Source code for psi4.driver.p4util.numpy_helper

# Psi4: an open-source quantum chemistry software package
# Copyright (c) 2007-2017 The Psi4 Developers.
# The copyrights for code used from other parties are included in
# the corresponding files.
# This file is part of Psi4.
# Psi4 is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, version 3.
# Psi4 is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License along
# with Psi4; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

import sys
import numpy as np

from psi4 import core
from .exceptions import *

### Matrix and Vector properties

# The next three functions make me angry
def translate_interface(interface):
    This is extra stupid with unicode

    if sys.version_info[0] > 2:
        return interface

    nouni_interface = {}
    for k, v in interface.items():
        if k == 'typestr':
            nouni_interface[k.encode('ascii', 'ignore')] = v.encode('ascii', 'ignore')
            nouni_interface[k.encode('ascii', 'ignore')] = v

    return nouni_interface

class numpy_holder(object):
    Blank object, stupid. Apparently you cannot create a view directly from a dictionary
    def __init__(self, interface):
        self.__array_interface__ = translate_interface(interface)

def _get_raw_views(self, copy=False):
    Gets simple raw view of the passed in object.
    ret = []
    for data in self.array_interface():

        # Yet another hack
        if isinstance(data["shape"], list):
            data["shape"] = tuple(data["shape"])

        if 0 in data["shape"]:
            ret.append(np.array(numpy_holder(data), copy=copy))
    return ret

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
        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)]
        raise ValidationError("Input array does not have a valid shape.")

[docs]def array_to_matrix(self, arr, name="New Matrix", dim1=None, dim2=None): """ Converts a numpy array or list of numpy arrays into a Psi4 Matrix (irreped if list). Parameters ---------- arr : array or list of arrays Numpy array or list of arrays to use as the data for a new core.Matrix name : str Name to give the new core.Matrix dim1 : list, tuple, or core.Dimension (optional) 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.Dimension object. Returns ------- matrix : :py:class:`~psi4.core.Matrix` or :py:class:`~psi4.core.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) >>> vector = array_to_matrix(data) >>> irrep_data = [np.random.rand(2, 2), np.empty(shape=(0,3)), np.random.rand(4, 4)] >>> matrix = array_to_matrix(irrep_data) >>> print matrix.rowspi().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]) view = _get_raw_views(ret)[0] view[:] = 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])[:] = arr return ret else: raise ValidationError("Array_to_Matrix: type '%s' is not recognized." % str(arr_type))
[docs]def _to_array(matrix, copy=True, dense=False): """ Converts a Psi4 Matrix or Vector to a numpy array. Either copies the data or simply consturcts a view. Parameters ---------- matrix : :py:class:`~psi4.core.Matrix` or :py:class:`~psi4.core.Vector` Pointers to which Psi4 core class should be used in the construction. copy : bool Copy the data if True, return a view otherwise dense : bool Converts irreped Psi4 objects to diagonally blocked dense arrays. Returns a list of arrays otherwise. Returns ------- array : np.array or list of of np.array 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.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 ret = _get_raw_views(matrix, copy=copy) # Return the list of arrays if dense is False: return ret # 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 ret: # 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, ret): 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, ret): if d1 == 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]
def _build_view(matrix): """ Builds a view of the vector or matrix """ views = _to_array(matrix, copy=False, dense=False) if matrix.nirrep() > 1: return tuple(views) else: return views def get_view(self): if hasattr(self, '_np_view_data'): return self._np_view_data else: self._np_view_data = _build_view(self) return self._np_view_data @property def _np_shape(self): """ Shape of the Psi4 data object """ view_data = get_view(self) if self.nirrep() > 1: return tuple(view_data[x].shape for x in range(self.nirrep())) else: return view_data.shape @property def _np_view(self): """ View without only one 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_view(self) @property def _nph_view(self): """ View with irreps. """ if self.nirrep() > 1: return get_view(self) else: return get_view(self), @property def _array_conversion(self): if self.nirrep() > 1: raise ValidationError("__array__interface__ can only be called on Psi4 data object with only one irrep!") else: return def _np_write(self, filename=None, prefix=""): ret = {} ret[prefix + "Irreps"] = self.nirrep() ret[prefix + "Name"] = for h, v in enumerate(self.nph): 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=""): if isinstance(filename, np.lib.npyio.NpzFile): data = filename elif (sys.version_info[0] == 2) and isinstance(filename, (str, unicode)): if not filename.endswith('.npz'): filename = filename + '.npz' data = np.load(filename) elif (sys.version_info[0] > 2) and 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.fromstring(json_data["data"][n], dtype=np.double) return ret # Matrix attributes core.Matrix.from_array = classmethod(array_to_matrix) core.Matrix.to_array = _to_array core.Matrix.shape = _np_shape = _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) # Vector attributes core.Vector.from_array = classmethod(array_to_matrix) core.Vector.to_array = _to_array core.Vector.shape = _np_shape = _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) ### CIVector properties @property def _civec_view(self): "Returns a view of the CIVector's buffer" return np.asarray(self) = _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