Moved things around! Packages should now all be capitalized. may need to to tweak git to ensure this...

SimPEG/Model.py

@@ -0,0 +1,94 @@
+from SimPEG import Utils, np, sp
+
+
+class BaseModel(object):
+    """SimPEG Model"""
+
+    __metaclass__ = Utils.Save.Savable
+
+    counter = None   #: A SimPEG.Utils.Counter object
+
+    def __init__(self):
+        pass
+
+    def transform(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: numpy.array
+            :return: transformed model
+
+            The *transform* changes the model into the physical property.
+
+            A common example of this is to invert for electrical conductivity
+            in log space. In this case, your model will be log(sigma) and to
+            get back to sigma, you can take the exponential:
+
+        """
+        return m
+
+    def transformDeriv(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: scipy.csr_matrix
+            :return: derivative of transformed model
+
+            The *transform* changes the model into the physical property.
+            The *transformDeriv* provides the derivative of the *transform*.
+        """
+        return sp.identity(m.size)
+
+    def example(self, mesh, type=None):
+        return np.random.rand(mesh.nC)
+
+
+
+class LogModel(BaseModel):
+    """SimPEG LogModel"""
+
+    def __init__(self, **kwargs):
+        BaseModel.__init__(self, **kwargs)
+
+    def transform(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: numpy.array
+            :return: transformed model
+
+            The *transform* changes the model into the physical property.
+
+            A common example of this is to invert for electrical conductivity
+            in log space. In this case, your model will be log(sigma) and to
+            get back to sigma, you can take the exponential:
+
+            .. math::
+
+                m = \log{\sigma}
+
+                \exp{m} = \exp{\log{\sigma}} = \sigma
+        """
+        return np.exp(Utils.mkvc(m))
+
+    def transformDeriv(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: scipy.csr_matrix
+            :return: derivative of transformed model
+
+            The *transform* changes the model into the physical property.
+            The *transformDeriv* provides the derivative of the *transform*.
+
+            If the model *transform* is:
+
+            .. math::
+
+                m = \log{\sigma}
+
+                \exp{m} = \exp{\log{\sigma}} = \sigma
+
+            Then the derivative is:
+
+            .. math::
+
+                \\frac{\partial \exp{m}}{\partial m} = \\text{sdiag}(\exp{m})
+        """
+        return Utils.sdiag(np.exp(Utils.mkvc(m)))