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Aug 27, 2017
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Cleanup: broadcasting #7562

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4 changes: 2 additions & 2 deletions doc/api/axis_api.rst
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Expand Up @@ -109,8 +109,8 @@ Ticks, tick labels and Offset text
Axis.axis_date


Data and view internvals
------------------------
Data and view intervals
-----------------------

.. autosummary::
:toctree: _as_gen
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2 changes: 1 addition & 1 deletion examples/animation/unchained.py
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Expand Up @@ -25,7 +25,7 @@
# Generate random data
data = np.random.uniform(0, 1, (64, 75))
X = np.linspace(-1, 1, data.shape[-1])
G = 1.5 * np.exp(-4 * X * X)
G = 1.5 * np.exp(-4 * X ** 2)
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In most cases I think this construct is marginally faster.

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# float is probably the most common case
In [8]: x = np.arange(100000).astype(float)

In [9]: %timeit x * x
10000 loops, best of 3: 65.5 µs per loop

In [10]: %timeit x ** 2
10000 loops, best of 3: 66.4 µs per loop

(and you can easily get them reversed on another run).

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interesting. That is an apparently wrong rule of thumb I have been using for a while.

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Why would you think this would be the case? (I am honestly puzzled.)

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It dates back to the early days of computers and compilers, when computers were slow and compilers were not so bright. Multiplication is faster than taking a power, in general. But now compilers recognize things like this and find the best algorithm. In the case of numpy, I'm pretty sure there is explicit internal optimization of small integer powers so that we wouldn't have to use that ancient manual optimization.

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I respectfully request the children remain off of my lawn. 😈

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@eric-wieser eric-wieser Dec 15, 2017 •
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As @efiring says, numpy hard-codes power(x, 2) to fall back on square(x), along with a handful of other constants (I think (-1, 0, 0.5, 1, 2))


# Generate line plots
lines = []
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12 changes: 4 additions & 8 deletions examples/api/custom_projection_example.py
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Expand Up @@ -434,15 +434,11 @@ def __init__(self, resolution):
self._resolution = resolution

def transform_non_affine(self, xy):
x = xy[:, 0:1]
y = xy[:, 1:2]

quarter_x = 0.25 * x
half_y = 0.5 * y
z = np.sqrt(1.0 - quarter_x*quarter_x - half_y*half_y)
longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0)))
x, y = xy.T
z = np.sqrt(1 - (x / 4) ** 2 - (y / 2) ** 2)
longitude = 2 * np.arctan((z * x) / (2 * (2 * z ** 2 - 1)))
latitude = np.arcsin(y*z)
return np.concatenate((longitude, latitude), 1)
return np.column_stack([longitude, latitude])
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

def inverted(self):
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36 changes: 17 additions & 19 deletions examples/api/scatter_piecharts.py
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Expand Up @@ -7,7 +7,7 @@

Thanks to Manuel Metz for the example
"""
import math

import numpy as np
import matplotlib.pyplot as plt

Expand All @@ -16,35 +16,33 @@
r2 = r1 + 0.4 # 40%

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

# calculate the points of the first pie marker
#
# these are just the origin (0,0) +
# some points on a circle cos,sin
x = [0] + np.cos(np.linspace(0, 2*math.pi*r1, 10)).tolist()
y = [0] + np.sin(np.linspace(0, 2*math.pi*r1, 10)).tolist()

x = [0] + np.cos(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
y = [0] + np.sin(np.linspace(0, 2 * np.pi * r1, 10)).tolist()
xy1 = list(zip(x, y))
s1 = max(max(x), max(y))
s1 = np.max(xy1)

# ...
x = [0] + np.cos(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
y = [0] + np.sin(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
x = [0] + np.cos(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
y = [0] + np.sin(np.linspace(2 * np.pi * r1, 2 * np.pi * r2, 10)).tolist()
xy2 = list(zip(x, y))
s2 = max(max(x), max(y))
s2 = np.max(xy2)

x = [0] + np.cos(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
y = [0] + np.sin(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
x = [0] + np.cos(np.linspace(2 * np.pi * r2, 2 * np.pi, 10)).tolist()
y = [0] + np.sin(np.linspace(2 * np.pi * r2, 2 * np.pi, 10)).tolist()
xy3 = list(zip(x, y))
s3 = max(max(x), max(y))
s3 = np.max(xy3)

fig, ax = plt.subplots()
ax.scatter(np.arange(3), np.arange(3), marker=(xy1, 0),
s=[s1*s1*_ for _ in sizes], facecolor='blue')
ax.scatter(np.arange(3), np.arange(3), marker=(xy2, 0),
s=[s2*s2*_ for _ in sizes], facecolor='green')
ax.scatter(np.arange(3), np.arange(3), marker=(xy3, 0),
s=[s3*s3*_ for _ in sizes], facecolor='red')
ax.scatter(range(3), range(3), marker=(xy1, 0),
s=s1 ** 2 * sizes, facecolor='blue')
ax.scatter(range(3), range(3), marker=(xy2, 0),
s=s2 ** 2 * sizes, facecolor='green')
ax.scatter(range(3), range(3), marker=(xy3, 0),
s=s3 ** 2 * sizes, facecolor='red')

plt.show()
2 changes: 1 addition & 1 deletion examples/event_handling/timers.py
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Expand Up @@ -18,7 +18,7 @@ def update_title(axes):
fig, ax = plt.subplots()

x = np.linspace(-3, 3)
ax.plot(x, x*x)
ax.plot(x, x ** 2)

# Create a new timer object. Set the interval to 100 milliseconds
# (1000 is default) and tell the timer what function should be called.
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24 changes: 11 additions & 13 deletions examples/event_handling/trifinder_event_demo.py
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Expand Up @@ -11,16 +11,15 @@
from matplotlib.tri import Triangulation
from matplotlib.patches import Polygon
import numpy as np
import math


def update_polygon(tri):
if tri == -1:
points = [0, 0, 0]
else:
points = triangulation.triangles[tri]
xs = triangulation.x[points]
ys = triangulation.y[points]
points = triang.triangles[tri]
xs = triang.x[points]
ys = triang.y[points]
polygon.set_xy(list(zip(xs, ys)))


Expand All @@ -39,23 +38,22 @@ def motion_notify(event):
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
angles[:, 1::2] += np.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
triangulation = Triangulation(x, y)
xmid = x[triangulation.triangles].mean(axis=1)
ymid = y[triangulation.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triangulation.set_mask(mask)
triang = Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

# Use the triangulation's default TriFinder object.
trifinder = triangulation.get_trifinder()
trifinder = triang.get_trifinder()

# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triangulation, 'bo-')
plt.triplot(triang, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
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14 changes: 6 additions & 8 deletions examples/images_contours_and_fields/tricontour_demo.py
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Expand Up @@ -8,7 +8,6 @@
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
import math

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

angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
angles[:, 1::2] += np.pi / n_angles

x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
z = (np.cos(radii) * np.cos(angles * 3.0)).flatten()
z = (np.cos(radii) * np.cos(3 * angles)).flatten()

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

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triang.set_mask(mask)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

###############################################################################
# pcolor plot.
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12 changes: 5 additions & 7 deletions examples/images_contours_and_fields/tricontour_smooth_user.py
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Expand Up @@ -10,7 +10,6 @@
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import math


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

angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
angles[:, 1::2] += np.pi / n_angles

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

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triang.set_mask(mask)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

#-----------------------------------------------------------------------------
# Refine data
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16 changes: 7 additions & 9 deletions examples/images_contours_and_fields/trigradient_demo.py
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Expand Up @@ -5,12 +5,11 @@

Demonstrates computation of gradient with matplotlib.tri.CubicTriInterpolator.
"""
from matplotlib.tri import Triangulation, UniformTriRefiner,\
CubicTriInterpolator
from matplotlib.tri import (
Triangulation, UniformTriRefiner, CubicTriInterpolator)
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import math


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

angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi/n_angles
angles[:, 1::2] += np.pi / n_angles

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

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triang.set_mask(mask)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

#-----------------------------------------------------------------------------
# Refine data - interpolates the electrical potential V
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14 changes: 6 additions & 8 deletions examples/images_contours_and_fields/tripcolor_demo.py
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Expand Up @@ -8,7 +8,6 @@
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
import math

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

angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
angles[:, 1::2] += np.pi / n_angles

x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
z = (np.cos(radii) * np.cos(angles * 3.0)).flatten()
z = (np.cos(radii) * np.cos(3 * angles)).flatten()

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

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triang.set_mask(mask)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

###############################################################################
# tripcolor plot.
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12 changes: 5 additions & 7 deletions examples/images_contours_and_fields/triplot_demo.py
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Expand Up @@ -8,7 +8,6 @@
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
import math

###############################################################################
# Creating a Triangulation without specifying the triangles results in the
Expand All @@ -20,9 +19,9 @@
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)

angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
angles[:, 1::2] += np.pi / n_angles

x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
Expand All @@ -31,10 +30,9 @@
triang = tri.Triangulation(x, y)

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triang.set_mask(mask)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)

###############################################################################
# Plot the triangulation.
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2 changes: 1 addition & 1 deletion examples/lines_bars_and_markers/nan_test.py
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Expand Up @@ -9,7 +9,7 @@
import matplotlib.pyplot as plt

t = np.arange(0.0, 1.0 + 0.01, 0.01)
s = np.cos(2 * 2 * np.pi * t)
s = np.cos(2 * 2*np.pi * t)
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Just curious: why is this the one where you don't have spaces around it?

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nm...I guess I see it now.

t[41:60] = np.nan

plt.subplot(2, 1, 1)
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2 changes: 1 addition & 1 deletion examples/misc/multipage_pdf.py
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Expand Up @@ -35,7 +35,7 @@

plt.rc('text', usetex=False)
fig = plt.figure(figsize=(4, 5))
plt.plot(x, x*x, 'ko')
plt.plot(x, x ** 2, 'ko')
plt.title('Page Three')
pdf.savefig(fig) # or you can pass a Figure object to pdf.savefig
plt.close()
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4 changes: 2 additions & 2 deletions examples/misc/pythonic_matplotlib.py
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Expand Up @@ -64,7 +64,7 @@
fig = figure(1)

ax1 = fig.add_subplot(211)
ax1.plot(t, sin(2*pi*t))
ax1.plot(t, sin(2*pi * t))
ax1.grid(True)
ax1.set_ylim((-2, 2))
ax1.set_ylabel('1 Hz')
Expand All @@ -74,7 +74,7 @@


ax2 = fig.add_subplot(212)
ax2.plot(t, sin(2*2*pi*t))
ax2.plot(t, sin(2 * 2*pi * t))
ax2.grid(True)
ax2.set_ylim((-2, 2))
l = ax2.set_xlabel('Hi mom')
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2 changes: 1 addition & 1 deletion examples/misc/table_demo.py
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Expand Up @@ -29,7 +29,7 @@
bar_width = 0.4

# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.array([0.0] * len(columns))
y_offset = np.zeros(len(columns))

# Plot bars and create text labels for the table
cell_text = []
Expand Down
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