def run(args):
assert len(args) == 0
if '--exercise-retrieve-unless-exists' in args:
exercise_retrieve_unless_exists()
else:
print('Skipping exercise_retrieve_unless_exists')
exercise_misc()
assert utils.sequence_index_dict(["a", "b"]) == {"a": 0, "b": 1}
assert utils.flat_list(0) == [0]
assert utils.flat_list([1,2,3]) == [1,2,3]
assert utils.flat_list([1,[2,3,4],3]) == [1,2,3,4,3]
assert utils.flat_list([1,[2,3,4],[[3,4],[5,6]]]) == [1,2,3,4,3,4,5,6]
try:
raise RuntimeError("Trial")
except KeyboardInterrupt: raise
except Exception:
assert utils.format_exception() == "RuntimeError: Trial"
else: raise Exception_expected
try:
assert 1 == 2
except KeyboardInterrupt: raise
except Exception:
s = utils.format_exception()
assert s.startswith("AssertionError: ")
assert s.find("tst_utils.py line ") >= 0
else: raise Exception_expected
exercise_indented_display()
exercise_approx_equal()
exercise_file_utils()
exercise_dir_utils()
exercise_group_args()
exercise_round2()
print(utils.format_cpu_times())
def run(args):
assert len(args) == 0
exercise_forward_compatibility()
exercise_misc()
assert utils.sequence_index_dict(["a", "b"]) == {"a": 0, "b": 1}
assert utils.flat_list(0) == [0]
assert utils.flat_list([1, 2, 3]) == [1, 2, 3]
assert utils.flat_list([1, [2, 3, 4], 3]) == [1, 2, 3, 4, 3]
assert utils.flat_list([1, [2, 3, 4],
[[3, 4], [5, 6]]]) == [1, 2, 3, 4, 3, 4, 5, 6]
try:
raise RuntimeError("Trial")
except KeyboardInterrupt:
raise
except Exception:
assert utils.format_exception() == "RuntimeError: Trial"
else:
raise Exception_expected
try:
assert 1 == 2
except KeyboardInterrupt:
raise
except Exception:
s = utils.format_exception()
assert s.startswith("AssertionError: ")
assert s.find("tst_utils.py line ") >= 0
else:
raise Exception_expected
exercise_indented_display()
exercise_approx_equal()
exercise_file_utils()
exercise_dir_utils()
print utils.format_cpu_times()
def run(args):
assert len(args) == 0
if '--exercise-retrieve-unless-exists' in args:
exercise_retrieve_unless_exists()
else:
print 'Skipping exercise_retrieve_unless_exists'
exercise_forward_compatibility()
exercise_misc()
assert utils.sequence_index_dict(["a", "b"]) == {"a": 0, "b": 1}
assert utils.flat_list(0) == [0]
assert utils.flat_list([1,2,3]) == [1,2,3]
assert utils.flat_list([1,[2,3,4],3]) == [1,2,3,4,3]
assert utils.flat_list([1,[2,3,4],[[3,4],[5,6]]]) == [1,2,3,4,3,4,5,6]
try:
raise RuntimeError("Trial")
except KeyboardInterrupt: raise
except Exception:
assert utils.format_exception() == "RuntimeError: Trial"
else: raise Exception_expected
try:
assert 1 == 2
except KeyboardInterrupt: raise
except Exception:
s = utils.format_exception()
assert s.startswith("AssertionError: ")
assert s.find("tst_utils.py line ") >= 0
else: raise Exception_expected
exercise_indented_display()
exercise_approx_equal()
exercise_file_utils()
exercise_dir_utils()
print utils.format_cpu_times()
def as_miller_arrays(self,
data_block_name=None,
crystal_symmetry=None,
force_symmetry=False,
merge_equivalents=True,
base_array_info=None):
if base_array_info is None:
base_array_info = miller.array_info(source=self.file_path,
source_type="cif")
if data_block_name is not None:
arrays = self.build_miller_arrays(
data_block_name=data_block_name,
base_array_info=base_array_info).values()
else:
arrays = flat_list([
arrays.values() for arrays in self.build_miller_arrays(
base_array_info=base_array_info).values()
])
other_symmetry = crystal_symmetry
for i, array in enumerate(arrays):
if crystal_symmetry is not None:
crystal_symmetry_from_file = array.crystal_symmetry()
crystal_symmetry = crystal_symmetry_from_file.join_symmetry(
other_symmetry=other_symmetry, force=force_symmetry)
arrays[i] = array.customized_copy(
crystal_symmetry=crystal_symmetry)
arrays[i].set_info(array.info())
return arrays
def AddUntrustedPolygon(self, vertices):
if len(vertices) < 4:
return
vertices.append(vertices[0])
vertices = [self._pyslip.ConvertView2Geo(v) for v in vertices]
vertices = [
self._pyslip.tiles.map_relative_to_picture_fast_slow(*v) for v in vertices
]
point_ = []
panel_id = None
for p in vertices:
p1, p0, p_id = self._pyslip.tiles.flex_image.picture_to_readout(p[1], p[0])
assert p_id >= 0, "Point must be within a panel"
if panel_id is not None:
assert (
panel_id == p_id
), "All points must be contained within a single panel"
panel_id = p_id
point_.append((p0, p1))
vertices = point_
from libtbx.utils import flat_list
from dials.util import masking
region = masking.phil_scope.extract().untrusted[0]
points = flat_list(vertices)
region.polygon = [int(p) for p in points]
region.panel = panel_id
self.params.masking.untrusted.append(region)
def as_miller_arrays(self,
data_block_name=None,
crystal_symmetry=None,
force_symmetry=False,
merge_equivalents=True,
base_array_info=None,
anomalous=None):
if base_array_info is None:
base_array_info = miller.array_info(source=self.file_path,
source_type="cif")
if data_block_name is not None:
arrays = list(
self.build_miller_arrays(
data_block_name=data_block_name,
base_array_info=base_array_info).values())
else:
arrays = flat_list([
list(arrays.values()) for arrays in self.build_miller_arrays(
base_array_info=base_array_info).values()
])
other_symmetry = crystal_symmetry
for i in range(len(arrays)):
if crystal_symmetry is not None:
crystal_symmetry_from_file = arrays[i].crystal_symmetry()
crystal_symmetry = crystal_symmetry_from_file.join_symmetry(
other_symmetry=other_symmetry, force=force_symmetry)
arrays[i] = arrays[i].customized_copy(
crystal_symmetry=crystal_symmetry, info=arrays[i].info())
if anomalous is not None:
arrays[i] = arrays[i].customized_copy(anomalous_flag=anomalous,
info=arrays[i].info())
return arrays
def as_miller_arrays(self, data_block_name=None,
crystal_symmetry=None,
force_symmetry=False,
merge_equivalents=True,
base_array_info=None):
if base_array_info is None:
base_array_info = miller.array_info(
source=self.file_path, source_type="cif")
if data_block_name is not None:
arrays = self.build_miller_arrays(
data_block_name=data_block_name,
base_array_info=base_array_info).values()
else:
arrays = flat_list([
arrays.values() for arrays in
self.build_miller_arrays(base_array_info=base_array_info).values()])
other_symmetry=crystal_symmetry
for i, array in enumerate(arrays):
if crystal_symmetry is not None:
crystal_symmetry_from_file = array.crystal_symmetry()
crystal_symmetry = crystal_symmetry_from_file.join_symmetry(
other_symmetry=other_symmetry,
force=force_symmetry)
arrays[i] = array.customized_copy(crystal_symmetry=crystal_symmetry)
arrays[i].set_info(array.info())
return arrays
def check(a, b, c, expected_free_vars, expected_sol):
m = []
t = []
for i in range(3):
m.append([a[i], b[i]])
t.append(c[i])
m_orig = matrix.rec(flat_list(m), (3,2))
t_orig = list(t)
free_vars = row_echelon.form_rational(m, t)
assert free_vars == expected_free_vars
sol = row_echelon.back_substitution_rational(m, t, free_vars, [3, 11])
assert sol == expected_sol
if (sol is not None):
assert list(m_orig * sol) == t_orig
def check(a, b, c, expected_free_vars, expected_sol):
m = []
t = []
for i in xrange(3):
m.append([a[i], b[i]])
t.append(c[i])
m_orig = matrix.rec(flat_list(m), (3,2))
t_orig = list(t)
free_vars = row_echelon.form_rational(m, t)
assert free_vars == expected_free_vars
sol = row_echelon.back_substitution_rational(m, t, free_vars, [3, 11])
assert sol == expected_sol
if (sol is not None):
assert list(m_orig * sol) == t_orig
def calculate_state_uncertainties(self, var_cov):
"""Given a variance-covariance array for the parameters of this model,
propagate those estimated errors into the uncertainties of the model state"""
# the gradients are in an n-element list, each element of which is an
# object of length m with the same dimensions as the model state. The
# elements of this object contains the gradient of the state element in
# the equivalent position
grads = self.get_ds_dp()
if len(grads) == 0: return
if self._is_multi_state:
# in this case the gradients are an n-element list, each element of which
# is a list of length of the number of states. Each element of the list
# is an object of length m with the same dimensions as the model state.
# Reshape this data so that the list over the number of states becomes
# the outer level.
reshaped = []
for i in range(len(grads[0])):
reshaped.append([g[i] for g in grads])
grads = reshaped
else:
# we only have a single state, so put gradients in a 1-elt list to mimic
# the structure of the multi-state case
grads = [grads]
# the jacobian is the m*n matrix of partial derivatives of the m state
# elements wrt the n parameters
from libtbx.utils import flat_list
from scitbx.array_family import flex
state_covs = []
for grads_one_state in grads:
jacobian_t = flex.double(flat_list(grads_one_state))
jacobian_t.reshape(flex.grid(len(grads_one_state),
len(grads_one_state[0].elems)))
# propagation of errors takes the variance-covariance matrix of parameters,
# along with the jacobian mapping changes in parameter values to changes
# in the model state elements, to calculate an approximate variance-
# covariance matrix of the state elements. That is, state_cov is the
# matrix product: jacobian * var_cov * jacobian_t
tmp = var_cov.matrix_multiply(jacobian_t)
state_cov = jacobian_t.matrix_transpose_multiply(tmp).as_scitbx_matrix()
state_covs.append(state_cov)
return state_covs
def calculate_state_uncertainties(self, var_cov):
"""Given a variance-covariance array for the parameters of this model,
propagate those estimated errors into the uncertainties of the model state"""
grads = []
if self._is_multi_state:
i = 0
while True:
try:
grads.append(self.get_ds_dp(multi_state_elt=i))
i += 1
except IndexError:
break
else:
grads.append(self.get_ds_dp())
if len(grads[0]) == 0: return
# the jacobian is the m*n matrix of partial derivatives of the m state
# elements wrt the n parameters
from libtbx.utils import flat_list
from scitbx.array_family import flex
state_covs = []
for grads_one_state in grads:
jacobian_t = flex.double(flat_list(grads_one_state))
jacobian_t.reshape(
flex.grid(len(grads_one_state), len(grads_one_state[0].elems)))
# propagation of errors takes the variance-covariance matrix of parameters,
# along with the jacobian mapping changes in parameter values to changes
# in the model state elements, to calculate an approximate variance-
# covariance matrix of the state elements. That is, state_cov is the
# matrix product: jacobian * var_cov * jacobian_t
tmp = var_cov.matrix_multiply(jacobian_t)
state_cov = jacobian_t.matrix_transpose_multiply(
tmp).as_scitbx_matrix()
state_covs.append(state_cov)
return state_covs
def calculate_state_uncertainties(self, var_cov):
"""Given a variance-covariance array for the parameters of this model,
propagate those estimated errors into the uncertainties of the model state"""
grads = []
if self._is_multi_state:
i = 0
while True:
try:
grads.append(self.get_ds_dp(multi_state_elt=i))
i += 1
except IndexError:
break
else:
grads.append(self.get_ds_dp())
if len(grads[0]) == 0: return
# the jacobian is the m*n matrix of partial derivatives of the m state
# elements wrt the n parameters
from libtbx.utils import flat_list
from scitbx.array_family import flex
state_covs = []
for grads_one_state in grads:
jacobian_t = flex.double(flat_list(grads_one_state))
jacobian_t.reshape(flex.grid(len(grads_one_state),
len(grads_one_state[0].elems)))
# propagation of errors takes the variance-covariance matrix of parameters,
# along with the jacobian mapping changes in parameter values to changes
# in the model state elements, to calculate an approximate variance-
# covariance matrix of the state elements. That is, state_cov is the
# matrix product: jacobian * var_cov * jacobian_t
tmp = var_cov.matrix_multiply(jacobian_t)
state_cov = jacobian_t.matrix_transpose_multiply(tmp).as_scitbx_matrix()
state_covs.append(state_cov)
return state_covs
def exercise_rational():
from scitbx.matrix import row_echelon
from scitbx import matrix
from libtbx.utils import flat_list
from boost_adaptbx.boost import rational
import random
rng = random.Random(0)
#
m = [[0]]
t = [0]
free_vars = row_echelon.form_rational(m, t)
assert m == [[0]]
assert t == [0]
assert free_vars == [0]
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol == [1]
sol = row_echelon.back_substitution_rational(m, None, free_vars, [2])
assert sol == [2]
#
m = [[0]]
t = [1]
free_vars = row_echelon.form_rational(m, t)
assert m == [[0]]
assert t == [1]
assert free_vars == [0]
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol is None
#
m = [[1]]
t = [2]
free_vars = row_echelon.form_rational(m, t)
assert m == [[1]]
assert t == [2]
assert free_vars == []
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol == [2]
#
def rr():
return rational.int(rng.randrange(-5,6), rng.randrange(1,10))
#
for i_trial in range(10):
for nr in [1,2,3]:
for nc in [1,2,3]:
m = []
for ir in range(nr):
m.append([rr() for ic in range(nc)])
m_orig = matrix.rec(flat_list(m), (nr,nc))
sol_orig = [rr() for ic in range(nc)]
t_orig = list(m_orig * matrix.col(sol_orig))
t = list(t_orig)
free_vars = row_echelon.form_rational(m, t)
sol = [None] * nc
for ic in free_vars:
sol[ic] = sol_orig[ic]
sol = row_echelon.back_substitution_rational(m, t, free_vars, sol)
assert sol is not None
assert sol.count(None) == 0
assert sol == sol_orig
sol = [1] * nc
sol = row_echelon.back_substitution_rational(m, None, free_vars, sol)
assert sol is not None
assert (m_orig * matrix.col(sol)).dot() == 0
#
for i_trial in range(10):
from itertools import count
for i in count(10):
a = matrix.col([rr(), rr(), rr()])
b = matrix.col([rr(), rr(), rr()])
if (a.cross(b).dot() != 0):
break
else:
raise RuntimeError
p = rng.randrange(-5,6)
q = rng.randrange(-5,6)
def check(a, b, c, expected_free_vars, expected_sol):
m = []
t = []
for i in range(3):
m.append([a[i], b[i]])
t.append(c[i])
m_orig = matrix.rec(flat_list(m), (3,2))
t_orig = list(t)
free_vars = row_echelon.form_rational(m, t)
assert free_vars == expected_free_vars
sol = row_echelon.back_substitution_rational(m, t, free_vars, [3, 11])
assert sol == expected_sol
if (sol is not None):
assert list(m_orig * sol) == t_orig
check(a, b, p*a+q*b, [], [p,q])
check(a, b, a.cross(b), [], None)
check(a, 5*a, -7*a, [1], [-62,11])
check(a, 5*a, b, [1], None)
check([0,0,0], [0,0,0], [0,0,0], [0,1], [3,11])
def find_basis_vector_combinations_cluster_analysis(self):
# hijack the xray.structure class to facilitate calculation of distances
xs = xray.structure(crystal_symmetry=self.crystal_symmetry)
for i, site in enumerate(self.sites):
xs.add_scatterer(xray.scatterer("C%i" %i, site=site))
xs = xs.sites_mod_short()
xs = xs.select(xs.sites_frac().norms() < 0.45)
cell_multiplier = 10
xs1 = xs.customized_copy(
unit_cell=uctbx.unit_cell([xs.unit_cell().parameters()[0]*cell_multiplier]*3))
xs1.set_sites_cart(xs.sites_cart())
xs = xs1
sites_cart = xs.sites_cart()
lengths = flex.double([matrix.col(sc).length() for sc in sites_cart])
xs = xs.select(flex.sort_permutation(lengths))
if self.params.debug:
with open('peaks.pdb', 'wb') as f:
print >> f, xs.as_pdb_file()
vector_heights = flex.double()
sites_frac = xs.sites_frac()
pair_asu_table = xs.pair_asu_table(distance_cutoff=self.params.max_cell)
asu_mappings = pair_asu_table.asu_mappings()
distances = crystal.calculate_distances(pair_asu_table, sites_frac)
vectors = []
difference_vectors = []
pairs = []
for di in distances:
if di.distance < self.params.min_cell: continue
i_seq, j_seq = di.i_seq, di.j_seq
if i_seq > j_seq: continue
pairs.append((i_seq, j_seq))
rt_mx_ji = di.rt_mx_ji
site_frac_ji = rt_mx_ji * sites_frac[j_seq]
site_cart_ji = xs.unit_cell().orthogonalize(site_frac_ji)
site_cart_i = xs.unit_cell().orthogonalize(sites_frac[i_seq])
vectors.append(matrix.col(site_cart_ji))
diff_vec = matrix.col(site_cart_i) - matrix.col(site_cart_ji)
if diff_vec[0] < 0:
# only one hemisphere of difference vector space
diff_vec = -diff_vec
difference_vectors.append(diff_vec)
params = self.params.multiple_lattice_search.cluster_analysis
if params.method == 'dbscan':
i_cluster = self.cluster_analysis_dbscan(difference_vectors)
min_cluster_size = 1
elif params.method == 'hcluster':
i_cluster = self.cluster_analysis_hcluster(difference_vectors)
i_cluster -= 1 # hcluster starts counting at 1
min_cluster_size = params.min_cluster_size
if self.params.debug_plots:
self.debug_plot_clusters(
difference_vectors, i_cluster, min_cluster_size=min_cluster_size)
clusters = []
min_cluster_size = params.min_cluster_size
for i in range(max(i_cluster)+1):
isel = (i_cluster == i).iselection()
if len(isel) < min_cluster_size:
continue
clusters.append(isel)
cluster_point_sets = []
centroids = []
cluster_sizes = flex.int()
difference_vectors = flex.vec3_double(difference_vectors)
from libtbx.utils import flat_list
for cluster in clusters:
points = flat_list([pairs[i] for i in cluster])
cluster_point_sets.append(set(points))
d_vectors = difference_vectors.select(cluster)
cluster_sizes.append(len(d_vectors))
centroids.append(d_vectors.mean())
# build a graph where each node is a centroid from the difference vector
# cluster analysis above, and an edge is defined when there is a
# significant overlap between the sets of peaks in the FFT map that
# contributed to the difference vectors in two clusters
import networkx as nx
G = nx.Graph()
G.add_nodes_from(range(len(cluster_point_sets)))
cutoff_frac = 0.25
for i in range(len(cluster_point_sets)):
for j in range(i+1, len(cluster_point_sets)):
intersection_ij = cluster_point_sets[i].intersection(
cluster_point_sets[j])
union_ij = cluster_point_sets[i].union(cluster_point_sets[j])
frac_connected = len(intersection_ij)/len(union_ij)
if frac_connected > cutoff_frac:
G.add_edge(i, j)
# iteratively find the maximum cliques in the graph
# break from the loop if there are no cliques remaining or there are
# fewer than 3 vectors in the remaining maximum clique
# Allow 1 basis vector to be shared between two cliques, to allow for
# cases where two lattices share one basis vectors (e.g. two plate
# crystals exactly aligned in one direction, but not in the other two)
distinct_cliques = []
cliques = list(nx.find_cliques(G))
cliques = sorted(cliques, key=len, reverse=True)
for i, clique in enumerate(cliques):
clique = set(clique)
if len(clique) < 3:
break
is_distinct = True
for c in distinct_cliques:
if len(c.intersection(clique)) > 1:
is_distinct = False
break
if is_distinct:
distinct_cliques.append(clique)
this_set = set()
for i_cluster in clique:
this_set = this_set.union(cluster_point_sets[i_cluster])
logger.info("Clique %i: %i lattice points" %(i+1, len(this_set)))
assert len(distinct_cliques) > 0
logger.info("Estimated number of lattices: %i" %len(distinct_cliques))
self.candidate_basis_vectors = []
self.candidate_crystal_models = []
for clique in distinct_cliques:
sel = flex.size_t(list(clique))
vectors = flex.vec3_double(centroids).select(sel)
perm = flex.sort_permutation(vectors.norms())
vectors = [matrix.col(vectors[p]) for p in perm]
# exclude vectors that are (approximately) integer multiples of a shorter
# vector
unique_vectors = []
for v in vectors:
is_unique = True
for v_u in unique_vectors:
if is_approximate_integer_multiple(v_u, v,
relative_tolerance=0.01,
angular_tolerance=0.5):
is_unique = False
break
if is_unique:
unique_vectors.append(v)
vectors = unique_vectors
self.candidate_basis_vectors.extend(vectors)
candidate_orientation_matrices \
= self.find_candidate_orientation_matrices(
vectors,
max_combinations=self.params.basis_vector_combinations.max_try)
if len(candidate_orientation_matrices) == 0:
continue
crystal_model, n_indexed = self.choose_best_orientation_matrix(
candidate_orientation_matrices)
if crystal_model is None: continue
# map to minimum reduced cell
crystal_symmetry = crystal.symmetry(
unit_cell=crystal_model.get_unit_cell(),
space_group=crystal_model.get_space_group())
cb_op = crystal_symmetry.change_of_basis_op_to_minimum_cell()
crystal_model = crystal_model.change_basis(cb_op)
self.candidate_crystal_models.append(crystal_model)
if self.params.debug:
file_name = "vectors.pdb"
a = self.params.max_cell
cs = crystal.symmetry(unit_cell=(a,a,a,90,90,90), space_group="P1")
xs = xray.structure(crystal_symmetry=cs)
for v in difference_vectors:
v = matrix.col(v)
xs.add_scatterer(xray.scatterer("C", site=v/(a/10)))
xs.sites_mod_short()
with open(file_name, 'wb') as f:
print >> f, xs.as_pdb_file()
for crystal_model in self.candidate_crystal_models:
logger.debug(crystal_model)
def exercise_rational():
from scitbx.matrix import row_echelon
from scitbx import matrix
from libtbx.utils import flat_list
from boost import rational
import random
rng = random.Random(0)
#
m = [[0]]
t = [0]
free_vars = row_echelon.form_rational(m, t)
assert m == [[0]]
assert t == [0]
assert free_vars == [0]
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol == [1]
sol = row_echelon.back_substitution_rational(m, None, free_vars, [2])
assert sol == [2]
#
m = [[0]]
t = [1]
free_vars = row_echelon.form_rational(m, t)
assert m == [[0]]
assert t == [1]
assert free_vars == [0]
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol is None
#
m = [[1]]
t = [2]
free_vars = row_echelon.form_rational(m, t)
assert m == [[1]]
assert t == [2]
assert free_vars == []
sol = row_echelon.back_substitution_rational(m, t, free_vars, [1])
assert sol == [2]
#
def rr():
return rational.int(rng.randrange(-5,6), rng.randrange(1,10))
#
for i_trial in xrange(10):
for nr in [1,2,3]:
for nc in [1,2,3]:
m = []
for ir in xrange(nr):
m.append([rr() for ic in xrange(nc)])
m_orig = matrix.rec(flat_list(m), (nr,nc))
sol_orig = [rr() for ic in xrange(nc)]
t_orig = list(m_orig * matrix.col(sol_orig))
t = list(t_orig)
free_vars = row_echelon.form_rational(m, t)
sol = [None] * nc
for ic in free_vars:
sol[ic] = sol_orig[ic]
sol = row_echelon.back_substitution_rational(m, t, free_vars, sol)
assert sol is not None
assert sol.count(None) == 0
assert sol == sol_orig
sol = [1] * nc
sol = row_echelon.back_substitution_rational(m, None, free_vars, sol)
assert sol is not None
assert (m_orig * matrix.col(sol)).dot() == 0
#
for i_trial in xrange(10):
from itertools import count
for i in count(10):
a = matrix.col([rr(), rr(), rr()])
b = matrix.col([rr(), rr(), rr()])
if (a.cross(b).dot() != 0):
break
else:
raise RuntimeError
p = rng.randrange(-5,6)
q = rng.randrange(-5,6)
def check(a, b, c, expected_free_vars, expected_sol):
m = []
t = []
for i in xrange(3):
m.append([a[i], b[i]])
t.append(c[i])
m_orig = matrix.rec(flat_list(m), (3,2))
t_orig = list(t)
free_vars = row_echelon.form_rational(m, t)
assert free_vars == expected_free_vars
sol = row_echelon.back_substitution_rational(m, t, free_vars, [3, 11])
assert sol == expected_sol
if (sol is not None):
assert list(m_orig * sol) == t_orig
check(a, b, p*a+q*b, [], [p,q])
check(a, b, a.cross(b), [], None)
check(a, 5*a, -7*a, [1], [-62,11])
check(a, 5*a, b, [1], None)
check([0,0,0], [0,0,0], [0,0,0], [0,1], [3,11])
