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 test_wrong_arguments(self):
freqs = [[1, 0], [1, 2]]
h = Histogram2D(binnings=(range(3), range(3)), frequencies=freqs)
with pytest.raises(RuntimeError):
h0 = h.partial_normalize(2)
with pytest.raises(RuntimeError):
h0 = h.partial_normalize(-2)
def test_with_zeros(self):
freqs = [
[0, 0],
[0, 2]
]
h = Histogram2D(binnings=(range(3), range(3)), frequencies=freqs)
h1 = h.partial_normalize(1)
assert np.allclose(h1.frequencies, [[0, 0], [0, 1.0]])
def test_inplace(self):
freqs = [[1, 0], [1, 2]]
h = Histogram2D(binnings=(range(3), range(3)), frequencies=freqs)
h1 = h.partial_normalize(0, inplace=False)
assert np.allclose(h.frequencies, freqs)
assert not np.allclose(h1.frequencies, h.frequencies)
h.partial_normalize(0, inplace=True)
assert h1 == h
def test_values(self):
freqs = [[1, 0], [1, 2]]
h = Histogram2D(binnings=(range(3), range(3)), frequencies=freqs)
h0 = h.partial_normalize(0)
h1 = h.partial_normalize(1)
assert np.allclose(h0.frequencies, [[.5, 0], [.5, 1.0]])
assert np.allclose(h1.frequencies,
[[1, 0], [.333333333333, .6666666666]])
def test_axis_names(self):
freqs = [
[1, 0],
[1, 2]
]
h = Histogram2D(binnings=(range(3), range(3)), frequencies=freqs, axis_names=["first_axis", "second_axis"])
h1 = h.partial_normalize("second_axis")
assert np.allclose(h1.frequencies, [[1, 0], [.333333333333, .6666666666]])
with pytest.raises(ValueError):
h0 = h.partial_normalize("third_axis")
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 map(h2: Histogram2D, *, show_zero: bool = True, show_values: bool = False, **kwargs) -> dict:
"""Heat-map of two-dimensional histogram."""
vega = _create_figure(kwargs)
values_arr = get_data(h2, kwargs.pop("density", None), kwargs.pop("cumulative", None))
values = values_arr.tolist()
value_format = get_value_format(kwargs.pop("value_format", None))
_add_title(h2, vega, kwargs)
_create_scales(h2, vega, kwargs)
_create_axes(h2, vega, kwargs)
_create_cmap_scale(values_arr, vega, kwargs)
_create_colorbar(vega, kwargs)
x = h2.get_bin_centers(0)
y = h2.get_bin_centers(1)
x1 = h2.get_bin_left_edges(0)
x2 = h2.get_bin_right_edges(0)
y1 = h2.get_bin_left_edges(1)
y2 = h2.get_bin_right_edges(1)
data = []
for i in range(h2.shape[0]):
for j in range(h2.shape[1]):
if not show_zero and values[i][j] == 0:
continue
item = {
"x": float(x[i]),
"x1": float(x1[i]),
"x2": float(x2[i]),
"y": float(y[j]),
"y1": float(y1[j]),
"y2": float(y2[j]),
"c": float(values[i][j])
}
if show_values:
item["label"] = value_format(values[i][j])
data.append(item)
vega["data"] = [{
"name": "table",
"values": data
}]
vega["marks"] = [
{
"type": "rect",
"from": {"data": "table"},
"encode": {
"enter": {
"x": {"scale": "xscale", "field": "x1"},
"x2": {"scale": "xscale", "field": "x2"},
"y": {"scale": "yscale", "field": "y1"},
"y2": {"scale": "yscale", "field": "y2"},
"fill": {"scale": "color", "field": "c"},
"stroke": {"value": 0},
# "strokeWidth": {"value": 0},
# "fillColor": {"value": "#ffff00"}
},
"update": {
"fillOpacity": {"value": kwargs.pop("alpha", 1)}
},
# "hover": {
# "fillOpacity": {"value": 0.5}
# }
}
}
]
if show_values:
vega["marks"].append(
{
"type": "text",
"from": {"data": "table"},
"encode": {
"enter": {
"align": {"value": "center"},
"baseline": {"value": "middle"},
"fontSize": {"value": 13},
"fontWeight": {"value": "bold"},
"text": {"field": "label"},
"x": {"scale": "xscale", "field": "x"},
"y": {"scale": "yscale", "field": "y"},
}
}
}
)
return vega
def map(h2: Histogram2D, *, show_zero: bool = True, show_values: bool = False, **kwargs) -> dict:
"""Heat-map of two-dimensional histogram."""
vega = _create_figure(kwargs)
values_arr = get_data(h2, kwargs.pop("density", None), kwargs.pop("cumulative", None))
values = values_arr.tolist()
value_format = get_value_format(kwargs.pop("value_format", None))
_add_title(h2, vega, kwargs)
_create_scales(h2, vega, kwargs)
_create_axes(h2, vega, kwargs)
_create_cmap_scale(values_arr, vega, kwargs)
_create_colorbar(vega, kwargs)
x = h2.get_bin_centers(0)
y = h2.get_bin_centers(1)
x1 = h2.get_bin_left_edges(0)
x2 = h2.get_bin_right_edges(0)
y1 = h2.get_bin_left_edges(1)
y2 = h2.get_bin_right_edges(1)
data = []
for i in range(h2.shape[0]):
for j in range(h2.shape[1]):
if not show_zero and values[i][j] == 0:
continue
item = {
"x": float(x[i]),
"x1": float(x1[i]),
"x2": float(x2[i]),
"y": float(y[j]),
"y1": float(y1[j]),
"y2": float(y2[j]),
"c": float(values[i][j])
}
if show_values:
item["label"] = value_format(values[i][j])
data.append(item)
vega["data"] = [{
"name": "table",
"values": data
}]
vega["marks"] = [
{
"type": "rect",
"from": {"data": "table"},
"encode": {
"enter": {
"x": {"scale": "xscale", "field": "x1"},
"x2": {"scale": "xscale", "field": "x2"},
"y": {"scale": "yscale", "field": "y1"},
"y2": {"scale": "yscale", "field": "y2"},
"fill": {"scale": "color", "field": "c"},
"stroke": {"value": 0},
# "strokeWidth": {"value": 0},
# "fillColor": {"value": "#ffff00"}
},
"update": {
"fillOpacity": {"value": kwargs.pop("alpha", 1)}
},
# "hover": {
# "fillOpacity": {"value": 0.5}
# }
}
}
]
if show_values:
vega["marks"].append(
{
"type": "text",
"from": {"data": "table"},
"encode": {
"enter": {
"align": {"value": "center"},
"baseline": {"value": "middle"},
"fontSize": {"value": 13},
"fontWeight": {"value": "bold"},
"text": {"field": "label"},
"x": {"scale": "xscale", "field": "x"},
"y": {"scale": "yscale", "field": "y"},
}
}
}
)
return vega
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)