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add working version

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This commit is contained in:
Andy Eschbacher 2016-03-24 11:34:28 -04:00
parent 1de90a7d39
commit cd3790860a
1 changed files with 13 additions and 9 deletions
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22
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
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@ -6,7 +6,7 @@ Spatial dynamics measurements using Spatial Markov
import numpy as np
import pysal as ps
import plpy
from crankshaft.clustering import get_query
from crankshaft.clustering import get_query, get_weight
def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, geom_col, id_col, w_type, num_ngbrs):
"""
@ -44,7 +44,9 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
try:
query_result = plpy.execute(query)
except:
zip([None],[None],[None])
plpy.notice('** Query failed: %s' % query)
plpy.error('Query failed: check the input parameters')
return zip([None], [None], [None], [None], [None])
## build weight
weights = get_weight(query_result, w_type)
@ -58,17 +60,17 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
sp_markov_result = ps.Spatial_Markov(t_data, weights, k=7, fixed=False)
## get lags
lags = ps.lag_spatial(weights, t_data)
## get lags of last time slice
lags = ps.lag_spatial(weights, t_data[:, -1])
## get lag classes
lag_classes = ps.Quantiles(lags.flatten(), k=7).yb
lag_classes = ps.Quantiles(lags, k=7).yb
## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(lag_classes, sp_markov_result.classes)
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)
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
## output the results
@ -78,7 +80,9 @@ def get_time_data(markov_data, time_cols):
"""
Extract the time columns and bin appropriately
"""
return np.array([[x[t_col] for x in query_result] for t_col in time_cols], dtype=float)
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).T
def rebin_data(time_data, num_time_per_bin):
"""
@ -139,7 +143,7 @@ def get_prob_stats(prob_dist, unit_indices):
movements
"""
num_elements = len(prob_dist)
num_elements = len(unit_indices)
trend_up = np.empty(num_elements, dtype=float)
trend_down = np.empty(num_elements, dtype=float)
trend = np.empty(num_elements, dtype=float)