fromcontextlibimportExitStackfrommatplotlib.axesimportAxesimportmatplotlib.axisasmaxisfrommatplotlib.projectionsimportregister_projectionimportmatplotlib.spinesasmspinesimportmatplotlib.transformsastransforms# 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.classSkewXTick(maxis.XTick):defdraw(self,renderer):# When adding the callbacks with `stack.callback`, we fetch the current# visibility state of the artist with `get_visible`; the ExitStack will# restore these states (`set_visible`) at the end of the block (after# the draw).withExitStack()asstack:forartistin[self.gridline,self.tick1line,self.tick2line,self.label1,self.label2]:stack.callback(artist.set_visible,artist.get_visible())needs_lower=transforms.interval_contains(self.axes.lower_xlim,self.get_loc())needs_upper=transforms.interval_contains(self.axes.upper_xlim,self.get_loc())self.tick1line.set_visible(self.tick1line.get_visible()andneeds_lower)self.label1.set_visible(self.label1.get_visible()andneeds_lower)self.tick2line.set_visible(self.tick2line.get_visible()andneeds_upper)self.label2.set_visible(self.label2.get_visible()andneeds_upper)super().draw(renderer)defget_view_interval(self):returnself.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 tickclassSkewXAxis(maxis.XAxis):def_get_tick(self,major):returnSkewXTick(self.axes,None,major=major)defget_view_interval(self):returnself.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 gridlinesclassSkewSpine(mspines.Spine):def_adjust_location(self):pts=self._path.verticesifself.spine_type=='top':pts[:,0]=self.axes.upper_xlimelse: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.classSkewXAxes(Axes):# The projection must specify a name.  This will be used be the# user to select the projection, i.e. ``subplot(projection='skewx')``.name='skewx'def_init_axis(self):# Taken from Axes and modified to use our modified X-axisself.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')}returnspinesdef_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 classsuper()._set_lim_and_transforms()# 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 boundsself.transDataToAxes=(self.transScale+self.transLimits+transforms.Affine2D().skew_deg(rot,0))# Create the full transform from Data to Pixelsself.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)@propertydeflower_xlim(self):returnself.axes.viewLim.intervalx@propertydefupper_xlim(self):pts=[[0.,1.],[1.,1.]]returnself.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.fromioimportStringIOimportmatplotlib.pyplotaspltimportnumpyasnpfrommatplotlib.tickerimportMultipleLocator,NullFormatter,ScalarFormatter# Some example data.data_txt='''        978.0    345    7.8    0.8        971.0    404    7.2    0.2        946.7    610    5.2   -1.8        944.0    634    5.0   -2.0        925.0    798    3.4   -2.6        911.8    914    2.4   -2.7        906.0    966    2.0   -2.7        877.9   1219    0.4   -3.2        850.0   1478   -1.3   -3.7        841.0   1563   -1.9   -3.8        823.0   1736    1.4   -0.7        813.6   1829    4.5    1.2        809.0   1875    6.0    2.2        798.0   1988    7.4   -0.6        791.0   2061    7.6   -1.4        783.9   2134    7.0   -1.7        755.1   2438    4.8   -3.1        727.3   2743    2.5   -4.4        700.5   3048    0.2   -5.8        700.0   3054    0.2   -5.8        698.0   3077    0.0   -6.0        687.0   3204   -0.1   -7.1        648.9   3658   -3.2  -10.9        631.0   3881   -4.7  -12.7        600.7   4267   -6.4  -16.7        592.0   4381   -6.9  -17.9        577.6   4572   -8.1  -19.6        555.3   4877  -10.0  -22.3        536.0   5151  -11.7  -24.7        533.8   5182  -11.9  -25.0        500.0   5680  -15.9  -29.9        472.3   6096  -19.7  -33.4        453.0   6401  -22.4  -36.0        400.0   7310  -30.7  -43.7        399.7   7315  -30.8  -43.8        387.0   7543  -33.1  -46.1        382.7   7620  -33.8  -46.8        342.0   8398  -40.5  -53.5        320.4   8839  -43.7  -56.7        318.0   8890  -44.1  -57.1        310.0   9060  -44.7  -58.7        306.1   9144  -43.9  -57.9        305.0   9169  -43.7  -57.7        300.0   9280  -43.5  -57.5        292.0   9462  -43.7  -58.7        276.0   9838  -47.1  -62.1        264.0  10132  -47.5  -62.5        251.0  10464  -49.7  -64.7        250.0  10490  -49.7  -64.7        247.0  10569  -48.7  -63.7        244.0  10649  -48.9  -63.9        243.3  10668  -48.9  -63.9        220.0  11327  -50.3  -65.3        212.0  11569  -50.5  -65.5        210.0  11631  -49.7  -64.7        200.0  11950  -49.9  -64.9        194.0  12149  -49.9  -64.9        183.0  12529  -51.3  -66.3        164.0  13233  -55.3  -68.3        152.0  13716  -56.5  -69.5        150.0  13800  -57.1  -70.1        136.0  14414  -60.5  -72.5        132.0  14600  -60.1  -72.1        131.4  14630  -60.2  -72.2        128.0  14792  -60.9  -72.9        125.0  14939  -60.1  -72.1        119.0  15240  -62.2  -73.8        112.0  15616  -64.9  -75.9        108.0  15838  -64.1  -75.1        107.8  15850  -64.1  -75.1        105.0  16010  -64.7  -75.7        103.0  16128  -62.9  -73.9        100.0  16310  -62.5  -73.5    '''# Parse the datasound_data=StringIO(data_txt)p,h,T,Td=np.loadtxt(sound_data,unpack=True)# Create a new figure. The dimensions here give a good aspect ratiofig=plt.figure(figsize=(6.5875,6.2125))ax=fig.add_subplot(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 plotax.semilogy(T,p,color='C3')ax.semilogy(Td,p,color='C2')# An example of a slanted line at constant Xl=ax.axvline(0,color='C0')# Disables the log-formatting that comes with semilogyax.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()

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