def test_nd_h5_illegal(self):
with h5py.File(file_path, mode='r') as h5_f:
with self.assertRaises(TypeError):
_ = dtype_utils.flatten_compound_to_real(h5_f['real'])
with self.assertRaises(TypeError):
_ = dtype_utils.flatten_compound_to_real(h5_f['complex'])
def test_illegal(self):
num_elems = (2, 3)
r_vals = np.random.random(size=num_elems)
with self.assertRaises(TypeError):
_ = dtype_utils.flatten_compound_to_real(r_vals)
with self.assertRaises(TypeError):
_ = dtype_utils.flatten_compound_to_real(14)
def _get_guess_chunk(self):
"""
Read the next chunk of the Guess to use for fitting
Parameters
----------
verbose : Boolean (optional)
Whether or not to print debugging statements
"""
if self._start_pos < self.max_pos:
self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,
self.sho_spec_inds_per_forc * (self._current_forc + 1))
self._end_pos = int(min(self.h5_projected_loops.shape[0], self._start_pos + self.max_pos))
self.data = self.h5_projected_loops[self._start_pos:self._end_pos, self._current_sho_spec_slice]
elif self._current_forc < self._num_forcs - 1:
# Resest for next FORC
self._current_forc += 1
self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,
self.sho_spec_inds_per_forc * (self._current_forc + 1))
self._current_met_spec_slice = slice(self.metrics_spec_inds_per_forc * self._current_forc,
self.metrics_spec_inds_per_forc * (self._current_forc + 1))
self._get_dc_offset()
self._start_pos = 0
self._end_pos = int(min(self.h5_projected_loops.shape[0], self._start_pos + self.max_pos))
self.data = self.h5_projected_loops[self._start_pos:self._end_pos, self._current_sho_spec_slice]
else:
self.data = None
guess = self.h5_guess[self._start_pos:self._end_pos,
self._current_met_spec_slice].reshape([-1, 1])
self.guess = flatten_compound_to_real(guess)[:, :-1]
def extract_loop_parameters(h5_loop_fit, nuc_threshold=0.03):
"""
Method to extract a set of physical loop parameters from a dataset of fit parameters
Parameters
----------
h5_loop_fit : h5py.Dataset
Dataset of loop fit parameters
nuc_threshold : float
Nucleation threshold to use in calculation physical parameters
Returns
-------
h5_loop_parm : h5py.Dataset
Dataset of physical parameters
"""
dset_name = h5_loop_fit.name.split('/')[-1] + '_Loop_Parameters'
h5_loop_parameters = create_empty_dataset(h5_loop_fit, dtype=switching32,
dset_name=dset_name,
new_attrs={'nuc_threshold': nuc_threshold})
loop_coef_vec = flatten_compound_to_real(np.reshape(h5_loop_fit, [-1, 1]))
switching_coef_vec = calc_switching_coef_vec(loop_coef_vec, nuc_threshold)
h5_loop_parameters[:, :] = switching_coef_vec.reshape(h5_loop_fit.shape)
h5_loop_fit.file.flush()
return h5_loop_parameters
def test_compound_to_real_nd_h5_legal(self):
with h5py.File(file_path, mode='r') as h5_f:
h5_comp = h5_f['compound']
actual = dtype_utils.flatten_compound_to_real(h5_comp)
expected = np.concatenate(
[h5_comp['r'], h5_comp['g'], h5_comp['b']],
axis=len(h5_comp.shape) - 1)
self.assertTrue(np.allclose(actual, expected))
def base_nd_h5_legal(self, lazy):
with h5py.File(file_path, mode='r') as h5_f:
h5_comp = h5_f['compound']
actual = dtype_utils.flatten_compound_to_real(h5_comp, lazy=lazy)
if lazy:
self.assertIsInstance(actual, da.core.Array)
actual = actual.compute()
expected = np.concatenate([h5_comp['r'], h5_comp['g'], h5_comp['b']], axis=len(h5_comp.shape) - 1)
self.assertTrue(np.allclose(actual, expected))
def test_1d(self):
num_elems = 5
structured_array = np.zeros(shape=num_elems, dtype=struc_dtype)
structured_array['r'] = r_vals = np.random.random(size=num_elems)
structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems)
structured_array['b'] = b_vals = np.random.random(size=num_elems)
expected = np.concatenate((r_vals, g_vals, b_vals))
actual = dtype_utils.flatten_compound_to_real(structured_array)
self.assertTrue(np.allclose(actual, expected))
def base_nd(self, lazy_in, lazy):
num_elems = (5, 7, 2, 3)
structured_array = np.zeros(shape=num_elems, dtype=struc_dtype)
structured_array['r'] = r_vals = np.random.random(size=num_elems)
structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems)
structured_array['b'] = b_vals = np.random.random(size=num_elems)
if lazy_in:
structured_array = da.from_array(structured_array, chunks=structured_array.shape)
expected = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1)
actual = dtype_utils.flatten_compound_to_real(structured_array, lazy=lazy)
if lazy:
self.assertIsInstance(actual, da.core.Array)
actual = actual.compute()
self.assertTrue(np.allclose(actual, expected))
