Merge branch 'pr-2946-interp1d' into master.

Reviewed at #2946

scipy/interpolate/interpolate.py

@@ -277,7 +277,7 @@ def __call__(self, x, y, dx=0, dy=0):
 class interp1d(_Interpolator1D):
     """
     interp1d(x, y, kind='linear', axis=-1, copy=True, bounds_error=True,
-             fill_value=np.nan)
+             fill_value=np.nan, assume_sorted=False)
 
     Interpolate a 1-D function.
 
@@ -288,7 +288,7 @@ class interp1d(_Interpolator1D):
     Parameters
     ----------
     x : (N,) array_like
-        A 1-D array of monotonically increasing real values.
+        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`.
@@ -314,6 +314,10 @@ class interp1d(_Interpolator1D):
         If provided, then this value will be used to fill in for requested
         points outside of the data range. If not provided, then the default
         is NaN.
+    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.
+
 
     See Also
     --------
@@ -337,7 +341,8 @@ class interp1d(_Interpolator1D):
     """
 
     def __init__(self, x, y, kind='linear', axis=-1,
-                 copy=True, bounds_error=True, fill_value=np.nan):
+                 copy=True, bounds_error=True, fill_value=np.nan,
+                 assume_sorted=False):
         """ Initialize a 1D linear interpolation class."""
         _Interpolator1D.__init__(self, x, y, axis=axis)
 
@@ -358,6 +363,11 @@ def __init__(self, x, y, kind='linear', axis=-1,
         x = array(x, copy=self.copy)
         y = array(y, copy=self.copy)
 
+        if not assume_sorted:
+            ind = np.argsort(x)
+            x = x[ind]
+            np.take(y, ind, axis=axis, out=y)
+
         if x.ndim != 1:
             raise ValueError("the x array must have exactly one dimension.")
         if y.ndim == 0:
@@ -578,7 +588,7 @@ def extend(self, c, x, right=True):
         """
         c = np.asarray(c)
         x = np.asarray(x)
-        
+
         if c.ndim < 2:
             raise ValueError("invalid dimensions for c")
         if x.ndim != 1:
@@ -1035,7 +1045,7 @@ class BPoly(_PPolyBase):
     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
 
@@ -1076,9 +1086,9 @@ def derivative(self, nu=1):
         if nu == 0:
             c2 = self.c.copy()
         else:
-            # For a polynomial 
+            # For a polynomial
             #    B(x) = \sum_{a=0}^{k} c_a b_{a, k}(x),
-            # we use the fact that 
+            # 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}
@@ -1151,29 +1161,29 @@ def from_derivatives(cls, xi, yi, orders=None, direction=None,
             ``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. 
+            derivatives are ignored.
         axis : int, optional
             Interpolation axis, default is 0.
         extrapolate : bool, optional
             Whether to extrapolate to ouf-of-bounds points based on first
-            and last intervals, or to return NaNs. Default: True.        
+            and last intervals, or to return NaNs. Default: True.
 
         Notes
         -----
         If ``k`` derivatives are specified at a breakpoint ``x``, the
-        constructed polynomial is exactly ``k`` times continuously 
+        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.
 
@@ -1206,7 +1216,7 @@ def from_derivatives(cls, xi, yi, orders=None, direction=None,
             raise NotImplementedError
         if direction is not None:
             raise NotImplementedError
-            
+
         xi = np.asarray(xi)
         if len(xi) != len(yi):
             raise ValueError("xi and yi need to have the same length")
@@ -1224,7 +1234,7 @@ def from_derivatives(cls, xi, yi, orders=None, direction=None,
             if isinstance(orders, integer_types):
                 orders = [orders] * m
             k = max(k, max(orders))
-            
+
             if any(o <= 0 for o in orders):
                 raise ValueError("Orders must be positive.")
 
@@ -1256,7 +1266,7 @@ def from_derivatives(cls, xi, yi, orders=None, direction=None,
 
     @staticmethod
     def _construct_from_derivatives(xa, xb, ya, yb):
-        """Compute the coefficients of a polynomial in the Bernstein basis 
+        """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
@@ -1303,10 +1313,10 @@ def _construct_from_derivatives(xa, xb, ya, yb):
 
           .. 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 
+        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`. 
+        At `x = xb` it's the same with `a = n - q`.
 
         """
         ya, yb = np.asarray(ya), np.asarray(yb)
@@ -1344,7 +1354,7 @@ def _construct_from_derivatives(xa, xb, ya, yb):
     def _raise_degree(c, d):
         """Raise a degree of a polynomial in the Bernstein basis.
 
-        Given the coefficients of a polynomial degree `k`, return (the 
+        Given the coefficients of a polynomial degree `k`, return (the
         coefficients of) the equivalent polynomial of degree `k+d`.
 
         Parameters