def lengths(a, close=False, prn=False):
"""Calls `e_leng` to calculate lengths for many types of nested objects.
This would include object arrays, list of lists and similar constructs.
Each part is considered separately.
Parameters
----------
a : array-like
An array like sequence of points that are assumed to make up lengths or
perimeters of geometries
close : boolean
True, computes the closed-loop length for polylines and check to ensure
polygons have duplicate first and last points.
prn : boolean
True, prints the output as well as returning the result
Notes
-----
e_leng is required by this function. Make sure you ensure that you are
aware of the `close` parameter.
Returns
-------
A list with total length and segment lengths, for multipoint sequences.
"""
def _prn_(a_s):
"""optional result printing"""
hdr = "{!s:<12} {}\n".format("Tot. Length", "Seg. Length")
r = ["{:12.3f} {!r:}".format(*a_s[i]) for i in range(len(a_s))]
print(hdr + "\n".join(r))
#
# a = np.asarray(a).squeeze()
if isinstance(a, (list, tuple)):
return sum([e_leng(i) for i in a])
if a.dtype == 'O':
# tmp = [np.asarray(i).squeeze() for i in a]
# a_s = [e_leng(i, close) for i in tmp]
tmp = pieces(a) # ---- did some fixing
a_s = sum([e_leng(i, close)[0] for i in tmp])
if prn:
_prn_(a_s)
return a_s
if len(a.dtype) == 1:
a = _reshape_(a)
if len(a.dtype) > 1:
a = _reshape_(a)
if isinstance(a, (list, tuple)):
return [e_leng(i, close) for i in a]
if a.ndim == 2:
a = a.reshape((1,) + a.shape)
a_s = [e_leng(i, close) for i in a]
if prn:
_prn_(a_s)
return a_s
def centroids(a):
"""batch centroids (ie centroid_)
"""
a = np.asarray(a)
if a.dtype == 'O':
tmp = [_reshape_(i) for i in a]
return np.asarray([centroid_(i) for i in tmp])
if len(a.dtype) >= 1:
a = _reshape_(a)
if a.ndim == 2:
a = a.reshape((1,) + a.shape)
c = np.asarray([centroid_(i) for i in a]).squeeze()
return c
def centers(a, remove_dup=True):
"""batch centres (ie center_)
"""
a = np.asarray(a)
if a.dtype == 'O':
tmp = [_reshape_(i) for i in a]
return np.asarray([center_(i, remove_dup) for i in tmp])
if len(a.dtype) >= 1:
a = _reshape_(a)
if a.ndim == 2:
a = a.reshape((1,) + a.shape)
c = np.asarray([center_(i, remove_dup) for i in a]).squeeze()
return c
def areas(a):
"""Calls e_area to calculate areas for many types of nested objects.
This would include object arrays, list of lists and similar constructs.
Each part is considered separately.
Returns
-------
A list with one or more areas.
"""
def _e_area(a):
"""mini e_area with a twist, shoelace formula using einsum"""
x0, y1 = (a.T)[:, 1:]
x1, y0 = (a.T)[:, :-1]
e0 = np.einsum('...i,...i->...i', x0, y0)
e1 = np.einsum('...i,...i->...i', x1, y1)
return np.nansum((e0-e1)*0.5)
#
# a = np.asarray(a).squeeze()
if isinstance(a, (list, tuple)):
a = np.asarray(a)
if a.dtype == 'O':
tmp = pieces(a) # ---- did some fixing
return sum([_e_area(i) for i in tmp])
if len(a.dtype) >= 1:
a = _reshape_(a)
if a.ndim == 2:
has_nan = np.any(np.isnan(a[:, 0]))
if has_nan:
tmp = _nan_sp_(a)
return sum([_e_area(i) for i in tmp])
a = a.reshape((1,) + a.shape)
a_s = np.array(sum([_e_area(i) for i in a]))
return a_s
def min_(a):
"""Array minimums. No `finite_check`. See Note above
"""
a = _reshape_(a)
if (a.dtype.kind == 'O') or (len(a.shape) > 2):
mins = np.asanyarray([np.nanmin(i, axis=0) for i in a])
else:
mins = np.nanmin(a, axis=0)
return mins
def extent_(a):
"""Array extent values. Only 2D extent is returned, Z extent not covered.
"""
a = _reshape_(a)
if isinstance(a, (list, tuple)):
a = np.asanyarray(a)
if (a.dtype.kind == 'O') or (len(a.shape) > 2):
mins = min_(a)
maxs = max_(a)
ret = np.hstack((mins, maxs))
else:
L, B = min_(a)[:2]
R, T = max_(a)[:2]
ret = np.asarray([L, B, R, T])
return ret