# ) * sin(Y*2*pi*3) *
a = numpy.exp(-0.5 * (((X - 0.3) / 0.2)**2 + ((Y - 0) / 0.2)**2 * 0 + (
(Z - 0.6) / 0.3)**2)) * (sin(Y * 2 * pi * 3) + 1)
print a.shape, a.min(), a.max()
xv = numpy.linspace(0, 1, 33)
yv = numpy.linspace(0, 1, 33)
zv = numpy.linspace(0, 1, 33)
print 'evaluating'
values = []
for xi in xv:
yvalues = []
for yi in yv:
zvalues = []
for zi in zv:
v = interpn(x, y, z, a, xi, zi, yi)
#z = numpy.exp(-0.5 * (((xi-0.3)/0.2)**2 + ((yi-0.6)/0.3)**2))
zvalues.append(v)
yvalues.append(zvalues)
values.append(yvalues)
values = numpy.array(values)
print 'plotting'
import matplotlib.pyplot as plt
def imshow(arr, title):
plt.title(title)
plt.imshow(
arr, extent=(0, 1, 1, 0), vmin=0, vmax=a.max(), interpolation='none')
plt.colorbar()
for y in [0, 1, 0.5]:
print x, y, interpn([0,1], [0,1], [[1, 2], [3, 4]], x, y, method='linear')
if __name__ == '__main__':
numpy.random.seed(0)
limits = [(-10,10), (-3,3)]
breaks = [numpy.array([lo] + sorted(numpy.random.uniform(lo,hi, size=50)) + [hi])
for lo,hi in limits]
npoints = numpy.product([len(b) for b in breaks])
inputvalues = []
values = []
for x in breaks[0]:
vx = []
for y in breaks[1]:
z = numpy.random.normal(0, 1)**2.
z = numpy.exp(-0.5 * ((x - -3)**2 + (y - 1)**2.))
inputvalues.append([x, y, z])
vx.append(z)
values.append(vx)
outputvalues = []
for i in range(1000):
x = [numpy.random.uniform(lo, hi) for lo,hi in limits]
z = interpn(*(breaks + [values] + x), method='linear')
outputvalues.append(x + [z])
numpy.savetxt("interpn_test_input.txt", inputvalues)
numpy.savetxt("interpn_test_output.txt", outputvalues)
def test_interpn_grid(): for x in [0, 1, 0.5]: for y in [0, 1, 0.5]: print x, y, interpn([0,1], [0,1], [[1, 2], [3, 4]], x, y, method='linear')
for j in range(1, len(z)): if j0 - j >= 0: a[:, j0 - j, :] = a[:, j0 - j + 1, :] * 0.9 if j0 + j < len(z): a[:, j0 + j, :] = a[:, j0 + j - 1, :] * 0.9 del j xv = numpy.linspace(0, 1, 33) yv = numpy.linspace(0, 1, 33) zv = numpy.linspace(0, 1, 33) #xi = numpy.array([0.5, 0.3]).transpose() xi = 0.5 yi = xi zi = xi print interpn(x, y, z, a, xi, zi, yi) print 'evaluating' values = [] for xi in xv: yvalues = [] for yi in yv: zvalues = [] for zi in zv: v = interpn(x, y, z, a, xi, zi, yi) #z = numpy.exp(-0.5 * (((xi-0.3)/0.2)**2 + ((yi-0.6)/0.3)**2)) zvalues.append(v) yvalues.append(zvalues) values.append(yvalues) values = numpy.array(values) print 'plotting'
