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LaTeX asterisks and chi-square test update #33

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123 changes: 89 additions & 34 deletions statannot/statannot.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
# pip install statannot

import warnings

import matplotlib.pyplot as plt
Expand All @@ -8,6 +10,7 @@
import pandas as pd
import seaborn as sns
from seaborn.utils import remove_na
import pdb

from .utils import raise_expected_got, assert_is_in
from .StatResult import StatResult
Expand Down Expand Up @@ -41,6 +44,7 @@ def stat_test(
- `t-test_paired`
- `Wilcoxon`
- `Kruskal`
- `Chi squared`
comparisons_correction: str or None, default None
Method to use for multiple comparisons correction. Currently only the
Bonferroni correction is implemented.
Expand Down Expand Up @@ -137,6 +141,16 @@ def stat_test(
result = StatResult(
'Kruskal-Wallis paired samples', 'Kruskal', 'stat', stat, pval
)

elif test == 'chisquare':
stat, pval = stats.chisquare([box_data1.count(), box_data2.count()],
**stats_params)
test_short_name = 'ChiSquare'
result = StatResult(
'ChiSquare categorical groups', 'ChiSquare', 'stat', stat, pval
)


else:
result = StatResult(None, '', None, None, np.nan)

Expand Down Expand Up @@ -263,8 +277,8 @@ def simple_text(pval, pvalue_format, pvalue_thresholds, test_short_name=None):

return text + pval_text


def add_stat_annotation(ax, plot='boxplot',
# ='boxplot' removed after the word plot
def add_stat_annotation(ax, plot,
data=None, x=None, y=None, hue=None, units=None, order=None,
hue_order=None, box_pairs=None, width=0.8,
perform_stat_test=True,
Expand All @@ -279,24 +293,30 @@ def add_stat_annotation(ax, plot='boxplot',
color='0.2', linewidth=1.5,
fontsize='medium', verbose=1):
"""
Optionally computes statistical test between pairs of data series, and add statistical annotation on top
of the boxes/bars. The same exact arguments `data`, `x`, `y`, `hue`, `order`, `width`,
`hue_order` (and `units`) as in the seaborn boxplot/barplot function must be passed to this function.

This function works in one of the two following modes:
a) `perform_stat_test` is True: statistical test as given by argument `test` is performed.
b) `perform_stat_test` is False: no statistical test is performed, list of custom p-values `pvalues` are
used for each pair of boxes. The `test_short_name` argument is then used as the name of the
custom statistical test.

:param plot: type of the plot, one of 'boxplot' or 'barplot'.
:param line_height: in axes fraction coordinates
:param text_offset: in points
:param box_pairs: can be of either form: For non-grouped boxplot: `[(cat1, cat2), (cat3, cat4)]`. For boxplot grouped by hue: `[((cat1, hue1), (cat2, hue2)), ((cat3, hue3), (cat4, hue4))]`
:param pvalue_format_string: defaults to `"{.3e}"`
:param pvalue_thresholds: list of lists, or tuples. Default is: For "star" text_format: `[[1e-4, "****"], [1e-3, "***"], [1e-2, "**"], [0.05, "*"], [1, "ns"]]`. For "simple" text_format : `[[1e-5, "1e-5"], [1e-4, "1e-4"], [1e-3, "0.001"], [1e-2, "0.01"]]`
:param pvalues: list or array of p-values for each box pair comparison.
:param comparisons_correction: Method for multiple comparisons correction. `bonferroni` or None.
Optionally computes statistical test between pairs of data series, and add
statistical annotation on top of the boxes/bars. The same exact arguments
`data`, `x`, `y`, `hue`, `order`, `width`, `hue_order` (and `units`) as in
the seaborn boxplot/barplot function must be passed to this function.

This function works in one of the two following modes: a)
`perform_stat_test` is True: statistical test as given by argument `test`
is performed. b) `perform_stat_test` is False: no statistical test is
performed, list of custom p-values `pvalues` are used for each pair of
boxes. The `test_short_name` argument is then used as the name of the
custom statistical test.

:param plot: type of the plot, one of 'boxplot' or 'barplot'. :param
line_height: in axes fraction coordinates :param text_offset: in points
:param box_pairs: can be of either form: For non-grouped boxplot: `[(cat1,
cat2), (cat3, cat4)]`. For boxplot grouped by hue: `[((cat1, hue1), (cat2,
hue2)), ((cat3, hue3), (cat4, hue4))]` :param pvalue_format_string:
defaults to `"{.3e}"` :param pvalue_thresholds: list of lists, or tuples.
Default is: For "star" text_format: `[[1e-4, "****"], [1e-3, "***"], [1e-2,
"**"], [0.05, "*"], [1, "ns"]]`. For "simple" text_format : `[[1e-5,
"1e-5"], [1e-4, "1e-4"], [1e-3, "0.001"], [1e-2, "0.01"]]` :param
pvalues: list or array of p-values for each box pair comparison. :param
comparisons_correction: Method for multiple comparisons correction.
`bonferroni` or None.
"""

def find_x_position_box(box_plotter, boxName):
Expand Down Expand Up @@ -348,8 +368,10 @@ def get_box_data(box_plotter, boxName):

if pvalue_thresholds is DEFAULT:
if text_format == "star":
pvalue_thresholds = [[1e-4, "****"], [1e-3, "***"],
[1e-2, "**"], [0.05, "*"], [1, "ns"]]
pvalue_thresholds = [[0.0001, r"${****}$"], [0.001, r"${***}$"],
[0.01, r"${**}$"], [0.05, r"$*$"], [1, "ns"]]
# [[0.0001, r"$\ast\ast\ast\ast$"], [0.001, r"$\ast\ast\ast$"],
# [0.01, r"${\ast\ast}$"], [0.05, r"$\ast*$"], [1, "ns"]]
else:
pvalue_thresholds = [[1e-5, "1e-5"], [1e-4, "1e-4"],
[1e-3, "0.001"], [1e-2, "0.01"]]
Expand All @@ -365,7 +387,7 @@ def get_box_data(box_plotter, boxName):
"or `test_short_name` must be `None`.")
valid_list = ['t-test_ind', 't-test_welch', 't-test_paired',
'Mann-Whitney', 'Mann-Whitney-gt', 'Mann-Whitney-ls',
'Levene', 'Wilcoxon', 'Kruskal']
'Levene', 'Wilcoxon', 'Kruskal', 'chisquare']
if test not in valid_list:
raise ValueError("test value should be one of the following: {}."
.format(', '.join(valid_list)))
Expand Down Expand Up @@ -433,31 +455,51 @@ def get_box_data(box_plotter, boxName):
box_plotter = sns.categorical._BoxPlotter(
x, y, hue, data, order, hue_order, orient=None, width=width, color=None,
palette=None, saturation=.75, dodge=True, fliersize=5, linewidth=None)

elif plot == 'barplot':
# Create the same plotter object as seaborn's barplot
box_plotter = sns.categorical._BarPlotter(
x, y, hue, data, order, hue_order,
estimator=np.mean, ci=95, n_boot=1000, units=None,
estimator=np.mean, ci=95, n_boot=1000, units=None, seed=None,
orient=None, color=None, palette=None, saturation=.75,
errcolor=".26", errwidth=None, capsize=None, dodge=True)

elif plot == 'countplot':
# Create the same plotter object as seaborn's countplot
box_plotter = sns.categorical._CountPlotter(
x, y, hue, data, order, hue_order,
estimator=np.mean, ci=95, n_boot=1000, units=None, seed=None,
orient=None, color=None, palette=None, saturation=.75,
errcolor=".26", errwidth=None, capsize=None, dodge=True)

# Build the list of box data structures with the x and ymax positions
group_names = box_plotter.group_names
hue_names = box_plotter.hue_names

if box_plotter.plot_hues is None:
box_names = group_names
labels = box_names
else:
box_names = [(group_name, hue_name) for group_name in group_names for hue_name in hue_names]
labels = ['{}_{}'.format(group_name, hue_name) for (group_name, hue_name) in box_names]

box_structs = [{'box':box_names[i],
if test == 'chisquare':
box_structs = [{'box':box_names[i],
'label':labels[i],
'x':find_x_position_box(box_plotter, box_names[i]),
'box_data':get_box_data(box_plotter, box_names[i]),
'ymax':np.amax(get_box_data(box_plotter, box_names[i])) if
'ymax':np.amax(get_box_data(box_plotter, box_names[i]).count()) if
len(get_box_data(box_plotter, box_names[i])) > 0 else np.nan}
for i in range(len(box_names))]
else:
box_structs = [{'box':box_names[i],
'label':labels[i],
'x':find_x_position_box(box_plotter, box_names[i]),
'box_data':get_box_data(box_plotter, box_names[i]),
'ymax':np.amax(get_box_data(box_plotter, box_names[i])) if
len(get_box_data(box_plotter, box_names[i])) > 0 else np.nan}
for i in range(len(box_names))]

# Sort the box data structures by position along the x axis
box_structs = sorted(box_structs, key=lambda x: x['x'])
# Add the index position in the list of boxes along the x axis
Expand All @@ -467,6 +509,7 @@ def get_box_data(box_plotter, boxName):

# Build the list of box data structure pairs
box_struct_pairs = []

for i_box_pair, (box1, box2) in enumerate(box_pairs):
valid = box1 in box_names and box2 in box_names
if not valid:
Expand All @@ -480,11 +523,11 @@ def get_box_data(box_plotter, boxName):
else:
pair = (box_struct2, box_struct1)
box_struct_pairs.append(pair)

# Draw first the annotations with the shortest between-boxes distance, in order to reduce
# overlapping between annotations.
box_struct_pairs = sorted(box_struct_pairs, key=lambda x: abs(x[1]['x'] - x[0]['x']))

# Build array that contains the x and y_max position of the highest annotation or box data at
# a given x position, and also keeps track of the number of stacked annotations.
# This array will be updated when a new annotation is drawn.
Expand All @@ -499,7 +542,7 @@ def get_box_data(box_plotter, boxName):
y_stack = []

for box_struct1, box_struct2 in box_struct_pairs:

box1 = box_struct1['box']
box2 = box_struct2['box']
label1 = box_struct1['label']
Expand Down Expand Up @@ -542,6 +585,14 @@ def get_box_data(box_plotter, boxName):
result.box2 = box2
test_result_list.append(result)

# Don't plot lines that are not significantly different to only plot significant bars
# (https://github.com/webermarcolivier/statannot/issues/25)
if result.pval > 0.05:
print(result.box1, 'and' ,result.box2, 'did not show significant differences and the p value = {}'.format(result.pval))
continue
else:
print(result.box1, 'and' ,result.box2, 'did show significant differences and the p value = {}'.format(result.pval))

if verbose >= 1:
print("{} v.s. {}: {}".format(label1, label2, result.formatted_output))

Expand Down Expand Up @@ -624,10 +675,14 @@ def get_box_data(box_plotter, boxName):
# Increment the counter of annotations in the y_stack array
y_stack_arr[2, xi1:xi2 + 1] = y_stack_arr[2, xi1:xi2 + 1] + 1

y_stack_max = max(ymaxs)
if loc == 'inside':
ax.set_ylim((ylim[0], max(1.03*y_stack_max, ylim[1])))
elif loc == 'outside':
ax.set_ylim((ylim[0], ylim[1]))
# Check to see if there are actual significant differences
if len(ymaxs) == 0:
pass
else:
y_stack_max = max(ymaxs)
if loc == 'inside':
ax.set_ylim((ylim[0], max(1.03*y_stack_max, ylim[1])))
elif loc == 'outside':
ax.set_ylim((ylim[0], ylim[1]))

return ax, test_result_list