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move to class based markov

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This commit is contained in:
Andy Eschbacher 2016-11-19 09:05:35 +00:00
parent 2738c1f29c
commit 224fbc2fc5
3 changed files with 366 additions and 317 deletions
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5
src/pg/sql/11_markov.sql
View File

@ -22,10 +22,11 @@ CREATE OR REPLACE FUNCTION
RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
AS $$
from crankshaft.space_time_dynamics import spatial_markov_trend
from crankshaft.space_time_dynamics import Markov
markov = Markov()
## TODO: use named parameters or a dictionary
return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
$$ LANGUAGE plpythonu;
-- input table format: identical to above but in a predictable format

93
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
View File

@ -8,34 +8,56 @@ import pysal as ps
import plpy
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_classes=7,
class QueryRunner:
def get_result(self, query):
try:
data = plpy.execute(query)
if len(data) == 0:
return zip([None], [None], [None], [None], [None])
return data
except plpy.SPIError, err:
plpy.error('Analysis failed: %s' % err)
class Markov:
def __init__(self, query_runner=None):
if query_runner is None:
self.query_runner = QueryRunner()
else:
self.query_runner = query_runner
def spatial_trend(self, subquery, time_cols, num_classes=7,
w_type='knn', num_ngbrs=5, permutations=0,
geom_col='the_geom', id_col='cartodb_id'):
"""
Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
1. history of its transitions to different classes (e.g., 1st
quantile -> 2nd quantile)
2. average class of its neighbors
Inputs:
@param subquery string: e.g., SELECT the_geom, cartodb_id,
interesting_time_column FROM table_name
@param time_cols list of strings: list of strings of column names
@param num_classes (optional): number of classes to break distribution
of values into. Currently uses quantile bins.
@param num_classes (optional): number of classes to break
distribution of values into. Currently uses quantile bins.
@param w_type string (optional): weight type ('knn' or 'queen')
@param num_ngbrs int (optional): number of neighbors (if knn type)
@param permutations int (optional): number of permutations for test
stats
@param geom_col string (optional): name of column which contains the
geometries
@param id_col string (optional): name of column which has the ids of
the table
@param geom_col string (optional): name of column which contains
the geometries
@param id_col string (optional): name of column which has the ids
of the table
Outputs:
@param trend_up float: probablity that a geom will move to a higher
class
@param trend_down float: probablity that a geom will move to a lower
class
@param trend_down float: probablity that a geom will move to a
lower class
@param trend float: (trend_up - trend_down) / trend_static
@param volatility float: a measure of the volatility based on
probability stddev(prob array)
@ -50,51 +72,41 @@ def spatial_markov_trend(subquery, time_cols, num_classes=7,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
try:
query_result = plpy.execute(
pu.construct_neighbor_query(w_type, qvals)
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, e:
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
plpy.error('Analysis failed: %s' % e)
return zip([None], [None], [None], [None], [None])
query = pu.construct_neighbor_query(w_type, qvals)
## build weight
query_result = self.query_runner.get_result(query)
# build weight
weights = pu.get_weight(query_result, w_type)
weights.transform = 'r'
## prep time data
# prep time data
t_data = get_time_data(query_result, time_cols)
plpy.debug('shape of t_data %d, %d' % t_data.shape)
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
plpy.debug('first num elements: %f' % t_data[0, 0])
sp_markov_result = ps.Spatial_Markov(t_data,
weights,
k=num_classes,
fixed=False,
permutations=permutations)
## get lag classes
# get lag classes
lag_classes = ps.Quantiles(
ps.lag_spatial(weights, t_data[:, -1]),
k=num_classes).yb
## look up probablity distribution for each unit according to class and lag class
# look up probablity distribution for each unit according to class and
# lag class
prob_dist = get_prob_dist(sp_markov_result.P,
lag_classes,
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])
# 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])
## output the results
# 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
@ -103,7 +115,8 @@ def get_time_data(markov_data, 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
# 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
@ -131,15 +144,17 @@ def rebin_data(time_data, num_time_per_bin):
"""
if time_data.shape[1] % num_time_per_bin == 0:
## if fit is perfect, then use it
# if fit is perfect, then use it
n_max = time_data.shape[1] / num_time_per_bin
else:
## fit remainders into an additional column
# 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)
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
@ -157,6 +172,7 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices):
"""
get the statistics of the probability distributions
@ -179,11 +195,12 @@ def get_prob_stats(prob_dist, unit_indices):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
if prob_dist[i, unit_indices[i]] > 0.0:
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
trend[i] = (trend_up[i] - trend_down[i]) / (
prob_dist[i, unit_indices[i]])
else:
trend[i] = None
## calculate volatility of distribution
# calculate volatility of distribution
volatility = prob_dist.std(axis=1)
return trend_up, trend_down, trend, volatility

367
src/py/crankshaft/test/test_space_time_dynamics.py
View File

@ -9,26 +9,39 @@ import unittest
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
from helper import fixture_file
from crankshaft.space_time_dynamics import Markov
import crankshaft.space_time_dynamics as std
from crankshaft import random_seeds
from crankshaft.clustering import QueryRunner
import json
class FakeQueryRunner(QueryRunner):
def __init__(self, data):
self.mock_result = data
def get_result(self, query):
return self.mock_result
class SpaceTimeTests(unittest.TestCase):
"""Testing class for Markov Functions."""
def setUp(self):
plpy._reset()
# plpy._reset()
self.params = {"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
self.neighbors_data = json.loads(
open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
self.time_data = np.array([i * np.ones(10, dtype=float)
for i in range(10)]).T
self.transition_matrix = np.array([
[[0.96341463, 0.0304878, 0.00609756, 0., 0.],
@ -77,13 +90,19 @@ class SpaceTimeTests(unittest.TestCase):
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
print(str(data[0]))
plpy._define_result('select', data)
# print(str(data[0]))
markov = Markov(FakeQueryRunner(data))
random_seeds.set_random_seeds(1234)
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
result = markov.spatial_trend('subquery',
['y1995', 'y1996', 'y1997', 'y1998',
'y1999', 'y2000', 'y2001', 'y2002',
'y2003', 'y2004', 'y2005', 'y2006',
'y2007', 'y2008', 'y2009'],
5, 'knn', 5, 0, 'the_geom',
'cartodb_id')
self.assertTrue(result != None)
self.assertTrue(result is not None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
expected = self.markov_data
@ -110,157 +129,162 @@ class SpaceTimeTests(unittest.TestCase):
'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'])
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 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]])
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))
@ -268,20 +292,22 @@ class SpaceTimeTests(unittest.TestCase):
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
# 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))
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
# 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))
self.assertTrue(
np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
def test_get_prob_dist(self):
"""Test get_prob_dist"""
@ -293,7 +319,8 @@ class SpaceTimeTests(unittest.TestCase):
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
[0., 0., 0., 0.02352941, 0.97647059]
])
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
result = std.get_prob_dist(self.transition_matrix,
lag_indices, unit_indices)
self.assertTrue(np.array_equal(result, answer))
@ -309,8 +336,12 @@ class SpaceTimeTests(unittest.TestCase):
unit_indices = np.array([1, 3, 2, 4])
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
answer_trend = np.array([-0.03301887 / 0.88207547,
-0.05882353 / 0.87058824,
0.02475248 / 0.77722772,
-0.02352941 / 0.97647059])
answer_volatility = np.array([0.34221495, 0.33705421,
0.29226542, 0.38834223])
result = std.get_prob_stats(probs, unit_indices)
result_up = result[0]