

'''
Test functions for Cramer
'''

from unittest import TestCase

import numpy as np
import numpy.testing as npt

from ..statistics import Cramer_Distance
from ._testing_data import \
    dataset1, dataset2, computed_data, computed_distances


class testCramer(TestCase):

    def test_cramer(self):
        self.tester = \
            Cramer_Distance(dataset1["cube"],
                            dataset2["cube"],
                            noise_value1=0.1,
                            noise_value2=0.1).distance_metric(normalize=False)

        npt.assert_allclose(self.tester.data_matrix1,
                            computed_data["cramer_val"])
        npt.assert_almost_equal(self.tester.distance,
                                computed_distances['cramer_distance'])

    def test_cramer_spatial_diff(self):

        small_data = dataset1["cube"][0][:, :26, :26]

        self.tester2 = Cramer_Distance(small_data, dataset2["cube"])
        self.tester2.distance_metric(normalize=False)
        self.tester3 = Cramer_Distance(dataset2["cube"], small_data)
        self.tester3.distance_metric(normalize=False)

        npt.assert_almost_equal(self.tester2.distance, self.tester3.distance)



'''
Test functions for Cramer
'''

import numpy.testing as npt

from ..statistics import Cramer_Distance
from ._testing_data import \
    dataset1, dataset2, computed_data, computed_distances


def test_cramer():
    tester = \
        Cramer_Distance(dataset1["cube"],
                        dataset2["cube"],
                        noise_value1=0.1,
                        noise_value2=0.1).distance_metric(normalize=False)

    npt.assert_allclose(tester.data_matrix1,
                        computed_data["cramer_val"])
    npt.assert_almost_equal(tester.distance,
                            computed_distances['cramer_distance'])


def test_cramer_spatial_diff():

    small_data = dataset1["cube"][0][:, :26, :26]

    tester2 = Cramer_Distance(small_data, dataset2["cube"])
    tester2.distance_metric(normalize=False)
    tester3 = Cramer_Distance(dataset2["cube"], small_data)
    tester3.distance_metric(normalize=False)

    npt.assert_almost_equal(tester2.distance, tester3.distance)

Remove importing UnitCase from Cramer tests
Remove importing UnitCase from Cramer tests

  
  
  '''
  Test functions for Cramer
  '''
  
- from unittest import TestCase
- 
- import numpy as np
  import numpy.testing as npt
  
  from ..statistics import Cramer_Distance
  from ._testing_data import \
      dataset1, dataset2, computed_data, computed_distances
  
  
- class testCramer(TestCase):
+ def test_cramer():
+     tester = \
+         Cramer_Distance(dataset1["cube"],
+                         dataset2["cube"],
+                         noise_value1=0.1,
+                         noise_value2=0.1).distance_metric(normalize=False)
  
+     npt.assert_allclose(tester.data_matrix1,
+                         computed_data["cramer_val"])
+     npt.assert_almost_equal(tester.distance,
+                             computed_distances['cramer_distance'])
-     def test_cramer(self):
-         self.tester = \
-             Cramer_Distance(dataset1["cube"],
-                             dataset2["cube"],
-                             noise_value1=0.1,
-                             noise_value2=0.1).distance_metric(normalize=False)
  
-         npt.assert_allclose(self.tester.data_matrix1,
-                             computed_data["cramer_val"])
-         npt.assert_almost_equal(self.tester.distance,
-                                 computed_distances['cramer_distance'])
  
-     def test_cramer_spatial_diff(self):
+ def test_cramer_spatial_diff():
  
-         small_data = dataset1["cube"][0][:, :26, :26]
+     small_data = dataset1["cube"][0][:, :26, :26]
  
-         self.tester2 = Cramer_Distance(small_data, dataset2["cube"])
+     tester2 = Cramer_Distance(small_data, dataset2["cube"])
-         self.tester2.distance_metric(normalize=False)
+     tester2.distance_metric(normalize=False)
-         self.tester3 = Cramer_Distance(dataset2["cube"], small_data)
+     tester3 = Cramer_Distance(dataset2["cube"], small_data)
-         self.tester3.distance_metric(normalize=False)
+     tester3.distance_metric(normalize=False)
  
-         npt.assert_almost_equal(self.tester2.distance, self.tester3.distance)
+     npt.assert_almost_equal(tester2.distance, tester3.distance)
  
Remove importing UnitCase from Cramer tests
## Code Before:


'''
Test functions for Cramer
'''

from unittest import TestCase

import numpy as np
import numpy.testing as npt

from ..statistics import Cramer_Distance
from ._testing_data import \
    dataset1, dataset2, computed_data, computed_distances


class testCramer(TestCase):

    def test_cramer(self):
        self.tester = \
            Cramer_Distance(dataset1["cube"],
                            dataset2["cube"],
                            noise_value1=0.1,
                            noise_value2=0.1).distance_metric(normalize=False)

        npt.assert_allclose(self.tester.data_matrix1,
                            computed_data["cramer_val"])
        npt.assert_almost_equal(self.tester.distance,
                                computed_distances['cramer_distance'])

    def test_cramer_spatial_diff(self):

        small_data = dataset1["cube"][0][:, :26, :26]

        self.tester2 = Cramer_Distance(small_data, dataset2["cube"])
        self.tester2.distance_metric(normalize=False)
        self.tester3 = Cramer_Distance(dataset2["cube"], small_data)
        self.tester3.distance_metric(normalize=False)

        npt.assert_almost_equal(self.tester2.distance, self.tester3.distance)

## Instruction:
Remove importing UnitCase from Cramer tests
## Code After:


'''
Test functions for Cramer
'''

import numpy.testing as npt

from ..statistics import Cramer_Distance
from ._testing_data import \
    dataset1, dataset2, computed_data, computed_distances


def test_cramer():
    tester = \
        Cramer_Distance(dataset1["cube"],
                        dataset2["cube"],
                        noise_value1=0.1,
                        noise_value2=0.1).distance_metric(normalize=False)

    npt.assert_allclose(tester.data_matrix1,
                        computed_data["cramer_val"])
    npt.assert_almost_equal(tester.distance,
                            computed_distances['cramer_distance'])


def test_cramer_spatial_diff():

    small_data = dataset1["cube"][0][:, :26, :26]

    tester2 = Cramer_Distance(small_data, dataset2["cube"])
    tester2.distance_metric(normalize=False)
    tester3 = Cramer_Distance(dataset2["cube"], small_data)
    tester3.distance_metric(normalize=False)

    npt.assert_almost_equal(tester2.distance, tester3.distance)
