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Commit 128ee17

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anntzeranntzer
committed
Remove/rework uses of np.where where possible.
In a few places, np.where can be replaced by direct use of boolean arrays, or other constructs. In colors.py, the use of putmask may be slightly obscure (so I left a comment explaining it), but avoids repeating the modified array twice, so I think is more legible. In polar.py, preallocating xy is just pointless given that we're constructing intermediate arrays for x and y and filling them into xy afterwards.
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‎examples/images_contours_and_fields/barcode_demo.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/barcode_demo.py
+2-4Lines changed: 2 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -11,12 +11,11 @@
1111
# Fixing random state for reproducibility
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np.random.seed(19680801)
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14-
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# the bar
16-
x = np.where(np.random.rand(500) > 0.7, 1.0, 0.0)
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x = np.random.rand(500) > 0.7
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1817
axprops = dict(xticks=[], yticks=[])
19-
barprops = dict(aspect='auto', cmap=plt.cm.binary, interpolation='nearest')
18+
barprops = dict(aspect='auto', cmap='binary', interpolation='nearest')
2019

2120
fig = plt.figure()
2221

@@ -28,7 +27,6 @@
2827
ax2 = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
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ax2.imshow(x.reshape((1, -1)), **barprops)
3029

31-
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plt.show()
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#############################################################################

‎examples/images_contours_and_fields/specgram_demo.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/specgram_demo.py
+2-4Lines changed: 2 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -3,8 +3,7 @@
33
Spectrogram Demo
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================
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6-
Demo of a spectrogram plot
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(:meth:`~.axes.Axes.specgram`).
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Demo of a spectrogram plot (`~.axes.Axes.specgram`).
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"""
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import matplotlib.pyplot as plt
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import numpy as np
@@ -18,8 +17,7 @@
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s2 = 2 * np.sin(2 * np.pi * 400 * t)
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2019
# create a transient "chirp"
21-
mask = np.where(np.logical_and(t > 10, t < 12), 1.0, 0.0)
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s2 = s2 * mask
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s2[t <= 10] = s2[12 <= t] = 0
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# add some noise into the mix
2523
nse = 0.01 * np.random.random(size=len(t))

‎examples/mplot3d/trisurf3d_2.py

Copy file name to clipboardExpand all lines: examples/mplot3d/trisurf3d_2.py
+3-3Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
'''
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"""
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===========================
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More triangular 3D surfaces
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===========================
@@ -8,7 +8,7 @@
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The first demonstrates use of plot_trisurf's triangles argument, and the
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second sets a Triangulation object's mask and passes the object directly
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to plot_trisurf.
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'''
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"""
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import numpy as np
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import matplotlib.pyplot as plt
@@ -71,7 +71,7 @@
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# Mask off unwanted triangles.
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xmid = x[triang.triangles].mean(axis=1)
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ymid = y[triang.triangles].mean(axis=1)
74-
mask = np.where(xmid**2 + ymid**2 < min_radius**2, 1, 0)
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mask = xmid**2 + ymid**2 < min_radius**2
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triang.set_mask(mask)
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# Plot the surface.

‎lib/matplotlib/colors.py

Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+13-22Lines changed: 13 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -1775,28 +1775,19 @@ def blend_hsv(self, rgb, intensity, hsv_max_sat=None, hsv_max_val=None,
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hsv = rgb_to_hsv(rgb[:, :, 0:3])
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# modify hsv values to simulate illumination.
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hsv[:, :, 1] = np.where(np.logical_and(np.abs(hsv[:, :, 1]) > 1.e-10,
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intensity > 0),
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((1. - intensity) * hsv[:, :, 1] +
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intensity * hsv_max_sat),
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hsv[:, :, 1])
1783-
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hsv[:, :, 2] = np.where(intensity > 0,
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((1. - intensity) * hsv[:, :, 2] +
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intensity * hsv_max_val),
1787-
hsv[:, :, 2])
1788-
1789-
hsv[:, :, 1] = np.where(np.logical_and(np.abs(hsv[:, :, 1]) > 1.e-10,
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intensity < 0),
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((1. + intensity) * hsv[:, :, 1] -
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intensity * hsv_min_sat),
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hsv[:, :, 1])
1794-
hsv[:, :, 2] = np.where(intensity < 0,
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((1. + intensity) * hsv[:, :, 2] -
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intensity * hsv_min_val),
1797-
hsv[:, :, 2])
1798-
hsv[:, :, 1:] = np.where(hsv[:, :, 1:] < 0., 0, hsv[:, :, 1:])
1799-
hsv[:, :, 1:] = np.where(hsv[:, :, 1:] > 1., 1, hsv[:, :, 1:])
1778+
np.putmask(hsv[:, :, 1], # i.e. A[mask] = B[mask].
1779+
(np.abs(hsv[:, :, 1]) > 1.e-10) & (intensity > 0),
1780+
(1 - intensity) * hsv[:, :, 1] + intensity * hsv_max_sat)
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np.putmask(hsv[:, :, 2],
1782+
intensity > 0,
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(1 - intensity) * hsv[:, :, 2] + intensity * hsv_max_val)
1784+
np.putmask(hsv[:, :, 1],
1785+
(np.abs(hsv[:, :, 1]) > 1.e-10) & (intensity < 0),
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(1 + intensity) * hsv[:, :, 1] - intensity * hsv_min_sat)
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np.putmask(hsv[:, :, 2],
1788+
intensity < 0,
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(1 + intensity) * hsv[:, :, 2] - intensity * hsv_min_val)
1790+
np.clip(hsv[:, :, 1:], 0, 1, out=hsv[:, :, 1:])
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# convert modified hsv back to rgb.
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return hsv_to_rgb(hsv)
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‎lib/matplotlib/projections/polar.py

Copy file name to clipboardExpand all lines: lib/matplotlib/projections/polar.py
+3-14Lines changed: 3 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -44,27 +44,16 @@ def __str__(self):
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def transform_non_affine(self, tr):
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# docstring inherited
47-
xy = np.empty(tr.shape, float)
48-
49-
t = tr[:, 0:1]
50-
r = tr[:, 1:2]
51-
x = xy[:, 0:1]
52-
y = xy[:, 1:2]
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47+
t, r = np.transpose(tr)
5448
# PolarAxes does not use the theta transforms here, but apply them for
5549
# backwards-compatibility if not being used by it.
5650
if self._apply_theta_transforms and self._axis is not None:
5751
t *= self._axis.get_theta_direction()
5852
t += self._axis.get_theta_offset()
59-
6053
if self._use_rmin and self._axis is not None:
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r = (r - self._axis.get_rorigin()) * self._axis.get_rsign()
62-
63-
mask = r < 0
64-
x[:] = np.where(mask, np.nan, r * np.cos(t))
65-
y[:] = np.where(mask, np.nan, r * np.sin(t))
66-
67-
return xy
55+
r = np.where(r >= 0, r, np.nan)
56+
return np.column_stack([r * np.cos(t), r * np.sin(t)])
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6958
def transform_path_non_affine(self, path):
7059
# docstring inherited

‎lib/matplotlib/tri/triinterpolate.py

Copy file name to clipboardExpand all lines: lib/matplotlib/tri/triinterpolate.py
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1341,7 +1341,7 @@ def _cg(A, b, x0=None, tol=1.e-10, maxiter=1000):
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# Jacobi pre-conditioner
13421342
kvec = A.diag
13431343
# For diag elem < 1e-6 we keep 1e-6.
1344-
kvec = np.where(kvec > 1.e-6, kvec, 1.e-6)
1344+
kvec = np.maximum(kvec, 1e-6)
13451345

13461346
# Initial guess
13471347
if x0 is None:

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