SkewT-logP图：使用变换和自定义投影

这可以作为matplotlib变换和自定义投影API的强化练习。 这个例子产生了一个所谓的SkewT-logP图，它是气象学中用于显示温度垂直剖面的常见图。 就matplotlib而言，复杂性来自于X轴和Y轴不正交。 这是通过在基本Axes变换中包含一个偏斜分量来处理的。 处理上下X轴具有不同数据范围的事实带来了额外的复杂性，这需要一系列用于刻度，棘轮和轴的自定义类来处理这一问题。

  1. from matplotlib.axes import Axes
  2. import matplotlib.transforms as transforms
  3. import matplotlib.axis as maxis
  4. import matplotlib.spines as mspines
  5. from matplotlib.projections import register_projection
  8. # The sole purpose of this class is to look at the upper, lower, or total
  9. # interval as appropriate and see what parts of the tick to draw, if any.
  10. class SkewXTick(maxis.XTick):
  11.     def update_position(self, loc):
  12.         # This ensures that the new value of the location is set before
  13.         # any other updates take place
  14.         self._loc = loc
  15.         super().update_position(loc)
  17.     def _has_default_loc(self):
  18.         return self.get_loc() is None
  20.     def _need_lower(self):
  21.         return (self._has_default_loc() or
  22.                 transforms.interval_contains(self.axes.lower_xlim,
  23.                                              self.get_loc()))
  25.     def _need_upper(self):
  26.         return (self._has_default_loc() or
  27.                 transforms.interval_contains(self.axes.upper_xlim,
  28.                                              self.get_loc()))
  30.     @property
  31.     def gridOn(self):
  32.         return (self._gridOn and (self._has_default_loc() or
  33.                 transforms.interval_contains(self.get_view_interval(),
  34.                                              self.get_loc())))
  36.     @gridOn.setter
  37.     def gridOn(self, value):
  38.         self._gridOn = value
  40.     @property
  41.     def tick1On(self):
  42.         return self._tick1On and self._need_lower()
  44.     @tick1On.setter
  45.     def tick1On(self, value):
  46.         self._tick1On = value
  48.     @property
  49.     def label1On(self):
  50.         return self._label1On and self._need_lower()
  52.     @label1On.setter
  53.     def label1On(self, value):
  54.         self._label1On = value
  56.     @property
  57.     def tick2On(self):
  58.         return self._tick2On and self._need_upper()
  60.     @tick2On.setter
  61.     def tick2On(self, value):
  62.         self._tick2On = value
  64.     @property
  65.     def label2On(self):
  66.         return self._label2On and self._need_upper()
  68.     @label2On.setter
  69.     def label2On(self, value):
  70.         self._label2On = value
  72.     def get_view_interval(self):
  73.         return self.axes.xaxis.get_view_interval()
  76. # This class exists to provide two separate sets of intervals to the tick,
  77. # as well as create instances of the custom tick
  78. class SkewXAxis(maxis.XAxis):
  79.     def _get_tick(self, major):
  80.         return SkewXTick(self.axes, None, '', major=major)
  82.     def get_view_interval(self):
  83.         return self.axes.upper_xlim[0], self.axes.lower_xlim[1]
  86. # This class exists to calculate the separate data range of the
  87. # upper X-axis and draw the spine there. It also provides this range
  88. # to the X-axis artist for ticking and gridlines
  89. class SkewSpine(mspines.Spine):
  90.     def _adjust_location(self):
  91.         pts = self._path.vertices
  92.         if self.spine_type == 'top':
  93.             pts[:, 0] = self.axes.upper_xlim
  94.         else:
  95.             pts[:, 0] = self.axes.lower_xlim
  98. # This class handles registration of the skew-xaxes as a projection as well
  99. # as setting up the appropriate transformations. It also overrides standard
  100. # spines and axes instances as appropriate.
  101. class SkewXAxes(Axes):
  102.     # The projection must specify a name.  This will be used be the
  103.     # user to select the projection, i.e. ``subplot(111,
  104.     # projection='skewx')``.
  105.     name = 'skewx'
  107.     def _init_axis(self):
  108.         # Taken from Axes and modified to use our modified X-axis
  109.         self.xaxis = SkewXAxis(self)
  110.         self.spines['top'].register_axis(self.xaxis)
  111.         self.spines['bottom'].register_axis(self.xaxis)
  112.         self.yaxis = maxis.YAxis(self)
  113.         self.spines['left'].register_axis(self.yaxis)
  114.         self.spines['right'].register_axis(self.yaxis)
  116.     def _gen_axes_spines(self):
  117.         spines = {'top': SkewSpine.linear_spine(self, 'top'),
  118.                   'bottom': mspines.Spine.linear_spine(self, 'bottom'),
  119.                   'left': mspines.Spine.linear_spine(self, 'left'),
  120.                   'right': mspines.Spine.linear_spine(self, 'right')}
  121.         return spines
  123.     def _set_lim_and_transforms(self):
  124.         """
  125.         This is called once when the plot is created to set up all the
  126.         transforms for the data, text and grids.
  127.         """
  128.         rot = 30
  130.         # Get the standard transform setup from the Axes base class
  131.         Axes._set_lim_and_transforms(self)
  133.         # Need to put the skew in the middle, after the scale and limits,
  134.         # but before the transAxes. This way, the skew is done in Axes
  135.         # coordinates thus performing the transform around the proper origin
  136.         # We keep the pre-transAxes transform around for other users, like the
  137.         # spines for finding bounds
  138.         self.transDataToAxes = self.transScale + \
  139.             self.transLimits + transforms.Affine2D().skew_deg(rot, 0)
  141.         # Create the full transform from Data to Pixels
  142.         self.transData = self.transDataToAxes + self.transAxes
  144.         # Blended transforms like this need to have the skewing applied using
  145.         # both axes, in axes coords like before.
  146.         self._xaxis_transform = (transforms.blended_transform_factory(
  147.             self.transScale + self.transLimits,
  148.             transforms.IdentityTransform()) +
  149.             transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
  151.     @property
  152.     def lower_xlim(self):
  153.         return self.axes.viewLim.intervalx
  155.     @property
  156.     def upper_xlim(self):
  157.         pts = [[0., 1.], [1., 1.]]
  158.         return self.transDataToAxes.inverted().transform(pts)[:, 0]
  161. # Now register the projection with matplotlib so the user can select
  162. # it.
  163. register_projection(SkewXAxes)
  165. if __name__ == '__main__':
  166.     # Now make a simple example using the custom projection.
  167.     from io import StringIO
  168.     from matplotlib.ticker import (MultipleLocator, NullFormatter,
  169.                                    ScalarFormatter)
  170.     import matplotlib.pyplot as plt
  171.     import numpy as np
  173.     # Some examples data
  174.     data_txt = '''
  175.   978.0    345    7.8    0.8     61   4.16    325     14  282.7  294.6  283.4
  176.   971.0    404    7.2    0.2     61   4.01    327     17  282.7  294.2  283.4
  177.   946.7    610    5.2   -1.8     61   3.56    335     26  282.8  293.0  283.4
  178.   944.0    634    5.0   -2.0     61   3.51    336     27  282.8  292.9  283.4
  179.   925.0    798    3.4   -2.6     65   3.43    340     32  282.8  292.7  283.4
  180.   911.8    914    2.4   -2.7     69   3.46    345     37  282.9  292.9  283.5
  181.   906.0    966    2.0   -2.7     71   3.47    348     39  283.0  293.0  283.6
  182.   877.9   1219    0.4   -3.2     77   3.46      0     48  283.9  293.9  284.5
  183.   850.0   1478   -1.3   -3.7     84   3.44      0     47  284.8  294.8  285.4
  184.   841.0   1563   -1.9   -3.8     87   3.45    358     45  285.0  295.0  285.6
  185.   823.0   1736    1.4   -0.7     86   4.44    353     42  290.3  303.3  291.0
  186.   813.6   1829    4.5    1.2     80   5.17    350     40  294.5  309.8  295.4
  187.   809.0   1875    6.0    2.2     77   5.57    347     39  296.6  313.2  297.6
  188.   798.0   1988    7.4   -0.6     57   4.61    340     35  299.2  313.3  300.1
  189.   791.0   2061    7.6   -1.4     53   4.39    335     33  300.2  313.6  301.0
  190.   783.9   2134    7.0   -1.7     54   4.32    330     31  300.4  313.6  301.2
  191.   755.1   2438    4.8   -3.1     57   4.06    300     24  301.2  313.7  301.9
  192.   727.3   2743    2.5   -4.4     60   3.81    285     29  301.9  313.8  302.6
  193.   700.5   3048    0.2   -5.8     64   3.57    275     31  302.7  313.8  303.3
  194.   700.0   3054    0.2   -5.8     64   3.56    280     31  302.7  313.8  303.3
  195.   698.0   3077    0.0   -6.0     64   3.52    280     31  302.7  313.7  303.4
  196.   687.0   3204   -0.1   -7.1     59   3.28    281     31  304.0  314.3  304.6
  197.   648.9   3658   -3.2  -10.9     55   2.59    285     30  305.5  313.8  305.9
  198.   631.0   3881   -4.7  -12.7     54   2.29    289     33  306.2  313.6  306.6
  199.   600.7   4267   -6.4  -16.7     44   1.73    295     39  308.6  314.3  308.9
  200.   592.0   4381   -6.9  -17.9     41   1.59    297     41  309.3  314.6  309.6
  201.   577.6   4572   -8.1  -19.6     39   1.41    300     44  310.1  314.9  310.3
  202.   555.3   4877  -10.0  -22.3     36   1.16    295     39  311.3  315.3  311.5
  203.   536.0   5151  -11.7  -24.7     33   0.97    304     39  312.4  315.8  312.6
  204.   533.8   5182  -11.9  -25.0     33   0.95    305     39  312.5  315.8  312.7
  205.   500.0   5680  -15.9  -29.9     29   0.64    290     44  313.6  315.9  313.7
  206.   472.3   6096  -19.7  -33.4     28   0.49    285     46  314.1  315.8  314.1
  207.   453.0   6401  -22.4  -36.0     28   0.39    300     50  314.4  315.8  314.4
  208.   400.0   7310  -30.7  -43.7     27   0.20    285     44  315.0  315.8  315.0
  209.   399.7   7315  -30.8  -43.8     27   0.20    285     44  315.0  315.8  315.0
  210.   387.0   7543  -33.1  -46.1     26   0.16    281     47  314.9  315.5  314.9
  211.   382.7   7620  -33.8  -46.8     26   0.15    280     48  315.0  315.6  315.0
  212.   342.0   8398  -40.5  -53.5     23   0.08    293     52  316.1  316.4  316.1
  213.   320.4   8839  -43.7  -56.7     22   0.06    300     54  317.6  317.8  317.6
  214.   318.0   8890  -44.1  -57.1     22   0.05    301     55  317.8  318.0  317.8
  215.   310.0   9060  -44.7  -58.7     19   0.04    304     61  319.2  319.4  319.2
  216.   306.1   9144  -43.9  -57.9     20   0.05    305     63  321.5  321.7  321.5
  217.   305.0   9169  -43.7  -57.7     20   0.05    303     63  322.1  322.4  322.1
  218.   300.0   9280  -43.5  -57.5     20   0.05    295     64  323.9  324.2  323.9
  219.   292.0   9462  -43.7  -58.7     17   0.05    293     67  326.2  326.4  326.2
  220.   276.0   9838  -47.1  -62.1     16   0.03    290     74  326.6  326.7  326.6
  221.   264.0  10132  -47.5  -62.5     16   0.03    288     79  330.1  330.3  330.1
  222.   251.0  10464  -49.7  -64.7     16   0.03    285     85  331.7  331.8  331.7
  223.   250.0  10490  -49.7  -64.7     16   0.03    285     85  332.1  332.2  332.1
  224.   247.0  10569  -48.7  -63.7     16   0.03    283     88  334.7  334.8  334.7
  225.   244.0  10649  -48.9  -63.9     16   0.03    280     91  335.6  335.7  335.6
  226.   243.3  10668  -48.9  -63.9     16   0.03    280     91  335.8  335.9  335.8
  227.   220.0  11327  -50.3  -65.3     15   0.03    280     85  343.5  343.6  343.5
  228.   212.0  11569  -50.5  -65.5     15   0.03    280     83  346.8  346.9  346.8
  229.   210.0  11631  -49.7  -64.7     16   0.03    280     83  349.0  349.1  349.0
  230.   200.0  11950  -49.9  -64.9     15   0.03    280     80  353.6  353.7  353.6
  231.   194.0  12149  -49.9  -64.9     15   0.03    279     78  356.7  356.8  356.7
  232.   183.0  12529  -51.3  -66.3     15   0.03    278     75  360.4  360.5  360.4
  233.   164.0  13233  -55.3  -68.3     18   0.02    277     69  365.2  365.3  365.2
  234.   152.0  13716  -56.5  -69.5     18   0.02    275     65  371.1  371.2  371.1
  235.   150.0  13800  -57.1  -70.1     18   0.02    275     64  371.5  371.6  371.5
  236.   136.0  14414  -60.5  -72.5     19   0.02    268     54  376.0  376.1  376.0
  237.   132.0  14600  -60.1  -72.1     19   0.02    265     51  380.0  380.1  380.0
  238.   131.4  14630  -60.2  -72.2     19   0.02    265     51  380.3  380.4  380.3
  239.   128.0  14792  -60.9  -72.9     19   0.02    266     50  381.9  382.0  381.9
  240.   125.0  14939  -60.1  -72.1     19   0.02    268     49  385.9  386.0  385.9
  241.   119.0  15240  -62.2  -73.8     20   0.01    270     48  387.4  387.5  387.4
  242.   112.0  15616  -64.9  -75.9     21   0.01    265     53  389.3  389.3  389.3
  243.   108.0  15838  -64.1  -75.1     21   0.01    265     58  394.8  394.9  394.8
  244.   107.8  15850  -64.1  -75.1     21   0.01    265     58  395.0  395.1  395.0
  245.   105.0  16010  -64.7  -75.7     21   0.01    272     50  396.9  396.9  396.9
  246.   103.0  16128  -62.9  -73.9     21   0.02    277     45  402.5  402.6  402.5
  247.   100.0  16310  -62.5  -73.5     21   0.02    285     36  406.7  406.8  406.7
  248.     '''
  250.     # Parse the data
  251.     sound_data = StringIO(data_txt)
  252.     p, h, T, Td = np.loadtxt(sound_data, usecols=range(0, 4), unpack=True)
  254.     # Create a new figure. The dimensions here give a good aspect ratio
  255.     fig = plt.figure(figsize=(6.5875, 6.2125))
  256.     ax = fig.add_subplot(111, projection='skewx')
  258.     plt.grid(True)
  260.     # Plot the data using normal plotting functions, in this case using
  261.     # log scaling in Y, as dictated by the typical meteorological plot
  262.     ax.semilogy(T, p, color='C3')
  263.     ax.semilogy(Td, p, color='C2')
  265.     # An example of a slanted line at constant X
  266.     l = ax.axvline(0, color='C0')
  268.     # Disables the log-formatting that comes with semilogy
  269.     ax.yaxis.set_major_formatter(ScalarFormatter())
  270.     ax.yaxis.set_minor_formatter(NullFormatter())
  271.     ax.set_yticks(np.linspace(100, 1000, 10))
  272.     ax.set_ylim(1050, 100)
  274.     ax.xaxis.set_major_locator(MultipleLocator(10))
  275.     ax.set_xlim(-50, 50)
  277.     plt.show()

SkewT-logP图示例

参考

此示例中显示了以下函数，方法，类和模块的使用：

  1. import matplotlib
  2. matplotlib.transforms
  3. matplotlib.spines
  4. matplotlib.spines.Spine
  5. matplotlib.spines.Spine.register_axis
  6. matplotlib.projections
  7. matplotlib.projections.register_projection

下载这个示例