def __init__(self, Y, w, k=<NUM_LIT:8>):
self.Y = Y<EOL>self.w = w<EOL>self.k = k<EOL>self.permtuations = <NUM_LIT:0><EOL>self.sw = <NUM_LIT:2> * np.pi / self.k<EOL>self.cuts = np.arange(<NUM_LIT:0.0>, <NUM_LIT:2> * np.pi + self.sw, self.sw)<EOL>observed = self._calc(Y, w, k)<EOL>self.theta = observed['<STR_LIT>']<EOL>self.bins = observed['<STR_LIT>']<EOL>self.counts = observed['<STR_LIT>']<EOL>self.r = observed['<STR_LIT:r>']<EOL>self.lag = observed['<STR_LIT>']<EOL>self._dx = observed['<STR_LIT>']<EOL>self._dy = observed['<STR_LIT>']<EOL>
Calculation of rose diagram for local indicators of spatial
association.

Parameters
----------
Y :  (n, 2) ndarray
    Variable observed on n spatial units over 2 time periods
w : W
    Spatial weights object.
k : int
    number of circular sectors in rose diagram (the default is 8).


Notes
-----
Based on :cite:`Rey2011`.

Examples
--------
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 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
array([0.        , 1.57079633, 3.14159265, 4.71238898, 6.28318531])

How many vectors fell in each sector

>>> r4.counts
array([32,  5,  9,  2])

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
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.counts
array([19, 13,  3,  2,  7,  2,  1,  1])
>>> r8.permute()
>>> r8.p
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
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
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(fig1)