def line(h1: Union[Histogram1D, "HistogramCollection"],
ax: Axes,
*,
errors: bool = False,
**kwargs):
"""Line plot of 1D histogram."""
show_stats = kwargs.pop("show_stats", False)
show_values = kwargs.pop("show_values", False)
density = kwargs.pop("density", False)
cumulative = kwargs.pop("cumulative", False)
value_format = kwargs.pop("value_format", None)
text_kwargs = pop_kwargs_with_prefix("text_", kwargs)
kwargs["label"] = kwargs.get("label", h1.name)
data = get_data(h1, cumulative=cumulative, density=density)
_apply_xy_lims(ax, h1, data, kwargs)
_add_ticks(ax, h1, kwargs)
_add_labels(ax, h1, kwargs)
if errors:
err_data = get_err_data(h1, cumulative=cumulative, density=density)
ax.errorbar(h1.bin_centers,
data,
yerr=err_data,
fmt=kwargs.pop("fmt", "-"),
ecolor=kwargs.pop("ecolor", "black"),
**kwargs)
else:
ax.plot(h1.bin_centers, data, **kwargs)
if show_stats:
_add_stats_box(h1, ax, stats=show_stats)
if show_values:
_add_values(ax, h1, data, value_format=value_format, **text_kwargs)
def polar_map(hist: Histogram2D,
ax: Axes,
*,
show_zero: bool = True,
show_colorbar: bool = True,
**kwargs):
"""Polar map of polar histograms.
Similar to map, but supports less parameters."""
data = get_data(hist,
cumulative=False,
flatten=True,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
rpos, phipos = (arr.flatten() for arr in hist.get_bin_left_edges())
dr, dphi = (arr.flatten() for arr in hist.get_bin_widths())
rmax, _ = (arr.flatten() for arr in hist.get_bin_right_edges())
bar_args = {}
if "zorder" in kwargs:
bar_args["zorder"] = kwargs.pop("zorder")
alphas = _get_alpha_data(cmap_data, kwargs)
if np.isscalar(alphas):
alphas = np.ones_like(data) * alphas
for i in range(len(rpos)):
if data[i] > 0 or show_zero:
bin_color = colors[i]
# TODO: align = "edge"
bars = ax.bar(phipos[i],
dr[i],
width=dphi[i],
bottom=rpos[i],
align='edge',
color=bin_color,
edgecolor=kwargs.get("grid_color", cmap(0.5)),
lw=kwargs.get("lw", 0.5),
alpha=alphas[i],
**bar_args)
ax.set_rmax(rmax.max())
if show_colorbar:
_add_colorbar(ax, cmap, cmap_data, norm)
def cylinder_map(hist: Union[Histogram2D, CylinderSurfaceHistogram],
ax: Axes3D,
*,
show_zero: bool = True,
**kwargs):
"""Heat map plotted on the surface of a cylinder."""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
if hasattr(hist, "radius"):
r = kwargs.pop("radius", hist.radius)
else:
r = kwargs.pop("radius", 1)
xs = r * np.outer(np.cos(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
ys = r * np.outer(np.sin(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
zs = np.outer(np.ones(hist.shape[0] + 1), hist.numpy_bins[1])
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b")
ax.set_xlim(-r * 1.1, r * 1.1)
ax.set_ylim(-r * 1.1, r * 1.1)
ax.set_zlim(zs.min(), zs.max())
def globe_map(hist: Union[Histogram2D, DirectionalHistogram],
ax: Axes3D,
*,
show_zero: bool = True,
**kwargs):
"""Heat map plotted on the surface of a sphere."""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
lw = kwargs.pop("lw", 1)
r = 1
xs = r * np.outer(np.sin(hist.numpy_bins[0]), np.cos(hist.numpy_bins[1]))
ys = r * np.outer(np.sin(hist.numpy_bins[0]), np.sin(hist.numpy_bins[1]))
zs = r * np.outer(np.cos(hist.numpy_bins[0]), np.ones(hist.shape[1] + 1))
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
col.set_edgecolor("black")
col.set_linewidth(lw)
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b")
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-1.1, 1.1)
ax.set_zlim(-1.1, 1.1)
return ax
def image(h2: Histogram2D,
ax: Axes,
*,
show_colorbar: bool = True,
interpolation: str = "nearest",
**kwargs):
"""Plot of 2D histograms based on pixmaps.
Similar to map, but it:
- has fewer options
- is much more effective (enables thousands)
- does not support irregular bins
Parameters
----------
interpolation: interpolation parameter passed to imshow, default: "nearest" (creates rectangles)
"""
cmap = _get_cmap(kwargs) # h2 as well?
data = get_data(h2, cumulative=False, density=kwargs.pop("density", False))
norm, cmap_data = _get_cmap_data(data, kwargs)
# zorder = kwargs.pop("zorder", None)
for binning in h2._binnings:
if not binning.is_regular():
raise RuntimeError(
"Histograms with irregular bins cannot be plotted using image method."
)
kwargs["interpolation"] = interpolation
if kwargs.get("xscale") == "log" or kwargs.get("yscale") == "log":
raise RuntimeError("Cannot use logarithmic axes with image plots.")
_apply_xy_lims(ax, h2, data=data, kwargs=kwargs)
_add_labels(ax, h2, kwargs)
ax.imshow(data.T[::-1, :],
cmap=cmap,
norm=norm,
extent=(h2.bins[0][0, 0], h2.bins[0][-1, 1], h2.bins[1][0, 0],
h2.bins[1][-1, 1]),
aspect="auto",
**kwargs)
if show_colorbar:
_add_colorbar(ax, cmap, cmap_data, norm)
def bar3d(h2: Histogram2D, ax: Axes3D, **kwargs):
"""Plot of 2D histograms as 3D boxes."""
density = kwargs.pop("density", False)
data = get_data(h2, cumulative=False, flatten=True, density=density)
if "cmap" in kwargs:
cmap = _get_cmap(kwargs)
_, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
else:
colors = kwargs.pop("color", kwargs.pop("c", "blue"))
xpos, ypos = (arr.flatten() for arr in h2.get_bin_centers())
zpos = np.zeros_like(ypos)
dx, dy = (arr.flatten() for arr in h2.get_bin_widths())
_add_labels(ax, h2, kwargs)
ax.bar3d(xpos, ypos, zpos, dx, dy, data, color=colors, **kwargs)
ax.set_zlabel("density" if density else "frequency")
def bar(h1: Histogram1D, ax: Axes, *, errors: bool = False, **kwargs):
"""Bar plot of 1D histograms."""
show_stats = kwargs.pop("show_stats", False)
show_values = kwargs.pop("show_values", False)
value_format = kwargs.pop("value_format", None)
density = kwargs.pop("density", False)
cumulative = kwargs.pop("cumulative", False)
label = kwargs.pop("label", h1.name)
lw = kwargs.pop("linewidth", kwargs.pop("lw", 0.5))
text_kwargs = pop_kwargs_with_prefix("text_", kwargs)
data = get_data(h1, cumulative=cumulative, density=density)
if "cmap" in kwargs:
cmap = _get_cmap(kwargs)
_, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
else:
colors = kwargs.pop("color", kwargs.pop("c", None))
_apply_xy_lims(ax, h1, data, kwargs)
_add_ticks(ax, h1, kwargs)
if errors:
err_data = get_err_data(h1, cumulative=cumulative, density=density)
kwargs["yerr"] = err_data
if "ecolor" not in kwargs:
kwargs["ecolor"] = "black"
_add_labels(ax, h1, kwargs)
ax.bar(h1.bin_left_edges,
data,
h1.bin_widths,
align="edge",
label=label,
color=colors,
linewidth=lw,
**kwargs)
if show_values:
_add_values(ax, h1, data, value_format=value_format, **text_kwargs)
if show_stats:
_add_stats_box(h1, ax, stats=show_stats)
def fill(h1: Histogram1D, ax: Axes, **kwargs):
"""Fill plot of 1D histogram."""
show_stats = kwargs.pop("show_stats", False)
# show_values = kwargs.pop("show_values", False)
density = kwargs.pop("density", False)
cumulative = kwargs.pop("cumulative", False)
kwargs["label"] = kwargs.get("label", h1.name)
data = get_data(h1, cumulative=cumulative, density=density)
_apply_xy_lims(ax, h1, data, kwargs)
_add_ticks(ax, h1, kwargs)
_add_labels(ax, h1, kwargs)
ax.fill_between(h1.bin_centers, 0, data, **kwargs)
if show_stats:
_add_stats_box(h1, ax, stats=show_stats)
# if show_values:
# _add_values(ax, h1, data)
return ax
def step(h1: Histogram1D, ax: Axes, **kwargs):
"""Step line-plot of 1D histogram."""
show_stats = kwargs.pop("show_stats", False)
show_values = kwargs.pop("show_values", False)
density = kwargs.pop("density", False)
cumulative = kwargs.pop("cumulative", False)
value_format = kwargs.pop("value_format", None)
text_kwargs = pop_kwargs_with_prefix("text_", kwargs)
kwargs["label"] = kwargs.get("label", h1.name)
data = get_data(h1, cumulative=cumulative, density=density)
_apply_xy_lims(ax, h1, data, kwargs)
_add_ticks(ax, h1, kwargs)
_add_labels(ax, h1, kwargs)
ax.step(h1.numpy_bins, np.concatenate([data[:1], data]), **kwargs)
if show_stats:
_add_stats_box(h1, ax, stats=show_stats)
if show_values:
_add_values(ax, h1, data, value_format=value_format, **text_kwargs)
def scatter(h1: Histogram1D, ax: Axes, *, errors: bool = False, **kwargs):
"""Scatter plot of 1D histogram."""
show_stats = kwargs.pop("show_stats", False)
show_values = kwargs.pop("show_values", False)
density = kwargs.pop("density", False)
cumulative = kwargs.pop("cumulative", False)
value_format = kwargs.pop("value_format", None)
text_kwargs = pop_kwargs_with_prefix("text_", kwargs)
label = kwargs.pop("label", h1.name)
data = get_data(h1, cumulative=cumulative, density=density)
if "cmap" in kwargs:
cmap = _get_cmap(kwargs)
_, cmap_data = _get_cmap_data(data, kwargs)
kwargs["color"] = cmap(cmap_data)
elif "color" in kwargs or "c" in kwargs:
kwargs["color"] = kwargs.pop("color", kwargs.get("c", None))
_apply_xy_lims(ax, h1, data, kwargs)
_add_ticks(ax, h1, kwargs)
_add_labels(ax, h1, kwargs)
if errors:
err_data = get_err_data(h1, cumulative=cumulative, density=density)
ax.errorbar(h1.bin_centers,
data,
yerr=err_data,
fmt=kwargs.pop("fmt", "o"),
ecolor=kwargs.pop("ecolor", "black"),
ms=0)
ax.scatter(h1.bin_centers, data, label=label, **kwargs)
if show_values:
_add_values(ax, h1, data, value_format=value_format, **text_kwargs)
if show_stats:
_add_stats_box(h1, ax, stats=show_stats)
def surface_map(hist,
ax: Axes3D,
*,
show_zero: bool = True,
x=(lambda x, y: x),
y=(lambda x, y: y),
z=(lambda x, y: 0),
**kwargs):
"""Coloured-rectangle plot of 2D histogram, placed on an arbitrary surface.
Each bin is mapped to a rectangle in 3D space using the x,y,z functions.
Parameters
----------
hist : Histogram2D
show_zero : Optional[bool]
Whether to show coloured box for bins with 0 frequency (otherwise background).
x : function
Function with 2 parameters used to map bins to spatial x coordinate
y : function
Function with 2 parameters used to map bins to spatial y coordinate
z : function
Function with 2 parameters used to map bins to spatial z coordinate
Returns
-------
matplotlib.axes._subplots.Axes3DSubplot
See Also
--------
map, cylinder_map, globe_map
"""
data = get_data(hist,
cumulative=False,
flatten=False,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
xs = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
ys = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
zs = np.ndarray((hist.shape[0] + 1, hist.shape[1] + 1), dtype=float)
edges_x = hist.numpy_bins[0]
edges_y = hist.numpy_bins[1]
for i in range(hist.shape[0] + 1):
for j in range(hist.shape[1] + 1):
xs[i, j] = x(edges_x[i], edges_y[j])
ys[i, j] = y(edges_x[i], edges_y[j])
zs[i, j] = z(edges_x[i], edges_y[j])
for i in range(hist.shape[0]):
for j in range(hist.shape[1]):
if not show_zero and not data[i, j]:
continue
x = xs[i, j], xs[i, j + 1], xs[i + 1, j + 1], xs[i + 1, j]
y = ys[i, j], ys[i, j + 1], ys[i + 1, j + 1], ys[i + 1, j]
z = zs[i, j], zs[i, j + 1], zs[i + 1, j + 1], zs[i + 1, j]
verts = [list(zip(x, y, z))]
col = Poly3DCollection(verts)
col.set_facecolor(colors[i, j])
ax.add_collection3d(col)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
if matplotlib.__version__ < "2":
ax.plot_surface([], [], [], color="b") # Dummy plot
ax.set_xlim(xs.min(), xs.max())
ax.set_ylim(ys.min(), ys.max())
ax.set_zlim(zs.min(), zs.max())
# ax.plot_surface(x, y, z, rstride=hist.shape[0], color="b")
return ax
def map(h2: Histogram2D,
ax: Axes,
*,
show_zero: bool = True,
show_values: bool = False,
show_colorbar: bool = True,
x=None,
y=None,
**kwargs):
"""Coloured-rectangle plot of 2D histogram.
Parameters
----------
show_zero : Whether to show coloured box for bins with 0 frequency (otherwise background).
show_values : Whether to show labels with frequencies/densities in the middle of the bin
text_color : Optional
Colour of text descriptions
text_alpha : Optional[float]
Alpha for the text labels only
x : Optional[Callable]
Transformation of x bin coordinates
y : Optional[Callable]
Transformation of y bin coordinates
zorder : float
z-order in the axis (higher number above lower)
See Also
--------
image, polar_map, surface_map
Notes
-----
If you transform axes using x or y parameters, the deduction of axis limits
does not work well automatically. Please, make sure to attend to it yourself.
The densities in transformed maps are calculated from original bins.
"""
# Detect transformation
transformed = False
if x is not None or y is not None:
if not x:
x = lambda x, y: x
if not y:
y = lambda x, y: y
transformed = True
value_format = kwargs.pop("value_format", lambda x: str(x))
# TODO: Implement correctly the text_kwargs
if isinstance(value_format, str):
format_str = "{0:" + value_format + "}"
value_format = lambda x: format_str.format(x)
rect_args = {}
if "zorder" in kwargs:
rect_args["zorder"] = kwargs.pop("zorder")
data = get_data(h2,
cumulative=False,
flatten=True,
density=kwargs.pop("density", False))
cmap = _get_cmap(kwargs)
norm, cmap_data = _get_cmap_data(data, kwargs)
colors = cmap(cmap_data)
xpos, ypos = (arr.flatten() for arr in h2.get_bin_left_edges())
dx, dy = (arr.flatten() for arr in h2.get_bin_widths())
text_x, text_y = (arr.flatten() for arr in h2.get_bin_centers())
_apply_xy_lims(ax, h2, data=data, kwargs=kwargs)
_add_labels(ax, h2, kwargs)
ax.autoscale_view()
alphas = _get_alpha_data(cmap_data, kwargs)
if np.isscalar(alphas):
alphas = np.ones_like(data) * alphas
for i in range(len(xpos)):
bin_color = colors[i]
alpha = alphas[i]
if data[i] != 0 or show_zero:
if not transformed:
rect = plt.Rectangle([xpos[i], ypos[i]],
dx[i],
dy[i],
facecolor=bin_color,
edgecolor=kwargs.get(
"grid_color", cmap(0.5)),
lw=kwargs.get("lw", 0.5),
alpha=alpha,
**rect_args)
tx, ty = text_x[i], text_y[i]
else:
# See http://matplotlib.org/users/path_tutorial.html
points = ((xpos[i], ypos[i]), (xpos[i] + dx[i], ypos[i]),
(xpos[i] + dx[i], ypos[i] + dy[i]),
(xpos[i], ypos[i] + dy[i]), (xpos[i], ypos[i]))
verts = [(x(*p), y(*p)) for p in points]
codes = [
path.Path.MOVETO,
path.Path.LINETO,
path.Path.LINETO,
path.Path.LINETO,
path.Path.CLOSEPOLY,
]
rect_path = path.Path(verts, codes)
rect = patches.PathPatch(rect_path,
facecolor=bin_color,
edgecolor=kwargs.get(
"grid_color", cmap(0.5)),
lw=kwargs.get("lw", 0.5),
alpha=alpha,
**rect_args)
tx = x(text_x[i], text_y[i])
ty = y(text_x[i], text_y[i])
ax.add_patch(rect)
if show_values:
text = value_format(data[i])
yiq_y = np.dot(bin_color[:3], [0.299, 0.587, 0.114])
text_color = kwargs.get("text_color", None)
if not text_color:
if yiq_y > 0.5:
text_color = (0.0, 0.0, 0.0,
kwargs.get("text_alpha", alpha))
else:
text_color = (1.0, 1.0, 1.0,
kwargs.get("text_alpha", alpha))
ax.text(tx,
ty,
text,
horizontalalignment='center',
verticalalignment='center',
color=text_color,
clip_on=True,
**rect_args)
if show_colorbar:
_add_colorbar(ax, cmap, cmap_data, norm)
