816 lines
23 KiB
Python
816 lines
23 KiB
Python
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from os.path import join, dirname
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_equal
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import pytest
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from pytest import raises as assert_raises
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from scipy.fftpack._realtransforms import (
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dct, idct, dst, idst, dctn, idctn, dstn, idstn)
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# Matlab reference data
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MDATA = np.load(join(dirname(__file__), 'test.npz'))
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X = [MDATA['x%d' % i] for i in range(8)]
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Y = [MDATA['y%d' % i] for i in range(8)]
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# FFTW reference data: the data are organized as follows:
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# * SIZES is an array containing all available sizes
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# * for every type (1, 2, 3, 4) and every size, the array dct_type_size
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# contains the output of the DCT applied to the input np.linspace(0, size-1,
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# size)
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FFTWDATA_DOUBLE = np.load(join(dirname(__file__), 'fftw_double_ref.npz'))
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FFTWDATA_SINGLE = np.load(join(dirname(__file__), 'fftw_single_ref.npz'))
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FFTWDATA_SIZES = FFTWDATA_DOUBLE['sizes']
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def fftw_dct_ref(type, size, dt):
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x = np.linspace(0, size-1, size).astype(dt)
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dt = np.result_type(np.float32, dt)
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if dt == np.double:
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data = FFTWDATA_DOUBLE
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elif dt == np.float32:
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data = FFTWDATA_SINGLE
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else:
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raise ValueError()
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y = (data['dct_%d_%d' % (type, size)]).astype(dt)
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return x, y, dt
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def fftw_dst_ref(type, size, dt):
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x = np.linspace(0, size-1, size).astype(dt)
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dt = np.result_type(np.float32, dt)
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if dt == np.double:
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data = FFTWDATA_DOUBLE
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elif dt == np.float32:
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data = FFTWDATA_SINGLE
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else:
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raise ValueError()
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y = (data['dst_%d_%d' % (type, size)]).astype(dt)
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return x, y, dt
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def dct_2d_ref(x, **kwargs):
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"""Calculate reference values for testing dct2."""
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x = np.array(x, copy=True)
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for row in range(x.shape[0]):
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x[row, :] = dct(x[row, :], **kwargs)
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for col in range(x.shape[1]):
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x[:, col] = dct(x[:, col], **kwargs)
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return x
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def idct_2d_ref(x, **kwargs):
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"""Calculate reference values for testing idct2."""
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x = np.array(x, copy=True)
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for row in range(x.shape[0]):
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x[row, :] = idct(x[row, :], **kwargs)
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for col in range(x.shape[1]):
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x[:, col] = idct(x[:, col], **kwargs)
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return x
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def dst_2d_ref(x, **kwargs):
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"""Calculate reference values for testing dst2."""
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x = np.array(x, copy=True)
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for row in range(x.shape[0]):
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x[row, :] = dst(x[row, :], **kwargs)
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for col in range(x.shape[1]):
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x[:, col] = dst(x[:, col], **kwargs)
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return x
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def idst_2d_ref(x, **kwargs):
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"""Calculate reference values for testing idst2."""
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x = np.array(x, copy=True)
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for row in range(x.shape[0]):
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x[row, :] = idst(x[row, :], **kwargs)
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for col in range(x.shape[1]):
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x[:, col] = idst(x[:, col], **kwargs)
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return x
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def naive_dct1(x, norm=None):
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"""Calculate textbook definition version of DCT-I."""
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x = np.array(x, copy=True)
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N = len(x)
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M = N-1
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y = np.zeros(N)
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m0, m = 1, 2
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if norm == 'ortho':
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m0 = np.sqrt(1.0/M)
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m = np.sqrt(2.0/M)
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for k in range(N):
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for n in range(1, N-1):
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y[k] += m*x[n]*np.cos(np.pi*n*k/M)
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y[k] += m0 * x[0]
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y[k] += m0 * x[N-1] * (1 if k % 2 == 0 else -1)
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if norm == 'ortho':
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y[0] *= 1/np.sqrt(2)
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y[N-1] *= 1/np.sqrt(2)
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return y
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def naive_dst1(x, norm=None):
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"""Calculate textbook definition version of DST-I."""
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x = np.array(x, copy=True)
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N = len(x)
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M = N+1
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y = np.zeros(N)
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for k in range(N):
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for n in range(N):
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y[k] += 2*x[n]*np.sin(np.pi*(n+1.0)*(k+1.0)/M)
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if norm == 'ortho':
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y *= np.sqrt(0.5/M)
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return y
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def naive_dct4(x, norm=None):
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"""Calculate textbook definition version of DCT-IV."""
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x = np.array(x, copy=True)
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N = len(x)
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y = np.zeros(N)
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for k in range(N):
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for n in range(N):
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y[k] += x[n]*np.cos(np.pi*(n+0.5)*(k+0.5)/(N))
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if norm == 'ortho':
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y *= np.sqrt(2.0/N)
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else:
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y *= 2
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return y
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def naive_dst4(x, norm=None):
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"""Calculate textbook definition version of DST-IV."""
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x = np.array(x, copy=True)
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N = len(x)
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y = np.zeros(N)
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for k in range(N):
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for n in range(N):
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y[k] += x[n]*np.sin(np.pi*(n+0.5)*(k+0.5)/(N))
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if norm == 'ortho':
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y *= np.sqrt(2.0/N)
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else:
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y *= 2
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return y
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class TestComplex:
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def test_dct_complex64(self):
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y = dct(1j*np.arange(5, dtype=np.complex64))
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x = 1j*dct(np.arange(5))
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assert_array_almost_equal(x, y)
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def test_dct_complex(self):
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y = dct(np.arange(5)*1j)
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x = 1j*dct(np.arange(5))
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assert_array_almost_equal(x, y)
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def test_idct_complex(self):
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y = idct(np.arange(5)*1j)
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x = 1j*idct(np.arange(5))
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assert_array_almost_equal(x, y)
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def test_dst_complex64(self):
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y = dst(np.arange(5, dtype=np.complex64)*1j)
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x = 1j*dst(np.arange(5))
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assert_array_almost_equal(x, y)
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def test_dst_complex(self):
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y = dst(np.arange(5)*1j)
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x = 1j*dst(np.arange(5))
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assert_array_almost_equal(x, y)
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def test_idst_complex(self):
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y = idst(np.arange(5)*1j)
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x = 1j*idst(np.arange(5))
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assert_array_almost_equal(x, y)
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class _TestDCTBase:
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def setup_method(self):
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self.rdt = None
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self.dec = 14
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self.type = None
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def test_definition(self):
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for i in FFTWDATA_SIZES:
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x, yr, dt = fftw_dct_ref(self.type, i, self.rdt)
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y = dct(x, type=self.type)
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assert_equal(y.dtype, dt)
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# XXX: we divide by np.max(y) because the tests fail otherwise. We
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# should really use something like assert_array_approx_equal. The
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# difference is due to fftw using a better algorithm w.r.t error
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# propagation compared to the ones from fftpack.
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assert_array_almost_equal(y / np.max(y), yr / np.max(y), decimal=self.dec,
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err_msg="Size %d failed" % i)
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def test_axis(self):
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nt = 2
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for i in [7, 8, 9, 16, 32, 64]:
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x = np.random.randn(nt, i)
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y = dct(x, type=self.type)
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for j in range(nt):
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assert_array_almost_equal(y[j], dct(x[j], type=self.type),
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decimal=self.dec)
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x = x.T
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y = dct(x, axis=0, type=self.type)
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for j in range(nt):
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assert_array_almost_equal(y[:,j], dct(x[:,j], type=self.type),
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decimal=self.dec)
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class _TestDCTIBase(_TestDCTBase):
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def test_definition_ortho(self):
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# Test orthornomal mode.
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dt = np.result_type(np.float32, self.rdt)
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for xr in X:
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x = np.array(xr, dtype=self.rdt)
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y = dct(x, norm='ortho', type=1)
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y2 = naive_dct1(x, norm='ortho')
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assert_equal(y.dtype, dt)
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assert_array_almost_equal(y / np.max(y), y2 / np.max(y), decimal=self.dec)
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class _TestDCTIIBase(_TestDCTBase):
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def test_definition_matlab(self):
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# Test correspondence with MATLAB (orthornomal mode).
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dt = np.result_type(np.float32, self.rdt)
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for xr, yr in zip(X, Y):
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x = np.array(xr, dtype=dt)
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y = dct(x, norm="ortho", type=2)
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assert_equal(y.dtype, dt)
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assert_array_almost_equal(y, yr, decimal=self.dec)
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class _TestDCTIIIBase(_TestDCTBase):
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def test_definition_ortho(self):
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# Test orthornomal mode.
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dt = np.result_type(np.float32, self.rdt)
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for xr in X:
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x = np.array(xr, dtype=self.rdt)
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y = dct(x, norm='ortho', type=2)
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xi = dct(y, norm="ortho", type=3)
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assert_equal(xi.dtype, dt)
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assert_array_almost_equal(xi, x, decimal=self.dec)
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class _TestDCTIVBase(_TestDCTBase):
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def test_definition_ortho(self):
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# Test orthornomal mode.
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dt = np.result_type(np.float32, self.rdt)
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for xr in X:
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x = np.array(xr, dtype=self.rdt)
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y = dct(x, norm='ortho', type=4)
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y2 = naive_dct4(x, norm='ortho')
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assert_equal(y.dtype, dt)
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assert_array_almost_equal(y / np.max(y), y2 / np.max(y), decimal=self.dec)
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class TestDCTIDouble(_TestDCTIBase):
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def setup_method(self):
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self.rdt = np.double
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self.dec = 10
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self.type = 1
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class TestDCTIFloat(_TestDCTIBase):
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def setup_method(self):
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self.rdt = np.float32
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self.dec = 4
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self.type = 1
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class TestDCTIInt(_TestDCTIBase):
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def setup_method(self):
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self.rdt = int
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self.dec = 5
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self.type = 1
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class TestDCTIIDouble(_TestDCTIIBase):
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def setup_method(self):
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self.rdt = np.double
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self.dec = 10
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self.type = 2
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class TestDCTIIFloat(_TestDCTIIBase):
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def setup_method(self):
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self.rdt = np.float32
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self.dec = 5
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self.type = 2
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class TestDCTIIInt(_TestDCTIIBase):
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def setup_method(self):
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self.rdt = int
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self.dec = 5
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self.type = 2
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class TestDCTIIIDouble(_TestDCTIIIBase):
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def setup_method(self):
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self.rdt = np.double
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self.dec = 14
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self.type = 3
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class TestDCTIIIFloat(_TestDCTIIIBase):
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def setup_method(self):
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self.rdt = np.float32
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self.dec = 5
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self.type = 3
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class TestDCTIIIInt(_TestDCTIIIBase):
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def setup_method(self):
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self.rdt = int
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self.dec = 5
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self.type = 3
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class TestDCTIVDouble(_TestDCTIVBase):
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def setup_method(self):
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self.rdt = np.double
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self.dec = 12
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self.type = 3
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class TestDCTIVFloat(_TestDCTIVBase):
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def setup_method(self):
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self.rdt = np.float32
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self.dec = 5
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self.type = 3
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class TestDCTIVInt(_TestDCTIVBase):
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def setup_method(self):
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self.rdt = int
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self.dec = 5
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self.type = 3
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class _TestIDCTBase:
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def setup_method(self):
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self.rdt = None
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self.dec = 14
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self.type = None
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def test_definition(self):
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for i in FFTWDATA_SIZES:
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xr, yr, dt = fftw_dct_ref(self.type, i, self.rdt)
|
||
|
|
x = idct(yr, type=self.type)
|
||
|
|
if self.type == 1:
|
||
|
|
x /= 2 * (i-1)
|
||
|
|
else:
|
||
|
|
x /= 2 * i
|
||
|
|
assert_equal(x.dtype, dt)
|
||
|
|
# XXX: we divide by np.max(y) because the tests fail otherwise. We
|
||
|
|
# should really use something like assert_array_approx_equal. The
|
||
|
|
# difference is due to fftw using a better algorithm w.r.t error
|
||
|
|
# propagation compared to the ones from fftpack.
|
||
|
|
assert_array_almost_equal(x / np.max(x), xr / np.max(x), decimal=self.dec,
|
||
|
|
err_msg="Size %d failed" % i)
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIDouble(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 10
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIFloat(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIInt(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIDouble(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 10
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIFloat(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIInt(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIIDouble(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 14
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIIFloat(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIIIInt(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
class TestIDCTIVDouble(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 12
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIVFloat(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDCTIVInt(_TestIDCTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
class _TestDSTBase:
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = None # dtype
|
||
|
|
self.dec = None # number of decimals to match
|
||
|
|
self.type = None # dst type
|
||
|
|
|
||
|
|
def test_definition(self):
|
||
|
|
for i in FFTWDATA_SIZES:
|
||
|
|
xr, yr, dt = fftw_dst_ref(self.type, i, self.rdt)
|
||
|
|
y = dst(xr, type=self.type)
|
||
|
|
assert_equal(y.dtype, dt)
|
||
|
|
# XXX: we divide by np.max(y) because the tests fail otherwise. We
|
||
|
|
# should really use something like assert_array_approx_equal. The
|
||
|
|
# difference is due to fftw using a better algorithm w.r.t error
|
||
|
|
# propagation compared to the ones from fftpack.
|
||
|
|
assert_array_almost_equal(y / np.max(y), yr / np.max(y), decimal=self.dec,
|
||
|
|
err_msg="Size %d failed" % i)
|
||
|
|
|
||
|
|
|
||
|
|
class _TestDSTIBase(_TestDSTBase):
|
||
|
|
def test_definition_ortho(self):
|
||
|
|
# Test orthornomal mode.
|
||
|
|
dt = np.result_type(np.float32, self.rdt)
|
||
|
|
for xr in X:
|
||
|
|
x = np.array(xr, dtype=self.rdt)
|
||
|
|
y = dst(x, norm='ortho', type=1)
|
||
|
|
y2 = naive_dst1(x, norm='ortho')
|
||
|
|
assert_equal(y.dtype, dt)
|
||
|
|
assert_array_almost_equal(y / np.max(y), y2 / np.max(y), decimal=self.dec)
|
||
|
|
|
||
|
|
class _TestDSTIVBase(_TestDSTBase):
|
||
|
|
def test_definition_ortho(self):
|
||
|
|
# Test orthornomal mode.
|
||
|
|
dt = np.result_type(np.float32, self.rdt)
|
||
|
|
for xr in X:
|
||
|
|
x = np.array(xr, dtype=self.rdt)
|
||
|
|
y = dst(x, norm='ortho', type=4)
|
||
|
|
y2 = naive_dst4(x, norm='ortho')
|
||
|
|
assert_equal(y.dtype, dt)
|
||
|
|
assert_array_almost_equal(y, y2, decimal=self.dec)
|
||
|
|
|
||
|
|
class TestDSTIDouble(_TestDSTIBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 12
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIFloat(_TestDSTIBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIInt(_TestDSTIBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIDouble(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 14
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIFloat(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIInt(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIIDouble(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 14
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIIFloat(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 7
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIIIInt(_TestDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 7
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIVDouble(_TestDSTIVBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 12
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIVFloat(_TestDSTIVBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestDSTIVInt(_TestDSTIVBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 5
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class _TestIDSTBase:
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = None
|
||
|
|
self.dec = None
|
||
|
|
self.type = None
|
||
|
|
|
||
|
|
def test_definition(self):
|
||
|
|
for i in FFTWDATA_SIZES:
|
||
|
|
xr, yr, dt = fftw_dst_ref(self.type, i, self.rdt)
|
||
|
|
x = idst(yr, type=self.type)
|
||
|
|
if self.type == 1:
|
||
|
|
x /= 2 * (i+1)
|
||
|
|
else:
|
||
|
|
x /= 2 * i
|
||
|
|
assert_equal(x.dtype, dt)
|
||
|
|
# XXX: we divide by np.max(x) because the tests fail otherwise. We
|
||
|
|
# should really use something like assert_array_approx_equal. The
|
||
|
|
# difference is due to fftw using a better algorithm w.r.t error
|
||
|
|
# propagation compared to the ones from fftpack.
|
||
|
|
assert_array_almost_equal(x / np.max(x), xr / np.max(x), decimal=self.dec,
|
||
|
|
err_msg="Size %d failed" % i)
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIDouble(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 12
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIFloat(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIInt(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 4
|
||
|
|
self.type = 1
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIDouble(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 14
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIFloat(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIInt(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 2
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIIDouble(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 14
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIIFloat(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIIIInt(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 3
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIVDouble(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.double
|
||
|
|
self.dec = 12
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIVFloat(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = np.float32
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestIDSTIVnt(_TestIDSTBase):
|
||
|
|
def setup_method(self):
|
||
|
|
self.rdt = int
|
||
|
|
self.dec = 6
|
||
|
|
self.type = 4
|
||
|
|
|
||
|
|
|
||
|
|
class TestOverwrite:
|
||
|
|
"""Check input overwrite behavior."""
|
||
|
|
|
||
|
|
real_dtypes = [np.float32, np.float64]
|
||
|
|
|
||
|
|
def _check(self, x, routine, type, fftsize, axis, norm, overwrite_x, **kw):
|
||
|
|
x2 = x.copy()
|
||
|
|
routine(x2, type, fftsize, axis, norm, overwrite_x=overwrite_x)
|
||
|
|
|
||
|
|
sig = "%s(%s%r, %r, axis=%r, overwrite_x=%r)" % (
|
||
|
|
routine.__name__, x.dtype, x.shape, fftsize, axis, overwrite_x)
|
||
|
|
if not overwrite_x:
|
||
|
|
assert_equal(x2, x, err_msg="spurious overwrite in %s" % sig)
|
||
|
|
|
||
|
|
def _check_1d(self, routine, dtype, shape, axis):
|
||
|
|
np.random.seed(1234)
|
||
|
|
if np.issubdtype(dtype, np.complexfloating):
|
||
|
|
data = np.random.randn(*shape) + 1j*np.random.randn(*shape)
|
||
|
|
else:
|
||
|
|
data = np.random.randn(*shape)
|
||
|
|
data = data.astype(dtype)
|
||
|
|
|
||
|
|
for type in [1, 2, 3, 4]:
|
||
|
|
for overwrite_x in [True, False]:
|
||
|
|
for norm in [None, 'ortho']:
|
||
|
|
self._check(data, routine, type, None, axis, norm,
|
||
|
|
overwrite_x)
|
||
|
|
|
||
|
|
def test_dct(self):
|
||
|
|
for dtype in self.real_dtypes:
|
||
|
|
self._check_1d(dct, dtype, (16,), -1)
|
||
|
|
self._check_1d(dct, dtype, (16, 2), 0)
|
||
|
|
self._check_1d(dct, dtype, (2, 16), 1)
|
||
|
|
|
||
|
|
def test_idct(self):
|
||
|
|
for dtype in self.real_dtypes:
|
||
|
|
self._check_1d(idct, dtype, (16,), -1)
|
||
|
|
self._check_1d(idct, dtype, (16, 2), 0)
|
||
|
|
self._check_1d(idct, dtype, (2, 16), 1)
|
||
|
|
|
||
|
|
def test_dst(self):
|
||
|
|
for dtype in self.real_dtypes:
|
||
|
|
self._check_1d(dst, dtype, (16,), -1)
|
||
|
|
self._check_1d(dst, dtype, (16, 2), 0)
|
||
|
|
self._check_1d(dst, dtype, (2, 16), 1)
|
||
|
|
|
||
|
|
def test_idst(self):
|
||
|
|
for dtype in self.real_dtypes:
|
||
|
|
self._check_1d(idst, dtype, (16,), -1)
|
||
|
|
self._check_1d(idst, dtype, (16, 2), 0)
|
||
|
|
self._check_1d(idst, dtype, (2, 16), 1)
|
||
|
|
|
||
|
|
|
||
|
|
class Test_DCTN_IDCTN:
|
||
|
|
dec = 14
|
||
|
|
dct_type = [1, 2, 3, 4]
|
||
|
|
norms = [None, 'ortho']
|
||
|
|
rstate = np.random.RandomState(1234)
|
||
|
|
shape = (32, 16)
|
||
|
|
data = rstate.randn(*shape)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('fforward,finverse', [(dctn, idctn),
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||
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|
(dstn, idstn)])
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@pytest.mark.parametrize('axes', [None,
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|
1, (1,), [1],
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||
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|
0, (0,), [0],
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||
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|
(0, 1), [0, 1],
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||
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|
(-2, -1), [-2, -1]])
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@pytest.mark.parametrize('dct_type', dct_type)
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@pytest.mark.parametrize('norm', ['ortho'])
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def test_axes_round_trip(self, fforward, finverse, axes, dct_type, norm):
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|
tmp = fforward(self.data, type=dct_type, axes=axes, norm=norm)
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|
tmp = finverse(tmp, type=dct_type, axes=axes, norm=norm)
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|
assert_array_almost_equal(self.data, tmp, decimal=12)
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||
|
|
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||
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|
@pytest.mark.parametrize('fforward,fforward_ref', [(dctn, dct_2d_ref),
|
||
|
|
(dstn, dst_2d_ref)])
|
||
|
|
@pytest.mark.parametrize('dct_type', dct_type)
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||
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|
@pytest.mark.parametrize('norm', norms)
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||
|
|
def test_dctn_vs_2d_reference(self, fforward, fforward_ref,
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||
|
|
dct_type, norm):
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||
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|
y1 = fforward(self.data, type=dct_type, axes=None, norm=norm)
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||
|
|
y2 = fforward_ref(self.data, type=dct_type, norm=norm)
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||
|
|
assert_array_almost_equal(y1, y2, decimal=11)
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||
|
|
|
||
|
|
@pytest.mark.parametrize('finverse,finverse_ref', [(idctn, idct_2d_ref),
|
||
|
|
(idstn, idst_2d_ref)])
|
||
|
|
@pytest.mark.parametrize('dct_type', dct_type)
|
||
|
|
@pytest.mark.parametrize('norm', [None, 'ortho'])
|
||
|
|
def test_idctn_vs_2d_reference(self, finverse, finverse_ref,
|
||
|
|
dct_type, norm):
|
||
|
|
fdata = dctn(self.data, type=dct_type, norm=norm)
|
||
|
|
y1 = finverse(fdata, type=dct_type, norm=norm)
|
||
|
|
y2 = finverse_ref(fdata, type=dct_type, norm=norm)
|
||
|
|
assert_array_almost_equal(y1, y2, decimal=11)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('fforward,finverse', [(dctn, idctn),
|
||
|
|
(dstn, idstn)])
|
||
|
|
def test_axes_and_shape(self, fforward, finverse):
|
||
|
|
with assert_raises(ValueError,
|
||
|
|
match="when given, axes and shape arguments"
|
||
|
|
" have to be of the same length"):
|
||
|
|
fforward(self.data, shape=self.data.shape[0], axes=(0, 1))
|
||
|
|
|
||
|
|
with assert_raises(ValueError,
|
||
|
|
match="when given, axes and shape arguments"
|
||
|
|
" have to be of the same length"):
|
||
|
|
fforward(self.data, shape=self.data.shape[0], axes=None)
|
||
|
|
|
||
|
|
with assert_raises(ValueError,
|
||
|
|
match="when given, axes and shape arguments"
|
||
|
|
" have to be of the same length"):
|
||
|
|
fforward(self.data, shape=self.data.shape, axes=0)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('fforward', [dctn, dstn])
|
||
|
|
def test_shape(self, fforward):
|
||
|
|
tmp = fforward(self.data, shape=(128, 128), axes=None)
|
||
|
|
assert_equal(tmp.shape, (128, 128))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('fforward,finverse', [(dctn, idctn),
|
||
|
|
(dstn, idstn)])
|
||
|
|
@pytest.mark.parametrize('axes', [1, (1,), [1],
|
||
|
|
0, (0,), [0]])
|
||
|
|
def test_shape_is_none_with_axes(self, fforward, finverse, axes):
|
||
|
|
tmp = fforward(self.data, shape=None, axes=axes, norm='ortho')
|
||
|
|
tmp = finverse(tmp, shape=None, axes=axes, norm='ortho')
|
||
|
|
assert_array_almost_equal(self.data, tmp, decimal=self.dec)
|