def test_constructors():
int_row = 10
int_col = 20
# int row/col
m1 = Matrix(int_row, int_col)
check_dense_mat(m1, int_row, int_col)
# int row/col w/ name
m2 = Matrix("m2", int_row, int_col)
check_dense_mat(m2, int_row, int_col, "m2")
dim_row = Dimension([3, 2, 1, 4])
dim_col = Dimension([4, 2, 0, 2])
# dim row/col (default sym)
m3 = Matrix("m3", dim_row, dim_col)
check_block_sparse_mat(m3, 4, dim_row, dim_col, "m3")
# dim row/col symm specified
m4 = Matrix("m4", dim_row, dim_col, 2)
check_block_sparse_mat(m4, 4, dim_row, dim_col, "m4", 2)
def test_clone():
dim = Dimension([1, 2, 3])
vec = Vector(dim)
copy = vec.clone()
assert copy.dimpi() == dim
a_name = "A^T"
else:
a_name = "A "
if bt:
b_name = "B^T"
else:
b_name = "B "
return f" N(G){gsz} || G(A): {Ga} || G(B): {Gb} || doublet({a_name} x {b_name}) || {sq_or_rec.upper()}"
dim_choices1 = [2, 3, 4, 5, 6, 7, 8, 9]
dim_choices2 = [x + 1 for x in dim_choices1]
doublet_args = []
group_size = 4
for group_size in [1, 2, 4, 8]:
d1 = Dimension([dim_choices1[x] for x in range(group_size)])
d2 = Dimension([dim_choices2[x] for x in range(group_size)])
a11_set = [(d1, d1, H) for H in range(group_size)]
b11_set = [(d1, d1, H) for H in range(group_size)]
for aargs, bargs, at, bt in itertools.product(a11_set, b11_set,
[True, False],
[True, False]):
adl, adr, Ga = aargs
bdl, bdr, Gb = bargs
doublet_args.append(
(group_size, adl, adr, Ga, bdl, bdr, Gb, at, bt, 'square'))
a12_set = [(d1, d2, H) for H in range(group_size)]
b12_set = [(d1, d2, H) for H in range(group_size)]
b21_set = [(d2, d1, H) for H in range(group_size)]
def array_to_matrix(
self,
arr: Union[np.ndarray, List[np.ndarray]],
name: str = "New Matrix",
dim1: Union[List, Tuple, core.Dimension] = None,
dim2: core.Dimension = None) -> Union[core.Matrix, core.Vector]:
"""
Converts a numpy array or list of numpy arrays into a Psi4 Matrix (irreped if list).
Parameters
----------
arr
Numpy array or list of arrays to use as the data for a new core.Matrix
name
Name to give the new core.Matrix
dim1
If a single dense numpy array is given, a dimension can be supplied to
apply irreps to this array. Note that this discards all extra information
given in the matrix besides the diagonal blocks determined by the passed
dimension.
dim2
Same as dim1 only if using a psi4.core.Dimension object.
Returns
-------
Matrix or Vector
Returns the given Psi4 object
Notes
-----
This is a generalized function to convert a NumPy array to a Psi4 object
Examples
--------
>>> data = np.random.rand(20,1)
>>> vector = psi4.core.Matrix.from_array(data)
>>> irrep_data = [np.random.rand(2, 2), np.empty(shape=(0,3)), np.random.rand(4, 4)]
>>> matrix = psi4.core.Matrix.from_array(irrep_data)
>>> print(matrix.rowdim().to_tuple())
(2, 0, 4)
"""
# What type is it? MRO can help.
arr_type = self.__mro__[0]
# Irreped case
if isinstance(arr, (list, tuple)):
if (dim1 is not None) or (dim2 is not None):
raise ValidationError(
"Array_to_Matrix: If passed input is list of arrays dimension cannot be specified."
)
irreps = len(arr)
if arr_type == core.Matrix:
sdim1 = core.Dimension(irreps)
sdim2 = core.Dimension(irreps)
for i in range(irreps):
d1, d2 = _find_dim(arr[i], 2)
sdim1[i] = d1
sdim2[i] = d2
ret = self(name, sdim1, sdim2)
elif arr_type == core.Vector:
sdim1 = core.Dimension(irreps)
for i in range(irreps):
d1 = _find_dim(arr[i], 1)
sdim1[i] = d1[0]
ret = self(name, sdim1)
else:
raise ValidationError(
"Array_to_Matrix: type '%s' is not recognized." %
str(arr_type))
for view, vals in zip(ret.nph, arr):
if 0 in view.shape: continue
view[:] = vals
return ret
# No irreps implied by list
else:
if arr_type == core.Matrix:
# Build an irreped array back out
if dim1 is not None:
if dim2 is None:
raise ValidationError(
"Array_to_Matrix: If dim1 is supplied must supply dim2 also"
)
dim1 = core.Dimension.from_list(dim1)
dim2 = core.Dimension.from_list(dim2)
if dim1.n() != dim2.n():
raise ValidationError(
"Array_to_Matrix: Length of passed dim1 must equal length of dim2."
)
ret = self(name, dim1, dim2)
start1 = 0
start2 = 0
for num, interface in enumerate(ret.nph):
d1 = dim1[num]
d2 = dim2[num]
if (d1 == 0) or (d2 == 0):
continue
view = np.asarray(interface)
view[:] = arr[start1:start1 + d1, start2:start2 + d2]
start1 += d1
start2 += d2
return ret
# Simple case without irreps
else:
ret = self(name, arr.shape[0], arr.shape[1])
ret.np[:] = arr
return ret
elif arr_type == core.Vector:
# Build an irreped array back out
if dim1 is not None:
if dim2 is not None:
raise ValidationError(
"Array_to_Matrix: If dim2 should not be supplied for 1D vectors."
)
dim1 = core.Dimension.from_list(dim1)
ret = self(name, dim1)
start1 = 0
for num, interface in enumerate(ret.nph):
d1 = dim1[num]
if (d1 == 0):
continue
view = np.asarray(interface)
view[:] = arr[start1:start1 + d1]
start1 += d1
return ret
# Simple case without irreps
else:
ret = self(name, arr.shape[0])
ret.np[:] = arr
return ret
else:
raise ValidationError(
"Array_to_Matrix: type '%s' is not recognized." %
str(arr_type))