Thanks to visit codestin.com
Credit goes to github.com

Skip to content

Added support for displaying percentiles to violinplot function #9971

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Added support for displaying percentiles to violinplot function #9971

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 35 additions & 5 deletions lib/matplotlib/axes/_axes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7374,7 +7374,7 @@ def matshow(self, Z, **kwargs):
@_preprocess_data(replace_names=["dataset"], label_namer=None)
def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
showmeans=False, showextrema=True, showmedians=False,
points=100, bw_method=None):
percentiles=[], points=100, bw_method=None):
"""
Make a violin plot.

Expand Down Expand Up @@ -7410,6 +7410,10 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
showmedians : bool, default = False
If `True`, will toggle rendering of the medians.

percentiles : array-like, default = []
Displays percentiles in the plot for all the given percentiles
in the set

points : scalar, default = 100
Defines the number of points to evaluate each of the
gaussian kernel density estimations at.
Expand Down Expand Up @@ -7457,6 +7461,11 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
:class:`matplotlib.collections.LineCollection` instance
created to identify the median values of each of the
violin's distribution.

- ``cpercentiles`` : A
:class:`matplotlib.collections.LineCollection` instance
created to identify the percentile values of each of the
violins' distributions
"""

def _kde_method(X, coords):
Expand All @@ -7466,7 +7475,8 @@ def _kde_method(X, coords):
kde = mlab.GaussianKDE(X, bw_method)
return kde.evaluate(coords)

vpstats = cbook.violin_stats(dataset, _kde_method, points=points)
vpstats = cbook.violin_stats(dataset, _kde_method, points=points,
percentiles=percentiles)
return self.violin(vpstats, positions=positions, vert=vert,
widths=widths, showmeans=showmeans,
showextrema=showextrema, showmedians=showmedians)
Expand Down Expand Up @@ -7501,6 +7511,8 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,

- ``max``: The maximum value for this violin's dataset.

- ``percentiles``: The percentiles for this violin's dataset.

positions : array-like, default = [1, 2, ..., n]
Sets the positions of the violins. The ticks and limits are
automatically set to match the positions.
Expand All @@ -7523,6 +7535,10 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
showmedians : bool, default = False
If true, will toggle rendering of the medians.

percentiles : array-like, default = []
Displays percentiles in the plot for all the given percentiles in the set.


Returns
-------
result : dict
Expand Down Expand Up @@ -7558,6 +7574,11 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
:class:`matplotlib.collections.LineCollection` instance
created to identify the median values of each of the
violin's distribution.

- ``cpercentiles``: A
:class:`matplotlib.collections.LineCollection` instance
created to identify the percentiles values of each of the
violin's distribution.

"""

Expand All @@ -7566,6 +7587,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
mins = []
maxes = []
medians = []
percentiles = []

# Collections to be returned
artists = {}
Expand Down Expand Up @@ -7622,6 +7644,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
mins.append(stats['min'])
maxes.append(stats['max'])
medians.append(stats['median'])
percentiles.append(stats['percentiles'])
artists['bodies'] = bodies

# Render means
Expand All @@ -7640,11 +7663,18 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,

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

# Render percentiles
artists['cpercentiles'] = []
for pcs, pmin, pmax in zip(percentiles, pmins, pmaxes):
linelen = pmax - pmin
artists['cpercentiles'].append(perp_lines(pcs,
pmin + linelen * 0.17,
pmax - linelen * 0.17,
colors=edgecolor))

return artists

def tricontour(self, *args, **kwargs):
Expand Down
15 changes: 13 additions & 2 deletions lib/matplotlib/cbook/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2047,7 +2047,7 @@ def _reshape_2D(X, name):
raise ValueError("{} must have 2 or fewer dimensions".format(name))


def violin_stats(X, method, points=100):
def violin_stats(X, method, points=100, percentiles=[]):
"""
Returns a list of dictionaries of data which can be used to draw a series
of violin plots. See the `Returns` section below to view the required keys
Expand All @@ -2071,6 +2071,9 @@ def violin_stats(X, method, points=100):
Defines the number of points to evaluate each of the gaussian kernel
density estimates at.

percentiles : array-like, default = []
Defines a set of percentiles, if any, that will be displayed per plotted point

Returns
-------

Expand All @@ -2085,6 +2088,7 @@ def violin_stats(X, method, points=100):
- median: The median value for this column of data.
- min: The minimum value for this column of data.
- max: The maximum value for this column of data.
- percentiles: The set of percentiles that will be rendered for this data.
"""

# List of dictionaries describing each of the violins.
Expand All @@ -2093,7 +2097,13 @@ def violin_stats(X, method, points=100):
# Want X to be a list of data sequences
X = _reshape_2D(X, "X")

for x in X:
if percentiles is None:
percentiles = []
percentiles = _reshape_2D(percentiles, "percentiles")
while len(percentiles) < len(X):
percentiles.append([])

for x, pcs in zip(X, percentiles):
# Dictionary of results for this distribution
stats = {}

Expand All @@ -2111,6 +2121,7 @@ def violin_stats(X, method, points=100):
stats['median'] = np.median(x)
stats['min'] = min_val
stats['max'] = max_val
stats['percentiles'] = np.percentile(x, pcs)

# Append to output
vpstats.append(stats)
Expand Down
7 changes: 4 additions & 3 deletions lib/matplotlib/pyplot.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3652,8 +3652,8 @@ def triplot(*args, **kwargs):
# changes will be lost
@_autogen_docstring(Axes.violinplot)
def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
showextrema=True, showmedians=False, points=100, bw_method=None,
hold=None, data=None):
showextrema=True, showmedians=False, percentiles=[], points=100,
bw_method=None, hold=None, data=None):
ax = gca()
# Deprecated: allow callers to override the hold state
# by passing hold=True|False
Expand All @@ -3668,7 +3668,8 @@ def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
ret = ax.violinplot(dataset, positions=positions, vert=vert,
widths=widths, showmeans=showmeans,
showextrema=showextrema, showmedians=showmedians,
points=points, bw_method=bw_method, data=data)
points=points, bw_method=bw_method, data=data,
percentiles=percentiles)
finally:
ax._hold = washold

Expand Down