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+# Constructing data for illustration of directional LISA analytics.
+# Data is for the 48 lower US states over the period 1969-2009 and
+# includes per capita income normalized to the national average.
+#
+# Load comma delimited data file in and convert to a numpy array
+#
+import libpysal
+from giddy.directional import Rose
+import numpy as np
+import matplotlib.pyplot as plt
+file_path = libpysal.examples.get_path("spi_download.csv")
+f=open(file_path,'r')
+lines=f.readlines()
+f.close()
+lines=[line.strip().split(",") for line in lines]
+names=[line[2] for line in lines[1:-5]]
+data=np.array([list(map(int,line[3:])) for line in lines[1:-5]])
+#
+# Bottom of the file has regional data which we don't need for this
+# example so we will subset only those records that match a state name
+#
+sids=list(range(60))
+out=['"United States 3/"',
+     '"Alaska 3/"',
+     '"District of Columbia"',
+     '"Hawaii 3/"',
+     '"New England"',
+     '"Mideast"',
+     '"Great Lakes"',
+     '"Plains"',
+     '"Southeast"',
+     '"Southwest"',
+     '"Rocky Mountain"',
+     '"Far West 3/"']
+snames=[name for name in names if name not in out]
+sids=[names.index(name) for name in snames]
+states=data[sids,:]
+us=data[0]
+years=np.arange(1969,2009)
+#
+# Now we convert state incomes to express them relative to the national
+# average
+#
+rel=states/(us*1.)
+#
+# Create our contiguity matrix from an external GAL file and row
+# standardize the resulting weights
+#
+gal=libpysal.io.open(libpysal.examples.get_path('states48.gal'))
+w=gal.read()
+w.transform='r'
+#
+# Take the first and last year of our income data as the interval to do
+# the directional directional analysis
+#
+Y=rel[:,[0,-1]]
+#
+# Set the random seed generator which is used in the permutation based
+# inference for the rose diagram so that we can replicate our example
+# results
+#
+np.random.seed(100)
+#
+# Call the rose function to construct the directional histogram for the
+# dynamic LISA statistics. We will use four circular sectors for our
+# histogram
+#
+r4=Rose(Y,w,k=4)
+#
+# What are the cut-offs for our histogram - in radians
+#
+r4.cuts
+# Expected:
+## array([0.        , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
+#
+# We can test whether these counts are different than what would be
+# expected if there was no association between the movement of the
+# focal unit and its spatial lag.
+#
+# To do so we call the `permute` method of the object
+#
+r4.permute()
+#
+# and then inspect the `p` attibute:
+#
+r4.p
+# Expected:
+## array([0.04, 0.  , 0.02, 0.  ])
+#
+# Repeat the exercise but now for 8 rather than 4 sectors
+#
+r8 = Rose(Y, w, k=8)
+r8.permute()
+r8.p
+# Expected:
+## array([0.86, 0.08, 0.16, 0.  , 0.02, 0.2 , 0.56, 0.  ])
+#
+# The default is a two-sided alternative. There is an option for a
+# directional alternative reflecting positive co-movement of the focal
+# series with its spatial lag. In this case the number of vectors in
+# quadrants I and III should be much larger than expected, while the
+# counts of vectors falling in quadrants II and IV should be much lower
+# than expected.
+#
+r8.permute(alternative='positive')
+r8.p
+# Expected:
+## array([0.51, 0.04, 0.28, 0.02, 0.01, 0.14, 0.57, 0.03])
+#
+# Finally, there is a second directional alternative for examining the
+# hypothesis that the focal unit and its lag move in opposite directions.
+#
+r8.permute(alternative='negative')
+r8.p
+# Expected:
+## array([0.69, 0.99, 0.92, 1.  , 1.  , 0.97, 0.74, 1.  ])
+#
+# We can call the plot method to visualize directional LISAs as a
+# rose diagram conditional on the starting relative income:
+#
+fig1, _ = r8.plot(attribute=Y[:,0])
+plt.show(block=False)
+#
+# Close plot when finished viewing.
+#
+plt.close("all")