Mixing
Plot borders of NaNs region in contour
I'm trying to contour plot some data with NaNs (no solution). I want to indicate the border of the NaNs with a black line. So far I've only found how to hatch the whole NaN region (hatch a NaN region in a contourplot in matplotlib) but I just want the outline.
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
d[2, 2], d[3, 5] = np.nan, np.nan
plt.contour(d)
plt.show()
I get:
I would want:
python matplotlib nan contour
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
add a comment |
I'm trying to contour plot some data with NaNs (no solution). I want to indicate the border of the NaNs with a black line. So far I've only found how to hatch the whole NaN region (hatch a NaN region in a contourplot in matplotlib) but I just want the outline.
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
d[2, 2], d[3, 5] = np.nan, np.nan
plt.contour(d)
plt.show()
I get:
I would want:
python matplotlib nan contour
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
add a comment |
I'm trying to contour plot some data with NaNs (no solution). I want to indicate the border of the NaNs with a black line. So far I've only found how to hatch the whole NaN region (hatch a NaN region in a contourplot in matplotlib) but I just want the outline.
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
d[2, 2], d[3, 5] = np.nan, np.nan
plt.contour(d)
plt.show()
I get:
I would want:
python matplotlib nan contour
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
I'm trying to contour plot some data with NaNs (no solution). I want to indicate the border of the NaNs with a black line. So far I've only found how to hatch the whole NaN region (hatch a NaN region in a contourplot in matplotlib) but I just want the outline.
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
d[2, 2], d[3, 5] = np.nan, np.nan
plt.contour(d)
plt.show()
I get:
I would want:
python matplotlib nan contour
python matplotlib nan contour
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
edited Nov 19 '18 at 15:48
DavidG
10.5k102941
10.5k102941
asked Nov 19 '18 at 15:33
an1234
132
asked Nov 19 '18 at 15:33
an1234
132
asked Nov 19 '18 at 15:33
an1234
132
132
add a comment |
add a comment |
1 Answer
1
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oldest
votes
You could draw another contour of the masked out regions. To this end one would mask the data out with a numpy.ma array. Then use its mask to plot another contour at a level close to (but not exactly) zero.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
mask = np.zeros(d.shape, dtype=bool)
mask[2, 2], mask[3, 5] = 1, 1
masked_d = np.ma.array(d, mask=mask)
plt.contour(masked_d)
plt.contour(mask, [0.01], colors="k", linewidths=3)
plt.show()
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You could draw another contour of the masked out regions. To this end one would mask the data out with a numpy.ma array. Then use its mask to plot another contour at a level close to (but not exactly) zero.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
mask = np.zeros(d.shape, dtype=bool)
mask[2, 2], mask[3, 5] = 1, 1
masked_d = np.ma.array(d, mask=mask)
plt.contour(masked_d)
plt.contour(mask, [0.01], colors="k", linewidths=3)
plt.show()
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
add a comment |
You could draw another contour of the masked out regions. To this end one would mask the data out with a numpy.ma array. Then use its mask to plot another contour at a level close to (but not exactly) zero.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
mask = np.zeros(d.shape, dtype=bool)
mask[2, 2], mask[3, 5] = 1, 1
masked_d = np.ma.array(d, mask=mask)
plt.contour(masked_d)
plt.contour(mask, [0.01], colors="k", linewidths=3)
plt.show()
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
add a comment |
You could draw another contour of the masked out regions. To this end one would mask the data out with a numpy.ma array. Then use its mask to plot another contour at a level close to (but not exactly) zero.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
mask = np.zeros(d.shape, dtype=bool)
mask[2, 2], mask[3, 5] = 1, 1
masked_d = np.ma.array(d, mask=mask)
plt.contour(masked_d)
plt.contour(mask, [0.01], colors="k", linewidths=3)
plt.show()
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
You could draw another contour of the masked out regions. To this end one would mask the data out with a numpy.ma array. Then use its mask to plot another contour at a level close to (but not exactly) zero.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.random.rand(10, 10)
mask = np.zeros(d.shape, dtype=bool)
mask[2, 2], mask[3, 5] = 1, 1
masked_d = np.ma.array(d, mask=mask)
plt.contour(masked_d)
plt.contour(mask, [0.01], colors="k", linewidths=3)
plt.show()
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
answered Nov 19 '18 at 16:03
ImportanceOfBeingErnest
125k11128204
125k11128204
add a comment |
add a comment |
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