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# sympy.matrices.zeros

All Samples(148)  |  Call(119)  |  Derive(0)  |  Import(29)
```Create zero matrix of dimensions dims = (d1,d2)
```

```        def zeros(dims):
"""Create zero matrix of dimensions dims = (d1,d2)"""
n, m = _dims_to_nm(dims)
return Matrix(n, m, [S.Zero]*m*n)
```

```"""Low-level linear systems solver. """

from __future__ import print_function, division

from sympy.matrices import Matrix, zeros
```
```def eqs_to_matrix(eqs, ring):
"""Transform from equations to matrix form. """
xs = ring.gens
M = zeros(len(eqs), len(xs)+1, cls=RawMatrix)

```

```           z d/dz B = M B.
"""
from sympy.matrices import Matrix, eye, zeros

afactors = [_x + a for a in self.func.ap]
```
```
m = eye(n)
m = m.col_insert(0, zeros(n, 1))
l = poly.all_coeffs()[1:]
l.reverse()
```
```    # We don't need to implement the reduction to polylog here, this
# is handled by expand_func.
from sympy.matrices import Matrix, zeros
from sympy.polys import apart

```
```    for b, c in coeffs.items():
M = zeros(len(B))
for b, l in deriv.items():
for c, b2 in l:
```

```           z d/dz B = M B.
"""
from sympy.matrices import Matrix, eye, zeros

afactors = [_x + a for a in self.func.ap]
```
```
m = eye(n)
m = m.col_insert(0, zeros(n, 1))
l = poly.all_coeffs()[1:]
l.reverse()
```
```    # We don't need to implement the reduction to polylog here, this
# is handled by expand_func.
from sympy.matrices import Matrix, zeros
from sympy.polys import apart

```
```    for b, c in coeffs.items():
M = zeros(len(B))
for b, l in deriv.items():
for c, b2 in l:
```

```from sympy.core import Dummy, ilcm, Add, Mul, Pow, S

from sympy.matrices import Matrix, zeros, eye

from sympy import im, sqrt, re
```
```    if all(qi.is_zero for qi in Q):
dc = -1
M = zeros(0, 2)
else:
dc = max([qi.degree(t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
```
```            dc = max([qi.degree(DE.t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
c = eye(m)
A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c))
```
```        gia, gid in G]
Q, M = prde_linear_constraints(A, B, G, DE)
M, _ = constant_system(M, zeros(M.rows, 1), DE)
# Reduce number of constants at this point
try:
```

```from sympy.core import Dummy, ilcm, Add, Mul, Pow, S

from sympy.matrices import Matrix, zeros, eye

from sympy import im, sqrt, re
```
```    if all(qi.is_zero for qi in Q):
dc = -1
M = zeros(0, 2)
else:
dc = max([qi.degree(t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
```
```            dc = max([qi.degree(DE.t) for qi in Q])
M = Matrix(dc + 1, m, lambda i, j: Q[j].nth(i))
A, u = constant_system(M, zeros(dc + 1, 1), DE)
c = eye(m)
A = A.row_join(zeros(A.rows, m)).col_join(c.row_join(-c))
```
```        gia, gid in G]
Q, M = prde_linear_constraints(A, B, G, DE)
M, _ = constant_system(M, zeros(M.rows, 1), DE)
# Reduce number of constants at this point
try:
```

```    def nullspace(self, simplified=False, simplify=False):
"""Returns list of vectors (Matrix objects) that span nullspace of self
"""
from sympy.matrices import zeros

```
```        # create a set of vectors for the basis
for i in range(self.cols - len(pivots)):
basis.append(zeros(self.cols, 1))
# contains the variable index to which the vector corresponds
basiskey, cur = [-1]*len(basis), 0
```
```        berkowitz_eigenvals
"""
from sympy.matrices import zeros

if not self.is_square:
```
```
for n in range(N, 1, -1):
T, k = zeros(n + 1, n), n - 1

R, C = -A[k, :k], A[:k, k]
```
```        vec
"""
from sympy.matrices import zeros

c = self.cols
```

```    def nullspace(self, simplified=False, simplify=False):
"""Returns list of vectors (Matrix objects) that span nullspace of self
"""
from sympy.matrices import zeros

```
```        # create a set of vectors for the basis
for i in range(self.cols - len(pivots)):
basis.append(zeros(self.cols, 1))
# contains the variable index to which the vector corresponds
basiskey, cur = [-1]*len(basis), 0
```
```        berkowitz_eigenvals
"""
from sympy.matrices import zeros

if not self.is_square:
```
```
for n in range(N, 1, -1):
T, k = zeros(n + 1, n), n - 1

R, C = -A[k, :k], A[:k, k]
```
```        vec
"""
from sympy.matrices import zeros

c = self.cols
```

```
from sympy import Matrix, I, Expr, Integer
from sympy.matrices import eye, zeros
from sympy.external import import_module

```
```    spmatrix = options.get('spmatrix', 'csr')
if format == 'sympy':
return zeros(m, n)
elif format == 'numpy':
return _numpy_zeros(m, n, **options)
```
```def matrix_to_zero(e):
"""Convert a zero matrix to the scalar zero."""
if isinstance(e, Matrix):
if zeros(*e.shape) == e:
e = Integer(0)
```

```from sympy.core.basic import sympify
from sympy.core.compatibility import string_types, xrange
from sympy.matrices import Matrix, zeros
from sympy.printing.pretty.stringpict import prettyForm

```
```    output_matrices = []
for i in range(1 << len(bits)):
output_matrices.append(zeros(2**nqubits, 1))

# Bitmasks will help sort how to determine possible outcomes.
```

```                   Tuple, Dummy)
from sympy.core.compatibility import u, unicode
from sympy.matrices import zeros
from sympy.printing.pretty.stringpict import prettyForm, stringPict
from sympy.printing.pretty.pretty_symbology import pretty_symbol
```
```    def _represent_base(self, basis, **options):
j = options.get('j', Rational(1, 2))
size, mvals = m_values(j)
result = zeros(size, size)
for p in range(size):
```
```        evaluate = sympify(options.get('doit'))
size, mvals = m_values(j)
result = zeros(size, size)
for p in range(size):
for q in range(size):
```
```        gamma = sympify(options.get('gamma', 0))
size, mvals = m_values(j)
result = zeros(size, 1)
# TODO: Use KroneckerDelta if all Euler angles == 0
# breaks finding angles on L930
```
```            raise ValueError(
'State must not have symbolic j values to represent')
result = zeros(self.hilbert_space.dimension, 1)
if self.j == int(self.j):
start = self.j**2
```

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