def shiiba(a,b, gridSize=20, searchWindowHeight=9, searchWindowWidth=9,\
display=False, useRecursion=True, toFile="",
centre=(0,0),
):
"""Shiiba global analysis
"""
# 2014-01-24
# imports
from shiiba import regression2 as regression
from shiiba import regressionCFLfree as cflfree
results = cflfree.regressGlobal(a,b, gridSize, searchWindowHeight, searchWindowWidth,\
display, useRecursion, centre=centre)
topResult = results[0]
(m,n), C, Rsquared = topResult
prediction = regression.getPrediction(C, a)
prediction6 = regression.getPrediction(C, a, coeffsUsed=6)
corr = b.corr(prediction)
vect = regression.convert(C,a)
C1 = C.copy()
C1[2] = 0
C1[5] = 0
deformation = regression.convert(C1, a)
README = 'Results for shiiba global regression. "mn" = shift, where the first coordinate is i=y, the second is j=x, "vect" = total vector field, "deformation" = deformation vector field, "corr" = correlation between prediction and ground truth, "prediction6"= prediction with 6 shiiba coeffs (instead of 9), "results" = list of (m,n), C, Rsquared in descending order of Rsquared'
return {"mn" : (m,n),
"C" : C,
"Rsquared" : Rsquared,
"prediction" : prediction,
"prediction6" : prediction6,
"vect" : vect,
"deformation" : deformation,
"corr" : corr,
"results" : results,
"README" : README}
def shiibaLocal(a, b, windowSize=100, iRange=range(000, 881, 100),\
jRange=range(000, 921, 100), searchWindowHeight=11,\
searchWindowWidth=11, useRecursion=True, plotting=True ):
# imports
from shiiba import regression2 as regression
from shiiba import regressionCFLfree as cflfree
import numpy.ma as ma
# results =dictionary with
# {'mn': mn, 'C':C, 'Rsquared':Rsquared, 'CR2':CR2, 'timeSpent':timeSpent}
results=cflfree.regressLocalAll(a, b, windowSize, iRange, jRange, searchWindowHeight,\
searchWindowWidth, useRecursion, plotting)
mn = results['mn']
C = results['C']
Rsquared = results['Rsquared']
# constructing the prediction
a1 = dbz(name = 'shiiba prediction for %s and %s' % (a.name, b.name),
matrix = ma.zeros(a.matrix.shape))
a1.matrix.fill_value = a.matrix.fill_value
a1.mask = True
# constructing the vector field
U = ma.zeros(a.matrix.shape)
U.fill_value = a.matrix.fill_value
U.mask = True
V = ma.zeros(a.matrix.shape)
V.fill_value = a.matrix.fill_value
V.mask = True
vect = pattern.VectorField(U=U, V=V, \
title='shiiba prediction for %s and %s' % (a.name, b.name))
# filling in the local values
for i, j in mn.keys():
aa = a.getWindow(i, j, windowSize)
a1.matrix[i:i+windowSize, j:j+windowSize] = regression.getPrediction(C[(i,j)], aa)
vectLocal = regression.convert(C[(i,j)], aa)
vect.U[i:i+windowSize, j:j+windowSize] = vectLocal.U
vect.V[i:i+windowSize, j:j+windowSize] = vectLocal.V
#########
# added 15 july 2013 ; doesn't make sense to have a global vector map without adding in the mn[(i,j)]'s
# adding the shift back to the regression result
# (see pattern.py and regression2.py)
vect.U[i:i+windowSize, j:j+windowSize] += mn[(i,j)][0] # U = first (i-) component ; V = j-component
vect.V[i:i+windowSize, j:j+windowSize] += mn[(i,j)][1]
#
##########
results['prediction'] = a1
results['vect'] = vect
return results