def test_local_multijoincount(self):
columbus = pygeoda.open("./data/columbus.shp")
columbus_q = pygeoda.queen_weights(columbus)
nsa = columbus.GetRealCol("nsa")
nsb = columbus.GetRealCol("nsb")
nndata = (nsa, nsb)
#The following two lines will raise warning to use local_bijoincount()
#nsa_inv = [1-i for i in nsa]
#lisa = pygeoda.local_multijoincount(columbus_q, (nsa, nsa_inv))
lisa = pygeoda.local_multijoincount(columbus_q, nndata)
lvals = lisa.lisa_values()
self.assertEqual(lvals[0], 2)
self.assertEqual(lvals[1], 3)
self.assertEqual(lvals[2], 4)
pvals = lisa.lisa_pvalues()
self.assertEqual(pvals[0], 0.213000)
self.assertEqual(pvals[1], 0.070000)
self.assertEqual(pvals[2], 0.017000)
nnvals = lisa.lisa_num_nbrs()
self.assertEqual(nnvals[0], 2)
self.assertEqual(nnvals[1], 3)
self.assertEqual(nnvals[2], 4)
def setUp(self):
self.guerry = pygeoda.open("./data/Guerry.shp")
self.queen_w = pygeoda.queen_weights(self.guerry)
self.crm_prp = self.guerry.GetIntegerCol("Crm_prp")
self.litercy = self.guerry.GetIntegerCol("Litercy")
slect_vars = ['Crm_prp','Crm_prs']
self.data = [self.guerry.GetRealCol(v) for v in slect_vars]
def setUp(self):
self.guerry = pygeoda.open("./data/Guerry.shp")
self.queen_w = pygeoda.queen_weights(self.guerry)
self.data = self.guerry[[
"Crm_prs", "Crm_prp", "Litercy", "Donatns", "Infants", "Suicids"
]]
self.bound_variable = self.guerry.GetRealCol("Pop1831")
self.min_bound = 3236.67 # 10% of Pop1831
def setUp(self):
self.guerry = pygeoda.open("./data/Guerry.shp")
self.queen_w = pygeoda.queen_weights(self.guerry)
select_vars = [
"Crm_prs", "Crm_prp", "Litercy", "Donatns", "Infants", "Suicids"
]
self.data = [self.guerry.GetRealCol(v) for v in select_vars]
self.Crm_prs = self.guerry.GetRealCol('Crm_prs')
self.Crm_prp = self.guerry.GetRealCol('Crm_prp')
def test_local_bijoincount(self):
columbus = pygeoda.open("./data/columbus.shp")
columbus_q = pygeoda.queen_weights(columbus)
nsa = columbus.GetRealCol("nsa")
nsa_inv = [1-i for i in nsa]
lisa = pygeoda.local_bijoincount(columbus_q, [nsa, nsa_inv])
jc = lisa.lisa_values()
self.assertEqual(jc[7], 0)
self.assertEqual(jc[8], 1)
self.assertEqual(jc[9], 1)
pvals = lisa.lisa_pvalues()
self.assertEqual(pvals[8], 0.002)
self.assertEqual(pvals[9], 0.034)
nn = lisa.lisa_num_nbrs()
self.assertEqual(nn[0], 2)
self.assertEqual(nn[1], 3)
self.assertEqual(nn[2], 4)
def test_local_joincount(self):
columbus = pygeoda.open("./data/columbus.shp")
columbus_q = pygeoda.queen_weights(columbus)
nsa = columbus.GetRealCol("nsa")
lisa = pygeoda.local_joincount(columbus_q, nsa)
lvals = lisa.lisa_values()
self.assertEqual(lvals[0], 2)
self.assertEqual(lvals[1], 3)
self.assertEqual(lvals[2], 4)
pvals = lisa.lisa_pvalues()
self.assertEqual(pvals[0], 0.21299999999999999)
self.assertEqual(pvals[1], 0.070000000000000007)
self.assertEqual(pvals[2], 0.017000000000000001)
nnvals = lisa.lisa_num_nbrs()
self.assertEqual(nnvals[0], 2)
self.assertEqual(nnvals[1], 3)
self.assertEqual(nnvals[2], 4)
import pygeoda import os from sys import argv import matplotlib import matplotlib.pyplot as plt import geopandas # 输入shp文件 dataPath = argv[1] feild = argv[2] randomSed = int(argv[3]) filter = float(argv[4]) cpu=int(argv[5]) data = pygeoda.open(dataPath) queen_w = pygeoda.weights.queen(data) fieldCol = data.GetIntegerCol(feild) lisa = pygeoda.local_moran(queen_w, fieldCol) lisa.SetPermutations(randomSed) lisa.SetThreads(cpu) lisa.Run() lisa_colors = lisa.GetColors() lisa_labels = lisa.GetLabels() lisa_cluster = lisa.GetClusterIndicators() list_p_value = lisa.GetPValues() fig, ax = plt.subplots(figsize=(10, 10))
def setUp(self):
self.guerry = pygeoda.open("./data/Guerry.shp")
self.crm_prp = self.guerry.GetIntegerCol("Crm_prp")
import time
import sys
"""
Usage:
python3 perf_pygeoda.py FILE_PATH VARIABLE_NAME PERMUTATIONS PERMUTATION_METHOD CPU_THREADS
python3 perf_pygeoda.py ./data/natregimes.shp HR60 999 brutal-force 1
python3 perf_pygeoda.py ./data/natregimes.shp HR60 999 lookup-table 8
"""
data_path = sys.argv[1]
var_name = sys.argv[2]
perms = int(sys.argv[3])
perm_method = sys.argv[4]
cpu_threads = int(sys.argv[5])
# prepare the data and weights
dt = pygeoda.open(data_path)
if data_path == './data/Chicago_parcels_points.shp':
w = pygeoda.weights.knn_weights(dt, 10)
else:
w = pygeoda.weights.queen_weights(dt)
x = dt.GetRealCol(var_name)
# time it
start_time = time.time()
lisa = pygeoda.local_moran(w,
x,
permutations=perms,
permutation_method=perm_method,
cpu_threads=cpu_threads)
run_time = time.time() - start_time
def setUp(self):
self.nat = pygeoda.open("./data/natregimes.shp")
self.poly_id = self.nat.GetIntegerCol("POLY_ID")
import pygeoda
sdoh = pygeoda.open("./data/us-sdoh-2014-chi_utm.shp")
v = ("1_SES", "2_MOB", "3_URB", "4_MICA")
chsdoh = pygeoda.neighbor_match_test(sdoh, v, 8)
print(chsdoh)
import pygeoda
snow = pygeoda.open("./data/deaths_nd_by_house.shp")
v = snow.GetIntegerCol("death_dum")
d20 = pygeoda.weights.distance_weights(snow, dist_thres=20)
print(d20)
snowjc = pygeoda.local_joincount(d20,
v,
permutations=99999,
significance_cutoff=0.01)
snowsig = snowjc.lisa_clusters()
pval = snowjc.lisa_pvalues()
print(sum(snowsig))