def test_trapz_complex128(self): x = np.asarray(np.random.rand(10)+1j*np.random.rand(10), np.complex128) x_gpu = gpuarray.to_gpu(x) z = integrate.trapz(x_gpu) assert np.allclose(np.trapz(x), z)
import pycuda.autoinit import pycuda.gpuarray import numpy as np from scikits.cuda import integrate import pycuda.gpuarray as gpuarray import pycuda.autoinit integrate.init() x = np.asarray(np.random.rand(10), np.float32) x_gpu = gpuarray.to_gpu(x) z = integrate.trapz(x_gpu, 0.1) np.allclose(np.trapz(x), z)
def test_trapz_float64(self): x = np.asarray(np.random.rand(10), np.float64) x_gpu = gpuarray.to_gpu(x) z = integrate.trapz(x_gpu) assert np.allclose(np.trapz(x), z)
def test_trapz_complex128(self): x = np.asarray( np.random.rand(10) + 1j * np.random.rand(10), np.complex128) x_gpu = gpuarray.to_gpu(x) z = integrate.trapz(x_gpu) assert np.allclose(np.trapz(x), z)
import pycuda.autoinit import pycuda.gpuarray import numpy as np import pycuda.gpuarray as gpuarray #import integrate from scikits.cuda import integrate integrate.init() #x = np.asarray(np.random.rand(10), np.float32) #x = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0] #np.asarray(x) n = 100000 #x = np.arange( n, dtype = np.float32)## x = np.random.rand(n) #print 1 x_gpu = gpuarray.to_gpu(x) #print 2 z = integrate.trapz(x_gpu) #print 3 #print x[99] print z
import numpy as np import pycuda.autoinit import pycuda.gpuarray as gpuarray import scikits.cuda.integrate as integrate integrate.init() a = np.asarray(np.random.rand(10), np.float32) #x = np.asarray(np.random.rand(10, 10), np.float32) x = np.ones( (2000, 2000) ) * 10 x_gpu = gpuarray.to_gpu(x) a_gpu = gpuarray.to_gpu(a) b = integrate.trapz(a_gpu) z = integrate.trapz2d(x_gpu) print(z) np.allclose(np.trapz(np.trapz(x)), z)