def stacked_bar(data,
axes=None,
labels=None,
lighten_by=0.1,
cmap=None,
**kwargs):
"""Create a stacked bar graph
Parameters
----------
data : :class:`numpy.ndarray`
Array of data, in which each row is a stack, each column a value in that stack.
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
labels : list, optional
Labels for each stack. If `None`, stacks are labeled sequentially by number.
(Default: `None`)
lighten_by : float, optional
Amount by which to lighten sequential blocks in each stack. (Default: 0.10)
cmap : :class:`matplotlib.colors.Colormap`, optional
Colormap from which to generate bar colors. If supplied, will override
any `color` attribute in `**kwargs`. (Default: `None`)
**kwargs : keyword arguments
Other keyword arguments to pass to :func:`matplotlib.pyplot.bar`
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, ax = get_fig_axes(axes)
rows, cols = data.shape
labels = labels if labels is not None else range(rows)
defaults = [("align", "center"), ("width", 0.8)]
if cmap is not None:
kwargs["color"] = cmap(numpy.linspace(0, 1.0, num=10))
elif kwargs.get("color", None) is None:
kwargs["color"] = [next(get_color_cycle(ax)) for _ in range(rows)]
x = numpy.arange(rows) + 0.5
xaxis = ax.xaxis
xaxis.set_ticks(x)
xaxis.set_ticklabels(labels)
bottoms = numpy.zeros(rows)
for k, v in defaults:
if k not in kwargs:
kwargs[k] = v
for i in range(cols):
color = kwargs["color"]
if i > 0:
kwargs["color"] = lighten(color, amt=lighten_by)
heights = data[:, i]
plt.bar(x, heights, bottom=bottoms, **kwargs)
heights.shape
bottoms += heights
ax.set_xlim(-0.5, rows + 0.5)
return fig, ax
def stacked_bar(data,axes=None,labels=None,lighten_by=0.1,cmap=None,**kwargs):
"""Create a stacked bar graph
Parameters
----------
data : :class:`numpy.ndarray`
Array of data, in which each row is a stack, each column a value in that stack.
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
labels : list, optional
Labels for each stack. If `None`, stacks are labeled sequentially by number.
(Default: `None`)
lighten_by : float, optional
Amount by which to lighten sequential blocks in each stack. (Default: 0.10)
cmap : :class:`matplotlib.colors.Colormap`, optional
Colormap from which to generate bar colors. If supplied, will override
any `color` attribute in `**kwargs`. (Default: `None`)
**kwargs : keyword arguments
Other keyword arguments to pass to :func:`matplotlib.pyplot.bar`
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, ax = get_fig_axes(axes)
rows, cols = data.shape
labels = labels if labels is not None else range(rows)
defaults = [("align","center"),
("width",0.8)]
if cmap is not None:
kwargs["color"] = cmap(numpy.linspace(0,1.0,num=10))
elif kwargs.get("color",None) is None:
kwargs["color"] = [next(get_color_cycle(ax)) for _ in range(rows)]
x = numpy.arange(rows) + 0.5
xaxis = ax.xaxis
xaxis.set_ticks(x)
xaxis.set_ticklabels(labels)
bottoms = numpy.zeros(rows)
for k,v in defaults:
if k not in kwargs:
kwargs[k] = v
for i in range(cols):
color = kwargs["color"]
if i > 0:
kwargs["color"] = lighten(color,amt=lighten_by)
heights = data[:,i]
plt.bar(x,heights,bottom=bottoms,**kwargs)
heights.shape
bottoms += heights
ax.set_xlim(-0.5,rows+0.5)
return fig, ax
def kde_plot(data,
axes=None,
color=None,
label=None,
alpha=0.7,
vert=False,
log=False,
base=10,
points=500,
bw_method="scott",
rescale=False,
zorder=None):
"""Plot a kernel density estimate of `data` on `axes`.
Parameters
----------
data : :class:`numpy.ndarray`
Array of data
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
color : matplotlib colorspec, optional
Color to use for plotting (Default: use next in matplotlibrc)
label : str, optional
Name of data series (used for legend; default: `None`)
alpha : float, optional
Amount of alpha transparency to use (Default: 0.7)
vert : bool, optional
If true, plot kde vertically
log : bool, optional
If `True`, `data` is log-transformed before the kde is estimated.
Data are converted back to non-log space afterwards.
base : 2, 10, or :obj:`numpy.e`, optional
If `log` is `True`, this serves as the base of the log space.
If `log` is `False`, this is ignored. (Default: 2)
points : int
Number of points over which to evaluate kde. (Default: 100)
bw_method : str
Bandwith estimation method. See documentation for
:obj:`scipy.stats.gaussian_kde`. (Default: "scott")
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, axes = get_fig_axes(axes)
if color is None:
color = next(get_color_cycle(axes))
a, b = get_kde(data,
log=log,
base=base,
points=points,
bw_method=bw_method)
if rescale == True:
b /= b.max()
fbargs = {"alpha": alpha, "facecolor": lighten(color), "edgecolor": color}
if label is not None:
fbargs["label"] = label
if vert == True:
axes.fill_betweenx(a, b, 0, **fbargs)
axes.plot(
b, a, color=color, alpha=alpha, label=label
) # this is a bit of a hack to get labels to print; fill_between doesn't work with legends
if log == True:
axes.semilogy()
else:
axes.fill_between(a, b, 0, **fbargs)
axes.plot(a, b, color=color, alpha=alpha, label=label)
if log == True:
axes.semilogx()
return fig, axes
def kde_plot(data,axes=None,color=None,label=None,alpha=0.7,vert=False,
log=False,base=10,points=500,bw_method="scott",rescale=False,
zorder=None):
"""Plot a kernel density estimate of `data` on `axes`.
Parameters
----------
data : :class:`numpy.ndarray`
Array of data
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
color : matplotlib colorspec, optional
Color to use for plotting (Default: use next in matplotlibrc)
label : str, optional
Name of data series (used for legend; default: `None`)
alpha : float, optional
Amount of alpha transparency to use (Default: 0.7)
vert : bool, optional
If true, plot kde vertically
log : bool, optional
If `True`, `data` is log-transformed before the kde is estimated.
Data are converted back to non-log space afterwards.
base : 2, 10, or :obj:`numpy.e`, optional
If `log` is `True`, this serves as the base of the log space.
If `log` is `False`, this is ignored. (Default: 2)
points : int
Number of points over which to evaluate kde. (Default: 100)
bw_method : str
Bandwith estimation method. See documentation for
:obj:`scipy.stats.gaussian_kde`. (Default: "scott")
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, axes = get_fig_axes(axes)
if color is None:
color = next(get_color_cycle(axes))
a, b = get_kde(data,log=log,base=base,points=points,bw_method=bw_method)
if rescale == True:
b /= b.max()
fbargs = { "alpha" : alpha,
"facecolor" : lighten(color),
"edgecolor" : color
}
if label is not None:
fbargs["label"] = label
if vert == True:
axes.fill_betweenx(a,b,0,**fbargs)
axes.plot(b,a,color=color,alpha=alpha,label=label) # this is a bit of a hack to get labels to print; fill_between doesn't work with legends
if log == True:
axes.semilogy()
else:
axes.fill_between(a,b,0,**fbargs)
axes.plot(a,b,color=color,alpha=alpha,label=label)
if log == True:
axes.semilogx()
return fig, axes
