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tacaswelltacaswell
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Merge pull request #9151 from dstansby/mlab-deprecate
API: Deprecate mlab functions
2 parents 0f42e10 + 4c61a1d commit 3e37306

39 files changed

+356
-482
lines changed

‎examples/api/affine_image.py

Lines changed: 3 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,6 @@
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"""
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import numpy as np
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import matplotlib.mlab as mlab
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import matplotlib.pyplot as plt
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import matplotlib.transforms as mtransforms
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@@ -18,9 +17,9 @@ def get_image():
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delta = 0.25
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x = y = np.arange(-3.0, 3.0, delta)
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X, Y = np.meshgrid(x, y)
21-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
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Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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Z = Z2 - Z1 # difference of Gaussians
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = (Z1 - Z2)
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return Z
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‎examples/frontpage/contour.py

Lines changed: 8 additions & 8 deletions
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@@ -8,27 +8,27 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import mlab, cm
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from matplotlib import cm
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extent = (-3, 3, -3, 3)
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delta = 0.5
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x = np.arange(-3.0, 4.001, delta)
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y = np.arange(-4.0, 3.001, delta)
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X, Y = np.meshgrid(x, y)
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Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, -0.5)
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Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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Z = (Z1 - Z2) * 10
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = Z1 - Z2
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23-
levels = np.linspace(-2.0, 1.601, 40)
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norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
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fig, ax = plt.subplots()
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cset1 = ax.contourf(
28-
X, Y, Z, levels,
27+
X, Y, Z, 40,
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norm=norm)
30-
ax.set_xlim(-3, 3)
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ax.set_ylim(-3, 3)
29+
ax.set_xlim(-2, 2)
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ax.set_ylim(-2, 2)
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ax.set_xticks([])
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ax.set_yticks([])
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fig.savefig("contour_frontpage.png", dpi=25) # results in 160x120 px image
34+
plt.show()

‎examples/images_contours_and_fields/contour_demo.py

Lines changed: 3 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,6 @@
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import matplotlib
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import numpy as np
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import matplotlib.cm as cm
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import matplotlib.mlab as mlab
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import matplotlib.pyplot as plt
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matplotlib.rcParams['xtick.direction'] = 'out'
@@ -21,10 +20,9 @@
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x = np.arange(-3.0, 3.0, delta)
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y = np.arange(-2.0, 2.0, delta)
2322
X, Y = np.meshgrid(x, y)
24-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
25-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
26-
# difference of Gaussians
27-
Z = 10.0 * (Z2 - Z1)
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = (Z1 - Z2) * 2
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###############################################################################
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# Create a simple contour plot with labels using default colors. The

‎examples/images_contours_and_fields/contour_image.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -13,7 +13,7 @@
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"""
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import matplotlib.pyplot as plt
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import numpy as np
16-
from matplotlib import mlab, cm
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from matplotlib import cm
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# Default delta is large because that makes it fast, and it illustrates
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# the correct registration between image and contours.
@@ -24,9 +24,9 @@
2424
x = np.arange(-3.0, 4.001, delta)
2525
y = np.arange(-4.0, 3.001, delta)
2626
X, Y = np.meshgrid(x, y)
27-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
28-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
29-
Z = (Z1 - Z2) * 10
27+
Z1 = np.exp(-X**2 - Y**2)
28+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
29+
Z = (Z1 - Z2) * 2
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# Boost the upper limit to avoid truncation errors.
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levels = np.arange(-2.0, 1.601, 0.4)

‎examples/images_contours_and_fields/contour_label_demo.py

Lines changed: 3 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,6 @@
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import matplotlib
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import numpy as np
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import matplotlib.cm as cm
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import matplotlib.mlab as mlab
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import matplotlib.ticker as ticker
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import matplotlib.pyplot as plt
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@@ -25,10 +24,9 @@
2524
x = np.arange(-3.0, 3.0, delta)
2625
y = np.arange(-2.0, 2.0, delta)
2726
X, Y = np.meshgrid(x, y)
28-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
29-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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# difference of Gaussians
31-
Z = 10.0 * (Z2 - Z1)
27+
Z1 = np.exp(-X**2 - Y**2)
28+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = (Z1 - Z2) * 2
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###############################################################################
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# Make contour labels using creative float classes

‎examples/images_contours_and_fields/contourf_demo.py

Lines changed: 3 additions & 3 deletions
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@@ -14,9 +14,9 @@
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x = y = np.arange(-3.0, 3.01, delta)
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X, Y = np.meshgrid(x, y)
17-
Z1 = plt.mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
18-
Z2 = plt.mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
19-
Z = 10 * (Z1 - Z2)
17+
Z1 = np.exp(-X**2 - Y**2)
18+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
19+
Z = (Z1 - Z2) * 2
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nr, nc = Z.shape
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‎examples/images_contours_and_fields/contourf_log.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -10,19 +10,19 @@
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import numpy as np
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from numpy import ma
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from matplotlib import ticker, cm
13-
from matplotlib.mlab import bivariate_normal
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1514
N = 100
1615
x = np.linspace(-3.0, 3.0, N)
1716
y = np.linspace(-2.0, 2.0, N)
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1918
X, Y = np.meshgrid(x, y)
2019

21-
# A low hump with a spike coming out of the top right.
20+
# A low hump with a spike coming out.
2221
# Needs to have z/colour axis on a log scale so we see both hump and spike.
2322
# linear scale only shows the spike.
24-
z = (bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0)
25-
+ 0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0))
23+
Z1 = np.exp(-(X)**2 - (Y)**2)
24+
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
25+
z = Z1 + 50 * Z2
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2727
# Put in some negative values (lower left corner) to cause trouble with logs:
2828
z[:5, :5] = -1
@@ -39,10 +39,10 @@
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4040
# Alternatively, you can manually set the levels
4141
# and the norm:
42-
#lev_exp = np.arange(np.floor(np.log10(z.min())-1),
42+
# lev_exp = np.arange(np.floor(np.log10(z.min())-1),
4343
# np.ceil(np.log10(z.max())+1))
44-
#levs = np.power(10, lev_exp)
45-
#cs = P.contourf(X, Y, z, levs, norm=colors.LogNorm())
44+
# levs = np.power(10, lev_exp)
45+
# cs = P.contourf(X, Y, z, levs, norm=colors.LogNorm())
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4747
# The 'extend' kwarg does not work yet with a log scale.
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‎examples/images_contours_and_fields/griddata_demo.py

Lines changed: 0 additions & 34 deletions
This file was deleted.

‎examples/images_contours_and_fields/image_demo.py

Lines changed: 6 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -14,7 +14,6 @@
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import numpy as np
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import matplotlib.cm as cm
17-
import matplotlib.mlab as mlab
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
2019
from matplotlib.path import Path
@@ -26,9 +25,9 @@
2625
delta = 0.025
2726
x = y = np.arange(-3.0, 3.0, delta)
2827
X, Y = np.meshgrid(x, y)
29-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
30-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
31-
Z = Z2 - Z1 # difference of Gaussians
28+
Z1 = np.exp(-X**2 - Y**2)
29+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
30+
Z = (Z1 - Z2) * 2
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3332
im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
3433
origin='lower', extent=[-3, 3, -3, 3],
@@ -156,9 +155,9 @@
156155
delta = 0.025
157156
x = y = np.arange(-3.0, 3.0, delta)
158157
X, Y = np.meshgrid(x, y)
159-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
160-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
161-
Z = Z2 - Z1 # difference of Gaussians
158+
Z1 = np.exp(-X**2 - Y**2)
159+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
160+
Z = (Z1 - Z2) * 2
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163162
path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
164163
patch = PathPatch(path, facecolor='none')

‎examples/images_contours_and_fields/image_masked.py

Lines changed: 3 additions & 4 deletions
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@@ -13,17 +13,16 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.colors as colors
16-
import matplotlib.mlab as mlab
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1817
# compute some interesting data
1918
x0, x1 = -5, 5
2019
y0, y1 = -3, 3
2120
x = np.linspace(x0, x1, 500)
2221
y = np.linspace(y0, y1, 500)
2322
X, Y = np.meshgrid(x, y)
24-
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
25-
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
26-
Z = 10*(Z2 - Z1) # difference of Gaussians
23+
Z1 = np.exp(-X**2 - Y**2)
24+
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
25+
Z = (Z1 - Z2) * 2
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2827
# Set up a colormap:
2928
# use copy so that we do not mutate the global colormap instance

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