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add docs for ParametrizedBlockInLayer, moved docs from rst to python files and automodule the docs for maps

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This commit is contained in:
Lindsey Heagy
2016-05-26 23:06:57 -07:00
parent 39ece11d8a
commit efbc8f9057
2 changed files with 75 additions and 94 deletions
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SimPEG/Maps.py
+74 -11
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@@ -131,7 +131,15 @@ class IdentityMap(object):
class ComboMap(IdentityMap):
"""Combination of various maps."""
"""
Combination of various maps.
The ComboMap holds the information for multiplying and combining
maps. It also uses the chain rule to create the derivative.
Remember, any time that you make your own combination of mappings
be sure to test that the derivative is correct.
"""
def __init__(self, maps, **kwargs):
IdentityMap.__init__(self, None, **kwargs)
@@ -180,6 +188,12 @@ class ComboMap(IdentityMap):
class ExpMap(IdentityMap):
"""
Electrical conductivity varies over many orders of magnitude, so it is a common
technique when solving the inverse problem to parameterize and optimize in terms
of log conductivity. This makes sense not only because it ensures all conductivities
will be positive, but because this is fundamentally the space where conductivity
lives (i.e. it varies logarithmically).
Changes the model into the physical property.
A common example of this is to invert for electrical conductivity
@@ -451,6 +465,32 @@ class Mesh2Mesh(IdentityMap):
"""
Takes a model on one mesh are translates it to another mesh.
.. plot::
from SimPEG import *
import matplotlib.pyplot as plt
M = Mesh.TensorMesh([100,100])
h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
h1 = h1/h1.sum()
M2 = Mesh.TensorMesh([h1,h1])
V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
v = Utils.mkvc(V)
modh = Maps.Mesh2Mesh([M,M2])
modH = Maps.Mesh2Mesh([M2,M])
H = modH * v
h = modh * H
ax = plt.subplot(131)
M.plotImage(v, ax=ax)
ax.set_title('Fine Mesh (Original)')
ax = plt.subplot(132)
M2.plotImage(H,clim=[0,1],ax=ax)
ax.set_title('Course Mesh')
ax = plt.subplot(133)
M.plotImage(h,clim=[0,1],ax=ax)
ax.set_title('Fine Mesh (Interpolated)')
plt.show()
"""
def __init__(self, meshes, **kwargs):
@@ -695,13 +735,13 @@ class CircleMap(IdentityMap):
Parameterize the model space using a circle in a wholespace.
..math::
.. math::
\sigma(m) = \sigma_1 + (\sigma_2 - \sigma_1)\left(\\arctan\left(100*\sqrt{(\\vec{x}-x_0)^2 + (\\vec{y}-y_0)}-r\\right) \pi^{-1} + 0.5\\right)
Define the model as:
..math::
.. math::
m = [\sigma_1, \sigma_2, x_0, y_0, r]
@@ -1057,19 +1097,42 @@ class SplineMap(IdentityMap):
# need to construct a parametrized mapping for the block in a layered space
# m = [val_back, val_layer, val_block, layer_top, layer_bottom, x0_block, y0_block, dx_block, dy_block]
class ParametrizedBlockInLayer(IdentityMap):
"""
Parametrized Block in a Layered Space
slope_fact = 1e3 # will be scaled by the mesh.
slope = None
indActive = None
.. plot::
:include-source:
from SimPEG import Mesh, Maps, np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1,1,figsize=(2,3))
mesh = Mesh.TensorMesh([50,50],x0='CC')
mapping = Maps.ParametrizedBlockInLayer(mesh)
m = np.hstack(np.r_[1., 2., 3., -0.2, 0.0, 0.3, 0.2])
rho = mapping._transform(m)
mesh.plotImage(rho, ax=ax)
**Required**
:param Mesh mesh: SimPEG Mesh, 2D or 3D
**Optional**
:param float slope_fact: arctan slope factor - divided by the minimum h spacing to give the slope of the arctan functions
:param float slope: slope of the arctan function
:param numpy.ndarray indActive: bool vector with
"""
slope_fact = 1e3 # slope factor of the arctan function - will be scaled by the mesh.
slope = None # slope of the arctan functions
indActive = None # bool array of the active indicies
def __init__(self, mesh, **kwargs):
# super(Parametrized_Block_in_Layer, self).__init__(mesh, **kwargs)
IdentityMap.__init__(self, mesh, **kwargs)
if self.slope is None:
docs/api_Maps.rst
+1 -83
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@@ -122,92 +122,10 @@ When these are used in the inverse problem, this is extremely important!!
The API
=======
.. autoclass:: SimPEG.Maps.IdentityMap
.. automodule:: SimPEG.Maps
:members:
:undoc-members:
Common Maps
===========
Exponential Map
---------------
Electrical conductivity varies over many orders of magnitude, so it is a common
technique when solving the inverse problem to parameterize and optimize in terms
of log conductivity. This makes sense not only because it ensures all conductivities
will be positive, but because this is fundamentally the space where conductivity
lives (i.e. it varies logarithmically).
.. autoclass:: SimPEG.Maps.ExpMap
:members:
:undoc-members:
Vertical 1D Map
---------------
.. autoclass:: SimPEG.Maps.Vertical1DMap
:members:
:undoc-members:
Map 2D Cross-Section to 3D Model
--------------------------------
.. autoclass:: SimPEG.Maps.Map2Dto3D
:members:
:undoc-members:
Mesh to Mesh Map
----------------
.. plot::
from SimPEG import *
import matplotlib.pyplot as plt
M = Mesh.TensorMesh([100,100])
h1 = Utils.meshTensor([(6,7,-1.5),(6,10),(6,7,1.5)])
h1 = h1/h1.sum()
M2 = Mesh.TensorMesh([h1,h1])
V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
v = Utils.mkvc(V)
modh = Maps.Mesh2Mesh([M,M2])
modH = Maps.Mesh2Mesh([M2,M])
H = modH * v
h = modh * H
ax = plt.subplot(131)
M.plotImage(v, ax=ax)
ax.set_title('Fine Mesh (Original)')
ax = plt.subplot(132)
M2.plotImage(H,clim=[0,1],ax=ax)
ax.set_title('Course Mesh')
ax = plt.subplot(133)
M.plotImage(h,clim=[0,1],ax=ax)
ax.set_title('Fine Mesh (Interpolated)')
plt.show()
.. autoclass:: SimPEG.Maps.Mesh2Mesh
:members:
:undoc-members:
Some Extras
===========
Combo Map
---------
The ComboMap holds the information for multiplying and combining
maps. It also uses the chain rule to create the derivative.
Remember, any time that you make your own combination of mappings
be sure to test that the derivative is correct.
.. autoclass:: SimPEG.Maps.ComboMap
:members:
:undoc-members: