from ..libgeoda import gda_localmoran, gda_bi_localmoran, VecBool, gda_batchlocalmoran, VecVecBool, gda_localmoran_eb
from .lisa import lisa, batchlisa
__author__ = "Xun Li <lixun910@gmail.com>"
[docs]def local_moran(w, data, **kwargs):
"""Local Moran statistics.
Args:
w (Weight): An instance of Weight class.
data (tuple/list/pandas.Series): A tuple of numeric values of selected variable
undefs (tuple/list, optional): A tuple of boolean values to indicate which value is undefined or null
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of lisa class represents the results of lisa computation
"""
if w == None:
raise ValueError("Weights is None.")
if w.num_obs != len(data):
raise ValueError("The size of data doesnt not match the number of observations.")
undefs = VecBool() if 'undefs' not in kwargs else kwargs['undefs']
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
lisa_obj = gda_localmoran(w.gda_w, list(data), list(undefs), significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return lisa(lisa_obj)
[docs]def local_bimoran(w, data1, data2, **kwargs):
"""Bivariate local Moran statistics.
The bivariate Local Moran’s I captures the relationship between the value for one variable at location i, and the average of the neighboring values for another variable.
Args:
w (Weight): An instance of Weight class.
data1 (tuple/list/pandas.Series): A tuple of numeric values of first variable
data1 (tuple/list/pandas.Series): A tuple of numeric values of second variable
undefs (tuple/list, optional): A tuple of boolean values to indicate which value is undefined or null
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of lisa class represents the results of lisa computation
"""
if w == None:
raise ValueError("Weights is None.")
if w.num_obs != len(data1) or len(data1) != len(data2):
raise ValueError("The size of data doesnt not match the number of observations.")
undefs = VecBool() if 'undefs' not in kwargs else kwargs['undefs']
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
lisa_obj = gda_bi_localmoran(w.gda_w, list(data1), list(data2), list(undefs), significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return lisa(lisa_obj)
def local_moran_eb(w, event_data, base_data, **kwargs):
"""The function to apply local Moran with EB Rate statistics.
The EB rate is first computed from "event" and "base" variables, and then used in local moran statistics.
Args:
w (Weight): An instance of Weight class
event_data (tuple/list/pandas.Series): A numeric tuple of selected "event" variable
base_data (tuple/list/pandas.Series): A numeric tuple of selected "base" variable
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of lisa class represents the results of lisa computation
"""
if w == None:
raise ValueError("Weights is None.")
if w.num_obs != len(event_data) or w.num_obs != len(base_data):
raise ValueError("The size of data doesnt not match the number of observations.")
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
lisa_obj = gda_localmoran_eb(w.gda_w, list(event_data), list(base_data), significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return lisa(lisa_obj)
[docs]def batch_local_moran(w, data, **kwargs):
"""Apply local moran statistics on a set of variables
Args:
w (Weight): An instance of Weight class.
data (list/pandas.dataframe): A list of numeric array of selected variables
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of BatchLisa class represents the results of lisa computations
"""
if w == None:
raise ValueError("Weights is None.")
if (not isinstance(data, list)) or (len(data) < 1):
raise ValueError("The data has to be a list of numeric array of selected variables.")
if w.num_obs != len(data[0]):
raise ValueError("The size of data doesnt not match the number of observations.")
if type(data).__name__ == "DataFrame":
data = data.values.transpose().tolist()
undefs = VecVecBool() if 'undefs' not in kwargs else kwargs['undefs']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
lisa_obj = gda_batchlocalmoran(w.gda_w, data, undefs, significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return batchlisa(lisa_obj)