[ENH]: Multi hatching in `ax.stackplot()` · Issue #27146 · matplotlib/matplotlib · GitHub

Problem

ax.stackplot() is used for conveniently plotting stacked area plot.

The documentation says that any **kwargs is passed on to ax.fill_between.

So, it all works fine if I work with colours, but not so much with hatching pattern.

If I want to add hatching, this is what I get by default:

import matplotlib.pyplot as plt
import numpy as np

# Data
cols = 10
rows = 4

data = (
    np.reshape(np.arange(0, cols, 1), (1, -1)) ** 2
    + np.reshape(np.arange(0, rows), (-1, 1))
    + np.random.random((rows, cols))*5
)

# Plot
fig, ax = plt.subplots(
    figsize=(10,6),
    facecolor="white",
    layout="constrained"
)
fig.suptitle(
    "with hatching",
    fontsize=25,
    weight="bold"
)

x = range(data.shape[1])
ax.stackplot(
    x, data,
    hatch="x"
)

ax.set_xlim(0, 9)
ax.set_ylim(0, 350)

The outcome:

But I would be more interested in getting something like this:

Proposed solution

The calling signature would be the same as before with exception that the hatch parameter would accept e.g. a list with hatching styles you want to use.

Example:

ax.stackplot(
    x, data,
    hatch=["x", "\\", ".", "-"]
)

The example I've used above in the problem was create using the code below and in this case it varies the hatching by the density:

fig, ax = plt.subplots(
    figsize=(10,6),
    facecolor="white",
    layout="constrained"
)
fig.suptitle(
    "with individual hatching",
    fontsize=25,
    weight="bold"
)

x = range(data.shape[1])
stack_baseline = np.zeros((data.shape[1]))
for i, y in enumerate(data):
    ax.fill_between(
        x, stack_baseline, y+stack_baseline,
        facecolor=(1, 1, 1, 0),
        hatch="x"*(data.shape[0]-i)
    )
    stack_baseline += y

ax.set_xlim(0, 9)
ax.set_ylim(0, 350)

Hello!
Don't you think 'with hatching' looks cleaner and makes it easier to extract insights? It's never a bad idea to have another style of representation at our disposal, but what is your opinion on this?

To me this request seems analogous to the hatch parameter added to pie at v3.7.
https://matplotlib.org/stable/users/prev_whats_new/whats_new_3.7.0.html#hatch-parameter-for-pie

Good first issue - notes for new contributors

This issue is suited to new contributors because it does not require understanding of the Matplotlib internals. To get started, please see our contributing guide.

We do not assign issues. Check the Development section in the sidebar for linked pull requests (PRs). If there are none, feel free to start working on it. If there is an open PR, please collaborate on the work by reviewing it rather than duplicating it in a competing PR.

If something is unclear, please reach out on any of our communication channels.

This is a reaonable request.

It's a good first issue because the change is local in

matplotlib/lib/matplotlib/stackplot.py

Lines 18 to 127 in 3958242

    
           def stackplot(axes, x, *args, 
        
                         labels=(), colors=None, baseline='zero', 
        
                         **kwargs): 
        
               """ 
        
               Draw a stacked area plot. 
        
               Parameters 
        
               ---------- 
        
               x : (N,) array-like 
        
               y : (M, N) array-like 
        
                   The data is assumed to be unstacked. Each of the following 
        
                   calls is legal:: 
        
                       stackplot(x, y)           # where y has shape (M, N) 
        
                       stackplot(x, y1, y2, y3)  # where y1, y2, y3, y4 have length N 
        
               baseline : {'zero', 'sym', 'wiggle', 'weighted_wiggle'} 
        
                   Method used to calculate the baseline: 
        
                   - ``'zero'``: Constant zero baseline, i.e. a simple stacked plot. 
        
                   - ``'sym'``:  Symmetric around zero and is sometimes called 
        
                     'ThemeRiver'. 
        
                   - ``'wiggle'``: Minimizes the sum of the squared slopes. 
        
                   - ``'weighted_wiggle'``: Does the same but weights to account for 
        
                     size of each layer. It is also called 'Streamgraph'-layout. More 
        
                     details can be found at http://leebyron.com/streamgraph/. 
        
               labels : list of str, optional 
        
                   A sequence of labels to assign to each data series. If unspecified, 
        
                   then no labels will be applied to artists. 
        
               colors : list of color, optional 
        
                   A sequence of colors to be cycled through and used to color the stacked 
        
                   areas. The sequence need not be exactly the same length as the number 
        
                   of provided *y*, in which case the colors will repeat from the 
        
                   beginning. 
        
                   If not specified, the colors from the Axes property cycle will be used. 
        
               data : indexable object, optional 
        
                   DATA_PARAMETER_PLACEHOLDER 
        
               **kwargs 
        
                   All other keyword arguments are passed to `.Axes.fill_between`. 
        
               Returns 
        
               ------- 
        
               list of `.PolyCollection` 
        
                   A list of `.PolyCollection` instances, one for each element in the 
        
                   stacked area plot. 
        
               """ 
        
               y = np.vstack(args) 
        
               labels = iter(labels) 
        
               if colors is not None: 
        
                   colors = itertools.cycle(colors) 
        
               else: 
        
                   colors = (axes._get_lines.get_next_color() for _ in y) 
        
               # Assume data passed has not been 'stacked', so stack it here. 
        
               # We'll need a float buffer for the upcoming calculations. 
        
               stack = np.cumsum(y, axis=0, dtype=np.promote_types(y.dtype, np.float32)) 
        
               _api.check_in_list(['zero', 'sym', 'wiggle', 'weighted_wiggle'], 
        
                                  baseline=baseline) 
        
               if baseline == 'zero': 
        
                   first_line = 0. 
        
               elif baseline == 'sym': 
        
                   first_line = -np.sum(y, 0) * 0.5 
        
                   stack += first_line[None, :] 
        
               elif baseline == 'wiggle': 
        
                   m = y.shape[0] 
        
                   first_line = (y * (m - 0.5 - np.arange(m)[:, None])).sum(0) 
        
                   first_line /= -m 
        
                   stack += first_line 
        
               elif baseline == 'weighted_wiggle': 
        
                   total = np.sum(y, 0) 
        
                   # multiply by 1/total (or zero) to avoid infinities in the division: 
        
                   inv_total = np.zeros_like(total) 
        
                   mask = total > 0 
        
                   inv_total[mask] = 1.0 / total[mask] 
        
                   increase = np.hstack((y[:, 0:1], np.diff(y))) 
        
                   below_size = total - stack 
        
                   below_size += 0.5 * y 
        
                   move_up = below_size * inv_total 
        
                   move_up[:, 0] = 0.5 
        
                   center = (move_up - 0.5) * increase 
        
                   center = np.cumsum(center.sum(0)) 
        
                   first_line = center - 0.5 * total 
        
                   stack += first_line 
        
               # Color between x = 0 and the first array. 
        
               coll = axes.fill_between(x, first_line, stack[0, :], 
        
                                        facecolor=next(colors), label=next(labels, None), 
        
                                        **kwargs) 
        
               coll.sticky_edges.y[:] = [0] 
        
               r = [coll] 
        
               # Color between array i-1 and array i 
        
               for i in range(len(y) - 1): 
        
                   r.append(axes.fill_between(x, stack[i, :], stack[i + 1, :], 
        
                                              facecolor=next(colors), 
        
                                              label=next(labels, None), 
        
                                              **kwargs)) 
        
               return r

and you can simply handle hatching analogous to the existing color handling.

Hello! Don't you think 'with hatching' looks cleaner and makes it easier to extract insights? It's never a bad idea to have another style of representation at our disposal, but what is your opinion on this?

To me, uniform hatching covering the whole data area is purely decorative and you still need colours to understand the data. As oppose to when you have individual hatching styles. Then you can go only with the hatching fill and remove the colours. Scientific papers are often required to be made in a way so you can print them in black and white. And they often use hatching for this purpose to distinguish between different areas.

Hi! I agree on the usefulness of having different hatching styles in a stack plot, and had a go at implementing this following the guidance above (many thanks for making this so clear and newbie-friendly BTW!) in the draft PR: #27158

[ENH]: Multi hatching in `ax.stackplot()` · Issue #27146 · matplotlib/matplotlib · GitHub

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[ENH]: Multi hatching in ax.stackplot() #27146

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	def stackplot(axes, x, *args,
	labels=(), colors=None, baseline='zero',
	**kwargs):
	"""
	Draw a stacked area plot.

	Parameters
	----------
	x : (N,) array-like

	y : (M, N) array-like
	The data is assumed to be unstacked. Each of the following
	calls is legal::

	stackplot(x, y) # where y has shape (M, N)
	stackplot(x, y1, y2, y3) # where y1, y2, y3, y4 have length N

	baseline : {'zero', 'sym', 'wiggle', 'weighted_wiggle'}
	Method used to calculate the baseline:

	- ``'zero'``: Constant zero baseline, i.e. a simple stacked plot.
	- ``'sym'``: Symmetric around zero and is sometimes called
	'ThemeRiver'.
	- ``'wiggle'``: Minimizes the sum of the squared slopes.
	- ``'weighted_wiggle'``: Does the same but weights to account for
	size of each layer. It is also called 'Streamgraph'-layout. More
	details can be found at http://leebyron.com/streamgraph/.

	labels : list of str, optional
	A sequence of labels to assign to each data series. If unspecified,
	then no labels will be applied to artists.

	colors : list of color, optional
	A sequence of colors to be cycled through and used to color the stacked
	areas. The sequence need not be exactly the same length as the number
	of provided y, in which case the colors will repeat from the
	beginning.

	If not specified, the colors from the Axes property cycle will be used.

	data : indexable object, optional
	DATA_PARAMETER_PLACEHOLDER

	**kwargs
	All other keyword arguments are passed to `.Axes.fill_between`.

	Returns
	-------
	list of `.PolyCollection`
	A list of `.PolyCollection` instances, one for each element in the
	stacked area plot.
	"""

	y = np.vstack(args)

	labels = iter(labels)
	if colors is not None:
	colors = itertools.cycle(colors)
	else:
	colors = (axes._get_lines.get_next_color() for _ in y)

	# Assume data passed has not been 'stacked', so stack it here.
	# We'll need a float buffer for the upcoming calculations.
	stack = np.cumsum(y, axis=0, dtype=np.promote_types(y.dtype, np.float32))

	_api.check_in_list(['zero', 'sym', 'wiggle', 'weighted_wiggle'],
	baseline=baseline)
	if baseline == 'zero':
	first_line = 0.

	elif baseline == 'sym':
	first_line = -np.sum(y, 0) * 0.5
	stack += first_line[None, :]

	elif baseline == 'wiggle':
	m = y.shape[0]
	first_line = (y * (m - 0.5 - np.arange(m)[:, None])).sum(0)
	first_line /= -m
	stack += first_line

	elif baseline == 'weighted_wiggle':
	total = np.sum(y, 0)
	# multiply by 1/total (or zero) to avoid infinities in the division:
	inv_total = np.zeros_like(total)
	mask = total > 0
	inv_total[mask] = 1.0 / total[mask]
	increase = np.hstack((y[:, 0:1], np.diff(y)))
	below_size = total - stack
	below_size += 0.5 * y
	move_up = below_size * inv_total
	move_up[:, 0] = 0.5
	center = (move_up - 0.5) * increase
	center = np.cumsum(center.sum(0))
	first_line = center - 0.5 * total
	stack += first_line

	# Color between x = 0 and the first array.
	coll = axes.fill_between(x, first_line, stack[0, :],
	facecolor=next(colors), label=next(labels, None),
	**kwargs)
	coll.sticky_edges.y[:] = [0]
	r = [coll]

	# Color between array i-1 and array i
	for i in range(len(y) - 1):
	r.append(axes.fill_between(x, stack[i, :], stack[i + 1, :],
	facecolor=next(colors),
	label=next(labels, None),
	**kwargs))
	return r

[ENH]: Multi hatching in `ax.stackplot()` · Issue #27146 · matplotlib/matplotlib · GitHub

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[ENH]: Multi hatching in ax.stackplot() #27146

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KartikeyBartwal commented on Oct 20, 2023

rcomer commented on Oct 20, 2023

github-actions commented on Oct 20, 2023

Good first issue - notes for new contributors

timhoffm commented on Oct 20, 2023

pawjast commented on Oct 20, 2023

nbarlowATI commented on Oct 20, 2023

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