line_tracking_of_fish_movement/statisitic_functions.py

79 lines
2.5 KiB
Python

import numpy as np
import matplotlib as mpl
def cohen_d(x, y):
"""
Calculates the effect size Cohens d' between the two data x and y
:param x: data array 1
:param y: data array 2
:return: float, d value of the effect size
"""
nx = len(x)
ny = len(y)
dof = nx + ny - 2
d = np.abs((np.mean(x) - np.mean(y)) / (((nx - 1) / dof) * (np.std(x) ** 2) + ((ny - 1) / dof) * (np.std(y) ** 2)))
return d
def significance_bar(ax, p, d, x0, x1, y, **kwargs):
"""
A horizontal bar with asterisks indicating significance level.
Plot a horizontal bar from x0 to x1 at height y
for indicating significance. On top of the bar plot
asterisks according to the significance value p are drawn.
If p > 0.05 nothing is plotted.
p<0.001: '***', p<0.01: '**', p<0.05: '*'.
Note: call this function AFTER ylim has been set!
Parameters
----------
ax: matplotlib axes
Axes to which the inset is added.
p: float
Significance level.
x0: float
x-coordinate of starting point of significance bar in data units.
x1: float
x-coordinate of ending point of significance bar in data units.
y: float
y-coordinate of significance bar in data units.
kwargs: key-word arguments
Passed on to `ax.text()` used to print the asterisks.
"""
if d is not None:
if d > 0.0:
ps = '%.2f' %(d)
else:
return
elif p is not None:
# set label:
if p < 0.001:
ps = '***'
elif p < 0.01:
ps = '**'
elif p < 0.05:
ps = '*'
else:
return
# ax dimensions:
pixely = np.abs(np.diff(ax.get_window_extent().get_points()[:, 1]))[0]
unity = np.abs(np.diff(ax.get_ylim()))[0]
dyu = unity / pixely
fs = mpl.rcParams['font.size']
if 'fontsize' in kwargs and isinstance(kwargs['fontsize'], (float, int)):
fs = kwargs['fontsize']
dy = 0.3 * fs * dyu
lw = 1.0
lh = ax.plot([x0, x0, x1, x1], [y - dy, y, y, y - dy], color='black', lw=lw,
solid_capstyle='butt', solid_joinstyle='miter', clip_on=False)
# get y position of line in figure pixel coordinates:
ly = np.array(lh[0].get_window_extent(ax.get_figure().canvas.get_renderer()))[1, 1]
th = ax.text(0.5 * (x0 + x1), y, ps, ha='center', va='bottom', **kwargs)
ty = np.array(th.get_window_extent(ax.get_figure().canvas.get_renderer()))[0, 1]
dty = ly + 5 * lw - 0.4 * fs - ty
th.set_position((0.5 * (x0 + x1), y + dty * dyu))