def __init__(self, band1, band2, expand = False):
"""
@param band1 First band (numpy masked array)
@param band2 Second band (numpy masked array)
@param expand If the param is True, use union of categories of the bands and compute NxN crosstable
"""
QObject.__init__(self)
if not sizes_equal(band1, band2):
raise CrossTabError('Sizes of rasters are not equal!')
band1, band2 = masks_identity(band1, band2, dtype=np.uint8)
self.X = np.ma.compressed(band1).flatten()
self.Y = np.ma.compressed(band2).flatten()
# Compute gradations of the bands
self.graduation_x = get_gradations(self.X)
self.graduation_y = get_gradations(self.Y)
if expand:
self.graduation_x = list(set(self.graduation_x + self.graduation_y))
self.graduation_y = self.graduation_x
rows, cols = len(self.graduation_x), len(self.graduation_y)
self.shape = (rows, cols)
self._T = None # Crosstable
self.n = None # Count of elements in the crosstable
def __init__(self, band1, band2, expand=False):
"""
@param band1 First band (numpy masked array)
@param band2 Second band (numpy masked array)
@param expand If the param is True, use union of categories of the bands and compute NxN crosstable
"""
QObject.__init__(self)
if not sizes_equal(band1, band2):
raise CrossTabError('Sizes of rasters are not equal!')
band1, band2 = masks_identity(band1, band2, dtype=np.uint8)
self.X = np.ma.compressed(band1).flatten()
self.Y = np.ma.compressed(band2).flatten()
# Compute gradations of the bands
self.graduation_x = get_gradations(self.X)
self.graduation_y = get_gradations(self.Y)
if expand:
self.graduation_x = list(set(self.graduation_x +
self.graduation_y))
self.graduation_y = self.graduation_x
rows, cols = len(self.graduation_x), len(self.graduation_y)
self.shape = (rows, cols)
self._T = None # Crosstable
self.n = None # Count of elements in the crosstable
def __init__(self, band1, band2):
if not sizes_equal(band1, band2):
raise CrossTabError('Sizes of rasters are not equal!')
band1, band2 = masks_identity(band1, band2)
X = np.ma.compressed(band1)
Y = np.ma.compressed(band2)
# Compute gradations of the bands
self.graduation_x = get_gradations(X)
self.graduation_y = get_gradations(Y)
rows, cols = len(self.graduation_x), len(self.graduation_y)
self.shape = (rows, cols)
# Compute crosstable
self.T = np.zeros([rows, cols], dtype=int)
self.n = len(X) # Count of unmasked elements (= sum of all elements of the table)
for i in range(self.n):
class_num_x = self.graduation_x.index(X[i])
class_num_y = self.graduation_y.index(Y[i])
self.T[class_num_x][class_num_y] +=1
def test_Size_no_equals(self):
self.assertEqual(sizes_equal(self.X2, self.Y), False, 'sizes are equal')
def test_size_equals(self):
self.assertEqual(sizes_equal(self.X, self.Y), True, 'incorrent size')
def test_Size_no_equals(self):
self.assertEqual(sizes_equal(self.X2, self.Y), False,
'sizes are equal')
def test_size_equals(self):
self.assertEqual(sizes_equal(self.X, self.Y), True, 'incorrent size')
