def at(directory: str or tuple or list or math.Tensor or 'Scene', id: int or math.Tensor or None = None) -> 'Scene':
"""
Creates a `Scene` for an existing directory.
See Also:
`Scene.create()`, `Scene.list()`.
Args:
directory: Either directory containing scene folder if `id` is given, or scene path if `id=None`.
id: (Optional) Scene `id`, will be determined from `directory` if not specified.
Returns:
`Scene` object for existing scene.
"""
if isinstance(directory, Scene):
assert id is None, f"Got id={id} but directory is already a Scene."
return directory
if isinstance(directory, (tuple, list)):
directory = math.wrap(directory, batch('scenes'))
directory = math.map(lambda d: expanduser(d), math.wrap(directory))
if id is None:
paths = directory
else:
id = math.wrap(id)
paths = math.map(lambda d, i: join(d, f"sim_{i:06d}"), directory, id)
# test all exist
for path in math.flatten(paths):
if not isdir(path):
raise IOError(f"There is no scene at '{path}'")
return Scene(paths)
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}")
def create(parent_directory: str,
shape: math.Shape = math.EMPTY_SHAPE,
copy_calling_script=True,
**dimensions) -> 'Scene':
"""
Creates a new `Scene` or a batch of new scenes inside `parent_directory`.
See Also:
`Scene.at()`, `Scene.list()`.
Args:
parent_directory: Directory to hold the new `Scene`. If it doesn't exist, it will be created.
shape: Determines number of scenes to create. Multiple scenes will be represented by a `Scene` with `is_batch=True`.
copy_calling_script: Whether to copy the Python file that invoked this method into the `src` folder of all created scenes.
See `Scene.copy_calling_script()`.
dimensions: Additional batch dimensions
Returns:
Single `Scene` object representing the new scene(s).
"""
shape = (shape & math.shape(**dimensions)).to_batch()
parent_directory = expanduser(parent_directory)
abs_dir = abspath(parent_directory)
if not isdir(abs_dir):
os.makedirs(abs_dir)
next_id = 0
else:
indices = [
int(name[4:]) for name in os.listdir(abs_dir)
if name.startswith("sim_")
]
next_id = max([-1] + indices) + 1
ids = math.wrap(tuple(range(next_id, next_id +
shape.volume))).vector.split(shape)
paths = math.map(lambda id_: join(parent_directory, f"sim_{id_:06d}"),
ids)
scene = Scene(paths)
scene.mkdir()
if copy_calling_script:
try:
scene.copy_calling_script()
except IOError as err:
warnings.warn(
f"Failed to copy calling script to scene during Scene.create(): {err}"
)
return scene
def subpath(self, name: str, create: bool = False) -> str or tuple:
"""
Resolves the relative path `name` with this `Scene` as the root folder.
Args:
name: Relative path with this `Scene` as the root folder.
create: Whether to create a directory of that name.
Returns:
Relative path including the path to this `Scene`.
In batch mode, returns a `tuple`, else a `str`.
"""
def single_subpath(path):
path = join(path, name)
if create and not isdir(path):
os.mkdir(path)
return path
result = math.map(single_subpath, self._paths)
if result.rank == 0:
return result.native()
else:
return result
def shape_type(self) -> Tensor:
return math.map(lambda s: f"rot{s}", self._geometry.shape_type)
def single_read(name):
name = _slugify_filename(name)
files = math.map(lambda dir_: _filename(dir_, name, frame), directory)
return read(files, convert_to_backend=convert_to_backend)
def plot_scalars(scene: str or tuple or list or Scene or math.Tensor,
names: str or tuple or list or math.Tensor = None,
reduce: str or tuple or list or math.Shape = 'names',
down='',
smooth=1,
smooth_alpha=0.2,
smooth_linewidth=2.,
size=(8, 6),
transform: Callable = None,
tight_layout=True,
grid: str or dict = 'y',
log_scale='',
legend='upper right',
x='steps',
xlim=None,
ylim=None,
titles=True,
labels: math.Tensor = None,
xlabel: str = None,
ylabel: str = None,
colors: math.Tensor = 'default'):
"""
Args:
scene: `str` or `Tensor`. Scene paths containing the data to plot.
names: Data files to plot for each scene. The file must be located inside the scene directory and have the name `log_<name>.txt`.
reduce: Tensor dimensions along which all curves are plotted in the same diagram.
down: Tensor dimensions along which diagrams are ordered top-to-bottom instead of left-to-right.
smooth: `int` or `Tensor`. Number of data points to average, -1 for all.
smooth_alpha: Opacity of the non-smoothed curves under the smoothed curves.
smooth_linewidth: Line width of the smoothed curves.
size: Figure size in inches.
transform: Function `T(x,y) -> (x,y)` transforming the curves.
tight_layout:
grid:
log_scale:
legend:
x:
xlim:
ylim:
titles:
labels:
xlabel:
ylabel:
colors: Line colors as `str`, `int` or `Tensor`. Integers are interpreted as indices of the default color list.
Returns:
MatPlotLib [figure](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure)
"""
scene = Scene.at(scene)
additional_reduce = ()
if names is None:
first_path = next(iter(math.flatten(scene.paths)))
names = [_str(n) for n in os.listdir(first_path)]
names = [n[4:-4] for n in names if n.endswith('.txt') and n.startswith('log_')]
names = math.wrap(names, batch('names'))
additional_reduce = ['names']
elif isinstance(names, str):
names = math.wrap(names)
elif isinstance(names, (tuple, list)):
names = math.wrap(names, batch('names'))
else:
assert isinstance(names, math.Tensor), f"Invalid argument 'names': {type(names)}"
if not isinstance(colors, math.Tensor):
colors = math.wrap(colors)
if xlabel is None:
xlabel = 'Iterations' if x == 'steps' else 'Time (s)'
shape = (scene.shape & names.shape)
batches = shape.without(reduce).without(additional_reduce)
cycle = list(plt.rcParams['axes.prop_cycle'].by_key()['color'])
fig, axes = plt.subplots(batches.only(down).volume, batches.without(down).volume, figsize=size)
axes = axes if isinstance(axes, numpy.ndarray) else [axes]
for b, axis in zip(batches.meshgrid(), axes):
assert isinstance(axis, plt.Axes)
names_equal = names[b].rank == 0
paths_equal = scene.paths[b].rank == 0
if titles is not None and titles is not False:
if isinstance(titles, str):
axis.set_title(titles)
elif names_equal:
axis.set_title(display_name(str(names[b])))
elif paths_equal:
axis.set_title(os.path.basename(scene.paths[b].native()))
if labels is not None:
curve_labels = labels
elif names_equal:
curve_labels = math.map(os.path.basename, scene.paths[b])
elif paths_equal:
curve_labels = names[b]
else:
curve_labels = math.map(lambda p, n: f"{os.path.basename(p)} - {n}", scene.paths[b], names[b])
def single_plot(name, path, label, i, color, smooth):
logging.debug(f"Reading {os.path.join(path, f'log_{name}.txt')}")
curve = numpy.loadtxt(os.path.join(path, f"log_{name}.txt"))
if curve.ndim == 2:
x_values, values, *_ = curve.T
else:
values = curve
x_values = np.arange(len(values))
if x == 'steps':
pass
else:
assert x == 'time', f"x must be 'steps' or 'time' but got {x}"
logging.debug(f"Reading {os.path.join(path, 'log_step_time.txt')}")
_, x_values, *_ = numpy.loadtxt(os.path.join(path, "log_step_time.txt")).T
values = values[:len(x_values)]
x_values = np.cumsum(x_values[:len(values)])
if transform:
x_values, values = transform(np.stack([x_values, values]))
if color == 'default':
color = cycle[i]
try:
color = int(color)
except ValueError:
pass
if isinstance(color, Number):
color = cycle[int(color)]
logging.debug(f"Plotting curve {label}")
axis.plot(x_values, values, color=color, alpha=smooth_alpha, linewidth=1)
curve = np.stack([x_values, values], -1)
axis.plot(*smooth_uniform_curve(curve, smooth), color=color, linewidth=smooth_linewidth, label=label)
if grid:
if isinstance(grid, dict):
axis.grid(**grid)
else:
grid_axis = 'both' if 'x' in grid and 'y' in grid else grid
axis.grid(which='both', axis=grid_axis, linestyle='--', linewidth=size[1] * 0.3)
if 'x' in log_scale:
axis.set_xscale('log')
if 'y' in log_scale:
axis.set_yscale('log')
if xlim:
axis.set_xlim(xlim)
if ylim:
axis.set_ylim(ylim)
if xlabel:
axis.set_xlabel(xlabel)
if ylabel:
axis.set_ylabel(ylabel)
return name
math.map(single_plot, names[b], scene.paths[b], curve_labels, math.range_tensor(shape.after_gather(b)), colors, smooth)
if legend:
axis.legend(loc=legend)
# Final touches
if tight_layout:
plt.tight_layout()
return fig
