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generic.py
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generic.py
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"""Functions that will be useful irrespective of backend."""
import warnings
from collections.abc import Callable, Iterable
import matplotlib.cm as cm
import numpy as np
from matplotlib import get_backend
from matplotlib.colors import TABLEAU_COLORS, XKCD_COLORS, to_rgba_array
from matplotlib.path import Path
from matplotlib.pyplot import close, subplots
from matplotlib.widgets import LassoSelector
from numpy import asanyarray, asarray, max, min, swapaxes
from .controller import gogogo_controls, prep_scalars
from .helpers import *
from .utils import figure, ioff, nearest_idx
from .xarray_helpers import get_hs_axes, get_hs_extent, get_hs_fmts
# functions that are methods
__all__ = [
"heatmap_slicer",
"zoom_factory",
"panhandler",
"image_segmenter",
"hyperslicer",
]
def heatmap_slicer(
X,
Y,
heatmaps,
slices="horizontal",
heatmap_names=None,
linecolor="k",
labels=("X", "Y"),
interaction_type="move",
fig=None,
figsize=(18, 9),
**pcolormesh_kwargs,
):
"""
Compare horizontal and/or vertical slices across multiple arrays.
Parameters
----------
X,Y : 1D array
heatmaps : array_like
must be 2-D or 3-D. If 3-D the last two axes should be (X,Y)
slices : {'horizontal', 'vertical', 'both'}
Direction to draw slice on heatmap. both will draw horizontal and vertical traces on
the same plot, while both_separate will make a line plot for each.
heatmap_names : (String, String, ...)
An iterable with the names of the heatmaps. If provided it must have as many names
as there are heatmaps
figsize : tuple of number, default: (18, 9)
The size of the created figure. Ignored if *fig* is not None.
linecolor : colorlike, default: 'k'
The color of the cursor showing the slices. Must be a valid Matplotlib linecolor.
labels : (string, string), default: ("X", "Y")
The labels for the x and y axes.
interaction_type : str
Update on mouse movement or mouse click. Options are {'move','click'}
fig : matplotlib figure, optional
The figure to use for the heatmap_slicer. Useful when embedding into a gui.
If you are embedding into a gui make sure you set up the gui canvas first
and then pass the figure to this function
**pcolormesh_kwargs
kwargs passed to ``ax.pcolormesh``.
Returns
-------
fig : matplotlib.Figure.figure
ax : tuple of matplotlib.axes.Axes
"""
horiz = vert = False
if slices == "both":
num_line_axes = 2
horiz_axis = -2
vert_axis = -1
horiz = vert = True
else:
horiz_axis = -1
vert_axis = -1
num_line_axes = 1
if slices == "horizontal":
horiz = True
elif slices == "vertical":
vert = True
else:
raise ValueError("Valid options for slices are {horizontal, vertical, both}")
heatmaps = asarray(heatmaps)
if heatmap_names is None:
heatmap_names = [f"heatmap_{i}" for i in range(heatmaps.shape[0])]
if heatmaps.ndim == 3:
num_axes = num_line_axes + heatmaps.shape[0]
if type(heatmap_names) is str or (len(heatmap_names) != heatmaps.shape[0]):
raise ValueError("need to provide at least as many heatmap_names as heatmaps")
elif heatmaps.ndim == 2:
heatmaps = heatmaps.reshape(1, *heatmaps.shape)
if type(heatmap_names) is str:
heatmap_names = [heatmap_names]
num_axes = num_line_axes + 1
else:
raise ValueError(f"heatmaps must be 2D or 3D but is {heatmaps.ndim}D")
if fig is None:
fig, axes = subplots(1, num_axes, figsize=figsize)
else:
axes = fig.subplots(1, num_axes)
hlines = []
vlines = []
init_idx = 0
axes[0].set_ylabel(labels[1])
X = asarray(X)
Y = asarray(Y)
# mpl pcolormesh from version 3.3+ handles len(X), len(Y) equal to Z shape
# differently than <2. (Unquestionably better, but different enough to justify a shim)
# https://github.com/matplotlib/matplotlib/pull/16258
shading = pcolormesh_kwargs.pop("shading", "auto")
x_centered = X[:-1] + (X[1:] - X[:-1]) / 2
y_centered = Y[:-1] + (Y[1:] - Y[:-1]) / 2
for i, ax in enumerate(axes[:-num_line_axes]):
ax.pcolormesh(X, Y, heatmaps[i], shading=shading, **pcolormesh_kwargs)
ax.set_xlabel(labels[0])
ax.set_title(heatmap_names[i])
hmap_shape = asanyarray(heatmaps[i]).shape
if i > 0:
ax.set_yticklabels([])
if horiz:
same_shape = X.shape[0] == hmap_shape[1]
if same_shape:
x = X
else:
x = x_centered
data_line = axes[horiz_axis].plot(
x, heatmaps[i, init_idx, :], label=f"{heatmap_names[i]}"
)[0]
hlines.append((same_shape, ax.axhline(Y[init_idx], color=linecolor), data_line))
if vert:
same_shape = Y.shape[0] == hmap_shape[0]
if same_shape:
y = Y
else:
y = y_centered
data_line = axes[vert_axis].plot(
y, heatmaps[i, :, init_idx], label=f"{heatmap_names[i]}"
)[0]
vlines.append((same_shape, ax.axvline(X[init_idx], color=linecolor), data_line))
minimum = min(heatmaps)
maximum = max(heatmaps)
if vert:
axes[vert_axis].set_title("Vertical")
axes[vert_axis].set_ylim([minimum, maximum])
axes[vert_axis].legend()
if horiz:
axes[horiz_axis].set_title("Horizontal")
axes[horiz_axis].set_ylim([minimum, maximum])
axes[horiz_axis].legend()
def _gen_idxs(orig, centered, same_shape, event_data):
"""
is there a better way? probably, but this gets the job done
so here we are...
"""
if same_shape:
data_idx = nearest_idx(orig, event_data)
disp_idx = nearest_idx(orig, event_data)
arr = orig
else:
disp_idx = nearest_idx(centered, event_data)
data_idx = nearest_idx(centered, event_data)
arr = centered
return arr, data_idx, disp_idx
def update_lines(event):
if event.inaxes in axes[:-num_line_axes]:
y = None
for i, (same_shape, display_line, data_line) in enumerate(hlines):
if y is None:
y, data_idx, disp_idx = _gen_idxs(Y, y_centered, same_shape, event.ydata)
display_line.set_ydata(y[disp_idx])
data_line.set_ydata(heatmaps[i, data_idx])
x = None
for i, (same_shape, display_line, data_line) in enumerate(vlines):
if x is None:
x, data_idx, disp_idx = _gen_idxs(X, x_centered, same_shape, event.xdata)
display_line.set_xdata(x[disp_idx])
data_line.set_ydata(heatmaps[i, :, data_idx])
fig.canvas.draw_idle()
if interaction_type == "move":
fig.canvas.mpl_connect("motion_notify_event", update_lines)
elif interaction_type == "click":
fig.canvas.mpl_connect("button_press_event", update_lines)
else:
close(fig)
raise ValueError(
f"{interaction_type} is not a valid option for interaction_type, valid options are 'click' or 'move'"
)
return fig, axes
# based on https://gist.github.com/tacaswell/3144287
def zoom_factory(ax, base_scale=1.1):
"""
Add ability to zoom with the scroll wheel.
Parameters
----------
ax : matplotlib axes object
axis on which to implement scroll to zoom
base_scale : float
how much zoom on each tick of scroll wheel
Returns
-------
disconnect_zoom : function
call this to disconnect the scroll listener
"""
def limits_to_range(lim):
return lim[1] - lim[0]
fig = ax.get_figure() # get the figure of interest
fig.canvas.capture_scroll = True
has_toolbar = hasattr(fig.canvas, "toolbar") and fig.canvas.toolbar is not None
if has_toolbar:
# it might be possible to have an interactive backend without
# a toolbar. I'm not sure so being safe here
toolbar = fig.canvas.toolbar
toolbar.push_current()
orig_xlim = ax.get_xlim()
orig_ylim = ax.get_ylim()
orig_yrange = limits_to_range(orig_ylim)
orig_xrange = limits_to_range(orig_xlim)
orig_center = ((orig_xlim[0] + orig_xlim[1]) / 2, (orig_ylim[0] + orig_ylim[1]) / 2)
def zoom_fun(event):
if not event.inaxes is ax:
return
# get the current x and y limits
cur_xlim = ax.get_xlim()
cur_ylim = ax.get_ylim()
# set the range
cur_xrange = (cur_xlim[1] - cur_xlim[0]) * 0.5
cur_yrange = (cur_ylim[1] - cur_ylim[0]) * 0.5
xdata = event.xdata # get event x location
ydata = event.ydata # get event y location
if event.button == "up":
# deal with zoom in
scale_factor = base_scale
elif event.button == "down":
# deal with zoom out
scale_factor = 1 / base_scale
else:
# deal with something that should never happen
scale_factor = 1
# set new limits
new_xlim = [
xdata - (xdata - cur_xlim[0]) / scale_factor,
xdata + (cur_xlim[1] - xdata) / scale_factor,
]
new_ylim = [
ydata - (ydata - cur_ylim[0]) / scale_factor,
ydata + (cur_ylim[1] - ydata) / scale_factor,
]
new_yrange = limits_to_range(new_ylim)
new_xrange = limits_to_range(new_xlim)
if abs(new_yrange) > abs(orig_yrange):
new_ylim = orig_center[1] - new_yrange / 2, orig_center[1] + new_yrange / 2
if abs(new_xrange) > abs(orig_xrange):
new_xlim = orig_center[0] - new_xrange / 2, orig_center[0] + new_xrange / 2
ax.set_xlim(new_xlim)
ax.set_ylim(new_ylim)
if has_toolbar:
toolbar.push_current()
ax.figure.canvas.draw_idle() # force re-draw
# attach the call back
cid = fig.canvas.mpl_connect("scroll_event", zoom_fun)
def disconnect_zoom():
fig.canvas.mpl_disconnect(cid)
# return the disconnect function
return disconnect_zoom
class panhandler:
"""
Enable panning a plot with any mouse button.
button determines which button will be used (default right click)
Left: 1
Middle: 2
Right: 3
"""
def __init__(self, fig, button=3):
self.fig = fig
self._id_drag = None
self.button = button
self.fig.canvas.mpl_connect("button_press_event", self.press)
self.fig.canvas.mpl_connect("button_release_event", self.release)
def _cancel_action(self):
self._xypress = []
if self._id_drag:
self.fig.canvas.mpl_disconnect(self._id_drag)
self._id_drag = None
def press(self, event):
if event.button != self.button:
self._cancel_action()
return
x, y = event.x, event.y
self._xypress = []
for i, a in enumerate(self.fig.get_axes()):
if (
x is not None
and y is not None
and a.in_axes(event)
and a.get_navigate()
and a.can_pan()
):
a.start_pan(x, y, event.button)
self._xypress.append((a, i))
self._id_drag = self.fig.canvas.mpl_connect("motion_notify_event", self._mouse_move)
def release(self, event):
self._cancel_action()
self.fig.canvas.mpl_disconnect(self._id_drag)
for a, _ind in self._xypress:
a.end_pan()
if not self._xypress:
self._cancel_action()
return
self._cancel_action()
def _mouse_move(self, event):
for a, _ind in self._xypress:
# safer to use the recorded button at the _press than current
# button: # multiple button can get pressed during motion...
a.drag_pan(1, event.key, event.x, event.y)
self.fig.canvas.draw_idle()
class image_segmenter:
"""
Manually segment an image with the lasso selector.
"""
def __init__(
self,
img,
nclasses=1,
mask=None,
mask_colors=None,
mask_alpha=0.75,
lineprops=None,
lasso_mousebutton="left",
pan_mousebutton="middle",
ax=None,
figsize=(10, 10),
**kwargs,
):
"""
Create an image segmenter. Any ``kwargs`` will be passed through to the ``imshow``
call that displays *img*.
Parameters
----------
img : array_like
A valid argument to imshow
nclasses : int, default 1
mask : arraylike, optional
If you want to pre-seed the mask
mask_colors : None, color, or array of colors, optional
the colors to use for each class. Unselected regions will always be totally transparent
mask_alpha : float, default .75
The alpha values to use for selected regions. This will always override the alpha values
in mask_colors if any were passed
lineprops : dict, default: None
lineprops passed to LassoSelector. If None the default values are:
{"color": "black", "linewidth": 1, "alpha": 0.8}
lasso_mousebutton : str, or int, default: "left"
The mouse button to use for drawing the selecting lasso.
pan_mousebutton : str, or int, default: "middle"
The button to use for `~mpl_interactions.generic.panhandler`. One of 'left', 'middle' or
'right', or 1, 2, 3 respectively.
ax : `matplotlib.axes.Axes`, optional
The axis on which to plot. If *None* a new figure will be created.
figsize : (float, float), optional
passed to plt.figure. Ignored if *ax* is given.
**kwargs
All other kwargs will passed to the imshow command for the image
"""
# ensure mask colors is iterable and the same length as the number of classes
# choose colors from default color cycle?
self.mask_alpha = mask_alpha
if mask_colors is None:
# this will break if there are more than 10 classes
if nclasses <= 10:
self.mask_colors = to_rgba_array(list(TABLEAU_COLORS)[:nclasses])
else:
# up to 949 classes. Hopefully that is always enough....
self.mask_colors = to_rgba_array(list(XKCD_COLORS)[:nclasses])
else:
self.mask_colors = to_rgba_array(np.atleast_1d(mask_colors))
# should probably check the shape here
self.mask_colors[:, -1] = self.mask_alpha
self._img = np.asarray(img)
if mask is None:
self.mask = np.zeros(self._img.shape[:2])
"""See :doc:`/examples/image-segmentation`."""
else:
self.mask = mask
self._overlay = np.zeros((*self._img.shape[:2], 4))
self.nclasses = nclasses
for i in range(nclasses + 1):
idx = self.mask == i
if i == 0:
self._overlay[idx] = [0, 0, 0, 0]
else:
self._overlay[idx] = self.mask_colors[i - 1]
if ax is not None:
self.ax = ax
self.fig = self.ax.figure
else:
with ioff:
self.fig = figure(figsize=figsize)
self.ax = self.fig.gca()
self.displayed = self.ax.imshow(self._img, **kwargs)
self._mask = self.ax.imshow(self._overlay)
if lineprops is None:
lineprops = {"color": "black", "linewidth": 1, "alpha": 0.8}
useblit = False if "ipympl" in get_backend().lower() else True
button_dict = {"left": 1, "middle": 2, "right": 3}
if isinstance(pan_mousebutton, str):
pan_mousebutton = button_dict[pan_mousebutton.lower()]
if isinstance(lasso_mousebutton, str):
lasso_mousebutton = button_dict[lasso_mousebutton.lower()]
self.lasso = LassoSelector(
self.ax, self._onselect, lineprops=lineprops, useblit=useblit, button=lasso_mousebutton
)
self.lasso.set_visible(True)
pix_x = np.arange(self._img.shape[0])
pix_y = np.arange(self._img.shape[1])
xv, yv = np.meshgrid(pix_y, pix_x)
self.pix = np.vstack((xv.flatten(), yv.flatten())).T
self.ph = panhandler(self.fig, button=pan_mousebutton)
self.disconnect_zoom = zoom_factory(self.ax)
self.current_class = 1
self.erasing = False
def _onselect(self, verts):
self.verts = verts
p = Path(verts)
self.indices = p.contains_points(self.pix, radius=0).reshape(self.mask.shape)
if self.erasing:
self.mask[self.indices] = 0
self._overlay[self.indices] = [0, 0, 0, 0]
else:
self.mask[self.indices] = self.current_class
self._overlay[self.indices] = self.mask_colors[self.current_class - 1]
self._mask.set_data(self._overlay)
self.fig.canvas.draw_idle()
def _ipython_display_(self):
display(self.fig.canvas)
def hyperslicer(
arr,
cmap=None,
norm=None,
aspect=None,
interpolation=None,
alpha=None,
vmin=None,
vmax=None,
vmin_vmax=None,
origin=None,
extent=None,
autoscale_cmap=True,
filternorm=True,
filterrad=4.0,
resample=None,
url=None,
ax=None,
slider_formats=None,
title=None,
force_ipywidgets=False,
play_buttons=False,
is_color_image=False,
controls=None,
display_controls=True,
**kwargs,
):
"""
View slices from a hyperstack of images selected by sliders. Also accepts Xarray.DataArrays
in which case the axes names and coordinates will be inferred from the xarray dims and coords.
Parameters
----------
arr : arraylike or xarray
Hyperstack of images. The last 2 or 3 dimensions will be treated as individiual images.
If an xarray.DataArray then the dimensions will be automatically inferred.
cmap : str or `~matplotlib.colors.Colormap`
The Colormap instance or registered colormap name used to map
scalar data to colors. This parameter is ignored for RGB(A) data.
forwarded to matplotlib
norm : `~matplotlib.colors.Normalize`, optional
The `~matplotlib.colors.Normalize` instance used to scale scalar data to the [0, 1]
range before mapping to colors using *cmap*. By default, a linear
scaling mapping the lowest value to 0 and the highest to 1 is used.
This parameter is ignored for RGB(A) data.
forwarded to matplotlib
autoscale_cmap : bool
If True rescale the colormap for every function update. Will not update
if vmin and vmax are provided or if the returned image is RGB(A) like.
forwarded to matplotlib
aspect : {'equal', 'auto'} or float
forwarded to matplotlib
interpolation : str
forwarded to matplotlib
ax : matplotlib axis, optional
if None a new figure and axis will be created
slider_formats : None, string, or dict
If None a default value of decimal points will be used. Uses the new {} style formatting
title : None or string
If a string then you can have it update automatically using string formatting of the names
of the parameters. i.e. to include the current value of tau: title='the value of tau is: {tau:.2f}'
force_ipywidgets : boolean
If True ipywidgets will always be used, even if not using the ipympl backend.
If False the function will try to detect if it is ok to use ipywidgets
If ipywidgets are not used the function will fall back on matplotlib widgets
play_buttons : bool or str or dict, optional
Whether to attach an ipywidgets.Play widget to any sliders that get created.
If a boolean it will apply to all kwargs, if a dictionary you choose which sliders you
want to attach play buttons too.
- None: no sliders
- True: sliders on the lft
- False: no sliders
- 'left': sliders on the left
- 'right': sliders on the right
is_color_image : boolean
If True, will treat the last 3 dimensions as comprising a color images and will only set up sliders for the first arr.ndim - 3 dimensions.
controls : mpl_interactions.controller.Controls
An existing controls object if you want to tie multiple plot elements to the same set of
controls
display_controls : boolean
Whether the controls should display on creation. Ignored if controls is specified.
Returns
-------
controls
"""
arr = np.squeeze(arr)
arr_type = "numpy"
if "xarray.core.dataarray.DataArray" in str(arr.__class__):
arr_type = "xarray"
elif "dask.array.core.Array" in str(arr.__class__):
arr_type = "dask"
if arr.ndim < 3 + is_color_image:
raise ValueError(
f"arr must be at least {3+is_color_image}D but it is {arr.ndim}D. mpl_interactions.imshow for 2D images."
)
if is_color_image:
im_dims = 3
else:
im_dims = 2
ipympl = notebook_backend()
fig, ax = gogogo_figure(ipympl, ax)
use_ipywidgets = ipympl or force_ipywidgets
slider_format_strings = create_slider_format_dict(slider_formats)
name_to_dim = {}
slices = [0 for i in range(arr.ndim - im_dims)]
names = None
axes = None
if arr_type != "xarray":
if "names" in kwargs:
names = kwargs.pop("names")
elif "axes" in kwargs:
axes = kwargs.pop("axes")
else:
axes = get_hs_axes(arr, is_color_image=is_color_image)
# Just pass in an array - no kwargs
for i in range(arr.ndim - im_dims):
slider_arr_passed = False
start, stop = None, None
name = f"axis{i}"
if name in kwargs:
if len(kwargs[name]) == 2:
start, stop = kwargs.pop(name)
else:
slider_arr_passed = True
slider_arr = kwargs.pop(name)
if axes is not None and axes[i] is not None:
# now we assume the axes[i] has one of the following forms
# ('mu', (0,1))
# ('mu', np.array)
# ('mu', 0, 1)
# (0, 1)
# 'mu'
# np.array or a list
a = axes[i]
if isinstance(a, str):
# axes = ['mu', ]
name = a
elif isinstance(a, tuple):
if len(a) == 3:
# axes = [('mu', 0, 1)]
name = a[0]
kwargs[name] = (*a[1:], arr.shape[i])
elif len(a) == 2:
if isinstance(a[0], str):
# axes = [('mu', (0,1))]
# axes = [('mu', np.linspace())]
# axes = [('mu', {('type1', 'type2', 'type3')}]
name = a[0]
if isinstance(a[1], tuple) or (isinstance(a[1], list) and len(a[1]) == 2):
kwargs[name] = (*a[1], arr.shape[i])
elif isinstance(a[1], np.ndarray) or isinstance(a[1], list):
kwargs[name] = a[1]
elif isinstance(a[1], set):
kwargs[name] = a[1]
elif np.isscalar(a[0]) and np.isscalar(a[1]):
# axes = [(0,1)]
kwargs[name] = (a[0], a[1], arr.shape[i])
slider_format_strings[name] = "{:.0f}"
elif isinstance(a, list) or isinstance(np.ndarray):
# no name only values
kwargs[name] = a
elif names is not None and names[i] is not None:
name = names[i]
name_to_dim[name] = i
if not name in kwargs:
slider_format_strings[name] = "{:.0f}"
kwargs[name] = np.arange(arr.shape[i])
if arr_type == "xarray":
slider_format_strings = get_hs_fmts(arr, is_color_image=is_color_image)
if extent is None:
extent = get_hs_extent(arr, is_color_image=is_color_image)
else:
if "extent" not in kwargs:
extent = None
extra_ctrls = []
funcs, extra_ctrls, param_excluder = prep_scalars(kwargs, vmin=vmin, vmax=vmax, alpha=alpha)
vmin = funcs["vmin"]
vmax = funcs["vmax"]
alpha = funcs["alpha"]
if vmin_vmax is not None:
if isinstance(vmin_vmax, tuple) and not isinstance(vmin_vmax[0], str):
vmin_vmax = ("r", *vmin_vmax)
kwargs["vmin_vmax"] = vmin_vmax
controls, params = gogogo_controls(
kwargs,
controls,
display_controls,
slider_format_strings,
play_buttons,
extra_ctrls,
allow_dupes=True,
)
if vmin_vmax is not None:
params.pop("vmin_vmax")
params["vmin"] = controls.params["vmin"]
params["vmax"] = controls.params["vmax"]
def vmin(**kwargs):
return kwargs["vmin"]
def vmax(**kwargs):
return kwargs["vmax"]
def update(params, indices, cache):
if title is not None:
ax.set_title(title.format(**params))
for k, v in indices.items():
try:
slices[name_to_dim[k]] = v
except KeyError:
# this is necessary to allow things
# like vmax = (240, 250)
pass
new_data = arr[tuple(slices)]
im.set_data(new_data)
if autoscale_cmap and (new_data.ndim != 3) and vmin is None and vmax is None:
im.norm.autoscale(new_data)
if isinstance(vmin, Callable):
im.norm.vmin = vmin(**param_excluder(params, "vmin"))
if isinstance(vmax, Callable):
im.norm.vmax = vmax(**param_excluder(params, "vmax"))
if isinstance(alpha, Callable):
im.set_alpha(callable_else_value_no_cast(alpha, param_excluder(params, "alpha"), cache))
controls._register_function(update, fig, params.keys())
# make it once here so we can use the dims in update
new_data = arr[tuple(0 for i in range(arr.ndim - im_dims))]
im = ax.imshow(
new_data,
cmap=cmap,
norm=norm,
aspect=aspect,
interpolation=interpolation,
alpha=alpha,
vmin=callable_else_value(vmin, params),
vmax=callable_else_value(vmax, params),
origin=origin,
extent=extent,
filternorm=filternorm,
filterrad=filterrad,
resample=resample,
url=url,
)
# this is necessary to make calls to plt.colorbar behave as expected
# i know it's bad news to use private methods :(
# but idk how else to accomplish being a psuedo-pyplot
ax._sci(im)
if title is not None:
ax.set_title(title.format(**params))
return controls