def __call__(self, data, state): data = state['data'] data_shape = state['data_shape'] compression = self.compression if compression is None or compression == "" or compression == "no": C = lo.identity(2 * (data.size, )) factor = 1 else: C = compression(data_shape, self.factor) # compress data data = compress(data, C, self.factor) # update pipeline state state['data'] = data state['compression'] = C state['compression_factor'] = self.factor state['compressed_data_shape'] = data.shape return data
""" Testing of the lo package """ import nose from numpy.testing import * import numpy as np import linear_operators as lo from linear_operators.iterative import utils n = 128 k = n - 1 nk = 3 # collection of linear operators to test I = lo.identity((n, n)) D = lo.diag(1. + np.arange(n)) C = lo.convolve((n,), kernel=1. + np.arange(nk)) lo_list = [I, D, C.T * C] # collection of vectors ones16 = np.ones(n) def check_cond(A): Adec = utils.eigendecomposition(A, k=k, which='BE') assert_almost_equal(Adec.cond(), np.linalg.cond(A.todense())) def test_cond(): for A in lo_list: yield check_cond, A
def identity(tod, factor): shape = tod.shape return lo.identity(2 * (np.prod(shape),), dtype=np.float64)
#!/usr/bin/env python """ Testing of the lo.iterative module """ import nose from numpy.testing import * import numpy as np import linear_operators as lo from linear_operators import iterative # collection of linear operators to test mat16 = lo.aslinearoperator(np.random.rand(16, 16)) id16 = lo.identity((16, 16)) diag16 = lo.diag(np.random.rand(16)) conv16 = lo.convolve(16, np.random.rand(4), mode="same") lo_list = [mat16, id16, diag16, conv16] # collection of vectors ones16 = np.ones(16) zeros16 = np.zeros(16) rand16 = np.random.rand(16) v_list = [ones16, zeros16, rand16] # collection of methods methods = [ iterative.acg, ]
def identity(tod, factor): shape = tod.shape return lo.identity(2 * (np.prod(shape), ), dtype=np.float64)
#!/usr/bin/env python """ Testing of the lo package """ import nose from numpy.testing import * import numpy as np import linear_operators as lo from linear_operators.iterative import utils n = 128 k = n - 1 nk = 3 # collection of linear operators to test I = lo.identity((n, n)) D = lo.diag(1. + np.arange(n)) C = lo.convolve((n, ), kernel=1. + np.arange(nk)) lo_list = [I, D, C.T * C] # collection of vectors ones16 = np.ones(n) def check_cond(A): Adec = utils.eigendecomposition(A, k=k, which='BE') assert_almost_equal(Adec.cond(), np.linalg.cond(A.todense())) def test_cond(): for A in lo_list:
#!/usr/bin/env python """ Testing of the lo.iterative module """ import nose from numpy.testing import * import numpy as np import linear_operators as lo from linear_operators import iterative # collection of linear operators to test mat16 = lo.aslinearoperator(np.random.rand(16, 16)) id16 = lo.identity((16, 16)) diag16 = lo.diag(np.random.rand(16)) conv16 = lo.convolve(16, np.random.rand(4), mode="same") lo_list = [mat16, id16, diag16, conv16] # collection of vectors ones16 = np.ones(16) zeros16 = np.zeros(16) rand16 = np.random.rand(16) v_list = [ones16, zeros16, rand16 ] # collection of methods methods = [iterative.acg, ] # tests def check_inv(method, A, x):