def test_02_test_shape(self, persons_gi):
"""
Test if the shapes of array are controlled correctly
"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
# jobs_j should have the shape (zones)
# correct shape
arr = np.ones((zones))
example.jobs_j = arr
# try to set values with the wrong shape
arr = np.ones((groups))
message = """
Arrays are not equal
jobs_j: shape target: [3], actual: (2,)
(mismatch 100.0%)
x: array([2])
y: array([3])
"""
with pytest.raises(AssertionError,
message=message):
example.jobs_j = arr
# change zones and try again
example.destinations = groups
# now the array fits to the shape defined
example.jobs_j = arr
def test042_test_nan(self, persons_gi):
"""Test nan"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
assert example.isnan_py(np.NAN)
assert not example.isnan_py(np.NINF)
assert not example.isnan_py(np.Inf)
assert not example.isnan_py(0)
assert not example.isnan_py(1)
assert not example.isnan_py(-1)
def example(km_ij, jobs, params_groups, persons_gi):
"""
different values for home zone
"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
example.set_array('km_ij', km_ij)
example.set_array('jobs_j', jobs)
example.set_array('param_g', params_groups)
example.set_array('persons_gi', persons_gi)
return example
def test_33_del_property(self):
"""Test the Example CDefClass creation"""
example = Example(groups=7, origins=5)
print(example.km_ij)
# remove the shape
example.dtypes['km_ij'].shape = None
# reset to default (not defined)
example.reset_array('km_ij')
np.testing.assert_array_equal(example.km_ij, np.NAN)
# reset to default (new value defined)
example.dtypes['km_ij'].default = 11
example.reset_array('km_ij')
np.testing.assert_array_equal(example.km_ij, 11)
# delete values
del example.km_ij
np.testing.assert_array_equal(example.km_ij.shape, (0, 0))
# set new values
new_array = np.arange(10).reshape(2, 5)
example.set_array('km_ij', value=new_array, shape=(2, 5))
np.testing.assert_array_equal(example.km_ij, new_array)
def test_20_save_and_read_ds(self, example, tempfile_h5):
"""Test that saving and re-reading the data works"""
example.zonenumbers_i = np.array([100, 200, 300])
example.groupnames_g = np.array(['Female', 'Male'], dtype='O')
example.create_ds()
example.save_dataset_to_netcdf(tempfile_h5)
# read the data into new class
new_example = Example.from_netcdf(tempfile_h5)
# assert that the data match
np.testing.assert_array_equal(example.jobs_j, new_example.jobs_j)
np.testing.assert_array_equal(example.groupnames_g,
new_example.groupnames_g)
# assert, that the array are correctly linked to the Dataset
new_example.calc_model()
old_value = new_example.ds.trips_ij.sum()
# calculate the model with 77 persons more
new_example.persons_gi[1, 0] += 77
new_example.calc_model()
new_value = new_example.ds.trips_ij.sum()
np.testing.assert_allclose(old_value + 77, new_value)
print(new_example.ds)
def test_04_test_dtype(self, persons_gi):
"""Test the dimensions"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
# dtype of jobs_j: f8
# dtype of not_initialized_ij: i4
# dtype of n_threads: char
# test if n_threads is really a signed char
example.n_threads = 127
assert example.n_threads == 127
with pytest.raises(OverflowError,
message='value too large to convert to char'):
example.n_threads = 128
# test if jobs_j is really an array of double precision
arr_i4 = np.array([2, 3, 4], dtype='i4')
example.jobs_j = arr_i4
assert example.jobs_j.dtype == np.dtype('f8')
# because the array set to jobs_j is of different dtype,
# it had to be converted to double. So no view is used,
# but a copy of the data
arr_i4[1] = -1
# the data in jobs_j stays untouched
assert example.jobs_j[1] == 3
# test if not_initialized_ij is really an array of 32bit integer
arr_i4 = np.array([[2, 3, 4], [5, 6, 7]], dtype='i4')
example.not_initialized_ij = arr_i4
assert example.not_initialized_ij.dtype == np.dtype('i4')
# because the array set to not_initialized_ij is of the same dtype,
# a view on the data could have been used
arr_i4[1, 2] = -1
# the data in not_initialized_ij changes
assert example.not_initialized_ij[1, 2] == -1
def test_01_test_init_array(self, persons_gi):
"""Test the Example CDefClass creation"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
# set correct shape
values = np.arange(zones)
example.set_array('jobs_j', np.arange((zones)))
np.testing.assert_array_equal(example.jobs_j, values)
np.testing.assert_array_equal(example._jobs_j, values)
# init the array with a default value
example.init_array('jobs_j', default=0)
np.testing.assert_array_equal(example.jobs_j, np.zeros(zones))
# init the array with the other default value
example.init_array('jobs_j', default=2)
np.testing.assert_array_equal(example.jobs_j,
np.full(zones, 2, dtype='d'))
# new default value will be stored
example.reset_array('jobs_j')
np.testing.assert_array_equal(example.jobs_j,
np.full(zones, 2, dtype='d'))
# set new shape, keep old default value
example.init_array('jobs_j', shape='groups')
np.testing.assert_array_equal(example.jobs_j,
np.full(groups, 2, dtype='d'))
def test_34_save_ds(self, tempfile_h5):
"""Test the Example CDefClass creation"""
example = Example(groups=7, origins=5, )
example.save_dataset_to_netcdf(tempfile_h5)
def test_32_test_not_init_array(self):
"""Test the Example CDefClass creation"""
example = Example(groups=7, origins=5,
init_arrays=False, threading=False)
with pytest.raises(AttributeError):
print(example.not_initialized_ij)
def test_30_test_init_array(self, persons_gi):
"""Test the Example CDefClass creation"""
example = Example(groups=7, origins=5)
example.init_array('param_g', 7)
assert example.param_g.shape == (7, )
def test_03_test_dimensions(self, persons_gi):
"""Test the dimensions"""
groups, zones = persons_gi.shape
example = Example(groups, zones)
# try to set shape with the wrong number of dimensions
msg = "builtins.ValueError: 1 Dimensions required, shape ['groups', 'zones'] has 2 dimensions"
with pytest.raises(ValueError,
message=msg):
example.init_array('jobs_j', shape='groups, zones')
# access a non-initialized array
target = np.empty((0, 0))
actual = example.not_initialized_ij
np.testing.assert_array_equal(actual, target)
# set arbitrary values to non-initialized 2D-Array
a = np.array([2, 3, 4], dtype='i4')
# with less dimensions than required ...
example.not_initialized_ij = a
b = example.not_initialized_ij
# another dimension is added
assert b.ndim == 2
assert b.shape == (1, 3)
print(example.not_initialized_ij)
# change value in cython-memoryview
example._not_initialized_ij[0, 1] = 99
target = np.array([[2, 99, 4]], dtype='i4')
# the value will be changed in the numpy-view of the array
np.testing.assert_array_equal(example.not_initialized_ij, target)
# as this is a view on the input-data, it will be changed there, too
np.testing.assert_array_equal(a[1], 99)
# the right number of dimensions should be fine
c = np.array([[0, 1],
[2, 3]], dtype='i4')
example.not_initialized_ij = c
d = example.not_initialized_ij
np.testing.assert_array_equal(c, d)
# too many dimensions will raise an error
e = np.array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]], dtype='i4')
with pytest.raises(AssertionError,
message="not_initialized_ij: ndim target: 2, actual: 3"):
example.not_initialized_ij = e
# when the dimensions can be reduced to the target dimensions,
# it should be fine
f = np.array([[[0, 1],
[2, 3]]], dtype='i4')
example.not_initialized_ij = f
g = example.not_initialized_ij
np.testing.assert_array_equal(g, f[0])
# change value in cython-memoryview
example._not_initialized_ij[1, 1] = 88
target = np.array([[0, 1],
[2, 88]], dtype='i4')
# the value will be changed in the numpy-view of the array
np.testing.assert_array_equal(example.not_initialized_ij, target)
# as this is a view on the input-data, it will be changed there, too
np.testing.assert_array_equal(f[0, 1, 1], 88)
# use more dimensions than required
# reset the array first
del example.not_initialized_ij
print(example.not_initialized_ij)
c = np.array([[[2, 3, 4]]], dtype='i4')
example.not_initialized_ij = c
b = example.not_initialized_ij
# one dimension can be taken away
assert b.ndim == 2
assert b.shape == (1, 3)
print(example.not_initialized_ij)
# use the correct number of dimensions
# reset the array first
del example.not_initialized_ij
print(example.not_initialized_ij)
d = np.array([[2, 3, 4]], dtype='i4')
example.not_initialized_ij = d
b = example.not_initialized_ij
# one dimension can be taken away
assert b.ndim == 2
assert b.shape == (1, 3)
print(example.not_initialized_ij)