def _scatterhist_help(
axes=None,
top_height=0,
left_width=0,
right_width=0,
bottom_height=0,
):
"""Create a scatter plot with the marginal distribution for `x`
plotted in a separate pain as a kernel density estimate.
Parameters
----------
color : matplotlib colorspec, or None, optional
Color to plot data series
axes : :class:`matplotlib.axes.Axes`, dict, or `None`, optional
If a :class:`matplotlib.axes.Axes`, an axes in which to place plot.
This axes will be split into relevant panels.
If a dict, this is expected to be a group of pre-split axes.
If `None`, a new figure is generated, and axes are split.
(Default: `None`)
top_height, left_width, right_width, bottom_height : float, optional
If not `None`, a panel on the corresponding side of the `ax` will
be created, using whatever fraction is specified (e.g. 0.1 to use
10% of total height).
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
dict of :class:`matplotlib.axes.Axes`
axes containing plot space
"""
if axes is None:
fig = plt.figure()
axes = plt.gca()
if isinstance(axes, matplotlib.axes.Axes):
fig = axes.figure
axes = split_axes(axes,
top_height=top_height,
left_width=left_width,
right_width=right_width,
bottom_height=bottom_height)
elif isinstance(axes, dict):
fig = axes["main"].figure
if left_width > 0:
assert "left" in axes
if right_width > 0:
assert "right" in axes
if bottom_height > 0:
assert "bottom" in axes
if top_height > 0:
assert "top" in axes
return fig, axes
def _scatterhist_help(axes=None,
top_height=0,left_width=0,right_width=0,bottom_height=0,
):
"""Create a scatter plot with the marginal distribution for `x`
plotted in a separate pain as a kernel density estimate.
Parameters
----------
color : matplotlib colorspec, or None, optional
Color to plot data series
axes : :class:`matplotlib.axes.Axes`, dict, or `None`, optional
If a :class:`matplotlib.axes.Axes`, an axes in which to place plot.
This axes will be split into relevant panels.
If a dict, this is expected to be a group of pre-split axes.
If `None`, a new figure is generated, and axes are split.
(Default: `None`)
top_height, left_width, right_width, bottom_height : float, optional
If not `None`, a panel on the corresponding side of the `ax` will
be created, using whatever fraction is specified (e.g. 0.1 to use
10% of total height).
Returns
-------
:class:`matplotlib.figure.Figure`
Parent figure of axes
dict of :class:`matplotlib.axes.Axes`
axes containing plot space
"""
if axes is None:
fig = plt.figure()
axes = plt.gca()
if isinstance(axes,matplotlib.axes.Axes):
fig = axes.figure
axes = split_axes(axes,top_height=top_height,left_width=left_width,
right_width=right_width,bottom_height=bottom_height)
elif isinstance(axes,dict):
fig = axes["main"].figure
if left_width > 0:
assert "left" in axes
if right_width > 0:
assert "right" in axes
if bottom_height > 0:
assert "bottom" in axes
if top_height > 0:
assert "top" in axes
return fig, axes
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 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