Compare commits
5 Commits
model-stor
...
add-max-p
| Author | SHA1 | Date | |
|---|---|---|---|
|
691ce95638 | ||
|
|
f8db8d9ba4 | ||
|
|
c6eb5d9d19 | ||
|
|
ae767fc903 | ||
|
|
a9bef6ba1d |
17
src/pg/sql/22_maxp.sql
Normal file
17
src/pg/sql/22_maxp.sql
Normal file
@@ -0,0 +1,17 @@
|
||||
-- max-p regionalization
|
||||
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_MaxP(
|
||||
subquery TEXT,
|
||||
colnames TEXT[],
|
||||
floor_variable TEXT,
|
||||
min_size int default 1,
|
||||
initial int default 99,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (region_class text, p_val numeric, rowid bigint)
|
||||
AS $$
|
||||
from crankshaft.clustering import MaxP
|
||||
maxp = MaxP()
|
||||
return maxp.maxp(subquery, colnames, floor_variable, floor=min_size)
|
||||
$$ LANGUAGE plpythonu;
|
||||
@@ -65,3 +65,17 @@ class AnalysisDataProvider:
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
|
||||
def get_maxp(self, params):
|
||||
"""fetch data for max-p"""
|
||||
try:
|
||||
query = pu.construct_neighbor_query('queen', params)
|
||||
data = plpy.execute(query)
|
||||
|
||||
if len(data) == 0:
|
||||
# TODO: replace with better message in PR#157
|
||||
plpy.error('No non-null valued rows')
|
||||
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
|
||||
@@ -2,3 +2,4 @@
|
||||
from moran import *
|
||||
from kmeans import *
|
||||
from getis import *
|
||||
from maxp import *
|
||||
|
||||
@@ -38,7 +38,7 @@ class Getis:
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
result = self.data_provider.get_getis(w_type, qvals)
|
||||
attr_vals = pu.get_attributes(result)
|
||||
attr_vals = pu.get_attribute(result)
|
||||
|
||||
# build PySAL weight object
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
85
src/py/crankshaft/crankshaft/clustering/maxp.py
Normal file
85
src/py/crankshaft/crankshaft/clustering/maxp.py
Normal file
@@ -0,0 +1,85 @@
|
||||
"""
|
||||
max-p clustering
|
||||
"""
|
||||
|
||||
import pysal as ps
|
||||
import numpy as np
|
||||
import random
|
||||
import time
|
||||
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
|
||||
class MaxP:
|
||||
def __init__(self, data_provider=None):
|
||||
if data_provider:
|
||||
self.data_provider = data_provider
|
||||
else:
|
||||
self.data_provider = AnalysisDataProvider()
|
||||
|
||||
def maxp(self, subquery, colnames, floor_variable=None,
|
||||
floor=1,
|
||||
geom_col='the_geom', id_col='cartodb_id',
|
||||
):
|
||||
"""
|
||||
Inputs:
|
||||
@param subquery (text): subquery to expose the data need for the
|
||||
analysis. This query needs to expose all
|
||||
of the columns in `colnames`, `id_col`, and
|
||||
`geom_col`
|
||||
@param colnames (list): list of column names (as strings). This is
|
||||
used to calculate intra-regional homogeneity.
|
||||
@param floor_variable (text): name of column variable for the floor
|
||||
@param floor (int): the minimum bound for a variable that has to be
|
||||
obtained in each region,
|
||||
@param geom_col (text): geometry column used for calculating the
|
||||
spatial neighborhood
|
||||
@param id_col (text): id column used for keeping the identity of
|
||||
the data
|
||||
Outputs: a list of tuples with the following columns:
|
||||
|
||||
classification_id: group that the geometry belongs to
|
||||
rowid: identifier from id_col
|
||||
"""
|
||||
params = {'subquery': subquery,
|
||||
'colnames': colnames,
|
||||
'id_col': id_col,
|
||||
'geom_col': geom_col,
|
||||
'floor':
|
||||
'floor_variable':floor_variable,
|
||||
}
|
||||
|
||||
resp = self.data_provider.get_maxp(params)
|
||||
attr_vals = pu.get_attributes(resp, len(colnames))
|
||||
weight = pu.get_weight(resp, w_type='queen')
|
||||
|
||||
if floor_variable == None:
|
||||
floor_variable = np.ones((weight.n, 1))
|
||||
else:
|
||||
floor_column_id = colnames.index(floor_variable)
|
||||
floor_variable = attr_vals.transpose()[floor_column_id]
|
||||
|
||||
start_time = time.time()
|
||||
r = ps.Maxp(weight, attr_vals,
|
||||
floor=floor,
|
||||
floor_variable= floor_variable,
|
||||
initial=10)
|
||||
# print r.regions
|
||||
cluster_classes = get_cluster_classes(weight.id_order, r.regions)
|
||||
r.inference()
|
||||
# print "elapsed time: ", time.time() - start_time
|
||||
return zip(cluster_classes, [r.pvalue] * len(weight.id_order),
|
||||
weight.id_order)
|
||||
|
||||
|
||||
def get_cluster_classes(ids, clusters):
|
||||
"""
|
||||
|
||||
"""
|
||||
cluster_classes = []
|
||||
for i in ids:
|
||||
for r_id, r in enumerate(clusters):
|
||||
if i in r:
|
||||
cluster_classes.append(r_id)
|
||||
return cluster_classes
|
||||
@@ -39,7 +39,7 @@ class Moran:
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
attr_vals = pu.get_attributes(result)
|
||||
attr_vals = pu.get_attribute(result, 1)
|
||||
|
||||
# calculate weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
@@ -68,7 +68,7 @@ class Moran:
|
||||
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
attr_vals = pu.get_attributes(result)
|
||||
attr_vals = pu.get_attribute(result, 1)
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
@@ -96,8 +96,8 @@ class Moran:
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
numer = pu.get_attribute(result, 1)
|
||||
denom = pu.get_attribute(result, 2)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
@@ -126,8 +126,8 @@ class Moran:
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
numer = pu.get_attribute(result, 1)
|
||||
denom = pu.get_attribute(result, 2)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
@@ -157,8 +157,8 @@ class Moran:
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
attr1_vals = pu.get_attributes(result, 1)
|
||||
attr2_vals = pu.get_attributes(result, 2)
|
||||
attr1_vals = pu.get_attribute(result, 1)
|
||||
attr2_vals = pu.get_attribute(result, 2)
|
||||
|
||||
# create weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
@@ -13,10 +13,10 @@ def construct_neighbor_query(w_type, query_vals):
|
||||
@param query_vals dict: values used to construct the query
|
||||
"""
|
||||
|
||||
if w_type.lower() == 'knn':
|
||||
return knn(query_vals)
|
||||
else:
|
||||
if w_type.lower() == 'queen':
|
||||
return queen(query_vals)
|
||||
else:
|
||||
return knn(query_vals)
|
||||
|
||||
|
||||
# Build weight object
|
||||
@@ -60,16 +60,17 @@ def query_attr_select(params):
|
||||
attr_string = ""
|
||||
template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
|
||||
|
||||
if 'time_cols' in params:
|
||||
if ('time_cols' in params) or ('colnames' in params):
|
||||
# if markov analysis
|
||||
attrs = params['time_cols']
|
||||
attrs = (params['time_cols'] if 'time_cols' in params
|
||||
else params['colnames'])
|
||||
|
||||
for idx, val in enumerate(attrs):
|
||||
attr_string += template % {"col": val, "alias_num": idx + 1}
|
||||
else:
|
||||
# if moran's analysis
|
||||
attrs = [k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery',
|
||||
if k not in ('id_col', 'geom_col',
|
||||
'num_ngbrs', 'subquery')]
|
||||
|
||||
for idx, val in enumerate(attrs):
|
||||
@@ -100,9 +101,12 @@ def query_attr_where(params):
|
||||
attr_string = []
|
||||
template = "idx_replace.\"%s\" IS NOT NULL"
|
||||
|
||||
if 'time_cols' in params:
|
||||
# markov where clauses
|
||||
attrs = params['time_cols']
|
||||
# TODO: generalize to colnames or not only?
|
||||
# this would reduce the complexity of the code here
|
||||
if ('time_cols' in params) or ('colnames' in params):
|
||||
# markov and max-p where clauses
|
||||
attrs = (params['time_cols'] if 'time_cols' in params
|
||||
else params['colnames'])
|
||||
# add values to template
|
||||
for attr in attrs:
|
||||
attr_string.append(template % attr)
|
||||
@@ -111,7 +115,7 @@ def query_attr_where(params):
|
||||
|
||||
# get keys
|
||||
attrs = [k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery',
|
||||
if k not in ('id_col', 'geom_col',
|
||||
'num_ngbrs', 'subquery')]
|
||||
|
||||
# add values to template
|
||||
@@ -190,10 +194,24 @@ def queen(params):
|
||||
# to add more weight methods open a ticket or pull request
|
||||
|
||||
|
||||
def get_attributes(query_res, attr_num=1):
|
||||
def get_attributes(query_resp, n_cols):
|
||||
"""
|
||||
@param query_res: query results with attributes and neighbors
|
||||
Extract the time columns and bin appropriately
|
||||
"""
|
||||
return np.array([[x['attr' + str(i + 1)] for x in query_resp]
|
||||
for i in range(n_cols)], dtype=float).transpose()
|
||||
|
||||
|
||||
def get_attribute(query_res, attr_num=1):
|
||||
"""
|
||||
Inputs:
|
||||
@param query_res: query results with attributes and other info, of the
|
||||
form [{'attr1': ..., 'id_col': ...},
|
||||
{'attr1': ..., 'id_col': ...},
|
||||
...]
|
||||
@param attr_num: attribute number (1, 2, ...)
|
||||
Returns:
|
||||
a numpy array that represents the column in 'attr' number attr_num
|
||||
"""
|
||||
return np.array([x['attr' + str(attr_num)] for x in query_res],
|
||||
dtype=np.float)
|
||||
|
||||
@@ -68,7 +68,7 @@ class Markov:
|
||||
weights.transform = 'r'
|
||||
|
||||
# prep time data
|
||||
t_data = get_time_data(query_result, time_cols)
|
||||
t_data = pu.get_attributes(query_result, len(time_cols))
|
||||
|
||||
sp_markov_result = ps.Spatial_Markov(t_data,
|
||||
weights,
|
||||
@@ -88,61 +88,15 @@ class Markov:
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
# find the ups and down and overall distribution of each cell
|
||||
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
|
||||
trend_up, trend_down, trend, \
|
||||
volatility = get_prob_stats(
|
||||
prob_dist,
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
# output the results
|
||||
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
|
||||
|
||||
|
||||
|
||||
def get_time_data(markov_data, time_cols):
|
||||
"""
|
||||
Extract the time columns and bin appropriately
|
||||
"""
|
||||
num_attrs = len(time_cols)
|
||||
return np.array([[x['attr' + str(i)] for x in markov_data]
|
||||
for i in range(1, num_attrs+1)], dtype=float).transpose()
|
||||
|
||||
|
||||
# not currently used
|
||||
def rebin_data(time_data, num_time_per_bin):
|
||||
"""
|
||||
Convert an n x l matrix into an (n/m) x l matrix where the values are
|
||||
reduced (averaged) for the intervening states:
|
||||
1 2 3 4 1.5 3.5
|
||||
5 6 7 8 -> 5.5 7.5
|
||||
9 8 7 6 8.5 6.5
|
||||
5 4 3 2 4.5 2.5
|
||||
|
||||
if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
|
||||
|
||||
This process effectively resamples the data at a longer time span n
|
||||
units longer than the input data.
|
||||
For cases when there is a remainder (remainder(5/3) = 2), the remaining
|
||||
two columns are binned together as the last time period, while the
|
||||
first three are binned together for the first period.
|
||||
|
||||
Input:
|
||||
@param time_data n x l ndarray: measurements of an attribute at
|
||||
different time intervals
|
||||
@param num_time_per_bin int: number of columns to average into a new
|
||||
column
|
||||
Output:
|
||||
ceil(n / m) x l ndarray of resampled time series
|
||||
"""
|
||||
|
||||
if time_data.shape[1] % num_time_per_bin == 0:
|
||||
# if fit is perfect, then use it
|
||||
n_max = time_data.shape[1] / num_time_per_bin
|
||||
else:
|
||||
# fit remainders into an additional column
|
||||
n_max = time_data.shape[1] / num_time_per_bin + 1
|
||||
|
||||
return np.array(
|
||||
[time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
|
||||
for i in range(n_max)]).T
|
||||
|
||||
|
||||
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
|
||||
"""
|
||||
Given an array of transition matrices, look up the probability
|
||||
|
||||
1
src/py/crankshaft/test/fixtures/maxp.json
vendored
Normal file
1
src/py/crankshaft/test/fixtures/maxp.json
vendored
Normal file
File diff suppressed because one or more lines are too long
62
src/py/crankshaft/test/test_clustering_maxp.py
Normal file
62
src/py/crankshaft/test/test_clustering_maxp.py
Normal file
@@ -0,0 +1,62 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import fixture_file
|
||||
from crankshaft.clustering import MaxP
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
import crankshaft.clustering as cc
|
||||
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class FakeDataProvider(AnalysisDataProvider):
|
||||
def __init__(self, fixturedata):
|
||||
self.your_maxp_data = fixturedata
|
||||
|
||||
def get_maxp(self, params):
|
||||
"""
|
||||
Replace this function name with the one used in your algorithm,
|
||||
and make sure to use the same function signature that is written
|
||||
for this algo in analysis_data_provider.py
|
||||
"""
|
||||
return self.your_maxp_data
|
||||
|
||||
class MaxPTest(unittest.TestCase):
|
||||
"""Testing class for max-p regionalization"""
|
||||
|
||||
def setUp(self):
|
||||
self.neighbor_data = json.loads(
|
||||
open(fixture_file('maxp.json')).read())
|
||||
self.neighbor_data = self.neighbor_data['rows']
|
||||
self.params = {"subquery": "select * from fake_table",
|
||||
"colnames": ['population','median_hh_income'],
|
||||
"floor_variable": 'population',
|
||||
"floor": 260000}
|
||||
|
||||
def test_maxp(self):
|
||||
"""
|
||||
"""
|
||||
data = [{'id': d['id'],
|
||||
'attr1': d['attr1'],
|
||||
'attr2':d['attr2'],
|
||||
'neighbors': d['neighbors']} for d in self.neighbor_data]
|
||||
|
||||
random_seeds.set_random_seeds(1234)
|
||||
maxp = MaxP(FakeDataProvider(data))
|
||||
regions = maxp.maxp('select * from research_nothing',['population','median_hh_income'],floor_variable='population',floor=260000)
|
||||
region_labels = [a[0] for a in regions]
|
||||
data_regionalized = zip(data,region_labels)
|
||||
for i in set(region_labels):
|
||||
sum_pop = 0
|
||||
for n in data_regionalized:
|
||||
if n[1] == i:
|
||||
sum_pop += n[0]['attr1']
|
||||
self.assertGreaterEqual(sum_pop, 260000)
|
||||
@@ -1,8 +1,10 @@
|
||||
import unittest
|
||||
import json
|
||||
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
from collections import OrderedDict
|
||||
from helper import fixture_file
|
||||
|
||||
|
||||
class PysalUtilsTest(unittest.TestCase):
|
||||
@@ -35,6 +37,8 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.neighbors_data = json.loads(
|
||||
open(fixture_file('neighbors_markov.json')).read())
|
||||
|
||||
def test_query_attr_select(self):
|
||||
"""Test query_attr_select"""
|
||||
@@ -158,3 +162,184 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
ans4 = [(None, None, None, None)]
|
||||
self.assertEqual(pu.empty_zipped_array(2), ans2)
|
||||
self.assertEqual(pu.empty_zipped_array(4), ans4)
|
||||
|
||||
def test_get_attributes(self):
|
||||
"""Test get_time_data"""
|
||||
import numpy as np
|
||||
data = [{'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009']} for d in self.neighbors_data]
|
||||
|
||||
result = pu.get_attributes(
|
||||
data, len(['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009']))
|
||||
|
||||
# expected was prepared from PySAL example:
|
||||
# f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
# pci = np.array([f.by_col[str(y)]
|
||||
# for y in range(1995, 2010)]).transpose()
|
||||
# rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array(
|
||||
[[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
|
||||
0.83271652, 0.83786314, 0.85012593, 0.85509656, 0.86416612,
|
||||
0.87119375, 0.86302631, 0.86148267, 0.86252252, 0.86746356],
|
||||
[0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388,
|
||||
0.90746978, 0.89830489, 0.89431991, 0.88924794, 0.89815176,
|
||||
0.91832091, 0.91706054, 0.90139505, 0.87897455, 0.86216858],
|
||||
[0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
|
||||
0.78964559, 0.80584442, 0.8084998, 0.82258551, 0.82668196,
|
||||
0.82373724, 0.81814804, 0.83675961, 0.83574199, 0.84647177],
|
||||
[1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
|
||||
1.14506948, 1.12151133, 1.11160697, 1.10888621, 1.11399806,
|
||||
1.12168029, 1.13164797, 1.12958508, 1.11371818, 1.09936775],
|
||||
[1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
|
||||
1.16898201, 1.17212488, 1.14752303, 1.11843284, 1.11024964,
|
||||
1.11943471, 1.11736468, 1.10863242, 1.09642516, 1.07762337],
|
||||
[1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
|
||||
1.44184737, 1.44782832, 1.41978227, 1.39092208, 1.4059372,
|
||||
1.40788646, 1.44052766, 1.45241216, 1.43306098, 1.4174431],
|
||||
[1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
|
||||
1.10888138, 1.11856629, 1.13062931, 1.11944984, 1.12446239,
|
||||
1.11671008, 1.10880034, 1.08401709, 1.06959206, 1.07875225],
|
||||
[1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
|
||||
0.99854316, 0.9880258, 0.99669587, 0.99327676, 1.01400905,
|
||||
1.03176742, 1.040511, 1.01749645, 0.9936394, 0.98279746],
|
||||
[0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845,
|
||||
0.99127006, 0.97925917, 0.9683482, 0.95335147, 0.93694787,
|
||||
0.94308213, 0.92232874, 0.91284091, 0.89689833, 0.88928858],
|
||||
[0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044,
|
||||
0.8578708, 0.86036185, 0.86107306, 0.8500772, 0.86981998,
|
||||
0.86837929, 0.87204141, 0.86633032, 0.84946077, 0.83287146],
|
||||
[1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
|
||||
1.14450183, 1.12349752, 1.12596664, 1.12213996, 1.1119989,
|
||||
1.10257792, 1.10491258, 1.11059842, 1.10509795, 1.10020097],
|
||||
[0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687,
|
||||
0.95831051, 0.94480909, 0.94804195, 0.95430286, 0.94103989,
|
||||
0.92122519, 0.91010201, 0.89280392, 0.89298243, 0.89165385],
|
||||
[0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
|
||||
0.9480927, 0.93539182, 0.95388718, 0.94597005, 0.96918424,
|
||||
0.94781281, 0.93466815, 0.94281559, 0.96520315, 0.96715441],
|
||||
[0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
|
||||
0.98687073, 0.99237486, 0.98209969, 0.9877653, 0.97399471,
|
||||
0.96910087, 0.98416665, 0.98423613, 0.99823861, 0.99545704],
|
||||
[0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
|
||||
0.86191535, 0.84981451, 0.85472102, 0.84564835, 0.83998883,
|
||||
0.83478547, 0.82803648, 0.8198736, 0.82265395, 0.8399404],
|
||||
[0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
|
||||
0.82785597, 0.86008789, 0.86776298, 0.86720209, 0.8676334,
|
||||
0.89179317, 0.94202108, 0.9422231, 0.93902708, 0.94479184],
|
||||
[0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
|
||||
0.90906632, 0.92693339, 0.93695966, 0.94242697, 0.94338265,
|
||||
0.91981796, 0.91108804, 0.90543476, 0.91737138, 0.94793657],
|
||||
[1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723,
|
||||
1.20172869, 1.21328691, 1.22624778, 1.22397075, 1.23857042,
|
||||
1.24419893, 1.23929384, 1.23418676, 1.23626739, 1.26754398],
|
||||
[1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
|
||||
1.34790023, 1.34399863, 1.32575181, 1.30795492, 1.30544841,
|
||||
1.30303302, 1.32107766, 1.32936244, 1.33001241, 1.33288462],
|
||||
[1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
|
||||
1.05059016, 1.03405057, 1.02747623, 1.03162734, 0.9961416,
|
||||
0.97356208, 0.94241549, 0.92754547, 0.92549227, 0.92138102],
|
||||
[1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
|
||||
1.13889622, 1.12442212, 1.13367018, 1.13982256, 1.14029944,
|
||||
1.11979401, 1.10905389, 1.10577769, 1.11166825, 1.09985155],
|
||||
[0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
|
||||
0.74480073, 0.76098396, 0.76156903, 0.76651952, 0.76533288,
|
||||
0.78205934, 0.76842416, 0.77487118, 0.77768683, 0.78801192],
|
||||
[0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
|
||||
0.97370819, 0.96419154, 0.97209861, 0.97441313, 0.96356162,
|
||||
0.94745352, 0.93965462, 0.93069645, 0.94020973, 0.94358232],
|
||||
[0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
|
||||
0.80071489, 0.83358256, 0.83451613, 0.85175032, 0.85954307,
|
||||
0.86790024, 0.87170334, 0.87863799, 0.87497981, 0.87888675],
|
||||
[0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
|
||||
0.98733195, 0.99644997, 0.99669587, 1.02559097, 1.01116651,
|
||||
0.99988024, 0.97906749, 0.99323123, 1.00204939, 0.99602148],
|
||||
[1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
|
||||
1.08312397, 1.05192626, 1.04230892, 1.05577278, 1.08569751,
|
||||
1.12443486, 1.08891079, 1.08603695, 1.05997314, 1.02160943],
|
||||
[1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272,
|
||||
1.18257029, 1.16226243, 1.16009196, 1.14467789, 1.14820235,
|
||||
1.12386598, 1.12680236, 1.12357937, 1.1159258, 1.12570828],
|
||||
[1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
|
||||
1.31210239, 1.29989156, 1.29203193, 1.27183516, 1.26830786,
|
||||
1.2617743, 1.28656675, 1.29734097, 1.29390205, 1.29345446],
|
||||
[0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
|
||||
0.78772975, 0.82848011, 0.8259679, 0.82435705, 0.83108634,
|
||||
0.84373784, 0.83891093, 0.84349247, 0.85637272, 0.86539395],
|
||||
[1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626,
|
||||
1.2256767, 1.21126648, 1.19377804, 1.18355337, 1.19674434,
|
||||
1.21536573, 1.23653297, 1.27962009, 1.27968392, 1.25907738],
|
||||
[0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282,
|
||||
0.96480308, 0.94686376, 0.93679073, 0.92540049, 0.92988835,
|
||||
0.93442917, 0.92100464, 0.91475304, 0.90249622, 0.9021363],
|
||||
[0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368,
|
||||
0.88937573, 0.894401, 0.90448993, 0.95495898, 0.92698333,
|
||||
0.94745352, 0.92562488, 0.96635366, 1.02520312, 1.0394296],
|
||||
[1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
|
||||
1.00759019, 0.99192968, 0.99747298, 0.99550759, 0.97583768,
|
||||
0.9610168, 0.94779638, 0.93759089, 0.93353431, 0.94121705],
|
||||
[0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
|
||||
0.83434854, 0.85813595, 0.84667961, 0.84374558, 0.85951183,
|
||||
0.87194227, 0.89455097, 0.88283929, 0.90349491, 0.90600675],
|
||||
[1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
|
||||
1.00581626, 0.98850522, 0.99291168, 0.98983209, 0.97511924,
|
||||
0.96134615, 0.96382634, 0.95011401, 0.9434686, 0.94637765],
|
||||
[1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
|
||||
1.04800023, 1.03024941, 1.04200483, 1.0402554, 1.03296979,
|
||||
1.02191682, 1.02476275, 1.02347523, 1.02517684, 1.04359571],
|
||||
[1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
|
||||
1.0531801, 1.07452771, 1.09383478, 1.1052447, 1.10322136,
|
||||
1.09167939, 1.08772756, 1.08859544, 1.09177338, 1.1096083],
|
||||
[0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
|
||||
0.86287327, 0.85169796, 0.85411285, 0.84886336, 0.84517414,
|
||||
0.84843858, 0.84488343, 0.83374329, 0.82812044, 0.82878599],
|
||||
[0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
|
||||
0.92652175, 0.94278865, 0.93682452, 0.98655146, 0.992237,
|
||||
0.9798497, 0.93869677, 0.96947771, 1.00362626, 0.98102351],
|
||||
[0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
|
||||
0.93092109, 0.92662519, 0.93412152, 0.93501274, 0.92879506,
|
||||
0.92110542, 0.91035556, 0.90430364, 0.89994694, 0.90073864],
|
||||
[0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
|
||||
0.98882205, 0.97662234, 0.95601578, 0.94905385, 0.94934888,
|
||||
0.97152609, 0.97163004, 0.9700702, 0.97158948, 0.95884908],
|
||||
[0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751,
|
||||
0.84818516, 0.85265681, 0.84502402, 0.82645665, 0.81743586,
|
||||
0.83550406, 0.83338919, 0.83511679, 0.82136617, 0.80921874],
|
||||
[0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441,
|
||||
0.95440787, 0.96364363, 0.96804412, 0.97136214, 0.97583768,
|
||||
0.95571724, 0.96895368, 0.97001634, 0.97082733, 0.98782366],
|
||||
[1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
|
||||
1.10558188, 1.1214086, 1.12292577, 1.13021031, 1.13342735,
|
||||
1.14686068, 1.14502975, 1.14474747, 1.14084037, 1.16142926],
|
||||
[1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
|
||||
1.11856702, 1.09764283, 1.08815849, 1.08044313, 1.09278827,
|
||||
1.07003204, 1.08398066, 1.09831768, 1.09298232, 1.09176125],
|
||||
[0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
|
||||
0.77751194, 0.79902974, 0.81437881, 0.80788828, 0.79603865,
|
||||
0.78966436, 0.79949807, 0.80172182, 0.82168155, 0.85587911],
|
||||
[1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561,
|
||||
1.00162979, 0.99860739, 1.00814981, 1.00574316, 0.99030032,
|
||||
0.97682565, 0.97292596, 0.96519561, 0.96173403, 0.95890284],
|
||||
[0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289,
|
||||
0.96608031, 0.99727185, 1.00781194, 1.03484236, 1.05333619,
|
||||
1.0983263, 1.1704974, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
self.assertTrue(result.shape == expected.shape)
|
||||
|
||||
@@ -105,204 +105,6 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_trend, exp_trend)
|
||||
|
||||
def test_get_time_data(self):
|
||||
"""Test get_time_data"""
|
||||
data = [{'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009']} for d in self.neighbors_data]
|
||||
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009'])
|
||||
|
||||
# expected was prepared from PySAL example:
|
||||
# f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
# pci = np.array([f.by_col[str(y)]
|
||||
# for y in range(1995, 2010)]).transpose()
|
||||
# rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array(
|
||||
[[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
|
||||
0.83271652, 0.83786314, 0.85012593, 0.85509656, 0.86416612,
|
||||
0.87119375, 0.86302631, 0.86148267, 0.86252252, 0.86746356],
|
||||
[0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388,
|
||||
0.90746978, 0.89830489, 0.89431991, 0.88924794, 0.89815176,
|
||||
0.91832091, 0.91706054, 0.90139505, 0.87897455, 0.86216858],
|
||||
[0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
|
||||
0.78964559, 0.80584442, 0.8084998, 0.82258551, 0.82668196,
|
||||
0.82373724, 0.81814804, 0.83675961, 0.83574199, 0.84647177],
|
||||
[1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
|
||||
1.14506948, 1.12151133, 1.11160697, 1.10888621, 1.11399806,
|
||||
1.12168029, 1.13164797, 1.12958508, 1.11371818, 1.09936775],
|
||||
[1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
|
||||
1.16898201, 1.17212488, 1.14752303, 1.11843284, 1.11024964,
|
||||
1.11943471, 1.11736468, 1.10863242, 1.09642516, 1.07762337],
|
||||
[1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
|
||||
1.44184737, 1.44782832, 1.41978227, 1.39092208, 1.4059372,
|
||||
1.40788646, 1.44052766, 1.45241216, 1.43306098, 1.4174431],
|
||||
[1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
|
||||
1.10888138, 1.11856629, 1.13062931, 1.11944984, 1.12446239,
|
||||
1.11671008, 1.10880034, 1.08401709, 1.06959206, 1.07875225],
|
||||
[1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
|
||||
0.99854316, 0.9880258, 0.99669587, 0.99327676, 1.01400905,
|
||||
1.03176742, 1.040511, 1.01749645, 0.9936394, 0.98279746],
|
||||
[0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845,
|
||||
0.99127006, 0.97925917, 0.9683482, 0.95335147, 0.93694787,
|
||||
0.94308213, 0.92232874, 0.91284091, 0.89689833, 0.88928858],
|
||||
[0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044,
|
||||
0.8578708, 0.86036185, 0.86107306, 0.8500772, 0.86981998,
|
||||
0.86837929, 0.87204141, 0.86633032, 0.84946077, 0.83287146],
|
||||
[1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
|
||||
1.14450183, 1.12349752, 1.12596664, 1.12213996, 1.1119989,
|
||||
1.10257792, 1.10491258, 1.11059842, 1.10509795, 1.10020097],
|
||||
[0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687,
|
||||
0.95831051, 0.94480909, 0.94804195, 0.95430286, 0.94103989,
|
||||
0.92122519, 0.91010201, 0.89280392, 0.89298243, 0.89165385],
|
||||
[0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
|
||||
0.9480927, 0.93539182, 0.95388718, 0.94597005, 0.96918424,
|
||||
0.94781281, 0.93466815, 0.94281559, 0.96520315, 0.96715441],
|
||||
[0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
|
||||
0.98687073, 0.99237486, 0.98209969, 0.9877653, 0.97399471,
|
||||
0.96910087, 0.98416665, 0.98423613, 0.99823861, 0.99545704],
|
||||
[0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
|
||||
0.86191535, 0.84981451, 0.85472102, 0.84564835, 0.83998883,
|
||||
0.83478547, 0.82803648, 0.8198736, 0.82265395, 0.8399404],
|
||||
[0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
|
||||
0.82785597, 0.86008789, 0.86776298, 0.86720209, 0.8676334,
|
||||
0.89179317, 0.94202108, 0.9422231, 0.93902708, 0.94479184],
|
||||
[0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
|
||||
0.90906632, 0.92693339, 0.93695966, 0.94242697, 0.94338265,
|
||||
0.91981796, 0.91108804, 0.90543476, 0.91737138, 0.94793657],
|
||||
[1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723,
|
||||
1.20172869, 1.21328691, 1.22624778, 1.22397075, 1.23857042,
|
||||
1.24419893, 1.23929384, 1.23418676, 1.23626739, 1.26754398],
|
||||
[1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
|
||||
1.34790023, 1.34399863, 1.32575181, 1.30795492, 1.30544841,
|
||||
1.30303302, 1.32107766, 1.32936244, 1.33001241, 1.33288462],
|
||||
[1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
|
||||
1.05059016, 1.03405057, 1.02747623, 1.03162734, 0.9961416,
|
||||
0.97356208, 0.94241549, 0.92754547, 0.92549227, 0.92138102],
|
||||
[1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
|
||||
1.13889622, 1.12442212, 1.13367018, 1.13982256, 1.14029944,
|
||||
1.11979401, 1.10905389, 1.10577769, 1.11166825, 1.09985155],
|
||||
[0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
|
||||
0.74480073, 0.76098396, 0.76156903, 0.76651952, 0.76533288,
|
||||
0.78205934, 0.76842416, 0.77487118, 0.77768683, 0.78801192],
|
||||
[0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
|
||||
0.97370819, 0.96419154, 0.97209861, 0.97441313, 0.96356162,
|
||||
0.94745352, 0.93965462, 0.93069645, 0.94020973, 0.94358232],
|
||||
[0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
|
||||
0.80071489, 0.83358256, 0.83451613, 0.85175032, 0.85954307,
|
||||
0.86790024, 0.87170334, 0.87863799, 0.87497981, 0.87888675],
|
||||
[0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
|
||||
0.98733195, 0.99644997, 0.99669587, 1.02559097, 1.01116651,
|
||||
0.99988024, 0.97906749, 0.99323123, 1.00204939, 0.99602148],
|
||||
[1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
|
||||
1.08312397, 1.05192626, 1.04230892, 1.05577278, 1.08569751,
|
||||
1.12443486, 1.08891079, 1.08603695, 1.05997314, 1.02160943],
|
||||
[1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272,
|
||||
1.18257029, 1.16226243, 1.16009196, 1.14467789, 1.14820235,
|
||||
1.12386598, 1.12680236, 1.12357937, 1.1159258, 1.12570828],
|
||||
[1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
|
||||
1.31210239, 1.29989156, 1.29203193, 1.27183516, 1.26830786,
|
||||
1.2617743, 1.28656675, 1.29734097, 1.29390205, 1.29345446],
|
||||
[0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
|
||||
0.78772975, 0.82848011, 0.8259679, 0.82435705, 0.83108634,
|
||||
0.84373784, 0.83891093, 0.84349247, 0.85637272, 0.86539395],
|
||||
[1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626,
|
||||
1.2256767, 1.21126648, 1.19377804, 1.18355337, 1.19674434,
|
||||
1.21536573, 1.23653297, 1.27962009, 1.27968392, 1.25907738],
|
||||
[0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282,
|
||||
0.96480308, 0.94686376, 0.93679073, 0.92540049, 0.92988835,
|
||||
0.93442917, 0.92100464, 0.91475304, 0.90249622, 0.9021363],
|
||||
[0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368,
|
||||
0.88937573, 0.894401, 0.90448993, 0.95495898, 0.92698333,
|
||||
0.94745352, 0.92562488, 0.96635366, 1.02520312, 1.0394296],
|
||||
[1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
|
||||
1.00759019, 0.99192968, 0.99747298, 0.99550759, 0.97583768,
|
||||
0.9610168, 0.94779638, 0.93759089, 0.93353431, 0.94121705],
|
||||
[0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
|
||||
0.83434854, 0.85813595, 0.84667961, 0.84374558, 0.85951183,
|
||||
0.87194227, 0.89455097, 0.88283929, 0.90349491, 0.90600675],
|
||||
[1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
|
||||
1.00581626, 0.98850522, 0.99291168, 0.98983209, 0.97511924,
|
||||
0.96134615, 0.96382634, 0.95011401, 0.9434686, 0.94637765],
|
||||
[1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
|
||||
1.04800023, 1.03024941, 1.04200483, 1.0402554, 1.03296979,
|
||||
1.02191682, 1.02476275, 1.02347523, 1.02517684, 1.04359571],
|
||||
[1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
|
||||
1.0531801, 1.07452771, 1.09383478, 1.1052447, 1.10322136,
|
||||
1.09167939, 1.08772756, 1.08859544, 1.09177338, 1.1096083],
|
||||
[0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
|
||||
0.86287327, 0.85169796, 0.85411285, 0.84886336, 0.84517414,
|
||||
0.84843858, 0.84488343, 0.83374329, 0.82812044, 0.82878599],
|
||||
[0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
|
||||
0.92652175, 0.94278865, 0.93682452, 0.98655146, 0.992237,
|
||||
0.9798497, 0.93869677, 0.96947771, 1.00362626, 0.98102351],
|
||||
[0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
|
||||
0.93092109, 0.92662519, 0.93412152, 0.93501274, 0.92879506,
|
||||
0.92110542, 0.91035556, 0.90430364, 0.89994694, 0.90073864],
|
||||
[0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
|
||||
0.98882205, 0.97662234, 0.95601578, 0.94905385, 0.94934888,
|
||||
0.97152609, 0.97163004, 0.9700702, 0.97158948, 0.95884908],
|
||||
[0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751,
|
||||
0.84818516, 0.85265681, 0.84502402, 0.82645665, 0.81743586,
|
||||
0.83550406, 0.83338919, 0.83511679, 0.82136617, 0.80921874],
|
||||
[0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441,
|
||||
0.95440787, 0.96364363, 0.96804412, 0.97136214, 0.97583768,
|
||||
0.95571724, 0.96895368, 0.97001634, 0.97082733, 0.98782366],
|
||||
[1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
|
||||
1.10558188, 1.1214086, 1.12292577, 1.13021031, 1.13342735,
|
||||
1.14686068, 1.14502975, 1.14474747, 1.14084037, 1.16142926],
|
||||
[1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
|
||||
1.11856702, 1.09764283, 1.08815849, 1.08044313, 1.09278827,
|
||||
1.07003204, 1.08398066, 1.09831768, 1.09298232, 1.09176125],
|
||||
[0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
|
||||
0.77751194, 0.79902974, 0.81437881, 0.80788828, 0.79603865,
|
||||
0.78966436, 0.79949807, 0.80172182, 0.82168155, 0.85587911],
|
||||
[1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561,
|
||||
1.00162979, 0.99860739, 1.00814981, 1.00574316, 0.99030032,
|
||||
0.97682565, 0.97292596, 0.96519561, 0.96173403, 0.95890284],
|
||||
[0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289,
|
||||
0.96608031, 0.99727185, 1.00781194, 1.03484236, 1.05333619,
|
||||
1.0983263, 1.1704974, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
self.assertTrue(result.shape == expected.shape)
|
||||
|
||||
def test_rebin_data(self):
|
||||
"""Test rebin_data"""
|
||||
# sample in double the time (even case since 10 % 2 = 0):
|
||||
# (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
# = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
|
||||
for i in range(0, 10, 2)]).T
|
||||
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
|
||||
# sample in triple the time (uneven since 10 % 3 = 1):
|
||||
# (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
# = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
|
||||
def test_get_prob_dist(self):
|
||||
"""Test get_prob_dist"""
|
||||
lag_indices = np.array([1, 2, 3, 4])
|
||||
|
||||
Reference in New Issue
Block a user