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Cleanup SkewT example.
- Don't give the impression to call an Axes private method when it's just calling the super()-method (we can have a discussion about inheritance of private methods at some other time...) - Make definitions of transDataAxes, _xaxis_transform show the individual component transforms more. - Shorten the sample data to only include the relevant parts.
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‎examples/specialty_plots/skewt.py

Lines changed: 91 additions & 88 deletions
Original file line numberDiff line numberDiff line change
@@ -3,15 +3,14 @@
33
SkewT-logP diagram: using transforms and custom projections
44
===========================================================
55
6-
This serves as an intensive exercise of matplotlib's transforms and custom
6+
This serves as an intensive exercise of Matplotlib's transforms and custom
77
projection API. This example produces a so-called SkewT-logP diagram, which is
88
a common plot in meteorology for displaying vertical profiles of temperature.
9-
As far as matplotlib is concerned, the complexity comes from having X and Y
9+
As far as Matplotlib is concerned, the complexity comes from having X and Y
1010
axes that are not orthogonal. This is handled by including a skew component to
1111
the basic Axes transforms. Additional complexity comes in handling the fact
1212
that the upper and lower X-axes have different data ranges, which necessitates
1313
a bunch of custom classes for ticks, spines, and axis to handle this.
14-
1514
"""
1615

1716
from contextlib import ExitStack
@@ -108,25 +107,30 @@ def _set_lim_and_transforms(self):
108107
rot = 30
109108

110109
# Get the standard transform setup from the Axes base class
111-
Axes._set_lim_and_transforms(self)
110+
super()._set_lim_and_transforms()
112111

113112
# Need to put the skew in the middle, after the scale and limits,
114113
# but before the transAxes. This way, the skew is done in Axes
115114
# coordinates thus performing the transform around the proper origin
116115
# We keep the pre-transAxes transform around for other users, like the
117116
# spines for finding bounds
118-
self.transDataToAxes = self.transScale + \
119-
self.transLimits + transforms.Affine2D().skew_deg(rot, 0)
120-
117+
self.transDataToAxes = (
118+
self.transScale
119+
+ self.transLimits
120+
+ transforms.Affine2D().skew_deg(rot, 0)
121+
)
121122
# Create the full transform from Data to Pixels
122123
self.transData = self.transDataToAxes + self.transAxes
123124

124125
# Blended transforms like this need to have the skewing applied using
125126
# both axes, in axes coords like before.
126-
self._xaxis_transform = (transforms.blended_transform_factory(
127-
self.transScale + self.transLimits,
128-
transforms.IdentityTransform()) +
129-
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
127+
self._xaxis_transform = (
128+
transforms.blended_transform_factory(
129+
self.transScale + self.transLimits,
130+
transforms.IdentityTransform())
131+
+ transforms.Affine2D().skew_deg(rot, 0)
132+
+ self.transAxes
133+
)
130134

131135
@property
132136
def lower_xlim(self):
@@ -138,8 +142,7 @@ def upper_xlim(self):
138142
return self.transDataToAxes.inverted().transform(pts)[:, 0]
139143

140144

141-
# Now register the projection with matplotlib so the user can select
142-
# it.
145+
# Now register the projection with matplotlib so the user can select it.
143146
register_projection(SkewXAxes)
144147

145148
if __name__ == '__main__':
@@ -150,86 +153,86 @@ def upper_xlim(self):
150153
import matplotlib.pyplot as plt
151154
import numpy as np
152155

153-
# Some examples data
156+
# Some example data.
154157
data_txt = '''
155-
978.0 345 7.8 0.8 61 4.16 325 14 282.7 294.6 283.4
156-
971.0 404 7.2 0.2 61 4.01 327 17 282.7 294.2 283.4
157-
946.7 610 5.2 -1.8 61 3.56 335 26 282.8 293.0 283.4
158-
944.0 634 5.0 -2.0 61 3.51 336 27 282.8 292.9 283.4
159-
925.0 798 3.4 -2.6 65 3.43 340 32 282.8 292.7 283.4
160-
911.8 914 2.4 -2.7 69 3.46 345 37 282.9 292.9 283.5
161-
906.0 966 2.0 -2.7 71 3.47 348 39 283.0 293.0 283.6
162-
877.9 1219 0.4 -3.2 77 3.46 0 48 283.9 293.9 284.5
163-
850.0 1478 -1.3 -3.7 84 3.44 0 47 284.8 294.8 285.4
164-
841.0 1563 -1.9 -3.8 87 3.45 358 45 285.0 295.0 285.6
165-
823.0 1736 1.4 -0.7 86 4.44 353 42 290.3 303.3 291.0
166-
813.6 1829 4.5 1.2 80 5.17 350 40 294.5 309.8 295.4
167-
809.0 1875 6.0 2.2 77 5.57 347 39 296.6 313.2 297.6
168-
798.0 1988 7.4 -0.6 57 4.61 340 35 299.2 313.3 300.1
169-
791.0 2061 7.6 -1.4 53 4.39 335 33 300.2 313.6 301.0
170-
783.9 2134 7.0 -1.7 54 4.32 330 31 300.4 313.6 301.2
171-
755.1 2438 4.8 -3.1 57 4.06 300 24 301.2 313.7 301.9
172-
727.3 2743 2.5 -4.4 60 3.81 285 29 301.9 313.8 302.6
173-
700.5 3048 0.2 -5.8 64 3.57 275 31 302.7 313.8 303.3
174-
700.0 3054 0.2 -5.8 64 3.56 280 31 302.7 313.8 303.3
175-
698.0 3077 0.0 -6.0 64 3.52 280 31 302.7 313.7 303.4
176-
687.0 3204 -0.1 -7.1 59 3.28 281 31 304.0 314.3 304.6
177-
648.9 3658 -3.2 -10.9 55 2.59 285 30 305.5 313.8 305.9
178-
631.0 3881 -4.7 -12.7 54 2.29 289 33 306.2 313.6 306.6
179-
600.7 4267 -6.4 -16.7 44 1.73 295 39 308.6 314.3 308.9
180-
592.0 4381 -6.9 -17.9 41 1.59 297 41 309.3 314.6 309.6
181-
577.6 4572 -8.1 -19.6 39 1.41 300 44 310.1 314.9 310.3
182-
555.3 4877 -10.0 -22.3 36 1.16 295 39 311.3 315.3 311.5
183-
536.0 5151 -11.7 -24.7 33 0.97 304 39 312.4 315.8 312.6
184-
533.8 5182 -11.9 -25.0 33 0.95 305 39 312.5 315.8 312.7
185-
500.0 5680 -15.9 -29.9 29 0.64 290 44 313.6 315.9 313.7
186-
472.3 6096 -19.7 -33.4 28 0.49 285 46 314.1 315.8 314.1
187-
453.0 6401 -22.4 -36.0 28 0.39 300 50 314.4 315.8 314.4
188-
400.0 7310 -30.7 -43.7 27 0.20 285 44 315.0 315.8 315.0
189-
399.7 7315 -30.8 -43.8 27 0.20 285 44 315.0 315.8 315.0
190-
387.0 7543 -33.1 -46.1 26 0.16 281 47 314.9 315.5 314.9
191-
382.7 7620 -33.8 -46.8 26 0.15 280 48 315.0 315.6 315.0
192-
342.0 8398 -40.5 -53.5 23 0.08 293 52 316.1 316.4 316.1
193-
320.4 8839 -43.7 -56.7 22 0.06 300 54 317.6 317.8 317.6
194-
318.0 8890 -44.1 -57.1 22 0.05 301 55 317.8 318.0 317.8
195-
310.0 9060 -44.7 -58.7 19 0.04 304 61 319.2 319.4 319.2
196-
306.1 9144 -43.9 -57.9 20 0.05 305 63 321.5 321.7 321.5
197-
305.0 9169 -43.7 -57.7 20 0.05 303 63 322.1 322.4 322.1
198-
300.0 9280 -43.5 -57.5 20 0.05 295 64 323.9 324.2 323.9
199-
292.0 9462 -43.7 -58.7 17 0.05 293 67 326.2 326.4 326.2
200-
276.0 9838 -47.1 -62.1 16 0.03 290 74 326.6 326.7 326.6
201-
264.0 10132 -47.5 -62.5 16 0.03 288 79 330.1 330.3 330.1
202-
251.0 10464 -49.7 -64.7 16 0.03 285 85 331.7 331.8 331.7
203-
250.0 10490 -49.7 -64.7 16 0.03 285 85 332.1 332.2 332.1
204-
247.0 10569 -48.7 -63.7 16 0.03 283 88 334.7 334.8 334.7
205-
244.0 10649 -48.9 -63.9 16 0.03 280 91 335.6 335.7 335.6
206-
243.3 10668 -48.9 -63.9 16 0.03 280 91 335.8 335.9 335.8
207-
220.0 11327 -50.3 -65.3 15 0.03 280 85 343.5 343.6 343.5
208-
212.0 11569 -50.5 -65.5 15 0.03 280 83 346.8 346.9 346.8
209-
210.0 11631 -49.7 -64.7 16 0.03 280 83 349.0 349.1 349.0
210-
200.0 11950 -49.9 -64.9 15 0.03 280 80 353.6 353.7 353.6
211-
194.0 12149 -49.9 -64.9 15 0.03 279 78 356.7 356.8 356.7
212-
183.0 12529 -51.3 -66.3 15 0.03 278 75 360.4 360.5 360.4
213-
164.0 13233 -55.3 -68.3 18 0.02 277 69 365.2 365.3 365.2
214-
152.0 13716 -56.5 -69.5 18 0.02 275 65 371.1 371.2 371.1
215-
150.0 13800 -57.1 -70.1 18 0.02 275 64 371.5 371.6 371.5
216-
136.0 14414 -60.5 -72.5 19 0.02 268 54 376.0 376.1 376.0
217-
132.0 14600 -60.1 -72.1 19 0.02 265 51 380.0 380.1 380.0
218-
131.4 14630 -60.2 -72.2 19 0.02 265 51 380.3 380.4 380.3
219-
128.0 14792 -60.9 -72.9 19 0.02 266 50 381.9 382.0 381.9
220-
125.0 14939 -60.1 -72.1 19 0.02 268 49 385.9 386.0 385.9
221-
119.0 15240 -62.2 -73.8 20 0.01 270 48 387.4 387.5 387.4
222-
112.0 15616 -64.9 -75.9 21 0.01 265 53 389.3 389.3 389.3
223-
108.0 15838 -64.1 -75.1 21 0.01 265 58 394.8 394.9 394.8
224-
107.8 15850 -64.1 -75.1 21 0.01 265 58 395.0 395.1 395.0
225-
105.0 16010 -64.7 -75.7 21 0.01 272 50 396.9 396.9 396.9
226-
103.0 16128 -62.9 -73.9 21 0.02 277 45 402.5 402.6 402.5
227-
100.0 16310 -62.5 -73.5 21 0.02 285 36 406.7 406.8 406.7
158+
978.0 345 7.8 0.8
159+
971.0 404 7.2 0.2
160+
946.7 610 5.2 -1.8
161+
944.0 634 5.0 -2.0
162+
925.0 798 3.4 -2.6
163+
911.8 914 2.4 -2.7
164+
906.0 966 2.0 -2.7
165+
877.9 1219 0.4 -3.2
166+
850.0 1478 -1.3 -3.7
167+
841.0 1563 -1.9 -3.8
168+
823.0 1736 1.4 -0.7
169+
813.6 1829 4.5 1.2
170+
809.0 1875 6.0 2.2
171+
798.0 1988 7.4 -0.6
172+
791.0 2061 7.6 -1.4
173+
783.9 2134 7.0 -1.7
174+
755.1 2438 4.8 -3.1
175+
727.3 2743 2.5 -4.4
176+
700.5 3048 0.2 -5.8
177+
700.0 3054 0.2 -5.8
178+
698.0 3077 0.0 -6.0
179+
687.0 3204 -0.1 -7.1
180+
648.9 3658 -3.2 -10.9
181+
631.0 3881 -4.7 -12.7
182+
600.7 4267 -6.4 -16.7
183+
592.0 4381 -6.9 -17.9
184+
577.6 4572 -8.1 -19.6
185+
555.3 4877 -10.0 -22.3
186+
536.0 5151 -11.7 -24.7
187+
533.8 5182 -11.9 -25.0
188+
500.0 5680 -15.9 -29.9
189+
472.3 6096 -19.7 -33.4
190+
453.0 6401 -22.4 -36.0
191+
400.0 7310 -30.7 -43.7
192+
399.7 7315 -30.8 -43.8
193+
387.0 7543 -33.1 -46.1
194+
382.7 7620 -33.8 -46.8
195+
342.0 8398 -40.5 -53.5
196+
320.4 8839 -43.7 -56.7
197+
318.0 8890 -44.1 -57.1
198+
310.0 9060 -44.7 -58.7
199+
306.1 9144 -43.9 -57.9
200+
305.0 9169 -43.7 -57.7
201+
300.0 9280 -43.5 -57.5
202+
292.0 9462 -43.7 -58.7
203+
276.0 9838 -47.1 -62.1
204+
264.0 10132 -47.5 -62.5
205+
251.0 10464 -49.7 -64.7
206+
250.0 10490 -49.7 -64.7
207+
247.0 10569 -48.7 -63.7
208+
244.0 10649 -48.9 -63.9
209+
243.3 10668 -48.9 -63.9
210+
220.0 11327 -50.3 -65.3
211+
212.0 11569 -50.5 -65.5
212+
210.0 11631 -49.7 -64.7
213+
200.0 11950 -49.9 -64.9
214+
194.0 12149 -49.9 -64.9
215+
183.0 12529 -51.3 -66.3
216+
164.0 13233 -55.3 -68.3
217+
152.0 13716 -56.5 -69.5
218+
150.0 13800 -57.1 -70.1
219+
136.0 14414 -60.5 -72.5
220+
132.0 14600 -60.1 -72.1
221+
131.4 14630 -60.2 -72.2
222+
128.0 14792 -60.9 -72.9
223+
125.0 14939 -60.1 -72.1
224+
119.0 15240 -62.2 -73.8
225+
112.0 15616 -64.9 -75.9
226+
108.0 15838 -64.1 -75.1
227+
107.8 15850 -64.1 -75.1
228+
105.0 16010 -64.7 -75.7
229+
103.0 16128 -62.9 -73.9
230+
100.0 16310 -62.5 -73.5
228231
'''
229232

230233
# Parse the data
231234
sound_data = StringIO(data_txt)
232-
p, h, T, Td = np.loadtxt(sound_data, usecols=range(0, 4), unpack=True)
235+
p, h, T, Td = np.loadtxt(sound_data, unpack=True)
233236

234237
# Create a new figure. The dimensions here give a good aspect ratio
235238
fig = plt.figure(figsize=(6.5875, 6.2125))

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