libpysal.weights.
W
(neighbors, weights=None, id_order=None, silence_warnings=False, ids=None)[source]¶Spatial weights class.
Parameters: |
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Examples
>>> from libpysal.weights import W
>>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
>>> weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
>>> w = W(neighbors, weights)
>>> "%.3f"%w.pct_nonzero
'29.630'
Read from external gal file
>>> import libpysal
>>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
>>> w.n
78
>>> "%.3f"%w.pct_nonzero
'6.542'
Set weights implicitly
>>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
>>> w = W(neighbors)
>>> round(w.pct_nonzero,3)
29.63
>>> from libpysal.weights import lat2W
>>> w = lat2W(100, 100)
>>> w.trcW2
39600.0
>>> w.trcWtW
39600.0
>>> w.transform='r'
>>> round(w.trcW2, 3)
2530.722
>>> round(w.trcWtW, 3)
2533.667
Cardinality Histogram >>> w.histogram [(2, 4), (3, 392), (4, 9604)]
Disconnected observations (islands)
>>> from libpysal.weights import W
>>> w = W({1:[0],0:[1],2:[], 3:[]})
UserWarning: The weights matrix is not fully connected: There are 3 disconnected components. There are 2 islands with ids: 2, 3.
Attributes: |
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Methods
asymmetry ([intrinsic]) |
Asymmetry check. |
from_adjlist (adjlist[, focal_col, …]) |
Return an adjacency list representation of a weights object. |
from_networkx (graph[, weight_col]) |
Convert a networkx graph to a PySAL W object. |
full () |
Generate a full numpy array. |
get_transform () |
Getter for transform property. |
plot (gdf[, indexed_on, ax, color, node_kws, …]) |
Plot spatial weights objects. |
remap_ids (new_ids) |
In place modification throughout W of id values from w.id_order to new_ids in all |
set_shapefile (shapefile[, idVariable, full]) |
Adding meta data for writing headers of gal and gwt files. |
set_transform ([value]) |
Transformations of weights. |
symmetrize ([inplace]) |
Construct a symmetric KNN weight. |
to_WSP () |
Generate a WSP object. |
to_adjlist ([remove_symmetric, focal_col, …]) |
Compute an adjacency list representation of a weights object. |
to_networkx () |
Convert a weights object to a networkx graph |
from_WSP | |
from_file | |
from_shapefile |
__init__
(neighbors, weights=None, id_order=None, silence_warnings=False, ids=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
Methods
__init__ (neighbors[, weights, id_order, …]) |
Initialize self. |
asymmetry ([intrinsic]) |
Asymmetry check. |
from_WSP (WSP[, silence_warnings]) |
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from_adjlist (adjlist[, focal_col, …]) |
Return an adjacency list representation of a weights object. |
from_file ([path, format]) |
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from_networkx (graph[, weight_col]) |
Convert a networkx graph to a PySAL W object. |
from_shapefile (*args, **kwargs) |
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full () |
Generate a full numpy array. |
get_transform () |
Getter for transform property. |
plot (gdf[, indexed_on, ax, color, node_kws, …]) |
Plot spatial weights objects. |
remap_ids (new_ids) |
In place modification throughout W of id values from w.id_order to new_ids in all |
set_shapefile (shapefile[, idVariable, full]) |
Adding meta data for writing headers of gal and gwt files. |
set_transform ([value]) |
Transformations of weights. |
symmetrize ([inplace]) |
Construct a symmetric KNN weight. |
to_WSP () |
Generate a WSP object. |
to_adjlist ([remove_symmetric, focal_col, …]) |
Compute an adjacency list representation of a weights object. |
to_networkx () |
Convert a weights object to a networkx graph |
Attributes
asymmetries |
List of id pairs with asymmetric weights. |
cardinalities |
Number of neighbors for each observation. |
component_labels |
Store the graph component in which each observation falls. |
diagW2 |
Diagonal of \(WW\). |
diagWtW |
Diagonal of \(W^{'}W\). |
diagWtW_WW |
Diagonal of \(W^{'}W + WW\). |
histogram |
Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit. |
id2i |
Dictionary where the key is an ID and the value is that ID’s index in W.id_order. |
id_order |
Returns the ids for the observations in the order in which they would be encountered if iterating over the weights. |
id_order_set |
Returns True if user has set id_order, False if not. |
islands |
List of ids without any neighbors. |
max_neighbors |
Largest number of neighbors. |
mean_neighbors |
Average number of neighbors. |
min_neighbors |
Minimum number of neighbors. |
n |
Number of units. |
n_components |
Store whether the adjacency matrix is fully connected. |
neighbor_offsets |
Given the current id_order, neighbor_offsets[id] is the offsets of the id’s neighbors in id_order. |
nonzero |
Number of nonzero weights. |
pct_nonzero |
Percentage of nonzero weights. |
s0 |
s0 is defined as |
s1 |
s1 is defined as |
s2 |
s2 is defined as |
s2array |
Individual elements comprising s2. |
sd |
Standard deviation of number of neighbors. |
sparse |
Sparse matrix object. |
transform |
Getter for transform property. |
trcW2 |
Trace of \(WW\). |
trcWtW |
Trace of \(W^{'}W\). |
trcWtW_WW |
Trace of \(W^{'}W + WW\). |