def insert_multicategory_histogram(plt_axis: Axes,
stat_dicts: List[Dict[str, Any]],
key_order: List[str] = None,
bar_titles: List[str] = None,
title: str = None) -> Axes:
"""
Given a final log dictionaries creates a histogram on the given Axes object.
:param plt_axis:
A matplotlib.axes.Axes object representing the matplotlib axes you wish
to draw on.
:param stat_dicts:
A List[Dict[str, Any]] containing all the final log dictionaries you
wish to draw a histogram for.
:param key_order:
(Optional) A List[str] containing the keys of the final log dictionary
you wish to draw form. Defaults to None (draws nothing).
:param bar_titles:
(Optional) A List[str] containing the histogram x-axis labels. Defaults
to None (takes key_order as reference).
:param title:
(Optional) A str representing the title of the subplot. Defaults to
None (no title).
:return:
A matplotlib.axes.Axes object (same as the one passed). Useful for
chaining calls.
"""
if key_order is None:
key_order = list(stat_dicts[0].keys())
if bar_titles is None:
bar_titles = list(key_order)
width = (1 - 0.2) / len(stat_dicts)
offsets = [
-(width * (len(stat_dicts) // 2)) + (x * width)
for x in range(len(stat_dicts))
]
for i, key in enumerate(key_order):
plt_axis.bar(x=[i + x for x in offsets],
height=[stat_dict[key] for stat_dict in stat_dicts],
width=width - 0.01,
align="center")
plt_axis.set_xticks(range(len(bar_titles)))
plt_axis.set_xticklabels(bar_titles, fontsize=16)
if title is not None:
plt_axis.set_title(title, fontsize=16)
plt_axis.autoscale(tight=True)
return plt_axis
def imscatter(
x: np.ndarray,
y: np.ndarray,
ax: Axes,
imageData: torch.Tensor,
unnormalize_fn: Optional[Callable] = None,
zoom: int = 1,
) -> NoReturn:
"""Scatter plot with images instead of points on ax."""
images = []
for i in range(len(x)):
x0, y0 = x[i], y[i]
# Convert to image
img = imageData[i]
if unnormalize_fn is not None:
img = unnormalize_fn(img)
img *= 255.0
img = img.permute([1, 2, 0]).numpy().astype(np.uint8)
image = OffsetImage(img, zoom=zoom)
ab = AnnotationBbox(image, (x0, y0), xycoords="data", frameon=False)
images.append(ax.add_artist(ab))
ax.update_datalim(np.column_stack([x, y]))
ax.autoscale()
