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adding module refs for pysaul utils

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This commit is contained in:
Andy Eschbacher 2016-03-31 09:35:10 -04:00
parent 314d1851db
commit c18baf26d8
1 changed files with 13 additions and 5 deletions
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18
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, get_weight
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
permutations, geom_col, id_col, w_type, num_ngbrs):
@ -43,7 +43,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
query = get_query(w_type, qvals)
query = pu.construct_neighbor_query(w_type, qvals)
try:
query_result = plpy.execute(query)
@ -53,7 +53,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
return zip([None], [None], [None], [None], [None])
## build weight
weights = get_weight(query_result, w_type)
weights = pu.get_weight(query_result, w_type)
## prep time data
t_data = get_time_data(query_result, time_cols)
@ -81,6 +81,14 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
def spatial_markov_predict(subquery, time_cols, num_time_per_bin,
permutations, geom_col, id_col, w_type, num_ngbrs):
"""
Filler for this future function
"""
return None
def get_time_data(markov_data, time_cols):
"""
Extract the time columns and bin appropriately
@ -98,13 +106,13 @@ def rebin_data(time_data, num_time_per_bin):
9 8 7 6 8.5 6.5
5 4 3 2 4.5 2.5
if m = 2
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.
first three are binned together for the first period.
Input:
@param time_data n x l ndarray: measurements of an attribute at