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1D ndsplines vs. scipy.interpolateΒΆ
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import numpy as np
import gc
import time
from scipy import interpolate
import ndsplines
import matplotlib.pyplot as plt
# number of time measurements per input/query size
n_iter = 10
def timeit(func, n_iter=1, return_samps=True, **func_kwargs):
results = np.empty(n_iter, dtype=np.double)
for i in range(n_iter):
# gc.collect()
tstart = time.time()
func(**func_kwargs)
delta = time.time() - tstart
results[i] = delta
if return_samps:
return results
else:
return np.mean(results)
def gen_xy(size):
x = np.pi * np.linspace(-1, 1, size)
y = np.sin(x)
return x, y
def gen_xx(size):
return 3 * np.pi * np.linspace(-1, 1, size)
# make_interp_spline timing
x_sizes = np.logspace(1, 3, 10, dtype=int)
t_scipy_build = np.empty((2, x_sizes.size))
t_ndspl_build = np.empty((2, x_sizes.size))
for i, size in enumerate(x_sizes):
x, y = gen_xy(size)
t_scipy = 10e3 * timeit(interpolate.make_interp_spline, x=x.copy(), y=y,
n_iter=n_iter)
t_ndspl = 10e3 * timeit(ndsplines.make_interp_spline, x=x.copy(), y=y,
n_iter=n_iter)
t_scipy_build[:, i] = np.mean(t_scipy), np.std(t_scipy)
t_ndspl_build[:, i] = np.mean(t_ndspl), np.std(t_ndspl)
# spline query timing
x, y = gen_xy(7)
xx_sizes = np.logspace(0, 3, 10, dtype=int)
t_scipy_call = np.empty((2, xx_sizes.size))
t_ndspl_npy_call = np.empty((2, xx_sizes.size))
t_ndspl_pyx_call = np.empty((2, xx_sizes.size))
for i, size in enumerate(xx_sizes):
xx = gen_xx(size)
spl_scipy = interpolate.make_interp_spline(x.copy(), y)
spl_ndspl = ndsplines.make_interp_spline(x.copy(), y)
spl_ndspl.allocate_workspace_arrays(size)
t_scipy = 10e3 * timeit(spl_scipy, x=xx.copy(), n_iter=n_iter)
ndsplines.set_impl('cython')
t_ndspl_pyx = 10e3 * timeit(spl_ndspl, x=xx.copy(), n_iter=n_iter)
ndsplines.set_impl('numpy')
t_ndspl_npy = 10e3 * timeit(spl_ndspl, x=xx.copy(), n_iter=n_iter)
t_scipy_call[:, i] = np.mean(t_scipy), np.std(t_scipy)
t_ndspl_npy_call[:, i] = np.mean(t_ndspl_npy), np.std(t_ndspl_npy)
t_ndspl_pyx_call[:, i] = np.mean(t_ndspl_pyx), np.std(t_ndspl_pyx)
# plot results
fig, axes = plt.subplots(nrows=2)
axes[0].errorbar(x_sizes, t_scipy_build[0], capsize=3, yerr=t_scipy_build[1],
label='scipy')
axes[0].errorbar(x_sizes, t_ndspl_build[0], capsize=3, yerr=t_ndspl_build[1],
label='ndsplines')
axes[0].set_title('make_interp_spline')
axes[1].errorbar(xx_sizes, t_scipy_call[0], capsize=3, yerr=t_scipy_call[1],
label='scipy.interpolate')
axes[1].errorbar(xx_sizes, t_ndspl_npy_call[0], capsize=3, yerr=t_ndspl_npy_call[1],
label='ndsplines npy')
axes[1].errorbar(xx_sizes, t_ndspl_pyx_call[0], capsize=3, yerr=t_ndspl_pyx_call[1],
label='ndsplines pyx')
axes[1].set_title('spline.__call__')
for ax in axes:
ax.set_xlabel('input array size')
ax.set_ylabel('time [ms]')
ax.set_xscale('log')
ax.grid()
axes[-1].legend()
fig.tight_layout()
plt.show()
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Total running time of the script: ( 0 minutes 0.637 seconds)