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Improved parsing from HDF5 attr dict to yaml compatible dict. Now we can parse HDF5 compound attributes (structured np arrays).

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This commit is contained in:
florez_j
2024-05-28 11:27:44 +02:00
parent 41c7660be3
commit f6a46168ec
1 changed files with 25 additions and 17 deletions
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42
src/hdf5_vis.py
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@ -59,31 +59,38 @@ def display_group_hierarchy_on_a_treemap(filename: str):
#pio.write_image(fig,file_name + ".png",width=800,height=600,format='png')
#
def make_dtype_yaml_compatible(value):
try:
if isinstance(value, np.generic):
#if np.issubdtype(value.dtype, np.string_):
#value = value.astype(str)
if np.issubdtype(value.dtype, np.bytes_):
value = value.decode('utf-8')
elif np.issubdtype(value.dtype, np.unicode_):
value = str(value)
value = str(value)
elif np.issubdtype(value.dtype, np.number):
value = float(value)
else:
print('Yaml-compatible data-type was not found. Value has been set to Nan.')
print('Yaml-compatible data-type was not found. Value has been set to NaN.')
value = np.nan
elif isinstance(value, np.ndarray):
if np.issubdtype(value.dtype, np.string_) or np.issubdtype(value.dtype, np.generic):
value = [str(item) for item in value] if len(value)>1 else str(value[0]) # value.astype(str).tolist()
elif np.issubdtype(value.dtype, np.integer) :
value = [int(item) for item in value] if len(value)>1 else int(value[0]) # value.astype(int).tolist()
elif np.issubdtype(value.dtype, np.floating):
value = [float(item) for item in value] if len(value)>1 else float(value[0]) # value.astype(float).tolist()
elif isinstance(value, np.ndarray):
# Handling structured array types (with fields)
if value.dtype.names:
value = {field: make_dtype_yaml_compatible(value[field]) for field in value.dtype.names}
else:
# Handling regular array NumPy types
if np.issubdtype(value.dtype, np.bytes_):
value = [item.decode('utf-8') for item in value] if len(value) > 1 else value[0].decode('utf-8')
elif np.issubdtype(value.dtype, np.unicode_):
value = [str(item) for item in value] if len(value) > 1 else str(value[0])
elif np.issubdtype(value.dtype, np.integer):
value = [int(item) for item in value] if len(value) > 1 else int(value[0])
elif np.issubdtype(value.dtype, np.floating):
value = [float(item) for item in value] if len(value) > 1 else float(value[0])
else:
print('Yaml-compatible data-type was not found. Value has been set to NaN.')
value = np.nan
except:
print('Yaml-compatible data-type was not found. Value has been set to Nan.')
except Exception as e:
print(f'Error converting value: {e}. Value has been set to NaN.')
value = np.nan
return value
@ -104,8 +111,9 @@ def construct_attributes_dict(attrs_obj):
if not key in ['file_list','filtered_file_list']:
if is_structured_array(value):
for subattr in value.dtype.names:
attr_dict[key][subattr] = make_dtype_yaml_compatible(value[subattr])
#for subattr in value.dtype.names:
#attr_dict[key][subattr] = make_dtype_yaml_compatible(value[subattr])
attr_dict[key] = make_dtype_yaml_compatible(value)
else:
value = make_dtype_yaml_compatible(value)
attr_dict[key] = {"rename_as" : key,
@ -156,7 +164,7 @@ def print_metadata(name, obj, folder_depth, yaml_dict):
yaml_dict[obj.name] = group_dict
elif isinstance(obj, h5py.Dataset):
parent_name = '/'.join(name_to_list[:-1])
yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes":dict(obj.attrs)}
yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes": construct_attributes_dict(obj.attrs)}
#print(yaml.dump(group_dict,sort_keys=False))
#elif len(obj.name.split('/')) == 3: