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Merge pull request #652 from jGaboardi/lint_format_weights_part_4
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lint & format `weights/*.py part 4
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@martinfleis
martinfleis authored Nov 14, 2023
2 parents 6112e8d + f80ef93 commit 6404937
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28 changes: 13 additions & 15 deletions libpysal/weights/spatial_lag.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,12 @@
"""
Spatial lag operations.
"""
__author__ = "Sergio J. Rey <[email protected]>, David C. Folch <[email protected]>, Levi John Wolf <[email protected]"
__author__ = (
"Sergio J. Rey <[email protected]>, "
"David C. Folch <[email protected]>, "
"Levi John Wolf <[email protected]"
)

__all__ = ["lag_spatial", "lag_categorical"]

import numpy as np
Expand All @@ -16,21 +21,18 @@ def lag_spatial(w, y):
Parameters
----------
w : W
libpysal spatial weightsobject
y : array
numpy array with dimensionality conforming to w (see examples)
Returns
-------
wy : array
array of numeric values for the spatial lag
Examples
--------
Setup a 9x9 binary spatial weights matrix and vector of data; compute the
spatial lag of the vector.
Expand All @@ -50,7 +52,6 @@ def lag_spatial(w, y):
array([2. , 2. , 3. , 3.33333333, 4. ,
4.66666667, 5. , 6. , 6. ])
Explicitly define data vector as 9x1 and recompute the spatial lag
>>> y.shape = (9, 1)
Expand All @@ -66,7 +67,6 @@ def lag_spatial(w, y):
[6. ],
[6. ]])
Take the spatial lag of a 9x2 data matrix
>>> yr = np.arange(8, -1, -1)
Expand All @@ -83,7 +83,6 @@ def lag_spatial(w, y):
[5. , 3. ],
[6. , 2. ],
[6. , 2. ]])
"""
return w.sparse * y

Expand All @@ -97,7 +96,6 @@ def lag_categorical(w, y, ties="tryself"):
Parameters
----------
w : W
PySAL spatial weightsobject
y : iterable
Expand All @@ -111,6 +109,7 @@ def lag_categorical(w, y, ties="tryself"):
and break a tie. If this does not resolve the tie,
a winner is chosen randomly. To just use random choice to
break ties, pass "random" instead.
Returns
-------
an (n x k) column vector containing the most common neighboring observation
Expand All @@ -124,7 +123,6 @@ def lag_categorical(w, y, ties="tryself"):
Examples
--------
Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of
categorical variables with no ties.
Expand All @@ -141,7 +139,9 @@ def lag_categorical(w, y, ties="tryself"):
>>> yvect = np.array(y).reshape(9,1)
>>> yvect_l = libpysal.weights.lag_categorical(w,yvect)
>>> check = np.array( [ [i] for i in ['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']] )
>>> check = np.array(
... [ [i] for i in ['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']]
... )
>>> np.array_equal(yvect_l, check)
True
Expand All @@ -153,14 +153,12 @@ def lag_categorical(w, y, ties="tryself"):
>>> check = np.array([['b', 'd'], ['a', 'c'], ['b', 'c'], ['c', 'd'], ['b', 'd'], ['c', 'c'], ['b', 'd'], ['c', 'd'], ['b', 'c']])
>>> np.array_equal(check, ym_lag)
True
"""
""" # noqa E501
if isinstance(y, list):
y = np.array(y)
orig_shape = y.shape
if len(orig_shape) > 1:
if orig_shape[1] > 1:
return np.vstack([lag_categorical(w, col) for col in y.T]).T
if len(orig_shape) > 1 and orig_shape[1] > 1:
return np.vstack([lag_categorical(w, col) for col in y.T]).T
y = y.flatten()
output = np.zeros_like(y)
labels = np.unique(y)
Expand Down
78 changes: 38 additions & 40 deletions libpysal/weights/spintW.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,12 +4,16 @@
"""

# ruff: noqa: N802, N803, N999

__author__ = "Taylor Oshan <[email protected]> "

from collections import OrderedDict

from scipy.sparse import kron
from .weights import W, WSP

from .distance import DistanceBand
from collections import OrderedDict
from .weights import WSP, W


def ODW(Wo, Wd, transform="r", silence_warnings=True):
Expand All @@ -22,22 +26,19 @@ def ODW(Wo, Wd, transform="r", silence_warnings=True):
----------
Wo : W object for origin locations
o x o spatial weight object amongst o origins
Wd : W object for destination locations
d x d spatial weight object amongst d destinations
transform : Transformation for standardization of final OD spatial weight; default
is 'r' for row standardized
Returns
-------
W : spatial contiguity W object for assocations between flows
ww : spatial contiguity W object for assocations between flows
o*d x o*d spatial weight object amongst o*d flows between o
origins and d destinations
Examples
--------
>>> import libpysal
>>> O = libpysal.weights.lat2W(2,2)
>>> D = libpysal.weights.lat2W(2,2)
Expand All @@ -49,33 +50,32 @@ def ODW(Wo, Wd, transform="r", silence_warnings=True):
>>> OD.full()[0][0]
array([0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. , 0.25, 0.25,
0. , 0. , 0. , 0. , 0. ])
"""
if Wo.transform != "b":
try:
Wo.tranform = "b"
except:
except: # noqa E722
raise AttributeError(
"Wo is not binary and cannot be transformed to "
"binary. Wo must be binary or suitably transformed to binary."
)
) from None
if Wd.transform != "b":
try:
Wd.tranform = "b"
except:
except: # noqa E722
raise AttributeError(
"Wd is not binary and cannot be transformed to "
"binary. Wd must be binary or suitably transformed to binary."
)
Wo = Wo.sparse
Wo.eliminate_zeros()
Wd = Wd.sparse
Wd.eliminate_zeros()
Ww = kron(Wo, Wd, format="csr")
Ww.eliminate_zeros()
Ww = WSP(Ww).to_W(silence_warnings=silence_warnings)
Ww.transform = transform
return Ww
) from None
wo = Wo.sparse
wo.eliminate_zeros()
wd = Wd.sparse
wd.eliminate_zeros()
ww = kron(wo, wd, format="csr")
ww.eliminate_zeros()
ww = WSP(ww).to_W(silence_warnings=silence_warnings)
ww.transform = transform
return ww


def netW(link_list, share="A", transform="r", **kwargs):
Expand All @@ -88,19 +88,22 @@ def netW(link_list, share="A", transform="r", **kwargs):
link_list : list
of tuples where each tuple is of the form (o,d) where o is an
origin id and d is a destination id
share : string
denoting how to define the nodal relationship used to determine neighboring edges; defualt is 'A' for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.
denoting how to define the nodal relationship used to determine
neighboring edges; defualt is 'A' for any shared nodes between two
network edges; options include: O a shared origin node; D a shared
destination node; OD; a shared origin or a shared destination node;
C a shared node that is the destination of the first edge and the
origin of the second edge - i.e., a directed chain is formed moving
from edge one to edge two.
transform : Transformation for standardization of final OD spatial weight; default
is 'r' for row standardized
**kwargs : keyword arguments
optional arguments for :class:`pysal.weights.W`
Returns
-------
W : nodal contiguity W object for networkd edges or flows
net_w : nodal contiguity W object for networkd edges or flows
W Object representing the binary adjacency of the network edges
given a definition of nodal relationshilibpysal.weights.spintW.
Expand All @@ -117,7 +120,6 @@ def netW(link_list, share="A", transform="r", **kwargs):
>>> any_common = libpysal.weights.netW(links, share='A')
>>> any_common.neighbors[('a', 'b')]
[('a', 'c'), ('a', 'd'), ('c', 'b'), ('c', 'a')]
"""
neighbors = {}
neighbors = OrderedDict()
Expand Down Expand Up @@ -151,9 +153,9 @@ def netW(link_list, share="A", transform="r", **kwargs):
raise AttributeError(
"Parameter 'share' must be 'O', 'D'," " 'OD', or 'C'"
)
netW = W(neighbors, **kwargs)
netW.tranform = transform
return netW
net_w = W(neighbors, **kwargs)
net_w.tranform = transform
return net_w


def vecW(
Expand All @@ -166,8 +168,8 @@ def vecW(
alpha=-1.0,
binary=True,
ids=None,
build_sp=False,
**kwargs
build_sp=False, # noqa ARG001
**kwargs,
):
"""
Distance-based spatial weight for vectors that is computed using a
Expand Down Expand Up @@ -198,7 +200,6 @@ def vecW(
distance decay parameter for weight (default -1.0)
if alpha is positive the weights will not decline with
distance. If binary is True, alpha is ignored
ids : list
values to use for keys of the neighbors and weights dicts
build_sp : boolean
Expand All @@ -209,10 +210,9 @@ def vecW(
**kwargs : keyword arguments
optional arguments for :class:`pysal.weights.W`
Returns
-------
W : DistanceBand W object that uses 4-dimenional distances between
w : DistanceBand W object that uses 4-dimenional distances between
vectors origin and destination coordinates.
Examples
Expand All @@ -228,20 +228,19 @@ def vecW(
>>> W2 = libpysal.weights.vecW(x1, y2, x1, y2, threshold=8.5)
>>> list(W2.neighbors[0])
[1, 2]
"""
data = list(zip(origin_x, origin_y, dest_x, dest_y))
W = DistanceBand(
data = list(zip(origin_x, origin_y, dest_x, dest_y, strict=True))
w = DistanceBand(
data,
threshold=threshold,
p=p,
binary=binary,
alpha=alpha,
ids=ids,
build_sp=False,
**kwargs
**kwargs,
)
return W
return w


def mat2L(edge_matrix):
Expand All @@ -261,7 +260,6 @@ def mat2L(edge_matrix):
edge_list : list
of tuples where each tuple is of the form (o,d) where o is an
origin id and d is a destination id
"""
if len(edge_matrix.shape) != 2:
raise AttributeError(
Expand Down
Empty file removed libpysal/weights/test.py
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