8000 Merge pull request #11580 from timhoffm/subplots · pganssle/matplotlib@90e30e4 · GitHub
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Merge pull request matplotlib#11580 from timhoffm/subplots
DOC: Use plt.subplots()
2 parents 184a408 + 365d54c commit 90e30e4

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+85
-195
lines changed

‎examples/lines_bars_and_markers/simple_plot.py

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@@ -14,8 +14,6 @@
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t = np.arange(0.0, 2.0, 0.01)
1515
s = 1 + np.sin(2 * np.pi * t)
1616

17-
# Note that using plt.subplots below is equivalent to using
18-
# fig = plt.figure() and then ax = fig.add_subplot(111)
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fig, ax = plt.subplots()
2018
ax.plot(t, s)
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‎examples/misc/load_converter.py

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@@ -17,8 +17,7 @@
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converters={0: bytespdate2num('%d-%b-%y')},
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skiprows=1, usecols=(0, 2), unpack=True)
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20-
fig = plt.figure()
21-
ax = fig.add_subplot(111)
20+
fig, ax = plt.subplots()
2221
ax.plot_date(dates, closes, '-')
2322
fig.autofmt_xdate()
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plt.show()

‎examples/pyplots/annotate_transform.py

Lines changed: 1 addition & 2 deletions
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@@ -13,8 +13,7 @@
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x = np.arange(0, 10, 0.005)
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y = np.exp(-x/2.) * np.sin(2*np.pi*x)
1515

16-
fig = plt.figure()
17-
ax = fig.add_subplot(111)
16+
fig, ax = plt.subplots()
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ax.plot(x, y)
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ax.set_xlim(0, 10)
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ax.set_ylim(-1, 1)

‎examples/pyplots/auto_subplots_adjust.py

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@@ -13,8 +13,7 @@
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"""
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import matplotlib.pyplot as plt
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import matplotlib.transforms as mtransforms
16-
fig = plt.figure()
17-
ax = fig.add_subplot(111)
16+
fig, ax = plt.subplots()
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ax.plot(range(10))
1918
ax.set_yticks((2,5,7))
2019
labels = ax.set_yticklabels(('really, really, really', 'long', 'labels'))

‎examples/ticks_and_spines/tick_labels_from_values.py

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@@ -17,8 +17,7 @@
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import matplotlib.pyplot as plt
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from matplotlib.ticker import FuncFormatter, MaxNLocator
20-
fig = plt.figure()
21-
ax = fig.add_subplot(111)
20+
fig, ax = plt.subplots()
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xs = range(26)
2322
ys = range(26)
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labels = list('abcdefghijklmnopqrstuvwxyz')

‎lib/matplotlib/lines.py

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@@ -1372,8 +1372,7 @@ def process_selected(self, ind, xs, ys):
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self.markers.set_data(xs, ys)
13731373
self.canvas.draw()
13741374
1375-
fig = plt.figure()
1376-
ax = fig.add_subplot(111)
1375+
fig, ax = plt.subplots()
13771376
x, y = np.random.rand(2, 30)
13781377
line, = ax.plot(x, y, 'bs-', picker=5)
13791378

‎lib/matplotlib/mlab.py

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@@ -3876,8 +3876,7 @@ def cross_from_below(x, threshold):
38763876
t = np.arange(0.0, 2.0, 0.1)
38773877
s = np.sin(2*np.pi*t)
38783878
3879-
fig = plt.figure()
3880-
ax = fig.add_subplot(111)
3879+
fig, ax = plt.subplots()
38813880
ax.plot(t, s, '-o')
38823881
ax.axhline(0.5)
38833882
ax.axhline(-0.5)

‎lib/matplotlib/tests/test_artist.py

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@@ -129,8 +129,7 @@ def test_cull_markers():
129129
x = np.random.random(20000)
130130
y = np.random.random(20000)
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132-
fig = plt.figure()
133-
ax = fig.add_subplot(111)
132+
fig, ax = plt.subplots()
134133
ax.plot(x, y, 'k.')
135134
ax.set_xlim(2, 3)
136135

‎lib/matplotlib/tests/test_axes.py

Lines changed: 34 additions & 83 deletions
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@@ -821,9 +821,7 @@ def test_axhspan_epoch():
821821
remove_text=True, extensions=['png'])
822822
def test_hexbin_extent():
823823
# this test exposes sf bug 2856228
824-
fig = plt.figure()
825-
826-
ax = fig.add_subplot(111)
824+
fig, ax = plt.subplots()
827825
data = (np.arange(2000) / 2000).reshape((2, 1000))
828826
x, y = data
829827

@@ -832,8 +830,7 @@ def test_hexbin_extent():
832830
# Reuse testcase from above for a labeled data test
833831
data = {"x": x, "y": y}
834832

835-
fig = plt.figure()
836-
ax = fig.add_subplot(111)
833+
fig, ax = plt.subplots()
837834
ax.hexbin("x", "y", extent=[.1, .3, .6, .7], data=data)
838835

839836

@@ -852,9 +849,7 @@ def __init__(self, x, y):
852849
self.x = x
853850
self.y = y
854851

855-
fig = plt.figure()
856-
857-
ax = fig.add_subplot(111)
852+
fig, ax = plt.subplots()
858853
data = (np.arange(200) / 200).reshape((2, 100))
859854
x, y = data
860855
hb = ax.hexbin(x, y, extent=[.1, .3, .6, .7], picker=-1)
@@ -867,32 +862,29 @@ def __init__(self, x, y):
867862
extensions=['png'])
868863
def test_hexbin_log():
869864
# Issue #1636
870-
fig = plt.figure()
871-
872865
np.random.seed(0)
873866
n = 100000
874867
x = np.random.standard_normal(n)
875868
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
876869
y = np.power(2, y * 0.5)
877-
ax = fig.add_subplot(111)
870+
871+
fig, ax = plt.subplots()
878872
ax.hexbin(x, y, yscale='log')
879873

880874

881875
def test_inverted_limits():
882876
# Test gh:1553
883877
# Calling invert_xaxis prior to plotting should not disable autoscaling
884878
# while still maintaining the inverted direction
885-
fig = plt.figure()
886-
ax = fig.gca()
879+
fig, ax = plt.subplots()
887880
ax.invert_xaxis()
888881
ax.plot([-5, -3, 2, 4], [1, 2, -3, 5])
889882

890883
assert ax.get_xlim() == (4, -5)
891884
assert ax.get_ylim() == (-3, 5)
892885
plt.close()
893886

894-
fig = plt.figure()
895-
ax = fig.gca()
887+
fig, ax = plt.subplots()
896888
ax.invert_yaxis()
897889
ax.plot([-5, -3, 2, 4], [1, 2, -3, 5])
898890

@@ -908,8 +900,7 @@ def test_nonfinite_limits():
908900
with np.errstate(divide='ignore'):
909901
y = np.log(x)
910902
x[len(x)//2] = np.nan
911-
fig = plt.figure()
912-
ax = fig.add_subplot(111)
903+
fig, ax = plt.subplots()
913904
ax.plot(x, y)
914905

915906

@@ -924,9 +915,7 @@ def test_imshow():
924915
r = np.sqrt(x**2+y**2-x*y)
925916

926917
# Create a contour plot at N/4 and extract both the clip path and transform
927-
fig = plt.figure()
928-
ax = fig.add_subplot(111)
929-
918+
fig, ax = plt.subplots()
930919
ax.imshow(r)
931920

932921
# Reuse testcase from above for a labeled data test
@@ -948,8 +937,7 @@ def test_imshow_clip():
948937
r = np.sqrt(x**2+y**2-x*y)
949938

950939
# Create a contour plot at N/4 and extract both the clip path and transform
951-
fig = plt.figure()
952-
ax = fig.add_subplot(111)
940+
fig, ax = plt.subplots()
953941

954942
c = ax.contour(r, [N/4])
955943
x = c.collections[0]
@@ -970,8 +958,7 @@ def test_polycollection_joinstyle():
970958

971959
from matplotlib import collections as mcoll
972960

973-
fig = plt.figure()
974-
ax = fig.add_subplot(111)
961+
fig, ax = plt.subplots()
975962
verts = np.array([[1, 1], [1, 2], [2, 2], [2, 1]])
976963
c = mcoll.PolyCollection([verts], linewidths=40)
977964
ax.add_collection(c)
@@ -991,8 +978,7 @@ def test_polycollection_joinstyle():
991978
]
992979
)
993980
def test_fill_between_input(x, y1, y2):
994-
fig = plt.figure()
995-
ax = fig.add_subplot(211)
981+
fig, ax = plt.subplots()
996982
with pytest.raises(ValueError):
997983
ax.fill_between(x, y1, y2)
998984

@@ -1009,8 +995,7 @@ def test_fill_between_input(x, y1, y2):
1009995
]
1010996
)
1011997
def test_fill_betweenx_input(y, x1, x2):
1012-
fig = plt.figure()
1013-
ax = fig.add_subplot(211)
998+
fig, ax = plt.subplots()
1014999
with pytest.raises(ValueError):
10151000
ax.fill_betweenx(y, x1, x2)
10161001

@@ -1022,23 +1007,21 @@ def test_fill_between_interpolate():
10221007
y1 = np.sin(2*np.pi*x)
10231008
y2 = 1.2*np.sin(4*np.pi*x)
10241009

1025-
fig = plt.figure()
1026-
ax = fig.add_subplot(211)
1027-
ax.plot(x, y1, x, y2, color='black')
1028-
ax.fill_between(x, y1, y2, where=y2 >= y1, facecolor='white', hatch='/',
1029-
interpolate=True)
1030-
ax.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
1031-
interpolate=True)
1010+
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
1011+
ax1.plot(x, y1, x, y2, color='black')
1012+
ax1.fill_between(x, y1, y2, where=y2 >= y1, facecolor='white', hatch='/',
1013+
interpolate=True)
1014+
ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
1015+
interpolate=True)
10321016

10331017
# Test support for masked arrays.
10341018
y2 = np.ma.masked_greater(y2, 1.0)
10351019
# Test that plotting works for masked arrays with the first element masked
10361020
y2[0] = np.ma.masked
1037-
ax1 = fig.add_subplot(212, sharex=ax)
1038-
ax1.plot(x, y1, x, y2, color='black')
1039-
ax1.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green',
1021+
ax2.plot(x, y1, x, y2, color='black')
1022+
ax2.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green',
10401023
interpolate=True)
1041-
ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
1024+
ax2.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
10421025
interpolate=True)
10431026

10441027

@@ -1049,8 +1032,7 @@ def test_fill_between_interpolate_decreasing():
10491032
t = np.array([9.4, 7, 2.2])
10501033
prof = np.array([7.9, 6.6, 3.8])
10511034

1052-
fig = plt.figure(figsize=(9, 9))
1053-
ax = fig.add_subplot(1, 1, 1)
1035+
fig, ax = plt.subplots(figsize=(9, 9))
10541036

10551037
ax.plot(t, p, 'tab:red')
10561038
ax.plot(prof, p, 'k')
@@ -1069,8 +1051,7 @@ def test_symlog():
10691051
x = np.array([0, 1, 2, 4, 6, 9, 12, 24])
10701052
y = np.array([1000000, 500000, 100000, 100, 5, 0, 0, 0])
10711053

1072-
fig = plt.figure()
1073-
ax = fig.add_subplot(111)
1054+
fig, ax = plt.subplots()
10741055
ax.plot(x, y)
10751056
ax.set_yscale('symlog')
10761057
ax.set_xscale('linear')
@@ -1083,37 +1064,12 @@ def test_symlog2():
10831064
# Numbers from -50 to 50, with 0.1 as step
10841065
x = np.arange(-50, 50, 0.001)
10851066

1086-
fig = plt.figure()
1087-
ax = fig.add_subplot(511)
1088-
# Plots a simple linear function 'f(x) = x'
1089-
ax.plot(x, x)
1090-
ax.set_xscale('symlog', linthreshx=20.0)
1091-
ax.grid(True)
1092-
1093-
ax = fig.add_subplot(512)
1094-
# Plots a simple linear function 'f(x) = x'
1095-
ax.plot(x, x)
1096-
ax.set_xscale('symlog', linthreshx=2.0)
1097-
ax.grid(True)
1098-
1099-
ax = fig.add_subplot(513)
1100-
# Plots a simple linear function 'f(x) = x'
1101-
ax.plot(x, x)
1102-
ax.set_xscale('symlog', linthreshx=1.0)
1103-
ax.grid(True)
1104-
1105-
ax = fig.add_subplot(514)
1106-
# Plots a simple linear function 'f(x) = x'
1107-
ax.plot(x, x)
1108-
ax.set_xscale('symlog', linthreshx=0.1)
1109-
ax.grid(True)
1110-
1111-
ax = fig.add_subplot(515)
1112-
# Plots a simple linear function 'f(x) = x'
1113-
ax.plot(x, x)
1114-
ax.set_xscale('symlog', linthreshx=0.01)
1115-
ax.grid(True)
1116-
ax.set_ylim(-0.1, 0.1)
1067+
fig, axs = plt.subplots(5, 1)
1068+
for ax, linthreshx in zip(axs, [20., 2., 1., 0.1, 0.01]):
1069+
ax.plot(x, x)
1070+
ax.set_xscale('symlog', linthreshx=linthreshx)
1071+
ax.grid(True)
1072+
axs[-1].set_ylim(-0.1, 0.1)
11171073

11181074

11191075
def test_pcolorargs_5205():
@@ -1145,15 +1101,10 @@ def test_pcolormesh():
11451101
# The color array can include masked values:
11461102
Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
11471103

1148-
fig = plt.figure()
1149-
ax = fig.add_subplot(131)
1150-
ax.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')
1151-
1152-
ax = fig.add_subplot(132)
1153-
ax.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])
1154-
1155-
ax = fig.add_subplot(133)
1156-
ax.pcolormesh(Qx, Qz, Z, shading="gouraud")
1104+
fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
1105+
ax1.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')
1106+
ax2.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])
1107+
ax3.pcolormesh(Qx, Qz, Z, shading="gouraud")
11571108

11581109

11591110
@image_comparison(baseline_images=['pcolormesh_datetime_axis'],

‎lib/matplotlib/tests/test_backend_svg.py

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@@ -18,8 +18,7 @@
1818

1919

2020
def test_visibility():
21-
fig = plt.figure()
22-
ax = fig.add_subplot(111)
21+
fig, ax = plt.subplots()
2322

2423
x = np.linspace(0, 4 * np.pi, 50)
2524
y = np.sin(x)

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