1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382
|
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
"""
Internal use module for manipulating dims specifications.
"""
__all__ = [] # Everything should be explicitly imported, not made available
# by default.
import numpy as np
from operator import getitem
from functools import partial
def is_scalar(dims):
"""
Returns True if a dims specification is effectively
a scalar (has dimension 1).
"""
return np.prod(flatten(dims)) == 1
def is_vector(dims):
return (
isinstance(dims, list) and
isinstance(dims[0], (int, np.integer))
)
def is_vectorized_oper(dims):
return (
isinstance(dims, list) and
isinstance(dims[0], list)
)
def type_from_dims(dims, enforce_square=False):
bra_like, ket_like = map(is_scalar, dims)
if bra_like:
if is_vector(dims[1]):
return 'bra'
elif is_vectorized_oper(dims[1]):
return 'operator-bra'
if ket_like:
if is_vector(dims[0]):
return 'ket'
elif is_vectorized_oper(dims[0]):
return 'operator-ket'
elif is_vector(dims[0]) and (dims[0] == dims[1] or not enforce_square):
return 'oper'
elif (
is_vectorized_oper(dims[0]) and
(
(
dims[0] == dims[1] and
dims[0][0] == dims[1][0]
) or not enforce_square
)
):
return 'super'
return 'other'
def flatten(l):
"""Flattens a list of lists to the first level.
Given a list containing a mix of scalars and lists,
flattens down to a list of the scalars within the original
list.
Examples
--------
>>> print(flatten([[[0], 1], 2]))
[0, 1, 2]
"""
if not isinstance(l, list):
return [l]
else:
return sum(map(flatten, l), [])
def deep_remove(l, *what):
"""Removes scalars from all levels of a nested list.
Given a list containing a mix of scalars and lists,
returns a list of the same structure, but where one or
more scalars have been removed.
Examples
--------
>>> print(deep_remove([[[[0, 1, 2]], [3, 4], [5], [6, 7]]], 0, 5))
[[[[1, 2]], [3, 4], [], [6, 7]]]
"""
if isinstance(l, list):
# Make a shallow copy at this level.
l = l[:]
for to_remove in what:
if to_remove in l:
l.remove(to_remove)
else:
l = list(map(lambda elem: deep_remove(elem, to_remove), l))
return l
def unflatten(l, idxs):
"""Unflattens a list by a given structure.
Given a list of scalars and a deep list of indices
as produced by `flatten`, returns an "unflattened"
form of the list. This perfectly inverts `flatten`.
Examples
--------
>>> l = [[[10, 20, 30], [40, 50, 60]], [[70, 80, 90], [100, 110, 120]]]
>>> idxs = enumerate_flat(l)
>>> print(unflatten(flatten(l)), idxs) == l
True
"""
acc = []
for idx in idxs:
if isinstance(idx, list):
acc.append(unflatten(l, idx))
else:
acc.append(l[idx])
return acc
def _enumerate_flat(l, idx=0):
if not isinstance(l, list):
# Found a scalar, so return and increment.
return idx, idx + 1
else:
# Found a list, so append all the scalars
# from it and recurse to keep the increment
# correct.
acc = []
for elem in l:
labels, idx = _enumerate_flat(elem, idx)
acc.append(labels)
return acc, idx
def _collapse_composite_index(dims):
"""
Given the dimensions specification for a composite index
(e.g.: [2, 3] for the right index of a ket with dims [[1], [2, 3]]),
returns a dimensions specification for an index of the same shape,
but collapsed to a single "leg." In the previous example, [2, 3]
would collapse to [6].
"""
return [np.prod(dims)]
def _collapse_dims_to_level(dims, level=1):
"""
Recursively collapses all indices in a dimensions specification
appearing at a given level, such that the returned dimensions
specification does not represent any composite systems.
"""
if level == 0:
return _collapse_composite_index(dims)
else:
return [_collapse_dims_to_level(index, level=level - 1) for index in dims]
def collapse_dims_oper(dims):
"""
Given the dimensions specifications for a ket-, bra- or oper-type
Qobj, returns a dimensions specification describing the same shape
by collapsing all composite systems. For instance, the bra-type
dimensions specification ``[[2, 3], [1]]`` collapses to
``[[6], [1]]``.
Parameters
----------
dims : list of lists of ints
Dimensions specifications to be collapsed.
Returns
-------
collapsed_dims : list of lists of ints
Collapsed dimensions specification describing the same shape
such that ``len(collapsed_dims[0]) == len(collapsed_dims[1]) == 1``.
"""
return _collapse_dims_to_level(dims, 1)
def collapse_dims_super(dims):
"""
Given the dimensions specifications for an operator-ket-, operator-bra- or
super-type Qobj, returns a dimensions specification describing the same shape
by collapsing all composite systems. For instance, the super-type
dimensions specification ``[[[2, 3], [2, 3]], [[2, 3], [2, 3]]]`` collapses to
``[[[6], [6]], [[6], [6]]]``.
Parameters
----------
dims : list of lists of ints
Dimensions specifications to be collapsed.
Returns
-------
collapsed_dims : list of lists of ints
Collapsed dimensions specification describing the same shape
such that ``len(collapsed_dims[i][j]) == 1`` for ``i`` and ``j``
in ``range(2)``.
"""
return _collapse_dims_to_level(dims, 2)
def enumerate_flat(l):
"""Labels the indices at which scalars occur in a flattened list.
Given a list containing a mix of scalars and lists,
returns a list of the same structure, where each scalar
has been replaced by an index into the flattened list.
Examples
--------
>>> print(enumerate_flat([[[10], [20, 30]], 40]))
[[[0], [1, 2]], 3]
"""
return _enumerate_flat(l)[0]
def deep_map(fn, collection, over=(tuple, list)):
if isinstance(collection, over):
return type(collection)(deep_map(fn, el, over) for el in collection)
else:
return fn(collection)
def dims_to_tensor_perm(dims):
"""
Given the dims of a Qobj instance, returns a list representing
a permutation from the flattening of that dims specification to
the corresponding tensor indices.
Parameters
----------
dims : list
Dimensions specification for a Qobj.
Returns
-------
perm : list
A list such that ``data[flatten(dims)[idx]]`` gives the
index of the tensor ``data`` corresponding to the ``idx``th
dimension of ``dims``.
"""
# We figure out the type of the dims specification,
# relaxing the requirement that operators be square.
# This means that dims_type need not coincide with
# Qobj.type, but that works fine for our purposes here.
dims_type = type_from_dims(dims, enforce_square=False)
perm = enumerate_flat(dims)
# If type is oper, ket or bra, we don't need to do anything.
if dims_type in ('oper', 'ket', 'bra'):
return flatten(perm)
# If the type is other, we need to figure out if the
# dims is superlike on its outputs and inputs
# This is the case if the dims type for left or right
# are, respectively, oper-like.
if dims_type == 'other':
raise NotImplementedError("Not yet implemented for type='other'.")
# If we're still here, the story is more complicated. We'll
# follow the strategy of creating a permutation by using
# enumerate_flat then transforming the result to swap
# input and output indices of vectorized matrices, then flattening
# the result. We'll then rebuild indices using this permutation.
if dims_type in ('operator-ket', 'super'):
# Swap the input and output spaces of the right part of
# perm.
perm[1] = list(reversed(perm[1]))
if dims_type in ('operator-bra', 'super'):
# Ditto, but for the left indices.
perm[0] = list(reversed(perm[0]))
return flatten(perm)
def dims_to_tensor_shape(dims):
"""
Given the dims of a Qobj instance, returns the shape of the
corresponding tensor. This helps, for instance, resolve the
column-stacking convention for superoperators.
Parameters
----------
dims : list
Dimensions specification for a Qobj.
Returns
-------
tensor_shape : tuple
NumPy shape of the corresponding tensor.
"""
perm = dims_to_tensor_perm(dims)
dims = flatten(dims)
return tuple(map(partial(getitem, dims), perm))
def dims_idxs_to_tensor_idxs(dims, indices):
"""
Given the dims of a Qobj instance, and some indices into
dims, returns the corresponding tensor indices. This helps
resolve, for instance, that column-stacking for superoperators,
oper-ket and oper-bra implies that the input and output tensor
indices are reversed from their order in dims.
Parameters
----------
dims : list
Dimensions specification for a Qobj.
indices : int, list or tuple
Indices to convert to tensor indices. Can be specified
as a single index, or as a collection of indices.
In the latter case, this can be nested arbitrarily
deep. For instance, [0, [0, (2, 3)]].
Returns
-------
tens_indices : int, list or tuple
Container of the same structure as indices containing
the tensor indices for each element of indices.
"""
perm = dims_to_tensor_perm(dims)
return deep_map(partial(getitem, perm), indices)
|