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response to Dayne's review

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levita_a
2025-03-20 10:46:24 +01:00
parent 5b6fd79b28
commit 2fd917350d
2 changed files with 25 additions and 39 deletions
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src/cdtools/tools/data/data.py
+7 -15
View File
@@ -813,7 +813,7 @@ def nested_dict_to_h5(h5_file, d):
for key in d.keys():
value = d[key]
if isinstance(value, numbers.Number):
if isinstance(value, (numbers.Number, np.bool_)):
arr = np.array(value)
h5_file.create_dataset(key, data=arr)
elif isinstance(value, np.ndarray):
@@ -832,7 +832,7 @@ def nested_dict_to_h5(h5_file, d):
def h5_to_nested_dict(h5_file):
"""Saves a nested dictionary to an h5 file object
"""Loads a nested dictionary from an h5 file object
Parameters
----------
@@ -842,7 +842,7 @@ def h5_to_nested_dict(h5_file):
Returns
-------
d : dict
A dictionary whose keys are all strings and whose values are numpy arrays, scalars, or python strings. Will raise an error if the data cannot be loadedinto this format
A dictionary whose keys are all strings and whose values are numpy arrays, scalars, or python strings. Will raise an error if the data cannot be loaded into this format
"""
# If a bare string is passed
@@ -884,19 +884,15 @@ def nested_dict_to_numpy(d):
Returns
-------
d_out : dict
new_dict : dict
A new dictionary with all array like objects sent to numpy
"""
new_dict = {}
for key in d.keys():
value = d[key]
if isinstance(value, numbers.Number):
new_dict[key] = value
# bools are an instance of number, but not np.bool_...
elif isinstance(value, np.bool_):
new_dict[key] = value
elif isinstance(value, np.ndarray):
if isinstance(value, (numbers.Number, np.bool_, np.ndarray)):
new_dict[key] = value
elif t.is_tensor(value):
new_dict[key] = value.cpu().numpy()
@@ -927,19 +923,15 @@ def nested_dict_to_torch(d, device=None):
Returns
-------
d_out : dict
new_dict : dict
A new dictionary with all array like objects sent to torch tensors
"""
new_dict = {}
for key in d.keys():
value = d[key]
if isinstance(value, numbers.Number):
new_dict[key] = t.as_tensor(value, device=device)
# bools are an instance of number, but not np.bool_...
elif isinstance(value, np.bool_):
new_dict[key] = t.as_tensor(value, device=device)
elif isinstance(value, np.ndarray):
if isinstance(value, (numbers.Number, np.bool_, np.ndarray)):
new_dict[key] = t.as_tensor(value, device=device)
elif t.is_tensor(value):
new_dict[key] = value.to(device=device)
tests/tools/test_data.py
+18 -24
View File
@@ -296,17 +296,15 @@ def test_nested_dict_to_h5(tmp_path, example_nested_dicts):
def check_dict_equality(truth, to_test):
for key in truth.keys():
if type(truth[key]) == type({'a': 1}):
if isinstance(truth[key], dict):
check_dict_equality(truth[key], to_test[key])
elif type(truth[key]) == type(t.as_tensor(np.array([1]))):
assert type(to_test[key]) == type(np.array([1]))
elif t.is_tensor(truth[key]):
assert isinstance(to_test[key], np.ndarray)
assert np.allclose(truth[key].numpy(), to_test[key])
elif type(truth[key]) == type(np.array([1])):
assert type(to_test[key]) == type(np.array([1]))
elif isinstance(truth[key], np.ndarray):
assert isinstance(to_test[key], np.ndarray)
assert np.allclose(truth[key], to_test[key])
elif type(truth[key]) == type(1.3) or \
type(truth[key]) == type(1) or \
type(truth[key]) == type('1'):
elif isinstance(truth[key], (float, int, str)):
assert truth[key] == to_test[key]
else:
assert 0
@@ -328,17 +326,15 @@ def test_nested_dict_to_numpy(example_nested_dicts):
def check_dict_numpyness(truth, to_test):
for key in truth.keys():
if type(truth[key]) == type({'a': 1}):
if isinstance(truth[key], dict):
check_dict_numpyness(truth[key], to_test[key])
elif type(truth[key]) == type(t.as_tensor(np.array([1]))):
assert type(to_test[key]) == type(np.array([1]))
elif t.is_tensor(truth[key]):
assert isinstance(to_test[key], np.ndarray)
assert np.allclose(truth[key].numpy(), to_test[key])
elif type(truth[key]) == type(np.array([1])):
assert type(to_test[key]) == type(np.array([1]))
elif isinstance(truth[key], np.ndarray):
assert isinstance(to_test[key], np.ndarray)
assert np.allclose(truth[key], to_test[key])
elif type(truth[key]) == type(1.3) or \
type(truth[key]) == type(1) or \
type(truth[key]) == type('1'):
elif isinstance(truth[key], (float, int, str)):
assert truth[key] == to_test[key]
else:
assert 0
@@ -352,17 +348,15 @@ def test_nested_dict_to_torch(example_nested_dicts):
def check_dict_torchiness(truth, to_test):
for key in truth.keys():
if type(truth[key]) == type({'a': 1}):
if isinstance(truth[key], dict):
check_dict_torchiness(truth[key], to_test[key])
elif type(truth[key]) == type(t.as_tensor(np.array([1]))):
assert type(to_test[key]) == type(t.as_tensor(np.array([1])))
elif t.is_tensor(truth[key]):
assert t.is_tensor(to_test[key])
assert t.allclose(truth[key], to_test[key])
elif type(truth[key]) == type(np.array([1])):
assert type(to_test[key]) == type(t.as_tensor(np.array([1])))
elif isinstance(truth[key], np.ndarray):
assert t.is_tensor(to_test[key])
assert t.allclose(t.as_tensor(truth[key]), to_test[key])
elif type(truth[key]) == type(1.3) or \
type(truth[key]) == type(1) or \
type(truth[key]) == type('1'):
elif isinstance(truth[key], (float, int, str)):
assert truth[key] == to_test[key]
else:
assert 0