def _get_schema_for_property(schema, attr):
subschema = schema.get('properties', {}).get(attr, None)
if subschema is not None:
return subschema
for combiner in ['allOf', 'anyOf']:
for subschema in schema.get(combiner, []):
subsubschema = _get_schema_for_property(subschema, attr)
if subsubschema != {}:
return subsubschema
return {}
def _get_schema_for_property(schema, attr):
subschema = schema.get('properties', {}).get(attr, None)
if subschema is not None:
return subschema
for combiner in ['allOf', 'anyOf']:
for subschema in schema.get(combiner, []):
subsubschema = _get_schema_for_property(subschema, attr)
if subsubschema != {}:
return subsubschema
return {}
def _make_default(attr, schema, ctx):
if 'max_ndim' in schema or 'ndim' in schema or 'datatype' in schema:
return _make_default_array(attr, schema, ctx)
elif 'default' in schema:
return schema['default']
elif (schema.get('type') == 'object' or schema.get('allOf')
or schema.get('anyOf')):
return {}
elif schema.get('type') == 'array':
return []
return copy.deepcopy(schema.get('default'))
def _make_default(attr, schema, ctx):
if 'max_ndim' in schema or 'ndim' in schema or 'datatype' in schema:
return _make_default_array(attr, schema, ctx)
elif 'default' in schema:
return schema['default']
elif (schema.get('type') == 'object' or
schema.get('allOf') or
schema.get('anyOf')):
return {}
elif schema.get('type') == 'array':
return []
return copy.deepcopy(schema.get('default'))
def _get_schema_for_index(schema, i):
items = schema.get('items', {})
if isinstance(items, list):
if i >= len(items):
return {}
else:
return items[i]
else:
return items
def _get_schema_for_index(schema, i):
items = schema.get('items', {})
if isinstance(items, list):
if i >= len(items):
return {}
else:
return items[i]
else:
return items
def _make_default_array(attr, schema, ctx):
dtype = schema.get('datatype')
if dtype is not None:
dtype = ndarray.asdf_datatype_to_numpy_dtype(dtype)
ndim = schema.get('ndim', schema.get('max_ndim'))
default = schema.get('default', None)
primary_array_name = ctx.get_primary_array_name()
if attr == primary_array_name:
if ctx.shape is not None:
shape = ctx.shape
elif ndim is not None:
shape = tuple([0] * ndim)
else:
shape = (0, )
else:
if dtype.names is not None:
if ndim is None:
shape = (0, )
else:
shape = tuple([0] * ndim)
default = None
else:
has_primary_array_shape = False
if primary_array_name is not None:
primary_array = getattr(ctx, primary_array_name, None)
has_primary_array_shape = primary_array is not None
if has_primary_array_shape:
if ndim is None:
shape = primary_array.shape
else:
shape = primary_array.shape[-ndim:]
elif ndim is None:
shape = (0, )
else:
shape = tuple([0] * ndim)
array = np.empty(shape, dtype=dtype)
if default is not None:
array[...] = default
return array
def _make_default_array(attr, schema, ctx):
dtype = schema.get('datatype')
if dtype is not None:
dtype = ndarray.asdf_datatype_to_numpy_dtype(dtype)
ndim = schema.get('ndim', schema.get('max_ndim'))
default = schema.get('default', None)
primary_array_name = ctx.get_primary_array_name()
if attr == primary_array_name:
if ctx.shape is not None:
shape = ctx.shape
elif ndim is not None:
shape = tuple([0] * ndim)
else:
shape = (0,)
else:
if dtype.names is not None:
if ndim is None:
shape = (0,)
else:
shape = tuple([0] * ndim)
default = None
else:
has_primary_array_shape = False
if primary_array_name is not None:
primary_array = getattr(ctx, primary_array_name, None)
has_primary_array_shape = primary_array is not None
if has_primary_array_shape:
if ndim is None:
shape = primary_array.shape
else:
shape = primary_array.shape[-ndim:]
elif ndim is None:
shape = (0,)
else:
shape = tuple([0] * ndim)
array = np.empty(shape, dtype=dtype)
if default is not None:
array[...] = default
return array
def _cast(val, schema):
val = _unmake_node(val)
if val is not None:
if 'datatype' in schema:
val = util.gentle_asarray(
val, ndarray.asdf_datatype_to_numpy_dtype(schema['datatype']))
if 'ndim' in schema and len(val.shape) != schema['ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected {0}, got {1}".
format(schema['ndim'], len(val.shape)))
if 'max_ndim' in schema and len(val.shape) > schema['max_ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected <= {0}, got {1}"
.format(schema['max_ndim'], len(val.shape)))
tag = schema.get('tag')
if tag is not None:
val = tagged.tag_object(tag, val)
return val
def _cast(val, schema):
val = _unmake_node(val)
if val is not None:
if 'datatype' in schema:
val = util.gentle_asarray(
val, ndarray.asdf_datatype_to_numpy_dtype(schema['datatype']))
if 'ndim' in schema and len(val.shape) != schema['ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected {0}, got {1}".format(
schema['ndim'], len(val.shape)))
if 'max_ndim' in schema and len(val.shape) > schema['max_ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected <= {0}, got {1}".format(
schema['max_ndim'], len(val.shape)))
tag = schema.get('tag')
if tag is not None:
val = tagged.tag_object(tag, val)
return val
