def test_boxplots_empty_distributions(self):
fig = boxplots([[1, 2, 3], [], [4, 5, 6]], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
# second distribution (empty) should have nans since it is hidden.
# boxplots in mpl < 1.4.0 have 8 lines per boxplot, while mpl 1.4.0 has
# 7. in either case, the line at index 8 should have a nan for its y
# value
lines = ax.get_lines()
self.assertTrue(np.isnan(lines[8].get_xydata()[0][1]))
# line in first distribution should *not* have nan for its y value
self.assertFalse(np.isnan(lines[0].get_xydata()[0][1]))
# All distributions are empty.
fig = boxplots([[], [], []], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
lines = ax.get_lines()
self.assertTrue(np.isnan(lines[0].get_xydata()[0][1]))
self.assertTrue(np.isnan(lines[8].get_xydata()[0][1]))
self.assertTrue(np.isnan(lines[16].get_xydata()[0][1]))
def test_boxplots_box_colors(self):
# Coloring works with all empty distributions.
fig = boxplots([[], [], []], box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
# patch colors should match what we specified
self.assertEqual(ax.patches[0].get_facecolor(), (0.0, 0.0, 1.0, 1.0))
self.assertEqual(ax.patches[1].get_facecolor(), (1.0, 0.0, 0.0, 1.0))
self.assertEqual(ax.patches[2].get_facecolor(), (1.0, 1.0, 0.0, 1.0))
# patch location should include at least one nan since the distribution
# is empty, and thus hidden
for patch in ax.patches:
self.assertTrue(np.isnan(patch.xy[0][1]))
fig = boxplots([[], [], []], box_colors='pink')
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
for patch in ax.patches:
npt.assert_almost_equal(
patch.get_facecolor(),
(1.0, 0.7529411764705882, 0.796078431372549, 1.0))
self.assertTrue(np.isnan(patch.xy[0][1]))
# Coloring works with some empty distributions.
fig = boxplots([[], [1, 2, 3.5], []],
box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertEqual(ax.patches[0].get_facecolor(), (0.0, 0.0, 1.0, 1.0))
self.assertEqual(ax.patches[1].get_facecolor(), (1.0, 0.0, 0.0, 1.0))
self.assertEqual(ax.patches[2].get_facecolor(), (1.0, 1.0, 0.0, 1.0))
self.assertTrue(np.isnan(ax.patches[0].xy[0][1]))
self.assertFalse(np.isnan(ax.patches[1].xy[0][1]))
self.assertTrue(np.isnan(ax.patches[2].xy[0][1]))
def test_boxplots_empty_distributions(self):
fig = boxplots([[1, 2, 3], [], [4, 5, 6]], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
# second distribution (empty) should have nans since it is hidden.
# boxplots in mpl < 1.4.0 have 8 lines per boxplot, while mpl 1.4.0 has
# 7. in either case, the line at index 8 should have a nan for its y
# value
lines = ax.get_lines()
self.assertTrue(np.isnan(lines[8].get_xydata()[0][1]))
# line in first distribution should *not* have nan for its y value
self.assertFalse(np.isnan(lines[0].get_xydata()[0][1]))
# All distributions are empty.
fig = boxplots([[], [], []], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
lines = ax.get_lines()
self.assertTrue(np.isnan(lines[0].get_xydata()[0][1]))
self.assertTrue(np.isnan(lines[8].get_xydata()[0][1]))
self.assertTrue(np.isnan(lines[16].get_xydata()[0][1]))
def test_boxplots_box_colors(self):
# Coloring works with all empty distributions.
fig = boxplots([[], [], []], box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
# patch colors should match what we specified
self.assertEqual(ax.patches[0].get_facecolor(), (0.0, 0.0, 1.0, 1.0))
self.assertEqual(ax.patches[1].get_facecolor(), (1.0, 0.0, 0.0, 1.0))
self.assertEqual(ax.patches[2].get_facecolor(), (1.0, 1.0, 0.0, 1.0))
# patch location should include at least one nan since the distribution
# is empty, and thus hidden
for patch in ax.patches:
self.assertTrue(np.isnan(patch.xy[0][1]))
fig = boxplots([[], [], []], box_colors='pink')
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
for patch in ax.patches:
npt.assert_almost_equal(
patch.get_facecolor(),
(1.0, 0.7529411764705882, 0.796078431372549, 1.0))
self.assertTrue(np.isnan(patch.xy[0][1]))
# Coloring works with some empty distributions.
fig = boxplots([[], [1, 2, 3.5], []],
box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertEqual(ax.patches[0].get_facecolor(), (0.0, 0.0, 1.0, 1.0))
self.assertEqual(ax.patches[1].get_facecolor(), (1.0, 0.0, 0.0, 1.0))
self.assertEqual(ax.patches[2].get_facecolor(), (1.0, 1.0, 0.0, 1.0))
self.assertTrue(np.isnan(ax.patches[0].xy[0][1]))
self.assertFalse(np.isnan(ax.patches[1].xy[0][1]))
self.assertTrue(np.isnan(ax.patches[2].xy[0][1]))
def generate_alpha_diversity_boxplots(rarefaction_lines,
mapping_lines,
category,
depth=None):
rarefaction_data = parse_rarefaction(rarefaction_lines)
category_value_to_sample_ids = \
get_category_value_to_sample_ids(mapping_lines,
category)
per_sample_average_diversities = \
get_per_sample_average_diversities(rarefaction_data,
depth)
per_category_value_average_diversities = \
collapse_sample_diversities_by_category_value(
category_value_to_sample_ids,
per_sample_average_diversities)
# sort the data alphabetically
sorted_per_category_value_average_diversities = \
sorted(per_category_value_average_diversities.items())
x_tick_labels = []
distributions = []
for cat, avg_diversities in sorted_per_category_value_average_diversities:
x_tick_labels.append("%s (n=%d)" % (cat, len(avg_diversities)))
distributions.append(avg_diversities)
return boxplots(distributions, x_tick_labels=x_tick_labels)
def test_boxplots_box_colors(self):
"""Test correctly handles coloring of box plots."""
# Coloring works with all empty distributions.
fig = boxplots([[], [], []], box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
fig = boxplots([[], [], []], box_colors='pink')
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
# Coloring works with some empty distributions.
fig = boxplots([[], [1, 2, 3.5], []],
box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
def test_boxplots_box_colors(self):
"""Test correctly handles coloring of box plots."""
# Coloring works with all empty distributions.
fig = boxplots([[], [], []], box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
fig = boxplots([[], [], []], box_colors='pink')
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
# Coloring works with some empty distributions.
fig = boxplots([[], [1, 2, 3.5], []],
box_colors=['blue', 'red', 'yellow'])
ax = fig.get_axes()[0]
self.assertEqual(len(ax.get_xticklabels()), 3)
def generate_alpha_diversity_boxplots(rarefaction_lines,
mapping_lines,
category,
depth=None):
rarefaction_data = parse_rarefaction(rarefaction_lines)
category_value_to_sample_ids = \
get_category_value_to_sample_ids(mapping_lines,
category)
per_sample_average_diversities = \
get_per_sample_average_diversities(rarefaction_data,
depth)
per_category_value_average_diversities = \
collapse_sample_diversities_by_category_value(
category_value_to_sample_ids,
per_sample_average_diversities)
# sort the data alphabetically
sorted_per_category_value_average_diversities = \
sorted(per_category_value_average_diversities.items())
x_tick_labels = []
distributions = []
for cat, avg_diversities in sorted_per_category_value_average_diversities:
x_tick_labels.append("%s (n=%d)" % (cat, len(avg_diversities)))
distributions.append(avg_diversities)
return boxplots(distributions, x_tick_labels=x_tick_labels)
def test_boxplots(self):
fig = boxplots(self.ValidTypicalBoxData, [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label",
legend=(('blue', 'red'), ('foo', 'bar')))
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
def test_boxplots_empty_distributions(self):
"""Test functions correctly with empty distributions."""
fig = boxplots([[1, 2, 3], [], [4, 5, 6]], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
# All distributions are empty.
fig = boxplots([[], [], []], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
def test_boxplots_empty_distributions(self):
"""Test functions correctly with empty distributions."""
fig = boxplots([[1, 2, 3], [], [4, 5, 6]], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
# All distributions are empty.
fig = boxplots([[], [], []], [1, 4, 10],
["Data 1", "Data 2", "Data 3"], "Test", "x-axis label",
"y-axis label")
ax = fig.get_axes()[0]
self.assertEqual(ax.get_title(), "Test")
self.assertEqual(ax.get_xlabel(), "x-axis label")
self.assertEqual(ax.get_ylabel(), "y-axis label")
self.assertEqual(len(ax.get_xticklabels()), 3)
self.assertTrue(np.array_equal(ax.get_xticks(), [1, 4, 10]))
def test_boxplots_invalid_input(self):
# Non-numeric entries in distribution.
with npt.assert_raises(ValueError):
boxplots([[1, 'foo', 3]])
# Number of colors doesn't match number of distributions.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3], [], [4, 5, 6]], box_colors=['blue', 'red'])
# Invalid legend.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3]], legend=('foo', 'bar', 'baz'))
def test_boxplots_invalid_input(self):
# Non-numeric entries in distribution.
with npt.assert_raises(ValueError):
boxplots([[1, 'foo', 3]])
# Number of colors doesn't match number of distributions.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3], [], [4, 5, 6]], box_colors=['blue', 'red'])
# Invalid legend.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3]], legend=('foo', 'bar', 'baz'))
def test_boxplots_invalid_input(self):
# Non-numeric entries in distribution.
with npt.assert_raises(ValueError):
boxplots([[1, "foo", 3]])
# Number of colors doesn't match number of distributions.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3], [], [4, 5, 6]], box_colors=["blue", "red"])
# Invalid legend.
with npt.assert_raises(ValueError):
boxplots([[1, 2, 3]], legend=("foo", "bar", "baz"))
def make_distance_boxplots(dm_f,
map_f,
fields,
width=None,
height=6.0,
suppress_all_within=False,
suppress_all_between=False,
suppress_individual_within=False,
suppress_individual_between=False,
y_min=0.0,
y_max=1.0,
whisker_length=1.5,
box_width=0.5,
box_color=None,
color_individual_within_by_field=None,
sort=None):
"""Generates various types of boxplots for distance comparisons.
Returns a list of tuples, one for each field. Each tuple contains the
following:
1) the name of the field (string)
2) a matplotlib.figure.Figure object containing the boxplots
3) a list of lists containing the raw plot data that was passed to mpl
4) a list of labels for each of the boxplots (string)
5) a list of mpl-compatible colors (one for each boxplot)
The Figure can be saved, and the raw data and labels can be useful (for
example) performing statistical tests or writing the raw data to disk.
The input arguments are exactly derived from the make_distance_boxplots.py
script (see the script options for details). To avoid duplicated effort,
their descriptions are not reproduced here.
"""
# Parse data files and do some preliminary error checking.
dm_header, dm_data = parse_distmat(dm_f)
map_data, map_header, map_comments = parse_mapping_file(map_f)
if fields is None or len(fields) < 1:
raise ValueError("You must provide at least one field to analyze.")
for field in fields:
if field not in map_header:
raise ValueError("The field '%s' is not in the provided mapping "
"file. Please supply correct fields "
"corresponding to fields in the mapping file." %
field)
# Make sure the y_min and y_max options make sense, as they can be either
# 'auto' or a number.
y_min = _cast_y_axis_extrema(y_min)
y_max = _cast_y_axis_extrema(y_max)
# Collate the distributions of distances that will comprise each boxplot.
# Suppress the generation of the indicated types of boxplots.
results = []
for field in fields:
plot_data = []
plot_labels = []
plot_colors = []
legend = None
# Little bit of duplicate code here... not sure it's worth the effort
# to clean up though.
if not suppress_all_within:
plot_data.append(
get_all_grouped_distances(dm_header,
dm_data,
map_header,
map_data,
field,
within=True))
plot_labels.append("All within %s" % field)
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
if not suppress_all_between:
plot_data.append(
get_all_grouped_distances(dm_header,
dm_data,
map_header,
map_data,
field,
within=False))
plot_labels.append("All between %s" % field)
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
if not suppress_individual_within:
within_dists = get_grouped_distances(dm_header,
dm_data,
map_header,
map_data,
field,
within=True)
field_states = []
for grouping in within_dists:
plot_data.append(grouping[2])
plot_labels.append("%s vs. %s" % (grouping[0], grouping[1]))
field_states.append(grouping[0])
# If we need to color these boxplots by a field, build up a
# list of colors and a legend.
if color_individual_within_by_field is not None:
colors, color_mapping = _color_field_states(
format_mapping_file(map_header,
map_data).split('\n'), dm_header,
field, field_states, color_individual_within_by_field)
plot_colors.extend(colors)
legend = (color_mapping.values(), color_mapping.keys())
else:
plot_colors.extend([box_color] * len(field_states))
if not suppress_individual_between:
between_dists = get_grouped_distances(dm_header,
dm_data,
map_header,
map_data,
field,
within=False)
for grouping in between_dists:
plot_data.append(grouping[2])
plot_labels.append("%s vs. %s" % (grouping[0], grouping[1]))
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
assert (len(plot_data) == len(plot_labels) and
len(plot_labels) == len(plot_colors)), "The number " +\
"of boxplot labels and colors do not match the number of " +\
"boxplots."
# We now have our data and labels ready, so plot them!
if plot_data:
if sort is not None:
plot_data, plot_labels, plot_colors = _sort_distributions(
plot_data, plot_labels, plot_colors, sort)
if width is None:
width = len(plot_data) * box_width + 2
if width <= 0 or height <= 0:
raise ValueError("The specified width and height of the plot "
"must be greater than zero.")
plot_figure = boxplots(plot_data,
x_tick_labels=plot_labels,
title="%s Distances" % field,
x_label="Grouping",
y_label="Distance",
x_tick_labels_orientation='vertical',
y_min=y_min,
y_max=y_max,
whisker_length=whisker_length,
box_width=box_width,
box_colors=plot_colors,
figure_width=width,
figure_height=height,
legend=legend)
results.append(
(field, plot_figure, plot_data, plot_labels, plot_colors))
else:
raise ValueError("The generation of all plots was suppressed. At "
"least one type of plot must be unsuppressed.")
return results
from skbio.draw import boxplots fig = boxplots([[2, 2, 1, 3, 4, 4.2, 7], [0, -1, 4, 5, 6, 7]])
from skbio.draw import boxplots
fig = boxplots([[2, 2, 1, 3], [0, -1, 0, 0.1, 0.3], [4, 5, 6, 3]],
x_tick_labels=('Control', 'Treatment 1', 'Treatment 2'),
box_colors=('green', 'blue', 'red'))
def make_distance_boxplots(dm_f,
map_f,
fields,
width=None,
height=6.0,
suppress_all_within=False,
suppress_all_between=False,
suppress_individual_within=False,
suppress_individual_between=False,
y_min=0.0,
y_max=1.0,
whisker_length=1.5,
box_width=0.5,
box_color=None,
color_individual_within_by_field=None,
sort=None):
"""Generates various types of boxplots for distance comparisons.
Returns a list of tuples, one for each field. Each tuple contains the
following:
1) the name of the field (string)
2) a matplotlib.figure.Figure object containing the boxplots
3) a list of lists containing the raw plot data that was passed to mpl
4) a list of labels for each of the boxplots (string)
5) a list of mpl-compatible colors (one for each boxplot)
The Figure can be saved, and the raw data and labels can be useful (for
example) performing statistical tests or writing the raw data to disk.
The input arguments are exactly derived from the make_distance_boxplots.py
script (see the script options for details). To avoid duplicated effort,
their descriptions are not reproduced here.
"""
# Parse data files and do some preliminary error checking.
dm_header, dm_data = parse_distmat(dm_f)
map_data, map_header, map_comments = parse_mapping_file(map_f)
if fields is None or len(fields) < 1:
raise ValueError("You must provide at least one field to analyze.")
for field in fields:
if field not in map_header:
raise ValueError("The field '%s' is not in the provided mapping "
"file. Please supply correct fields "
"corresponding to fields in the mapping file." %
field)
# Make sure the y_min and y_max options make sense, as they can be either
# 'auto' or a number.
y_min = _cast_y_axis_extrema(y_min)
y_max = _cast_y_axis_extrema(y_max)
# Collate the distributions of distances that will comprise each boxplot.
# Suppress the generation of the indicated types of boxplots.
results = []
for field in fields:
plot_data = []
plot_labels = []
plot_colors = []
legend = None
# Little bit of duplicate code here... not sure it's worth the effort
# to clean up though.
if not suppress_all_within:
plot_data.append(get_all_grouped_distances(dm_header, dm_data,
map_header, map_data, field, within=True))
plot_labels.append("All within %s" % field)
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
if not suppress_all_between:
plot_data.append(get_all_grouped_distances(dm_header, dm_data,
map_header, map_data, field, within=False))
plot_labels.append("All between %s" % field)
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
if not suppress_individual_within:
within_dists = get_grouped_distances(dm_header, dm_data,
map_header, map_data, field,
within=True)
field_states = []
for grouping in within_dists:
plot_data.append(grouping[2])
plot_labels.append("%s vs. %s" % (grouping[0], grouping[1]))
field_states.append(grouping[0])
# If we need to color these boxplots by a field, build up a
# list of colors and a legend.
if color_individual_within_by_field is not None:
colors, color_mapping = _color_field_states(
format_mapping_file(map_header, map_data).split('\n'),
dm_header, field, field_states,
color_individual_within_by_field)
plot_colors.extend(colors)
legend = (color_mapping.values(), color_mapping.keys())
else:
plot_colors.extend([box_color] * len(field_states))
if not suppress_individual_between:
between_dists = get_grouped_distances(dm_header, dm_data,
map_header, map_data, field, within=False)
for grouping in between_dists:
plot_data.append(grouping[2])
plot_labels.append("%s vs. %s" % (grouping[0], grouping[1]))
if color_individual_within_by_field is not None:
plot_colors.append(None)
else:
plot_colors.append(box_color)
assert (len(plot_data) == len(plot_labels) and
len(plot_labels) == len(plot_colors)), "The number " +\
"of boxplot labels and colors do not match the number of " +\
"boxplots."
# We now have our data and labels ready, so plot them!
if plot_data:
if sort is not None:
plot_data, plot_labels, plot_colors = _sort_distributions(
plot_data, plot_labels, plot_colors, sort)
if width is None:
width = len(plot_data) * box_width + 2
if width <= 0 or height <= 0:
raise ValueError("The specified width and height of the plot "
"must be greater than zero.")
plot_figure = boxplots(plot_data, x_tick_labels=plot_labels,
title="%s Distances" % field,
x_label="Grouping", y_label="Distance",
x_tick_labels_orientation='vertical',
y_min=y_min, y_max=y_max,
whisker_length=whisker_length,
box_width=box_width, box_colors=plot_colors,
figure_width=width, figure_height=height,
legend=legend)
results.append((field, plot_figure, plot_data, plot_labels,
plot_colors))
else:
raise ValueError("The generation of all plots was suppressed. At "
"least one type of plot must be unsuppressed.")
return results