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# Psi4: an open-source quantum chemistry software package
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"""
Array function, including NumPy interface and Python extensions to core array
classes:
- Matrix (constructor, view, access, serialization)
- Vector (constructor, view, access, serialization)
- Dimension (constructor)
- CIVector (view)
"""
__all__ = [
"array_to_matrix",
"block_diagonal_array",
]
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
import numpy as np
from psi4 import core
from .exceptions import ValidationError
### Matrix and Vector properties
def _get_raw_views(self, copy=False):
"""
Gets simple raw view of the passed in object.
"""
if copy:
return tuple([np.array(x) for x in self.array_interface()])
else:
return tuple(self.array_interface())
def _find_dim(arr, ndim):
"""
Helper function to help deal with zero or sized arrays
"""
# Zero arrays
if (arr is None) or (arr is False):
return [0] * ndim
# Make sure this is a numpy array like thing
if not hasattr(arr, 'shape'):
raise ValidationError("Expected numpy array, found object of type '%s'" % type(arr))
if len(arr.shape) == ndim:
return [arr.shape[x] for x in range(ndim)]
else:
raise ValidationError("Input array does not have a valid shape.")
[docs]
def array_to_matrix(
self: Union[core.Matrix, core.Vector],
arr: Union[np.ndarray, List[np.ndarray]],
name: str = "New Matrix",
dim1: Optional[Union[List, Tuple, core.Dimension]] = None,
dim2: Optional[core.Dimension] = None,
) -> Union[core.Matrix, core.Vector]:
"""
Converts a `NumPy array
<https://numpy.org/doc/stable/reference/arrays.ndarray.html>`_ or list of
NumPy arrays into a |PSIfour| :class:`~psi4.core.Matrix` or
:class:`~psi4.core.Vector` (irrepped if list).
Parameters
----------
self
Matrix or Vector class.
arr
NumPy array or list of arrays to use as the data for a new
:class:`~psi4.core.Matrix` or :class:`~psi4.core.Vector`.
name
Name to give the new :class:`~psi4.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 :class:`~psi4.core.Dimension` object.
Returns
-------
Matrix or Vector
Returns the given (`self`) 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]
# Irrepped 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 irrepped 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 irrepped 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))
[docs]
def _to_array(
matrix: Union[core.Matrix, core.Vector],
copy: bool = True,
dense: bool = False,
) -> Union[np.ndarray, List[np.ndarray]]:
"""
Converts a |PSIfour| Matrix or Vector to a NumPy array. Either copies the
data or simply constructs a view.
Parameters
----------
matrix
Pointers to which Psi4 core class should be used in the construction.
copy
Copy the data if `True`, return a view otherwise
dense
Converts irrepped Psi4 objects to diagonally blocked dense arrays if
`True`. Returns a list of arrays otherwise.
Returns
-------
~numpy.ndarray or ~typing.List[~numpy.ndarray]
Returns a single or list of NumPy arrays depending on options.
Notes
-----
This is a generalized function to convert a Psi4 object to a NumPy array
Examples
--------
>>> data = psi4.core.Matrix(3, 3)
>>> data.to_array()
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
"""
if matrix.nirrep() > 1:
# We will copy when we make a large matrix
if dense:
copy = False
matrix_views = _get_raw_views(matrix, copy=copy)
# Return the list of arrays
if dense is False:
return matrix_views
# Build the dense matrix
if isinstance(matrix, core.Vector):
ret_type = '1D'
elif isinstance(matrix, core.Matrix):
ret_type = '2D'
else:
raise ValidationError("Array_to_Matrix: type '%s' is not recognized." % type(matrix))
dim1 = []
dim2 = []
for h in matrix_views:
# Ignore zero dim irreps
if 0 in h.shape:
dim1.append(0)
dim2.append(0)
else:
dim1.append(h.shape[0])
if ret_type == '2D':
dim2.append(h.shape[1])
ndim1 = np.sum(dim1)
ndim2 = np.sum(dim2)
if ret_type == '1D':
dense_ret = np.zeros(shape=(ndim1))
start = 0
for d1, arr in zip(dim1, matrix_views):
if d1 == 0: continue
dense_ret[start:start + d1] = arr
start += d1
else:
dense_ret = np.zeros(shape=(ndim1, ndim2))
start1 = 0
start2 = 0
for d1, d2, arr in zip(dim1, dim2, matrix_views):
if (d1 == 0) or (d2 == 0): continue
dense_ret[start1:start1 + d1, start2:start2 + d2] = arr
start1 += d1
start2 += d2
return dense_ret
else:
return _get_raw_views(matrix, copy=copy)[0]
@property
def _np_shape(self):
"""
Shape of the Psi4 data object.
"""
view_data = _get_raw_views(self)
if self.nirrep() > 1:
return tuple(view_data[x].shape for x in range(self.nirrep()))
else:
return view_data[0].shape
@property
def _np_view(self):
"""
View with single irrep.
"""
if self.nirrep() > 1:
raise ValidationError("Attempted to call .np on a Psi4 data object with multiple irreps."
"Please use .nph for objects with irreps.")
return _get_raw_views(self)[0]
@property
def _nph_view(self):
"""
View with irreps.
"""
return _get_raw_views(self)
@property
def _array_conversion(self):
"""
Provides the array interface to simply classes so that np.array(core.Matrix(5, 5)) works flawlessly.
"""
if self.nirrep() > 1:
raise ValidationError("__array__interface__ can only be called on Psi4 data object with only one irrep!")
else:
return self.np.__array_interface__
def _np_write(
self: Union[core.Matrix, core.Vector],
filename: Optional[str] = None,
prefix: str = "",
) -> Optional[Dict[str, Any]]:
"""
Writes the irrepped matrix to a NumPy uncompressed file using :func:`numpy.savez`.
Can return the packed data for saving many matrices into the same file.
Parameters
----------
self
Instance to be serialized.
filename
File name where the data will be saved.
prefix
Name of instance prepared for NumPy.
Returns
-------
None or ~typing.Dict[str, ~typing.Any]
When `filename` given, it and dict serialization passed to
:func:`numpy.savez`, so ``.npz`` file saved and None returned.
When `filename` None, dict serialization returned.
"""
ret = {}
ret[prefix + "Irreps"] = self.nirrep()
ret[prefix + "Name"] = self.name
for h, v in enumerate(self.nph):
# If returning arrays to user, we want to return copies (snapshot), not
# views of the core.Matrix's memory.
if filename is None and not v.flags['OWNDATA']:
v = np.copy(v)
ret[prefix + "IrrepData" + str(h)] = v
if isinstance(self, core.Matrix):
ret[prefix + "Dim1"] = self.rowdim().to_tuple()
ret[prefix + "Dim2"] = self.coldim().to_tuple()
if isinstance(self, core.Vector):
ret[prefix + "Dim"] = [self.dim(x) for x in range(self.nirrep())]
if filename is None:
return ret
np.savez(filename, **ret)
def _np_read(
self: Union[core.Matrix, core.Vector],
filename: str,
prefix: str = "",
) -> Union[core.Matrix, core.Vector]:
"""Reads the data from a NumPy compressed or uncompressed file using :func:`numpy.load`.
Parameters
----------
self
Pointer to which class to be constructed.
filename
File name to read.
prefix
Name under which array was saved for NumPy.
"""
if isinstance(filename, np.lib.npyio.NpzFile):
data = filename
elif isinstance(filename, str):
if not filename.endswith('.npz'):
filename = filename + '.npz'
data = np.load(filename)
else:
raise Exception("Filename not understood: %s" % filename)
ret_data = []
if ((prefix + "Irreps") not in data.keys()) or ((prefix + "Name") not in data.keys()):
raise ValidationError("File %s does not appear to be a numpyz save" % filename)
for h in range(data[prefix + "Irreps"]):
ret_data.append(data[prefix + "IrrepData" + str(h)])
arr_type = self.__mro__[0]
if arr_type == core.Matrix:
dim1 = core.Dimension.from_list(data[prefix + "Dim1"])
dim2 = core.Dimension.from_list(data[prefix + "Dim2"])
ret = self(str(data[prefix + "Name"]), dim1, dim2)
elif arr_type == core.Vector:
dim1 = core.Dimension.from_list(data[prefix + "Dim"])
ret = self(str(data[prefix + "Name"]), dim1)
for h in range(data[prefix + "Irreps"]):
ret.nph[h][:] = ret_data[h]
return ret
def _to_serial(self: Union[core.Matrix, core.Vector]) -> Dict[str, Any]:
"""
Converts an object with a ``.nph`` accessor to a serialized dictionary
Parameters
----------
self
Matrix or Vector instance.
Returns
-------
~typing.Dict[str, ~typing.Any]
Serialized dictionary with keys:
- shape
- data : List[str]
- type : {'matrix', 'vector'}
"""
json_data = {}
json_data["shape"] = []
json_data["data"] = []
for view in self.nph:
json_data["shape"].append(view.shape)
json_data["data"].append(view.tostring())
if len(json_data["shape"][0]) == 1:
json_data["type"] = "vector"
elif len(json_data["shape"][0]) == 2:
json_data["type"] = "matrix"
else:
raise ValidationError("_to_json is only used for vector and matrix objects.")
return json_data
def _from_serial(self, json_data: Dict[str, Any]) -> Union[core.Matrix, core.Vector]:
"""
Converts serialized data to the correct Psi4 data type
Parameters
----------
self
Pointer to which class to be constructed.
json_data
Serialization of class. See :meth:`to_serial` for data layout.
"""
if json_data["type"] == "vector":
dim1 = core.Dimension.from_list([x[0] for x in json_data["shape"]])
ret = self("Vector from JSON", dim1)
elif json_data["type"] == "matrix":
dim1 = core.Dimension.from_list([x[0] for x in json_data["shape"]])
dim2 = core.Dimension.from_list([x[1] for x in json_data["shape"]])
ret = self("Matrix from JSON", dim1, dim2)
else:
raise ValidationError("_from_json did not recognize type option of %s." % str(json_data["type"]))
for n in range(len(ret.nph)):
ret.nph[n].flat[:] = np.frombuffer(json_data["data"][n], dtype=np.double)
return ret
def _chain_dot(*args, **kwargs) -> core.Matrix:
"""Chains dot products together from a series of Psi4 Matrix classes. Uses :func:`~psi4.core.doublet`.
Parameters
----------
args
Arbitrary number of :class:`~psi4.core.Matrix` arguments to be
multiplied.
trans
Optional iterable of booleans of length number of `args` to designate
transposes, if any.
"""
trans = kwargs.pop("trans", None)
if trans is None:
trans = [False for x in range(len(args))]
else:
if len(trans) != len(args):
raise ValidationError(
"Chain dot: The length of the transpose arguements is not equal to the length of args.")
# Setup chain
ret = args[0]
if trans[0]:
ret = ret.transpose()
# Run through
for n, mat in enumerate(args[1:]):
ret = core.doublet(ret, mat, False, trans[n + 1])
return ret
def _irrep_access(self, *args, **kwargs):
"""
Warns user when iterating/accessing an irrepped object.
"""
raise ValidationError("Attempted to access by index/iteration a Psi4 data object that supports multiple"
" irreps. Please use .np or .nph explicitly.")
# Matrix attributes
core.Matrix.from_array = classmethod(array_to_matrix)
core.Matrix.from_list = classmethod(lambda self, x: array_to_matrix(self, np.array(x)))
core.Matrix.to_array = _to_array
core.Matrix.shape = _np_shape
core.Matrix.np = _np_view
core.Matrix.nph = _nph_view
core.Matrix.__array_interface__ = _array_conversion
core.Matrix.np_write = _np_write
core.Matrix.np_read = classmethod(_np_read)
core.Matrix.to_serial = _to_serial
core.Matrix.from_serial = classmethod(_from_serial)
core.Matrix.chain_dot = _chain_dot
core.Matrix.__iter__ = _irrep_access
core.Matrix.__getitem__ = _irrep_access
# Vector attributes
core.Vector.from_array = classmethod(array_to_matrix)
core.Vector.from_list = classmethod(lambda self, x: array_to_matrix(self, np.array(x)))
core.Vector.to_array = _to_array
core.Vector.shape = _np_shape
core.Vector.np = _np_view
core.Vector.nph = _nph_view
core.Vector.__array_interface__ = _array_conversion
core.Vector.np_write = _np_write
core.Vector.np_read = classmethod(_np_read)
core.Vector.to_serial = _to_serial
core.Vector.from_serial = classmethod(_from_serial)
core.Vector.__iter__ = _irrep_access
core.Vector.__getitem__ = _irrep_access
### CIVector properties
@property
def _civec_view(self):
"""
Returns a view of the CIVector's buffer
"""
return np.asarray(self)
core.CIVector.np = _civec_view
### Dimension properties
@classmethod
def _dimension_from_list(
self,
dims: Union[Tuple[int], List[int], np.ndarray, core.Dimension],
name="New Dimension",
) -> core.Dimension:
"""
Builds a Dimension object from a Python list or tuple. If a :class:`~psi4.core.Dimension` object is passed, a copy will be returned.
Parameters
----------
dims
Iterable of integers defining irrep dimensions.
name
Name for new instance.
"""
if isinstance(dims, (tuple, list, np.ndarray)):
irreps = len(dims)
elif isinstance(dims, core.Dimension):
irreps = dims.n()
else:
raise ValidationError("Dimension from list: Type '%s' not understood" % type(dims))
ret = core.Dimension(irreps, name)
for i in range(irreps):
ret[i] = dims[i]
return ret
def _dimension_to_tuple(self: core.Dimension) -> Tuple[int]:
"""Serializes :class:`~psi4.core.Dimension` to a tuple."""
if isinstance(self, (tuple, list)):
return tuple(self)
irreps = self.n()
ret = []
for i in range(irreps):
ret.append(self[i])
return tuple(ret)
def _dimension_iter(dim) -> Iterator[int]:
"""
Provides an iterator class for the Dimension object.
Example
-------
>>> dim = psi4.core.Dimension(...)
>>> list(dim)
"""
for i in range(dim.n()):
yield dim[i]
# Dimension attributes
core.Dimension.from_list = _dimension_from_list
core.Dimension.to_tuple = _dimension_to_tuple
core.Dimension.__iter__ = _dimension_iter
# General functions for NumPy array manipulation
[docs]
def block_diagonal_array(*args: List[np.ndarray]) -> np.ndarray:
"""
Convert square NumPy array to a single block diagonal array. Mimic of SciPy's :func:`scipy.linalg.block_diag`.
Parameters
----------
args
Arbitrary number of square arrays.
"""
# Validate the input matrices.
dim = 0
for matrix in args:
try:
shape = matrix.shape
dim += shape[0]
except (AttributeError, TypeError):
raise ValidationError("Cannot construct block diagonal from non-arrays.")
if len(shape) != 2:
raise ValidationError("Cannot construct block diagonal from non-2D arrays.")
if shape[0] != shape[1]:
raise ValidationError("Cannot construct block diagonal from non-square arrays.")
# If this is too slow, try a sparse matrix?
block_diag = np.zeros((dim, dim))
start = 0
for matrix in args:
next_block = slice(start, start + matrix.shape[0])
block_diag[next_block, next_block] = matrix
start += matrix.shape[0]
return block_diag
