def exercise_matrix():
a = sparse.matrix(10, 7)
assert a.n_rows == 10 and a.n_cols == 7
for c in a.cols():
assert c.is_structurally_zero()
a[0, 1] = 1.
a[9, 5] = 2.
assert a.non_zeroes == 2
for i in xrange(10):
for j in xrange(7):
if (i, j) == (0, 1): assert a[i, j] == 1.
elif (i, j) == (9, 5): assert a[i, j] == 2.
else: assert a[i, j] == 0, (i, j, a[i, j])
a = sparse.matrix(6, 3)
assert a.n_rows == 6
a[1, 1] = 1.
a[3, 2] = 2.
a[5, 1] = 2.
a[4, 0] = 1.
assert a.non_zeroes == 4
assert a.n_rows == 6
a[7, 0] = 1.
assert a[7, 0] == 0
assert a.n_rows == 6
a = sparse.matrix(4, 3)
a[0, 1] = 1.01
b = sparse.matrix(4, 3)
b[0, 1] = 1.02
b[3, 2] = 0.001
approx_equal = sparse.approx_equal(tolerance=0.1)
assert approx_equal(a, b)
m = 10
a = sparse.matrix(m, 2)
columns = (sparse.matrix_column(m, {
1: 0.1,
2: 0.2
}), sparse.matrix_column(m, {
4: 0.4,
8: 0.8
}))
a[:, 0] = columns[0]
a[:, 1] = columns[1]
assert a[:, 0], a[:, 1] == columns
try:
a[1, :] = sparse.vector(2, {1: 1})
raise Exception_expected
except RuntimeError, e:
assert str(e)
def exercise_matrix():
a = sparse.matrix(10,7)
assert a.n_rows == 10 and a.n_cols == 7
for c in a.cols():
assert c.is_structurally_zero()
a[0,1] = 1.
a[9,5] = 2.
assert a.non_zeroes == 2
for i in xrange(10):
for j in xrange(7):
if (i,j) == (0,1): assert a[i,j] == 1.
elif (i,j) == (9,5): assert a[i,j] == 2.
else: assert a[i,j] == 0, (i, j, a[i,j])
a = sparse.matrix(6, 3)
assert a.n_rows == 6
a[1,1] = 1.
a[3,2] = 2.
a[5,1] = 2.
a[4,0] = 1.
assert a.non_zeroes == 4
assert a.n_rows == 6
a[7,0] = 1.
assert a[7,0] == 0
assert a.n_rows == 6
a = sparse.matrix(4,3)
a[0,1] = 1.01
b = sparse.matrix(4,3)
b[0,1] = 1.02
b[3,2] = 0.001
approx_equal = sparse.approx_equal(tolerance=0.1)
assert approx_equal(a,b)
m = 10
a = sparse.matrix(m, 2)
columns = ( sparse.matrix_column(m, {1:0.1, 2:0.2}),
sparse.matrix_column(m, {4:0.4, 8:0.8}) )
a[:,0] = columns[0]
a[:,1] = columns[1]
assert a[:,0], a[:,1] == columns
try:
a[1,:] = sparse.vector(2, {1:1})
raise Exception_expected
except RuntimeError, e:
assert str(e)
def _extend_gradient_vectors(results, m, n, keys=("dX_dp", "dY_dp", "dZ_dp")):
"""Extend results list by n empty results. These will each be a dictionary
indexed by the given keys. The value for each key will be an empty vector of
size m, to store the derivatives of n parameters, for m reflections.
This is the sparse vector version."""
new_results = [{key: sparse.matrix_column(m) for key in keys} for _ in range(n)]
results.extend(new_results)
return results
def _extend_gradient_vectors(results, m, n,
keys=("dX_dp", "dY_dp", "dZ_dp")):
"""Extend results list by n empty results. These will each be a dictionary
indexed by the given keys. The value for each key will be an empty vector of
size m, to store the derivatives of n parameters, for m reflections.
This is the sparse vector version."""
from scitbx import sparse
new_results = []
for i in range(n):
result = {}
for key in keys:
result[key] = sparse.matrix_column(m)
new_results.append(result)
results.extend(new_results)
return results
def _extend_gradient_vectors(results,
m,
n,
keys=("dX_dp", "dY_dp", "dZ_dp")):
"""Extend results list by n empty results. These will each be a dictionary
indexed by the given keys. The value for each key will be an empty vector of
size m, to store the derivatives of n parameters, for m reflections.
This is the sparse vector version."""
from scitbx import sparse
new_results = []
for i in range(n):
result = {}
for key in keys:
result[key] = sparse.matrix_column(m)
new_results.append(result)
results.extend(new_results)
return results