def scatter(self, *args, **kwargs):
""" attr1/2 default to canvas.scatter """
multi= kwargs.pop('multi', False)
colors = kwargs.pop('colors', self._request_plotcolors())
annotate = kwargs.pop('annotate', False)
axes, kwargs = putil._parse_ax(*args, **kwargs)
# Multiplot style
if multi:
raise NotImplementedError
# Overlaid plot style
else:
if not axes:
fig, axes = plt.subplots()
for idx, c in enumerate(self.canvii):
# Only pass annotate once to avoid re-write axis labels/title
if idx == 0 and annotate:
addlabel = True
else:
addlabel = False
c.scatter(axes, color=colors[idx], annotate=addlabel, **kwargs)
if annotate:
axes.legend(self.names)
return axes
def _show(self, *args, **kwargs):
""" show() and patchshow()** wrap their respective Canvas methods,
so any valid arguments (like colormaps, grids etc...) should just
work. In addition, multicanvas show methods have the following
additional keyword arguments:
Parameters
----------
names: bool (False)
Show multicanvas names at top of plot
colors: bool (True):
Map stored color to each particle in subplot.
**kwargs:
Any valid splot arg (ncols, figsize etc...) or show/patchshow
args, such as cmap, grid etc...
If passing a pre-constructed axes/subplots to mc.show(), it must be
as a keyword. As a positional, it will not work!
"""
names = kwargs.pop('names', False)
colors = kwargs.pop('colors', True)
showstyle = kwargs.pop('showstyle', 'show')
if showstyle not in ['show', 'patchshow']:
raise MultiError("showstyle must be show or patchshow, "
"not %s" % showstyle)
axes, kwargs = putil._parse_ax(*args, **kwargs)
if not axes:
axes, kwargs = putil.multi_axes(len(self), **kwargs)
if len(axes) < len(self):
logger.warn("MultiCanvas has %s canvas, but only %s axes recieved"
" in show()" % (len(self), len(axes)))
upperlim = len(axes)
else:
upperlim = len(self)
pcolors = self._request_plotcolors()
for idx in range(upperlim):
c = self.canvii[idx]
if colors:
def cmap(p):
p.color = pcolors[idx]
return p
c = c.pmap(cmap)
if names:
kwargs['title'] = self.names[idx]
getattr(c, showstyle)(axes[idx], **kwargs)
return axes
def scatter(self, *args, **kwargs):
""" Scatter plot of two particles attributes (eg area vs ccircularity).
Parameters
----------
attr1: str
X-attribute
attr2: str
Y-attribute
annotate: False
Adds title, x and y labels
"""
# Would it make more sense to default these to something
attr1 = kwargs.pop('attr1', None)
attr2 = kwargs.pop('attr2', None)
if not attr1 or not attr2:
raise CanvasPlotError('Scatter attributes must be specified as'
' keywords (IE c.scatter(attr1=area, attr2=eccentricity ...)')
annotate = kwargs.pop('annotate', False)
title = kwargs.pop('title', None)
fancy = kwargs.pop('fancy', False)
axes, kwargs = _parse_ax(*args, **kwargs)
if fancy:
raise NotImplementedError("Fancy kwarg not yet supported.")
if not axes:
fig, axes = plt.subplots()
x, y = getattr(self, attr1), getattr(self, attr2)
axes.scatter(x, y, **kwargs)
if annotate:
if not title:
title = '%s - %s' % (attr1.title(), attr2.title())
axes.set_xlabel(attr1)
axes.set_ylabel(attr2)
if title:
axes.set_title(title)
return axes
def show(self, *args, **kwargs):
""" Wrapper to imshow. Converts to gray to allow color maps.
Notes
-----
Differs from patchshow in that the collective image (bg, grid, particles)
is a series of masks, so they have to be drawn onto a single ndarray and
then plotted. Sicne patchshow is writing patchs, it can plot the background
separate from the grid and particles, which is slightly easier"""
# This will pull out "ax", leaving remaing args/kwargs
axes, kwargs = _parse_ax(*args, **kwargs)
title = kwargs.pop('title', None)
save = kwargs.pop('save', None)
bgonly = kwargs.pop('bgonly', False)
annotate = kwargs.pop('annotate', False)
grid = kwargs.pop('grid', False)
gcolor = kwargs.pop('gcolor', None)
gunder = kwargs.pop('gunder', False)
gstyle = kwargs.pop('gstyle', None) #NOT USED
nolabel = kwargs.pop('nolabel', False)
zoom = kwargs.pop('zoom', None)
if gstyle:
raise CanvasPlotError('"gstyle" only valid for patchshow()')
if 'pmap' in kwargs:
raise CanvasPlotError('"pmap" is only valid for patchshow() method')
PBINARY = False
if 'cmap' in kwargs:
if kwargs['cmap'] == 'pbinary' or kwargs['cmap'] == 'pbinary_r':
PBINARY = kwargs['cmap']
del kwargs['cmap']
# Get the background
if bgonly:
if 'cmap' not in kwargs:
raise CanvasPlotError('"bgonly" is only valid when a colormap is'
' passed.')
bg = kwargs['cmap'](self.graybackground)[... , :3]
del kwargs['cmap']
else:
bg = self.background
if PBINARY:
if PBINARY == 'pbinary_r':
bg = np.ones(bg.shape).astype(bool)
else:
bg = np.zeros(bg.shape).astype(bool)
# grid overlay
if gcolor or gunder and not grid:
grid = True
# If user enters gcolor, assume default grid
if grid and not gcolor:
gcolor = GCOLOR
# Map attributes from grid (centers, corners, grid)
gattr = np.zeros(bg.shape).astype(bool) #IE pass
if grid:
if not gcolor:
gcolor = GCOLOR
if grid == True:
grid = 'grid'
# Validate grid keyword
try:
gattr=getattr( self.grid, grid.lower() )
except Exception:
raise CanvasPlotError('Invalid grid argument, "%s". Choose from: '
'True, "grid", "centers", "corners", "hlines", "vlines"'
% grid)
gcolor = to_normrgb(gcolor)
#Draw grid over or under?
if gunder:
bg[gattr] = gcolor
image = self._draw_particles(bg, force_binary=PBINARY)
else:
image = self._draw_particles(bg, force_binary=PBINARY)
image[gattr] = gcolor
# GRAY CONVERT
if 'cmap' in kwargs:
image = rgb2uint(image)
# Matplotlib
if axes:
axes.imshow(image, **kwargs)
else:
axes = plt.imshow(image, **kwargs).axes
axes = self._annotate_plot(axes, annotate, title)
# SHOW DOESNT ACTUALLY CROP ANYTHING WHEN ZOOMING
if zoom:
xi, yi, xf, yf = zoom
axes.set_xlim(xi, xf)
axes.set_ylim(yf, yi)
if nolabel:
axes.xaxis.set_visible(False)
axes.yaxis.set_visible(False)
if nolabel == 'x':
axes.yaxis.set_visible(True)
elif nolabel == 'y':
axes.xaxis.set_visible(True)
if save:
path = _parse_path(save)
skimage.io.imsave(path, image)
return axes
def patchshow(self, *args, **kwargs):
""" ...
args/kwargs include alpha, edgecolors, linestyles
Notes:
Matplotlib API is setup that args or kwargs can be entered. Order is
important for args, but the correspond to same kwargs.
"""
axes, kwargs = _parse_ax(*args, **kwargs)
title = kwargs.pop('title', None)
bgonly = kwargs.pop('bgonly', False)
annotate = kwargs.pop('annotate', False)
zoom = kwargs.pop('zoom', None)
grid = kwargs.pop('grid', False)
gcolor = kwargs.pop('gcolor', None)
gstyle = kwargs.pop('gstyle', None)
gunder = kwargs.pop('gunder',False)
pmap = kwargs.pop('pmap', None)
nolabel = kwargs.pop('nolabel', None)
alpha = kwargs.get('alpha', None)
edgecolor = kwargs.get('edgecolor', None)
linewidth = kwargs.get('linewidth', None)
linestyle = kwargs.get('linestyle', None)
# Some keywords to savefig; not all supported
save = kwargs.pop('save', None)
dpi = kwargs.pop('dpi', None)
bbox_inches = kwargs.pop('bbox_inches', None)
# GET NOT POP
cmap = kwargs.get('cmap', None)
# Implement later
if cmap in ['pbinary', 'pbinary_r']:
raise CanvasPlotError('"pbinary(_r)" color map only valid for .show()')
# grid defaults
if gcolor or gunder or gstyle and not grid:
grid = True
# If user enters gcolor/gstyle, assume default grid
if grid and not gcolor:
gcolor = GCOLOR
if grid and not gstyle:
gstyle = 'solid'
# Corner case, don't touch
if pmap and cmap and bgonly:
bgonly = False
if bgonly and not cmap:
raise CanvasPlotError('"bgonly" is only valid when a colormap is'
' passed.')
if cmap:
bg = self.graybackground
else:
bg = self.background
if zoom:
xi, yi, xf, yf = zoom
bg = crop(bg, zoom)
#Overwrite axes image
if not axes:
fig, axes = plt.subplots()
else:
axes.images=[]
# DONT PASS ALL KWARGS
axes.imshow(bg, cmap=cmap)
# FOR PATICLES IN IMAGE ONLY.
in_and_edges = self.pin + self.pedge
# PARTICLE FACECOLOR, ALPHA and other PATCH ARGS
# http://matplotlib.org/api/artist_api.html#matplotlib.patches.Patch
patches = [p.particle.as_patch(facecolor=p.color, alpha=alpha,
edgecolor=edgecolor, linestyle=linestyle, linewidth=linewidth)
for p in in_and_edges]
# If no particles or grid, just pass to avoid deep mpl exceptiosn
if patches or grid:
if patches:
if pmap:
kwargs['cmap'] = pmap
if 'cmap' in kwargs and not bgonly:
ppatch = PatchCollection(patches, **kwargs) #cmap and Patch Args
ppatch.set_array(np.arange(len(patches)))
# Use settings passed to "patches"
else:
ppatch = PatchCollection(patches, match_original=True, **kwargs) #
# Grid under particles
if gunder:
axes.add_collection(self.grid.as_patch(
edgecolors=gcolor, linestyles=gstyle))
if patches:
axes.add_collection(ppatch)
# Grid over particles
else:
if patches:
axes.add_collection(ppatch)
if grid:
axes.add_collection(self.grid.as_patch(
edgecolors=gcolor, linestyles=gstyle))
axes = self._annotate_plot(axes, annotate, title)
if zoom:
axes.set_xlim(xi, xf)
axes.set_ylim(yf, yi)
if nolabel:
axes.xaxis.set_visible(False)
axes.yaxis.set_visible(False)
if nolabel == 'x':
axes.yaxis.set_visible(True)
elif nolabel == 'y':
axes.xaxis.set_visible(True)
if save:
path = _parse_path(save)
plt.savefig(path, dpi=dpi, bbox_inches=bbox_inches)
return axes
def hist(self, *histargs, **histkwargs):
""" Matplotlib histogram wrapper.
Parameters
----------
**annotate:** bool - True
Add general legend, title, axis labels.
**attr:** str - "area"
Particle attribute for data. (Also a pie chart keyword).
**xlim:** range(start, stop) - None
Shortcut to set x-limits. If **xlim=auto**, absolute min and
absolute max of data will be used.
This crops data AND sets axis limits; to only change plot axes,
use *axes.set_xlim()*.
Notes
-----
We see that the **annotate** option adds a legend, title and axis
labels. The default attribute of the histogram is *area*,
corresponding to the **attr** kwargs. All other valid matplotlib
histogram kwargs should work.
"""
annotate = histkwargs.pop('annotate', True)
attr = histkwargs.pop('attr', 'area')
histkwargs.setdefault('stacked', True)
histkwargs.setdefault('label', self.names)
histkwargs.setdefault('bins', 10)
metavar = histkwargs.pop('metavar', None)
xlim = histkwargs.pop('xlim', None)
#MPL api asymmetry with pie
if 'colors' in histkwargs:
histkwargs['color'] = histkwargs.pop('colors')
# If not colors, set colors based on _colors
if 'color' not in histkwargs:
histkwargs['color'] = self._request_plotcolors()
axes, histkwargs = putil._parse_ax(*histargs, **histkwargs)
if not axes:
fig, axes = plt.subplots()
# Get list of arrays for descriptor, slice if xlim
attr_list = [getattr(c, attr) for c in self.canvii]
if xlim:
if xlim == 'auto':
xi, xf = min(map(min, attr_list)), max(map(max, attr_list))
else:
xi, xf = xlim
attr_list = [array[(array >= xi) & (array <= xf)]
for array in attr_list]
axes.set_xlim(xi, xf)
# Validate attr_list for empy arrays; avoid ambiguous mpl error
for idx, array in enumerate(attr_list):
if len(array) == 0:
raise MultiError('Empty array returned for "%s" attribute'
' on "%s" Canvas.' % (attr, self.names[idx]) )
axes.hist(attr_list, **histkwargs)
if annotate:
axes.set_xlabel(attr.title()) #Capitalize first letter
axes.set_ylabel('Counts')
if metavar:
attr = metavar
axes.set_title('%s Distribution (%s bins)' %
(attr.title(), histkwargs['bins']) )
axes.legend()
return axes
def pie(self, *chartargs, **chartkwargs):
""" Pie chart wrapper to matplotlib.
Parameters
----------
attr : Str or None
Sum of variable to show in the pie. Generally particle
descriptor (eg area). If None or "count", the particle
counts by species are used.
annotate : Bool (True)
Add auto title to piechart.
autopct : str or fcn or None
Label of pie slices. Some built in short cuts include
"count", "percentage", "both". Note results in no labels.
See matplotlib.pie for more.
usetex : bool (False)
Label of pie slcies use latex rendering. If matplotlib.rcparams
usetex = True, then set this to True.
"""
attr = chartkwargs.pop('attr', None)
annotate = chartkwargs.pop('annotate', True)
usetex = chartkwargs.pop('usetex', False)
chartkwargs.setdefault('shadow', False)
metavar = chartkwargs.pop('metavar', None)
# If not colors, set colors based on _colors
if 'colors' not in chartkwargs:
chartkwargs['colors'] = self._request_plotcolors()
if annotate:
autopct = chartkwargs.get('autopct', 'percent')
chartkwargs.setdefault('labels', self.names)
else:
autopct = chartkwargs.get('autopct', None)
axes, chartkwargs = putil._parse_ax(*chartargs, **chartkwargs)
if not axes:
fig, axes = plt.subplots()
if attr is None or attr == 'count':
attr_list = [len(c) for c in self.canvii]
attr = 'number' # for title
else:
attr_list = [sum(getattr(c, attr)) for c in self.canvii]
# Percentage or true values
if autopct == 'percent':
if usetex:
chartkwargs['autopct'] = r'\bf{%.1f\%%}' #Label size and position
else:
chartkwargs['autopct'] = '%.1f%%' #Label size and position
elif autopct == 'count':
if usetex:
chartkwargs['autopct'] = \
lambda(p): r'\bf{:.0f}'.format(p * sum(attr_list) / 100)
else:
chartkwargs['autopct'] = \
lambda(p): '{:.0f}'.format(p * sum(attr_list) / 100)
elif autopct == 'both':
def double_autopct(pct):
total = sum(attr_list)
val = int(round(pct*total/100.0, 0))
if usetex:
texstring = r'{v} ({p:.1f}\%)'.format(p=pct,v=val)
return r'\bf{%s}' % texstring
else:
return '{v}\n({p:.1f}%)'.format(p=pct,v=val)
chartkwargs['autopct'] = double_autopct
axes.pie(attr_list, **chartkwargs)
if annotate:
if metavar:
attr = metavar
axes.set_title('%s Distribution' % attr.title())
return axes