__author__ = "Xun Li <lixun910@gmail.com>"
from ..libgeoda import gda_distance_weights, gda_min_distthreshold, gda_knn_weights
from .weight import Weight
[docs]def distance_weights(geoda_obj, dist_thres, **kwargs):
'''Distance-based Spatial Weights
Create a distance-based weights
Args:
geoda_obj (geoda): An instance of geoda class.
dist_thres (float): A positive numeric value of distance threshold used to find neighbors.
For example, one can use the pygeoda.min_distthreshold() to get a distance that
guarantees that every observation has at least 1 neighbor.
power (float, optional): The power (or exponent) of a number indicates how many times to use the number in a multiplication.
is_inverse (bool, optional): A bool flag indicates whether or not to apply inverse on distance value.
Defaults to False.
is_arc (bool, optional): A bool flag indicates if compute arc distance or Euclidean distance. Defaults to False (Euclidean distance)
is_mile (bool, optional): A bool flag indicates if the distance unit is mile or km. Defaults to True (mile).
Returns:
Weight: An instance of Weight class
'''
power = 1.0 if 'power' not in kwargs else kwargs['power']
is_inverse = False if 'is_inverse' not in kwargs else kwargs['is_inverse']
is_arc = False if 'is_arc' not in kwargs else kwargs['is_arc']
is_mile = True if 'is_mile' not in kwargs else kwargs['is_mile']
poly_id = ""
kernel = ""
diagonal = False
gda_w = gda_distance_weights(geoda_obj.gda, dist_thres, poly_id, power, is_inverse, is_arc, is_mile, kernel, diagonal)
return Weight(gda_w)
[docs]def min_distthreshold(geoda_obj, is_arc=False, is_mile=True):
'''Minimum Distance Threshold for Distance-based Weights
Get minimum threshold of distance that makes sure each observation has at least one neighbor
Args:
geoda_obj (geoda): An instance of geoda class.
is_arc (bool, optional): A bool flag indicates if compute arc distance or Euclidean distance. Defaults to False (Euclidean distance)
is_mile (bool, optional): A bool flag indicates if the distance unit is mile or km. Defaults to True (mile).
Returns:
thres (float): A float value of minimum threhold for distance based weights.
'''
return gda_min_distthreshold(geoda_obj.gda, is_arc, is_mile)
[docs]def knn_weights(geoda_obj, k, **kwargs):
'''K-Nearest Neighbors-based Spatial Weights
Create a k-nearest neighbors based spatial weights
Args:
geoda_obj (geoda): An instance of geoda class.
k (int): A positive integer number for k-nearest neighbors
power (float, optional): The power (or exponent) of a number indicates how many times to use the number in a multiplication.
is_inverse (bool, optional): A bool flag indicates whether or not to apply inverse on distance value.
Defaults to False.
is_arc (bool, optional): A bool flag indicates if compute arc distance or Euclidean distance. Defaults to False (Euclidean distance)
is_mile (bool, optional): A bool flag indicates if the distance unit is mile or km. Defaults to True (mile).
Returns:
Weight: An instance of Weight class
'''
power = 1.0 if 'power' not in kwargs else kwargs['power']
is_inverse = False if 'is_inverse' not in kwargs else kwargs['is_inverse']
is_arc = False if 'is_arc' not in kwargs else kwargs['is_arc']
is_mile = True if 'is_mile' not in kwargs else kwargs['is_mile']
# not used
kernel = ""
bandwidth = 0
adaptive_bandwidth = False
use_kernel_diagnals = False
polyid = ""
gda_w = gda_knn_weights(geoda_obj.gda, k, power, is_inverse, is_arc, is_mile, kernel, bandwidth, adaptive_bandwidth, use_kernel_diagnals, polyid)
return Weight(gda_w)