How to use the pysal.examples.get_path function in pysal

model.w = w_r
    return model


def _test():
    import doctest
    start_suppress = np.get_printoptions()['suppress']
    np.set_printoptions(suppress=True)
    doctest.testmod()
    np.set_printoptions(suppress=start_suppress)

if __name__ == '__main__':
    _test()
    import numpy as np
    import pysal
    db = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
    y_var = 'CRIME'
    y = np.array([db.by_col(y_var)]).reshape(49, 1)
    x_var = ['INC', 'HOVAL']
    x = np.array([db.by_col(name) for name in x_var]).T
    r_var = 'NSA'
    regimes = db.by_col(r_var)
    w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
    w.transform = 'r'
    olsr = OLS_Regimes(y, x, regimes, w=w, constant_regi='many', nonspat_diag=False, spat_diag=False, name_y=y_var, name_x=['INC', 'HOVAL'],
                       name_ds='columbus', name_regimes=r_var, name_w='columbus.gal', regime_err_sep=True, cols2regi=[True, True], sig2n_k=True, robust='white')
    print olsr.summary

def _test():
    import doctest
    start_suppress = np.get_printoptions()['suppress']
    np.set_printoptions(suppress=True)
    doctest.testmod()
    np.set_printoptions(suppress=start_suppress)

if __name__ == '__main__':
    _test()
    import numpy as np
    import pysal
    dbf = pysal.open(pysal.examples.get_path('columbus.dbf'), 'r')
    y = np.array([dbf.by_col('CRIME')]).T
    var_x = ['INC', 'HOVAL']
    x = np.array([dbf.by_col(name) for name in var_x]).T
    w = pysal.open(pysal.examples.get_path("columbus.gal"), 'r').read()
    w.transform = 'r'
    probit1 = Probit(
        (y > 40).astype(float), x, w=w, name_x=var_x, name_y="CRIME",
        name_ds="Columbus", name_w="columbus.dbf")
    print probit1.summary

np.set_printoptions(suppress=start_suppress)

if __name__ == "__main__":
    _test()
    import numpy as np
    import pysal as ps

    db = ps.open(ps.examples.get_path("baltim.dbf"), 'r')
    ds_name = "baltim.dbf"
    y_name = "PRICE"
    y = np.array(db.by_col(y_name)).T
    y.shape = (len(y), 1)
    x_names = ["NROOM", "NBATH", "PATIO", "FIREPL",
               "AC", "GAR", "AGE", "LOTSZ", "SQFT"]
    x = np.array([db.by_col(var) for var in x_names]).T
    ww = ps.open(ps.examples.get_path("baltim_q.gal"))
    w = ww.read()
    ww.close()
    w_name = "baltim_q.gal"
    w.transform = 'r'

    regimes = []
    y_coord = np.array(db.by_col("Y"))
    for i in y_coord:
        if i > 544.5:
            regimes.append("North")
        else:
            regimes.append("South")

    mlerror = ML_Error_Regimes(y, x, regimes, w=w, method='full', name_y=y_name,
                               name_x=x_names, name_w=w_name, name_ds=ds_name, regime_err_sep=False,
                               name_regimes="North")

start_suppress = np.get_printoptions()['suppress']
    np.set_printoptions(suppress=True)
    doctest.testmod()
    np.set_printoptions(suppress=start_suppress)

if __name__ == '__main__':
    _test()

    import numpy as np
    import pysal
    db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
    y_var = 'CRIME'
    y = np.array([db.by_col(y_var)]).reshape(49, 1)
    x_var = ['INC', 'HOVAL']
    x = np.array([db.by_col(name) for name in x_var]).T
    w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
    w.transform = 'r'
    ols = OLS(
        y, x, w=w, nonspat_diag=True, spat_diag=True, name_y=y_var, name_x=x_var,
        name_ds='columbus', name_w='columbus.gal', robust='white', sig2n_k=True, moran=True)
    print ols.summary

if __name__ == '__main__':
    _test()
    import numpy as np
    import pysal
    db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
    y_var = 'CRIME'
    y = np.array([db.by_col(y_var)]).reshape(49, 1)
    x_var = ['INC']
    x = np.array([db.by_col(name) for name in x_var]).T
    yd_var = ['HOVAL']
    yd = np.array([db.by_col(name) for name in yd_var]).T
    q_var = ['DISCBD']
    q = np.array([db.by_col(name) for name in q_var]).T
    r_var = 'NSA'
    regimes = db.by_col(r_var)
    w = pysal.queen_from_shapefile(pysal.examples.get_path("columbus.shp"))
    w.transform = 'r'
    model = GM_Lag_Regimes(y, x, regimes, yend=yd, q=q, w=w, constant_regi='many', spat_diag=True, sig2n_k=False, lag_q=True, name_y=y_var,
                           name_x=x_var, name_yend=yd_var, name_q=q_var, name_regimes=r_var, name_ds='columbus', name_w='columbus.gal', regime_err_sep=True, robust='white')
    print model.summary

'''
        values = np.array(dbf.by_col("SIDR74"))
        #values[: values.shape[0]/2] = 1
        #values[values.shape[0]/2: ] = 0
        '''
        patchco = map_poly_shp(ps.open(shp_link))
        #patchco = base_choropleth_classif(shp_link, np.random.random(3))
        #patchco = plot_choropleth(shp_link, np.random.random(3), 'quantiles')

    if data == 'point':
        shp_link = ps.examples.get_path("burkitt.shp")
        dbf = ps.open(shp_link.replace('.shp', '.dbf'))
        patchco = map_point_shp(ps.open(shp_link))

    if data == 'line':
        shp_link = ps.examples.get_path("eberly_net.shp")
        dbf = ps.open(shp_link.replace('.shp', '.dbf'))
        values = np.array(dbf.by_col('TNODE'))
        mobj = map_line_shp(ps.open(shp_link))
        patchco = base_choropleth_unique(mobj, values)

    '''
    which = values > 1.

    for shp_link in [shp_link]:

        fig = plt.figure()
        patchco = map_poly_shp(shp_link)
        patchcoB = map_poly_shp(shp_link, which=which)
        patchco.set_facecolor('none')
        ax = setup_ax([patchco, patchcoB])
        fig.add_axes(ax)

path = markerobj.get_path().transformed(
            markerobj.get_transform())
    scales = np.array([2, 2])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    pc = PathCollection((path,), scales, offsets=xy, \
            facecolors='r', transOffset=mpl.transforms.IdentityTransform())
    #pc.set_transform(mpl.transforms.IdentityTransform())
    #_ = _add_axes2col(pc, [0, 0, 5, 5])
    ax.add_collection(pc)
    fig.add_axes(ax)
    #ax = setup_ax([pc], ax)
    plt.show()
    '''

    shp_link = ps.examples.get_path('columbus.shp')
    values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL'))
    w = ps.queen_from_shapefile(shp_link)
    lisa = ps.Moran_Local(values, w, permutations=999)
    _ = plot_lisa_cluster(shp_link, lisa)
    #_ = plot_choropleth(shp_link, values, 'fisher_jenks')