def draw_visa(base_line, up_base_line, start, duration, color, text):
rect_w = duration * 0.1
rect_h = 0.08
x = start * 0.1 + rect_w / 2
if up_base_line:
y = base_line - 0.1
else:
y = base_line - 0.15
rect = Ellipse((x, y), rect_w, rect_h, alpha=1, facecolor=color)
cx, cy = rect.get_center()
currentAxis.annotate(str(start), (cx, cy),
color='w',
weight='bold',
fontsize=6,
ha='center',
va='center')
currentAxis.add_patch(rect)
def test_corner_center():
loc = [10, 20]
width = 1
height = 2
# Rectangle
# No rotation
corners = ((10, 20), (11, 20), (11, 22), (10, 22))
rect = Rectangle(loc, width, height)
assert_array_equal(rect.get_corners(), corners)
assert_array_equal(rect.get_center(), (10.5, 21))
# 90 deg rotation
corners_rot = ((10, 20), (10, 21), (8, 21), (8, 20))
rect.set_angle(90)
assert_array_equal(rect.get_corners(), corners_rot)
assert_array_equal(rect.get_center(), (9, 20.5))
# Rotation not a multiple of 90 deg
theta = 33
t = mtransforms.Affine2D().rotate_around(*loc, np.deg2rad(theta))
corners_rot = t.transform(corners)
rect.set_angle(theta)
assert_almost_equal(rect.get_corners(), corners_rot)
# Ellipse
loc = [loc[0] + width / 2,
loc[1] + height / 2]
ellipse = Ellipse(loc, width, height)
# No rotation
assert_array_equal(ellipse.get_corners(), corners)
# 90 deg rotation
corners_rot = ((11.5, 20.5), (11.5, 21.5), (9.5, 21.5), (9.5, 20.5))
ellipse.set_angle(90)
assert_array_equal(ellipse.get_corners(), corners_rot)
# Rotation shouldn't change ellipse center
assert_array_equal(ellipse.get_center(), loc)
# Rotation not a multiple of 90 deg
theta = 33
t = mtransforms.Affine2D().rotate_around(*loc, np.deg2rad(theta))
corners_rot = t.transform(corners)
ellipse.set_angle(theta)
assert_almost_equal(ellipse.get_corners(), corners_rot)
def confidence_ellipse(self,
x,
y,
ax,
n_std=1,
facecolor='none',
**kwargs):
"""
Create a plot of the covariance confidence ellipse of *x* and *y*.
Parameters
----------
x, y : array-like, shape (n, )
Input data.
ax : matplotlib.axes.Axes
The axes object to draw the ellipse into.
n_std : float
The number of standard deviations to determine the ellipse's radiuses.
Returns
-------
matplotlib.patches.Ellipse
Other parameters
----------------
kwargs : `~matplotlib.patches.Patch` properties
"""
if x.size != y.size:
raise ValueError("x and y must be the same size")
cov = np.cov(x, y)
pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])
# Using a special case to obtain the eigenvalues of this
# two-dimensionl dataset.
ell_radius_x = np.sqrt(1 + pearson)
ell_radius_y = np.sqrt(1 - pearson)
ellipse = Ellipse((0, 0),
width=ell_radius_x * 2,
height=ell_radius_y * 2,
facecolor=facecolor,
**kwargs)
self.g_ell_center = ellipse.get_center()
self.g_ell_width = ell_radius_x * 2
self.g_ell_height = ell_radius_y * 2
self.angle = 45
# Calculating the stdandard deviation of x from
# the squareroot of the variance and multiplying
# with the given number of standard deviations.
scale_x = np.sqrt(cov[0, 0]) * n_std
mean_x = np.mean(x)
# calculating the stdandard deviation of y ...
scale_y = np.sqrt(cov[1, 1]) * n_std
mean_y = np.mean(y)
transf = transforms.Affine2D() \
.rotate_deg(45) \
.scale(scale_x, scale_y) \
.translate(mean_x, mean_y)
ellipse.set_transform(transf + ax.transData)
self.ellipse = ellipse
return ax.add_patch(ellipse)
