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):