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1D ndsplines vs. scipy.interpolateΒΆ

make_interp_spline, spline.__call__
  7 import numpy as np
  8 import gc
  9 import time
 10
 11 from scipy import interpolate
 12 import ndsplines
 13
 14 import matplotlib.pyplot as plt
 15
 16 # number of time measurements per input/query size
 17 n_iter = 10
 18
 19
 20 def timeit(func, n_iter=1, return_samps=True, **func_kwargs):
 21     results = np.empty(n_iter, dtype=np.double)
 22     for i in range(n_iter):
 23         # gc.collect()
 24
 25         tstart = time.time()
 26         func(**func_kwargs)
 27         delta = time.time() - tstart
 28
 29         results[i] = delta
 30
 31     if return_samps:
 32         return results
 33     else:
 34         return np.mean(results)
 35
 36
 37 def gen_xy(size):
 38     x = np.pi * np.linspace(-1, 1, size)
 39     y = np.sin(x)
 40     return x, y
 41
 42
 43 def gen_xx(size):
 44     return 3 * np.pi * np.linspace(-1, 1, size)
 45
 46
 47 # make_interp_spline timing
 48 x_sizes = np.logspace(1, 3, 10, dtype=int)
 49 t_scipy_build = np.empty((2, x_sizes.size))
 50 t_ndspl_build = np.empty((2, x_sizes.size))
 51 for i, size in enumerate(x_sizes):
 52     x, y = gen_xy(size)
 53     t_scipy = 10e3 * timeit(interpolate.make_interp_spline, x=x.copy(), y=y,
 54                             n_iter=n_iter)
 55     t_ndspl = 10e3 * timeit(ndsplines.make_interp_spline, x=x.copy(), y=y,
 56                             n_iter=n_iter)
 57     t_scipy_build[:, i] = np.mean(t_scipy), np.std(t_scipy)
 58     t_ndspl_build[:, i] = np.mean(t_ndspl), np.std(t_ndspl)
 59
 60 # spline query timing
 61 x, y = gen_xy(7)
 62 xx_sizes = np.logspace(0, 3, 10, dtype=int)
 63 t_scipy_call = np.empty((2, xx_sizes.size))
 64 t_ndspl_npy_call = np.empty((2, xx_sizes.size))
 65 t_ndspl_pyx_call = np.empty((2, xx_sizes.size))
 66 for i, size in enumerate(xx_sizes):
 67     xx = gen_xx(size)
 68     spl_scipy = interpolate.make_interp_spline(x.copy(), y)
 69     spl_ndspl = ndsplines.make_interp_spline(x.copy(), y)
 70     spl_ndspl.allocate_workspace_arrays(size)
 71     t_scipy = 10e3 * timeit(spl_scipy, x=xx.copy(), n_iter=n_iter)
 72     ndsplines.set_impl('cython')
 73     t_ndspl_pyx = 10e3 * timeit(spl_ndspl, x=xx.copy(), n_iter=n_iter)
 74     ndsplines.set_impl('numpy')
 75     t_ndspl_npy = 10e3 * timeit(spl_ndspl, x=xx.copy(), n_iter=n_iter)
 76     t_scipy_call[:, i] = np.mean(t_scipy), np.std(t_scipy)
 77     t_ndspl_npy_call[:, i] = np.mean(t_ndspl_npy), np.std(t_ndspl_npy)
 78     t_ndspl_pyx_call[:, i] = np.mean(t_ndspl_pyx), np.std(t_ndspl_pyx)
 79
 80 # plot results
 81 fig, axes = plt.subplots(nrows=2)
 82
 83 axes[0].errorbar(x_sizes, t_scipy_build[0], capsize=3, yerr=t_scipy_build[1],
 84                  label='scipy')
 85 axes[0].errorbar(x_sizes, t_ndspl_build[0], capsize=3, yerr=t_ndspl_build[1],
 86                  label='ndsplines')
 87 axes[0].set_title('make_interp_spline')
 88
 89 axes[1].errorbar(xx_sizes, t_scipy_call[0], capsize=3, yerr=t_scipy_call[1],
 90                  label='scipy.interpolate')
 91 axes[1].errorbar(xx_sizes, t_ndspl_npy_call[0], capsize=3, yerr=t_ndspl_npy_call[1],
 92                  label='ndsplines npy')
 93 axes[1].errorbar(xx_sizes, t_ndspl_pyx_call[0], capsize=3, yerr=t_ndspl_pyx_call[1],
 94                  label='ndsplines pyx')
 95 axes[1].set_title('spline.__call__')
 96
 97 for ax in axes:
 98     ax.set_xlabel('input array size')
 99     ax.set_ylabel('time [ms]')
100     ax.set_xscale('log')
101     ax.grid()
102
103 axes[-1].legend()
104 fig.tight_layout()
105
106 plt.show()

Total running time of the script: (0 minutes 0.654 seconds)

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