#
# @BEGIN LICENSE
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# Psi4: an open-source quantum chemistry software package
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# Copyright (c) 2007-2021 The Psi4 Developers.
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# The copyrights for code used from other parties are included in
# the corresponding files.
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# This file is part of Psi4.
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# Psi4 is free software; you can redistribute it and/or modify
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# Psi4 is distributed in the hope that it will be useful,
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# with Psi4; if not, write to the Free Software Foundation, Inc.,
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# @END LICENSE
#
import sys
from typing import List, 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, 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))
[docs]def _to_array(matrix: Union[core.Matrix, core.Vector], copy: bool = True, dense: bool = False) -> Union[np.ndarray, List[np.ndarray]]:
"""
Converts a Psi4 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 irreped Psi4 objects to diagonally blocked dense arrays if `True`. Returns a list of arrays otherwise.
Returns
-------
numpy.ndarray
Returns either a list of np.array's or the base array 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, filename=None, prefix=""):
"""
Writes the irreped matrix to a NumPy zipped file.
Can return the packed data for saving many matrices into the same file.
"""
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, filename, prefix=""):
"""
Reads the data from a NumPy compress file.
"""
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(data):
"""
Converts an object with a .nph accessor to a serialized dictionary
"""
json_data = {}
json_data["shape"] = []
json_data["data"] = []
for view in data.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):
"""
Converts serialized data to the correct Psi4 data type
"""
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):
"""
Chains dot products together from a series of Psi4 Matrix classes.
By default there is no transposes, an optional vector of booleans can be passed in.
"""
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 irreped 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, name="New Dimension"):
"""
Builds a core.Dimension object from a python list or tuple. If a dimension
object is passed a copy will be returned.
"""
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(dim):
"""
Converts a core.Dimension object to a tuple.
"""
if isinstance(dim, (tuple, list)):
return tuple(dim)
irreps = dim.n()
ret = []
for i in range(irreps):
ret.append(dim[i])
return tuple(ret)
def _dimension_iter(dim):
"""
Provides an iterator class for the Dimension object.
Allows:
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
def block_diagonal_array(*args):
"""
Convert square NumPy array to a single block diagonal array.
Mimic of SciPy's block_diag.
"""
# 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
