def test_array_maskna_reshape():
# Simple reshape 1D -> 2D
a = np.arange(6, maskna=True)
a[1] = np.NA
a[5] = np.NA
# Reshape from 1D to C order
b = a.reshape(2,3)
assert_(b.base is a)
assert_equal(b.shape, (2,3))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [[0,1,0],[0,0,1]])
# Reshape from 1D to F order
b = a.reshape(2,3,order='F')
assert_(b.base is a)
assert_equal(b.shape, (2,3))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [[0,0,0],[1,0,1]])
# Add a new axis using 'newaxis'
a = np.array(np.NA, maskna=True)
assert_equal(np.isna(a[np.newaxis]), [True])
def test_array_maskna_diagonal():
# ndarray.diagonal
a = np.arange(6, maskna=True)
a.shape = (2, 3)
a[0, 1] = np.NA
# Should produce a view into a
res = a.diagonal()
assert_(res.base is a)
assert_(res.flags.maskna)
assert_(not res.flags.ownmaskna)
assert_equal(res, [0, 4])
res = a.diagonal(-1)
assert_equal(res, [3])
res = a.diagonal(-2)
assert_equal(res, [])
# This diagonal has the NA
res = a.diagonal(1)
assert_equal(np.isna(res), [1, 0])
assert_equal(res[~np.isna(res)], [5])
res = a.diagonal(2)
assert_equal(res, [2])
res = a.diagonal(3)
assert_equal(res, [])
def test_array_maskna_conjugate_method():
# ndarray.conjugate
a = np.array([1j, 2+4j, np.NA, 2-1.5j, np.NA], maskna=True)
b = a.conjugate()
assert_equal(np.isna(b), [0,0,1,0,1])
assert_equal(b[~np.isna(b)], [-1j, 2-4j, 2+1.5j])
def test_array_maskna_diagonal():
# ndarray.diagonal
a = np.arange(6, maskna=True)
a.shape = (2,3)
a[0,1] = np.NA
# Should produce a view into a
res = a.diagonal()
assert_(res.base is a)
assert_(res.flags.maskna)
assert_(not res.flags.ownmaskna)
assert_equal(res, [0, 4])
res = a.diagonal(-1)
assert_equal(res, [3])
res = a.diagonal(-2)
assert_equal(res, [])
# This diagonal has the NA
res = a.diagonal(1)
assert_equal(np.isna(res), [1,0])
assert_equal(res[~np.isna(res)], [5])
res = a.diagonal(2)
assert_equal(res, [2])
res = a.diagonal(3)
assert_equal(res, [])
def t_test(var, by, df):
by_column = df[by]
by_column = by_column.drop_duplicates()
p = []
comparison_array = []
for x in by_column:
if np.isna(x): continue
df_x = df[df[by] == x]
s_x = df_x[var]
for y in by_column:
if np.isna(y): continue
if not x == y:
if not [x, y] in comparison_array and not [
y, x
] in comparison_array:
df_y = df[df[by] == y]
s_y = df_y[var]
sig = sp.stats.ttest_ind(s_x, s_y)
p.append({
'Var': var,
'By': by,
'X': x,
'Y': y,
't': sig[0],
'p': sig[1]
})
comparison_array.append([x, y])
p = pd.DataFrame(p)
return p
def check_ufunc_max_1D(max_func):
a_orig = np.array([0, 3, 2, 10, -1, 5, 7, -2])
a = a_orig.view(maskna=True)
# Straightforward reduce with no NAs
b = max_func(a)
assert_equal(b, 10)
# Set the biggest value to NA
a[3] = np.NA
b = max_func(a)
assert_(np.isna(b))
# Skip the NA
b = max_func(a, skipna=True)
assert_(not b.flags.maskna)
assert_(not np.isna(b))
assert_equal(b, 7)
# Set the first value to NA
a[0] = np.NA
b = max_func(a, skipna=True)
assert_(not b.flags.maskna)
assert_(not np.isna(b))
assert_equal(b, 7)
# Set all the values to NA - should raise the same error as
# for an empty array
a[...] = np.NA
assert_raises(ValueError, max_func, a, skipna=True)
def test_array_maskna_reshape():
# Simple reshape 1D -> 2D
a = np.arange(6, maskna=True)
a[1] = np.NA
a[5] = np.NA
# Reshape from 1D to C order
b = a.reshape(2, 3)
assert_(b.base is a)
assert_equal(b.shape, (2, 3))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [[0, 1, 0], [0, 0, 1]])
# Reshape from 1D to F order
b = a.reshape(2, 3, order='F')
assert_(b.base is a)
assert_equal(b.shape, (2, 3))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [[0, 0, 0], [1, 0, 1]])
# Add a new axis using 'newaxis'
a = np.array(np.NA, maskna=True)
assert_equal(np.isna(a[np.newaxis]), [True])
def test_array_maskna_conjugate_method():
# ndarray.conjugate
a = np.array([1j, 2 + 4j, np.NA, 2 - 1.5j, np.NA], maskna=True)
b = a.conjugate()
assert_equal(np.isna(b), [0, 0, 1, 0, 1])
assert_equal(b[~np.isna(b)], [-1j, 2 - 4j, 2 + 1.5j])
def check_ufunc_max_1D(max_func):
a_orig = np.array([0, 3, 2, 10, -1, 5, 7, -2])
a = a_orig.view(maskna=True)
# Straightforward reduce with no NAs
b = max_func(a)
assert_equal(b, 10)
# Set the biggest value to NA
a[3] = np.NA
b = max_func(a)
assert_(np.isna(b))
# Skip the NA
b = max_func(a, skipna=True)
assert_(not b.flags.maskna)
assert_(not np.isna(b))
assert_equal(b, 7)
# Set the first value to NA
a[0] = np.NA
b = max_func(a, skipna=True)
assert_(not b.flags.maskna)
assert_(not np.isna(b))
assert_equal(b, 7)
# Set all the values to NA - should raise the same error as
# for an empty array
a[...] = np.NA
assert_raises(ValueError, max_func, a, skipna=True)
def test_array_maskna_column_stack():
# np.column_stack
a = np.array((1, 2, 3), maskna=True)
b = np.array((2, 3, 4), maskna=True)
b[2] = np.NA
res = np.column_stack((a, b))
assert_equal(np.isna(res), [[0, 0], [0, 0], [0, 1]])
assert_equal(res[~np.isna(res)], [1, 2, 2, 3, 3])
def test_array_maskna_column_stack():
# np.column_stack
a = np.array((1,2,3), maskna=True)
b = np.array((2,3,4), maskna=True)
b[2] = np.NA
res = np.column_stack((a,b))
assert_equal(np.isna(res), [[0,0], [0,0], [0,1]])
assert_equal(res[~np.isna(res)], [1,2,2,3,3])
def test_maskna_ufunc_1D():
a_orig = np.arange(3)
a = a_orig.view(maskna=True)
b_orig = np.array([5,4,3])
b = b_orig.view(maskna=True)
c_orig = np.array([0,0,0])
c = c_orig.view(maskna=True)
# An NA mask is produced if an operand has one
res = a + b_orig
assert_(res.flags.maskna)
assert_equal(res, [5,5,5])
res = b_orig + a
assert_(res.flags.maskna)
assert_equal(res, [5,5,5])
# Can still output to a non-NA array if there are no NAs
np.add(a, b, out=c_orig)
assert_equal(c_orig, [5,5,5])
# Should unmask everything if the output has NA support but
# the inputs don't
c_orig[...] = 0
c[...] = np.NA
np.add(a_orig, b_orig, out=c)
assert_equal(c, [5,5,5])
# If the input has NA support but an output parameter doesn't,
# should work as long as the inputs contain no NAs
c_orig[...] = 0
np.add(a, b, out=c_orig)
assert_equal(c_orig, [5,5,5])
# An NA is produced if either operand has one
a[0] = np.NA
b[1] = np.NA
res = a + b
assert_equal(np.isna(res), [1,1,0])
assert_equal(res[2], 5)
# If the output contains NA, can't have out= parameter without
# NA support
assert_raises(ValueError, np.add, a, b, out=c_orig)
# Divide in-place with NA
a_orig = np.array([[3], [12.]])
a = a_orig.view(maskna=True)
a[0,0] = np.NA
a /= 3
# Shouldn't have touched the masked element
assert_array_equal(a_orig, [[3], [4.]])
assert_array_equal(a, [[np.NA], [4.]])
# double-check assertions
assert_equal(np.isna(a), [[1], [0]])
assert_equal(a[~np.isna(a)], [4.])
def test_maskna_ufunc_1D():
a_orig = np.arange(3)
a = a_orig.view(maskna=True)
b_orig = np.array([5, 4, 3])
b = b_orig.view(maskna=True)
c_orig = np.array([0, 0, 0])
c = c_orig.view(maskna=True)
# An NA mask is produced if an operand has one
res = a + b_orig
assert_(res.flags.maskna)
assert_equal(res, [5, 5, 5])
res = b_orig + a
assert_(res.flags.maskna)
assert_equal(res, [5, 5, 5])
# Can still output to a non-NA array if there are no NAs
np.add(a, b, out=c_orig)
assert_equal(c_orig, [5, 5, 5])
# Should unmask everything if the output has NA support but
# the inputs don't
c_orig[...] = 0
c[...] = np.NA
np.add(a_orig, b_orig, out=c)
assert_equal(c, [5, 5, 5])
# If the input has NA support but an output parameter doesn't,
# should work as long as the inputs contain no NAs
c_orig[...] = 0
np.add(a, b, out=c_orig)
assert_equal(c_orig, [5, 5, 5])
# An NA is produced if either operand has one
a[0] = np.NA
b[1] = np.NA
res = a + b
assert_equal(np.isna(res), [1, 1, 0])
assert_equal(res[2], 5)
# If the output contains NA, can't have out= parameter without
# NA support
assert_raises(ValueError, np.add, a, b, out=c_orig)
# Divide in-place with NA
a_orig = np.array([[3], [12.]])
a = a_orig.view(maskna=True)
a[0, 0] = np.NA
a /= 3
# Shouldn't have touched the masked element
assert_array_equal(a_orig, [[3], [4.]])
assert_array_equal(a, [[np.NA], [4.]])
# double-check assertions
assert_equal(np.isna(a), [[1], [0]])
assert_equal(a[~np.isna(a)], [4.])
def check_maskna_ufunc_sum_1D(sum_func):
a = np.arange(3.0, maskna=True)
b = np.array(0.5)
c_orig = np.array(0.5)
c = c_orig.view(maskna=True)
# Since 'a' has no NA values, this should work
sum_func(a, out=b)
assert_equal(b, 3.0)
b[...] = 7
sum_func(a, skipna=True, out=b)
assert_equal(b, 3.0)
ret = sum_func(a)
assert_equal(ret, 3.0)
ret = sum_func(a, skipna=True)
assert_equal(ret, 3.0)
# With an NA value, the reduce should throw with the non-NA output param
a[1] = np.NA
assert_raises(ValueError, sum_func, a, out=b)
# With an NA value, the output parameter can still be an NA-array
c_orig[...] = 0.5
sum_func(a, out=c)
assert_equal(c_orig, 0.5)
assert_(np.isna(c))
# Should not touch the out= element when assigning NA
b[...] = 1.0
d = b.view(maskna=True)
sum_func(a, out=d)
assert_(np.isna(d))
assert_equal(b, 1.0)
# Without an output parameter, return NA
ret = sum_func(a)
assert_(np.isna(ret))
# With 'skipna=True'
ret = sum_func(a, skipna=True)
assert_equal(ret, 2.0)
# With 'skipna=True', and out= parameter
b[...] = 0.5
sum_func(a, skipna=True, out=b)
assert_equal(b, 2.0)
# With 'skipna=True', and out= parameter with a mask
c[...] = 0.5
c[...] = np.NA
sum_func(a, skipna=True, out=c)
assert_(not np.isna(c))
assert_equal(c, 2.0)
def check_maskna_ufunc_sum_1D(sum_func):
a = np.arange(3.0, maskna=True)
b = np.array(0.5)
c_orig = np.array(0.5)
c = c_orig.view(maskna=True)
# Since 'a' has no NA values, this should work
sum_func(a, out=b)
assert_equal(b, 3.0)
b[...] = 7
sum_func(a, skipna=True, out=b)
assert_equal(b, 3.0)
ret = sum_func(a)
assert_equal(ret, 3.0)
ret = sum_func(a, skipna=True)
assert_equal(ret, 3.0)
# With an NA value, the reduce should throw with the non-NA output param
a[1] = np.NA
assert_raises(ValueError, sum_func, a, out=b)
# With an NA value, the output parameter can still be an NA-array
c_orig[...] = 0.5
sum_func(a, out=c)
assert_equal(c_orig, 0.5)
assert_(np.isna(c))
# Should not touch the out= element when assigning NA
b[...] = 1.0
d = b.view(maskna=True)
sum_func(a, out=d)
assert_(np.isna(d))
assert_equal(b, 1.0)
# Without an output parameter, return NA
ret = sum_func(a)
assert_(np.isna(ret))
# With 'skipna=True'
ret = sum_func(a, skipna=True)
assert_equal(ret, 2.0)
# With 'skipna=True', and out= parameter
b[...] = 0.5
sum_func(a, skipna=True, out=b)
assert_equal(b, 2.0)
# With 'skipna=True', and out= parameter with a mask
c[...] = 0.5
c[...] = np.NA
sum_func(a, skipna=True, out=c)
assert_(not np.isna(c))
assert_equal(c, 2.0)
def test_array_maskna_squeeze():
# np.squeeze
a = np.zeros((1,3,1,1,4,2,1), maskna=True)
a[0,1,0,0,3,0,0] = np.NA
res = np.squeeze(a)
assert_equal(res.shape, (3,4,2))
assert_(np.isna(res[1,3,0]))
res = np.squeeze(a, axis=(0,2,6))
assert_equal(res.shape, (3,1,4,2))
assert_(np.isna(res[1,0,3,0]))
def test_array_maskna_squeeze():
# np.squeeze
a = np.zeros((1, 3, 1, 1, 4, 2, 1), maskna=True)
a[0, 1, 0, 0, 3, 0, 0] = np.NA
res = np.squeeze(a)
assert_equal(res.shape, (3, 4, 2))
assert_(np.isna(res[1, 3, 0]))
res = np.squeeze(a, axis=(0, 2, 6))
assert_equal(res.shape, (3, 1, 4, 2))
assert_(np.isna(res[1, 0, 3, 0]))
def test_array_maskna_setasflat():
# Copy from a C to a F array with some NAs
a_orig = np.empty((2, 3), order='C')
b_orig = np.empty((3, 2), order='F')
a = a_orig.view(maskna=True)
b = b_orig.view(maskna=True)
a[...] = 1
a[0, 1] = np.NA
a[1, 2] = np.NA
b[...] = 2
b.setasflat(a)
assert_equal(np.isna(a), [[0, 1, 0], [0, 0, 1]])
assert_equal(b_orig, [[1, 2], [1, 1], [1, 2]])
assert_equal(np.isna(b), [[0, 1], [0, 0], [0, 1]])
def test_array_maskna_setasflat():
# Copy from a C to a F array with some NAs
a_orig = np.empty((2,3), order='C')
b_orig = np.empty((3,2), order='F')
a = a_orig.view(maskna=True)
b = b_orig.view(maskna=True)
a[...] = 1
a[0,1] = np.NA
a[1,2] = np.NA
b[...] = 2
b.setasflat(a)
assert_equal(np.isna(a), [[0,1,0],[0,0,1]])
assert_equal(b_orig, [[1,2],[1,1],[1,2]])
assert_equal(np.isna(b), [[0,1],[0,0],[0,1]])
def test_isna():
# Objects which are not np.NA or ndarray all return False
assert_equal(np.isna(True), False)
assert_equal(np.isna("abc"), False)
assert_equal(np.isna([1, 2, 3]), False)
assert_equal(np.isna({3: 5}), False)
# Various NA values return True
assert_equal(np.isna(np.NA), True)
assert_equal(np.isna(np.NA()), True)
assert_equal(np.isna(np.NA(5)), True)
assert_equal(np.isna(np.NA(dtype='f4')), True)
assert_equal(np.isna(np.NA(12, dtype='f4')), True)
def test_isna():
# Objects which are not np.NA or ndarray all return False
assert_equal(np.isna(True), False)
assert_equal(np.isna("abc"), False)
assert_equal(np.isna([1,2,3]), False)
assert_equal(np.isna({3:5}), False)
# Various NA values return True
assert_equal(np.isna(np.NA), True)
assert_equal(np.isna(np.NA()), True)
assert_equal(np.isna(np.NA(5)), True)
assert_equal(np.isna(np.NA(dtype='f4')), True)
assert_equal(np.isna(np.NA(12,dtype='f4')), True)
def test_array_maskna_astype():
dtsrc = [np.dtype(d) for d in '?bhilqpBHILQPefdgFDGSUO']
#dtsrc.append(np.dtype([('b', np.int, (1,))]))
dtsrc.append(np.dtype('datetime64[D]'))
dtsrc.append(np.dtype('timedelta64[s]'))
dtdst = [np.dtype(d) for d in '?bhilqpBHILQPefdgFDGSUO']
#dtdst.append(np.dtype([('b', np.int, (1,))]))
dtdst.append(np.dtype('datetime64[D]'))
dtdst.append(np.dtype('timedelta64[s]'))
warn_ctx = WarningManager()
warn_ctx.__enter__()
try:
warnings.simplefilter("ignore", np.ComplexWarning)
for dt1 in dtsrc:
a = np.ones(2, dt1, maskna=1)
a[1] = np.NA
for dt2 in dtdst:
msg = 'type %s to %s conversion' % (dt1, dt2)
b = a.astype(dt2)
assert_(b.flags.maskna, msg)
assert_(b.flags.ownmaskna, msg)
assert_(np.isna(b[1]), msg)
finally:
warn_ctx.__exit__()
def test_array_maskna_astype():
dtsrc = [np.dtype(d) for d in '?bhilqpBHILQPefdgFDGSUO']
#dtsrc.append(np.dtype([('b', np.int, (1,))]))
dtsrc.append(np.dtype('datetime64[D]'))
dtsrc.append(np.dtype('timedelta64[s]'))
dtdst = [np.dtype(d) for d in '?bhilqpBHILQPefdgFDGSUO']
#dtdst.append(np.dtype([('b', np.int, (1,))]))
dtdst.append(np.dtype('datetime64[D]'))
dtdst.append(np.dtype('timedelta64[s]'))
warn_ctx = WarningManager()
warn_ctx.__enter__()
try:
warnings.simplefilter("ignore", np.ComplexWarning)
for dt1 in dtsrc:
a = np.ones(2, dt1, maskna=1)
a[1] = np.NA
for dt2 in dtdst:
msg = 'type %s to %s conversion' % (dt1, dt2)
b = a.astype(dt2)
assert_(b.flags.maskna, msg)
assert_(b.flags.ownmaskna, msg)
assert_(np.isna(b[1]), msg)
finally:
warn_ctx.__exit__()
def test_copyto_fromscalar():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Simple copy
np.copyto(a, 1.5)
assert_equal(a, 1.5)
np.copyto(a.T, 2.5)
assert_equal(a, 2.5)
# Where-masked copy
mask = np.array([[0, 1, 0], [0, 0, 1]], dtype='?')
np.copyto(a, 3.5, where=mask)
assert_equal(a, [[2.5, 3.5, 2.5], [2.5, 2.5, 3.5]])
mask = np.array([[0, 1], [1, 1], [1, 0]], dtype='?')
np.copyto(a.T, 4.5, where=mask)
assert_equal(a, [[2.5, 4.5, 4.5], [4.5, 4.5, 3.5]])
# Simple copy to NA-masked
a_orig = a
a = a_orig.view(maskna=True)
a[...] = np.NA
np.copyto(a, 0.5)
assert_equal(a, 0.5)
a[...] = np.NA
np.copyto(a.T, 1.5)
assert_equal(a, 1.5)
# Where-masked copy to NA-masked
a[0, 0] = np.NA
a[1, 1] = np.NA
mask = np.array([[1, 0, 1], [0, 0, 1]], dtype='?')
np.copyto(a, 2.5, where=mask)
assert_equal(np.isna(a), [[0, 0, 0], [0, 1, 0]])
assert_equal(a_orig, [[2.5, 1.5, 2.5], [1.5, 1.5, 2.5]])
# Simple preservena=True copy
a[0, 0] = np.NA
np.copyto(a, 3.5, preservena=True)
assert_equal(np.isna(a), [[1, 0, 0], [0, 1, 0]])
assert_equal(a_orig, [[2.5, 3.5, 3.5], [3.5, 1.5, 3.5]])
# Where-masked preservena=True copy
mask = np.array([[1, 0, 1], [0, 0, 1]], dtype='?')
np.copyto(a, 4.5, where=mask, preservena=True)
assert_equal(np.isna(a), [[1, 0, 0], [0, 1, 0]])
assert_equal(a_orig, [[2.5, 3.5, 4.5], [3.5, 1.5, 4.5]])
def test_copyto_fromscalar():
a = np.arange(6, dtype='f4').reshape(2,3)
# Simple copy
np.copyto(a, 1.5)
assert_equal(a, 1.5)
np.copyto(a.T, 2.5)
assert_equal(a, 2.5)
# Where-masked copy
mask = np.array([[0,1,0],[0,0,1]], dtype='?')
np.copyto(a, 3.5, where=mask)
assert_equal(a, [[2.5,3.5,2.5],[2.5,2.5,3.5]])
mask = np.array([[0,1],[1,1],[1,0]], dtype='?')
np.copyto(a.T, 4.5, where=mask)
assert_equal(a, [[2.5,4.5,4.5],[4.5,4.5,3.5]])
# Simple copy to NA-masked
a_orig = a
a = a_orig.view(maskna=True)
a[...] = np.NA
np.copyto(a, 0.5)
assert_equal(a, 0.5)
a[...] = np.NA
np.copyto(a.T, 1.5)
assert_equal(a, 1.5)
# Where-masked copy to NA-masked
a[0,0] = np.NA
a[1,1] = np.NA
mask = np.array([[1,0,1],[0,0,1]], dtype='?')
np.copyto(a, 2.5, where=mask)
assert_equal(np.isna(a), [[0,0,0],[0,1,0]])
assert_equal(a_orig, [[2.5,1.5,2.5],[1.5,1.5,2.5]])
# Simple preservena=True copy
a[0,0] = np.NA
np.copyto(a, 3.5, preservena=True)
assert_equal(np.isna(a), [[1,0,0],[0,1,0]])
assert_equal(a_orig, [[2.5,3.5,3.5],[3.5,1.5,3.5]])
# Where-masked preservena=True copy
mask = np.array([[1,0,1],[0,0,1]], dtype='?')
np.copyto(a, 4.5, where=mask, preservena=True)
assert_equal(np.isna(a), [[1,0,0],[0,1,0]])
assert_equal(a_orig, [[2.5,3.5,4.5],[3.5,1.5,4.5]])
def test_array_maskna_construction():
# Construction with NA inputs
a = np.array([1.0, 2.0, np.NA, 7.0], maskna=True)
assert_equal(a.dtype, np.dtype('f8'))
assert_(a.flags.maskna)
assert_equal(type(a[2]), np.NAType)
# Without the 'maskna=True', still produces an NA mask if NA is there
a = np.array([1.0, 2.0, np.NA, 7.0])
assert_equal(a.dtype, np.dtype('f8'))
assert_(a.flags.maskna)
assert_equal(type(a[2]), np.NAType)
# Without any NAs, does not produce an NA mask
a = np.array([1.0, 2.0, 4.0, 7.0])
assert_equal(a.dtype, np.dtype('f8'))
assert_(not a.flags.maskna)
# From np.NA as a straight scalar
a = np.array(np.NA, maskna=True)
assert_equal(type(a), np.ndarray)
assert_(np.isna(a))
# As a special case, converting np.NA to an array produces
# a zero-dimensional masked array
a = np.array(np.NA)
assert_equal(type(a), np.ndarray)
assert_(np.isna(a))
# The data type defaults to the same as an empty array if all is NA
a = np.array([np.NA], maskna=True)
b = np.array([])
assert_equal(a.dtype, b.dtype)
assert_(np.isna(a))
a = np.zeros((3, ))
assert_(not a.flags.maskna)
a = np.zeros((3, ), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), False)
a = np.ones((3, ))
assert_(not a.flags.maskna)
a = np.ones((3, ), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), False)
# np.empty returns all NAs if maskna is set to True
a = np.empty((3, ))
assert_(not a.flags.maskna)
a = np.empty((3, ), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), True)
# np.empty_like returns all NAs if maskna is set to True
tmp = np.arange(3)
a = np.empty_like(tmp)
assert_(not a.flags.maskna)
a = np.empty_like(tmp, maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), True)
def test_maskna_take_1D():
a = np.arange(5, maskna=True)
b = np.arange(3)
c = b.view(maskna=True)
# Take without any NAs
assert_equal(a.take([0, 2, 4]), [0, 2, 4])
# Take without any NAs, into non-NA output parameter
a.take([0, 2, 4], out=b)
assert_equal(b, [0, 2, 4])
# Take without any NAs, into NA output parameter
b[...] = 1
c[...] = np.NA
a.take([0, 2, 4], out=c)
assert_equal(c, [0, 2, 4])
# Take with some NAs
a[2] = np.NA
a[3] = np.NA
ret = a.take([0, 2, 4])
assert_equal([ret[0], ret[2]], [0, 4])
assert_equal(np.isna(ret), [0, 1, 0])
# Take with some NAs, into NA output parameter
b[...] = 1
c[...] = np.NA
a.take([0, 2, 4], out=c)
assert_equal(b, [0, 1, 4])
assert_equal([c[0], c[2]], [0, 4])
assert_equal(np.isna(c), [0, 1, 0])
c[...] = 1
a.take([0, 2, 4], out=c)
assert_equal(b, [0, 1, 4])
assert_equal([c[0], c[2]], [0, 4])
assert_equal(np.isna(c), [0, 1, 0])
# Take with an NA just at the start
a = np.arange(5, maskna=True)
a[0] = np.NA
res = a.take([1, 2, 3, 4])
assert_equal(res, [1, 2, 3, 4])
def test_maskna_take_1D():
a = np.arange(5, maskna=True)
b = np.arange(3)
c = b.view(maskna=True)
# Take without any NAs
assert_equal(a.take([0,2,4]), [0,2,4])
# Take without any NAs, into non-NA output parameter
a.take([0,2,4], out=b)
assert_equal(b, [0,2,4])
# Take without any NAs, into NA output parameter
b[...] = 1
c[...] = np.NA
a.take([0,2,4], out=c)
assert_equal(c, [0,2,4])
# Take with some NAs
a[2] = np.NA
a[3] = np.NA
ret = a.take([0,2,4])
assert_equal([ret[0], ret[2]], [0,4])
assert_equal(np.isna(ret), [0,1,0])
# Take with some NAs, into NA output parameter
b[...] = 1
c[...] = np.NA
a.take([0,2,4], out=c)
assert_equal(b, [0,1,4])
assert_equal([c[0], c[2]], [0,4])
assert_equal(np.isna(c), [0,1,0])
c[...] = 1
a.take([0,2,4], out=c)
assert_equal(b, [0,1,4])
assert_equal([c[0], c[2]], [0,4])
assert_equal(np.isna(c), [0,1,0])
# Take with an NA just at the start
a = np.arange(5, maskna=True)
a[0] = np.NA
res = a.take([1,2,3,4])
assert_equal(res, [1,2,3,4])
def test_array_maskna_construction():
# Construction with NA inputs
a = np.array([1.0, 2.0, np.NA, 7.0], maskna=True)
assert_equal(a.dtype, np.dtype('f8'))
assert_(a.flags.maskna)
assert_equal(type(a[2]), np.NAType)
# Without the 'maskna=True', still produces an NA mask if NA is there
a = np.array([1.0, 2.0, np.NA, 7.0])
assert_equal(a.dtype, np.dtype('f8'))
assert_(a.flags.maskna)
assert_equal(type(a[2]), np.NAType)
# Without any NAs, does not produce an NA mask
a = np.array([1.0, 2.0, 4.0, 7.0])
assert_equal(a.dtype, np.dtype('f8'))
assert_(not a.flags.maskna)
# From np.NA as a straight scalar
a = np.array(np.NA, maskna=True)
assert_equal(type(a), np.ndarray)
assert_(np.isna(a))
# As a special case, converting np.NA to an array produces
# a zero-dimensional masked array
a = np.array(np.NA)
assert_equal(type(a), np.ndarray)
assert_(np.isna(a))
# The data type defaults to the same as an empty array if all is NA
a = np.array([np.NA], maskna=True)
b = np.array([])
assert_equal(a.dtype, b.dtype)
assert_(np.isna(a))
a = np.zeros((3,))
assert_(not a.flags.maskna)
a = np.zeros((3,), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), False)
a = np.ones((3,))
assert_(not a.flags.maskna)
a = np.ones((3,), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), False)
# np.empty returns all NAs if maskna is set to True
a = np.empty((3,))
assert_(not a.flags.maskna)
a = np.empty((3,), maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), True)
# np.empty_like returns all NAs if maskna is set to True
tmp = np.arange(3)
a = np.empty_like(tmp)
assert_(not a.flags.maskna)
a = np.empty_like(tmp, maskna=True)
assert_(a.flags.maskna)
assert_equal(np.isna(a), True)
def test_array_maskna_view_NA_assignment_1D():
a = np.arange(10)
a_ref = a.copy()
# Make sure that assigning NA doesn't affect the original data
b = a.view(maskna=True)
b[...] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[:] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3:5] = np.NA
assert_equal(np.isna(b), [0,0,0,1,1,0,0,0,0,0])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3:10:3] = np.NA
assert_equal(np.isna(b), [0,0,0,1,0,0,1,0,0,1])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3] = np.NA
assert_equal(np.isna(b), [0,0,0,1,0,0,0,0,0,0])
assert_equal(a, a_ref)
b = a.view(maskna=True)
mask = np.array([0,1,0,1,1,0,0,0,1,1], dtype='?')
b[mask] = np.NA
assert_equal(np.isna(b), mask)
assert_equal(a, a_ref)
def test_array_maskna_view_NA_assignment_1D():
a = np.arange(10)
a_ref = a.copy()
# Make sure that assigning NA doesn't affect the original data
b = a.view(maskna=True)
b[...] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[:] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3:5] = np.NA
assert_equal(np.isna(b), [0, 0, 0, 1, 1, 0, 0, 0, 0, 0])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3:10:3] = np.NA
assert_equal(np.isna(b), [0, 0, 0, 1, 0, 0, 1, 0, 0, 1])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[3] = np.NA
assert_equal(np.isna(b), [0, 0, 0, 1, 0, 0, 0, 0, 0, 0])
assert_equal(a, a_ref)
b = a.view(maskna=True)
mask = np.array([0, 1, 0, 1, 1, 0, 0, 0, 1, 1], dtype='?')
b[mask] = np.NA
assert_equal(np.isna(b), mask)
assert_equal(a, a_ref)
def test_array_maskna_ravel():
# From a C array
a = np.zeros((2, 3), maskna=True, order='C')
a[0, 1] = np.NA
a[1, 2] = np.NA
# Ravel in C order returns a view
b = np.ravel(a)
assert_(b.base is a)
assert_equal(b.shape, (6, ))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [0, 1, 0, 0, 0, 1])
# Ravel in F order returns a copy
b = np.ravel(a, order='F')
assert_(b.base is None)
assert_equal(b.shape, (6, ))
assert_(b.flags.maskna)
assert_(b.flags.ownmaskna)
assert_equal(np.isna(b), [0, 0, 1, 0, 0, 1])
a = np.arange(12, maskna=True).reshape(2, 3, 2).swapaxes(1, 2)
assert_equal(a.ravel(order='K'), np.arange(12))
def test_array_maskna_ravel():
# From a C array
a = np.zeros((2,3), maskna=True, order='C')
a[0,1] = np.NA
a[1,2] = np.NA
# Ravel in C order returns a view
b = np.ravel(a)
assert_(b.base is a)
assert_equal(b.shape, (6,))
assert_(b.flags.maskna)
assert_(not b.flags.ownmaskna)
assert_equal(np.isna(b), [0,1,0,0,0,1])
# Ravel in F order returns a copy
b = np.ravel(a, order='F')
assert_(b.base is None)
assert_equal(b.shape, (6,))
assert_(b.flags.maskna)
assert_(b.flags.ownmaskna)
assert_equal(np.isna(b), [0,0,1,0,0,1])
a = np.arange(12, maskna=True).reshape(2,3,2).swapaxes(1,2)
assert_equal(a.ravel(order='K'), np.arange(12))
def test_maskna_nonzero_1D():
a = np.zeros((5,), maskna=True)
# The nonzeros without any NAs
assert_equal(np.count_nonzero(a), 0)
assert_equal(np.nonzero(a)[0], [])
a[2] = 3
assert_equal(np.count_nonzero(a), 1)
assert_equal(np.nonzero(a)[0], [2])
a[3:] = 2
assert_equal(np.count_nonzero(a), 3)
assert_equal(np.nonzero(a)[0], [2,3,4])
# The nonzeros with an NA
a[2] = np.NA
assert_(np.isna(np.count_nonzero(a)))
assert_raises(ValueError, np.nonzero, a)
def test_maskna_nonzero_1D():
a = np.zeros((5, ), maskna=True)
# The nonzeros without any NAs
assert_equal(np.count_nonzero(a), 0)
assert_equal(np.nonzero(a)[0], [])
a[2] = 3
assert_equal(np.count_nonzero(a), 1)
assert_equal(np.nonzero(a)[0], [2])
a[3:] = 2
assert_equal(np.count_nonzero(a), 3)
assert_equal(np.nonzero(a)[0], [2, 3, 4])
# The nonzeros with an NA
a[2] = np.NA
assert_(np.isna(np.count_nonzero(a)))
assert_raises(ValueError, np.nonzero, a)
def test_array_maskna_std_mean_methods():
# ndarray.std, ndarray.mean
a = np.array([[2, np.NA, 10], [4, 8, 7], [12, 4, np.NA]], maskna=True)
res = a.mean(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [np.array([2, 4, 12]).mean()])
res = a.mean(axis=-1)
assert_equal(np.isna(res), [1, 0, 1])
assert_equal(res[~np.isna(res)], [np.array([4, 8, 7]).mean()])
res = a.std(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [np.array([2, 4, 12]).std()])
res = a.std(axis=-1)
assert_equal(np.isna(res), [1, 0, 1])
assert_equal(res[~np.isna(res)], [np.array([4, 8, 7]).std()])
def test_array_maskna_item():
# With a zero-dimensional array
a = np.array(np.NA, maskna=True)
# Should return NA as the item
assert_equal(type(a.item()), np.NAType)
# Should be able to set the item
a.itemset(1.5)
assert_(not np.isna(a))
assert_equal(a, 1.5)
a.itemset(np.NA)
assert_(np.isna(a))
# With a one-dimensional array
a = np.array([1, np.NA, 2, np.NA], maskna=True)
# Should return the scalar or NA as the item
assert_(not np.isna(a.item(0)))
assert_equal(type(a.item(1)), np.NAType)
# Should be able to set the items
a.itemset(0, np.NA)
assert_(np.isna(a[0]))
a.itemset(1, 12)
assert_(not np.isna(a[1]))
assert_equal(a[1], 12)
# With a two-dimensional array
a = np.arange(6, maskna=True).reshape(2,3)
a[0,1] = np.NA
# Should return the scalar or NA as the item
assert_(not np.isna(a.item((0,0))))
assert_equal(type(a.item((0,1))), np.NAType)
# Should be able to set the items
a.itemset((0,1), 8)
assert_(not np.isna(a[0,1]))
assert_equal(a[0,1], 8)
a.itemset((1,1), np.NA)
assert_(np.isna(a[1,1]))
def test_array_maskna_item():
# With a zero-dimensional array
a = np.array(np.NA, maskna=True)
# Should return NA as the item
assert_equal(type(a.item()), np.NAType)
# Should be able to set the item
a.itemset(1.5)
assert_(not np.isna(a))
assert_equal(a, 1.5)
a.itemset(np.NA)
assert_(np.isna(a))
# With a one-dimensional array
a = np.array([1, np.NA, 2, np.NA], maskna=True)
# Should return the scalar or NA as the item
assert_(not np.isna(a.item(0)))
assert_equal(type(a.item(1)), np.NAType)
# Should be able to set the items
a.itemset(0, np.NA)
assert_(np.isna(a[0]))
a.itemset(1, 12)
assert_(not np.isna(a[1]))
assert_equal(a[1], 12)
# With a two-dimensional array
a = np.arange(6, maskna=True).reshape(2, 3)
a[0, 1] = np.NA
# Should return the scalar or NA as the item
assert_(not np.isna(a.item((0, 0))))
assert_equal(type(a.item((0, 1))), np.NAType)
# Should be able to set the items
a.itemset((0, 1), 8)
assert_(not np.isna(a[0, 1]))
assert_equal(a[0, 1], 8)
a.itemset((1, 1), np.NA)
assert_(np.isna(a[1, 1]))
def test_array_maskna_view_NA_assignment_2D():
a = np.arange(6).reshape(2, 3)
a_ref = a.copy()
# Make sure that assigning NA doesn't affect the original data
b = a.view(maskna=True)
b[...] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[:] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[0, :] = np.NA
assert_equal(np.isna(b[0]), True)
assert_equal(np.isna(b[1]), False)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[1:, 1:3] = np.NA
assert_equal(np.isna(b), [[0, 0, 0], [0, 1, 1]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[1, ::2] = np.NA
assert_equal(np.isna(b), [[0, 0, 0], [1, 0, 1]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[0, 2] = np.NA
assert_equal(np.isna(b), [[0, 0, 1], [0, 0, 0]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
mask = np.array([[1, 0, 1], [1, 1, 0]], dtype='?')
b[mask] = np.NA
assert_equal(np.isna(b), mask)
assert_equal(a, a_ref)
def test_array_maskna_view_NA_assignment_2D():
a = np.arange(6).reshape(2,3)
a_ref = a.copy()
# Make sure that assigning NA doesn't affect the original data
b = a.view(maskna=True)
b[...] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[:] = np.NA
assert_equal(np.isna(b), True)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[0,:] = np.NA
assert_equal(np.isna(b[0]), True)
assert_equal(np.isna(b[1]), False)
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[1:,1:3] = np.NA
assert_equal(np.isna(b), [[0,0,0],[0,1,1]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[1,::2] = np.NA
assert_equal(np.isna(b), [[0,0,0],[1,0,1]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
b[0,2] = np.NA
assert_equal(np.isna(b), [[0,0,1],[0,0,0]])
assert_equal(a, a_ref)
b = a.view(maskna=True)
mask = np.array([[1,0,1],[1,1,0]], dtype='?')
b[mask] = np.NA
assert_equal(np.isna(b), mask)
assert_equal(a, a_ref)
def test_array_maskna_std_mean_methods():
# ndarray.std, ndarray.mean
a = np.array([[2, np.NA, 10],
[4, 8, 7],
[12, 4, np.NA]], maskna=True)
res = a.mean(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [np.array([2,4,12]).mean()])
res = a.mean(axis=-1)
assert_equal(np.isna(res), [1,0,1])
assert_equal(res[~np.isna(res)], [np.array([4,8,7]).mean()])
res = a.std(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [np.array([2,4,12]).std()])
res = a.std(axis=-1)
assert_equal(np.isna(res), [1,0,1])
assert_equal(res[~np.isna(res)], [np.array([4,8,7]).std()])
def test_array_maskna_clip_method():
# ndarray.clip
a = np.array([2, np.NA, 10, 4, np.NA, 7], maskna=True)
b = np.clip(a, 3, None)
assert_equal(np.isna(b), [0, 1, 0, 0, 1, 0])
assert_equal(b[~np.isna(b)], [3, 10, 4, 7])
res = np.clip(a, None, 6)
assert_equal(np.isna(res), [0, 1, 0, 0, 1, 0])
assert_equal(res[~np.isna(res)], [2, 6, 4, 6])
res = np.clip(a, 4, 7)
assert_equal(np.isna(res), [0, 1, 0, 0, 1, 0])
assert_equal(res[~np.isna(res)], [4, 7, 4, 7])
def test_array_maskna_clip_method():
# ndarray.clip
a = np.array([2, np.NA, 10, 4, np.NA, 7], maskna=True)
b = np.clip(a, 3, None)
assert_equal(np.isna(b), [0,1,0,0,1,0])
assert_equal(b[~np.isna(b)], [3, 10, 4, 7])
res = np.clip(a, None, 6)
assert_equal(np.isna(res), [0,1,0,0,1,0])
assert_equal(res[~np.isna(res)], [2, 6, 4, 6])
res = np.clip(a, 4, 7)
assert_equal(np.isna(res), [0,1,0,0,1,0])
assert_equal(res[~np.isna(res)], [4, 7, 4, 7])
def test_na_comparison():
# NA cannot be converted to a boolean
assert_raises(ValueError, bool, np.NA)
# Comparison results should be np.NA(dtype='bool')
def check_comparison_result(res):
assert_(np.isna(res))
assert_(res.dtype == np.dtype('bool'))
# Comparison with different objects produces an NA with boolean type
check_comparison_result(np.NA < 3)
check_comparison_result(np.NA <= 3)
check_comparison_result(np.NA == 3)
check_comparison_result(np.NA != 3)
check_comparison_result(np.NA >= 3)
check_comparison_result(np.NA > 3)
# Should work with NA on the other side too
check_comparison_result(3 < np.NA)
check_comparison_result(3 <= np.NA)
check_comparison_result(3 == np.NA)
check_comparison_result(3 != np.NA)
check_comparison_result(3 >= np.NA)
check_comparison_result(3 > np.NA)
# Comparison with an array should produce an array
a = np.array([0,1,2]) < np.NA
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.array([0,1,2]) == np.NA
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.array([0,1,2]) != np.NA
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
# Comparison with an array should work on the other side too
a = np.NA > np.array([0,1,2])
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.NA == np.array([0,1,2])
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.NA != np.array([0,1,2])
assert_equal(np.isna(a), [1,1,1])
assert_equal(a.dtype, np.dtype('bool'))
def test_na_comparison():
# NA cannot be converted to a boolean
assert_raises(ValueError, bool, np.NA)
# Comparison results should be np.NA(dtype='bool')
def check_comparison_result(res):
assert_(np.isna(res))
assert_(res.dtype == np.dtype('bool'))
# Comparison with different objects produces an NA with boolean type
check_comparison_result(np.NA < 3)
check_comparison_result(np.NA <= 3)
check_comparison_result(np.NA == 3)
check_comparison_result(np.NA != 3)
check_comparison_result(np.NA >= 3)
check_comparison_result(np.NA > 3)
# Should work with NA on the other side too
check_comparison_result(3 < np.NA)
check_comparison_result(3 <= np.NA)
check_comparison_result(3 == np.NA)
check_comparison_result(3 != np.NA)
check_comparison_result(3 >= np.NA)
check_comparison_result(3 > np.NA)
# Comparison with an array should produce an array
a = np.array([0, 1, 2]) < np.NA
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.array([0, 1, 2]) == np.NA
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.array([0, 1, 2]) != np.NA
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
# Comparison with an array should work on the other side too
a = np.NA > np.array([0, 1, 2])
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.NA == np.array([0, 1, 2])
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
a = np.NA != np.array([0, 1, 2])
assert_equal(np.isna(a), [1, 1, 1])
assert_equal(a.dtype, np.dtype('bool'))
def test_array_maskna_concatenate():
# np.concatenate
a = np.arange(6, maskna=True, dtype='i4').reshape(2,3)
a[1,0] = np.NA
b = np.array([[12],[13]], dtype='i4')
res = np.concatenate([a, b], axis=1)
assert_equal(np.isna(res), [[0,0,0,0], [1,0,0,0]])
assert_equal(res[~np.isna(res)], [0,1,2,12,4,5,13])
assert_equal(res.strides, (16, 4))
b = np.array([[10, np.NA, 11]], maskna=True, dtype='i4')
res = np.concatenate([a,b], axis=0)
assert_equal(np.isna(res), [[0,0,0], [1,0,0], [0,1,0]])
assert_equal(res[~np.isna(res)], [0,1,2,4,5,10,11])
assert_equal(res.strides, (12, 4))
b = np.array([[np.NA, 10]], order='F', maskna=True, dtype='i4')
res = np.concatenate([a.T, b], axis=0)
assert_equal(np.isna(res), [[0,1], [0,0], [0,0], [1,0]])
assert_equal(res[~np.isna(res)], [0,1,4,2,5,10])
assert_equal(res.strides, (4, 16))
def test_array_maskna_concatenate():
# np.concatenate
a = np.arange(6, maskna=True, dtype='i4').reshape(2, 3)
a[1, 0] = np.NA
b = np.array([[12], [13]], dtype='i4')
res = np.concatenate([a, b], axis=1)
assert_equal(np.isna(res), [[0, 0, 0, 0], [1, 0, 0, 0]])
assert_equal(res[~np.isna(res)], [0, 1, 2, 12, 4, 5, 13])
assert_equal(res.strides, (16, 4))
b = np.array([[10, np.NA, 11]], maskna=True, dtype='i4')
res = np.concatenate([a, b], axis=0)
assert_equal(np.isna(res), [[0, 0, 0], [1, 0, 0], [0, 1, 0]])
assert_equal(res[~np.isna(res)], [0, 1, 2, 4, 5, 10, 11])
assert_equal(res.strides, (12, 4))
b = np.array([[np.NA, 10]], order='F', maskna=True, dtype='i4')
res = np.concatenate([a.T, b], axis=0)
assert_equal(np.isna(res), [[0, 1], [0, 0], [0, 0], [1, 0]])
assert_equal(res[~np.isna(res)], [0, 1, 4, 2, 5, 10])
assert_equal(res.strides, (4, 16))
def test_array_maskna_array_function_1D():
a = np.arange(10)
a_ref = a.copy()
b = a.view(maskna=True)
b[3:10:2] = np.NA
b_view = b.view()
# Ensure the setup is correct
assert_(not a.flags.maskna)
assert_(b.flags.maskna)
assert_(b.flags.ownmaskna)
assert_(b_view.flags.maskna)
assert_(not b_view.flags.ownmaskna)
# Should be able to add a mask with 'maskna='
c = np.array(a, maskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_(not (c is b))
# Should be able to add a mask with 'ownmaskna='
c = np.array(a, ownmaskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_(not (c is b))
# Should propagate mask
c = np.array(b)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should propagate mask with 'maskna=True'
c = np.array(b, maskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should propagate mask with 'ownmaskna=True'
c = np.array(b, ownmaskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should be able to pass it through
c = np.array(b, copy=False)
assert_(c is b)
# Should be able to pass it through with 'maskna=True'
c = np.array(b, copy=False, maskna=True)
assert_(c is b)
# Should be able to pass it through with 'maskna=True'
c = np.array(b_view, copy=False, maskna=True)
assert_(c is b_view)
# Should be able to pass an owned mask through with 'ownmaskna=True'
c = np.array(b, copy=False, ownmaskna=True)
assert_(c is b)
# Should produce a view with an owned mask with 'ownmaskna=True'
c = np.array(b_view, copy=False, ownmaskna=True)
assert_(c.base is b_view.base)
assert_(c.flags.ownmaskna)
assert_(not (c is b_view))
# Should produce a view whose base is 'c', because 'c' owns
# the data for its mask
d = c.view()
assert_(d.base is c)
assert_(d.flags.maskna)
assert_(not d.flags.ownmaskna)
def test_array_maskna_max_min_ptp_methods():
# ndarray.max, ndarray.min, ndarray.ptp
a = np.array([[2, np.NA, 10], [4, 8, 7], [12, 4, np.NA]], maskna=True)
res = a.max(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [12])
res = a.max(axis=-1)
assert_equal(np.isna(res), [1, 0, 1])
assert_equal(res[~np.isna(res)], [8])
res = a.min(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [2])
res = a.min(axis=-1)
assert_equal(np.isna(res), [1, 0, 1])
assert_equal(res[~np.isna(res)], [4])
res = a.ptp(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [10])
res = a.ptp(axis=-1)
assert_equal(np.isna(res), [1, 0, 1])
assert_equal(res[~np.isna(res)], [4])
def test_array_maskna_max_min_ptp_methods():
# ndarray.max, ndarray.min, ndarray.ptp
a = np.array([[2, np.NA, 10],
[4, 8, 7],
[12, 4, np.NA]], maskna=True)
res = a.max(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [12])
res = a.max(axis=-1)
assert_equal(np.isna(res), [1,0,1])
assert_equal(res[~np.isna(res)], [8])
res = a.min(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [2])
res = a.min(axis=-1)
assert_equal(np.isna(res), [1,0,1])
assert_equal(res[~np.isna(res)], [4])
res = a.ptp(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [10])
res = a.ptp(axis=-1)
assert_equal(np.isna(res), [1,0,1])
assert_equal(res[~np.isna(res)], [4])
def test_array_maskna_sum_prod_methods():
# ndarray.sum, ndarray.prod
a = np.array([[2, np.NA, 10],
[4, 8, 7],
[12, 4, np.NA],
[3, 2, 5]], maskna=True)
res = a.sum(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [21])
res = a.sum(axis=-1)
assert_equal(np.isna(res), [1,0,1,0])
assert_equal(res[~np.isna(res)], [19,10])
res = a.prod(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [2*4*12*3])
res = a.prod(axis=-1)
assert_equal(np.isna(res), [1,0,1,0])
assert_equal(res[~np.isna(res)], [4*8*7,3*2*5])
# Check also with Fortran-order
a = np.array([[2, np.NA, 10],
[4, 8, 7],
[12, 4, np.NA],
[3, 2, 5]], maskna=True, order='F')
res = a.sum(axis=0)
assert_equal(np.isna(res), [0,1,1])
assert_equal(res[~np.isna(res)], [21])
res = a.sum(axis=-1)
assert_equal(np.isna(res), [1,0,1,0])
assert_equal(res[~np.isna(res)], [19,10])
def test_array_maskna_view_array_assignment_1D():
a = np.arange(5)
b = a.view(maskna=True)
# Assigning a constant scalar should unmask the values
b[...] = np.NA
b[...] = 3
assert_equal(a, 3)
assert_equal(np.isna(b), False)
# Assigning from a list should unmask the values
b[...] = np.NA
b[...] = [2]
assert_equal(a, [2,2,2,2,2])
assert_equal(np.isna(b), False)
# Assigning from a list should unmask the values
b[...] = np.NA
b[...] = [2,3,4,5,6]
assert_equal(a, [2,3,4,5,6])
assert_equal(np.isna(b), False)
# Assigning from a list with NAs should unmask the non-NA values
b[...] = np.NA
b[...] = [7,np.NA,2,0,np.NA]
assert_equal(a, [7,3,2,0,6])
assert_equal(np.isna(b), [0,1,0,0,1])
# Assigning from an unmasked array should unmask the values
b[...] = np.NA
b[...] = np.arange(5)
assert_equal(a, np.arange(5))
assert_equal(np.isna(b), False)
# Assigning from a masked array with no NAs should unmask the values
b[...] = np.NA
tmp = np.arange(5) + 1
tmp.flags.maskna = True
b[...] = tmp
assert_equal(a, np.arange(5) + 1)
assert_equal(np.isna(b), False)
# Assigning from a masked array with some NAs should unmask most
# of the values, and leave the value behind the NAs untouched
b[...] = np.NA
tmp = np.arange(5) + 5
tmp.flags.maskna = True
tmp[2] = np.NA
b[...] = tmp
assert_equal(a, [5,6,3,8,9])
assert_equal(np.isna(b), [0,0,1,0,0])
# Assigning to a single element should unmask the value
b[...] = np.NA
b[2] = 10
assert_equal(a, [5,6,10,8,9])
assert_equal(np.isna(b), [1,1,0,1,1])
# Assigning to a simple slice should unmask the values
b[...] = np.NA
b[2:] = 4
assert_equal(a, [5,6,4,4,4])
assert_equal(np.isna(b), [1,1,0,0,0])
# Assigning to a strided slice should unmask the values
b[...] = np.NA
b[3::-2] = 12
assert_equal(a, [5,12,4,12,4])
assert_equal(np.isna(b), [1,0,1,0,1])
# Assigning to a boolean index should unmask the values
b[...] = np.NA
mask = np.array([0,1,1,0,1], dtype='?')
b[mask] = 7
assert_equal(a, [5,7,7,12,7])
assert_equal(np.isna(b), [1,0,0,1,0])
# Assigning a list to a boolean index should unmask the values
b[...] = np.NA
mask = np.array([1,0,0,0,1], dtype='?')
b[mask] = [8,1]
assert_equal(a, [8,7,7,12,1])
assert_equal(np.isna(b), [0,1,1,1,0])
# Assigning a list with NA to a boolean index should unmask non-NA values
b[...] = np.NA
mask = np.array([0,1,1,0,0], dtype='?')
b[mask] = np.array([8,np.NA], maskna=True)
assert_equal(a, [8,8,7,12,1])
assert_equal(np.isna(b), [1,0,1,1,1])
def test_array_maskna_isna_2D():
a = np.zeros((3, 4))
# With no mask, it returns all False
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (3, 4))
# With a mask but no NAs, it still returns all False
a.flags.maskna = True
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (3, 4))
# Checking isna of a single value
assert_equal(np.isna(a[1, 2]), False)
# Assigning NA to a single value
a[1, 2] = np.NA
assert_equal(np.isna(a), [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0]])
# Checking isna of a single value
assert_equal(np.isna(a[1, 2]), True)
# Checking isna of a slice
assert_equal(np.isna(a[1:4, 1:3]), [[0, 1], [0, 0]])
# Assigning NA to a slice
a[1:3, 0:2] = np.NA
assert_equal(np.isna(a), [[0, 0, 0, 0], [1, 1, 1, 0], [1, 1, 0, 0]])
# Checking isna of a strided slice
assert_equal(np.isna(a[1:, 1:5:2]), [[1, 0], [1, 0]])
# Assigning NA to a strided slice
a[::2, ::2] = np.NA
assert_equal(np.isna(a), [[1, 0, 1, 0], [1, 1, 1, 0], [1, 1, 1, 0]])
# Checking isna of a boolean mask index
mask = np.array([[1, 1, 0, 0], [0, 1, 0, 1], [0, 0, 1, 0]], dtype='?')
assert_equal(np.isna(a[mask]), [1, 0, 1, 0, 1])
# Assigning NA to a boolean masked index
a[mask] = np.NA
assert_equal(np.isna(a), [[1, 1, 1, 0], [1, 1, 1, 1], [1, 1, 1, 0]])
def test_array_maskna_sum_prod_methods():
# ndarray.sum, ndarray.prod
a = np.array([[2, np.NA, 10], [4, 8, 7], [12, 4, np.NA], [3, 2, 5]],
maskna=True)
res = a.sum(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [21])
res = a.sum(axis=-1)
assert_equal(np.isna(res), [1, 0, 1, 0])
assert_equal(res[~np.isna(res)], [19, 10])
res = a.prod(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [2 * 4 * 12 * 3])
res = a.prod(axis=-1)
assert_equal(np.isna(res), [1, 0, 1, 0])
assert_equal(res[~np.isna(res)], [4 * 8 * 7, 3 * 2 * 5])
# Check also with Fortran-order
a = np.array([[2, np.NA, 10], [4, 8, 7], [12, 4, np.NA], [3, 2, 5]],
maskna=True,
order='F')
res = a.sum(axis=0)
assert_equal(np.isna(res), [0, 1, 1])
assert_equal(res[~np.isna(res)], [21])
res = a.sum(axis=-1)
assert_equal(np.isna(res), [1, 0, 1, 0])
assert_equal(res[~np.isna(res)], [19, 10])
def test_array_maskna_isna_1D():
a = np.arange(10)
# With no mask, it returns all False
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (10, ))
# With a mask but no NAs, it still returns all False
a.flags.maskna = True
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (10, ))
# Checking isna of a single value
assert_equal(np.isna(a[4]), False)
# Assigning NA to a single value
a[3] = np.NA
assert_equal(np.isna(a), [0, 0, 0, 1, 0, 0, 0, 0, 0, 0])
# Checking isna of a single value
assert_equal(np.isna(a[3]), True)
# Checking isna of a slice
assert_equal(np.isna(a[1:6]), [0, 0, 1, 0, 0])
# Assigning NA to a slice
a[5:7] = np.NA
assert_equal(np.isna(a), [0, 0, 0, 1, 0, 1, 1, 0, 0, 0])
# Checking isna of a strided slice
assert_equal(np.isna(a[1:8:2]), [0, 1, 1, 0])
# Assigning NA to a strided slice
a[2:10:3] = np.NA
assert_equal(np.isna(a), [0, 0, 1, 1, 0, 1, 1, 0, 1, 0])
# Checking isna of a boolean mask index
mask = np.array([1, 1, 0, 0, 0, 1, 0, 1, 1, 0], dtype='?')
assert_equal(np.isna(a[mask]), [0, 0, 1, 0, 1])
# Assigning NA to a boolean masked index
a[mask] = np.NA
assert_equal(np.isna(a), [1, 1, 1, 1, 0, 1, 1, 1, 1, 0])
def test_array_maskna_isna_1D():
a = np.arange(10)
# With no mask, it returns all False
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (10,))
# With a mask but no NAs, it still returns all False
a.flags.maskna = True
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (10,))
# Checking isna of a single value
assert_equal(np.isna(a[4]), False)
# Assigning NA to a single value
a[3] = np.NA
assert_equal(np.isna(a), [0,0,0,1,0,0,0,0,0,0])
# Checking isna of a single value
assert_equal(np.isna(a[3]), True)
# Checking isna of a slice
assert_equal(np.isna(a[1:6]), [0,0,1,0,0])
# Assigning NA to a slice
a[5:7] = np.NA
assert_equal(np.isna(a), [0,0,0,1,0,1,1,0,0,0])
# Checking isna of a strided slice
assert_equal(np.isna(a[1:8:2]), [0,1,1,0])
# Assigning NA to a strided slice
a[2:10:3] = np.NA
assert_equal(np.isna(a), [0,0,1,1,0,1,1,0,1,0])
# Checking isna of a boolean mask index
mask = np.array([1,1,0,0,0,1,0,1,1,0], dtype='?')
assert_equal(np.isna(a[mask]), [0,0,1,0,1])
# Assigning NA to a boolean masked index
a[mask] = np.NA
assert_equal(np.isna(a), [1,1,1,1,0,1,1,1,1,0])
def test_array_maskna_array_function_1D():
a = np.arange(10)
a_ref = a.copy()
b = a.view(maskna=True)
b[3:10:2] = np.NA
b_view = b.view()
# Ensure the setup is correct
assert_(not a.flags.maskna)
assert_(b.flags.maskna)
assert_(b.flags.ownmaskna)
assert_(b_view.flags.maskna)
assert_(not b_view.flags.ownmaskna)
# Should be able to add a mask with 'maskna='
c = np.array(a, maskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_(not (c is b))
# Should be able to add a mask with 'ownmaskna='
c = np.array(a, ownmaskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_(not (c is b))
# Should propagate mask
c = np.array(b)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should propagate mask with 'maskna=True'
c = np.array(b, maskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should propagate mask with 'ownmaskna=True'
c = np.array(b, ownmaskna=True)
assert_(c.flags.maskna)
assert_(c.flags.ownmaskna)
assert_equal(np.isna(b), np.isna(c))
assert_(not (c is b))
# Should be able to pass it through
c = np.array(b, copy=False)
assert_(c is b)
# Should be able to pass it through with 'maskna=True'
c = np.array(b, copy=False, maskna=True)
assert_(c is b)
# Should be able to pass it through with 'maskna=True'
c = np.array(b_view, copy=False, maskna=True)
assert_(c is b_view)
# Should be able to pass an owned mask through with 'ownmaskna=True'
c = np.array(b, copy=False, ownmaskna=True)
assert_(c is b)
# Should produce a view with an owned mask with 'ownmaskna=True'
c = np.array(b_view, copy=False, ownmaskna=True)
assert_(c.base is b_view)
assert_(c.flags.ownmaskna)
assert_(not (c is b_view))
# Should produce a view whose base is 'c', because 'c' owns
# the data for its mask
d = c.view()
assert_(d.base is c)
assert_(d.flags.maskna)
assert_(not d.flags.ownmaskna)
def test_array_maskna_isna_2D():
a = np.zeros((3,4))
# With no mask, it returns all False
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (3,4))
# With a mask but no NAs, it still returns all False
a.flags.maskna = True
assert_equal(np.isna(a), False)
assert_equal(np.isna(a).shape, (3,4))
# Checking isna of a single value
assert_equal(np.isna(a[1,2]), False)
# Assigning NA to a single value
a[1,2] = np.NA
assert_equal(np.isna(a), [[0,0,0,0],[0,0,1,0],[0,0,0,0]])
# Checking isna of a single value
assert_equal(np.isna(a[1,2]), True)
# Checking isna of a slice
assert_equal(np.isna(a[1:4,1:3]), [[0,1],[0,0]])
# Assigning NA to a slice
a[1:3,0:2] = np.NA
assert_equal(np.isna(a), [[0,0,0,0],[1,1,1,0],[1,1,0,0]])
# Checking isna of a strided slice
assert_equal(np.isna(a[1:,1:5:2]), [[1,0],[1,0]])
# Assigning NA to a strided slice
a[::2,::2] = np.NA
assert_equal(np.isna(a), [[1,0,1,0],[1,1,1,0],[1,1,1,0]])
# Checking isna of a boolean mask index
mask = np.array([[1,1,0,0],[0,1,0,1],[0,0,1,0]], dtype='?')
assert_equal(np.isna(a[mask]), [1,0,1,0,1])
# Assigning NA to a boolean masked index
a[mask] = np.NA
assert_equal(np.isna(a), [[1,1,1,0],[1,1,1,1],[1,1,1,0]])
def check_comparison_result(res):
assert_(np.isna(res))
assert_(res.dtype == np.dtype('bool'))
def test_array_maskna_view_array_assignment_1D():
a = np.arange(5)
b = a.view(maskna=True)
# Assigning a constant scalar should unmask the values
b[...] = np.NA
b[...] = 3
assert_equal(a, 3)
assert_equal(np.isna(b), False)
# Assigning from a list should unmask the values
b[...] = np.NA
b[...] = [2]
assert_equal(a, [2, 2, 2, 2, 2])
assert_equal(np.isna(b), False)
# Assigning from a list should unmask the values
b[...] = np.NA
b[...] = [2, 3, 4, 5, 6]
assert_equal(a, [2, 3, 4, 5, 6])
assert_equal(np.isna(b), False)
# Assigning from a list with NAs should unmask the non-NA values
b[...] = np.NA
b[...] = [7, np.NA, 2, 0, np.NA]
assert_equal(a, [7, 3, 2, 0, 6])
assert_equal(np.isna(b), [0, 1, 0, 0, 1])
# Assigning from an unmasked array should unmask the values
b[...] = np.NA
b[...] = np.arange(5)
assert_equal(a, np.arange(5))
assert_equal(np.isna(b), False)
# Assigning from a masked array with no NAs should unmask the values
b[...] = np.NA
tmp = np.arange(5) + 1
tmp.flags.maskna = True
b[...] = tmp
assert_equal(a, np.arange(5) + 1)
assert_equal(np.isna(b), False)
# Assigning from a masked array with some NAs should unmask most
# of the values, and leave the value behind the NAs untouched
b[...] = np.NA
tmp = np.arange(5) + 5
tmp.flags.maskna = True
tmp[2] = np.NA
b[...] = tmp
assert_equal(a, [5, 6, 3, 8, 9])
assert_equal(np.isna(b), [0, 0, 1, 0, 0])
# Assigning to a single element should unmask the value
b[...] = np.NA
b[2] = 10
assert_equal(a, [5, 6, 10, 8, 9])
assert_equal(np.isna(b), [1, 1, 0, 1, 1])
# Assigning to a simple slice should unmask the values
b[...] = np.NA
b[2:] = 4
assert_equal(a, [5, 6, 4, 4, 4])
assert_equal(np.isna(b), [1, 1, 0, 0, 0])
# Assigning to a strided slice should unmask the values
b[...] = np.NA
b[3::-2] = 12
assert_equal(a, [5, 12, 4, 12, 4])
assert_equal(np.isna(b), [1, 0, 1, 0, 1])
# Assigning to a boolean index should unmask the values
b[...] = np.NA
mask = np.array([0, 1, 1, 0, 1], dtype='?')
b[mask] = 7
assert_equal(a, [5, 7, 7, 12, 7])
assert_equal(np.isna(b), [1, 0, 0, 1, 0])
# Assigning a list to a boolean index should unmask the values
b[...] = np.NA
mask = np.array([1, 0, 0, 0, 1], dtype='?')
b[mask] = [8, 1]
assert_equal(a, [8, 7, 7, 12, 1])
assert_equal(np.isna(b), [0, 1, 1, 1, 0])
# Assigning a list with NA to a boolean index should unmask non-NA values
b[...] = np.NA
mask = np.array([0, 1, 1, 0, 0], dtype='?')
b[mask] = np.array([8, np.NA], maskna=True)
assert_equal(a, [8, 8, 7, 12, 1])
assert_equal(np.isna(b), [1, 0, 1, 1, 1])
