def test_setattr(self):
dat = AttrArray(np.random.rand(10),name='randvals')
# add a custom attribute:
dat.custom = 'attribute'
self.assertEquals(dat.custom,'attribute')
# _required_attrs is read only:
self.assertRaises(AttributeError,dat.__setattr__,'_required_attrs','test')
def test_setattr(self):
dat = AttrArray(np.random.rand(10), name='randvals')
# add a custom attribute:
dat.custom = 'attribute'
self.assertEquals(dat.custom, 'attribute')
# _required_attrs is read only:
self.assertRaises(AttributeError, dat.__setattr__, '_required_attrs',
'test')
def test_pickle(self):
# make sure we can pickle this thing
dat = AttrArray(np.random.rand(10), name='randvals')
# dump to string
pstr = pickle.dumps(dat)
# load to new variable
dat2 = pickle.loads(pstr)
# make sure data same
assert_array_equal(dat, dat2)
# make sure has attr and it's correct
self.assertTrue(hasattr(dat2, '_attrs'))
self.assertTrue(hasattr(dat2, 'name'))
self.assertEquals(dat2.name, 'randvals')
# make sure has required attr
self.assertTrue(hasattr(dat2, '_required_attrs'))
def test_nanmean(self):
arr = np.random.rand(10,10,10)
dat = AttrArray(arr,name='randvals')
# if there are no NaN's, mean and nanmean should give equal
# results:
self.assertEquals(dat.mean(),dat.nanmean())
assert_array_almost_equal(dat.mean(0),dat.nanmean(0))
self.assertEquals(dat.nanmean(0).name, 'randvals')
assert_array_almost_equal(dat.mean(1),dat.nanmean(1))
self.assertEquals(dat.nanmean(1).name, 'randvals')
assert_array_almost_equal(dat.mean(2),dat.nanmean(2))
self.assertEquals(dat.nanmean(2).name, 'randvals')
# Now, make sure results are as expected with NaN present:
arr[0,0,0] = np.nan
dat = AttrArray(arr,name='randvals')
self.assertEquals(dat[~np.isnan(dat)].mean(),dat.nanmean())
for i in range(len(arr.shape)):
tmp1 = dat.mean(i)
tmp1[0,0] = 0
tmp2 = dat.nanmean(i)
tmp2[0,0] = 0
assert_array_almost_equal(tmp1,tmp2)
self.assertEquals(tmp2.name, 'randvals')
arr[3,6,2] = np.nan
dat = AttrArray(arr,name='randvals')
self.assertEquals(dat[~np.isnan(dat)].mean(),dat.nanmean())
tmp1 = dat.mean(0)
tmp1[0,0] = 0
tmp1[6,2] = 0
tmp2 = dat.nanmean(0)
tmp2[0,0] = 0
tmp2[6,2] = 0
assert_array_almost_equal(tmp1,tmp2)
self.assertEquals(tmp2.name, 'randvals')
tmp1 = dat.mean(1)
tmp1[0,0] = 0
tmp1[3,2] = 0
tmp2 = dat.nanmean(1)
tmp2[0,0] = 0
tmp2[3,2] = 0
assert_array_almost_equal(tmp1,tmp2)
self.assertEquals(tmp2.name, 'randvals')
tmp1 = dat.mean(2)
tmp1[0,0] = 0
tmp1[3,6] = 0
tmp2 = dat.nanmean(2)
tmp2[0,0] = 0
tmp2[3,6] = 0
assert_array_almost_equal(tmp1,tmp2)
self.assertEquals(tmp2.name, 'randvals')
def test_nanmean(self):
arr = np.random.rand(10, 10, 10)
dat = AttrArray(arr, name='randvals')
# if there are no NaN's, mean and nanmean should give equal
# results:
self.assertEquals(dat.mean(), dat.nanmean())
assert_array_almost_equal(dat.mean(0), dat.nanmean(0))
self.assertEquals(dat.nanmean(0).name, 'randvals')
assert_array_almost_equal(dat.mean(1), dat.nanmean(1))
self.assertEquals(dat.nanmean(1).name, 'randvals')
assert_array_almost_equal(dat.mean(2), dat.nanmean(2))
self.assertEquals(dat.nanmean(2).name, 'randvals')
# Now, make sure results are as expected with NaN present:
arr[0, 0, 0] = np.nan
dat = AttrArray(arr, name='randvals')
self.assertEquals(dat[~np.isnan(dat)].mean(), dat.nanmean())
for i in range(len(arr.shape)):
tmp1 = dat.mean(i)
tmp1[0, 0] = 0
tmp2 = dat.nanmean(i)
tmp2[0, 0] = 0
assert_array_almost_equal(tmp1, tmp2)
self.assertEquals(tmp2.name, 'randvals')
arr[3, 6, 2] = np.nan
dat = AttrArray(arr, name='randvals')
self.assertEquals(dat[~np.isnan(dat)].mean(), dat.nanmean())
tmp1 = dat.mean(0)
tmp1[0, 0] = 0
tmp1[6, 2] = 0
tmp2 = dat.nanmean(0)
tmp2[0, 0] = 0
tmp2[6, 2] = 0
assert_array_almost_equal(tmp1, tmp2)
self.assertEquals(tmp2.name, 'randvals')
tmp1 = dat.mean(1)
tmp1[0, 0] = 0
tmp1[3, 2] = 0
tmp2 = dat.nanmean(1)
tmp2[0, 0] = 0
tmp2[3, 2] = 0
assert_array_almost_equal(tmp1, tmp2)
self.assertEquals(tmp2.name, 'randvals')
tmp1 = dat.mean(2)
tmp1[0, 0] = 0
tmp1[3, 6] = 0
tmp2 = dat.nanmean(2)
tmp2[0, 0] = 0
tmp2[3, 6] = 0
assert_array_almost_equal(tmp1, tmp2)
self.assertEquals(tmp2.name, 'randvals')
def test_new(self):
# test new instantiation (attributes are defined by kwargs)
# instatiation with a numpy ndarray:
shape = (10, )
arr = np.random.random_sample(shape)
dat_array = AttrArray(arr, name='randvals')
self.assertTrue(dat_array.name == 'randvals')
self.assertEquals(shape, dat_array.shape)
self.assertTrue((dat_array == arr).all())
# another instatioation with an ndarray, but this time with
# dtype set:
shape = (1, 2, 3, 4)
arr = np.random.random_sample(shape)
dat_array = AttrArray(arr, name='randvals', dtype=np.float32)
self.assertTrue(dat_array.name == 'randvals')
self.assertEquals(shape, dat_array.shape)
# "almost" equal because of the casting to np.float32:
assert_array_almost_equal(dat_array, arr)
self.assertTrue(dat_array.dtype == np.float32)
# # another ndarray, with copy = True vs. copy = False
shape = (10, 9, 8, 7, 6, 1, 8)
arr = np.random.random_sample(shape)
dat_array = AttrArray(arr,
name='randvals',
test1=33,
test2='test',
copy=True)
self.assertTrue(dat_array.name == 'randvals')
self.assertTrue(dat_array.test1 == 33)
self.assertTrue(dat_array.test2 == 'test')
self.assertEquals(shape, dat_array.shape)
assert_array_equal(dat_array, arr)
dat_array[0] += 5
# # "almost" equal because of slight inaccuracies in the the
# # representation of floats:
assert_array_almost_equal((dat_array[0] - 5), arr[0])
dat_array = AttrArray(arr,
name='randvals',
test1=33,
test2='test',
copy=False)
self.assertTrue(dat_array.name == 'randvals')
self.assertTrue(dat_array.test1 == 33)
self.assertTrue(dat_array.test2 == 'test')
self.assertEquals(shape, dat_array.shape)
self.assertTrue((dat_array == arr).all())
dat_array[0] += 5
assert_array_equal(dat_array[0], arr[0])
# instantiation with a list:
lst = range(10)
dat_list = AttrArray(lst, name='range')
self.assertTrue(dat_list.name == 'range')
self.assertTrue((lst == dat_list).all())
lst = [['a', 'b', 'c']]
dat_list = AttrArray(lst, name='list')
self.assertTrue(dat_list.name == 'list')
self.assertTrue((lst == dat_list).all())
lst = [[1, 2, 3], [4.5, 6, 7]]
dat_list = AttrArray(lst, name='list')
self.assertTrue(dat_list.name == 'list')
self.assertTrue((lst == dat_list).all())
# instantiation with a AttrArray:
dat_attrarray = AttrArray(dat_array, name='attrarray')
self.assertTrue(dat_attrarray.name == 'attrarray')
dat_attrarray = AttrArray(dat_list, newname='attrarray', test=44)
self.assertTrue(dat_attrarray.newname == 'attrarray')
self.assertTrue(dat_attrarray.test == 44)
def test_method(self):
# make sure ndarray methods work and return a new AttrArray
# instance with the attributes intact
dat = AttrArray(np.random.rand(10), name='randvals')
sdat = np.sqrt(dat)
self.assertEquals(sdat.name, 'randvals')
def test_getattr(self):
dat = AttrArray(np.random.rand(10), name='randvals')
self.assertEquals(dat.name, 'randvals')
def test_new(self):
# should raise Error if dims contains non-Dim instances:
self.assertRaises(AttributeError,DimArray,np.random.rand(5,10),
dims = np.arange(4))
self.assertRaises(AttributeError,DimArray,np.random.rand(5,10),
dims=[Dim(range(5),name='freqs',unit='Hz'),
AttrArray(range(10),name='time',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(5,10),
dims=[AttrArray(range(5),name='freqs',unit='Hz'),
Dim(range(10),name='time',unit='sec')])
# should throw Error if dims do not match data shape:
self.assertRaises(AttributeError,DimArray,np.random.rand(5,10),
dims=[Dim(range(10),name='freqs',unit='Hz'),
Dim(range(5),name='time',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(5,10),
dims=[Dim(range(5),name='freqs',unit='Hz')])
# should throw Error if 2 dims have the same name:
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(5),name='dim1',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,3,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(3),name='dim2',unit='Hz'),
Dim(range(5),name='dim1',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,3,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(3),name='dim1',unit='Hz'),
Dim(range(5),name='dim1',unit='sec')])
# should throw Error if a dim name is not a valid identifier:
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(5),name='dim 2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim 1',unit='Hz'),
Dim(range(5),name='dim2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim 1',unit='Hz'),
Dim(range(5),name='dim 2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(5),name='dim$2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='$dim1',unit='Hz'),
Dim(range(5),name='dim2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='1dim1',unit='Hz'),
Dim(range(5),name='dim:2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='dim1',unit='Hz'),
Dim(range(5),name='',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='',unit='Hz'),
Dim(range(5),name='dim2',unit='sec')])
self.assertRaises(AttributeError,DimArray,np.random.rand(10,5),
dims=[Dim(range(10),name='',unit='Hz'),
Dim(range(5),name='',unit='sec')])
# this is a proper initialization:
dat = DimArray(np.random.rand(5,10),
dims=[Dim(range(5),name='freqs',unit='Hz'),
Dim(range(10),name='time',unit='sec')])
# should raise Attribute Error if dims is removed:
self.assertRaises(AttributeError,dat.__setattr__,'dims',None)
# ensure dim_names attribute is set properly:
self.assertEquals(dat.dim_names,['freqs','time'])
# ensure proper shape
self.assertEquals(dat.shape,(5,10))
# ensure dims have proper lengths:
self.assertEquals(len(dat.dims[0]),5)
self.assertEquals(len(dat.dims[1]),10)
# ensure that dims attributes are copied properly:
self.assertEquals(dat.dims[0].unit,'Hz')
self.assertEquals(dat.dims[1].unit,'sec')
# check that dims values are preserved:
self.assertEquals(dat.dims[0][-1],4)
self.assertEquals(dat.dims[1][-1],9)
dat = DimArray(np.random.rand(2,4,5),
dims=[Dim(range(2),name='dim1',unit='Hz'),
Dim(range(4),name='dim2',bla='bla'),
Dim(range(5),name='dim3',attr1='attr1',
attr2='attr2')])
# ensure dim_names attribute is set properly:
self.assertEquals(dat.dim_names,['dim1','dim2','dim3'])
# ensure proper shape
self.assertEquals(dat.shape,(2,4,5))
# ensure dims have proper lengths:
self.assertEquals(len(dat.dims[0]),2)
self.assertEquals(len(dat.dims[1]),4)
self.assertEquals(len(dat.dims[2]),5)
# ensure that dims attributes are copied properly:
self.assertEquals(dat.dims[0].unit,'Hz')
self.assertEquals(dat.dims[1].bla,'bla')
self.assertEquals(dat.dims[2].attr1,'attr1')
self.assertEquals(dat.dims[2].attr2,'attr2')
# check that dims values are preserved:
self.assertEquals(dat.dims[0][-1],1)
self.assertEquals(dat.dims[1][-1],3)
self.assertEquals(dat.dims[2][-1],4)
# check filling in of default dims if left out
dat = DimArray(np.random.rand(4,3))
self.assertEquals(dat.dim_names, ['dim1','dim2'])
assert_array_equal(dat['dim1'],np.arange(dat.shape[0]))
assert_array_equal(dat['dim2'],np.arange(dat.shape[1]))
