FIX: conda bug: reenabled cache=True option

Author: albop

interpolation/cartesian.py

@@ -67,7 +67,7 @@ def mlinspace(a, b, nums, order='C'):
     return cartesian(nodes, order=order)
 
 
-@njit(cache=False)
+@njit(cache=True)
 def _repeat_1d(x, K, out):
     '''Repeats each element of a vector many times and repeats the whole result many times
 

interpolation/splines/eval_cubic_numba.py

@@ -33,7 +33,7 @@ for i in range(1,4):
 # SLOWER
 #
 # {{for d in range(1,max_order+1)}}
-# @njit(cache=False)
+# @njit(cache=True)
 # def vec_eval_cubic_spline_{{d}}(a, b, orders, coefs, points, values):
 #
 #    N = points.shape[0]
@@ -46,7 +46,7 @@ for i in range(1,4):
 
 
 {{for d in range(1,max_order+1)}}
-@njit(cache=False)
+@njit(cache=True)
 def eval_cubic_spline_{{d}}(a, b, orders, coefs, point):
 
     {{for i in range(d)}}
@@ -104,7 +104,7 @@ def eval_cubic_spline_{{d}}(a, b, orders, coefs, point):
 
 
 {{for d in range(1,max_order+1)}}
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_{{d}}(a, b, orders, coefs, points, out):
 
     d = a.shape[0]
@@ -165,7 +165,7 @@ def vec_eval_cubic_spline_{{d}}(a, b, orders, coefs, points, out):
 
 
 {{for d in range(1,max_order+1)}}
-@njit(cache=False)
+@njit(cache=True)
 def eval_cubic_splines_{{d}}(a, b, orders, coefs, point, vals):
 
     n_vals = coefs.shape[{{d}}]
@@ -224,7 +224,7 @@ def eval_cubic_splines_{{d}}(a, b, orders, coefs, point, vals):
 
 
 {{for d in range(1,max_order+1)}}
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_splines_{{d}}(a, b, orders, coefs, points, vals):
 
     n_vals = coefs.shape[{{d}}]
@@ -287,7 +287,7 @@ def vec_eval_cubic_splines_{{d}}(a, b, orders, coefs, points, vals):
 
 
 {{for d in range(1,max_order+1)}}
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_splines_G_{{d}}(a, b, orders, coefs, points, vals, dvals):
 
     n_vals = coefs.shape[{{d}}]

interpolation/splines/eval_cubic_numba.py.in

@@ -8,7 +8,7 @@
 basis = np.array([1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0, 0.0])
 
 
-@njit(cache=False)
+@njit(cache=True)
 def solve_deriv_interp_1d(bands, coefs):
 
     M = coefs.shape[0] - 2
@@ -55,7 +55,7 @@ def solve_deriv_interp_1d(bands, coefs):
 
 
 
-@njit(cache=False)
+@njit(cache=True)
 def find_coefs_1d(delta_inv, M, data, coefs):
 
     bands = np.zeros((M + 2, 4))
@@ -88,7 +88,7 @@ def find_coefs_1d(delta_inv, M, data, coefs):
     solve_deriv_interp_1d(bands, coefs)
 
 
-@njit(cache=False)
+@njit(cache=True)
 def filter_coeffs_1d(dinv, data):
 
     M = data.shape[0]
@@ -100,7 +100,7 @@ def filter_coeffs_1d(dinv, data):
     return coefs
 
 
-@njit(cache=False)
+@njit(cache=True)
 def filter_coeffs_2d(dinv, data):
 
     Mx = data.shape[0]
@@ -124,7 +124,7 @@ def filter_coeffs_2d(dinv, data):
     return coefs
 
 
-@njit(cache=False)
+@njit(cache=True)
 def filter_coeffs_3d(dinv, data):
 
     Mx = data.shape[0]
@@ -154,7 +154,7 @@ def filter_coeffs_3d(dinv, data):
     return coefs
 
 
-@njit(cache=False)
+@njit(cache=True)
 def filter_coeffs_4d(dinv, data):
 
     Mx = data.shape[0]

interpolation/splines/filter_cubic.py

@@ -3,8 +3,6 @@
 import numpy
 import numpy as np
 
-from interpolation.splines.multilinear_numba import multilinear_interpolation, vec_multilinear_interpolation
-
 
 def mlinspace(smin,smax,orders):
     if len(orders) == 1:
@@ -83,6 +81,8 @@ def set_values(self,values):
 
 
     def interpolate(self,s):
+
+        from .multilinear_numba import multilinear_interpolation
         s = numpy.ascontiguousarray(s, dtype=self.dtype)
         a = multilinear_interpolation(self.smin,self.smax,self.orders,self.values,s)
         return a
@@ -92,7 +92,6 @@ def __call__(self,s):
         if s.ndim == 1:
             res = self.__call__( numpy.atleast_2d(s) )
             return res[0]
-
         return self.interpolate(s)
 
 
@@ -169,6 +168,8 @@ def set_values(self,mvalues):
 
 
     def interpolate(self,s):
+
+        from .multilinear_numba import vec_multilinear_interpolation
         s = numpy.ascontiguousarray(s, dtype=self.dtype)
         a = vec_multilinear_interpolation(self.smin,self.smax,self.orders,self.mvalues,s)
         return a

interpolation/splines/multilinear.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+from numba import njit
 
 def vec_multilinear_interpolation(smin, smax, orders, values, s, out=None):
 
@@ -39,6 +39,7 @@ def multilinear_interpolation(smin, smax, orders, values, s, out=None):
     return out
 
 
+@njit(cache=True)
 def multilinear_interpolation_1d(smin, smax, orders, V, s, output):
 
     d = 1
@@ -68,6 +69,7 @@ def multilinear_interpolation_1d(smin, smax, orders, V, s, output):
             output[n] = (1-lam_0)*(v_0) + (lam_0)*(v_1)
 
 
+@njit(cache=True)
 def multilinear_interpolation_2d(smin, smax, orders, V, s, output):
 
     d = 2
@@ -103,9 +105,8 @@ def multilinear_interpolation_2d(smin, smax, orders, V, s, output):
             # interpolated/extrapolated value
             output[n] = (1-lam_0)*((1-lam_1)*(v_00) + (lam_1)*(v_01)) + (lam_0)*((1-lam_1)*(v_10) + (lam_1)*(v_11))
 
-from numba import njit
 
-@njit(cache=False)
+@njit(cache=True)
 def multilinear_interpolation_3d(smin, smax, orders, V, s, output):
 
     d = 3
@@ -151,6 +152,7 @@ def multilinear_interpolation_3d(smin, smax, orders, V, s, output):
             output[n] = (1-lam_0)*((1-lam_1)*((1-lam_2)*(v_000) + (lam_2)*(v_001)) + (lam_1)*((1-lam_2)*(v_010) + (lam_2)*(v_011))) + (lam_0)*((1-lam_1)*((1-lam_2)*(v_100) + (lam_2)*(v_101)) + (lam_1)*((1-lam_2)*(v_110) + (lam_2)*(v_111)))
 
 
+@njit(cache=True)
 def multilinear_interpolation_4d(smin, smax, orders, V, s, output):
 
     d = 4
@@ -209,6 +211,7 @@ def multilinear_interpolation_4d(smin, smax, orders, V, s, output):
             output[n] = (1-lam_0)*((1-lam_1)*((1-lam_2)*((1-lam_3)*(v_0000) + (lam_3)*(v_0001)) + (lam_2)*((1-lam_3)*(v_0010) + (lam_3)*(v_0011))) + (lam_1)*((1-lam_2)*((1-lam_3)*(v_0100) + (lam_3)*(v_0101)) + (lam_2)*((1-lam_3)*(v_0110) + (lam_3)*(v_0111)))) + (lam_0)*((1-lam_1)*((1-lam_2)*((1-lam_3)*(v_1000) + (lam_3)*(v_1001)) + (lam_2)*((1-lam_3)*(v_1010) + (lam_3)*(v_1011))) + (lam_1)*((1-lam_2)*((1-lam_3)*(v_1100) + (lam_3)*(v_1101)) + (lam_2)*((1-lam_3)*(v_1110) + (lam_3)*(v_1111))))
 
 
+@njit(cache=True)
 def multilinear_interpolation_5d(smin, smax, orders, V, s, output):
 
     d = 5

interpolation/splines/multilinear_numba.py

@@ -36,7 +36,7 @@ def multilinear_interpolation(smin, smax, orders, values, s, out=None):
 
 {{for d in range(1,max_d+1)}}
 
-@njit(cache=False)
+@njit(cache=True)
 def multilinear_interpolation_{{d}}d(smin, smax, orders, V, s, output):
 
     d = {{d}}

interpolation/splines/multilinear_numba.py.in

@@ -46,7 +46,7 @@
     d2Ad[:,i] = dAd[:,i-1]*(4-i)
 
 
-@njit(cache=False)
+@njit(cache=True)
 def eval_cubic_spline_3(a, b, orders, coefs, point, Ad, dAd):
 
     M0 = orders[0]
@@ -142,7 +142,7 @@ def eval_cubic_spline_3(a, b, orders, coefs, point, Ad, dAd):
     return t
 
 
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_3(a, b, orders, coefs, points, values):
 
     N = points.shape[0]
@@ -151,7 +151,7 @@ def vec_eval_cubic_spline_3(a, b, orders, coefs, points, values):
         point = points[n, :]
         values[n] = eval_cubic_spline_3(a, b, orders, coefs, point, Ad, dAd)
 
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_3_inlined(a, b, orders, coefs, points, values):
 
     N = points.shape[0]
@@ -255,7 +255,7 @@ def vec_eval_cubic_spline_3_inlined(a, b, orders, coefs, points, values):
 
         values[n] = Phi0_0*(Phi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0]) + Phi2_1*(coefs[i0+0,i1+0,i2+1]) + Phi2_2*(coefs[i0+0,i1+0,i2+2]) + Phi2_3*(coefs[i0+0,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0]) + Phi2_1*(coefs[i0+0,i1+1,i2+1]) + Phi2_2*(coefs[i0+0,i1+1,i2+2]) + Phi2_3*(coefs[i0+0,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0]) + Phi2_1*(coefs[i0+0,i1+2,i2+1]) + Phi2_2*(coefs[i0+0,i1+2,i2+2]) + Phi2_3*(coefs[i0+0,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0]) + Phi2_1*(coefs[i0+0,i1+3,i2+1]) + Phi2_2*(coefs[i0+0,i1+3,i2+2]) + Phi2_3*(coefs[i0+0,i1+3,i2+3]))) + Phi0_1*(Phi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0]) + Phi2_1*(coefs[i0+1,i1+0,i2+1]) + Phi2_2*(coefs[i0+1,i1+0,i2+2]) + Phi2_3*(coefs[i0+1,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0]) + Phi2_1*(coefs[i0+1,i1+1,i2+1]) + Phi2_2*(coefs[i0+1,i1+1,i2+2]) + Phi2_3*(coefs[i0+1,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0]) + Phi2_1*(coefs[i0+1,i1+2,i2+1]) + Phi2_2*(coefs[i0+1,i1+2,i2+2]) + Phi2_3*(coefs[i0+1,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0]) + Phi2_1*(coefs[i0+1,i1+3,i2+1]) + Phi2_2*(coefs[i0+1,i1+3,i2+2]) + Phi2_3*(coefs[i0+1,i1+3,i2+3]))) + Phi0_2*(Phi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0]) + Phi2_1*(coefs[i0+2,i1+0,i2+1]) + Phi2_2*(coefs[i0+2,i1+0,i2+2]) + Phi2_3*(coefs[i0+2,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0]) + Phi2_1*(coefs[i0+2,i1+1,i2+1]) + Phi2_2*(coefs[i0+2,i1+1,i2+2]) + Phi2_3*(coefs[i0+2,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0]) + Phi2_1*(coefs[i0+2,i1+2,i2+1]) + Phi2_2*(coefs[i0+2,i1+2,i2+2]) + Phi2_3*(coefs[i0+2,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0]) + Phi2_1*(coefs[i0+2,i1+3,i2+1]) + Phi2_2*(coefs[i0+2,i1+3,i2+2]) + Phi2_3*(coefs[i0+2,i1+3,i2+3]))) + Phi0_3*(Phi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0]) + Phi2_1*(coefs[i0+3,i1+0,i2+1]) + Phi2_2*(coefs[i0+3,i1+0,i2+2]) + Phi2_3*(coefs[i0+3,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0]) + Phi2_1*(coefs[i0+3,i1+1,i2+1]) + Phi2_2*(coefs[i0+3,i1+1,i2+2]) + Phi2_3*(coefs[i0+3,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0]) + Phi2_1*(coefs[i0+3,i1+2,i2+1]) + Phi2_2*(coefs[i0+3,i1+2,i2+2]) + Phi2_3*(coefs[i0+3,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0]) + Phi2_1*(coefs[i0+3,i1+3,i2+1]) + Phi2_2*(coefs[i0+3,i1+3,i2+2]) + Phi2_3*(coefs[i0+3,i1+3,i2+3])))
 
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_3_inlined_columns(a, b, orders, coefs, points, values):
 
     # N = points.shape[0]
@@ -361,7 +361,7 @@ def vec_eval_cubic_spline_3_inlined_columns(a, b, orders, coefs, points, values)
 
         values[n] = Phi0_0*(Phi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0]) + Phi2_1*(coefs[i0+0,i1+0,i2+1]) + Phi2_2*(coefs[i0+0,i1+0,i2+2]) + Phi2_3*(coefs[i0+0,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0]) + Phi2_1*(coefs[i0+0,i1+1,i2+1]) + Phi2_2*(coefs[i0+0,i1+1,i2+2]) + Phi2_3*(coefs[i0+0,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0]) + Phi2_1*(coefs[i0+0,i1+2,i2+1]) + Phi2_2*(coefs[i0+0,i1+2,i2+2]) + Phi2_3*(coefs[i0+0,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0]) + Phi2_1*(coefs[i0+0,i1+3,i2+1]) + Phi2_2*(coefs[i0+0,i1+3,i2+2]) + Phi2_3*(coefs[i0+0,i1+3,i2+3]))) + Phi0_1*(Phi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0]) + Phi2_1*(coefs[i0+1,i1+0,i2+1]) + Phi2_2*(coefs[i0+1,i1+0,i2+2]) + Phi2_3*(coefs[i0+1,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0]) + Phi2_1*(coefs[i0+1,i1+1,i2+1]) + Phi2_2*(coefs[i0+1,i1+1,i2+2]) + Phi2_3*(coefs[i0+1,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0]) + Phi2_1*(coefs[i0+1,i1+2,i2+1]) + Phi2_2*(coefs[i0+1,i1+2,i2+2]) + Phi2_3*(coefs[i0+1,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0]) + Phi2_1*(coefs[i0+1,i1+3,i2+1]) + Phi2_2*(coefs[i0+1,i1+3,i2+2]) + Phi2_3*(coefs[i0+1,i1+3,i2+3]))) + Phi0_2*(Phi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0]) + Phi2_1*(coefs[i0+2,i1+0,i2+1]) + Phi2_2*(coefs[i0+2,i1+0,i2+2]) + Phi2_3*(coefs[i0+2,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0]) + Phi2_1*(coefs[i0+2,i1+1,i2+1]) + Phi2_2*(coefs[i0+2,i1+1,i2+2]) + Phi2_3*(coefs[i0+2,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0]) + Phi2_1*(coefs[i0+2,i1+2,i2+1]) + Phi2_2*(coefs[i0+2,i1+2,i2+2]) + Phi2_3*(coefs[i0+2,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0]) + Phi2_1*(coefs[i0+2,i1+3,i2+1]) + Phi2_2*(coefs[i0+2,i1+3,i2+2]) + Phi2_3*(coefs[i0+2,i1+3,i2+3]))) + Phi0_3*(Phi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0]) + Phi2_1*(coefs[i0+3,i1+0,i2+1]) + Phi2_2*(coefs[i0+3,i1+0,i2+2]) + Phi2_3*(coefs[i0+3,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0]) + Phi2_1*(coefs[i0+3,i1+1,i2+1]) + Phi2_2*(coefs[i0+3,i1+1,i2+2]) + Phi2_3*(coefs[i0+3,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0]) + Phi2_1*(coefs[i0+3,i1+2,i2+1]) + Phi2_2*(coefs[i0+3,i1+2,i2+2]) + Phi2_3*(coefs[i0+3,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0]) + Phi2_1*(coefs[i0+3,i1+3,i2+1]) + Phi2_2*(coefs[i0+3,i1+3,i2+2]) + Phi2_3*(coefs[i0+3,i1+3,i2+3])))
 
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_3_inlined_lesswork(orders, coefs, points, values, Ad, dAd):
 
     N = points.shape[0]
@@ -417,7 +417,7 @@ def vec_eval_cubic_spline_3_inlined_lesswork(orders, coefs, points, values, Ad,
 
 
 
-@njit(cache=False)
+@njit(cache=True)
 def kernel(n, a, b, orders, coefs, points, values):
 
     x0 = points[n,0]
@@ -518,7 +518,7 @@ def kernel(n, a, b, orders, coefs, points, values):
 
     values[n] = t
 
-@njit(cache=False)
+@njit(cache=True)
 def vec_eval_cubic_spline_3_kernel(a, b, orders, coefs, points, values):
 
     N = points.shape[0]

misc/alternative_implementations.py

@@ -8,7 +8,7 @@
     # Versions should comply with PEP440.  For a discussion on single-sourcing
     # the version across setup.py and the project code, see
     # http://packaging.python.org/en/latest/tutorial.html#version
-    version='0.1.5',
+    version='0.1.6',
 
     description='Interpolation in Python',
     # long_description=long_description,