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Cleanups: Broadcasting, np.hypot, np.pi, etc.
Also fixes a bug in fill_between with masked data. In the modified test figures, the area in green is supposed to correspond to the part of the hatched area where the curve is below y=2. The new behavior is the correct one. Also fixes cbook._reshape2D for scalar object inputs. Before the fix, `plt.hist(None)` would fail with `x must have 2 or fewer dimensions`, which it does have. Now it fails with a bit later with `unsupported operands type(s) for +: 'NoneType' and 'float'`, which is hopefully clearer.
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‎doc/api/axis_api.rst

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -99,8 +99,8 @@ Ticks, tick labels and Offset text
9999
Axis.axis_date
100100

101101

102-
Data and view internvals
103-
------------------------
102+
Data and view intervals
103+
-----------------------
104104

105105
.. autosummary::
106106
:toctree: _as_gen

‎examples/animation/unchained.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@
2525
# Generate random data
2626
data = np.random.uniform(0, 1, (64, 75))
2727
X = np.linspace(-1, 1, data.shape[-1])
28-
G = 1.5 * np.exp(-4 * X * X)
28+
G = 1.5 * np.exp(-4 * X ** 2)
2929

3030
# Generate line plots
3131
lines = []

‎examples/api/custom_projection_example.py

Lines changed: 4 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -434,15 +434,11 @@ def __init__(self, resolution):
434434
self._resolution = resolution
435435

436436
def transform_non_affine(self, xy):
437-
x = xy[:, 0:1]
438-
y = xy[:, 1:2]
439-
440-
quarter_x = 0.25 * x
441-
half_y = 0.5 * y
442-
z = np.sqrt(1.0 - quarter_x*quarter_x - half_y*half_y)
443-
longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0)))
437+
x, y = xy.T
438+
z = np.sqrt(1 - (x / 4) ** 2 - (y / 2) ** 2)
439+
longitude = 2 * np.arctan((z * x) / (2 * (2 * z ** 2 - 1)))
444440
latitude = np.arcsin(y*z)
445-
return np.concatenate((longitude, latitude), 1)
441+
return np.column_stack([longitude, latitude])
446442
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
447443

448444
def inverted(self):

‎examples/api/scatter_piecharts.py

Lines changed: 17 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -7,7 +7,7 @@
77
88
Thanks to Manuel Metz for the example
99
"""
10-
import math
10+
1111
import numpy as np
1212
import matplotlib.pyplot as plt
1313

@@ -16,35 +16,33 @@
1616
r2 = r1 + 0.4 # 40%
1717

1818
# define some sizes of the scatter marker
19-
sizes = [60, 80, 120]
19+
sizes = np.array([60, 80, 120])
2020

2121
# calculate the points of the first pie marker
2222
#
2323
# these are just the origin (0,0) +
2424
# some points on a circle cos,sin
25-
x = [0] + np.cos(np.linspace(0, 2*math.pi*r1, 10)).tolist()
26-
y = [0] + np.sin(np.linspace(0, 2*math.pi*r1, 10)).tolist()
27-
25+
x = [0] + np.cos(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
26+
y = [0] + np.sin(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
2827
xy1 = list(zip(x, y))
29-
s1 = max(max(x), max(y))
28+
s1 = np.max(xy1)
3029

31-
# ...
32-
x = [0] + np.cos(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
33-
y = [0] + np.sin(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
30+
x = [0] + np.cos(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
31+
y = [0] + np.sin(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
3432
xy2 = list(zip(x, y))
35-
s2 = max(max(x), max(y))
33+
s2 = np.max(xy2)
3634

37-
x = [0] + np.cos(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
38-
y = [0] + np.sin(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
35+
x = [0] + np.cos(np.linspace(2 * np.pi * r2, 2 * np.pi, 10)).tolist()
36+
y = [0] + np.sin(np.linspace(2 * np.pi * r2, 2 * np.pi, 10)).tolist()
3937
xy3 = list(zip(x, y))
40-
s3 = max(max(x), max(y))
38+
s3 = np.max(xy3)
4139

4240
fig, ax = plt.subplots()
43-
ax.scatter(np.arange(3), np.arange(3), marker=(xy1, 0),
44-
s=[s1*s1*_ for _ in sizes], facecolor='blue')
45-
ax.scatter(np.arange(3), np.arange(3), marker=(xy2, 0),
46-
s=[s2*s2*_ for _ in sizes], facecolor='green')
47-
ax.scatter(np.arange(3), np.arange(3), marker=(xy3, 0),
48-
s=[s3*s3*_ for _ in sizes], facecolor='red')
41+
ax.scatter(range(3), range(3), marker=(xy1, 0),
42+
s=s1 ** 2 * sizes, facecolor='blue')
43+
ax.scatter(range(3), range(3), marker=(xy2, 0),
44+
s=s2 ** 2 * sizes, facecolor='green')
45+
ax.scatter(range(3), range(3), marker=(xy3, 0),
46+
s=s3 ** 2 * sizes, facecolor='red')
4947

5048
plt.show()

‎examples/event_handling/timers.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,7 @@ def update_title(axes):
1818
fig, ax = plt.subplots()
1919

2020
x = np.linspace(-3, 3)
21-
ax.plot(x, x*x)
21+
ax.plot(x, x ** 2)
2222

2323
# Create a new timer object. Set the interval to 100 milliseconds
2424
# (1000 is default) and tell the timer what function should be called.

‎examples/event_handling/trifinder_event_demo.py

Lines changed: 11 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -11,16 +11,15 @@
1111
from matplotlib.tri import Triangulation
1212
from matplotlib.patches import Polygon
1313
import numpy as np
14-
import math
1514

1615

1716
def update_polygon(tri):
1817
if tri == -1:
1918
points = [0, 0, 0]
2019
else:
21-
points = triangulation.triangles[tri]
22-
xs = triangulation.x[points]
23-
ys = triangulation.y[points]
20+
points = triang.triangles[tri]
21+
xs = triang.x[points]
22+
ys = triang.y[points]
2423
polygon.set_xy(list(zip(xs, ys)))
2524

2625

@@ -39,23 +38,22 @@ def motion_notify(event):
3938
n_radii = 5
4039
min_radius = 0.25
4140
radii = np.linspace(min_radius, 0.95, n_radii)
42-
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
41+
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
4342
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
44-
angles[:, 1::2] += math.pi / n_angles
43+
angles[:, 1::2] += np.pi / n_angles
4544
x = (radii*np.cos(angles)).flatten()
4645
y = (radii*np.sin(angles)).flatten()
47-
triangulation = Triangulation(x, y)
48-
xmid = x[triangulation.triangles].mean(axis=1)
49-
ymid = y[triangulation.triangles].mean(axis=1)
50-
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
51-
triangulation.set_mask(mask)
46+
triang = Triangulation(x, y)
47+
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
48+
y[triang.triangles].mean(axis=1))
49+
< min_radius)
5250

5351
# Use the triangulation's default TriFinder object.
54-
trifinder = triangulation.get_trifinder()
52+
trifinder = triang.get_trifinder()
5553

5654
# Setup plot and callbacks.
5755
plt.subplot(111, aspect='equal')
58-
plt.triplot(triangulation, 'bo-')
56+
plt.triplot(triang, 'bo-')
5957
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
6058
update_polygon(-1)
6159
plt.gca().add_patch(polygon)

‎examples/images_contours_and_fields/tricontour_demo.py

Lines changed: 6 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,6 @@
88
import matplotlib.pyplot as plt
99
import matplotlib.tri as tri
1010
import numpy as np
11-
import math
1211

1312
###############################################################################
1413
# Creating a Triangulation without specifying the triangles results in the
@@ -20,22 +19,21 @@
2019
min_radius = 0.25
2120
radii = np.linspace(min_radius, 0.95, n_radii)
2221

23-
angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
22+
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
2423
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
25-
angles[:, 1::2] += math.pi / n_angles
24+
angles[:, 1::2] += np.pi / n_angles
2625

2726
x = (radii * np.cos(angles)).flatten()
2827
y = (radii * np.sin(angles)).flatten()
29-
z = (np.cos(radii) * np.cos(angles * 3.0)).flatten()
28+
z = (np.cos(radii) * np.cos(3 * angles)).flatten()
3029

3130
# Create the Triangulation; no triangles so Delaunay triangulation created.
3231
triang = tri.Triangulation(x, y)
3332

3433
# Mask off unwanted triangles.
35-
xmid = x[triang.triangles].mean(axis=1)
36-
ymid = y[triang.triangles].mean(axis=1)
37-
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
38-
triang.set_mask(mask)
34+
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
35+
y[triang.triangles].mean(axis=1))
36+
< min_radius)
3937

4038
###############################################################################
4139
# pcolor plot.

‎examples/images_contours_and_fields/tricontour_smooth_user.py

Lines changed: 5 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,6 @@
1010
import matplotlib.pyplot as plt
1111
import matplotlib.cm as cm
1212
import numpy as np
13-
import math
1413

1514

1615
#-----------------------------------------------------------------------------
@@ -36,9 +35,9 @@ def function_z(x, y):
3635
min_radius = 0.15
3736
radii = np.linspace(min_radius, 0.95, n_radii)
3837

39-
angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
38+
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
4039
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
41-
angles[:, 1::2] += math.pi / n_angles
40+
angles[:, 1::2] += np.pi / n_angles
4241

4342
x = (radii * np.cos(angles)).flatten()
4443
y = (radii * np.sin(angles)).flatten()
@@ -50,10 +49,9 @@ def function_z(x, y):
5049
triang = tri.Triangulation(x, y)
5150

5251
# Mask off unwanted triangles.
53-
xmid = x[triang.triangles].mean(axis=1)
54-
ymid = y[triang.triangles].mean(axis=1)
55-
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
56-
triang.set_mask(mask)
52+
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
53+
y[triang.triangles].mean(axis=1))
54+
< min_radius)
5755

5856
#-----------------------------------------------------------------------------
5957
# Refine data

‎examples/images_contours_and_fields/trigradient_demo.py

Lines changed: 7 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -5,12 +5,11 @@
55
66
Demonstrates computation of gradient with matplotlib.tri.CubicTriInterpolator.
77
"""
8-
from matplotlib.tri import Triangulation, UniformTriRefiner,\
9-
CubicTriInterpolator
8+
from matplotlib.tri import (
9+
Triangulation, UniformTriRefiner, CubicTriInterpolator)
1010
import matplotlib.pyplot as plt
1111
import matplotlib.cm as cm
1212
import numpy as np
13-
import math
1413

1514

1615
#-----------------------------------------------------------------------------
@@ -33,9 +32,9 @@ def dipole_potential(x, y):
3332
min_radius = 0.2
3433
radii = np.linspace(min_radius, 0.95, n_radii)
3534

36-
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
35+
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
3736
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
38-
angles[:, 1::2] += math.pi/n_angles
37+
angles[:, 1::2] += np.pi / n_angles
3938

4039
x = (radii*np.cos(angles)).flatten()
4140
y = (radii*np.sin(angles)).flatten()
@@ -46,10 +45,9 @@ def dipole_potential(x, y):
4645
triang = Triangulation(x, y)
4746

4847
# Mask off unwanted triangles.
49-
xmid = x[triang.triangles].mean(axis=1)
50-
ymid = y[triang.triangles].mean(axis=1)
51-
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
52-
triang.set_mask(mask)
48+
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
49+
y[triang.triangles].mean(axis=1))
50+
< min_radius)
5351

5452
#-----------------------------------------------------------------------------
5553
# Refine data - interpolates the electrical potential V

‎examples/images_contours_and_fields/tripcolor_demo.py

Lines changed: 6 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,6 @@
88
import matplotlib.pyplot as plt
99
import matplotlib.tri as tri
1010
import numpy as np
11-
import math
1211

1312
###############################################################################
1413
# Creating a Triangulation without specifying the triangles results in the
@@ -20,22 +19,21 @@
2019
min_radius = 0.25
2120
radii = np.linspace(min_radius, 0.95, n_radii)
2221

23-
angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
22+
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
2423
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
25-
angles[:, 1::2] += math.pi / n_angles
24+
angles[:, 1::2] += np.pi / n_angles
2625

2726
x = (radii * np.cos(angles)).flatten()
2827
y = (radii * np.sin(angles)).flatten()
29-
z = (np.cos(radii) * np.cos(angles * 3.0)).flatten()
28+
z = (np.cos(radii) * np.cos(3 * angles)).flatten()
3029

3130
# Create the Triangulation; no triangles so Delaunay triangulation created.
3231
triang = tri.Triangulation(x, y)
3332

3433
# Mask off unwanted triangles.
35-
xmid = x[triang.triangles].mean(axis=1)
36-
ymid = y[triang.triangles].mean(axis=1)
37-
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
38-
triang.set_mask(mask)
34+
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
35+
y[triang.triangles].mean(axis=1))
36+
< min_radius)
3937

4038
###############################################################################
4139
# tripcolor plot.

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