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https://github.com/wassname/simpeg.git
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Rowan Cockett
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
from SimPEG import Mesh, Maps, np
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def run(plotIt=True):
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"""
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Maps: ComboMaps
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===============
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We will use an example where we want a 1D layered earth as
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our model, but we want to map this to a 2D discretization to do our forward
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modeling. We will also assume that we are working in log conductivity still,
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so after the transformation we want to map to conductivity space.
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To do this we will introduce the vertical 1D map (:class:`SimPEG.Maps.SurjectVertical1D`),
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which does the first part of what we just described. The second part will be
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done by the :class:`SimPEG.Maps.ExpMap` described above.
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.. code-block:: python
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:linenos:
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M = Mesh.TensorMesh([7,5])
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v1dMap = Maps.SurjectVertical1D(M)
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expMap = Maps.ExpMap(M)
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myMap = expMap * v1dMap
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m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
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sig = myMap * m
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If you noticed, it was pretty easy to combine maps. What is even cooler is
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that the derivatives also are made for you (if everything goes right).
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Just to be sure that the derivative is correct, you should always run the test
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on the mapping that you create.
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"""
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M = Mesh.TensorMesh([7,5])
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v1dMap = Maps.SurjectVertical1D(M)
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expMap = Maps.ExpMap(M)
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myMap = expMap * v1dMap
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m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
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sig = myMap * m
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if not plotIt: return
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import matplotlib.pyplot as plt
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figs, axs = plt.subplots(1,2)
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axs[0].plot(m, M.vectorCCy, 'b-o')
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axs[0].set_title('Model')
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axs[0].set_ylabel('Depth, y')
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axs[0].set_xlabel('Value, $m_i$')
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axs[0].set_xlim(0,3)
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axs[0].set_ylim(0,1)
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clbar = plt.colorbar(M.plotImage(sig,ax=axs[1],grid=True,gridOpts=dict(color='grey'))[0])
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axs[1].set_title('Physical Property')
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axs[1].set_ylabel('Depth, y')
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clbar.set_label('$\sigma = \exp(\mathbf{P}m)$')
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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run()
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