def qzeros(size, **kwargs):
'''
Produces a qmatrix of zeroes, similar to numpy.zeros. Accepts dtype argument.
>>> qzeros((2,3))
1 1 1
1 0.0 0.0 0.0
1 0.0 0.0 0.0
>>> qzeros(2)
1 1
1 0.0 0.0
>>> qzeros((2,3), dtype=int16)
1 1 1
1 0 0 0
1 0 0 0
'''
if (not isinstance(size, tuple)):
size = (1, size)
if len(size) > 2:
raise QuantMatrixError, "Enter a tuple of size <= 2."
if kwargs.has_key('dtype'):
return QuantMatrix(numpy.zeros(size, dtype=kwargs['dtype']), [
qhomogeneous(size[0],
dimensionless().dim),
qhomogeneous(size[1],
dimensionless().dim)
], True)
return QuantMatrix(numpy.zeros(size), [
qhomogeneous(size[0],
dimensionless().dim),
qhomogeneous(size[1],
dimensionless().dim)
], True)
def qzeros(size, **kwargs):
'''
Produces a qmatrix of zeroes, similar to numpy.zeros. Accepts dtype argument.
>>> qzeros((2,3))
1 1 1
1 0.0 0.0 0.0
1 0.0 0.0 0.0
>>> qzeros(2)
1 1
1 0.0 0.0
>>> qzeros((2,3), dtype=int16)
1 1 1
1 0 0 0
1 0 0 0
'''
if (not isinstance(size,tuple)):
size = (1,size)
if len(size)>2:
raise QuantMatrixError, "Enter a tuple of size <= 2."
if kwargs.has_key('dtype'):
return QuantMatrix(numpy.zeros(size, dtype=kwargs['dtype']), [qhomogeneous(size[0], dimensionless().dim), qhomogeneous(size[1],dimensionless().dim)], True)
return QuantMatrix(numpy.zeros(size), [qhomogeneous(size[0], dimensionless().dim), qhomogeneous(size[1],dimensionless().dim)], True)
def qmat(array):
'''
Casts array into a QuantMatrix.
>>> a = array([[1,2],[3,4]])
>>> qmat(a)
1 1
1 1 2
1 3 4
'''
if (not isinstance(array, numpy.ndarray)) or (not len(array.shape) == 2):
raise QuantMatrixError, "qmat takes a two dimensional ndarray as its argument."
return QuantMatrix(array, [
qhomogeneous(array.shape[0],
dimensionless().dim),
qhomogeneous(array.shape[1],
dimensionless().dim)
], True)
def qmat(array):
'''
Casts array into a QuantMatrix.
>>> a = array([[1,2],[3,4]])
>>> qmat(a)
1 1
1 1 2
1 3 4
'''
if (not isinstance(array, numpy.ndarray)) or (not len(array.shape)==2):
raise QuantMatrixError, "qmat takes a two dimensional ndarray as its argument."
return QuantMatrix(array, [qhomogeneous(array.shape[0], dimensionless().dim), qhomogeneous(array.shape[1], dimensionless().dim)], True)
def qidentity(size, **kwargs):
'''
Creates the identitiy matrix of size size.
>>> qidentity(3)
1 1 1
1 1.0 0.0 0.0
1 0.0 1.0 0.0
1 0.0 0.0 1.0
'''
if kwargs.has_key('dtype'):
return QuantMatrix(numpy.identity(size, dtype=kwargs['dtype']), [qhomogeneous(size, dimensionless().dim), qhomogeneous(size, dimensionless().dim)], True)
return QuantMatrix(numpy.identity(size), [qhomogeneous(size, dimensionless().dim), qhomogeneous(size, dimensionless().dim)], True)
def qidentity(size, **kwargs):
'''
Creates the identitiy matrix of size size.
>>> qidentity(3)
1 1 1
1 1.0 0.0 0.0
1 0.0 1.0 0.0
1 0.0 0.0 1.0
'''
if kwargs.has_key('dtype'):
return QuantMatrix(numpy.identity(size, dtype=kwargs['dtype']), [
qhomogeneous(size,
dimensionless().dim),
qhomogeneous(size,
dimensionless().dim)
], True)
return QuantMatrix(numpy.identity(size), [
qhomogeneous(size,
dimensionless().dim),
qhomogeneous(size,
dimensionless().dim)
], True)
def qcolumn_vector(raw_numbers, quantities):
'''
A quick constructor for a column vector.
>>> qcolumn_vector((1,2,3),(meter,second,mole))
1
m 1.0
s 2.0
mol 3.0
'''
matrix = []
for element in raw_numbers:
matrix.append([element])
return QuantMatrix(numpy.array(matrix), [list(quantities),[dimensionless()]])
def qcolumn_vector(raw_numbers, quantities):
'''
A quick constructor for a column vector.
>>> qcolumn_vector((1,2,3),(meter,second,mole))
1
m 1.0
s 2.0
mol 3.0
'''
matrix = []
for element in raw_numbers:
matrix.append([element])
return QuantMatrix(numpy.array(matrix),
[list(quantities), [dimensionless()]])
