File support-sympy-1.12.patch of Package python-qsymm
diff --git a/qsymm/_scipy_patch.py b/qsymm/_scipy_patch.py
deleted file mode 100644
index ac5a5d1918f43d2203fcdd5ff510313c415fe743..0000000000000000000000000000000000000000
--- a/qsymm/_scipy_patch.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# This file has been taken from Scipy 1.1.0 under the
-# BSD 3-Clause license, reproduced below:
-#
-# Copyright (c) 2001-2002 Enthought, Inc. 2003-2019, SciPy Developers.
-# 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 copyright holder 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
-# OWNER 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.
-
-from pkg_resources import parse_version
-
-import scipy.sparse
-
-# Scipy 1.1 implemented sparse matrix reshaping. In order to allow a lower
-# version of scipy (to remain compatible with Kwant's requirements) we do
-# not want to use this more recent version of scipy. We monkey patch the
-# "reshape" method onto the appropriate sparse matrix classes.
-#
-# This module should be imported in all modules that use the "reshape"
-# method of sparse matrices (this is a sufficient but not necessary
-# condition for the monkey patching to work, because the patch may already
-# have been applied by other modules that themselves import this).
-#
-# TODO: remove this file (and all the places it is imported) when we can
-# depend on scipy >= 1.1
-
-if parse_version(scipy.__version__) < parse_version("1.1"):
-
- import operator
-
- import numpy as np
- from scipy.sparse import coo_matrix
-
-
- def check_shape(args, current_shape=None):
- """Imitate numpy.matrix handling of shape arguments"""
- if len(args) == 0:
- raise TypeError("function missing 1 required positional argument: "
- "'shape'")
- elif len(args) == 1:
- try:
- shape_iter = iter(args[0])
- except TypeError:
- new_shape = (operator.index(args[0]), )
- else:
- new_shape = tuple(operator.index(arg) for arg in shape_iter)
- else:
- new_shape = tuple(operator.index(arg) for arg in args)
-
- if current_shape is None:
- if len(new_shape) != 2:
- raise ValueError('shape must be a 2-tuple of positive integers')
- elif new_shape[0] < 0 or new_shape[1] < 0:
- raise ValueError("'shape' elements cannot be negative")
-
- else:
- # Check the current size only if needed
- current_size = np.prod(current_shape, dtype=int)
-
- # Check for negatives
- negative_indexes = [i for i, x in enumerate(new_shape) if x < 0]
- if len(negative_indexes) == 0:
- new_size = np.prod(new_shape, dtype=int)
- if new_size != current_size:
- raise ValueError('cannot reshape array of size {} into shape {}'
- .format(current_size, new_shape))
- elif len(negative_indexes) == 1:
- skip = negative_indexes[0]
- specified = np.prod(new_shape[0:skip] + new_shape[skip+1:])
- unspecified, remainder = divmod(current_size, specified)
- if remainder != 0:
- err_shape = tuple('newshape' if x < 0 else x for x in new_shape)
- raise ValueError('cannot reshape array of size {} into shape {}'
- ''.format(current_size, err_shape))
- new_shape = new_shape[0:skip] + (unspecified,) + new_shape[skip+1:]
- else:
- raise ValueError('can only specify one unknown dimension')
-
- # Add and remove ones like numpy.matrix.reshape
- if len(new_shape) != 2:
- new_shape = tuple(arg for arg in new_shape if arg != 1)
-
- if len(new_shape) == 0:
- new_shape = (1, 1)
- elif len(new_shape) == 1:
- new_shape = (1, new_shape[0])
-
- if len(new_shape) > 2:
- raise ValueError('shape too large to be a matrix')
-
- return new_shape
-
-
- def check_reshape_kwargs(kwargs):
- """Unpack keyword arguments for reshape function.
- This is useful because keyword arguments after star arguments are not
- allowed in Python 2, but star keyword arguments are. This function unpacks
- 'order' and 'copy' from the star keyword arguments (with defaults) and
- throws an error for any remaining.
- """
-
- order = kwargs.pop('order', 'C')
- copy = kwargs.pop('copy', False)
- if kwargs: # Some unused kwargs remain
- raise TypeError('reshape() got unexpected keywords arguments: {}'
- .format(', '.join(kwargs.keys())))
- return order, copy
-
-
- def get_index_dtype(arrays=(), maxval=None, check_contents=False):
-
- int32min = np.iinfo(np.int32).min
- int32max = np.iinfo(np.int32).max
-
- dtype = np.intc
- if maxval is not None:
- if maxval > int32max:
- dtype = np.int64
-
- if isinstance(arrays, np.ndarray):
- arrays = (arrays,)
-
- for arr in arrays:
- arr = np.asarray(arr)
- if not np.can_cast(arr.dtype, np.int32):
- if check_contents:
- if arr.size == 0:
- # a bigger type not needed
- continue
- elif np.issubdtype(arr.dtype, np.integer):
- maxval = arr.max()
- minval = arr.min()
- if minval >= int32min and maxval <= int32max:
- # a bigger type not needed
- continue
-
- dtype = np.int64
- break
-
- return dtype
-
-
- def sparse_reshape(self, *args, **kwargs):
- # If the shape already matches, don't bother doing an actual reshape
- # Otherwise, the default is to convert to COO and use its reshape
- shape = check_shape(args, self.shape)
- order, copy = check_reshape_kwargs(kwargs)
- if shape == self.shape:
- if copy:
- return self.copy()
- else:
- return self
-
- return self.tocoo(copy=copy).reshape(shape, order=order, copy=False)
-
-
- def coo_reshape(self, *args, **kwargs):
- shape = check_shape(args, self.shape)
- order, copy = check_reshape_kwargs(kwargs)
-
- # Return early if reshape is not required
- if shape == self.shape:
- if copy:
- return self.copy()
- else:
- return self
-
- nrows, ncols = self.shape
-
- if order == 'C':
- # Upcast to avoid overflows: the coo_matrix constructor
- # below will downcast the results to a smaller dtype, if
- # possible.
- dtype = get_index_dtype(maxval=(ncols * max(0, nrows - 1) + max(0, ncols - 1)))
-
- flat_indices = np.multiply(ncols, self.row, dtype=dtype) + self.col
- new_row, new_col = divmod(flat_indices, shape[1])
- elif order == 'F':
- dtype = get_index_dtype(maxval=(nrows * max(0, ncols - 1) + max(0, nrows - 1)))
-
- flat_indices = np.multiply(nrows, self.col, dtype=dtype) + self.row
- new_col, new_row = divmod(flat_indices, shape[0])
- else:
- raise ValueError("'order' must be 'C' or 'F'")
-
- # Handle copy here rather than passing on to the constructor so that no
- # copy will be made of new_row and new_col regardless
- if copy:
- new_data = self.data.copy()
- else:
- new_data = self.data
-
- return coo_matrix((new_data, (new_row, new_col)),
- shape=shape, copy=False)
-
-
- # Apply monkey patches
- scipy.sparse.spmatrix.reshape = sparse_reshape
- scipy.sparse.coo_matrix.reshape = coo_reshape
diff --git a/qsymm/kwant_continuum.py b/qsymm/kwant_continuum.py
index 842bf125c3212b079f366b13ea8ba85ec8f04b31..c2e5695e4368bf838f4af8b4c57944831fdefdde 100644
--- a/qsymm/kwant_continuum.py
+++ b/qsymm/kwant_continuum.py
@@ -45,14 +45,10 @@ import sympy.abc
import sympy.physics.quantum
from sympy.core.function import AppliedUndef
from sympy.core.sympify import converter
-from sympy.core.core import all_classes as sympy_classes
from sympy.physics.matrices import msigma as _msigma
import warnings
-# TODO: remove when sympy correctly includes MutableDenseMatrix (lol).
-sympy_classes = set(sympy_classes) | {sympy.MutableDenseMatrix}
-
momentum_operators = sympy.symbols('k_x k_y k_z', commutative=False)
position_operators = sympy.symbols('x y z', commutative=False)
@@ -178,7 +174,7 @@ def sympify(expr, locals=None):
stored_value = None
# if ``expr`` is already a ``sympy`` object we may terminate a code path
- if isinstance(expr, tuple(sympy_classes)):
+ if isinstance(expr, sympy.Basic):
if locals:
warnings.warn('Input expression is already SymPy object: '
'"locals" will not be used.',
diff --git a/qsymm/linalg.py b/qsymm/linalg.py
index 05a4a7413e4715734122ebb8464beb6c6d4a38c1..f4a125e795e41e9009a0e411c5cf45d507da87cf 100644
--- a/qsymm/linalg.py
+++ b/qsymm/linalg.py
@@ -4,14 +4,11 @@ import scipy.linalg as la
import scipy.sparse.linalg as sla
import scipy
from numbers import Number
-from scipy.optimize import minimize
from scipy.spatial.distance import cdist
from scipy.sparse.csgraph import connected_components
import itertools as it
import tinyarray as ta
-from . import _scipy_patch
-
def commutator(A, B):
return np.dot(A, B) - np.dot(B, A)
@@ -97,11 +94,17 @@ def matrix_basis(dim, traceless=False, antihermitian=False, real=False, sparse=F
A generator that returns matrices that span the vector space.
"""
if sparse:
- null = lambda: scipy.sparse.lil_matrix((dim, dim), dtype=complex)
- set_type = lambda x: x.tocsr()
+ def null():
+ return scipy.sparse.lil_matrix((dim, dim), dtype=complex)
+
+ def set_type(x):
+ return x.tocsr()
else:
- null = lambda: np.zeros((dim, dim), dtype=complex)
- set_type = lambda x: x
+ def null():
+ return np.zeros((dim, dim), dtype=complex)
+
+ def set_type(x):
+ return x
# Matrix basis for dim x dim matrices. With real coefficients spans
# Hermitian matrices, with complex spans all matrices
coeff = (1j if antihermitian else 1)
@@ -424,13 +427,15 @@ def solve_mat_eqn(HL, HR=None, hermitian=False, traceless=False,
sparse = isinstance(HL[0], scipy.sparse.spmatrix)
if HR is None:
HR = HL
- if len(HL) != len(HR) or not all(l.shape == r.shape for l, r in zip(HL, HR)):
+ if len(HL) != len(HR) or not all(
+ left.shape == right.shape for left, right in zip(HL, HR)
+ ):
raise ValueError('HL and HR must have the same shape.')
if isinstance(conjugate, bool):
conjugate = [conjugate] * len(HL)
if len(conjugate) != len(HL):
- raise ValueError('Conugate must have the same length as HL.')
- if not all(l.shape[0] == l.shape[1] == r.shape[0] == r.shape[1] for l, r in zip(HL, HR)):
+ raise ValueError('Conjugate must have the same length as HL.')
+ if not all(term.shape[0] == term.shape[1] for term in HL):
raise ValueError('HL and HR must be a list of square matrices.')
dim = HL[0].shape[-1]
@@ -441,16 +446,20 @@ def solve_mat_eqn(HL, HR=None, hermitian=False, traceless=False,
# Prepare for differences in sparse and dense algebra
if sparse:
- basis = lambda: matrix_basis(dim, traceless=traceless, sparse=True)
+ def basis():
+ return matrix_basis(dim, traceless=traceless, sparse=True)
vstack = scipy.sparse.vstack
bmat = scipy.sparse.bmat
# Cast it to coo format and reshape
- flatten = lambda x: scipy.sparse.coo_matrix(x).reshape((x.shape[0]*x.shape[1], 1))
+ def flatten(x):
+ return scipy.sparse.coo_matrix(x).reshape((x.shape[0]*x.shape[1], 1))
else:
- basis = lambda: matrix_basis(dim, traceless=traceless, sparse=False)
+ def basis():
+ return matrix_basis(dim, traceless=traceless, sparse=False)
vstack = np.vstack
bmat = np.block
- flatten = lambda x: x.reshape((-1, 1))
+ def flatten(x):
+ return x.reshape((-1, 1))
# Calculate coefficients from commutators of the basis matrices
null_mat = []
@@ -472,7 +481,8 @@ def solve_mat_eqn(HL, HR=None, hermitian=False, traceless=False,
ns = nullspace(null_mat, sparse=sparse, k_max=k_max)
# Make all solutions
# 'ij,jkl->ikl'
- basis = lambda: matrix_basis(dim, traceless=traceless, sparse=False)
+ def basis():
+ return matrix_basis(dim, traceless=traceless, sparse=False)
return np.array([sum((v * mat for v, mat in zip(vec, basis()))) for vec in ns])
diff --git a/qsymm/model.py b/qsymm/model.py
index 48ad102a8ebdb5ed32b919a71ee454549a975d78..2ec3b23909be7d1c9a68587669c5223672ca6c0f 100644
--- a/qsymm/model.py
+++ b/qsymm/model.py
@@ -3,26 +3,24 @@ from copy import copy
from numbers import Number
from warnings import warn
from functools import lru_cache
-from collections import defaultdict, abc, UserDict
+from collections import abc, UserDict
import numpy as np
import scipy
-import tinyarray as ta
-import scipy.linalg as la
import sympy
from sympy.core.numbers import One
from sympy.matrices.matrices import MatrixBase
from sympy.core.basic import Basic
from sympy.core.function import AppliedUndef
-from .linalg import prop_to_id, allclose
-from . import kwant_continuum, _scipy_patch
+from .linalg import allclose
+from . import kwant_continuum
_commutative_momenta = [kwant_continuum.make_commutative(k, k)
for k in kwant_continuum.momentum_operators]
e = kwant_continuum.sympify('e')
-I = kwant_continuum.sympify('I')
+I = kwant_continuum.sympify('I') # noqa: E741
def substitute_exponents(expr):
