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 triangle_plot(data,
axes=None,
fn="scatter",
vertex_labels=None,
grid=None,
clip=True,
do_setup=True,
**kwargs):
"""Plot data lying in a plane x + y + z = k in a homogenous triangular space.
Parameters
----------
data : :class:`numpy.ndarray`
Mx2 or Mx3 list or array of points in triangular space, where
the first column is the first coordinate, the second column
the second, and the third, the third.
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
fn : str, optional
Name of plotting function. Must correspond to an attribute of a
:class:`matplotlib.axes.Axes` (e.g. 'scatter', 'plot', 'hexbin'et c.),
that is be able to take an Nx2 :class:`numpy.ndarray` in Cartesian space
as input (e.g. 'plot', 'scatter', 'hexbin'; Default: 'scatter').
vertex_labels : list or None, optional
Labels for vertex. If `None`, vertices aren't labeled. (Default: `None`)
grid : :class:`numpy.ndarray` or None, optional
If not `None`, draw gridlines at intervals specified in `grid`,
as long as the grid coordinate is > 0.33333 (center of triangle)
and <= 1.0 (border).
clip : bool, optional
If `True` clipping masks corresponding to the triangle boundaries
will be applied to all plot elements (Default: `True`)
do_setup : bool, optional
If `True`, the plot area will be prepared. A triangle will be drawn,
gridlines drawn, et c. Specify `False` if plotting a second dataset
ontop of an already-prepared axes (Default: `True`)
**kwargs : keyword arguments
Other keyword arguments to pass to function specified by `fn`.
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, ax = get_fig_axes(axes)
mplrc = matplotlib.rcParams
if do_setup == True:
triverts = trianglize(_triverts)
tripatch = matplotlib.patches.Polygon(
triverts,
closed=True,
facecolor=mplrc["axes.facecolor"],
edgecolor=mplrc["axes.edgecolor"],
linewidth=mplrc["axes.linewidth"],
zorder=-10)
ax.add_patch(tripatch)
# format axes
ax.set_xlim((0, 1))
ax.set_ylim((0, _triverts[:, 1].max()))
ax.set_frame_on(False)
ax.set_xticks([])
ax.set_yticks([])
# label vertices
if vertex_labels is not None:
l1, l2, l3 = vertex_labels
tkwargs = {"fig": fig, "units": "points"}
p1trans = matplotlib.transforms.offset_copy(ax.transData,
x=0,
y=8,
**tkwargs)
p2trans = matplotlib.transforms.offset_copy(ax.transData,
x=-10,
y=-12,
**tkwargs)
p3trans = matplotlib.transforms.offset_copy(ax.transData,
x=10,
y=-12,
**tkwargs)
ax.text(triverts[0, 0], triverts[0, 1], l1, transform=p1trans)
ax.text(triverts[1, 0], triverts[1, 1], l2, transform=p2trans)
ax.text(triverts[2, 0], triverts[2, 1], l3, transform=p3trans)
# add gridlines
grid_kwargs = {
K.replace("grid.", ""): V
for (K, V) in mplrc.items() if K.startswith("grid")
}
if grid is not None:
grid = numpy.array(grid)
remainders = (1.0 - grid) / 2
for i, r in zip(grid, remainders):
if i >= 1.0 / 3:
points = [numpy.array([i, r, r])]
for _ in range(3):
points.append(rotate.dot(points[-1]))
points = numpy.array(points)
points = trianglize(points[:, [0, 2]])
myline = matplotlib.lines.Line2D(points[:, 0], points[:,
1],
**grid_kwargs)
ax.add_line(myline)
# scale data
data = trianglize(data)
# plot data
artists = []
fn = getattr(ax, fn)
res = fn(*zip(*data), **kwargs)
if isinstance(res, Artist):
artists.append(res)
elif isinstance(res, list):
artists.extend([X for X in res if isinstance(X, Artist)])
# clip
if clip == True:
for artist in artists:
artist.set_clip_path(tripatch)
artist.set_clip_on(True)
return fig, ax
def profile_heatmap(data,
profile=None,
x=None,
axes=None,
sort_fn=sort_max_position,
cmap=None,
nancolor="#666666",
im_args={},
plot_args={}):
"""Create a dual-paned plot in which `profile` is displayed in a top
panel, above a heatmap showing the intensities of each row of `data`,
optionally sorted top-to-bottom by `sort_fn`.
Parameters
----------
data : :class:`numpy.ndarray`
Array of data, in which each row is an individual aligned vector of data
for region of interest, and each column a position in that vector
profile : :class:`numpy.ndarray` or None
Reduced profile of data, often a column-wise median. If not
supplied, it will be calculated.
x : :class:`numpy.ndarray`
Array of values for X-axis
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
sort_fn : function, optional
Sort rows in `data` by this function before plotting. Function must
return a :class:`numpy.ndarray` of indices corresponding to rows in `data`
(Default: sort by ascending argmax of each row)
cmap : :class:`~matplotlib.colors.Colormap`, optional
Colormap to use in heatmap. It not `None`, overrides any value
in `im_args`. (Default: `None`)
nancolor : str or matplotlib colorspec
Color used for plotting `nan` and other illegal or masked values
im_args : dict
Keyword arguments to pass to :func:`matplotlib.pyplot.imshow`
plot_args : dict
Keyword arguments to pass to :func:`matplotlib.pyplot.plot`
for plotting metagene average
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
dict
Dictionary of :class:`matplotlib.axes.Axes`. "top" refers to the
panel containing the summary profile. "main" refers to the heatmap
of individual values
"""
fig, ax = get_fig_axes(axes)
axes = split_axes(ax, top_height=0.2)
if sort_fn is None:
sort_indices = numpy.arange(data.shape[0])
else:
sort_indices = sort_fn(data)
if x is None:
x = numpy.arange(0, data.shape[1])
if profile is None:
profile = numpy.nanmedian(data, axis=0)
im_args = copy.deepcopy(im_args)
# populate with defaults
for k, v in _heatmap_defaults.items():
if k not in im_args:
im_args[k] = v
if "extent" not in im_args:
im_args["extent"] = [x.min(), x.max(), 0, data.shape[0]] #,0]
if "vmin" not in im_args:
im_args["vmin"] = numpy.nanmin(data)
if "vmax" not in im_args:
im_args["vmax"] = numpy.nanmax(data)
if cmap is not None:
im_args["cmap"] = cmap
elif "cmap" in im_args:
cmap = matplotlib.cm.get_cmap(im_args["cmap"])
else:
cmap = matplotlib.cm.get_cmap()
cmap.set_bad(nancolor, 1.0)
axes["top"].plot(x, profile, **plot_args)
axes["top"].set_ylim(0, profile.max())
axes["top"].set_xlim(x.min(), x.max())
#axes["top"].set_yticks([])
axes["top"].set_yticks([0, profile.max()])
axes["top"].xaxis.tick_bottom()
axes["top"].grid(True, which="both")
axes["main"].xaxis.tick_bottom()
axes["main"].imshow(data[sort_indices, :], **im_args)
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
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 profile_heatmap(data,profile=None,x=None,axes=None,sort_fn=sort_max_position,
cmap=None,nancolor="#666666",im_args={},plot_args={}):
"""Create a dual-paned plot in which `profile` is displayed in a top
panel, above a heatmap showing the intensities of each row of `data`,
optionally sorted top-to-bottom by `sort_fn`.
Parameters
----------
data : :class:`numpy.ndarray`
Array of data, in which each row is an individual aligned vector of data
for region of interest, and each column a position in that vector
profile : :class:`numpy.ndarray` or None
Reduced profile of data, often a column-wise median. If not
supplied, it will be calculated.
x : :class:`numpy.ndarray`
Array of values for X-axis
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
sort_fn : function, optional
Sort rows in `data` by this function before plotting. Function must
return a :class:`numpy.ndarray` of indices corresponding to rows in `data`
(Default: sort by ascending argmax of each row)
cmap : :class:`~matplotlib.colors.Colormap`, optional
Colormap to use in heatmap. It not `None`, overrides any value
in `im_args`. (Default: `None`)
nancolor : str or matplotlib colorspec
Color used for plotting `nan` and other illegal or masked values
im_args : dict
Keyword arguments to pass to :func:`matplotlib.pyplot.imshow`
plot_args : dict
Keyword arguments to pass to :func:`matplotlib.pyplot.plot`
for plotting metagene average
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
dict
Dictionary of :class:`matplotlib.axes.Axes`. "top" refers to the
panel containing the summary profile. "main" refers to the heatmap
of individual values
"""
fig, ax = get_fig_axes(axes)
axes = split_axes(ax,top_height=0.2)
if sort_fn is None:
sort_indices = numpy.arange(data.shape[0])
else:
sort_indices = sort_fn(data)
if x is None:
x = numpy.arange(0,data.shape[1])
if profile is None:
profile = numpy.nanmedian(data,axis=0)
im_args = copy.deepcopy(im_args)
# populate with defaults
for k,v in _heatmap_defaults.items():
if k not in im_args:
im_args[k] = v
if "extent" not in im_args:
im_args["extent"] = [x.min(),x.max(),0,data.shape[0]]#,0]
if "vmin" not in im_args:
im_args["vmin"] = numpy.nanmin(data)
if "vmax" not in im_args:
im_args["vmax"] = numpy.nanmax(data)
if cmap is not None:
im_args["cmap"] = cmap
elif "cmap" in im_args:
cmap = matplotlib.cm.get_cmap(im_args["cmap"])
else:
cmap = matplotlib.cm.get_cmap()
cmap.set_bad(nancolor,1.0)
axes["top"].plot(x,profile,**plot_args)
axes["top"].set_ylim(0,profile.max())
axes["top"].set_xlim(x.min(),x.max())
#axes["top"].set_yticks([])
axes["top"].set_yticks([0,profile.max()])
axes["top"].xaxis.tick_bottom()
axes["top"].grid(True,which="both")
axes["main"].xaxis.tick_bottom()
axes["main"].imshow(data[sort_indices,:],**im_args)
return fig, axes
def triangle_plot(data,axes=None,fn="scatter",vertex_labels=None,grid=None,clip=True,do_setup=True,**kwargs):
"""Plot data lying in a plane x + y + z = k in a homogenous triangular space.
Parameters
----------
data : :class:`numpy.ndarray`
Mx2 or Mx3 list or array of points in triangular space, where
the first column is the first coordinate, the second column
the second, and the third, the third.
axes : :class:`matplotlib.axes.Axes` or `None`, optional
Axes in which to place plot. If `None`, a new figure is generated.
(Default: `None`)
fn : str, optional
Name of plotting function. Must correspond to an attribute of a
:class:`matplotlib.axes.Axes` (e.g. 'scatter', 'plot', 'hexbin'et c.),
that is be able to take an Nx2 :class:`numpy.ndarray` in Cartesian space
as input (e.g. 'plot', 'scatter', 'hexbin'; Default: 'scatter').
vertex_labels : list or None, optional
Labels for vertex. If `None`, vertices aren't labeled. (Default: `None`)
grid : :class:`numpy.ndarray` or None, optional
If not `None`, draw gridlines at intervals specified in `grid`,
as long as the grid coordinate is > 0.33333 (center of triangle)
and <= 1.0 (border).
clip : bool, optional
If `True` clipping masks corresponding to the triangle boundaries
will be applied to all plot elements (Default: `True`)
do_setup : bool, optional
If `True`, the plot area will be prepared. A triangle will be drawn,
gridlines drawn, et c. Specify `False` if plotting a second dataset
ontop of an already-prepared axes (Default: `True`)
**kwargs : keyword arguments
Other keyword arguments to pass to function specified by `fn`.
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
:class:`matplotlib.axes.Axes`
Axes containing plot
"""
fig, ax = get_fig_axes(axes)
mplrc = matplotlib.rcParams
if do_setup == True:
triverts = trianglize(_triverts)
tripatch = matplotlib.patches.Polygon(triverts,
closed=True,
facecolor=mplrc["axes.facecolor"],
edgecolor=mplrc["axes.edgecolor"],
linewidth=mplrc["axes.linewidth"],
zorder=-10
)
ax.add_patch(tripatch)
# format axes
ax.set_xlim((0,1))
ax.set_ylim((0,_triverts[:,1].max()))
ax.set_frame_on(False)
ax.set_xticks([])
ax.set_yticks([])
# label vertices
if vertex_labels is not None:
l1,l2,l3 = vertex_labels
tkwargs = { "fig" : fig,
"units" : "points"
}
p1trans = matplotlib.transforms.offset_copy(ax.transData,x=0, y=8, **tkwargs)
p2trans = matplotlib.transforms.offset_copy(ax.transData,x=-10,y=-12,**tkwargs)
p3trans = matplotlib.transforms.offset_copy(ax.transData,x=10, y=-12,**tkwargs)
ax.text(triverts[0,0],triverts[0,1],l1,transform=p1trans)
ax.text(triverts[1,0],triverts[1,1],l2,transform=p2trans)
ax.text(triverts[2,0],triverts[2,1],l3,transform=p3trans)
# add gridlines
grid_kwargs = { K.replace("grid.","") : V for (K,V) in mplrc.items() if K.startswith("grid") }
if grid is not None:
grid = numpy.array(grid)
remainders = (1.0 - grid)/2
for i, r in zip(grid,remainders):
if i >= 1.0/3:
points = [numpy.array([i,r,r])]
for _ in range(3):
points.append(rotate.dot(points[-1]))
points = numpy.array(points)
points = trianglize(points[:,[0,2]])
myline = matplotlib.lines.Line2D(points[:,0],
points[:,1],
**grid_kwargs)
ax.add_line(myline)
# scale data
data = trianglize(data)
# plot data
artists = []
fn = getattr(ax,fn)
res = fn(*zip(*data),**kwargs)
if isinstance(res,Artist):
artists.append(res)
elif isinstance(res,list):
artists.extend([X for X in res if isinstance(X,Artist)])
# clip
if clip == True:
for artist in artists:
artist.set_clip_path(tripatch)
artist.set_clip_on(True)
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
