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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<h1>api example code: skewt.py<a class="headerlink" href="#api-example-code-skewt-py" title="Permalink to this headline">¶</a></h1>

(<a class="reference external" href="../../mpl_examples/api/skewt.py">Source code</a>, <a class="reference external" href="../../mpl_examples/api/skewt.png">png</a>, <a class="reference external" href="../../mpl_examples/api/skewt.hires.png">hires.png</a>, <a class="reference external" href="../../mpl_examples/api/skewt.pdf">pdf</a>)

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<div class="highlight-python"><div class="highlight"><pre># 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.

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

# 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__)

lower_interval = self.axes.xaxis.lower_interval

upper_interval = self.axes.xaxis.upper_interval

if self.gridOn and transforms.interval_contains(

self.axes.xaxis.get_view_interval(), self.get_loc()):

self.gridline.draw(renderer)

if transforms.interval_contains(lower_interval, self.get_loc()):

if self.tick1On:

self.tick1line.draw(renderer)

if self.label1On:

self.label1.draw(renderer)

if transforms.interval_contains(upper_interval, self.get_loc()):

if self.tick2On:

self.tick2line.draw(renderer)

if self.label2On:

self.label2.draw(renderer)

renderer.close_group(self.__name__)

# 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

def _get_tick(self, major):

return SkewXTick(self.axes, 0, '', major=major)

@property

def lower_interval(self):

return self.axes.viewLim.intervalx

def get_view_interval(self):

return self.upper_interval[0], self.axes.viewLim.intervalx[1]

# 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]

pts = self._path.vertices

pts[0, 0] = left

pts[1, 0] = right

self.axis.upper_interval = (left, right)

# 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'

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)

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

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

#Get the standard transform setup from the Axes base class

Axes._set_lim_and_transforms(self)

# 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))

# Create the full transform from Data to Pixels

self.transData = self.transDataToAxes + self.transAxes

# 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()) +

# Now register the projection with matplotlib so the user can select

# it.

register_projection(SkewXAxes)

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

#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'''

# Parse the data

sound_data = StringIO(data_txt)

p,h,T,Td = np.loadtxt(sound_data, usecols=range(0,4), unpack=True)

# 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')

plt.grid(True)

# 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')

# An example of a slanted line at constant X

l = ax.axvline(0, color='b')

# 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)

ax.xaxis.set_major_locator(MultipleLocator(10))

ax.set_xlim(-50,50)

plt.show()

</pre></div>

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Keywords: python, matplotlib, pylab, example, codex (see <a class="reference internal" href="../../faq/howto_faq.html#how-to-search-examples">Search examples</a>)

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Last updated on Oct 18, 2014.

Created using <a

href="http://sphinx-doc.org/">Sphinx</a> 1.3b2.

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