Reconstruction Updated

pyramid/costfunction.py

@@ -3,7 +3,7 @@
 # Author: J. Caron
 #
 """This module provides the :class:`~.Costfunction` class which represents a strategy to calculate
-the so called `cost` of a threedimensional magnetization distribution."""
+the so called `cost` of a three dimensional magnetization distribution."""
 
 import logging
 
@@ -20,7 +20,7 @@ class Costfunction(object):
     Represents a strategy for the calculation of the `cost` of a 3D magnetic distribution in
     relation to two-dimensional phase maps. The `cost` is a measure for the difference of the
     simulated phase maps from the magnetic distributions to the given set of phase maps and from
-    a priori knowledge represented by a :class:`~.Regularisator` object. Furthermore this class
+    a prior knowledge represented by a :class:`~.Regularisator` object. Furthermore this class
     provides convenient methods for the calculation of the derivative :func:`~.jac` or the product
     with the Hessian matrix :func:`~.hess_dot` of the costfunction, which can be used by
     optimizers. All required data should be given in a :class:`~DataSet` object.
@@ -41,7 +41,7 @@ class Costfunction(object):
         Size of the input space.
     Se_inv : :class:`~numpy.ndarray` (N=2), optional
         Inverted covariance matrix of the measurement errors. The matrix has size `m x m` with m
-        being the length of the targetvector y.
+        being the length of the target vector y.
 
     """
 

pyramid/reconstruction.py

@@ -16,15 +16,15 @@ import logging
 
 import numpy as np
 
-from pyramid.fielddata import VectorData
+from pyramid.fielddata import VectorData, ScalarData
 
-__all__ = ['optimize_linear', 'optimize_nonlin', 'optimize_splitbregman']
+__all__ = ['optimize_linear', 'optimize_linear_charge', 'optimize_nonlin', 'optimize_splitbregman']
 _log = logging.getLogger(__name__)
 
 
 def optimize_linear(costfunction, mag_0=None, ramp_0=None, max_iter=None, verbose=False):
     """Reconstruct a three-dimensional magnetic distribution from given phase maps via the
-    conjugate gradient optimizaion method :func:`~.scipy.sparse.linalg.cg`.
+    conjugate gradient optimization method :func:`~.scipy.sparse.linalg.cg`.
     Blazingly fast for l2-based cost functions.
 
     Parameters
@@ -35,11 +35,11 @@ def optimize_linear(costfunction, mag_0=None, ramp_0=None, max_iter=None, verbos
     mag_0: :class:`~.VectorData`
         The starting magnetisation distribution used for the reconstruction. A zero vector will be
         used if no VectorData object is specified.
-    mag_0: :class:`~.Ramp`
+    ramp_0: :class:`~.Ramp`
         The starting ramp for the reconstruction. A zero vector will be
         used if no Ramp object is specified.
     max_iter : int, optional
-        The maximum number of iterations for the opimization.
+        The maximum number of iterations for the optimization.
     verbose: bool, optional
         If set to True, information like a progressbar is displayed during reconstruction.
         The default is False.
@@ -78,8 +78,8 @@ def optimize_linear(costfunction, mag_0=None, ramp_0=None, max_iter=None, verbos
 
 
 def optimize_linear_charge(costfunction, charge_0=None, ramp_0=None, max_iter=None, verbose=False):
-    """Reconstruct a three-dimensional magnetic distribution from given phase maps via the
-    conjugate gradient optimizaion method :func:`~.scipy.sparse.linalg.cg`.
+    """Reconstruct a three-dimensional charge distribution from given phase maps via the
+    conjugate gradient optimization method :func:`~.scipy.sparse.linalg.cg`.
     Blazingly fast for l2-based cost functions.
 
     Parameters
@@ -87,22 +87,22 @@ def optimize_linear_charge(costfunction, charge_0=None, ramp_0=None, max_iter=No
     costfunction : :class:`~.Costfunction`
         A :class:`~.Costfunction` object which implements a specified forward model and
         regularisator which is minimized in the optimization process.
-    mag_0: :class:`~.VectorData`
+    charge_0: :class:`~.VectorData`
         The starting magnetisation distribution used for the reconstruction. A zero vector will be
         used if no VectorData object is specified.
-    mag_0: :class:`~.Ramp`
+    ramp_0: :class:`~.Ramp`
         The starting ramp for the reconstruction. A zero vector will be
         used if no Ramp object is specified.
     max_iter : int, optional
-        The maximum number of iterations for the opimization.
+        The maximum number of iterations for the optimization.
     verbose: bool, optional
         If set to True, information like a progressbar is displayed during reconstruction.
         The default is False.
 
     Returns
     -------
-    magdata : :class:`~pyramid.fielddata.VectorData`
-        The reconstructed magnetic distribution as a :class:`~.VectorData` object.
+    elecdata : :class:`~pyramid.fielddata.ScalarData`
+        The reconstructed charge distribution as a :class:`~.ScalarData` object.
 
     """
     import jutil.cg as jcg
@@ -113,8 +113,8 @@ def optimize_linear_charge(costfunction, charge_0=None, ramp_0=None, max_iter=No
     # Get starting distribution vector x_0:
     x_0 = np.empty(costfunction.n)
     if charge_0 is not None:
-        costfunction.fwd_model.magdata = charge_0
-    x_0[:data_set.n] = costfunction.fwd_model.magdata.get_vector(mask=data_set.mask)
+        costfunction.fwd_model.elecdata = charge_0
+    x_0[:data_set.n] = costfunction.fwd_model.elecdata.get_vector(mask=data_set.mask)
     if ramp_0 is not None:
         ramp_vec = ramp_0.param_cache.ravel()
     else:
@@ -126,10 +126,10 @@ def optimize_linear_charge(costfunction, charge_0=None, ramp_0=None, max_iter=No
     _log.info('Cost after optimization: {:.3e}'.format(costfunction(x_opt)))
     # Cut ramp parameters if necessary (this also saves the final parameters in the ramp class!):
     x_opt = costfunction.fwd_model.ramp.extract_ramp_params(x_opt)
-    # Create and return fitting VectorData object:
-    mag_opt = VectorData(data_set.a, np.zeros((3,) + data_set.dim))
-    mag_opt.set_vector(x_opt, data_set.mask)
-    return mag_opt
+    # Create and return fitting ScalarData object:
+    charge_opt = ScalarData(data_set.a, np.zeros(data_set.dim))
+    charge_opt.set_vector(x_opt, data_set.mask)
+    return charge_opt
 
 
 def optimize_nonlin(costfunction, first_guess=None):