def plot_collection(
collection: DatasetCollection,
order: Optional[list] = list(),
styles: Optional[dict] = dict(),
interactive: bool = True,
n: int = 0,
) -> Figure:
# check if collection is not empty
if not collection.store:
raise ValueError("Collection is empty.")
# check if time and iteration arrays are equal
for check in ["iteration", "time"]:
arr = [
np.array(dataset.axes[check])
for dataset in collection.store.values()
]
if not all(np.array_equal(arr[0], i) for i in arr):
raise ValueError(
f"Attribute `{check}` is not the same for all datasets " +
"in the collection.")
f_a = []
for dataset in collection.store.values():
i_style = styles[
dataset.name] if dataset.name in styles.keys() else None
p_plan = PlotPlan(
dataset=dataset,
style=filter_style(dataset.plot_type(), i_style),
)
a_plan = AxesPlan(axes=None,
plots=[p_plan],
style=filter_style(Axes, i_style))
f_a.append(a_plan)
f_plan = FigurePlan(
fig=None,
axes=f_a,
style=filter_style(Figure,
styles["fig"] if "fig" in styles.keys() else None),
)
if len(dataset) > 1 and inside_notebook() and interactive:
f_plan.build_interactive(n)
else:
return f_plan.build()
def plot_particle_dataset(
dataset: ParticleDataset,
fig: Optional[Figure] = None,
axes: Optional[Axes] = None,
style: dict = dict(),
interactive: bool = True,
n: int = 0,
):
"""Plots a single/multiple iteration :class:`nata.containers.ParticleDataset`\
using a :class:`nata.plots.types.ScatterPlot`.
Parameters
----------
fig: :class:`nata.plots.Figure`, optional
If provided, the plot is drawn on ``fig``. The plot is drawn on
``axes`` if it is a child axes of ``fig``, otherwise a new axes
is created on ``fig``. If ``fig`` is not provided, a new
:class:`nata.plots.Figure` is created.
axes: :class:`nata.plots.Axes`, optional
If provided, the plot is drawn on ``axes``, which must be an axes
of ``fig``. If ``axes`` is not provided or is provided without a
corresponding ``fig``, a new :class:`nata.plots.Axes` is created in
a new :class:`nata.plots.Figure`.
style: ``dict``, optional
Dictionary that takes a mix of style properties of
:class:`nata.plots.Figure`, :class:`nata.plots.Axes` and any plot
type (see :class:`nata.plots.types.ScatterPlot`).
interactive: ``bool``, optional
Controls wether interactive widgets should be shown with the plot
to allow for temporal navigation. Only applicable if ``dataset``
has multiple iterations.
n: ``int``, optional
Selects the index of the iteration to be shown initially. Only
applicable if ``dataset`` has multiple iterations, .
Returns
------
:class:`nata.plots.Figure` or ``None``:
Figure with plot built based on ``dataset``. Interactive widgets
are shown with the figure if ``dataset`` has multiple iterations,
in which case this method returns ``None``.
Examples
--------
To get a plot with default style properties in a new figure, simply
call the ``.plot()`` method. The first two quantities in the dataset
``quantities`` dictionary will be represented in the horizontal and
vertical plot axes, respectively. If a third quantity is available, it
will be represented in colors.
>>> from nata.containers import ParticleDataset
>>> import numpy as np
>>> arr = np.arange(30).reshape(1,10,3)
>>> ds = ParticleDataset("path/to/file")
>>> fig = ds.plot()
The list of quantities in the dataset can be filtered with the
:meth:`nata.containers.ParticleDataset.filter` method.
>>> fig = ds.filter(quantities=["x1", "p1", "ene"]).plot()
"""
p_plan = PlotPlan(
dataset=dataset,
style=filter_style(dataset.plot_type(), style),
)
a_plan = AxesPlan(axes=axes,
plots=[p_plan],
style=filter_style(Axes, style))
f_plan = FigurePlan(fig=fig,
axes=[a_plan],
style=filter_style(Figure, style))
if len(dataset) > 1:
if inside_notebook():
if interactive:
f_plan.build_interactive(n)
else:
return f_plan[n].build()
else:
# TODO: remove last line from warn
warn(f"Plotting only iteration with index n={str(n)}." +
" Interactive plots of multiple iteration datasets are not" +
" supported outside notebook environments.")
return f_plan[n].build()
else:
return f_plan.build()
def plot_grid_dataset(
dataset: GridDataset,
fig: Optional[Figure] = None,
axes: Optional[Axes] = None,
style: Optional[dict] = {},
interactive: Optional[bool] = True,
n: Optional[int] = 0,
) -> Union[Figure, None]:
"""Plots a single/multiple iteration :class:`nata.containers.GridDataset`\
using a :class:`nata.plots.types.LinePlot` or\
:class:`nata.plots.types.ColorPlot` if the dataset is one- or\
two-dimensional, respectively.
Parameters
----------
fig: :class:`nata.plots.Figure`, optional
If provided, the plot is drawn on ``fig``. The plot is drawn on
``axes`` if it is a child axes of ``fig``, otherwise a new axes
is created on ``fig``. If ``fig`` is not provided, a new
:class:`nata.plots.Figure` is created.
axes: :class:`nata.plots.Axes`, optional
If provided, the plot is drawn on ``axes``, which must be an axes
of ``fig``. If ``axes`` is not provided or is provided without a
corresponding ``fig``, a new :class:`nata.plots.Axes` is created in
a new :class:`nata.plots.Figure`.
style: ``dict``, optional
Dictionary that takes a mix of style properties of
:class:`nata.plots.Figure`, :class:`nata.plots.Axes` and any plot
type (see :class:`nata.plots.types.LinePlot` or
:class:`nata.plots.types.ColorPlot`).
interactive: ``bool``, optional
Controls wether interactive widgets should be shown with the plot
to allow for temporal navigation. Only applicable if ``dataset``
has multiple iterations.
n: ``int``, optional
Selects the index of the iteration to be shown initially. Only
applicable if ``dataset`` has multiple iterations, .
Returns
------
:class:`nata.plots.Figure` or ``None``:
Figure with plot built based on ``dataset``. Interactive widgets
are shown with the figure if ``dataset`` has multiple iterations,
in which case this method returns ``None``.
Examples
--------
To get a plot with default style properties in a new figure, simply
call the ``.plot()`` method of the dataset.
>>> from nata.containers import GridDataset
>>> import numpy as np
>>> arr = np.arange(10)
>>> ds = GridDataset.from_array(arr)
>>> fig = ds.plot()
In case a :class:`nata.plots.Figure` is returned by the method, it can
be shown by calling the :func:`nata.plots.Figure.show` method.
>>> fig.show()
To draw a new plot on ``fig``, we can pass it as an argument to the
``.plot()`` method. If ``axes`` is provided, the new plot is drawn on
the selected axes.
>>> ds2 = GridDataset.from_array(arr**2)
>>> fig = ds2.plot(fig=fig, axes=fig.axes[0])
"""
p_plan = PlotPlan(dataset=dataset,
style=filter_style(dataset.plot_type(), style))
a_plan = AxesPlan(axes=axes,
plots=[p_plan],
style=filter_style(Axes, style))
f_plan = FigurePlan(fig=fig,
axes=[a_plan],
style=filter_style(Figure, style))
if len(dataset) > 1:
if inside_notebook():
if interactive:
f_plan.build_interactive(n)
else:
return f_plan[n].build()
else:
# TODO: remove last line from warn
warn(f"Plotting only iteration with index n={str(n)}." +
" Interactive plots of multiple iteration datasets are not" +
" supported outside notebook environments.")
return f_plan[n].build()
else:
return f_plan.build()