def imshow(arr, x=None, ax=None, vmin=None, vmax=None, percentile=True,
strip=False, features=None, conf=0.95, sort_by=None,
line_kwargs=None, fill_kwargs=None, imshow_kwargs=None, figsize=(5, 12),
width_ratios=(4, 1), height_ratios=(4, 1),
subplot_params=dict(wspace=0.1, hspace=0.1),
subset_by=None, subset_order=None,):
"""
Do-it-all function to help with plotting heatmaps
Parameters
----------
arr : array-like
x : 1D array
X values to use. If None, use range(arr.shape[1])
ax : matplotlib.Axes
If not None, then only plot the array on the provided axes. This will
ignore any additional arguments provided that apply to figure-level
configuration or to the average line plot. For example, `figsize`,
`width_ratios`, `height_ratios`, `subplot_params`, `line_kwargs`, and
`fill_kwargs` will all be ignored.
vmin, vmax : float
percentile : bool
If True, then treat values for `vmin` and `vmax` as percentiles rather
than absolute values.
strip : bool
Include a strip plot alongside the array
features : pybedtools.BedTool or string filename
Features used to construct the array
conf : float
Confidence interval to use in line plot.
sort_by : array-like
Use the provided array to sort the array (e.g., an array of expression
values). This array will be argsorted to get the proper order.
line_kwargs, fill_kwargs : dict
Passed directly to `ci_plot`.
figsize : tuple
(Width, height) of the figure to create.
imshow_kwargs : dict
Passed directly to matplotlib.pyplot.imshow. By default, arguments
used are `origin='lower'`, `aspect="auto"` and a colormap from
colormap_adjust.smart_colormap generated using the provided `vmin` and
`vmax`.
width_ratios, height_ratios: tuple
These tuples are passed to the `new_shell` function. The default
values set up a 2x2 configuration of panels for heatmap, line plot,
colorbar axes, and optional strip plot. However modifying
`width_ratios` or `height_ratios` can be used to create more or fewer panels.
subplot_params : dict
Passed to Figure.subplots_adjust
subset_by : array
An array of any type (but usually int or str) that contains a class
label for each row in the heatmap array. For example, to subset by
expression, an array the values of "up", "down", or "unchanged" at each
of the positions could be provided.
Note that the heatmap array is first sorted by `sort_by` and then split
into groups according to `subset_by`, so each subset remains sorted by
`sort_by`.
subset_order : list-like
This provides the order in which the subsets are plotted. Since the
default imshow arguments contain `origin="lower"`, these will be
plotted in order starting at the bottom of the heatmap.
"""
if ax is None:
fig = new_shell(
figsize=figsize,
strip=strip,
subplot_params=subplot_params,
width_ratios=width_ratios,
height_ratios=height_ratios)
if x is None:
x = np.arange(arr.shape[1] + 1)
if percentile:
if vmin is None:
vmin = arr.min()
else:
vmin = mlab.prctile(arr.ravel(), vmin)
if vmax is None:
vmax = arr.max()
else:
vmax = mlab.prctile(arr.ravel(), vmax)
else:
if vmin is None:
vmin = arr.min()
if vmax is None:
vmax = arr.max()
cmap = colormap_adjust.smart_colormap(vmin, vmax)
_imshow_kwargs = dict(origin='lower', cmap=cmap, vmin=vmin, vmax=vmax,
aspect='auto')
if imshow_kwargs is not None:
_imshow_kwargs.update(imshow_kwargs)
# previously we did an argsort first; with subsetting we don't want to do
# that yet....
#if sort_by is not None:
# ind = np.argsort(sort_by)
#else:
# ind = np.arange(arr.shape[0])
if sort_by is None:
sort_by = np.arange(arr.shape[0])
if ax is None:
array_ax = fig.array_axes
else:
array_ax = ax
# If not provided, assume all in the same subset.
if subset_by is None:
subset_by = np.zeros(arr.shape[0])
# Ensure always array, since we're doing indexing tricks
if not isinstance(subset_by, np.ndarray):
subset_by = np.array(subset_by)
# If not provided, use sorted order
if subset_order is None:
subset_order = sorted(np.unique(subset_by))
inds = []
for cls in subset_order:
subset_ind = np.nonzero(subset_by == cls)[0]
subset_sort_by = sort_by[subset_ind]
subset_argsort_by = np.argsort(subset_sort_by)
inds.append(subset_ind[subset_argsort_by])
ind = np.concatenate(inds)
mappable = array_ax.imshow(
arr[ind, :],
extent=(x.min(), x.max(), 0, arr.shape[0]),
**_imshow_kwargs
)
if line_kwargs is None:
line_kwargs = {}
if fill_kwargs is None:
fill_kwargs = {}
if isinstance(line_kwargs, dict):
line_kwargs = [line_kwargs]
if isinstance(fill_kwargs, dict):
fill_kwargs = [fill_kwargs]
_line_kwargs = itertools.cycle(line_kwargs)
_fill_kwargs = itertools.cycle(fill_kwargs)
if ax is None:
plt.colorbar(mappable, fig.cax)
for subset_ind, label, _lkw, _fkw in zip(inds, subset_order, _line_kwargs, _fill_kwargs):
ci_plot(
x,
arr[subset_ind],
ax=fig.line_axes,
line_kwargs=_lkw,
fill_kwargs=_fkw,
)
return fig
else:
return ax.figure
def imshow(arr, x=None, ax=None, vmin=None, vmax=None, percentile=True,
strip=False, features=None, conf=0.95, line_kwargs=None,
sort_by=None, fill_kwargs=None, figsize=(5, 12),
width_ratios=(4, 1), height_ratios=(4, 1),
subplot_params=dict(wspace=0.1, hspace=0.1), imshow_kwargs=None):
"""
Parameters
----------
arr : array-like
x : 1D array
X values to use. If None, use range(arr.shape[1])
ax : matplotlib.Axes
If not None, then only plot the array on the provided axes. This will
ignore any additional arguments provided that apply to figure-level
configuration or to the average line plot. For example, `figsize`,
`width_ratios`, `height_ratios`, `subplot_params`, `line_kwargs`, and
`fill_kwargs` will be ignored.
vmin, vmax : float
percentile : bool
If True, then treat values for `vmin` and `vmax` as percentiles rather
than absolute values.
strip : bool
Include a strip plot alongside the array
features : pybedtools.BedTool or string filename
Features used to construct the array
sort_by : array-like
Use the provided array to sort the array (e.g., expression). This
array is argsorted to get the proper order.
line_kwargs, fill_kwargs : dict
Passed directly to `ci_plot`.
figsize : tuple
(Width, height) of the figure to create.
"""
if ax is None:
fig = new_shell(
figsize=figsize,
strip=strip,
subplot_params=subplot_params,
width_ratios=width_ratios,
height_ratios=height_ratios)
if x is None:
x = np.arange(arr.shape[1])
if percentile:
if vmin is None:
vmin = arr.min()
else:
vmin = mlab.prctile(arr.ravel(), vmin)
if vmax is None:
vmax = arr.max()
else:
vmax = mlab.prctile(arr.ravel(), vmax)
else:
if vmin is None:
vmin = arr.min()
if vmax is None:
vmax = arr.max()
if imshow_kwargs is None:
imshow_kwargs = {}
cmap = colormap_adjust.smart_colormap(vmin, vmax)
if sort_by is not None:
ind = np.argsort(sort_by)
else:
ind = np.arange(arr.shape[0])
if ax is None:
array_ax = fig.array_axes
else:
array_ax = ax
mappable = array_ax.imshow(
arr[ind, :],
aspect='auto',
cmap=cmap,
vmin=vmin,
vmax=vmax,
origin='lower',
extent=(x.min(), x.max(), 0, arr.shape[0]),
**imshow_kwargs
)
if ax is None:
plt.colorbar(mappable, fig.cax)
ci_plot(
x,
arr,
ax=fig.line_axes,
line_kwargs=line_kwargs,
fill_kwargs=fill_kwargs,
)
return fig
else:
return ax.figure
def imshow(
arr,
x=None,
ax=None,
vmin=None,
vmax=None,
percentile=True,
strip=False,
features=None,
conf=0.95,
sort_by=None,
line_kwargs=None,
fill_kwargs=None,
imshow_kwargs=None,
figsize=(5, 12),
width_ratios=(4, 1),
height_ratios=(4, 1),
subplot_params=dict(wspace=0.1, hspace=0.1),
subset_by=None,
subset_order=None,
):
"""
Do-it-all function to help with plotting heatmaps
Parameters
----------
arr : array-like
x : 1D array
X values to use. If None, use range(arr.shape[1])
ax : matplotlib.Axes
If not None, then only plot the array on the provided axes. This will
ignore any additional arguments provided that apply to figure-level
configuration or to the average line plot. For example, `figsize`,
`width_ratios`, `height_ratios`, `subplot_params`, `line_kwargs`, and
`fill_kwargs` will all be ignored.
vmin, vmax : float
percentile : bool
If True, then treat values for `vmin` and `vmax` as percentiles rather
than absolute values.
strip : bool
Include a strip plot alongside the array
features : pybedtools.BedTool or string filename
Features used to construct the array
conf : float
Confidence interval to use in line plot.
sort_by : array-like
Use the provided array to sort the array (e.g., an array of expression
values). This array will be argsorted to get the proper order.
line_kwargs, fill_kwargs : dict
Passed directly to `ci_plot`.
figsize : tuple
(Width, height) of the figure to create.
imshow_kwargs : dict
Passed directly to matplotlib.pyplot.imshow. By default, arguments
used are `origin='lower'`, `aspect="auto"` and a colormap from
colormap_adjust.smart_colormap generated using the provided `vmin` and
`vmax`.
width_ratios, height_ratios: tuple
These tuples are passed to the `new_shell` function. The default
values set up a 2x2 configuration of panels for heatmap, line plot,
colorbar axes, and optional strip plot. However modifying
`width_ratios` or `height_ratios` can be used to create more or fewer panels.
subplot_params : dict
Passed to Figure.subplots_adjust
subset_by : array
An array of any type (but usually int or str) that contains a class
label for each row in the heatmap array. For example, to subset by
expression, an array the values of "up", "down", or "unchanged" at each
of the positions could be provided.
Note that the heatmap array is first sorted by `sort_by` and then split
into groups according to `subset_by`, so each subset remains sorted by
`sort_by`.
subset_order : list-like
This provides the order in which the subsets are plotted. Since the
default imshow arguments contain `origin="lower"`, these will be
plotted in order starting at the bottom of the heatmap.
"""
if ax is None:
fig = new_shell(figsize=figsize,
strip=strip,
subplot_params=subplot_params,
width_ratios=width_ratios,
height_ratios=height_ratios)
if x is None:
x = np.arange(arr.shape[1] + 1)
if percentile:
if vmin is None:
vmin = arr.min()
else:
vmin = mlab.prctile(arr.ravel(), vmin)
if vmax is None:
vmax = arr.max()
else:
vmax = mlab.prctile(arr.ravel(), vmax)
else:
if vmin is None:
vmin = arr.min()
if vmax is None:
vmax = arr.max()
cmap = colormap_adjust.smart_colormap(vmin, vmax)
_imshow_kwargs = dict(origin='lower',
cmap=cmap,
vmin=vmin,
vmax=vmax,
aspect='auto')
if imshow_kwargs is not None:
_imshow_kwargs.update(imshow_kwargs)
# previously we did an argsort first; with subsetting we don't want to do
# that yet....
#if sort_by is not None:
# ind = np.argsort(sort_by)
#else:
# ind = np.arange(arr.shape[0])
if sort_by is None:
sort_by = np.arange(arr.shape[0])
if ax is None:
array_ax = fig.array_axes
else:
array_ax = ax
# If not provided, assume all in the same subset.
if subset_by is None:
subset_by = np.zeros(arr.shape[0])
# Ensure always array, since we're doing indexing tricks
if not isinstance(subset_by, np.ndarray):
subset_by = np.array(subset_by)
# If not provided, use sorted order
if subset_order is None:
subset_order = sorted(np.unique(subset_by))
inds = []
for cls in subset_order:
subset_ind = np.nonzero(subset_by == cls)[0]
subset_sort_by = sort_by[subset_ind]
subset_argsort_by = np.argsort(subset_sort_by)
inds.append(subset_ind[subset_argsort_by])
ind = np.concatenate(inds)
mappable = array_ax.imshow(arr[ind, :],
extent=(x.min(), x.max(), 0, arr.shape[0]),
**_imshow_kwargs)
if line_kwargs is None:
line_kwargs = {}
if fill_kwargs is None:
fill_kwargs = {}
if isinstance(line_kwargs, dict):
line_kwargs = [line_kwargs]
if isinstance(fill_kwargs, dict):
fill_kwargs = [fill_kwargs]
_line_kwargs = itertools.cycle(line_kwargs)
_fill_kwargs = itertools.cycle(fill_kwargs)
if ax is None:
plt.colorbar(mappable, fig.cax)
for subset_ind, label, _lkw, _fkw in zip(inds, subset_order,
_line_kwargs, _fill_kwargs):
ci_plot(
x,
arr[subset_ind],
ax=fig.line_axes,
line_kwargs=_lkw,
fill_kwargs=_fkw,
)
return fig
else:
return ax.figure
