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Shorten the implementation of violin(). #19467

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May 13, 2021
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Shorten the implementation of violin(). #19467

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100 changes: 36 additions & 64 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8100,18 +8100,18 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
- ``cquantiles``: A `~.collections.LineCollection` instance created
to identify the quantiles values of each of the violin's
distribution.

"""

# Statistical quantities to be plotted on the violins
means = []
mins = []
maxes = []
medians = []
quantiles = np.asarray([])
quantiles = []

qlens = [] # Number of quantiles in each dataset.

# Collections to be returned
artists = {}
artists = {} # Collections to be returned

N = len(vpstats)
datashape_message = ("List of violinplot statistics and `{0}` "
Expand All @@ -8129,84 +8129,56 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
elif len(widths) != N:
raise ValueError(datashape_message.format("widths"))

# Calculate ranges for statistics lines
pmins = -0.25 * np.array(widths) + positions
pmaxes = 0.25 * np.array(widths) + positions
# Calculate ranges for statistics lines (shape (2, N)).
line_ends = [[-0.25], [0.25]] * np.array(widths) + positions

# Colors.
if rcParams['_internal.classic_mode']:
fillcolor = 'y'
linecolor = 'r'
else:
fillcolor = linecolor = self._get_lines.get_next_color()

# Check whether we are rendering vertically or horizontally
if vert:
fill = self.fill_betweenx
perp_lines = self.hlines
par_lines = self.vlines
perp_lines = functools.partial(self.hlines, colors=linecolor)
par_lines = functools.partial(self.vlines, colors=linecolor)
else:
fill = self.fill_between
perp_lines = self.vlines
par_lines = self.hlines

if rcParams['_internal.classic_mode']:
fillcolor = 'y'
edgecolor = 'r'
else:
fillcolor = edgecolor = self._get_lines.get_next_color()
perp_lines = functools.partial(self.vlines, colors=linecolor)
par_lines = functools.partial(self.hlines, colors=linecolor)

# Render violins
bodies = []
for stats, pos, width in zip(vpstats, positions, widths):
# The 0.5 factor reflects the fact that we plot from v-p to
# v+p
# The 0.5 factor reflects the fact that we plot from v-p to v+p.
vals = np.array(stats['vals'])
vals = 0.5 * width * vals / vals.max()
bodies += [fill(stats['coords'],
-vals + pos,
vals + pos,
facecolor=fillcolor,
alpha=0.3)]
bodies += [fill(stats['coords'], -vals + pos, vals + pos,
facecolor=fillcolor, alpha=0.3)]
means.append(stats['mean'])
mins.append(stats['min'])
maxes.append(stats['max'])
medians.append(stats['median'])
q = stats.get('quantiles')
if q is not None:
# If exist key quantiles, assume it's a list of floats
quantiles = np.concatenate((quantiles, q))
q = stats.get('quantiles') # a list of floats, or None
if q is None:
q = []
quantiles.extend(q)
qlens.append(len(q))
artists['bodies'] = bodies

# Render means
if showmeans:
artists['cmeans'] = perp_lines(means, pmins, pmaxes,
colors=edgecolor)

# Render extrema
if showextrema:
artists['cmaxes'] = perp_lines(maxes, pmins, pmaxes,
colors=edgecolor)
artists['cmins'] = perp_lines(mins, pmins, pmaxes,
colors=edgecolor)
artists['cbars'] = par_lines(positions, mins, maxes,
colors=edgecolor)

# Render medians
if showmedians:
artists['cmedians'] = perp_lines(medians,
pmins,
pmaxes,
colors=edgecolor)

# Render quantile values
if quantiles.size > 0:
# Recalculate ranges for statistics lines for quantiles.
# ppmins are the left end of quantiles lines
ppmins = np.asarray([])
# pmaxes are the right end of quantiles lines
ppmaxs = np.asarray([])
for stats, cmin, cmax in zip(vpstats, pmins, pmaxes):
q = stats.get('quantiles')
if q is not None:
ppmins = np.concatenate((ppmins, [cmin] * np.size(q)))
ppmaxs = np.concatenate((ppmaxs, [cmax] * np.size(q)))
# Start rendering
artists['cquantiles'] = perp_lines(quantiles, ppmins, ppmaxs,
colors=edgecolor)
if showmeans: # Render means
artists['cmeans'] = perp_lines(means, *line_ends)
if showextrema: # Render extrema
artists['cmaxes'] = perp_lines(maxes, *line_ends)
artists['cmins'] = perp_lines(mins, *line_ends)
artists['cbars'] = par_lines(positions, mins, maxes)
if showmedians: # Render medians
artists['cmedians'] = perp_lines(medians, *line_ends)
if quantiles: # Render quantiles: each width is repeated qlen times.
artists['cquantiles'] = perp_lines(
quantiles, *np.repeat(line_ends, qlens, axis=1))

return artists

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