def read(self, obj, frame=0):
if struct.isstruct(obj):
obj = _transform_for_writing(obj)
names = struct.flatten(obj)
if not np.all([isinstance(n, six.string_types) for n in names]):
names = struct.names(obj)
data = struct.map(lambda name: self.read_array(self._filename(name), frame), names)
return data
else:
return self.read_array('unnamed', frame)
def load_state(state):
if isinstance(state, StateProxy):
state = state.state
assert isinstance(state, State)
state = _transform_for_writing(state)
names = struct.names(state)
with _unsafe():
placeholders = placeholder(state.shape)
state_in = struct.map(
lambda x: x,
placeholders) # validates fields, splits staggered tensors
return state_in, {placeholders: names}
def write(self, obj, names=None, frame=0):
if struct.isstruct(obj):
obj = _transform_for_writing(obj)
if names is None:
names = struct.names(obj)
values = struct.flatten(obj)
names = struct.flatten(names)
names = [_slugify_filename(name) for name in names]
self.write_sim_frame(values, names, frame)
else:
name = str(names) if names is not None else 'unnamed'
self.write_sim_frame([obj], [name], frame)
def build_graph_input(obj, input_type='placeholder', frames=None):
"""
Create placeholders for tensors in the supplied state.
:param obj: struct or StateProxy
:param input_type: 'placeholder' or 'dataset_handle'
:param frames: Number of input frames. If not None, returns a list of input structs.
:return:
1. Valid state containing or derived from created placeholders or dataset handle
2. dict mapping from placeholders to their default names (using struct.names)
"""
if isinstance(obj, StateProxy):
obj = obj.state
assert struct.isstruct(obj)
# --- Shapes and names ---
writable_obj = _transform_for_writing(obj)
shape = _writing_staticshape(obj)
names = struct.names(writable_obj)
if input_type == 'placeholder':
if frames is not None: raise NotImplementedError()
with _unsafe():
placeholders = placeholder(shape)
graph_in = struct.map(
lambda x: x,
placeholders) # validates fields, splits staggered tensors
return graph_in, {placeholders: names}
elif input_type == 'dataset_handle':
with _unsafe():
dtypes = struct.dtype(writable_obj)
dataset_nodes, iterator_handle = dataset_handle(shape,
dtypes,
frames=frames)
graph_in = struct.map(
lambda x: x,
dataset_nodes) # validates fields, splits staggered tensors
shapes = struct.flatten(struct.staticshape(dataset_nodes),
leaf_condition=is_static_shape)
dtypes = struct.flatten(struct.dtype(dataset_nodes))
return graph_in, {
'names': struct.flatten(names),
'iterator_handle': iterator_handle,
'shapes': shapes,
'dtypes': dtypes,
'frames': frames
}
else:
raise ValueError(input_type)
def write_sim_frame(directory: math.Tensor,
fields: Field or tuple or list or dict or struct.Struct,
frame: int,
names: str or tuple or list or struct.Struct
or None = None):
"""
Write a Field or structure of Fields to files.
The filenames are created from the provided names and the frame index in accordance with the
scene format specification at https://tum-pbs.github.io/PhiFlow/Scene_Format_Specification.html .
This method can be used in batch mode.
Batch mode is active if a list of directories is given instead of a single directory.
Then, all fields are unstacked along the batch_dim dimension and matched with the directories list.
Args:
directory: directory name or list of directories.
If a list is provided, all fields are unstacked along batch_dim and matched with their respective directory.
fields: single field or structure of Fields to save.
frame: Number < 1000000, typically time step index.
names: (Optional) Structure matching fields, holding the filename for each respective Field.
If not provided, names are automatically generated based on the structure of fields.
"""
if names is None:
names = struct.names(fields)
if frame > 1000000:
warnings.warn(
f"frame too large: {frame}. Data will be saved but filename might cause trouble in the future."
)
def single_write(f, name):
name = _slugify_filename(name)
files = math.map(lambda dir_: _filename(dir_, name, frame), directory)
if isinstance(f, SampledField):
write(f, files)
elif isinstance(f, math.Tensor):
raise NotImplementedError()
elif isinstance(f, Field):
raise ValueError(
"write_sim_frame: only SampledField instances are saved. Resample other Fields before saving them."
)
else:
raise ValueError(
f"write_sim_frame: only SampledField instances can be saved but got {f}"
)
struct.foreach(single_write, fields, names)