def test_funcs(self):
"""Test the numpy functions"""
# make ndarray an Dimaray with identical data
arr = np.random.rand(5,12,3,1)
dat = DimArray(arr,dims=[Dim(range(5),name='one'),
Dim(range(12),name='two'),
Dim(range(3),name='three'),
Dim(range(1),name='four')],test='tst')
# these are functions that take an axis argument:
funcs = [np.mean,np.all,np.any,np.argmax,np.argmin,np.argsort,
np.cumprod,np.cumsum,np.max,np.mean,np.min,np.prod,
np.ptp,np.std,np.sum,np.var]
# The axes for the ndarray:
axes_arr = [None,0,1,2,3,0,1,2,3]
# The axes for the DimArray (we want to test indexing them by
# number and name):
axes_dat = [None,0,1,2,3,'one','two','three','four']
# loop through the functions and axes:
for func in funcs:
for a in range(len(axes_arr)):
# apply the function to the ndarray and the DimArray
arr_func = func(arr,axis=axes_arr[a])
dat_func = func(dat,axis=axes_dat[a])
# make sure they are the same:
assert_array_equal(arr_func,dat_func)
if not(axes_dat[a] is None):
# ensure we still have a DimArray
self.assertTrue(isinstance(dat_func,DimArray))
# ensure that the attributes are preserved
self.assertEquals(dat_func.test,'tst')
# same tests as above but this time calling the DimArray
# methods directly (this test is necessary because it is in
# principle possible for the numpy function to work and the
# DimArray method not to work (or vice versa):
for a in range(len(axes_arr)):
assert_array_equal(arr.all(axes_arr[a]),
dat.all(axes_dat[a]))
assert_array_equal(arr.any(axes_arr[a]),
dat.any(axes_dat[a]))
assert_array_equal(arr.argmax(axes_arr[a]),
dat.argmax(axes_dat[a]))
assert_array_equal(arr.argmin(axes_arr[a]),
dat.argmin(axes_dat[a]))
assert_array_equal(arr.argsort(axes_arr[a]),
dat.argsort(axes_dat[a]))
assert_array_equal(arr.cumprod(axes_arr[a]),
dat.cumprod(axes_dat[a]))
assert_array_equal(arr.cumsum(axes_arr[a]),
dat.cumsum(axes_dat[a]))
assert_array_equal(arr.max(axes_arr[a]),
dat.max(axes_dat[a]))
assert_array_equal(arr.mean(axes_arr[a]),
dat.mean(axes_dat[a]))
assert_array_equal(arr.min(axes_arr[a]),
dat.min(axes_dat[a]))
assert_array_equal(arr.prod(axes_arr[a]),
dat.prod(axes_dat[a]))
assert_array_equal(arr.ptp(axes_arr[a]),
dat.ptp(axes_dat[a]))
assert_array_equal(arr.std(axes_arr[a]),
dat.std(axes_dat[a]))
assert_array_equal(arr.sum(axes_arr[a]),
dat.sum(axes_dat[a]))
assert_array_equal(arr.var(axes_arr[a]),
dat.var(axes_dat[a]))
if not(axes_dat[a] is None):
self.assertTrue(isinstance(dat.all(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.any(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argmax(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argmin(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argsort(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.cumprod(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.cumsum(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.max(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.mean(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.min(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.prod(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.ptp(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.std(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.sum(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.var(axes_arr[a]),DimArray))
self.assertEquals(dat.all(axes_arr[a]).test,'tst')
self.assertEquals(dat.any(axes_arr[a]).test,'tst')
self.assertEquals(dat.argmax(axes_arr[a]).test,'tst')
self.assertEquals(dat.argmin(axes_arr[a]).test,'tst')
self.assertEquals(dat.argsort(axes_arr[a]).test,'tst')
self.assertEquals(dat.cumprod(axes_arr[a]).test,'tst')
self.assertEquals(dat.cumsum(axes_arr[a]).test,'tst')
self.assertEquals(dat.max(axes_arr[a]).test,'tst')
self.assertEquals(dat.mean(axes_arr[a]).test,'tst')
self.assertEquals(dat.min(axes_arr[a]).test,'tst')
self.assertEquals(dat.prod(axes_arr[a]).test,'tst')
self.assertEquals(dat.ptp(axes_arr[a]).test,'tst')
self.assertEquals(dat.std(axes_arr[a]).test,'tst')
self.assertEquals(dat.sum(axes_arr[a]).test,'tst')
self.assertEquals(dat.var(axes_arr[a]).test,'tst')
# test functions that require function specific input:
for a in range(len(axes_arr)):
if axes_arr[a] is None:
length = len(arr)
else:
length = np.shape(arr)[axes_arr[a]]
cond = np.random.random(length)>0.5
# calling the compress method directly:
arr_func = arr.compress(cond,axis=axes_arr[a])
dat_func = dat.compress(cond,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,DimArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy compress function:
arr_func = np.compress(cond,arr,axis=axes_arr[a])
dat_func = np.compress(cond,dat,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,DimArray))
self.assertEquals(dat_func.test,'tst')
# the below tests should not run with axis==None:
if axes_arr[a] is None:
continue
reps = np.random.random_integers(low=1, high=10, size=length)
# calling the repeat method directly:
arr_func = arr.repeat(reps,axis=axes_arr[a])
dat_func = dat.repeat(reps,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy repeat function:
arr_func = np.repeat(arr,reps,axis=axes_arr[a])
dat_func = np.repeat(dat,reps,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# skip the last dimension for this test for
# convenience (the last dimension only has 1 level):
if a >= 3:
continue
indcs = np.arange(len(arr.shape))
# calling the take method directly (squeeze, to get rid of
# the last dimension):
arr_func = arr.squeeze().take(indcs,axis=axes_arr[a])
dat_func = dat.squeeze().take(indcs,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a]:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy take function directly (squeeze, to get rid of
# the last dimension):
arr_func = np.take(arr.squeeze(),indcs,axis=axes_arr[a])
dat_func = np.take(dat.squeeze(),indcs,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a]:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# This should work with numpy 1.2 but doesn't
# with 1.1.1 or below (therfore commented out for now):
# arr_func = arr.clip(0.4,0.6)
# dat_func = dat.clip(0.4,0.6)
# assert_array_equal(arr_func,dat_func)
#self.assertTrue(isinstance(dat_func,DimArray))
#self.assertEquals(dat_func.test,'tst')
#arr_func = np.clip(arr,0.4,0.6)
#dat_func = np.clip(dat,0.4,0.6)
#assert_array_equal(arr_func,dat_func)
#self.assertTrue(isinstance(dat_func,DimArray))
#self.assertEquals(dat_func.test,'tst')
# other functions that don't necessarily take return a
# DimArray:
funcs = [np.diagonal,np.nonzero,np.ravel,np.squeeze,
np.sort,np.trace,np.transpose]
for func in funcs:
arr_func = func(arr)
dat_func = func(dat)
assert_array_equal(arr_func,dat_func)
# same tests as above, but calling the methods directly:
assert_array_equal(arr.diagonal(),dat.diagonal())
assert_array_equal(arr.nonzero(),dat.nonzero())
assert_array_equal(arr.ravel(),dat.ravel())
assert_array_equal(arr.squeeze(),dat.squeeze())
assert_array_equal(arr.sort(),dat.sort())
assert_array_equal(arr.trace(),dat.trace())
assert_array_equal(arr.transpose(),dat.transpose())
# there is no numpy.flatten() function, so we only call the
# method directly:
assert_array_equal(arr.flatten(),dat.flatten())
assert_array_equal(arr.swapaxes(0,1),dat.swapaxes(0,1))
self.assertTrue(isinstance(dat.swapaxes(0,1),DimArray))
self.assertEquals(dat.swapaxes(0,1).test,'tst')
assert_array_equal(arr.swapaxes(0,1),dat.swapaxes('one','two'))
self.assertTrue(isinstance(dat.swapaxes('one','two'),DimArray))
self.assertEquals(dat.swapaxes('one','two').test,'tst')
assert_array_equal(arr.swapaxes(1,3),dat.swapaxes(1,3))
self.assertTrue(isinstance(dat.swapaxes(1,3),DimArray))
self.assertEquals(dat.swapaxes(1,3).test,'tst')
assert_array_equal(arr.swapaxes(1,3),dat.swapaxes('two','four'))
self.assertTrue(isinstance(dat.swapaxes('two','four'),DimArray))
self.assertEquals(dat.swapaxes('two','four').test,'tst')
def test_funcs(self):
"""Test the numpy functions"""
# make ndarray an Dimaray with identical data
arr = np.random.rand(5,12,3,1)
dat = DimArray(arr,dims=[Dim(range(5),name='one'),
Dim(range(12),name='two'),
Dim(range(3),name='three'),
Dim(range(1),name='four')],test='tst')
# these are functions that take an axis argument:
funcs = [np.mean,np.all,np.any,np.argmax,np.argmin,np.argsort,
np.cumprod,np.cumsum,np.max,np.mean,np.min,np.prod,
np.ptp,np.std,np.sum,np.var]
# The axes for the ndarray:
axes_arr = [None,0,1,2,3,0,1,2,3]
# The axes for the DimArray (we want to test indexing them by
# number and name):
axes_dat = [None,0,1,2,3,'one','two','three','four']
# loop through the functions and axes:
for func in funcs:
for a in range(len(axes_arr)):
# apply the function to the ndarray and the DimArray
arr_func = func(arr,axis=axes_arr[a])
dat_func = func(dat,axis=axes_dat[a])
# make sure they are the same:
assert_array_equal(arr_func,dat_func)
if not(axes_dat[a] is None):
# ensure we still have a DimArray
self.assertTrue(isinstance(dat_func,DimArray))
# ensure that the attributes are preserved
self.assertEquals(dat_func.test,'tst')
# same tests as above but this time calling the DimArray
# methods directly (this test is necessary because it is in
# principle possible for the numpy function to work and the
# DimArray method not to work (or vice versa):
for a in range(len(axes_arr)):
assert_array_equal(arr.all(axes_arr[a]),
dat.all(axes_dat[a]))
assert_array_equal(arr.any(axes_arr[a]),
dat.any(axes_dat[a]))
assert_array_equal(arr.argmax(axes_arr[a]),
dat.argmax(axes_dat[a]))
assert_array_equal(arr.argmin(axes_arr[a]),
dat.argmin(axes_dat[a]))
assert_array_equal(arr.argsort(axes_arr[a]),
dat.argsort(axes_dat[a]))
assert_array_equal(arr.cumprod(axes_arr[a]),
dat.cumprod(axes_dat[a]))
assert_array_equal(arr.cumsum(axes_arr[a]),
dat.cumsum(axes_dat[a]))
assert_array_equal(arr.max(axes_arr[a]),
dat.max(axes_dat[a]))
assert_array_equal(arr.mean(axes_arr[a]),
dat.mean(axes_dat[a]))
assert_array_equal(arr.min(axes_arr[a]),
dat.min(axes_dat[a]))
assert_array_equal(arr.prod(axes_arr[a]),
dat.prod(axes_dat[a]))
assert_array_equal(arr.ptp(axes_arr[a]),
dat.ptp(axes_dat[a]))
assert_array_equal(arr.std(axes_arr[a]),
dat.std(axes_dat[a]))
assert_array_equal(arr.sum(axes_arr[a]),
dat.sum(axes_dat[a]))
assert_array_equal(arr.var(axes_arr[a]),
dat.var(axes_dat[a]))
if not(axes_dat[a] is None):
self.assertTrue(isinstance(dat.all(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.any(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argmax(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argmin(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.argsort(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.cumprod(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.cumsum(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.max(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.mean(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.min(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.prod(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.ptp(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.std(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.sum(axes_arr[a]),DimArray))
self.assertTrue(isinstance(dat.var(axes_arr[a]),DimArray))
self.assertEquals(dat.all(axes_arr[a]).test,'tst')
self.assertEquals(dat.any(axes_arr[a]).test,'tst')
self.assertEquals(dat.argmax(axes_arr[a]).test,'tst')
self.assertEquals(dat.argmin(axes_arr[a]).test,'tst')
self.assertEquals(dat.argsort(axes_arr[a]).test,'tst')
self.assertEquals(dat.cumprod(axes_arr[a]).test,'tst')
self.assertEquals(dat.cumsum(axes_arr[a]).test,'tst')
self.assertEquals(dat.max(axes_arr[a]).test,'tst')
self.assertEquals(dat.mean(axes_arr[a]).test,'tst')
self.assertEquals(dat.min(axes_arr[a]).test,'tst')
self.assertEquals(dat.prod(axes_arr[a]).test,'tst')
self.assertEquals(dat.ptp(axes_arr[a]).test,'tst')
self.assertEquals(dat.std(axes_arr[a]).test,'tst')
self.assertEquals(dat.sum(axes_arr[a]).test,'tst')
self.assertEquals(dat.var(axes_arr[a]).test,'tst')
# test functions that require function specific input:
for a in range(len(axes_arr)):
if axes_arr[a] is None:
length = len(arr)
else:
length = np.shape(arr)[axes_arr[a]]
cond = np.random.random(length)>0.5
# calling the compress method directly:
arr_func = arr.compress(cond,axis=axes_arr[a])
dat_func = dat.compress(cond,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,DimArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy compress function:
arr_func = np.compress(cond,arr,axis=axes_arr[a])
dat_func = np.compress(cond,dat,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,DimArray))
self.assertEquals(dat_func.test,'tst')
# the below tests should not run with axis==None:
if axes_arr[a] is None:
continue
reps = np.random.random_integers(low=1, high=10, size=length)
# calling the repeat method directly:
arr_func = arr.repeat(reps,axis=axes_arr[a])
dat_func = dat.repeat(reps,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy repeat function:
arr_func = np.repeat(arr,reps,axis=axes_arr[a])
dat_func = np.repeat(dat,reps,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a] is not None:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# skip the last dimension for this test for
# convenience (the last dimension only has 1 level):
if a >= 3:
continue
indcs = np.arange(len(arr.shape))
# calling the take method directly (squeeze, to get rid of
# the last dimension):
arr_func = arr.squeeze().take(indcs,axis=axes_arr[a])
dat_func = dat.squeeze().take(indcs,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a]:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# calling the numpy take function directly (squeeze, to get rid of
# the last dimension):
arr_func = np.take(arr.squeeze(),indcs,axis=axes_arr[a])
dat_func = np.take(dat.squeeze(),indcs,axis=axes_dat[a])
assert_array_equal(arr_func,dat_func)
if axes_dat[a]:
self.assertTrue(isinstance(dat_func,AttrArray))
self.assertEquals(dat_func.test,'tst')
# This should work with numpy 1.2 but doesn't
# with 1.1.1 or below (therfore commented out for now):
# arr_func = arr.clip(0.4,0.6)
# dat_func = dat.clip(0.4,0.6)
# assert_array_equal(arr_func,dat_func)
#self.assertTrue(isinstance(dat_func,DimArray))
#self.assertEquals(dat_func.test,'tst')
#arr_func = np.clip(arr,0.4,0.6)
#dat_func = np.clip(dat,0.4,0.6)
#assert_array_equal(arr_func,dat_func)
#self.assertTrue(isinstance(dat_func,DimArray))
#self.assertEquals(dat_func.test,'tst')
# other functions that don't necessarily take return a
# DimArray:
funcs = [np.diagonal,np.nonzero,np.ravel,np.squeeze,
np.sort,np.trace,np.transpose]
for func in funcs:
arr_func = func(arr)
dat_func = func(dat)
assert_array_equal(arr_func,dat_func)
# same tests as above, but calling the methods directly:
assert_array_equal(arr.diagonal(),dat.diagonal())
assert_array_equal(arr.nonzero(),dat.nonzero())
assert_array_equal(arr.ravel(),dat.ravel())
assert_array_equal(arr.squeeze(),dat.squeeze())
assert_array_equal(arr.sort(),dat.sort())
assert_array_equal(arr.trace(),dat.trace())
assert_array_equal(arr.transpose(),dat.transpose())
# there is no numpy.flatten() function, so we only call the
# method directly:
assert_array_equal(arr.flatten(),dat.flatten())
assert_array_equal(arr.swapaxes(0,1),dat.swapaxes(0,1))
self.assertTrue(isinstance(dat.swapaxes(0,1),DimArray))
self.assertEquals(dat.swapaxes(0,1).test,'tst')
assert_array_equal(arr.swapaxes(0,1),dat.swapaxes('one','two'))
self.assertTrue(isinstance(dat.swapaxes('one','two'),DimArray))
self.assertEquals(dat.swapaxes('one','two').test,'tst')
assert_array_equal(arr.swapaxes(1,3),dat.swapaxes(1,3))
self.assertTrue(isinstance(dat.swapaxes(1,3),DimArray))
self.assertEquals(dat.swapaxes(1,3).test,'tst')
assert_array_equal(arr.swapaxes(1,3),dat.swapaxes('two','four'))
self.assertTrue(isinstance(dat.swapaxes('two','four'),DimArray))
self.assertEquals(dat.swapaxes('two','four').test,'tst')
