

.. _sphx_glr_gallery_api_skewt.py:


===========================================================
SkewT-logP diagram: using transforms and custom projections
===========================================================

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.





.. image:: /gallery/api/images/sphx_glr_skewt_001.png
    :align: center





.. code-block:: python


    from matplotlib.axes import Axes
    import matplotlib.transforms as transforms
    import matplotlib.axis as maxis
    import matplotlib.spines as mspines
    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 update_position(self, loc):
            # This ensures that the new value of the location is set before
            # any other updates take place
            self._loc = loc
            super(SkewXTick, self).update_position(loc)

        def _has_default_loc(self):
            return self.get_loc() is None

        def _need_lower(self):
            return (self._has_default_loc() or
                    transforms.interval_contains(self.axes.lower_xlim,
                                                 self.get_loc()))

        def _need_upper(self):
            return (self._has_default_loc() or
                    transforms.interval_contains(self.axes.upper_xlim,
                                                 self.get_loc()))

        @property
        def gridOn(self):
            return (self._gridOn and (self._has_default_loc() or
                    transforms.interval_contains(self.get_view_interval(),
                                                 self.get_loc())))

        @gridOn.setter
        def gridOn(self, value):
            self._gridOn = value

        @property
        def tick1On(self):
            return self._tick1On and self._need_lower()

        @tick1On.setter
        def tick1On(self, value):
            self._tick1On = value

        @property
        def label1On(self):
            return self._label1On and self._need_lower()

        @label1On.setter
        def label1On(self, value):
            self._label1On = value

        @property
        def tick2On(self):
            return self._tick2On and self._need_upper()

        @tick2On.setter
        def tick2On(self, value):
            self._tick2On = value

        @property
        def label2On(self):
            return self._label2On and self._need_upper()

        @label2On.setter
        def label2On(self, value):
            self._label2On = value

        def get_view_interval(self):
            return self.axes.xaxis.get_view_interval()


    # 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 _get_tick(self, major):
            return SkewXTick(self.axes, None, '', major=major)

        def get_view_interval(self):
            return self.axes.upper_xlim[0], self.axes.lower_xlim[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):
            pts = self._path.vertices
            if self.spine_type == 'top':
                pts[:, 0] = self.axes.upper_xlim
            else:
                pts[:, 0] = self.axes.lower_xlim


    # 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()) +
                transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes

        @property
        def lower_xlim(self):
            return self.axes.viewLim.intervalx

        @property
        def upper_xlim(self):
            pts = [[0., 1.], [1., 1.]]
            return self.transDataToAxes.inverted().transform(pts)[:, 0]


    # 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 (MultipleLocator, NullFormatter,
                                       ScalarFormatter)
        import matplotlib.pyplot as plt
        from six 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 dictated by the typical meteorological plot
        ax.semilogy(T, p, color='C3')
        ax.semilogy(Td, p, color='C2')

        # An example of a slanted line at constant X
        l = ax.axvline(0, color='C0')

        # Disables the log-formatting that comes with semilogy
        ax.yaxis.set_major_formatter(ScalarFormatter())
        ax.yaxis.set_minor_formatter(NullFormatter())
        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()

**Total running time of the script:** ( 0 minutes  0.049 seconds)



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     :download:`Download Python source code: skewt.py <skewt.py>`



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