import pysal.model.spreg as EC
from pysal.model.spreg import sputils as spu
from warnings import warn as Warn, filterwarnings
import unittest as ut
import numpy as np
import scipy.sparse as spar

filterwarnings('ignore', category=spar.SparseEfficiencyWarning)

ALL_FUNCS = [f for f,v in list(spu.__dict__.items()) \
	     if (callable(v) \
		 and not f.startswith('_'))]
COVERAGE = ['spinv', 'splogdet', 'spisfinite', 'spmin', 'spfill_diagonal', \
	    'spmax', 'spbroadcast', 'sphstack', 'spmultiply', 'spdot']

NOT_COVERED = set(ALL_FUNCS).difference(COVERAGE)

if len(NOT_COVERED) > 0:
    Warn('The following functions in {} are not covered:\n'
         '{}'.format(spu.__file__, NOT_COVERED))

class Test_Sparse_Utils(ut.TestCase):
    def setUp(self):
        np.random.seed(8879)

        self.n = 20
        self.dense0 = np.random.randint(2, size=(self.n,self.n))
        self.d0td0 = self.dense0.T.dot(self.dense0)
        self.dense1 = np.eye(self.n)
        self.sparse0 = spar.csc_matrix(self.dense0)
        self.s0ts0 = self.sparse0.T.dot(self.sparse0)
        self.sparse1 = spar.csc_matrix(spar.identity(self.n))

    def test_inv(self):
        r = spu.spinv(self.d0td0)
        rs = spu.spinv(self.s0ts0)
        rs2d = rs.toarray()

        self.assertIsInstance(r, np.ndarray)
        self.assertTrue(spar.issparse(rs))
        self.assertIsInstance(rs2d, np.ndarray)

        np.testing.assert_allclose(r, rs2d)
    
    def test_spdot(self):
        dd = spu.spdot(self.dense0, self.dense1)
        ds = spu.spdot(self.dense0, self.sparse1)
        sd = spu.spdot(self.sparse0, self.dense1)
        ss = spu.spdot(self.sparse0, self.sparse1, array_out=False)

        # typing tests
        self.assertIsInstance(dd, np.ndarray)
        self.assertIsInstance(ds, np.ndarray)
        self.assertIsInstance(sd, np.ndarray)
        self.assertIsInstance(ss, spar.csc_matrix)

        # product test
        np.testing.assert_array_equal(dd, ds)
        np.testing.assert_array_equal(dd, sd)
        np.testing.assert_array_equal(dd, ss.toarray())

    def test_logdet(self):
        dld = spu.splogdet(self.d0td0)
        sld = spu.splogdet(self.s0ts0)

        np.testing.assert_allclose(dld, sld)
   
    def test_isfinite(self):
        self.assertTrue(spu.spisfinite(self.dense0))
        self.assertTrue(spu.spisfinite(self.sparse0))
        
        dense_inf = np.float64(self.dense0.copy())
        dense_inf[0,0] = np.nan
        sparse_inf = spar.csc_matrix(dense_inf)

        self.assertTrue(not spu.spisfinite(dense_inf))
        self.assertTrue(not spu.spisfinite(sparse_inf))

    def test_min(self):
        self.assertEqual(spu.spmin(self.dense0), 0)
        self.assertEqual(spu.spmin(self.sparse0), 0)

    def test_max(self):
        self.assertEqual(spu.spmax(self.dense1), 1)
        self.assertEqual(spu.spmax(self.sparse1), 1)
    
    def test_fill_diagonal(self):
        current_dsum = self.dense0.trace()
        current_ssum = self.sparse0.diagonal().sum()
        self.assertEqual(current_dsum, 7)
        self.assertEqual(current_ssum, 7)

        tmpd = self.dense0.copy()
        tmps = self.sparse0.copy()
        d_4diag = spu.spfill_diagonal(tmpd, 4)
        s_4diag = spu.spfill_diagonal(tmps, 4)
        
        known = 4 * self.n

        self.assertEqual(known, d_4diag.trace())
        self.assertEqual(known, s_4diag.diagonal().sum())

    def test_broadcast(self):
        test_vec = np.ones((self.n,1)) * .2
        
        tmpd = spu.spbroadcast(self.dense0, test_vec)
        tmps = spu.spbroadcast(self.sparse0.tocsr(), test_vec) 

        self.assertIsInstance(tmpd, np.ndarray)
        self.assertIsInstance(tmps, spar.csr_matrix)

        np.testing.assert_allclose(tmpd, tmps.toarray())

    def test_hstack(self):
        tmpd = spu.sphstack(self.dense0, self.dense1)
        tmps = spu.sphstack(self.sparse0.tocsr(), self.sparse1.tocsr())

        self.assertIsInstance(tmpd, np.ndarray)
        self.assertIsInstance(tmps, spar.csr_matrix)

        np.testing.assert_allclose(tmpd, tmps.toarray())

    def test_multiply(self):
        dd = spu.spmultiply(self.dense0, self.dense1)
        ss = spu.spmultiply(self.sparse0, self.sparse1, array_out=False)

        #typing
        self.assertIsInstance(dd, np.ndarray)
        self.assertIsInstance(ss, spar.csc_matrix)

        #equality
        np.testing.assert_array_equal(dd, ss.toarray())


if __name__ == '__main__':
    ut.main()
