def _example():
from stars import Project
s = Project("s")
s.ReadData("csiss")
income = s.getVariable("pcincome")
ym = ClMarkov(income)
ym.transMat
s.ReadGalMatrix("states48")
w = s.getMatrix("states48")
sm = SpMarkov(income, w=w)
return [ym, income.regionNames, sm]
def _example():
from stars import Project
s=Project("s")
s.ReadData("csiss")
income=s.getVariable("pcincome")
ym=ClMarkov(income)
ym.transMat
s.ReadGalMatrix("states48")
w=s.getMatrix("states48")
sm=SpMarkov(income,w=w)
return [ym,income.regionNames,sm]
# create an instance
#t=DataTable(root,values,name="Variable Name Here",rowLabels=rowLabels,
# columnLabels=columnLabels,fmt=[8,0])
#
from stars import Project
from Markov import *
s=Project('s')
s.ReadData("data/csiss")
y = s.getVariable('pcr')
n,k = shape(y)
#values=y
#ijs = [ (i,j) for i in range(n) for j in range(k) ]
#svalues = [ format([8,3], values[i,j]) for i,j in ijs ]
s.ReadGalMatrix('states48')
w=s.getMatrix('states48')
sm = SpMarkov(y,w=w)
classes = [ "%5.3f"% value for value in sm.upperBounds ]
k=len(classes)
rowLabels = [ c+" | "+r for r in classes for c in classes ]
mat = sm.pMat
mat = reshape(mat,(k*k,k))
st = DataTable(root,mat,rowLabels=rowLabels,
columnLabels=classes,fmt=[8,3],
name='Spatial Markov Transition Probabilities')
top = Toplevel(root)
yt = DataTable(top,y,
rowLabels=s.regionNames,
columnLabels=s.timeString,
fmt=[8,3],name="PCR")
#mtop = Toplevel(root)
# create an instance
#t=DataTable(root,values,name="Variable Name Here",rowLabels=rowLabels,
# columnLabels=columnLabels,fmt=[8,0])
#
from stars import Project
from Markov import *
s=Project('s')
s.ReadData("data/csiss")
y = s.getVariable('pcr')
n,k = shape(y)
#values=y
#ijs = [ (i,j) for i in range(n) for j in range(k) ]
#svalues = [ format([8,3], values[i,j]) for i,j in ijs ]
s.ReadGalMatrix('states48')
w=s.getMatrix('states48')
sm = SpMarkov(y,w=w)
classes = [ "%5.3f"% value for value in sm.upperBounds ]
k=len(classes)
rowLabels = [ c+" | "+r for r in classes for c in classes ]
mat = sm.pMat
mat = reshape(mat,(k*k,k))
st = DataTable(root,mat,rowLabels=rowLabels,
columnLabels=classes,fmt=[8,3],
name='Spatial Markov Transition Probabilities')
top = Toplevel(root)
yt = DataTable(top,y,
rowLabels=s.regionNames,
columnLabels=s.timeString,
fmt=[8,3],name="PCR")
#mtop = Toplevel(root)
# t=DataTable(root,values,name="Variable Name Here",rowLabels=rowLabels,
# columnLabels=columnLabels,fmt=[8,0])
#
from stars import Project
from Markov import *
s = Project("s")
s.ReadData("data/csiss")
y = s.getVariable("pcr")
n, k = shape(y)
# values=y
# ijs = [ (i,j) for i in range(n) for j in range(k) ]
# svalues = [ format([8,3], values[i,j]) for i,j in ijs ]
s.ReadGalMatrix("states48")
w = s.getMatrix("states48")
sm = SpMarkov(y, w=w)
classes = ["%5.3f" % value for value in sm.upperBounds]
k = len(classes)
rowLabels = [c + " | " + r for r in classes for c in classes]
mat = sm.pMat
mat = reshape(mat, (k * k, k))
st = DataTable(
root, mat, rowLabels=rowLabels, columnLabels=classes, fmt=[8, 3], name="Spatial Markov Transition Probabilities"
)
top = Toplevel(root)
yt = DataTable(top, y, rowLabels=s.regionNames, columnLabels=s.timeString, fmt=[8, 3], name="PCR")
# mtop = Toplevel(root)
# ym=ClMarkov(y)
# labels = [ "%8.3f"%value for value in ym.upperBounds ]
# tm = DataTable(mtop,ym.pMat,rowLabels=labels,