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Merge pull request #1664 from dopplershift/skew
Support for skewed transforms.
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# This serves as an intensive exercise of matplotlib's transforms | ||
# and custom projection API. This example produces a so-called | ||
# SkewT-logP diagram, which is a common plot in meteorology for | ||
# displaying vertical profiles of temperature. As far as matplotlib is | ||
# concerned, the complexity comes from having X and Y axes that are | ||
# not orthogonal. This is handled by including a skew component to the | ||
# basic Axes transforms. Additional complexity comes in handling the | ||
# fact that the upper and lower X-axes have different data ranges, which | ||
# necessitates a bunch of custom classes for ticks,spines, and the axis | ||
# to handle this. | ||
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from matplotlib.axes import Axes | ||
import matplotlib.transforms as transforms | ||
import matplotlib.axis as maxis | ||
import matplotlib.spines as mspines | ||
import matplotlib.path as mpath | ||
from matplotlib.projections import register_projection | ||
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# The sole purpose of this class is to look at the upper, lower, or total | ||
# interval as appropriate and see what parts of the tick to draw, if any. | ||
class SkewXTick(maxis.XTick): | ||
def draw(self, renderer): | ||
if not self.get_visible(): return | ||
renderer.open_group(self.__name__) | ||
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lower_interval = self.axes.xaxis.lower_interval | ||
upper_interval = self.axes.xaxis.upper_interval | ||
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if self.gridOn and transforms.interval_contains( | ||
self.axes.xaxis.get_view_interval(), self.get_loc()): | ||
self.gridline.draw(renderer) | ||
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if transforms.interval_contains(lower_interval, self.get_loc()): | ||
if self.tick1On: | ||
self.tick1line.draw(renderer) | ||
if self.label1On: | ||
self.label1.draw(renderer) | ||
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if transforms.interval_contains(upper_interval, self.get_loc()): | ||
if self.tick2On: | ||
self.tick2line.draw(renderer) | ||
if self.label2On: | ||
self.label2.draw(renderer) | ||
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renderer.close_group(self.__name__) | ||
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# This class exists to provide two separate sets of intervals to the tick, | ||
# as well as create instances of the custom tick | ||
class SkewXAxis(maxis.XAxis): | ||
def __init__(self, *args, **kwargs): | ||
maxis.XAxis.__init__(self, *args, **kwargs) | ||
self.upper_interval = 0.0, 1.0 | ||
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def _get_tick(self, major): | ||
return SkewXTick(self.axes, 0, '', major=major) | ||
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@property | ||
def lower_interval(self): | ||
return self.axes.viewLim.intervalx | ||
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def get_view_interval(self): | ||
return self.upper_interval[0], self.axes.viewLim.intervalx[1] | ||
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# This class exists to calculate the separate data range of the | ||
# upper X-axis and draw the spine there. It also provides this range | ||
# to the X-axis artist for ticking and gridlines | ||
class SkewSpine(mspines.Spine): | ||
def _adjust_location(self): | ||
trans = self.axes.transDataToAxes.inverted() | ||
if self.spine_type == 'top': | ||
yloc = 1.0 | ||
else: | ||
yloc = 0.0 | ||
left = trans.transform_point((0.0, yloc))[0] | ||
right = trans.transform_point((1.0, yloc))[0] | ||
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pts = self._path.vertices | ||
pts[0, 0] = left | ||
pts[1, 0] = right | ||
self.axis.upper_interval = (left, right) | ||
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# This class handles registration of the skew-xaxes as a projection as well | ||
# as setting up the appropriate transformations. It also overrides standard | ||
# spines and axes instances as appropriate. | ||
class SkewXAxes(Axes): | ||
# The projection must specify a name. This will be used be the | ||
# user to select the projection, i.e. ``subplot(111, | ||
# projection='skewx')``. | ||
name = 'skewx' | ||
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def _init_axis(self): | ||
#Taken from Axes and modified to use our modified X-axis | ||
self.xaxis = SkewXAxis(self) | ||
self.spines['top'].register_axis(self.xaxis) | ||
self.spines['bottom'].register_axis(self.xaxis) | ||
self.yaxis = maxis.YAxis(self) | ||
self.spines['left'].register_axis(self.yaxis) | ||
self.spines['right'].register_axis(self.yaxis) | ||
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def _gen_axes_spines(self): | ||
spines = {'top':SkewSpine.linear_spine(self, 'top'), | ||
'bottom':mspines.Spine.linear_spine(self, 'bottom'), | ||
'left':mspines.Spine.linear_spine(self, 'left'), | ||
'right':mspines.Spine.linear_spine(self, 'right')} | ||
return spines | ||
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def _set_lim_and_transforms(self): | ||
""" | ||
This is called once when the plot is created to set up all the | ||
transforms for the data, text and grids. | ||
""" | ||
rot = 30 | ||
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#Get the standard transform setup from the Axes base class | ||
Axes._set_lim_and_transforms(self) | ||
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# Need to put the skew in the middle, after the scale and limits, | ||
# but before the transAxes. This way, the skew is done in Axes | ||
# coordinates thus performing the transform around the proper origin | ||
# We keep the pre-transAxes transform around for other users, like the | ||
# spines for finding bounds | ||
self.transDataToAxes = self.transScale + (self.transLimits + | ||
transforms.Affine2D().skew_deg(rot, 0)) | ||
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# Create the full transform from Data to Pixels | ||
self.transData = self.transDataToAxes + self.transAxes | ||
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# Blended transforms like this need to have the skewing applied using | ||
# both axes, in axes coords like before. | ||
self._xaxis_transform = (transforms.blended_transform_factory( | ||
self.transScale + self.transLimits, | ||
transforms.IdentityTransform()) + | ||
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes | ||
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# Now register the projection with matplotlib so the user can select | ||
# it. | ||
register_projection(SkewXAxes) | ||
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if __name__ == '__main__': | ||
# Now make a simple example using the custom projection. | ||
from matplotlib.ticker import ScalarFormatter, MultipleLocator | ||
from matplotlib.collections import LineCollection | ||
import matplotlib.pyplot as plt | ||
from StringIO import StringIO | ||
import numpy as np | ||
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#Some examples data | ||
data_txt = ''' | ||
978.0 345 7.8 0.8 61 4.16 325 14 282.7 294.6 283.4 | ||
971.0 404 7.2 0.2 61 4.01 327 17 282.7 294.2 283.4 | ||
946.7 610 5.2 -1.8 61 3.56 335 26 282.8 293.0 283.4 | ||
944.0 634 5.0 -2.0 61 3.51 336 27 282.8 292.9 283.4 | ||
925.0 798 3.4 -2.6 65 3.43 340 32 282.8 292.7 283.4 | ||
911.8 914 2.4 -2.7 69 3.46 345 37 282.9 292.9 283.5 | ||
906.0 966 2.0 -2.7 71 3.47 348 39 283.0 293.0 283.6 | ||
877.9 1219 0.4 -3.2 77 3.46 0 48 283.9 293.9 284.5 | ||
850.0 1478 -1.3 -3.7 84 3.44 0 47 284.8 294.8 285.4 | ||
841.0 1563 -1.9 -3.8 87 3.45 358 45 285.0 295.0 285.6 | ||
823.0 1736 1.4 -0.7 86 4.44 353 42 290.3 303.3 291.0 | ||
813.6 1829 4.5 1.2 80 5.17 350 40 294.5 309.8 295.4 | ||
809.0 1875 6.0 2.2 77 5.57 347 39 296.6 313.2 297.6 | ||
798.0 1988 7.4 -0.6 57 4.61 340 35 299.2 313.3 300.1 | ||
791.0 2061 7.6 -1.4 53 4.39 335 33 300.2 313.6 301.0 | ||
783.9 2134 7.0 -1.7 54 4.32 330 31 300.4 313.6 301.2 | ||
755.1 2438 4.8 -3.1 57 4.06 300 24 301.2 313.7 301.9 | ||
727.3 2743 2.5 -4.4 60 3.81 285 29 301.9 313.8 302.6 | ||
700.5 3048 0.2 -5.8 64 3.57 275 31 302.7 313.8 303.3 | ||
700.0 3054 0.2 -5.8 64 3.56 280 31 302.7 313.8 303.3 | ||
698.0 3077 0.0 -6.0 64 3.52 280 31 302.7 313.7 303.4 | ||
687.0 3204 -0.1 -7.1 59 3.28 281 31 304.0 314.3 304.6 | ||
648.9 3658 -3.2 -10.9 55 2.59 285 30 305.5 313.8 305.9 | ||
631.0 3881 -4.7 -12.7 54 2.29 289 33 306.2 313.6 306.6 | ||
600.7 4267 -6.4 -16.7 44 1.73 295 39 308.6 314.3 308.9 | ||
592.0 4381 -6.9 -17.9 41 1.59 297 41 309.3 314.6 309.6 | ||
577.6 4572 -8.1 -19.6 39 1.41 300 44 310.1 314.9 310.3 | ||
555.3 4877 -10.0 -22.3 36 1.16 295 39 311.3 315.3 311.5 | ||
536.0 5151 -11.7 -24.7 33 0.97 304 39 312.4 315.8 312.6 | ||
533.8 5182 -11.9 -25.0 33 0.95 305 39 312.5 315.8 312.7 | ||
500.0 5680 -15.9 -29.9 29 0.64 290 44 313.6 315.9 313.7 | ||
472.3 6096 -19.7 -33.4 28 0.49 285 46 314.1 315.8 314.1 | ||
453.0 6401 -22.4 -36.0 28 0.39 300 50 314.4 315.8 314.4 | ||
400.0 7310 -30.7 -43.7 27 0.20 285 44 315.0 315.8 315.0 | ||
399.7 7315 -30.8 -43.8 27 0.20 285 44 315.0 315.8 315.0 | ||
387.0 7543 -33.1 -46.1 26 0.16 281 47 314.9 315.5 314.9 | ||
382.7 7620 -33.8 -46.8 26 0.15 280 48 315.0 315.6 315.0 | ||
342.0 8398 -40.5 -53.5 23 0.08 293 52 316.1 316.4 316.1 | ||
320.4 8839 -43.7 -56.7 22 0.06 300 54 317.6 317.8 317.6 | ||
318.0 8890 -44.1 -57.1 22 0.05 301 55 317.8 318.0 317.8 | ||
310.0 9060 -44.7 -58.7 19 0.04 304 61 319.2 319.4 319.2 | ||
306.1 9144 -43.9 -57.9 20 0.05 305 63 321.5 321.7 321.5 | ||
305.0 9169 -43.7 -57.7 20 0.05 303 63 322.1 322.4 322.1 | ||
300.0 9280 -43.5 -57.5 20 0.05 295 64 323.9 324.2 323.9 | ||
292.0 9462 -43.7 -58.7 17 0.05 293 67 326.2 326.4 326.2 | ||
276.0 9838 -47.1 -62.1 16 0.03 290 74 326.6 326.7 326.6 | ||
264.0 10132 -47.5 -62.5 16 0.03 288 79 330.1 330.3 330.1 | ||
251.0 10464 -49.7 -64.7 16 0.03 285 85 331.7 331.8 331.7 | ||
250.0 10490 -49.7 -64.7 16 0.03 285 85 332.1 332.2 332.1 | ||
247.0 10569 -48.7 -63.7 16 0.03 283 88 334.7 334.8 334.7 | ||
244.0 10649 -48.9 -63.9 16 0.03 280 91 335.6 335.7 335.6 | ||
243.3 10668 -48.9 -63.9 16 0.03 280 91 335.8 335.9 335.8 | ||
220.0 11327 -50.3 -65.3 15 0.03 280 85 343.5 343.6 343.5 | ||
212.0 11569 -50.5 -65.5 15 0.03 280 83 346.8 346.9 346.8 | ||
210.0 11631 -49.7 -64.7 16 0.03 280 83 349.0 349.1 349.0 | ||
200.0 11950 -49.9 -64.9 15 0.03 280 80 353.6 353.7 353.6 | ||
194.0 12149 -49.9 -64.9 15 0.03 279 78 356.7 356.8 356.7 | ||
183.0 12529 -51.3 -66.3 15 0.03 278 75 360.4 360.5 360.4 | ||
164.0 13233 -55.3 -68.3 18 0.02 277 69 365.2 365.3 365.2 | ||
152.0 13716 -56.5 -69.5 18 0.02 275 65 371.1 371.2 371.1 | ||
150.0 13800 -57.1 -70.1 18 0.02 275 64 371.5 371.6 371.5 | ||
136.0 14414 -60.5 -72.5 19 0.02 268 54 376.0 376.1 376.0 | ||
132.0 14600 -60.1 -72.1 19 0.02 265 51 380.0 380.1 380.0 | ||
131.4 14630 -60.2 -72.2 19 0.02 265 51 380.3 380.4 380.3 | ||
128.0 14792 -60.9 -72.9 19 0.02 266 50 381.9 382.0 381.9 | ||
125.0 14939 -60.1 -72.1 19 0.02 268 49 385.9 386.0 385.9 | ||
119.0 15240 -62.2 -73.8 20 0.01 270 48 387.4 387.5 387.4 | ||
112.0 15616 -64.9 -75.9 21 0.01 265 53 389.3 389.3 389.3 | ||
108.0 15838 -64.1 -75.1 21 0.01 265 58 394.8 394.9 394.8 | ||
107.8 15850 -64.1 -75.1 21 0.01 265 58 395.0 395.1 395.0 | ||
105.0 16010 -64.7 -75.7 21 0.01 272 50 396.9 396.9 396.9 | ||
103.0 16128 -62.9 -73.9 21 0.02 277 45 402.5 402.6 402.5 | ||
100.0 16310 -62.5 -73.5 21 0.02 285 36 406.7 406.8 406.7''' | ||
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# Parse the data | ||
sound_data = StringIO(data_txt) | ||
p,h,T,Td = np.loadtxt(sound_data, usecols=range(0,4), unpack=True) | ||
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# Create a new figure. The dimensions here give a good aspect ratio | ||
fig = plt.figure(figsize=(6.5875, 6.2125)) | ||
ax = fig.add_subplot(111, projection='skewx') | ||
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plt.grid(True) | ||
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# Plot the data using normal plotting functions, in this case using | ||
# log scaling in Y, as dicatated by the typical meteorological plot | ||
ax.semilogy(T, p, 'r') | ||
ax.semilogy(Td, p, 'g') | ||
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# An example of a slanted line at constant X | ||
l = ax.axvline(0, color='b') | ||
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# Disables the log-formatting that comes with semilogy | ||
ax.yaxis.set_major_formatter(ScalarFormatter()) | ||
ax.set_yticks(np.linspace(100,1000,10)) | ||
ax.set_ylim(1050,100) | ||
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ax.xaxis.set_major_locator(MultipleLocator(10)) | ||
ax.set_xlim(-50,50) | ||
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plt.show() |
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