def mformat(self, X):
# X is an ndarray value to be display
# self provides set type for formatting
out = ''
n = self.N # dimension of rotation submatrix
for rownum, row in enumerate(X):
rowstr = ' '
# format the columns
for colnum, element in enumerate(row):
if sym.issymbol(element):
s = '{:<12s}'.format(str(element))
else:
if tol > 0 and abs(element) < tol * _eps:
element = 0
s = '{:< 12g}'.format(element)
if rownum < n:
if colnum < n:
# rotation part
s = rotcol + bgcol + s + reset
else:
# translation part
s = trcol + bgcol + s + reset
else:
# bottom row
s = constcol + bgcol + s + reset
rowstr += s
out += rowstr + bgcol + ' ' + reset + '\n'
return out
def getvector(v, dim=None, out="array", dtype=np.float64):
"""
Return a vector value
:param v: passed vector
:param dim: required dimension, or None if any length is ok
:type dim: int or None
:param out: output format, default is 'array'
:type out: str
:param dtype: datatype for numPy array return (default np.float64)
:type dtype: numPy type
:return: vector value in specified format
:raises TypeError: value is not a list or NumPy array
:raises ValueError: incorrect number of elements
- ``getvector(vec)`` is ``vec`` converted to the output format ``out``
where ``vec`` is any of:
- a Python native int or float, a 1-vector
- Python native list or tuple
- numPy real 1D array, ie. shape=(N,)
- numPy real 2D array with a singleton dimension, ie. shape=(1,N)
or (N,1)
- ``getvector(vec, N)`` as above but must be an ``N``-element vector.
The returned vector will be in the format specified by ``out``:
========== ===============================================
format return type
========== ===============================================
'sequence' Python list, or tuple if a tuple was passed in
'list' Python list
'array' 1D numPy array, shape=(N,) [default]
'row' row vector, a 2D numPy array, shape=(1,N)
'col' column vector, 2D numPy array, shape=(N,1)
========== ===============================================
.. runblock:: pycon
>>> from spatialmath.base import getvector
>>> import numpy as np
>>> getvector([1,2]) # list
>>> getvector([1,2], out='row') # list
>>> getvector([1,2], out='col') # list
>>> getvector((1,2)) # tuple
>>> getvector(np.r_[1,2,3], out='sequence') # numpy array
>>> getvector(1) # scalar
>>> getvector([1])
>>> getvector([[1]])
.. note::
- For 'array', 'row' or 'col' output the NumPy dtype defaults to the
``dtype`` of ``v`` if it is a NumPy array, otherwise it is
set to the value specified by the ``dtype`` keyword which defaults
to ``np.float64``.
- If ``v`` is symbolic the ``dtype`` is retained as ``'O'``
:seealso: :func:`isvector`
"""
dt = dtype
if isinstance(v, _scalartypes): # handle scalar case
v = [v]
if isinstance(v, (list, tuple)):
# list or tuple was passed in
if sym.issymbol(v):
dt = None
if dim is not None and v and len(v) != dim:
raise ValueError("incorrect vector length")
if out == "sequence":
return v
elif out == "list":
return list(v)
elif out == "array":
return np.array(v, dtype=dt)
elif out == "row":
return np.array(v, dtype=dt).reshape(1, -1)
elif out == "col":
return np.array(v, dtype=dt).reshape(-1, 1)
else:
raise ValueError("invalid output specifier")
elif isinstance(v, np.ndarray):
s = v.shape
if dim is not None:
if not (s == (dim, ) or s == (1, dim) or s == (dim, 1)):
raise ValueError(
"incorrect vector length: expected {}, got {}".format(
dim, s))
v = v.flatten()
if v.dtype.kind == "O":
dt = "O"
if out in ("sequence", "list"):
return list(v.flatten())
elif out == "array":
return v.astype(dt)
elif out == "row":
return v.astype(dt).reshape(1, -1)
elif out == "col":
return v.astype(dt).reshape(-1, 1)
else:
raise ValueError("invalid output specifier")
else:
raise TypeError("invalid input type")
def getvector(v, dim=None, out='array', dtype=np.float64):
"""
Return a vector value
:param v: passed vector
:param dim: required dimension, or None if any length is ok
:type dim: int or None
:param out: output format, default is 'array'
:type out: str
:param dtype: datatype for numPy array return (default np.float64)
:type dtype: numPy type
:return: vector value in specified format
The passed vector can be any of:
- Python native list or tuple
- numPy 1D array, ie. shape=(N,)
- numPy 2D array with a singleton dimension, ie. shape=(1,N) or (N,1)
The returned vector will be in the format specified by ``out``:
========== ===============================================
format return type
========== ===============================================
'sequence' Python list, or tuple if a tuple was passed in
'array' 1D numPy array, shape=(N,)
'row' row vector, a 2D numPy array, shape=(1,N)
'col' column vector, 2D numPy array, shape=(N,1)
========== ===============================================
For 'array', 'row' or 'col' the numPy dtype defaults to ``np.float64`` but
can be overriden using the ``dtype`` argument.
"""
dt = dtype
if isinstance(v, _scalartypes): # handle scalar case
v = [v]
if isinstance(v, (list, tuple)):
# list or tuple was passed in
if sym.issymbol(v):
dt = None
if dim is not None and v and len(v) != dim:
raise ValueError("incorrect vector length")
if out == 'sequence':
return v
elif out == 'array':
return np.array(v, dtype=dt)
elif out == 'row':
return np.array(v, dtype=dt).reshape(1, -1)
elif out == 'col':
return np.array(v, dtype=dt).reshape(-1, 1)
else:
raise ValueError("invalid output specifier")
elif isinstance(v, np.ndarray):
s = v.shape
if dim is not None:
if not (s == (dim, ) or s == (1, dim) or s == (dim, 1)):
raise ValueError(
"incorrect vector length: expected {}, got {}".format(
dim, s))
v = v.flatten()
if v.dtype.kind != 'O':
dt = dtype
if out == 'sequence':
return list(v.flatten())
elif out == 'array':
return v.astype(dt)
elif out == 'row':
return v.astype(dt).reshape(1, -1)
elif out == 'col':
return v.astype(dt).reshape(-1, 1)
else:
raise ValueError("invalid output specifier")
else:
raise TypeError("invalid input type")