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DOC: clearing out some instances of using pylab in the docs #6957

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7 changes: 4 additions & 3 deletions doc/faq/installing_faq.rst
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Expand Up @@ -23,9 +23,10 @@ complexities. Open up a UNIX shell or a DOS command prompt and cd into a
directory containing a minimal example in a file. Something like
:file:`simple_plot.py` for example::

from pylab import *
plot([1,2,3])
show()
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1,2,3])
plt.show()

and run it with::

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92 changes: 45 additions & 47 deletions doc/pyplots/boxplot_demo.py
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Expand Up @@ -4,62 +4,60 @@
# Example boxplot code
#

from pylab import *
import numpy as np
import matplotlib.pyplot as plt

# fake up some data
spread= rand(50) * 100
center = ones(25) * 50
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
data =concatenate((spread, center, flier_high, flier_low), 0)

# basic plot
boxplot(data)
#savefig('box1')

# notched plot
figure()
boxplot(data,1)
#savefig('box2')

# change outlier point symbols
figure()
boxplot(data,0,'gD')
#savefig('box3')

# don't show outlier points
figure()
boxplot(data,0,'')
#savefig('box4')
np.random.seed(0)

# horizontal boxes
figure()
boxplot(data,0,'rs',0)
#savefig('box5')

# change whisker length
figure()
boxplot(data,0,'rs',0,0.75)
#savefig('box6')
# fake up some data
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
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@QuLogic QuLogic Aug 19, 2016 •
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Are these comments wrong? Why remove them?

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I added them to the figures as titles -- figure that just as informative when reading the code.

data = np.concatenate((spread, center, flier_high, flier_low), 0)

fig1, ax1 = plt.subplots()
ax1.set_title('Basic Plot')
ax1.boxplot(data)

fig2, ax2 = plt.subplots()
ax2.set_title('Notched boxes')
ax2.boxplot(data, notch=True)

green_diamond = dict(markerfacecolor='g', marker='D')
fig3, ax3 = plt.subplots()
ax3.set_title('Changed Outlier Symbols')
ax3.boxplot(data, flierprops=green_diamond)

fig4, ax4 = plt.subplots()
ax4.set_title('Hide Outlier Points')
ax4.boxplot(data, showfliers=False)

red_square = dict(markerfacecolor='r', marker='s')
fig5, ax5 = plt.subplots()
ax5.set_title('Horizontal Boxes')
ax5.boxplot(data, vert=False, flierprops=red_square)

fig6, ax6 = plt.subplots()
ax6.set_title('Shorter Whisker Length')
ax6.boxplot(data, flierprops=red_square, vert=False, whis=0.75)

# fake up some more data
spread= rand(50) * 100
center = ones(25) * 40
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
d2 = concatenate( (spread, center, flier_high, flier_low), 0 )
spread = np.random.rand(50) * 100
center = np.ones(25) * 40
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
d2 = np.concatenate((spread, center, flier_high, flier_low), 0)
data.shape = (-1, 1)
d2.shape = (-1, 1)
#data = concatenate( (data, d2), 1 )

# Making a 2-D array only works if all the columns are the
# same length. If they are not, then use a list instead.
# This is actually more efficient because boxplot converts
# a 2-D array into a list of vectors internally anyway.
data = [data, d2, d2[::2,0]]
# multiple box plots on one figure
figure()
boxplot(data)
#savefig('box7')

show()
fig7, ax7 = plt.subplots()
ax7.set_title('Multiple Samples with Different sizes')
ax7.boxplot(data)

plt.show()
7 changes: 3 additions & 4 deletions doc/pyplots/whats_new_98_4_fill_between.py
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@@ -1,17 +1,16 @@
#!/usr/bin/env python
import matplotlib.mlab as mlab
from pylab import figure, show
import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)

fig = figure()
ax = fig.add_subplot(111)
fig, ax = plt.subplots()
ax.plot(x, y1, x, y2, color='black')
ax.fill_between(x, y1, y2, where=y2>y1, facecolor='green')
ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='red')
ax.set_title('fill between where')

show()
plt.show()
15 changes: 8 additions & 7 deletions doc/pyplots/whats_new_99_mplot3d.py
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@@ -1,17 +1,18 @@
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import pylab
import random

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D

fig = pylab.figure()
ax = Axes3D(fig)
X = np.arange(-5, 5, 0.25)
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Maybe move data pieces above plotting code?

Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet)

pylab.show()
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.viridis)

plt.show()
6 changes: 3 additions & 3 deletions examples/api/colorbar_only.py
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Expand Up @@ -2,11 +2,11 @@
Make a colorbar as a separate figure.
'''

from matplotlib import pyplot
import matplotlib.pyplot as plt
import matplotlib as mpl

# Make a figure and axes with dimensions as desired.
fig = pyplot.figure(figsize=(8, 3))
fig = plt.figure(figsize=(8, 3))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
ax3 = fig.add_axes([0.05, 0.15, 0.9, 0.15])
Expand Down Expand Up @@ -71,4 +71,4 @@
orientation='horizontal')
cb3.set_label('Custom extension lengths, some other units')

pyplot.show()
plt.show()
19 changes: 8 additions & 11 deletions examples/units/units_scatter.py
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Expand Up @@ -9,28 +9,25 @@
arrays.
"""
import numpy as np
import matplotlib.pyplot as plt
from basic_units import secs, hertz, minutes
from matplotlib.pylab import figure, show

# create masked array
data = (1, 2, 3, 4, 5, 6, 7, 8)
mask = (1, 0, 1, 0, 0, 0, 1, 0)
xsecs = secs * np.ma.MaskedArray(data, mask, float)


xsecs = secs*np.ma.MaskedArray((1, 2, 3, 4, 5, 6, 7, 8), (1, 0, 1, 0, 0, 0, 1, 0), float)
#xsecs = secs*np.arange(1,10.)

fig = figure()
ax1 = fig.add_subplot(3, 1, 1)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True)
ax1.scatter(xsecs, xsecs)
#ax1.set_ylabel('seconds')
ax1.yaxis.set_units(secs)
ax1.axis([0, 10, 0, 10])

ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
ax2.scatter(xsecs, xsecs, yunits=hertz)
ax2.axis([0, 10, 0, 1])

ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
ax3.scatter(xsecs, xsecs, yunits=hertz)
ax3.yaxis.set_units(minutes)
ax3.axis([0, 10, 0, 1])

show()
fig.tight_layout()
plt.show()
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consistency of fig.show() or plt.show()? (my preference is for fig.show()...)

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mine too, but some examples have more than 1 figure, so pyplot.show() shows them all.

Happy to change this one though.

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also, an advantage of pyplot.show() is that it pulls up a blocking interactive window when fig.show() won't.

IOW, if I run python doc/path/to/some/example.py and it has fig.show(), the figure closes before I can look it. With pyplot.show(), it stays open until I close it.

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Hmm, the new I guess use plt.show() for all?

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I would lean towards plt.show() over fig.show()