def barh(*args, **kwargs):
"""
Creates a bar plot, with white outlines and a fill color that defaults to
the first teal-ish green in ColorBrewer's Set2. Optionally accepts
grid='y' or grid='x' to draw a white grid over the bars,
to show the scale. Almost like "erasing" some of the plot,
but it adds more information!
Can also add an annotation of the width of the barplots directly onto
the bars with the `annotate` parameter, which can either be True,
which will annotate the values, or a list of strings, which will annotate
with the supplied strings.
Can support stacked bars with the value of each stack shown on the stack
(Added by Salil Banerjee)
@param ax: matplotlib.axes instance
@param top: Vector of values of where to put the top side of the bar
@param width: Vector of values of the bar widths
@param ytickabels: Vector of labels of the bar widths
@param kwargs: Any additional arguments to matplotlib.bar()
"""
ax, args, kwargs = maybe_get_ax(*args, **kwargs)
kwargs.setdefault('color', set2[0])
kwargs.setdefault('edgecolor', 'white')
middle = 0.4 if 'width' not in kwargs else kwargs['width'] / 2.0
# Check if data contains stacks
stacked = kwargs.pop('stacked', False)
# Check if stack text should be included
stack_text = kwargs.pop('stack_text', False)
# Get legend if available
legend = kwargs.pop('legend', False)
top = args[0]
width = np.array(args[1])
# Label each individual bar, if xticklabels is provided
ytickabels = kwargs.pop('yticklabels', None)
# left+0.4 is the center of the bar
yticks = np.array(top) + middle
# Whether or not to annotate each bar with the width value
annotate = kwargs.pop('annotate', False)
# If no grid specified, don't draw one.
grid = kwargs.pop('grid', None)
cmap = kwargs.pop('cmap', False)
if cmap:
kwargs['edgecolor'] = almost_black
if not stacked:
kwargs['color'] = getcolors(cmap, width, 0)
# Check if stacked and plot data accordingly
if stacked:
num_stacks, num_data = width.shape
left = np.zeros(num_data)
for i in np.arange(num_stacks):
lst = list(args)
lst[1] = width[i]
args = tuple(lst)
if cmap:
kwargs['color'] = getcolors(cmap, width[i], i)
else:
kwargs['color'] = set2[i]
kwargs['left'] = left
rectangles = ax.barh(*args, **kwargs)
left += width[i]
else:
rectangles = ax.barh(*args, **kwargs)
# add legend
if isinstance(legend, collections.Iterable):
ax.legend(legend,
loc='upper center',
bbox_to_anchor=(0.5, 1.11),
ncol=5)
# add whitespace padding on left
ymin, ymax = ax.get_ylim()
ymin -= 0.2
if stacked:
ymax = num_data
ax.set_ylim(ymin, ymax)
# If there are negative counts, remove the bottom axes
# and add a line at y=0
if any(w < 0 for w in width.tolist()):
axes_to_remove = ['top', 'right', 'bottom']
ax.vlines(x=0, ymin=ymin, ymax=ymax, linewidths=0.75)
#ax.hlines(y=0, xmin=xmin, xmax=xmax,
# linewidths=0.75)
else:
axes_to_remove = ['top', 'right']
#Remove excess axes
remove_chartjunk(ax, axes_to_remove, grid=grid)
if stacked:
data = width
width = width.sum(axis=0)
# Add the yticklabels if they are there
if ytickabels is not None:
ax.set_yticks(yticks)
ax.set_yticklabels(ytickabels)
if annotate or isinstance(annotate, collections.Iterable):
annotate_xrange_factor = 0.050
xmin, xmax = ax.get_xlim()
xrange = xmax - xmin
# Reset ymax and ymin so there's enough room to see the annotation of
# the top-most
if xmax > 0:
xmax += xrange * 0.1
if xmin < 0:
xmin -= xrange * 0.1
ax.set_xlim(xmin, xmax)
xrange = xmax - xmin
offset_ = xrange * annotate_xrange_factor
if isinstance(annotate, collections.Iterable):
annotations = map(str, annotate)
else:
annotations = [
'%.3f' % w if type(w) is np.float_ else str(w) for w in width
]
for y, w, annotation in zip(yticks, width, annotations):
# Adjust the offset to account for negative bars
offset = offset_ if w >= 0 else -1 * offset_
# Finally, add the text to the axes
ax.annotate(annotation, (w + offset, y),
verticalalignment='center',
horizontalalignment='center',
color=almost_black)
# Text for each block of stack
# This was partially inspired by the following article by Tableau software
# http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812
if stack_text:
left = np.zeros(num_data)
max_w = max(width)
for i in np.arange(num_stacks):
for y, d, l in zip(yticks, data[i], left):
if (d * 100.0 / max_w) > 2.0:
ax.text(l + d / 2.0,
y,
d,
ha='center',
va='center',
color=almost_black)
left += data[i]
return rectangles
def bar(*args, **kwargs):
"""
Creates a bar plot, with white outlines and a fill color that defaults to
the first teal-ish green in ColorBrewer's Set2. Optionally accepts
grid='y' or grid='x' to draw a white grid over the bars,
to show the scale. Almost like "erasing" some of the plot,
but it adds more information!
Can also add an annotation of the height of the barplots directly onto
the bars with the `annotate` parameter, which can either be True,
which will annotate the values, or a list of strings, which will annotate
with the supplied strings.
Can support stacked bars with the value of each stack shown on the stack
(Added by Salil Banerjee)
@param ax: matplotlib.axes instance
@param left: Vector of values of where to put the left side of the bar
@param height: Vector of values of the bar heights
@param kwargs: Besides xticklabels, which is a prettyplotlib-specific
argument, any additional arguments to matplotlib.bar(): http://matplotlib
.org/api/axes_api.html#matplotlib.axes.Axes.bar is accepted.
"""
ax, args, kwargs = maybe_get_ax(*args, **kwargs)
kwargs.setdefault('color', set2[0])
kwargs.setdefault('edgecolor', 'white')
middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0
# Check if data contains stacks
stacked = kwargs.pop('stacked',False)
# Check if stack text should be included
stack_text = kwargs.pop('stack_text',False)
# Get legend if available
legend = kwargs.pop('legend',False)
left = args[0]
height = np.array(args[1])
# Label each individual bar, if xticklabels is provided
xtickabels = kwargs.pop('xticklabels', None)
# left+0.4 is the center of the bar
xticks = np.array(left) + middle
# Whether or not to annotate each bar with the height value
annotate = kwargs.pop('annotate', False)
show_ticks = kwargs.pop('show_ticks', False)
# If no grid specified, don't draw one.
grid = kwargs.pop('grid', None)
cmap = kwargs.pop('cmap', False)
if cmap:
kwargs['edgecolor'] = almost_black
if not stacked:
kwargs['color'] = getcolors(cmap, height, 0)
# Check if stacked and plot data accordingly
color = kwargs.get('color', None)
if stacked:
num_stacks, num_data = height.shape
bottom = np.zeros(num_data)
for i in np.arange(num_stacks):
lst = list(args)
lst[1] = height[i]
args = tuple(lst)
# make sure number of user specified colors equals to the stacks
if not color or len(color) != num_stacks:
if cmap:
kwargs['color'] = getcolors(cmap, height[i], i)
else:
kwargs['color'] = set2[i]
else:
kwargs['color'] = color[i]
kwargs['bottom'] = bottom
rectangles = ax.bar(*args, **kwargs)
bottom += height[i]
else:
rectangles = ax.bar(*args, **kwargs)
# add legend
if isinstance(legend, collections.Iterable):
ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)
# add whitespace padding on left
xmin, xmax = ax.get_xlim()
xmin -= 0.2
if stacked:
xmax = num_data
ax.set_xlim(xmin, xmax)
# If the user is only plotting one bar, make it an iterable
if not isinstance(height, collections.Iterable):
height = [height]
# If there are negative counts, remove the bottom axes
# and add a line at y=0
if any(h < 0 for h in height.tolist()):
axes_to_remove = ['top', 'right', 'bottom']
ax.hlines(y=0, xmin=xmin, xmax=xmax,
linewidths=0.75)
else:
axes_to_remove = ['top', 'right']
# Remove excess axes
remove_chartjunk(ax, axes_to_remove, grid=grid, show_ticks=show_ticks)
if stacked:
data = height
height = height.sum(axis=0)
# Add the xticklabels if they are there
if xtickabels is not None:
ax.set_xticks(xticks)
ax.set_xticklabels(xtickabels)
if annotate or isinstance(annotate, collections.Iterable):
annotate_yrange_factor = 0.025
ymin, ymax = ax.get_ylim()
yrange = ymax - ymin
# Reset ymax and ymin so there's enough room to see the annotation of
# the top-most
if ymax > 0:
ymax += yrange * 0.1
if ymin < 0:
ymin -= yrange * 0.1
ax.set_ylim(ymin, ymax)
yrange = ymax - ymin
offset_ = yrange * annotate_yrange_factor
if isinstance(annotate, collections.Iterable):
annotations = map(str, annotate)
else:
annotations = ['%.3f' % h if type(h) is np.float_ else str(h)
for h in height]
for x, h, annotation in zip(xticks, height, annotations):
# Adjust the offset to account for negative bars
offset = offset_ if h >= 0 else -1 * offset_
verticalalignment = 'bottom' if h >= 0 else 'top'
# Finally, add the text to the axes
ax.annotate(annotation, (x, h + offset),
verticalalignment=verticalalignment,
horizontalalignment='center',
color=almost_black)
# Text for each block of stack
# This was partially inspired by the following article by Tableau software
# http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812
if stack_text:
bottom = np.zeros(num_data)
max_h = max(height)
for i in np.arange(num_stacks):
for x, d, b in zip(xticks, data[i], bottom):
if (d*100.0/max_h) > 4.0:
ax.text(x,b+d/2.0,d, ha='center', va='center', color=almost_black)
bottom += data[i]
return rectangles
def barh(*args, **kwargs):
"""
Creates a bar plot, with white outlines and a fill color that defaults to
the first teal-ish green in ColorBrewer's Set2. Optionally accepts
grid='y' or grid='x' to draw a white grid over the bars,
to show the scale. Almost like "erasing" some of the plot,
but it adds more information!
Can also add an annotation of the width of the barplots directly onto
the bars with the `annotate` parameter, which can either be True,
which will annotate the values, or a list of strings, which will annotate
with the supplied strings.
Can support stacked bars with the value of each stack shown on the stack
(Added by Salil Banerjee)
@param ax: matplotlib.axes instance
@param top: Vector of values of where to put the top side of the bar
@param width: Vector of values of the bar widths
@param ytickabels: Vector of labels of the bar widths
@param kwargs: Any additional arguments to matplotlib.bar()
"""
ax, args, kwargs = maybe_get_ax(*args, **kwargs)
kwargs.setdefault('color', set2[0])
kwargs.setdefault('edgecolor', 'white')
middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0
# Check if data contains stacks
stacked = kwargs.pop('stacked',False)
# Check if stack text should be included
stack_text = kwargs.pop('stack_text',False)
# Get legend if available
legend = kwargs.pop('legend',False)
top = args[0]
width = np.array(args[1])
# Label each individual bar, if xticklabels is provided
ytickabels = kwargs.pop('yticklabels', None)
# left+0.4 is the center of the bar
yticks = np.array(top) + middle
# Whether or not to annotate each bar with the width value
annotate = kwargs.pop('annotate', False)
# If no grid specified, don't draw one.
grid = kwargs.pop('grid', None)
cmap = kwargs.pop('cmap', False)
if cmap:
kwargs['edgecolor'] = almost_black
if not stacked:
kwargs['color'] = getcolors(cmap, width, 0)
# Check if stacked and plot data accordingly
if stacked:
num_stacks, num_data = width.shape
left = np.zeros(num_data)
for i in np.arange(num_stacks):
lst = list(args)
lst[1] = width[i]
args = tuple(lst)
if cmap:
kwargs['color'] = getcolors(cmap, width[i], i)
else:
kwargs['color'] = set2[i]
kwargs['left'] = left
rectangles = ax.barh(*args, **kwargs)
left += width[i]
else:
rectangles = ax.barh(*args, **kwargs)
# add legend
if isinstance(legend, collections.Iterable):
ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)
# add whitespace padding on left
ymin, ymax = ax.get_ylim()
ymin -= 0.2
if stacked:
ymax = num_data
ax.set_ylim(ymin, ymax)
# If there are negative counts, remove the bottom axes
# and add a line at y=0
if any(w < 0 for w in width.tolist()):
axes_to_remove = ['top', 'right', 'bottom']
ax.vlines(x=0, ymin=ymin, ymax=ymax,
linewidths=0.75)
#ax.hlines(y=0, xmin=xmin, xmax=xmax,
# linewidths=0.75)
else:
axes_to_remove = ['top', 'right']
#Remove excess axes
remove_chartjunk(ax, axes_to_remove, grid=grid)
if stacked:
data = width
width = width.sum(axis=0)
# Add the yticklabels if they are there
if ytickabels is not None:
ax.set_yticks(yticks)
ax.set_yticklabels(ytickabels)
if annotate or isinstance(annotate, collections.Iterable):
annotate_xrange_factor = 0.050
xmin, xmax = ax.get_xlim()
xrange = xmax - xmin
# Reset ymax and ymin so there's enough room to see the annotation of
# the top-most
if xmax > 0:
xmax += xrange * 0.1
if xmin < 0:
xmin -= xrange * 0.1
ax.set_xlim(xmin, xmax)
xrange = xmax - xmin
offset_ = xrange * annotate_xrange_factor
if isinstance(annotate, collections.Iterable):
annotations = map(str, annotate)
else:
annotations = ['%.3f' % w if type(w) is np.float_ else str(w)
for w in width]
for y, w, annotation in zip(yticks, width, annotations):
# Adjust the offset to account for negative bars
offset = offset_ if w >= 0 else -1 * offset_
# Finally, add the text to the axes
ax.annotate(annotation, (w + offset, y),
verticalalignment='center',
horizontalalignment='center',
color=almost_black)
# Text for each block of stack
# This was partially inspired by the following article by Tableau software
# http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812
if stack_text:
left = np.zeros(num_data)
max_w = max(width)
for i in np.arange(num_stacks):
for y, d, l in zip(yticks, data[i], left):
if (d*100.0/max_w) > 2.0:
ax.text(l+d/2.0,y,d, ha='center', va='center', color=almost_black)
left += data[i]
return rectangles