomial(s). If the PPoly object describes multiple polynomials, the return value is an object array whose each element is an ndarray containing the roots. Notes ----- This routine works only on real-valued polynomials. If the piecewise polynomial contains sections that are identically zero, the root list will contain the start point of the corresponding interval, followed by a nan value. If the polynomial is discontinuous across a breakpoint, and there is a sign change across the breakpoint, this is reported if the discont parameter is True. Examples -------- Finding roots of [x**2 - 1, (x - 1)**2] defined on intervals [-2, 1], [1, 2]: >>> from scipy.interpolate import PPoly >>> pp = PPoly(np.array([[1, -4, 3], [1, 0, 0]]).T, [-2, 1, 2]) >>> pp.roots() array([-1., 1.]) """ if extrapolate is None: extrapolate = self.extrapolate self._ensure_c_contiguous() if np.issubdtype(self.c.dtype, np.complexfloating): raise ValueError("Root finding is only for " "real-valued polynomials") y = float(y) r = _ppoly.real_roots(self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, y, bool(discontinuity), bool(extrapolate)) if self.c.ndim == 2: return r[0] else: r2 = np.empty(prod(self.c.shape[2:]), dtype=object) # this for-loop is equivalent to r2[...] = r, but that's broken # in numpy 1.6.0 for ii, root in enumerate(r): r2[ii] = root return r2.reshape(self.c.shape[2:]) def roots(self, discontinuity=True, extrapolate=None): """ Find real roots of the the piecewise polynomial. Parameters ---------- discontinuity : bool, optional Whether to report sign changes across discontinuities at breakpoints as roots. extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to return roots from the polynomial extrapolated based on first and last intervals, 'periodic' works the same as False. If None (default), use self.extrapolate. Returns ------- roots : ndarray Roots of the polynomial(s). If the PPoly object describes multiple polynomials, the return value is an object array whose each element is an ndarray containing the roots. See Also -------- PPoly.solve """ return self.solve(0, discontinuity, extrapolate) @classmethod def from_spline(cls, tck, extrapolate=None): """ Construct a piecewise polynomial from a spline Parameters ---------- tck A spline, as returned by splrep or a BSpline object. extrapolate : bool or 'periodic', optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. """ if isinstance(tck, BSpline): t, c, k = tck.tck if extrapolate is None: extrapolate = tck.extrapolate else: t, c, k = tck cvals = np.empty((k + 1, len(t)-1), dtype=c.dtype) for m in xrange(k, -1, -1): y = fitpack.splev(t[:-1], tck, der=m) cvals[k - m, :] = y/spec.gamma(m+1) return cls.construct_fast(cvals, t, extrapolate) @classmethod def from_bernstein_basis(cls, bp, extrapolate=None): """ Construct a piecewise polynomial in the power basis from a polynomial in Bernstein basis. Parameters ---------- bp : BPoly A Bernstein basis polynomial, as created by BPoly extrapolate : bool or 'periodic', optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. """ dx = np.diff(bp.x) k = bp.c.shape[0] - 1 # polynomial order rest = (None,)*(bp.c.ndim-2) c = np.zeros_like(bp.c) for a in range(k+1): factor = (-1)**a * comb(k, a) * bp.c[a] for s in range(a, k+1): val = comb(k-a, s-a) * (-1)**s c[k-s] += factor * val / dx[(slice(None),)+rest]**s if extrapolate is None: extrapolate = bp.extrapolate return cls.construct_fast(c, bp.x, extrapolate, bp.axis) class BPoly(_PPolyBase): """Piecewise polynomial in terms of coefficients and breakpoints. The polynomial between x[i] and x[i + 1] is written in the Bernstein polynomial basis:: S = sum(c[a, i] * b(a, k; x) for a in range(k+1)), where k is the degree of the polynomial, and:: b(a, k; x) = binom(k, a) * t**a * (1 - t)**(k - a), with t = (x - x[i]) / (x[i+1] - x[i]) and binom is the binomial coefficient. Parameters ---------- c : ndarray, shape (k, m, ...) Polynomial coefficients, order k and m intervals x : ndarray, shape (m+1,) Polynomial breakpoints. Must be sorted in either increasing or decreasing order. extrapolate : bool, optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. axis : int, optional Interpolation axis. Default is zero. Attributes ---------- x : ndarray Breakpoints. c : ndarray Coefficients of the polynomials. They are reshaped to a 3-dimensional array with the last dimension representing the trailing dimensions of the original coefficient array. axis : int Interpolation axis. Methods ------- __call__ extend derivative antiderivative integrate construct_fast from_power_basis from_derivatives See also -------- PPoly : piecewise polynomials in the power basis Notes ----- Properties of Bernstein polynomials are well documented in the literature. Here's a non-exhaustive list: .. [1] http://en.wikipedia.org/wiki/Bernstein_polynomial .. [2] Kenneth I. Joy, Bernstein polynomials, http://www.idav.ucdavis.edu/education/CAGDNotes/Bernstein-Polynomials.pdf .. [3] E. H. Doha, A. H. Bhrawy, and M. A. Saker, Boundary Value Problems, vol 2011, article ID 829546, :doi:10.1155/2011/829543. Examples -------- >>> from scipy.interpolate import BPoly >>> x = [0, 1] >>> c = [[1], [2], [3]] >>> bp = BPoly(c, x) This creates a 2nd order polynomial .. math:: B(x) = 1 \\times b_{0, 2}(x) + 2 \\times b_{1, 2}(x) + 3 \\times b_{2, 2}(x) \\\\ = 1 \\times (1-x)^2 + 2 \\times 2 x (1 - x) + 3 \\times x^2 """ def _evaluate(self, x, nu, extrapolate, out): _ppoly.evaluate_bernstein( self.c.reshape(self.c.shape[0], self.c.shape[1], -1), self.x, x, nu, bool(extrapolate), out) def derivative(self, nu=1): """ Construct a new piecewise polynomial representing the derivative. Parameters ---------- nu : int, optional Order of derivative to evaluate. Default is 1, i.e. compute the first derivative. If negative, the antiderivative is returned. Returns ------- bp : BPoly Piecewise polynomial of order k - nu representing the derivative of this polynomial. """ if nu < 0: return self.antiderivative(-nu) if nu > 1: bp = self for k in range(nu): bp = bp.derivative() return bp # reduce order if nu == 0: c2 = self.c.copy() else: # For a polynomial # B(x) = \sum_{a=0}^{k} c_a b_{a, k}(x), # we use the fact that # b'_{a, k} = k ( b_{a-1, k-1} - b_{a, k-1} ), # which leads to # B'(x) = \sum_{a=0}^{k-1} (c_{a+1} - c_a) b_{a, k-1} # # finally, for an interval [y, y + dy] with dy != 1, # we need to correct for an extra power of dy rest = (None,)*(self.c.ndim-2) k = self.c.shape[0] - 1 dx = np.diff(self.x)[(None, slice(None))+rest] c2 = k * np.diff(self.c, axis=0) / dx if c2.shape[0] == 0: # derivative of order 0 is zero c2 = np.zeros((1,) + c2.shape[1:], dtype=c2.dtype) # construct a compatible polynomial return self.construct_fast(c2, self.x, self.extrapolate, self.axis) def antiderivative(self, nu=1): """ Construct a new piecewise polynomial representing the antiderivative. Parameters ---------- nu : int, optional Order of antiderivative to evaluate. Default is 1, i.e. compute the first integral. If negative, the derivative is returned. Returns ------- bp : BPoly Piecewise polynomial of order k + nu representing the antiderivative of this polynomial. Notes ----- If antiderivative is computed and self.extrapolate='periodic', it will be set to False for the returned instance. This is done because the antiderivative is no longer periodic and its correct evaluation outside of the initially given x interval is difficult. """ if nu <= 0: return self.derivative(-nu) if nu > 1: bp = self for k in range(nu): bp = bp.antiderivative() return bp # Construct the indefinite integrals on individual intervals c, x = self.c, self.x k = c.shape[0] c2 = np.zeros((k+1,) + c.shape[1:], dtype=c.dtype) c2[1:, ...] = np.cumsum(c, axis=0) / k delta = x[1:] - x[:-1] c2 *= delta[(None, slice(None)) + (None,)*(c.ndim-2)] # Now fix continuity: on the very first interval, take the integration # constant to be zero; on an interval [x_j, x_{j+1}) with j>0, # the integration constant is then equal to the jump of the bp at x_j. # The latter is given by the coefficient of B_{n+1, n+1} # *on the previous interval* (other B. polynomials are zero at the # breakpoint). Finally, use the fact that BPs form a partition of unity. c2[:,1:] += np.cumsum(c2[k, :], axis=0)[:-1] if self.extrapolate == 'periodic': extrapolate = False else: extrapolate = self.extrapolate return self.construct_fast(c2, x, extrapolate, axis=self.axis) def integrate(self, a, b, extrapolate=None): """ Compute a definite integral over a piecewise polynomial. Parameters ---------- a : float Lower integration bound b : float Upper integration bound extrapolate : {bool, 'periodic', None}, optional Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), use self.extrapolate. Returns ------- array_like Definite integral of the piecewise polynomial over [a, b] """ # XXX: can probably use instead the fact that # \int_0^{1} B_{j, n}(x) \dx = 1/(n+1) ib = self.antiderivative() if extrapolate is None: extrapolate = self.extrapolate # ib.extrapolate shouldn't be 'periodic', it is converted to # False for 'periodic. in antiderivative() call. if extrapolate != 'periodic': ib.extrapolate = extrapolate if extrapolate == 'periodic': # Split the integral into the part over period (can be several # of them) and the remaining part. # For simplicity and clarity convert to a <= b case. if a <= b: sign = 1 else: a, b = b, a sign = -1 xs, xe = self.x[0], self.x[-1] period = xe - xs interval = b - a n_periods, left = divmod(interval, period) res = n_periods * (ib(xe) - ib(xs)) # Map a and b to [xs, xe]. a = xs + (a - xs) % period b = a + left # If b <= xe then we need to integrate over [a, b], otherwise # over [a, xe] and from xs to what is remained. if b <= xe: res += ib(b) - ib(a) else: res += ib(xe) - ib(a) + ib(xs + left + a - xe) - ib(xs) return sign * res else: return ib(b) - ib(a) def extend(self, c, x, right=None): k = max(self.c.shape[0], c.shape[0]) self.c = self._raise_degree(self.c, k - self.c.shape[0]) c = self._raise_degree(c, k - c.shape[0]) return _PPolyBase.extend(self, c, x, right) extend.__doc__ = _PPolyBase.extend.__doc__ @classmethod def from_power_basis(cls, pp, extrapolate=None): """ Construct a piecewise polynomial in Bernstein basis from a power basis polynomial. Parameters ---------- pp : PPoly A piecewise polynomial in the power basis extrapolate : bool or 'periodic', optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. """ dx = np.diff(pp.x) k = pp.c.shape[0] - 1 # polynomial order rest = (None,)*(pp.c.ndim-2) c = np.zeros_like(pp.c) for a in range(k+1): factor = pp.c[a] / comb(k, k-a) * dx[(slice(None),)+rest]**(k-a) for j in range(k-a, k+1): c[j] += factor * comb(j, k-a) if extrapolate is None: extrapolate = pp.extrapolate return cls.construct_fast(c, pp.x, extrapolate, pp.axis) @classmethod def from_derivatives(cls, xi, yi, orders=None, extrapolate=None): """Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array_like sorted 1D array of x-coordinates yi : array_like or list of array_likes yi[i][j] is the j-th derivative known at xi[i] orders : None or int or array_like of ints. Default: None. Specifies the degree of local polynomials. If not None, some derivatives are ignored. extrapolate : bool or 'periodic', optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. Default is True. Notes ----- If k derivatives are specified at a breakpoint x, the constructed polynomial is exactly k times continuously differentiable at x, unless the order is provided explicitly. In the latter case, the smoothness of the polynomial at the breakpoint is controlled by the order. Deduces the number of derivatives to match at each end from order and the number of derivatives available. If possible it uses the same number of derivatives from each end; if the number is odd it tries to take the extra one from y2. In any case if not enough derivatives are available at one end or another it draws enough to make up the total from the other end. If the order is too high and not enough derivatives are available, an exception is raised. Examples -------- >>> from scipy.interpolate import BPoly >>> BPoly.from_derivatives([0, 1], [[1, 2], [3, 4]]) Creates a polynomial f(x) of degree 3, defined on [0, 1] such that f(0) = 1, df/dx(0) = 2, f(1) = 3, df/dx(1) = 4 >>> BPoly.from_derivatives([0, 1, 2], [[0, 1], [0], [2]]) Creates a piecewise polynomial f(x), such that f(0) = f(1) = 0, f(2) = 2, and df/dx(0) = 1. Based on the number of derivatives provided, the order of the local polynomials is 2 on [0, 1] and 1 on [1, 2]. Notice that no restriction is imposed on the derivatives at x = 1 and x = 2. Indeed, the explicit form of the polynomial is:: f(x) = | x * (1 - x), 0 <= x < 1 | 2 * (x - 1), 1 <= x <= 2 So that f'(1-0) = -1 and f'(1+0) = 2 """ xi = np.asarray(xi) if len(xi) != len(yi): raise ValueError("xi and yi need to have the same length") if np.any(xi[1:] - xi[:1] <= 0): raise ValueError("x coordinates are not in increasing order") # number of intervals m = len(xi) - 1 # global poly order is k-1, local orders are <=k and can vary try: k = max(len(yi[i]) + len(yi[i+1]) for i in range(m)) except TypeError: raise ValueError("Using a 1D array for y? Please .reshape(-1, 1).") if orders is None: orders = [None] * m else: if isinstance(orders, (integer_types, np.integer)): orders = [orders] * m k = max(k, max(orders)) if any(o <= 0 for o in orders): raise ValueError("Orders must be positive.") c = [] for i in range(m): y1, y2 = yi[i], yi[i+1] if orders[i] is None: n1, n2 = len(y1), len(y2) else: n = orders[i]+1 n1 = min(n//2, len(y1)) n2 = min(n - n1, len(y2)) n1 = min(n - n2, len(y2)) if n1+n2 != n: mesg = ("Point %g has %d derivatives, point %g" " has %d derivatives, but order %d requested" % ( xi[i], len(y1), xi[i+1], len(y2), orders[i])) raise ValueError(mesg) if not (n1 <= len(y1) and n2 <= len(y2)): raise ValueError("order input incompatible with" " length y1 or y2.") b = BPoly._construct_from_derivatives(xi[i], xi[i+1], y1[:n1], y2[:n2]) if len(b) < k: b = BPoly._raise_degree(b, k - len(b)) c.append(b) c = np.asarray(c) return cls(c.swapaxes(0, 1), xi, extrapolate) @staticmethod def _construct_from_derivatives(xa, xb, ya, yb): r"""Compute the coefficients of a polynomial in the Bernstein basis given the values and derivatives at the edges. Return the coefficients of a polynomial in the Bernstein basis defined on [xa, xb] and having the values and derivatives at the endpoints xa and xb as specified by ya and yb. The polynomial constructed is of the minimal possible degree, i.e., if the lengths of ya and yb are na and nb, the degree of the polynomial is na + nb - 1. Parameters ---------- xa : float Left-hand end point of the interval xb : float Right-hand end point of the interval ya : array_like Derivatives at xa. ya[0] is the value of the function, and ya[i] for i > 0 is the value of the i-th derivative. yb : array_like Derivatives at xb. Returns ------- array coefficient array of a polynomial having specified derivatives Notes ----- This uses several facts from life of Bernstein basis functions. First of all, .. math:: b'_{a, n} = n (b_{a-1, n-1} - b_{a, n-1}) If B(x) is a linear combination of the form .. math:: B(x) = \sum_{a=0}^{n} c_a b_{a, n}, then :math: B'(x) = n \sum_{a=0}^{n-1} (c_{a+1} - c_{a}) b_{a, n-1}. Iterating the latter one, one finds for the q-th derivative .. math:: B^{q}(x) = n!/(n-q)! \sum_{a=0}^{n-q} Q_a b_{a, n-q}, with .. math:: Q_a = \sum_{j=0}^{q} (-)^{j+q} comb(q, j) c_{j+a} This way, only a=0 contributes to :math: B^{q}(x = xa), and c_q are found one by one by iterating q = 0, ..., na. At x = xb it's the same with a = n - q. """ ya, yb = np.asarray(ya), np.asarray(yb) if ya.shape[1:] != yb.shape[1:]: raise ValueError('ya and yb have incompatible dimensions.') dta, dtb = ya.dtype, yb.dtype if (np.issubdtype(dta, np.complexfloating) or np.issubdtype(dtb, np.complexfloating)): dt = np.complex_ else: dt = np.float_ na, nb = len(ya), len(yb) n = na + nb c = np.empty((na+nb,) + ya.shape[1:], dtype=dt) # compute coefficients of a polynomial degree na+nb-1 # walk left-to-right for q in range(0, na): c[q] = ya[q] / spec.poch(n - q, q) * (xb - xa)**q for j in range(0, q): c[q] -= (-1)**(j+q) * comb(q, j) * c[j] # now walk right-to-left for q in range(0, nb): c[-q-1] = yb[q] / spec.poch(n - q, q) * (-1)**q * (xb - xa)**q for j in range(0, q): c[-q-1] -= (-1)**(j+1) * comb(q, j+1) * c[-q+j] return c @staticmethod def _raise_degree(c, d): r"""Raise a degree of a polynomial in the Bernstein basis. Given the coefficients of a polynomial degree k, return (the coefficients of) the equivalent polynomial of degree k+d. Parameters ---------- c : array_like coefficient array, 1D d : integer Returns ------- array coefficient array, 1D array of length c.shape[0] + d Notes ----- This uses the fact that a Bernstein polynomial b_{a, k} can be identically represented as a linear combination of polynomials of a higher degree k+d: .. math:: b_{a, k} = comb(k, a) \sum_{j=0}^{d} b_{a+j, k+d} \ comb(d, j) / comb(k+d, a+j) """ if d == 0: return c k = c.shape[0] - 1 out = np.zeros((c.shape[0] + d,) + c.shape[1:], dtype=c.dtype) for a in range(c.shape[0]): f = c[a] * comb(k, a) for j in range(d+1): out[a+j] += f * comb(d, j) / comb(k+d, a+j) return out class NdPPoly(object): """ Piecewise tensor product polynomial The value at point xp = (x', y', z', ...) is evaluated by first computing the interval indices i such that:: x[0][i[0]] <= x' < x[0][i[0]+1] x[1][i[1]] <= y' < x[1][i[1]+1] ... and then computing:: S = sum(c[k0-m0-1,...,kn-mn-1,i[0],...,i[n]] * (xp[0] - x[0][i[0]])**m0 * ... * (xp[n] - x[n][i[n]])**mn for m0 in range(k[0]+1) ... for mn in range(k[n]+1)) where k[j] is the degree of the polynomial in dimension j. This representation is the piecewise multivariate power basis. Parameters ---------- c : ndarray, shape (k0, ..., kn, m0, ..., mn, ...) Polynomial coefficients, with polynomial order kj and mj+1 intervals for each dimension j. x : ndim-tuple of ndarrays, shapes (mj+1,) Polynomial breakpoints for each dimension. These must be sorted in increasing order. extrapolate : bool, optional Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. Default: True. Attributes ---------- x : tuple of ndarrays Breakpoints. c : ndarray Coefficients of the polynomials. Methods ------- __call__ construct_fast See also -------- PPoly : piecewise polynomials in 1D Notes ----- High-order polynomials in the power basis can be numerically unstable. """ def __init__(self, c, x, extrapolate=None): self.x = tuple(np.ascontiguousarray(v, dtype=np.float64) for v in x) self.c = np.asarray(c) if extrapolate is None: extrapolate = True self.extrapolate = bool(extrapolate) ndim = len(self.x) if any(v.ndim != 1 for v in self.x): raise ValueError("x arrays must all be 1-dimensional") if any(v.size < 2 for v in self.x): raise ValueError("x arrays must all contain at least 2 points") if c.ndim < 2*ndim: raise ValueError("c must have at least 2*len(x) dimensions") if any(np.any(v[1:] - v[:-1] < 0) for v in self.x): raise ValueError("x-coordinates are not in increasing order") if any(a != b.size - 1 for a, b in zip(c.shape[ndim:2*ndim], self.x)): raise ValueError("x and c do not agree on the number of intervals") dtype = self._get_dtype(self.c.dtype) self.c = np.ascontiguousarray(self.c, dtype=dtype) @classmethod def construct_fast(cls, c, x, extrapolate=None): """ Construct the piecewise polynomial without making checks. Takes the same parameters as the constructor. Input arguments c and x must be arrays of the correct shape and type. The c array can only be of dtypes float and complex, and x array must have dtype float. """ self = object.__new__(cls) self.c = c self.x = x if extrapolate is None: extrapolate = True self.extrapolate = extrapolate return self def _get_dtype(self, dtype): if np.issubdtype(dtype, np.complexfloating) \ or np.issubdtype(self.c.dtype, np.complexfloating): return np.complex_ else: return np.float_ def _ensure_c_contiguous(self): if not self.c.flags.c_contiguous: self.c = self.c.copy() if not isinstance(self.x, tuple): self.x = tuple(self.x) def __call__(self, x, nu=None, extrapolate=None): """ Evaluate the piecewise polynomial or its derivative Parameters ---------- x : array-like Points to evaluate the interpolant at. nu : tuple, optional Orders of derivatives to evaluate. Each must be non-negative. extrapolate : bool, optional Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. Returns ------- y : array-like Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of x. Notes ----- Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals are considered half-open, [a, b), except for the last interval which is closed [a, b]. """ if extrapolate is None: extrapolate = self.extrapolate else: extrapolate = bool(extrapolate) ndim = len(self.x) x = _ndim_coords_from_arrays(x) x_shape = x.shape x = np.ascontiguousarray(x.reshape(-1, x.shape[-1]), dtype=np.float_) if nu is None: nu = np.zeros((ndim,), dtype=np.intc) else: nu = np.asarray(nu, dtype=np.intc) if nu.ndim != 1 or nu.shape[0] != ndim: raise ValueError("invalid number of derivative orders nu") dim1 = prod(self.c.shape[:ndim]) dim2 = prod(self.c.shape[ndim:2*ndim]) dim3 = prod(self.c.shape[2*ndim:]) ks = np.array(self.c.shape[:ndim], dtype=np.intc) out = np.empty((x.shape[0], dim3), dtype=self.c.dtype) self._ensure_c_contiguous() _ppoly.evaluate_nd(self.c.reshape(dim1, dim2, dim3), self.x, ks, x, nu, bool(extrapolate), out) return out.reshape(x_shape[:-1] + self.c.shape[2*ndim:]) def _derivative_inplace(self, nu, axis): """ Compute 1D derivative along a selected dimension in-place May result to non-contiguous c array. """ if nu < 0: return self._antiderivative_inplace(-nu, axis) ndim = len(self.x) axis = axis % ndim # reduce order if nu == 0: # noop return else: sl = [slice(None)]*ndim sl[axis] = slice(None, -nu, None) c2 = self.c[sl] if c2.shape[axis] == 0: # derivative of order 0 is zero shp = list(c2.shape) shp[axis] = 1 c2 = np.zeros(shp, dtype=c2.dtype) # multiply by the correct rising factorials factor = spec.poch(np.arange(c2.shape[axis], 0, -1), nu) sl = [None]*c2.ndim sl[axis] = slice(None) c2 *= factor[sl] self.c = c2 def _antiderivative_inplace(self, nu, axis): """ Compute 1D antiderivative along a selected dimension May result to non-contiguous c array. """ if nu <= 0: return self._derivative_inplace(-nu, axis) ndim = len(self.x) axis = axis % ndim perm = list(range(ndim)) perm[0], perm[axis] = perm[axis], perm[0] perm = perm + list(range(ndim, self.c.ndim)) c = self.c.transpose(perm) c2 = np.zeros((c.shape[0] + nu,) + c.shape[1:], dtype=c.dtype) c2[:-nu] = c # divide by the correct rising factorials factor = spec.poch(np.arange(c.shape[0], 0, -1), nu) c2[:-nu] /= factor[(slice(None),) + (None,)*(c.ndim-1)] # fix continuity of added degrees of freedom perm2 = list(range(c2.ndim)) perm2[1], perm2[ndim+axis] = perm2[ndim+axis], perm2[1] c2 = c2.transpose(perm2) c2 = c2.copy() _ppoly.fix_continuity(c2.reshape(c2.shape[0], c2.shape[1], -1), self.x[axis], nu-1) c2 = c2.transpose(perm2) c2 = c2.transpose(perm) # Done self.c = c2 def derivative(self, nu): """ Construct a new piecewise polynomial representing the derivative. Parameters ---------- nu : ndim-tuple of int Order of derivatives to evaluate for each dimension. If negative, the antiderivative is returned. Returns ------- pp : NdPPoly Piecewise polynomial of orders (k[0] - nu[0], ..., k[n] - nu[n]) representing the derivative of this polynomial. Notes ----- Derivatives are evaluated piecewise for each polynomial segment, even if the polynomial is not differentiable at the breakpoints. The polynomial intervals in each dimension are considered half-open, [a, b), except for the last interval which is closed [a, b]. """ p = self.construct_fast(self.c.copy(), self.x, self.extrapolate) for axis, n in enumerate(nu): p._derivative_inplace(n, axis) p._ensure_c_contiguous() return p def antiderivative(self, nu): """ Construct a new piecewise polynomial representing the antiderivative. Antiderivative is also the indefinite integral of the function, and derivative is its inverse operation. Parameters ---------- nu : ndim-tuple of int Order of derivatives to evaluate for each dimension. If negative, the derivative is returned. Returns ------- pp : PPoly Piecewise polynomial of order k2 = k + n representing the antiderivative of this polynomial. Notes ----- The antiderivative returned by this function is continuous and continuously differentiable to order n-1, up to floating point rounding error. """ p = self.construct_fast(self.c.copy(), self.x, self.extrapolate) for axis, n in enumerate(nu): p._antiderivative_inplace(n, axis) p._ensure_c_contiguous() return p def integrate_1d(self, a, b, axis, extrapolate=None): r""" Compute NdPPoly representation for one dimensional definite integral The result is a piecewise polynomial representing the integral: .. math:: p(y, z, ...) = \int_a^b dx\, p(x, y, z, ...) where the dimension integrated over is specified with the axis parameter. Parameters ---------- a, b : float Lower and upper bound for integration. axis : int Dimension over which to compute the 1D integrals extrapolate : bool, optional Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. Returns ------- ig : NdPPoly or array-like Definite integral of the piecewise polynomial over [a, b]. If the polynomial was 1-dimensional, an array is returned, otherwise, an NdPPoly object. """ if extrapolate is None: extrapolate = self.extrapolate else: extrapolate = bool(extrapolate) ndim = len(self.x) axis = int(axis) % ndim # reuse 1D integration routines c = self.c swap = list(range(c.ndim)) swap.insert(0, swap[axis]) del swap[axis + 1] swap.insert(1, swap[ndim + axis]) del swap[ndim + axis + 1] c = c.transpose(swap) p = PPoly.construct_fast(c.reshape(c.shape[0], c.shape[1], -1), self.x[axis], extrapolate=extrapolate) out = p.integrate(a, b, extrapolate=extrapolate) # Construct result if ndim == 1: return out.reshape(c.shape[2:]) else: c = out.reshape(c.shape[2:]) x = self.x[:axis] + self.x[axis+1:] return self.construct_fast(c, x, extrapolate=extrapolate) def integrate(self, ranges, extrapolate=None): """ Compute a definite integral over a piecewise polynomial. Parameters ---------- ranges : ndim-tuple of 2-tuples float Sequence of lower and upper bounds for each dimension, [(a[0], b[0]), ..., (a[ndim-1], b[ndim-1])] extrapolate : bool, optional Whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. Returns ------- ig : array_like Definite integral of the piecewise polynomial over [a[0], b[0]] x ... x [a[ndim-1], b[ndim-1]] """ ndim = len(self.x) if extrapolate is None: extrapolate = self.extrapolate else: extrapolate = bool(extrapolate) if not hasattr(ranges, '__len__') or len(ranges) != ndim: raise ValueError("Range not a sequence of correct length") self._ensure_c_contiguous() # Reuse 1D integration routine c = self.c for n, (a, b) in enumerate(ranges): swap = list(range(c.ndim)) swap.insert(1, swap[ndim - n]) del swap[ndim - n + 1] c = c.transpose(swap) p = PPoly.construct_fast(c, self.x[n], extrapolate=extrapolate) out = p.integrate(a, b, extrapolate=extrapolate) c = out.reshape(c.shape[2:]) return c class RegularGridInterpolator(object): """ Interpolation on a regular grid in arbitrary dimensions The data must be defined on a regular grid; the grid spacing however may be uneven. Linear and nearest-neighbour interpolation are supported. After setting up the interpolator object, the interpolation method (*linear* or *nearest*) may be chosen at each evaluation. Parameters ---------- points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ) The points defining the regular grid in n dimensions. values : array_like, shape (m1, ..., mn, ...) The data on the regular grid in n dimensions. method : str, optional The method of interpolation to perform. Supported are "linear" and "nearest". This parameter will become the default for the object's __call__ method. Default is "linear". bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then fill_value is used. fill_value : number, optional If provided, the value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Methods ------- __call__ Notes ----- Contrary to LinearNDInterpolator and NearestNDInterpolator, this class avoids expensive triangulation of the input data by taking advantage of the regular grid structure. If any of points have a dimension of size 1, linear interpolation will return an array of nan values. Nearest-neighbor interpolation will work as usual in this case. .. versionadded:: 0.14 Examples -------- Evaluate a simple example function on the points of a 3D grid: >>> from scipy.interpolate import RegularGridInterpolator >>> def f(x, y, z): ... return 2 * x**3 + 3 * y**2 - z >>> x = np.linspace(1, 4, 11) >>> y = np.linspace(4, 7, 22) >>> z = np.linspace(7, 9, 33) >>> data = f(*np.meshgrid(x, y, z, indexing='ij', sparse=True)) data is now a 3D array with data[i,j,k] = f(x[i], y[j], z[k]). Next, define an interpolating function from this data: >>> my_interpolating_function = RegularGridInterpolator((x, y, z), data) Evaluate the interpolating function at the two points (x,y,z) = (2.1, 6.2, 8.3) and (3.3, 5.2, 7.1): >>> pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]]) >>> my_interpolating_function(pts) array([ 125.80469388, 146.30069388]) which is indeed a close approximation to [f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)]. See also -------- NearestNDInterpolator : Nearest neighbour interpolation on unstructured data in N dimensions LinearNDInterpolator : Piecewise linear interpolant on unstructured data in N dimensions References ---------- .. [1] Python package *regulargrid* by Johannes Buchner, see https://pypi.python.org/pypi/regulargrid/ .. [2] Trilinear interpolation. (2013, January 17). In Wikipedia, The Free Encyclopedia. Retrieved 27 Feb 2013 01:28. http://en.wikipedia.org/w/index.php?title=Trilinear_interpolation&oldid=533448871 .. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear and multilinear table interpolation in many dimensions." MATH. COMPUT. 50.181 (1988): 189-196. http://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf """ # this class is based on code originally programmed by Johannes Buchner, # see https://github.com/JohannesBuchner/regulargrid def __init__(self, points, values, method="linear", bounds_error=True, fill_value=np.nan): if method not in ["linear", "nearest"]: raise ValueError("Method '%s' is not defined" % method) self.method = method self.bounds_error = bounds_error if not hasattr(values, 'ndim'): # allow reasonable duck-typed values values = np.asarray(values) if len(points) > values.ndim: raise ValueError("There are %d point arrays, but values has %d " "dimensions" % (len(points), values.ndim)) if hasattr(values, 'dtype') and hasattr(values, 'astype'): if not np.issubdtype(values.dtype, np.inexact): values = values.astype(float) self.fill_value = fill_value if fill_value is not None: fill_value_dtype = np.asarray(fill_value).dtype if (hasattr(values, 'dtype') and not np.can_cast(fill_value_dtype, values.dtype, casting='same_kind')): raise ValueError("fill_value must be either 'None' or " "of a type compatible with values") for i, p in enumerate(points): if not np.all(np.diff(p) > 0.): raise ValueError("The points in dimension %d must be strictly " "ascending" % i) if not np.asarray(p).ndim == 1: raise ValueError("The points in dimension %d must be " "1-dimensional" % i) if not values.shape[i] == len(p): raise ValueError("There are %d points and %d values in " "dimension %d" % (len(p), values.shape[i], i)) self.grid = tuple([np.asarray(p) for p in points]) self.values = values def __call__(self, xi, method=None): """ Interpolation at coordinates Parameters ---------- xi : ndarray of shape (..., ndim) The coordinates to sample the gridded data at method : str The method of interpolation to perform. Supported are "linear" and "nearest". """ method = self.method if method is None else method if method not in ["linear", "nearest"]: raise ValueError("Method '%s' is not defined" % method) ndim = len(self.grid) xi = _ndim_coords_from_arrays(xi, ndim=ndim) if xi.shape[-1] != len(self.grid): raise ValueError("The requested sample points xi have dimension " "%d, but this RegularGridInterpolator has " "dimension %d" % (xi.shape[1], ndim)) xi_shape = xi.shape xi = xi.reshape(-1, xi_shape[-1]) if self.bounds_error: for i, p in enumerate(xi.T): if not np.logical_and(np.all(self.grid[i][0] <= p), np.all(p <= self.grid[i][-1])): raise ValueError("One of the requested xi is out of bounds " "in dimension %d" % i) indices, norm_distances, out_of_bounds = self._find_indices(xi.T) if method == "linear": result = self._evaluate_linear(indices, norm_distances, out_of_bounds) elif method == "nearest": result = self._evaluate_nearest(indices, norm_distances, out_of_bounds) if not self.bounds_error and self.fill_value is not None: result[out_of_bounds] = self.fill_value return result.reshape(xi_shape[:-1] + self.values.shape[ndim:]) def _evaluate_linear(self, indices, norm_distances, out_of_bounds): # slice for broadcasting over trailing dimensions in self.values vslice = (slice(None),) + (None,)*(self.values.ndim - len(indices)) # find relevant values # each i and i+1 represents a edge edges = itertools.product(*[[i, i + 1] for i in indices]) values = 0. for edge_indices in edges: weight = 1. for ei, i, yi in zip(edge_indices, indices, norm_distances): weight *= np.where(ei == i, 1 - yi, yi) values += np.asarray(self.values[edge_indices]) * weight[vslice] return values def _evaluate_nearest(self, indices, norm_distances, out_of_bounds): idx_res = [] for i, yi in zip(indices, norm_distances): idx_res.append(np.where(yi <= .5, i, i + 1)) return self.values[idx_res] def _find_indices(self, xi): # find relevant edges between which xi are situated indices = [] # compute distance to lower edge in unity units norm_distances = [] # check for out of bounds xi out_of_bounds = np.zeros((xi.shape[1]), dtype=bool) # iterate through dimensions for x, grid in zip(xi, self.grid): i = np.searchsorted(grid, x) - 1 i[i < 0] = 0 i[i > grid.size - 2] = grid.size - 2 indices.append(i) norm_distances.append((x - grid[i]) / (grid[i + 1] - grid[i])) if not self.bounds_error: out_of_bounds += x < grid[0] out_of_bounds += x > grid[-1] return indices, norm_distances, out_of_bounds def interpn(points, values, xi, method="linear", bounds_error=True, fill_value=np.nan): """ Multidimensional interpolation on regular grids. Parameters ---------- points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ) The points defining the regular grid in n dimensions. values : array_like, shape (m1, ..., mn, ...) The data on the regular grid in n dimensions. xi : ndarray of shape (..., ndim) The coordinates to sample the gridded data at method : str, optional The method of interpolation to perform. Supported are "linear" and "nearest", and "splinef2d". "splinef2d" is only supported for 2-dimensional data. bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then fill_value is used. fill_value : number, optional If provided, the value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Extrapolation is not supported by method "splinef2d". Returns ------- values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:] Interpolated values at input coordinates. Notes ----- .. versionadded:: 0.14 See also -------- NearestNDInterpolator : Nearest neighbour interpolation on unstructured data in N dimensions LinearNDInterpolator : Piecewise linear interpolant on unstructured data in N dimensions RegularGridInterpolator : Linear and nearest-neighbor Interpolation on a regular grid in arbitrary dimensions RectBivariateSpline : Bivariate spline approximation over a rectangular mesh """ # sanity check 'method' kwarg if method not in ["linear", "nearest", "splinef2d"]: raise ValueError("interpn only understands the methods 'linear', " "'nearest', and 'splinef2d'. You provided %s." % method) if not hasattr(values, 'ndim'): values = np.asarray(values) ndim = values.ndim if ndim > 2 and method == "splinef2d": raise ValueError("The method spline2fd can only be used for " "2-dimensional input data") if not bounds_error and fill_value is None and method == "splinef2d": raise ValueError("The method spline2fd does not support extrapolation.") # sanity check consistency of input dimensions if len(points) > ndim: raise ValueError("There are %d point arrays, but values has %d " "dimensions" % (len(points), ndim)) if len(points) != ndim and method == 'splinef2d': raise ValueError("The method spline2fd can only be used for " "scalar data with one point per coordinate") # sanity check input grid for i, p in enumerate(points): if not np.all(np.diff(p) > 0.): raise ValueError("The points in dimension %d must be strictly " "ascending" % i) if not np.asarray(p).ndim == 1: raise ValueError("The points in dimension %d must be " "1-dimensional" % i) if not values.shape[i] == len(p): raise ValueError("There are %d points and %d values in " "dimension %d" % (len(p), values.shape[i], i)) grid = tuple([np.asarray(p) for p in points]) # sanity check requested xi xi = _ndim_coords_from_arrays(xi, ndim=len(grid)) if xi.shape[-1] != len(grid): raise ValueError("The requested sample points xi have dimension " "%d, but this RegularGridInterpolator has " "dimension %d" % (xi.shape[1], len(grid))) for i, p in enumerate(xi.T): if bounds_error and not np.logical_and(np.all(grid[i][0] <= p), np.all(p <= grid[i][-1])): raise ValueError("One of the requested xi is out of bounds " "in dimension %d" % i) # perform interpolation if method == "linear": interp = RegularGridInterpolator(points, values, method="linear", bounds_error=bounds_error, fill_value=fill_value) return interp(xi) elif method == "nearest": interp = RegularGridInterpolator(points, values, method="nearest", bounds_error=bounds_error, fill_value=fill_value) return interp(xi) elif method == "splinef2d": xi_shape = xi.shape xi = xi.reshape(-1, xi.shape[-1]) # RectBivariateSpline doesn't support fill_value; we need to wrap here idx_valid = np.all((grid[0][0] <= xi[:, 0], xi[:, 0] <= grid[0][-1], grid[1][0] <= xi[:, 1], xi[:, 1] <= grid[1][-1]), axis=0) result = np.empty_like(xi[:, 0]) # make a copy of values for RectBivariateSpline interp = RectBivariateSpline(points[0], points[1], values[:]) result[idx_valid] = interp.ev(xi[idx_valid, 0], xi[idx_valid, 1]) result[np.logical_not(idx_valid)] = fill_value return result.reshape(xi_shape[:-1]) # backward compatibility wrapper class _ppform(PPoly): """ Deprecated piecewise polynomial class. New code should use the PPoly class instead. """ def __init__(self, coeffs, breaks, fill=0.0, sort=False): warnings.warn("_ppform is deprecated -- use PPoly instead", category=DeprecationWarning) if sort: breaks = np.sort(breaks) else: breaks = np.asarray(breaks) PPoly.__init__(self, coeffs, breaks) self.coeffs = self.c self.breaks = self.x self.K = self.coeffs.shape[0] self.fill = fill self.a = self.breaks[0] self.b = self.breaks[-1] def __call__(self, x): return PPoly.__call__(self, x, 0, False) def _evaluate(self, x, nu, extrapolate, out): PPoly._evaluate(self, x, nu, extrapolate, out) out[~((x >= self.a) & (x <= self.b))] = self.fill return out @classmethod def fromspline(cls, xk, cvals, order, fill=0.0): # Note: this spline representation is incompatible with FITPACK N = len(xk)-1 sivals = np.empty((order+1, N), dtype=float) for m in xrange(order, -1, -1): fact = spec.gamma(m+1) res = _fitpack._bspleval(xk[:-1], xk, cvals, order, m) res /= fact sivals[order-m, :] = res return cls(sivals, xk, fill=fill) # The 3 private functions below can be called by splmake(). def _dot0(a, b): """Similar to numpy.dot, but sum over last axis of a and 1st axis of b""" if b.ndim <= 2: return dot(a, b) else: axes = list(range(b.ndim)) axes.insert(-1, 0) axes.pop(0) return dot(a, b.transpose(axes)) def _find_smoothest(xk, yk, order, conds=None, B=None): # construct Bmatrix, and Jmatrix # e = J*c # minimize norm(e,2) given B*c=yk # if desired B can be given # conds is ignored N = len(xk)-1 K = order if B is None: B = _fitpack._bsplmat(order, xk) J = _fitpack._bspldismat(order, xk) u, s, vh = scipy.linalg.svd(B) ind = K-1 V2 = vh[-ind:,:].T V1 = vh[:-ind,:].T A = dot(J.T,J) tmp = dot(V2.T,A) Q = dot(tmp,V2) p = scipy.linalg.solve(Q, tmp) tmp = dot(V2,p) tmp = np.eye(N+K) - tmp tmp = dot(tmp,V1) tmp = dot(tmp,np.diag(1.0/s)) tmp = dot(tmp,u.T) return _dot0(tmp, yk) # conds is a tuple of an array and a vector # giving the left-hand and the right-hand side # of the additional equations to add to B def _find_user(xk, yk, order, conds, B): lh = conds[0] rh = conds[1] B = np.concatenate((B, lh), axis=0) w = np.concatenate((yk, rh), axis=0) M, N = B.shape if (M > N): raise ValueError("over-specification of conditions") elif (M < N): return _find_smoothest(xk, yk, order, None, B) else: return scipy.linalg.solve(B, w) # Remove the 3 private functions above as well when removing splmake @np.deprecate(message="splmake is deprecated in scipy 0.19.0, " "use make_interp_spline instead.") def splmake(xk, yk, order=3, kind='smoothest', conds=None): """ Return a representation of a spline given data-points at internal knots Parameters ---------- xk : array_like The input array of x values of rank 1 yk : array_like The input array of y values of rank N. yk can be an N-d array to represent more than one curve, through the same xk points. The first dimension is assumed to be the interpolating dimension and is the same length of xk. order : int, optional Order of the spline kind : str, optional Can be 'smoothest', 'not_a_knot', 'fixed', 'clamped', 'natural', 'periodic', 'symmetric', 'user', 'mixed' and it is ignored if order < 2 conds : optional Conds Returns ------- splmake : tuple Return a (xk, cvals, k) representation of a spline given data-points where the (internal) knots are at the data-points. """ yk = np.asanyarray(yk) order = int(order) if order < 0: raise ValueError("order must not be negative") if order == 0: return xk, yk[:-1], order elif order == 1: return xk, yk, order try: func = eval('_find_%s' % kind) except: raise NotImplementedError # the constraint matrix B = _fitpack._bsplmat(order, xk) coefs = func(xk, yk, order, conds, B) return xk, coefs, order @np.deprecate(message="spleval is deprecated in scipy 0.19.0, " "use BSpline instead.") def spleval(xck, xnew, deriv=0): """ Evaluate a fixed spline represented by the given tuple at the new x-values The xj values are the interior knot points. The approximation region is xj[0] to xj[-1]. If N+1 is the length of xj, then cvals should have length N+k where k is the order of the spline. Parameters ---------- (xj, cvals, k) : tuple Parameters that define the fixed spline xj : array_like Interior knot points cvals : array_like Curvature k : int Order of the spline xnew : array_like Locations to calculate spline deriv : int Deriv Returns ------- spleval : ndarray If cvals represents more than one curve (cvals.ndim > 1) and/or xnew is N-d, then the result is xnew.shape + cvals.shape[1:] providing the interpolation of multiple curves. Notes ----- Internally, an additional k-1 knot points are added on either side of the spline. """ (xj, cvals, k) = xck oldshape = np.shape(xnew) xx = np.ravel(xnew) sh = cvals.shape[1:] res = np.empty(xx.shape + sh, dtype=cvals.dtype) for index in np.ndindex(*sh): sl = (slice(None),) + index if issubclass(cvals.dtype.type, np.complexfloating): res[sl].real = _fitpack._bspleval(xx,xj, cvals.real[sl], k, deriv) res[sl].imag = _fitpack._bspleval(xx,xj, cvals.imag[sl], k, deriv) else: res[sl] = _fitpack._bspleval(xx, xj, cvals[sl], k, deriv) res.shape = oldshape + sh return res # When spltopp gets removed, also remove the _ppform class. @np.deprecate(message="spltopp is deprecated in scipy 0.19.0, " "use PPoly.from_spline instead.") def spltopp(xk, cvals, k): """Return a piece-wise polynomial object from a fixed-spline tuple.""" return _ppform.fromspline(xk, cvals, k) @np.deprecate(message="spline is deprecated in scipy 0.19.0, " "use Bspline class instead.") def spline(xk, yk, xnew, order=3, kind='smoothest', conds=None): """ Interpolate a curve at new points using a spline fit Parameters ---------- xk, yk : array_like The x and y values that define the curve. xnew : array_like The x values where spline should estimate the y values. order : int Default is 3. kind : string One of {'smoothest'} conds : Don't know Don't know Returns ------- spline : ndarray An array of y values; the spline evaluated at the positions xnew. """ return spleval(splmake(xk, yk, order=order, kind=kind, conds=conds), xnew)